Document [original]

Technische Universität Berlin

Essays on Regional Trust Cues in

the Context of Green Energy

Platform Economics

vorgelegt von

M. Sc. Carl Max Tobias Menzel

ORCID: 0000-0002-6657-2293

an der Fakultät VII – Wirtschaft und Management

der Technischen Universität Berlin

zur Erlangung des akademischen Grades

Doktor der Wirtschaftswissenschaften

– Dr. rer. oec. –

Promotionsausschuss:

Vorsitzender: Prof. Dr. Christian von Hirschhausen

Gutachter: Prof. Dr. Timm Teubner

Prof. Dr. Jan Kratzer

Tag der wissenschaftlichen Aussprache: 14. Dezember 2022

Berlin, 2023

Abstract

Leading academics have repeatedly called for research in the field of Information Systems

(IS) to address one of the major issues of mankind: global warming. One area in which IS

research can contribute to this challenge is the design of digital solutions to support decision-

making for more sustainable practices. Addressing this call for research, I engaged in an

extensive research agenda on how to design User Interfaces (UI) so that they support user

decisions for regional – an thus, commonly more sustainable – products and services. This

cumulative dissertation presents the results of this research. I found a very suitable object of

study in the electricity market – and thanks to the sector’s characteristics, findings should be

easily transferable to other contexts. At first, the thesis provides a look at the sector which is

currently disrupted by digitization, decarbonization, and decentralization. Subsequently, the

consumer perspective is taken to assess whether consumers value regionality when buying

electricity. In a next step, this work investigates how regional design elements (i.e., trust

cues) are used on UIs in practice. Thereafter, I present the results from multiple experiments

in which participants engaged with regional trust cues on UIs. In these experiments, various

objective and subjective measures for user attitudes and behavior were collected. I present

empirical evidence that regional trust cues positively affect these measures. Throughout,

implications for research, practitioners, policy makers, but also the broader society are

drawn from this work. I conclude with offering an outlook on recent developments

surrounding regional design elements on UI in research and practice.

Key words: Sustainability, User Interface Design, Information Systems, Human-Computer

Interaction, Reginal Trust Cues, Energy Sector, Renewable Energies, Regional Green Power,

Digital Business Models, Platform Economy, Experiment, Survey, Eye-Tracking, Content

Analysis, Web Scraping

Zusammenfassung

Führende Wissenschaftler haben wiederholt gefordert, dass die Forschung im Bereich der

Informationssysteme (IS) eines der größten Probleme der Menschheit adressieren sollte: die

globale Erwärmung. Ein Bereich, in dem die IS-Forschung einen Beitrag zu dieser

Herausforderung leisten kann, ist die Entwicklung digitaler Lösungen zur Unterstützung der

Entscheidungsfindung für nachhaltigere Praktiken. Diesem Aufruf folgend, habe ich mich auf

eine umfassende Forschungsreise begeben, um herauszufinden, wie digitale

Nutzerschnittstellen (UI) so gestaltet werden können, dass sie die Entscheidungen der

Benutzenden im Hinblick auf die Auswahl regionaler Produkte und Dienstleistungen

unterstützen. Mit der vorliegenden kumulativen Dissertation präsentiere ich die Ergebnisse

dieses Vorhabens. Der Strommarkt stellt dafür ein sehr passendes Forschungsobjekt dar –

und dank der Eigenschaften des Sektors sollten die Ergebnisse auf andere Kontexte

übertragbar sein. Zunächst wirft die Arbeit einen Blick auf den Sektor, der derzeit einen

tiefgreifenden Umbruch befeuert durch Digitalisierung, Dekarbonisierung und

Dezentralisierung durchläuft. Anschließend wird die Verbraucherperspektive eingenommen,

um zu analysieren, ob Verbrauchende beim Stromkauf Wert auf Regionalität legen. In einem

nächsten Schritt wird untersucht, wie regionale Gestaltungselemente auf UIs in der Praxis

eingesetzt werden. Danach stelle ich die Ergebnisse mehrerer Experimente vor, in denen sich

die Teilnehmenden mit regionalen Designelementen auf UIs auseinandersetzten. In diesen

Experimenten wurden objektive und subjektive Messgrößen für Verhalten und Einstellung

der Nutzenden erhoben. Ich präsentiere empirische Belege dafür, dass sich regionale

Designelemente positiv auf diese Messgrößen auswirken. In dieser Arbeit werden

Implikationen für Forschung, Praxis, Politik wie auch für die Gesellschaft im Allgemeinen

diskutiert. Ich schließe mit einem Ausblick auf die jüngsten Entwicklungen rund um regionale

Designelemente auf UIs in Forschung und Praxis.

Schlüsselwörter: Nachhaltigkeit, User Interface Design, Informationssysteme, Mensch-

Computer-Interaktion, Regional Trust Cues, Energiewirtschaft, Erneuerbare Energien,

Regionaler Ökostrom, Digitale Geschäftsmodelle, Plattformökonomie, Experiment,

Umfrage, Eye-Tracking, Inhaltsanalyse, Web Scraping

Acknowledgements

It’s a common phrase that it takes a village to raise a child. I believe the same applies to

writing a thesis. Therefore, I embrace this opportunity to thank all the major and minor

contributors to this endeavor. First and foremost, I thank Prof. Dr. Timm Teubner for his

supervision and guidance. He has been an outstanding mentor for all academic matters and

beyond. Second, I greatly appreciate the work of everyone involved in the research projects:

My co-authors Prof. Dr. Marc Adam and Dr. Peyman Toreini (and again, Prof. Dr. Timm

Teubner) but also my students Thanh Ngo Chi, Catayoun Azarm, Daniel Lawall, Janis Piskol,

Stefano Schlinke, and Maximilian Dreyer who supported in data collection and experiment

execution. Next, I would like to thank my colleagues at TU Berlin and the Einstein Center

Digital Future for their companionship, feedback, and advice. Further, I am thankful for the

support of my employer KEARNEY and the Stiftung der Deutschen Wirtschaft. Last, my

deepest gratitude extends to my wife Melissa for always having my back and tirelessly

listening to my academic stories.

 
5 
Contents 
ABSTRACT ................................................................................................................................. 2 
ZUSAMMENFASSUNG ............................................................................................................. 3 
ACKNOWLEDGEMENTS .......................................................................................................... 4 
CONTENTS ................................................................................................................................ 5 
LIST OF FIGURES ..................................................................................................................... 8 
LIST OF TABLES ....................................................................................................................... 9 
LIST OF ABBREVIATIONS ......................................................................................................10 
LIST OF PUBLICATIONS ......................................................................................................... 11 
CHAPTER I: INTRODUCTION ................................................................................................ 12 
RESEARCH MOTIVATION ............................................................................................................................ 12 
STRUCTURE OF THE THESIS AND OVERARCHING RESEARCH AIMS .............................................................. 13 
METHODOLOGY ......................................................................................................................................... 14 
CHAPTER II: AN OVERVIEW ON GREEN ENERGY PLATFORM ECONOMICS ................. 17 
INTRODUCTION .......................................................................................................................................... 17 
Problem Statement ............................................................................................................................. 17 
Study Significance .............................................................................................................................. 18 
Assumptions/Definitions ................................................................................................................... 18 
Objectives ............................................................................................................................................ 18 
Contribution/Originality ................................................................................................................... 19 
Structure ............................................................................................................................................. 19 
RELATED WORK ......................................................................................................................................... 19 
Energy Comparison Platforms .........................................................................................................20 
Charging Integrator Platforms .........................................................................................................20 
Peer-to-Peer (P2P) Energy Trading Platforms ...............................................................................20 
P2P EV Charging Platforms .............................................................................................................. 21 
Residential-To-Grid (R2G) Platforms .............................................................................................. 21 
V2G Platforms .................................................................................................................................... 22 
METHODOLOGY ......................................................................................................................................... 22 
Step 1: Research Objectives ............................................................................................................... 23 
Step 2: Literature Search / Provider Search and Classification .................................................... 23 
Step 3: Draft Framework Development ........................................................................................... 24 
Step 4: Framework Validation .......................................................................................................... 24 
Step 5: Application of Framework .................................................................................................... 25 
RESULTS .................................................................................................................................................... 25 
Research Framework, Provider Landscape, and Literature Classification (RO1)........................ 25 
Value Chain Evolution (RO2) ............................................................................................................. 28 
Research Agenda (RO3) ..................................................................................................................... 30 
DISCUSSION ............................................................................................................................................... 30 
Findings ............................................................................................................................................... 30 
Theoretical Implications .................................................................................................................... 31 
Managerial Implications ................................................................................................................... 31 
Policy Implications ............................................................................................................................. 32 
CONCLUSION .............................................................................................................................................. 33 
CHAPTER III: THE VALUE OF REGIONALITY IN THE ELECTRICITY SECTOR ............... 34 
INTRODUCTION .......................................................................................................................................... 34 
THEORETICAL BACKGROUND AND HYPOTHESES DEVELOPMENT ................................................................ 36 
Provider Perspective: Pricing Regionality ....................................................................................... 36 
Consumer Perspective: Consumer Ethnocentrism and the Value of Regionality ......................... 36 
METHODOLOGY AND DATA SET .................................................................................................................. 37 
Step 1 .................................................................................................................................................... 38 

 
6 
Step 2 ................................................................................................................................................... 38 
Step 3 ................................................................................................................................................... 39 
Step 4 ................................................................................................................................................... 39 
RESULTS ....................................................................................................................................................40 
The Value of Geographic Regionality (H1) .......................................................................................40 
The Value of Entrepreneurial Regionality (H2) ............................................................................... 41 
Interaction of the two Interpretations of Regionality (H3)............................................................. 41 
DISCUSSION AND CONCLUDING REMARKS .................................................................................................. 42 
Key Findings ....................................................................................................................................... 42 
Practical Implications for Design of User Interfaces ...................................................................... 42 
Theoretical Implications .................................................................................................................... 44 
Implications for Power Sector Sustainabilization ........................................................................... 44 
Limitations and Work in Progress .................................................................................................... 44 
CHAPTER IV: A DESCRIPTIVE ANALYSIS OF REGIONAL TRUST CUES ON USER 
INTERFACES ........................................................................................................................... 46 
INTRODUCTION .......................................................................................................................................... 46 
MATERIALS AND METHODS ........................................................................................................................ 51 
Study 1 ................................................................................................................................................. 51 
Study 2 ................................................................................................................................................. 53 
Study 3 ................................................................................................................................................. 54 
RESULTS .................................................................................................................................................... 54 
Study 1 ................................................................................................................................................. 54 
Study 2 ................................................................................................................................................. 58 
Study 3 .................................................................................................................................................60 
DISCUSSION ............................................................................................................................................... 61 
Key Findings ....................................................................................................................................... 61 
Theoretical Contributions .................................................................................................................. 62 
Practical Implications ........................................................................................................................ 63 
Consumer and Policy Implications ................................................................................................... 64 
LIMITATIONS AND FUTURE WORK .............................................................................................................. 65 
CONCLUSION .............................................................................................................................................. 66 
CHAPTER V: ON THE EFFECTS OF REGIONAL TRUST CUES ON USER BEHAVIOR –
IMAGERY ................................................................................................................................. 67 
INTRODUCTION .......................................................................................................................................... 67 
BACKGROUND AND THEORY ....................................................................................................................... 69 
Effects of Geographic Cues on User Attitudes and Behaviors ........................................................ 69 
Consumer Ethnocentrism Theory and Perceived Regional Presence ............................................ 69 
Disentangling Regional from Other Cues in UI Imagery ............................................................... 70 
Hypotheses Development and Research Model ............................................................................... 71 
STUDY 1: EYE-TRACKING LAB EXPERIMENT ............................................................................................... 72 
Materials and Methods ...................................................................................................................... 73 
Results ................................................................................................................................................. 74 
STUDY 2: ONLINE SURVEY ......................................................................................................................... 77 
Materials and Methods ...................................................................................................................... 77 
Results ................................................................................................................................................. 78 
DISCUSSION ............................................................................................................................................... 84 
Theoretical Implications .................................................................................................................... 84 
Implications for UI Design ................................................................................................................ 85 
Societal Implications .......................................................................................................................... 86 
Limitations and Future Work ............................................................................................................ 86 
Conclusions ......................................................................................................................................... 87 
CHAPTER VI: ON THE EFFECTS OF REGIONAL TRUST CUES ON USER BEHAVIOR – 
LABELS .................................................................................................................................... 88 
INTRODUCTION ......................................................................................................................................... 88 
BACKGROUND AND RESEARCH MODEL....................................................................................................... 89 

 
7 
Regional Green Electricity ................................................................................................................. 89 
Regional and Green Labels ................................................................................................................90 
Signaling Theory and Hypotheses Development ............................................................................. 91 
MATERIALS AND METHODS ........................................................................................................................ 93 
Scenario and Treatment Design ....................................................................................................... 94 
Stimulus Material and Label Design ................................................................................................ 94 
Measures ............................................................................................................................................. 95 
Sample and Procedure ....................................................................................................................... 96 
Randomization Check ........................................................................................................................ 97 
RESULTS .................................................................................................................................................... 97 
Visual Attention (H1) .......................................................................................................................... 98 
Time to Decision (H2) ....................................................................................................................... 100 
Trust (H3) .......................................................................................................................................... 101 
Moderating Role of Label Familiarity (H4a,b,c) .............................................................................. 101 
Supplementary Analysis: Disentangling Regional and Green Effects ........................................ 102 
DISCUSSION ............................................................................................................................................. 103 
Theoretical Implications .................................................................................................................. 103 
Policy Implications ........................................................................................................................... 104 
Takeaways for Practitioners ........................................................................................................... 104 
Applicability of Findings .................................................................................................................. 105 
Limitations and Paths for Future Work ......................................................................................... 105 
CHAPTER VII: CONCLUSION AND OUTLOOK ................................................................... 107 
RE-VISITING THE RESEARCH OBJECTIVES ................................................................................................ 107 
IMPLICATIONS, LIMITATIONS, AND FUTURE RESEARCH ............................................................................ 108 
Societal Implications ........................................................................................................................ 108 
Policy Recommendations ................................................................................................................. 109 
Theoretical Contributions ................................................................................................................ 109 
Advice for UI designers .................................................................................................................... 109 
Limitations and Paths for Future Research ................................................................................... 110 
OUTLOOK ................................................................................................................................................. 110 
Recent market trends ....................................................................................................................... 110 
Ongoing research projects ................................................................................................................ 111 
CONCLUSION ............................................................................................................................................. 113 
REFERENCES ......................................................................................................................... 115 
APPENDIX .............................................................................................................................. 133 
APPENDIX TO CHAPTER II ........................................................................................................................ 134 
APPENDIX TO CHAPTER IV ....................................................................................................................... 143 
APPENDIX TO CHAPTER V ........................................................................................................................ 145 
APPENDIX TO CHAPTER VI ....................................................................................................................... 147 
 

 
8 
List of Figures 
Figure 1. Research projects and structure of this thesis ........................................................... 15 
Figure 2. Research methodology for framework development and application...................... 23 
Figure 3. GEPE Framework and selected platform providers ................................................. 26 
Figure 4. GEPE Framework and descriptive statistics of literature review ............................. 28 
Figure 5. Evolution of the electricity value chain through GEPE ............................................ 29 
Figure 6. Research methodology .............................................................................................. 38 
Figure 7. Spatial sample distribution ....................................................................................... 38 
Figure 8. Frequency distribution of observation groups ......................................................... 40 
Figure 9. Overall price estimates (Error bars indicate standard errors) ..................................41 
Figure 10. Dummy effect estimates and SE across zip codes ................................................... 42 
Figure 11. Example of website with regional cues in the form of imagery and text ................. 48 
Figure 12. Example of a website with regional, social, and nature cues .................................. 49 
Figure 13. Methodology for analysis of imagery cues .............................................................. 52 
Figure 14. Methodology for analysis of textual cues ................................................................ 52 
Figure 15. Example of a website where textual areas for transcription are highlighted .......... 53 
Figure 16. Classification of imagery cues ................................................................................. 55 
Figure 17. Website examples illustrating types of regional motifs .......................................... 55 
Figure 18. Subclassification of regional cues ........................................................................... 56 
Figure 19. Classification of textual cues ................................................................................... 57 
Figure 20. Top-20 most frequent keywords for Study 1 providers .......................................... 58 
Figure 21. Top-12 most frequent keywords for Study 2 providers ........................................... 59 
Figure 22. Comparison of relative frequency of imagery and textual cues .............................. 60 
Figure 23. Website examples with regional cues based on location of web request.................61 
Figure 24. Website examples of national providers with regional cues .................................. 65 
Figure 25. Research model ........................................................................................................ 71 
Figure 26. Example heatmap of treatment (left) and control imagery (right) ........................ 75 
Figure 27. Breakdown of visual attention metrics per electricity plan .................................... 75 
Figure 28. Results of structural model ..................................................................................... 80 
Figure 29. Model variation ....................................................................................................... 80 
Figure 30. Decomposition of regional stimulus imagery. ........................................................ 82 
Figure 31. Research model (note: label translated) ................................................................. 92 
Figure 32. Stimulus material (note: labels translated) ............................................................ 95 
Figure 33. Experiment procedure ............................................................................................ 97 
Figure 34. Heat map examples without (left) and with regional green label (right). .............. 98 
Figure 35. Interaction effects of regional green label vs. no label with label familiarity ........ 101 
Figure 36. Comparison of no label, regional label, green label, and regional green label. .... 103 
Figure 37. Example for geographic UI customization on website .......................................... 111 
Figure 38. Stimulus material for field experiment ................................................................. 112 
Figure 39. Strategy to obtain user location ............................................................................. 113 
   

 
9 
List of Tables 
Table 1. Summary of research projects in this thesis ................................................................16 
Table 2. Regression results .......................................................................................................41 
Table 3. Summary of research designs for RQ1 to 3 ................................................................. 51 
Table 4. Key terms and definitions. ......................................................................................... 68 
Table 5. Eye-tracking regression summary .............................................................................. 76 
Table 6. Experimental conditions: Distribution of stimulus combinations ............................ 78 
Table 7. Construct descriptive statistics, consistency, and validity ......................................... 79 
Table 8. Randomization checks. .............................................................................................. 79 
Table 9. Summary CB-SEM results for variations of control variables. .................................. 81 
Table 10. Drivers of perceived regional presence regression summary ................................... 83 
Table 11. Spillover effects ......................................................................................................... 84 
Table 12. Sample characteristics .............................................................................................. 96 
Table 13. Tests for random condition assignment ................................................................... 97 
Table 14. Descriptive statistics ................................................................................................. 98 
Table 15. Regression results ................................................................................................... 100 
Table 16. Interaction effect regression models ...................................................................... 102 
 
  

List of Abbreviations

Area of Interest (AOI)

Business-to-Consumer (B2C)

Carbon Dioxide (CO2)

Comparative Fit Index (CFI)

Consumer Ethnocentrism Theory (CET)

Consumer-to-Consumer (C2C)

Consumer-to-Grid (C2G)

Coronavirus Disease 2019 (COVID-19)

Covariance-Based Structural Equation Modeling (CB-SEM)

Dependent Variable (DV)

Design Science Research (DSR)

Electric Vehicle (EV)

European Energy Exchange (EEX))

European Union (EU)

Global Positioning System (GPS)

Green Energy Platform Economics (GEPE)

Heating, Ventilation, and Air-Conditioning Systems (HVACs)

Human-Computer Interaction (HCI)

Hydrogen (H2)

Hypothesis (H)

Information and Communication Technology (ICT)

Information System (IS)

Internet Protocol (IP)

Least Square Dummy Variable (LSDV)

Ordinary Least Squares (OLS)

Over the Counter (OTC)

Peer-to-Peer (P2P)

Perceived Nature Presence (PNP)

Perceived Regional Presence (PRP)

Perceived Social Presence (PSP)

Photovoltaic (PV)

Research Objective (RO)

Research Question (RQ)

Residential-to-Grid (R2G)

Root Mean Square Error of Approximation (RMSEA)

Standard Error (SE)

Standardized Root Mean Square Residual (SRMR)

System for Guarantees of Regional Origin (SGRO)

Tucker Lewis Index (TLI)

United Kingdom (UK)

United States (US)

User Interface (UI)

Vehicle-to-Grid (V2G)

List of Publications

Chapter II:

Menzel, T., & Teubner, T. (2021). Green Energy Platform Economics – Understanding

Platformization and Sustainabilization in the Energy Sector. International Journal of

Energy Sector Management, 15(3), 456–475. doi.org/10.1108/IJESM-05-2020-0022

The chapter is based on the accepted manuscript.

Chapter III:

Menzel, T., & Teubner, T. (2021a). But Keep your Customers Closer: The Value of Regionality

in Electronic Commerce. European Conference on Information Systems (ECIS), 1–10.

aisel.aisnet.org/ecis2021_rip/2/

The chapter is based on the accepted manuscript.

Chapter IV:

Menzel, T., & Teubner, T. (2021e). How Regional Trust Cues Could Drive Decentralisation in

the Energy Sector—An Exploratory Approach. Sustainability, 13(6), 3010.

doi.org/10.3390/su13063010

The chapter is based on the accepted manuscript.

Chapter V:

Menzel, T., Teubner, T., Adam, M. T. P., & Toreini, P. (2022). Home is where your Gaze is –

Evaluating effects of embedding regional cues in user interfaces. Computers in Human

Behavior, 136, 107369. doi.org/10.1016/j.chb.2022.107369

The chapter is based on the accepted manuscript.

Chapter VI:

Menzel, T., Teubner, T., (tbd). Signaling sustainability and regionality in the electricity

market: An eye-tracking study on labels.

Currently under review. The chapter is based on the initially submitted manuscript.

Chapter I

Chapter I: Introduction

Research Motivation

Addressing climate change is among the key challenges for mankind. Consequently, leading

scholars in the field of Information Systems (IS) have called for research on this issue through

the use of information and communication technology (ICT; Dedrick, 2010; Melville, 2010;

Watson et al., 2010). IS research in this area is critical for the success of many approaches to

limit climate change as “all sustainable objectives and targets need ICTs as key catalysts”

(Koliouska & Andreopoulou, 2020, p. 4869). Most importantly, research with tangible and

implementable results is needed (vom Brocke et al., 2013). In their editorial to a special issue

on solutions for environmental sustainability in the Journal of the Association for Information

Systems, Gholami et al. (2016) suggest that “too few information systems […] academics

engage in impactful research that offers solutions to global warming despite the fact that

climate change is one of the most critical challenges facing this generation” (p.521). According

to their editorial, one research avenue in which the IS community could add meaningful value

to the efforts against climate change is to design solutions that “support decision-

making for more sustainable practices” (Gholami et al., 2016, p. 527). Addressing this

call, my main research motivation is to contribute to the overarching research agenda on how

to design IS solutions to support more sustainable decision-making. Within this realm I focus

on the design of user interfaces (UI) since it is the gate for human-computer interaction (HCI)

and hence a core aspect in consumers’ decision-making process.

Within this context a so far mostly overlooked aspect is the “surprisingly understudied” topic

of regionality (Herz & Diamantopoulos, 2019, p. 44). Consumer decisions in favor of regional

products and services are sustainable in many dimensions such as biodiversity, animal welfare,

governance, and resilience (Schmitt et al., 2017)

. Related research suggests that trust cues

(e.g., images of humans or nature) are powerful design tools for UI design in the sense of

affecting consumers’ attitudes towards the interface, underlying product, and provider, and

ultimately influencing their decision-making (e.g., Gefen & Straub, 2004; Rendell et al., 2021).

My work shows that regional cues are frequently used in practice and provides evidence that

such cues are used intentionally to promote regionality (Chapter IV). However, academia has

to my best knowledge shed little light on whether regional cues actually affect user behavior.

Therefore, this thesis aims to evaluate whether the use of regional trust cues on digital user

interfaces affects user attitudes and behavior and could hence support decision-making in

favor of more regional products and services.

Against this background, I identified the German electricity market as a very suitable object of

study for multiple reasons:

• Relevance of digital UI. In Germany, the vast majority of electricity plans is sold

through digital sales channels (provider websites, comparison portals, etc.; YouGov,

2015). Hence, decisions are made when consumers engage with digital user interfaces.

Regarding other aspects such as carbon footprint, land use, energy, or water consumption, the

academic debate on whether to favor regional over non-regional consumption is still undecided as

outcomes depend on “a diverse range of system boundaries, produce types, varied assumptions and a

multiplicity of foot printing methods” (Rothwell et al., 2016, p. 421).

Chapter I

• Sustainability of regional decision-making. In the electricity sector, there is a

relationship of making regional consumer decisions and sustainability (beyond the

above outlined benefits): Motivating consumers to purchase regional green electricity

(i.e., decentrally generated close to their home) is a sustainable practice in the sense

that it reduces transmission losses (Bauknecht et al., 2020), contributes to a higher

reliability of the system (Zerriffi et al., 2007), and avoids grid expansions (Allard et al.,

2020)

• Transferability of findings: Electricity represents a homogenous and credence

good. Thus, study results are not confounded by quality discrepancies of regional and

non-regional products (e.g., driven by shorter transportation distance). Also, it is

transported through grids and transmission costs are independent of the distance from

generation to consumption. Accordingly, observable effects can be attributed to the

very idea of regionality and should be transferable to other contexts.

Structure of the Thesis and Overarching Research Aims

This thesis consists of seven chapters. This first chapter motivates the research, provides an

overarching research agenda, gives a high-level description of the seven research projects

included in this work, and explains how those seven projects are reflected in the following

chapters. Chapter II lays the basis for further research by taking a closer look at the object of

study: the electricity sector. This industry is currently undergoing an in-depth transformation

of decarbonization, decentralization, and digitalization (di Silvestre et al., 2018). The chapter

draws on joint work with Prof. Dr. Timm Teubner and was published in the International

Journal of Energy Sector Management (Menzel & Teubner, 2021c). We offer a perspective on

how this transformation will shape the sector and, in particular, describe how platform

business models will disrupt the industry. In brief, the research objective of that chapter is:

RO1: Provide an overview on how current developments (i.e., decarbonization,

decentralization, and digitalization) are shaping the energy sector of the future.

Chapter II assesses the electricity sector from the angle of consumer preferences. The chapter

was published in the proceedings of the European Conference on Information Systems

(Menzel & Teubner, 2021a). As a starting point for further assessments of regional trust cues,

the chapter analyzes whether consumers prefer regional product characteristics when

purchasing electricity. Hence, the research aim addressed in Chapter III is:

RO2: Assess whether consumers value regionality in the electricity context.

Next, Chapter IV sheds light on the use of regional trust cues in practice. It represents joint

work with Prof. Dr. Timm Teubner which was published in the proceedings of the

A more detailed discussion of regional green electricity is provided in Chapter 6

Chapter I

Internationale Tagung Wirtschaftsinformatik

(Menzel & Teubner, 2021b) and in

Sustainability (Menzel & Teubner, 2021e). The research aim of this chapter is:

RO3: Understand how regional trust cues are used on user interfaces in practice.

Chapters V and VI are the core of this thesis. They build on findings from the previous chapters

that consumers prefer regional electricity and that regional trust cues are frequently used in

practice. The section analysis whether and how regional trust cues trigger this user preference

for regionality and, in turn, affect user attitudes and behavior. Chapter V analyzes the effects

of regional imagery and stems from joint research with Prof. Dr. Timm Teubner, Prof. Dr. Marc

Adam, and Dr. Peyman Toreini. The chapter was published in Computers in Human Behavior

(Menzel et al., 2022). Initial results and research design were also published in the proceedings

of the Internationale Tagung Wirtschaftsinformatik

(Menzel & Teubner, 2021b) and the

European Conference on Information Systems (Menzel & Teubner, 2021d)

. While Chapter V

assesses effects of regional imagery, Chapter VI turns to labels as a different incarnation of

regional trust cues. The chapter draws on joint work with Prof. Dr. Timm Teubner and is

currently under review at an international journal. Both chapters deal with the question

whether and how regional trust cues (imagery in Chapter V, labels in Chapter VI) affect

different measures of user attitudes and behavior. Hence, the overarching research objective

of those two chapters is:

RO4: Evaluate whether and how regional trust cues (i.e., images, labels) affect user

attitudes and behavior.

In the concluding Chapter VII, I revisit these outlined research aims, discuss implications for

research, policy, and practice, provide limitations and paths for future work, and give an

outlook on current developments in research and practice.

Methodology

As mentioned above, this thesis endeavor consists of seven research projects. Their design,

preliminary findings, and final results have been published in seven articles in conference

proceedings and journals. The five key publications which cover all major findings are included

as chapters in this thesis (see Figure 1).

Please note, however, that this research-in-progress paper is not part of this dissertation as it

presents only preliminary results of Chapter IV and research design of one study in Chapter V.

Please note, however, that this short paper is not part of this dissertation as it presents only

preliminary results of Chapter IV and research design of one study in Chapter V.

Please note, however, that this research-in-progress paper is not part of this dissertation as it

provides only preliminary results of one study and the research design of another study in Chapter V.

Chapter I

FIGURE 1. RESEARCH PROJECTS AND STRUCTURE OF THIS THESIS

These studies feature a wide range of methodologies and apply different theoretical

frameworks. Methodological pluralism adds value in IS research by offering a “richer

understanding of a research topic” (Mingers, 2001, p. 241) and drawing attention to different

aspects of the object of research (Niehaves, 2005). In addition, combining multiple

methodologies may mitigate limitations that a single approach would encompass. For

instance, adding eye-tracking to a survey-based experiment mitigates a key shortcoming of

self-reported assessments, namely, “that it does not allow us to conclude if a particular

characteristic is not relevant for the participant or if it does not catch his/her attention and

consequently is not processed” (Meyerding & Merz, 2018, p. 782). For this reason, not all seven

research projects led to independent research articles but instead, some of the studies were

combined for publication (and resultingly chapters in this thesis). The studies are combined

to chapters as follows:

• Chapter II: In a first study (#1 Figure 1), an extensive literature (162 papers in the

final shortlist) and provider (52) review on platform business models in the energy

sector was conducted. The study is presented in Chapter II of this thesis. Most

importantly, a conceptual framework for Green Energy Platform Economics and a

taxonomy for platform business models in the energy sector is developed.

• Chapter III: In the next project (2), we devised a web scraper to crawl data from a

German comparison portal for household electricity plans (n=22,890 observations).

We evaluated a hedonic regression model with key outcome that consumers value both

geographic and entrepreneurial attributes of regional electricity providers. The study

is covered in Chapter IV.

• Chapter IV: Chapter IV presents the results from two research projects (3 and 4). The

first project (3) analyses 318 regional energy provider websites by means of a

qualitative content analysis. The study identifies key design elements and categorizes

Chapter I

regional image and text cues. In the other project (4), a new set of regional energy

provider websites was compared to national energy provider websites in a quantitative

content analysis (n=136). The study highlights that regional trust cues appear

significantly more frequent on regional provider websites compared to national

provider websites suggesting an intentional use by regional providers to highlight their

regional attributes.

• Chapter V: The key finding in the next study (5) is that regional imagery appears to

trigger regional presence which, in turn, is associated with higher levels of trust. The

study included a between-subject online experiment (n=329) in which participants

engaged with a fictive energy provider website in which we systematically variated the

presence of regional, social, and nature cues. These findings are confirmed in a within-

subject lab experiment (6, n=18 participants, 138 observations) in which participants

were confronted with another fictive energy provider website with and without regional

cues. Eye-tracking and survey data were collected. In addition to the above-mentioned

effects on regional presence and trust, findings suggest that regional imagery is also

associated with higher levels of visual attention. Both studies are covered in Chapter V.

• Chapter VI: The most recent project (7) features another multi-method lab

experiment (n=38 participants, 304 observations) and is described in Chapter VI. We

collected gaze data, stated trust measures, and time to decision of participants while

they engaged with a fictive comparison portal for household electricity plans that

featured different labels with regionality and sustainability claims. We find that a label

for regional green electricity captures visual attention, reduces time to decision, and

increases trust.

A detailed breakdown of methodologies, theories and key results of all research projects is

provided in in Table 1.

TABLE 1. SUMMARY OF RESEARCH PROJECTS IN THIS THESIS

Methodology

Sample

size

Theoretical

frameworks

Chapter

Key results/ confirmed hypotheses

Literature & Provider

Review

161 + 52

N/A

Conceptual framework

Taxonomy

Web Scraping,

Hedonic Regression

Model

22,890

Consumer

Ethnocentrism

III

Geographic regionality → +WTP

Geographic x entrepreneurial

regionality → +WTP

Qualitative Content

Analysis

318

Social Presence

Theory, Biophilia

Hypothesis,

Consumer

Ethnocentrism

Classification of regional text and

imagery trust cues

Quantitative Content

Analysis

136

# of regional trust cues used by regional

providers > national providers

Online-Survey

329

Regional imagery → +Regional Presence

Regional Presence → +Trust

Eye-Tracking, Survey

18/138

Regional imagery → +Visual Attention

Regional imagery → +Regional Presence

Regional Presence → +Trust

Eye-Tracking, Survey

38/304

Signaling Theory

Regional label → +Trust

Regional label → +Visual Attention

Regional label → -Time to Decision

Chapter II

Chapter II: An Overview on Green Energy

Platform Economics

Paving the ground for further study and discussion, I provide an overview on the energy

sector in this section. In particular, the chapter discusses how the recent mega trends

digitization and decarbonization may shape the sector in the future. Until recently, the power

sector was characterized by a rigid value chain building mainly on centralized generation in

nuclear and coal power plants, transmission through monopolized grids, and consumption

by customers unenthusiastic about switching providers. But the tide is turning as

liberalization, digitization (e.g., smart homes), electrification (e.g., e-mobility), and

decentralization (e.g., rise of the prosumer) are changing the rules of the game in the energy

industry. But platform business models – having disrupted many industries in recent years

– are approaching to fundamentally disrupt the sector. Ultimately, this development could

not only turn the industry upside down but also substantially accelerate its green

transformation: Platform business models will promote adoption of green technology by

providing additional income streams and lowering entrance barriers for new, renewable

assets (e.g., rooftop PV, home storage batteries, electric vehicles) and managing increased

complexity caused by flexible power generation and demand. This chapter provides a first

review of literature on this matter. It describes the upcoming transformation in the sector,

presents a taxonomy of platform business models in the energy sector, and describes

worthwhile paths for future work.

Tobias Menzel, Timm Teubner

Introduction

Problem Statement

In response to global climate change, the energy sector has to become ecologically sustainable

by substituting conventional generation with renewable energy technologies (IPCC, 2014) and

by reducing energy consumption (Stankeviciute & Criqui, 2008). Achieving the goals set out

in the Paris Climate Change Agreement will require a 70% reduction in energy-related CO2

emissions from 2015 levels by the year 2050 (Aberg et al., 2019). At the same time,

digitalization continues to disrupt many industries and represents one of the key challenges

for business and politics. The energy sector in particular is expected to be heavily affected by

the latest wave of digitalization (Ringel, 2018): by 2025 one out of four energy providers could

go bankrupt due to the pressure of digitalization (Schwieters et al., 2016). The industry is

therefore facing the challenge of transforming its value chain to become green and digital in

order to live up to economic and environmental demands of shareholders and stakeholders

alike.

This chapter was published in the International Journal of Energy Sector Management with the title

“Green energy platform economics – understanding platformization and sustainabilization in the energy

sector”, doi.org/10.1108/IJESM-05-2020-0022

Chapter II

Digital platforms have already disrupted other industries such as the hospitality sector (e.g.,

Airbnb) and the retail sector (e.g., Amazon) and their growth has caught the attention of

academics and the public at large. At the same time, platform businesses have become

increasingly prominent within policy debates (Kiesling et al., n.d.). This prompts the question

of how and to what extent this potential could be leveraged in the energy sector to help drive

the required transition to a low-carbon energy system.

Study Significance

At present, the main obstacle to achieving the transformation of the energy sector is not the

lack of technology, but rather the lack of its application (Flamos, 2010). Platforms can provide

a means of addressing this problem and are considered a key element in the transition to a

low-carbon energy sector (Weiller & Pollitt, 2016). Digital platforms for green energy are a

dynamic combination of powerful economic, social, and technological factors that can disrupt

traditional markets (Ilieva & Rajasekharan, 2018). Indeed, some scholars go so far as to claim

that these platforms are critical to the successful transformation of the energy sector (e.g., C.

Zhang et al., 2018). Up until now, the academic debate around platformization in the energy

sector has focused on distinct platform applications, such as local energy markets (C. Rosen &

Madlener, 2016), plug-sharing platforms (Matzner et al., 2016), or vehicle-to-grid (V2G)

services (Schmidt et al., 2015). However, there has been no attempt to take a more holistic

view of platformization and sustainabilization, something this paper will attempt to address.

Assumptions/Definitions

To be as precise as possible, we will use the following definitions:

Platform

Market

A platform market is a market where user interactions are mediated by an

intermediary, the platform provider, and are subject to network effects. As opposed to

a marketplace or trading exchange, a platform intermediary must offer inherent value

beyond the simple mediation process for the two sides of the market. This added value

usually comes from [information and communication technology] (ICT) and the

associated complementary innovation that increases utility and attractiveness of the

platform to all user groups.

(Weiller & Pollitt, 2016, p. 7)

GEPE

Green Energy Platform Economics (GEPE) is the study of digital platform markets

that either facilitate the trading of energy from renewable sources or enable the

integration of renewable energy into the energy system.

Own definition

Objectives

As outlined above, a more holistic perspective has been missing from academic debate on the

platformization and sustainabilization in the energy sector. Such a perspective would support

policy makers, business leaders, and scholars. Therefore, we set out to structure the field with

the help of a research framework, leading to our first overarching research objective (RO):

Chapter II

RO1: Develop a framework to research platformization and sustainabilization in the energy

sector

In the next step, we dissect the sector along its value chain. Understanding the value chain is

critical to identifying the drivers of and challenges to innovation (Ferroukhi et al., 2013),

tracing economic value and risk flows (Furlonge, 2011), and responding to technological

change (Kolloch & Reck, 2017). Our second research objective can therefore be specified as

follows:

RO2: Determine how Green Energy Platforms affect the energy value chain

Finally, to develop the field further, we aim to identify existing research gaps and to derive a

corresponding research agenda. Our third and last research objective is therefore reads:

RO3: Uncover research gaps and derive a research agenda

We address these three objectives by means of (1) a literature review, (2) research on the

provider landscape, and (3) discussions with scholars and industry experts. We develop a

framework to structure Green Energy Platforms and then apply this framework to (a)

showcase how it could affect the value chain and (b) derive a research agenda.

Contribution/Originality

Our contribution to understanding platformization and sustainabilization in the energy sector

is fourfold. First, we provide a holistic framework for business leaders, policy makers, and

academics and describe the relevant terms, concepts, actors, and mechanisms. To the best of

our knowledge, this is the first study to provide such a holistic view of this important

contemporary trend. Second, we provide a comprehensive aggregated review of the relevant

literature and of the platform landscape. Third, we lay out a research agenda to further develop

the field. Fourth, the unique characteristics of energy, namely that it is homogenous and

intangible (Baye & Morgan, 2001) and that it can be classified as a credence good (Emons,

1997), offer a new perspective on platform economics research in general.

Structure

The remainder of the paper is structured as follows. We discuss related work in Section 2. Our

methodology is outlined in Section 3. Section 4 presents the results generated by this

methodology: a research framework, a comprehensive overview of the relevant literature and

of current platform providers, value chain mapping, and a future research agenda. Section 5

discusses our findings and their theoretical, practical, and policy implications. Our conclusions

are presented in Section 6.

Related Work

In this section, we review the academic debate on digital platform markets in the energy sector

and structure it in terms of six different platform types.

Chapter II

Energy Comparison Platforms

This platform type includes price comparison platforms for electricity, gas, and hydrogen.

These platforms serve as an intermediary between the supply side (energy retailers / utilities)

and the demand side (households / industrial consumers). We consider such platforms to be

Green Energy Platforms if they exclusively trade in energy from renewable sources (or at least

offer an option to purchase it). Green products allow providers to demand price markups over

conventional generation (Hast et al., 2014). Beyond matchmaking, these platforms add value

by enabling transparent comparison of complex products and pricing structures (Laffey,

2010).

Charging Integrator Platforms

On this type of platform, the product/price-comparison function is enriched by the

geographical dimension relevant to mobility applications. These platforms are able to provide

value by, for instance, showing users the closest available charging point and navigating them

there (e.g., Kuby et al., 2014). It has also been argued that this feature can reduce user

reservations towards e-mobility and accelerate its adoption (Bedogni et al., 2014). These

platforms generate additional value for users (especially in the domain of e-mobility) by

integrating multiple retailers so that users do not have to register with different charging point

providers (e.g., Noyen et al., 2013). Kim et al. (2017) introduce a blockchain-based billing

platform for this use case. As electric, gas, and hydrogen mobility is considered a key enabler

for the integration of renewable energy into the energy system, we include such platforms

within the GEPE framework.

Peer-to-Peer (P2P) Energy Trading Platforms

On this type of platform, both demand and supply are provided by non-commercial agents

(i.e., ordinary citizens). This is the reason why consumers are often referred to as prosumers

in this context, that is, consumers who by owning decentral generation units, such as rooftop

solar panels or electric vehicles (EV), are also producers. Accordingly, applications of this type

are also referred to as P2P (Sousa et al., 2019) or sharing platforms (C. Park & Yong, 2017).

The intermediary is typically a utility who, besides operating the platform, supplies residual

energy in case the platform community does not generate sufficient amounts of energy

(Mengelkamp et al., 2018). The traded good is often renewable energy generated from rooftop

solar panels or residential windmills, but the concept works similarly for sharing hydrogen

(Amoretti, 2011; Xiao et al., 2018) or heat (Block et al., 2008). Even though hydrogen and heat

may not necessarily be from renewable sources, their inclusion in the GEPE framework is

justified, as both technologies can contribute to increasing the share of renewable energy and

substitute conventional, emission-heavy sources such as fossil oil, gas, and coal.

A recent body of work has studied community and connectivity aspects of P2P energy trading

platforms, such as microgrids, which can be understood as a physically connected community

(e.g., Marzal et al., 2018). They have also been regarded as socially cohesive communities

connected by a local energy market (e.g., Lezama et al., 2019) and discussed in terms of

community-based energy trading (e.g., Koirala et al., 2016). In terms of added value,

customers get access to locally produced renewable energy (e.g., Kahrobaee et al., 2014) while

the acceptance and adoption of distributed renewable energy can be increased due to the

additional value streams created for prosumers (Kiesling et al., n.d.). The research literature

Chapter II

focuses primarily on market design and implementation, such as bilateral trading (e.g.,

Morstyn et al., 2019), broker-based markets (e.g., Chen & Su, 2019), consensus-based

approaches (e.g., Sorin et al., 2018), and auction mechanisms (e.g., Paudel et al., 2019).

Another frequently discussed topic is the use of blockchain architectures (e.g., S. Wang et al.,

2019). Regulatory challenges have also been addressed in a number of publications, such as

Soshinskaya et al. (2014) who consider interconnection rules with the main grid as barriers to

the further adoption of microgrids.

P2P EV Charging Platforms

In the mobility sphere, P2P platforms are designed to match owners of private EV charging

points with drivers (Matzner et al., 2016). According to Madina et al. (2016), this is the first

type of mobility platform with a viable business model, as the total cost of ownership is lower

than that for public charge points or V2G applications. Even though the electricity used in EVs

does not necessarily stem from renewable sources, the technology qualifies as a green energy

platform since it is widely considered a key contributor to the decarbonization of the economy

(e.g., IPCC, 2014). P2P EV charging platforms provide an income stream for owners of private

charging points. Drivers of EVs also benefit as value is added by the opportunity to search for

and compare charging points (Plenter, 2017). The most frequently debated topic in this context

is the architecture of the information systems (IS) used in such platforms (e.g., Radi et al.,

2019), some of which are based on blockchain technology (e.g., Kang et al., 2017).

Residential-To-Grid (R2G) Platforms

R2G platforms are a particular feature of electricity markets. Since the electricity grid is very

sensitive to fluctuations in its operating frequency (50 Hz in Europe, 60 Hz in Northern

America), grid operators rely on specialist service providers to ensure that demand and supply

are balanced at all times. These providers offer products ranging from those with highly

responsive supply capacities, such as primary reserve frequency regulation (where the energy

providing asset has to be able to ramp production up or down within seconds), to less

responsive load-management solutions with ramp-up times of the order of hours. Apart from

these services procured by grid operators, grid services also include applications such as peak

shaving or load shifting where excess energy is stored and fed back to the grid when needed

(López et al., 2015). In contrast to the four platforms mentioned above, in which a certain

amount of energy is traded, the commodity traded on R2G platforms is capacity, that is, the

flexibility to feed electricity into the grid when demand is higher than supply, and vice versa.

Up to now, conventional power plants fueled by coal, gas, or nuclear power have provided

these services by flexibly adjusting production. New technology for balancing grid services is

thus essential when replacing conventional energy production and transforming the energy

system towards renewable sources (Motalleb et al., 2016). Among these new load-

management technologies, energy storage assets such as batteries will be critical to the

integration of renewable resources (Debia et al., 2019). Ilieva and Rajasekharan (2018)

describe how multi-sided platforms can pool, coordinate, and monetize an array of storage

assets at the consumer level. Typically, an individual EV or home appliance does not provide

enough capacity to be attractive for the demand side and pooling is typically required to

generate a marketable product (e.g., C. Rosen & Madlener, 2016). In addition to pooling, these

platforms add value by offering an additional income stream to vehicle and homeowners and

hence facilitate adoption of EVs or storage systems. The data generated can also be used to

Chapter II

optimize demand and supply schedules (e.g., Eid et al., 2016). Flexibility on R2G platforms is

provided by residential battery capacity (e.g., C. Rosen & Madlener, 2013), flexible loads such

as heating, ventilation, and air-conditioning systems (HVACs) (e.g., Jin et al., 2020), or

adjustable residential production units such as solar panels. Batteries can operate in two

directions, that is they are able to both store and re-supply excess energy. In contrast, flexible

loads and generation units can typically only provide flexibility in one direction by shifting or

cutting consumption or generation (Ströhle & Flath, 2016). Most research into R2G platforms

has centered around market design, such as hierarchical market models (e.g., Gkatzikis et al.,

2013), real-time pricing mechanisms (e.g., Cardell, 2007), and auction mechanisms (e.g.,

Dauer et al., 2015).

V2G Platforms

V2G platforms offer a similar solution to the R2G platforms described above but differ in that

they use the storage capacity of pure and hybrid EVs. In contrast to residential assets, EVs can

operate at different feed-in locations (Kempton & Tomić, 2005b). Fuel cell vehicles can also

participate in V2G platforms, but with the constraint that only upward regulation is feasible

(Kempton & Tomić, 2005a). However, research publications (e.g., Weiller & Neely, 2014)

remain skeptical about the viability of V2G platforms because, as things currently stand, the

costs of providing balancing services exceed the earning potential in the flexibility market

(Brandt et al., 2017). However, this business model will become increasingly attractive as

battery costs decline (Uddin et al., 2018). Initially, services with short charging intervals (e.g.,

frequency regulation services) will become financially viable, while those services with longer

charging cycles (e.g., peak shaving, load shifting) will require a further decrease in battery costs

before they become economically feasible (Tomić & Kempton, 2007). Research into IS

architecture also addresses data privacy concerns (e.g., Ghosh et al., 2013) and the

implementation of V2G platforms into the smart grid (Guille & Gross, 2009). Zhao et al. (2016)

have even suggested that V2G platforms could facilitate the breakthrough of electric trucks.

Methodology

To develop the framework in this paper, we followed a five-step approach as outlined in Figure

2, starting with the definition of initial research objectives. In the following, we outline and

provide specifics for each step.

Chapter II

FIGURE 2. RESEARCH METHODOLOGY FOR FRAMEWORK DEVELOPMENT AND APPLICATION

Step 1: Research Objectives

As outlined in the previous section, our research objectives are to develop a conceptual

framework for GEPE (RO1), to assess how platformization in the energy sector affects the value

chain (RO2), and to use these results to derive a future research agenda (RO3).

Step 2: Literature Search / Provider Search and Classification

We began by identifying relevant literature via keyword searches on Google Scholar with a

review of the top 50 search results as well as backward and forward searches of the references.

We selected Google Scholar over other databases as it is currently the most comprehensive

collection of papers, books, and conference proceedings for academic searches (Gusenbauer,

2019) and is expanding rapidly compared with other databases (de Winter et al., 2014). Google

Scholar can be regarded as a combination of multiple databases and offers “substantial extra

coverage” (Martín-Martín et al., 2018, p. 1) compared with other services such as Scopus or

Web of Science. In addition, we searched for platform providers using a regular internet

search. We used keyword combinations that ranged from generic terms such as “platform

economics” in combination with different energy carriers (e.g., electricity, hydrogen, gas, etc.)

to concrete applications such as “peer-to-peer sharing”, “comparison websites”, or “vehicle-to-

grid”. We also used the names of some of the established platforms (e.g., “Verivox”). The full

list of keywords is provided in the Appendix. We applied the following inclusion/exclusion

criteria resulting in a total of 161 publications in the final set:

• Quality – As quality criterion, only journals with SCImago H-index scores above 50 and

conferences with H-index scores greater than 10 are considered (Mengelkamp, Weinhardt,

et al., 2019). SCImago Journal & Country Rank is a publicly available website providing

indicators to evaluate scientific outlets including all major conferences and journals. The

H-index provides the “number of articles (h) that have at least h citations” (SCImago

Journal & Country Rank, 2020, p. 1). Exceptions apply for a low number of highly relevant

preprints or publications.

Chapter II

• Subject – The platform must be the publication’s main subject. We therefore excluded

publications in which, for instance, the platform is only the data source for other

calculations. We also excluded publications on the (technical) optimization of energy flows,

energy management, or voltage control. Moreover, we removed publications that targeted

the architecture of physical (non-IS) components, communication protocols, or battery

degradation.

• Market properties – As defined above, the platform must be a multi-sided marketplace.

We therefore excluded publications dealing with monopolistic market structures, such as

blink or newmotion, where a single company is both the seller and the platform provider.

We also excluded publications on microgrids when the paper focused only on the physics

of the grid.

The final set of 161 publications was classified using a cross-sectional approach that involved

the following categories (the full classified list is provided in the Appendix).

• Type of publication – This distinguishes practitioner journals and conferences (e.g., the

IEEE universe) from scholarly publications.

• Context – This classifies a paper’s content in terms of specific aspects of Green Energy

Platforms. Categories are IS architecture (e.g., algorithms), business model, user interface,

social interaction and community, and regulatory or policy framework. Further

classifications include optimization (e.g., improvement of cost, bidding strategy, and

forecasting), the presentation and discussion of concrete artifacts, market design, and

publications discussing the acceptance and adoption of such platforms. For a particular

research paper, more than one category may apply.

• Form of energy transmitted – This identifies which form of energy is being

considered: electricity, gas, hydrogen, or heat. It is important to note that the focus here is

on the form of energy that ultimately flows between trade partners. For instance, if a

hydrogen-powered fuel cell car uses its battery to take part in a power balancing service

market, the transmitted form of energy is electricity and not hydrogen. Multiple selections

are possible.

• Research methodology – This describes the methodological approach used in the

paper: case study, conceptual work, design science research (DSR), experiment, expert

interview, field study, framework, literature review, model, protocol, prototype,

simulation, and survey. Multiple selections are possible.

Step 3: Draft Framework Development

Once Step 2 had been completed, we developed a draft framework that structured papers and

platform providers. This framework defines six platform types within a 2x3 matrix. The two

rows represent residential applications and mobile applications respectively, while the three

columns are assigned to the type of business interaction: business-to-consumer (B2C),

consumer-to-consumer (C2C), and consumer-to-grid (C2Grid).

Step 4: Framework Validation

The framework was further refined through an iterative process involving presentations to and

discussions with scholars and industry experts. Specifically, we held meetings with two

academic experts in the field of platform economics, three research scholars with an interest

in the energy sector, an employee of an energy utility, and a partner in an energy sector strategy

Chapter II

consultancy. After each meeting, we further optimized the framework by iterating Steps 3

and 4.

Step 5: Application of Framework

Having developed the conceptual framework, we summarized and visualized the results of our

review of the research literature and the provider landscape (RO1). Next, we turned our

attention to the value chain. To do this we focused on the electricity value chain because the

literature review revealed that electricity is by far the most frequently researched form of

energy (i.e., covered in 160 of 161 research publications). Starting with the conventional value

chain, we assessed how each of the six platform types can affect or disrupt the value chain

(RO2). In the final stage, we used the classification scheme described above to identify gaps in

the research literature and - based on these findings - formulated an agenda for future research

(RO3).

Results

Research Framework, Provider Landscape, and Literature Classification (RO1)

The framework that we have developed is a 2x3 matrix in which existing platforms and relevant

research literature can be presented in a structured manner. The two rows of the matrix are

used to distinguish between the spatial characteristics of the platform models: residential and

mobile. Residential applications include both household and industry customers and are

characterized by a fixed geographic point of consumption or production. In contrast, platforms

are treated as mobility applications whenever vehicles are involved. In these cases, the point

where energy is consumed or fed into the grid is not fixed and may vary over time. The columns

of the matrix distinguish between the platform business models B2C, C2C, and C2Grid. While

a number of B2B platforms also exist in this context, they do not satisfy the platform definition

used in the present analysis, either because they are used solely for market clearing purposes

(e.g., spot exchanges for electricity or gas – such as the European Energy Exchange (EEX)) or

because they do not represent a multi-sided market. The resulting six fields within the matrix

each feature one of the platform types described in Section 2. Figure 3 displays the framework

matrix together with a non-exhaustive summary of platform providers.

Chapter II

FIGURE 3. GEPE FRAMEWORK AND SELECTED PLATFORM PROVIDERS

• B2C Residential (No. 1 in Figure 2 to Figure 4) – Energy comparison platforms feature

websites such as Verivox and Check24 in Germany, uSwitch in the United Kingdom (UK),

and chooseenergy in some states of the United States (US). It is worth mentioning that the

geographic scope of this business model is limited to liberalized markets (i.e., it is relevant

in only 16 US states). Household and business consumers can compare various providers

and tariffs for green or conventional electricity and gas contracts. Most comparison

websites began as price comparison services for insurance policies, phone or broadband

plans, or credit card offers and later expanded into the energy market.

• B2C Mobility (No. 2) – In addition to electricity and gas, charging integrator platforms

include solutions for hydrogen. However, electricity is still by far the predominant form of

energy. Owners of electric cars can use applications such as Plugsurfing, ChargeNow,

PlugShare to find the closest public charge point, start the charging process, and execute

payments. Gibgas and H2 offer similar services for gas and hydrogen, respectively. The

enterprises behind these platforms are typically either start-ups (as in the case of

Plugsurfing) or mobility incumbents (such as BMW and Daimler in the case of

ChargeNow). Energy providers operating their own on-street charge points tend to offer

applications that only cover their own assets. Hence, these applications do not qualify as

platforms as defined here, as they do not represent a multi-sided market.

• C2C Residential (No. 3) – The most prominent example of a P2P energy trading

platform is LO3Energy, which is the technology partner for the Brooklyn Microgrid, one

of the earliest and largest pilot programs for local electricity sharing. Platforms of this type

include start-ups with a technology-heavy background that often emphasize their

capabilities in blockchain or other distributed ledger technologies such as Grid+ Energy,

Power Ledger, Prosume, or Hive Power. Other platforms in this area include

SonnenCommunity, which enables owners of its PV-coupled battery storage systems to

trade surplus power.

Chapter II

• C2C Mobility (No. 4) – Among P2P EV charging platforms, EVmatch is probably the

most advanced platform. While platforms like EVmatch and elbnb focus exclusively on

private charging points, share&charge and CHRG network also integrate public EV

charging stations. Electricity is the only form of energy currently traded on both C2C

residential and C2C mobility platforms.

• C2Grid Residential (No. 5) – The companies active on R2G platforms are essentially the

same as those involved in P2P energy trading. They either trade load balancing services via

the same platform they use for wholesale energy trading (vlux, Hive Power) or they offer

different products under the same brand umbrella such as VPP2.0 by Power Ledger. The

start-up Piclo is the only market player that we identified that focuses exclusively on the

flexibility market.

• C2Grid Mobility (No. 6) – As noted earlier, electricity is the only form of energy traded

on C2Grid applications. Interestingly, our research into V2G platforms shows that the

large mobility or energy players are not particularly engaged in this area, with most activity

coming from the independent US-based start-ups Fermata Energy and Nuvve as well as

the German start-up The Mobility House, all of which were specifically founded with the

purpose of exploiting V2G technology.

We have also used our framework to present the descriptive statistics generated from our

analysis of relevant research publications (see Figure 4). Overall, the number of publications

on GEPE has increased markedly over the last five years compared with the decade before,

indicating a significant growth in interest in the field. The distribution of articles is fairly

balanced between practitioner research and purely academic research, with a slightly higher

share of practitioner literature in papers dealing with mobility applications and a slight excess

of scholarly literature concerned with residential applications. Furthermore, electricity is by

far the predominant form of energy discussed in the literature. Of all 161 papers, hydrogen is

considered in only three studies, trading of gas is covered twice, and heat is considered only

once. P2P energy trading platforms (No. 3) are the most frequently researched platform type

(69 publications), almost half of which examine market design. V2G platforms (No. 6) and

R2G platforms (No. 5) are the next most popular research subjects with 47 and 33 publications

respectively. The literature on V2G platforms deals mostly with business models, while the

dominant topic in articles on R2G platforms is, again, market design. Charging integrator

platforms (No. 2) were the subject of 22 papers, while 17 articles analyzed P2P EV charging

platforms (No. 4), where the IS architecture was the most frequently discussed topic. Energy

comparison platforms (No. 1) were considered in seven publications with no topic being

particularly dominant.

Chapter II

FIGURE 4. GEPE FRAMEWORK AND DESCRIPTIVE STATISTICS OF LITERATURE REVIEW

Value Chain Evolution (RO2)

In this section we use the framework developed above to study how the electricity value chain

is affected by the rise of platform markets. The fact that 1) electricity is considered in all but

one of the 161 papers examined and 2) that other energy carriers, such as heat, gas, or

hydrogen, are discussed in only four, justifies our focus on the electricity value chain. The

conventional electricity value chain (e.g., Simmonds, 2002) starts with conventional,

centralized power generation (e.g., in coal or nuclear power plants). Electricity flows via high-

voltage transmission grids to lower voltage distribution grids. Traditionally, retailers, often

utility companies, controlled practically all aspects of end-customer interaction, which

typically included an over-the-counter (OTC) contractual relationship and services such as

billing and metering. In terms of marketplace transactions and bilateral trading, the

conventional value chain involved retailers purchasing electricity from the power generators

on the basis of long-term contracts as well as via electricity exchanges to cover short-term and

intraday demand. The transmission grid operators responsible for grid stability, purchased

balancing services from the power generating companies through balancing service markets.

As summarized in Figure 5, this conventional set-up will be affected by Green Energy

Platforms at almost every level – a transition that is already underway.

Chapter II

FIGURE 5. EVOLUTION OF THE ELECTRICITY VALUE CHAIN THROUGH GEPE

Looking first at the market players and the structure of the future electricity value chain, new

power generation units, such as wind or solar power plants, have entered the market on a

substantial scale, feeding into both transmission and distribution grids. In addition, the

conventional consumer has evolved into a prosumer who can both provide and consume

electricity at different times. During periods of low energy consumption or high self-

production (e.g., from solar panels), the prosumer feeds energy into the grid – and vice versa.

At the residential level, the rise of the prosumer has been driven predominantly by solar

photovoltaic systems, but also by other forms of decentralized generation, battery storage

capacities, and smart loads. The increase of EVs is also adding an additional element at the

consumer level. With the introduction of a platform economy model, new markets are

beginning to arise within the value chain. Lastly, the socio-political context has evolved as well.

Although the energy sector has always been subject to extensive regulation, it is now also being

confronted by societal and political developments, some of which involve new actors such as

the youth-driven Fridays-for-Future movement.

The current state of platformization in the energy sector can be described as follows: B2C

platforms (No. 1 and 2) are now situated between retailers and consumers/prosumers (both

static and mobile) while the P2P energy trading platforms (No. 3) operate among prosumers.

P2P EV charging platforms are used when mobile consumers charge their EVs with energy

from prosumer households (No. 4). The two C2Grid platform types (No. 5 and 6), which are

located between the prosumers/EVs and the transmission grid operators, are the most

complex ones within our framework. As outlined earlier, these markets may well expand to

encompass distribution grids, where operators could, for instance, purchase flexibility to avoid

congestion losses. Utilities could also procure flexibility from prosumers and EVs either by

pooling these providers with their own assets in order to bring them to market or to avoid

compensation payments to the transmission grid operator, during periods in which their

balancing group is not balanced. In summary, platformization acts a disruptor along the entire

Chapter II

electricity value chain by enabling consumers/prosumers to market their assets at basically

every level.

Research Agenda (RO3)

By combining our cross-sectional review and classification of the research literature with the

results of applying our research framework we have established a foundation on which we are

now able to identify areas where further research is needed.

• Platform type: Price comparison websites were the subject of only seven of the 161

papers. Moreover, only four of these papers focused exclusively on comparison websites,

while the other three publications also addressed other platform types. This seems

somewhat surprising given that comparison websites represent a rather mature business

field. Assessing these sites by means of web scraping could deliver novel insights not only

for comparison website research, but also for understanding Green Energy Platforms in

general.

• Context: An aggregated view of articles examined reveals that particular aspects of GEPE,

such as market design (60 papers), business models (40), IS architecture (34), and

regulatory topics (27), have been addressed quite extensively. In contrast, however, very

little research appears to have been done into platform user interfaces (4) and social

interactions on such platforms (3). It is worth noting in this regard that research in other

sectors has shown that topics of this type, such as trust and reputation mechanisms, are

crucial factors in determining a platform’s success.

• Form of energy transmitted: As discussed earlier, in practically every one of the papers

analyzed (160 out of 161) the form of energy traded was electricity. Other (potentially

renewable) resources such as hydrogen, heat, and gas are barely considered in studies of

platform economy models. Nevertheless, these forms of energy all play an important role

in the ongoing transformation of the energy sector and their platformization should

therefore be the subject of future research.

• Research methodology: Our examination of the research literature also identified a

lack of empirical work, especially the analysis of real market data relating to Green Energy

Platforms. In contrast, there is an abundance of market simulations (79 papers),

theoretical models (44), and case studies (22). This could be due to the fact that those

platforms that are most frequently the subject of research, such as P2P energy trading or

V2G applications, are to a large extent still at the project stage or in the early phases of

their development and hence cannot provide dependable market data at the present time.

However, web scraping of energy comparison sites and charging integrator platforms or

partnering with them would provide robust insights based on real-life data.

Discussion

Findings

Green Energy Platforms are fundamentally disrupting the energy value chain in multiple ways.

These platforms are creating new markets and trading possibilities, introducing new forms of

cooperation between market players and thus unlocking the so-far untapped potential of

thousands of small-scale generation and storage units through coordination and bundling. The

positive impact of platformization can be illustrated by taking a look at how it has influenced

other sectors. Take, for instance, the case of the ride sharing platform BlaBlaCar. While drivers

Chapter II

used to drive an otherwise empty car from A to B, the platform brokers this unused capacity

and hence makes an otherwise idle resource available.

At the same time, platformization helps to manage the increasing complexity that is being

driven by the decentralization of the energy sector. By boosting transparency, platformization

is also helping to make markets more efficient. Finally, enabling new players to enter the

markets adds a healthy level of competition in markets that only a few decades ago were

dominated by monopolies and that still tend to exhibit oligopolistic characteristics (Strunz,

2014).

Theoretical Implications

In this work, we are attempting to broaden the perspectives typically adopted in the few

existing research papers on the platformization in the energy sector. The framework we

propose broadens the classification of business models used by Giehl et al. (2019) to discuss

the ongoing and future transformation of the energy sector. First, we highlight the “two-

sidedness” of markets as a key characteristic of the platform economy. Second, we take into

account the importance of (environmental) sustainability of platform models whereas Giehl et

al. (2019) did not explicitly account for such environmental impacts. Last, we systematize the

various platform types by arranging them within a 2x3 matrix structure. We build on the work

of Kupferschmidt et al. (2018), positing that the energy sector’s platformization is a key enabler

for handling the growing complexity caused by increasing decentralization. Expanding on

Kupferschmidt et al. (2018),we attempt to create a comprehensive overview of the field by

including C2Grid applications as well as platforms involving mobile prosumers. Our

framework contributes to the academic debate by enriching platform-agnostic discussions of

such topics as social interactions (e.g., Y. Huang et al., 2015; Skopik, 2014) or technological

implementation issues (e.g., Albrecht et al., 2018).

In the broader context of sustainable business models in the energy sector, Richter (2013) has

drawn attention to a lack of viable business models for small-scale renewable generational and

storage assets, which is an issue directly tackled by GEPE. Green Energy Platforms also

address the need for new forms of collaboration to manage the increasing complexity as has

been highlighted by other scholars (e.g., Engelken et al., 2016).

Managerial Implications

Managers and business decision makers need to be aware that their business can adopt one of

a number of different roles within a platform set-up: seller, platform provider, buyer, or

ecosystem partner. It is critical for business leaders to understand which platform type they

want to employ and for what purpose and which role they want their company to play within

the platform ecosystem. Depending on whether the digital platform is supposed to serve as a

revenue stream, act as a sourcing path, or provide access to a customer base will affect the

company’s role on the platform. Take for instance the case of a traditional utility provider. This

type of company might act as a seller on a comparison platform, as a platform provider for a

P2P energy trading market, and/or as a buyer on a V2G platform. A start-up on the other hand

could develop the platform for a P2P plug-sharing service while simultaneously acting as a

buyer on a price comparison website.

For utilities in particular, platformization requires a paradigm shift. To successfully manage

the transition to a platform-enabled marketplace, they may need to abandon their currently

Chapter II

preferred model of a few large-scale projects (Richter, 2012) in favor of more small-scale

customer-side initiatives.

In terms of business strategy and the timing of when best to adopt platform business models,

we have identified notable differences in the maturity of the platforms, with decreasing states

of sophistication from left to right in our framework matrix. While established comparison

platforms such as Verivox have already been operating for years, the providers of P2P

applications are predominantly in the test and pilot phases. C2Grid platforms are even further

away from launching commercial operations. These observations are in line with other

publications. Weinhardt et al. (2019), for instance, report that most local energy market

projects are still in the proof-of-concept stage, and Laurischkat et al. (2016) state that V2G is

currently only a theoretical concept due primarily to the high degree of sophistication of such

projects and the technology and commercial relationships involved (San Román et al., 2011).

Managers looking to develop digital platform business models in the energy sector will also

need to acquire a new understanding of risk, as these models are much more technology-

driven and dependent on a platform ecosystem than conventional business models in this

sector (Dellermann et al., 2017). For instance, monitoring the financial viability of all partners

within the platform’s ecosystem is significantly more complex than in traditional bilateral

business relationships.

Policy Implications

Although energy sector regulation has undergone fundamental changes in recent years

(Wagner et al., 2020), a number of important issues still need to be addressed by policy makers

if shareholders and stakeholders are to harvest the benefits of platformization and

sustainabilization in the energy sector. In a number of cases, current regulations do not

provide the reliable legal framework that is necessary for long-term planning and investment

decisions (Engelken et al., 2016). However, a reliable legal foundation is critical if decision

makers are to take the ambitious steps necessary to achieve the transition to a green and digital

energy economy. As a critical element of public infrastructure, the energy sector is subject to

extensive regulation which creates bureaucracy, complexity, and numerous operational

limitations (Kotilainen et al., 2016). This results in additional challenges that digital business

platforms in other sectors may not face. Take the case of P2P plug-sharing in Germany: in an

attempt to harmonize billing of EV charging transactions, the German government has issued

a law that only allows for quantity-based payment schemes. This poses a severe problem for

P2P plug-sharing systems, as it requires plug owners to install specific metering technology.

In contrast, P2P platforms such as EVmatch have been successfully implemented in other

markets using a time-based remuneration mechanism that is much simpler to administer.

Our review of the research literature and of the current provider landscape also suggests that

there is still little interest in other sources of energy beyond electricity. However, hydrogen in

particular is projected to become a major factor in achieving sustainability in the energy sector

(IEA, 2019). Policy makers should therefore seriously consider funding schemes for research

in this area.

Finally, if consumers are to build trust in the Green Energy Platform Economy, policy makers

need to establish a clear and transparent legal footing that guarantees data protection.

Chapter II

Conclusion

This paper can be understood as an overview and mapping of the field of Green Energy

Platform Economics. The platformization of the energy sector promises to make a significant

contribution towards a decarbonized, decentralized, and digitalized energy economy. We

propose a two-dimensional framework that structures the field in terms of the spatial

characteristics of the application (residential or mobile) and the type of business interaction

involved (B2C, C2C, C2Grid). The framework was developed through 1) a careful review of the

research literature, 2) an examination of the provider landscape, and 3) insights from

academic and industry experts. The framework was then applied in order to structure and

classify 161 relevant publications, summarize the provider landscape, and showcase how Green

Energy Platforms impact the electricity value chain. Lastly, we identified key research gaps

and derived a corresponding research agenda. Our main conclusions can be summarized as

follows:

• Green Energy Platforms will fundamentally affect the conventional electricity value chain

by enabling prosumers to market their assets, creating new stages for trading and

collaboration, increasing transparency, and boosting competition in the sector.

• Further research, especially empirical work, on energy forms other than electricity is

needed. In addition, previously underrepresented aspects of Green Energy Platforms, such

as user interface and social interactions, should be made the subject of future work.

• Business leaders will be forced to adjust their strategies to identify the most appropriate

role for their business in different platform set-ups and will need to acquire a new

understanding of risk in order to succeed in any future platformized energy sector.

• Policy makers must strive to decrease complexity and bureaucracy in this highly regulated

sector and provide a reliable legal framework that can be used to implement

platformization and sustainabilization in the energy sector and thus harvest the associated

benefits.

Naturally, this paper is not without limitations. On the one hand, we have restricted our

analysis to few core platform topics. Associated aspects such as optimization of energy flows,

energy management, or voltage control and the architecture of physical (non-IS) components

(e.g., degradation of batteries) may be addressed in future work. On the other hand, our

analysis of how digital platforms can influence value chains has been restricted to the

electricity value chain. As outlined above, however, other forms of energy – in particular

hydrogen – are likely to play a major role in the ongoing transition to a green energy sector

and should therefore clearly be the subject of further research.

Chapter III

Chapter III: The Value of Regionality in the

Electricity Sector

As a starting point for the discussion on regional trust cues, this chapter analyses whether

consumers value regionality when purchasing energy. Hence, I assess whether there is a

consumer preference which regional trust cues can attain to. According to the study results,

two dimensions of regionality are important to consumers. First, they value regionality in a

geographic sense, in other words, electricity sold from providers in proximity to them.

Second, regionality can also be understood as an entrepreneurial attribute. The study

suggests that consumers value electricity providers with ties to their region.

Tobias Menzel, Timm Teubner

Introduction

Climate change is one of – if not the – greatest challenges humanity faces today. While the

coronavirus pandemic captures much of the political, business, and academic attention,

climate change has not lost its actuality and urgency. In the Information Systems (IS)

community, leading scholars have shaped the field Green IS to address climate change through

information and communication technology (ICT) (e.g., Dedrick, 2010; Melville, 2010;

Watson et al., 2010). In their editorial to a JAIS special issue on solutions for environmental

sustainability, Gholami et al. (2016) have ascertained the fact that “too few information

systems […] academics engage in impactful research that offers solutions to global warming

despite the fact that climate change is one of the most critical challenges facing this generation”

(p. 521). Importantly, also practical research that goes beyond theory is needed (vom Brocke

et al., 2013).

According to Gholami et al. (2016), one of the areas in which the IS community could add

meaningful value to the efforts against climate change is to design solutions that “support

decision-making for more sustainable practices” (p. 527). Addressing this call, we engage in a

broader research agenda on how to design user interfaces to support more sustainable

decision-making. A so far mostly overlooked aspect in this realm is the “surprisingly

understudied” topic of regionality (Herz & Diamantopoulos, 2019, p. 44). Buying regional

represents a sustainable choice in many dimensions such as biodiversity, animal welfare,

governance, and resilience (Schmitt et al., 2017). With regard to other aspects such as carbon

footprint, land use, energy, or water consumption, the academic debate on whether to favor

regional over non-regional consumption is still undecided as outcomes depend on “a diverse

range of system boundaries, produce types, varied assumptions and a multiplicity of foot

printing methods” (Rothwell et al., 2016, p. 421). In this paper, we aim to provide practicable

and impactful research on the design of user interfaces to support decision-making in favor of

regional products and services. We provide an indication for users in fact valuing regionality

in online contexts. Note that this valuation can be triggered by means of regional cues on user

This chapter was published as Research-in-Progress Paper in the proceedings of the European

Conference on Information Systems 2021 with the title “But Keep your Customers Closer: The Value of

Regionality in Electronic Commerce”, https://aisel.aisnet.org/ecis2021_rip/2

Acknowledgement: I thank our students Daniel Lawall, Janis Piskol, Stefano Schlinke, and

Maximilian Dreyer for their support in building and operating the web-crawler

Chapter III

interfaces (Menzel & Teubner, 2021d). Based on this, we seek to assess how such cues ought

to be designed in view of user decision-making.

Purchasing regional products is an established and still emerging trend in many offline

markets, in particular in the food sector (Darby et al., 2006). Yet, the question arises whether

this trend translates to electronic commerce as the Internet is considered a “window to the

world” (Hongladarom, 1999, p. 400) and a means to (explicitly) overcome geographic

boundaries (Forman & van Zeebroeck, 2018). Nevertheless, earlier work has shown that

regional cues are frequently used in practice and provides evidence that such cues are used

intentionally to promote regionality (Menzel & Teubner, 2021e). Academia seems to be lagging

behind in studying this trend. Therefore, we investigate whether consumers actually prefer

regional products when buying on digital user interfaces and if so, which factors drive such

preferences. This yields the following research question:

RQ: Do consumers value regionality in electronic commerce? In other words, are they

willing to pay price mark-ups for regional products and services when purchasing on

digital user interfaces?

To address this question, we consider actual market data from Verivox, a leading German price

comparison platform for electricity and gas plans. This market provides a compelling case to

study for multiple reasons. First, as electricity can (with some limitations) be considered a

homogenous credence good, other product properties which could explain users’ preferences

for regional sourcing can largely be ruled out (e.g., consumers may prefer regional strawberries

for their higher freshness). Second, electricity is transmitted through networks, eliminating

differences in transportation cost for consumers (Obstfeld & Rogoff, 2000) and trade barriers

(Wolf, 2000). We can hence assume that any preferences for regional electricity are driven by

the very idea of regionality. Note that the German electricity market is highly fragmented and

hence offers a large sample of providers. In addition, the historic market genesis has led to a

situation in which different types of providers operate (more on this below). Also, the fact that

comparison platforms are the most important sales channel for household energy plans in the

German market (YouGov, 2015) ensures a certain level of robustness for the analysis. As

platform providers are hesitant to share transactional data such as click streams and

conversion rates, we consider publicly available price data for our analysis. Applying hedonic

pricing models, we assume that providers’ pricing strategy reflect consumer preferences (to at

least some degree). In other words, hedonic pricing assumes that higher prices for regional

offers are related to consumer preferences for such offers. Further, we draw on the theoretical

lenses of Consumer Ethnocentrism (Shimp & Sharma, 1987) to describe households’

consumption patterns. We find evidence that consumers indeed prefer regional offers if a

combination of criteria for regionality is met (i.e., geographic and entrepreneurial).

In the following, we illustrate related work and theory (Section 2), describe the study’s

methods (Section 3), present results (Section 4), and discuss its findings, implications, and

limitations (Section 5).

Chapter III

Theoretical Background and Hypotheses Development

Provider Perspective: Pricing Regionality

The Hedonic Pricing Model, going back to Rosen (1974), assumes that every aspect of a product

or service that adds value to customers will – in the long run – be reflected in market prices.

Based on observed prices, the model attributes “shadow prices” to product attributes,

reflecting their values to consumers (Greening et al. 1997, p.183). These attributes can go

beyond tangible product characteristics and include, for instance, branding or market

segmentation (Baltas & Freeman, 2001). Applications of the concept are wide-ranging and are

applied to evaluate the (monetary) value of criteria such as the energy efficiency of

refrigerators (Greening et al., 1997) or the value of accommodation amenities (Teubner et al.,

2017). Most frequently, the theory is applied in the context of real estate (e.g., Gibbons, 2004),

public goods (Cavailhès et al., 2009; e.g., van Praag & Baarsma, 2005), and tourism

(Vanslembrouck et al., 2005). While, to the best of our knowledge, hedonic pricing has not

been applied to electricity, it is well-suited for assessing the intangible characteristic of

regionality.

Consumer Perspective: Consumer Ethnocentrism and the Value of Regionality

The literature provides several approaches to explain consumers’ preferences for regional

products and services, including the notion of familiarity (Huberman, 2001), trust toward

transaction partners (Lai & Teo, 2008), ambiguity aversion (Boyle et al., 2012), homeland

sympathy (Morse & Shive, 2011), sustainability, and support for local businesses (Darby et al.,

2006). Importantly, consumers do not necessarily need to be driven by sustainability motives

in order to make more sustainable decisions. In this study, we draw on the established theory

of Consumer Ethnocentrism (Shimp & Sharma, 1987), developing two perspectives on

regionality and narrowing down this multi-faceted term in the following.

Consumer Ethnocentrism explains consumers’ preferences for regional products and services

with an evolutionary psychological pattern. Throughout the early days of mankind, survival

was dependent of cohesion and solidarity within a (geographically bounded) social group such

as tribes and families (van den Berghe, 1981). Therefore, the well-being of this group was in

the center of decision-making. This pattern is hence deeply rooted within the human brain and

today, still leads to ethnocentric consumer behavior (Bizumic, 2019) in the sense that

purchasing from the in-group (defined as the group of people with “which an individual

identifies” (Shimp & Sharma, 1987, p. 280)) is unconsciously preferred over buying from the

out-group. Consumers perceive themselves as “center of the universe” and will therefore avoid

buying non-regional products and services because “it hurts the domestic economy, causes

loss of jobs, and is plainly unpatriotic” (Shimp & Sharma, 1987, p. 280).

We first consider the (obvious) geographic aspect of regionality. Accordingly, geographic

regionality is a preference of goods and services offered by providers in geographic proximity

to consumers. Originally, Consumer Ethnocentrism posits that consumers prefer domestic

over imported products, but the concept is equally applicable to the regional context (Bryła,

2019). Considering an example from the food sector, Darby et al. (2006) demonstrated that

consumers are willing to pay higher prices for strawberries grown nearby compared to

elsewhere from (within) their country. In a recent study, one participant stated that regionality

was a dominant factor when evaluating energy provider websites (Menzel & Teubner, 2021d).

Chapter III

This fosters our belief that regional preferences in the geographic sense can a) be independent

of product quality such as freshness and b) translate into electronic commerce. Accordingly,

we hypothesize:

H1: Consumer preferences for regional products and services are reflected in price mark-

ups for electricity plans offered by providers in greater geographic proximity to them.

While Consumer Ethnocentrism is typically interpreted from this geographical perspective, we

offer another (less obvious) interpretation of regionality in the sense that the in-group is

understood as set of entities “with which a person identifies” (Shimp & Sharma, 1987, p. 280).

Looping back to the examples above, the preference for strawberries grown nearby would

become even stronger when sold on a farmer’s market compared to a grocery store (Darby et

al., 2006). In this sense, “the fact that the provider seems to be regionally embedded generates

trust” (Menzel & Teubner, 2021d, p. 8). Apparently, consumers not only care about where the

product stems from but also from whom they buy it and prefer to buy from entities that they

identify with. Hu et al. (2012) laid out how the use of a fictious small farmer association logo

on blackberry jam led to increased likelihood of purchase and willingness to pay. We capture

this provider attribute by the notion of entrepreneurial regionality. A regional provider in this

entrepreneurial sense is strongly connected with and within the region it operates in (e.g., as

an employer, charity sponsor, investor, etc.). Typically, these providers are small or medium-

sized firms, potentially in public ownership, and the majority of their operations is

concentrated within their vicinity (i.e., their home turf). Accordingly, our second hypothesis

reads:

H2: Consumer preferences for regional products and services are reflected in price mark-

ups for electricity plans offered by providers with higher entrepreneurial regionality.

Note that the German electricity market is fully liberalized, and providers are free to sell their

products nationwide. Therefore, entrepreneurial regionality can be assessed independently

from the notion of geographic regionality as providers are free to sell their products outside of

their home region too. Yet, these two concepts can be assumed to be interconnected, leading

us to the third hypothesis:

H3: There occurs a positive interaction between geographic and entrepreneurial

regionality with regard to consumers’ valuations.

Methodology and Data Set

To evaluate our hypotheses, we draw on data from the German electricity retail market. The

market is fully liberalized; providers can hence offer in any location and freely vary prices

across regions. Also, the market is highly fragmented leading to a large sample of providers,

and platformization of the market has evolved to an extent that most providers generate a

significant share of sales via comparison platforms such as Verivox (KEARNEY et al., 2019).

Figure 6 summarizes our research approach.

Chapter III

FIGURE 6. RESEARCH METHODOLOGY

Step 1

We devised a web scraper (python) that randomly selected 468 out of the roughly 8,000

German zip code areas (Figure 7). The web scraper issued queries to the Verivox website,

searching for electricity plans, using the zip codes and an assumed annual consumption of

2,500 kWh (the typical consumption level for two-person households). From the search

results, data on providers, products, and prices were retrieved and stored, resulting in a total

of 31,785 observations from 133 distinct providers.

FIGURE 7. SPATIAL SAMPLE DISTRIBUTION

Step 2

Next, we retrieved each provider’s address and flagged observations as geographically regional

where consumer (i.e., the queried zip code) and provider headquarter were located in the same

zip code area. We excluded providers with headquarter outside of Germany, providers that

offered in only one zip code area, and those without offers in the zip code of their headquarters

as, for these providers, within-comparisons (with vs. without geographic regionality) are not

Chapter III

possible. This yielded a set of 22,890 observations from 71 distinct providers. To measure the

economic effect of geographic regionality, we employ a hedonic pricing approach and consider

the effect of the “geographic regionality” flag on posted prices where the hypothesized

valuation of geographic regionality should be reflected in price mark-ups for plans by

providers based in the consumer’s region.

Step 3

To assess the entrepreneurial interpretation of regionality, we take a closer look at provider

characteristics. Again, the hypothesized valuation of entrepreneurial regionality should be

reflected in price mark-ups for plans by providers with entrepreneurial regionality over plans

by providers without this characteristic. While the grouping in Step 2 is dependent on a

combination of zip codes of consumer and provider, this grouping builds on provider

characteristics only. Providers of entrepreneurial regionality are characterized by small or

medium company size, public ownership, operational focus on, and close ties to a certain

region. In the German electricity market, local (i.e., municipal and regional) utilities exhibit

these characteristics. Their connection with and within the region is even expressed through a

reference to a city or region in the company name (e.g., “Stadtwerke Heidelberg” – that is,

Municipal Utilities of Heidelberg). We exploit this circumstance and flag all observations

where the company name references a city or region to be regional in the entrepreneurial

sense. In addition, provider websites were consulted for all providers without clear reference

to identify local utilities which may be using abbreviations or acronyms instead. Overall, 37 of

the 71 providers met the criteria for entrepreneurial regionality. Note that despite of their

operational focus on a specific region, local utilities are free to sell electricity nationwide. This

circumstance allows us to analyze the effect of entrepreneurial regionality independent of

geographic properties.

Step 4

Following Steps 2 and 3, observations are structured in four pseudo-treatment groups: Offers

from providers without entrepreneurial regionality outside (NN in Figure 8) and inside (YN)

the areas in which these providers are considered geographically regional (i.e., their “home

turf”), and offers from providers with entrepreneurial regionality, again outside (NY) and

inside (YY) of areas of geographic regionality. Note that the two observation groups with

geographic regionality are markedly smaller than the other two groups. This is driven by the

fact that – by definition – for each provider, only offers in one or a few of all zip code areas are

considered regional in the geographic sense. Accounting for this discrepancy, we start the

empirical analysis with an effect size assessment using Cohen’s d as measure for the effect’s

expressiveness (Cohen, 1988). Since our data offers structural similarities to an unbalanced

panel (with geographic instead of time dimension), we apply a Within Fixed Effects model

executed as least square dummy variable regression with geography dummies. This enables us

to control for omitted spatial effects on zip code level (Wooldridge, 2002) such as purchasing

power, population density, and grid fees (which vary across regions and account for a

significant cost component in Germany), leading to following model specification:

𝑌𝑖𝑗 = 𝛽0+ 𝛽1𝑅𝐺,𝑖𝑗

⏟

𝐼+ 𝛽2𝑅𝐸,𝑖

⏟

𝐼𝐼

⏟

𝐼𝐼𝐼𝑎

+ 𝛽3𝑅𝐺,𝑖𝑗𝑅𝐸,𝑖

⏟

𝐼𝐼𝐼𝑏

+ ∑ 𝛿𝑛𝑍𝑛

𝑁(𝑗)−1=467

𝑛=1 +𝑢𝑖𝑗

Chapter III

In this regression equation, Yij refers to the annual price with i and j as the indices for providers

and zip code areas. Whenever a provider offers an electricity plan in its own region (geographic

regionality), the binary variable RG,ij is 1, otherwise it is 0. For the assessment of the mark-up

for entrepreneurial regionality, we include RE,i which captures whether provider i exhibits

entrepreneurial regionality (=1) or not (=0). The model allows for the interaction of RG and RE.

Accordingly, coefficients represent mark-up for geographic regionality (β1), mark-up for

entrepreneurial regionality (β2), and the interaction effect (β3). The coefficients (δ) and binary

variables for zip code areas (Z) capture regional effects, while uij captures the residual. We

performed F-tests to decide whether to prefer the panel model over ordinary least squares

(OLS), which is the case for all model specifications (p<.001).

FIGURE 8. FREQUENCY DISTRIBUTION OF OBSERVATION GROUPS

Results

The Value of Geographic Regionality (H1)

We assess the value of geographic regionality by the price mark-ups for offers by providers

headquartered in the same area as the consumer. Table 2 summarizes results of the regression

analysis. Our findings confirm our initial hypothesis that consumers value geographic

regionality (H1). We identify a mark-up of around 20€ regardless of controlling for

entrepreneurial regionality (Model IIIa, β1= €20.4, p<.001) or not (Model I, β1= €19.9,

p<.001). Effect size analysis delivers a Cohen’s d of 0.36 which is in the range between small

(0.2) and a medium (0.5) sized effect. On the first look, this effect might not appear as a major

influence but considering the market’s typical profit margins of one to three percent

(Dringenberg, 2020), such relatively small mark-ups already have meaningful effects on

profitability. Accordingly, a mark-up of around 20 € (β1) on the base price of roughly 690 €

(β0) would at least double the regional provider’s profit margin.

Chapter III

TABLE 2. REGRESSION RESULTS

DV: Price (EUR), n=22,890

IIIa

IIIb

Constant (β0)

692.1***

692.5***

692.6***

(14.49)

(14.50)

Geographic Regionality (β1)

19.94***

20.44***

-2.47

(4.09)

(4.10)

(8.17)

Entrepreneurial Regionality (β2)

-1.122

-1.381+

-1.590*

(0.76)

(0.77)

Interaction (β3)

30.58**

(9.43)

Zip Code Fixed Effects

yes

19.3***

19.2***

Adj. R2

0.272

0.271

0.272

Note: *** p<.001; ** p<.01; * p<.05; + p<0.1; DV = Dependent variable

The Value of Entrepreneurial Regionality (H2)

For the assessment of consumers’ valuation of entrepreneurial regionality, we obtain a Cohen’s

d of 0.02 and no (or only weakly) significant coefficients in the regression models (Model II:

β2= €-1.12, n.s.; Model IIIa: β2= €-1.38, p<.01). This suggests this aspect on its own is

negligible both statistically and economically.

Interaction of the two Interpretations of Regionality (H3)

Considering the interaction of geographic and entrepreneurial regionality, the interaction

model (IIIb) adds a relevant perspective. When adding the interaction term, both β1 and β2

(denoting the effects of geographic/ entrepreneurial regionality in the absence of the

respective other) are either statistically insignificant (β1= €-2.47, n.s.) or economically

irrelevant (β2= €-1.59, p<.05) while the interaction between them is even larger than the

individual mark-ups in the previous models (β3= €30.6, p<.01).

In essence, these findings suggest that household consumers indeed value regionality in the

electricity market under the condition that the provider exhibits both geographic and

entrepreneurial regionality (Figure 9).

FIGURE 9. OVERALL PRICE ESTIMATES (ERROR BARS INDICATE STANDARD ERRORS)

Chapter III

The absolute price estimate fluctuates noticeably across zip code areas as outlined in Figure 10

(displaying estimates and standard errors (SE)), justifying the inclusion of zip code dummies

into the models. For robustness, we have tested further control variables such as share of

renewable electricity in the offered products and providers’ user ratings. This does not alter

the coefficients in terms of magnitude, sign, or significance and has a negligible effect on the

R2 values.

FIGURE 10. DUMMY EFFECT ESTIMATES AND SE ACROSS ZIP CODES

Discussion and Concluding Remarks

Key Findings

Our analysis provides some indication that consumers indeed value regionality. More

precisely, they are willing to pay price mark-ups for regional products and services when

purchasing through digital user interfaces. In particular, this preference pertains to the

geographic interpretation of regionality, that is, proximity of consumer and provider (H1). In

contrast, we did not identify consumer preferences for entrepreneurial regionality per se (H2).

However, the two types of regionality interact in the sense that only geographically regional

providers with entrepreneurial regionality are able to achieve mark-ups. Apparently,

consumers value providers as long as they are both 1) based in close proximity to consumers

(geographic regionality) and 2) strongly connected with and within the region (entrepreneurial

regionality). Accordingly, consumers seem to value providers which are located in, owned by,

operationally focused on, and tied to their region. To further explore these findings, we sought

out conversations with municipal utility employees and industry experts. All interviewees

assured that providers indeed consider regionality as a key product characteristic that affects

their pricing strategies.

Practical Implications for Design of User Interfaces

These findings carry implications for our above outlined research aim to provide practicable

and impactful research on the design of IS solutions to “support decision-making for more

sustainable practices” (Gholami et al., 2016, p. 527). Also, we answer calls for IS-driven

contributions fostering environmental sustainability (Gholami et al., 2016; Malhotra et al.,

2013) and impactful IS research to counter global warming (vom Brocke et al., 2013). In the

Chapter III

following, we describe practical implications for the design of two types of user interfaces –

direct sales channels (e.g., provider websites) and platforms (e.g., comparison platforms).

For user interfaces with direct customer interaction (e.g., websites), our findings suggest that

providers should emphasize regionality when applicable – especially if they are attributed with

entrepreneurial regionality. In this context, regional trust cues provide a powerful means to

signal regionality which in turn increases trust in the provider and purchase intentions on user

interfaces (Menzel & Teubner, 2021d). In contrast to these insights, recent studies of regional

energy provider websites unveiled that merely 25% of the providers used pictorial (Menzel &

Teubner, 2021b) and only 33% applied textual cues to promote regionality of their products

(Menzel & Teubner, 2021e). This is little surprising as those providers often lack the skill or

resources to properly optimize their interfaces to the customer. According to our findings,

there is a massive optimization opportunity for these entities. Moreover, the study results

indicate that the potential of using regional trust cues on user interfaces heavily depends on

the geographic position of the user. If a request to a provider’s user interface is issued from an

area in which this provider is based, regional cues should affect consumer valuation to a larger

degree than otherwise. This raises the questions of how to tailor the design of user interfaces

in view of user location. Most critically, providers need to capture the users’ geographic

location in order to adjust their interface design accordingly. Identifying the IP address, the

use of cookies, or the processing of user profile and transactional data could be routes to

further explore in this regard.

For user interfaces of platform business models (e.g., comparison platforms), our study yields

design insights for platform operators to enhance user experience. As per our findings, users

prefer regional products and services over non-regional offers. Therefore, providing

information on the regionality of offers to consumers could substantiate a competitive

advantage over other platforms. To support the decision-making of regionality-aware

consumers, multiple design elements are conceivable:

• Filters: Platform operators may build filters to sort offers by regional and non-regional

providers. Note that users are prompted to provide their zip code on all major comparison

platforms which makes dealing with/ filtering by geographic regionality an easy fix.

Moreover, platform operators could also assess entrepreneurial regionality and offer filters

for this property. Most importantly, a combination of filters should be feasible as our

findings suggest an interaction of both effects.

• Icons/Labels: In similar fashion, platforms could implement icons or labels to signal the

above described attributes to consumers.

• Text/Pictorial Cues: Implementing slots on the platform in which providers can outline

their (products’) regionality via text or images will further enhance the user experience.

Note that these design elements not only improve user experience, they could also develop new

income streams for platform operators. Platform operators could skim off some of the mark-

ups generated by regional offers through providing sellers the possibility to purchase regional

icons, labels, space for regional messaging, or the appearance in certain filters.

Since the beginning of this research project, we have observed how several platforms have

implemented some of the above-mentioned measures. Verivox, for instance, has introduced a

filter for geographic regionality. When applied, only providers based within a 100 km range

Chapter III

from the user’s address are displayed. Also, the platform created a label to highlight providers

with entrepreneurial regionality that needs to be purchased by the seller.

Theoretical Implications

In terms of theoretical implications, Consumer Ethnocentrism assumes consumer preferences

based on geographic match of consumers with product origin. We offer a new interpretation

of the in-group, in other words the set of entities “with which a person identifies” (Shimp &

Sharma, 1987, p. 280), in the sense that the entrepreneurial regionality of a provider may serve

as an identity-establishing feature as well. Referring back to the farmer’s market example, this

would mean that consumers go and purchase there not only because they consider the farmer

someone from here but also one of us. This perspective appears reasonable in view of the

evolutionary psychological roots of this theory according to which cohesion of and solidarity

within a (geographically bounded) social group was critical to survival (van den Berghe, 1981).

Our findings suggest that the notion of “us versus them” (Klein, 2002, p. 1) not only applies in

the geographic context but also in the sense of common people (which includes small,

regionally focused businesses) against the large corporates. Nevertheless, following this

study’s results, this entrepreneurial interpretation of regionality does not exist on its own and

necessarily needs to be considered in the geographic context.

Implications for Power Sector Sustainabilization

While we chose electricity as subject of this study mainly for its properties (homogenous good,

transported in networks, etc.), the power sector also features a pressing need for solutions to

support consumers in pro-regional decision-making. With the aim of carbon neutrality, the

sector is currently undergoing a drastic transformation in terms of decarbonization,

digitization, and decentralization (di Silvestre et al., 2018). Our findings could support this

transition in two ways. First, local utilities (i.e., companies with high entrepreneurial

regionality) are considered key drivers for the transition of the power sector in their region

(Berlo & Wagner, 2011), because they operate decentral renewable generation units such as

solar and wind parks, manage heat district concepts, provide energy management solutions

(Richter, 2013), and organize local energy markets (Weinhardt et al., 2019). Therefore,

nudging consumers into the direction of these companies will accelerate the sector’s

sustainabilization. Second, the sectors sustainabilization could be substantially accelerated by

its ongoing platformization (Menzel & Teubner, 2021c). However, well-designed user

interfaces are a perquisite for the swift adoption of platform business models. We suggest that

insights gained here on comparison platforms can (with some constraints) be brought to good

use also for other platform types in the energy sector and hence contribute substantially to the

sectors’ overall platformization and, in turn, sustainabilization.

Limitations and Work in Progress

Alike any study, this one is not without limitations. First, the hedonic pricing regression builds

on the assumption that consumer preferences are reflected in providers’ pricing strategies

which may only partly be true in many cases. To strengthen our analyses, we plan to expand

our data model by adding secondary data in the spatial dimension (e.g., purchase power,

population density, grid fees, etc.) and on company level (e.g., ownership structure, employees,

turnover, etc.). Further, we seek to validate this assumption through collaboration with a

comparison platform and analysis of actual purchase data (e.g., click rates, conversion rates)

Chapter III

rather than just pricing data. Second, we acknowledge that data clusters of geographic

regionality are comparatively small. To some degree this is unavoidable, as each provider has

only one home region but is free to offer in all other territories. Still, we are in the process of

geocoding provider and customer locations which will enable the measuring of geographic

regionality as a continuous variable and allow for more fine-tuned, assessments. Third,

regional providers do not necessarily generate electricity in their region – even though this is

increasingly the case for green technology such as wind, solar, or biomass. Accounting for this

aspect, for instance, through adding the share of regionally produced energy into the model,

will further strengthen the link between consumers’ valuation of regional energy providers and

the energy sector’s sustainabilization.

Chapter IV

Chapter IV: A Descriptive Analysis of

Regional Trust Cues on User Interfaces

This section assesses how energy providers attempt to trigger the consumer preference for

regionality described in the previous chapter. I provide a review of >450 German energy

provider websites illustrating their use of regional, social and environmental trust cues. The

study provides insights into the practical use of regional trust cues and suggest that such cues

in form of text and imagery play a pivotal role in user interface design (in particular, for

providers with regional ties).

Tobias Menzel, Timm Teubner

Introduction

Climate change is a global phenomenon with implications on a local level (Borowski, 2020a).

Roughly a decade ago, leading scholars in the information systems (IS) community initiated

the research field of Green IS to identify solutions to mitigate climate change driven by

information and communications technology (ICT) (Dedrick, 2010; Malhotra et al., 2013;

Melville, 2010; Watson et al., 2010). More recently, Koliouska and Andreopoulou suggested in

this journal that “all sustainable objectives and targets need ICTs as key catalysts” (Koliouska

& Andreopoulou, 2020, p. 4869). Gholami et al. pointed out that “too few information systems

[…] academics engage in impactful research that offers solutions to global warming despite the

fact that climate change is one of the most critical challenges facing this generation” (Gholami

et al., 2016, p. 521). Most importantly, the community needs to deliver practical and

implementable research results that go beyond theory (vom Brocke et al., 2013).

For the energy sector, this translates into the question of how ICT can contribute to the sector’s

transition to climate neutrality (Goebel et al., 2014; Watson et al., 2010). This transition will

require fundamental disruptive shifts towards decarbonization, decentralization, and

digitalization (di Silvestre et al., 2018). While considerable IS contributions have been

provided in recent years on the matters of decarbonization and digitalization of the energy

sector (Goebel et al., 2014), research on the sector’s decentralization has been less emphasized.

Therefore, we set out to contribute to the Green IS debate on the subject of decentralization of

the energy sector with the aim of providing impactful and practical results.

So far, IS research on the decentralization of the energy sector has mainly focused on the

supply of energy (e.g., virtual power plants, decentral generation) or the transmission of

This chapter was published in Sustainability with the title “How Regional Trust Cues Could Drive

Decentralisation in the Energy Sector—An Exploratory Approach”,

https://doi.org/10.3390/su13063010, This article is an open access article distributed under the terms

and conditions of the Creative Commons Attribution (CC BY) license

(https://creativecommons.org/licenses/by/4.0/).

Preliminary results of Study 1 in this chapter were published as short paper in the proceedings of

Internationale Tagung Wirtschaftsinformatik 2021 with the title “Buy Online, Trust Local – The Use

of Regional Imagery on Web Interfaces and its Effect on User Behavior”,

aisel.aisnet.org/wi2021/PHuman/Track11/1

Funding: I acknowledge support by the German Research Foundation and the Open Access

Publication Fund of TU Berlin, who funded this article’s processing charges.

Acknowledgments: I thank my student Thanh Ngo Chi for his support in coding the imagery.

Chapter IV

energy (e.g., smart grids). In contrast, we focus on the demand side and emphasize consumer

decision-making. Gholami et al. suggest that the design of solutions which “support decision-

making for more sustainable practices” (Gholami et al., 2016, p. 527) is an area of research

where IS can contribute insights to drive environmental sustainability. In the context of

decentralization in the energy sector, this translates into the question as to how consumers can

be supported in making decisions in favor of decentral – or, in other words regional – energy.

The role of consumers in the energy sector is drastically changing and gaining importance

(Borowski, 2020b) as they are evolving from passive consumers to “active energy citizens”

(Campos et al., 2020, p. 1) – also known as prosumers. Therefore, user-centricity is essential

when designing solutions for the future energy sector (Immonen et al., 2020). Today, in

liberalized energy markets the majority of consumer energy plans are sold – and hence,

decisions are made – via digital sales channels (Dringenberg, 2020; YouGov, 2015). We

therefore define energy provider websites as the subject of our study.

When exploring regional energy provider websites, we noticed that these providers frequently

use regional textual and pictorial cues (in addition to social and nature cues). An example is

provided in Figure 11, which shows a regional energy provider website with a cityscape image

of a town within the provider’s geographic area of operation. In IS and marketing literature,

the use of social cues (e.g., Gefen & Straub, 2004) and nature cues (e.g., Schmuck et al., 2018)

is well established. However, regional cues have to our knowledge not been subject to research

in the energy sector and in online contexts. Therefore, the study takes an exploratory approach

to shed light on the “surprisingly understudied topic of regionality” (Herz & Diamantopoulos,

2019, p. 44). Our aim is to increase understanding of the use of regional imagery and text cues

on energy provider websites, systematically capturing this new phenomenon in IS research

(Trauth, 2001), and identifying implications for the design of IS solutions to support decision-

making in favor of regional energy. We employ the well-established (Rourke & Anderson,

2004) method of content analysis to address this objective and lay the foundation for future

quantitative research (Kruse & Lenger, 2014) such as experiments on the behavioral effects of

regional cues. This research promises both impactful and practical results thanks to the high

usage of these websites and our desire to provide implementable suggestions.

Chapter IV

FIGURE 11. EXAMPLE OF WEBSITE WITH REGIONAL CUES IN THE FORM OF IMAGERY AND TEXT13

For a theoretical framework, we draw on social presence theory (Short et al., 1976), biophilia

hypothesis (Wilson, 1984), and consumer ethnocentrism (Shimp & Sharma, 1987) to develop

the construct of regional presence based on the established construct of social presence. The

use of imagery and text as trust cues to affect consumer behavior is well established in

marketing and IS literature (e.g., Gefen & Straub, 2004; Hassanein & Head, 2005; M. Kim &

Lennon, 2008). IS and marketing research has primarily focused on social cues to generate

consumer trust and trigger purchase intentions. Social cues on websites generate the

perception of “personal, sociable, and sensitive human contact” (Gefen & Straub, 2004, p.

410), when, in fact, looking at a website on a screen is characterized by a lack of such contact.

For an explanation of this effect, we need to go far back in the evolution of humankind: social

cues trigger an evolutionary psychological pattern according to which humans increase their

chances of survival through collaboration with other humans (K. Lee, 2004; Riva et al., 2015).

In other words, as claimed by Aristotle, humans are social animals (Barker, 1968). More

recently, similar effects have been attested to using nature cues (Schmuck et al., 2018). In

similarity to social presence, nature cues generate a perception of the natural environment in

the absence of real nature. This again triggers an evolutionary psychological pattern because

natural surroundings were critical for human survival as a source of water and nutrition and

also provided “security and defense advantages” (Ulrich, 1993, p. 19). The biophilia hypothesis

is a theory with origins in biology and claims that humans are endowed with an affinity to

nature (Wilson, 1984). It explains this behavioral pattern as an “urge to affiliate with nature”

Source: Stadtwerke Oberkirch, available online: https://www.stadtwerke-oberkirch.de/ (accessed on

16 February 2021).

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(Wilson, 1984, p. 85). The perceived experience of nature reduces stress (Ulrich, 1993) and

restores attention (Kaplan & Kaplan, 1989) which ultimately turns into positive brand attitude

and purchase intention (Schmuck et al., 2018). In a similar fashion, we explain the

functionality of regional cues using evolutionary psychology. In the evolutionary logic, human

survival depended heavily on a cohesive social group (van den Berghe, 1981). Following

consumer ethnocentrism theory (Shimp & Sharma, 1987), this translates into ethnocentric

behavior on a regional or national level in contemporary consumer decisions. Accordingly,

consumers prefer to buy from their ingroup, in other words a (geographically bounded) set of

people with which “an individual identifies” (Shimp & Sharma, 1987, p. 280). Buying from the

outgroup “is wrong because […] it hurts the domestic economy, causes loss of jobs, and is

plainly unpatriotic” (Shimp & Sharma, 1987, p. 280). Based on the similarities with the social

presence construct (in other words, the perception of social contact in a human-free setting),

we use analogous phrasing for the perception of regionality in an online context that is in fact

considered a means of overcoming geographic boundaries (Forman & van Zeebroeck, 2018).

Regional presence can therefore be understood as the sensation of regionality in a set-up

characterized by geographic independence. Figure 12 provides a website example in which

icons highlight pictorial cues to trigger perceptions of social contact, nature experience, and

regionality.

FIGURE 12. EXAMPLE OF A WEBSITE WITH REGIONAL, SOCIAL, AND NATURE CUES14

The first step in capturing a new phenomenon involves both demonstrating its existence and

classifying its major constituent parts (Vartiainen et al., 2011). We will therefore put our initial

observation on an empirical footing by assessing a sample of energy provider websites.

Research Question 1 (RQ1) looks at whether these providers systematically employ regional

cues on their websites. And, if this is the case, how providers apply these cues – in other words,

what image motifs and text keywords are used. Because we expect energy providers with a

regional operational focus to use regional cues more often, this group is the focus of this first

analysis. Hence, our first research question is:

Source: Stadtwerke Görlitz, available online: https://www.stadtwerke-goerlitz.de/privatkunden/

(accessed on 5 November 2020).

Chapter IV

RQ1: Do regional providers systematically apply regional cues on their websites? If so, how

are they doing it (i.e., what types of regional imagery and text cues are used)?

We address this question through qualitative content analysis of 318 regional energy provider

websites to explore the use of regional, social and nature cues in a real-life e-commerce use

case. We identified the German household electricity market as a compelling object of study

since this market is highly fragmented and therefore offers a large sample of companies. In

addition, due to the market’s genesis, it features an abundance of regional providers whose

operations focus is a particular city or region (Stadtwerke). Nevertheless, these providers have

been able to sell energy nationwide since market liberalization in 1998, so their websites target

a national audience.

Our analysis addressing RQ1 shows that almost half of the assessed 318 regional energy

providers apply regional cues in the form of either text or images on their websites. Primary

motifs are cityscapes and buildings, while frequently used textual cues include direct

references to a city or region and the terms “region” and “regional”. This frequency suggests

but does not prove an intentional use of regional imagery and text by regional providers. To

validate these findings and provide empirical evidence for this intentionality, we adjust our

research design and analyze a second set of 136 provider websites including both regional and

national providers. In order to support the hypothesis that regional providers intentionally

employ regional cues on their website to outline their offering’s regionality, we must identify

a significant discrepancy in the use of regional cues between the two groups. Therefore, our

second research question is:

RQ2: Do regional providers apply regional cues structurally more often than national

providers?

Addressing this question, we examine 136 regional and national energy provider websites by

means of quantitative content analysis. We find a significantly higher use of regional text and

imagery cues by regional providers through a set of Chi-squared tests. Also, national providers

tend to use more nature imagery as well as price and quality text cues. However, discrepancies

are not statistically significant.

In addition to these findings, the previously described analysis also provided us with an

interesting case study about regionality on user interfaces. Since the use of regional cues is still

quite a new phenomenon in the online context, analyzing trends may increase our

understanding of the direction in which the industry is evolving (Yazdanifard et al., 2011) and

provide promising avenues for further research (Watson IV et al., 2015). We therefore

conclude this paper with an emerging trend and our third and final research question is:

RQ3: What is the next trend in the application of regional cues on user interfaces?

To study this question, we describe how a provider tailors its web interface content, including

regional imagery and text, to the user’s geographic location. This provider appears to possess

the tools to trace the location of users when they submit a request to the website and uses this

information to adjust the website’s imagery and text elements. Apparently, the provider

Chapter IV

expects that the application of regionality cues positively effects user behavior and provides a

compelling template for the application of geo-specific regional references.

The remainder of the paper is structured as follows: Section 2 discusses materials and methods

of the three different studies on this matter, while Section 3 outlines their results. In Section

4, we discuss the findings and implications for theorists, practitioners, and consumers.

Limitations and future work are discussed in Section 5. Section 6 concludes.

Materials and Methods

As described above, we performed one study for each research question. In each of the three

studies, textual and pictorial cues were assessed as outlined in Table 3. Study 1 performs a

qualitative content analysis of 318 regional energy provider websites to analyze the use of text

and image cues on those websites. In Study 2, we enrich the qualitative investigation with an

empirical analysis to test for structural differences in the use of said cues between 65 regional

and 71 national providers. We conclude with a case study highlighting the latest trends

concerning the use of regional textual and pictorial cues on energy provider websites in Study

3. Using a mixed-method approach provides a greater flexibility in undertaking research and

promises better-supported arguments (Borowski, 2021).

TABLE 3. SUMMARY OF RESEARCH DESIGNS FOR RQ1 TO 3

Study 1

Study 2

Study 3

Research Question

RQ1

RQ2

RQ3

Method

Qualitative content

analysis

Quantitative content

analysis

Case Study

Sample

318 regional energy

providers

136 energy providers

(65 regional, 71

national)

1 national energy

provider

Study 1

In examining RQ1, we shortlisted 318 regional energy providers from an online resource

(Stadtwerke in Deutschland, 2020) by selecting corporations (“AG” or “GmbH”) referring to

themselves as regional utilities (“Stadtwerke”). Next, we devised a web-scraper to take

screenshots of all 318 landing pages (Step 0 in Figure 13 and Figure 14). We analysed the

content of these screenshots with different approaches for pictorial and textual cues as

described below.

Imagery Analysis. We draw on Bell (2001), Callahan (2006), Xi et al. (2007), and Vilnai-

Yavetz and Tifferet (2013) to develop the approach illustrated in Figure 13.

Chapter IV

FIGURE 13. METHODOLOGY FOR ANALYSIS OF IMAGERY CUES

• Step 1: Define variables and values: in the first step, it was critical to define explicit and

unambiguous categories in order to yield meaningful evidence (Bell, 2001). In our case, we

utilized the three concepts regional, social, and nature and used a binary scale.

• Step 2: Two researchers individually coded the website screenshots following the

variables and values defined in step 1 (example provided in Figure 12). We calculated

Cohen’s kappa (Cohen, 1960) to measure inter-rater reliability. With kappa of 0.74 for

regional, 0.75 for social, and 0.68 for nature cues, the inter-rater reliability is in the range

of substantial agreement (Viera & Garrett, 2005). We therefore conclude that the coding

provides reliable data, and we can proceed with the analysis.

• Step 3: Conflicting cases were resolved by a third researcher.

• Step 4: Coding results were aggregated and visualized.

• Step 5: For imagery classified as regional, we added two layers of detail by classifying

them with regard to their content (e.g., riverside cityscape, church, fountain, sports event,

etc.) and structured these subclassifications into the following clusters: cityscapes,

buildings, monuments and bridges.

Textual Analysis. Concerning textual cues, we drew on Braun and Clarke (2006) and

applied their approach for thematic analysis to our context. The approach is outlined in Figure

14.

FIGURE 14. METHODOLOGY FOR ANALYSIS OF TEXTUAL CUES

• Step 1: The textual analysis involved transcribing each website’s tag lines. We therefore

focused on the three main messages: headline, subtitle, and company slogan. An example

is provided in Figure 15. These text fragments were cleansed of non-contextual words.

• Step 2: In the next step, we grouped together words from the same word families (e.g.,

nature, natural) and with similar meaning (e.g., cheap, low-cost, affordable). We also

excluded greetings (e.g., “hello”, “welcome”) and news items (e.g., “information center

closed during holidays”). Further, we introduced a placeholder (“[city name]”) where

providers referred to a specific city; this enabled us to track this effect as a pattern.

• Step 3: Building on the results of imagery analysis and related theory, we started off by

grouping keywords into social, nature, and regional cues and a general category.

Chapter IV

• Step 4: When reviewing step 3, we realized that the remaining keywords could be further

grouped and so created two additional clusters for price and quality.

• Step 5: After another round of reviewing keywords, we defined our final set of themes

consisting of social, nature, and regional cues as well as price, quality, and a general theme.

• Step 6: For reporting, we aggregated and visualized the findings in a similar way to the

approach for imagery. In addition, we generated bar charts with the most frequent key

words.

FIGURE 15. EXAMPLE OF A WEBSITE WHERE TEXTUAL AREAS FOR TRANSCRIPTION ARE

HIGHLIGHTED15

Study 2

In addressing RQ2, we generated a set of 136 providers by web-scraping a price comparison

portal. Based on how they described themselves (e.g., Stadtwerke, similar to Study 1) and their

operational focus, this list was divided into 65 regional and 71 national energy providers. We

chose this approach over just expanding the list used in Study 1 for two reasons: focusing on

the analysis of providers with sales presence on a comparison portal ensures a certain level of

digital savviness of all (and in particular the regional) providers. This would ensure that the

selected providers are comparable and hence increase the robustness of the findings. Secondly,

using a price comparison portal as a source ensures that all relevant national providers would

be included. As these portals represent a significant share of all newly signed household energy

contracts (YouGov, 2015), this is one of the largest sales channels for energy providers and

hence should attract all major players. While national providers might not use specific regional

cues (e.g., a specific city name) they can still apply unspecific regional cues such as “regional”,

“for your city”, etc.

Source: Stadtwerke Kierspe, available online: https://stadtwerke-kierspe.de/ (accessed on 5

November 2020).

Chapter IV

In a similar way to the procedure in Study 1, we devised a web-scraper to take screenshots of

all 136 provider websites. For imagery, we repeated steps 0 to 4 as described in Study 1. Again,

inter-rater reliability for regional cues was in the range of substantial agreement (kappa =

0.74). For social cues (0.94) and nature cues (0.84), it was in the range of almost perfect

agreement (Viera & Garrett, 2005). Textual analysis followed the same steps as described in

Study 1 with transcription, cleansing, grouping and classification. Further, we analysed

whether each category’s frequency was statistically independent from the provider type

(regional vs. national) by means of Chi-squared tests in R. The Chi-squared test evaluates the

hypothesis (H0) that the frequency in which regional (and other) cues are used is independent

from the provider type. Put in simple terms, H0 claims that there is no relationship between

frequency of cues and provider type. Accordingly, rejecting this hypothesis by means of the

Chi-squared test provides empirical evidence that the discrepancies in the use of regional (and

other) cues is driven by the provider type (H1).

Study 3

Regarding RQ3, we provide a case study on the Greenpeace Energy landing page. We explain

how a national provider is using information on the geographic origin of a request to tailor

textual and pictorial cues to the user’s region. This case study provides a fascinating

perspective on the use of regional cues on websites.

Results

Study 1

Addressing RQ1, we assessed 318 regional energy provider websites by analysing imagery and

text cues used on those sites. This provides insights how frequently regional providers use

regional (and other) cues and offers insights on applied keywords and motifs.

Study 1a: Imagery. As displayed in Figure 16, the majority of the energy providers evaluated

(215 of 318) employ at least one of the three constructs. Social imagery is the dominant cue

(125) but regional (78) and nature (69) imagery are often used. The most frequent combination

of cues is regional and nature imagery (27), while only three websites embed imagery

combining all three concepts.

Chapter IV

FIGURE 16. CLASSIFICATION OF IMAGERY CUES

Taking a closer look at regional imagery, we identify four clusters of visual motifs (examples

provided in Figure 17). Used in more than half of cases (44 out of 78), cityscape is the dominant

cluster. Less frequently, providers display buildings (15), monuments (13) and cultural events

(6) on their websites.

FIGURE 17. WEBSITE EXAMPLES ILLUSTRATING TYPES OF REGIONAL MOTIFS16

Sources: Stadtwerke Neckargemünd, available online: https://www.stadtwerke-neckargemuend.de/

(accessed on 5 November 2020); Westfalica Stadtwerke, available online:

https://www.westfalica.de/privatkunden (accessed on 5 November 2020); Stadtwerke München,

available online: https://www.swm.de/ (accessed on 5 November 2020); Leipziger Stadtwerke,

available online: https://www.l.de/stadtwerke/# (accessed on 5 November 2020).

Chapter IV

Within these clusters, there is an additional layer of classification for the main motifs used as

a regional cue (frequencies provided in Figure 18). The cityscape concept is mainly represented

by aerial photographs of a certain city (25). But riverside panoramas (8) and snapshots of

market squares (7) are also frequently used. Providers use churches (4) and secular historic

buildings (9) like palaces and castles. The monument cluster comprises towers (5), statues (3),

fountains (3), and bridges (2), while culture cues feature either sports (3) or cultural events (3,

e.g., concerts, carnival parades). Additional website screenshots with examples for each

regional motif are provided in the Appendix.

FIGURE 18. SUBCLASSIFICATION OF REGIONAL CUES

Study 1b: Text. Continuing the analysis with a focus on textual cues, we observe an even

higher frequency of regional cues (105) on energy provider websites. As shown in Figure 19,

almost every third provider applies a textual reference to a city or region on their web interface.

In contrast, social (82) and nature (54) cues are less frequently used. The most frequent

combination of cues in this context is regional with social keywords (31). We further note that

the overall number of providers to use either regional, social, or nature cues or a combination

in textual form (174) is lower than the overall number using imagery across those categories

(215).

Chapter IV

FIGURE 19. CLASSIFICATION OF TEXTUAL CUES

The fact that less providers use regional, social, and nature cues in textual form compared to

imagery might also be explained by the additional possibilities available for text. Alongside

regional, social, and nature cues, we introduce price and quality as additional categories in

textual analysis. The bar chart in Figure 20 illustrates the 20 most frequent keywords with

icons highlighting these five categories. Unsurprisingly, the most often used words are general

keywords such as the product sold (“energy”: 97 websites; “electricity”: 49; “gas”: 24) and the

provider’s name (one of the selection criteria for this list of providers was the use of

“Stadtwerke” – which means “regional utility”– in the company name). Outside the general

category, the most frequent keywords are the regional cues “region”/“regional” (35) and a

reference to a particular city or region (27). Also frequently mentioned are two keywords with

social cues (“for you”/”there for you”: 21; “care”, 21) and one with nature affiliation

(“eco”/“ecological”: 17). The most frequent keyword in the price category is “price”/“price

stability”, (14) while the most frequently used word connoting quality is the term

“simple”/“easy” (8, not on the chart).

Chapter IV

FIGURE 20. TOP-20 MOST FREQUENT KEYWORDS FOR STUDY 1 PROVIDERS

Study 2

With focus on RQ2, we assessed 65 regional and 71 national energy provider websites to analyse

structural differences in the use of regional (and other) trust cues between these provider

types. Focusing first on discrepancies in textual cues, Figure 21 shows the relative frequency of

keywords used on regional and national provider websites. Notably, regional providers use

regional cues most frequently (e.g., a reference to a particular city or region: 25%;

“region”/“regional”: 14%), while national providers tend to emphasize price competitiveness

in their messaging (e.g., “low-cost”: 20%; “fair”: 9%; “switch”: 9%). When using regional cues,

regional companies specifically refer to their region (e.g., using the name of their city), while

national providers tend to use much more unspecific terms (e.g., “at home”, “neighbour”,

“region”/“regional”). Both provider types embed social and nature cues with similar frequency

and employ similar keywords. In fact, the most frequently used keyword in each category is

identical for regional and national providers (“there for you”/“for you” appearing on 8% of

regional provider websites and 9% of national provider websites; “eco”/“ecological” appearing

on 17% and 13% of websites respectively).

Chapter IV

FIGURE 21. TOP-12 MOST FREQUENT KEYWORDS FOR STUDY 2 PROVIDERS

The empirical analysis of text cues supports these findings (Figure 22). However, only the

discrepancy in regional cues is statistically significant (p < 0.01): while almost half of the

regional providers (48%) apply regional text cues on their websites, only 7% of national

providers do so. Although the use of price and quality text cues is considerably lower on

regional provider sites (price: 22%; quality: 15%) than on national provider websites (price:

35% of websites; quality: 25%), the test statistic fails to reject independence of price (p=0.12)

and quality (p=0.22) from the provider type.

Chapter IV

FIGURE 22. COMPARISON OF RELATIVE FREQUENCY OF IMAGERY AND TEXTUAL CUES

The analysis provides similar results for imagery. Discrepancies in the use of regional cues by

regional (31%) compared to national providers (4%) are statistically significant (p<0.01) and

practically relevant. Social cues are used in similar frequencies (46% regional; 48% national).

Interestingly, only a third (34%) of regional providers employs nature cues in their imagery

versus half of the national providers (49%), which is statistically speaking on the edge of

significance (p=0.099).

Study 3

Regarding RQ3, our objective of this study is to highlight upcoming trends in the use of regional

trust cues on energy provider websites. During our analysis in Study 2, we noticed that one of

the national providers used regional and text cues that were very specific to the researchers’

city of residence. This caught our attention because it is counterintuitive to our earlier findings.

A national provider using regional and text material for a very particular region would limit

the effect of those regional cues to that one specific area in Germany. However, their

operations are nationwide. We therefore reached out to a network of researchers in different

German cities requesting them to go to this providers’ website and send us a screenshot.

Interestingly, each screenshot contained imagery and textual cues referring to the user’s

particular city of origin (see examples in Figure 23). This provider appears to be adjusting the

website according to user’s geographic location (e.g., by locating the IP address). While the

overall layout is identical, the background image as well as text fragments within the headline

are tailored to the city of origin. For instance, when accessing the website from a Berlin-based

internet connection, users see a cityscape image of Berlin with the headline “Good news from

the Spree. Berlin goes green”, while a website visitor from Hamburg is met with a Hamburg

cityscape with the message “Good news from the Elbe. Hamburg goes green” (the Spree and

the Elbe are the main rivers in Berlin and Hamburg respectively). We did not identify a similar

Chapter IV

provider strategy on any other websites analysed in Studies 1 and 2. We therefore conclude

that this seems to be a rather innovative approach which, however, may motivate other

providers to follow suit in the near future.

FIGURE 23. WEBSITE EXAMPLES WITH REGIONAL CUES BASED ON LOCATION OF WEB REQUEST17

Discussion

Key Findings

In response to RQ1, we find in Study 1 that almost half of the regional energy providers (47%)

use regional cues in imagery and/or text form on their websites. The fact that the use of

regional cues is so frequent suggests that it is an intentional strategy on the part of providers

to influence consumer behaviour. When using regional imagery (25%), we observe a variety of

different motifs, with a slight tendency towards cityscapes (in particular from an aerial

perspective). A consensus among practitioners on the types of image that best promotes the

desired behaviour appears not to have emerged as yet; this is an area where this study could

provide useful insights. Regional providers reference regionality at an even higher rate when

it comes to textual cues: one in every three websites (33%) features regional text cues. The

most frequent keywords are direct references to a city or region and the use of the term

“region”/“regional”.

Our findings in Study 2 validate these results and also provide empirical evidence that regional

providers intentionally employ regional cues on their websites. The relative frequency of

Source: Greenpeace Energy, available online: https://www.greenpeace-

energy.de/privatkunden.html (accessed on 5 November 2020).

Chapter IV

regional cues is considerably higher on regional provider websites compared to national

provider web interfaces for text (48% vs. 7%; p<0.01) and imagery cues (31% vs. 4%; p<0.01)

(RQ2). Therefore, we conclude that regional providers apply these cues intentionally, expecting

positive effects on user behaviour such as trust in providers and purchase decisions.

Moreover, by comparing relative frequencies among the 318 providers in Study 1 (selected

based on how they describe themselves) and the 65 regional providers in Study 2 (selected

based on a listing on a price comparison platform), we gain insights into how providers’ digital

savviness may affect their awareness and use of regional cues. This builds on the assumption

that a listing on a comparison platform requires certain digital capabilities within the

company. We therefore assume that all regional providers in Study 2 have such capabilities at

their disposal. 60% of regional providers in Study 2 use either regional text or imagery (or

both), which is even higher than in Study 1 (47%). Text cues are most significant here (48% vs.

33%), but imagery cues are also frequent (31% vs. 25%). We hypothesize that providers with

better digital capabilities are either more often aware of the benefits of regional cues or more

often capable of implementing these cues on their websites.

Regarding RQ3, we identified a compelling case study demonstrating how providers are taking

the use of regional cues to the next level. National providers typically limit themselves to

generic regionality cues (e.g., the use of “regional”/“regionality”) when operating one website

for a nationwide audience. However, Greenpeace Energy has introduced location-specific

regional cues tailored to a regional audience. This provides further evidence for our

suggestions that providers believe regional cues have a positive effect on user behaviour.

Theoretical Contributions

By analysing the “surprisingly understudied topic of regionality” (Herz & Diamantopoulos,

2019) (p. 44) in the online context, our work has several implications for IS, energy and

marketing research.

First, it responds to calls from leading IS researchers and enriches the discussion on ICT-

driven solutions to counter climate change (Malhotra et al., 2013; Melville, 2010; Watson et

al., 2010). In particular, we provide tangible implications (vom Brocke et al., 2013) for the

design of user interfaces in the energy sector. By systematically capturing regional imagery

and text cues, we enrich the debate on trust cues in the energy sector as a means to “support

decision-making for more sustainable practices” (Gholami et al., 2016) (p. 527).

Second, while the effects of social cues (e.g., Gefen & Straub, 2004; Hassanein & Head, 2005)

and, to a lesser extent, nature cues (e.g., Schmuck et al., 2018) on trust and user behaviour are

commonly accepted in IS and marketing research, we argue that regional cues should be

included in future debates in the field. At first glance, the idea of using regional cues to

motivate users’ online purchase behaviour may seem somewhat counter-intuitive, because the

internet is often considered the “window to the world” (Hongladarom, 1999) (p. 400) and a

means of overcoming geographic boundaries (Forman & van Zeebroeck, 2018). However, our

observation of the frequent use of regional cues online suggests otherwise. By deriving the

concept of regional presence, we offer a new angle to understand ethnocentric consumer

behaviour when interacting with web interfaces. Consumer ethnocentrism theory suggests

that consumers prefer purchasing products or services from providers based in the same

Chapter IV

region as the consumer (Shimp & Sharma, 1987). We extend this perspective in the sense that

already the perception of regionality may cause said provider preference.

Third, we build on the knowledge of regional cues such as labels (Hu et al., 2012) and imagery

(Kneafsey & Ilbery, 2001) on packaging (Bruwer & Johnson, 2010) and offline print

advertising (Luceri et al., 2016) and expand on the discussion by (a) applying it to the online

context and (b) taking it beyond the food sector. Looking at electricity and gas offers new

perspectives compared to the marketing of food products. First, electricity and gas are

homogenous and credence goods. This allows us to control for potential confounding effects

based on product quality. For instance, consumer preference for regional strawberries (i.e.,

those produced in geographic proximity to the consumer) may be driven by freshness more

than anything else. In contrast, it is impossible for consumers to distinguish regional from

non-regional electricity or gas as they are physically identical at the point of consumption.

Second, electricity and gas are supplied through networks. This eliminates the transportation

cost effect since network fees are charged to consumers regardless of the product’s geographic

origin. For these two reasons, we assume that any observed user preferences for regional

products in this context are purely driven by the very idea of regionality.

Fourth, in terms of methodology we enhance the content analysis toolbox by combining two

quantitative approaches: using Chi-squared tests to assess statistical differences in the

frequency of motifs used on websites (e.g., Hamid, 2017) and analysing differences in the use

of website content across two provider types (e.g., Vilnai-Yavetz & Tifferet, 2013).

Practical Implications

Considering practical implications, our study yields new insights for the design of user

interfaces in the energy sector. The role of regional products and services is of particular

importance in the energy segment since decentralization is one of three major disruptions

(alongside digitization and decarbonization) that the industry needs to undergo in order to live

up to climate policy ambitions (di Silvestre et al., 2018). In the narrower context of provider

websites, a well-designed user interface is important for energy providers as their websites are

one of their major sales channels (Dringenberg, 2020). In a broader context, the rise of the

platform economy in the energy sector creates a need for trust-building user interfaces in the

coming years (Menzel & Teubner, 2021c). New platform technologies such as peer-to-peer

local energy markets (Weinhardt et al., 2019), plug-sharing platforms (Matzner et al., 2016),

and vehicle-to-grid solutions (Hoang et al., 2017) will become more important in the energy

sector and are projected to play a pivotal role in its decarbonization, digitization, and

decentralization (Menzel & Teubner, 2021c). However, for these new technologies to be

adopted quickly by consumers, trust-building user interfaces will be key (e.g., Hesse et al.,

2020) because trust in the (energy) provider is a critical driver for IS adoption (Ableitner et

al., 2020; Söllner et al., 2016; Stenner et al., 2017).

Regarding the implementation of regional cues on websites and other user interfaces, a new

challenge arises for operators. While social and nature cues can be generically applied, regional

cues need to be adjusted according to the physical location of the website user. For instance, a

picture of a scenic landscape will trigger similar effects for users in City A and City B, but a

picture of the market square in City A may not resonate equally well with users located in City

B. The provider must therefore determine how best to ascertain user location. We briefly

discuss the advantages and limitations of four approaches:

Chapter IV

• IP address: Potentially the simplest way is to use IP addresses, but this has significant

constraints. For instance, if the users employ VPN tunnels, their IP addresses and locations

are not aligned. In addition, in cases where users make purchases online while at work or

away from home, a regional cue based on the user’s IP address will produce flawed results.

• Cookies: Cookies might provide a more accurate estimate of geographic location, but they

are often limited by user privacy settings.

• User data provided during customer journey: A third option is to collect user data

directly. For instance, the shipping or billing address provided during an online shopping

process should be a precise estimate of the user location. However, such information is

often only provided after the purchase decision has been made.

• User profile data: A final option is to use profile data. In particular in platform solutions,

providers encourage users to create profiles which typically include geographic details.

These details should provide a sound basis for tailoring regional cues to the user. However,

this option is also limited to customers deciding to set-up a user profile.

In summary, none of the options mentioned is perfect, and the decision as to whether the

advantages outweigh the limitations will depend on the specific application.

Consumer and Policy Implications

While the previous section looked at how consumers could be supported in their decision-

making in favor of regional products, an improved understanding of regional trust cues could

also yield negative consequences for consumers: providers could use regional trust cues to

deceive consumers through regional washing. We derive this term from the idea of “green

washing”, which is defined as “[companies] misleading consumers about their environmental

performance or the environmental benefits of a product or service” (Delmas & Burbano, 2011)

(p. 64). Analogously, non-regional providers could regional wash their company image; in

other words, they could use regional trust cues to deceive consumers regarding their

regionality and decentralization.

Since beginning our research, we have observed such regional washing approaches on user

interfaces in the energy sector. In Figure 24, we provide two examples of national suppliers

using regional cues to encourage customers to purchase decentrally produced energy – one

provider merely pretends to be regional (Regionale Energiewerke, on the left in the figure),

while the other backs up their claims with actions (enyway, on the right).

• Regionale Energiewerke (left in Figure 24): This is an example of regional (and also

green) washing because the company uses both regionality and sustainability to promote

itself on its website, but does not substantiate these claims. First, the company name

contains the German word for regional and the term “Energiewerke”, which mirrors the

phrasing of the German term “Stadtwerke” (“regional utilities”). Second, they use nature

imagery to signal ecological sustainability on the website. Third, in the text on their

website, they describe themselves as a “regional energy provider”, a company that “stands

for sustainability” and that their energy plans are “environmentally aware” (Regionale

Energiewerke, 2020, p. 1). However, following the customer journey to purchase an energy

plan shows that the company does not offer energy from renewable sources. The company

also has none of the attributes of a typical regional energy provider (e.g., operational focus

on a region, public ownership by a municipality or region, historical genesis in a region).

Chapter IV

Their claims of regionality and sustainability are therefore a marketing strategy that is not

supported by corporate behavior. Both consumers and key policy makers need to be aware

of this evolution.

• Enyway (right): In contrast, an example of how regional cues can encourage customers to

purchase regional energy is provided by the firm enyway. The marketing claim of enyway

promotes decentralization in the energy sector in multiple ways. Firstly, the phrase

“ecological electricity from your region” (Enyway, 2020, p. 1) promotes decentralization

through regional energy generation. Secondly, the phrase “Goodbye corporations”

(Enyway, 2020, p. 1) suggests an interpretation of decentralization as a shift away from

large, centralized providers to smaller regional companies. Enyway’s claims are backed up

by its activities. The company’s business model is to provide a platform that matches

residential energy generators (e.g., rooftop PV plants, small farmers with wind turbines,

etc.) with household consumers in their region.

FIGURE 24. WEBSITE EXAMPLES OF NATIONAL PROVIDERS WITH REGIONAL CUES18

In terms of mitigation strategies, research on green washing suggests increased transparency,

ethical leadership and employee trainings (Delmas & Burbano, 2011). Other scholars propose

eco labels (Gutierrez et al., 2020). These approaches could be adapted to the regional washing

context as well.

Limitations and Future Work

This paper is of course not without limitations. First and most importantly, we have analysed

provider behaviour by analysing their websites. Our findings suggest that the evaluated

providers assume that using regional imagery will have positive effects on customer behaviour

(e.g., trust in the provider and purchases). However, this does not necessarily imply that

consumers are actually affected by regional cues and, if so, whether they are affected in the

way that providers intend. Therefore, further research should shift focus and assess the effects

of regional cues on consumer behaviour. We have in this study defined terms and concepts

Sources: Regionale Energiewerke, available online: https://regionale-energiewerke.de/home

(accessed on 10 November 2020); Enyway, available online: https://www.enyway.com/de/power

(accessed on 10 November 2020).

Chapter IV

and systematically captured design elements as a foundation for future work. More

specifically, we propose three methodologies and briefly discuss advantages and limitations.

• Online experiment/survey: An online experiment could assess how perceived

regionality affects trust and purchase intentions. This approach would enable a large

sample size and a wide range of control variables. However, only intentions and not actual

behavior could be captured.

• Eye-tracking experiment: Eye-tracking offers the analysis of cognitive processes of

participants by analyzing their eye movement and hence offers a substantial addition to

insights gained in a survey. However, for practical reasons the sample would generally be

limited. Also, the experiment takes place in a laboratory set-up and hence does still not

reflect actual purchase settings.

• A/B testing field experiment: In cooperation with an energy provider, research

findings should be tested in a field experiment. This could be implemented by means of

A/B testing and would provide data on real consumer decisions. However, A/B testing in

a live environment provides fewer options to gain control variables.

We propose a combination of these methods for future work.

Second, the link between the energy sector’s decentralization and consumer decisions in favor

of regional providers hinges on the fact that these providers produce the energy in that region.

This is typically the case for regional energy providers (e.g., with waste-to-energy plants) and

the share of regionally generated energy is increasing with the expansion of renewable energy

technologies. In particular, regional providers are increasingly investing in onshore wind and

solar plants within their area of operation. Nevertheless, other examples exist as we have

shown in Section 4.4, and this aspect should be considered in future work.

Conclusion

Motivated by the need for ICT-driven solutions to fight climate change, this study offers an

exploratory analysis of the use of regional trust cues on user interfaces in the energy sector.

The application of regional trust cues on user interfaces in the energy sector could motivate

consumers to purchase regional energy products. Decision-making in favor of more regional

energy providers would accelerate decentralization in the energy sector and avoid expensive

and unpopular power grid expansions. We performed qualitative and quantitative content

analysis of energy provider websites. Our findings highlight the relevance of this emerging

phenomenon and provide a groundwork for future experimental research by providing terms,

concepts, and a theoretical foundation. Further, we contextualised our work within the

theoretical conversations around visual trust cues in the IS community as well as text and

imagery in regional offline marketing. We drew conclusions for the design of user interfaces

in the energy sector and outlined technical approaches that tailor regional cues to the user’s

geographical location. Next, we explained how providers are using these cues to give an

impression of regionality, and we presented strategies to mitigate such examples of regional

washing. Last, we pointed to future avenues of research by outlining three potential study

methodologies for analysing the behavioural effects of regional trust cues.

Chapter V

Chapter V: On the Effects of Regional Trust

Cues on User Behavior –Imagery

This chapter builds on the findings outlined in the previous chapters that a) energy

consumers value regionality and b) regional trust cues are a frequently used element in user

interface design. Going one step further, the section analysis whether and how regional trust

cues can trigger this user preference for regionality. If this were the case, regional trust cues

should affect user attitudes and behavior. This section provides the results of two studies in

which I assessed the effects of regional imagery on users’ attitudes and behavior. More

precisely, the findings suggest that regional imagery captures significant visual attention

and increases stated trusting intention and belief.

Tobias Menzel, Timm Teubner, Marc Adam, Peyman Toreini

Introduction

The impact of visual cues in user interface (UI) design is an important aspect of human-

computer interaction (HCI). Visual cues such as images, badges, or icons convey information

“more directly and with more immediacy than […] words” (Rogers & Oborne, 1987, p. 99).

While research has studied the positive effects of social (e.g., images of human faces; Gefen &

Straub, 2004) and nature cues (e.g., images of natural landscapes; Rendell et al., 2021) in UI

design, regional cues have received only limited attention. Addressing the “surprisingly

understudied topic of regionality” (Herz & Diamantopoulos, 2019, p. 44) in the context of UI

design could provide a meaningful contribution for two reasons. First, UI designers appear to

frequently use regional cues to trigger consumer preferences for regional products and services

in practice (e.g., images of iconic buildings, landmarks, or cityscapes; Menzel & Teubner,

2021). Yet, it is unclear whether, and if so, how regional cues influence and interact with these

preferences to ultimately affect user attitudes and behaviors. Second, a better understanding

of regional cues could facilitate regional consumption decisions and, in turn, more sustainable,

transparent, and resilient value chains (Curtis, 2003; Schmitt et al., 2017). We hence pose the

following research question:

RQ1: How does embedding regional cues in UIs affect user attitudes and behaviors?

In this paper, we build on Consumer Ethnocentrism Theory (CET; Shimp & Sharma, 1987) to

study the impact of regional cues on visual attention, perceptions of regional presence, and

This chapter was published in Computers in Human Behavior with the title “Home is where your Gaze

is – Evaluating effects of embedding regional cues in user interfaces”,

doi.org/10.1016/j.chb.2022.107369

The experiment design of Study 1 in this chapter was published in a short paper in the proceedings

of Internationale Tagung Wirtschaftsinformatik 2021 with the title “Buy Online, Trust Local – The

Use of Regional Imagery on Web Interfaces and its Effect on User Behavior”,

aisel.aisnet.org/wi2021/PHuman/Track11/1

Preliminary results of Study 1 and the design of Study 2 in this chapter were published in a

Research-in-Progress Paper in the proceedings of the European Conference on Information Systems

2021 with the title “Home Sweet Home – The Effect of Regional Presence on Trust in Electronic

Commerce”, aisel.aisnet.org/ecis2021_rip/3/

Chapter V

trust. Our research model is evaluated through a multi-method approach based on objective

and subjective measures for user attitudes and behaviors. In Study 1, we conduct a lab

experiment to analyze participants’ gaze patterns (using eye-tracking) and trust (using a

survey) when engaging with a fictive electricity provider website. In Study 2, we conduct an

online experiment to investigate the effects of regional cues on trust in greater depth while, at

the same time, considering potential interdependencies of regional, social, and nature cues.

Our contribution is threefold. First, this paper is among the first to assess the use of regional

cues in UI design and its effects on perceptions of regional presence and trust, suggesting that

regional cues can be an effective design element. Second, we discuss practical implications for

UI design. Most importantly, regional cues need to be tailored to the users’ location. We

discuss different options to gather this information and discuss advantages and challenges.

Third, we sketch out how the use of regional cues in UIs could contribute to more sustainable

consumer decisions. To provide the reader with a better understanding of the paper’s

constructs and terms, Table 4 summarizes the key definitions upfront.

TABLE 4. KEY TERMS AND DEFINITIONS.

Term

Definition

Source

Perceived Regional

Presence (PRP)

The extent to which a UI allows the user to sense a

feeling of regionality.

Own definition

Perceived Social

Presence (PSP)

The perception that there is personal, sociable, and

sensitive human contact in the medium.

(Gefen &

Straub, 2004, p.

410)

Perceived Nature

Presence (PNP)

The extent to which the website allows a user to

experience the natural environment as being present.

(Rendell et al.,

2021, p. 2)

Visual Attention

At any given time, the environment presents far more

perceptual information than can be effectively

processed. Visual attention allows people to select the

information that is most relevant to ongoing behavior.

(Chun & Wolfe,

2005, p. 273)

Trust

The willingness of a party to be vulnerable to the actions

of another party based on the expectation that the other

will perform a particular action important to the trustor,

irrespective of the ability to monitor or control that other

party.

(Mayer et al.,

1995, p. 712)

Trusting Belief

Trusting belief describes the fact that “one believes that

the other party has one or more characteristics

beneficial to oneself.”

(McKnight &

Chervany, 2001,

p. 46)

Trusting Intention

Trusting intention occurs if “one is willing to depend on,

or intends to depend on, the other party even though

one cannot control that party.”

(McKnight &

Chervany, 2001,

p. 46)

The remainder of the paper is organized as follows: In Section 2, we provide the theoretical

context and develop our hypotheses. Sections 3 and 4 illustrate method and results for Study 1

and 2, respectively. Section 5 discusses overall findings, draws conclusions for UI design, and

outlines theoretical and societal implications, limitations, as well as paths for future work.

Chapter V

Background and Theory

Effects of Geographic Cues on User Attitudes and Behaviors

The effects of geographic cues (e.g., images, labels, text referencing a country, a region, etc.)

have been studied to some degree by researchers in HCI, psychology, and marketing (e.g., in

the form of place-of-origin cues). However, regional cues that adjust UIs in response to the

user’s location (i.e., the user’s city or region) has received only little research attention.

Place-of-origin cues. Place-of-origin cues “reflect the implications that the geographical

provenance of the product [origin] has for consumers” and typically manifest as country-of-

origin cues or, on a geographic micro-level, as region-of-origin cues (Chamorro et al., 2015, p.

820). Most prominently, the country-of-origin effect (Schooler, 1965) states that cues

referencing a product’s manufacturing country (e.g., Swiss made watches) influence human

information processing (Halkias et al., 2021) and product quality perceptions (Hong & Kang,

2006). How such cues affect users depends on the reputation of the referenced country

(Diamantopoulos et al., 2021). Country-of-origin cues can increase trust in products and

providers (Jiménez & San Martín, 2010), purchase intentions (Verlegh & Steenkamp, 1999),

and willingness-to-pay (Bernard & Zarrouk-Karoui, 2014). Similarly, region-of-origin cues

reference a city or region with the aim to impact user attitudes and behaviors (e.g., the

European Union’s designation of origin label). Region-of-origin cues are also considered to

increase quality perceptions (García-Gallego & Chamorro Mera, 2018) and, in turn, purchase

intentions and willingness-to-pay (Bruwer & Johnson, 2010; Chamorro et al., 2015). Note that

these two geographic cues are agnostic of the user’s location.

Tailoring UI design to user geography. As for tailoring UIs to users’ location, UI

designers have thus far mainly considered the user’s country (e.g., Reinecke & Bernstein, 2011)

or even larger domains (e.g., the Arab countries; Aljaroodi et al., 2020) when adjusting

language, imagery, and other design elements. More recently, tailoring UI design to user

location has been taken to the micro-level, focusing on specific regions or cities (Menzel &

Teubner, 2021e). Despite first accounts describing this practice (e.g., Herbes & Ramme, 2014;

Menzel & Teubner, 2021e), there has been no research into whether and, if so, how it affects

user attitudes and behaviors.

Consumer Ethnocentrism Theory and Perceived Regional Presence

Scholars often turn to evolutionary psychology when explaining the mechanisms underlying

the effects of visual UI cues (e.g., Gefen & Straub, 2004; Rendell et al., 2021). The rationale is

that these cues trigger psychological responses deeply rooted in human evolution (Riva et al.,

2015). Following this school of thought, CET posits that people consider their own (regional)

group as the “center of the universe” and will therefore refrain from buying non-regional

products as this “hurts the domestic economy, causes loss of jobs, and is plainly unpatriotic”

(Shimp & Sharma, 1987, p. 280). Therefore, they prefer brands considered as local (Ma et al.,

2019). This regionality preference seems natural from the evolutionary perspective, as for the

greater part of humankind, survival depended on cohesion and solidarity within a

geographically-bounded social group, family, or tribe (van den Berghe, 1981). In modern

times, this translates into ethnocentric consumer decisions on a regional or national level

(Bizumic, 2019). Previous studies have shown that ethnocentrism plays an important role for

Chapter V

consumers’ readiness to purchase regional products not only in mature, but also in developing

countries and markets (Yen, 2018).

Digital interfaces often have no distinct regional context as the Internet provides “a window to

the world” (Hongladarom, 1999, p. 400), that is, a tool to overcome borders and geographic

constraints (Forman & van Zeebroeck, 2018). Here, we investigate whether regional UI cues

can induce Perceived Regional Presence (PRP), referring to the extent to which a UI allows the

user to sense a feeling of regionality

. In the following, we theorize on how the use of regional

UI cues can trigger PRP and, in turn, affect user attitudes and behaviors.

Disentangling Regional from Other Cues in UI Imagery

Beyond regional cues, the most frequent cue type in UI design are social and nature images

(Menzel & Teubner, 2021e). Positive behavioral effects of social cues are established in the HCI

literature (e.g., Gefen & Straub, 2004). Social Presence Theory describes the ability of a

communication medium to transmit social cues and has its origins in social psychology (Short

et al., 1976). It describes how social cues provoke perceptions of “personal, sociable, and

sensitive human contact” which in fact lack such contact (Gefen & Straub, 2004, p. 410). An

explanation for this behavioral pattern is provided by evolutionary psychology. Interactions

with other humans have been critical throughout human evolution as chances of survival

increased through cooperation – and cooperation is inherently social (K. Lee, 2004; Riva et

al., 2015). Unsurprisingly, already Aristotle understood that humans are social animals

(Barker, 1968). In modern humans’ brains, this underlying pattern is still at work and lets

social imagery reduce anxiety towards online transactions which, in turn, promotes trust

(Hassanein & Head, 2005). Importantly, this evolutionary pattern can be triggered by artificial

social cues, that is, in the absence of actual social interaction or humans (Gefen & Straub,

2004).

Positive effects on trusting belief and intention have also been attributed to nature cues

(Rendell et al., 2021; Schmuck et al., 2018). While explanations for this phenomenon also link

to evolutionary psychology, the underlying rationale is different. Edward Wilson (1984)

argued in his Biophilia Hypothesis that humans are endowed with a biological attraction to

nature. Others pointed out that (perceived) nature has the ability to restore attention (Kaplan

& Kaplan, 1989) and reduce stress (Ulrich, 1993). This is because nature was (and is) a critical

factor for survival, for instance, as a source of water, nutrition, and shelter (Ulrich, 1993).

Crucially, this psychological pattern can be triggered also by virtual nature experience

(Hartmann & Apaolaza-Ibáñez, 2008, p. 821). Rendell et al. (2021) introduced the concept of

Perceived Nature Presence (PNP), referring to “the extent to which the website allows a user

to experience the natural environment as being present” (p. 2). They demonstrated that the

use of nature imagery engenders perceptions of visual aesthetics, as well as trusting belief and

intention.

Note that these cues often appear in combination. For instance, iconic public parks (e.g.,

Tiergarten in Berlin, Central Park in New York City, or the Gardens by the Bay in Singapore)

are likely to affect perceptions of both regional and nature presence. Images of humans in

The concept of Local Presence describes a UI’s ability to “create the illusion of the product being

present in the consumer’s physical environment” which can, for instance, be increased by using 360-

degree spins or virtual mirrors instead of plain photos on e-commerce websites (Verhagen et al., 2014,

p. 271). This concept is hence not related to our context.

Chapter V

traditional clothing or regional celebrities (e.g., the quarterback of the high-school football

team) potentially raise both regional and social presence perceptions. Therefore, we include

PSP and PNP in our assessment to disentangle the three effects.

Hypotheses Development and Research Model

We draw on CET to theorize how regional cues affect user attitudes and behaviors, focusing on

visual attention (integral component of human information processing; e.g., Just & Carpenter,

1980) and trust (key success factor for HCI; e.g., Beldad et al., 2010) as main outcome

variables. Figure 25depicts our research model.

Effects of regional cues on visual attention. First, we consider visual attention as an

integral component of human information processing (Just & Carpenter, 1980; Kowler, 2011).

Assessing visual attention can uncover unconscious effects that self-reported measures may

not be able to reveal (Dimoka et al., 2012). As outlined above, CET explains consumer

preferences for regional goods and services through an underlying evolutionary psychological

pattern. Viewing a regional cue should hence trigger this pattern. This, in turn, should be

reflected in visual attention to the cue as human information processing and visual attention

are closely associated (Schulte-Mecklenbeck et al., 2017). This link is based on assumptions on

the interplay of eye movements and cognitive processes (Just & Carpenter, 1980): First, the

eye-mind assumption suggests a strong connection between line of gaze and thought. Second,

the immediacy assumption claims that visually-apprehended information is immediately

processed and hence, the fixation duration on a visual object is in line with the duration of

Sources: Regional cue: commons.wikimedia.org/wiki/File:The_Grand_Louvre_(235493607).jpeg;

Social cue: commons.wikimedia.org/wiki/File:Confident_Eye_Contact_(Unsplash).jpg ; Nature cue:

commons.wikimedia.org/wiki/File:Fjallabak_Nature_Reserve.jpg

FIGURE 25. RESEARCH MODEL23

Chapter V

information processing. Higher visual attention on an object suggests that this object is

important (Poole & Ball, 2006) or interesting (Cyr et al., 2006) to the viewer and actively used

for decision-making (Gloeckner & Herbold, 2011). Previous studies showed that social cues

attract higher visual attention than control imagery (Djamasbi, 2014; Sivaji et al., 2011).

Similar effects on visual attention have recently been presented for nature cues (T. C. Wang et

al., 2018). Therefore, we hypothesize:

H1: The presence of regional cues embedded in UIs captures more visual attention than

control images.

Effects of regional cues on PRP and trust. Second, we turn to trust as a key success factor

for HCI design (e.g., Beldad et al., 2010; Y. Wang & Emurian, 2005). While H1 considers

whether regional cues affect decision-making at all, the following deals with the question how

human information processing is affected. For this purpose, we conflate our reasoning with

the e-trust model (Gefen & Straub, 2004). In its pure form (paths (i–iii) in Figure 25), the e-

trust model studies the effect of PSP on different aspects of trusting belief and, in turn, trusting

intention. While the focus of the present study is on PRP, we also include PSP and PNP to our

model to disentangle these effects as many cues may affect more than one construct.

Considering path (i), it is commonly accepted that PSP drives trusting belief (e.g., Gefen &

Straub, 2004; Hassanein & Head, 2005; Lu et al., 2016). Further, PSP (ii) as well as trusting

belief (iii) are commonly accepted to drive trusting intention (e.g., Gefen & Straub, 2004; Lu

et al., 2016; Oliveira et al., 2017). We hence tested a model variation including PRP and PNP

as well as their effects on trusting belief and intention. More recently, Rendell et al. (2021)

found empirical evidence for the role of PNP for trusting belief, too (path iv). Also, Schmuck

et al. (2018) and Rendell et al. (2021) suggested that PNP is positively associated with trusting

intention (path v).

Drawing on CET, consumers prefer buying products and service from their region (Bizumic,

2019; Ma et al., 2019; Shimp & Sharma, 1987). This is, for instance, reflected in higher levels

of trust and willingness-to-buy (Guo et al., 2018). Expanding on this, we hypothesize that using

regional cues in a UI increases PRP which, in turn, drives trusting belief and intention:

H2a: Higher PRP is associated with increased levels of trusting belief.

H2b: Higher PRP is associated with increased levels of trusting intention.

Study 1: Eye-Tracking Lab Experiment

To evaluate how regional cues affect visual attention (H1) and to gain first insights on their

effect on trust (H2a and H2b), we conducted an eye-tracking lab experiment. In this experiment,

we tracked participants’ gaze patterns while they engaged in a set of fictive electricity provider

websites. Further, we surveyed participants on trusting belief and intention. Such multi-

method approaches are useful to capture the full richness of a phenomenon (Niehaves, 2005,

p. 4). While self-reported measures rely on users’ subjective ratings, analyzing gaze patterns

provides a more objective measurement (Djamasbi, 2014). We use the context of electricity for

two main reasons. First, electricity can (to a certain degree) be considered a homogenous

Chapter V

credence good. Potential confounding effects due to product properties are hence eliminated

(e.g., strawberries from one’s region are actually likely to be fresher and tastier due to faster

delivery etc.). Second, considering electricity eliminates the confounding effect of

transportation cost as network fees are charged to consumers regardless of the electricity’s

physical origin. Given this high abstractness of electricity as a product, it can be assumed that

observable effects are driven by the very idea of regionality.

Materials and Methods

Scenario & Task. Participants were shown an e-commerce scenario with fictive electricity

provider websites. The stimulus material was integrated into the website. To achieve a realistic

setup, we developed the materials based on actual provider websites, adapting the imagery

and product information for the experimental conditions. Also, we eliminated all provider

details and logos to avoid that participants’ evaluation would be biased by associations with

actual providers. As the experimental task, we asked participants to evaluate several electricity

plans (in randomized order), featuring different prices, contract lengths, and imagery.

Treatments & Stimulus Material. We applied a within-subjects design with the presence

of a regional cue as treatment condition (neutral imagery as control condition). We chose a

within-subjects design as it allows us to control for potential participant-level effects and

increases statistical power (Neuman et al., 2019). Aiming for a realistic scenario, we provided

four different electricity plans sourced from actual provider websites with variations in price

(between 73.35 and 78.09 €/month) and contract length (“short”, 24 months, flexible, 12

months). Using a full-factorial design, each participant was asked to evaluate 8 combinations

in randomized order (2 treatment conditions × 4 electricity plans). Stimulus imagery was

drawn from real provider websites and randomly assigned to the electricity plans. We surveyed

participants on how realistic they deemed the scenario (average of 5.7 on a 1-7 Likert scale).

High internal validity was ensured by identical size and similar style of treatment and control

imagery (Orquin & Holmqvist, 2018).

Apparatus. We used a Gazepoint GP3 HD eye-tracker with a sampling rate of 150Hz to record

gaze data and 5-point calibration (Gazept, 2022).

Eye-Tracking Measures. Eye movement data is frequently used as a proxy for visual

attention (Djamasbi et al., 2008) and well-established to study consumer behavior (Ho, 2014;

C. Liu et al., 2017; Luan et al., 2016). In this study, we draw on two eye-tracking metrics to

assess the effects of regional cues on visual attention. First, we consider the number of fixations

(or fixation count). This metric refers to a pattern in which the eye focus rests motionless on a

certain area of interest (AOI) for some time – typically lasting 200-300 milliseconds

(Djamasbi, 2014) – and is an established measure for user attention (Ahn et al., 2018; Q. Wang

et al., 2014). This information can be used to distinguish between superficial information

scanning and active consideration for decision-making (Gloeckner & Herbold, 2011). The

number of fixations on a certain design element hence indicates the element’s importance to

the viewer (Poole & Ball, 2006). Second, we measure fixation duration, where longer fixations

suggest that an object is of interest to the viewer (Cyr & Head, 2013). We pre-defined three

AOIs as non-overlapping rectangles for regional/control images (AOI1 in the example in the

Appendix), electricity plan details (AOI2), and website header (AOI3). The screen layout and

AOIs are depicted in the Appendix.

Chapter V

Survey Measures. We used single-item 7-point Likert scales for all constructs. To measure

PRP we drew on the social presence instrument provided by Gefen and Straub (2003) and

adapted it to the PRP context. For trusting belief and intention, we drew on validated scales

with adjustments to the electricity context (Everard & Galletta, 2005; Gefen & Straub, 2003).

A comprehensive list of these items is provided in the Appendix. Also, we measured disposition

to trust (Gefen, 2000), disposition to ethnocentric consumer behavior (Shimp & Sharma,

1987), attitude towards city (derived from Lentz et al., 2006), duration of residence, experience

level, age, gender, and employment status as control variables.

Sample & Procedure. Typically, eye-tracking studies feature smaller sample sizes than

most surveys and behavioral lab experiments (Riedl et al., 2020). Caine (2016) reports a mean

of 21 participants in HCI studies using eye-tracking. We scheduled 20 participants with two

dropping out on short notice due to Covid-19 quarantine requirements (Age avg: 33; Age min:

22; Age max: 43; 50% female). This resulted in 18*8=144 observations, of which we excluded

six due to failed manipulation checks (resulting in 138 observations for analysis). Average

experiment duration was 9.6 minutes (range: 7 to 14 minutes). All participants were recruited

from the city in which the lab is situated to tailor the regional stimulus material to one single

area. After welcoming them to the lab, participants were seated in a room with negligible

ambient lighting approximately 60 cm from a 21 inch screen. Following eye-tracking

calibration and validation, participants were provided with the experiment instructions. Then,

the actual experiment started in which participants evaluated the eight websites. After each

website, the measurement items appeared upon participant request. Participants did not face

any time constraints as this provides the most realistic scenario and more accurate task

completion (Cyr & Head, 2008). Last, we surveyed participants on demographic and other

control variables and asked for a brief textual explanation of their evaluation. We also surveyed

participants for whether they had an eye health condition as this could potentially affect the

eye-tracking (none mentioned by any participant).

Results

For a first qualitative analysis, we visualized participants’ gaze patterns using heat maps

(Figure 26). In line with H1, participants paid more attention to the treatment (left) than to the

control imagery (right). Also, the experimental task of evaluating electricity plans worked as

desired as a considerable share of visual attention was allocated to the respective AOI. On

average, participants spent 28% of their viewing time on the imagery (AOI1), 61% on product

information (AOI2), and 11% on the website header (AOI3).

Chapter V

FIGURE 26. EXAMPLE HEATMAP OF TREATMENT (LEFT) AND CONTROL IMAGERY (RIGHT)24

Following up this first visual assessment, Figure 27 shows average number of fixations and

duration on AOI1 (regional/control imagery). After Levene’s tests indicated variance

heterogeneity for both attention measures (Number of Fixations: F-value=5.8, p<0.05;

Fixation Duration: F-value=14.3, p<0.001), we employed one-tailed Welch’s t-tests to assess

whether the means of the treatment observations are significantly higher than the means of

the control group. Supporting H1, for all electricity plans, visual attention to the regional cue

was significantly higher than on the control image, reflected both in number of fixations and

fixation duration.

FIGURE 27. BREAKDOWN OF VISUAL ATTENTION METRICS PER ELECTRICITY PLAN

Outlier Filter = 5%.

Chapter V

In addition, we conducted a set of multivariate regressions with number of fixations (Model A

in Table 5) and fixation duration (Model B) as dependent variables and participant fixed effects

to capture personal idiosyncrasies. Substantial R² and Adjusted R² values speak to the models’

overall fit. We found that the use of regional cues positively affects visual attention in terms of

fixations and fixation durations compared to control imagery (supporting H1).

For an initial analysis of H2a and H2b, we first conducted a manipulation check and compared

the levels of PRP of websites with regional cue (6.11 on a scale from 1 to 7) to websites without

(1.10). The difference is statistically significant (Welch’s t-test with p<0.001) and practically

relevant. Hence, we conclude that the manipulation was successful. Respective regression

models for trusting belief (Models D in Table 5) and trusting intention (Models F and H)

provide initial evidence for a positive relationship between PRP and trusting belief (supporting

H2a) and intention (supporting H2b). The effect of PRP on trusting intention is partially

mediated by trusting belief (Model H). These relationships also hold when we use the (binary)

regional cue variable instead of the PRP measure as independent variable (Models C, E and

G).

TABLE 5. EYE-TRACKING REGRESSION SUMMARY

n = 138 observations

Dependent Variable

Model

Regional Cue

(yes=1, no=0)

5.86***

(1.12)

2.42***

(0.40)

1.21***

(0.15)

1.23***

(0.18)

0.46*

(0.19)

PRP

0.23***

(0.03)

0.24***

(0.03)

0.10**

(0.04)

0.64***

(0.10)

0.62***

(0.10)

Control

Visual Aesthetics

-0.46

(0.52)

-0.09

(0.19)

-0.02

(0.07)

-0.06

(0.07)

0.21*

(0.08)

0.17+

(0.08)

0.22**

(0.07)

0.20**

(0.07)

Price

-0.67*

(0.29)

-0.25*

(0.10)

-0.07+

(0.04)

-0.08+

(0.04)

-0.16**

(0.05)

-0.16**

(0.05)

-0.11**

(0.04)

-0.11**

(0.04)

Contract Length

0.02

(0.06)

0.02

(0.02)

0.01

(0.01)

0.01

(0.01)

-0.01

(0.01)

-0.01

(0.01)

-0.01

(0.01)

-0.01

(0.01)

Participant FE

yes

Constant

69.6**

(22.5)

22.7**

(7.91)

10.2***

(2.94)

10.3***

(2.95)

15.4***

(3.57)

15.5***

(3.54)

8.96**

(3.21)

9.21**

(3.19)

0.52

0.49

0.61

0.59

0.60

0.70

0.71

Adjusted R2

0.43

0.40

0.54

0.52

0.53

0.65

Notes: + p<0.1; * p<0.05; ** p<0.01; *** p<0.001; FD: Fixation Duration (in seconds); NF: Number of Fixations;

PRP: Perceived Regional Presence; TB: Trusting Belief, TI: Trusting Intention; FE: Fixed Effects. Standard error in

parentheses.

Chapter V

We observe consistent effects of the control variables across the models. Specifically, higher

prices are associated with lower trusting intention, while higher perceived visual aesthetics are

associated with higher trusting intention (Models E to H). Further, when replacing participant

fixed effects dummies with the participant-level control variables, we find a positive

relationship of disposition to trust with trusting belief (p<0.05) and lower trusting intention

for female participants (p<0.05). All other control variables (i.e., disposition to ethnocentric

consumer behavior, attitude towards city, duration of residence, experience level, age, and

employment status) are insignificant. Moreover, all coefficients of the research model are

robust in terms of sign, magnitude, and significance.

Study 2: Online Survey

To validate and expand the findings of Study 1, we conducted a follow-up experimental online

survey (n=329) to disentangle the effects of PRP from those of PSP and PNP. We again used

the context of electricity for the reasons outlined in Study 1.

Materials and Methods

Scenario. We presented a fictive purchasing scenario for an electricity plan, including five

steps. First, participants were welcomed and introduced to the scenario. On the second page,

they stated their city of residence, the duration they have been living in that city, and

demographic variables. Third, participants faced the fictive provider website with a request to

provide household size (i.e., number of persons) and to indicate whether they want to search

for ecological plans only. This input was used to populate the fourth view (see Appendix) in

which participants saw four different electricity plans. This forth view was individually

adjusted to each participant based on the provided information and a randomly assigned

treatment combination. In this view, they responded to items below the image (randomized

order). Last, participants were asked to provide additional control variables and to provide a

brief (textual) explanation of their choices made in the experiment.

Sample & Treatment Structure. We used the large online participant pool Prolific.ac

(Palan & Schitter, 2018) to recruit 350 participants, residing in Germany at the time of the

experiment. Three incomplete submissions were excluded. Moreover, 18 participants failed to

correctly answer attention checks. The final sample size hence was 329 (35% female; age

between 18 and 65 years with an average of 29 years). Participants were compensated with an

average payment of 7.32 €/h. Average completion time was 8.6 minutes. We applied a full-

factorial 2 (regional cue: present/ not present) × 2 (social cue: present/ not present) × 2

(nature cue: present/ not present) between-subjects design. Participants engaged on the

electricity provider’s website with randomly assigned stimulus material combinations. The

distribution of stimulus combinations across the sample is shown in Table 6. Note that we

recruited an entirely new set of participants for Study 2.

Chapter V

TABLE 6. EXPERIMENTAL CONDITIONS: DISTRIBUTION OF STIMULUS COMBINATIONS

n=329 participants

Social Cue = Yes

Social Cue = No

Nature Cue = Yes

Nature Cue = No

Nature Cue = Yes

Nature Cue = No

Regional Cue =

Yes

YYY

53 participants

YNY

41 participants

NYY

39 participants

NNY

39 participants

Regional Cue =

YYN

43 participants

YNN

39 participants

NYN

40 participants

NNN

35 participants

Stimulus Material. Social and nature imagery was drawn from actual provider websites

while regional imagery was tailored to each participant’s home region. To achieve this,

participants were asked to state their city of residence in the experiments’ first step. To be able

to customize the survey to the participant’s actual city of residence, we prepared a library of

landmark images for the largest 360 German cities. For example, a participant stating to be

from the city of Heidelberg would have been shown an image of Heidelberg’s iconic castle. To

ensure a realistic experiment setting, we selected a real provider website as starting point and

then adjusted the design to our setup. Also, actual electricity prices fluctuate significantly

across regions. Hence, we retrieved price data for all 360 cities and different variables

(household size, with/without ecological preferences) from the price comparison website

verivox.com to make sure that the price information shown on the fictive website was

plausible. We included two items to evaluate as how realistic participants perceived this

scenario (5.1 on the 1-7 point Likert scale on average).

Measures. For all measurement items, we used 7-point Likert scales. To measure PRP, PSP,

and PNP, we drew on the social presence instrument provided by Gefen and Straub (2003),

adjusting it to the electricity plan context. For trusting belief and intention, we also drew on

validated scales with slight adjustments (Everard & Galletta, 2005; Gefen & Straub, 2003).

The full list of all measured instruments is provided in the Appendix. Further, we measured

demographic data (i.e., age, employment status, gender, nationality), online experience,

participants’ perceptions of the website’s aesthetics (Cyr et al., 2006), trusting disposition

(Gefen, 2000), environmental concerns (Schuhwerk & Lefkoff-Hagius, 1995), and attitude

towards city of resident (derived from Lentz et al., 2006).

Results

Consistency, Validity, and Manipulation Checks. Table 7 provides summary statistics

for all constructs. All constructs exhibit internal consistency with Cronbach’s alpha values

within the generally accepted lower (0.70) and upper (0.95) bounds (Taber, 2018). Also,

HTMT ratios (Henseler et al., 2015) were below the generally accepted threshold of 0.85

indicating discriminant validity (e.g., Voorhees et al., 2016). As a second norm for discriminant

validity, also the Fornell-Larcker criterion is met (Fornell & Larcker, 1981). We conducted

manipulation checks to confirm that the stimulus material yielded differences in PRP, PSP,

and PNP. Participants scored higher levels of PRP in response to stimulus combinations

involving regional cues (average PRP score of 4.35for websites with regional cue and 3.21 for

websites without). The analysis provides similar results for PSP and PNP on the respective

scales (social cue/PSP: 3.74 vs. 3.46; nature cue/PNP: 4.81 vs. 3.88). Welch’s tests confirmed

Chapter V

statistically significant differences for the regional (t=7.50, p<0.001), social (t=1.94, p<0.05),

and nature stimuli (t=6.10, p<0.001). We conclude that the manipulation was successful.

TABLE 7. CONSTRUCT DESCRIPTIVE STATISTICS, CONSISTENCY, AND VALIDITY

Statistics

HTMT Matrix

Fornell-Larcker Matrix



PRP

PSP

PNP

PRP

PSP

PNP

PRP

3.81

1.49

0.84

1.00

0.80

PSP

3.61

1.28

0.82

0.66

1.00

0.55

0.77

PNP

4.48

1.43

0.88

0.52

0.77

1.00

0.44

0.65

0.84

4.47

1.07

0.79

0.59

0.69

0.54

1.00

0.48

0.56

0.46

0.75

4.09

1.34

0.83

0.52

0.63

0.45

0.84

1.00

0.43

0.52

0.39

0.68

0.85

Notes: TB: Trusting Belief; TI: Trusting Intention; M: Mean, SD: Standard Deviation; α: Cronbach’s alpha; HTMT

Matrix: Heterotrait-Monotrait ratios; Fornell-Larcker Matrix: square root of average value extracted (AVE) on the

diagonal in italic and correlation coefficients off-diagonal.

Randomization Checks. We ran a set of ordinary least squares (OLS) and logit regressions

to ensure that participants were adequately randomized into the eight treatment groups

(Nguyen & Kim, 2019). To do so, we tested whether the key participant demographic variables

are independent from treatment conditions. We used age, employment status (full-time = 1,

other = 0), gender (female = 1, other = 0), and nationality (German = 1, other = 0) as dependent

variables and the three treatment conditions (regional, social, nature cue present or not) as

independent variables. The results are displayed in Table 8. As expected, all key participant

variables are statistically independent from the treatment conditions. We hence conclude that

the randomization worked as intended.

TABLE 8. RANDOMIZATION CHECKS.

Dependent Variable

n = 329 participants

Age

Employment Status

= Full-Time

Gender =

Female

Nationality =

German

Regional Cue

(present/not present)

-0.56

(0.98)

0.03

(0.05)

-0.06

(0.05)

0.03

(0.04)

Social Cue

(present/not present)

0.24

(0.98)

0.08

(0.05)

-0.004

(0.05)

-0.01

(0.05)

Nature Cue

(present/not present)

0.10

(0.98)

0.02

(0.05)

-0.04

(0.05)

-0.01

(0.04)

Constant

28.86***

(1.00)

0.29***

(0.05)

0.40***

(0.05)

0.79***

(0.05)

Notes: + p<0.1; * p<0.05; ** p<0.01; *** p<0.001; Standard error in parentheses.

Structural Equation Modelling. To evaluate our research model (Figure 25), we used

covariance-based structural equation modeling (CB-SEM). Results are displayed in Figure 28.

The model exhibits good fit according to common indicators (Comparative Fit Index

(CFI)=0.976; Tucker Lewis Index (TLI)=0.968; Root Mean Square Error of Approximation

Chapter V

(RMSEA)=0.053; Standardized Root Mean Square Residual (SRMR)=0.045). Our analysis

confirms H2a as PRP is positively related to trusting belief (0.19, p<0.01). Trusting belief, in

turn, is significantly associated with trusting intention (0.96, p<0.001). Moreover, the model

indicates significant and positive relationships between PSP and trusting belief (0.38, p<0.01)

and, to a lesser degree, trusting intention (0.27, p<0.05), and no significant effects involving

PNP (p>0.1). To test H2b, we excluded trusting belief from the model (Figure 29). Doing so

reveals significant direct effects of PRP (0.17, p<0.05) and PSP (0.63, p<0.001) on trusting

intention. In terms of model fit, this adjusted, parsimonious model performs even better

(CFI=0.988, TLI=0.983, RMSEA=0.043, SRMR =0.023). Hence, our findings provide

support for H2b, suggesting that higher levels of PRP are associated with higher levels of

trusting intention (where this effect is fully mediated via trusting belief).

FIGURE 28. RESULTS OF STRUCTURAL MODEL

FIGURE 29. MODEL VARIATION

We corroborate the above findings by including the control variables into the model (i.e., visual

aesthetics (Model 1 in Table 9), disposition to trust (2), nature care (3), and attitude towards

city (4)). We find that visual aesthetics (0.43, p<0.001), disposition to trust (0.83, p<0.001),

and nature care (0.20, p<0.01) have positive and significant relations with trusting belief,

while not so with trusting intention. Importantly, the positive and significant relationships

between PRP, trusting belief, and trusting intention remain robust across all model variations

in terms of sign, magnitude, and significance.

Chapter V

TABLE 9. SUMMARY CB-SEM RESULTS FOR VARIATIONS OF CONTROL VARIABLES.

n=329 participants

Dependent Variable

Independent Variables

PRP

.153**

-.008

.122*

-.011

.179**

-.009

.178**

.009

PSP

.120

.242

.374***

.271*

.395***

.286*

.383**

.254*

PNP

-.010

-.090

-.014

-.088

-.009

-.093

.008

-.089

Visual Aesthetics

.425***

.862

Disposition to Trust

.383***

.017

Nature Care

.195**

.088

Attitude towards City

.022

-.040

Trusting Belief

.898***

.959***

.943***

.972***

Fit

CFI

.980

.965

.974

.976

TLI

.973

.955

.967

RMSEA

.045

.053

.045

.050

SRMR

.041

.049

.043

.044

Notes: * p<0.05; ** p<0.01; *** p<0.001; CFI: Comparative Fit Index; TB: Trusting Belief; TI: Trusting Intention;

TLI: Tucker Lewis Index; RMSEA: Root Mean Square Error of Approximation; SRMR: Standardized Root Mean

Square Residual

Further, we controlled for demographics and experience levels (including the above-

mentioned constructs). For trusting intention (as DV), the dummy for German nationality

(p<0.05) and the gender dummy are significant (yes = female, p<0.05). All other control

variables (e.g., age, employment status, duration of residence in the city, online shopping

experience) did not exhibit significant coefficients. Again, the positive and significant

relationships of PRP with trusting belief and, in turn, trusting intention remain robust across

all models in terms of sign, magnitude, and significance.

Supplementary Analysis: Drivers of PRP. Aiming to better understand the stimulus

characteristics that drive PRP, we now inspect the regional stimulus images in greater detail.

Specifically, we enriched our model by secondary data based on these images. First, we

conducted a color decomposition analysis (example provided in Figure 30) using web tools

that decomposed each image’s colors, extracted the five most frequent colors’ hexadecimal

codes, and matched these codes to a palette of eight principal colors

. The respective shares

of these colors were then included into OLS regression models with PRP as dependent

variables.

Web-tool for color decomposition: www.geotests.net/couleurs/frequences_en.html; Web-tool for

clustering and color coding: mkweb.bcgsc.ca/color-summarizer; Web-tool for aggregation:

https://encycolorpedia.com.

Chapter V

FIGURE 30. DECOMPOSITION OF REGIONAL STIMULUS IMAGERY.26

Second, all images were manually coded for light conditions (daylight, night/twilight),

perspective (panoramic, close-up), type of sight shown (church, monument, palace, etc.), and

other sight attributes (modern/historic). Third, we collected data from the online travel

platform TripAdvisor.com. We used the number of ratings and the aggregated star rating as a

proxy for the popularity of the displayed sight. To assess a sight’s popularity relative to other

sights within the same city, we used the rank of a sight compared to others in the city (e.g.,

“No. 3 of 67 activities in Heidelberg”). To control for popularity in the model, we used binary

dummies if 1) the sight was ranked first in the city or 2) within the city’s top 5%. This enables

us to assess four different dimensions (Table 10): Image colors, image style, sight

characteristics, and sight popularity (absolute and relative to other sights in the city).

Source for image: commons.wikimedia.org/wiki/File:Nuernberger_Burg_0154.jpg

Chapter V

TABLE 10. DRIVERS OF PERCEIVED REGIONAL PRESENCE REGRESSION SUMMARY

n = 75 images with

region cues

Dependent Variable:

Perceived Regional Presence

III

VII

VIII

Image Colors1

n.s.

Image Style2

n.s.

Sight Characteristics3

n.s.

Sight Popularity

5-Star Score

n.s.

Number of Ratings4

n.s.

Rank within City

n.s.

Top 1 ranked

1.33***

(0.32)

1.28**

(0.47)

Top 5% ranked

0.80**

(0.30)

1.10**

(0.35)

Constant

3.31

(2.18)

4.55***

(0.27)

4.45***

(0.64)

3.81

(2.21)

4.11***

(0.16)

4.10***

(0.20)

2.55

(2.27)

4.69

(2.41)

0.15

0.01

0.02

0.001

0.19

0.09

0.33

0.35

Adjusted R2

0.05

-0.02

-0.10

-0.04

0.18

0.08

0.09

0.12

Notes: * p<0.05; ** p<0.01; *** p<0.001; n.s. = not significant; Standard error in parentheses.

1Image Colors: %-share of cyan, blue, magenta, red, orange, yellow, green, and brown in the image.

2Image Style: Dummy variables for light conditions (daylight, night/twilight), perspective (panoramic, close-up).

3Sight Characteristics: Dummy variables for type of sight (church, monument, palace, etc.), age of sight

(modern/historic).

4 centered and normalized

We find that image colors (shares of 8 main colors in the image, Models I, VII, VIII), image

style (perspective, light conditions, Models II, VII, VIII), and sight characteristics (type of

sight, age of sight, Models III, VII, VIII) all seem to be unrelated to perceptions of regionality.

Also, the absolute popularity of the displayed sight does not seem to play a role in this context

(Model IV). However, what jumps out is the strong effect of highly ranked sights on regionality

perceptions (Model V, β= 1.33, p<0.001 for images with top 1 ranked sights; Model VI, β= 0.8,

p<0.01 for top 5% ranked sights). Apparently, to effectively trigger PRP, it makes a big

difference whether just any sight out of a city is displayed or instead (one of) the representative

sight is shown. The effect size is quite remarkable (1.3 points on a 7-point scale for images

showing the top 1 ranked sight). For additional robustness checks, we ran all OLS regressions

in isolation. Further, we controlled for city-related demographic data of survey participants

(duration of residence, attitude towards city) and the size of the city. None of these checks

changed outcomes in terms of sign, magnitude, or significance.

Chapter V

Supplementary Analysis: Interplay of PRP, PSP, PNP. For a better understanding of

interdependencies between PRP, PSP, and PNP, we analyzed spillover and interaction effects.

Results of the spillover analysis are displayed in Table 11. Beyond all cue types’ (regional,

social, nature) effectiveness in triggering outcomes within their respective category, we

observe a negative cross-category effect of regional cues on PNP. This is reasonable since the

regional cues typically display man-made objects and hence present an opposite to nature. To

shed light on the interactions between cue types, we conducted OLS regressions with all two-

way interactions (i.e., PRP×PSP, PRP×PNP, PSP×PNP). All interactions were insignificant.

TABLE 11. SPILLOVER EFFECTS

n = 329 participants

Dependent Variable

PRP

PSP

PNP

Regional Cue [present=1, otherwise=0]

1.15***

(0.15)

-0.04

(0.14)

-0.28*

(0.14)

Social Cue [present=1, otherwise=0]

-0.20

(0.15)

0.28*

(0.14)

-0.02

(0.15)

Nature Cue [present=1, otherwise=0]

-0.008

(0.15)

0.01

(0.14)

0.93***

(0.15)

Constant

3.32***

(0.16)

3.48***

(0.15)

4.05***

(0.15)

Adjusted R2

0.14

0.003

0.11

Notes: + p<0.1; * p<0.05; ** p<0.01; *** p<0.001

Discussion

In this study, we investigated the impact of embedding regional cues in UIs. In Study 1, we

evaluated how regional cues affect users’ information processing by means of an eye-tracking

experiment. Our findings suggest that regional cues capture a significant amount of visual

attention compared to neutral imagery. The increased visual attention to regional cues

suggests that these cues were important (Poole & Ball, 2006) and of interest to participants

(Cyr & Head, 2013). Further, higher visual attention on a design element signals that the

information provided by this element is actively made use of, for instance, for decision-making

(Gloeckner & Herbold, 2011). Diving deeper into the matter, in Study 2 we conducted an online

experiment, assessing the effects of regional cues on perceptions of regional presence and, in

turn, on trust. Our findings confirm that embedding regional cues in UIs increases PRP with

positive effects on trusting belief and trusting intention.

Theoretical Implications

Our findings have theoretical implications for the broader research community. First, while

the effectiveness of social and nature cues is commonly accepted (e.g., Gefen & Straub, 2004;

Lu et al., 2016; Rendell et al., 2021), our findings suggest that future research should consider

regional cues as well. We shed first light on the rather novel concept of PRP which may capture

an important aspect of user perceptions in the interaction with UIs. In doing so, we extend the

academic debate on the use of visual cues in UIs and their effects on user attitudes and

behaviors. Alike social and nature cues, regional cues seem to be capable of triggering

Chapter V

evolutionary psychological patterns. We confirm established relations between PSP and visual

aesthetics with trusting belief and intention (e.g., Gefen & Straub, 2004; Li & Yeh, 2010; D.

Zhang et al., 2010) which corroborates the robustness and validity of the overall model and

approach. However, unlike earlier studies (e.g., Rendell et al., 2021; Schmuck et al., 2018), we

did not find significant relationships of PNP. This could be caused by the relatively small image

size in the survey, stalling any meaningful virtual nature experience. In contrast, it is

reasonable to assume that seeing a face or a regional landmark in small size may be sufficient

to trigger PSP and PRP. In conclusion, regional cues seem to be a potent design element for

UIs and should receive more attention in future research and practice.

Second, we provide a new perspective on CET which assumes favorable consumer attitudes

towards products, services, and providers based on a match of product/service/provider origin

and consumers’ own geographic location (Shimp & Sharma, 1987). However, a geographic

match may not always be clear or a dichotomous matter – and the line between region and

non-regional products/services/providers becomes even more blurry online. Our findings

extend CET by suggesting that already the perception of regionality (rather than an actual

geographic match) may be sufficient to trigger ethnocentric consumption patterns.

Interestingly, the analysis of the open-ended responses in Study 2 showed that none of the

participants directly stated the products’/providers’ regionality as a factor that influenced their

evaluation. In contrast, most participants pointed to product properties (e.g., price, contract

length) or website design (e.g., overall aesthetics) as reasons for their assessment. This

circumstance further nourishes the assumption that the regional cues trigger some form of

unconscious psychological effect. Our analysis also provides insight into which image

characteristics trigger ethnocentric consumer behavior. Specifically, PRP is primarily driven

by the most iconic sights of a city and less so by less famous landmarks. This finding provides

an indication that the recognition of or familiarity with the object shown in the image alone

may not be sufficient to trigger ethnocentric consumption patterns. It rather seems that a

reference to a truly iconic, identity-establishing landmark is required to trigger this

evolutionary psychological effect.

Implications for UI Design

Our study shows that regional cues represent an effective design element for online trust

building, visual attention, and – ultimately – behavior. This has an important implication: In

contrast to social and nature cues, regional cues require a user-specific design tailored to the

user’s location. We addressed this challenge for the experimental design of Study 2 by

compiling a database of images with regional cues and using region-specific stimulus material

for participants depending on their city/ region as provided earlier in the process. However,

asking users for their location is typically not feasible in practice. Therefore, we take a brief

look at some approaches to assess user location, along with the most striking advantages and

shortcomings:

• IP address. A common approach to localize users is to match their internet protocol (IP)

address in geolocation databases (Shavitt & Zilberman, 2011). This is a convenient method

because the IP information is easy to capture and can quickly be matched to location data

using geolocation services. As a disadvantage, this procedure is not accurate if people use

virtual private networks or at times when they are not at home (e.g., at work, travelling).

Chapter V

• Cookies. Providers may also generate insights on user geolocation by analyzing earlier

user transactions through cookies. Cookies enable providers to identify users without them

going through log-in procedures (Englehardt et al., 2015). Therefore, providers can link

the current session to earlier ones and use the data collected during historic user

interactions to draw conclusions on the user’s location. Yet, this approach is limited as

users may manually deny the use of Cookies. Also, this procedure is limited to repeat users.

• User profile data. Potentially the most accurate path in this context is to ask users what

area they consider their home region (e.g., by providing their address). This can be

implemented via user profiles. Still, the method hinges on users’ willingness to create a

profile and provide this information.

• Third party services. Third party services such as Google Ads can predict user

preferences with high accuracy (Castelluccia et al., 2012). Such a service can also be used

to gain insights on user geolocation. However, these services will typically come at a cost

and are subject to public headwinds due to their interference with data privacy.

• GPS data. For applications on mobile devices, geo-location can be gained via the device’s

global positioning system (GPS; Al-Suwaidi & Zemerly, 2009). However, users may choose

to deny access to this information.

In summary, there seems to be no one-size-fits-all approach and future work should further

analyze which method is best suited for specific circumstances.

Societal Implications

While not the primary focus, this paper also contributes to the debate around the design of UI

and information systems in general to foster sustainability. Leading scholars have called for

research to fight climate change by means of developing and designing better information

systems (e.g., Watson et al., 2010). Most importantly, practical research with implications

beyond theory is needed (vom Brocke et al., 2013). In recent years, this topic has caught

increasing scholarly attention. One way research could contribute is the design of solutions

that “support decision-making for more sustainable practices” (Gholami et al., 2016, p. 527).

Buying regional is usually considered as a sustainable choice in many dimensions such as

biodiversity, animal welfare, governance, and resilience (Schmitt et al., 2017). Regarding other

aspects such as carbon footprint, land use, energy, or water consumption, the debate is still

out on whether to favor regional over non-regional consumption. After all, outcomes depend

on “a diverse range of system boundaries, produce types, varied assumptions and a multiplicity

of foot printing methods” (Rothwell et al., 2016, p. 421). In contrast, other researchers claim

that only a shift towards a more regional economy will enable us to reach ecologic

sustainability (Curtis, 2003). We provide starting points for UI design to nudge consumers

towards more regional and hence – potentially – more sustainable decisions.

Limitations and Future Work

Like any empirical study, this one is not without limitations. First, both studies are somewhat

limited in that they survey intentions and perceptions rather than actual behavior. To increase

external validity, future work should expand on our design and analyze actual user behavior.

For instance, researchers could cooperate with electricity providers to analyze their website

design by A/B testing with/without regional cues. Second, we have focused on imagery in this

paper, but other cue types such as badges, labels, or icons are frequently used in practice, too.

Chapter V

As future work, we suggest investigating them and their effects on user attitudes and

behaviors. Third, the sample in the eye-tracking study featured 65% male participants which

are generally more affected by the issue of color blindness. While we had surveyed all eye-

tracking participants for eye health conditions (none indicated by any participant), we did not

specifically check for color blindness and hence cannot judge how this potentially affected our

results. However, since the supplementary analysis in Study 2 suggests that image colors do

not drive PRP, we believe this risk is negligible. Nevertheless, it should be included in future

eye-tracking studies on this subject. Fourth, future work could further disentangle the concept

of regionality. In our studies, we interpret regionality as the geography in which the

experiment participant is living at the time of the experiment. In Study 1, this interpretation

was implemented by drawing participants from one city. In Study 2, we surveyed participants

for their current area of residency and tailored the stimulus material to this geography.

However, users may also feel a sense of regional belonging to other cities, such as, their place

of birth, the city they grew up, the city they spent their semester abroad, and so forth. In some

cases, these effects even overlap (e.g., if city of birth and current city of residence are identical).

In our analysis of Study 2, we tackled this issue to a certain degree by controlling for the

duration of residence (did not have significant effects). To shed further light on this matter,

future work could, for instance, repeat Study 2 but use the city of birth instead of the city of

residence as trigger for the stimulus material generation.

Conclusions

The present study is one of the first to investigate the impact of embedding regional cues in UI

on user attitudes and behaviors. Drawing on CET, we developed a theoretical model and

evaluated it by means of a multi-method approach – including an eye-tracking lab experiment

and an experimental online survey. As regional cues in fact capture visual attention and

increase trust, the results of our study have important implications for UI researchers and

practitioners. Accordingly, regional cues emerge as a powerful tool for UI design in a wide

range of application areas.

Chapter VI

Chapter VI: On the Effects of Regional

Trust Cues on User Behavior – Labels

While the previous section focused on regional imagery, this chapter assesses how regional

labels affect user attitudes and behavior. Again, the findings suggest that a regional trust cue

(this time the label) captures significant visual attention and increases trusting intention and

belief. In this case, the study further indicates that the use of the label reduces consumers’

time to decision.

Tobias Menzel, Timm Teubner

Introduction

Decarbonizing the electricity market is a pivotal challenge in the efforts to limit global warming

(IPCC, 2014). This requires replacing fossil fueled generation assets by renewable energy

technologies such as wind, solar, and hydro power plants (W. Huang et al., 2017). In recent

years, major progress has been made in this area. For example, in 2020, the share of electricity

from renewable sources has reached 28% of total power generation globally, where country-

specific figures range between 20% in the US, 29% in China, and 39% in the EU (IEA, 2021).

Nevertheless, major social hurdles persist in the further and faster expansion of the renewable

technology roll-out (Fait et al., 2022): While the general support for the power sectors’ green

transformation is high, people resist to renewable power assets being installed in close

proximity to them (e.g., Dimitropoulos & Kontoleon, 2009; Dugstad et al., 2020; Kalkbrenner

et al., 2017; Ki et al., 2022; Thomas et al., 2022).

Aiming to address this issue, Germany – being considered a leader in this transformation (Fait

et al., 2022; Grimm et al., 2021) – has introduced a new classification system in 2019. It

enables power suppliers to market green energy generated in close proximity to consumers as

regional green electricity

(BMWK, 2016; UBA, 2019). Through this approach

, the

“legislator intends to promote the identification of consumers with renewable electricity

installations in their region, in particular to avoid negative attitudes to the expansion of

renewable energies” (UBA, 2019, p. 1). A recent representative survey indicates that, on the

one hand, consumers indeed appreciate this approach (UBA, 2021): A large majority of

consumers considers regional green electricity an important contribution to a successful

energy transition and a potent means to increase local acceptance. On the other hand,

however, the survey also unveils a lack of transparency and credibility of the current

communication of regional green electricity (UBA, 2021). With electricity being a homogenous

good, it is virtually impossible for consumers to monitor the product quality of their electricity

This chapter was submitted to a major international journal, is currently in the review process, and

carries the title “Signaling sustainability and regionality in the electricity market: An eye-tracking study

on labels”. This version is based on the initially submitted manuscript.

Acknowledgement: I thank my student Catayoun Azarm for her support in the experiment

execution.

While local green electricity would be a more accurate translation for the German regionaler

Grünstrom, we stick with regional green electricity in this article as this terminology is predominantly

used in related work (e.g., Fait et al., 2022; Lehmann et al., 2021, 2022).

see Appendix for a detailed explanation on the definition and certification process.

Chapter VI

supply (Rommel et al., 2018): At the point of consumption, the physical flow of electricity

generated in regional renewable power plants is identical to that from nuclear or coal power

plants far away. Hence, consumers are confronted with the issue of information asymmetry

around sustainability and regionality of their power supply.

Research in related contexts has explored the use of visual labels as a means to overcome such

information asymmetry (e.g., Atkinson & Rosenthal, 2014; Bougherara & Combris, 2009;

Markard & Truffer, 2006). In fact, 71% of participants in the aforementioned survey state that

an official (visual) label would be useful for their decision-making and increase the

classification’s transparency and credibility (UBA, 2021) . However, the use of visual labels

has, to our best knowledge, not been studied in the context of regional green electricity and it

is hence unclear whether and how such a label would actually affect consumer attitudes and

behavior. A better understanding of this relationship could contribute to reducing the outlined

information asymmetry and, in the long run, to mitigating this hurdle to further and faster

roll-out of renewable energy generation. We hence pose the following research question:

RQ: Can a visual label for regional green electricity affect consumers’ attitudes and

behavior?

In this paper, we draw on Signaling Theory (Spence, 1973) to hypothesize on the label’s effects

on consumers’ visual attention, time to decision, and stated trust in supplier and product. We

present results from a multi-method lab experiment (38 participants, 304 observations),

including survey and eye-tracking data. In a nutshell, we find that a (hypothetical) visual label

for regional green electricity captures significant visual attention, reduces time to decision,

and has positive effects on trust-related response variables. Further, the label’s effect on visual

attention and trust is moderated by participants’ familiarity with the label. Our findings

suggest that policymakers and regulators should explore the introduction of visual indicators

for regional green electricity and invest such labels’ propagation and popularity.

The remainder of the paper is organized as follows: Section 2 presents related work and

develops the research model. Section 3 describes the methodology for the multi-method lab

experiment followed by its empirical results in Section 4. Section 5 discusses findings,

theoretical implications, and limitations. Section 6 concludes with policy implications.

Background and Research Model

Regional Green Electricity

With the aim to “promote the identification of consumers with renewable electricity

installations in their region” (UBA, 2021, p. 1), Germany introduced a system for guarantees

of regional origin (SGRO) in 2019 (BMWK, 2016; UBA, 2019). This system allows operators

of renewable generation assets to receive guarantees-of-origin certificates for their electricity

production. These proof-of-origin certificates enable energy providers to market regional

green electricity if a) the generation asset is situated within a 50-kilometer range of the

consumer’s location and b) the energy supplier has a contractual relationship with the

generation asset operator (Fait et al., 2022; Lehmann et al., 2022).

Chapter VI

Note that this concept differs considerably from other geography-related certifications. In the

realm of electricity, the European Union’s system for guarantees-of-origin (European Union,

2009) provides a proof-of-origin for renewable electricity but lacks the geographic

component (see Lehmann et al., 2021 for a short discussion). Outside the electricity domain,

guarantees-of-origin (e.g., the Swiss-made label for watches, a designation-of-origin

certificate, etc.) build on geographic areas of production (e.g., Ham from Parma, Bourbon

Whiskey from Kentucky) but are independent of the consumers’ location. In contrast, the

regional green electricity certification requires a match of the locations of supply and demand.

It has become evident that consumers appreciate electricity from renewable sources (e.g.,

Amador et al., 2013; Borriello et al., 2021; Buryk et al., 2015; Dimitropoulos & Kontoleon,

2009; Koto & Yiridoe, 2019; Xie & Zhao, 2018), generated in some geographic proximity (i.e.,

not necessarily the 50km range: Groh, 2022; Kaenzig et al., 2013; Kalkbrenner et al., 2017; K.

S. Lee et al., 2021; Mengelkamp, Schönland, et al., 2019). Most recently, several studies have

confirmed these findings in light of the regional green electricity classification (Fait et al.,

2022; Lehmann et al., 2022). Also, energy providers have started to embrace the

advertisement and marketing opportunities that regional electricity production presents

(Herbes & Ramme, 2014; Menzel & Teubner, 2021e).

However, consumers are skeptical regarding the credibility and transparency of the current

communication of regional green electricity (UBA, 2021). This comes to no surprise, as the

product’s sustainability and regionality can be considered credence features (Nelson, 1970,

1974). While many product characteristics can be verified either through search or experience,

credence claims must be accepted at face value by consumers (Atkinson & Rosenthal, 2014).

Nelson (1970, 1974) provides the example of tuna cans for which consumers can verify claims

about its taste by consuming it. Yet, claims about other attributes such as the fishing method

or the catch location cannot be verified by consumers per se (Bottega & de Freitas, 2009). The

same applies to the features of regionality and sustainability in the context of electricity: The

origin and production technology of electricity are, at the point of consumption, physically

undiscernible. Accordingly, consumers face an information asymmetry regarding these

features.

Regional and Green Labels

Addressing the issue of information asymmetry between consumer and producer generated by

credence features, visual labels can serve as both a quality assurance for consumers and a

communication tool (Markard & Truffer, 2006; Truffer et al., 2001).

Regarding regionality claims, designation-of-origin labels are considered means to overcome

information asymmetry between producers and consumers regarding a product’s origin (e.g.,

Halkias et al., 2021). Designation-of-origin cues “reflect the implications that the geographical

provenance of the product [origin] has for consumers” and include country-of-origin or, on a

smaller level, region-of-origin labels (Chamorro et al., 2015, p. 820). Most prominently, the

country-of-origin effect (first described by Schooler, 1965) explains that respective labels can

influence human information processing (Halkias et al., 2021) and perceptions of product

quality (Hong & Kang, 2006). Similarly, region-of-origin labels are considered to affect user

attitudes and behavior in the sense of increasing quality perceptions (García-Gallego &

Chamorro Mera, 2018), purchase intention, and willingness-to-pay (Bruwer & Johnson, 2010;

Chamorro et al., 2015).

Chapter VI

Considering sustainability features, eco labels can provide a relevant signal to consumers

holding concerns about the environmental qualities of a good or service (Atkinson &

Rosenthal, 2014; Bougherara & Combris, 2009). An eco label is “any symbol appearing on [a]

product […] that seeks to inform consumers that a particular product is in some significant

way less harmful to the environment than purchase alternatives” (Tang et al., 2004, p. 87).

Eco labels have been shown to effectively affect consumer attitudes and behavior (Jain et al.,

2018; Murray & Mills, 2011; J. Y. Park, 2017; Z. Wang et al., 2019; Ward et al., 2011). The

combination of sustainability and regional features, however, has thus far received little

research attention.

Signaling Theory and Hypotheses Development

Signaling theory provides a suitable theoretical framework to explain how eco and regional

labels affect user attitudes and behavior in situations of information asymmetry around a

product’s quality (Erdem & Swait, 1998). First employed in the economic context by Spence

(1973), the theory considers markets with information asymmetry (in Spence’s example job

seekers and employers). According to the theory, the more informed side can use signals to

unveil their otherwise unobservable quality (e.g., talent, skills, product quality). A

fundamental principle for signaling to function properly is that obtaining those signals is

inherently costly and associated with prohibitively high cost for owners of low-quality traits

and (comparatively) low cost for owners of high-quality traits. This ultimately results in a

market equilibrium in which only the owners of high-quality traits have an incentive to acquire

the signal (“separating equilibrium”; Bergh et al., 2014, p. 1335). In turn, the signal itself

becomes a quality differentiator (Dann et al., 2022). Applied to situations in which producers

hold better information on credence claims around product quality, consumers draw on

information cues (e.g., labels) in their evaluation of product quality (Basoglu & Hess, 2014;

Kirmani, 1997; Kirmani & Rao, 2000; Nelson, 1970, 1974). In this sense, a label becomes “a

marketer-controlled, easy-to-acquire informational cue, extrinsic to the product itself, that

consumers use to form inferences about the quality or value of that product” (Bloom & Reve,

1990, p. 59).

In the case of regional green electricity, the preconditions for effective signaling are met in the

sense that certification requires a lengthy process. Accordingly, producers of regional green

electricity will be able to obtain the certificate at manageable cost, while other producers can

only obtain the certification through fraud, which, in turn, goes along considerable legal and

reputational risks and associated costs. Therefore, labels tied to the regional green electricity

certification should be a powerful tool to overcome the information asymmetry between

suppliers and sellers around the electricity’s regionality and sustainability claims. Drawing on

this theoretical groundwork, we focus on three dimensions to provide a holistic assessment of

whether and how a visual label for regional green electricity affects consumer attitudes and

behavior (Figure 31).

Chapter VI

First, we consider visual attention (path H1 in Figure 31): If the label were to be effective in the

sense of signaling theory, it should influence consumers’ decision-making processes in some

form. In this regard, attention to visual elements such as labels is associated with human

information processing (Schulte-Mecklenbeck et al., 2017). The idea of drawing on visual

attention to understand cognitive processes builds on two established assumptions (Just &

Carpenter, 1980). First, the eye-mind assumption claims a strong connection between

thoughts and line of gaze. Second, the immediacy assumption suggests that information

received through the eyes is immediately processed and hence the visual attention to an object

is in line with the time of information processing of the object. Put in simpler terms, if a label

attracts significant visual attention, it is considered to be of some sort of interest to the

consumer (Cyr et al., 2006), important (Poole & Ball, 2006), or even actively influences the

decision-making process (Gloeckner & Herbold, 2011). Some recent work showed that eco and

regional labels can attract visual attention (e.g., Fabianek et al., 2020; Halkias et al., 2021;

Song et al., 2019). Therefore, we hypothesize:

H1: A label for regional green electricity captures significant shares of consumers’ visual

attention.

Second, we evaluate whether a label affects consumers’ time for decision-making (H2). While

the previous hypothesis reflects on whether a label affects decision-making at all, this (and the

next) hypothesis covers the question how consumers decision-making is concerned. In light of

signaling theory, labels are considered as a “simplifying strategy for consumers” in situations

of asymmetric information (Atkinson & Rosenthal, 2014, p. 34). The use of a label is

considered to be convenient (Narayanan & Huebscher, 1998) and reduce complexity for the

recipient of a message (Rogers, 1986). Accordingly, a potent label should reduce complexity

for the consumer. Following Hick’s law (Hick, 1952) this should, in turn, reduce the time

needed for decision-making. We hence hypothesize:

FIGURE 31. RESEARCH MODEL (NOTE: LABEL TRANSLATED)

Chapter VI

H2: A label for regional green electricity reduces consumers’ time for decision-making.

Third, we assess consumers’ trust in supplier and product (H3). Trust is a perquisite for

basically any human interaction, but in particular so for commercial relationships (e.g., Mayer

et al., 1995; McKnight & Chervany, 2001). From the viewpoint of signaling theory, labels are a

means to overcome the information asymmetry between sellers and buyers and hence increase

consumers’ trust in seller and product quality (Kirmani & Rao, 2000). In particular, when

faced with sustainability-related product features, consumers are often skeptical whether

producers are truthful about their quality claims (Kalafatis et al., 1999). Therefore, their

intention to make sustainable purchase decisions is often hindered by their lack of trust

concerning green product claims (Leire & Thidell, 2005). In such situations, a label generates

trust and “acts as a lubricant” for decision-making (Atkinson & Rosenthal, 2014, p. 34).

Related studies suggested that eco labels and designation-of-origin labels are drivers of trust

in supplier and product (e.g., Atkinson & Rosenthal, 2014; Jiménez & San Martín, 2010) which

ultimately drives business-relevant outcome variables such as purchase intention and

willingness-to-pay (Bernard & Zarrouk-Karoui, 2014; Bruwer & Johnson, 2010; Chamorro et

al., 2015; Z. Wang et al., 2019).

H3: A label for regional green electricity increases consumers’ trust in supplier and

product.

Capping the holistic assessment, we intend to address a common critique of labels: An

frequently raised argument against using eco labels builds on the preconception that they are

limited by their familiarity to consumers (Song et al., 2019; Teisl et al., 2002; van Amstel et

al., 2008). Hence, eco labels are assumed to require extensive “brand building” (Tang et al.,

2004, p. 101) and efforts to increase consumer knowledge to effectively alter consumers’

attitudes and behavior (Rex & Baumann, 2007) – after all, people tend to trust what they know

and, vice versa, tend to be skeptical of what they don’t. Against the background of signaling

theory, a signals’ credibility is critical for its function in overcoming information asymmetry

(Atkinson & Rosenthal, 2014; Sun et al., 2021). Accordingly, we hypothesize that consumers’

familiarity of a label for regional green electricity will enforce its effects on visual attention

(even higher), time to decision (even faster), and trust (even more; paths H4a,b,c):

H4: Consumers’ familiarity with the label for regional green electricity will moderate the

label’s effect on visual attention (H4a), time to decision (H4b), and trust in supplier

and product (H4c).

Materials and Methods

We evaluate the proposed research model by means of a scenario-based lab experiment. In

this scenario, participants were asked to evaluate electricity contracts on a fictive price

comparison website. On this website, we systematically varied whether the described label was

shown or not as the main treatment variable. Such an experiment provides a high degree of

control and allows for assessing the causal effects by the exogenous treatment variable (in this

case the label; Cassar & Friedman, n.d.).

Chapter VI

Scenario and Treatment Design

Participants acted as consumers seeking to book a household electricity plan. As the majority

of electricity contracts in Germany is purchased via price comparison platforms (YouGov,

2015), we based our experiment on this sales channel. To evaluate the label’s effects on visual

attention, time to decision, and trust (H1, H2, and H3), we used a within-subject design as it

allows controlling for between-subject variations which improves the power to identify

differences and reduces error (Neuman et al., 2019). Aiming for a realistic scenario, we

provided two different electricity plans with variations in price and contract length drawn from

an actual comparison website (plan 1: 46.79€ per month, 12 months contract duration; plan 2:

49.81€ per month, variable contract duration). All tariffs were based on the consumption of a

2-person household (2,200 kWh p.a.). As treatment condition, a label for regional green

electricity was either present or not. We also included labels for regional (but not green) and

green (but not regional) electricity. This enables us to disentangle the effects of regional and

green claims and provides a somewhat more realistic search-and-comparison scenario.

Applying a full-factorial design, all participants were hence asked to evaluate 2 × 2 × 2 = 8

electricity plans in randomized order (i.e., [plan 1, plan 2] × [no label, regional label

component] × [no label, green label component]).

In addition, we included a between-subject design element to evaluate how the label’s

familiarity moderates the aforementioned outcome effects (H4). Half of all participants (at

random) were briefed with information on the labels prior to engaging with the comparison

website (familiarity group). The other half did not receive any information on the labels

(control group). Since the labels were developed deliberately for this experiment, the control

group is not familiar with the labels (by design).

Stimulus Material and Label Design

For a realistic setup, we based the stimulus material on an actual price comparison website for

household electricity plans (verivox.com). We changed the website provider name, blurred

adjacent offers, and replaced the electricity supplier logo with a generic logo to avoid that

associations with real providers would affect participants’ evaluation. Building on this setup,

the eight stimulus conditions were produced by variations of contract details and price as well

as the addition of the 3 different labels (see Figure 32).

Regarding label design, we conducted a pre-study collecting green and regional labels through

content analysis of 318 energy supplier websites and 8 leading price comparison platforms. In

addition, earlier work indicates that eco labels are most effective if they combine visual and

text elements (e.g., M. Kim & Lennon, 2008; Tang et al., 2004). To ensure high internal

validity, labels should further be prominently placed on the screen (Faraday, 2000) and be of

similar style and size (Orquin & Holmqvist, 2018). Finally, labels should differ sufficiently

from existing ones to avoid confounding effects due to associations with existing labels. Taking

this into account, we created the stimulus material as displayed in Figure 32. We included a

realism check in the measurement instrument as part of the experiment. The average

participant score of 5.9 on a 7-point Likert scale indicates that the stimulus material provided

a fairly realistic scenario.

Chapter VI

FIGURE 32. STIMULUS MATERIAL (NOTE: LABELS TRANSLATED)

Measures

To assess visual attention, we draw on eye-tracking measures. Analyzing eye-movements is a

frequently used method to evaluate consumer behavior in general (e.g., Meyerding & Merz,

2018; Song et al., 2019) and visual attention in particular (e.g., Djamasbi et al., 2008).

Therefore, we defined four areas of interest (AOI) in our stimulus material. These none-

overlapping rectangles cover the areas in which label (AOI_label), supplier logo (AOI_logo),

contract details (AOI_text), and price details (AOI_price) are situated. Using an eye-tracking

device, we measured participants’ fixations of those AOIs. A fixation is “a period of relative

stability during which an object can be viewed” (Jacob, 1995, p. 260). It refers to a gaze pattern

in which the eyeball stays motionless for a certain period of time (typically 200-300

milliseconds; Rayner, 1998). Fixation data offers insights on whether information is merely

scanned or actively made use of for decision-making (Gloeckner & Herbold, 2011). As

measures, we collected the fixation count (i.e., how often an AOI is viewed) and fixation

duration (i.e., how long an AOI is viewed). Since we hypothesize a relationship of label and

time to decision, we use relative values for the visual attention measures (i.e., fixation count

and fixation duration as share of all fixations and total fixation duration) to control that

systematically shorter or longer time to decisions distort the measurement of visual attention.

Gaze data was collected with a Gazepoint GP3 HD eye-tracker with sampling rate of 150 Hz.

To measure time to decision, we collected participants’ response time. For each of the eight

stimulus combinations, participants were at first presented with the stimulus material without

survey items. Only upon participants’ request, the items would appear, and participants could

fill in their responses. We extracted the time from appearance of the stimulus material to the

request for the survey items from the system to generate a proxy for time to decision.

For the measurement of trust, we draw on validated scales for trusting belief and trusting

intention with adjustments to the electricity context (Everard & Galletta, 2005; Gefen &

Straub, 2003). Since the within-subject design requires participants to score each item

Chapter VI

multiple times, we condensed the scales to a single item in order to manage the overall volume

(Trusting Belief: “This electricity provider is trustworthy”; Trusting Intention: “I am very likely

to buy an electricity plan from this website”; details provided in the Appendix). We used 11-

point Likert scales for both items.

We further collected control variables at the end of the experiment. Specifically, we control for

disposition to trust (Gefen, 2000), disposition to ethnocentric consumer behavior (Shimp &

Sharma, 1987), nature concern (Schuhwerk & Lefkoff-Hagius, 1995), age, gender, profession,

and prior experience with buying electricity plans via comparison websites. Also, addressing

potential spill-over effects, the order in which a stimulus combination appeared during the

experiment (from one to eight) is used as control variable.

Sample and Procedure

Caine (2016) reports an average sample size of 21 for eye-tracking lab experiments in a

literature review of 560 articles. As we divide the sample in two groups (with label briefing,

and without) we aimed to roughly double this mark and scheduled 40 experiment participants

– with two dropping out short notice. The resulting participant sample of 38 participants

consists of 40% female participants and 50% students. Age ranged between 18 and 85 years

with an average of 31 years. Details are provided in Table 12. With each participant responding

to 8 stimulus conditions, the total number of observations is hence 304. The sample was drawn

by approaching people on and around campus in August and September 2021. No

renumeration was offered to participants.

TABLE 12. SAMPLE CHARACTERISTICS

Treatment Group

Control Group

Total

Participants

Observations

152

304

Age – avg

31.11

31.84

31.47

Age – range

18 - 85

18 – 69

18 - 85

Female participants

Students

The entire experiment was run under a strict Covid-19 protocol. The procedure is described in

Figure 33. Upon arrival, we welcomed participants and placed them approximately 60 cm

from a 21-inch screen on which the experiment was conducted. Next, the eye-tracker was

calibrated and validated by the experimenter. Participants were then provided with welcome

message, instructions, and terms of participation on the screen. The familiarity group received

additional information on the labels at the end of this step. After that, the actual experiment

was conducted in which participants engaged with the fictive comparison platform while we

tracked their eye-movement and surveyed trust-related items. Participants first saw the

stimulus without items and formed their evaluation. Then, the items appeared upon

participants’ request and participants provided their input. After a short break, the next

stimulus appeared. We refrained from setting any time constraints for the sake of realism (Cyr

Chapter VI

& Head, 2008). Last, participants were surveyed for demographic and control items as well as

a short textual explanation of their evaluation.

FIGURE 33. EXPERIMENT PROCEDURE

Randomization Check

To check whether the random assignment of participants to the two groups (with label

briefing, and without) actually worked, we consider key demographic variables using a set of

OLS and logit regressions (Nguyen & Kim, 2019). Assuming successful random assignment,

participants’ characteristics should not systematically differ across the two groups (Atkinson

& Rosenthal, 2014). The analysis (see Table 13) suggests no statistical effects of the group

assignment. We hence conclude that the randomization was successful.

TABLE 13. TESTS FOR RANDOM CONDITION ASSIGNMENT

n = 38 participants

OLS

Logit

DV: Age

DV: Gender =

Female

DV: Profession =

Student

Familiarity group

[assigned/not assigned]

-0.74

(4.80)

0.05

(0.16)

0.16

(0.16)

Constant

31.84***

(3.40)

0.37**

(0.12)

0.42**

(0.12)

R²

0.001

Log Likelihood

-27.66

-28.10

Notes: + p<0.1; * p<0.05; ** p<0.01; *** p<0.001; Standard error in parentheses.

DV = Dependent variable, OLS = Ordinary least squares

Results

As a first visual assessment of the data, we consider heat maps. We then go on using

multivariate regression models to evaluate our hypotheses. Supplementary analysis breaks

down the labels’ effect into regionality and sustainability claims.

Chapter VI

A summary of all outcome variables is shown in Table 14. As expected, the two measures for

visual attention (i.e., fixation count and fixation duration, r = 0.9) are highly correlated which

speaks to dependability and robustness of the experiment. The same applies to the measures

for trust (i.e., trusting belief, trusting intention, r = 0.7).

TABLE 14. DESCRIPTIVE STATISTICS

Measure

AVG

Correlation Matrix

Fixation Count a

12.37

0.009

1.00

Fixation Duration b

12.29

0.12

0.90

1.00

Response Time c

13.9

6.74

-0.24

-0.22

1.00

Trusting Belief d

5.8

1.88

0.23

0.27

-0.10

1.00

Trusting Intention d

5.6

2.04

0.27

0.26

-0.12

0.69

1.00

Notes:

a Fixation Count of label AOI as percentage of all fixations on the stimulus; b Fixation duration of label

AOI as percentage of total fixation duration on the stimulus; c in seconds; d Likert scale from 0 to 10.

OV= Outcome variable, AVG = Average, SD = Standard deviation, FC = Fixation count,

FD = Fixation duration, RT = Response time, TB = Trusting belief, TI = Trusting intention,

VA = Visual attention, TD = Time to decision, TR = Trust

Visual Attention (H1)

For an initial assessment of visual attention, we visualize participants’ gaze patterns with the

help of heat maps (Figure 34; outlier filter = 10%, gaze duration = 20 seconds). Overall, the

scenario of evaluating electricity plans seems to have worked as desired as the large majority

of participants’ attention is attributed to areas on the screen with electricity plan information

while other elements of the comparison website have received very little attention (90.3% of

fixation duration attributed to the 4 AOIs). As shown in Figure 34, the regional green electricity

label captures significant shares of participants’ visual attention while the same area in the

control image (i.e., without label) basically draws no attention at all.

FIGURE 34. HEAT MAP EXAMPLES WITHOUT (LEFT) AND WITH REGIONAL GREEN LABEL (RIGHT).

For a quantitative analysis, we conduct two regression models. First, we employ an ordinary

least square model (OLS in Table 15) including participant-level control variables. In the

Chapter VI

second model, we include participant fixed effects to capture participant-driven idiosyncrasies

(least square dummy variable, LSDV). The findings are in line with the previous analysis and

confirm H1. The presence of the regional green label increases visual attention in terms of

fixation count (β = 14.1%, p < 0.001 in both models) and fixation duration (β = 16.1%, p <

0.001 in both models). Consistency of both models and high R² values speak to the overall

robustness and credibility of the analysis. Note that minor spill-over effects exist, but the effect

size is rather small. Apparently, visual attention shifted towards the label during the course of

the experiment (i.e., the effect of the experiment round (1 to 8) on fixation count and fixation

duration is positive and significant: e.g., β = 0.7%, p < 0.01 in the fixation count OLS model).

This seems reasonable since other elements such as the supplier logo or the overall design do

not change over time while the label is presented in varying versions along the course of the

experiment (within-subject design).

Chapter VI

100

TABLE 15. REGRESSION RESULTS

n = 152

Visual attention

Trust

Measure/DV

DV: FC

DV: FD

DV: RT

DV: TB

DV: TI

Model

OLS

LSDV

OLS

LSDV

OLS

LSDV

OLS

LSDV

OLS

LSDV

Regional Green Label

[present/not present]

.141***

(.012)

.141***

(.012)

.161***

(.014)

.161***

(.014)

-2.28*

(1.12)

-2.28*

(.975)

1.86***

(.282)

1.86***

(.240)

2.70***

(.309)

2.70***

(.268)

Price [low/high]

.009

(.012)

.009

(.012)

.008

(.015)

.008

(.014)

-.064

(1.12)

-.056

(.976)

-.143

(.282)

-.148

(.240)

-.179

(.309)

-.179

(.268)

Experiment Round [1-8]

.007**

(.003)

.006*

(.003)

.006*

(.003)

.005

(.003)

-1.10***

(.241)

-1.04***

(.217)

.012

(.061)

-.026

(.053)

-.066

(.066)

-.063

(.060)

Age [years]

.001

(.001)

.001

(.001)

.027

(.053)

.002

(.013)

-.010

(.015)

Gender [female=1/ male=0]

.010

(.014)

.029+

(.014)

.144

(1.25)

-.552+

(.314)

-.240

(.345)

Profession [student=1]

.016

(.016)

.009

(.019)

-.192

(1.48)

.250

(.371)

.249

(.407)

Disposition to Trust

.007

(.006)

.006

(.007)

.637

(.569)

.450**

(.143)

.435**

(.157)

Ethnocentrism

-.005

(.006)

-.003

(.007)

.620

(.517)

.233+

(.130)

.025

(.143)

Nature Care

.009

(.011)

.003

(.013)

-1.04

(.969)

-.241

(.244)

-.091

(.267)

Experience

.009

(.015)

.020

(.018)

-2.41+

(1.347)

.202

(.339)

.008

(.371)

Participant dummy

—

yes

—

yes

—

yes

—

yes

—

yes

Constant

-.111+

(.060)

-.006

(.039)

-.095

(.071)

-.037

(.045)

20.3***

(5.43)

27.4***

(3.19)

2.91*

(1.37)

6.99***

(.785)

3.25*

(1.50)

6.23***

(.878)

R²

.507

.647

.486

.649

.205

.526

.311

.607

.386

.635

Adj. R²

.472

.519

.450

.522

.148

.355

.262

.465

.343

.504

F statistic

14.5***

5.08***

13.3***

5.13***

3.63***

3.08***

6.36***

4.28***

8.88***

4.83***

Notes: + p<0.1; * p<0.05; ** p<0.01; *** p<0.001; Standard error in parentheses.

TD = Time to decision, DV = Dependent variable, FC = Fixation count, FD = Fixation duration, RT = Response

time, TB = Trusting belief, TI = Trusting intention

Time to Decision (H2)

Our second hypothesis (label’s effect on time to decision; H2) is also supported by the data. On

average, participants responded more than two seconds faster when the label was present (i.e.,

13.03 sec vs. 15.35 sec). The effect is significant in both regression models (p < 0.05 in both

models). Expectedly, we also observe a learning effect (i.e., decreasing response time over the

course of the experiment; experiment round: β = -1.10 in OLS, β = -1.04 in LSDV, p < 0.001 in

both models).

Chapter VI

101

Trust (H3)

According to our analysis, the label for regional green electricity is also a driver of trust (H3).

Drawing on regression results, participants attributed almost two additional points on a 0-10

point Likert scale of trusting belief when engaging with stimulus combinations with regional

green label compared to control material (β = 1.86, p < 0.001 in both models) and close to

three points for the trusting intention measure (β = 2.70, p < 0.001 in both models).

Considering control variables, participants’ disposition to trust increases stated trust levels as

expected (trusting belief: β = 0.45, p < 0.01; trusting intention: β = 0.44, p < 0.01). Note that

we do not identify spill-over effects in this context (experiment round: p > 0.1 in all four trust-

related models).

Moderating Role of Label Familiarity (H4a,b,c)

Next, we turn towards the moderating role of label familiarity. As displayed in Figure 35,

briefing participants with label information prior to engaging with the comparison website

appears to strengthen the regional green label’s effect on visual attention (H4a) and trust (H4c).

FIGURE 35. INTERACTION EFFECTS OF REGIONAL GREEN LABEL VS. NO LABEL WITH LABEL

FAMILIARITY

We deep dive into the analysis by means of interaction regression models (Table 16). For the

sake of simplicity and since OLS and LSDV models produced very similar results in the

previous analysis, we focus in LSDV models in this analysis. We find that familiarity indeed

moderates the label’s effect on visual attention (fixation count: β = 4.8%, p < 5.2%; fixation

duration: β = 1.38, p < 0.1) and on trust (trusting belief: β = 1.02, p < 0.05; trusting intention:

β = 1.38, p < 0.01). However, the interaction effect in the context of time to decision is not

significant (p > 0.1).

Chapter VI

102

TABLE 16. INTERACTION EFFECT REGRESSION MODELS

n = 152

Visual Attention

Trust

Measure/DV

Model

LSDV

Regional Green Label

[present/not present]

.117***

(.016)

.135***

(.019)

-1.58

(1.38)

1.35***

(.334)

2.01***

(.370)

Label familiarity

[Familiarity/control group]

-.034

(.053)

-.042

(.061)

5.45

(4.372)

1.48

(1.05)

1.79

(1.17)

Label × Familiarity

.048*

(.023)

.052+

(.023)

-1.40

(1.96)

1.02*

(.472)

1.38**

(.523)

Price [low/high]

.009

(.012)

.008

(.014)

-.057

(.978)

-.147

(.236)

-.178

(.261)

Experiment Round [1-8]

.006*

(.003)

.005+

(.003)

-1.04***

(.217)

-.023

(.052)

-.060

(.058)

Participant dummy

yes

Constant

.016

(.039)

-.009

(.045)

22.3***

(3.25)

5.24***

(.784)

4.08***

(.868)

R²

.660

.528

.623

.657

Adj. R²

.533

.352

.482

.529

F statistic

5.20***

3.00***

4.29***

5.14***

Notes: + p<0.1; * p<0.05; ** p<0.01; *** p<0.001; Standard error in parentheses.

TD = Time to decision, DV = Dependent variable, FC = Fixation count, FD = Fixation duration,

RT = Response time, TB = Trusting belief, TI = Trusting intention

Supplementary Analysis: Disentangling Regional and Green Effects

Last, to disentangle the effects of regional and green label components, we consider both

components as well as their combination. For the sake of readability, we here focus on one

measure per outcome variable in this analysis (fixation duration, response time, and trusting

intention, Figure 36). All three labels attracted more visual attention than the control stimuli

(p < 0.001) and reduced time to decision (p < 0.05). We do not identify significant differences

between the three labels concerning these two outcome variables (p > 0.1). However, there are

significant differences with regard to trust. Stimulus material with the regional green label

facilitated significantly higher trust scores than when the green component was present, but

the regional component was missing (p < 0.01), which again received higher scores than

stimuli with the regional label (vs. regional green label: p < 0.001; vs. green label: p < 0.001).

All three labels received higher trusting intention scores than control condition (i.e., no label

at all; p < 0.001).

Chapter VI

103

FIGURE 36. COMPARISON OF NO LABEL, REGIONAL LABEL, GREEN LABEL, AND REGIONAL GREEN

LABEL.

Discussion

In this study, we aimed to address the issue that consumers prefer to purchase green electricity

generated in their region but face uncertainty about energy providers’ regional and green

marketing claims. A label based on the recently implemented classification of regional green

electricity could address this information asymmetry. We investigate whether and how such a

label provides a capable tool to affect consumers attitudes and behavior. We find that the label

in fact captures significant amounts of visual attention, suggesting that it is of interest to

consumers (e.g., Cyr & Head, 2013) and actively influences decision-making (Gloeckner &

Herbold, 2011). Further, we observe faster decisions. This is particularly relevant as quicker

time to decision is associated with higher ease of use of a user interface (Lin et al., 2017). Also,

participants reported higher trust in provider and product when the label was present. Trust

is a perquisite for basically any commercial relationship (e.g., Mayer et al., 1995; McKnight &

Chervany, 2001). In addition, familiarity with the label moderated the effects on visual

attention and trust. These findings carry relevant implications for theory and practice.

Theoretical Implications

This study contributes to three streams of literature. First, we employ Signaling Theory

(Spence, 1973) to theorize how the label for regional green electricity affects consumers’

attitudes and behavior. Our findings suggest that visual labels can provide a meaningful signal

in this context. In other words, the label represents a means to overcome the information

asymmetry concerning the source of electricity (both geographically and technologically)

between providers and consumers.

Chapter VI

104

Second, literature on eco and regional labels has thus far mainly assessed each dimension in

isolation. In this study, we differentiate the effects of regional, green, as well as regional and

green labels and find that also the individual components are capable of capturing some visual

attention, enable faster decisions, and engender trust (as compared to neutral stimulus

material). This finding is in line with earlier work on eco labels (e.g., Atkinson & Rosenthal,

2014; Song et al., 2019) and regional labels (e.g., Halkias et al., 2021; Jiménez & San Martín,

2010). However, while visual attention and time to decision are on similar levels for regional,

green, and regional green labels, our findings suggest that the combination of both

components can foster these outcome variables even further than each component can in

isolation (see Figure 36).

Third, this is the first study in the nascent field of regional green electricity research with a

focus on signaling the product’s underlying qualities by means of visual design elements.

Earlier work has highlighted that consumers appreciate green electricity generated in

geographic proximity to them (e.g., Kalkbrenner et al., 2017; Mengelkamp, Schönland, et al.,

2019) and are willing to pay a premium for electricity when classified as regional and green

(Fait et al., 2022; Lehmann et al., 2022). We expand on those studies by putting a specific label

for regional green electricity to the test and studying its effects on consumers’ attitudes and

behavior.

Policy Implications

These findings carry the following implications for policymakers. First, our findings suggest

that establishing a label for regional green electricity is a potent policy initiative to accelerate

the transition towards a carbon neutral energy sector. Second, promoting and brand building

of the label should be emphasized as familiarity with the label is a critical success factor for

this policy initiative. Implementing this policy measure in Germany could be executed in a

(rather) timely manner as the classification processes for regional green electricity have

already been established.

However, the question arises whether promoting regional green electricity through labels is

the most effective policy measure towards the legislator’s declared goal of increasing “the

identification of consumers with renewable electricity installations in their region” (UBA,

2019, p. 1). The political toolbox features other means to foster regional green electricity

generation such as, community energy projects (e.g., Mirzania et al., 2019; Zade et al., 2022),

peer-to-peer energy platforms (e.g., Cortade & Poudou, 2022; Sousa et al., 2019), and co-

ownership models (Johansen & Emborg, 2018). Future work should assess which scheme is

most effective regarding both environmental as well as economic impact and assess potential

trade-offs.

Takeaways for Practitioners

The generation of renewable electricity in or close to urban areas is considered a core lever to

reach the goals of CO2 emission reduction (Schenone & Delponte, 2021). From a micro-

economic perspective, this translates into a marketing challenge for providers in that they need

to convince consumers to make sustainable decisions (Herbes & Ramme, 2014). A successful

transition towards a carbon-free system requires “effective branding and marketing

communications strategies designed to enhance consumers’ benefit perception” (Hartmann &

Chapter VI

105

Apaolaza-Ibáñez, 2012, p. 1254). Our findings suggest that a label for regional green electricity

can play a role in that transition where it may serve as a powerful communication tool for

marketing practitioners and user interface designers. Our results indicate that the label’s

effects are consistent across demographic parameters and experience levels (i.e., with and

without prior electricity comparison portal usage), rendering them a promising device for

broader audiences.

Applicability of Findings

This study investigates the use of a label for regional green electricity in the context of

comparison websites in the German electricity retail market. Yet, our findings may also

provide insights beyond this scope. First, while comparison portals are the predominant sales

channel for electricity providers especially in Germany (YouGov, 2015), a label for regional

green electricity could be used in other sales channels too, including provider websites and

customer letters. Second, regional green electricity labels could offer similar benefits in other

liberalized electricity retail markets (i.e., where consumers can choose between multiple

electricity providers: e.g., the United Kingdom and 18 states in the United States of America).

Third, even in non-liberalized electricity markets (e.g., China), a label for regional green

electricity may be applied to some applications outside of the traditional electricity retail. For

example, regionality and sustainability of electricity supply have been identified as a key

criterion in the evaluation of electric vehicles charging services (Fabianek et al., 2020).

Charging station operators could hence use a label for regional green electricity as a

differentiator in the market.

Limitations and Paths for Future Work

Like any empirical study, this one is not without limitations and the nascent field of research

on regional green electricity offers numerous paths for future work. First, our study does not

emphasize the source of the label which, in practice, is typically issued by either a

governmental organization, the providers themselves, or an independent third party. Prior

work on eco labels suggests that governmental labels are most effective (e.g., Banerjee &

Solomon, 2003), but future work should evaluate whether these results apply to the regional

green electricity context.

Second, the study does not investigate different provider properties. For example, earlier

studies suggest that consumers prefer energy providers with ties to their region (Kalkbrenner

et al., 2017) or headquarters in their region (Sagebiel et al., 2014). Also, providers in municipal

(Rommel et al., 2016) or local (Ndebele et al., 2019) ownership are preferred by consumers.

Future work could assess whether and how the label’s effects on consumer attitudes and

behavior differ between those provider attributes.

Third, this study focuses on trust in provider and product. Other targets of trust could consider

the label itself. If consumers fail to trust the label, they are not likely to use it as source of

information for decision-making (Boulding & Kirmani, 1993). Future work could investigate

what drives consumers’ trust in the label (e.g., whether it is issued from a governmental or

private institution as outlined above) and whether and how trust in the label mediates the

label’s effects on consumers attitudes and behavior.

Chapter VI

106

Fourth, this study is built on an experimental footing. External validity could be enhanced in

future work by investigating real consumer behavior – for example through A/B testing in

cooperation with price comparison portals.

Chapter VII

107

Chapter VII: Conclusion and Outlook

I conclude this thesis by revisiting the research objectives outlined in the introduction,

discussing big picture implications, and giving an outlook on recent developments in

research and practice.

Re-visiting the Research Objectives

In this thesis, I investigate regional trust cues in the context of green energy platform

economics. Therefore, four overarching research aims were derived in the introduction. In the

following, I will briefly discuss main findings to each research objective:

RO1: Provide an overview on how current developments (i.e., decarbonization,

decentralization, and digitalization) are shaping the energy sector of the future.

As discussed in Chapter II, platform business models are emerging in the sector and promise

to shape its transformation. These business models will fundamentally affect the conventional

electricity value chain by enabling prosumers to market their assets, creating new stages for

trading and collaboration, increasing transparency, and boosting competition in the sector.

This work provides a two-dimensional framework (Figure 3) along the spatial characteristics

of the application (residential or mobile) and the type of business interaction involved (B2C,

C2C, C2Grid). Further, an in-depth discussion how platform business models will affect the

value chain is provided (Figure 5). Also, an analysis of existing literature suggests that future

work should address so far underrepresented aspects of GEPE, such as, for example, user

interfaces and social interactions.

RO2: Assess whether consumers value regionality in the electricity context.

The analysis in Chapter III concludes that consumers indeed appear to value regionality when

purchasing electricity via digital UIs and are willing to pay a premium for it (Table 2). Most

importantly, the interaction of 1) geographic proximity of an electricity supplier to the

customer (i.e., geographic regionality) and 2) the supplier’s attachment to the customer’s

region (i.e., entrepreneurial regionality) is what drives this preference. In other words,

consumers seem to value providers which are located in, owned by, operationally focused on,

and tied to their region.

RO3: Understand how regional trust cues are used on user interfaces in practice.

Chapter IV offers a look at design elements on real provider websites in the German electricity

market. According to this snapshot, images with regional cues but also witch social and nature

cues are frequently embedded on energy provider websites (Figure 16). When it comes to text

cues, the websites also use those three cue types along with price and quality key words (Figure

19). Interestingly, the chapter unveils that regional energy providers use regional image and

Chapter VII

108

text cues significantly more often than national providers (Figure 22). Note that the analyzed

websites use regional cues referencing one particular region (typically the region around their

headquarters. E.g., Stadtwerke Heidelberg show an image of Heidelberg on their website) or

unspecific cues (e.g., the text cue: ”from your region”). Hence, the same region is referenced

regardless of the user location. A recent trend in UI design is taking this concept one step

further: Chapter IV discusses a case study in which an energy provider website is customized

to the user location (i.e., a user from Hamburg is shown an image of Hamburg while a user

from Berlin will see an image of Berlin; Figure 23).

RO4: Evaluate whether and how regional trust cues (i.e., images, labels) affect user

attitudes and behavior.

This thesis provides initial evidence that regional trust cues indeed affect user attitudes and

behavior. The experiments in Chapter V provide data insinuating that the use of regional

imagery is associated with higher visual attention and trust (Table 5, Table 9). In Chapter VI,

the findings offer evidence that regional labels also increase visual attention and trust, and, in

addition, decrease time for decision-making (Table 15). The increased visual attention to

regional cues hints that these cues are important (Poole & Ball, 2006) and of interest to the

users (Cyr & Head, 2013). Also, it suggests that the information provided by these design

elements is actively made use of for decision-making (Gloeckner & Herbold, 2011). The

observed quicker time to decision is associated with higher ease of use of the UI (Lin et al.,

2017). Trust is important because it is a perquisite for basically any commercial relationship

(e.g., Mayer et al., 1995; McKnight & Chervany, 2001).

Implications, Limitations, and Future Research

This work at hand analyses platformization, regionality, and regional trust cues in the energy

sector with multiple methodologies (e.g., content analysis, eye-tracking, online experiment,

etc.) and across various UI types (e.g., homepages, comparison portals, etc.). This section takes

a bird’s eye perspective to summarize the key implications of my thesis:

Societal Implications

Regarding my overarching research motivation, which is contributing to the knowledge on

how to design IS solutions to support more sustainable decision-making, the thesis offers the

general insight that regional trust cues seem to provide an effective tool to nudge consumers

towards more regional decision-making. This implies societal benefits on multiple levels: First,

on a general level, consumer decisions in favor of regional products and services are

considered sustainable in many dimensions such as biodiversity, animal welfare, governance,

and resilience (Schmitt et al., 2017). Second, zooming in on the electricity sector, regional

decision-making is sustainable in the sense that regional providers (i.e., companies with high

entrepreneurial regionality) are viewed as driving forces for the power sector’s transformation

(Berlo & Wagner, 2011): They install and operate renewable assets such as solar and wind

parks in their region, provide heat district and energy management solutions (Richter, 2013),

and coordinate local energy markets (Weinhardt et al., 2019). Therefore, nudging consumers

into the direction of these companies may accelerate the sector’s sustainabilization. Third,

Chapter VII

109

zooming in even further on a particular product within the electricity sector, promoting

regional green electricity contributes to the sectors sustainabilization as follows. The supply of

renewable electricity in or close to urban areas is a core lever to reduce carbon emissions

(Schenone & Delponte, 2021). It reduces transmission losses (Bauknecht et al., 2020),

contributes to a higher reliability of the system (Zerriffi et al., 2007), and avoids grid

expansions (Allard et al., 2020). Our findings suggest that a label for regional green electricity

can play a role in this transition where it may serve as a powerful communication tool.

Policy Recommendations

From a policy perspective, the work at hand offers three recommendations. First, in response

to the overall transition in the energy sector, policy makers should aim to decrease complexity

and bureaucracy in this highly regulated sector and provide a reliable legal framework that can

be used to implement platformization and sustainabilization in the energy sector. Second, the

findings suggest that introducing a label for regional green electricity is a powerful policy tool

to accelerate the green transformation in the sector. Especially so, if it goes along with a

marketing campaign for the label as familiarity with such a label is critical for its effectiveness.

Third, policy makers should also consider potential adverse effects that regional trust cues may

carry. As discussed in Chapter IV, providers may use regional cues to deceive consumers and

regional wash their company image. Just like in the green washing context, companies could

apply regional trust cues to pretend regional attributes of their products and services.

Theoretical Contributions

From a theoretical lens, I will focus on how this work contributes to two established theoretical

concepts. First, this work provides a new wrinkle to CET (Shimp & Sharma, 1987). The theory

assumes consumer preferences for regional products and services through triggering an

underlying evolutionary mechanism. This mechanism is based on a match of consumer

location and the product’s geographic origin. This work derives the concept of Regional

Presence to display that activating this evolutionary pattern does not necessarily require an

actual geographic match but instead can be triggered already by the perception of regionality.

Second, this thesis is among the first to apply Signaling Theory (Spence, 1973) to the context

of regional green electricity. In doing so, this work shows that a single label may

simultaneously provide a meaningful signal (in the sense of generating a “separating

equilibrium”; Bergh et al., 2014, p. 1335) in two dimensions of product claims, namely,

generation technology and location of generation asset.

Advice for UI designers

This work also features tangible recommendations for UI designers. The findings in this thesis

insinuate that regional trust cues are effective design elements in UI design across different

types of UI (e.g., websites, comparison portals, etc.). When embedding regional image cues on

UIs, it appears that the most iconic sights of a city have larger effects on user attitudes and

behavior than less famous landmarks. Further, we discuss a critical challenge for

geographically customized UI design: Adjusting UI design to a user’s geography requires

knowing the user’s location. This work discusses different approaches to capture a user’s

location (Chapters IV and V) and in the outlook, a peek into how a real platform operator is

approaching this challenge is provided.

Chapter VII

110

Limitations and Paths for Future Research

Like any research endeavor, this one is not without limitations. First and foremost, no real

behavior was observed in the studies. Future research should enhance external validity

through investigating actual user actions. I provide a glimpse into an ongoing research project

in the outlook. In that project, we cooperate with a comparison website outside the electricity

context to perform an A/B test on regional imagery. The project will also address a second

limitation, namely, that this work focuses on the electricity industry. As discussed in the thesis,

findings should be transferable to other contexts as electricity is a homogenous and credence

good which is transported though networks. However, future work should thrive to produce

the evidence for this assumption. Third, regionality is an abstract concept and I believe that

this work has just scratched the surface of understanding it. There are still many facets to

explore in future research, such as, for instance, isolating whether the associated effects are

driven by living in a certain area (i.e., place of residence) or being entrenched in it (i.e., a feeling

of Heimat). Another aspect to explore is the different geographic levels. For example, the

Brandenburg Gate offers an object of geographic identification for residents of the city of

Berlin but also for all Germans. Fourth and last, the experiments in this thesis have focused

on imagery and labels. Future work could investigate other trust cues, such as, for instance,

text. Further, a combination of different cue types and their interaction effects would be

worthwhile investigating.

Outlook

Recent market trends

Since I started my work on this dissertation, geographic UI customization has gained traction

in and beyond energy sector. While Greenpeace Energy may be considered the pioneer in this

regard (see Figure 23), other providers have followed suit. A recent example is the regional

utility entega (Figure 37). This provider has elected the simplest but also most accurate

approach to identifying the user location: they simply ask the users for their location. Users

are requested to click on a city name and are then redirected to a customized page for that city.

Note that both imagery and text cues are embedded.

Chapter VII

111

FIGURE 37. EXAMPLE FOR GEOGRAPHIC UI CUSTOMIZATION ON WEBSITE31

Ongoing research projects

Building on the research presented in this thesis, two research projects have been initiated by

me and my colleagues with the aim to take our research to practice. The first project coincides

with the recent market trend outlined in the previous paragraph. In cooperation with the

leading German prices comparison website (idealo.de) we are evaluating how regional imagery

affects click rates in an A/B test setting. This addresses two major limitations of this thesis by

measuring real user behavior and targeting a use case outside of the electricity sector.

Preliminary stimulus material is provided in Figure 38. Idealo.de has implemented a feature

called “in your region” which limits the displayed offers to vendors with brick-and-mortar

store within a certain distance to the user (the standard setting is 50km). The idea is to buy

the product online and picked it up in the store. The aim of the A/B test is to evaluate whether

click rates can be increased by adding regional trust cues in the form of an image or a map to

the UI.

Source entega, available online: https://www.entega.de/regionaler-oekostrom/;

https://www.entega.de/regionaler-oekostrom/stuttgart; https://www.entega.de/regionaler-

oekostrom/muenchen; accessed on 31.07.2022 (accessed on 30 July 2022).

Chapter VII

112

FIGURE 38. STIMULUS MATERIAL FOR FIELD EXPERIMENT

Again, the question arises how to gain the information on each user’s location when the feature

is requested. As hypothesized in this thesis (see Chapters IV and V), a combination of

approaches is applied in this use case (Figure 39). When available, the zip code from the user

account is considered to calculate which brick-and-mortar stores are within range. If this data

is not available in the account or the user does not have an account, users have the option to

manually entry their zip code outside of the feature – for example, in an earlier shopping

process on this website. If available, this data is used. Otherwise, the website tries to estimate

the user location based on the IP address. If all three approaches fail, a pop-up window appears

when the user requests the feature, and the zip code needs to be entered into this window

before the user can proceed to the feature. Once using the feature, users still have the option

to manually override the results from this calculation and provide another zip code.

Chapter VII

113

FIGURE 39. STRATEGY TO OBTAIN USER LOCATION

I conclude with an outlook on a second research project. Reverting all the way back to

Chapter II, we are partnering with a strategy consulting firm to discuss platform business

models in the energy sector. We aim to derive tangible implications for business leaders and

publish them in a leading outlet for management practitioners.

Conclusion

This work at hand responds to a call for IS research on the design of solutions that support

decision-making in favor of more sustainable practices. Having explored different regional

trust cues and their effects on user attitudes and behavior, I come to the conclusion that such

cues provide an effective design element in this regard. In particular, the context of green

energy platform economics offers an intriguing field of application as regional trust cues could

play a role in accelerating the energy sector’s transformation into a carbon free system.

Accordingly, regional trust cues should receive more attention among academics and

practitioners within the context of green energy platform economics and beyond.

114

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Appendix

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APPENDIX

134

Appendix to Chapter II

Literature Classification

GEPE Matrix: As per framework numeration

Outlet Type: S= Scholar, P= Practitioner

Source of energy: E= Electricity, G = Gas, H2= Hydrogen, H= Heat

Platform Topic: AR = IS Architecture, BM = Business Model, UI = User Interface, SI = Social

interaction (community), RF = Regulatory/Policy Framework, OP = Optimization, AT =

Artefact, MD = Market Design, AD = Acceptance

Methodology: LR = Literature Review, MO = Model, PR = Protocol, CS = Case Study, SI =

Simulation, FW = Framework, CW = Conceptual work, PT = Prototype, DS = Design Science

Research, SU = Survey, EI = Expert interviews, EX = Experiment, FS = Field Study

TABLE A1: LITERATURE CLASSIFICATION

Author (year)

Outlet

GEPE

Matrix

Type

Source of

energy

Context

Methodol

ogy

Alam et al. (2019)

Applied Energy

MO, SI

Albrecht et al. (2018)

Hawaii International Conference on

System Sciences (HICSS)

1 to 6

Amoretti (2011)

International Journal of Hydrogen

Energy

E, H2

PR, SI

An et al. (2020)

Applied Energy

CS, SI

Andersson et al. (2010)

Energy Policy

Bedogni et al. (2014)

International Conference on Next

Generation Mobile Applications,

Services and Technologies, NGMAST

AR, UI, AT

PT, SU

Bessa & Matos (2014)

Electric Power Systems Research

MO, SI

Bessa et al. (2011)

IEEE Transactions on Smart Grid

2, 6

Block et al. (2007)

Hawaii International Conference on

System Sciences (HICSS)

E, H

Brandt et al. (2012)

International Conference on

Information Systems (ICIS)

Brandt et al. (2017)

Transportation Research Part D:

Transport and Environment

Bremdal et al. (2017)

International Conference on Electricity

Distribution (CIRED)

Broneske & Wozabala

(2017)

Manufacturing and Service Operations

Management

BM, MD

MO, SI

Cardell (2007)

IEEE Lausanne Power Tech 2007

Chen & Su (2019)

IEEE Transactions on Smart Grid

MO, SI

Ciuciu et al. (2012)

2012 6th IEEE International

Conference on Digital Ecosystems

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Clairand et al. (2018)

IEEE Access

Cui et al. (2014)

IEEE PES Innovative Smart Grid

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Gkatzikis et al. (2013)

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MO, SI

Goebel & Jacobsen

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IEEE Transactions on Power Systems

MO, SI

San Roman et al. (2011)

Energy Policy

2, 4, 6

BM, RF

Gonzales et al. (2014)

IEEE Transactions on Power Systems

Guille & Gross (2009)

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AR, BM

Hackbarth & Loebbe

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RF, AD

Hahnel et al. (2019)

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RF, AD

Hast et al. (2014)

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Hermana et al. (2016)

IEEE Intelligent Transportation

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OP, MD

Hill et al. (2011)

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Hoang et al. (2017)

IEEE Access

BM, OP

MO, SI

Huang et al. (2015)

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Jargstorf & Wickert

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BM, RF

Jin et al. (2020)

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Jogunola et al. (2017)

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PR, SI

Johanning & Bruckner

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International Conference on the

European Energy Market, EEM

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OP, AT

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LR, FW, EI

Lee & Cho (2020)

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Lezama et al. (2019)

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MO, SI

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MO, SI

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IEEE Transactions on Power Systems

MO, SI

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IEEE Access

AR, MD

SI, CW

Liu et al. (2019)

The Electricity Journal

Loisel et al. (2014)

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MO, SI

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MO, SI

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Energy Policy

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Energy Policy

BM, RF

Majumder et al. (2014)

IEEE Symposium on Computational

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Martín et al. (2016)

Renewable and Sustainable Energy

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AR, BM

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International Conference on the

European Energy Market, EEM

MO, SI

Mengelkamp et al.

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Applied Energy

AR, BM,

RF, MD

LR, CS

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Energy Policy

RF, AD

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Michaels & Parag

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Mihaylov et al. (2014)

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RF, MD

FW, CW

Morstyn et al. (2019)

IEEE Transactions on Smart Grid

MO, SI

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IEEE Transactions on Power Systems

MO, SI

Motalleb et al. (2016)

Energy Conversion and Management

AR, OP

MO, SI

Mwasilu et al. (2014)

Renewable and Sustainable Energy

Reviews

Niesten & Alkemade

(2016)

Renewable and Sustainable Energy

Reviews

5, 6

LR, CS

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Applied Energy

AR, OP

MO, SI

Noyen et al. (2013)

International Conference on Mobile

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AR, AT

Olivella-Rosell et al.

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Energies

MO, SI

Parag & Sovacool

(2016)

Nature Energy

3, 5

Parag (2015)

ECEE Summer study proceedings

Park & Yong (2017)

Energy Procedia

Parsons et al. (2014)

Energy Economics

BM, AD

Paudel et al. (2019)

IEEE Transactions on Industrial

electronics

MO, SI

Paukstadt et al. (2019)

European Conference on Information

Systems (ECIS)

LR, CS

Plenter (2017)

IEEE Conference on Business

Informatics

2, 4, 6

Plenter et al. (2018)

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Journal of Power Sources

Radi et al. (2019)

IEEE International Conference on

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Ramos et al. (2016)

Utilities Energy

Rassaei et al. (2018)

IEEE Transactions on Smart Grid

MO, SI

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IEEE International Conference on

Mobile Ad Hoc and Sensor Systems

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International Conference on the

European Energy Market, EEM

BM, RF

Rosen & Madlener

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Decision Support Systems

MO, SI

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The Energy Journal

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IEEE Signal Processing Magazine

Siano et al. (2019)

IEEE Systems Journal

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AR, SI

SI, FW

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IEEE Transactions on Power Systems

MO, SI

Sortomme & El-

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IEEE Transactions on Smart Grid

MO, SI

Soshinskaya et al.

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Renewable and Sustainable Energy

Reviews

BM, RF

Sousa et al. (2019)

Renewable and Sustainable Energy

Reviews

LR, SI

Stroehle & Flath (2016)

European Journal of Operational

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MO, SI

Tomic & Kempton

(2007)

Journal of Power Sources

Torbaghan et al. (2016)

International Conference on the

European Energy Market, EEM

Tushar et al. (2018)

IEEE Access

MD, AD

MO, SI

Tushar et al. (2019)

Applied Energy

MD, AD

MO, SI

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Energy Policy

BM, RF

Vanrykel et al. (2018)

Competition and Regulation in

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Wang et al. (2019)

IEEE Transactions on Systems, Man,

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AR, OP

Weiller & Neely (2014)

Energy

Weiller & Pollitt (2013)

Cambridge Working Paper in

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5, 6

OP, MD

LR, CS

Weinhardt et al. (2019)

e-Energy ACM International

Conference on Future Energy

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AR, BM,

RF, MD

White & Zhang (2011)

Journal of Power Sources

Wörner et al. (2019)

International Conference on

Information Systems (ICIS)

Wu et al. (2012)

IEEE Transactions on Smart Grid

MO, SI

Wu et al. (2015)

IEEE Transactions on Industrial

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Xiao et al. (2018)

IEEE Transactions on Power Systems

E, H2

MO, SI

Yoon et al. (2016)

IEEE Transactions on Vehicular

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MO, SI

Zhang et al. (2016)

Energy Procedia

AR, BM

MO, SI

Zhang et al. (2017)

Energy Procedia

Zhang et al. (2018)

Applied Energy

AR, AT,

MO, SI

Zhang et al. (2018)

IEEE Transactions on Smart Grid

MO, SI

Zhang et al. (2019)

IEEE Transactions on Intelligent

Transportation Systems

PR, SI

Zhao et al. (2016)

Applied Energy

Zhou et al. (2017)

Energy Procedia

Zhou et al. (2018)

Applied Energy

MO, SI

Zhou et al. (2018)

Energy

MO, SI

Alam et al. (2019)

Applied Energy

MO, SI

Albrecht et al. (2018)

Hawaii International Conference on

System Sciences (HICSS)

1 to 6

Search Tags

TABLE A2: LITERATURE SEARCH TAGS

Topic

GEPE Matrix

Search Tags

Search results

Green Energy Platform

Economics

general

“platform economics” AND electricity

222

general

“platform economics” AND gas

133

general

“platform economics” AND hydrogen

general

“platform economics” AND energy

279

Comparison website

“comparison website” AND energy

672

“comparison website” AND electricity

439

“comparison website” AND gas

335

“comparison website” AND hydrogen

References

140

“web aggregator” AND energy

“web aggregator” AND electricity

“web aggregator” AND gas

“web aggregator” AND hydrogen

118

“comparison platform” AND energy

417

“comparison platform” AND electricity

334

“comparison platform” AND gas

139

“comparison platform” AND hydrogen

“Verivox”

637

“Check24”

418

“Uswitch”

645

“Bulb.co.uk”

“energywatch uk”

“chooseenergy.com”

“electricchoice.com”

Charging integrator

*EV charging AND “information service provider”

“charging information service”

“charging integrator”

*EV AND “charging aggregator”

“charging aggregator”

“comparison website” AND *EV charging

“Roaming platform” AND *EV charging

“charge point map” AND *EV charging

“comparison website” AND hydrogen

“*Roaming platform” AND hydrogen

“fuel station location” AND hydrogen

“*Roaming platform” AND CNG

“fuel station location” AND CNG

“140lugsurfing”

“GoingElectric”

“chargeNow”

“plugshare”

288

“chargeyourcar”

“chargemap”

146

“chargepoint”

781

“electromaps”

163

“CNG Trip Planner”

“gibgas”

“h2.live”

P2P energy trading

“Local energy market*” AND “Platform”

421

“Microgrid” AND “Platform”

21500

“P2P energy trading” AND “Platform”

359

“P2P electricity trading” AND “Platform”

133

“Peer to Peer energy trading” AND “Platform”

574

“Peer to Peer electricity trading” AND “Platform”

325

“Sharing economy” AND energy

15800

“Sharing economy” AND electricity

10700

“Brooklyn microgrid”

1390

References

141

“Power Ledger”

337

“Grid+” AND Energy

“LO3 Energy” OR “LO3Energy”

447

“Suncontract”

“Eemnes Energie” OR “EemnesEnergie”

“Elecbay”

“Hive Power”

“Verv VLUX”

“Sonnenbatterie” AND P2P

“Dajie” AND P2P

P2P EV charging

2920

“Sharing economy” AND “*EV charging”

247

“*EV charging” AND “peer to peer”

891

“*EV charging” AND “P2P”

365

“charger sharing”

“plug sharing”

“Share & charge” OR “share&charge”

“Evmatch”

“wecharge” OR “charg coin”

“CHRG network”

“Elbnb”

“Wattpop”

Home to grid

“Home to grid”

227

“Home 2 grid”

“Building to grid”

444

“Building 2 grid”

“Residential to grid”

“Residential 2 grid”

“smart home” AND “frequency regulation”

730

“smart home” AND “ancillary services”

1340

“smart home” AND “grid regulation”

112

“smart home” AND “demand response”

8040

“Local flexibility markets”

165

“Sonnenbatterie” AND “grid”

218

“Sonnenflat”

“Verv VLUX”

“Power Ledger”

337

“Piclo flex”

“Hive Power”

Vehicle to grid

“vehicle to grid” AND platform

6360

“vehicle 2 grid” AND platform

“V2G” AND platform”

5110

“electric vehicle” AND “frequency regulation”

6490

“electric vehicle” AND “ancillary services”

7850

“electric vehicle” AND “grid regulation”

1140

“electric vehicle” AND “demand response”

16600

*EV AND “frequency regulation”

7840

*EV AND “ancillary services”

10800

References

142

*EV AND “grid regulation”

1110

*EV AND “demand response”

17100

“Fermata Energy”

“Nuvve”

195

“the mobility house”

112

Note: Google scholar search area: all; excluding patents

References

143

Appendix to Chapter IV

Additional Website Examples

FIGURE A1: REGIONAL IMAGERY WEBSITE EXAMPLES: CITYSCAPES

FIGURE A2: REGIONAL IMAGERY WEBSITE EXAMPLES: BUILDINGS

Sources: Stadtwerke Oberkirch, available online: https://www.stadtwerke-oberkirch.de/ (accessed on

16 February 2021); Stadtwerke Neckargemünd, available online: https://www.stadtwerke-

neckargemuend.de/ (accessed on 5 November 2020); Stadtwerke Aschersleben, available online:

https://www.sw-aschersleben.de/startseite.html (accessed on 10 November 2020); Stadtwerke Langen,

available online: https://stadtwerke-langen.de/ (accessed on 10 November 2020).

Sources (all accessed on 5 November 2020): Westfalica Stadtwerke, available online:

https://www.westfalica.de/privatkunden; Stadtwerke Düsseldorf, available online: https://www.swd-

ag.de; Stadtwerke Güstrow, available online: https://www.stadtwerke-guestrow.de

References

144

FIGURE A3: REGIONAL IMAGERY WEBSITE EXAMPLES: MONUMENTS

FIGURE A4: REGIONAL IMAGERY WEBSITE EXAMPLES: CULTURE

Sources: Stadtwerke München, available online: https://www.swm.de/ (accessed on 5 November

2020); Stadtwerke Solingen, available online: https://www.stadtwerke-solingen.de/privat-

gewerbekunden/kundenservice/foerderprogramm-klingen-plus/haushalt/ (accessed on 10 November

2020). Stadtwerke Lehrte, available online: https://www.stadtwerke-lehrte.de/ (accessed on 10

November 2020). Stadtwerke Geesthacht, available online: https://www.stadtwerke-

geesthacht.de/startseite (accessed on 10 November 2020).

Sources: Leipziger Stadtwerke, available online: https://www.l.de/stadtwerke/# (accessed on 5

November 2020); Stadtwerke Stockach, available online: https://www.stadtwerke-

stockach.de/startseite.html (accessed on 10 November 2020).

References

145

Appendix to Chapter V

Stimulus Example and Measurement Items Study 1

FIGURE A5. SAMPLE STIMULUS UI WITH EMBEDDED REGIONAL CUE IN STUDY 136

Notes: For copyright reasons, we display a different image here (creative commons licensed – public

domain). We used different (but similar) images in the experiment. AOIs highlighted via dashed boxes.

TABLE A3. MEASUREMENT MODEL ITEMS STUDY 1.

Item

Mean (SD)

PRP

Looking at this website makes me think of the region I live in.

3.61 (2.67)

This electricity provider is trustworthy.

4.70 (1.28)

I am very likely to buy an electricity plan from this website.

4.28 (1.52)

Note: SD: Standard Deviation, PRP: Perceived Regional Presence, TB: Trusting Belief, TI: Trusting

Intention, Items were translated to German for the experiment

Source for website layout: www.stadtwerke-karlsruhe.de/de/; Source for image:

commons.wikimedia.org/wiki/File:Conspiracist_protest_Brandenburg_Gate_Berlin_2020-08-

29_03.jpg

References

146

Stimulus Example and Measurement Items Study 2

FIGURE A6. SAMPLE STIMULUS UI WITH EMBEDDED SOCIAL, NATURE, AND REGIONAL CUES IN

STUDY 237

Note: For copyright reasons, we display the images from the research model here (creative commons

licensed – public domain). We used different (but similar) images in the experiment.

TABLE A4. MEASUREMENT MODEL ITEMS STUDY 2

Item

Mean (SD)

Perceived

Regional

Presence (PRP)

PRP1: There is a sense of regionality in the website.

4.16 (1.61)

PRP2: Looking at this website makes me think of the region I live in.

3.75 (1.83)

PRP3: This website conveys a sense of regionality.

3.53 (1.66)

Perceived Social

Presence (PSP)

PSP1: There is a sense of human contact in the website.

3.43 (1.52)

PSP2: There is a sense of sociability in the website.

3.52 (1.49)

PSP3: There is a sense of human warmth in the website.

3.89 (1.48)

Perceived Nature

Presence (PNP)

PNP1: There is a sense of closeness to nature in the website.

4.28 (1.64)

PNP2: The website makes me think of nature.

4.33 (1.57)

PNP3: The website evokes the sensation of being in nature.

4.53 (1.55)

Trusting Belief

(TB)

TB1: This electricity provider is trustworthy.

4.58 (1.20)

TB2: I trust this electricity provider keeps my best interests in mind.

4.27 (1.40)

TB3: I would trust this electricity provider.

4.55 (1.22)

Trusting

Intention (TI)

TI1: I am very likely to buy an electricity plan from this website.

4.15 (1.46)

TI2: I would not hesitate to purchase electricity from this website.

4.04 (1.42)

Note: SD: Standard Deviation, Items were translated to German for the experiment

Source for website layout: https://www.stadtwerke-hef.de/; Sources for regional cue:

https://commons.wikimedia.org/wiki/File:The_Grand_Louvre_(235493607).jpeg; social cue:

https://commons.wikimedia.org/wiki/File:Confident_Eye_Contact_(Unsplash).jpg; nature cue:

https://commons.wikimedia.org/wiki/File:Fjallabak_Nature_Reserve.jpg

References

147

Appendix to Chapter VI

Measurement Items

TABLE A7. ITEM STATISTICS

Measure

Question

Scale

Mean

Standard

Deviation

Trusting

Belief

This electricity provider is trustworthy.

0-10 Likert

5.83

1,88

Trusting

Intention

I am very likely to buy an electricity plan

from this website.

0-10 Likert

5.59

2,04

TABLE A8. ITEM INFORMATION

Trusting Belief

Original Item (Everard & Galletta, 2005)

This online store is trustworthy

Adapted to context

This electricity provider is trustworthy

Translated to German

Dieser Stromanbieter ist vertrauenswürdig

Trusting Intention

Original Item (Gefen & Straub, 2003)

I am very likely to buy tickets from

Travelocity.com

Adapted to context

I am very likely to buy an electricity plan from

this website

Translated to German

Ich kann mir vorstellen, diesen Tarif

abzuschließen