Drivers, barriers and impacts of digitalisation in rural areas from the viewpoint of experts

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Drivers, barriers and impacts of digitalisation in rural areas from the viewpoint of experts © 2022 the Authors Published version Ferrari, Alessio; Bacco, Manlio; Gaber, Kirsten; Jedlitschka, Andreas; Hess, Steffen; Kaipainen, Jouni; Koltsida, Panagiota; Toli, Eleni; Brunori, Gianluca Ferrari, A., Bacco, M., Gaber, K., Jedlitschka, A., Hess, S., Kaipainen, J., Koltsida, P., Toli, E., & Brunori, G. (2022). Drivers, barriers and impacts of digitalisation in rural areas from the viewpoint of experts. Information and Software Technology, 145, Article 106816. https://doi.org/10.1016/j.infsof.2021.106816 2022 Information and Software Technology 145 (2022) 106816 Available online 10 January 2022 0950-5849/© 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Contents lists available at ScienceDirect Information and Software Technology journal homepage: www.elsevier.com/locate/infsof Drivers, barriers and impacts of digitalisation in rural areas from the viewpoint of experts Alessio Ferrari a,∗, Manlio Bacco a, Kirsten Gaber b, Andreas Jedlitschka c, Steffen Hess c, Jouni Kaipainen d, Panagiota Koltsida e, Eleni Toli e, Gianluca Brunori f aInstitute of Information Science and Technologies (ISTI), National Research Council (CNR), Pisa, 56124, Italy bInstitute for Technology Assessment and Systems Analysis, Karlsruhe Institute of Technology, P.O. Box 3640, Karlsruhe, 76021, Germany cFraunhofer IESE, Fraunhofer-Platz 1, Kaiserslautern, 67663, Germany dUniversity of Jyväskylä, Kokkola University Consortium Chydenius, Talonpojankatu 2B, Kokkola, 67701, Finland eAthena Research and Innovation Center in Information, Communication and Knowledge Technologies, Artemidos 6 & Epidavrou Maroussi, Athens, GR-151 25, Greece fDepartment of Agricultural, Food and Agro-Environmental Sciences (DISAAA), University of Pisa, Via del Borghetto 80, Pisa, 56124, Italy ARTICLE INFO MSC: 68-02 68U35 68N99 Keywords: Software engineering Requirements engineering Sustainability requirements Interviews Digitalisation Empirical study ABSTRACT Context: The domain of rural areas, including rural communities, agriculture, and forestry, is going through a process of deep digital transformation. Digitalisation can have positive impacts on sustainability in terms of greater environmental control, and community prosperity. At the same time, it can also have disruptive effects, with the marginalisation of actors that cannot cope with the change. When developing a novel system for rural areas, requirements engineers should carefully consider the specific socio-economic characteristics of the domain, so that potential positive effects can be maximised, while mitigating negative impacts. Objective: The goal of this paper is to support requirements engineers with a reference catalogue of drivers, barriers and potential impacts associated to the introduction of novel ICT solutions in rural areas. Method: To this end, we interview 30 cross-disciplinary experts in digitalisation of rural areas, and we analyse the transcripts to identify common themes. Results: According to the experts, main drivers are economic, with the possibility of reducing costs, and regulatory, as institutions push for more precise tracing and monitoring of production; barriers are the limited connectivity, but also distrust towards technology and other socio-cultural aspects; positive impacts are socioeconomic (e.g., reduction of manual labour, greater productivity), while negative ones include potential dependency from technology, with loss of hands-on expertise, and marginalisation of certain actors (e.g., small farms, subjects with limited education). Conclusion: This paper contributes to the literature with a domain-specific catalogue that characterises digitalisation in rural areas. The catalogue can be used as a reference baseline for requirements elicitation endeavours in rural areas, to support domain analysis prior to the development of novel solutions, as well as fit-gap analysis for the adaptation of existing technologies. 1. Introduction When transforming an existing environment through the introduction of a digital system, traditional requirements engineering (RE) approaches normally focus on the analysis of existing processes, stakeholders’ needs, and social relations [1–3]. While this can guide the engineering of suitable solutions that take into account costs, benefits, budget and time within a short-term perspective, it is not sufficient to guarantee that sustainability concerns are addressed in the long run [4,5]. As clearly stated by Becker et al. [4], ‘‘the system the customer ∗Corresponding author. E-mail address: [email protected] (A. Ferrari). wants and the system that should be built are quite different’’. Design choices may privilege some stakeholders while marginalising others, and may not consider silent stakeholders, such as the natural ecosystem and future generations. This is particularly relevant for the application domain of rural areas, including rural communities, agriculture and forestry. The field is currently facing profound technological transformations [6–8], with digitalisation being regarded as a strategic enabler towards sustainable growth at social, economic and environmental level [9,10]. ICT https://doi.org/10.1016/j.infsof.2021.106816 Received 20 May 2021; Received in revised form 17 December 2021; Accepted 20 December 2021 Information and Software Technology 145 (2022) 106816 2 A. Ferrari et al. solutions under the umbrella of precision agriculture, but also logistic systems that support the food value-chain, and even basic communication tools, are considered fundamental means to address sustainability concerns of this domain [9,11,12]. At the same time, the transformation of an existing and highly traditional context with the introduction of a digital system can produce undesired consequences [10,12,13]. To prevent this, RE activities oriented to the development or adaptation of ICT solutions for rural areas need to take into account the domainspecific conditions that influence and are influenced by the adoption of a certain technology [7,12]. In particular, requirements engineers need to know what are the socio-economic, regulatory and environmental drivers that can facilitate the introduction of their solution, as well as the barriers that can create obstacles towards its adoption. Furthermore, they need to evaluate what are the potential impacts, so that positive effects towards sustainability can be maximised, while mitigating negative consequences. This paper aims to support RE activities with a catalogue of drivers, barriers and potential impacts associated to the introduction of novel ICT solutions in the domain of rural areas. These three concepts incorporate traditional stakeholders’ goals among the drivers, but also account for other components that do not currently have a prominent place in RE. In particular, explicitly reflecting on socio-economic, regulatory and environmental concepts that comes before system design (drivers, barriers) and after its deployment (impacts), can help to extend the scope of RE activities so that societal and long-term effects are considered. To elicit information for these three core concepts, we perform a set of 30 semi-structured interviews with experts across the European Union (EU), who were recruited in the context of the Horizon 2020 DESIRA Project (Digitisation: Economic and Social Impacts in Rural Areas).1The experts have diversified knowledge about a wide range of ICT solutions applied in rural areas—e.g., precision agriculture, blockchain-based tracking, and automated milking systems (AMSs). They are selected because of their expertise on families of systems in the domain, and can therefore provide an informed opinion, with the right high-level perspective that gives a (filtered) voice to multiple stakeholders. We perform a thematic analysis of the interview transcripts to identify common categories and provide an expert-based reference catalogue to be placed before any project-specific requirements elicitation activity in the domain of rural areas. Our results show that typical barriers for the adoption of ICT solutions are the lack of connectivity in rural areas, but also fear and distrust towards technology. In addition, the cost of technology and regulatory issues, also related to unclear data governance are relevant barriers. Main drivers are economic, as technology can lead to cost reduction, but also institutional ones, since technology can improve monitoring as well as accountability. In this regard, regulators can play a crucial role by means of funding programmes and norms. Positive impacts are the replacement of repetitive labour and the possibility of exploiting economies of scale. On the other hand, negative impacts are the higher dependency from technology as well as the social exclusion of some players that cannot cope with the change, at least not fast enough. This work contributes with a catalogue of drivers, barriers and impacts associated to the adoption of technological solutions in rural areas. Our themes represent a reliable snapshot of the state of affairs in rural areas, and can be taken as reference for the development of socio-technical systems in this domain. Although the catalogue is domain-specific, the main concepts put forward can be also extended to other fields in which there is a strong interplay between the digital and the social dimension, and sustainability is a main concern. The remainder of the paper is structured as follows. In Section 2, we present the DESIRA project and related work. Section 3describes the conceptual meta-model that describes the constructs of drivers, barriers 1Project website: https://desira2020.eu. and impacts, relating them to more common RE terminology. Section 4 reports the research design, and Section 5presents the results, describing the different categories of drivers, barriers, and impacts. Section 6 summarises the main take-away messages and provides a discussion in relation to existing literature. Section 7provides conclusions and final remarks. 2. Background and related work 2.1. The H2020 DESIRA project The paradigm of cyber–physical systems [14] is often referred to as a model to describe how complex systems interact with the physical world, integrating computation and physical processes. Depending on the context, the cyber and physical spaces can be intertwined with the social space [15], giving birth to the concept of socio-cyber–physical systems [12,16], a paradigm in which humans are at the very centre, as opposed to cyber–physical systems that revolve around computation and physical processes. The socio-cyber–physical paradigm is the core of DESIRA (Digitisation: Economic and Social Impacts in Rural Areas), a four-year H2020 EU project started in June 2019, which focuses its attention on the digitalisation of rural areas, including agriculture, forestry and rural communities. The analysis conducted within DESIRA activities covers both the past and the present, and also aims at developing future scenarios in which the impacts of digital technology can be defined as game changing [17]. A digital game changer can be defined as a disruptive digital technology introduced or adopted in a context. The socio-economic impacts of potential digital game changers are discussed in twenty Living Labs2all across Europe, each around its own focal question that embodies a crucial need or desire in a geographical area. The Living Labs will perform the so-called scenario workshops to explore different future scenarios with respect to gamechanging events, such as the adoption of digital technologies that have the potential to reshape rural areas. The Living Labs will also codesign novel digital solutions tailored on the specificity of rural areas. The co-design will be carried out in the so-called use case workshops, involving relevant stakeholders from different sectors as in the scenario workshops. In this work, we focus on the creation of a baseline catalogue of drivers, barriers and impacts of digitalisation in rural areas, based on experts’ interviews. This is the starting point of the analysis in DESIRA. The catalogue will be further specialised, considering the specific contexts of the Living Labs as novel relevant elements will emerge along with the workshops. 2.2. Sustainability in requirements engineering Sustainability in system engineering has traditionally been interpreted as the ability of a system to evolve and be maintained in a cost-effective way, while managing technical debt [4,18–22]. This vision, which focuses only on the technical side of sustainability, has been criticised by the Karlskrona Manifesto [4], edited by a group of software engineering researchers to raise awareness on the relationship of ICT solutions with ecological and social systems. The manifesto calls for a more systemic view of sustainability during system design, and identifies RE as the key area where system-level thinking can be applied to escape the trap of solutionism [23] and broaden the perspective to reason on potential effects of technological change from the social, ecologic, and economic viewpoints. The call to arms of the Karlskrona Manifesto, which stems from reflections already well developed in the social science field [24], triggered research around the notion of sustainability requirements [22,25–28]. These are intended as quality goals that a system shall fulfil to provide long-term benefits for its 2The Living Labs can be seen on the DESIRA website: desira2020.eu. Information and Software Technology 145 (2022) 106816 3 A. Ferrari et al. environment and members therein, while minimising damage for other members and the environment as a whole [25].3 In recent years, several works have been conducted to address the challenge of eliciting, analysing and satisfying sustainability requirements. Part of the work focuses on experimenting and tailoring RE methods. Others are oriented to surveying the field and provide general frameworks. 2.2.1. RE methods for sustainability Research in RE and sustainability dates back to the late ‘00, with the seminal work of Cabot et al. [30]. The authors propose to use the wellknown 𝑖∗goal modelling framework to represents the sustainability effect of each business or design alternative. Sustainability is defined as asoftgoal (i.e., a nonfunctional/quality requirement) and is further decomposed into subgoals, such as reuse, recycle, etc. to build a reference taxonomy. Mussbacher et al. [31] introduce goal-oriented engineering for sustainability, and uses the Goal-oriented Requirements Language (GLR), extended with the notion of time to account for measurable aspects relate to this variable and its relation to sustainability. Roher and Richardson propose to use a recommender system for sustainability requirements, so to enable reuse of requirements archetypes [32], later refined into sustainability requirements patterns [33]. Mahaux et al. [27] take a more empirical perspective, with an experience report oriented to reflect on the process of discovering sustainability requirements. The paper observes that sustainability requirements can be analysed using traditional techniques, but specific checklists need to be defined and, in addition, a sustainability specialist needs to be involved in the RE process. Brito et al. [34] combine aspect-oriented requirements analysis with the hybrid assessment method, an approach for multi-criteria decision making. They define a meta-model to represent sustainability concerns, which includes the potential effect of a certain requirement, a notion similar to the one of impact that we consider in our paper. Seyff et al. [35] tailor the Win Win negotiation process to consider the impact of requirements on sustainability. The approach is applied on an industrial case study involving an ERP system vendor. Though the experience was considered successful, discussion on the impact of requirements was hampered by a lack of information to anticipate long-term effects, which leads to participants having different, and uncertain, opinions. Specific to the context of rural areas, Doerr et al. [7] present an RE framework to assess and derive new RE methods for social contexts. The authors highlight the need to consider different RE dimensions, including the attitude of people towards ICT systems, as well as the impact of the technology. In a recent work, Duboc et al. [36] present an RE approach to facilitate the elicitation of sustainability-related requirements. The framework consists of a set of questions to be asked to stakeholders during interviews or workshops, and also includes a diagrammatic notation to graphically support a coherent analysis of the relationship between different types of impacts. Saputri et al. [37,38] propose a complete framework, with guidelines to elicit and assess sustainability requirements and metrics. The approach is applied on multiple case studies, showing that the guidelines provided facilitate the identification of sustainability requirements. 3As pointed out, among others, by Venters et al. [25], the concept of sustainability requirement is not well defined in the literature. Here we provide an intuitive idea to clarify what is the topic of discussion, without any ambition for formality or completeness. With respect to the multiple interpretations analysed in the social science literature [29], sustainability is considered here as the integration of a set of social-environmental criteria or qualities to guide human actions or their products. 2.2.2. Surveys on RE for sustainability Based on previous works, also in the broader area of software engineering for sustainability [21,39], Chitchyan et al. [40] gives an overview of techniques that can be applied to support sustainability in each RE phase. On a similar note, García-Mireles et al. [41] present a mapping study on sustainability and software product quality, highlighting that this is a particularly lively area of research, but still at its exploratory stage, with works that are mostly focused on the development of energy-saving solutions, which are only one of the multiple facets of sustainability. In another contribution [42], the same authors focus on surveying RE methodologies for sustainability, pointing out the limited knowledge available on how to assess the achievement of sustainability requirements. While these works mostly focus on gathering data from the literature, Chitchyan et al. [28] look more into practice, performing an interview study with RE professionals to identify their viewpoints on sustainability requirements. Among the different aspects, the subjects generally complained about the absence of a clear development methodology to support sustainability in their companies, and the lack of support for engineers in understanding sustainability issues. Similarly, Condori-Fernandez and Lago [22] perform an online survey with different software professionals to identify how different quality requirements, framed according to the ISO/IEC 25010:2011 Quality model [43], contribute to sustainability. Building on a previous work from Lago et al. [26], they analyse the responses according to four sustainability dimensions, namely: social,technical,economic, and environmental. The results show that the different dimensions are intertwined, as a type of requirement can address multiple dimensions at once. For example, availability and efficiency requirements address the technical dimension, but are also strongly related to the environmental and economic ones. 2.2.3. Contribution Our work belongs to the line of research on RE and sustainability, as it enables reasoning on potential effects of technological change from the social, economic, and ecologic viewpoints, in line with the indications of the Karlskrona Manifesto [4]. Specifically, it falls into the group of studies concerned with expert surveys about sustainability and RE [22,28]. These previous surveys are domain-agnostic, and ask professionals about system requirements that have a relation with sustainability concern [22], or about the state of the practice of sustainability design [28]. In our case, we ask about concrete sustainabilityrelated effects of system adoption (impacts), and about domain-related factors that can affect the adoption itself (drivers, barriers). In addition, we focus on rural areas, and we collect domain-specific information. With respect to previous work, our contribution is thus three-fold: (1) we propose to broaden the scope of RE so to account for societal and long-term aspects [41] by reasoning on the concepts of drivers (which subsumes traditional stakeholders’ goals), barriers and impacts associated to the introduction of a novel ICT solution in a socio-physical domain; (2) differently from previous domain-agnostic work based on interviews and questionnaires [22,28], we focus on the specific field of rural areas, as domain-dependent lenses to support RE activities, especially when sustainability is a main concern [21]; (3) we present an expert-based catalogue of drivers, barriers and impacts specific to rural areas that can be used as a reference for RE endeavours in the field. 3. Conceptual meta-model The goal of this paper is to support RE activities with a catalogue of drivers,barriers and impacts associated to the introduction of ICT solutions in the domain of rural areas. These constructs enrich the set of concepts traditionally used in RE, such as stakeholders, actors, goals (or functional requirements), softgoals (or quality requirements, or nonfunctional requirements), domain assumptions, and specifications [2, Information and Software Technology 145 (2022) 106816 4 A. Ferrari et al. Fig. 1. Informal meta-model describing the relationship between the constructs of our study. Rounded and coloured elements are the specialisation of the meta-model for the domain of rural areas derived from our study. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 3]. The objective of this section is to describe the constructs of drivers, barriers and impacts in a way that resonates to requirements engineers, and relate them to more traditional RE terminology. To support the descriptions, we find it useful to represent these constructs through an informal diagram, reported in Fig. 1 (elements in squared boxes). This should be regarded as the conceptual meta-model behind our catalogue. The figure also includes the categories and themes identified by of our study, i.e., classes of drivers, barriers and impacts specific to the rural area domain (rounded boxes), which can be considered as the specialisation of the meta-model. In the following, we describe the main concepts and their relations, while the actual catalogue will be presented in Section 5. ADigital Technology represents a family of digital systems, or composition thereof, which aims at satisfying or satisficing a given set of hard- and soft-Goals, and in doing so it modifies an existing socio-cyber–physical Context.4For example, a vegetation monitoring technology based on hyper-spectral cameras and signal processing can have the goals of monitoring the field and ensure grain quality; the technology socially and physically modifies a context made of farmers (e.g., by introducing technological experts) and fields (e.g., by introducing cameras onboard drones). The introduction of technology in a context is favoured by Drivers and hindered by Barriers, and has certain Impacts on existing Stakeholders. Drivers include goals of some stakeholders, for example the need to improve wheat quality required by farmers, but also other higher-level aspects, for example the funding from institutions to support specific technologies. Similarly, Barriers include obstacles in KAOS terms [3], intended as elements preventing the achievement of a specific goal, but also more structural impediments that hamper the introduction of the digital technology as a whole in the given context. For example, the difficulty of farmers in interacting with the novel technology, or the regulatory problems related to the use of drones. The concept of Impact is analogous to that already considered, among others, by Brito et al. [34] and by Seyff et al. [35], and is intended as the expected effect that the digital technology can have 4We refrain from the usage of the term environment, which is more commonly used in RE, as the term is reserved to refer to ecosystems. from a sustainability standpoint, and thus in the mid- to long-term. The impact can be positive, as, e.g., reduction of manual labour, but also negative, for example due to the exclusion of small farmers that cannot afford the technology. AStakeholder includes actors, ‘‘standard’’ stakeholders, and any party that is indirectly impacted by the technology without voluntarily interacting with it or taking part to the decision process that leads to its deployment, such as the environment, the animals, or the community as a whole. Drivers, barriers and impacts are associated to different sustainability categories inspired by the literature. Reference categories, or dimensions, that we have considered and adapted to our domain are social,technical,economic,environmental, and individual, as in Duboc et al. [36], Lago [26]5and other authors [4,44,45]. In the following, we specialise the meta-model for the domain of rural areas by interviewing experts in relation to the introduction of digital technology, or digitalisation, for short. We focus on the main elements of drivers, barriers and impacts, and we relate them with sustainability categories, and impacted stakeholders. For each driver, barrier and impact, we will identify a theme (coloured rounded boxes in Fig. 1), and a code (cf. content of Tables 3,4, and 5). 4. Research design The present study can be regarded as a judgment study [46], which is a form of in-depth survey involving selected experts on a certain topic of interest—in our case digitalisation in rural areas. We use semistructured interviews as data collection technique. The study is carried out by first selecting a set of representative experts as participants, and then by interviewing them according to predefined interview scripts. The interviews are analysed to produce a coherent and complete view of the topic of interest based on the collected opinions. The study is exploratory and descriptive in nature and it is guided by the following research questions (RQs): RQ1: What are the barriers hindering digitalisation in rural areas? RQ2: What are the drivers facilitating digitalisation in rural areas? RQ3: What are the potential impacts of digitalisation in rural areas? 5Lago [26] does not explicitly include the individual dimension. Information and Software Technology 145 (2022) 106816 5 A. Ferrari et al. Table 1 Interviewed experts. We report specific expertise, background (social or ICT), and whether they work for academia (A), government (G), or R&D in industry (I). ID Sub-domain Geographical Area Expertise Social ICT A G I A G I 1 Agriculture France Researcher (remote sensing and computer vision) ✓ 2 Agriculture France Support in policy making ✓ ✓ 3 Agriculture France Operation director ✓ 4 Agriculture France Instructor and consultant for agricultural cooperatives ✓ 5 Agriculture Finland Automation technology in farms ✓ 6 Agriculture Belgium Researcher (precision agriculture and photonics) ✓ 7 Agriculture Greece Researcher (innovation and project management) ✓ 8 Agriculture Greece Researcher (agricultural engineering) ✓ 9 Agriculture Switzerland Agricultural research (precision agriculture and unmanned vehicles) ✓ 10 Agriculture Latvia Consultant, researcher (economy and management) ✓ ✓ 11 Agriculture Latvia Researcher (dairy farming) ✓ 12 Agriculture Germany Agronomist, researcher (precision farming and agricultural economics) ✓ ✓ 13 Agriculture UK Head of farms networks ✓ 14 Agriculture Hungary Farm manager ✓ 15 Agriculture Italy Agronomist, researcher (monitoring, protection, information systems) ✓ 16 Agriculture, rural communities Finland ICT project manager, researcher (digitalisation) ✓ 17 Agriculture, rural communities Spain Rural development, support for policy making ✓ 18 Agriculture, rural communities France Sociologist (health and social research) ✓ 19 Rural communities France Advisor, entrepreneur ✓ ✓ 20 Rural communities Netherlands Rural and community development ✓ 21 Rural communities Netherlands Ethics, impacts of innovation ✓ 22 Rural communities Spain Manager of a protected natural area ✓ ✓ 23 Rural communities Belgium Policy expert ✓ 24 Rural communities Germany Researcher (digitalisation, software engineering) ✓ 25 Rural communities Poland Agriculture and food economics ✓ 26 Rural communities Greece Researcher (sustainable development and economics) ✓ 27 Forestry Italy Manager of non-profit consortium, forest engineer ✓ 28 Forestry Italy Startup founder, renewable sources ✓ 29 Forestry Austria Education, training, research ✓ ✓ 30 Forestry Spain Head technical team of environmental information network ✓ Table 2 Interview scripts covering digital technologies (DTs). Q1 and Q6 are common for both profiles. ICT Expert Social Science Expert Q1 Which are the DTs you deal with or encounter most commonly in your work? What are their main uses in the three domains? Q2 Which is a plausible tomorrow’s use of the DTs you cited in Q1? What are the socio-economic impacts of the DTs you cited in Q1? Q3 Can you provide some examples of uses of DTs/new developments you are participating to/aware of? Do you think those developments have the potential to be game changers? What do you consider as drivers for the adoptions of DTs in the three domains? Q4 Which are the positive and negative impacts of technological advancement on SMEs, workers, and other actors, especially considering cases you have been involved into? What do you consider as barriers for the adoptions of DTs in the three domains? Q5 What do you consider as drivers and barriers for the adoption of DTs in the three domains? How new and deeper reflections/methodologies to assess the impacts of technology could help you in your work? Q6 Have you already been involved in any activities to assess the socio-economic impacts of DTs? 4.1. Study participants selection Participants of the study were selected by the authors based on opportunistic sampling. The goal was to involve experts that: (a) could cover the main sub-domains of rural areas, namely agriculture, forestry and rural communities; (b) covered ICT and social-science background; (c) could be representative of different geographical areas of the EU. The participants to the DESIRA project, who have interdisciplinary backgrounds including ICT, social science and agriculture, contacted specific subjects in their fields that were considered as reliable experts due to their professional position and their publicly recognised active role in the theme of digitalisation for rural areas. Table 1 lists the selected participants together with their reference subdomain, nationality, and main expertise. 4.2. Data collection and analysis To collect data, we first defined a set of interview scripts to guide the interviews and then we applied thematic analysis [47,48]. Interview scripts and delivery. The selected subjects have an interdisciplinary background, but broadly belong to two groups: social science and ICT experts. Therefore, we defined two main interview scripts, one for each group. The questions for the two groups are reported in Table 2. Interviews were conducted remotely, or in person, according to different conditions, by the different authors of this paper and by other partners of the consortium, and then transcribed. The transcription was checked by the interviewed subjects for misunderstanding. Interview analysis. Each interview was initially evaluated by the first author in two cycles. In the first coding cycle, from each interview, he extracted independent paragraphs (469 in total from 18,000 words, 38 words per paragraph on average) and coded them based on their content, and following the coding guidelines of Saldaña [49] for descriptive coding by associating descriptive codes to them. In a second cycle, the codes were selected, reviewed and aggregated into themes, and then into higher-level sustainability categories, by means of axial coding [49] and leveraging the sustainability dimensions from the literature [4,26,36,44]. For example, the paragraph Historically [...] there is solidarity among neighbours and people who live in rural areas, Information and Software Technology 145 (2022) 106816 6 A. Ferrari et al. Table 3 Barriers hindering digitalisation in rural areas. Socio-cultural barriers Demographic Age issues, social isolation, sparse population, seasonal work Distrust Distrust of funders, distrust of regulators, distrust of ICT supplier, distrust of technology Fear Fear of dependency from technology, fear of hidden costs, privacy concerns Values Attachment to tradition Competence Lack of education, lack of knowledge, lack of skills, digital debt Complexity Complexity of regulations, complexity of technology, paradox of choice Technical barriers Connectivity Absence of infrastructure, low quality of infrastructure Dependability Poor reliability, low efficiency Usability Poor ergonomic standards, poor usability in the field Scalability Limited data storage, limited computing capacity Economic barriers Costs Cost of technology, modernisation cost, maintenance cost, lack of evidence of cost-effectiveness, lack of funding Scale Small market size, small business size, atomised business structure Regulatory-institutional barriers Data management Unclear data ownership, unclear data governance Regulations Frequent change of regulations, legal restrictions on technology, inadequate grant schemes criteria and what the digital does is to allow that natural resilience and solidarity to come out more was initially coded with ‘‘solidarity spirit’’, ‘‘community support’’. Considering similarity with other codes, a higher-level theme called ‘‘cultural tendencies’’ was created as part of the axial coding process. The descriptive code ‘‘solidarity spirit’’ was kept as more representative of a driver, while ‘‘community support’’ was not considered as it was regarded more as an enabler than a driver. In addition, the category Socio-cultural Drivers was produced to aggregate the theme ‘‘cultural tendencies’’ with the one called ‘‘practical demands’’ (cf. Table 4), created in a similar manner. This process, here presented in a linear form, was iterative in practice, and codes, themes and categories were revised, selected and adjusted in multiple cycles. Furthermore, here we considered an example clearly belonging to a single theme in a specific category. In general, a paragraph could be associated to multiple codes, later associated to different themes. For example, the paragraph [...] However, not everybody corresponds the criteria set by the grant schemes and some farmers distrust these funds, coded as Socio-cultural Barriers→‘‘distrust’’→‘‘distrust of funders’’, and as Regulatory-Institutional Barriers→‘‘regulations’’→‘‘inadequate grant scheme criteria’’. The first author used a shared spreadsheet file (a Google sheet) to record themes and categories. From this hierarchical grouping, he produced a set of summary tables that answer the different RQs. The link between data, themes and categories were cross-checked by the third author, who commented for unclear links or theme names (11 disagreements were identified), to come to a consolidated output. 4.3. Threats to validity Validity of the findings is discussed according to the categories of validity, reliability, and generalisability outlined by Leung [50]. Validity. The main requirement for judgment studies is the adequate expertise of the subjects involved, so that the collected opinions are authoritative and informed ones [46]. The level of expertise of the selected subjects was checked by the DESIRA project consortium, which is formed by multiple institutions that study rural areas from different viewpoints (ICT, economic, legal, etc.), and have an up-to-date vision of relevant voices in the field. To balance the specific background of each subject, two types of script were defined, one for ICT experts and the other for social-science experts. To increase content validity, the scripts were reviewed and piloted within the consortium. Concerning the completeness of the information collected from each participant with respect to the RQs, we defined interview questions that are derived from the RQs, but are also sufficiently broad to allow interviewees to freely and completely express their opinions on the discussed topics. A limitation of the study is the reduced number of negative impacts elicited, as the subjects appeared to mostly emphasise positive aspects of digitalisation. Further work within the Living Labs will be conduced with interviews oriented to stress on negative aspects. Member checking was adopted to ensure descriptive validity, as the interviewee could review and correct their transcribed interviews. Reliability. Different forms of structured procedures were adopted to support triangulation and increase the reliability of the findings— correctness cannot be achieved within the constructivist paradigm inherent to our method: (a) the coding activities were applied to the whole text of each interview, and all codes and associated interview data were shared in a spreadsheet, to facilitate cross-checking; (b) one researcher performed the coding activity and a second one crosschecked the results with respect to the original data—the first has an RE background, while the second one is a social scientist, and native English speaker; (c) the resulting findings (i.e., the preliminary versions of the tables reported in Section 5) were further reviewed by the other authors of the paper; (d) excerpts are reported from the interviews that show evidence of the relation between codes and data. Generalisability. In our study, experts were selected to have a sufficient coverage of three main dimensions (subdomain, geographical EU area, and background), as reported in Table 1. Therefore, their opinions, and our findings, mainly reflect their background. In particular, the results are representative for the subdomains of agriculture and rural communities in both southern and northern EU countries, and for forestry, but mostly in southern EU countries. Different results may be obtained if other continents are considered. We cannot claim generalisation of our catalogue to other domains. However, a similar approach to ours can be applied to identify analogous concepts in, e.g., smart cities or industrial areas. 5. Execution and results Interviews were conducted between May 2020 and February 2021. Results were analysed between October 2020 and April 2021. This section reports the results with respect to the different RQs. Each RQ is associated to one of the main reference concepts of this paper, namely drivers, barriers and impacts. For each RQ, we report: •asummary table with categories associated to the concept, themes within a category, and codes within a theme; •a list of the main categories (e.g., social, technical, etc.) identified for the specific concept; •a textual explanation of the themes within a category; •a set of fragments that exemplify the themes, tagged with the specific code (in square brackets). Information and Software Technology 145 (2022) 106816 7 A. Ferrari et al. 5.1. RQ1: What are the barriers hindering digitalisation in rural areas? Barriers are reported in Table 3 and are categorised into sociocultural, technical, economic, environmental, and regulatory-institutional. Below, we discuss the different categories and internal themes. We report fragments of the interviews together with the codes associated to them, to provide evidence of the relation between data and themes. Socio-cultural barriers. Most of the barriers to digitalisation are rooted in the cultural, socio-demographic, and somewhat emotional aspects and inclinations of the individuals populating the rural communities. We identify six types of barriers: 1. demographic, related to age issues, the logistic isolation of rural communities, the sparse, low-density population, and the presence of seasonal work, which makes rural areas places in which there is a limited permanent human presence for large part of the year. [demographic6]Main limitations of these sectors are the atomised structure, the harsh working conditions, the seasonal work and the sparse and aged rural population. All of them facilitate the social and economic isolation. 2. distrust, which is oriented towards different players, from founders and regulators, to ICT suppliers and technology in general. [distrust of supplier] [There is] lack of trust in partners who use the data, which can be ICT companies (who may use the data for profiling, or on the stock market or who may sell the data) or other partners in the value chain (for example, if the farmers and the slaughterhouse start to share data, who will then harvest the benefits: the farmer or the slaughterhouse?) 3. fear, often based or real threats, such as the risk of dependency from technology, the presence of hidden costs such as those related to maintenance of installed technology, and the privacy concerns related to data sharing. [fear of dependency from technology] Finally, there is also a fear of dependency and loss of control among some farmers. For example, by investing in monitoring systems farmers are increasing their dependence from management systems that need internet and electricity to successfully operate. Thus, sudden shocks like electricity loss might have devastating impacts on the farm. [fear of hidden costs] It might also be that the farmer decides not to implement the solution because of the challenges associated with the maintenance of the novelty. 4. values, and in particular the attachment to traditional ways of working and identity. [attachment to tradition] So far, many of them are reluctant to use a lot of technology as it does not fit to their image of being a farmer (e.g. working with the soil). 5. competence, such as general lack of higher education, specific knowledge of technologies, as well as practical skills to deal with technology, and, when these aspects become endemic, the emergence of digital debt that increases the competence barrier to be covered. [lack of knowledge] Another key challenge for farmers is to find staff that would have agricultural education yet would also have the knowledge regarding the cutting-edge farming software and hardware. [digital debt] Because of the poor material connectivity, people managed to cope without digital connectivity, and now they lack the ‘‘digital capital’’ to join the bigger leap in digitalisation. 6This fragment is associated to all the codes in the demographic theme. 6. complexity, which deals with the relationship between the individual and the feeling of being overwhelmed by the complex systems of regulations, the complexity of technology, and the paradox of choice due to the wide variety of technological solutions available in the market. [paradox of choice] As barriers: cost, complexity, skills and the fact that people are lost in the profusion of existing solutions. When farmers are talking about this to their advisors, the latter are sometimes as lost as farmers and limit themselves to propose solution they control. Technical barriers. These are related to four main quality aspects: 1. connectivity, as the absence of a communication infrastructure in rural areas is one of the issues mentioned most often by the interviewed experts. [connectivity] In my research I have seen that rural communities have been, and still are, on the wrong side of a digital divide. Over the past two decades this was mainly a material matter, with a lack of connectivity as the prime issue. 2. dependability, since, when present, technologies need to be dependable especially in particular environmental conditions such as those of fields and forests. [malfunctioning] The agricultural environment is a relatively challenging environment. I have had that with the Near Infrared (NIR) sensors for manure tankers [...] And that is a challenging environment, especially manure is very corrosive. 3. usability, as standards required by the usage of a mobile phone in a field are not the same as those of the same device for daily usage. [usability in the field] [One of the promising technologies is the] use of natural languages recognition to facilitate the interactions with machine (e.g. manage crop operations and field log using voice interaction instead of manual entry). 4. scalability, in terms of size and time complexity, since the amount of environmental data coming from monitoring systems is large, and need to be efficiently processed to take informed decisions in acceptable time, possibly profiting from edge computing solutions. [scalability] Today our computer models are based on ‘‘cloud’’ which means that farmers are locally collecting information thanks to sensors, smartphones or computers. Then raw data are sent to a distant server which will treat them, make calculations, cartographies and recommendations. After that, those results are sent back to farmers’ terminal. But cloud needs that raw information leave from the place they are so it needs a big communication effort between server and data collection area. Economic barriers. Economic barriers are mostly related to the difficulty in dedicating financially sustainable investments in technological solutions, when margins are already limited as it happens in the primary sector. Main themes are: 1. costs, including cost of technology but also cost of modernisation of the physical infrastructure of farms, low evidence of costeffectiveness and the general lack of funds needed to afford the modernisation. [lack of funding, cost of technology] By far the most significant barrier is funding. The technologies are expensive and not all farmers have the funds needed to cover the expenses. [modernisation cost] Another important barrier is related to the properties of infrastructure. Installation of hardware needed to gather data for management systems or to install milking robots requires that farms correspond to certain characteristics. This might mean that farm building is too small, the ceiling is too low [...] In these cases, the modernisation is just too expensive and might include complete reconstruction of the farm. Information and Software Technology 145 (2022) 106816 8 A. Ferrari et al. Table 4 Drivers facilitating digitalisation in rural areas. Socio-cultural drivers Practical demands Demand for work flexibility, demand for workload reduction, demand for wealth, demand for employment, need to reduce isolation Cultural tendencies Cooperative spirit, solidarity spirit, need for inclusion, technological fascination, trust in technology Technical drivers Quality Simplicity of technology, specialisation of technology, proven reliability, proven efficiency Service More connectivity, availability of technology, data availability Economic drivers Market demands Competition, consumer health concerns, green company image, transparent company image, demand for certification, demand of organic products Organisational Presence of intermediary roles, collective forms of organisation, opportunity for cooperation Business needs Need for better control, need for simplification of legal compliance, need for process optimisation, need for better planning Financial Decreasing cost of technology, need for cost-effectiveness Labour Shortage of labour, cost of manual labour Environmental drivers Impact reduction Need to reduce environmental impacts, need to reduce fertilisers, need to reduce pesticides Control Need to decrease food waste, need to improve animal welfare, need to control natural disasters Regulatory-institutional drivers Regulatory restrictions Taxes, constraints, need for regulatory compliance Economic incentives Funding programmes, subsidies, incentives for technological adoption, support for cooperation Educational support Training programmes, technical mentorship, support of education, digital innovation centres Promotional Dissemination of results, promotion of digital entrepreneurship, promotion of digital innovation 2. scale, as rural communities in EU are normally small business and do not have the mass to invest in costly technological renewals. [small business size] Margins are often rather small/thin in rural businesses (small and micro family businesses often dominate the business landscape in rural areas) and this means that businesses can be caught up in trying to make break-even. [small market size] High value enterprises such as milk production will justify the technology many years before low value sectors such as lamb production. Regulatory-institutional barriers. Institutions are also responsible for some barriers, as inadequate or unclear policies can hamper access to funds and technology. In particular, in relation to: 1. data management, which is often unclear in terms of who owns the data coming, e.g., from farm monitoring systems and how these are managed. [unclear data ownership] ‘‘Data food consortium’’ [...] is about to develop a digital standard in order that all data can be integrated from one digital catalogue of products to another to decrease organisational costs and reinforce the control of data ownership. Farmers should only be able to give their agreement on data sharing for a precise and known use. 2. regulations, which are frequently changing, and are sometimes not appropriate for rural contexts when it comes to grant schemes, which tend to privilege endeavours from large-size players. [inadequate grant schemes criteria] The EU funds is an important mean to overcome the challenges associated with access to funds. However, not everybody corresponds the criteria set by the grant schemes. [frequent change of regulations] The legislative context is ultrachanging so the one who says he want to revolutionise the word of agriculture and food industry in general will not succeed. 5.2. RQ2: What are the drivers facilitating digitalisation in rural areas? Drivers are reported in Table 4, and are grouped into the same categories of barriers. The reader will notice that while for drivers we have most of the themes in the economic and regulatory-institutional categories, barriers are mostly socio-cultural and technical. Socio-cultural drivers. Socio-cultural drivers include all those aspects that are related to the main social needs of rural communities and to the typical inclinations and tendencies of stakeholders. We identify the following themes: 1. practical demands represent the social needs, and are related to: (i) reduction of isolation through better communication that can allow to identify and strengthen the links between needs and potential supply; (ii) demand for lighter work, as automation is expected to reduce the manual labour that is typical of rural activities. [need to reduce isolation] New technologies break the existing isolation in those areas, providing the necessary communication coverage. [...] So the technology allows you to mix and match, it allows you to identify where the needs are and where there is a potential supply and to improve the links between them. [demand for workload reduction] And from a social point of view [...] many technologies reduce the workload for everyone, [such as automatic] steering systems and these are the drivers. 2. cultural tendencies, which include the natural cooperative and solidarity spirit of small communities, the need for inclusion in the ‘‘local vibe’’, but also the fascination that technology can create. [solidarity spirit] Historically [...] there is solidarity among neighbours and people who live in rural areas, and what the digital does is to allow that natural resilience and solidarity to come out more. [need for inclusion] With community members [...] the driver seems to be to get included, to join others in (online) groups, and make sure one stays part of the local vibe. Social drivers such as inclusion, but also comparing with peers (other businesses) often turn out to be straightforward, and old-fashioned if you like, motivational factors. [technological fascination] When it comes to harvesting for big crops like barley, wheat etc there are these large scale harvesting machines that workers use for many days in a row. After interviewing farmers in Denmark, that have used yield monitoring tools on their machinery, they stated that their daily job has become more interesting. Technical drivers. A limited yet relevant part of the drivers is also technical, intended as relevant non-functional attributes of technology that can play a crucial role in facilitating digitalisation. We identify two main themes: Information and Software Technology 145 (2022) 106816 15 A. Ferrari et al. 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