Document [original]

Vol.:(0123456789)

Group Decision and Negotiation (2023) 32:435–467

https://doi.org/10.1007/s10726-023-09814-4

1 3

Facing Reciprocity: How Photos andAvatars Promote

Interaction inMicro‑communities

TimmTeubner1· SoniaCamacho2

Accepted: 10 January 2023 / Published online: 8 February 2023

Abstract

Online platforms facilitate the formation of micro-communities on the Internet by

enabling exchange between locally dispersed individuals. Since all interactions are

mediated through the online platform, user representation plays a critical role for

such communities. Grounded in Social Exchange Theory, we report results of a

behavioral experiment on the role of user profile photos and avatars for the emer-

gence of network structures over time. While overall network value increases

slightly, the underlying structures of exchange shift systematically from many weak

ties to fewer but significantly stronger reciprocal exchange relations. Interestingly,

despite representing users in a highly abstracted way, avatars yield outcomes compa-

rable to those when using actual photographs. We discuss theoretical and practical

implications of how online platforms can leverage social cues such as profile photos

and avatars to manage and support micro-communities.

Keywords Laboratory experiment· Micro-communities· Reciprocity· Social cues·

Social exchange theory

Mathematics Subject Classification C9· D61· D85· D91

* Timm Teubner

[email protected]

Sonia Camacho

[email protected]

1 Einstein Center Digital Future (ECDF), Technical University ofBerlin, Wilhelmstraße 67,

10117Berlin, Germany

2 School ofManagement, Universidad de los Andes, Carrera 1 18A-12, Bogotá111711,

Colombia

436

T.Teubner, S.Camacho

1 3

1 Introduction

Online platforms facilitate the formation of micro-communities by enabling

social exchange between locally dispersed individuals with mutual interests or

goals (Lasfer and Vaast 2018; Preece 2000). Examples include communities for

knowledge exchange (Buendía etal. 2018; Pedersen and Lupton 2018), resource

sharing (e.g., Nextdoor), as well as social and professional networking sites

(Grissa 2016). Value created in these communities stems from different forms of

exchange between members where typical interactions include the provision of

advice, answers, hints to job or accommodation vacancies, endorsements, testi-

monials, or simply the sharing of photos, links, or otherwise relevant content. In

a more abstract sense, one user creates value for another user at some cost (e.g.,

time and effort), where the cost is typically smaller than the value created (Bern-

inghaus etal. 2008). Thereby, the creation of value is often motivated by expecta-

tions about reciprocity, where helping someone else today is associated with the

prospect of own benefits in the future (Güth etal. 2010).

Importantly, in such micro-communities, members do not come together for

once-off interactions but multiple times over the course of days, moths, or even

years, establishing transactional histories and knowing with whom they have

interacted in the past (i.e., whom they helped and who helped them). As this cre-

ates path dependencies, it is hence important to take into account (at least the

recent) history of interactions when seeking to understand individual behavior at

any given point in time. Moreover, this also warrants a deeper investigation into

how behavior and outcomes develop over time—both on the individual and group

level.

Considering all interactions are mediated by the online platform, user repre-

sentation plays a pivotal role in this environment. A crucial design decision thus

pertains to how users should be represented in the micro-community. On most

platforms, they can upload a photograph of themselves as their digital represen-

tation (Hesse etal. 2020; Lee etal. 2014), typically in the form of portrait-like

profile pictures (Teubner etal. 2021). Thereby, the platform employs a key fea-

ture of virtually all forms of social exchange in the real world: the processing of

visual appearance in face-to-face encounters. After all, what automatically comes

to mind when thinking about other people is their faces (Tamir and Ward 2015).

Hence, it does not come as a surprise that online communities leverage visual

representations.

The increasing blending of online platforms, virtual worlds, and potential

future metaverse applications also introduces other forms of user representation,

specifically avatars. Avatars refer to stylized graphical user representations that

capture characteristic human features (Bailenson and Blascovich 2004). As pho-

tos have raised various concerns, mainly around privacy and discrimination, ava-

tars are becoming more and more popular with users (Micallef and Misra 2018;

Wolverton 2018). This trend is also evidenced in the popularity of applications

such as Bitmoji, Zepeto, or the Codec Avatar project (Apple 2022; Facebook

2019; Gaus 2018; Nusca 2017; Tech at Meta 2022). Vivid user representations

437

1 3

Photos and Avatars in Micro-Communities

have been found to facilitate exchange relations in various contexts (Bente etal.

2014), and avatars may serve as a viable extension, or even alternative, to photo-

graphs—in particular when privacy is a concern.

This gives rise to questions in view of expectations about reciprocity, the facilita-

tion of individual transactions, and the emergence of community value when different

forms of representation are being used (Hesse etal. 2020; Lee etal. 2014; Tamir and

Ward 2015). While user photographs and avatars have been studied for online social

networks (Steinbrück etal. 2002; Wang etal. 2010), the sharing economy (Ert etal.

2016; Fagerstrøm et al. 2017), public good experiments (Teubner etal. 2013), elec-

tronic commerce (Karimi and Wang 2017), and e-learning (Kear etal. 2014), little is

known about the specific differences between photos and avatars as well as the role of

such cues for facilitating multilateral relations over time (i.e., such as seen in micro-

communities). Prior research has mostly investigated the role of user representation

for static (i.e., snapshot) and bilateral community interactions. In contrast, the role of

user representation in dynamic scenarios as well as for multiple exchange relations has

remainded largely unexplored. Moreover, only few studies have deliberately considered

how and under which conditions photos and avatars differ in terms of outcomes—and

when they do not. Against this backdrop, this paper addresses the following overarch-

ing research question: To what extent does the availability of profile images (pho-

tographs/ avatars) affect the process of network formation and value creation in

online micro-communities, and how do outcomes depend on the employed type

of profile imagery? To address this question, we present behavioral evidence from a

controlled, multi-period laboratory experiment.

We find that while overall network value increases slightly over time, the under-

lying network structures of exchange shift systematically from many weak ties to

fewer but significantly stronger reciprocal exchange relations. Interestingly, despite

representing users in a highly abstracted way, avatars yield results similar to those of

using actual photographs. This study hence contributes to Social Exchange Theory

(SET) by empirically demonstrating two mechanisms that govern how specifically

reciprocal relations—as posited by SET—develop over time and what this means

on a network-level. In addition, results extend the literature on avatars’ influence on

user behavior by illustrating similarities and differences in the effects of avatars and

photos in the absence of other forms of communication.

The remainder of this paper is organized as follows. Section2 sketches out the

study’s theoretical background and develops our hypotheses. Section 3 then pre-

sents method and the experimental design. Next, Sect.4 reports the results which we

discuss in view of theoretical and practical implications as well as limitations and

future work in Sect.5. Last, Sect.6 concludes the manuscript.

2 Theoretical Background andHypotheses

2.1 Social Exchange Theory

Social Exchange Theory (SET) posits that human relationships are formed based

on subjective cost–benefit analysis (Blau 1967). Originating from the nexus of

438

T.Teubner, S.Camacho

1 3

economics, psychology, and sociology, SET conceptualizes interpersonal interac-

tions as a form of social trade, where individuals’ actions are contingent on the ben-

efits they (expect to) receive from others (Homans 1961; Kelley and Thibaut 1978;

Uehara 1990). Summarized across all actors of a network, this net benefit can be

thought of as value creation. Exchanges are prompted by resources’ value and their

dispersion across different individuals (Levine and White 1961). The value of those

resources is materialized through exchanges (e.g., the transfer of ideas, knowledge,

objects, goods, or services). As such, (social) network analysis lends itself well to

study how resources are exchanged within a micro-community. Specifically, network

analysis allows to assess key characteristics of the formation of social exchange in a

network through measures such as network density (Mitchell 1969) and reciprocity

(i.e., “a user strategy to return received favors in a similar way”; Lee etal. 2010, p.

136).

2.2 Reciprocity inSocial Exchange

The notion of reciprocity plays a major role in how social exchange unfolds. It refers

to bi- and multilateral relations of exchange between individuals where one actor

creates value for another that is expected to be returned in good faith, entailing some

agreed-upon standard of equivalence (Berninghaus etal. 2008; Gouldner 1960). As

such, the concept of reciprocity is widely used in Social Network Analysis where it

is referred as the “degree to which individuals report the same (or similar) intensi-

ties with each other” (Scott etal. 2008, p. 203). Here, intensity refers to the “strength

of the relation as indicated by the degree to which individuals honor obligations or

forego personal costs to carry out obligations” (Mitchell 1969; Tichy etal. 1979, p.

509).

Reciprocity represents a core success factor for social exchange relations in

the long run (Cook and Rice 2003; Emerson 1972). Reciprocal exchanges do not

involve explicit bargaining; they are contigent on each party’s actions and thus, often

emerge over time (Cropanzano and Mitchell 2005; Molm 2003). Considering a com-

munity of actors, tracing exchange relations reveals network structures (Cook and

Rice 2003; Surma 2016). Individuals have finite resources (e.g., time) which leads

them to be selective in choosing exchange partners (Levine and Kurzban 2006). In

this sense, reciprocal exchanges tend to function as a stabilizer within networks. As

individual cost–benefit analysis will ultimately define the network structure, reci-

procity is essential for network formation (Mitchell 1969; Scott etal. 2008; Tichy

etal. 1979).

Reciprocity has been found a critical factor in a range of contexts, including per-

sonal relations (Buunk and Schaufeli 1999), online reviews (Bolton et al. 2013),

elections (Finan and Schechter 2012), organizations (Settoon etal. 1996), and more

abstract economic exchange scenarios (Berg etal. 1995; Berninghaus etal. 2008).

One particular finding is that in many cases, factors that may have an impact on

the unilateral provision of resources (e.g., frequency of contact, similarity between

exchange partners) only play a secondary role: Whether an actor decides to provide

439

1 3

Photos and Avatars in Micro-Communities

resources or not is contingent on whether they received resources before (Plickert

etal. 2007), that is, whether a reciprocal relation could be established.

More recently, scholars have explored how reciprocity is manifested in social

networking sites. In this context, reciprocity is often applied to the notion of self-

disclosure: as individuals receive others’ personal information over time, they recip-

rocate by sharing their own personal information with them (Posey etal. 2010). In

the same vein, reacting to content posted by others (e.g., in the form of a “like” or

comment) is hoped to elicit reactions when posting own content (Surma 2016). In a

way, the process of value creation hence relies on an initial spark of value and then

enters a cycle of further contributions. Feelings of indebteness represent drivers of

reciprocation in knowledge exchange (Feng and Ye 2016) and gift giving on social

network sites (Kim etal. 2018). Chen etal. (2018) find that reciprocity is effective to

move users from low to medium motivational states to contribute within Q&A com-

munities. Ye etal. (2018) study reciprocity in an online book barter market, where

they find reciprocal relationships to help improving exchanges through “lower rejec-

tion rates and faster delivery speeds” (p. 521).

2.3 Photos andAvatars asCarriers ofSocial Cues

Before exchanges take place, individuals seek social cues to learn that resources are

available and that they are worth pursuing (Hobfoll 2002) and to assess the risk of

free riding, that is, that the contributions they provide in good faith will not be recip-

rocated (Das and Teng 2002). Importantly, for micro-communities, all interactions

are mediated through the platform which deliberately integrates social cues to sup-

port trust-building (Halbesleben etal. 2014; Lasfer and Vaast 2018). A commonly

used cue, in this regard, are prospective exchange partners’ photographs (Dai etal.

2018; Teubner etal. 2022; Zhang etal. 2018).

The importance of facial imagery as carriers of social cues can be understood

based on evolutionary psychology. To facilitate positive human interactions and

survival, humans have developed the ability to process the cues embedded in facial

imagery and to make assessments of trustworthiness thereupon (Bente etal. 2012;

Riedl etal. 2014). In this sense, the availability of a photograph represents a subtle

form of visual communication. Individuals can utilize profile photos to create posi-

tive self-presentations, for instance, to appear happy, friendly, successful, or self-

conscious to others (Wang etal. 2010; Wu etal. 2015). Smiling facial expressions

have been found to increase inferred trustworthiness (Ert and Fleischer 2017). For

the selection of profile images, users tend to choose posed images (Hum etal. 2011;

Wu etal. 2015) and attempt to appear genuine and authentic (Whitty etal. 2014).

As such, photographs may render interactions more personal (Stephenson etal.

1976). Since trust in online environments is not formed solely based on calculative

processes but also relies on “soft” signals, profile photos may serve as an effective

component towards this end (Qiu and Benbasat 2010; Steinbrück etal. 2002). Pro-

file photos can be assumed to be particularly powerful because the human brain is

“hardwired” to intuitively process faces (Anzellotti and Caramazza 2014), involving

a brain area referred to as the fusiform face area in the extrastriate cortex (Kanwisher

440

T.Teubner, S.Camacho

1 3

etal. 1997). This is exemplified by the fact that infants react to faces within the first

minutes after birth (Goren etal. 1975). In addition, detecting facial expressions hap-

pens unconsciously and fast (Willis and Todorov 2006). Representing an inherently

social signal, human faces foster trust in a variety of online environments (Cyr etal.

2009; Gefen and Straub 2003, 2004; Hassanein and Head 2007; Ou etal. 2014; Qiu

and Benbasat 2010; Steinbrück etal. 2002). Based on such photos’ trust effect and

its implications for social exchange behavior, we hypothesize that.

H1 Within micro-communities, the availability of profile photos yields higher net-

work (a) value, (b) density, and (c) reciprocity as compared to default images.

Similarly, an individual’s representation in the form of an avatar can also be con-

sidered as a social cue that allows to make inferences about them (Burgoon etal.

2008; Isbister et al. 2000; Morrison etal. 2012). Avatars do not display a photo-

realistic but a stylized depiction of the represented person. However, by depicting

human features (e.g., hair style, clothing, accessories), avatars also induce social

presence (Bailenson and Blascovich 2004), in particular for immersive virtual envi-

ronments (Lee etal. 2009; Nowak and Biocca 2003; Qiu and Benbasat 2005). Past

research on the use of avatars (see Table1 for summary of studies) suggests that

evaluations of avatars’ characteristics such as attractiveness, empathy, and perceived

social support influence positively the development of satisfying social interactions

(Banakou etal. 2009; Guadagno etal. 2011), intentions to shop online (Chattaraman

etal. 2012; Mull etal. 2015), compliance with favors (Waddell and Ivory 2015),

and improved task performance (Ratan etal. 2016; Ratan and Sah 2015). In fact,

Tong etal. (2000) showed that the fusiform face area is activated not only for pho-

tos of human faces, but also for animal and cartoon faces and concluded that “car-

toons are readily perceived as animate faces” (p. 264). In addition to humans, even

other primates (such as macaques) exhibit comparable brain activation for cartoon

and real faces, but not for non-face objects (Freiwald etal. 2009). In other words,

whatever appears to be a face is processed as such by the brain. This is reasonable

from an evolutionary point of view, as for most of human history—without artifi-

cial images—everything that looked like a face—in fact—was a face, and needed to

be recognized rapidly to assess sentiment and intentions. We hence suggest that the

above arguments extend to avatars:

H2 Within micro-communities, the availability of avatars yields higher network (a)

value, (b) density, and (c) reciprocity as compared to default images.

2.4 Avatars Versus Photos

When thinking about the potential differences between photos and avatars in view of

outcomes, it is important to note that avatars may differ greatly in terms of abstrac-

tion/detail, emotional expressiveness, realism, and likability (i.e., “cuteness”). Any

hypothesing on an avatar’s effect will have to be conducted in view of the specific

avatar design at hand—as avatar design may range from highly abstract “stickman”

441

1 3

Photos and Avatars in Micro-Communities

Table 1 Related Literature on avatars and human behavior

Study (Authors, Year) Focus Method Context Sample (size, origin) Theory/constructs

Banakou etal. (2009) Effect of avatar appear-

ance on users’ sociabil-

ity

Virtual ethnography Virtual world 9, Not indicated Number of social encoun-

ters (private and public)

von der Pütten etal.

(2010)

Differences in reaction to

interacting with agents

(i.e., controlled by arti-

ficial intelligence) and

avatars (i.e., controlled

by humans)

Lab experiment Self-disclosure tasks 83, US Threshold model of social

influence, Ethopoeia

concept

Saunders etal. (2011) Exploring the notions

of space, place, and

presence

Lab experiment Virtual world: Second

Life; (brainstorm tool,

organizer tool, voting

tool)

150, Alpine Executive

Center, Second Life

Theory of virtual space and

place, Social Presence,

Perceived Ease of Use,

Perceived Enjoyment,

Focused Immersion

Guadagno etal. (2011) Effect of smile behavior

on social evalua-

tions (e.g., perceived

empathy) of virtual peer

counsellors

Lab experiment Peer counselling 38, US empathy, general positivity,

supportiveness, like-

ability, comfort with the

interaction, interaction

satisfaction, and interac-

tion enjoyableness

Social presence, trust

Chattaraman etal. (2012) Evaluation of a virtual

agent that offers search,

navigational and proce-

dural support to older

adults

Lab experiment Online shopping 60, US social presence, perceived

social support, trust,

perceived risk, patronage

intention

Ang etal. (2013) Effects of gesture-based

avatar-mediated commu-

nication vs video-medi-

ated communication

Lab experiment Brainstorming and nego-

tiation

64, UK Perceived satisfaction,

likeability, trust, and intel-

ligence; number of ideas,

quality of ideas

442

T.Teubner, S.Camacho

1 3

Table 1 (continued)

Study (Authors, Year) Focus Method Context Sample (size, origin) Theory/constructs

Pinto etal. (2013) Efficacy of avatar-based

depression self-man-

agement intervention

among young adults

Randomized controlled

trial

Depression self-manage-

ment

28, US Depressive symptoms

Teubner etal. (2014) Effects of avatar

humanization on sharing

behavior

Lab experiment Sharing Economy 216, Germany Social Presence, Privacy

Calculus

Mull etal. (2015) Examining consum-

ers’ perceptions (e.g.,

credibility, homophily)

of using 3D avatars as

salespeople

Survey Online shopping 120, US Credibility, homophily,

attractiveness, and inten-

tion to interact

Ratan and Sah (2015) Effect of avatar customi-

zation (i.e., design-

ing an avatar) and

avatar embodiment

(i.e., perceiving avatar

is incorporated in body

schema) and gender on

post-game behavior

Lab experiment Online game 64, US Stereotype threat theory,

avatar embodiment

Waddell and Ivory (2015) Effect of interaction of

avatar’s and user’s fea-

tures on favor responses

Field experiment Online game 2300, North Ameri-

can World, World of

Warcraft

Avatar attractiveness, avatar

sex, user sex, magnitude

of request (i.e., small vs

large favor), compliance

(i.e., agreeing to the favor)

443

1 3

Photos and Avatars in Micro-Communities

Table 1 (continued)

Study (Authors, Year) Focus Method Context Sample (size, origin) Theory/constructs

Ratan etal. (2016) Examining how avatars

can be integrated to stu-

dents’ communication to

improve their perfor-

mance motivation

Field experiment Education 229, US Avatar type (i.e., actual-self,

ideal-self, superhero-

student), average number

of topics discussed

Canidate and Hart (2017) To identify preferences

for health informa-

tion provider avatars

and how such avatars

compare to seekers’ own

characteristics

Online survey Informational website 1119, US Age, gender, and ethnicity

origin

Gordon etal. (2017) A pilot proof-of-concept

feasibility study for

avatar-assisted therapy

in a virtual environment

Pilot study, interviews

before and after the

treatment

Outpatient substance

abuse treatment

58, US Avatar features: hair, skin,

eye color, facial features,

body type and size, and

clothing

Treatment completion,

urine drug-screen positive

samples, re-arrest infor-

mation

Heyselaar etal. (2017) Examining differences

between human–

computer language

interaction (e.g., with a

human-like avatar) and

human–human language

interaction (i.e., with

a human partner) in

a priming syntactic

activity

Lab experiment Virtual reality Experiment 1: 48;

The Netherlands

Experiment 2: 48;

The Netherlands

Relationship questionnaire

(opinions of partners),

conflict questionnaire

(how participants man-

aged conflict), target

responses (active, passive)

444

T.Teubner, S.Camacho

1 3

Table 1 (continued)

Study (Authors, Year) Focus Method Context Sample (size, origin) Theory/constructs

Joo and Kim (2017) Effect of an online game

avatar’s body shape

(obese vs. normal

weight) and health

behavior (healthy vs.

unhealthy) on user’s

health behavior (using

exercise machine or eat-

ing high-sugar cookies)

Lab experiment Online game 124, South Korea Exercise behavior, eating

behavior, Body Mass

Index (control)

Teng (2017) Extent to which regarding

an avatar as oneself (i.e.,

avatar identification)

impacts gamers’ con-

tinued intention to play

(i.e., gamer loyalty)

Online survey Online game 1384, Not indicated Social identity theory,

social capital theory / ava-

tar identification (four fac-

tors: feeling during play,

absorption during play,

positive attitude toward

the avatar, importance to

identity), participation

in gaming communities,

social presence, loyalty

Wu and Kraemer (2017) Examining how emotion

in language (i.e., posi-

tive or negative) affects

avatars’ judgment (e.g.,

likeability) and prefer-

ences (e.g., avatar as an

interacting partner)

Online survey Virtual environment 189, US and Amazon

Mechanical Turk (Ama-

zon MTurk)

Choices between a pair of

avatars: (i) avatar they

would prefer to interact

with; (ii) avatar they

would prefer to use for

self-representation; and

(iii) the avatar they attrib-

uted to human control

Variables: likeability

445

1 3

Photos and Avatars in Micro-Communities

Table 1 (continued)

Study (Authors, Year) Focus Method Context Sample (size, origin) Theory/constructs

Messinger etal. (2019) Analyzing how a person’s

avatar differs from the

individual the avatar

represents and how

in-world behavioral

traits are affected by the

relative attractiveness

of the avatar compared

with the real person

Virtual environment Study 1:

167, US

97, Second Life

Study 2: 38, US

Self-enhancement theory,

self-verification theory /

Variables: attractiveness

(avatar vs. real person),

behavioral traits

Yu etal. (2021) To compare a point cloud

reconstruction-based

avatar with a virtual

character-based (3DVC)

avatar in a 3D telepres-

ence system

Lab experiment VR/AR teleconsultation

system

24, Germany Performance at task,

co-presence, telepres-

ence, social presence,

self-location, uncanniness

and eeriness perceptions,

behavior impression (nat-

ural, realistic, synchro-

nous), system usability

scale, fast motion sickness

scale

446

T.Teubner, S.Camacho

1 3

figures to (near) photo-realistic representations (e.g., Meta’s Codec Avatars; Clark

2021; Skarredghost 2022). In addition to that, there exist several further aspects that

should be taken into account:

• First, avatars may fall into what has been labeled as the “uncanny valley”

(Cheong 2022; University of Cambridge 2019). This concept refers to a nega-

tive relation between an avatar’s resemblance to a human being and a user’s

emotional response to it, specifically when the avatar comes very eerily close to

human appearance but can still be identified as artificial. In this range, avatars

evoke feelings of uneasiness, and revulsion. Beyond avatars within digital appli-

cations, examples of the uncanny valley can be found in movies, robotics, and

lifelike dolls.

• Second, not only may an avatar affect the behavior of others that encounter it,

but also the user being represented by and/or controling the avatar. Being rep-

resented by an avatar of a certain style or appearance is known to create a back-

coupling, referred to as the Proteus effect (Yee etal. 2009; Yee and Bailenson

2007). The effect refers to people’s tendency to be affected by their digital rep-

resentations (e.g., in video games, on dating sites, or social networks), in a way

that their behavior conforms to common expectations towards the digital repre-

sentation (TechTarget 2014; Yee and Bailenson 2007). Research suggests that

the effect extends into users’ real life with small but fairly consistent effect sizes

(Clark 2020; Ratan etal. 2020). A recent review study indicates that three main

components drive the Proteus effect, namely similarity (i.e., self-identification),

desirability (i.e., wishful identification), and perceived embodiment (Praetorius

and Görlich 2020).

• Third, as another boundary condition, it is important to understand not only how

avatars look, but also how and under which restrictions they were created and

assigned to users. Will, for instance, avatars depict the actual user or are they free

to chose any ever so phantasmal identity? How is a potential matching ensured?

In this regard, research indicates that when users select (or are assigned to) ava-

tars that reflect their appearance well, they are less likely to engage in deception

(Galanxhi and Nah 2007; Hooi and Cho 2013). As we will describe in the next

section, the present study makes use of certified avatars, where an independent

third party (i.e., the research team) creates and assigns the avatars based on par-

ticipants’ photographs (as taken right before the experiment). This establishes

that (1) the avatars indeed reflect participants’ appearance and (2) that this link is

reliable and trustworthy.

The avatars used in this study fall into the middle range of a) having a rich set

of features (i.e., hair style and color, head shape, skin tone, accessory, etc.) but b)

being clearly cartoonish and far away from photoreaslism (proportions, no texture,

not even a nose, etc.)—see FiguresA1 and A2 in the Appendix. While the pho-

tos provide a clear and realistic view on the person behind the online identity, the

avatars also strip away quirky features and they appear particularly neat and cute—

keeping a safe distance to “uncanny” representations. We hence leave it as an open-

ended question here how (these specific) avatars will affect outcomes compared to

447

1 3

Photos and Avatars in Micro-Communities

photographs. Given the study’s specific setup with high (presumed) avatar likabiltiy

and a credible reflection of user appearance, we believe that avatars may be as effec-

tive as photosgraphs or even surpass them.

3 Method

3.1 Experimental Treatments

To evaluate our hypotheses, we emulate an online micro-community by means of

a 15-period Gift Exchange Experiment (Berninghaus etal. 2008). Building on the

gift exchange model by Akerlof and Yellen (1988), this setting allows to investi-

gate the formation of reciprocity in social exchange (Berninghaus etal. 2008; Güth

etal. 2010). Naturally, such experiments cannot simulate or reproduce actual micro-

communities; they represent stylized and abstracted models of such communities,

capturing the most important structural features.

We employed three different conditions. First, in the profile photos treatment,

participants were represented by an actual photograph of themselves. Second, in the

avatars treatment, participants were represented by an avatar a staff member created

based on the participant’s photo. Third, participants in the control group were repre-

sented by a default image. Examples for all conditions are provided in AppendixA.

Each participant was exposed to only one of the three conditions (between-subjects

design). Participants were randomly assigned to the treatments and to gender-bal-

anced cohorts of six (3 males, 3 females) and remained in the same cohort for the

entire experiment.

3.2 Experimental Task

Following the design of Berninghaus etal. (2008), in each period, all six partici-

pants i ∈ {1, 2, …, 6} of a cohort were endowed with E = 100 units of one out of six

unique resource types. Participants were then able to make transfers to other partici-

pants of their cohort. Thereby, each participant decided (simultaneously) how many

units of their own resource to transfer to each other participant (non-negative inte-

ger values only). The total number of units transferred could not exceed the initial

endowment of E = 100 units. A transfer from participant i to j in period t is denoted

by xij,t. Every transaction xij,t > 0 yielded transaction costs of c = 1 monetary unit

(MU) for the sender (i), representing the (small but positive) effort associate with

the transfer. Therefore, participants could face transaction costs varying between 0

MU (xij,t = 0, ∀j ≠ i) and 5 MU (xij,t > 0, ∀j ≠ i). After each period, participants thus

held between 0 and 100 units of the six resource types. These were then converted

into monetary units. In order to reflect decreasing marginal utility for each resource,

amounts were converted into monetary units by means of the square root function

(Berninghaus etal. 2008). Participant i’s payoff for period t is hence given as:

448

T.Teubner, S.Camacho

1 3

Given the concave nature of the square root function and the transaction costs,

the overall (i.e., social) optimum corresponds to an even distribution of all resources

among all participants. Since only integer numbers were possible, an allocation

of 17, 17, 17, 17, 16, and 16 represented the optimal approximation, yielding an

average outcome of 19.49 MU per participant and period. Note that the scenario

represents a social dilemma with a unique Nash equilibrium in not transferring any

resources at all. Participants may choose to follow this strategy, yielding a certain

payoff of (at least) 10 MU (from their own resource type). Overall, capturing the

nature of online micro-communities, mutually exchanging resources with others

yields the potential for better outcomes while it also involves 1) the inherent risk of

realizing a loss and 2) the temptation of free riding. Figure1 illustrates this by an

example.

3.3 Procedure

Altogether, 216 subjects participated (108 females, 108 males, average

age = 22.1 years) across the three conditions. Each condition comprised twelve

cohorts with 6 participants per cohort (216 = 3 × 12 × 6). Sessions took about

50–60 min. Participants were recruited from a pool of university students using

ORSEE (Greiner 2015). The experimental interface was implemented using zTree

(Fischbacher 2007). In order to incentivize behavior (Smith 1976), each participant’s

payoffs from ten randomly selected periods were paid out in cash (1 MU = 0.14

USD; average payoff of 26.2 USD).

After arriving at the lab, participants gave written consent for the use and pro-

cessing of their photograph. Specifically, this included the photos’ use within

the experiment (shown to the five other respective participants in their cohort),

pi,t=

�

E−

�

j≠i

xij,t+

�

j≠i

√

xji,t−c

�

j≠i

1xij,t>

Fig. 1 Exemplary illustration of a period outcome

449

1 3

Photos and Avatars in Micro-Communities

processing for scientific purposes, and use in presentations and publications. The

experimental design and execution adhered to the ethical and procedural guidelines

of the economic lab the study was conducted at.

A research assistant then took each participant’s photo. Appendix A provides

examples for the profile images used in the three conditions. In the avatars treat-

ment, a staff member created the avatar based on the photo by using a proprietary

tool with the aim of matching characteristic features (e.g., clothing, eye color, hair-

style and color, head shape). This process was conducted while participants were

settling into the lab (i.e., within a few minutes) so that the participant-specific ava-

tars were available upon the start of the actual experiment. A pre-recorded audio

version of the experiment instructions was played, and a written version was handed

out (Appendix B). Participants then answered twelve quiz questions testing their

comprehension of procedure and payoff rules. After every participant had success-

fully completed the quiz, the actual experiment started.

3.4 Measures

Participants repeatedly decided whether, how much, and with whom to share their

resources. At the network level, we analyze the emerging graph of connections for

each cohort in terms of network value, density, and reciprocity. At the individual

level, we investigate each transfer xij,t from participant i to another participant j in a

given period t. Further, we consider each participant’s total volume of transfers, as

well as the number of recipients they sent resources to. As control variables, we use

participants’ gender and risk attitude (Holt and Laury 2002). Appendix C provides

an overview of definitions and summary statistics for all measures.

Importantly, it is a well-established result in the behavioral sciences that strategic

interaction with a fixed time horizon (here: 15 periods) yields so-called “endgame

effects” (Berninghaus etal. 2008; Bolton etal. 2004). Thereby, the final few periods

usually exhibit a markedly different pattern of behavior (e.g., collapse of collabo-

ration). Hence, we follow the common practice of excluding the last three periods

from analysis and focus on the first twelve periods.1

4 Results

4.1 Hypotheses Testing

To evaluate our hypotheses, we consider (1) realized overall value (as compared to

the theoretical upper and lower bounds, normalized to the interval [0, 1]), (2) the

underlying network’s density, and (3) overall reciprocity within the network. Fig-

ure2 summarizes these measures by treatment (left) and over time (right). To assess

1 Hence, our dataset includes 432 observations at the cohort level (3 treatments × 12 cohorts × 12 peri-

ods), 2,592 observations at the participant level (3 treatments × 12 cohorts × 12 periods × 6 participants),

and 12,960 observations at the transfer level (3 treatments × 12 cohorts × 12 periods × 6 participants × 5

respective other participants).

450

T.Teubner, S.Camacho

1 3

the displayed treatment differences and time trends statistically, we use a set of panel

regressions that consider the dependent variables at the cohort level (see Table2).

The regression models account for the dynamics of network formation over time by

controlling for period effects (coded from 0 to 11) and treatment-period interactions.

As hypothesized, photos (b = 0.131, p < .05; H1a supported) and avatars

(b = 0.099, p < .10; H2a supported) have a positive overall effect on network value.2

Value also increases over time for photos but not for avatars or the control condi-

tion (photos: b = 0.004, p < .05; avatars: b = 0.003, p = .169; control: b = − 0.001,

p = .262). The results on network density and reciprocity provide further insight

into how specifically value is created within the network. Overall, the availability

.50

.60

.70

.80

.90

Control Avatars Photos

Network Density

Treatment

.50

.60

.70

.80

.90

12345678910 11 12 13 14 15

Photos AvatarsControl

endgame

Period

.50

.60

.70

.80

.90

ControlAvatars Photos

Network Reciprocity

Treatment

.50

.60

.70

.80

.90

123456789101112131415

Photos AvatarsControl endgame

Period

.50

.60

.70

.80

.90

ControlAvatars Photos

Treatment

Network Value

.50

.60

.70

.80

.90

123456789101112131415

Photos AvatarsControl

Period

endgame

Fig. 2 Network value creation, density, and reciprocity across treatments and over the course of the

experiment

2 Note that the treatment effect for avatars is only marginally significant (p < .10). However, the analysis

reported in Table2 is conducted at the cohort level and hence relatively conservative.

451

1 3

Photos and Avatars in Micro-Communities

Table 2 Panel regression models for network value, density, and reciprocity (random effects; cohort level)

Standard errors in parentheses. ***p < .001; **p < .01; *p < .05; +p < .10; Significant coefficients highlighted in bold face

(a) Network value (b) Network density (c) Network reciprocity

Treatment: Photos (H1).131*(.052) .108* (.053) .114* (.056) .099 (.058)+.120* (.053) .100+(.059)

Treatment: Avatars (H2).099+ (.052) .085 (.053) .073 (.056) .062 (.058) .048 (.053) .039 (.059)

Period (0–11) .001 (.001) −.001 (.001) −.006*** (.001) −.008*** (.002) .013*** (.002) .011** (.003)

Photos × Period .004* (.002) .003 (.003) .004 (.005)

Avatars × Period .003 (.002) .002 (.003) .002 (.005)

Constant .630*** (.037) .642*** (.038) .696*** (.040) .705*** (.041) .611*** (.039) .621*** (.042)

N 432 432 432 432 432 432

R2.018 .030 .084 .086 .104 .105

452

T.Teubner, S.Camacho

1 3

of photos has positive effects on network density (b = 0.114, p < .05; H1b supported)

and reciprocity (b = 0.120, p < .05; H1c supported). In contrast, the effects of ava-

tars on network density (b = 0.073, p = .192; H2b not supported) and reciprocity

(b = 0.048, p = .368; H2c not supported) are positive but insignificant.

Result 1a The availability of profile photos yields higher network value (H1a),

higher density (H1b), and higher reciprocity (H1c) in the micro-community than the

control condition with default images. By contrast, the availability of avatars only

yields marginally higher network value (H2a), while density (H2b) and reciprocity

(H2c) are not significantly different than in the control condition.

Moreover, we do not observe any statistical differences between photos and ava-

tars on network level (i.e., with regard to network value, density, or reciprocity).

Result 1b Photos and avatars yield similar results in terms of network value, density,

and reciprocity.

Interestingly, in the first periods, there emerge relatively dense networks (i.e.,

high number of ties). Then, however—and irrespective of treatment condition—

density decreases and reciprocity increases over time, that is, the network structure

shifts towards fewer but stronger links.

Result 1c While network value in the micro-community is stable over time, network

density decreases and reciprocity increases over time in all three conditions (photos,

avatars, control).

These results illustrate how participants form transfer relations within their

micro-community. Starting out from a broader “shotgun” approach (i.e., transferring

small(er) amounts to many others), they adapt their behavior towards larger transfers

to fewer recipients over time, where the enduring relations are also more likely to be

mutual. As can be seen from the treatment-time interaction coefficients in Table2

and Fig.2, this dynamic is consistent across treatment conditions.

4.2 Transfer Strategies

Next, to shed more light on the underlying behavior of network formation, we

investigate participants’ transfers in more detail. Specifically, we consider (1) total

transfer volumes, (2) number of recipients, and (3) average amounts transferred per

recipient. This analysis allows us to discern strategies (e.g., “shotgun” approaches

vs. targeted transfers to specific recipients). Table3 shows the results from a set of

panel regressions for these measures, considering models with and without treat-

ment-period interactions.

First, photos (b = 15.688, p < .001) and avatars (b = 13.128, p < .001) exhibit

higher overall transfer volumes than the control condition. There also exists an

increase over time (b = 0.495, p < .001). However, as can be seen in the second model

453

1 3

Photos and Avatars in Micro-Communities

specification, this is primarily driven by the photo treatment (slope = 0.180 + 0.649,

p < .001). Second, compared to the control condition, participants transfer resources

to a higher number of recipients in the photo condition (b = 0.568, p < .01) and in

the avatar condition (b = 0.371, p < .05). Further, irrespective of treatment condition,

this number decreases over time (b = –0.032, p < .001). Finally, the interplay of these

findings (increasing overall transfer volume and decreasing number of recipients) is

then also reflected in the average amount sent per recipient. Here, both treatments

(as compared to the control condition) yield higher amounts, and there is a marked

positive time effect (b = 0.231, p < .001).

These results indicate that profile images (either photos or avatars) were indeed

utilized as a cue to develop an impression of potential exchange partners that, in

turn, contributed to facilitate resource transfers.3 Moreover, we considered risk aver-

sion as a control variable and found it to be associated with lower overall transfer

Table 3 Panel regression models for individual transfers (random effects; participant level)

Standard errors in parentheses.***p < .001; **p < .01; * < .05; +p < .10; significant coefficients high-

lighted in bold face

Total transfer volume Number of recipients Average transfer /

recipient

Treatment: Photos 15.688*** 12.117** .568** .489* 2.822*** 2.740**

(3.561) (3.698) (.185) (.197) (.792) (.849)

Treatment: Avatars 13.128*** 11.501** .371* .317 3.023*** 2.855***

(3.562) (3.699) (.185) (.197) (.792) (.849)

Period (0–11) .495*** .180 −.032*** −.040*** .231*** .216***

(.074) (.128) (.005) (.009) (.023) (.039)

Photos × Period .649*** .014 .015

(.181) (.012) (.055)

Avatars × Period .296 .010 .031

(.181) (.012) (.055)

Risk Aversion −1.931+−1.931+−.035 −.035 −.517* −.517*

(1.042) (1.042) (.054) (.054) (.232) (.232)

Gender: Female −7.679** −7.679** −.371* −.371* −.270 −.270

(2.920) (2.920) (.152) (.152) (.649) (.649)

Constant 55.859*** 42.406*** 3.870*** 3.524*** 14.029*** 10.939***

(6.662) (2.667) (.347) (.140) (1.484) (.604)

N 2592 2592 2592 2592 2592 2592

R2.029 .032 .022 .020 .047 .047

3 To confirm that reciprocity in fact acts as a driver and sustainer of exchange relations, we conducted

a complementary analysis on the individual transfers at a peer-to-peer level (Appendix D). Indeed, the

amount that was received from the respective other participant in the previous period (t–1) predicts one’s

transfer in the current period (t). This analysis also shows that female participants tend to transfer less

(b = -1.142, p < .001) while neither male nor female subjects receive systematically higher transfers in

general when accounting for reciprocity (b = -.053, p = .616). Sender-receiver gender interactions and

gender identity/difference do not reveal any further effects.

454

T.Teubner, S.Camacho

1 3

volumes (b = − 1.931, p < .10), an effect that is primarily driven by the fact that

risk averse subjects tend to make lower average transfers per recipient (b = −0.517,

p < .05), rather than to fewer recipients (b = −0.035, p = .518). Additionally, female

participants also tend to transfer less overall (b = −7.679, p < .01) where this effect

is primarily driven by the lower number of recipients (b = − 0.371, p < .05) rather

than by lower average transfers (b = −0.270, p = .678).

Result 2a The availability of profile images (photos, avatars) yields higher total

transfer volumes, higher number of recipients, and higher transfers per recipient than

the control condition.

Result 2b Over time, the number of recipients decreases while the average transfer

per recipient increases—regardless of the treatment condition.

4.3 Effect Decomposition andNetwork Value

To better understand how profile images affect network formation and value over

time, we now differentiate between cross-treatment differences (in the first period)

and time effects (within a fixed treatment condition). As main dependent variables,

we focus on the average number of recipients participants engage with and the aver-

age amounts transferred to those recipients (x- and y-axis in Fig.3a, b). The total

transfer volumes can be approximated by the product of the two factors (i.e., area of

the resulting rectangle). This allows attributing and hence decomposing the overall

differences into the two underlying partial effects. Figure3a, b illustrate this and

include levels of equal network value (dotted grey lines).

Treatment effects (first period) Focusing on the first period, Fig.3a allows to

discern an immediate volume effect (“transfer more resources to the same peo-

ple”) and a spread effect (“transfer resources to more people”) of profile images

Fig. 3 Effect decomposition with a treatment differences (first period) and b within-treatment time

effects. Curved lines represent iso-value levels (v) where cost c = 1 MU and endowment E = 100 MU.

Note that the iso-value levels assume identical transfers for each subject as well as infinitesimal division

while the plotted data is based on actual (integer) numbers of recipients and transferred amounts

455

1 3

Photos and Avatars in Micro-Communities

regarding transfer volumes and how these translate into network value. Further,

there is an interplay surplus (emerging from the interaction of the two). Com-

pared to the control condition, participants in the photo treatment both shared

resources with a higher number of other participants (spread effect) and trans-

ferred more to each of them (volume effect). Considering the difference between

the photo and avatar conditions, it becomes evident that the photos’ entire surplus

is grounded in the spread effect. In other words, while the amounts transferred per

person are virtually identical, using photographs (rather than avatars) increases

the number of initial recipients—and hence the overall volume of resources trans-

ferred down the line. Again, these results corroborate the usefulness of profile

images as visual cues to engender initial trust towards potential interaction part-

ners and to engage in exchanges with them. Table4 summarizes all partial and

overall relative effects for the three treatment contrasts (photos vs. control, ava-

tars vs. control, and photos vs. avatars).

Result 3 While the average transfer per recipient is comparable between photos

and avatars, photos yield a higher number of recipients (spread effect) and hence a

higher level of value in the first period.

Time effects (within-treatment) Building on the treatment effects in the first

period, Fig.3b illustrates the dynamics of network formation that occur over the

course of the twelve periods. We can discern marked time-effects in how par-

ticipants adapt their behavior. When comparing the first period to the following

values, notice that over time, participants transfer resources to fewer people but

with higher amounts per recipient. This transition can be described as a “cut and

reinforce” strategy, where participants cut off non-functioning (i.e., low reciproc-

ity) relations and reinforce flourishing relations. Importantly, this behavior can be

observed in all three treatment conditions—with varying effect sizes.

Considering the described two-fold alteration of the rectangles allows attribut-

ing the relative changes of transfer volumes to the “cut” and “reinforce” portions.

As can be seen in Table5, the surplus due to reinforcing generally outweighs the

decline due to cutting (reinforce >|cut|). Moreover, the overall relative increase in

transfers is highest for the avatars condition (with only little cutting and much rein-

forcing), while behavior in the control condition is more governed by cutting, result-

ing in a lower overall effect. Also, in comparison to the treatment differences in the

first period (i.e., an area increase of up to 38.7% compared to control; see Table4),

the within-treatment time effects from first to last period are smaller (photos: 10.2%,

avatars: 16.2%; see Table 5). Importantly, the transition of the two transfer com-

ponents (number of recipients, average transfer per recipient) takes place along the

iso-value lines over time. Hence, the overall value levels remain stable by and large

(even though overall transfer volumes increase slightly).

Result 4 Over time, participants interact with fewer participants (“cut”) and transfer

higher amounts in reciprocal relations (“reinforce”).

456

T.Teubner, S.Camacho

1 3

Table 4 Treatment effect decomposition of total transfers (approximated by the product of average amount transferred and number of recipients; corresponds to rectangle

areas in Fig.3a

Treatment Comparison Volume effect (Δ avg. transfer) Spread effect (Δ avg. recipi-

ents)

Interplay surplus Total effect (Δ area) ΔValue (%)

Control vs. Photo 10.67⟶12.86 + 20.5% 3.58⟶4.13 + 15.1% + 3.1% 38.25⟶53.04 + 38.7% + 20.6

Control vs. Avatar 10.67⟶12.91 + 20.9% 3.58⟶3.79 + 5.8% + 1.2% 38.25⟶48.94 + 27.9% + 15.7

Avatar vs. Photo 12.91⟶12.86 −0.4% 3.79⟶4.13 + 8.8% < 0.1% 48.94⟶53.04 + 8.4% + 4.3

457

1 3

Photos and Avatars in Micro-Communities

5 Discussion

The emergence of exchange relations in networks is complex in nature. In this study,

we employed a multi-period experiment to study the role of photos and avatars for

dynamic network formation in micro-communities. We observe systematic use of

cut-and-reinforce strategies, where people shift towards fewer but stronger (and

more reciprocal) relations. While this behavior is not contingent of user representa-

tion, the availability of photos/avatars leads to higher network value, density, and

reciprocity as it induces more and higher transfers right from the get-go—and this

effect persists over time. Note that there only occur smaller differences between pho-

tos and avatars, suggesting that the effectiveness of photos in conveying social cues

can, in principle, also be achieved by avatars (taking into the account the specific

boundary conditions and design).

5.1 Theoretical Contributions

Micro-communities facilitate value creation through technology-mediated social

exchange. Our study sheds light on the formation of reciprocal relations over time,

and how these relations contribute to network formation and value creation. As pos-

ited by SET, the prospect of net benefits plays a pivotal role for users to engage

in social exchange relations (Blau 1967). Moreover, SET assumes the existence of

relationships in the long run as opposed to one-off interactions (Kim etal. 2018;

Molm 1997). By considering the dynamics of such exchanges over time, the present

study contributes to SET by empirically illustrating and differentiating two inherent

mechanisms for how such relations unfold. Specifically, we illustrate how users (1)

engage in a higher number of reciprocal exchange relations and (2) are willing to

contribute more strongly to those relations when profile images (photos or avatars)

are available. We demonstrate that the decision to “reach out” to others is taken early

on and affects exchanges from there on. This demonstrates that user representation

is a crucial factor for online network formation that needs to be taken into account.

But why is that? We argue that the facilitating role of profile images links back

to extant literature on affective information processing. Evolutionary, the abil-

ity to process social cues in human faces has been vital for humans to engage in

rewarding social interactions. As such, the human brain has evolved to readily pro-

cess human faces and these facial processing abilities are innate, involuntary, and

Table 5 Time effect decomposition of total transfers (approximated by rectangle areas in Fig.3b

Treatment Period Cut effect (Δ area) Reinforce effect (Δ

area)

Total effect (Δ

area)

ΔValue (%)

1 12

Control 38.3 41.8 −5.19 −13.6% + 8.72 + 22.8% + 3.53 + 9.2% −3.2

Avatar 48.9 56.9 −3.76 −7.7% + 11.72 + 23.9% + 7.95 + 16.2% + 5.0

Photo 53.0 58.5 −5.89 −11.1% + 11.30 + 21.3% + 5.41 + 10.2% + 1.2

458

T.Teubner, S.Camacho

1 3

fast (Kanwisher etal. 1997). Because online communities lack actual face-to-face

encounters, profile imagery represents an essential cue for facial processing (Bente

etal. 2012; Riedl etal. 2014). Hence, the availability of profile images enables users

to draw on their innate capacity to process human faces and, based on this, form

impressions and engender trust (Ert etal. 2016; Riedl etal. 2014). While previous

research has shown that this process can facilitate trust in individual one-to-one rela-

tions (Bente etal. 2012; Ert etal. 2016), our results provide insights into how these

processes emerge when the user can engage in multiple relations and when these

relations are carried out over time.

One intriguing result in this regard relates to the similarities and differences in

the effects of avatars and photos. The human brain continuously draws significance

from random and vague visual stimuli. Known as pareidolia, this process allows

users to “see faces where actual faces do not exist” (Hong et al. 2014, p. 352).

Thereby, the brain area that is dedicated to the processing of faces is also involved

in the processing of avatars (Riedl etal. 2014; Tong etal. 2000). Previous research

found that avatars can function as a potent social cue in user interface design (Lee

etal. 2009; Nowak and Biocca 2003; Qiu and Benbasat 2005). Applied to the con-

text of micro-communities, our results show that photos and avatars yield very simi-

lar outcomes. On one hand, this can be seen as surprising as the here-used avatars

only convey a highly stylized image of the human behind them. On the other hand,

however, avatars in fact trigger the same neurological processes and are interpreted

very much like actual faces. With this in mind, it is important to note that all avatars

used in our study have a friendly appearance and participants know that they can

be relied upon. Hence, some basic requirements for trusting behavior are met (e.g.,

credibility, perceptions of benevolence). This supports past research that indicates

that when an avatar resembles the person behind it closely, it is more likely to elicit

neural and behavioral responses similar to those evoked by the person themselves

(de Borst and de Gelder 2015; Fysh etal. 2022). Drilling down into the individual

transactions allows discerning how these differences come about and play out over

time. While the average transferred amounts per recipient are almost identical for

photos and avatars, the number of recipients is smaller in the avatar conditions. In

other words, users in the avatar condition engage in fewer social relations but the

strength of those relations is comparable to those of photos. One interpretation of

this would be to think of photos as guiding attention towards the micro-community

as a whole, whereas with avatars, people will be more likely to focus on fewer indi-

viduals. However, other approaches are well-conceivable: Most naturally, artificial

images may simply be not perceived as trustworthy as actual photographs. Also,

some avatars may convey “veto-features” that are not seen in photographs (although

the opposite is actually more likely, as avatars smoothen out a lot of the more strik-

ing facial features). Overall, this extends literature analyzing avatars’ influence on

users’ behavior. Previous research has focused mainly on user perceptions of avatar

features (e.g., Ang etal. 2013; Heyselaar etal. 2017; Wu and Kraemer 2017; Yu

etal. 2021), avatars’ behavior and performance (e.g., online worlds, online games;

Teng 2017; Waddell and Ivory 2015; Yu etal. 2021), and health behaviors (e.g.,

Gordon etal. 2017; Joo and Kim 2017; Pinto etal. 2013). Results from our study

indicate that avatars can have a positive influence on online behaviors (i.e., exchange

459

1 3

Photos and Avatars in Micro-Communities

of resources) in the complete absence of communication between interacting part-

ners (e.g., negotiating or messaging).

Finally, our approach allows for the investigation of community formation over

time. Existing research on profile imagery commonly employs snapshot approaches,

focusing on the impact of photos (and/or avatars) on one-to-one interactions at a spe-

cific point in time (Ert etal. 2016; Qiu etal. 2018). Complementing and extending

this work, we provide insights into how participants adapt their behavior over time

and how these effects shape network formation. Several interesting patterns appear.

First, we observe a consolidation of social exchange relations (“cut and reinforce”)

which occurs regardless of treatment condition. For exposition, Fig.4 illustrates this

“cut-and-reinforce” graphically for one cohort from the avatars condition.

The role of reciprocity appears to be equally important across all treatment condi-

tions. This consolidation of network relations reflects the formation of dense clus-

ters inside networks for the flow of resources, which has previously been identified

in the literature (Levine and Kurzban 2006). In the same vein, reciprocity has a

predominant role over other characteristics (e.g., gender) for the consolidation of

exchange relations, also in line with previous research (Plickert etal. 2007). Partici-

pants developed interdependent relationships with others through the exchange of

resources. Their assessment of reciprocity led them to cut those relationships per-

ceived as unbalanced (in terms of resources transferred and received) and to rein-

force those with higher perceptions of equity, in line with the theoretical assump-

tions and claims of SET (Fox and Gambino 2021; Kelley and Thibaut 1978).

Ultimately, the importance of reciprocity in this study corresponds to one of the

major tenets of SET, positing that such relationships “evolve over time into trusting,

loyal, and mutual commitments” (Cropanzano and Mitchell 2005, p. 875).

The evolving relationships evidenced in this study reflect the developmental

approach to interpersonal communication supported by Miller (1978). Participants

started to exchange resources with others in their cohort in an impersonal and trans-

actional manner, likely assessing others based on cultural and sociological informa-

tion. Over time, they developed more interpersonal interactions; in such cases, they

were likely able to predict, or make attributions about, the behaviors of others. The

assessment of others based on their behavior facilitated participants’ decision to

focus on reciprocal interactions.

Fig. 4 Example of network formation; edge width shows transfer volume; periods 1 to 12

460

T.Teubner, S.Camacho

1 3

5.2 Practical Contributions

The results of our study have direct implications for practitioners. Even though

online communities operate in a virtual space, the “rules” of how humans draw

meaning from social cues and decide to engage in social exchange readily apply. The

availability of profile imagery (either photos or avatars) plays a critical role for net-

work formation. Whenever there are concerns about user privacy or discrimination,

avatars may be advantageous.

A second implication relates to the dynamics of network formation. One striking

result here is that much of the subsequent behavior is determined already in the very

first period. Online communities should hence attempt guide their members early

on, for instance, to contact others, complete their profile, upload a photo, and sup-

port this by onboarding and scaffolding processes. In the initial stages, we observe

“shotgun” approaches, that is, transferring smaller amounts to many other people.

Over time, as people cut and reinforce, reciprocal relations form. Platform operators

may devise strategies to support this process in a way that (1) a higher amount of the

initial links is retained (e.g., by emphasizing the importance of reciprocity) and (2)

users are supported in sustaining rewarding relations. For instance, the user inter-

face may support transaction partners in “stabilizing” exchange relations by empha-

sizing the transaction history over time. This may encourage users to maintain and

strengthen relations in the long run.

A third implication relates to behaviors in online communities. Results from this

study indicate that individuals develop more reciprocal relations over time in con-

texts with profile imagery available, even in the absence of other forms of communi-

cation. This might prove useful not only for online communities attempting to mon-

etize user interactions (e.g., through gift giving), but also for organizational social

networking sites where geographical dispersed employees might need to develop

collaborative tasks (e.g., co-design of products) and work together in multiple pro-

jects over time.

5.3 Limitations andFuture Work

Naturally, this study is not without limitations. Generally speaking, users in a given

micro-community will not be represented homogenously but will usually have dif-

ferent profile image types. Scenarios in which only some users pick avatars while

others are represented by photos or default images would also allow for a more spe-

cific attribution of value. Vivid user representation could be more effective for those

users who employ it if only few do so (rather than when everyone does). In this

sense, our results may even under-estimate the power of photos and avatars.

Another limitation roots in the experiment design and how avatars are created

and assigned. Previous literature indicates that avatars might play the role of an

“actual mask”, where individuals can hide behind and engage in deceptive behaviors

(Galanxhi and Nah 2007; Hooi and Cho 2013). Thus, giving participants the option

to choose their own avatars (not necessarily reflecting their appearance) might have

461

1 3

Photos and Avatars in Micro-Communities

resulted in their engagement in different behaviors (e.g., free riding, tricking others).

However, it is worth remembering that the majority of people tends to select ava-

tars that share some similarities with them (e.g., age, height, and weight; Messinger

etal. 2019). Future studies may allow the free choice of profile imagery. In addition,

a voluntary certification process (on behalft of the platform) may issue a confirma-

tion for the employed avatar—based on a comparison of face, identification docu-

ment, and the avatar (similar as being used for KYC processes).

A third limitation is related to the low degree of variance explained by the regres-

sion models (i.e., low R-squared values). This is not uncommon in experimental

studies, where there might be some unobserved situational drivers of behavior (e.g.,

mood, distractions). To account for other factors that might explain the variance of

our outcome variables, future research may explore the influence of specific user

perceptions of others’ profile images (e.g., in terms of perceived similarity, attrac-

tiveness, etc.). Another avenue to pursue is to analyze what occurs in more immer-

sive environments (e.g., virtual worlds, metaverse applications), by incorporating the

concept of space (within which users could move) and place (i.e., a bounded space

imbued with meaning) (Saunders etal. 2011). Alternatively, the effect of using more

realistic and/or dynamic avatars in such environments should be explored, where

animated avatars could include facial expressions (e.g., rising eyebrows, smiling) or

other human behaviors (e.g., talking, providing non-verbal feedback).

6 Concluding Note

Social cues and reciprocity play fundamental roles for the creation of value in online

communities. This study sheds light on how profile images facilitate the formation

of individual transactions and the community. Hence, beyond analyzing individual

transactions or snapshots in time, we provide insights into how photos and avatars

facilitate the formation of online communities over the course of repeated interac-

tions. We demonstrate that users engage in a higher number of reciprocal exchanges

and are willing to contribute more strongly to those relations when profile images

are available. In view of their potential to foster social exchange, avatars may be

considered as a promising alternative to actual photographs, for instance, when user

privacy is a concern.

Supplementary Information The online version contains supplementary material available at https:// doi.

org/ 10. 1007/ s10726- 023- 09814-4.

Acknowledgement We thank Christoph Buring and Marc T. P. Adam for their invaluable support during

this research project.

Funding Open Access funding enabled and organized by Projekt DEAL.

Declarations

Conflict of interest The authors declare that they have no conflict of interest.

462

T.Teubner, S.Camacho

1 3

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,

which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as

you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com-

mons licence, and indicate if changes were made. The images or other third party material in this article

are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the

material. If material is not included in the article’s Creative Commons licence and your intended use is

not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission

directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen

ses/ by/4. 0/.

References

Akerlof GA, Yellen JL (1988) Fairness and unemployment. Am Econ Rev 78(2):44–49

Ang CS, Bobrowicz A, Siriaraya P, Trickey J, Winspear K (2013) Effects of gesture-based avatar-medi-

ated communication on brainstorming and negotiation tasks among younger users. Comput Hum

Behav 29(3):1204–1211

Anzellotti S, Caramazza A (2014) The neural mechanisms for the recognition of face identity in humans.

Front Psychol 5(672):1–6

Apple (2022) Bitmoji on the AppStore. Available at https:// apps. apple. com/ us/ app/ bitmo ji/ id868 077558.

Accessed 27 Oct 2022

Bailenson JN, Blascovich J (2004) Avatars. In: Encyclopedia of human-computer interaction. Berkshire

Publishing Group, pp 64–68

Banakou D, Chorianopoulos K, Anagnostou K (2009) Avatars’ appearance and social behavior in online

virtual worlds. In: 2009 13th panhellenic conference on informatics. IEEE, pp 207–211

Bente G, Baptist O, Leuschner H (2012) To buy or not to buy: influence of seller photos and reputation

on buyer trust and purchase behavior. Int J Hum Comput Stud 70(1):1–13

Bente G, Dratsch T, Rehbach S, Reyl M, Lushaj B (2014) Do you trust my avatar? Effects of photo-real-

istic seller avatars and reputation scores on trust in online transactions. In: International conference

on HCI in business, pp 461–470

Berg J, Dickhaut J, McCabe K (1995) Trust, reciprocity, and social history. Games Econom Behav

10(1):122–142

Berninghaus SK, Güth W, Vogt B (2008) Spontaneous evolution of social exchange—an experimental

study. J Socio-Econ 37(3):976–997

Blau PM (1967) Exchange and power in social life. Wiley

Bolton G, Greiner B, Ockenfels A (2013) Engineering trust: reciprocity in the production of reputation

information. Manage Sci 59(2):265–285

Bolton G, Katok E, Ockenfels A (2004) How effective are electronic reputation mechanisms? An experi-

mental investigation. Manage Sci 50(11):1587–1602

de Borst AW, de Gelder B (2015) Is it the real deal? Perception of virtual characters versus humans: an

affective cognitive neuroscience perspective. Front Psychol 6:576

Buendía F, Gayoso-Cabada J, Sierra J-L (2018) Using digital medical collections to support radiology

training in e-learning platforms. In: European conference on technology enhanced learning, pp

566–569

Burgoon JK, Blair JP, Strom RE (2008) Cognitive biases and nonverbal cue availability in detecting

deception. Hum Commun Res 34(4):572–599

Buunk BP, Schaufeli WB (1999) Reciprocity in interpersonal relationships: an evolutionary perspective

on its importance for health and well-being. Eur Rev Soc Psychol 10(1):259–291

Canidate S, Hart M (2017) The use of avatar counseling for HIV/AIDS health education: the examination

of self-identity in avatar preferences. J Med Internet Res 19(12):e365

Chattaraman V, Kwon W-S, Gilbert JE (2012) Virtual agents in retail web sites: benefits of simulated

social interaction for older users. Comput Hum Behav 28(6):2055–2066

Chen W, Wei X, Zhu KX (2018) Engaging voluntary contributions in online communities: a hidden

Markov model. MIS Q 42(1):83–100

Cheong BC (2022) Avatars in the metaverse: potential legal issues and remedies Avatare im Metaversum-

potenzielle Rechtsfragen und Lösungsansätze. Int Cyber Law Rev

463

1 3

Photos and Avatars in Micro-Communities

Clark M (2021) Facebook, now Meta, showed an impressive demo of its Codec Avatars - The Verge.

October 28 (available at https:// www. theve rge. com/ 2021/ 10/ 28/ 22751 177/ faceb ook- meta- codec-

avatar- real- time- envir onment- rende ring- neural- inter face. Accessed 1 Nov 2022

Clark OJ (2020) How to kill a Greek god: a meta-analysis and critical review of 14 years of Proteus effect

research. Working Paper

Cook KS, Rice E (2003) Handbook of Social psychology. Kluwer Academic/Plenum Publishers, New

York City

Cropanzano R, Mitchell MS (2005) Social exchange theory: an interdisciplinary review. J Manag

31(6):874–900

Cyr D, Head M, Larios H, Pan B (2009) Exploring human images in website design: a multi-method

approach. MIS Q 33(3):539–566

Dai Y, Viken G, Joo E, Bente G (2018) Risk assessment in e-commerce: how sellers’ photos, reputation

scores, and the stake of a transaction influence buyers’ purchase behavior and information process-

ing. Comput Hum Behav 84:342–351

Das TK, Teng B-S (2002) Note alliance constellations: a social exchange perspective. Acad Manag J

27(3):445–456

Emerson RM (1972) Exchange theory, Part II: Exchange relations and networks. In: Berger J, Zelditch M,

Anderson B (eds) Sociological theories in progress 2. Houghton Mifflin, Boston, pp 58–87

Ert E, Fleischer A (2017) What in a photo makes you trust a person online? A structural equation mod-

eling approach. Working Paper, pp 1–17

Ert E, Fleischer A, Magen N (2016) Trust and reputation in the sharing economy: the role of personal

photos in Airbnb. Tour Manage 55(1):62–73

Facebook (2019) Facebook is building the future of connection with lifelike avatars. March 13 (available

at https:// tech. fb. com/ ar- vr/ 2019/ 03/ codec- avata rs- faceb ook- reali ty- labs/. Accessed 27 Oct 2022

Fagerstrøm A, Pawar S, Sigurdsson V, Foxall GR, Yani-de-Soriano M (2017) That personal profile image

might jeopardize your rental opportunity! On the relative impact of the seller’s facial expressions

upon buying behavior on Airbnb™. Comput Hum Behav 72:123–131

Feng Y, Ye H (2016) Why do you return the favor in online knowledge communities? A study of the

motivations of reciprocity. Comput Hum Behav 63:342–349

Finan F, Schechter L (2012) Vote-buying and reciprocity. Econometrica 80(2):863–881

Fischbacher U (2007) z-Tree: Zurich toolbox for ready-made economic experiments. Exp Econ

10(2):171–178

Fox J, Gambino A (2021) Relationship development with humanoid social robots: applying interpersonal

theories to human-robot interaction. Cyberpsychol Behav Soc Netw 24(5):294–299

Freiwald WA, Tsao DY, Livingstone MS (2009) A face feature space in the macaque temporal lobe. Nat

Neurosci 12(9):1187–1196

Fysh MC, Trifonova IV, Allen J, Mccall C, Burton AM, Bindemann M (2022) Avatars with faces of

real people: a construction method for scientific experiments in virtual reality. Behav Res Methods

54:1461–1475

Galanxhi H, Nah FF-H (2007) Deception in cyberspace: a comparison of text-only vs. avatar-supported

medium. Int J Hum Comput Stud 65(9):770–783

Gaus A (2018) Who’s Afraid of Facebook? New Social Apps Could Chip Away at Facebook’s Empire

– TheStreet. December 1 (available at https:// www. thest reet. com/ techn ology/ faceb ook- social- apps-

killer- new- apps- 14797 599. Accessed 27 Oct 2022

Gefen D, Straub DW (2003) Managing user trust in B2C e-services. e-Serv J 2(2):7–24

Gefen D, Straub DW (2004) Consumer trust in B2C e-commerce and the importance of social presence:

experiments in e-products and e-services. Omega 32(6):407–424

Gordon MS, Carswell SB, Schadegg M, Mangen K, Merkel K, Tangires S, Vocci FJ (2017) Avatar-

assisted therapy: a proof-of-concept pilot study of a novel technology-based intervention to treat

substance use disorders. Am J Drug Alcohol Abuse 43(5):518–524

Goren CC, Sarty M, Wu PYK (1975) Visual following and pattern discrimination of face-like stimuli by

newborn infants. Pediatrics 56(4):544–549

Gouldner AW (1960) The norm of reciprocity: a preliminary statement. Am Sociol Rev 25(2):161–178

Greiner B (2015) Subject pool recruitment procedures: organizing experiments with ORSEE. J Econ Sci

Assoc 1(1):114–125

Grissa K (2016) What makes opinion leaders share brand content on professional networking sites (e.g

LinkedIn, Viadeo, Xing, SkilledAfricans). In: Proceedings of the international conference on digi-

tal economy: emerging technologies and business innovation, pp 8–15

464

T.Teubner, S.Camacho

1 3

Guadagno RE, Swinth KR, Blascovich J (2011) Social evaluations of embodied agents and avatars. Com-

put Hum Behav 27(6):2380–2385

Güth W, Levati MV, von Wangenheim G (2010) Mutual interdependence versus repeated interaction: an

experiment studying voluntary social exchange. Ration Soc 22(2):131–158

Halbesleben JRB, Neveu JP, Paustian-Underdahl SC, Westman M (2014) Getting to the ‘COR’: under-

standing the role of resources in conservation of resources theory. J Manag 40(5):1334–1364

Hassanein K, Head M (2007) Manipulating perceived social presence through the web interface and its

impact on attitude towards online shopping. Int J Hum Comput Stud 65(8):689–708

Hesse M, Dann D, Braesemann F, Teubner T (2020) Understanding the platform economy: signals, trust,

and social interaction. In: HICSS 2020 proceedings, pp 5139–5148

Heyselaar E, Hagoort P, Segaert K (2017) In dialogue with an avatar, language behavior is identical to

dialogue with a human partner. Behav Res Methods 49(1):46–60

Hobfoll SE (2002) Social and psychological resources and adaptation. Rev Gen Psychol 6(4):307–324

Holt CA, Laury SK (2002) Risk aversion and incentive effects. Am Econ Rev 92(5):1644–1655

Homans CG (1961) Human behavior: Its elementary forms. Harcourt, New York City

Hong K, Chalup SK, King RAR (2014) Affective visual perception using machine pareidolia of facial

expressions. IEEE Trans Affect Comput 5(4):352–363

Hooi R, Cho H (2013) Deception in avatar-mediated virtual environment. Comput Hum Behav

29(1):276–284

Hum NJ, Chamberlin PE, Hambright BL, Portwood AC, Schat AC, Bevan JL (2011) A picture is worth

a thousand words: a content analysis of Facebook profile photographs. Comput Hum Behav

27(5):1828–1833

Isbister K, Nakanishi H, Ishida T, Nass C (2000) Helper agent: designing an assistant for human-human

interaction in a virtual meeting space. In: CHI 2000 proceedings, pp 57–64

Joo YK, Kim K (2017) When you exercise your avatar in a virtual game: the role of avatars’ body shape

and behavior in users’ health behavior. Interact Comput:1–12

Kanwisher N, McDermott J, Chun MM (1997) The fusiform face area: a module in human extrastriate

cortex specialized for face perception. J Neurosci 17(11):4302–4311

Karimi S, Wang F (2017) Online review helpfulness: impact of reviewer profile image. Decis Support

Syst 96:39–48

Kear K, Chetwynd F, Jefferis H (2014) Social presence in online learning communities: the role of per-

sonal profiles. Res Learn Technol 22:1–15

Kelley HH, Thibaut JW (1978) Interpersonal relationships. Wiley, New York City

Kim H-W, Kankanhalli A, Lee S-H (2018) Examining gifting through social network services: a social

exchange theory perspective. Inf Syst Res 29(4):805–828

Lasfer A, Vaast E (2018) A relational approach on collaborative resource spending in online communi-

ties. In: ICIS 2018 proceedings, pp 1–17

Lee JG, Antoniadis P, Salamatian K (2010) Faving reciprocity in content sharing communities a compar-

ative analysis of Flickr and Twitter. In: Proceedings - 2010 international conference on advances in

social network analysis and mining, ASONAM 2010, pp 136–143

Lee MR, Yen DC, Hsiao CY (2014) Understanding the perceived community value of Facebook users.

Comput Hum Behav 35:350–358

Lee Y, Kozar KA, Larsen KR (2009) Avatar e-mail versus traditional e-mail: perceptual difference and

media selection difference. Decis Support Syst 46(1):451–467

Levine SS, Kurzban R (2006) Explaining clustering in social networks: towards an evolutionary theory of

cascading benefits. Manag Decis Econ 27(2–3):173–187

Levine S, White EP (1961) Exchange as a conceptual framework for the study of interorganizational rela-

tionships. Adm Sci Q 5(4):583–601

Messinger PR, Ge X, Smirnov K, Stroulia E, Lyons K (2019) Reflections of the extended self: visual self-

representation in avatar-mediated environments. J Bus Res 100:531–546

Micallef N, Misra G (2018) Towards designing a mobile app that creates avatars for privacy protection.

In: MobileHCI 2018 proceedings, pp 79–86

Miller GR (1978) The current status of theory and research in interpersonal communication. Hum Com-

mun Res 4(2):164–178

Mitchell JC (1969) The concept and use of social networks. Manchester University Press, Manchester

Molm LD (1997) Coercive power in social exchange. Cambridge University Press, Cambridge

Molm LD (2003) Theoretical comparisons of forms of exchange. Sociol Theory 21(1):1–17

465

1 3

Photos and Avatars in Micro-Communities

Morrison R, Cegielski CG, Rainer RK (2012) Trust, avatars, and electronic communications: implica-

tions for e-learning. J Comput Inf Syst 53(1):80–89

Mull I, Wyss J, Moon E, Lee S-E (2015) An exploratory study of using 3D avatars as online salespeople.

J Fash Mark Manag 19(2):154–168

Nowak KL, Biocca F (2003) The effect of the agency and anthropomorphism on users’ sense of telep-

resence, copresence, and social presence in virtual environments. Presence Teleoperators Virtual

Environ 12(5):481–494

Nusca A (2017) The most popular Google Android and Apple iOS Apps of 2017. (available at http:// fortu

ne. com/ 2017/ 12/ 29/ best- most- popul ar- apps/)

Ou CX, Pavlou PA, Davison RM (2014) Swift Guanxi in online marketplaces: the role of computer-

mediated communication technologies. MIS Q 38(1):209–230

Pedersen S, Lupton D (2018) ‘What are you feeling right now?’ Communities of maternal feeling on

Mumsnet. Emot Space Soc 26:57–63

Pinto MD, Hickman RL, Clochesy J, Buchner M (2013) Avatar-based depression self-management tech-

nology: promising approach to improve depressive symptoms among young adults. Appl Nurs Res

26(1):45–48

Plickert G, Côté RR, Wellman B (2007) It’s not who you know, it’s how you know them: Who exchanges

what with whom? Soc Net 29(3):405–429

Posey C, Lowry PB, Roberts TL, Ellis TS (2010) Proposing the online community self-disclosure model:

the case of working professionals in France and the U.K. who use online communities. Eur J Inf

Syst 19(2):181–195

Praetorius AS, Görlich D (2020) How avatars influence user behavior. In: International conference on the

foundations of digital games, New York, NY, USA: ACM, September 15, pp 1–9

Preece J (2000) Online communities: designing usability and supporting socialbilty. Wiley

von der Pütten AM, Krämer NC, Gratch J, Kang SH (2010) ‘It doesn’t matter what you are!’ Explaining

social effects of agents and avatars. Comput Hum Behav 26(6):1641–1650

Qiu L, Benbasat I (2005) Online consumer trust and live help interfaces: the effects of text-to-speech

voice and three-dimensional avatars. Int J Hum-Comput Interact 19(1):75–94

Qiu L, Benbasat I (2010) A study of demographic embodiments of product recommendation agents in

electronic commerce. Int J Hum Comput Stud 68(10):669–688

Qiu W, Parigi P, Abrahao B (2018) More stars or more reviews? Differential effects of reputation on trust

in the sharing economy. In: CHI 2018 proceedings, pp 1–11

Ratan R, Beyea D, Li BJ, Graciano L (2020) Avatar characteristics induce users’ behavioral conform-

ity with small-to-medium effect sizes: a meta-analysis of the proteus effect. Media Psychol

23(5):651–675

Ratan R, Rikard RV, Wanek C, McKinley M, Johnson L, Sah YJ (2016) Introducing avatarification: an

experimental examination of how avatars influence student motivation. In: Proceedings of the

annual Hawaii international conference on system sciences, (Vol. 2016-March), IEEE Computer

Society, March 7, pp 51–59

Ratan R, Sah YJ (2015) Leveling up on stereotype threat: the role of avatar customization and avatar

embodiment. Comput Hum Behav 50:367–374

Riedl R, Mohr PNC, Kenning PH, Davis FD, Heekeren HR (2014) Trusting humans and avatars: a brain

imaging study based on evolution theory. J Manag Inf Syst 30(4):83–114

Saunders C, Rutkowski AF, van Genuchten M, Vogel D, Orrego JM (2011) Virtual space and place:

theory and test. MIS Q 35(4):1079

Scott N, Baggio R, Cooper C (2008) Network analysis and tourism: from theory to practice. Channel

View Publications, Bristol

Settoon RP, Bennett N, Liden RC (1996) Social exchange in organizations: perceived organizational sup-

port, leader-member exchange, and employee reciprocity. J Appl Psychol 81(3):219–227

Skarredghost (2022) Impressive realistic Mark Zuckerberg avatar shown at Meta Connect – YouTube.

Youtube, October 15 (available at https:// www. youtu be. com/ watch?v= So8Gd QD0Qyc. Accessed

1 Nov 2022

Smith VL (1976) Experimental economics: induced value theory. Am Econ Rev 66(3):274–279

Steinbrück U, Schaumburg H, Duda S, Krüger T (2002) A picture says more than a thousand words: pho-

tographs as trust builders in e-commerce websites. In: CHI 2002 proceedings, pp 748–749

Stephenson GM, Ayling K, Rutter DR (1976) The role of visual communication in social exchange. Br J

Soc Clin Psychol 15(2):113–120

466

T.Teubner, S.Camacho

1 3

Surma J (2016) Social exchange in online social networks. The reciprocity phenomenon on Facebook.

Comput Commun 73:342–346

Tamir DI, Ward AF (2015) Old desires, new media. In: The psychology of desire. Guilford Press, pp

432–455

Tech at Meta (2022) The Future of Avatars and Identity in the Metaverse with Aigerim Shorman. April

12 (available at https:// tech. fb. com/ ideas/ 2022/ 04/ boz- to- the- future- episo de-8- the- future- of- avata

rs- and- ident ity- in- the- metav erse- with- aiger im- shorm an/. Accessed 27 Oct 2022

TechTarget (2014) What is Proteus effect?. January Available at https:// www. techt arget. com/ whatis/ defin

ition/ Prote us- effect. Accessed 10 Oct 2022

Teng C-I (2017) Impact of avatar identification on online gamer loyalty: perspectives of social identity

and social capital theories. Int J Inf Manage 37(6):601–610

Teubner T, Adam MTP, Camacho S, Hassanein K (2014) Understanding resource sharing in C2C plat-

forms: the role of picture humanization. In: ACIS 2014 proceedings, pp 1–10

Teubner T, Adam MTP, Camacho S, Hassanein K (2022) What you see is what you g(u)e(s)t: how profile

photos and profile information drive providers’ expectations of social reward in co-usage sharing.

Inf Syst Manag 39(1):64–81

Teubner T, Adam MTP, Hawlitschek F (2021) On the potency of online user representation: insights from

the sharing economy. In: Gimpel H, Krämer J, Neumann D, Pfeiffer J, Seifert S, Teubner T, Veit

DJ, Weidlich A (eds) Market engineering: insights from two decades of research on markets and

information. Springer, pp 167–181

Teubner T, Hawlitschek F, Adam MTP, Weinhardt C (2013) Social identity and reciprocity in online gift

giving networks. In: HICSS 2013 proceedings, pp 708–717

Tichy NM, Tushman ML, Fombrun C (1979) Social network analysis for organizations. Acad Manag Rev

4(4):507–519

Tong F, Nakayama K, Moscovitch M, Weinrib O, Kanwisher N (2000) Response properties of the human

fusiform face area. Cogn Neuropsychol 17(1–3):257–280

Uehara E (1990) Dual exchange theory, social networks, and informal social support. Am J Sociol

96(3):521–557

University of Cambridge (2019) Scientists identify possible source of the ‘Uncanny Valley’ in the brain |

University of Cambridge. July 1 Available at https:// www. cam. ac. uk/ resea rch/ news/ scien tists- ident

ify- possi ble- source- of- the- uncan ny- valley- in- the- brain. Accessed 27 Oct 2022

Waddell TF, Ivory JD (2015) It’s not easy trying to be one of the guys: the effect of avatar attractiveness,

avatar sex, and user sex on the success of help-seeking requests in an online game. J Broadcast

Electron Media 59(1):112–129

Wang SS, Moon SI, Kwon KH, Evans CA, Stefanone MA (2010) Face off: implications of visual cues on

initiating friendship on Facebook. Comput Hum Behav 26(2):226–234

Whitty M, Doodson J, Creese S, Hodges D (2014) Image choice to represent the self in different online

environments. In: International conference on social computing and social media, pp 528–537

Willis J, Todorov A (2006) First impressions making up your mind after a 100-ms exposure to a face.

Psychol Sci 17(7):592–598

Wolverton T (2018) Apple is bringing personalized emojis to the iPhone so you can create an avatar

that looks just like you — here’s how it works. Available at https:// www. busin essin sider. nl/ apple-

memoji- animo ji- wwdc- 2018- 6/? inter natio nal= true&r= US. Accessed 6 Feb 2019

Wu J, Kraemer P (2017) Positive preferences: the emotional valence of what an avatar says matters.

Cyberpsychol Behav Soc Netw 20(1):17–21

Wu YCJ, Chang WH, Yuan CH (2015) Do Facebook profile pictures reflect user’s personality? Comput

Hum Behav 51:880–889

Ye S, Viswanathan S, Hann I-H (2018) The value of reciprocity in online barter markets: an empirical

investigation. MIS Q 42(2):521–549

Yee N, Bailenson J (2007) The Proteus effect: the effect of transformed self-representation on behavior.

Hum Commun Res 33(3):271–290

Yee N, Bailenson JN, Ducheneaut N (2009) The Proteus effect: implications of transformed digital self-

representation on online and offline behavior. Commun Res 36(2):285–312

Yu K, Gorbachev G, Eck U, Pankratz F, Navab N, Roth D (2021) Avatars for teleconsultation: effects

of avatar embodiment techniques on user perception in 3D asymmetric telepresence. IEEE Trans

Visual Comput Graphics 27(11):4129–4139

467

1 3

Photos and Avatars in Micro-Communities

Zhang L, Yan Q, Zhang L (2018) A computational framework for understanding antecedents of guests’

perceived trust towards hosts on Airbnb. Decis Supp Syst 115:105–116

Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps

and institutional affiliations.