Manage Your Money Wisely: How Consumers and Marketers Can Effectively Communicate Money Issues

Washington University in St. Louis Washington University in St. Louis

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Manage Your Money Wisely: How Consumers and Marketers Can Manage Your Money Wisely: How Consumers and Marketers Can

Effectively Communicate Money Issues Effectively Communicate Money Issues

Alexander Park

Washington University in St. Louis

, [email protected]

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WASHINGTON UNIVERSITY IN ST. LOUIS

Olin Business School

Dissertation Examination Committee:

Cynthia Cryder, Chair

Rachel Gershon

Robyn LeBoeuf

Hannah Perfecto

Sydney Scott

Marissa Sharif

Manage Your Money Wisely:

How Consumers and Marketers Can Effectively Communicate Money Issues

Alexander B. Park

A dissertation presented to

Olin Business School

of Washington University in

partial fulfillment of the

requirements for the degree

of Doctor of Philosophy in Business Administration

May 2023

St. Louis, Missouri

ii 
 
Table of Contents 
List of Figures .............................................................................................................................v 
List of Tables ............................................................................................................................ vi 
Acknowledgements .................................................................................................................. vii 
Abstract .................................................................................................................................. viii 
1  Introduction ..................................................................................................................1 
2   Conceptual Background ................................................................................................3 
2.1  The Socio-psychological Complexity of Money ................................................................ 3 
2.2  Social Connection Strength ............................................................................................... 5 
2.3   Selecting the Approach – Social Richness Matters ............................................................ 7 
2.4   Overview of Studies ....................................................................................................... 10 
3   Study 1A ..................................................................................................................... 11 
3.1   Methods ......................................................................................................................... 11 
3.2   Results ........................................................................................................................... 12 
4   Study 1B ..................................................................................................................... 14 
4.1   Methods ......................................................................................................................... 14 
4.2   Results ........................................................................................................................... 15 
4.3  Discussion ...................................................................................................................... 17 
5   Study 2........................................................................................................................ 18 
5.1   Methods ......................................................................................................................... 18 
5.2   Results ........................................................................................................................... 19 
5.3  Discussion ...................................................................................................................... 20 
6   Study 3........................................................................................................................ 21 
6.1   Methods ......................................................................................................................... 21 
6.2   Results ........................................................................................................................... 22 
6.3  Discussion ...................................................................................................................... 24 
7   Study 4A ..................................................................................................................... 24 
7.1   Methods ......................................................................................................................... 25 
7.2   Results ........................................................................................................................... 26 
7.3  Discussion ...................................................................................................................... 27 
8   Study 4B ..................................................................................................................... 28 
8.1   Methods ......................................................................................................................... 28 
8.2   Results ........................................................................................................................... 29 
8.3  Discussion ...................................................................................................................... 30 

iii 
 
9   Study 5........................................................................................................................ 30 
9.1   Methods ......................................................................................................................... 31 
9.2   Results ........................................................................................................................... 32 
9.3  Discussion ...................................................................................................................... 35 
10   General Discussion ..................................................................................................... 35 
1  Introduction ................................................................................................................ 38 
2   Conceptual Background .............................................................................................. 41 
2.1  Corporate Social Responsibility: A Double-Edged Sword ............................................... 41 
2.2  Commitment and Charitable Credit ................................................................................. 42 
2.3   Periodic Donations on Commitment and Charitable Perceptions ..................................... 43 
2.4   Total Donation Size Matters ........................................................................................... 45 
2.5   Overview of Studies ....................................................................................................... 47 
3   Study 1A ..................................................................................................................... 48 
3.1   Methods ......................................................................................................................... 48 
3.2   Results ........................................................................................................................... 49 
4   Study 1B ..................................................................................................................... 50 
4.1   Methods ......................................................................................................................... 50 
4.2   Results ........................................................................................................................... 51 
4.3  Discussion ...................................................................................................................... 52 
5   Study 2........................................................................................................................ 52 
5.1   Methods ......................................................................................................................... 53 
5.2   Results ........................................................................................................................... 54 
5.3  Discussion ...................................................................................................................... 55 
6   Study 3........................................................................................................................ 55 
6.1   Methods ......................................................................................................................... 55 
6.2   Results ........................................................................................................................... 56 
6.3  Discussion ...................................................................................................................... 58 
7   Study 4........................................................................................................................ 58 
7.1   Methods ......................................................................................................................... 58 
7.2   Results ........................................................................................................................... 60 
7.3  Discussion ...................................................................................................................... 61 
8   Study 5........................................................................................................................ 62 
8.1   Methods ......................................................................................................................... 63 
8.2   Results ........................................................................................................................... 64 
8.3  Discussion ...................................................................................................................... 65 

2.9 Study 6........................................................................................................................ 66

2.9.1 Methods ......................................................................................................................... 66

2.9.2 Results ........................................................................................................................... 67

2.9.3 Discussion ...................................................................................................................... 68

2.10 General Discussion ..................................................................................................... 69

Appendix .................................................................................................................................. 70

3.1.1 Chapter 1: Appendix A ............................................................................................... 70

3.1.2 Chapter 1: Appendix B ................................................................................................ 78

3.2.1 Chapter 2: Appendix C ................................................................................................ 83

3.2.2 Chapter 2: Appendix D ............................................................................................... 89

References/Bibliography/Works Cited .................................................................................... 100

v 
 
List of Figures 
Figure 1.1: Perceived Discomfort of Discussing Sensitive Topics ...............................................5 
Figure 1.2: Study 3 Mediation Model ........................................................................................ 24 
Figure 1.3: Study 4A Results ..................................................................................................... 27 
Figure 1.4: Study 4B Results ..................................................................................................... 30 
Figure 2.1: Study 1A Stimuli ..................................................................................................... 49 
Figure 2.2: Study 1B Stimuli ..................................................................................................... 51 
Figure 2.3: Study 3 Mediation Model ........................................................................................ 57 
Figure 2.4: Study 4 Mediation Models ...................................................................................... 61 
Figure 2.5: Study 5 Results ....................................................................................................... 65 
Figure 2.6: Study 6 Results ....................................................................................................... 68 
 
 

List of Tables

Table 1.1: Hypotheses ............................................................................................................... 10

Table 1.2: Study 1A Results ...................................................................................................... 13

Table 1.3: Study 1B Results ...................................................................................................... 15

Table 1.4: Study 2 Results ......................................................................................................... 20

Table 1.5: Study 5 Results ......................................................................................................... 32

Table 2.1: Study 1B Results ...................................................................................................... 51

Table 2.2: Study 2 Results ......................................................................................................... 54

vii

Acknowledgments

First, I would like to express my deepest gratitude to the best advisor anyone could ask

for, Cynthia Cryder. Thank you, Cindy, for showing me what it means to be a phenomenal

scholar and mentor. I am forever grateful for your continuous inspiration, wisdom, and emotional

support. To Robyn LeBoeuf, Sydney Scott, and Hannah Perfecto, thank you all so much for your

invaluable advice and time throughout the doctoral program. My academic journey would not

have been possible without your support and patience. To Rachel Gershon and Marissa Sharif,

while the struggle has been so real, your unwavering encouragement and mentorship have made

the experience the most exciting, laughter-filled, and happy struggle one could ask for.

Second, I would like to thank the Consumer Behavior Decision Science Lab and the

Marketing Department at WashU for their endless generosity and encouragement. Moreover, I

would like to thank my fellow Consumer Behavior cohorts for their friendship and for being

such a wonderful group.

Finally, I would like to thank my amazing family and friends for their support and

encouragement. Thank you all for believing in me.

Alexander B. Park

Washington University in St. Louis

May 2023

viii

ABSTRACT OF THE DISSERTATION

Manage Your Money Wisely:

How Consumers and Marketers Can Effectively Communicate Money Issues

Alexander Park

Doctor of Philosophy in Business Administration

Washington University in St. Louis, 2023

Professor Cynthia Cryder, Chair

Consumers and marketers find it challenging to communicate money matters. For

example, consumers experience discomfort and uneasiness when discussing financial issues with

their social relationships. Firms often receive backlash when sharing their good financial deeds

(e.g., charitable efforts) with consumers. Because financial matters can be a sensitive topic, in

my research, I explore the difficulties consumers and marketers experience when communicating

money issues and how they can better navigate these problems.

In Chapter One, I investigate a particularly uneasy interaction that consumers often face

with their friends and acquaintances: the need to ask for money back. Seven fully preregistered

studies (N = 5,543) show that consumers’ approach to resolving peer debt varies based on their

closeness with the requestee. Specifically, consumers prefer communication methods low in

social richness (e.g., digital apps) when requesting money back from weak social connections

such as distant acquaintances. However, they prefer communication methods high in social

richness (e.g., in-person interactions) when requesting money back from strong social

connections such as close friends. Process evidence shows that this pattern occurs because 1)

consumers anticipate discomfort when requesting money back from distant acquaintances in

person, driving them away from in-person requests and toward digital apps, and 2) consumers

are more averse to appearing impersonal with close friends, driving them away from digital apps

and toward in-person requests. In sum, consumers adaptively approach financial interactions

based on the relationship dynamics at hand.

In Chapter Two, I investigate a domain where firms and marketers encounter issues

communicating their finances with consumers: sharing their corporate social responsibility

(CSR) activities. CSR is essential for a firm’s brand image and financial success, but consumers

often doubt whether these engagements are self-interested marketing strategies driven by profit.

Then, how might firms effectively communicate their CSR activities? Across seven preregistered

experiments (N = 147,996; two field experiments and five lab experiments), we find that

donations presented as a series of periodic donations (e.g., $1000 donation per month for 12

months) improve company favorability more so than equivalent donation pledges presented in an

aggregate frame (e.g., a donation of $12,000). We further show that the effectiveness of this

temporal framing is driven by the perceived commitment towards the cause – when a company

frames its donations periodically, consumers perceive the company to be more committed to

supporting the cause, increasing company favorability.

Chapter 1: Fighting Fiscal Awkwardness: To

Resolve Peer Indebtedness, Consumers

Adapt Based on Relationship Strength

1.1 Introduction

Suppose that you recently went to lunch with a co-worker. Your co-worker forgot their

wallet, so you covered their meal. Several days passed, and this co-worker still has not repaid

you, so you decide to ask for the money back. Would you make this request in person? Or would

you avoid the in-person confrontation and send a digital request instead?

Consumers often lend and borrow money from each other (Banerjee and Duflo 2007; Lee

and Persson 2016; Morvinski and Shani 2022; Rona-Tas and Guseva 2018). Although such

exchanges are frequent, navigating interpersonal debt can evoke unease because of money's

social and psychological complexities (Belk and Wallendorf 1990; Sun and Slepian 2020;

Zelizer 1989). Money can represent power (Zelizer 1989), status (Ivanic, Overbeck, and Nunes

2011), and achievement (Ridgeway 2014; Rose and Orr 2007). As such, thoughts about money

can trigger anxiety (Fitch et al. 2011; Sweet et al. 2013), distrust (Yamauchi and Templer 1982),

and even psychological pain (Prelec and Loewenstein 1998; Zellermayer 1996). Given the

complex social dynamics surrounding money, how do consumers navigate financial matters in

social contexts?

The current research investigates how consumers request owed money back from peers,

finding that consumers strategically tailor their approach to fit the relationship in question.

Specifically, we propose and find that controlling for how frequently consumers see the

requestee, the strength of consumers’ social connection with each other affects request behaviors.

We show that because consumers anticipate substantial discomfort when requesting owed money

from weak social connections, they request in ways that avoid face-to-face interactions (e.g.,

digitally). However, in the case of strong social connections, there is greater concern that a

request for repayment could appear impersonal. Therefore, with strong social ties, consumers

request via socially rich means (e.g., in-person) to avoid appearing impersonal to the requestee.

This research makes several contributions to the literature on financial decision-making,

social relationships, and consumers’ decisions in an expanding digital landscape. We examine

the important, yet understudied, domain of peer indebtedness. While informal loans are

commonplace among peers and households (e.g., Banerjee and Duflo 2007), little is understood

about how consumers navigate owing and recouping money from peers. Second, this research

contributes to the literature examining the intersection of social relationships and personal

finance. Despite the known perils of mixing money and relationships (Clark and Mills 1979,

2012; Fiske 1992), the two are frequently and inevitably intertwined (Corfman and Lehmann

1987; Ferber and Lee 1974; Simpson, Griskevicius, and Rothman 2012). This intersection of

money and relationships has been studied most extensively within romantic relationships

(Garbinsky and Gladstone 2019; Olson and Rick 2021; Rick, Small, and Finkel 2011); however,

there is limited work outside the romantic realm. Finally, we contribute to the growing literature

seeking to understand consumer finance in the digital world. Some recent work documents how

digital payment apps affect consumption experiences (e.g., Huang et al. 2020). However, to our

knowledge, we are the first to examine when and why digital payment apps are used for their

primary purpose – resolving peer debt.

The following sections develop a theoretical basis for why and when peer indebtedness

elicits discomfort. Relying on this theoretical foundation, we argue that request methods low in

social richness, such as digital apps, can minimize discomfort for interactions among distant

acquaintances in particular. By contrast, in the case of close personal relationships, the risk of

appearing impersonal becomes a dominant consideration, driving consumers away from digital

apps and toward options higher in social richness, including in-person requests. In short, we

propose that consumers adjust financial interactions in social contexts based on the relationship

priorities at hand.

1.2 Conceptual Background

1.2.1 The Socio-psychological Complexity of Money

Consumers associate money with status, achievement, security, and power (Furnham,

Wilson, and Telford 2012; Rose and Orr 2007). Depending on how it is used, money can be so

exalted that it is viewed as sacred (Baker and Jimmerson 1992; Belk and Wallendorf 1990;

McGraw, Tetlock, and Kristel 2003; Zelizer 1989). Possessions acquired with money can

become important reflections of consumers’ identities (Belk 1988), and accordingly, consumers

often perceive a “rich self” to be a “happy self” (Aknin, Norton, and Dunn 2009; Kahneman and

Deaton 2010; Luft 1957; Mogilner 2010; Mogilner and Norton 2016).

At the same time, money serves as a source of great insecurity and angst (Furnham 1984;

Furnham, Wilson, and Telford 2012; Rose and Orr 2007). Consumers perceive money to be a

signal of one’s competence and abilities (Furnham 1984), and therefore, indebtedness or a

perceived lack of money is associated with lower psychological and even physiological well-

being (Clayton et al. 2015; Fitch et al. 2011; Ong, Theseira, and Ng 2019; Rose and Orr 2007;

Sweet et al. 2013). Because money, or lack thereof, can trigger insecurities, discussing one’s

finances with others may elicit an array of negative emotions, including discomfort and

nervousness, ultimately making it one of the most avoided conversational topics in many cultures

(Belk and Wallendorf 1990; Hart, VanEpps, and Schweitzer 2021; Krueger 1991; Sun and

Slepian 2020; Trachtman 1999; Wherry, Seefeldt, and Alvarez 2019).

To explore this notion, we collected data on the discomfort of financial discussions

relative to other sensitive topics. A pilot study (N = 544) measured the discomfort of discussing

ten sensitive topics, including money issues, sex, relationship problems, and politics, with both

close friends and distant acquaintances. We found that “money issues” was rated as the second

most uncomfortable topic to discuss with these social connections (see Figure 1.1; for pilot study

details, see Appendix A). These results are broadly consistent with those from Sun and Slepian

(2020), showing that money and financial matters were the most commonly avoided discussion

topics, and similarly, Hart, VanEpps, and Scwheitzer (2021), finding that when individuals are

asked to generate highly sensitive questions, they often produce questions about money. We

further found that respondents consistently rated these topics as substantially more

uncomfortable to discuss with distant acquaintances relative to close friends. Given these results

and the prevalence of peer indebtedness, it is a high priority to understand how consumers can

better manage their fiscal discomfort with their social networks.

FIGURE 1.1: PERCEIVED DISCOMFORT OF DISCUSSING SENSITIVE TOPICS

WITH PEERS

NOTE. – Error bars represent 95% confidence intervals.

1.2.2 Social Connection Strength

We posit that social connection strength has an important influence on how consumers

approach financial matters with peers. Greater emotional intimacy and feelings of connectedness

between individuals dictate the strength of a social connection (Aron, Aron, Smollan 1992;

Granovetter 1973). Relationships such as those with family members, close friends, and

significant others tend to be characterized as strong social connections. Consumers have had

numerous interactions, shared experiences, and feelings of affection with these strong social ties;

thus, they tend to share a strong bond (Garcia-Rada et al. 2021; Krachardt, Nohria, and Eccles

2003). Conversely, relationships with acquaintances and distant others, defined as weak social

ties, tend to be less developed. There is a relative lack of emotional closeness, feelings of

connectedness, and importance attached to these relationships (Granovetter 1973; Lydon,

Jamieson, and Holmes 1997). Within this conceptualization, it is important to note that weak

social ties are not strangers nor disliked individuals, nor even necessarily mere transactional

connections (Clark and Mills 1993, 2012); rather, both strong and weak social ties generally refer

to positive social relationships, differing primarily in psychological closeness (Fowler and

Christakis 2008; Granovetter 1973).

Consumers’ peer networks consist of both strong (i.e., close friends) and weak social

connections (i.e., acquaintances). We propose that consumers have distinct approaches to

navigating financial interactions with each relationship type. Specifically, we argue that the

relationship priorities determine how consumers approach financial interactions.

An important feature of weak social ties is a lack of a close personal connection (Lydon,

Jamieson, and Holmes 1997). The lower levels of intimacy and emotional connection between

individuals with weak social ties can elicit greater discomfort and awkwardness during in-person

interactions, particularly when the topic is fraught. This is evident in the pilot study results,

where participants consistently rated awkward topics to be considerably more uncomfortable to

discuss with a distant acquaintance than a close friend (Figure 1.1). We predict that consumers

anticipate greater levels of discomfort when requesting owed money in person from weak social

ties (see Table 1.1, Hypothesis 2).

Although sensitive topics can evoke strong discomfort from weak social connections,

consumers may be less concerned about discomfort arising in interactions with close social

connections, consistent with our pilot study results (Figure 1.1). Consumers have a close

personal bond with their strong social connections. At the same time, these stronger bonds also

indicate that consumers value these relationships and might feel a need to signal care. Consumers

are generally more concerned about how their strong social connections view them (Garcia-Rada

et al. 2021; Wilcox and Stephen 2014), and consequently, strong social ties exert an especially

powerful influence on consumer behavior (Brown and Reingen 1987). For instance, consumers

avoid getting impersonal gifts for their close friends, sometimes even prioritizing gifts that signal

their personal relationship over prioritizing the recipient’s gift preferences (Ward and

Broniarczyk 2016). Greater concern for maintaining close relationships suggests that consumers

might be especially averse to appearing impersonal with this group in order to protect cherished

connections (see Table 1.1, Hypothesis 3).

1.2.3 Selecting the Approach – Social Richness Matters

As outlined above, we predict that 1) consumers anticipate greater discomfort when

interacting with weak social connections regarding financial matters, and 2) consumers are more

averse to appearing impersonal with strong social connections. Due to these forces, we propose

that consumers use approaches differing in social richness to resolve peer indebtedness with

these two groups.

Social richness is defined as the degree to which a medium of communication varies in

intimacy and immediacy (Daft and Lengel 1986; Short, Williams, and Christie, 1976). Under this

theorization, face-to-face interactions are high in social richness because they are interpersonal

and synchronous, allowing for various social cues, immediate feedback, and personal

connection. On the other hand, digital interactions are low in social richness due to their indirect

communication and asynchronous features, rendering limited social cues, a delayed response,

and impersonality.

A second pilot test (N = 100) gauged the social richness of seven different approaches for

communicating about financial matters with peers: 1) in person, 2) phone calls, 3) texts, 4)

emails, 5) Venmo, 6) PayPal, and 7) a standard bank app (for pilot study details, see Appendix

A). Based on conceptualizations of social richness (Daft and Lengel 1986; Short, Williams, and

Christie, 1976), participants answered three questions: 1) “To what extent is [communication

method] a socially rich way to communicate with others?”, 2) “To what extent would you feel

that you are in the presence of others when communicating [communication method]?”, and 3)

“To what extent would you feel connected with others when communicating [communication

method]” on a 11-point Likert scale (1 = Not at all, 11 = Extremely; α = .94). The order in which

participants saw the seven communication methods was randomized. Consistent with prior

theories of social richness, post-hoc comparisons showed that participants rated in-person

interactions as the most socially rich, followed by phone calls, texts, emails, Venmo, PayPal, and

then a standard bank app (see Appendix A). In the current research, we focus the bulk of our

investigations on the communication media that are the highest on this social richness dimension

(in-person requests) versus the lowest (digital payment apps). Moreover, we focus on how these

communication media are used for financial discussions in particular.

Because digital apps are judged as low in social richness, they might allow consumers to

minimize the awkwardness inherent in resolving peer indebtedness. Indeed, one proposed benefit

of digital apps is that they can smooth some of the friction of indebtedness by allowing

consumers to avoid direct fiscal confrontation when requesting money (PYMNTS 2017). Digital

apps are often used to split bills, request or repay money among peers, and even pay vendors for

purchases (Unger et al. 2020; Zhang et al. 2017). While consumers frequently use digital apps to

request money because of convenience, we propose that this communication method can also

minimize the social discomfort inherent in fiscal interactions. Thus, we hypothesize that

consumers prefer less socially rich request methods when requesting owed money from weak

social ties because they expect substantial discomfort from discussing financial topics with

distant social connections.

In contrast, we predict that consumers will rely more heavily on socially rich methods,

such as in-person requests, to resolve peer indebtedness with close social connections. Digital

communication, unlike face-to-face interactions, lacks social cues and reduces personalization

(Kiesler, Siegel, and McGuire 1984; Sproull and Kiesler 1986; Walther 1995, 1996). The lack of

social features in digital communication has potential negative downstream consequences, such

as miscommunication (Kruger et al. 2005), the risk of appearing impersonal (Sproull and Kiesler

1986), or even dehumanizing the communicator (Schroeder, Kardas, and Epley 2017; Walther

1996). Drawing on these findings, we expect that for interpersonal contexts such as peer-to-peer

indebtedness, the use of digital apps poses a concern that the requester might appear impersonal

to the requestee, and this concern is greater for strong social ties. Thus, we hypothesize that

consumers prefer socially rich options, such as in-person requests, when requesting owed money

from strong social connections.

Based on the conceptualization outlined earlier regarding the key psychological dynamics

at play in social-financial interactions and insights from our two pilot tests, we propose three

main hypotheses for how consumers approach interpersonal indebtedness with strong and weak

social ties (see Table 1.1).

TABLE 1.1: HYPOTHESES

Hypotheses

Studies

When consumers request owed money from weak social

ties, they prefer media low in social richness (e.g., digital

apps). When they request from strong social ties, they

prefer media high in social richness (e.g., in-person

1A, 1B, 2, & 5

Hypotheses

Studies

requests).

Consumers anticipate more discomfort when requesting

money in person from weak versus strong social ties,

which in turn increases requests low in social richness.

3 (mediation) &

4A (moderation)

Consumers are more averse to appearing impersonal with

strong versus weak social ties, which in turn increases

requests high in social richness.

3 (mediation) &

4B (moderation)

1.2.4 Overview of Studies

We test these hypotheses across seven preregistered studies. Studies 1A and 1B use

retrospective recall paradigms to provide behavioral evidence that consumers adjust the way they

request owed money depending on the strength of their social connection with the requestee.

Study 2 replicates these findings across a variety of social scenarios while relying on

experimental manipulation of social connection strength, carefully controlling for potential

confounds. Study 3 relies on mediation to measure and test the proposed mechanisms. Studies

4A and 4B directly manipulate discomfort and impersonality, respectively. Finally, Study 5

broadens the investigation to explore how consumers resolve indebtedness with non-friend or

acquaintance relationships (i.e., a store vendor) and examines consumer choice when given the

option not to request the money back at all.

We report all conditions, data exclusions (if any), and measures for each study (Simmons,

Nelson, and Simonsohn 2012). Preregistrations for all studies as well as study materials,

including complete stimuli, measures, data, and code can be found here:

https://researchbox.org/144&PEER_REVIEW_passcode=TQPTF.

1.3 Study 1A: Housemate Retrospective Study

Study 1A tests H1 that consumers prefer requesting money via methods low in social

richness with weak social connections and methods high in social richness with strong social

connections. We used a retrospective paradigm with a large sample of consumers to study real

monetary requests. Given that the frequency with which consumers see a requestee may

influence how requests are made, Study 1A relied on a natural setting, house or apartment

sharing, where consumers see the other person frequently. We expect that participants who

report weaker social connections with their roommates would be more likely to request owed

money via digital apps than those who report stronger connections, while participants who report

stronger connections with their roommates would be more likely to request in person.

1.3.1 Methods

We recruited 1198 Amazon Mechanical Turk (MTurk) participants via CloudResearch

between the ages of 18 and 40 (M

Age

= 30.33, 48.16% female). After excluding participants who

failed our preregistered exclusion criteria (described below), a final sample of 803 participants

remained. The preregistration for this study is here: https://aspredicted.org/SRV_FPX.

Participants first answered two “yes or no” questions about whether 1) within the past

five years, they had a housemate or roommate who was not a family member, and 2) they had

ever been in a situation where a roommate owed them money and they requested the money

back. Participants who answered “yes” to both questions were eligible for the study and typed

the first name or initials of the roommate from whom they had requested money back.

Participants then answered our main dependent variable question – how they requested the owed

funds from their roommates – among six choice options: 1) in-person (face-to-face), 2) over a

phone call, 3) via text message, 4) via a payment app such as Venmo, Zelle, PayPal, or Facebook

Messenger, 5) email or 6) other. Next, participants answered how close they were with their

roommates – our key independent variable. Participants were asked, “How close were you to

your roommate, [name], at the time you requested the money?” on a 20-point Likert scale (1 =

[name] was like a complete stranger to me, 20 = [name] was like a close family member to me).

Lastly, participants answered whether they had the option to request the owed money via a

payment app (yes or no), how much money their roommate owed, how long they waited until

requesting their money back, and demographic variables such as age and gender. Following our

preregistration, only participants who correctly answered the attention check question about

whom they had to request back owed money and answered “yes” to having the option to request

owed money via a payment app were included in the final analysis.

1.3.2 Results

On average, the roommates owed $284.62, and participants waited 24.88 days before

requesting back the owed money. In-person requests were the most frequent (57.66%), followed

by texts (22.54%), digital payment apps (17.68%), emails (.88%), other (.87%), and phone calls

(.37%); see Appendix A for details).

Following our preregistration, we concentrated on the two request methods that most

differ in social richness based on the previously mentioned social richness pilot test (Appendix

A): digital payment apps versus in-person requests. To test our hypothesis, we created a

combination of dummy variables by comparing the choice of request made via digital payment

apps (coded as “1”) or in person (coded as “0”). With this combination of the dummy variables

(n = 605), we conducted a logistic regression predicting the choice of request method based on

how close they were with their roommate.

As hypothesized, regression analyses (see Table 1.2) revealed that the closeness with

one’s roommate is negatively associated with the choice of request made via digital apps (β = -

.22, SE = .10, z = -2.35, p = .019). This association persisted even after controlling for the

amount of money that was owed (β = -.22, SE = .10, z = -2.25, p = .024) and the number of days

waited until requesting the money back (β = -.22, SE = .10, z = -2.19, p = .029)

. These results

are consistent with H1: consumers adapt their approach to resolving peer indebtedness based on

the strength of their social connections with the requestee. Specifically, we found that, even

when participants had the opportunity to interact with their roommates frequently, weaker social

connections between participants and their roommates predicted a greater likelihood of

requesting money using digital apps and a lower likelihood of requesting in person.

TABLE 1.2: STUDY 1A RESULTS

Dependent variable: Choice of digital app (1) versus in-person (0) request

Independent variable

Model 1

Model 2

Model 3

Model 4

Closeness

-.23*

(.10)

-.22*

(.10)

-.22*

(.10)

-.22*

(.10)

Amount of Money Owed

-.88*

(.35)

-.46

(.36)

Number of days waited

-1.16***

(.34)

-1.02**

(.36)

NOTE. – *p < .05, **p < .01, *** p < .001

Although non-preregistered, because text requests were common, comparisons between 1) digital payment app

versus text and 2) text versus in-person requests can be found in Appendix A.

1.4 Study 1B: Retrospective Study Across Contexts and

Connections

The goal of Study 1B was to replicate the patterns from Study 1A, but this time using a

broader set of contexts. As in Study 1A, we expected consumers who requested money from

weak versus strong social ties would prefer to use digital apps rather than ask in person.

1.4.1 Methods

We recruited 252 MTurk participants via CloudResearch between the ages of 18 and 35

Age

= 28.10, 53.17% female). After excluding participants who failed our preregistered

exclusion criteria, a final sample of 192 participants remained. The preregistration for this study

is here: https://aspredicted.org/blind.php?x=gu8v7x.

Participants first answered two questions about whether they had ever been in a situation

where 1) a close friend and/or 2) a distant acquaintance owed them money, and they had to

request the money back from that person. Participants who answered “yes” to either question

were eligible for the study and typed the first name or initials of the close friend and/or distant

acquaintance from whom they had requested money back. Participants next answered open-

ended questions describing the situation (e.g., why they lent the money), how much money they

lent, and how long they waited (in days) before requesting the money back. After describing

each situation, participants answered our main dependent variable question – how they requested

the owed funds from their close friend or distant acquaintance – from among five options: 1) in-

person (face-to-face), 2) over a phone call, 3) via text message, 4) via a payment app such as

Venmo, Zelle, PayPal, or Facebook Messenger, or 5) other.

1.4.2 Results

Out of 192 participants, 178 (92.71%) reported that they had been in a situation where a

close friend owed them money, and they had to request it back, and 94 (48.96%) reported that

they had been in a situation where a distant acquaintance owed them money and they had to

request it back (see Appendix A for details).

Using an approach similar to Study 1, we created three combinations of dummy variables

to code the choice of request method, following our preregistration: 1) digital payment apps

versus in-person, 2) digital payment apps versus phone calls and texts, and 3) phone calls and

texts versus in-person. While comparison 1 (digital apps versus in-person) is our primary

interest, we also compared phone and text requests to explore how media that are moderate in

social richness (Appendix A) differ from the media on both ends of the social richness scale.

We conducted three separate chi-square tests comparing the choice of request method by

requestee relationship (strength of social tie) with each combination of the dummy variables.

Table 1.3 displays the raw counts and percentages of the request methods for each combination.

TABLE 1.3: STUDY 1B RESULTS

Requestee

Relationship

Request Method

Chi-square test

Comparison 1

Digital Payment

App

In-Person

Close Friend

18.87%

(20/106)

81.13%

(86/106)

(1, N = 170) = 8.36, p =

.004

Distant

Acquaintance

39.06%

(25/64)

60.94%

(39/64)

Comparison 2

Requestee

Relationship

Request Method

Chi-square test

Digital Payment

App

Phone & Text

Close Friend

22.47%

(20/89)

77.53%

(69/89)

(1, N = 140) = 10.48, p =

.001

Distant

Acquaintance

49.02%

(25/51)

50.98%

(26/51)

Comparison 3

Phone & Text

In-person

Close Friend

44.52%

(69/155)

55.48%

(86/155)

(1, N = 220) = .38, p =

.54

Distant

Acquaintance

40%

(26/65)

60%

(39/65)

Our preregistered analyses showed a significant relationship between the request method

choice and the requestee's relationship when comparing digital payment apps versus in-person

requests. In support of H1, when looking only at participants who requested either via digital

apps or in person (comparison 1), participants requested money using digital apps more

frequently when requesting from distant acquaintances (39.06%) than when requesting from

close friends (18.87%; χ2(1, N = 170) = 8.36, p = .004). Correspondingly, more participants

requested money in person (81.13%) from close friends than from distant acquaintances

(60.94%). That is, participants were more likely to use digital apps and less likely to request in

person when requesting money from weak versus strong social ties.

Further, we found a significant relationship when comparing digital payment app requests

versus phone and text requests (comparison 2). When looking only at participants who requested

either via digital apps or phone/text, participants once again more frequently requested money

via digital payment apps from distant acquaintances (49.02%) than from close friends (22.47%;

χ2(1, N = 140) = 10.48, p = .001); correspondingly, more participants requested via phone or text

with close friends (77.53%) than distant acquaintances (50.98%). This pattern eases any

remaining concerns that patterns in Study 1A occurred because roommates who are socially

closer to each other have more opportunities to request in-person. In this comparison, we focus

only on phone-based options and still see that participants select socially rich approaches more

often when requesting from close social connections. For the final comparison (comparison 3),

no difference arose comparing phone and text versus in-person requests between close friends

(44.52%) and distant acquaintances (40%; χ2(1, N = 220) = .38, p = .54).

1.4.3 Discussion

Study 1A and 1B both show support for H1 – consumers’ approach to resolving peer

indebtedness depends on the strength of their social ties with the requestee when we look at

reports of their actual requesting behavior. Consumers frequently request repayment using

socially rich methods (i.e., in-person requests) from their peers. However, with weaker social ties

such as distant acquaintances, consumers’ preference for less socially rich methods (e.g., digital

app requests) increases.

One advantage of the methods in Studies 1A and 1B is that we examined participants’

recall of real instances of their financial request behaviors. Moreover, the roommate setting in

Study 1A suggests that these patterns occur even in settings where consumers see the requestee

frequently. However, these studies do not demonstrate causality. Various factors such as

payment amount and loan circumstances may also vary across these relationship types,

potentially contributing to the differences in their choice of request method. Thus, in the

following studies, we use experimental designs to investigate and isolate whether the strength of

the social tie between the requester and requestee drives the choice of request method.

1.5 Study 2: Stimulus Sampling

Study 2 provides further support for H

by directly manipulating the strength of the social

connection between the requester and requestee and otherwise holding the situation constant. In

addition, we implemented stimulus sampling, systematically presenting nine different scenarios

across participants to verify the generalizability of the phenomena under study and reduce the

threat of idiosyncratic features within any individual scenario (Judd, Westfall, and Kenny 2012;

Wells and Windschitl 1999). We expect that, across scenarios, individuals requesting from a

distant acquaintance relative to a close friend would be more likely to request owed money via

digital apps and, accordingly, would be less likely to request in person.

1.5.1 Methods

We recruited 1505 Amazon MTurk participants via CloudResearch (M

Age

= 39.58,

49.97% female). Participants were randomly assigned to one of two experimental conditions in a

2-cell (requestee: close friend vs. distant acquaintance) between-subjects design. Each participant

was then randomly assigned to view one of nine scenarios (amusement park, car troubles,

concert ticket, food, gas money, happy hour, housing, movie ticket, Uber ride). The amount of

money lent in each scenario varied from $10 to $100.

Participants imagined that either a close friend or a distant acquaintance named Charlie

had borrowed money (see Appendix B for materials). We describe the concert ticket scenario

below, but all scenarios follow a similar pattern. Participants in the concert ticket scenario

imagined that Charlie had forgotten their wallet and asked the participant to cover the cost of

their concert ticket. Participants were told that it had now been a couple of weeks since Charlie

had borrowed the money, and there were two ways to request the money back: either 1) request

via Venmo or 2) request in person. Venmo was described as a digital payment service that allows

users to request money through its phone app. Requesting in person was described as, “You can

request the money from Charlie face to face (assume you see Charlie twice a week and the

money will be paid through a digital payment method).” We specified these assumptions for the

in-person request to hold constant other factors that could affect the choice of request method,

such as how often the participant sees Charlie and the method by which they would receive

payment due to convenience. Participants then answered the question, “How would you ask for

the money Charlie, [one of your closest friends/one of your distant acquaintances], owed?”

Participants could respond either “Ask via Venmo” or “Ask in person.”

1.5.2 Results

As outlined in our preregistration (https://aspredicted.org/K9M_Q6D), only participants

who correctly answered the attention check questions such as who the requestee was and how

often they see the requestee (N = 1140) were included in the final analysis. We conducted a

logistic regression to regress participants’ choice of request method (1 = Ask via Venmo; 0 =

Ask in person) on the requestee (1 = distant acquaintance, 0 = close friend), including fixed

effects for scenarios. As predicted, participants in the distant acquaintance condition (45.49%;

237/521) were more likely to request owed money via a digital app than were participants in the

close friend condition (27.30%; 169/619; z = 6.45, p < .001); correspondingly, participants in the

close friend condition were more likely to request in person (72.70%; 450/619) than were

participants in the distant acquaintance condition (54.51%; 284/521). That is, when participants

requested owed money from a weak social tie (i.e., a distant acquaintance) compared to a strong

social tie (i.e., a close friend), they were more likely to request via a digital app and less likely to

ask in person. The pattern of results was also directionally consistent when looking at each

scenario separately (see Table 1.4).

TABLE 1.4: STUDY 2 RESULTS

Scenario

Close friend

Distant acquaintance

p-value

Amusement Park

31.75%

54.90%

p = .013

Bar happy hour

22.86%

42.11%

p = .02

Car troubles

18.42%

32.20%

p = .06

Concert ticket

27.50%

49.15%

p = .009

Food

25.35%

35.82%

p = .18

Gas money

33.96%

46.00%

p = .21

Housing

23.73%

50.00%

p = .003

Movie ticket

24.64%

47.69%

p = .005

Uber ride

38.46%

54.72%

p = .07

Collapsed

27.30%

45.49%

p < .001

1.5.3 Discussion

Study 2 provided additional evidence that, across multiple consumer contexts, individuals

have different preferred methods for requesting owed money based on the strength of social

connection with the requestee. When the requestee is an acquaintance relative to a close friend,

consumers are more likely to request via a digital app and less likely to request in person.

1.6 Study 3: Mediating Role of In-Person Request

Discomfort and Aversion to Appearing Impersonal

Our framework predicts that both the discomfort of requesting in person and the aversion

to appearing impersonal mediate the relationship between one’s social tie strength with the

requestee and their choice of request method. In Study 3, we expect to replicate the results of

Studies 1A-2: people will be more likely to request owed money via digital apps with a distant

acquaintance versus a close friend. Further, we predict that when requesting money from a

distant acquaintance (vs. a close friend), individuals would feel more discomfort requesting in

person, which in turn increases requests via digital apps (H

). We additionally predict that

individuals would be less averse to appearing impersonal when requesting money from a distant

acquaintance (vs. a close friend), further contributing to the preference for using digital apps with

distant social connections to request funds (H

1.6.1 Methods

We recruited 401 Amazon MTurk participants via CloudResearch (M

Age

= 39.91, 46.13%

female). Participants were randomly assigned to one of two conditions (requestee: close friend

vs. distant acquaintance) in a between-subjects design. Each participant was then randomly

assigned to view one of three scenarios (concert ticket, food, Uber ride). Following our

preregistration (https://aspredicted.org/blind.php?x=5un8ta), we planned to collapse analyses

across all scenarios, focusing our analysis and interpretation on the two different requestee

relationship conditions.

In Study 3, participants provided the first name of either a close friend or a distant

acquaintance. Participants imagined requesting owed money from that individual following one

of three scenarios (concert ticket, food, or Uber ride; see Appendix B) and were asked, “How

would you request the money that [name of the close friend/distant acquaintance] owed?” We

then measured our two proposed mechanisms: 1) the discomfort of requesting in person and 2)

the aversion to appearing impersonal. We asked participants in all conditions to rate their

discomfort of requesting the money in person using three items (adapted from Jiang, Hoegg, and

Dahl 2013): a) “How uncomfortable…, b) “How awkward…, and c) “How confrontational

would you feel when requesting money from [name of the requestee], your [close friend/distant

acquaintance] in person?” (1 = Not at all, 7 = Very much; α = .89). To measure the aversion to

appearing impersonal, we again used three items (adapted from Short, Williams, and Christie

1976): “How much of a problem would it be if [name of the requestee], your [close friend/distant

acquaintance] saw you as a) a cold person, b) impersonal, and c) insincere?” (1 = Not much of a

problem, 7 = Very much of a problem; α = .95). The order in which participants saw the two sets

of items was randomized.

1.6.2 Results

Replicating previous results, we found that participants were more likely to request owed

money via a digital app in the distant acquaintance condition (44.88%; 92/205) than in the close

friend condition (31.63%; 62/196; χ

(1, N = 401) = 7.43, p = .006). That is, when requesting

owed money from a weak (versus a strong) social tie, participants were more likely to request via

a digital app and, consequently, less likely to request in person. The pattern of results was also

consistent when looking at each scenario separately (see Appendix A).

As predicted, participants in the distant acquaintance condition (M = 4.14, SD = 1.74)

reported more discomfort requesting in person than those in the close friend condition (M = 3.43,

SD = 1.73; t(399) = 4.09, p < .001, d = .41, 95% CI [.21, .61]). Also as predicted, participants

were less averse to appearing impersonal in the distant acquaintance condition (M = 3.16, SD =

1.66) compared to the close friend condition (M = 4.57, SD = 1.74; t(399) = -8.30, p < .001, d = -

.83, 95% CI [-1.03, -.63]).

We tested whether the effect of the requestee relationship on the choice of request

method was mediated by the 1) discomfort of requesting in person and 2) aversion to appearing

impersonal (Hayes 2017, Model 4). Contrasting the close friend (0) and distant acquaintance (1)

conditions, we simultaneously tested the significance of both measured mediators by calculating

the standardized effects for 10,000 bootstrapped samples. We found a statistically significant

indirect effect of the discomfort of requesting in person (indirect effect = .24; 95% CI [.11, .41]),

and the indirect effect of the aversion to appearing impersonal was marginally significant

(indirect effect = .17; 95% CI [-.02, .38]).

As illustrated in Figure 1.2, including the two

mediators in the model, the direct effect of requestee relationship on the choice of request

method was no longer significant, indicating full mediation (direct effect = .20, p = .41, 95% CI

[-.27, .67]). In sum, the proposed mechanisms jointly and fully mediated the effect of requestee

relationship on the choice of request method. These results suggest that participants were more

uncomfortable requesting in person and less averse to appearing impersonal with distant

acquaintances versus close friends, thus increasing requests via digital apps for those weak social

ties.

When excluding participants who never used mobile payment apps, we found a significant indirect effect of

aversion to appearing impersonal (indirect effect = .26, 95% CI [.02, .52]).

FIGURE 1.2: STUDY 3 MEDIATION MODEL

NOTE. –

†

p <.10, *p < .05, **p < .01, *** p < .001

1.6.3 Discussion

Study 3 provided evidence for the hypothesized mediators driving our effect. Consumers

feel more discomfort requesting money in person from an acquaintance versus a close friend,

which increases their likelihood to request through digital means instead. In addition, individuals

are less averse to appearing impersonal when requesting money from an acquaintance versus a

close friend, thus increasing their digital (vs. in-person) requests.

1.7 Study 4A: Moderation via Decreased Discomfort

Study 3 found initial evidence that the increased discomfort of requesting in person and

the decreased aversion to appearing impersonal when interacting with distant acquaintances

increase consumers’ preference for requesting money using digital apps. Studies 4A and 4B

sought additional evidence of our proposed mechanisms via moderation (Spencer, Zanna, and

Fong 2005).

In Study 4A, we directly manipulated the discomfort of requesting in person by

describing the requestee as someone who is approachable. If consumers feel discomfort when

requesting owed money from distant acquaintances in person, then assuring minimal discomfort

should influence their choice of approach. Accordingly, we expect a reduction in the preference

for digital requests when a distant acquaintance is described as a person who is easy to talk to.

However, given that discomfort is already low for close friends, we expect a more modest

decrease in digital requests when this description is used for a close friend.

1.7.1 Methods

Amazon MTurk participants (N = 1201; MAge = 37.16, 50.12% female) were recruited

via CloudResearch. Participants were randomly assigned to one of four conditions in a 2

(requestee: close friend vs. distant acquaintance) x 2 (discomfort: control vs. decreased)

between-subjects design. Each participant was then randomly assigned to view one of three

scenarios (concert ticket, food, Uber ride). In accordance with our preregistration

(https://aspredicted.org/blind.php?x=7jf7ep), only participants that answered the attention check

questions correctly (N = 1085) were included in the analysis.

As in Study 3, participants imagined either a close friend or a distant acquaintance named

Charlie had borrowed money for a concert ticket, food, or an Uber ride and that they had to

request the money that was owed. Half of the participants read that Charlie is very friendly and

easy to talk to for the decreased discomfort manipulation, while the other half (control) did not

read this description. Then participants chose how they would prefer to request money from

Charlie (either “using Venmo” or “Ask in person”).

1.7.2 Results

Following our preregistration, we collapsed across the three scenarios and conducted a

logistic regression of the choice of request method on dummy variables representing close friend

versus distant acquaintance, discomfort conditions, and the interaction between these terms. This

analysis yielded a significant main effect of requestee relationship (z = -5.34, p < .001) and a

main effect of the discomfort manipulation (z = -3.80, p < .001). Most important, we found a

marginally significant interaction between requestee relationship and discomfort (z = 1.75, p =

.081). Probing this interaction, we found that participants were more likely to request the money

via a digital app from a distant acquaintance (58.36%; 157/269) than from a close friend

(35.46%; 100/282; χ

(1, N = 551) = 29.02, p < .001) in the discomfort-control condition where

no additional information about the requestee was provided. In the discomfort-decreased

condition, however, when Charlie was described as “very friendly and easy to talk to,” the

difference in digital app requests between a distant acquaintance (41.76%; 109/261) versus a

close friend (30.4%; 83/273; χ

(1, N = 534) = 7.48, p = .006) was still significant, but smaller

(see Figure 1.3). The pattern of results was also consistent when looking at each scenario

separately (see Appendix A).

FIGURE 1.3: STUDY 4A RESULTS

1.7.3 Discussion

If individuals prefer using digital apps to request money from acquaintances to avoid the

discomfort of fiscal confrontation, reducing discomfort should influence their preferred

approach. Results from Study 4A are in line with this conjecture – informing participants that an

acquaintance (versus a close friend) is approachable resulted in a greater decrease in the choice

to request via a digital app and, consequently, increased the choice to request in person.

Therefore, we found causal evidence that individuals choose to request via digital apps with

acquaintances partly due to the discomfort they expect when requesting in person; this

discomfort looms larger with acquaintances than with close friends.

1.8 Study 4B: Moderation via Impersonal Discomfort

Study 4B examines our other proposed mechanism: the aversion to appearing impersonal.

We manipulated participants’ aversion to appearing impersonal by describing the requestee as

someone who values warm interactions, thus making it important not to appear impersonal when

requesting the money. We predict that consumers are typically highly averse to appearing

impersonal when requesting owed money from close friends but less so when requesting money

back from distant acquaintances. Therefore, emphasizing that participants should avoid

appearing impersonal should have less of an effect on participants requesting from close friends,

who already focus on this dimension, but a larger effect on participants requesting from distant

acquaintances.

1.8.1 Methods

We recruited 1402 Prolific Academic participants (M

Age

= 28.36, 44.01% female).

Participants were randomly assigned to one of four conditions in a 2 (requestee: close friend vs.

distant acquaintance) x 2 (impersonal: control vs. averse) between-subjects design. Each

participant was then randomly assigned to view one of three scenarios (concert ticket, food, Uber

ride). Per our preregistration (https://aspredicted.org/blind.php?x=a2nv23), only participants who

correctly answered the attention check questions (N = 1291) were included in the analysis.

As in Study 4A, participants imagined either a close friend or a distant acquaintance

named Charlie had borrowed money for a concert ticket, food, or an Uber ride and that they had

to request the money that was owed. Participants in the impersonal-averse conditions read that

Charlie is someone who values warm interactions, so it is critical in this situation for them to not

appear impersonal when requesting the money. Participants in the impersonal-control conditions

did not have this information. Then participants chose how they would prefer to request money

from Charlie (either “using PayPal” or “Ask in person”).

1.8.2 Results

We collapsed across the three scenarios, following our preregistration, and conducted a

logistic regression of the choice of request method on dummy variables representing close friend

versus distant acquaintance, impersonal conditions, and the interaction between these terms. This

analysis yielded a non-significant main effect of requestee relationship (z = -.83, p = .41) and a

main effect of the impersonal manipulation (z = 7.90, p < .001). Most important, we found a

significant interaction between requestee and the impersonal manipulation (z = -2.95, p = .003).

In the impersonal-control conditions, participants were more likely to request the money using a

digital app with distant acquaintances (41.74%; 134/321) than with close friends (18.60%;

64/344; χ

(1, N = 665) = 42.52, p < .001). However, when we highlighted an aversion to

appearing impersonal (impersonal-averse conditions), this difference decreased and was no

longer significant (distant acquaintance: 11.78%; 35/297; close friend: 9.73%; 32/329; χ

(1, N =

626) = .69, p = .41; see Figure 1.4). The pattern of results was consistent when looking at each

scenario separately (see Appendix A).

FIGURE 1.4: STUDY 4B RESULTS

We changed the digital app choice option from “Venmo” to “PayPal” because the recruited sample contains

participants from the United Kingdom who most widely use PayPal.

1.8.3 Discussion

Study 4B provided further evidence for the role of consumers’ aversion to appearing

impersonal in their choice of repayment request method. When consumers request owed money

from a distant acquaintance (vs. a close friend), they are more likely to use digital apps because

they are less averse to appearing impersonal in these relationships. However, we find an

attenuation of this effect when the distant acquaintance is described as someone with whom the

consumer does not wish to appear impersonal.

1.9 Study 5: Requesting Money Back from a Vendor

In our final study, we sought to broaden our investigation in two ways. First, we wished

to test a logical extension of our conceptualization: as consumers’ relationship with a requestee

becomes increasingly distant, we predict that financial interactions will become less socially rich

as well. We therefore added a third experimental condition in which participants requested

money back from a store vendor. We also included a new choice option, the least socially rich

request method from our pilot test (i.e., a “standard bank app”), to extend the boundaries of our

stimuli on social richness. While Venmo and a standard bank app are similar in that they are both

primarily used for digital transactions, a standard bank app lacks some of the social

communication features of Venmo (e.g., text and emoji features), leading to less socially rich

evaluations, as verified by our second pilot study. We primarily expect to replicate the main

pattern – consumers are more likely to request owed money from weak social connections

(distant acquaintances and store vendors) using less socially rich media (Venmo and a standard

bank app) than when requesting from strong social ties (close friends). Moreover, we predict that

consumers would be more likely to request owed money via a standard bank app, the least social

option, with the most distant relationship (a vendor) than when requesting from a close friend or

a distant acquaintance.

Second, we wanted to explore how the strength of social connection affects consumers’

decision to opt out of requesting altogether In practice, the potential discomfort and relationship

threat consumers anticipate when requesting owed money may deter them from asking (e.g.,

Jaroszewicz 2020). We wished to test whether the previously observed differences in

communication choice persist when consumers can opt out of requesting money back.

1.9.1 Methods

We recruited 652 Prolific Academic participants (MAge = 33.36, 51.84% female).

Participants were randomly assigned to one of three conditions (close friend, distant

acquaintance, vendor) in a between-subjects experimental design. Following our preregistration

(https://aspredicted.org/blind.php?x=yd6rn7), only participants who correctly answered the

attention check question, such as how often they see the requestee (N = 631), were included in

the final analysis.

Participants in the close friend and distant acquaintance conditions imagined that either

their close friend or distant acquaintance named Charlie owed them money for breaking a set of

their dishes. Participants in the vendor condition imagined that they had bought a set of dishes

from a store vendor but discovered that the dishes were broken when they arrived home. The

vendor’s policy is that if any of the dishes are broken, consumers can request a refund in person

during their next visit or take a photo of the broken dishes and request the refund electronically

(see Appendix B for scenario details).

Participants then answered the question, “How would you request the money from [name

of close friend/name of distant acquaintance/the pottery vendor]?” Participants could respond

“Use Venmo,” “Ask in person,” “Use a standard bank app,” or “Do not request the money back.”

Venmo was described as a digital payment service that allows users to request money through its

phone app. Requesting in person was described as, “You will request the money from

[Charlie/the vendor] face-to-face. The standard bank app was described as, “another standard

bank app on your phone that allows users to request money through a phone app.”

1.9.2 Results

Table 1.5 displays the raw counts and percentages of the request methods for each

requestee relationship. A chi-square analysis revealed a significant relationship between

requestee and the choice of request method (χ

(6, N = 631) = 52.93, p < .001).

TABLE 1.5: STUDY 5 RESULTS

Requestee

Relationship

Request Method

Venmo

In-person

Bank app

Do not request the money

back

Close Friend

(n = 199)

18.59%

(37/199)

56.28%

(112/199)

5.03%

(10/199)

20.1%

(40/199)

Distant

Acquaintance

(n = 228)

30.26%

(69/228)

47.81%

(109/228)

7.89%

(18/228)

14.04%

(32/228)

Vendor

(n = 204)

35.29%

(72/204)

49.02%

(100/204)

14.22%

(29/204)

1.47%

(3/204)

Following our preregistration, we created three combinations of dummy variables to code

the choice of request method: 1) Venmo and bank app (combined as “digital apps”) versus in-

person, 2) bank app versus Venmo and in-person, and 3) Do not request the money back versus

Venmo, in-person, and bank app. The first combination of dummy variables (Venmo and bank

app versus in-person) tested our prediction that when participants choose to request owed money

from weaker ties, such as a distant acquaintance or a vendor (relative to a close friend), they are

more likely to request using less social rich methods such as digital apps. The second

combination of dummy variables (bank app versus Venmo and in-person) tested our prediction

that the least socially rich request method (bank app) will more likely be used with the least

social relationship (vendor). The last combination of dummy variables (do not request the money

back versus Venmo, in-person, and bank app) explores how the strength of social ties affects

consumers’ decision to forgo owed money.

As predicted, pairwise comparisons showed that participants in the distant acquaintance

condition (44.39%; 87/196) were more likely to request owed money via digital apps (Venmo

and bank app) than participants in the close friend condition (29.56%; 47/159; χ

(1, N = 355) =

8.21, p = .004). That is, replicating previous findings, when consumers chose to request owed

money from a distant acquaintance compared to a close friend, they were more likely to request

using digital apps and less likely to request in person. We found no difference in the choice to

request using the bank app between the distant acquaintance (9.18%; 18/196) and close friend

(6.29%; 10/159) conditions (χ

(1, N = 355) = 1.01, p = .31). Lastly, participants in the close

friend condition (20.1%; 40/199) were marginally significantly more likely to forgo requesting

owed money than those in the distant acquaintance condition (14.04%; 32/228; χ

(1, N = 427) =

2.79, p = .095).

Participants in the vendor condition (50.25%; 101/201) were also more likely to request

owed money using digital apps than participants in the close friend condition (29.56%; 47/159;

(1, N = 360) = 15.70, p < .001). Furthermore, participants in the vendor condition (14.43%;

29/201) were more likely to request owed money via a standard bank app than were participants

in the close friend condition (6.29%; 10/159; χ

(1, N = 360) = 6.09, p = .014). Lastly,

participants in the close friend condition (20.1%; 40/199) were more likely to opt out of

requesting back owed money compared to the vendor condition (1.47%; 3/204; χ

(1, N = 303) =

36.68, p < .001).

We found no difference in the choice to use digital apps with a distant acquaintance

(44.39%; 87/196) and a vendor (44.39%; 87/196; χ

(1, N = 397) = 1.37, p = .24). Contrary to our

prediction, we also found no difference in the request made between the vendor (14.43%;

29/201) and distant acquaintance (9.18%; 18/196) conditions via a standard bank app (χ

(1, N =

397) = 2.61, p = .11), though patterns are directionally consistent with the prediction. Lastly,

participants in the distant acquaintance condition (14.04%; 32/228) were more likely to opt out

of requesting back owed money compared to the vendor condition (1.47%; 3/204; χ

(1, N = 303)

= 22.83, p < .001). The greater likelihood to forgo requesting money in the distant acquaintance

condition suggests that consumers do not view distant acquaintances as purely transactional

relationships but likely do view vendors this way.

1.9.3 Discussion

The current findings provided further support for our framework, showing that consumers

adaptively resolve indebtedness depending on the strength of their social connection. Replicating

previous studies, we found that consumers were more likely to request owed money using digital

apps with weak (versus strong) social ties. In addition, we found that more consumers requested

using the least socially rich means (i.e., a standard bank app) when interacting with weaker social

ties, such as a vendor, relative to stronger social ties, such as a close friend. This pattern indicates

that as relationships become more distant, the preferred means of financial interactions become

more distant as well. Moreover, we found evidence that consumers were more likely to forgo

requesting owed money with close social ties than with weaker social ties. This suggests that

opting out of settling financial scores with peers is relatively common, even if this means

absorbing the financial cost; however, consumers are unwilling to forgo this financial loss with

highly distant and potentially transaction-based social contacts, such as store vendors.

Importantly, we also show that our main findings persist even when consumers are offered the

option to opt out of requesting owed money back.

1.10 General Discussion

Requesting owed money from peers can be uncomfortable and potentially fraught with

relationship hazards. In the present research, we study how consumers navigate this challenge.

Across seven studies, we find that consumers tailor their approach to requesting owed money

based on the strength of their social connection with the requestee. In both retrospective recall

paradigms (Studies 1A and 1B) and a stimulus-sampling experiment (Study 2), we document that

consumers are more likely to request owed money using digital apps with weaker social

connections, controlling for how frequently they see the requestee. We further find via mediation

(Study 3) and moderation (Studies 4A and 4B) that the difference in request method based on

social closeness arises because when interacting with weak social ties, consumers anticipate

greater discomfort from requesting in person and are less averse to appearing impersonal,

resulting in greater preference for digital request options.

One could wonder whether the current findings align with work on communal versus

exchange relationship norms (Clark and Mills 1979, 1993, 2012). This influential body of

research broadly conceptualizes friends versus acquaintances as communal versus exchange

relationships, respectively (e.g., Ryu and Han 2009), due to the differing norms of reciprocity for

each relationship type. However, the theory of communal and exchange relationships does not

answer the key questions posed in the current research: 1) how do consumers resolve peer debt

with each relationship, and 2) why is there a difference in request preferences? The theory does

make clear predictions about whether consumers would try to resolve peer debt with friends

versus acquaintances. The norms surrounding reciprocity are stronger for exchange relationships

(acquaintances), so consumers should be more likely to request owed money from weaker social

ties. We find a version of this pattern in Study 5, where consumers are more likely to opt out of

requesting owed money from close friends relative to store vendors. However, we also find that

request rates look largely the same between close friends and distant acquaintances. We do not

rely on the communal versus exchange distinction in this work because it does not explain how

consumers behave differently with friends versus acquaintances in this domain, and it does not

clearly predict how consumers resolve indebtedness depending on relationship type. Instead, we

draw from research on social tie strength (Granovetter 1973) and social richness (Daft and

Lengel 1986; Short, Williams, and Christie, 1976) to generate predictions ultimately supported

by our findings.

If anything, communal and exchange relationship theory might predict the opposite of

our proposition regarding anticipated discomfort if weak social ties are conceptualized as

exchange relationships. Under their theorization, exchange relationships prioritize equity and the

amount of benefit that is exchanged. Therefore, consumers could anticipate less discomfort

requesting back owed money in person from weak (vs. strong) social ties, given the expectations

of receiving back what is owed. However, our results suggest this is not the case.

Lastly, communal versus exchange relationship theory might predict that consumers

actually have lower concerns about appearing impersonal with strong ties because close

relationship members are more willing to forgive and accept each other’s behaviors. However,

while it might be true that individuals in close relationships are more forgiving of each other’s

behavior, our findings suggest that concern for the greater value of these relationships (e.g.,

Garcia-Rada et al. 2021) is a stronger driver of choices in this domain.

Interpersonal indebtedness evokes substantial unease. The current research shows that the

strength of consumers’ social connections affects how they attempt to resolve such indebtedness.

To fight the fiscal awkwardness of requesting money in person, consumers rely on media low in

social richness to communicate with weak social connections about owed money. At the same

time, consumers use more sociable means to communicate about money issues with strong social

connections to avoid appearing impersonal. Thus, consumers selectively maneuver to resolve

indebtedness based on the needs of the social relationship at hand.

Chapter 2: When Bigger Is Not Always

Better: Periodic Donations Enhance

Perceived Corporate Social Responsibility

and Company Engagement

2.1 Introduction

Corporate social responsibility (CSR), a company’s status and actions regarding its

perceived societal obligations (Brown and Dacin 1997; Sen and Bhattacharya 2001), has gained

significant attention from marketers and consumers over the past few decades. Partaking in

socially responsible activities not only benefits the firm’s image through charitable credit but

also the societal issue they support. With the idea that firms bring shared value to society and the

company, CSR has become an essential marketing strategy for companies (Kramer and Porter

2011; Porter and Kramer 2006).

However, the critical challenge firms face when engaging in socially responsible

activities is getting the consumers to believe the firm’s authentic prosocial motives and

charitable commitment that go beyond financial obligations. Consumers doubt the authenticity of

charitable activities, seeing them as self-interested marketing strategies driven by profit. As a

result, they look for evidence of genuine prosocial motivation (Barasch et al. 2014; Newman and

Cain 2014). Despite the shared values CSR brings, skepticism towards these actions can

diminish or even harm the charitable reputation of companies (Berman et al. 2015; Yoon,

Gurhan-Canli, and Schwarz 2006). Thus, marketers are highly motivated to share their socially

responsible activities in ways that show their commitment to the prosocial cause.

The current research investigates how firms can mitigate consumers’ doubt of prosocial

commitment when advertising their CSR activities. Specifically, we propose that firms can

leverage a temporal framing strategy for their donations. Counter to the intuition that a larger

aggregate amount (e.g., donate $120,000 this year) is perceived to be more charitable, we show

that a smaller periodic amount (e.g., donate $10,000 each month this year) increases positive

perceptions of the company and engagement with the firm. We further show that the

effectiveness of periodic framing is driven by perceived commitment – breaking a donation into

several instances helps to increase the goodwill consumers attribute to CSR because repeated

donations reinforce that a company is authentically committed to a cause. Thus, when companies

frame their donations periodically, consumers believe firms are more committed to the prosocial

cause, increasing positive perceptions and engagement.

Our findings make contributions to several bodies of literature. First, we contribute to the

literature on charitable credit and commitment. Previous research has largely emphasized the

importance intentions, motives, and commitment have on charitable credit (Berman et al. 2015;

Lin-Healy and Small 2013; Newman and Cain 2014; Small and Cryder 2016). The more genuine

and committed prosocial actions are, the greater the charitable credit they receive. Moreover,

prior findings have shown that consumers interpret repeated behaviors as more diagnostic of

others’ characteristics and intentions (D’Souza and Rao 1995; Skowronski and Carlston 1987;

Valsesia and Diehl 2022). Therefore, we extend this literature by showing how periodic framing

of donations – a repeated action – can increase perceptions of charitable commitment.

Second, we document an important theoretical moderator of temporal framing on

charitable credit. In contrast to the current findings, recent work showed that periodic donations

could actually harm the perceptions of the donors (Basu 2021). Specifically, the author found

that donors who made periodic (vs. aggregate) donations were perceived to be less morally

praiseworthy because the smaller donations under periodic framing were perceived to make less

of a sacrifice. Reconciling Basu (2021) and our work, we extend the current literature by

showing that total donation size matters when temporally framing donations. Specifically, we

document that when the total donation size is relatively small (e.g., $120 this year), periodic

framing of donations (e.g., $10 a month) decreases charitable credit, consistent with Basu (2021).

However, in larger amounts (e.g., $120,000 this year), we find a reversal of the effect such that

periodic donations (e.g., $10,000 a month) increase charitable credit because of the greater

perceived commitment towards the prosocial cause.

Lastly, we contribute to the work on temporal framing by showing that the strategy's

effectiveness goes beyond consumer-centric domains. Past findings examined how periodic

pricing can impact consumers' donation and purchase intentions through the role of perceived

costs and benefits of the contracts (e.g., Gourville 1998; Atlas and Bartels 2018). However, prior

work has primarily focused on how temporal framing is utilized as a pricing strategy to induce

consumption (e.g., subscription, donation intentions). The current work takes a novel perspective

by focusing on the effectiveness of temporal framing on firm-centric domains, specifically CSR.

In the following sections, we review the literature on corporate social responsibility and

charitable commitment. Then, drawing on the work on temporal framing, we describe the impact

of periodic framing on perceived commitment, charitable credit, and firm engagement.

2.2 Conceptual Background

2.2.1 Corporate Social Responsibility: A Double-Edged Sword

Corporate social responsibility creates shared value: CSR activities yield positive benefits

for society and enhance a firm’s image and reputation (Kramer and Porter 2011; Porter and

Kramer 2006). Engaging in CSR can generate positive attitudes towards firms (Brown and Dacin

1997; Sen, Bhattacharya, and Korschun 2006), brands (Klein and Dawar 2004), retail stores

(Lichtenstein, Drumwright, and Braig 2004), and company identification (Sen and Bhattacharya

2001), which impacts a firm’s revenue growth (Lev, Petrovits, and Radhakrishnan 2010; Mohr

and Webb 2005). These multi-faceted effects of CSR have led firms to adopt CSR as a core

marketing strategy.

Although consumers generally favor CSR activities, evidence suggests that CSR is

ineffective and can lead to consumer backlash if done improperly. When a company's brand

image contradicts its CSR information, disfluency between the two concepts arises, leading to a

negative evaluation of the company (Torelli, Monga, and Kaikati 2012). Similarly, CSR

initiatives backfire if brand concepts are at odds with a CSR campaign's message (Yoon,

Gurhan-Canli, and Schwarz 2006). Hence, CSR is more complex than donating to a cause. A

company should maximize the goodwill associated with CSR by understanding the factors that

lead good deeds to favorable judgments.

Growing work suggests that a critical factor that sways consumer favorability of a firm’s

CSR activities is the perception of a company’s motive. In charitable giving contexts, consumers

look for signals of authentic prosocial motivation (Barasch et al. 2014). When a company’s

financial motives are salient, CSR can appear at odds with the altruism it promotes, a domain in

which people believe empathy should be the primary driver of behavior (Batson et al. 1991). So,

firms elicit selfish cognitions if consumers perceive CSR as driven primarily by self-centered

and egoistic motives (Ellen, Webb, and Mohr 2006). Such findings are consistent with work in

the social judgment literature, where actors sharing good deeds can create favorable inferences of

generosity while simultaneously generating negative attributions of selfishness (Berman et al.

2015). Newman and Cain (2014) found that doing nothing at all was sometimes better than

charitable actions that generated negative attributions of selfishness, likely due to consumers

questioning the selfish versus selfless nature of good deeds (Critcher and Dunning 2014). Pure

altruism involves sacrifice (Lin-Healy and Small 2014), and any donation must be weighed

against the self-serving behavior of a brand. Given this tension between charitable giving and

ulterior motives, CSR may fail to provide evidence that their behaviors are not self-serving. Said

another way, without indicators of authentic prosocial motivation, companies that announce

charitable contributions might actually risk consumer backlash. So, firms face the challenge of

conveying authentic commitment when sharing their CSR activities.

2.2.2 Commitment and Charitable Credit

People generally attribute another’s behaviors to internal motivations if those behaviors

are consistent over time (Kelley 1973). Consistent behavior is viewed positively (Suh 2002), as a

signal of authenticity (English and Chen 2011; Kraus, Chen, and Keltner 2011), and as more

diagnostic of one’s characteristics and intentions (Skowronski and Carlston 1987; Valsesia and

Diehl 2022). Moreover, people believe that those who make small commitments are likely to

follow through with larger commitments that are consistent with these values (Cialdini,

Cacioppo, Bassett, and Miller 1978). These findings suggest that consistency within a firm’s

CSR activities may convey authentic commitment as consumers perceive a firm’s frequent

charitable acts as a stronger cue of commitment. So, we propose that making such authentic

commitment (dedication to a cause) salient through repetition should increase the effectiveness

of CSR by generating goodwill toward the company. This conjecture is consistent with findings

in the attribution theory literature, which suggest that increases in perceived company

commitment should lead consumers to react more favorably to a company’s actions (Cui, Trent,

Sullivan, and Matiru 2003; Dean 2003; Forehand and Grier 2003; Sparkman and Locander

1980).

An open question remains: How can firms highlight the repetition of charitable actions

that signal commitment when communicating their CSR activities? An ideal solution would be to

engage in CSR activities more frequently to show commitment. The more often firms act

socially responsible, the more committed they are perceived to be in support of the prosocial

cause. However, a practical limitation to this solution is that resources can be limited. Thus,

firms must resolve the issue of highlighting commitment through frequency without increasing

the donation size. We suggest that firms can leverage a temporal framing strategy for their

donations. Specifically, we propose that firms framing their donations as a series of periodic

donations can highlight the repetition of charitable actions, increasing charitable perceptions and

firm engagement.

2.2.3 Periodic Donations on Commitment and Charitable Perceptions

The literature on temporal framing and periodic pricing offers a strategy whereby

corporate donations can leverage perceived commitment. Just as higher payment frequencies

lead consumers to believe they have more resources (De la Rosa and Tully 2022), a higher

frequency of donations may increase the degree to which consumers believe a company is

dedicating to a cause. Related evidence supports that repeated behaviors can impact consumer

attributions and engagement. For example, consumers read others’ characteristics and intentions

from repeated behaviors (Gershon and Smith 2020; Skowronski and Carlston 1987; Valsesia and

Diehl 2022) and make inferences about a company’s intentions when companies repeat

advertisements (D’Souza & Rao 1995). So, in the case of repeated donations, consumers would

infer that a firm’s series of periodic donations signal a greater commitment to charitable causes

than a single aggregate donation.

Moreover, single aggregate donations need help signaling commitment, despite their

larger numeric value. First, consumers are scope insensitive (Frederick and Fischhoff 1998;

Kahneman and Knetsch 1990). Although a stronger signal of charitable intent can be

synonymous with increasing the donation size for many companies, consumers’ valuations of the

information are not multiplicative relative to the magnitude. So, larger amounts do not

necessarily mean they will receive proportional charitable credit. Second, firms highlighting their

largest donations can create a “goodwill ceiling.” This means that in the absence of authentic

prosocial motivation cues (Barasch et al. 2014), the scope of a company’s perceived altruism is

capped at a donation amount (Frederick and Fischhoff 1998; Kahneman and Knetsch 1990).

Lastly, announcing a single large donation can backfire if consumers believe the donor has an

ulterior motive (such as rehabilitating a company’s negative reputation) or does not do enough to

address a cause. For example, people often believe companies donate to capitalize on the

publicity of a current event and are cynical about a company’s likelihood of continuing to help

the cause. Sometimes consumers are unaware of a company’s prior charitable commitments, or a

company is wading into a new donation context. In these instances, companies risk attracting

negative perceptions if consumers perceive them as opportunistic.

As such, consumers may use temporal cues to infer consistency and commitment. Indeed,

consumers perceive a company as more authentically committed to a cause when it enhances the

perceived consistency and commitment of its CSR activities. Thus, we hypothesize that when a

company presents its donations as a series of periodic donations (e.g., $10,000 each month)

instead of a single aggregate donation (e.g., $120,000 this year), consumers will likely recognize

the greater authentic commitment, which in turn increases charitable perceptions and firm

engagement.

2.2.4 Total Donation Size Matters

However, it is unclear whether the benefits of a periodic donation strategy always

outweigh the magnitude and weight of a single aggregated donation. Because periodic donations

sacrifice magnitude for frequency, consumers may view each firm’s periodic donation as too

small, leading to inferences that the firm is not doing enough. For example, consumers believe

disaggregated payments are smaller (compared to aggregated payments; Gourville 1998). Due to

conversational norms, consumers see changes expressed in smaller units of measurement as

smaller than an equivalent change expressed in larger units (Zhang and Schwarz 2012). If people

experience smaller-unit quantities as smaller overall, periodic donations may lead consumers to

judge companies as less authentically committed to a cause.

Moreover, donation size must be sufficiently large because research suggests that

consumers vary in their focus on unit size and quantity when making judgments. Monga and

Bagchi (2012) demonstrated evidence of a unitosity effect, whereby consumers evaluated

changes based on the relative size of the units. Whereas past research supports that people rely

on the numerosity heuristic and infer a greater overall size when there are greater units (Pelham,

Sumarta, and Myaskovsky 1994), Monga and Bagchi (2012) demonstrated that the reverse

occurs when the unit type is salient. That is, when the type of unit is salient, people rely on the

size of the unit itself (as opposed to the number of units) when inferring the scale of change (i.e.,

small units—like inches—equate to small changes and large units—like feet—equate to large

changes).

In the context of donations, the unit is likely salient because it provides consumers with a

sense of the scale of goodwill to which a company is committing. Said another way, if a donation

unit is small, consumers are likely to react negatively to the perception that a company is

committing so little to help people, regardless of the number of instances. In cases where the unit

is less salient, such as the numerosity heuristic, people rely on the number of units to inform their

judgments. For example, people believe delays framed in small (vs. large) units will be longer

when they focus on the number of units displayed in the delay (Pelham, Sumarta, and

Myaskovsky 1994). When consumers assessed a warranty and focused on the number of units

(rather than the type of unit), they saw warranties as better when these warranties were broken

into smaller (vs. larger) units (Pandelaere, Briers, and Lembregts 2011).

In fact, one paper consistent with this notion found that people perceive donors who

disaggregate their contributions (e.g., donating $10 a month) as less moral than those who make

aggregate contributions (e.g., donating $120 a year; Basu 2021). Although this finding

challenges the merit of periodic donations as a useful strategy for improving perceived company

commitment, Basu (2021) focused on charitable contributions that were relatively small

compared to those typically made by companies. Therefore, we hypothesize that firms donating

an earnest amount stand to benefit from the added temporal cues of periodic donations because

each donation instance reinforces a company’s dedication to a cause. However, in contexts

where the donation quantity per instance is small, consumers may focus on the small size of the

quantity rather than the benefits that come from each donation instance, harming charitable

perceptions.

2.2.5 Overview of Studies

We test our hypotheses across seven preregistered studies. Across our studies, we

examine how participants perceive and engage with periodic donations. We find that when

companies frame their donations in periodic (vs. aggregate) terms, customers have more positive

perceptions of the companies and are more likely to show interest and engage with them. We

examine the robustness of our effect across donation type, donation amount, messaging,

charitable cause, and company.

Studies 1A and 1B test the hypotheses via two preregistered field experiments. In Study

2, we use stimulus sampling to verify the effect's generalizability across various donation causes

and donation amounts. Study 3 tests the proposed mechanism of perceived commitment and

simultaneously rules out alternative mechanisms of perceived costs and benefits. Study 4

replicates the mechanism findings with a stronger three-item measure of commitment. Study 4,

also explores the downstream consequences of commitment on social media engagement,

controlling for consumer inferences. Study 5 examines the limits of the effect when another

temporal donation marker (past commitment) is introduced. Finally, we test an important

boundary condition of the effect – extremely small donations (Study 6). We report all conditions,

data exclusions (if any), and measures for each study (Simmons, Nelson, and Simonsohn 2012).

All studies are preregistered, and the study materials, including complete stimuli, measures, data,

and code can be found here:

https://researchbox.org/809&PEER_REVIEW_passcode=TQTQHA.

2.3 Study 1A: Field Experiment (Salt and Smoke)

In Study 1A, we partnered with a company called Salt and Smoke and conducted a field

experiment. Salt and Smoke is a restaurant in St. Louis, Missouri, that often donates to local

communities. We launched a marketing campaign that either promoted Salt and Smoke’s

donation pledge in a periodic or aggregate format and tested which campaign would elicit more

clicks to the company’s website. We predict that a periodic (vs. aggregate) framing of donations

will produce greater interest in the company.

2.3.1 Methods

Salt and Smoke had 75,514 customer subscribers to their email newsletter. Customers

were randomly assigned to one of two conditions in a 2-cell (donation framing: aggregate vs.

periodic) between-subjects design. In the periodic condition, participants saw a marketing

campaign that framed Salt and Smoke’s donation pledge in periodic terms, while participants in

the aggregate condition read the donation pledge framed in aggregate terms (see Figure 2.1).

Importantly, the total dollar amount was held constant across both conditions. Customers who

were interested in learning more about Salt and Smoke had the option to click a link that

redirected customers to the Salt and Smoke website. The number of customers that clicked on the

Salt and Smoke website was the outcome measure of interest.

2.3.2 Results

Following our preregistration (https://aspredicted.org/Y91_Z38), we examined the click-

through rate by donation framing. In support of our hypothesis, customers who have received the

periodic framing of donation pledges (.69%; 278/40,272) were more likely to click on the Salt

and Smoke website than customers who have received the aggregate framing of donations (.35%;

124/35,242); χ

(1) = 40.66, p < .001.

FIGURE 2.1: STUDY 1 STIMULI

2.4 Study 1B: Field Experiment (GiftAMeal)

In Study 1B, we aim to replicate and extend our findings by showing that the effect

persists not only in monetary donations but also in goods donations. We partnered with a

company called GiftAMeal and conducted a field experiment. GiftAMeal is a company that

donates meals to food banks each time customers take photos of their meal and shares them on

social media. We launched two Facebook advertisements that described GiftAMeal’s previous

donations in a periodic or aggregate format and tested which advertisement would elicit the most

link clicks to the company’s website. Again, we hypothesize that a periodic (vs. aggregate)

framing of donations will produce greater interest in the company.

2.4.1 Methods

Using Facebook’s split testing platform, we experimentally tested the effectiveness of

two advertisements (Orazi and Johnston 2020). We further specified that Facebook targets

individuals 18 years or older who speak English and live in the cities/states where GiftAMeal has

a presence. Following our preregistration (https://aspredicted.org/1Y2_WJL), the sample size

was determined by a total budget limit of $600 over one month. This budget resulted in a total

reach of 67,576 Facebook users.

We had three different ad designs (see Appendix D for materials). For each ad design,

two advertisements were launched: periodic and aggregate framing (see Figure 2.2). In the

periodic condition, participants saw ad designs that framed how many meals GiftAMeal donated

last year in periodic terms. Specifically, the ads read, “GiftAMeal donated 25,000 meals every

month to local communities last year.” In the aggregate condition, the donations were framed in

aggregate terms such that the ads read, “GiftAMeal donated 300,000 meals to local communities

last year.” All participants had the option to click on the advertisement, which would redirect

them to the GiftAMeal website. The number of participants that clicked on the GiftAMeal ad

was the outcome measure of interest.

2.4.2 Results

As preregistered, we collapsed across the three different types of designs in each

condition and examined the number of unique link clicks (i.e., the total number of users who

clicked on the advertisement) out of the total reach (i.e., the number of unique users exposed to

the advertisement). As predicted, Facebook users were more likely to click on the ads that

showed the donations in periodic terms (1.48%; 489/32,968) than the ads that showed the

donations in aggregate terms (1.22%; 421/34,608); χ

(1) = 9.04, p = .003 (see Table 2.1).

FIGURE 2.2: STUDY 1B STIMULI

TABLE 2.1: STUDY 1B RESULTS

Periodic Condition

Aggregate Condition

p-value

Smiling kid

1.36%

(318/23,328)

1.16%

(292/25,136)

.047

Join us

1.52%

(101/6,646)

1.15%

(84/7,332)

.053

Download app

1.19%

(75/6,310)

1.00%

(45/4,517)

.35

Collapsed

1.48%

(489/32,968)

1.22%

(421/34,608)

.003

2.4.3 Discussion

The results from Studies 1A and 1B support the hypothesis that periodic framing of donations

elicits greater customer engagement than aggregate donations. Moreover, it is interesting to note

that the effect holds for both monetary donations (Study 1A) and goods donations (Study 1B).

Next, we test how framing affects company perceptions across various donation scenarios and

donation amounts.

2.5 Study 2: Stimulus Sampling

Study 2 implements stimulus sampling by presenting nine different scenarios across

participants to verify the generalizability of the effect (Judd, Westfall, and Kenny 2012; Wells

and Windschitl 1999). We expect that, across scenarios, periodic framing of donations would

increase positive perceptions of the firm than aggregate donations.

2.5.1 Methods

We recruited 1,797 Prolific Academic participants (M

Age

= 36.17, 47.86% female).

Participants were randomly assigned to one of two conditions in a 2-cell (donation framing:

aggregate vs. periodic) between-subjects design. Each participant was then randomly assigned to

view one of nine scenarios (education, environmental, gender equality, health 1, health 2,

hunger, poverty, refugee, social activism; see Appendix D). The donation amount and donation

cause were different across the nine scenarios. Moreover, it is important to note that in all

scenarios, the total donation amount is smaller in the periodic (vs. aggregate) condition. In

accordance with our preregistration (https://aspredicted.org/5CS_9CG), only participants that

answered the attention check questions correctly (N = 1,665) were included in the analysis.

Participants read that a fictional company pledged to donate to a certain prosocial cause.

We describe the education scenario below, but all scenarios follow a similar pattern. Participants

in the education scenario read that a company named ANY Corporation recently pledged to

donate a portion of their earnings to support children’s education and public schools. The

donation pledge read,

“ANY Corporation recognizes that we can use part of our earnings to do a significant

amount of good. We pledge to donate a [aggregate condition: one-time donation of $1.2 million

in 2023/periodic condition: $90,000 every month between January 2023 and December 2023] to

non-profit organizations that support children’s education and public schools.

Participants then rated the key dependent variable of company perception using three

items: 1) “How much do you admire [company name]?”, 2) “How favorably do you view

[company name]?”, and 3) “How positively do you view [company name]?” (1 = Not at all, 7 =

Very much). The three items were averaged to create a “company perception” composite (α =

.95).

2.5.2 Results

Following our preregistered analysis, we regressed company perception on the framing of

donation pledges, including a fixed effect of scenarios. As predicted, participants in the periodic

condition (M = 5.47, SD = 1.37) had more positive perceptions of the company than the

aggregate condition (M = 5.18, SD = 1.27), β = .24, t(1655) = 4.87, p < .001. The pattern of

results is mostly consistent when looking at each scenario separately (see Table 2.2).

TABLE 2.2: STUDY 2 RESULTS

Scenario

Periodic Condition

Aggregate

Condition

p-value

Education cause

5.61

4.70

< .001

Environmental cause

5.63

5.40

.20

Gender equality cause

4.98

5.01

.88

Health 1 cause

5.82

5.44

.048

Health 2 cause

5.68

5.27

.018

Hunger cause

5.47

5.34

.47

Poverty cause

5.54

5.28

.18

Refugee cause

5.64

5.07

.006

Social activism cause

5.06

.99

2.5.3 Discussion

Across multiple donation contexts and amounts, we find that donations framed in

periodic (vs. aggregate) terms increase charitable perceptions of the company. Moreover, we find

the effect persists even when the total amount of donation is smaller in the periodic condition

than the aggregate condition. Next, we test our proposed mechanism of perceived company

commitment while controlling for participants’ inferences about the donation amount.

2.6 Study 3: Mechanisms

Study 3 examines perceived commitment as a mechanism while ruling out alternative

mechanisms of perceived costs and benefits suggested by previous work on periodic pricing

(Atlas and Bartels 2018; Gourville 1998). Past work has documented that periodic pricing (e.g.,

$1/day) leads to greater purchase intentions than aggregate pricing (e.g., $365 a year) because of

either the lesser perceived costs (Gourville 1998) or greater perceived benefits of the purchase

(Atlas and Bartels 2018). Therefore, it is possible that the favorability judgment of periodic

donations is driven by these reasons. If true, we should expect to see a significant indirect(s) of

perceived costs, benefit, or both. However, if our proposed mechanism is true, we should only

see a significant indirect effect of perceived commitment.

2.6.1 Methods

We recruited 600 Prolific Academic participants (M

Age

= 34.53, 47.50% female). As in

Study 2, participants were randomly assigned to one of two conditions in a 2-cell (donation

framing: aggregate vs. periodic) between-subjects design. Each participant was then randomly

assigned to view one of three scenarios (environmental, hunger, refugee donations; see Appendix

D). In accordance with our preregistration (https://aspredicted.org/MCR_ZZR), only participants

that answered the attention check questions correctly (N = 581) were included in the analysis.

The design mimics Study 2. Participants rated company perception (α = .94) after reading

a company’s donation pledge that was either framed periodically or in aggregate. We then

measured perceived commitment with the following item: “How committed is [company name]

to [donation cause]?” (1 = Not at all committed, 7 = Very committed). Moreover, we measured

the perceived costs (α = .82) and benefits (α = .64) of the donation using two sets of three seven-

point Likert questions (adapted from Atlas and Bartels 2018; see Appendix D). The order in

which participants saw the items of perceived commitment, costs, and benefits was randomized.

2.6.2 Results

Following our preregistered analysis, we regressed company perception on the framing of

donation pledges, including a fixed effect of scenarios. Replicating previous studies, participants

in the periodic condition (M = 5.27, SD = 1.22) had greater positive perceptions of the company

than the aggregate condition (M = 4.88, SD = 1.29), β = .29, t(577) = 3.58, p < .001. The pattern

of results is directionally consistent when looking at each scenario separately (see Appendix C).

Similarly, we found that participants in the periodic condition (M = 5.00, SD = 1.46) had

greater positive perceptions of the company than the aggregate condition (M = 4.49, SD = 1.52),

β = .32, t(577) = 3.99, p < .001. The pattern of results is directionally consistent when looking at

each scenario separately (see Appendix C).

Unlike previous work on periodic pricing (Atlas and Bartels 2018; Gourville 1998), we

found no significant differences of perceived costs, β = .09, t(577) = 1.16, p = .25, or benefits, β

= .07, t(577) = .82, p = .41, between participants in the periodic and aggregate conditions (see

Appendix C).

As shown in Figure 2.3, we conducted a parallel mediation model (Hayes 2017, Model 4)

with our donation framing as the independent variable (aggregate condition = 0, periodic

condition = 1); perceived commitment, costs, and benefits as parallel mediators; and company

perception as the dependent variable. As predicted, perceived commitment significantly

mediated the relationship between donation framing (indirect effect = .27; 95% CI [.14, .41]),

while perceived costs (indirect effect = .01; 95% CI [-.01, .03]) and benefits (indirect effect =

.02; 95% CI [-.02, .06] did not.

FIGURE 2.3: STUDY 3 MEDIATION MODEL

NOTE. –

*p < .05, **p < .01, *** p < .001

2.6.3 Discussion

Study 3 provides evidence for the hypothesized mechanism of perceived commitment

while ruling out alternative explanations of perceived costs and benefits. When donations are

framed in periodic (vs. aggregate) terms, consumers believe the company is more committed to

the prosocial cause, increasing positive perceptions of the company. Study 4 seeks additional

evidence of our proposed mechanism.

2.7 Study 4: Perceived Commitment

Study 4 tests our mechanism of perceived commitment using another measure of

commitment, while controlling for consumers’ inferences about the total donation. More

specifically, we test whether periodic donations signal greater charitable commitment and elicit

greater consumer engagement than aggregate donations, even when an observer knows the total

donation. We hypothesize that consumers who see a periodic donation (whether in the presence

or absence of the total donation amount) will perceive the company as more committed to a

charitable cause than those who see an aggregate donation, increasing consumer engagement

with the company that donates periodically.

2.7.1 Methods

We recruited 748 Amazon Mechanical Turk participants (M

Age

= 39.90, 61.10% female).

Participants were randomly assigned to one of three conditions in a 3-cell (donation framing:

aggregate vs. periodic vs. periodic-sum) between-subjects design. Following our preregistration

(https://aspredicted.org/blind.php?x=J6C_XXN), we excluded participants who either failed an

attention check or a manipulation check, leaving a total sample of 681 in the analysis.

Participants were asked to read a social media post. The post was a tweet by a materials

company touting its recent donation to child hunger charities. The tweet described the company’s

donation and a link to an article with more information (see Appendix D for materials).

Participants in the periodic condition saw the following text: “Today we are committing to

addressing child hunger by donating $10K a month for a year to multiple local charities. Click

the link to find out more.” Participants in the aggregate condition saw the following text: “Today

we are committing to child hunger by donating $120K to multiple local charities. Click the link

to find out more.” We also introduced a periodic-sum condition, where participants saw a tweet

with the following text, “Today we are committing to addressing child hunger by donating $10K

a month for a year (totaling $120K) to multiple local charities. Click the link to find out more.”

After seeing the tweet, all participants were instructed to briefly summarize the social

media post. We then measured our proposed mechanism of perceived commitment. Participants

rated the company’s commitment, dedication, and devotion by responding to the following

prompt: “Regarding child hunger efforts, how much do this company's actions show genuine

________?” (1 = Not at all, 7 = Extremely). These items showed strong reliability (α = .97).

Following our preregistration, we averaged the items to create a perceived commitment

composite. Then, participants responded to a series of items to assess their degree of engagement

with the company. More specifically, we asked, “If Barksdale Materials were to include the

following website links in their tweet, how likely would you be to click each:”, 1) “A link to

their website”, 2) “A link to a charity”, and 3) “The story linked in the tweet” (0 = Not at all, 10

= Definitely). These items showed high reliability (α = .90), so we created an engagement

composite using the average of these items. We additionally measured two exploratory

dependent variables (see Appendix C for results).

2.7.2 Results

We examined the effects of our periodic manipulations on the commitment composite

and found a significant difference between the conditions, F(2, 678) = 7.63, p < .001, η

= .022.

We then conducted a series of t-tests to examine differences across conditions. We first

examined whether the effect replicated. As predicted, participants believed a company was more

committed to a cause when announcing a periodic donation (M = 5.44, SD = 1.52) than when

announcing an aggregated donation (M = 5.01, SD = 1.51), t(464) = 3.05, p = .002, d = .28.

We next examined whether the periodic donation effect held when including the total

donation amount. As predicted, participants in the periodic-sum condition thought a company

was more committed to a cause (M = 5.50, SD = 1.37) than those in the aggregate condition,

t(453) = 3.60, p < .001, d = .34. There were no differences between the two periodic conditions,

t(439) = .44, p = .66.

We examined the effects of our periodic donation manipulations on our engagement

composite. Participants differed across conditions in the degree to which they were likely to

engage with the post, F(2, 678) = 3.33, p = .036, η

= .010. Participants in the periodic condition

were more likely to engage (M = 6.02, SD = 3.04) than those in the aggregate condition (M =

5.40, SD = 2.96), t(464) = 2.22, p = .027, d = .21. Participants in the periodic-sum condition (M

= 6.00, SD = 2.85) were also more likely to donate than those in the aggregate condition, t(453)

= 2.22, p = .027, d = .21. There was no difference between the two periodic conditions, t(439) =

.04, p = .97.

We tested whether the effect of donation framing on consumer engagement was mediated

by perceived commitment through a 3-cell multicategorical mediation (Hayes 2017, Model 4).

Contrasting the aggregate (0) and periodic (1) conditions, we tested the significance of

the measured mediator by calculating the standardized effects for 10,000 bootstrapped samples.

We found a statistically significant indirect effect of perceived commitment on engagement

(indirect effect = .43; 95% CI [.14, .73]; see Figure 2.4). Controlling for commitment, the direct

effect was non-significant (direct effect = .19, p = .43, 95% CI [-.28, .66]).

Contrasting the aggregate (0) and periodic-sum (1) conditions, we tested the significance

of the measured mediator by calculating the standardized effects for 10,000 bootstrapped

samples. Similarly, we found a statistically significant indirect effect of perceived commitment

on engagement (indirect effect = .49; 95% CI [.22, .77]; see Figure 2.4). Controlling for

commitment, the direct effect was non-significant (direct effect = .12, p = .63, 95% CI [-.36,

.59]).

FIGURE 2.4: STUDY 4 MEDIATION MODELS

NOTE. –

*p < .05, **p < .01, *** p < .001

2.7.3 Discussion

Study 4 provides initial evidence for the mechanism of perceived commitment. We find

that participants are more likely to engage with companies framing their donations in periodic

(vs. aggregate) terms because they perceive those companies to be more committed to the

charitable cause. Moreover, we find that even when participants are aware of the total donation

amount with periodic donations (periodic-sum condition), participants perceive the companies as

more committed to the charitable cause and are more likely to engage with the company.

2.8 Study 5: Perceived Commitment Moderation

In Study 5, we directly manipulate the perceived commitment towards the prosocial

cause. We examine whether an additional cue of commitment (i.e., longer donation span)

attenuates the effect of periodic donations on perceived commitment and social media

engagement. If consumers infer that the periodic donations are an indicator of broader charitable

commitment (and by contrast, that an aggregate donation is an indicator that companies are less

committed to a cause), expanding the timeframe of donations should increase perceived

commitment for companies donating in aggregate. To that effect, we ask participants to judge

periodic (quarterly) and aggregate (annual) donations for a single-year or four-year span.

Varying a second indicator of temporal commitment (years donating) should lead participants to

infer a company is committed to a cause, whether the company’s donations are in aggregate or

periodic.

In this study participants assess a company’s donation behavior by viewing part of a

company’s financial report. The report indicates donations per year and fiscal quarter. Presenting

donations in this way allows us to address concerns that companies announce aggregated

donations to manage impressions instead of from a place of genuine commitment to a cause.

2.8.1 Methods

We recruited 1,001 participants (M

Age

= 39.99, 42.86% female) on Amazon MTurk. We

randomly assigned participants to one of four conditions in a 2 (donation framing: aggregate vs.

periodic) X 2 (donation span: one year vs. four years) between-subjects design. Following our

preregistration (https://aspredicted.org/F8R_HRJ), we excluded participants who failed either of

the two attention checks, leaving a sample of 798 for our final.

All participants saw part of a company’s expense report that indicates the company’s

donation schedule. Participants read about a company named Bell Electrek. More specifically,

we told participants, “Below is part of an expense report highlighting a company's charitable

contributions toward child hunger relief efforts. The expense report shows the donation schedule

by fiscal year (see Appendix D for materials). Participants in the periodic conditions saw that

Bell Electrek donated $25K per quarter (four times per fiscal year) while participants in the

aggregate conditions saw an annual donation of $100K (once at the end of the fiscal year).

Additionally, participants in the one-year conditions saw how much Bell Electrek donated over

the last year, while participants in the four-year conditions saw donations over the last four years.

Then, similar to Study 3, participants rated the company’ commitment (α = .97).

Moreover, we measured participants’ degree of engagement with the company by asking, “After

learning about Bell Electrek’s donation behaviors, are you more or less likely to:” 1) “Visit their

website”, 2) “Visit their social media pages”, and 3) “Visit the website of their partner charities”

(-5 = Much less likely, 5 = Much more likely). These items showed high reliability (α = .92), so

we created an engagement composite using the average of these items. We additionally measured

two exploratory dependent variables (see Appendix C for results).

2.8.2 Results

A 2 (donation framing: aggregate vs. periodic) x 2 (donation span: one year vs. four

years) ANOVA yielded a significant main effect of donation framing, F(1, 794) = 28.41, p <

.001, η

= .03, and a significant main effect of donation span, F(1, 794) = 123.48, p < .001, η

=.13. Most importantly, we found a significant interaction, F(1, 794) = 41.75, p < .001, η

=.05,

such that over a one year span, the company donating periodically (M = 5.59, SD = 1.14) was

perceived to be more committed than donating in aggregate (M = 3.62, SD = 1.61), t(794) =

14.44, p < .001, d = 1.45. We observed the same pattern when the company donated over a four-

year span, but the difference between conditions diminished significantly. Participants saw the

company that made periodic donations over a four-year period (M = 5.85, SD = 1.15) as more

committed than those that made donations in aggregate (M = 5.13, SD = 1.46) over the same

span, t(794) = 5.33, p < .001, d = .53 (see Figure 2.5).

A 2 (donation framing: aggregate vs. periodic) x 2 (donation span: one year vs. four

years) ANOVA yielded a non-significant main effect of donation framing, F(1, 794) = 2.58, p =

.11, η

= .003, and a significant main effect of donation span, F(1, 794) = 20.29, p < .001, η

.02. Most importantly, we found a significant interaction between the two variables, F(1, 794) =

7.32, p = .007, η

=.01. Over a one-year period, participants were more likely to engage with the

company that donated periodically (M = 1.27, SD = 1.89) than in aggregate (M = .18, SD = 2.19),

t(794) = 5.42, p < .001, d = .54. However, when the company donated across four years, there

was no difference in engagement between the periodic (M = 1.40, SD = 1.85) and aggregate

conditions (M = 1.08, SD = 2.06), t(794) = 1.61, p = .11 (see Figure 2.5).

We conducted a moderated mediation (Hayes 2017, Model 8) to test that perceived

commitment mediated the relationship of donation framing and donation span on consumer

engagement. We tested the significance of the mediator by calculating the standardized indirect

effect for 10,000 bootstrap samples. Consistent with our hypothesis, we found that commitment

mediated the interaction between donation framing and donation span on engagement (index = -

.70; 95% CI [-.97, -.47]). As hypothesized, perceived commitment mediated the effect of

donation framing on engagement when the donation was made for one year (indirect effect =

1.12; 95% CI [.86, 1.39]), however, the indirect effect is smaller for participants in the four-year

conditions (indirect effect = .41; 95% CI [.25, .60]).

FIGURE 2.5: STUDY 5 RESULTS

NOTE. – Error bars represent 95% confidence intervals.

2.8.3 Discussion

If consumers perceive a company that make periodic (vs. aggregate) donations to be more

committed to a cause, then expanding the timeframe of donations from one year to four years

should increase this perceived commitment. Results from Study 5 are in line with this conjecture

– showing participants a company’s donation in a four-year span increases commitment and

engagement likelihood. Therefore, we find causal evidence that consumers are more likely to

engage with a company with periodic donations due to the company’s perceived commitment to

a cause.

2.9 Study 6: Boundary Condition

In our final study, we examine an important boundary condition of our periodic donation

effect – extremely small donations. Contrary to our findings, recent work shows that periodic

donations can harm perceptions of the donors (Basu 2021). Specifically, the author finds that

donors who make periodic (vs. aggregate) donations are perceived to be less morally

praiseworthy because people perceive donors donating periodically as making less of a sacrifice.

Reconciling the opposing effects, we identify a key difference between the materials used in

Basu (2021) and our studies. In the previous work, the author compares framing of donations

that total in small amounts (e.g., $250 donation, 400 cans, 35 boxes of toys). However, company

charitable donations typically happen on a much larger scale, which is largely why we observe

the periodic donation effect in our studies. Here, we extend the current literature by showing that

periodic effects are interpreted differently when the amount occurs at different scales. We predict

that when the total donation amounts are large, consumers will perceive a company more

positively with periodic framing. However, with smaller total donation amounts, we expect a

reversal such that people will perceive a company more positively with aggregate framing.

2.9.1 Methods

We recruited 1201 Prolific Academic participants (M

Age

= 36.24, 48.29% female).

Participants were randomly assigned to one of four conditions in a 2 (donation framing:

aggregate vs. periodic) x 2 (donation amount: large vs. small) between-subjects design. Per our

preregistration, only participants that correctly answered the attention check questions (N =

1,181) were included in the final analysis. The preregistration for this study can be found here:

https://aspredicted.org/SWP_H43.

Participants read that a fictional company named Alpha Manufacturing donated to a

charity focused on international disaster relief. Participants in the periodic-large condition read

that Alpha Manufacturing donated $10,000 every week, while the aggregate-large condition

donated $520,000. Participants in the periodic-small condition read that Alpha Manufacturing

donated $2 every week, while the aggregate-small condition donated $100.

Similar to Study 2, participants then rated the key dependent variable of company

perception (α = .95) and moral praiseworthiness (adapted from Basu 2021): “How morally

praiseworthy do you think the action of Alpha Manufacturing is?” (1 = Not at all, 7 =

Extremely).

2.9.2 Results

A 2 (donation framing: aggregate vs. periodic) x 2 (donation amount: large vs. small)

ANOVA yielded a significant main effect of donation framing, F(1, 1177) = 5.82, p = .016, η

=.004, and a significant main effect of donation amount, F(1, 1177) = 19.76, p < .001, η

=.02.

Importantly, we found a significant interaction F(1, 1177) = 9.27, p = .002, η

=.008, such that

when donation amounts were large, the periodic donation (M = 5.55, SD = 1.31) had greater

positive perceptions of the company than the aggregate donation (M = 5.29, SD = 1.30), t(1177)

= 2.41, p = .016, d = .20. However, when donation amounts were large, periodic framing (M =

4.58, SD = 1.43) had marginally less positive perceptions of the company than the aggregate

framing (M = 4.79, SD = 1.29), t(1177) = 1.89, p = .059, d = .15 (see Figure 2.6).

A 2 (donation framing: aggregate vs. periodic) x 2 (donation amount: large vs. small)

ANOVA yielded a significant main effect of donation framing, F(1, 1177) = 5.74, p = .017, η

=.005, and a significant main effect of donation amount F(1, 1177) = 29.87, p < .001, η

=.02.

Importantly, we found a significant interaction, F(1, 1177) = 14.79, p < .001, η

=.01. When

donation amounts were large, the periodic donation (M = 5.68, SD = 1.36) had greater positive

perceptions of the company than the aggregate donation (M = 5.38, SD = 1.43), t(1177) = 2.40, p

= .017, d = .20. However, replicating Basu (2021), when donation amounts were small, periodic

framing (M = 4.35, SD = 1.61) had lesser positive perceptions of the company than the aggregate

framing (M = 4.72, SD = 1.43), t(1177) = 3.05, p = .002, d = .25 (see Figure 2.6).

FIGURE 2.6: STUDY 6 RESULTS

NOTE. – Error bars represent 95% confidence intervals.

2.9.3 Discussion

Study 6 demonstrates that periodic donations do not always impact company perceptions

positively. We find that when donation amounts are significantly smaller, periodic donations

negatively impact company perceptions, consistent with findings from Basu (2021).

2.10 General Discussion

Across seven studies, we find evidence that a periodic donation strategy (compared to an

aggregate donation strategy) increases positive impressions of companies and increases

consumer engagement. Periodic donations accomplish this by increasing the degree to which

consumers see companies as authentically committed to a cause. Studies 1A and 1B examined

the effect of periodic donation framing using two preregistered field experiments. Both studies

found that consumers were more likely to engage with online campaigns that were framed as

providing periodic donations. Study 2 supported the generalizability of the effect by employing a

stimulus sampling approach. Across donation causes, amounts, and phrasings, we found support

that periodic donation framing increased the degree to which consumers judged a company

positively. Study 3 tested our perceived commitment mechanism and found that it mediated the

relationship between periodic donation framing and company perceptions. Study 3 also ruled out

alternative mechanisms. In Study 4, we replicated the mechanism findings using a stronger three-

item measure of commitment and provided evidence of the robustness of periodic donations

when consumers knew about the total donation sum. Study 4 also replicated the findings of our

field studies by showing that consumers were more likely engage on social media with

authentically committed companies. In Study 5, we demonstrated that other temporal donation

cues moderated our effect. Finally, in Study 6 we provided evidence of an important boundary

condition. Namely, a donation must be sufficiently large to benefit from periodic framing.

Appendix

3.1.1 Chapter 1: Appendix A

This appendix reports additional findings that were not reported in the main document.

Methods and Results from Figure 1.1

We recruited 544 undergraduates from a private university (M

Age

= 22.75, 54.6% female).

Participants were asked, “To what extent would you feel 1) uncomfortable, 2) awkward, and 3)

uneasy talking about the following topics with your [close friends/distant acquaintances]?” (1 =

Not at all, 7 = Very much; adapted from Jiang, Hoegg, and Dahl 2013). The three items were

averaged to create a “discomfort” composite (α = .96). The topics were money issues, politics,

religion, sex, death, gossip, offensive jokes, relationship problems, health problems, and race.

The perceived discomfort was tested using a 2 (relationship type: close friends vs. distant

acquaintances) x 10 (list of topics) repeated measures analysis of variance (ANOVA). We found

significant main effects of relationship type (F(1, 10,317) = 5,695.23, p < .001), list of topics

(F(9, 10,317) = 102.15, p < .001), and interaction (F(9, 10,317) = 6.92, p < .001).

Overall, we found that people have varying levels of discomfort talking about sensitive

topics with close friends and distant acquaintances. First, people feel more uncomfortable talking

about sensitive topics with distant acquaintances than close friends. Second, people feel more

uncomfortable talking about offensive jokes, followed by money issues, sex, relationship

problems, health problems, politics, race, death, religion, and gossip.

[70]

Methods and Results of Social Richness

We recruited 100 Amazon MTurk participants via CloudResearch (M

Age

= 37.11, 35%

female). We used three items (adapted from Oh, Bailenson, and Welch 2018) to measure

perceived social richness of communicating 1) in person, 2) over a phone call, 3) via text, 4) via

email, 5) via Venmo, 6) via PayPal, and 7) via a standard bank app: “To what extent is

[communication method] a socially rich way to communicate with others?”, “To what extent

would you feel that you are in the presence of others when communicating [communication

method]?”, and “To what extent would you feel connected with others when communicating

[communication method]” (1 = Not at all, 11 = Extremely; α = .94). The order in which

participants answered the social richness of each communication method was randomized. The

preregistration for this pilot test can be found here: https://aspredicted.org/ZXV_F8B

The perceived social richness was tested using a 7-cell (communication method: in-

person, phone call, text, email, Venmo, PayPal, bank app) repeated measures ANOVA. As

shown below, we found significant differences between the communication methods (F(6, 594)

= 239.50, p < .001). The table below displays the Bonferroni post-hoc analyses of the perceived

social richness of each communication method.

In summary, we found that each request method had different social richness levels that

are consistent with theories of social richness (Daft and Lengel 1986; Short, Williams, and

Christie, 1976). Specifically, in-person interactions were perceived to be most socially rich,

followed by phone calls, texts, email, Venmo and PayPal, and a standard bank app.

[71]

[72]

PERCEIVED SOCIAL RICHNESS BY COMMUNICATION METHODS

NOTE. – Error bars represent 95% confidence intervals.

[73]

BONFERRONI POST-HOC ANALYSES OF PERCEIVED SOCIAL RICHNESS

Communication method

Mean difference

In-person (M =

10.19)

Phone

2.06

.26

594

8.05

< .001

Text

3.42

.26

594

13.36

< .001

4.72

.26

594

18.46

< .001

Venmo

6.52

.26

594

25.49

< .001

PayPal

6.77

.26

594

26.45

< .001

Bank app

7.72

.26

594

30.19

< .001

Phone (M = 8.13)

Text

1.36

.26

594

5.31

< .001

2.66

.26

594

10.41

< .001

Venmo

4.46

.26

594

17.44

< .001

PayPal

4.71

.26

594

18.40

< .001

Bank app

5.67

.26

594

22.14

< .001

Text (M = 6.77)

1.30

.26

594

5.09

< .001

Venmo

3.10

.26

594

12.13

< .001

PayPal

3.35

.26

594

13.09

< .001

Bank app

4.31

.26

594

16.83

< .001

Email (M = 5.47)

Venmo

1.80

.26

594

7.03

< .001

PayPal

2.05

.26

594

8.00

< .001

Bank app

3.00

.26

594

11.73

< .001

Venmo (M = 3.67)

PayPal

.25

.26

594

.96

.99

Bank app

1.20

.26

594

4.70

< .001

PayPal (M = 3.42)

Bank app (M = 2.46)

.96

.26

594

3.74

.004

[74]

Additional Results from Study 1A

The tables below displays background descriptive statistics and logistic regression

models comparing 1) digital app versus text and 2) text versus in-person requests, respectively.

Despite the difference in perceived social richness between digital payment apps and text

requests, we found no significant association between relationship closeness and the choice of

request method when comparing digital app and text requests (ps > .05). However, we found a

significant negative association between relationship closeness and the choice of request method

when comparing text and in-person requests (β = -.27, SE = .08, z = -3.18, p = .001). This

association remained strong even after controlling for the amount of money that was owed (β = -

.28, SE = .09, z = -3.19, p = .001) and the number of days waited until requesting the money

back (β = -.27, SE = .08, z = -3.16, p = .002).

These patterns are consistent with H

such that with weaker social connections,

consumers chose to request through relatively less socially rich means, such as digital payment

apps and texts. However, they were more likely to request using more socially rich means such

as in person with stronger social ties.

DESCRIPTIVE STATISTICS (STUDY 1A)

N = 803

Mean Amount ($)

$284.62

Median Amount ($)

$100

Mean Days Until Request

24.88 days

Median Days Until Request

14 days

Choice of Request Method

[75]

In-person

57.66%

(463/803)

Phone Call

.37%

(3/803)

Text

22.54%

(181/803)

Digital Payment App

17.68%

(142/803)

.88%

(7/803)

Other

.87%

(7/803)

LOGISTIC REGRESSION MODELS COMPARING DIGITAL APP VERSUS TEXT

REQUESTS (STUDY 1A)

Dependent variable: Choice of digital app (1) versus text (0) request

n = 323

Independent variable

Model 1

Model 2

Model 3

Model 4

Closeness

.06

(.11)

.07

(.11)

.06

(.11)

.07

(.11)

Amount of Money Owed

-.82*

(.38)

-.53

(.40)

Number of days waited

-.91*

(.36)

-.73

(.38)

NOTE. – *p < .05, **p < .01, *** p < .001

[76]

LOGISTIC REGRESSION MODELS COMPARING TEXTS VERSUS IN PERSON

REQUESTS (STUDY 1A)

Dependent variable: Choice of text (1) versus in-person (0) request

n = 644

Independent variable

Model 1

Model 2

Model 3

Model 4

Closeness

-.27**

(.08)

-.28**

(.09)

-.27**

(.08)

-.27**

(.09)

Amount of Money Owed

.02

(.08)

.03

(.08)

Number of days waited

-.03

(.14)

-.05

(.15)

NOTE. – *p < .05, **p < .01, *** p < .001

Additional Results from Study 1B

This has the descriptive statistics from Study 1B of how much money was owed, how

long they waited until requesting back owed money, and their choice of request method by

requestee relationship. Because of extreme outliers in the variables of the number of days

participants waited before requesting the money and how much their close friend or distant

acquaintance owed, we winsorized the top 10% of responses for those variables in the

descriptive statistics.

DESCRIPTIVE STATISTICS (STUDY 1B)

Close Friend

Distant Acquaintance

N = 192

178 (92.71%)

94 (48.96%)

Amount ($)

$168

$89.27

[77]

Close Friend

Distant Acquaintance

Days Until Request

27.73

21.97

Choice of Request Method

In-person

48.31%

(86/178)

41.49%

(39/94)

Phone Call

5.06%

(9/178)

6.38%

(6/94)

Text

33.70%

(60/178)

21.28%

(20/94)

Digital Payment App

11.24%

(20/178)

26.60%

(25/94)

Other

1.69%

(3/178)

4.25%

(4/94)

Additional Results from Studies 3, 4A, and 4B

PERCENTAGE OF DIGITAL APP REQUESTS BY REQUESTEE RELATIONSHIP AND

SCENARIOS (STUDIES 3, 4A, AND 4B)

Study

friend

Distant

acquaintance

p-value

friend

Distant

acquaintance

p-value

Study 3

(N = 401)

Concert

ticket

35.94%

41.18%

p = .54

Food

27.59%

47.06%

p = .02

Uber ride

31.08%

46.38%

p = .06

Collapsed

31.63%

44.88%

p = .006

Study 4A

[78]

Study

friend

Distant

acquaintance

p-value

friend

Distant

acquaintance

p-value

(N = 1085)

Discomfort-Control

Discomfort-Decreased

Concert

ticket

38.26%

57.61%

p = .006

27.38%

36.90%

p = .19

Food

32.93%

49.38%

p = .03

32%

48.84%

p = .02

Uber ride

34.12%

66.67%

p < .001

31.46%

39.56%

p = .26

Collapsed

35.46%

58.36%

p < .001

30.40%

41.76%

p = .006

Study 4B

(N = 1291)

Impersonal-Control

Impersonal-Averse

Concert

ticket

18.87%

29.91%

p = .06

7.38%

7.48%

p = .98

Food

16.53%

42.27%

p < .001

13.51%

16.46%

p = .57

Uber ride

20.51%

54.21%

p < .001

8.33%

12.61%

p = .32

Collapsed

18.60%

41.74%

p < .001

9.73%

11.78%

p = .41

3.1.2 Chapter 1: Appendix B

This appendix has all the study stimuli.

STUDY SCENARIOS (STUDY 2)

Scenario

Amusement park

Imagine you and a group of people including Charlie, [one of your closest friends/one of your

distant acquaintances], have decided to go to an amusement park.

Because you bought everyone’s tickets, you paid the full price and everyone else will pay you

back their share which is $100 per person.

[79]

Scenario

Other than Charlie, everyone else paid you back the $100 they owed for the amusement park

tickets.

It has been a few days since Charlie borrowed the $100. However, Charlie still has not paid

you back the money.

Bar happy hour

Imagine you are attending a happy hour at a bar with a group of people including Charlie, [one

of your closest friends/one of your distant acquaintances].

As you are about to purchase your drink, Charlie informs you that they forgot their credit card

at home, and asks if you can pay for a few of their drinks which costs $20.

Charlie promises to pay you back as soon as possible.

It has been a couple of weeks since Charlie borrowed the $20. However, Charlie still has not

paid you back the money.

Car troubles

Imagine Charlie, [one of your closest friends/one of your distant acquaintances], is

experiencing some car troubles and they need to replace one of their tires.

Moreover, Charlie informs you that they don’t have enough money at the moment and asks if

they can borrow $80 to fix their car.

Charlie promises to pay you back as soon as they get paid in a few days.

It has been a week since Charlie borrowed the $80. However, Charlie still has not paid you

back the money.

Concert ticket

Imagine you are attending a concert with a group of people including Charlie, [one of your

closest friends/one of your distant acquaintances].

As you are about to purchase your ticket, Charlie informs you that they forgot their wallet in

the car, and asks if you can pay for their ticket which costs $50.

Charlie promises to pay you back as soon as possible.

[80]

Scenario

It has been a couple of weeks since Charlie borrowed the $50. However, Charlie still has not

paid you back the money.

Food

Imagine you are attending a dinner party with a group of people including Charlie, [one of

your closest friends/one of your distant acquaintances]. The group decides to order a couple of

appetizers and entrees to share.

After the meal, the group decides to evenly split the bill which comes out to $30 per person.

However, Charlie tells you that they forgot their wallet, and asks if you can cover their portion

of the bill.

Charlie promises to pay you back as soon as possible.

It has been a week since Charlie borrowed the $30. However, Charlie still has not paid you

back the money.

Gas money

Imagine you and a group of people including Charlie, [one of your closest friends/one of your

distant acquaintances], recently went on a road trip for a few days.

Because you drove your car, you ask everyone if they can pitch in for gas which is $25 per

person.

While everyone pitched in, Charlie has not given you the $25 they owed for gas.

It has been a few days since Charlie borrowed the $25. However, Charlie still has not paid you

back the money.

Housing

Imagine you and a group of people including Charlie, [one of your closest friends/one of your

distant acquaintances], have rented an Airbnb for a cabin trip in the mountains.

Because you were in charge of booking the cabin, you paid the full price and everyone else

will pay you back their share which is $75 per person.

Other than Charlie, everyone else paid you back the $75 they owed for the Airbnb.

[81]

Scenario

It has been a couple of weeks since Charlie borrowed the $75. However, Charlie still has not

paid you back the money.

Movie ticket

Imagine you are going to the theaters with a group of people including Charlie, [one of your

closest friends/one of your distant acquaintances].

As you are about to purchase your ticket, Charlie informs you that they forgot their wallet at

home and asks if you can pay for their ticket which costs $15.

Charlie promises to pay you back as soon as possible.

It has been a couple of days since Charlie borrowed the $15 for their ticket. However, Charlie

still has not paid you back the money.

Uber ride

Imagine you and a group of people including Charlie, [one of your closest friends/one of your

distant acquaintances], are planning to visit someone's house. You decide to call an Uber for

everyone to share.

Because you called the Uber, paid the full price and everyone else will pay you back their

share, which is $10 per person.

Other than Charlie, everyone else paid you back the $10 they owed for the Uber ride.

It has been a couple of days since Charlie borrowed the $10. However, Charlie still has not

paid you back the money.

STUDY SCENARIO (STUDY 5)

Close friend/Distant acquaintance

Vendor

Imagine that there is a local farmers' market

that you go to once a week on your way

home.

During one visit, you find out that a pottery

vendor is selling a set of dishes that you really

like for $100. You decide to buy them to take

Imagine that there is a local farmers' market

that you go to once a week on your way

home.

During one visit, you find out that a pottery

vendor is selling a set of dishes for sale

for $100 that you really like. You decide to

[82]

Close friend/Distant acquaintance

Vendor

home.

On your way home, you run into Charlie, [one

of your closest friends/one of your distant

acquaintances]. You and Charlie start talking

about the new set of dishes you just

purchased.

While showing Charlie the set of dishes,

they accidentally drop the set breaking all the

dishes.

Charlie apologizes and promises to pay back

the cost of the dishes as soon as possible.

It has been a week, however, Charlie still has

not paid you back for the broken set of dishes.

There are three ways to request the money

from Charlie: using Venmo, requesting in

person, or using a standard bank app.

Venmo: Venmo is a digital payment service

that allows users to request money through its

phone app.

In person: You can request the money

from Charlie face to face (assume you see

Charlie once a week and the money will be

paid through a digital payment method).

A standard bank app: You can use another

standard bank app on your phone that allows

users to request money through a phone app.

How would you request the $100 from

Charlie, [one of your closest friends/one of

your distant acquaintances]?

buy them to take home.

However, when you arrive home you discover

that some of the dishes are broken.

The vendor has a policy that if any of the

dishes are broken in the box when you get

home, you can either bring them back the next

time you come and request a refund in person,

or you can take a photo of the broken dishes

and request the refund electronically.

It has been a week since you purchased the

broken set of dishes from the pottery vendor.

There are three ways to request the money

from the vendor: using Venmo, requesting in

person, or using a standard bank app.

Venmo: Venmo is a digital payment service

that allows users to request money through its

phone app.

In person: You can request the money

from the vendor face to face (assume you go

to this farmers market once a week and the

money will be paid through a digital payment

method).

A standard bank app: You can use another

standard bank app on your phone that allows

users to request money through a phone app.

How would you request the $100 from the

pottery vendor?

[83]

3.2.1 Chapter 2: Appendix C

This appendix reports additional findings that were not reported in the main document.

Results from Study 3

MEANS OF DEPENDENT VARIABLES BY SCENARIO AND CONDITIONS

(STUDY 3)

Scenario

Dependent

Variable

Periodic

Condition

Aggregate

Condition

p-value

Environment

Donation

Company

Perception

5.53

5.18

.040

Perceived

Commitment

5.49

4.88

.001

Perceived Costs

4.39

3.98

.032

Perceived

Benefits

5.56

5.25

.031

Hunger Donation

Company

Perception

5.39

4.89

.003

Perceived

Commitment

4.91

4.28

.004

Perceived Costs

3.91

3.72

.35

Perceived

Benefits

5.35

5.22

.39

Refugee Donation

Company

Perception

4.81

4.57

.23

Scenario

Dependent

Variable

Periodic

Condition

Aggregate

Condition

p-value

Perceived

Commitment

4.49

4.31

.43

Perceived Costs

2.99

3.23

.24

Perceived

Benefits

4.66

4.91

.15

Collapsed

Company

Perception

5.27

4.88

< .001

Perceived

Commitment

5.00

4.49

< .001

Perceived Costs

3.81

3.64

.25

Perceived

Benefits

5.23

5.13

.33

Results from Study 4

In Study 4, we measured exploratory variables of perceived impact and donation

likelihood. Perceived impact was measured by the following item: “How impactful will this

company's involvement be in addressing child hunger?” (1 = Not at all, 5 = Extremely).

Donation likelihood was measured the following item: “If Barksdale Materials shared a link to

donate directly to one of the charities they are partnering with, how likely would you be to

donate?” (0 = Not at all, 10 = Extremely likely). The table below displays the series of t-tests for

each variable.

MEANS OF EXPLORATORY VARIABLES BY CONDITIONS

(STUDY 4)

[84]

[85]

Exploratory

Variable

Condition

(Mean)

Condition

(Mean)

t-tests

Perceived

Impact

Periodic

(3.73)

Aggregate

(3.46)

t(464) = 2.72, p = .007

Periodic-sum

(3.62)

t(439) = 1.08, p = .28

Aggregate

Periodic-sum

t(453) = 1.69, p = .092

Donation

Likelihood

Periodic

(5.28)

Aggregate

(4.91)

t(464) = 1.32, p = .19

Periodic-sum

(5.38)

t(439) = .34, p = .73

Aggregate

Periodic-sum

t(453) = 1.73, p = .085

Results from Study 5

In Study 5, we measured exploratory variables of perceived impact and donation

likelihood. Perceived impact was measured by averaging the following items: “This company’s

donation behavior will: 1) feed many people, 2) have a noticeable impact in their community,

and 3) significantly reduce child hunger (1 = Strongly disagree, 7 = Strongly agree) (α = .93).

Donation likelihood was measured averaging the following items: 1) “If Bell Electrek shared a

[86]

link to donate directly to one of the charities they are partnering with, how likely would you be

to donate?” (0 = Not at all, 10 = Extremely likely), 2) If Bell Electrek offered donation matching

(in which they donate a dollar for every dollar you donate) for donating to one of their partnering

charities, how likely would you be to donate?” (0 = Not at all, 10 = Extremely likely), and 3) If

you decided to donate to a child hunger relief organization, would you be more likely to donate

once to a charity or sign up for a monthly donation?” (-5 = I would definitely only donate once, 5

= I would definitely sign up for monthly donations). The last item was recoded so that the values

of the anchors matched the first two items. The table below displays the post hoc analyses for

each variable.

POST HOC ANALYSES OF EXPLORATORY VARIABLES BY CONDITIONS

(STUDY 5)

Exploratory

Variable

Donation

Framing-

Donation Span

Donation

Framing-

Donation Span

Mean

difference

Perceived

Impact

Periodic-One

Aggregate-One

.54

.13

794

4.08

.001

Periodic-Four

Aggregate-Four

.16

.13

794

1.19

.24

Donation

Likelihood

Periodic-One

Aggregate-One

.13

.24

794

.57

Periodic-Four

Aggregate-Four

-.08

.24

794

-.33

.74

Methods and Results of Additional Study

We recruited 602 Prolific Academic participants (M

Age

= 34.61, 49.17% female).

Participants were randomly assigned to one of two conditions in a 2-cell (donation framing:

aggregate vs. periodic) between-subjects design. Each participant was then randomly assigned to

view one of three scenarios (environmental, hunger, social activism donations; see Appendix D).

The donation amount and donation cause were different across the three scenarios. In accordance

with our preregistration (https://aspredicted.org/9NN_6QN), only participants that answered the

attention check questions correctly (N = 579) were included in the analysis.

Participants read that a fictional company pledged to donate to a certain prosocial cause.

We describe the environment scenario below, but all scenarios follow a similar pattern.

Participants in the environment scenario read that a company named Smith Enterprise recently

pledged to donate a portion of their earnings to protect the environment. The donation pledge

read,

“Smith Enterprise recognizes that we can use part of our earnings to do a significant

amount of good for the environment. We pledge to donate a [aggregate condition: one-

time donation of $1.2 million in 2023/periodic condition: $100,000 each month between

January 2023 and December 2023] to non-profit environmental organizations protecting

the environment and wildlife.”

Participants then rated the key dependent variable of company perception using three

items: 1) “How much do you admire [company name]?”, 2) “How favorably do you view

[company name]?”, and 3) “How positively do you view [company name]?” (1 = Not at all, 7 =

Very much). The three items were averaged to create a “company perception” composite (α =

.97). We also measured an exploratory variable of perceived authenticity using three items: 1)

[87]

[88]

“How authentic is [company name]’s support for the cause?”, 2) “How sincere is [company

name]’s donation?”, and 3) “How genuine is [company name]’s donation?” (1 = Not at all, 7 =

Very)

Following our preregistered analysis, we regressed company perception on the framing of

donation pledges, including a fixed effect of scenarios. As predicted, participants in the periodic

condition (M = 5.17, SD = 1.42) had greater positive perceptions of the company than the

aggregate condition (M = 4.50, SD = 1.64), t(575) = 5.24, p < .001. The pattern of results is

consistent when looking at each scenario separately (see table below).

Similar to our key dependent variable, we regressed perceived authenticity on the

framing of donation pledges, including a fixed effect of scenarios. Consistent with previous

results, participants in the periodic condition (M = 5.28, SD = 1.31) had greater positive

perceptions of the company than the aggregate condition (M = 4.75, SD = 1.44), t(575) = 4.48, p

< .001. The pattern of results is consistent when looking at each scenario separately (see table

below).

MEANS OF DEPENDENT VARIABLES BY SCENARIO AND CONDITIONS

Scenario

Dependent

Variable

Periodic

Condition

Aggregate

Condition

p-value

Environment

Donation

Company

Perception

5.29

4.77

.007

Perceived

Authenticity

5.38

4.82

.004

[89]

Scenario

Dependent

Variable

Periodic

Condition

Aggregate

Condition

p-value

Hunger Donation

Company

Perception

5.45

4.98

.026

Perceived

Authenticity

5.50

5.04

.013

Social Activism

Donation

Company

Perception

4.79

3.86

< .001

Perceived

Authenticity

4.96

4.45

.017

3.2.2 Chapter 2: Appendix D

This appendix has all the study stimuli.

GIFTAMEAL FIELD EXPERIMENT ADVERTISEMENTS (STUDY 1B)

[90]

[91]

STUDY SCENARIOS (STUDIES 2, 3, & APPENDIX C)

SCENARIOS

STUDY 2

Education cause

ANY Corporation is an American company that designs and sells children's apparel, with over

800 stores across North America, Europe, and Asia.

Imagine that ANY Corporation recently pledged to donate a portion of their earnings to

support children’s education and public schools. This was their pledging statement:

“ANY Corporation recognizes that we can use part of our earnings to do a significant amount

of good. We pledge to donate [aggregate condition: a one-time donation of $1.2

million/periodic condition: $90,000 every month between January 2023 and December 2023]

to non-profit organizations that support children’s education and public schools.”

Environmental cause

Smith Enterprise is an American company that sells various equipment for outdoor excursions

such as travel equipment, camping gear, clothing, and sports equipment.

Imagine that Smith Enterprise recently pledged to donate a portion of their earnings to support

wildlife and environmental conservation. This was their pledging statement:

“Smith Enterprise recognizes that we can use part of our earnings to do a significant amount of

good. We pledge to donate [aggregate condition: $2.5 million in 2023/periodic condition:

$200,000 every month between January 2023 and December 2023] to non-profit organizations

that support wildlife and environmental conservation.”

[92]

SCENARIOS

Gender equality cause

FYA Jewelry is an American retailer and producer of jewelry that is known for their

customizable bracelets, designer rings, earrings, and necklaces. The company markets its

products in more than 50 countries on six continents.

Imagine that FYA Jewelry recently pledged to donate a portion of their earnings to support

gender equality. This was their pledging statement:

“FYA Jewelry recognizes that we can use part of our earnings to do a significant amount of

good. We pledge to donate [aggregate condition: a one-time donation of $300,000 in

2023/periodic condition: $24,000 every month between January 2023 and December 2023] to

non-profit organizations that support gender equality.”

Health 1 cause

Alpha Manufacturing is an American company that manufactures various parts for

automobiles, home appliances, and textiles.

Imagine that Alpha Manufacturing recently pledged to donate a portion of their earnings to

support individuals, families, and caregivers affected by Alzheimer’s disease. This was their

pledging statement:

Alpha Manufacturing recognizes that we can use part of our earnings to do a significant

amount of good. We pledge to donate [aggregate condition: a one-time donation of $600,000

in 2023/periodic condition: $45,000 every month between January 2023 and December 2023]

to non-profit organizations that support individuals, families, and caregivers affected by

Alzheimer’s disease.”

Health 2 cause

Zeta LLC is a multinational investment bank and financial services company that provides

various financial services to different clients.

[93]

SCENARIOS

Imagine that Zeta LLC recently pledged to donate a portion of their earnings to support

individuals, families, and caregivers affected by Parkinson’s disease. This was their pledging

statement:

“Zeta LLC recognizes that we can use part of our earnings to do a significant amount of good.

We pledge to donate [aggregate condition: a total donation of $2 million in 2023/periodic

condition: $150,000 every month between January 2023 and December 2023] to non-profit

organizations that support individuals, families, and caregivers affected by Parkinson’s

disease.”

Hunger cause

YNA Corporation is an American fashion retailer company that sells accessories, beauty

products, and clothing for people of all ages, with over 700 stores across North America,

Europe, and Asia.

Imagine that YNA Corp recently pledged to donate a portion of their earnings to support the

ending of world hunger. This was their pledging statement:

“YNA Corp recognizes that we can use part of our earnings to do a significant amount of

good. We pledge to donate [aggregate condition: a total donation of $750,000 in

2023/periodic condition: $60,000 every month between January 2023 and December 2023] to

non-profit organizations that support the ending of world hunger.”

Poverty cause

Beta Tech is an American technology company that focuses on online advertising, cloud

computing, e-commerce, and consumer electronics.

Imagine that Beta Tech recently pledged to donate a portion of their earnings to support the

ending of global poverty. This was their pledging statement:

“Beta Tech recognizes that we can use part of our earnings to do a significant amount of good.

[94]

SCENARIOS

We pledge to donate [aggregate condition: $1 million in 2023/periodic condition: $80,000

every month between January 2023 and December 2023] to non-profit organizations that

support the ending of global poverty.”

Refugee cause

Johnson & Williams is an American electronics company that sells various office supplies

such as computers, printers, and laptops to firms across the globe.

Imagine that Johnson & Williams recently pledged to donate a portion of their earnings to

support war refugees and provide relief. This was their pledging statement:

“Johnson & Williams recognizes that we can use part of our earnings to do a significant

amount of good. We pledge to donate [aggregate condition: a total donation of $1.5 million in

2023/periodic condition: $120,000 every month between January 2023 and December 2023] to

non-profit organizations that support war refugees and provide relief.”

Social activism cause

J&L is an American food company that is one of the biggest food suppliers to restaurants and

groceries across the globe.

Imagine that J&L recently pledged to donate a portion of their earnings to support and protect

human rights. This was their pledging statement:

“J&L recognizes that we can use part of our earnings to do a significant amount of good. We

pledge to donate [aggregate condition: $200,000 in 2023/periodic condition: $16,000 every

month between January 2023 and December 2023] to non-profit organizations that support and

protect human rights.”

STUDY 3

Environmental cause

[95]

SCENARIOS

Smith Enterprise is an American company that sells various equipment for outdoor excursions

such as travel equipment, camping gear, clothing, and sports equipment.

Imagine that Smith Enterprise recently pledged to donate a portion of their earnings to protect

the environment. This was their pledging statement:

“Smith Enterprise recognizes that we can use part of our earnings to do a significant amount of

good for the environment. We pledge to donate [aggregate condition: a one-time donation of

$1.2 million in 2023/periodic condition: $100,000 every month between January 2023 and

December 2023] to non-profit environmental organizations protecting the environment and

wildlife.”

Hunger cause

YNA Corporation is an American fashion retailer company that sells accessories, beauty

products, and clothing for people of all ages, with over 700 stores across North America,

Europe, and Asia.

Imagine that YNA Corporation recently pledged to donate a portion of their earnings to

support the ending of World Hunger. This was their pledging statement:

“YNA Corporation recognizes that we can use part of our earnings to do a significant amount

of good to end world hunger. We pledge to donate [aggregate condition: a one-time donation

of $720,000 in 2023/periodic condition: $60,000 every month between January 2023 and

December 2023] to international non-profit organizations that aim to create a hunger-free

world.”

Refugee cause

Johnson & Williams is an American electronics company that sells various office supplies

[96]

SCENARIOS

such as computers, printers, and laptops to firms across the globe.

Imagine that Johnson & Williams recently pledged to donate a portion of their earnings to aid

war and refugee relief. This was their pledging statement:

“Johnson & Williams recognizes that we can use part of our earnings to do a significant

amount of good for aid refugees across the globe. We pledge to donate [aggregate condition: a

one-time donation of $60,000 in 2023/periodic condition: $5,000 every month between

January 2023 and December 2023] to non-profit organizations helping and protecting war

refugees.”

Environmental cause

Smith Enterprise is an American company that sells various equipment for outdoor excursions

such as travel equipment, camping gear, clothing, and sports equipment.

Imagine that Smith Enterprise recently pledged to donate a portion of their earnings to protect

the environment. This was their pledging statement:

“Smith Enterprise recognizes that we can use part of our earnings to do a significant amount of

good for the environment. We pledge to donate [aggregate condition: a one-time donation of

$1.2 million in 2023/periodic condition: $100,000 each month between January 2023 and

December 2023] to non-profit environmental organizations protecting the environment and

wildlife.”

Hunger cause

Greater Goods is an American chain store of bakery-café fast-casual restaurants with over

2,000 locations across the United States and Canada.

Imagine that Greater Goods recently pledged to donate a portion of their earnings to support

[97]

SCENARIOS

the ending of World Hunger. This was their pledging statement:

“Greater Goods recognizes that we can use part of our earnings to do a significant amount of

good to end world hunger. We pledge to donate [aggregate condition: a one-time donation of

$600,000 this year/periodic condition: $50,000 per month from June 2022 to May 2023] to

international non-profit organizations that aim to create a hunger-free world.

Social activism cause

YNA Corporation is an American fashion retailer company that sells accessories, beauty

products, and clothing for people of all ages, with over 700 stores across North America,

Europe, and Asia.

Imagine that YNA Corporation recently pledged to donate a portion of their earnings to the

Black Lives Matter social movement. This was their pledging statement:

“YNA Corporation recognizes that we can use part of our earnings to do a significant amount

of good for the Black Lives Matter social movement. We pledge to donate [aggregate

condition: a one-time donation of $720,000 in 2023/periodic condition: $60,000 every month

starting in January 2023 and going until December 2023] to non-profit organizations helping

and advocating for various policy changes and anti-racism.

PERCEIVED COST AND BENEFITS MEASURES (STUDY 3)

Perceived costs measures (7-point Likert Scale; 1 = strongly disagree; 7 = strongly agree)

1. <r> The cost of this donation is trivial to [company name]

2. The cost this donation is a lot of money

3. <r> [company name] would barely notice the cost of this donation.

[98]

Perceived benefits measures (7-point Likert Scale; 1 = strongly disagree; 7 = strongly agree)

1. Non-profit organizations would miss out on many benefits if [company name] did not

donate.

2. Non-profit organizations would benefit a lot from the donation.

3. <r> The donation would not be beneficial to non-profit organizations.

STUDY STIMULI: PERIODIC CONDITION (LEFT), PERIODIC-SUM CONDITION

(CENTER), AND AGGREGATE CONDITION (RIGHT) (STUDY 4)

STUDY STIMULI: PERIODIC-ONE YEAR CONDITION (FIRST), AGGREGATE-ONE

YEAR CONDITION (SECOND), PERIODIC-FOUR YEAR CONDITION (THIRD),

AGGREGATE-FOUR YEAR CONDITION (FOURTH) (STUDY 5)

Periodic-One Year

[99]

Aggregate-One Year

Periodic-Four Year

Aggregate-Four Year

100

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