THE EFFECT OF DESCRIPTIVE NORMS AND INVOLVEMENT ON
USER INTENTION TO ADOPT CAUSE-RELATED FACEBOOK PROFILE FILTER
AND TO DONATE TO NPOS
By
Xiaoyu Zhao

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Public Relations — Master of Arts
2017

ABSTRACT
THE EFFECT OF DESCRIPTIVE NORMS AND INVOLVEMENT ON
USER INTENTION TO ADOPT CAUSE-RELATED FACEBOOK PROFILE FILTER
AND TO DONATE TO NPOS
By
Xiaoyu Zhao
With the prevalence in the use of the Facebook profile filter for social causes and the
newly launched fund raising tool, Facebook is providing a unique opportunity for non-profit
organizations (NPOs) to raise public attention and funding. The current study aims to understand
how online descriptive norms and involvement affect Facebook users’ behavioral intentions
toward NPOs. A 2 (descriptive norms: low vs. high) X 2 (involvement: low vs. high) betweensubject factorial experiment was conducted. The manipulation of involvement through
geographic proximity was not successful in that it yielded a counter-hypothesized validation of
the manipulation. Results showed that the interaction between descriptive norms and
organizational geographic scope had a significant effect on participants’ profile filter adoption
intentions. Moreover, organizational geographic scope significantly influenced participants’
donation intentions. The study’s findings were discussed in relation to enhancing the social
norms approach within the social media context and in relation to varied levels of organizational
scope. Practically, the findings increase professional communicators’ understanding of the
nuances of using social media to influence behavioral change in relation to social causes.

Keywords: descriptive norms, involvement, non-profit organization, Facebook

Copyright by
XIAOYU ZHAO
2017

TABLE OF CONTENTS

LIST OF TABLES………………………………………………………………………………..vi
LIST OF FIGURES……………………………………………………………………………...vii
INTRODUCTION………………………………………………………………………………...1
LITERATURE REVIEW…...…………………………………………………………………….4
Social Media Marketing for Non-profit Organization…………………………………………….4
The Social Norms Approach………………………………………………………………………6
Involvement……………………………………………………………………………………….9
Involvement and Descriptive Norms…………………………………………………………….10
METHOD………………………………………………………………………………………..14
Study Design & Independent Variables………………………………………………….14
Descriptive Norms ………………………………………………………………14
Involvement……………………………………………………………………...14
Participants……………………………………………………………………………… 14
Dependent Variables……………………………………………………………………..15
Filter Adoption Intentions………………………………………………………..15
Donation Intentions………………………………………………………………15
Control Variables………………………………………………………………………...16
Preexisting Attitudes toward the Issue/Organization…………………………….16
Preexisting Involvement with the Issue………………………………………….16
Familiarity of the Facebook Filter Function/Organization………………………16
Previous Volunteering and Donation Behaviors…………………………………17
Facebook Usage Intensity………………………………………………………..17
Altruism………………………………………………………………………….17
Pretest…………………………………………………………………………………….18
Procedure………………………………………………………………………………...19
RESULTS………………………………………………………………………………………..21
Descriptive Results………………………………………………………………………21
Manipulation Check……………………………………………………………………...21
Hypotheses Testing………………………………………………………………………22
DISCUSSION……………………………………………………………………………………25
Summary of Findings…………………………………………………………………….25
Theoretical and Managerial Implications………………………………………………..29
Limitations and Future Studies…………………………………………………………..30
APPENDICES…………………………………………………………………………………...32
APPENDIX A: Factor Loading of Variables……………………………………………33

iv

APPENDIX B: Stimuli…………………………………………………………………..35
REFERENCES…………………………………………………………………………………..38

v

LIST OF TABLES

Table 1: Factor Analysis Results for Evaluations of Positive Status Updates (with Varimax
Rotation) ……………………………………………………………………………….34

vi

LIST OF FIGURES

Figure 1: Interaction Effect of Descriptive Norms and Geographic Scope on Adoption
Intentions………………………………………………………………………………23
Figure 2: Stimuli Example for Four Conditions (Descriptive Norms X Involvement)………….36
Figure 3: Face Selection to Match Participants’ Gender and Race……………………………...37

vii

INTRODUCTION
After the U.S. Supreme Court legalized gay marriage in 2015, Facebook launched a tool
allowing users to express their support for marriage equality by adding a semi-transparent filter
of a rainbow flag on top of their Facebook profile picture. Over 26 million people applied the
rainbow flag filter to “celebrate pride” (Dewey, 2015). This relatively new feature gained
attention again in November 2015 when Facebook users used the French flag filter in response to
the Paris terror attacks (Feeney, 2015). Recently, Facebook has been providing similar
opportunities for users to showcase their affinity for sports teams and universities/colleges
(Sanders, 2015). Whether a friend has adopted the filter or the organization promoting it pushes
it through promoted newsfeed updates, Facebook users can simply click on the “try it” button to
transform and redecorate their profile picture and showcase their support for the cause, event, or
organization. The Facebook profile filter is a Facebook user’s explicit expression of endorsement
for a social issue, sports fandom, or even a commercial brand.
People are motivated to share information on social media with which they have an
emotional connection on social media (Mangold & Faulds, 2009). As an outcome of new
technology, social networking sites (SNSs) are ideal platforms for users to link to and support
social causes. According to Facebook (2015), more than 150 million people worldwide connect
to a cause on Facebook. Therefore, the use of the Facebook filter as a promotional and
awareness-raising strategy could be beneficial to both nonprofit organizations and commercial
companies. The Facebook profile picture filter is a unique way of engaging with a cause. Unlike
donating money to a cause that could happen in private, the use of the filter feature entails a high

1

degree of visibility for a SNS user’s support for the cause through his/her public display of
endorsement to that cause. There have been a number of causes that inspired Facebook users to
change their Facebook profile pictures to show support or dissent. For example, the Childhood
Cartoon Faces campaign aims to end childhood abuse by inviting participants to change their
Facebook profile pictures to cartoon characters they liked when they were kids; the Breast
Cancer Site motivates Facebook users to adopt the pink ribbon image as profile photo to raise
awareness for the disease, especially during Breast Cancer Awareness Month each year —
October; Planned Parenthood invited Facebook users to “pink out” their profile picture by adding
a pink #StandwithPP filter to support women’s health and rights in 2015 (Desta, 2014; Demaria,
2015). The Facebook filter function could successfully raise awareness on the topic within the
online environment and help generate extensive discussion on social media (Sanders, 2015). The
question, then, becomes: How can nonprofits motivate Facebook users to adopt a profile picture
filter? And what effect does this adoption have on evaluations of and behavioral intentions
toward both the organization and the cause?
Lapinski and Rimal (2005) explicated that perceived popularity of a behavior strongly
influences human behavior. The ways in which perceived prevalence among one’s social group
governs his/her own behavior has been long studied under the social norms approach (Cialdini et
al., 1991; Borsari & Carey, 2003; Schultz et al., 2007). On SNSs like Facebook, the prevalence
and acceptance of a social norm can be visible to users through displays of the number of likes,
shares, comments, and even adoption of a profile filter. As descriptive norms deal with the
perceived prevalence of a certain behavior among one’s social group, displays of the descriptive
norm via SNSs can influence both online and offline behaviors (Alhabash et al., 2015). To this

2

end, the current study manipulates descriptive norms by varying the number of Facebook friends
who have already adopted the Facebook profile picture filter.
While the effect of social (descriptive) norms has been well documented (Rivis &
Sheeran, 2003; Bosari & Carey, 2003; Baumgartner et al., 2011), other factors come into play in
determining one’s online and offline behaviors. Involvement with the brand, issue, or cause has
been shown to affect both attitudes and behavior (Hajjat, 2003; Bigné-Alcañiz et al., 2010).
Rimal and Lapinski (2015) argued that involvement moderates the effect of descriptive norm on
behavior according to the theory of normative social behavior (TNSB). The current study
manipulates involvement with the non-profit organization and investigates its main effect and
interaction with descriptive norms on behavioral intentions.
This paper starts with a review of the use of social media marketing by non-profit
organizations. Second, the study reviews the social norms approach with a focus on descriptive
norms as a predictor of behavior. The third section conceptualizes involvement as a moderator of
the effect of social norms on behaviors using TNSB. The fourth section describes the
experimental method. The fifth and sixth sections reports and discuss the study’s findings,
respectively.

3

LITERATURE REVIEW
Social Media Marketing for Non-profit Organizations
The advent of new media platforms brought new modes of communication with vast
opportunities to foster relationships among users, as well as among consumers, businesses, and
organizations. Spreading commercial messages and interacting with consumers via social media
make it easier for marketers to perform integrated marketing activities with less cost (Kim & Ko,
2012). Research showed that social media marketing benefits organizations and brands in terms
of creating online communities, distributing content, increasing exposure, gaining website traffic,
generating two-way communication with audience members, and building brand awareness and
reputation (Kaplan & Haenlein, 2010; Kim & Ko, 2010; e-Marketer, 2010; Michaelidou et al.,
2011). Social media have become essential to marketers as they harness relationship building
among the brand or the cause and consumers, users, and stakeholders. Given the exponential
growth of social media adoption and use, businesses have capitalized on its power to effectively
reach users. In 2014, 13% of digital marketing spending of U.S. was allocated to social media;
estimated to reach 16% in 2019 (Forrester Research, 2014).
Social media have generated similar opportunities for non-profit organizations (NPOs) by
galvanizing two-way communication with various stakeholders, which serves to strategically and
efficiently mobilize the public (Briones et al., 2010; Lovejoy et al., 2012). With limited resources,
NPOs largely rely on general public awareness for their causes, volunteer support, and donations
to advocate for social, health, political, policy, and economic changes (Auger, 2013). Witman
(2013) argued that social media are beneficial for NPOs because of the combination of low
marketing costs to promote social causes and cultivate relationships with stakeholders (Witman,
2013). Nonprofit Benchmarks Study (2016) stated that social media were mainly used in

4

branding, new donor acquisition, and existing supporter conversion. Stakeholders’ online
interaction with NPOs, including those taking place via social media, blogging, and journaling,
increase money contributions both online and offline (Mano, 2014). Pressrove and Pardun (2016)
further argued that building a social media-based connection with stakeholders has a significant
impact on their offline behavioral intentions to support the NPO such as the intentions to
volunteer and donate.
With 1.71 billion active monthly users and more than one billion using it daily, Facebook
is the world’s leading social networking site (Facebook Newsroom, 2016). It allows users to
build profiles about themselves, connect to their social connections within the system, and create,
view and share user generated content on it (Boyd & Ellison, 2007; Kaplan & Haenlein, 2010).
Facebook has become an important marketing tool, following its exponential user growth after
the site expanded its registration beyond Ivy League college campuses (Facebook Newsroom,
2006). More than 150 million people worldwide connect to a social cause on Facebook
(Facebook Newsroom, 2015), which makes Facebook an ideal platform for NPOs to raise
awareness and funds. According to a University of Massachusetts Dartmouth (2014) report, of
the top 400 charities and NPOs, 92% have adopted Facebook to distribute content. On average,
NPOs post to Facebook 1.3 times per day. However, when NPOs post on Facebook, the post
might only reach 10% of their Facebook fans and gain 5.4% of engagement rate (Nonprofit
Benchmarks Study, 2016). With the Facebook profile filter, NPOs can extend awareness-raising
efforts beyond the boundaries of their registered fan base on the site, where a multiplier effect is
generated through adoption of a cause-related Facebook filter. In doing so, a Facebook user not
only endorses the issue, but this endorsement is shared publicly with his/her network of
Facebook friends. Considering the potential for a multiplier effect, the current study investigates

5

the ways in which Facebook users become motivated to adopt the Facebook filter and donate to
NPO via Facebook. The study questions whether the presence of descriptive norms cues,
recorded through the number of Facebook friends who have adopted the Facebook filter, would
affect their willingness to adopt the filter themselves. The next section uses the social norms
approach to conceptualize the effect of descriptive norms.
The Social Norms Approach
Perceptions of what other people are doing and what should be done are strong drivers of
human behavior (Cialdini et al., 1991; Lapinski et al., 2015). Cialdini et al. (1991) distinguished
two types of social norms — descriptive norms and injunctive norms. These social norms
provide evidence of what is adaptive in the social group and could guide human behavior (Reno
et al., 1993; Lapinski & Rimal, 2005). Descriptive norms refer to one’s perception of the
behavior’s prevalence among others in the social group (Cialdini et al., 1991; Rimal & Real
2005). It can serve as a heuristic cue in providing a decisional shortcut to govern one’s
behavioral outcome (Lapinski et al., 2015). On the other hand, injunctive norms refer to
perceptions of “what ought to be done” (Cialdini et al., 1991, p. 203; Lapinski & Rimal, 2005). It
expresses the perception of which behavior is and is not acceptable among member of the social
group (Bosari & Carey, 2003). The main difference between descriptive norms and injunctive
norms is that social sanction is involved in injunctive norms if one fails to perform a widely
accepted behavior (Rimal & Real, 2005; Lapinski & Rimal, 2005). Previous studies showed that
descriptive norms and injunctive norms have direct (Lapinski et al., 2007; Mollen et al., 2013)
effects on human behavior.
Social norms are formed and propagated via communication within a social group
(Kincaid, 2004; Lapinski & Rimal, 2005). Social media platforms can serve as a communication

6

context for social norms to be formulated and communicated. Yee et al. (2006) argued that social
norms govern how individuals behave in the digital world in a similar fashion as its does in the
physical environment. On social media, high numbers of likes, shares, comments and views
signify a high level of acceptance of a message and it was supported to influence people’s online
and offline civic behavioral intentions (Alhabash et al., 2015). Adopting an NPO-themed profile
filter on Facebook is a visible endorsement of NPO-congruent attitudes and behaviors, and it also
constitutes a form of online behavior in and of itself. If a user has not yet adopted the NPOthemed filter while his/her friends have done so, a sponsored story (often paid for by the
promotion entity, i.e., NPO) can appear with persuasive messages such as an explicit indication
of the number of friends who have already adopted the filter (Loomer, 2013; Facebook Business,
n.d.). Therefore, I argue that the prevalence of a filter – indicated by the number of friends who
have adopted it – signals the descriptive norm. Users are able to adopt filters based on whatever
social causes they care about, but no social sanction is involved. Rivis and Sheeran’s (2003)
meta-analysis documented medium to strong positive associations between descriptive norms
and behavioral intentions (Rivis & Sheeran, 2003). For example, research has also shown that
perceived descriptive norms strongly affect college students’ alcohol consumption (Borsari &
Carey, 2003; Larimer et al., 2004; Ridout & Campbell, 2014; Rimal, 2008).
Interestingly, Alhabash et al. (2013) found that descriptive norms expressed in number of
likes and shares could backfire in terms of driving favorable evaluations of the message in which
messages with low virality (low likes and low shares) result in more favorable evaluations. A
similar counter-intuitive finding was that YouTube cyberbullying videos with low views were
better at driving civic actions (the intentions to like, share, and comment on a social media post)
in certain situations (Alhabash et al., 2015). Alhabash et al. (2015) argued that novelty of content

7

and the need to be a social media pioneer could explain why social media posts with low views
were more successful at driving online and offline behaviors. It is also possible that for
participants, a social issue-related video with low views results in a feeling of an important social
issue that has not gained the attention it deserves, which leads to a stronger civic behavioral
intention. With these counter-intuitive results, Alhabash et al. (2013) suggested that the effect of
online descriptive norms should be explored with other contexts that vary regarding user
involvement and other message formats.
The current study examines the effect of descriptive norms on individuals’ intention to
adopt the NPO-themed Facebook filter. To manipulate descriptive norms, scholars have
manipulated the numbers in the descriptive norm messages. In the context of Facebook, how
viral a message is can demonstrate its prevalence of it and a descriptive norm can be manipulated
through number of likes or shares (Alhabash et al., 2015). Similarly, the current study will
manipulate descriptive norms by changing the number of Facebook friends who have already
adopted the filter. Hence, I hypothesize:
H1a: Participants will be more likely to adopt the NPO-themed Facebook profile filter
if a high than a low number of Facebook friends are using it.
In July 2016, Facebook officially launched a new tool — Fundraisers — for NPOs to
raise money (Facebook Newsroom, 2015). NPOs can add a donate button on their pages or posts
to raise money. Previous studies provided evidence on how social norms influence individuals’
donation behavior intention (Park & Smith, 2007; Martin & Randal, 2008, 2009; Croson et al.,
2009). These studies consistently showed a significant influence of descriptive norms on donors’
contributing behavior. Individuals’ perception of others’ donation results in intention to donate
and higher average donation amount (Martin & Randal, 2008, 2009). Croson et al. (2009) further

8

support that if existing donors are informed that others are making high contributions, they tend
to donate more. Thus, I hypothesize:
H2a: Participants will express greater donation intentions to the non-profit
organization if a high than a low number of their Facebook friends have adopted
the NPO-themed Facebook filter.
Involvement
Involvement refers to the importance of the particular issue to an individual (Apsler &
Sears, 1968; Kiesler et al., 1969; Petty & Cacioppo, 1979; Sherif et al., 1965). Involvement has
long been linked to an individual’s self-concept. Johnson and Eagly (1989) noted that even
though involvement is “a motivational state induced by an association between an active attitude
and the self-concept” (p.290), it can be triggered by different aspects of the self-concept. Johnson
and Eagly (1989) reviewed different bodies of involvement studies including social judgmentinvolvement approach (Sherif & Hovland, 1961; Sherif et al., 1965), Zimbardo (1960)’s response
involvement and a series of works from Petty and Cacioppo in which the construct was termed as
issue involvement or personal involvement (Petty & Cacioppo, 1979; Petty et al., 1981; Petty &
Cacioppo, 1981). Johnson and Eagly (1989) categorized involvement into three types: (1) valuerelevant involvement which is rooted in one’s intrinsic values; (2) impression-relevant
involvement, which concerns public perception of the self; and (3) outcome-relevant
involvement, which refers to “the relevance of an issue to one’s current important goals or
outcomes” (p. 292). Each type of involvement is thought to yield different persuasive effects.
Zaichowsky (1985, 1986) re-conceptualized involvement within the framework of an
attitude object’s relevance to an individual based on his/her inherent needs, values, and interests.
Zaichowksy (1985, 1986) explicated three antecedents to involvement: (1) personal

9

characteristics regarding inherent interests, needs, or values; (2) physical characteristics of the
object; and (3) situational characteristics that can have an impact one’s interest toward the object.
While Zaichowsky’s definition is widely accepted in the field of advertising, it is usually limited
to differentiating products, product categories, and brands as a function of their personal
relevance to the individual (e.g., smartphone vs. toothpaste; Zaichkowsky, 1987; Lockshin et al.,
1997; McWilliam, 1997; Lin &Chen, 2006). The current study attempts to keep the issues for
which the NPOs advocate constant while varying involvement as a function of the organization’s
importance to the participant. Past studies (Pelsmacker et al, 2002; Park et al., 2007; Te’eniHarari et al., 2009; Kong & Zhang, 2013; Te’eni-Harari, 2014) documented that individuals are
more likely to behave in accordance with the persuasive message if they feel personally involved
with the attitude object. Based on this, I hypothesize:
H1b: Participants assigned to the high involvement condition will be more likely to
adopt the NPO-themed Facebook filter than those assigned to the low
involvement condition.
H2b: Participants assigned to the high involvement condition will express greater
donation intentions to the NPO than those assigned to the low involvement
condition.
Involvement and Descriptive Norms
Rimal and Real (2005) proposed the theory of normative social behavior (TNSB) to
further distinguish between descriptive norms and injunctive norms and to explain how
injunctive norms, outcome expectation, and group identity work as moderators of the effect of
descriptive norms on behavioral intentions. The underlying assumption of TNSB is that one is
more likely to perform a prevalent behavior if he/she believed doing so could lead to the desired

10

benefits (Rimal & Lapinski, 2015). Additional moderators such as ego-involvement (Lapinski &
Rimal, 2005) and behavioral identity (Rimal, 2008) have also been documented in relation to
investigating the relationship between descriptive norms and behaviors. Rimal and Lapinski
(2015) expanded the scope of TNSB by categorizing potential moderators into individual-level
and group-level factors that moderate the effect of norms on behavior. Individual-level variables
include involvement, self-monitoring, self-efficacy, among others, whereas group-level variables
include group proximity and independence. Even though involvement was identified as an
individual-level moderator, it hasn’t been fully explored in descriptive norms studies and it is
“conceptually confusing” (Lapinski et. al, 2015, p.2).
Lapinski and Rimal (2005) argued that if one believes the behavior is consistent with
his/her self-concept, descriptive norms would compel the person to perform the behavior. They
adopted Johnson and Eagly’s (1989) concept of value-relevant involvement and defined egoinvolvement as “the extent to which individuals’ self-concept is connected with their position on
a particular issue and forms an integral part of how individuals define themselves” (Lapinski &
Rimal, 2005, p.136). Ego-involvement will have direct effect on human behavior and moderate
effect on descriptive norm-behavior relationship (Rimal et al., 2004; Lainski & Rimal, 2005). It
is hypothesized that descriptive norms’ influence on behavior will be stronger for people who
have higher level of ego-involvement with a behavior (Lapinski & Rimal, 2005). However,
Gockeritz et al. (2010) reported inconsistent results with TNSB regarding the moderating effect
of personal involvement on the relationship between descriptive norms and behavior. Based on
the work of Thomsen et al. (1995), personal involvement refers to “individuals are personally
involved with an issue, event, object, or person to the extent that they care about that entity and

11

perceive it as important” (p. 517). Results showed that positive descriptive norms-behavior
relationship was stronger as personal involvement was low.
In Lapinski et al. (2015), the moderating effect of value-relevant involvement,
impression-relevant involvement, and outcome-relevant involvement has been studied across
different contexts, such as alcohol consumption, fast food consumption, and recycling (Johnson
& Eagly, 1989; Cho & Boster, 2005; Lapinski et al., 2015). Results showed that the moderating
effect of value-relevant involvement varies across different behavioral domains. When valuerelevant involvement is low, the descriptive norm-behavior relationship is stronger in fast food
consumption condition but no significant moderation effects were found in the other two
conditions — alcohol consumption and recycling (Lapniski et al., 2015). In addition, the
interaction between outcome-/impression-relevant involvement and descriptive norms to
influence behavior was not significant across any of the three contexts. As Marshall et al. (2008)
demonstrated, the effect of involvement on behavioral outcomes varies due to different
behavioral attributes such as if the behavior happens publicly or not (Marshall et al., 2008; Rimal
et al., 2011). Thus, authors also suggested that involvement’s moderating role should be tested in
other behavioral domains (Lapniski et al., 2015; Rimal & Lapniski, 2015).
Past research findings (Gockeritz et al., 2010; Lapinski et al., 2015) were inconsistent
with the prediction of TNSB (Lapinski & Rimal, 2005), and instead were interpreted using
Elaboration Likelihood Model (ELM; Petty & Cacioppo, 1986). ELM posits that individuals
process persuasive information through the activation of either the central route or the peripheral
route. Central route processing is thought to involve greater elaboration and allocation of
cognitive resources to processing, while peripheral route processing relies on heuristic cues and
cognitive shortcuts for what is thought to be less-effortful processing. Petty and Cacioppo (1996)

12

argued that highly involved participants are more likely than those with low levels of
involvement to engage in central route processing of persuasive information, which is thought to
lead to more stable attitude. Thus, the prevalence of a behavior is not sufficient enough to
influence highly involved people’s behavior. On the other hand, when involvement is low and
peripheral route is activated for information processing, descriptive norms might serve as a
heuristic cue to provide a shortcut for an individual’s attitude and behavior change. Thus,
descriptive norms could have stronger influence on one’s behavior if the individual has lower
levels of involvement (Petty & Cacioppo, 1986; Lapinski et al., 2015). Lapinski et al. (2015)
noted that the moderating role of involvement should be examined in different behavioral
domains. Therefore, I hypothesize:
H1c:

Participants assigned to the low involvement condition will express greater
intentions to adopt the NPO-themed filter when the descriptive norm is high than
low compared to those in high involvement conditions who will express equal
likelihood of adopting the filter across both descriptive norms conditions.

H2c:

Participants assigned to the low involvement condition will express greater
donation intentions to the NPO when the descriptive norm is high than low
compared to those assigned to the high involvement conditions who will express
similar donation intentions across both descriptive norms conditions.

13

METHOD
Study Design & Independent Variables
The study used a 2 (descriptive norm: high vs. low) Χ 2 (involvement: high vs. low)
between-subject factorial design. Participants were exposed to non-profit organization themed
Facebook profile filter promotion posts with similar persuasive messages that were created for
this study.
Descriptive Norms. Descriptive norm was manipulated by varying the number of
Facebook friends who have adopted the filter/donated in the message. Prior to stimuli exposure,
participants reported the number of their Facebook friends. Per the random assignment to low or
high descriptive norms conditions, they received tailored messages with either 1% (low) or 25%
(high) of their Facebook friends who have adopted the profile picture filter. An automatic
calculator was set up on the Qualtrics survey system to calculate the number of friends who have
adopted the filter.
Involvement. Participants were also randomly assigned to one of two involvement
conditions that was manipulated through organization geographic proximity (Petty & Cacioppo,
1984). Participants read the cover story of the charity prior to stimuli exposure. For the high
involvement condition, the cover story introduced the NPO as the Greater Lansing Chapter
including brief history, mission of the NPO and how the NPO will help communities in Greater
Lansing area. For low involvement group, the cover story provided general information
including brief history and mission of the NPO that was headquartered in New York City and
mainly focused on the organization’s international work.
Participants
Participants (N=275) were recruited from MSU SONA online survey system and
received extra credit for participation. Participants were mostly female (66.9%). Participants’ age

14

ranged from 17 to 25 years old with (M = 20.35, SD = 1.78). As for current class standing, 39.6
percent of participants reported being juniors, followed by seniors (30.2%), sophomores (18.5%),
freshman (10.9%), and graduate students (0.7%). In terms of the ethnic background, the majority
of participants (67.3%) reported being White, followed by Asian (20%), Black or African
American (6.2%), Hispanic or Latino (3.3%), and other ethnicities (3.3%). The majority of
participants reported being religious (73.8%), including Catholic (33.8%), Protestant Christian
(18.9%), Jewish (6.9%), Buddhist (5.5%), Hindu (0.7%), Islam (0.7%) and other religion (7.3%).
Dependent Variables
Filter Adoption Intentions. Willingness to adopt the NPO themed Facebook profile
filter was measured using four seven-point Likert-type scale items anchored with strongly
disagree to strongly agree: I intend to adopt the [NPO name] Facebook profile filter; I plan to
adopt the [NPO name] Facebook profile filter; I am thinking about adopting the [NPO name]
Facebook profile filter; and I will adopt the [NPO name] Facebook profile filter (Park & Smith,
2007). Upon satisfactory factor and reliability (see Table 1 in Appendix), the mean value for
filter adoption intentions were computed for each participant.
Donation Intentions. Donation intentions were measured using four seven-point Likerttype scale items anchored with strongly disagree to strongly agree: I intend to donate to [NPO
name]; I plan to donate to [NPO name]; I am thinking about donating to [NPO name]; and I will
donate to [NPO name] (Park & Smith, 2007). Upon satisfactory factor and reliability (see Table
1 in Appendix), the mean value for donation intentions were computed for each participant. Each
participant was also given $100 virtual cash. They were informed that they would be seeing a
Facebook message, and would be asked to allocate the amount of money in donation to the NPO.

15

Upon exposure to the stimuli, participants were asked: “how much money out of $100 are you
willing to donate to [NPO name]?” with a slider ranged from $0 to $100.
Control Variables
The study included the following variables as controls:
Preexisting Attitudes toward the Issue/Organization. Preexisting attitude toward the
issue were measured using MacKenzie and Lutz’s (1989) attitudes toward the ad scale and Yi
and Yoo’s (2011) attitudes towards brand scale, where participants were asked to indicate their
evaluations of the organization using four seven-point semantic differential scales: bad/good,
unfavorable/favorable, unpleasant/pleasant, harmful/beneficial. Upon satisfactory factor and
reliability (see Table 1 in Appendix), the mean value for attitudes toward the issue and
organization were computed for each participant.
Preexisting Involvement with the Issue. Preexisting involvement with the issue was
measured using Zaichowsky’s (1985) seven-point semantic differential involvement scale:
unimportant/important,

boring/interesting,

irrelevant/relevant,

unexciting/exciting,

means

nothing/means a lot to me, unappealing/appealing, mundane/fascinating, worthless/valuable,
uninvolving/involving, not needed/needed. Factor analysis showed that the loadings of six items
were below 0.6 which were deleted. Four items (unimportant/important, irrelevant/relevant,
worthless/valuable, not needed/needed) remained and upon satisfactory factor and reliability (see
Table 1 in Appendix), the mean value for involvement with the issue was computed for each
participant.
Familiarity of the Facebook Filter Function/Organization. Familiarity with the
Facebook filter function/organization were measured using a seven-point semantic differential
scale anchored with “not at all familiar” and “extremely familiar.”

16

Previous Volunteering and Donation Behaviors. Previous volunteering and donation
behaviors were measured using questions adopted from Reuveni and Werner (2015): “have you
ever volunteered for any NPOs in the past” and “have you ever donated to any NPOs in the past?”
(yes or no).
Facebook Usage Intensity. Facebook general usage was examined by six seven-point
Likert scale items anchored with strongly disagree to strongly agree: Facebook is part of my
everyday activity; I am proud to tell people I’m on Facebook; Facebook has become part of my
daily routine; I feel out of touch when I haven’t logged onto Facebook for a while; I feel I am
part of the Facebook community; I would be sorry if Facebook shut down (Ellison et al., 2006).
Upon satisfactory factor and reliability (see Table 1 in Appendix), the mean value for Facebook
usage intensity were computed for each participant.
Altruism. Altruism was measured using Morgan and Miller’s (2002) 11 seven-point
Likert scale items anchored with strongly disagree to strongly agree: I enjoy doing small favors
every day for the people I care about; helping others is one of the most important aspects of life;
I enjoy working for the welfare of others; my family tends to do what we can to help those less
fortunate than ourselves; overall, I tend to be a cheerful person; I am not what I would call a
warm-hearted person; when people hurt me, I usually hold a grudge for a long time; I am an
affectionate and tender person; I am generally a sincere and truthful person; if I could help save
somebody’s life, I would do everything possible; I agree with the old saying, “It is better to give
than to receive.” Factor analysis showed that the loadings of three items (“my family tends to do
what we can to help those less fortune than ourselves”, “I am not what I would call a warmhearted person” and “when people hurt me, I usually hold a grudge for a long time”) were below

17

0.6. After dropping the items, upon satisfactory factor and reliability (see Table 1 in Appendix),
the mean value for altruism was computed for each participant.
Pretest
To select NPOs to develop stimuli for this study, a pretest was conducted (N = 57) to test
the familiarity and attitudes toward 11 NPOs and related issue involvement toward
corresponding social causes. NPO familiarity was tested using a single item measured with a
seven-point semantic differential scale anchored with “not at all familiar” and “extremely
familiar.” I also measured participants’ attitudes toward the NPO using MacKenzie and Lutz’s
(1989) attitudes toward the ad scale and Yi and Yoo’s (2011) attitudes towards brand scale,
where participants were asked to indicate their evaluations of the organization using four sevenpoint semantic differential scales: bad/good, unfavorable/favorable, unpleasant/pleasant,
harmful/beneficial (Cronbach’s α = .92). Finally, to select issues of moderate involvement and
without variability among a student population, the study measured issue involvement using
Zaichowsky’s

(1985)

unimportant/important,

seven-point
boring/interesting,

semantic

differential

irrelevant/relevant,

involvement

unexciting/exciting,

scale:
means

nothing/means a lot to me, unappealing/appealing, mundane/fascinating, worthless/valuable,
uninvolving/involving, not needed/needed (Cronbach’s α = .94).
Four NPOs (Charity: Water, Disabled American Veterans, Teach for America and
Human Rights Watch) were comparable in terms of issue involvement (F [3,168] = 1.94, ns),
and attitude toward the NPO (F [3,168] = 2.30, ns). However, the four selected NPOs varied in
familiarity (F [3,168] = 8.76, p<0.01). Charity: Water was finally selected as the only NPO that
was used in the experiment because of the ongoing drinking water contamination issue in Flint,
Michigan, which started in 2014. The experiment was conducted in the State of Michigan where

18

participants are involved and familiar with the local issue of drinking water safety. Therefore, the
manipulation on involvement with Charity: Water Greater Lansing Chapter would be more likely
to be successful. On the other hand, in the high involvement condition, the cover story would
introduce Charity: Water as a national organization headquartered in New York, which is a city
that geographically further.
Procedure
Participants were recruited through the College of Communication Arts & Sciences
online subject pool (SONA system). Participants took part in the study online via
www.Qualtrics.com. After signing the consent form, participants were asked to answer
demographic questions, Facebook usage related question, previous usage and familiarity of the
Facebook profile filter function, previous volunteer and donation behaviors, altruism, and
attitude toward and involvement with the social issue of drinking water safety. Participants were
then randomly assigned to one of the four conditions: (1) high descriptive norms and high
involvement; (2) high descriptive norms and low involvement; (3) low descriptive norms and
high involvement; and (4) low descriptive norms and low involvement. In their respective
conditions, participants were presented with a cover story of an NPO featuring either the Greater
Lansing Chapter of the NPO (high involvement) or general information about the NPO
headquartered in a different city (low involvement). Secondly, they were exposed to the NPO
themed Facebook profile filter promotion post with either a high number or a low number of
Facebook friends who have already adopted the filter (descriptive norms). The faces appearing in
the stimuli were matched to the participant’s race demonstrated in the demographic information
to alleviate any confounding cross-racial effects (see Appendix for stimuli and face selection).
Following stimuli exposure, participants answered questions about their intentions to adopt the

19

filter and indicated the amount of money they were willing to allocate to the NPO. Finally,
participants were asked to answer questions about their involvement with the issue of having
clean and safe drinking water, attitude toward and involvement with the NPO, and routed back to
the SONA system for automatic linking of credit.

20

RESULTS
Descriptive Results
All of the participants reported having a Facebook account. Participants indicated that
they have 594 Facebook friends on average (M = 593.99, SD = 462.44), and use Facebook for
less than one hour daily on average (M = 42.73 minutes, SD = 32.98). More than half (53.1%) of
participants reported having heard of the Facebook profile filter function and 28 percent of
participants have previously used a profile filter. In terms of volunteering and donation
experience, the majority of participants reported having volunteered (69.5%) or donated (68.7%)
for NPOs in the past. The average previous donation amount was 51.85 dollars (SD = 52.93).
Manipulation Check
To verify the manipulation on descriptive norms, participants were asked to evaluate the
number indicated in the Facebook post was small or large and to evaluate if the post was popular
or not on two seven-point semantic differential scales. Two Independent Samples T-tests were
conducted to compare the means of high and low descriptive norms conditions. Results showed
that there was a significant difference between high and low descriptive norms conditions on
both items: Number: t (273) = - 4.98, p < .001) and Popularity: t (273) = -3.53, p < .001.
Participants reported higher mean value in the high descriptive norms condition (MNumber = 4.04,
SD = 1.54 and MPopularity = 4.01, SD = 1.49) than in the low descriptive norms condition (MNumber
= 3.14, SD = 1.44 and MPopularity = 3.38, SD = 1.52). Based on this, the manipulation of
descriptive norm was successful.
For involvement, data for organizational involvement were submitted to an Independent
Samples T-test. Results showed that there was significant difference between the two
involvement conditions, t (273) = 2.73, p < .05. Interestingly, participants in the high

21

involvement condition reported lower involvement scores (M = 5.82, SD = 1.26) than those in
the low involvement condition (M = 6.21, SD = 1.11). Therefore, the manipulation of
involvement through geographic proximity was not successful in that it yielded counterhypothesized validation of the manipulation. The manipulation failure of involvement will be
further discussed in the discussion section. Nonetheless, the following report includes results
related to the two manipulated factors, irrespective of the failed manipulation.
Hypotheses Testing
Hypotheses 1a-c predicted the main effects of descriptive norms and involvement and the
interaction effect between them on Facebook users’ intentions to adopt the cause-related profile
filter. To test the hypotheses, data for filter adoption intentions were submitted to a 2 (descriptive
norms) x 2 (involvement) univariate analysis of covariance (ANCOVA), co-varying out the
effect of gender, religion, altruism, attitude toward the issue, involvement with the issue,
Facebook usage intensity, attitude toward the organization, organization familiarity, profile filter
familiarity, previous volunteer and donation experience, and Facebook friend number.
Organization familiarity (F (1, 257) = 13.81, p < .001, η2p = .05), and Facebook friend number (F
(1, 257) = 5.67, p < .05, η2p = .02) were significantly related to filter adoption intention.
The main effect of descriptive norms (F (1, 257) = .88, ns) and involvement (geographic
scope) (F (1,257) = .62, ns) were not significant. Therefore, H1a and H1b were not supported.
However, the interaction effect of descriptive norms and involvement (geographic scope) on
filter adoption intentions was significant, F (1, 257) = 8.36, p < .05, η2p = .03. Participants
exposed to a post by the national organization expressed greater filter adoption intentions if a
low number of Facebook friend had adopted the filter (M = 3.60, SD = 1.69) than a high number
(M = 2.88, SD = 1.50), and this difference was significant, t (139) = 2.66, p < .01. As for

22

participants exposed to the post by a local organization, they expressed greater filter adoption
intentions when a high (M = 3.29, SD = 1.53) than a low (M = 2.98, SD = 1.48) number of
Facebook friend adopted the filter, but this difference was not significant, t (139) = -1.16, ns (see
Figure 1). Therefore, H1c was not supported.
Figure 1: Interaction Effect of Descriptive Norms and Geographic Scope on Adoption Intentions

3.6
3.5
3.4
3.3
3.2
3.1
3
2.9
2.8
2.7

Low.number.of.FB.friends. High.number.of.FB.friends.
adopt.the.filter.
adopt.the.filter.
National.

Local.

Hypotheses 2a-c predicted the main effects of descriptive norms and involvement
(geographic scope) and the interaction effect between them on donation intentions measured
through four seven-point semantic differential scales and the amount of money participants
intended to donate to the NPO. To test the hypotheses, donation intentions were submitted to two
2 (descriptive norms) x 2 (involvement) univariate analyses of covariance (ANCOVA), covarying out the effects of gender, religion, altruism, attitude toward the issue, involvement with
the issue, Facebook usage intensity, attitude toward the organization, organization familiarity,
previous volunteer and donation experience, and Facebook friend number. Results showed that
23

altruism (F (1, 257) = 10.62, p < .05, η2p = .04), attitude toward the organization (F (1, 257) =
12.90, p < .05, η2p = .05) and organization familiarity (F (1, 257) = 5.46, p < .05, η2p = .02) were
significantly related to donation intentions.
Organizational geographic scope had a significant effect on donation intentions, F (1, 257)
= 5.18, p < .05, η2p = .02. Participants expressed greater donation intentions upon exposure to a
post by the national organization (M = 4.27, SD = 1.55) than a local one (M = 3.82, SD = 1.35).
Despite the fact that this effect is significant, yet it was not in the direction hypothesized, thus
H2a was not supported. Neither descriptive norms (F (1,257) = 1.52, ns) nor the interaction
between descriptive norms and geographic scope (F (1,257) = 1.60, ns) had significant effects on
donation intentions. Thus, H2a and H2c were not supported.
With regard to the amount of money participants indented to donate to the NPO,
ANCOVA yielded similar results. Attitude toward the organization was significantly related to
donation amount (F (1, 256) = 12.65, p < .05, η2p = .05). Organizational geographic scope was
significantly related to amount of money that intended to donate (F (1, 256) = 5.43, p < .05, η2p
= .02). Participants allocated a greater monetary amount to donate to the NPO when it had a
national (M = $38.39, SD = 32.02) which is 31.9% more than the amount they were willing to
donate to the local chapter (M =$29.10, SD = 24.02). Again, the main effect of descriptive norms
(F (1,256) = .58, ns) and interaction effect between descriptive norms and involvement
(geographic scope) (F (1,256) = 1.25, ns) on donation amount were not significant.

24

DISCUSSION
The current study explored the effects of online descriptive norms and organizational
involvement on Facebook users’ behavioral intentions toward NPOs, specifically, on intentions
to adopt a NPO-themed Facebook profile filter and donate. It aimed to expand the theory of
normative social behavior by examining how the interaction between involvement and
descriptive norms affect individuals’ behavioral intentions within online context. Meanwhile, the
current study helped NPO marketers to better understand cause-related behavioral change on
social media. The following discussion provides a summary of the study’s main findings and
offers theoretical and practical implications for them.
Summary of Findings
The result of manipulation on involvement was contrary to expectation. Participants
assigned to Charity: Water Greater Lansing Chapter (local scope) condition indicated lower
involvement compared to those assigned to the Charity: Water (national scope) condition. There
are a number of plausible explanations for this failed manipulation. First, it is possible that
participants perceived national Charity: Water as more influential than local organization so that
they expressed higher involvement toward it, since previous studies found that people tend to
identify themselves with prestigious social entities such as organizations or brands to meet the
need of self-enhancement and positive self-views (Dutton et al., 1994; Bhattacharya & Sen, 2003;
Kuenzel & Halliday, 2008). Thus, it is plausible that participants who were exposed to
information and breadth of experience nationally and internationally from the NPO with a
national scope showed higher organizational involvement, which rendered the manipulation of
involvement using geographic scope not successful. Another plausible explanation is that the
national NPO charity: water is an actual organization, whereas the localized chapter was a

25

fictitious one. It is plausible that participants’ familiarity, or inability to retrieve information
about the local chapter, resulted in greater believability for the national chapter and lack of
validity for the local chapter and thus exacerbated varied and counter-hypothesized involvement
perceptions. Finally, it is also plausible that associating with a national chapter of an NPO
provides greater exposure and better impression management via social media, thus the issue is
viewed more favorably.
For the first set of hypotheses, the interaction between online descriptive norms and
organizational geographic scope affected participants’ intention to adopt the NPO-themed
Facebook profile filter. Interestingly, the effects of descriptive norms were different as a function
of the NPOs’ geographic scope. For participants who were exposed to the post by the local
chapter of the NPO, descriptive norms worked in the hypothesized direction, where the greater
number of Facebook friends adopted the filter resulted in higher filter adoption intentions. This is
consistent with the elaboration likelihood model (Petty & Cacioppo, 1981), which indicated that
at times individuals rely on heuristic cues to process persuasive messages and make decisions.
Despite the fact that we hypothesized the local NPO would activate greater involvement due to
geographic proximity, the findings showed the opposite, where those participants reported lower
organizational involvement. Hence, from an ELM perspective, this low-involving, yet
geographically proximal situation yielded heuristic or peripheral route processing that relied on
descriptive norms to inform online behavioral intentions. Descriptive norms imply the
prevalence and acceptance of the behavior so that they could serve as heuristic cue to influence
one’s behavior (Petty & Cacioppo, 1981). It is important, however, to mention that despite the
fact that the means indicated in the results were in the hypothesized direction, yet the differences
were not significant. However, inconsistent with Gockeritz et al. (2010) and Lapinski et al.

26

(2015), results of the current study showed that descriptive norms were negatively associated
with participants’ intentions to adopt NPO-themed filter when the organization had a national
and wider geographic scope. Specifically, when descriptive norms were low, participants
indicated higher adoption intentions compared to participants exposed to the post by the national
NPO with more Facebook friends have adopted the filter. This finding was consistent with
previous studies (Alhabash et al., 2013; Alhabash et al., 2014) in which participants indicated
higher civic behavioral intentions toward cyberbullying YouTube videos with low share/likes.
One possible explanation is that social media users tend to engage with novel content that is not
yet popular (LaRose & Eastin, 2004; Lang, 2006; Alhabash et al., 2013; Alhabash et al., 2015).
This is especially important as it applies only to NPOs of national geographic scope and not a
localized scope because with the local scope, descriptive norms influenced users’ behavioral
intentions. Another possible reason would be that participants exposed to the post by the national
chapter of the NPO and who also felt more involved with the organization, as indicated by the
manipulation check scores, saw the need to support a cause and an organization they care about
at a time when its online post was not having considerable attention (Alhabash et al., 2015). This
finding showed evidence that online and offline descriptive norms might act differently on
guiding one’s behavioral intentions. Taken together, we see that descriptive norms were more
influential when the NPO had a national and not a local geographic scope. A plausible
explanation is that for the national NPO, there could be a higher expectation of greater virality
and online engagement due to its supposed national and international reach, yet these same
parameters were not seen variably when the scope of the organization was local.
Second, for donation intentions, the only significant effect found was for the main effect
of geographic scope. Participants were more likely to donate to an NPO that has national than

27

local scope. Previous studies have found that personal relevance with a cause/issue positively
associated with donation intentions (Reubsaet et al., 2001; Bea & Kang, 2008). Similarly, this
study’s findings showed that geographical proximal NPOs (low involved) did not activate the
desire to donate to the NPO to the extent that participants indicated donation intentions to the
national NPO. There are a number of plausible explanations for this particular trend of findings.
First, donating to the national NPO might be associated with greater impact for the donation
amount and a greater reach for their civic participation. In other words, participants could have
been thinking that by donating to the national NPO, they would be having a bigger impact that
transcends their immediate environment. Another plausible explanation stems from a possible
limitation of the study in that we chose an issue that has been heavily covered in local and
national press associated with the Flint water crisis. This association, and the potential
exhaustion from media coverage of the issue might have resulted in a boomerang effect and
withdrawal intention from contributing locally. Additionally, participants might have thought
that given the great media attention, their contribution would not be as impactful to the local
water-related NPO.
Descriptive norms played a role in influencing filter adoption intentions on Facebook
rather than donation intentions might due to the different behavior characteristics. Changing
profile filter on Facebook can be visible to all of one’s Facebook friends, which is a public
endorsement that is performed publicly. On the other hand, donation behaviors often happen
privately. Rimal and Lapinski (2015) argued that in the social environment or when a behavior is
social in nature, descriptive norms have stronger impact on behaviors. It is possible that
individuals are motivated to build a positive personal image on social media by publicly
supporting a social cause/NPO, while factors such as financial status, attitudes toward charitable

28

giving behavior could be weighted more heavily when making personal donation decisions.
Another explanation might be that in the current study, participants were only exposed to the
message showing descriptive norms of filter adoption. In other words, they did not have an
accurate sense of the prevalence of donation behavior. Therefore, descriptive norms of filter
adoption might not be strong enough to influence participants’ donation intentions. Smith et al.
(2007) argued that one of the reasons for descriptive norms predict volunteering (Warburton &
Terry, 2000) instead of charitable giving could be that individuals usually lack knowledge as to
what extent other people have engaged in this behavior, since donation is more of a private prosocial behavior.
Theoretical and Managerial Implications
The current study offers both theoretical and managerial implications. This paper
investigated how online descriptive norms and involvement influence people’s charitable
behavior intentions on social media. Based on the theory of normative social behavior (TNSB),
the current study applied Zaichowsky’s (1985) involvement on NPO and expanded the literature
regarding moderators of the relationship between descriptive norms-behaviors relationship.
Second, manipulating descriptive norms within the social media environment, the current study
showed a “backfire effect” of online descriptive norms, expressed through the number of friends
that have partaken in the advocated behavior, on individuals’ behavior intentions. Results
suggested that online descriptive norms act differently on changing behaviors compare to
previous studies in which descriptive norms were manipulated within offline context. More
research on comparing effects of descriptive norms in different media environment is needed.
Third, this study found that descriptive norms had influence on advocating behavior intentions
instead of charitable giving and discussed why this influence might due to different behavior

29

characteristics. The finding adds more evidence to theorizing varying normative influences on
behaviors with various characteristics.
From the managerial standpoint, the current study showed that social media provide a
great opportunity for NPOs who need to raise public awareness. Social media users tend to show
more support when fewer people have engaged in the cause when the organization has a national
scope. Moreover, the geographic scope of the organization plays a role in influencing both
advocacy and charitable giving behavioral intentions. Emphasizing the virality of an online
behavior about a local organization might not be as important as instances when the organization
has a national scope. As for national NPOs, social marketers should capitalize on the aspect of
novelty and the feeling of innovativeness related to online behaviors of consumers. At a national
level, being first or among a few showed greater online behavioral intentions. Marketers could
also address more personal relevance information in persuasive messages aiming to increase
audiences’ perceived involvement with the social cause.
Limitations and Future Studies
A few of limitations exist in this study. First, the current study used a student sample.
Thus, the generalizability of the results is limited. Meanwhile, a student sample generally has
limited financial ability, which could potentially decrease their intentions to donate to NPOs. A
future study could replicate the experiment with groups representing a wider range of age,
occupation, race, etc. Second, although the manipulation check showed that there was a
significant difference between two involvement groups, the manipulation on proximity for
involvement was not successful. Besides manipulating proximity, a future study could explore
more on involvement manipulation, such as using messages with customized information that
address salience personal relevance. Moreover, a future study could explore the reason of

30

participants indicated higher involvement with NPO headquarter instead of the local chapter.
Third, participants in the current study were only exposed to Facebook post featuring filter
adopting descriptive norms. The recent launched fundraising tool — Facebook Fundraiser, which
is able to show the number of people who have donated to a certain cause/NPO, makes the
descriptive norms on donation accessible to Facebook users. This provides a great opportunity
for future studies to explore how online descriptive norms could influence individuals’ donation
intentions.
In the current study, the backfire effect of high descriptive norms for participants in the
high involvement condition was consistent with the findings in Alhabash et al. (2013) and
Alhabash et al. (2015). It suggested that online and offline descriptive norms might influence
one’s behavioral intentions differently. Future studies could further examine how online
descriptive norms affect individuals’ behavioral intentions and the underlying reasons for the
different effects of online and offline descriptive norms. Finally, the current study found that
descriptive norms had an influence on different supportive behaviors toward NPOs. One of the
possible reasons could be different behavioral characteristics (public vs. private in the current
study) (Rimal & Lapinski, 2015; Lapinski et al., 2015). It is suggested that future studies could
focus on comparing the effect of descriptive norms on behaviors with different characteristics,
such as public vs. private, social vs. non-social, the ease of performing the behavior (antidrinking vs. supporting social cause by sharing/commenting on social media).

31

APPENDICES

32

APPENDIX A

Factor Loading of Variables

33

Table 1: Factor Analysis Results for Evaluations of Positive Status Updates (with Varimax
Rotation)
Item

Factor Loading

Eigenvalue

% of Var.
Explained

Adoption Intention 1
Adoption Intention 2
Adoption Intention 3
Adoption Intention 4
Donation Intention 1
Donation Intention 2
Donation Intention 3
Donation Intention 4
Issue Attitude 1
Issue Attitude 2
Issue Attitude 3
Issue Attitude 4
Organization
Attitude 1
Organization
Attitude 2
Organization
Attitude 3
Organization
Attitude 4
Issue INV 1
Issue INV 3
Issue INV 7
Issue INV 9
Facebook Usage
Intensity 1
Facebook Usage
Intensity 2
Facebook Usage
Intensity 3
Facebook Usage
Intensity 4
Facebook Usage
Intensity 5
Facebook Usage
Intensity 6
Altruism 1
Altruism 2
Altruism 3
Altruism 5
Altruism 8
Altruism 9
Altruism 10
Altruism 11

.917
.923
.864
.908

3.69

89.25

Cronbach’s
α
.96

3.49

88.24

.96

3.89

91.96

.97

3.44

87.83

.95

3.96

39.54

.92

4.20

69.10

.91

5.17

44.50

.89

.874
.894
.837
.870
.958
.934
.911
.956
.868
.869
.882
.855
.840
.798
.849
.851
.864
.772
.869
.815
.839
.767
.773
.751
.700
.669
.662
.768
.727
.666

34

APPENDIX B

Stimuli

35

Figure 2: Stimuli Example for Four Conditions (Descriptive Norms X Involvement)

Low Descriptive Norms
Low Involvement

High Descriptive Norms
Low Involvement

Low Descriptive Norms
High Involvement

High Descriptive Norms
High Involvement
36

Figure 3: Face Selection to Match Participants’ Gender and Race

37

REFERENCES

38

REFERENCES

Alhabash, S., Baek, J. H., Cunningham, C., & Hagerstrom, A. (2015). To comment or not to
comment?: How virality, arousal level, and commenting behavior on YouTube videos
affect civic behavioral intentions. Computers in Human Behavior, 51, 520-531.
Alhabash, S., McAlister, A. R., Hagerstrom, A., Quilliam, E. T., Rifon, N. J., & Richards, J. I.
(2013). Between likes and shares: Effects of emotional appeal and virality on the
persuasiveness of anticyberbullying messages on Facebook. Cyberpsychology, Behavior,
and Social Networking, 16(3), 175-182.
Apsler, R., & Sears, D. O. (1968). Warning, personal involvement, and attitude change. Journal
of Personality and Social Psychology, 9(2p1), 162.
Auger, G. A. (2013). Fostering democracy through social media: Evaluating diametrically
opposed nonprofit advocacy organizations’ use of Facebook, Twitter, and YouTube.
Public Relations Review, 39(4), 369-376.
Baumgartner, S. E., Valkenburg, P. M., & Peter, J. (2011). The influence of descriptive and
injunctive peer norms on adolescents' risky sexual online behavior. Cyberpsychology,
Behavior, and Social Networking, 14(12), 753-758.
Bhattacharya, C. B., & Sen, S. (2003). Consumer-company identification: A framework for
understanding consumers’ relationships with companies. Journal of marketing, 67(2), 7688.
Bigné-Alcañiz, E., Currás-Pérez, R., Ruiz-Mafé, C., & Sanz-Blas, S. (2010). Consumer
behavioral intentions in cause-related marketing. The role of identification and social
cause involvement. International Review on Public and Nonprofit Marketing, 7(2), 127143.
Borsari, B., & Carey, K. B. (2003). Descriptive and injunctive norms in college drinking: A
meta-analytic integration. Journal of studies on alcohol, 64(3), 331.
Briones, R. L., Kuch, B., Liu, B. F., & Jin, Y. (2011). Keeping up with the digital age: How the
American Red Cross uses social media to build relationships. Public relations review,
37(1), 37-43.
Cho, H., & Boster, F. J. (2005). Development and validation of value-, outcome-, and
impression-relevant involvement scales. Communication Research, 32(2), 235-264.
Cialdini, R.B., Kallgren, C.A., & Reno, R.R. (1991). A focus theory of normative conduct.
Advances in Experimental Social Psychology, 24, 201-234.

39

Croson, R., Handy, F., & Shang, J. (2009). Keeping up with the Joneses: The relationship of
perceived descriptive social norms, social information, and charitable giving. Nonprofit
Management and Leadership, 19(4), 467-489.
Demaria, M. (2015). How to Change Your Facebook Photo for Pink Out Day and Stand with
Planned Parenthood When They Need You Most. Retrieved from
http://www.bustle.com/articles/113708-how-to-change-your-facebook-photo-for-pinkout-day-and-stand-with-planned-parenthood-when
Desta, Y. (2014). 11 Times Everyone on Facebook Changed Their Profile Pictures. Retrieved
from http://mashable.com/2014/01/28/facebook-profile-pictures/#Igf3NJHGesqD.
Dewey, C. (2015). More Than 26 Million People Have Changed Their Facebook Picture to a
Rainbow
Flag.
Here’s
Why
that
Matters.
Retrieved
from
https://www.washingtonpost.com/news/the-intersect/wp/2015/06/29/more-than-26million-people-have-changed-their-facebook-picture-to-a-rainbow-flag-heres-why-thatmatters/.
De Pelsmacker, P., Geuens, M., & Anckaert, P. (2002). Media context and advertising
effectiveness: The role of context appreciation and context/ad similarity. Journal of
Advertising, 31(2), 49-61.
Dutton, J. E., Dukerich, J. M., & Harquail, C. V. (1994). Organizational images and member
identification. Administrative science quarterly, 239-263.
E-Marketer (2010). Leveraging Best Practices for Social Media. Retrieved
http://www.emarketer.com.proxy1.cl.msu.edu/Article.aspx?R=1008057.

from

Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of
Computer!Mediated Communication, 13(1), 210-230.
Ellison, N., Steinfield, C., & Lampe, C. (2006). Spatially bounded online social networks and
social capital. International Communication Association, 36(1-37).
Facebook (2015). Introducing New Tools for Nonprofits. Retrieved
http://newsroom.fb.com/news/2015/11/introducing-new-tools-for-nonprofits/.

from

Facebook Business (n.d.). Easy and Effective
https://www.facebook.com/business/products/ads.

from

Facebook

Ads.

Retrived

Facebook Newsroom (2006). Our History. Retrieved from http://newsroom.fb.com/companyinfo/.
Facebook Newsroom (2015). Introducing New Tools for Nonprofits. Retrieved from
http://newsroom.fb.com/news/2015/11/introducing-new-tools-for-nonprofits/.
Facebook Newsroom (2016). Our History. Retrieved from http://newsroom.fb.com/companyinfo/#.
40

Feeney. N (2015). Facebook’s New Photo Filter Lets You Show Solidarity with Paris. Retrieved
from http://time.com/4113171/paris-attacks-facebook-filter-french-flag-profile-picture/.
Forrester Research (2014). Digital marketing spending in the United States from 2014 to 2019,
by
segment
(in
billion
U.S.
dollars)
Retrieved
from
http://www.statista.com.proxy1.cl.msu.edu/statistics/275230/us-interactive-marketingspending-growth-from-2011-to-2016-by-segment/.
Göckeritz, S., Schultz, P., Rendón, T., Cialdini, R. B., Goldstein, N. J., & Griskevicius, V.
(2010). Descriptive normative beliefs and conservation behavior: The moderating roles
ofpersonal involvement and injunctive normative beliefs. European Journal of Social
Psychology, 40, 514–523.
Hajjat, M. M. (2003). Effect of cause-related marketing on attitudes and purchase intentions: the
moderating role of cause involvement and donation size. Journal of Nonprofit & Public
Sector Marketing, 11(1), 93-109
Johnson, B. T., & Eagly, A. H. (1989). Effects of involvement on persuasion: A meta-analysis.
Psychological Bulletin, 106(2), 290.
Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and
opportunities of Social Media. Business horizons, 53(1), 59-68.
Kiesler, C. A., Collins, B. E., & Miller, N. (1969). Attitude change.
Kim, A. J., & Ko, E. (2012). Do social media marketing activities enhance customer equity? An
empirical study of luxury fashion brand. Journal of Business Research, 65(10), 14801486.
Kim, A. J., & Ko, E. (2010). Impacts of luxury fashion brand’s social media marketing on
customer relationship and purchase intention. Journal of Global Fashion Marketing, 1(3),
164-171.
Kincaid, D. L. (2004). From innovation to social norm: Bounded normative influence. Journal of
health communication, 9(S1), 37-57.
Kong, Y., & Zhang, A. (2013). Consumer response to green advertising: the influence of product
involvement. Asian journal of communication, 23(4), 428-447.
Kuenzel, S., & Vaux Halliday, S. (2008). Investigating antecedents and consequences of brand
identification. Journal of Product & Brand Management, 17(5), 293-304.
Lang, A. (2006). Using the limited capacity model of motivated mediated message processing to
design effective cancer communication messages. Journal of communication, 56(s1),
S57-S80.

41

LaRose, R., & Eastin, M. S. (2004). A social cognitive theory of Internet uses and gratifications:
Toward a new model of media attendance. Journal of Broadcasting & Electronic Media,
48(3), 358-377.
Larimer, M. E., Turner, A. P., Mallett, K. A., & Geisner, I. M. (2004). Predicting drinking
behavior and alcohol-related problems among fraternity and sorority members:
examining the role of descriptive and injunctive norms. Psychology of Addictive
Behaviors, 18(3), 203.
Lapinski, M. K., & Rimal, R. N. (2005). An explication of social norms. Communication Theory,
15(2), 127-147.
Lapinski, M. K., Rimal, R. N., DeVries, R., & Lee, E. L. (2007). The role of group orientation
and descriptive norms on water conservation attitudes and behaviors. Health
communication, 22(2), 133-142.
Lapinski, M. K., Zhuang, J., Koh, H., & Shi, J. (2015). Descriptive Norms and Involvement in
Health and Environmental Behaviors. Communication Research, 0093650215605153.
Lin, L. Y., & Chen, C. S. (2006). The influence of the country-of-origin image, product
knowledge and product involvement on consumer purchase decisions: an empirical study
of insurance and catering services in Taiwan. Journal of consumer Marketing, 23(5),
248-265.
Lockshin, L. S., Spawton, A. L., & Macintosh, G. (1997). Using product, brand and purchasing
involvement for retail segmentation. Journal of Retailing and Consumer services, 4(3),
171-183.
Lommer, J. (2013). Everthing You Need to Know about Facebook Sponsored Stories. Retrieved
from http://www.jonloomer.com/2013/06/03/facebook-sponsored-stories/.
Lovejoy, K., & Saxton, G. D. (2012). Information, community, and action: How nonprofit
organizations use social media. Journal of Computer!Mediated Communication, 17(3),
337-353.
MacKenzie, S. B., & Lutz, R. J. (1989). An empirical examination of the structural antecedents
of attitude toward the ad in an advertising pretesting context. The Journal of Marketing,
48-65.
Mangold, W. G., & Faulds, D. J. (2009). Social media: The new hybrid element of the promotion
mix. Business horizons, 52(4), 357-365.
Mano, R. S. (2014). Social media, social causes, giving behavior and money contributions.
Computers in Human Behavior, 31, 287-293.
Marshall, H. M., Reinhart, A. M., Feeley, T. H., Tutzauer, F., & Anker, A. (2008). Comparing
college students' value-, outcome-, and impression-relevant involvement in health-related
issues. Health communication, 23(2), 171-183.
42

Martin, R., & Randal, J. (2008). How is donation behaviour affected by the donations of others?.
Journal of Economic Behavior & Organization, 67(1), 228-238.
Martin, R., & Randal, J. (2009). How Sunday, price, and social norms influence donation
behaviour. The Journal of Socio-Economics, 38(5), 722-727.
McWilliam, G. (1997). Low involvement brands: is the brand manager to blame?. Marketing
Intelligence & Planning, 15(2), 60-70.
Michaelidou, N., Siamagka, N. T., & Christodoulides, G. (2011). Usage, barriers and
measurement of social media marketing: An exploratory investigation of small and
medium B2B brands. Industrial Marketing Management, 40(7), 1153-1159.
Mollen, S., Rimal, R. N., Ruiter, R. A., Jang, S. A., & Kok, G. (2013). Intervening or
interfering? The influence of injunctive and descriptive norms on intervention behaviours
in alcohol consumption contexts. Psychology & health, 28(5), 561-578.
Morgan, S., & Miller, J. (2002). Communicating about gifts of life: The effect of knowledge,
attitudes, and altruism on behavior and behavioral intentions regarding organ donation.
Journal of Applied Communication Research, 30(2), 163-178.
Nonprofit Benchmarks Study (2016). Retrieved from http://mrbenchmarks.com/.
Park, D. H., Lee, J., & Han, I. (2007). The effect of on-line consumer reviews on consumer
purchasing intention: The moderating role of involvement. International Journal of
Electronic Commerce, 11(4), 125-148.
Park, H. S., & Smith, S. W. (2007). Distinctiveness and influence of subjective norms, personal
descriptive and injunctive norms, and societal descriptive and injunctive norms on
behavioral intent: A case of two behaviors critical to organ donation. Human
Communication Research, 33(2), 194-218.
Petty, R. E., & Cacioppo, J. T. (1979). Issue involvement can increase or decrease persuasion by
enhancing message-relevant cognitive responses. Journal of personality and social
psychology, 37(10), 1915.
Petty, R. E., & Cacioppo, J. T. (1981). Issue involvement as a moderator of the effects on
attitude of advertising content and context. NA-Advances in Consumer Research Volume
08.
Petty, R. E., Cacioppo, J. T., & Goldman, R. (1981). Personal involvement as a determinant of
argument-based persuasion. Journal of personality and social psychology, 41(5), 847.
Petty, R. E., Cacioppo, J. T., & Schumann, D. (1983). Central and peripheral routes to
advertising effectiveness: The moderating role of involvement. Journal of consumer
research, 10(2), 135-146.

43

Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. In
Communication and persuasion (pp. 1-24). Springer New York.
Pressrove, G., & Pardun, C. J. (2016). Relationship between Personal Technology Use and the
Donor/Volunteer: A Parasocial Approach. Journal of Promotion Management, 22(1),
137-150.
Reno, R. R., Cialdini, R. B., & Kallgren, C. A. (1993). The transsituational influence of social
norms. Journal of personality and social psychology, 64(1), 104.
Reuveni, Y., & Werner, P. (2015). Factors Associated with Teenagers’ Willingness to Volunteer
with Elderly Persons: Application of the Theory of Planned Behavior (TPB). Educational
Gerontology, 41(9), 623-634.
Ridout, B., & Campbell, A. (2014). Using Facebook to deliver a social norm intervention to
reduce problem drinking at university. Drug and alcohol review, 33(6), 667-673.
Rimal, R. N. (2008). Modeling the Relationship Between Descriptive Norms and Behaviors: A
Test and Extension of the Theory of Normative Social Behavior (TNSB) . Health
Communication, 23(2), 103-116.
Rimal, R. N., Lapinski, M. K., Cook, R. J., & Real, K. (2005). Moving toward a theory of
normative influences: How perceived benefits and similarity moderate the impact of
descriptive norms on behaviors. Journal of health communication, 10(5), 433-450.
Rimal, R. N., & Lapinski, M. K. (2015). A ReDExplication of Social Norms, Ten Years Later.
Communication Theory, 25(4), 393-409.
Rimal, R. N., Lapinski, M. K., Turner, M. M., & Smith, K. (2011). The attribute-centered
approach for understanding health behaviors: Initial ideas and future research directions.
Stud Commun Sci, 11(1), 15-34.
Rimal, R. N., & Real, K. (2005). How behaviors are influenced by perceived norms a test of the
theory of normative social behavior. Communication Research, 32(3), 389-414.
Rivis, A., & Sheeran, P. (2003). Descriptive norms as an additional predictor in the theory of
planned behaviour: A meta-analysis. Current Psychology, 22(3), 218-233.
Sanders, S. (2015). French Flags On Facebook. National Public Radio. Retrieved from
http://www.npr.org/2015/11/21/456820583/-memeoftheweek-french-flags-on-facebook.
Schultz, P. W., Nolan, J. M., Cialdini, R. B., Goldstein, N. J., & Griskevicius, V. (2007). The
constructive, destructive, and reconstructive power of social norms. Psychological
science, 18(5), 429-434.
Sherif, M., & Hovland, C. I. (1961). Social judgment: Assimilation and contrast effects in
communication and attitude change.

44

Sherif, C. W., Sherif, M., & Nebergall, R. E. (1965). Attitude and attitude change: The social
judgment-involvement approach (pp. 127-167). Philadelphia: Saunders.
Te’eni-Harari, T., Lehman-Wilzig, S. N., & Lampert, S. I. (2009). The importance of product
involvement for predicting advertising effectiveness among young people. International
Journal of Advertising, 28(2), 203-229.
Te’eni-Harari, T. (2014). Clarifying the relationship between involvement variables and
advertising effectiveness among young people. Journal of Consumer Policy, 37(2), 183203.
Thomsen, C. J., Borgida, E., & Lavine, H. (1995). The causes and consequences of personal
involvement. Attitude strength: Antecedents and consequences, 4, 191-214.
University of Massachusetts Dartmouth (2014). Social media sites and tools used by U.S. charity
and
non-profit
organizations
as
of
spring
2014.
Retrieved
from
http://www.statista.com.proxy1.cl.msu.edu/statistics/310006/us-charity-and-non-profitsocial-media-usage/.
Witman, P. (2013). Social media for social value. Computer, 46(7), 82-85.
Yee, N., Bailenson, J. N., Urbanek, M., Chang, F., & Merget, D. (2007). The unbearable likeness
of being digital: The persistence of nonverbal social norms in online virtual
environments. CyberPsychology & Behavior, 10(1), 115-121.
Yi, Y., & Yoo, J. (2011). The longDterm effects of sales promotions on brand attitude across
monetary and nonDmonetary promotions. Psychology & Marketing, 28(9), 879-896.
Zaichkowsky, J. L. (1985). Measuring the involvement construct. Journal of consumer research,
12(3), 341-352.
Zaichkowsky, J. L. (1986). Conceptualizing involvement. Journal of advertising, 15(2), 4-34.
Zaichkowsky, J. L. (1987). The emotional affect of product involvement. NA-Advances in
Consumer Research Volume 14.
Zimbardo, P. G. (1960). Involvement and communication discrepancy as determinants of
opinion conformity. The Journal of Abnormal and Social Psychology, 60(1), 86.

45