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Full Disclosure: How Smartphones Enhance Consumer Self-Disclosure

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Results from three large-scale field studies and two controlled experiments show that consumers tend to be more self-disclosing when generating content on their smartphone versus personal computer. This tendency is found in a wide range of domains including social media posts, online restaurant reviews, open-ended survey responses, and compliance with requests for personal information in web advertisements. The authors show that this increased willingness to self-disclose on one’s smartphone arises from the psychological effects of two distinguishing properties of the device: (1) feelings of comfort that many associate with their smartphone and (2) a tendency to narrowly focus attention on the disclosure task at hand due to the relative difficulty of generating content on the smaller device. The enhancing effect of smartphones on self-disclosure yields several important marketing implications, including the creation of content that is perceived as more persuasive by outside readers. The authors explore implications for how these findings can be strategically leveraged by managers, including how they may generalize to other emerging technologies.
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Article
Full Disclosure: How Smartphones Enhance
Consumer Self-Disclosure
Shiri Melumad and Robert Meyer
Abstract
Results from three large-scale field studies and two controlled experiments show that consumers tend to be more self-disclosing
when generating content on their smartphone versus personal computer. This tendency is found in a wide range of domains
including social media posts, online restaurant reviews, open-ended survey responses, and compliance with requests for personal
information in web advertisements. The authors show that this increased willingness to self-disclose on one’s smartphone arises
from the psychological effects of two distinguishing properties of the device: (1) feelings of comfort that many associate with their
smartphone and (2) a tendency to narrowly focus attention on the disclosure task at hand due to the relative difficulty of gen-
erating content on the smaller device. The enhancing effect of smartphones on self-disclosure yields several important marketing
implications, including the creation of content that is perceived as more persuasive by outside readers. The authors explore
implications for how these findings can be strategically leveraged by managers, including how they may generalize to other
emerging technologies.
Keywords
natural language processing, self-disclosure, technology, user-generated content
Online supplement: https://doi.org/10.1177/0022242920912732
Among the many recent changes in consumer markets, two
trends have been particularly transformative. The first is the
emergence of online communication as a central medium
through which firms and customers interact. This medium has
yielded a wealth of textual data including social media posts,
online reviews, and chats that can provide firms with real-time
insights into customer opinions, needs, and preferences (e.g.,
Wedel and Kannan 2016). The second trend is the emergence
of the smartphone as the dominant platform on which these
communications take place. Whereas online activities were
once limited to at-home or in-office sessions on personal com-
puters (PCs), with smartphones these activities can now occur
at virtually any time and place. As a consequence of these two
trends, when firms analyze user-generated content today, it is
increasingly likely that it was created by consumers on their
smartphone rather than their PC.
In this article, we explore a question that lies at the inter-
section of these two trends: As consumers continue to generate
content on their smartphone, might this shift be altering what
consumers share about themselves—and thus what firms can
learn about their customers? Across three field studies (and
three replication studies) examining thousands of customer-
generated posts from various contexts—as well as two prere-
gistered experiments—we provide evidence that content
created by consumers on their smartphones tends to be more
self-disclosing than that created on PCs. We show, for exam-
ple, that social media posts and customer reviews written on
smartphones tend to be composed in a more personal, intimate
linguistic style, and that consumers are more willing to
admit certain types of personal information when using their
smartphone, such as experiences with products that are pri-
vate or embarrassing. This effect is robust across different
measures (human judgments, automated measures) and dif-
ferent forms of disclosure (e.g., open-ended survey
responses, online reviews, compliance rates with calls to
action [CTAs] in web ads). Importantly, this effect has sig-
nificant marketing implications; for example, the more per-
sonal and intimate nature of smartphone-generated reviews
results in content that is more persuasive to outside readers,
in turn heightening purchase intentions.
We also investigate the mechanisms that underlie the
observed differences in disclosure, demonstrating that the
Shiri Melumad is Assistant Professor of Marketing, the Wharton School,
University of Pennsylvania, USA (email: melumad@wharton.upenn.edu).
Robert Meyer is Frederick Ecker/MetLife Professor of Marketing, the
Wharton School, University of Pennsylvania, USA (email: meyerr@wharton
.upenn.edu).
Journal of Marketing
2020, Vol. 84(3) 28-45
ªAmerican Marketing Association 2020
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DOI: 10.1177/0022242920912732
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greater tendency to self-disclose on smartphones arises from
the combination of two factors unique to the device. The first is
that the highly personal nature of smartphones—resulting from
both their constant accessibility and frequent use for personal or
intimate activities (e.g., textingwith family and friends)—fosters
distinct feelings of psychological comfort on the device that
facilitate users’ willingness to self-disclose. Second, the diffi-
culty of creating content on the smaller form of the device
(screen and keyboard) leads consumers to narrow their atten-
tional focus to the task at hand, which also facilitates disclosure.
Theoretical Background
What Factors Enhance Willingness to Self-Disclose?
The study of self-disclosure is among the oldest in the social
sciences, spanning the fields of psychology (e.g., Cozby 1973;
Derlega et al. 1993), human–computer interaction (e.g., Kim
and Dindia 2011; Walther 1996), and survey research (e.g.,
Heberlein and Baumgartner 1978; Weisband and Kiesler
1996). This topic is of growing interest to marketing research-
ers, who have explored how consumers’ willingness to provide
personal information over a computer is affected by factors
such as the nature of the web interface (e.g., John, Acquisti,
and Loewenstein 2011), online privacy policies (Andrade,
Kaltcheva, and Wietz 2002), and the degree of reciprocity in
an interaction with an agent (Moon 2000). Here, we follow
Altman and Taylor (1973) to define “self-disclosure” as the
voluntary communication of feelings, thoughts, or other infor-
mation deemed to be private and that might make the discloser
feel vulnerable (see also Cozby 1973; Omarzu 2000). For
example, disclosure might involve expressing one’s candid
feelings about a service experience or admitting to incriminat-
ing consumption behaviors.
A primary focus of research in this area has been to identify
situational factors that drive people’s willingness to self-
disclose. For example, while people tend to be inherently pro-
tective of their private feelings and thoughts, they are more
willing to share personal information when they feel a greater
sense of privacy in their environment (e.g., Derlega and Chai-
kin 1977; Dienlin 2014; John, Acquisti, and Loewenstein 2011)
or if they perceive their particular audience as more anonymous
(e.g., Kiesler, Siegel, and McGuire 1984; Spears and Lea 1994;
Wallace 1999). Likewise, the degree of psychological comfort
evoked by a context can drive self-disclosure. Therapists, for
example, find that patients tend to be more self-disclosing in
physical environments that foster feelings of security and
familiarity, or when they feel more at ease with a counterpart
in conversation (e.g., Chaikin, Derlega, and Miller 1976; Gif-
ford 1988).
The Role of Modality in Self-Disclosure
Relevant to our work is research examining how different
communication modalities affect people’s willingness to self-
disclose, particularly in computer-mediated versus face-to-face
environments (e.g., Bowling 2005; Joinson 2001; Kim and
Dindia 2011; Walther 1996; Weisband and Kiesler 1996). A
consistent finding in this literature is that people are often
willing to disclose more about themselves when communicat-
ing over a computer (e.g., email, instant messaging) than in
person (e.g., Bowling 2005; Ruppel et al. 2017; Walther
1996). While these effects have been discussed both in terms
of depth of disclosure—the sensitivity of what people reveal—
and breadth, or the amount revealed, a recent meta-analysis
found that communicating through a computer (vs. face-to-
face) has a larger effect on depth of disclosure (Ruppel et al.
2017). That is, while people may not necessarily disclose more
when communicating through a computer compared to face-to-
face, what they do disclose tends to be more intimate.
Although a few explanations have been proposed for why
people might disclose more through computers than in person
(e.g., Kim and Dindia 2011), accounts generally point to the
comparative anonymity of interacting through computer
screens (e.g., Walther 1996). Specifically, when people inter-
act in person they receive a wealth of social cues that make
them more concerned about how they come across to others
(Short, Williams, and Christie 1976). This concern about oth-
ers’ reactions then works to reduce willingness to disclose
personal or intimate information in face-to-face interaction
(e.g., Carver and Scheier 1981). When one expresses oneself
through a computer, however, these social cues are less salient
because of the physical distance or isolation of one’s audi-
ence, which can increase willingness to disclose (e.g., Kim
and Dindia 2011; Sassenberg, Boos, and Rabung 2005; Weis-
band and Kiesler 1996).
While there is consistent evidence that consumers often
disclose information that is more intimate or sensitive when
communicating over computer-mediated interfaces relative to
face-to-face, it is less clear whether depth of disclosure is influ-
enced by the computing interface itself—specifically, smart-
phones versus PCs. For example, communication on both types
of device involves interaction through a screen, suggesting that
the salience of social cues—and in turn, users’ willingness to
disclose—might be similar across devices. Indeed, some lim-
ited support for this invariance has been provided by Antoun,
Couper, and Conrad (2017), Mavletova and Couper (2013), and
Toninelli and Revilla (2016), who found that when sensitive
questions were first posed on a mobile device and then later on
a PC (and vice versa), respondents showed high test–retest
reliability, suggesting that device effects, if they exist, may
be small. However, there have been no prior attempts to exam-
ine whether the use of smartphones (vs. PCs) affects degrees of
self-disclosure in more complex real-world settings, such as
when consumers post on social media, write reviews, or
respond to open-ended questions on surveys.
How Smartphones Enhance Self-Disclosure Relative
to PCs
In this work, we hypothesize that while smartphones and PCs
share some commonalities, they differ along two dimensions
Melumad and Meyer 29
that, taken together, influence consumers’ willingness to self-
disclose: (1) the extent to which consumers experience psycho-
logical comfort while on the device and (2) the degree of
attentional narrowing arising from the form factor of the device
(e.g., size). These two elements, depicted in Figure 1, form the
foundation of our main hypothesis:
H
1
:Consumers will tend to exhibit enhanced depth of dis-
closure—sharing personal feelings, thoughts, and other
information deemed to be more intimate and private—when
creating content on their smartphone versus their PC.
Next, we discuss the process by which we hypothesize
that comfort and attentional narrowing lead to greater self-
disclosure.
The comforting role of smartphones. The first factor that we argue
enhances depth of disclosure on one’s smartphone versus PC is
the increased psychological comfort that consumers tend to
derive from their phone (e.g., Clayton, Leshner, and Almond
2015; Vahedi and Saiphoo 2018). For example, Melumad and
Pham (2020) found that after an induction of stress, participants
assigned to engage in a task on their smartphone reported a
greater increase in psychological comfort (and thus, greater
relief from stress) than those assigned to engage in the same
task on their laptop, or even an otherwise similar smartphone
belonging to someone else.
The enhanced feeling of comfort associated with one’s
smartphone (vs. other devices) is thought to arise from a unique
combination of positive, personal associations with the device
(Melumad and Pham 2020). For example, whereas PCs tend to
be used more for work purposes, smartphones are often relied
on for texting with friends and family, watching entertaining
videos, or catching up on social media updates (e.g., Panova
and Carbonell 2018; Skierkowski and Wood 2012). Moreover,
given their portability, smartphones are almost always within
arm’s reach—in one’s pocket or purse during the day, by one’s
bedside at night—such that consumers learn that they can rely
on their smartphone to engage in these personal activities
whenever and wherever they want (e.g., Cheever et al. 2014).
As a result, the device becomes a general source of comfort and
security for owners (Melumad and Pham 2020).
Critically, this difference in comfort bears important impli-
cations for depth of disclosure across devices. Prior work has
shown that situational factors—such as mere differences in
how a website is designed—can foster enhanced feelings of
privacy and security and, thus, greater willingness to self-
disclose in online surveys (John, Acquisti, and Loewenstein
2011). These results are consistent with research showing that
people are more likely to self-disclose in environments that
evoke positive affect (Forgas 2011), such as feelings of comfort
and security (e.g., Chaikin, Derlega, and Miller 1976; Gifford
1988; Miwa and Hanyu 2006). We therefore propose the
following:
H
2
:Consumers are more willing to share sensitive informa-
tion on their smartphone (vs. PC) in part because they tend
to experience greater psychological comfort while on the
device.
The smaller form of smartphones. Prior work shows that differ-
ences in form influence the process of content generation
across smartphones versus PCs. For example, because it is
more difficult to write on its smaller keyboard and screen, users
tend to generate shorter content on their smartphone (vs. PC)
when completing open-ended surveys (e.g., Buskirk and
Andrus 2014; Mavletova and Couper 2013; Wells, Bailey, and
Link 2014) and writing online reviews (Melumad, Inman,
and Pham 2019; Ransbotham, Lurie, and Liu 2019). Given
these form-factor constraints, the amount of information that
Smartphone
(vs. computer)
Enhanced
depth of
disclosure
(willingness to
reveal feelings,
thoughts, and
experiences that
are more personal
or intimate)
Greater attentional
focus while on
device
Greater
psychological
comfort while on
device
Figure 1. Theoretical process model showing how use of one’s smartphone (vs. PC) can lead to greater depth of disclosure.
Notes: The model hypothesizes two parallel causal paths of mediation: one stemming from greater focus on the disclosure at hand, and the other through feelings
of enhanced psychological comfort on the device.
30 Journal of Marketing 84(3)
people disclose—or the breadth of self-disclosure—should
similarly be lower when sharing from a smartphone versus PC.
Although the smaller form of smartphones (vs. PCs) may
limit the amount of information that consumers share, we argue
that it has the countervailing effect of enhancing depth of dis-
closure, or the intimacy of information disclosed. One line of
evidence consistent with this prediction comes from Melumad,
Inman, and Pham (2019), who found that when consumers
write reviews on their smartphone versus PC, they tend to use
a greater proportion of emotional words (e.g., “love,”
“amazing”). Although expressions of emotionality do not
necessarily imply more self-disclosure per se, enhanced emo-
tionality is widely considered one of several linguistic markers
of greater depth of disclosure (e.g., Houghton and Joinson
2012).
The rationale for our hypothesis is as follows. A large body
of research shows that when tasks are more difficult, people
tend to respond by focusing more intently on the most essential
aspects of the task in lieu of peripheral information (e.g., Cas-
tiello and Umilt`a 1990; Chen, Liao, and Yeh 2011; Murphy,
Groeger, and Greene 2016). As such, the relative difficulty of
engaging in activities on a smartphone due to its smaller key-
board and screen may similarly narrow users’ attention to the
task they are engaging in on the device. Consistent with this,
research shows that smartphone users often experience
“inattentional blindness” when using their device, narrowing
their focus to the activity onscreen while blocking out external
surroundings (e.g., Hyman et al. 2010; Lin and Huang 2017).
As an illustration of this narrowing effect, a large-scale field
study found that 46%of pedestrians who were on their smart-
phone failed to notice a unicycling clown passing within one
meter of them (Chen and Pai 2018).
Building on this, we propose that when engaging in disclo-
sure of personal information on one’s smartphone (vs. PC), the
relative difficulty of completing the task on its smaller key-
board and screen will similarly focus users on the most essen-
tial elements of the task—sharing one’s personal thoughts and
feelings—and less on peripheral thoughts and cues that might
otherwise inhibit disclosure. This prediction is consistent with
recent work demonstrating that people asked to complete an
online survey while under cognitive load (i.e., remembering
names)—possibly paralleling the difficulty of generating con-
tent on a smartphone (vs. PC)—were more willing to respond
to sensitive survey questions (Veltri and Ivchenko 2017). For-
mally, we hypothesize,
H
3
:The smaller form of smartphones (vs. PCs) narrows
consumers’ attention down to the communication at hand,
which, in turn, enhances depth of disclosure when generat-
ing content on the device.
Overview of Studies
We report the results of five empirical studies that support the
proposed effect of device use on depth of disclosure in user-
generated content, as well as explore the mechanisms
underlying the observed differences (Figure 1). We begin by
offering large-scale field evidence for the basic effect in analy-
ses of tweets about a variety of topics (Study 1) and analyses of
online restaurant reviews (Study 2). In two preregistered experi-
ments we then test for the causal effect of device use on self-
disclosure, as well as the proposed underlying mechanisms
(Studies 3 and 4). We conclude by demonstrating the real-
world generality of the effect by examining consumers’ compli-
ance with sensitive CTAs in web ads across devices (Study 5).
Study 1: Depth of Disclosure Across Devices
on Twitter
The purpose of the first field study was to test for the proposed
differences in depth of disclosure on a major social media plat-
form, Twitter. To control for potential differences across
devices in both the timing of posts and topical content, we
analyzed a data set of 369,161 original tweets
1
that each
included one of 203 “trending hashtags” within a single 12-
hour period in December 2015.
2
The hashtags covered a wide
range of topical domains—including pop culture, sports, and a
terrorist attack that occurred in San Bernardino, California—
which allowed us to test for the generalizability of any
observed differences between smartphones and PCs.
Data Preprocessing
Prior to the main analysis, the data underwent four waves of
preprocessing. First, a dichotomous indicator of whether a
tweet was written on a smartphone or PC was created by iden-
tifying each tweet’s originating platform (e.g., a tweet written
on a smartphone would be evidenced by “Twitter for iPhone,”
and on PC by “Twitter Web Client”); tweets originating from
ambiguous or unknown sources (e.g., third-party apps) were
removed, leaving 296,473 original tweets. Because we were
interested in analyzing tweets generated by typical users rather
than commercial networks or professional bloggers, in the sec-
ond stage of preprocessing we removed 1,305 tweets with
known commercial usernames (e.g., CNN) or that were posted
from accounts with exceptionally large followings, which we
defined a priori to be the top 1%of the distribution of followers
(more than 32,978 followers).
3
This yielded a final data set of
293,039 tweets (59.6%originating from smartphones). Next, to
allow for a within-user analysis of differences in depth of dis-
closure, the third stage of preprocessing involved identifying
the 2,121 users from the total set of qualified users who tweeted
from both a PC and smartphone.
1
These data come from a master corpus of 9 million tweets that were scraped
in early December 2015. This same master corpus was the basis for a separate
subset of data used in Melumad, Inman, and Pham (2019) to test a different
theory (using different measures).
2
Apreliminaryanalysisconfirmedthattherewerenosignificantdifferencesin
the average time of posting between devices across the hashtag categories.
3
Given their small representation in the data (1%), the statistical results are
robust to the exclusion of commercial accounts.
Melumad and Meyer 31
In the final wave of preprocessing, the 203 “trending
hashtags” in our data were topically categorized by human
judges. To achieve this, we recruited a sample of 150 Amazon
Mechanical Turk (MTurk) participants and first asked them to
familiarize themselves with ten randomly assigned hashtags (of
the possible 203) by clicking on a hyperlink that led them to a
set of tweets that had recently included the hashtag. After par-
ticipants felt familiar with the content associated with an
assigned hashtag, they were instructed to indicate whether it
belonged to one or more of seven possible categories: news
(e.g., #CaliforniaShooting), sports (e.g., #ArmyNavy), enter-
tainment/pop-culture (e.g., #GoldenGlobes), amusement (e.g.,
#ImAWreckCause), moral causes (#GenocideVictimsDay),
technology (e.g., #GoogleDemoDay), and economy/finance
(#futureofwork). This process yielded roughly 15 judgments
for each of the 203 hashtags, and each hashtag was categorized
on the basis of the grouping (e.g., “news,” “sports”) to which it
was most frequently assigned.
4
Method and Results
We undertook two approaches to analyzing whether tweets
created on smartphones showed greater depth of self-
disclosure than those created on PCs: an automated analysis
of linguistic markers (e.g., use of first-person pronouns; see,
e.g. Davis and Brock 1975) as well as human assessments of
the content. Each of these approaches will be described in turn.
Automated measures of depth of disclosure. Multiple researchers
have sought to identify linguistic markers indicative of greater
self-disclosure in text (e.g., Bak, Lin, and Oh 2014; Brock-
meyer et al. 2015; Davis and Brock 1975; Pennebaker and
Graybeal 2001; Wang, Burke, and Kraut 2016). To illustrate
these markers, in Web Appendix 1 we provide examples of
texts from each of our studies that were assessed by human
judges as being high or low in self-disclosure. Consistent with
prior work on the linguistic markers of self-disclosure, the
examples show how texts judged to be self-disclosing tend to
be accompanied by more extensive use of (1) first-person pro-
nouns (e.g., “I,” “me”), (2) references to family and friends,
and (3) words that convey emotionality—particularly negative
emotions (Houghton and Joinson 2012; Okdie 2011). The pres-
ence of these common linguistic markers forms the foundation
of algorithms designed to automatically detect depth of disclo-
sure in online texts (Bak, Kim, and Oh 2012; Balani and De
Choudhury 2015; Ravichander and Black 2018; Wang, Burke,
and Kraut 2016).
Drawing on this extant literature, we subjected the tweets to
analysis by Linguistic Inquiry and Word Count (LIWC; Pen-
nebaker, Boyd, and Jordan 2015), which contains dictionaries
for each of the aforementioned linguistic markers (first-person
pronouns, references to family and/or friends, and negative
emotionality).
5
We also examined LIWC measures for
“authentic” and “analytical” writing styles. According to Pen-
nebaker, Boyd, and Jordan (2015), writers using a more authen-
tic style create texts that are more personal and vulnerable, such
that higher authenticity scores point to greater depth of disclo-
sure. Moreover, texts written in a less analytical style, as indi-
cated by more narrative language and references to personal
experiences, would also be suggestive of greater depth of
disclosure.
Finally, to ensure that the LIWC analysis provided valid
measures of the degree of self-disclosure in tweets, we under-
took a cross-validation analysis that regressed human judg-
ments of the depth of disclosure for a sample of the tweets
on each of the six LIWC measures of disclosure (see Web
Appendix 2 for a description of the method and results). The
findings confirmed, for example, that tweets rated as more self-
disclosing by human judges tended to include a greater propor-
tion of first-person pronouns and references to family and
friends, and were written in a more authentic—but less analy-
tic—writing style as measured by LIWC.
Results: differences in depth of disclosure based on automated
measures. In Table 1 we report the results of two statistical
approaches to analyzing how the linguistic content of tweets
created on smartphones differs from that created on PCs. One
approach involved a set of six univariate analyses that modeled
each of the LIWC dimensions of interest (e.g., use of first-
person pronouns, analytical and authentic writing styles) as a
function of the originating device. The second was a multi-
variate logistic regression that predicted the likelihood of a
tweet being written on a smartphone versus PC as a function
of the six LIWC dimensions simultaneously. The analyses con-
trolled for the word count of the texts—which tends to be
greater for content written on PCs versus smartphones (Melu-
mad, Inman, and Pham 2019)—as well as for the hashtag cate-
gories.
6
Finally, the table reports separate results for the overall
corpus of tweets as well as a within-user analysis of the subset
of users who tweeted from both devices, which allowed us to
better control for possible issues of self-selection across
devices.
The results provide strong initial support for H
1
among the
full data set of tweets as well as the subset of users who tweeted
from both devices. For the full data set, across hashtag cate-
gories tweets written on smartphones tended to contain greater
proportions of first-person pronouns (M
smartphone
¼3.29 vs.
M
PC
¼2.23; F(1, 293,038) ¼2,742.45, p<.001), references
to family (M
smartphone
¼.76 vs. M
PC
¼.50; F(1, 293,038) ¼
4
For example, if a hashtag was judged by 62%of MTurk judges as focusing on
news and 38%as focusing on finance, it was categorized as a “news” hashtag
for the purpose of analysis.
5
Exploratory analyses of other LIWC categories indicated that tweets written
on smartphones (vs. PCs) also tended to use relatively more informal language
(i.e., netspeak, nonfluencies, swear words). While a more informal writing
style might also point to greater self-disclosure on smartphones, we focus on
the four linguistic markers that have been validated in prior work (e.g.,
Ravichander and Black 2018).
6
Models estimated controlling for neither the hashtag category nor word count
yielded similar results.
32 Journal of Marketing 84(3)
681.54, p<.001) and friends (M
smartphone
¼.32 vs. M
PC
¼.25;
F(1, 293,038) ¼142.79, p<.001), and negative emotional
words (M
smartphone
¼1.62 vs. M
PC
¼1.52; F(1, 293,038) ¼
40.73, p<.001). These results are further supported by differ-
ences in writing style across devices: tweets written on smart-
phones tended to display a less analytical but more authentic
writing style (analytical: M
smartphone
¼69.08 vs. M
PC
¼72.98;
F(1, 293,038) ¼1,059.54, p<.001; authentic: M
smartphone
¼
28.94 vs. M
PC
¼25.49; F(1, 293,038) ¼681.54, p<.001). As
shown in Table 1, the within-user analyses of tweets written by
the same users on their smartphone and PC yielded a similar
pattern of results.
Finally, we examined whether the size of these effects var-
ied by the particular hashtag category (e.g., sports, politics).
The results (reported in Web Appendix 3) suggest that while
many of the aggregate results hold across the hashtag cate-
gories, there was some variance in the size and, in some cases,
direction of the effects. For example, while the use of first-
person pronouns and references to family and friends were
consistently greater on smartphones in domains that are
intuitively more amenable to self-disclosure—such as news
(e.g., #CaliforniaShooting), moral causes (e.g., #GenocideVic-
timsDay), pop culture (e.g., #CouplesTherapy), and sports
(e.g., #FIFA)—these same markers were less evident in the
more impersonal domains of finance (e.g., #DiscussTheDeals)
and technology (#bufferchat). These latter two categories also
showed less authentic and more analytical writing styles, a
reversal that is perhaps unsurprising given the conversational
norms that generally surround discussions of finance and tech-
nology (e.g., a tone that is more objective than subjective).
Human judgments of depth of disclosure. To provide further evi-
dence for differences in disclosure, we subjected the full set of
tweets from two of the hashtag categories—2,261 tweets about
the San Bernardino terrorist attack (53%smartphone), and
1,009 tweets about pop culture (56%smartphone)—to assess-
ment by human judges. These two categories were selected so
that we could test for the effect across topics that differed
substantively in context and valence. We recruited an indepen-
dent sample of 1,925 MTurk participants to assess up to 10
Table 1. Study 1: Univariate Least-Square Mean Differences Between Smartphone- and PC-Generated Tweets Along LIWC Dimensions and
Coefficients of Multivariate Logistic Regression Modeling the Likelihood That Tweets Were Written on Smartphones (vs. PCs) as a Function of
These Dimensions.
Univariate Tests of Least-Square Means
Entire Corpus (N ¼293,039) Users Tweeting on Both Devices (N ¼41,452)
Trait PC Smartphone F Pr >F PC Smartphone F Pr >F
Analytical style 72.98 69.08 1,059.54 <.001 70.97 69.54 126.17 <.001
Authentic style 25.49 28.94 681.54 <.001 25.68 29.00 287.06 <.001
First person 2.23 3.29 2,742.45 <.001 2.52 3.20 244.55 <.001
Family .50 .76 714.18 <.001 .55 .64 112.70 <.001
Friends .25 .32 142.79 <.001 .28 .34 37.07 <.001
Negative emotion 1.52 1.62 40.73 <.001 1.34 1.55 98.18 <.001
Word count 14.70 13.12 3,816.09 <.001 13.50 12.56 201.08 <.001
Multivariate Logistic Models (Criterion: Smartphone-Generated Tweet)
Entire Corpus (N ¼293,039) Users Tweeting on Both Devices (N ¼41,452)
Word Frequencies Linguistic Style Word Frequencies Linguistic Style
Trait Estimate Pr >w
2
Estimate Pr >w
2
Estimate Pr >w
2
Estimate Pr >w
2
Analytical style ".004 <.001 ".001 .008
Authentic style .002 <.001 .003 <.001
First person .033 <.001 .03 <.001
Family .023 <.001 .01 <.001
Friends .022 <.001 .02 <.001
Negative emotion .005 <.001 .01 <.001
Word count ".041 <.001 ".0036 <.001 ".02 <.001 ".02 <.001
Model Fit Model Fit Model Fit Model Fit
LR w
2
% Concordant LR w
2
%Concordant LRw
2
% Concordant LR w
2
%Concordant
8,202.16 59.4 5,278.53 64.4 985.13 58.1 866.58 57.6
Notes: Analytical and authentic writing styles are measured as scores out of 100, whereas types of words are measured as percentages. The effects of writing styles
were modeled separately because the scores are composites of some of the word-use measures.
Melumad and Meyer 33
randomly selected tweets, yielding an average of 7.08 judg-
ments per tweet. Participants were blind to both the hypothesis
of this study and the originating device of the tweet.
To measure depth of disclosure in the tweets, participants
rated their agreement with a set of items used in prior
work (e.g., Barak and Gluck-Ofri 2007; Jourard and Lasakow
1958; Wang, Burke, and Kraut 2016) on a seven-point scale
(1 ¼“Not at all,” and 7 ¼“Very much so”):
1. Self-focus: “To what extent does the writer focus on
him/herself in this tweet (e.g., how he/she felt, what
he/she did)?”
2. Internal states: “To what extent does the writer reveal
his or her personal feelings, thoughts, or opinions?”
3. Personal information:“Towhatextentdoesthewriter
disclose personal information about him/herself?”
4. Vulnerable: “To what extent does the writer disclose
information that might make him/her feel emotionally
vulnerable?”
5. Controversial: “To what extent is the writer expressing
potentially controversial statements/views?”
6. Offensive: “To what extent is the writer expressing
views that may be offensive to others?”
7. Impulsive: “To what extent does it seem like the writer
was impulsive when writing his/her tweet?”
An exploratory factor analysis revealed that these items
loaded onto two dimensions of disclosure: “intimate
information” (self-focus, internal states, personal information,
vulnerable; a¼.78) and “lack of censorship” (controversial,
offensive, impulsive; a¼.85). Web Appendix 1 shows exam-
ples of tweets that scored high versus low on intimacy of infor-
mation disclosed.
Results: differences in depth of disclosure based on human
judgments. Paralleling the analyses of the automated measures,
we undertook two sets of analyses of the human assessments of
the tweets. The first set included two univariate analyses that
separately modeled our measures of perceived depth of inti-
mate disclosure and lack of censorship as a function of the
originating device, and the second set involved a multivariate
logistic analysis that predicted the likelihood that a tweet had
been written on a smartphone or a PC as a function of the
perceived depth of intimate disclosure and lack of censorship
(simultaneously). As with the automated analysis, each model
controlled for the word-count differences between devices.
The results provide convergent validity for the central
hypothesis that consumers express greater depth of disclosure
when writing on their smartphone versus PC. First, tweets writ-
ten on smartphones (vs. PCs) were assessed by judges as con-
veying more intimate information—an effect that held for the
tweets about both the San Bernardino attack (M
smartphone
¼
3.04 vs. M
PC
¼2.84; F(1, 7,854) ¼34.47, p<.001) and the
pop-culture topics (M
smartphone
¼5.01 vs. M
PC
¼2.76;
F(1, 5,180) ¼2,210.08, p<.001). In contrast, while tweets
written on smartphones were judged as more uncensored on
average (see Web Appendix 4), the effect was primarily driven
by the tweets about pop-culture topics (M
smartphone
¼5.73 vs.
M
PC
¼3.71; F(1, 5,169) ¼1,297.90, p<.001), as there was no
perceived difference in degree of censorship among tweets
about the San Bernardino attack (M
smartphone
¼4.15 vs.
M
PC
¼4.16; F <1). Thus, tweets posted from smartphones
were consistently viewed as more intimately self-disclosing
than those posted from PCs, and were less consistently seen
as more unfiltered or uncensored. (The results of the multi-
variate logistic regression, which models the likelihood of the
tweets being written on a smartphone versus PC as a function of
the two human-judged dimensions of disclosure, mirror these
results; see Web Appendix 4.)
Discussion and Replication Studies
The results of the first field study provide initial evidence that
user-generated content written on smartphones tends to convey
greater depth of disclosure than content written on PCs (H
1
).
This effect was robust across both automated measures (e.g.,
percentage of first-person pronouns, references to family) and
human judgments of disclosure. These results were also robust
across a variety of contexts that ranged from serious breaking-
news events (a terrorist attack) to frivolous online amusements
(e.g., “#TheWorstSecretSantaGifts”).
Although we find that the pattern of results consistently
held across the hashtag categories in our data set, to further
test for the robustness of this effect we conducted the same
analyses reported above for three additional Twitter data sets.
Two were obtained from public sources: (1) a corpus of
67,408 tweets about the 2018 FIFA World Cup posted on
Kaggle (Rituparna 2018), and (2) a corpus of 201,258 tweets
posted about the 2016 U.S. presidential election on election
day (King 2016). We also analyzed (3) an original corpus of
18,346 tweets on hashtags covering news, sports, and amuse-
ment/entertainment on a single day in January 2017. The
results of these analyses, reported in Web Appendix 5, closely
replicate those reported above: Whether users were tweeting
about a sporting event, election, or entertaining topic, tweets
written on smartphones (vs. PCs) consistently contained
greater proportions of first-person pronouns, references to
family and friends, and emotional words—particularly those
conveying negative affect. They also tended to have a more
authentic and less analytical style.
Study 2: Does Self-Disclosure Matter?
An Analysis of Online Reviews
Study 1 offered initial evidence that at least one type of user-
generated content—tweets about a variety of topics—tends to
contain greater depth of disclosure when written on smart-
phones versus PCs. One important question, however, is
whether the observed differences in depth of disclosure might
yield meaningful downstream marketing implications. Prior
work would suggest, for example, that content written in a
more self-disclosing manner would lead readers to feel a
34 Journal of Marketing 84(3)
greater sense of similarity to the writer, which may result in
content that is more persuasive to outside readers (Jiang et al.
2010; see also Faraji-Rad et al. 2015). In Study 2 we therefore
tested for the robustness of the effect in a domain in which self-
disclosure might have material impacts on consumer behavior:
online restaurant reviews on TripAdvisor.
Method
The data set contained a corpus of 10,185 TripAdvisor restaurant
reviews written on smartphones or PCs between April 2014 and
July 2017. The reviews were a random sample drawn from a
larger corpus utilized in previous work (Melumad, Inman, and
Pham 2019) and were comparably balanced between smart-
phones (N ¼5,097) and PCs (N ¼5,088). The data included
the name of the restaurant, date of the visit, and text of the
review. Mirroring the approach to analysis in Study 1, we under-
took two analyses to measure the depth of disclosure in the
reviews. The first was to subject the texts to analysis by LIWC,
which as in Study 1 yielded measures of a battery of linguistic
markers of self-disclosure (e.g., first-person pronouns).
The second approach subjected the same reviews to assess-
ment by MTurk judges who rated the reviews along two dimen-
sions. The first was the perceived depth of disclosure, measured
along two items adapted from Study 1 that were relevant in the
context ofrestaurant reviews:“To what extent did the writer focus
on him/herself in this review (e.g., how he/she felt, what he/she
did)?” and “To what extent did the writer reveal his or her per-
sonal feelings, thoughts, or opinions?” (a¼.63). Importantly,
two additional measures were now included to capture a potential
downstream consequence: “How persuasive would you find this
review to be if youwere considering goingto this restaurant?” and
“How interested would you be in visiting this restaurant?” As in
Study 1, all items were measuredon a seven-pointscale (1 ¼“Not
at all,” and 7 ¼“Very much so”), with participants blind to the
hypothesis and originating device of the review.
Results
Differences in depth of disclosure based on automated measures. As
in Study 1, we undertook both univariate and multivariate logis-
tic regression analyses of the degree to which content written on
smartphones differed from that written on PCs along a battery of
LIWC measures that are suggestive of self-disclosure: use of
first-person pronouns, references to family/friends, negative
emotionality, and authentic and analytical styles. Again, the
analysis controls for differences in word count, which was sig-
nificantly higher in PC-generated reviews (M
smartphone
¼69.87
words vs. M
PC
¼96.28 words; F(1, 10,182) ¼347.76, p<.001).
Note that because reviews are, by definition, personal—
typically first-person—accounts of one’s consumption experi-
ence, logically the vast majority of reviews should appear
self-disclosing (at least to some extent). Still, even in this com-
paratively self-disclosing context, the results conceptually
replicate those of Study 1. Reviews written on smartphones
tended to include greater proportions of first-person pronouns
(M
smartphone
¼2.35 vs. M
PC
¼2.19; F(1, 10,182) ¼9.55,
p¼.002), references to family (M
smartphone
¼.38 vs. M
PC
¼
.32; F(1, 10,182) ¼9.55, p¼.002) and friends (M
smartphone
¼
.52 vs. M
PC
¼.43; F(1, 10,182) ¼36.34, p<.001), and
negative emotional words (M
smartphone
¼.75 vs. M
PC
¼.67;
F(1, 10,182) ¼7.09, p¼.008). Finally, as in Study 1
smartphone-generated reviews had a less analytic writing style
(M
smartphone
¼64.43 vs. M
PC
¼65.95; F(1, 10,182) ¼9.40,
p¼.002), though here we did not find a difference in authentic
writing style (M
smartphone
¼34.63 vs. M
PC
¼35.07; F <1).
(Binary logistic regression analyses, presented in Web
Appendix 6, yield similar results.)
Differences in depth of disclosure and persuasiveness based on
human judgments. Next, we undertook the same analyses as in
Study 1 to test for differences in human assessments of the
content. Consistent with Study 1, the results show that reviews
written on smartphones (vs. PCs) were rated as containing
greater depth of disclosure (M
smartphone
¼4.67 vs. M
PC
¼
4.52; F(1, 9,551
7
)¼18.10, p<.001). The results also provide
evidence for a key downstream implication of this effect:
reviews written on smartphones were rated by judges as being
more persuasive than those written on PCs (M
smartphone
¼4.97
vs. M
PC
¼4.74; F(1, 9,540) ¼49.40, p<.001). Finally, read-
ers were more interested in visiting restaurants reviewed by
other customers on their smartphones than restaurants reviewed
on PCs (least square M
smartphone
¼4.68 vs. M
PC
¼4.61;
F(1, 9,840) ¼3.90, p¼.048)—an effect that was strengthened
after we controlled for valence of the review (as captured by the
percentage of negative emotional words; M
smartphone
¼4.69 vs.
M
PC
¼4.60; F(1, 9,539) ¼5.74, p¼.016).
Next, we conducted a serial mediation analysis (SAS Proc
Calis) to test whether reviews with greater depth of disclosure
in smartphone-generated content led to greater persuasiveness
and, thus, greater interest in the restaurant under review. The
results provided an excellent fit to the data (Bentler compara-
tive fit index ¼.998; root mean square residual ¼.008) and,
critically, supported the hypothesized model. Reviews written
on smartphones (vs. PCs) contained greater depth of disclosure
(b
device !disclosure
¼.05; t ¼4.79, p<.001); reviews
containing greater depth of disclosure were more persuasive
(b
disclosure !persuasive
¼.28; t ¼30.06, p<.001); and more
persuasive reviews heightened readers’ interest in visiting the
restaurant (b
persuasive !interest
¼.62; t ¼96.67, p<.001). Finally,
the model supported an overall positive indirect effect of device
on interest in visiting the restaurant (total indirect effect: b
device !
disclosure !persuasive !interest
¼.03; t ¼4.71, p<.001).
Discussion
Across two field studies we provide consistent evidence that
customers tend to convey greater depth of disclosure when
7
Differences in degrees of freedom across these analyses arose because of
missing responses to some scale items for certain reviews.
Melumad and Meyer 35
generating content on their smartphone than on their PC—as
evidenced by tweets about a variety of topics (Study 1) and by
restaurant reviews (Study 2). It is worth noting that the size of
the effects was somewhat smaller among the restaurant reviews
(e.g., Cohen’s d for human-judged depth of disclosure ¼.09)
compared with the tweets (Cohen’s d ¼.14)—a result that is
not surprising given that, by construction, customer-generated
reviews are first-person accounts of personal consumption
experiences, making it harder to observe differences in degree
of disclosure across devices. Nevertheless, even in this context,
reviews written on smartphones still exhibited greater depth of
disclosure than those written on PCs.
Study 2 also indicates that the greater depth of disclosure in
smartphone-generated content carries important downstream
consequences. Outside readers found reviews written on smart-
phones (vs. PCs) to be more persuasive, which, in turn, heigh-
tened their interest in visiting the restaurant under review.
These results are broadly consistent with those of Grewal and
Stephen (2019), who found that reviews containing a mobile
indicator (e.g., a “written on mobile” label) are more persuasive
to outside readers than those containing a PC indicator. They
argued that this occurred because readers infer that mobile-
generated reviews are more credible given the relative diffi-
culty of writing on the device. It is important to emphasize,
however, that the outside readers in our study were given no
information about the device on which the content was written,
such that reviews written on smartphones (vs. PCs) were rated
as more persuasive based solely on their content.
Finally, it is worth noting that one possible explanation for
why reviews and/or tweets written on smartphones (vs. PCs)
appeared more self-disclosing is that they were composed at
the same time as an event or experience, when personal feel-
ings may have been more salient. Two results, however, argue
against such a timing explanation: (1) restaurant reviews writ-
ten on smartphones included relatively more—not fewer—
references to the past (M ¼6.90) than those written on PCs
(M ¼6.33; F(1, 10,182) ¼42.39, p<.001) and (2) the tweets
in Study 1 were posted nearly simultaneously from smart-
phones and PCs. Nevertheless, because the association between
smartphone use and self-disclosure remains correlational, and
the underlying mechanism remains uncertain, in the next two
studies we attempt to increase our knowledge by investigating
the effect in a more controlled setting.
Study 3: Testing for Underlying Mechanisms
The purpose of Study 3 was twofold. The first aim was to test
whether the differences in depth of disclosure observed in the
first two field studies replicate in a more controlled setting
wherein participants are randomly assigned to generate con-
tent on their smartphone or PC. The second aim was to test
whether the greater depth of disclosure in smartphone-
generated content is driven by the proposed mechanisms for
the effect: first, a greater sense of psychological comfort (H
2
),
and second, greater attentional narrowing on one’s smart-
phone versus PC (H
3
).
Method
Study 3 involved two data-collection phases. In the first phase,
participants were randomly assigned to use their smartphone or
PC to write about an upsetting personal experience; in the
second phase, participants’ descriptions of their personal expe-
rience were evaluated by an independent sample of judges for
depth of disclosure. In this section, we describe each of these
phases in turn.
Phase 1: eliciting disclosures and measuring proposed mediators.
We preregistered this study on AsPredicted.org, which
included the preregistration of our predicted hypotheses as well
as exclusion criteria.
8
Our final data set included responses
from 715 participants from a Qualtrics panel (60%female) who
were randomly assigned to complete a two-part survey on
either their smartphone or their personal computer (for the
complete survey instrument, see Web Appendix 7). In the first
part of the survey—which served to administer the disclosure
task—participants were asked to use their assigned device to
describe an upsetting personal experience (in four to five sen-
tences). The specific instructions were as follows:
Think of a topic or event in your life that made you upset (e.g., an
article you read that made you angry; an argument you had with a
friend that upset you). In the space below please describe what
made you upset, including your thoughts and feelings about the
topic or event.
After participants completed the disclosure task, they were
asked to use the same device to respond to a set of scales that
measured the proposed drivers of depth of disclosure:
1. Psychological comfort. Participants responded to five
items adapted from Melumad and Pham (2020) that
measured the extent to which they associated feelings
of psychological comfort with the use of their assigned
device (1 ¼“Not true at all,” and 7 ¼“Very true”): (1)
“Using my smartphone (PC) provides a source of
comfort,” (2) “Having my smartphone (PC) with me
makes me feel secure,” (3) “When I am using my smart-
phone (PC) I feel I am in my safe space,” (4) “Just
holding my smartphone (PC), no matter what I do with
it, makes me feel comforted,” and (5) “Touching or
holding my smartphone (PC) makes me feel calmer.”
Responses to these items were averaged into an index of
“psychological comfort” (a¼.88).
8
The data set was originally composed of 1,040 descriptions that were
subjected to two preregistered exclusion criteria: descriptions were excluded
if they (1) contained content unrelated to the task (e.g., nonsense words, text
copied from unrelated sources) and/or (2) were either too brief (<15 words) or
too poorly written to be assessed by human judges. Of all the open
ended-responses, 229 (22%) were excluded on this basis. An additional 77
responses were deleted for failing two embedded attention checks.
Preregistration is available at http://aspredicted.org/blind.php?x¼d7vx69.
36 Journal of Marketing 84(3)
2. Attentional narrowing on disclosure. Participants indi-
cated the extent to which they agreed with each of three
statements about how they felt while writing about their
personal experience: (1) “I drowned out my environ-
ment when writing,” (2) “I got lost in what I was
writing,” and (3) “I felt a sense of privacy when
writing” (1 ¼“Not true at all,” and 5 ¼“Very true”).
Responses to these items were averaged to create an
index of “attentional narrowing” (a¼.66).
Next, although our theory does not make direct predictions
about whether consumers accurately perceive the depth of dis-
closure of their own writing, to explore this we asked partici-
pants to rate their beliefs about the sensitivity of the
information that they shared in their descriptions. This was
measured in terms of their agreement with four items (on a
five-point scale): “I would hesitate to share this experience
with someone I just met,” “I felt I was revealing something
very personal about myself when describing this experience,”
“The experience is a very private matter,” and “There were
sensitive parts of that experience that I intentionally chose not
to write about.” Responses to these four items were combined
to form a “self-judged disclosure” index (a¼.77).
9
Finally, to control for possible factors that might addition-
ally influence depth of disclosure across devices, we asked
participants to indicate (1) whether they had completed the
study in a private or public setting, and 2) the extent to which
they were generally concerned about privacy issues on their
assigned device. Responses were based on their agreement with
two items on a five-point scale: “There are some things that I
avoid doing on my smartphone (PC) (e.g., finance-related
activities)” and “I worry a lot about the privacy of the data
on my smartphone (PC)” (“general privacy concern” index;
a¼.83).
Phase 2: human judgments of depth of disclosure. To measure the
key dependent variable—depth of disclosure in the descriptions
as perceived by outside judges—we recruited an independent
sample of 649 judges from MTurk to assess up to ten randomly
assigned texts written by respondents in the main study (judges
were blind to both originating device and hypothesis). After
reading each text, participants were asked to rate it along the
same four items that were used to create the “intimate dis-
closure” index in Studies 1 and 2 (a¼.76). We obtained three
assessments for each description, yielding a total of 2,129
judgments.
Analyses and Results
Differences in depth of disclosure. As in the first two studies, to
test for differences in depth of disclosure across devices we
undertook analyses using both automated measures and human
judgments of the texts. For the automated analysis, the 715
descriptions were subject to analysis by LIWC (Pennebaker,
Boyd, and Jordan 2015), from which we extracted the same set
of linguistic markers of self-disclosure analyzed in the previous
studies. Similar to the previous results, descriptions written by
participants on their smartphones made greater use of personal
pronouns (M
smartphone
¼14.36 vs. M
PC
¼13.26; F(1, 713) ¼
4.97, p¼.026), contained more references to family
(M
smartphone
¼1.71 vs. M
PC
¼1.16; F(1, 713) ¼7.02, p¼
.008), and expressed greater negative emotionality, though
this effect did not reach the a priori level of significance
(M
smartphone
¼5.57 vs. M
PC
¼5.00; F(1, 713) ¼3.29, p¼
.07; Wald w
2
¼3.38, p¼.06). In contrast, here we did not see a
significant difference in references to friends (M
smartphone
¼.65
vs. M
PC
¼.62; F <1) or in writing styles (authentic:
M
smartphone
¼37.72 vs. M
PC
¼39.70; F <1; analytical:
M
smartphone
¼53.33 vs. M
PC
¼53.89; F <1).
External judges’ assessments of the descriptions provided
more direct evidence for differences in depth of disclosure. As
we predicted, participants assigned to use their smartphone to
write about an upsetting personal experience created content
that was rated by outside judges as more disclosing (M ¼
4.85) compared with content created by participants assigned
to use their PC (M ¼4.55; F(1, 1,911) ¼22.09, p<.001).
Importantly, this effect was sustained after controlling for
factors that might covary with depth of disclosure, such as
the length of the descriptions as well as the age and gender of
the writers (depth of disclosure: least square M
smartphone
¼
4.84 vs. M
PC
¼4.56; F(1, 1,908) ¼17.08, p<.001). The
results also hold after we control for the setting in which the
writers completed the study—though it is worth noting that,
across conditions, 93%of participants completed the study in
a personal (vs. public) place.
Evidence for proposed mechanisms. To investigate whether the
effect of device use on depth of self-disclosure could be
explained by the proposed mediation model (Figure 1), we
estimated a structural path model that included the hypothe-
sized drivers of the effect. The model hypothesized that the
direct effect of smartphone (vs. PC) use on human-judged
depth of self-disclosure is described by two causal paths: one
in which smartphone use evokes greater feelings of psycholo-
gical comfort, thereby enhancing depth of disclosure (device
!psychological comfort !disclosure), and another in which
smartphone use leads to more narrowed attention on the com-
munication at hand, which also enhances depth of disclosure
(device !attentional narrowing !disclosure).
We obtained maximum-likelihood estimates of the path
coefficients using SAS’s Proc Calis, and they supported the
hypothesized causal structure. Specifically, the analysis sup-
ported the parallel positive path from smartphone (vs. PC) to
psychological comfort (b
device !comfort
¼.05; t ¼1.98, p¼
.024), and a positive path from comfort to depth of disclosure
(b
comfort !disclosure
¼.35; t ¼1.79, p¼.037). Likewise, the
analysis confirmed a significant positive effect of smartphone
9
The original survey also included the item “The experience I wrote about
reveals something about who I am as a person.” An exploratory factor analysis,
however, indicated that this item loaded onto a second dimension that was
unrelated to the other items, and we therefore excluded it from the index.
Melumad and Meyer 37
(vs. PC) use on degree of attentional narrowing on the disclo-
sure task (b
device !attentional narrowing
¼.07; t ¼3.05, p¼.001),
and a significant positive path from attentional narrowing to
depth of disclosure (b
attentional narrowing !disclosure
¼1.27; t ¼
3.95, p<.001). The results also showed a significant total
indirect effect of smartphone (vs. PC) use on depth of disclo-
sure through the parallel paths of attentional narrowing and
psychological comfort (total indirect effect: b ¼.10; t ¼
4.61, p<.001).
Additional analyses: self-judged disclosure and privacy concerns. We
undertook two additional analyses for which we did not make a
priori predictions. We first examined whether the observed
differences in depth of disclosure arose for participants’ own
perceptions of their descriptions. Notably, the results revealed
that participants assigned to write on their smartphone indeed
rated their description as more disclosing (M ¼2.88) than did
those assigned to write on their PC (M ¼2.69; F(1, 713) ¼
5.22, p¼.023). Thus, both outside readers and the writers
themselves appeared to perceive the greater depth of disclosure
of smartphone-generated content.
We next examined whether participants’ general privacy
concerns might influence depth of disclosure across devices.
First, as might be expected, participants in the smartphone
condition were more likely to agree with the statement, “There
are some things that I avoid doing on my [device]; e.g. finance-
related activities” compared with those in the PC condition
(M
smartphone
¼3.47 vs. M
PC
¼3.11; F(1, 713) ¼11.21, p<
.001). Interestingly, however, this greater general privacy con-
cern on smartphones did not seem to influence depth of disclo-
sure on the device. Privacy concerns were not statistically
correlated with outside judges’ assessments of the depth of
disclosure (Pearson r ¼".01; p¼.729; N ¼1,913) but were
positively correlated with participants’ own perceptions of the
depth of disclosure in their accounts (Pearson r ¼.15; p<.001;
N¼715). As a result, inclusion of privacy concerns as a
covariate did not alter the effect of device on external judg-
ments of disclosure (least square M
smartphone
¼4.86 vs. M
PC
¼
4.54; F(1, 1,910) ¼22.84, p<.001), but it did temper the effect
of device on self-perceptions of disclosure (least square
M
smartphone
¼2.98 vs. M
PC
¼2.72; F(1, 712) ¼2.85, p¼.051).
Discussion
Study 3 provides a conceptual replication of the findings of the
two field studies, showing that participants randomly assigned
to write about an upsetting personal experience on their smart-
phone generated content that revealed greater depth of disclo-
sure than did those assigned to use their PC. This effect was
observed not only in terms of the automated measures and
external human judgments analyzed in the prior studies, but
in terms of the writers’ own perceptions of depth of disclosure
in their descriptions. The results also show that the effects
observed in the prior studies generalize to another domain of
user-generated content of potential interest to firms: a context
wherein consumers are asked to reveal private information in
an open-ended survey. Finally, and most importantly, the
results of Study 3 provide initial evidence in support of the
proposed mechanisms underlying the effect. As we hypothe-
sized, the greater depth of disclosure in smartphone-generated
content was driven by a greater sense of psychological comfort
on the device (H
2
) as well as more narrowed attention on the
disclosure task at hand (H
3
).
Study 4: Disclosure of Sensitive Consumer
Information
In a second experiment, we explored whether the findings of
the first three studies generalize to a context that is often of
importance to marketers: customer compliance with requests
for private or sensitive information. In Study 4 we therefore
asked participants to describe a private and potentially embar-
rassing product experience, with a focus on whether those
using their smartphone (vs. PC) would be more willing to com-
ply with the request rather than opt out of doing so.
Method
An independent sample of 1,389 participants was recruited
from a Qualtrics panel (71%female) and randomly assigned
to complete the study either on their smartphone or their PC.
We preregistered the study on AsPredicted.org,
10
which
included the preregistration of our predicted hypotheses as well
as the same exclusion criteria as in Study 3. The general pro-
cedure was similar to that used in Study 3, involving two data-
collection phases. The first phase asked participants to disclose
a product that they had purchased which they considered to be
private and potentially embarrassing (by responding in an
open-ended text box) and then to describe their experience with
that product (in a second open-ended text box on the subse-
quent screen). The specific instructions were as follows:
This survey is part of a market research study aimed at helping
companies better understand consumers’ experiences with differ-
ent types of products. Think of a product that you use, or have used
in the past, which you consider to be private and possibly embar-
rassing—that is, something that you might not want others to know
about. For example, perhaps you have purchased products to pre-
vent hair loss—or perhaps you sometimes buy certain foods to
binge on when you’re feeling sad. In the spaces below, please first
indicate what this product is (e.g., “weight loss supplement”).
Then, please tell us about your experience with this product, such
as what led you to buy it, and how you feel about using it.
The key dependent variables of interest were (1) whether
participants were willing to disclose such a product or whether
they opted out of doing so (e.g., by writing “N/A”) and, if they
complied with the request for information, (2) the depth of
disclosure expressed in their description of the product (which
we measured in phase 2). Finally, participants were asked to
10
Preregistration is available at http://aspredicted.org/blind.php?x¼kr7j3g.
38 Journal of Marketing 84(3)
use their assigned device to respond to the same items as those
used in Study 3 to measure the proposed mechanisms—their
psychological comfort (a¼.86) and attentional narrowing on
the task at hand (a¼.68)—as well as a series of questions
measuring possible covariates and demographic information
(see Web Appendix 8).
For phase 2 of the study, we first identified the subset of 975
participants who did disclose a private product (and met the
preregistered inclusion criteria),
11
and we then recruited a sep-
arate sample of 374 MTurk judges to rate the descriptions on
two dimensions. The first dimension was the sensitivity of the
product described, which was measured based on judges’
agreement with the following items (1 ¼“Not at all,” ¼7¼
“Very much so”): “This product was ...” (1) “very private,”
(2) “potentially embarrassing,” (3) “not one that would be dis-
cussed with a stranger,” (4) reveals something personal about
the user, and (5) “very intimate.” Responses to these items were
combined into an index of “product sensitivity” (a¼.92). The
second dimension was the depth of disclosure in the descrip-
tions, which was measured using the same items as in Study 3
(a¼.77). Thus, while our main analysis compared across
devices the rates of compliance (or participants’ willingness
to disclose the personal product vs. opting out), this second
phase enabled us to compare the depth of disclosure conveyed
in participants’ product descriptions, conditional on their hav-
ing disclosed one.
Results and Discussion
Differences in response compliance. Across conditions, 134 (11%)
of all participants refused to comply with the request to
describe a private or sensitive product, as indicated by
responses such as “none” (73%) or “I do not buy these types
of products” (27%). More importantly, as we predicted, rates of
compliance differed between conditions. Participants were sig-
nificantly more likely to disclose a private or embarrassing
product purchase when responding on their smartphone
(93%)versustheirPC(86%; likelihood-ratio w
2
¼13.35,
p¼.003). This effect still held after controlling for three mea-
sured factors that may have incidentally contributed to differ-
ences in compliance across devices: the gender of participants,
their age, and whether the study was completed in a public
place (least square M
smartphone
¼.94 vs. M
PC
¼.85;
likelihood-ratio w
2
¼23.14, p<.001).
Differences in depth of disclosure (conditional on compliance). We
next tested whether there were differences in the depth of dis-
closure in the product descriptions among the subset of parti-
cipants who were willing to disclose such a product. Again, the
results show that smartphone-generated content was assessed
by outside judges as more self-disclosing than that generated on
PCs. Specifically, products disclosed by participants on their
smartphone were rated by outside judges as more sensitive in
nature than those disclosed on PCs (M
smartphone
¼4.54 vs.
M
PC
¼4.35; F(1, 4,803) ¼4.54, p<.001), and the accompa-
nying descriptions of their products were also rated as convey-
ing greater depth of self-disclosure (M
smartphone
¼4.83 vs.
M
PC
¼4.70; F(1, 4,741) ¼13.41, p<.001).
12
In contrast,
unlike in Study 3, where all participants complied with the
writing task, members of this self-selected group of participants
were unaware that they were being more self-disclosing when
writing about their habits, as here we found no significant
difference in self-perceptions of depth of self-disclosure
between devices (M
smartphone
¼3.29 vs. M
PC
¼3.31; F <1).
Evidence for mechanisms. To test for the proposed drivers of
differences in depth of disclosure, we undertook two structural
equation analyses: one for the decision to comply with the
disclosure task (vs. opting out) and another for the depth of
disclosure in the product descriptions provided by those who
did comply. In this particular study, given that one of the pro-
posed mechanisms—degree of attentional narrowing on the
disclosure task—was meaningful only for participants who
actually agreed to disclose, our analysis of participants’ will-
ingness to comply examined only the mediating effect of the
degree of psychological comfort associated with the device.
Our analysis for differences in depth of disclosure among par-
ticipants who did comply, in contrast, examined the full set of
mediators (as in the prior studies).
For the analysis of differences in rates of compliance, path
model estimates derived using SAS’s Proc Calis supported the
theorized mediating effect of psychological comfort. As we
predicted, smartphones (vs. PCs) were associated with greater
psychological comfort (b
device !comfort
¼.12; t ¼4.25, p<
.001), and greater psychological comfort was associated with a
higher probability of compliance (b
comfort !compliance
¼.08;
t¼3.48, p<.001). Critically, we also observed a significant
indirect effect of device on compliance through comfort (b ¼
.01; t ¼2.68, p¼.007). Thus, the greater willingness to com-
ply with a request for private information was in part driven by
the enhanced feeling of psychological comfort that participants
associated with their smartphone versus PC (H
2
).
Next, to test for the proposed drivers of depth of disclosure
as in Study 3, we collected MTurk assessments of the product
descriptions provided by the subset of participants who dis-
closed the private product. As in Study 3, we estimated the
model in which depth of disclosure was driven by two parallel
paths: one in which smartphones yield greater psychological
comfort (device !comfort !self-disclosure) and another in
which smartphones induce greater attentional narrowing on the
disclosure task (device !attentional narrowing !self-disclo-
sure). For this study, we measured depth of disclosure as a
latent construct with two manifest measures: sensitivity of the
11
Of the 1,197 respondents who disclosed a private product, 222 provided
narratives that failed the preregistered criteria (e.g., too short for textual
analysis), leaving 975 descriptions for analysis.
12
The effect of device on depth of disclosure is strengthened when we
controlled for the length of the description, which tended to be longer on
PCs (M
smartphone
¼4.85 vs. M
PC
¼4.69; F(1, 4,740) ¼21.52, p<.001).
Melumad and Meyer 39
product described and sensitivity of the information conveyed
in the product description (r ¼.54; a¼.70).
13
The model provided an excellent fit to the data (Bentler
comparative fit index ¼.998; root mean square residual
¼.000) and offered support for the hypothesized causal struc-
ture. Consistent with H
2
,wefindsupportforpositivepath
coefficients leading from smartphone (vs. PC) to psychological
comfort (b
device !comfort
¼.12; t ¼8.37, p<.001), and
from psychological comfort to depth of disclosure (b
comfort !
disclosure
¼.39; t ¼5.43, p<.001). Likewise, consistent with
H
3
, we find support for positive path coefficients from smart-
phone (vs. PC) to attentional narrowing (b
device !attentional
narrowing
¼.03; t ¼1.91, p¼.028), and from attentional nar-
rowing to depth of disclosure (b
attentional narrowing !disclosure
¼
.10; t ¼6.13, p<.001).
Study 5: Compliance with CTAs in Web Ads
To test for the real-world generalizability of the results of Study
4, in the final field study we again examined whether consu-
mers were more willing to provide sensitive information on
their smartphone versus PC. To do this, we collaborated with
the advertising technology company Taboola (https://www.
taboola.com), which provided data on the daily performance
of 19,962 CTA web ad campaigns that were run on both smart-
phones and PCs between November 2018 and August 2019.
The data included a total of 631,013 observations representing
each of the dates on which the 19,962 campaigns were run.
Call-to-action ads are of interest because they request per-
sonal information from consumers to further interact with the
firm or brand. The ad campaigns spanned 22 different cate-
gories (e.g., finance, music, family) and varied widely in the
sensitivity of the product/service advertised (e.g., online
games, fitness products, financial services) as well as the depth
of personal information that was being requested (e.g., email
addresses, estimated credit scores). Web Appendix 9 provides
examples of the CTAs for several ad categories. For each ad
campaign run on a given date, the data included information
about the platform on which the ad was targeted (smartphone,
PC), the ad category to which it belonged (e.g., health, dating,
lifestyle), the number of consumers who were presented with
the ad (i.e., impressions
14
), and the number of consumers who
complied with the information request in the ad (i.e., conver-
sions). To test whether consumers were more responsive to
CTAs eliciting personal information on their smartphone or
PC (H
1
), we calculated the conversion rate (i.e., the number
of conversions divided by the number of impressions) for each
ad category on each platform, which served as our main depen-
dent variable.
Results and Discussion
The campaigns included in the data reached extremely large
audiences, achieving 75.8 billion impressions over the ten-
month period of study. As is commonly the case with web ads,
however, the rate at which consumers clicked on the ads and
provided all the requested information—recorded as a conver-
sion—was quite low (e.g., Manchanda et al. 2006), with
84.68%of all ad-dates reporting no conversions on either PC
or smartphone. To account for this highly skewed distribution
of responses, we subjected the conversion rates across devices
to a series of negative binomial regressions that modeled con-
version rates as a function of (1) the device on which the ad was
administered and (2) fixed effects that controlled for variance
in conversion rates across ad categories, as well as interactions
between the device and ad category.
The results of these analyses, summarized in Web Appendix
10, robustly support H
1
. Consistent with the results of Study 4,
consumers were more likely to comply with requests for per-
sonal information in ads when using their smartphone versus
PC. Specifically, CTA ads on smartphones had an average
conversion rate of .28%, whereas those on PCs had an average
conversion rate of .02%(t ¼12.48, p<.001; see Web Appen-
dix 10). This effect was larger when analyzing just the subset of
ad dates for which there were nonzero conversion rates. Here,
the average conversion rate on smartphones was .54%relative
to .03%on PCs (t ¼25.89, p<.001). While the difference in
the conversion rates is small in absolute terms, the .26%
increase in conversions on smartphones (vs. PCs) translates
to millions of customer responses when applied to the billions
of impressions achieved across the campaigns.
One natural concern with this analysis is that the higher
conversion rates on smartphones may have accrued to factors
other than users’ willingness to self-disclose per se, such as
differences in the contexts in which the devices are used or
how the ads are displayed/formatted. If consumers are indeed
more willing to self-disclose on their phone (vs. PC), differ-
ences in conversion rates should be larger among ad categories
where the information elicited is more personal or sensitive in
nature, such as ads for dating sites, financial services, and
health products; in contrast, conversion rates should be more
comparable for categories that are less sensitive, such as music,
food, and news. We therefore examined how differences in
conversion rates varied by ad category.
To test the association between the sensitivity of the ads and
compliance across devices, Taboola provided ad content for a
random sample of 1,061 ads from each of the 23 categories
that, importantly, included descriptive titles for each ad (e.g.,
“Calculate Your Maximum Social Security Benefit Instantly”
from the “finance” category). We then recruited 686 MTurk
participants to assess (on a seven-point scale) up to ten of the ad
titles along three correlated items measuring the personal
nature and sensitivity of the ad: “This ad is about a very sen-
sitive/private topic,” “If I responded to this ad I would expect to
be asked a number of personal questions (e.g., my address,
finances),” and “If I provided information requested in this
13
We obtain similar path-model results when the two measures of disclosure
are modeled separately.
14
We counted an impression in this data set only if an ad was successfully
served to viewers (e.g., an ad blocked by an ad blocker would not register as an
impression).
40 Journal of Marketing 84(3)
ad I feel I would be disclosing something intimate or private
about myself.” We averaged across these items to create a
“personal/sensitive nature” index for each ad title (a¼.70).
We then estimated the following negative binomial regres-
sion, which modeled the observed conversion rates for each of
the individual ad campaigns as a function of device, judged
sensitivity of each ad category (from which ads were sampled),
and the interaction between sensitivity and device:
CR ijk ¼boþb1Dkþb2Sij þb3Dk$Sij
where CR
ijk
is the observed conversion rate for ad campaign i
from ad category j on device k, D
k
is a dichotomous indicator
for device k (smartphone ¼1, desktop ¼"1), S
ij
is the judged
sensitivity of ad campaign i from category j, and b
0
,...,b
3
are
empirical parameters. The key parameter of interest is the
coefficient of the interaction between device and sensitivity
(D
k
$S
ij
).
The results, presented in Web Appendix 11, confirmed that
consumers were more likely to comply with calls to action in ad
categories that were more personal and/or sensitive on a smart-
phone versus a computer. Specifically, we find a positive effect
of smartphone (vs. PC) (b ¼1.98; t ¼4.27; p<.001), as well
as a negative main effect of perceived ad sensitivity (b ¼".34;
t¼"2.53; p¼.011) on conversion rates. More importantly,
we find a positive interaction between device and ad sensitivity
on conversion rates (b ¼.40; t ¼2.78; p¼.005), suggesting
that, as hypothesized, the tendency for conversion rates to be
higher on smartphones (vs. PCs) is enhanced for ads that are
more personal or sensitive in nature.
As an illustration of this interaction effect, the three cate-
gories judged to be the most sensitive on average—dating
(M ¼4.64), financial services (M ¼3.88), and health (M ¼
3.56)—were associated with the largest differences in average
conversion rates between smartphones and PCs (dating:
M
smartphone "PC diff.
¼.67%,p<.001; financial: M
smartphone "
PC diff.
¼.51%,p<.001; health: M
smartphone "PC diff.
¼.40%,
p<.001). In contrast, categories judged to be among the least
sensitive in nature—music (M ¼2.13), fashion (M ¼2.87), and
pets (M ¼2.93)—showed very limited compliance overall, and
no statistically significant difference between smartphones and
PCs (music: M
smartphone "PC diff.
¼".0004%,p¼.164; fashion:
M
smartphone "PC diff.
¼".002%,p¼.096; pets: M
smartphone "
PC diff.
¼".008%,p¼.514; see Web Appendix 12).
General Discussion
In recent years, smartphones have increasingly come to sup-
plant personal computers as the major medium through which
consumers provide and share information. In this work we offer
evidence that this change has served to alter not only how
consumers communicate but also what they share: across five
experimental and field studies, we find that consumers tend to
be more self-disclosing on their smartphones than their PCs.
We find this effect robustly across (1) multiple domains of
user-generated content (e.g., six field data sets, open-ended
survey responses), (2) different forms of self-disclosure
(e.g., self-generated posts, admissions of embarrassing infor-
mation), and (3) different measures of disclosure (automated
measures, external human judgments, the writers’ own percep-
tions of depth of disclosure, and compliance with sensitive
CTAs in ads). We also find evidence for the two proposed
parallel drivers of this effect, showing that enhanced disclosure
on smartphones (vs. PCs) is driven by greater feelings of psy-
chological comfort that consumers associate with their phone
and the relative difficulty of generating content on the device,
which narrows attention on the disclosure task at hand (and
away from peripheral cues or thoughts).
One question that remains is whether consumers are gener-
ally aware that they disclose differentially across their devices;
for example, when consumers use their phone to tweet, are they
aware that they may be revealing more about themselves?
While in Study 3 participants reported being more disclosing
after completing the disclosure task on their phone versus PC, it
is not clear whether they were aware of a general effect of this
device. To examine this issue more directly, we recruited an
independent sample of 544 MTurk participants and asked them
to indicate their beliefs about their willingness to disclose
across devices.
15
We also administered similar scales to those
used in Studies 3 and 4 to measure the degree to which parti-
cipants believed that they experience greater psychological
comfort and attentional narrowing on their smartphone versus
PC. All responses were this time rendered on comparative
scales, which were anchored at 1 (“Much more true of my
laptop”) and 5 (“Much more true of my smartphone”), with
3 (“Equally true of my laptop and smartphone”) serving as the
midpoint.
The results suggest that consumers seem to be generally
aware of the differences in self-disclosure observed in our
studies. Respondents indicated that they tend to be more self-
disclosing when creating content on their smartphone
compared with their PC (M
disclosure
¼3.13; t(545) ¼3.42,
p<.001). Furthermore, participants reported that they gener-
ally associate stronger feelings of psychological comfort
(M
comfort
¼3.69; t(545) ¼21.36, p<.001) and tend to feel a
greater attentional narrowing in activities (M
attentional narrowing
¼
3.52; t(545) ¼16.99, p<.001) when using their smartphone
versus PC. Thus, consumers seem to be at least somewhat aware
of the distinct psychological experiences they undergo on their
smartphone versus PC, as well as the differences in the types of
information they tend to disclose across devices.
Strategic Implications for Managers
The finding that consumers are more willing to self-disclose on
their smartphone (vs. PC)—and the identified mechanisms that
give rise to it—hold several actionable implications for
15
To measure participants’ self-reported disclosure behavior, we constructed a
new index based on four items: “When I use this device to post content on
social media, chat with friends, etc. I tend to be ...” (1) “less censored,” (2)
“less inhibited,” (3) “more honest in what I write,” and (4) “more disclosing of
what I really think or feel” (a¼.87).
Melumad and Meyer 41
marketers. Perhaps the most direct is that if a firm wants to
obtain sensitive or personal information from consumers, it
should target them on their smartphone rather than their PC.
We found evidence for this in Study 4, for example, where
participants who were asked to admit to purchasing a private
or embarrassing product were 6%less likely to do so when
asked on their PC than when asked on their smartphone. While
small in absolute terms, this difference would be quite mean-
ingful for any firm relying on consumer self-reports to gauge
consumption rates. Further evidence is provided from the large-
scale field data in Study 5, where consumers were more com-
pliant when ads requesting information were targeted on their
smartphone (vs. PC)—a difference that was especially large for
requests that were more sensitive in nature. Again, while the
absolute size of this effect mayseem small (M
smartphone "PC diff.
¼
.19%;M
smartphone
¼.28%vs. M
PC
¼.09%), when applied to the
billions of impressions produced by the ad campaigns, this dif-
ference in compliance rates potentially translates to millions of
additional customer leads for firms.
The finding that social media posts and open-ended survey
responses produced on smartphones were more self-disclosing
also suggests that smartphone-generated content may offer
more diagnostic or accurate insights into consumer prefer-
ences. Consistent with this, in Study 3 participants self-
reported that they had disclosed information that was more
private and personal on their smartphone than did participants
on their PC. Building on this result, future work might explore
whether the observed effects generalize to domains of disclo-
sure with a measurable benchmark of “truth” or accuracy of
information. For example, might consumer preferences dis-
closed on smartphones (vs. PCs) be more predictive of market
outcomes?
Finally, we found that the greater depth of disclosure in
smartphone-generated content has the major downstream con-
sequence of being more persuasive to outside readers. Study 2
demonstrated that restaurant reviews written on smartphones
were perceived as 5%more persuasive on average than those
written on PCs and, when positive, were associated with a 2%
increase in readers’ interest in visiting the restaurant. The effect
of device use was even larger on the high extremes of the
perceived persuasiveness and interest scales. Reviews written
on smartphones were 33%more likely to receive a “7” on a
seven-point scale of persuasiveness (raw difference þ4.5%)
and 28%more likely to receive a “7” on a seven-point scale
of interest in visiting the restaurant (raw difference þ3.9%).
Thus, firms could identify which reviews will be more persua-
sive to outside customers by simply identifying their originat-
ing device.
Leveraging the Psychological Drivers to Enhance
Self-disclosure
In our research we showed that content produced on smart-
phones (vs. PCs) tends to be more self-disclosing because of
two drivers: (1) the tendency for smartphones to be associated
with heightened psychological comfort and (2) the narrowing
of attention that often arises while completing a task on the
device. We conceptualize these drivers as two independent
factors with a relative influence that likely varies across con-
sumers as well as contexts. For example, consumers vary in the
degree to which they derive psychological comfort from their
phone as a function of whether they use the device more for
work versus hedonic purposes (Melumad and Pham 2020).
Nevertheless, according to our theory, consumers who derive
little psychological comfort from their phone might still be
more self-disclosing when generating content on the device
because of the narrowing of attention that tends to arise when
writing on its smaller keyboard and screen. Similarly, one
might conjecture that attentional narrowing would not arise
when performing a simple task on one’s phone, such as clicking
amultiple-choicebuttonwhenrespondingtosurveyquestions;
in such cases, however, consumers might still show enhanced
depth of disclosure due to the feelings of comfort that often
arise on the device.
These two paths suggest actionable levers by which firms
might influence consumers’ willingness to self-disclose. For
example, if firms wish to encourage consumers to be more
self-disclosing in survey responses, our findings suggest that
they should design surveys in a way that enhances respondents’
feelings of psychological comfort—such as by exposing them
throughout the survey to images or even sounds that are com-
forting or relaxing. From a consumer-welfare perspective, the
mechanisms also have implications for consumers who want to
avoid being too self-disclosing in certain contexts; for instance,
they might consider eschewing their phone for their laptop
when writing a work email or when responding to long,
open-ended survey questions.
Extensions to Emerging Technologies and Boundary
Conditions
One suggested area for future research is exploring whether the
observed effects extend to newer technologies. Here, we
argued that the small size of smartphone screens has an
attention-narrowing effect that heightens consumers’ willing-
ness to self-disclose when generating content. Given that some
new technologies—such as smart watches—have screens so
small that they render typing extremely difficult, we predict
that attempting to write on such small devices may prevent
consumers from disclosing altogether. Another emerging tech-
nology to consider is voice-enabled assistants such as iPhone’s
Siri or Amazon’s Alexa. Would the observed effects on smart-
phones versus PCs still hold if consumers used voice com-
mands instead of written responses to disclose personal
information? One might predict, for example, that sharing per-
sonal information verbally—rather than writing it—might
evoke the psychological experience of face-to-face interaction,
which (as noted previously) has been shown to reduce disclo-
sure relative to written communication through computers
(e.g., Joinson 2001). We believe that these are important and
intriguing questions that merit future investigation.
42 Journal of Marketing 84(3)
Future research could also further explore the role that the
psychological comfort associated with one’s smartphone plays
in enhancing depth of disclosure on the device. For example,
incidental experiences that precede disclosure—such as the
extent of comfort derived from browsing certain online content
on the device—may influence users’ subsequent willingness to
disclose in the short term, regardless of the device on which
they are responding. In this case, firms may want to expose
consumers to certain types of comforting or relaxing content
prior to eliciting sensitive information from them. Moreover, to
the extent that this psychological comfort arises from one’s
established associations with the device (Melumad and Pham
2020), consumers might be more willing to respond to personal
or sensitive questions on their own smartphone than on an
otherwise similar phone belonging to someone else. This bears
important implications for medical professionals, for example,
who have begun to increasingly administer surveys to patients
using in-office tablets. Our findings suggest that medical pro-
fessionals might consider sending sensitive survey questions to
their patients so that they can respond instead on their personal
smartphone.
Future research could also test for boundaries of the types of
information that consumers are willing to reveal on their smart-
phone versus PC. While we found evidence for the effect
among some highly sensitive disclosures (e.g., providing one’s
bankruptcy history or issues with substance abuse in Study 5),
many of the disclosures examined in our studies did not involve
highly sensitive information—for example, descriptions of res-
taurant experiences (Study 1). While we do find that the effect
extends to admissions of embarrassing or private purchases
(Study 4), it is possible that the observed differences across
devices may disappear when it comes to disclosures that could
be more personally harmful, such as sharing one’s social secu-
rity number or financial information.
Acknowledgments
The authors thank Rachel Zalta and Andres Salazar from Taboola for
their data contribution.
Associate Editor
Markus Giesler
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to
the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, author-
ship, and/or publication of this article.
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