2. devices facilitate mobile social media to collect more online and offline information of the users
(e.g. real-time location information) than traditional social media, enabling firms to be more
sensitive to their customer needs (Lin, Li and Wang, 2017). Furthermore, ubiquity is another
significant trait differentiating the mobile social media from the traditional social media, by
allowing users to conveniently access the social network anytime and anywhere (Shankar
et al., 2010; Watson et al., 2002). This trait results in the main trend that most of the social
media users constantly visit their profile and other social media accounts via the mobile
devices. In order to take the advantage of the potential benefits of mobile social media, many
firms are struggling to achieve high mobile social media popularity, which is referred to
as the number of users (Butler et al., 2014), views (Jang and Lee, 2016), comments and “likes”
(De Vries et al., 2012) and/or re-tweets (Zhang, Peng, Zhang, Wang and Zhu, 2014) on a mobile
account. Nowadays, an increasing number of firms are allocating more budgets for the design
and development of their mobile social media accounts in order to achieve high user
popularity and ultimately business success.
According to the definition of mobile social media popularity, high user popularity
means a great number of followers and active engagements in a firm’s mobile social media
account. As the followers are potential customers, whether the followers can be transformed
into actual customers is of course the main concern of the firms. Previous research suggests
that potential customers engaging in the social exchange relationships with firms are more
likely to convert into actual customers or to increase loyalty and satisfaction (Shiau and Luo,
2012; Wang et al., 2016). According to Emerson (1976), relationships between the account
followers and account owners can be regarded as the social exchange relationships[1],
meaning that there is a high possibility of the followers becoming actual customers due to
their engagement in the social exchange activities. Thus, high user popularity (i.e. numerous
account followers) may imply high customer acquisition. However, limited empirical
evidences have been found regarding the relationships between mobile social media
popularity and firm’s customer acquisition. The conversion value of mobile social media
popularity is not yet fully understood (Lu et al., 2016). In order to extend the knowledge
boundary of mobile social media popularity and to realize the business value of mobile
social media popularity, this paper attempts to confirm the conversion value of mobile social
media popularity by examining the relationship between firms’ mobile social media
popularity and firms’ customer acquisition.
Furthermore, research has consistently found that it is difficult for firms to achieve high
user popularity (Du, 2014). This situation remains or is even more serious in the mobile
context because of the challenges derived from the unique features of mobile social media.
First, users are forced to cope with information overload (Lee et al., 2016), which are even
more serious in the mobile environment, as the limited screen size of mobile devices restricts
functionality and content availability (Hoehle and Venkatesh, 2015). Second, information
oversharing (Kaplan and Haenlein, 2012) becomes an emerging concern for mobile users
(Lee and Rha, 2016; Eastin et al., 2016) because firms can easily and invisibly access users’
personal information via the mobile applications. Plus, the context-awareness becomes a
new requirement for the mobile social media because the mobile device is increasing
ubiquitous (Lee and Benbasat, 2004). We find that some challenges are bequeathed from
the online social media (e.g. information overload) but some challenges are emerging in the
mobile context (e.g. context-awareness). Confronted with the bequeathed challenges, we do
not know what previous experiences are still useful in the mobile context. Furthermore, the
new knowledge on how to overcome the challenges originated from the mobile context is
also limited. However, the lack of the clarification on the availability of previous experiences
learnt from traditional social media operations and the missing of new guidance for firms to
set up the mobile social media accounts for high popularity are both the key reasons for why
many firms fail to achieve high mobile social media popularity.
1387
Achieving
mobile social
media
popularity
3. In response to the issues of achieving high social media popularity, research has
introduced the social construction perspective to guide the studies. A large proportion of
these studies has focused on the influence from other social actors and/or external social
settings, such as social influence on individual’s behavioral changes and decision making
(e.g. Lin and Lu, 2015; Zhao et al., 2016). These studies have primarily considered socially
constructed behavior at the interpersonal level. However, besides influences from others and
social settings, the intrapersonal power of social cognition is also an important factor
affecting users’ online behavior (Zhang, Zhao, Cheung and Lee, 2014). Many studies state
that there are two coexisting processes in human social cognition (e.g. Chaiken, 1980; Zhang,
Zhao, Cheung and Lee, 2014): heuristic cognition, in which social actors rely on heuristic
cues (i.e. information cues that trigger heuristic cognition) to make an immediate decision
(Tam and Ho, 2005); and systematic cognition, in which social actors rely on systematic cues
(i.e. information cues that trigger systematic cognition), to make a deliberate decision
(Todorov et al., 2002). Heuristic and systematic cues can be embedded in the interfaces of
social media account mostly in the forms of information exhibition (Zhang, Peng, Zhang,
Wang and Zhu, 2014). The previous literature evidences the significant power of heuristic–
systematic cues on the social media popularity. Several studies have identified that some
heuristic cues, such as content freshness (Du, 2014), and systematic cues, such as degree of
attractiveness (Zhang, Peng, Zhang, Wang and Zhu, 2014), influences the user’s behaviors in
the social media. However, all of these studies are merely conducted in the context of
traditional social media. In order words, the limited research examines the effect of heuristic
cues and systematic cues on user decision making in the context of mobile social media,
indicating that we still cannot understand well what previous or new heuristic/systematic
cues are available in the mobile social media context. To bridge this gap, according to the
challenges facing the firms for high mobile social media popularity, this paper identifies two
heuristic cues (i.e. content freshness and source credibility) from the existing literature of
traditional social media which have not yet been tested in the mobile context. Furthermore,
in order to generate the new guidance for firms to achieve high mobile social media
popularity, this paper also develops a new systematic cue (i.e. transaction relevance) which
is only applicable to the context of mobile social media. To do so, this paper examines these
two heuristic cues and a systematic cue embedded in the media interfaces to determine their
effects on firms’ mobile social media popularity.
In this study, data are collected from the peer-to-peer (P2P) lending firms and their mobile
social media accounts. P2P lending is a new internet finance model (Cai et al., 2016) using
various online sources (e.g. mobile social media) to rally online individual customers for
collective funding (Burtch et al., 2014; Liu et al., 2015). The market of P2P lending is really
competitive because numerous firms get involved in this promising field (Yan et al., 2018).
Furthermore, the emergence of massive P2P lending firms and their high failure rate caused
by the turbulent market, moral risk and information asymmetry (Tao et al., 2017) lead to the
fact that many P2P lending firms are usually new and still in the entrepreneurial stage.
For their businesses to thrive, P2P lending firms need to constantly establish and maintain
close online relationships and initial trust with their customers (Jiang et al., 2018). With the
data from P2P lending firms, the findings of this paper are particularly for online enterprises
in the initial stage to understand the conversion value of mobile social media popularity and
to learn about what can be done to achieve high mobile social media popularity in the
competitive situation.
Therefore, based on the social exchange theory (SET), we attempt to confirm the positive
relationship between mobile social media popularity and firms’ customer acquisition.
The findings extend the understanding on the business value of mobile social media
popularity. Besides, this study also contributes to the literature of heuristic–systematic
model (HSM) by extending and examining the effect of heuristic–systematic cues in the
1388
INTR
29,6
4. context mobile social media. Specifically, this paper confirms the effect of heuristics cues on
mobile social media popularity as well as develops a new systematic cue to guide firms to
achieve high mobile social media popularity.
2. Literature review
This section gives an overview of the literature on social media popularity from the social
construction perspective and outlines the key theoretical building blocks (i.e. SET and HSM)
that we used to develop our hypotheses and research model.
2.1 Social media popularity and the emerging mobile context
Social media studies based on a social construction perspective (e.g. social cognition) have
attracted increasing attention because of their theoretical explanatory power with respect to
people’s cognition and behaviors (McFarland and Ployhart, 2015).
Based on our literature review, research on social media popularity falls into three
categories: social media adoption and continuing use (e.g. Hsu and Lin, 2008); factors
influencing popularity (e.g. Lee and Hong, 2016); and the general values and effects of
popularity (e.g. Uzunoğlu and Kip, 2014). Research in these three categories has primarily
focused on the popularity of traditional social media, such as blogs. Research on mobile
social media popularity is still rare. The earliest mobile-related study was carried out in 2015
(i.e. Hoehle et al., 2015). In addition, the volume of research on influential factors and the
values and effects of popularity is lower than that of research on social media adoption and
continuing use. More research is therefore needed on the factors influencing popularity as
well as their values and effects on (online) business (e.g. P2P lending) especially in the
mobile context. Table I gives a summary of the relevant research.
2.2 Social exchange theory (SET)
SET explains the behaviors/relationships related to the process of resource exchange
(Emerson, 1976). The differences between social exchange and economic exchange are
mainly the nature of the benefits gained, the exchange rules and the basis of exchange
relationships (Gefen and Ridings, 2002; Konovsky and Pugh, 1994). First, rewards from
social exchanges can be tangible or intangible (Liu et al., 2016). Second, there are no legal
rules or agreements governing social exchange relationships, implying that the relationship
is voluntary (Colquitt et al., 2014). Third, trust, commitment and good impressions are the
crucial factors in developing and maintaining a long-term reciprocal relationship between
social exchange partners (Lin and Huang, 2010).
SET has been adopted in various research areas because of its explanatory potential.
Many scholars have used SET to explain individual behaviors in e-business (e.g. Wang et al.,
2016) and on social media (e.g. Yu et al., 2015). Current research is mainly focused
on the social exchange relationships between individual users (e.g. Yan et al., 2016).
The relationships between individual users and online business accounts owned by firms
can also be regarded as social exchange relationships. Nevertheless, research on such
relationships between individual users and firms is limited.
2.3 Heuristic–systematic model (HSM)
The HSM is a classical social cognition model usually applied to explain how individuals
accept and process information (Chaiken, 1980). This model claims that both heuristic
cognition and systematic cognition are able to be simultaneously involved when people
process information during decision making. We review the HSM-based IS research to
summarize the heuristic–systematic cues used in the previous literature (see Table II).
1389
Achieving
mobile social
media
popularity
5. Heuristic cognition and heuristic cues. Heuristic cognition involves processing information
cues using primitive schemas or simple decision rules (Todorov et al., 2002). It is a process
whereby “people consider a few informational cues or even a single informational cue and
form a judgment based on these cues” (Tam and Ho, 2005). Heuristic thinkers usually put in
little effort and rely on accessible and minimal information cues to draw a quick conclusion
(Zhang, Zhao, Cheung and Lee, 2014). HSM holds that the availability and salience of certain
information cues allows them to become influential proxies to trigger individuals’ mental
shortcuts or cognitive heuristics. In this case, these information cues are called heuristic
cues as they trigger individuals’ heuristic cognition.
As shown in Table II, there are many heuristic cues impacting the users’ online behaviors
in different context. For example, the quantity of recommendation as a heuristic cue
positively affects user’s time spent in considering products (Gupta and Harris, 2010); user
activeness (Zhang, Peng, Zhang, Wang and Zhu, 2014) and content freshness (Du, 2014) as
the heuristic cues have a positive influence on the popularity of online social media;
source credibility as a heuristic cue positively influences individual purchasing intention
Category Research orientation Theoretical basis Key finding (source)
Adoption
and
continuance
Continuing blog use Social influence Community identification and attitude toward
blogging are important factors influencing blog
users’ continuing use intention (Hsu and Lin, 2008)
Social media
adoption
Social influence;
TAM
Social influence through platform quality affects
users’ usage intention (Wang and Lin, 2011)
Social media
satisfaction and
continuing use
Social exchange Users’ continuing social network use is not only
due to their satisfaction, but also to the trade-off
between what they received and what they
sacrificed in its use (Hu et al., 2015)
Mobile social media
continuing use
intentiona
Hofstede’s five
cultural values
Espoused national cultural values moderate the
relationships between mobile social media
application usability and continuing use
intention (Hoehle et al., 2015)
Mobile social
commerce continuing
usea
Expectation
confirmation model
Information privacy concerns influence social
media users’ perceptions of usefulness (Hew
et al., 2016)
Social media loyalty Psychological
ownership; social
influence; TAM
Social influence is a critical driver of
psychological ownership, which has a
significant effect on customers’ loyalty to social
media (Zhao et al., 2016)
Influential
factors
Social media post
popularity
Heuristic–systematic
model
Content factors outperform contextual factors in
affecting the popularity of a post (Zhang, Peng,
Zhang, Wang and Zhu, 2014)
Users’ active social
media behaviors
Social exchange;
social cognition;
social support
Ten social construction factors influence users’
motivation for information sharing and social
support (Oh and Syn, 2015)
Users’ reaction to
social media ads
Reasoned action;
social influence;
persuasion
Informativeness and advertising creativity are
key drivers of a favorable response to social
media ads (Lee and Hong, 2016)
Value and
effects
Followers’ behavior
in electronic
word-of-mouth
Consumer
socialization
Followers who use social media heavily and
follow many brands are most likely to tweet
brands (Chu and Sung, 2015)
Negative
word-of-mouth
communication
Social support;
cognitive dissonance
Contextual, individual and social networking
negatively influences word-of-mouth
communication (Balaji et al., 2016)
Note: a
This research includes the mobile context
Table I.
Social media
popularity studies
based-on the social
construction
perspective
1390
INTR
29,6
6. (Zhang, Zhao, Cheung and Lee, 2014) and this cue also influences users’ information
adoption behaviors on the online communities (Zhang and Watts, 2008). However, the
effects of these heuristic cues are only examined in the non-mobile context. Since the
differences between mobile social media and online social media are significant, we do not
know what heuristic cues are still suitable and available in the mobile context.
In this paper, according to the challenges derived from the unique feature of mobile social
media (i.e. the problems of more serious information overload and the information
oversharing), we thereby select two potentially valuable heuristic cues: content freshness
and source credibility to examine their effects in the mobile context.
Content freshness often refers to the “up-to-dateness” of a website’s content
(Lewandowski, 2008). It has been used as a criterion to evaluate the quality of a
website’s information (Zhang and Peng, 2015). In the context of mobile social media, mobile
users suffer from more serious information overload problem because the limited screen size
of mobile devices restricts functionality and content availability (Hoehle and Venkatesh,
2015). Moreover, users are highly likely to end the active participation in social media due to
the issue of information overload (Jones et al., 2004). Several papers confirmed that the
timely updated content positively influences user’s information processing and adopting in
the situation of information overload (e.g. Cheung et al., 2008; Filieri and McLeay, 2014).
Du’s (2014) study further proofs that the heuristic cue of content freshness significantly
Influencing factora
Research context Heuristic cue Systematic cue Outcome Literature source
Online purchasing Online review
credibility
Argument quality Consumers’
purchase
intentions
Ruiz-Mafe et al.
(2018)
Review valence;
review volume
– Consumers’
purchase
intentions
Maslowska et al.
(2017)
Reviewer’s expertise Review sidedness Helpfulness of
online reviews
Chen (2016)
Quality of review;
source credibility
Informativeness;
persuasiveness
Purchase
intention
Zhang, Zhao, Cheung
and Lee (2014)
The quality of the
recommendation
– Time spent in
considering
products
Gupta and Harris
(2010)
Blog, microblog
and online
communities
The use of external
links and hashtags
– Retweeting or
liking behaviors
Lahuerta-Otero et al.
(2018)
Content freshness;
source credibility
– Blog readership
popularity
Du (2014)
Contextual factors
(e.g. user’s activeness)
Content factors
(e.g. content
attractiveness)
Post popularity Zhang, Peng, Zhang,
Wang and Zhu (2014)
Source
trustworthiness,
expertise, popularity
Amount of
information; content
objectivity
Information
retweeting
Liu et al. (2012)
Source likeability Argument quality Information
adoption
Zhang and Watts
(2008)
E-tourism Non-content cues (e.g.
web page design)
Content cues
(e.g. interestingness)
Destination
image
Kim et al. (2017)
Video conferencing Source credibility Argument quality Information
adoption
Ferran and Watts
(2008)
Note: a
This table only highlights the influencing factors of heuristic–systematic cues
Table II.
The overview of the
HSM-based research
in IS field
1391
Achieving
mobile social
media
popularity
7. influences the popularity of traditional social media. This means that the content freshness
can help users find the fresh message from a good deal of information and hence appeal to
users to visit the website (Li et al., 2003). Moreover, from the overview of HSM-based studies
(see Table II), we know that the content freshness is an important heuristic cue in the
social media context (Du, 2014; Kim et al., 2017) to influence users’ decision making.
However, the study on effect of content freshness in the mobile context is limited.
Source credibility refers to the “believability” of sources (Choi and Stvilia, 2015; Risius and
Beck, 2015), suggesting that a credible source is viewed as trustworthy and capable of
providing expertise (Hovland and Weiss, 1951). In the mobile context, information
oversharing problem is the emerging problem (Lee and Rha, 2016; Eastin et al., 2016) because
of the power of mobile devices in accessing the user information. Recent research has also
found that mobile users become very cautious in selecting mobile social media accounts due to
the risk of their personal information disclosure caused by information oversharing problem
(Kaplan and Haenlein, 2012). The previous literature states that to be credible is the effective
way to decrease the user’s risk concerns or sense of unsafe (e.g. Mun et al., 2013; Trumbo and
McComas, 2003). As an important heuristic cue, source credibility has been widely studied.
A great deal of research claims that source credibility has an important influence on
individuals’ online decision making (e.g. Liu et al., 2012; Zhang, Zhao, Cheung and Lee, 2014).
Furthermore, the HSM-based studies (see Table II) also indicate source credibility is a
significant heuristic cue in the various contexts, such as social media (e.g. Liu et al., 2012) and
online purchasing (e.g. Ruiz-Mafe et al., 2018). Nevertheless, the research of source credibility
in the mobile social media context is also limited.
Systematic cognition and systematic cues. Systematic cognition involves first considering
all relevant pieces of information; then elaborating on them; and finally forming a judgment
based on these elaborations (Todorov et al., 2002). Unlike heuristic cognition, systematic
cognition requires significant cognitive effort to evaluate systematic cues and assess their
validity in making judgments (Zhang, Peng, Zhang, Wang and Zhu, 2014). HSM posits that
people tend to perform systematic information processing when they have sufficient
motivation, ability and cognitive resources (e.g. Zhang, Zhao, Cheung and Lee, 2014). The
information cues used when people conduct systematic cognition are called systematic cues.
Among the extant studies (see Table II), systematic cues usually rely on the quality of
articles posted on the social media platforms (e.g. Liu et al., 2012; Zhang and Watts, 2008),
as well as the topics and effectiveness of posts (e.g. Zhang, Peng, Zhang, Wang and Zhu,
2014). In the mobile context, recent studies also agree that content quality is still important
for user satisfaction in the mobile applications (Lin, Wang and Hsu, 2017; Tarute et al.,
2017). Besides the content quality (an author-dependent quality), context-aware quality
that is often designable on the mobile interface is also crucial to affect mobile users’ trust
and satisfaction (Nilashi et al., 2015). The context-aware quality is an emerging and
significant requirement in the mobile context because of the increasing ubiquity of mobile
application (Zou et al., 2016). Context-awareness is a mobile application capability
detecting and understanding the situational context in which users employ mobile devices
to adapt its behavior in the users preferred manner accordingly (Lee and Benbasat, 2004;
Zhang et al., 2009). For instance, context-aware applications can utilize information on the
user’s mobile setting to adapt the interface to the users’ specific circumstances (Want et al.,
1995). However, the study on the relevant systematic cues of context-aware quality which
is critical in the mobile context is limited.
In the mobile context, transaction relevance, an essential performance of context-aware
quality of the mobile interface (Lum and Lau, 2002), is defined as the extent to which a mobile
application offers functions relevant to the mobile user’s purposes and context (Agarwal and
Venkatesh, 2002; Hoehle and Venkatesh, 2015). Assessing the transaction relevance requires
1392
INTR
29,6
8. users to make an effort to browse the mobile website and then use the function to eventually
(rather than immediately) obtain their expected gain. This process takes time for users to first
assess the function availability and then judge its design quality based on the users’
circumstances, requiring the users to have enough motivation and ability which triggers
systematic cognition (Chaiken, 1980). Thus, the transaction relevance can be regarded as the
systematic cue embedded in the interface of mobile social media accounts.
3. Hypotheses development
Based on the theories of SET and HSM, four hypotheses are generated as follows.
The research model is shown in Figure 1.
3.1 Mobile social media popularity and customer acquisition
The popularity of a firm’s mobile social media account reflects the number of relationships
between followers/users and firms (Du, 2014). According to the definition of social exchange
(Emerson, 1976), the relationships between account followers and account owners (in this
paper, P2P lending firms) can be regarded as social exchange relationships, as users
voluntarily follow firms’ mobile social media accounts to establish basic interactive
relationships without restrictions from legal rules or regulations (Colquitt et al., 2014).
Furthermore, such relationships are highly likely to increase the willingness of users who
engage in this relationship to become actual customers (e.g. investing or borrowing money
on a P2P lending platform). This is corroborated by many studies that have found that
users’ trust (Chang et al., 2013), commitment (Wang et al., 2016) and other important factors
that enhance users’ purchase intention (Shiau and Luo, 2012) are formed through various
social exchange activities (e.g. reading/commenting on posts by mobile social media
accounts). Thus, the higher the popularity of a P2P lending firm’s mobile social media, the
more follower relationships it has. Consequently, more followers/users in such relationship
are likely to become actual customers due to the influence of social exchange activities on
the relationship, hence improving the firm’s customer acquisition.
Hence, from SET perspective, H1 is formulated as follows:
H1. The mobile social media popularity of a firm, such as a P2P lending firm, is
positively associated with its performance in customer acquisition.
3.2 The heuristic influence of content freshness and source credibility
Content freshness refers to the “up-to-dateness” of the content presented on a mobile social
media account. Freshness is something relating to the latest, relevant, regularly updated
content as opposed to old, stale or obsolete content (Du, 2014). Examples of content
freshness indicators include recentness of data inception date (Wang and Strong, 1996),
changes in anchor texts (Lewandowski et al., 2006) and frequency of content updates or
changes (Yen et al., 2007). In the mobile context, users are facing with the more serious issue
Content freshness
Source credibility
Heuristic cues
Transaction relevance
Mobile social media
popularity
Customer acquisition
H1
H2
H3
H4
Systematic cue
Figure 1.
Research model
1393
Achieving
mobile social
media
popularity
9. of information overload than that in other types of media context due to the small screen size
of the mobile device and the limited function availability of the mobile application (Hoehle
and Venkatesh, 2015). Maintaining the content fresh, in this case, by updating the content
frequently is a viable way to keep the most recent information closest to the users’ reach
among mass amount of available information in the mobile context with the most recently
updated information is always kept on top of the message feeds (Freyne et al., 2010; Kim
et al., 2017). Such content freshness cues prompt “an immediate assessment of information
need” heuristic to the mobile users. The recentness and frequency of information updates
are also structural features that express the “situational relevance” of the content regarding
current events or new information (Saracevic, 1996; Wang and Strong, 1996). In finance or
P2P lending context, current information is essential for investors to make decisions. Digital
media users also often use freshness of content and information to determine which
websites to browse (Sundar et al., 2007; Zhang and Peng, 2015). In addition, high content
freshness can trigger users’ “up-to-dateness” of heuristic information, which is an important
factor in motivating users to read and share firms’ posts bearing new information (Syn and
Oh, 2015). Furthermore, users’ information sharing behaviors may lead to the bandwagon
effect (Wu and Lin, 2017), prompting more users to follow a firm’s mobile social media.
Hence, drawing on the heuristic cognition of the HSM, H2 is formulated as follows:
H2. Content freshness, as a heuristic cue, is positively associated with the mobile social
media popularity of a firm, such as a P2P lending firm.
Source credibility refers to the believability and authentication of the content presented on a
mobile social media account. Sources (or content) are treated as credible and authentic when
they are trustworthy and offer expertise (Self, 1996). Because mobile users are mostly
independent and unrelated individuals, their decision making may be heavily influenced by
characteristics such as expertise and trustworthiness displayed on a mobile social media
account (Zhang, Peng, Zhang, Wang and Zhu, 2014). Furthermore, the information
oversharing is also the significant concern of mobile users because of the powerful capability
of mobile applications to access user’s information. On the P2P lending firm’s mobile social
media accounts, complete certification information on their home pages as heuristic cues for
source credibility can lead mobile users to employ the “trustworthiness” heuristic, which tends
to influence their assessment of the credibility of the information provided and the security of
the information they share. In order words, heuristic cues for source credibility provide
cognitive quality and authority to recipients (Zhang, Zhao, Cheung and Lee, 2014). A mobile
social media account with sources credibility attracts more users to follow because these
cognitive heuristics or judgmental shortcuts serve as filtering mechanisms that help users
who have concern about the information oversharing confirm and consume information
sources more efficiently in the virtually limitless mobile environment.
Hence, drawing on the heuristic cognition of the HSM, H3 is formulated as follows:
H3. Source credibility, as a heuristic cue, is positively associated with the mobile social
media popularity of a firm, such as a P2P lending firm.
3.3 The systematic influence of transaction relevance
In the mobile context, context-awareness is the capability concerned with the nature of the
dynamic circumstances in which users employ mobile devices (Lee and Benbasat, 2004).
Transaction relevance as an essential performance of context-aware quality of mobile
interface (Lum and Lau, 2002) refers to the extent to which a mobile application offers
functions that are relevant to its mobile users’ purposes and the contexts to which they
pertain (Agarwal and Venkatesh, 2002; Hoehle and Venkatesh, 2015). Pertinent functions
(related to user’s purposes) and adaption of these functions (related to user’s contexts)
1394
INTR
29,6
10. can help mobile users focus more on the main features of a mobile application, and hence
effectively facilitates the fulfillment of their purposes (Venkatesh and Agarwal, 2006; Wells
et al., 2011). Thus, for P2P lending firms, functions related to user’s purpose of investing
and/or borrowing as well as adapted to the mobile interface are most useful for mobile users
and help them fulfill their goals on the mobile device, which improve their use experience.
Therefore, high transaction relevance in a firm’s mobile social media account can help the
mobile users reach their final goals more easily, thus increasing their loyalty to the accounts
(Chen et al., 2017) and forming systematic cognition of its good parametric quality. Thus,
high transaction relevance of a firm’s mobile social media account as a systematic cue can
help user realize the possibility of high user satisfaction and triggers the users’ desire to
constantly follow the firm’s mobile social media account.
Hence, drawing on the systematic cognition of the HSM, H4 is formulated as follows:
H4. Transaction relevance, as a systematic cue, is positively associated with the mobile
social media popularity of a firm, such as a P2P lending firm.
4. Research method
We selected WeChat, the most pervasive mobile social media application in China, as the
specific mobile social media platform for this study. First released in 2011, WeChat
enjoyed over 900m active mobile users per month in 2017[2]. The official WeChat account
offered by WeChat is designed for organizations to provide information (posts) to
followers, interact with them, and even offer services to them by developing lightweight
apps within the account.
4.1 Sampling
The website “www.wdzj.com” was used as the sample source because it is the first and
most authoritative portal for P2P lending in China, listing majority of the firms in the P2P
lending industry in the country. The website daily updates and releases relevant data
(e.g. number of turnovers, number of lenders and number of borrowers) of the P2P lending
firms who authorize the website to track their performance. All the P2P lending firms
listed in the website were used as our sample set for data analysis. By clicking on the
“name of P2P lending firms” on the website, we can jump to the official website of that P2P
lending firm. Initially, there were 195 listed platforms of the P2P lending firms as our
sample set. In total, 12 cases were removed because 4 of which did not have official
WeChat accounts (for further data collection) and the accounts of the other 8 firms were
inaccessible. As a result, 183 P2P lending firms (sample cases) were finally identified from
the website on January 25, 2015.
4.2 Measurement and data collection
Relevant data of the identified samples were collected from multiple sources: the website
www.wdzj.com (which lists the P2P lending firms with hyperlinks to their official websites),
official websites of the P2P lending firms, the website www.gsdata.com, and the official
WeChat accounts of the firms. Table III shows the operational definitions, measures and
specific data sources of each variable used in the study. Some data of the P2P lending firms
(e.g. data for customer acquisition) were collected from the www.wdzj.com. Some data of the
firms’ official WeChat accounts were collected from the www.gsdata.cn. This website is a
well-established social media aggregator in China thatz can automatically trace detailed
public data (including views and “likes”) of any official WeChat account. With the samples
of 183 P2P lending firms, we identified the official WeChat account IDs of these firms on
their official websites. Then, the official WeChat account IDs of 183 P2P lending firms were
input into the www.gsdata.cn. After doing so, the www.gsdata.cn can automatically track
1395
Achieving
mobile social
media
popularity
11. and report the data related to the entered official WeChat account IDs per day. Note here
that some data (i.e. source credibility, transaction relevance, number of accounts and type of
loan) were collected manually on the firms’ websites or their official WeChat accounts
according to the measuring criteria descried in Table III. All the final data were collected
between March 29 and April 4, 2015.
Variable Operational definition Measures References
Content
freshness
Frequency of content
updates
Total number of weekly postings of
an official WeChat account.
Secondary data retrieved from
“www.gsdata.cn”
Du (2014), Sundar et al. (2007),
Yen et al. (2007)
Source
credibility
Extent of disclosure on
the certification of an
official WeChat account
An account is categorized as one of
the three levels of source credibility:
low: no disclosure of certification
information; middle: only one item of
certification information is disclosed;
high: more than one item of
certification information is disclosed
(observed data from WeChat account)
Zhang, Zhao, Cheung and Lee
(2014), Zhang, Peng, Zhang,
Wang and Zhu (2014)
Transaction
relevance
Availability and fit of a
trading function on an
official WeChat account
An account is categorized as one of
the three levels of transaction
relevance: low: no trading function
entrance on the chat page; middle:
trading function entrances on the
chat page, but the function page
(second page after tapping the
entrance button) is not suitable for
display on mobile devices; high:
trading function entrances and a
suitable function page for display on
mobile devices (observed data from
WeChat account)
Agarwal and Venkatesh
(2002), Hoehle and Venkatesh
(2015), Lee and Benbasat
(2004)
Mobile
social media
popularity
Popularity of an official
WeChat account
WCI is used as the proxy data;
(secondary data retrieved from
“www.gsdata.cn,” value⩾0)
De Vries et al. (2012), Jang and
Lee (2016)
Customer
acquisition
Performance in
acquiring customers
The number of investors and
borrowers are used as the proxy
data (secondary data retrieved from
“www.wdzj.com”)
Choi et al. (2012), Stahl et al.
(2012)
Number of
accounts
Number of official
WeChat accounts that a
firm owns
Collecting the data based on the
information in the firm’s online
platform and confirming the data
with the help of a WeChat search
engine (observed data)
Type of loan Type of loan that a
firm offers
An account is categorized to offer
one of the two types of loans: non-
comprehensive loans;
comprehensive loans. The loan
classification is based on each firm’s
introduction on its official WeChat
account (observed data from
WeChat account)
Average
interest rate
Average interest rate of
the total investment
products of a firm
Secondary data retrieved from
“www.wdzj.com”
Table III.
Variables and
measurements
1396
INTR
29,6
12. Content freshness. The content freshness as a heuristic cue is often measured by the
recentness or frequency of content updates (e.g. Sundar et al., 2007; Du, 2014; Yen et al.,
2007), as it prompts “up-to-dateness” and “an immediate assessment of information need”
heuristics hints to the users. Recentness or frequency of content updates implies the
assurance of the content regarding current events or new information (Lewandowski, 2008;
Ntoulas et al., 2004; Poojary et al., 2018). Therefore, following the measurement used in
previous literature (e.g. Du, 2014), we used the total number of weekly postings as the
indicator of content freshness in this study. The content freshness data were automatically
collected by “www.gsdata.cn” when 183 samples were entered to generate their mobile
social media popularity indices.
Source credibility. Based on Zhang, Zhao, Cheung and Lee’s (2014) and Zhang, Peng,
Zhang, Wang and Zhu’s (2014) studies, we considered the number of certification
information items disclosed on a profile as the proxy for source credibility. The three levels
of source credibility are defined in Table III.
Transaction relevance. Transaction relevance is an essential factor related to the
context-aware quality of the mobile interface. Trading (i.e. investing and borrowing money)
via a mobile device is the main purpose for the followers of P2P lending firms’ official
WeChat accounts. Therefore, regarding the transaction relevance of official WeChat
accounts, mobile trading is a task-related function that should be provided on the official
WeChat accounts of P2P lending firms. Hence, to be consistent with the definition of
transaction relevance, we selected the availability and fit of a mobile trading function as the
proxy measures for transaction relevance in this study. Availability measures whether a
trading function is available on the official WeChat account (whether there is a function
relevant to serve the user’s trading purpose). Fit measures whether the trading
function page is suitably designed for display on a mobile device (whether the function
has context relevance). The three levels of transaction relevance are detailed in Table III.
Mobile social media popularity. In this study, mobile social media popularity is
demonstrated by the popularity of a firm’s official WeChat accounts. The proxy data used
for mobile social media popularity was the WeChat communication index (WCI), which is a
comprehensive indicator combining the number of views and “likes.” The WCI can be
generated by calculations based on a formula originally found on “www.gsdata.cn”
(the detailed formula is given in the Appendix).
Customer acquisition. Customer-related data (e.g. number of buyers) reflects a firm’s
customer acquisition (Choi et al., 2012; Stahl et al., 2012). In the context of P2P lending,
investors and borrowers are both customers of firms. Hence, customer acquisition is the
variable which consists of the number of investors and borrowers.
Control variables. To control differences among the P2P lending firms, we follow the
common statistical method which is the inclusion of control variables in the research model.
We found that some P2P lending firms have more than one official WeChat account. These
firms can attract users to follow their main account from another account, which may
influence the popularity of the main account (the main accounts were chosen as the sample
for our study). Hence, the variable Number of (official WeChat) accounts (that a firm owns)
was controlled in the research model. In addition, the various types of loan that firms offer
are associated with the themes of their accounts, which may influence the users’ intention to
follow them. Therefore, the variable Type of loan (that a firm offers) was also controlled. In
the previous studies of P2P lending, research has examined the impact of the change of
interest rate on the selection of different investment products as it reflected the expected
investment profit to some extent. Cai et al.’s (2016) study, for instance, found that interest
rate is one of the main factors influencing the purchase decisions of investors and borrowers
in the context of a single P2P lending firm. However, the impact of overall interest rate of a
1397
Achieving
mobile social
media
popularity
13. firm among multiple P2P lending firms who are competing for investment products has not
yet been confirmed. So, in this research model we also controlled the Average interest rate
(of the total investment products of a firm).
5. Results and post hoc analysis
5.1 Results
The descriptive statistics (before standardization) of the data set are summarized in Table IV.
From these statistics, which show customer acquisition data ranging from 0 to 38,150 and
mobile social media popularity varying from 0 to 1,151.090, we found that both a firm’s
performance in acquiring customers and the popularity of its official WeChat account
contributed to significant differences among the firms. According to the descriptive statistic,
we can find the minimum value of each continuous variable is 0. We go back to the data and
find seven P2P lending firms have this 0 value. Especially, the zero value of the customer
acquisition and average interest rate means the companies are “dying” in the competition,
which also indicates the fierce competition of the P2P lending market.
Table V gives the correlations (after standardization) of all the variables used in the data
analysis. The variance inflation factors of these variables were all less than 3.00, suggesting
that there was not a significant multicollinearity problem in this data set.
The research model was examined using SmartPLS 2.0 (after standardization).
The results are shown in Figure 2. First, a positive relationship between mobile social media
popularity and customer acquisition was confirmed (B ¼ 0.531, po0.001), which supported
H1 and explained the 29.2 percent of variance in customer acquisition. Second, two heuristic
Variable Min. Max. M SD
Customer acquisition 0 38,150 2,252.404 5,255.994
Mobile social media popularity 0 1,151.090 272.337 187.801
Content freshness 0 7 4.520 2.216
Average interest rate (%) 0 0.330 0.148 5.271
f
Transaction relevance: 0 ¼ low; 1 ¼ middle; 2 ¼ high 0 ¼ 58; 1 ¼ 43; 2 ¼ 82
Source credibility: 0 ¼ low; 1 ¼ middle; 2 ¼ high 0 ¼ 26; 1 ¼ 54; 2 ¼ 103
Number of accountsa
1 ¼ 90; 2 ¼ 84; 3 ¼ 7; 4 ¼ 0; 5 ¼ 2
Type of loanb
0 ¼ 21; 1 ¼ 162
Notes: n¼ 183. a
“1” means the firm owns one account, “2” means it owns two accounts, and the subsequent
numbers of accounts follow this formula; b
“1” refers to comprehensive loans; “0” refers to non-comprehensive loans
Table IV.
Descriptive statistics
Variable 1 2 3 4 5 6 7 8
1 Content freshness 1.664 – – – – – – –
2 Source credibility 0.167* 1.078 – – – – – –
3 Transaction relevance 0.133 0.104 1.070 – – – – –
4 Number of accounts 0.007 0.049 0.104 1.056 – – – –
5 Type of loan 0.023 0.043 0.055 0.004 1.027 – – –
6 Mobile social media
popularity 0.565** 0.239** 0.255** 0.130 0.002 2.379 – –
7 Average interest rate −0.165* −0.096 −0.047 −0.098 0.104 −0.273** 1.104 –
8 Customer acquisition 0.070 0.189* 0.156* −0.016 0.073 0.513** −0.166* 1.565
Notes: Diagonal elements are VIFs values from their indicators. Off-diagonal elements are correlations
between variables. *po0.05, **po0.01
Table V.
Correlation matrix
1398
INTR
29,6
14. cue-related variables showed significant positive relationships with mobile social media
popularity: content freshness (B ¼ 0.525, po0.001) and source credibility (B ¼ 0.133,
po0.05), which supported H2 and H3. Third, regarding the systematic cue-related variable,
there was a significant positive relationship between transaction relevance and mobile
social media popularity (B ¼ 0.132, po0.05). Hence, H4 was also supported. The content
freshness, source credibility and transaction relevance findings explained the 37.2 percent of
variance in mobile social media popularity. None of the control variables were significant.
5.2 Post hoc analysis
In this subsection, two further analyses were conducted for the following two reasons. First,
there is a need of the robustness check for the model test results (i.e. PLS analysis) (Ou and
Davison, 2011). To some extent, different analysis tool and method were applied in this part to
re-examine the proposed hypotheses. The results of these analyses will be compared to the
results gained by the PLS method to see whether any inconsistency occurs. By doing this,
we can make sure that the results gained from the different methods is stable, which indicates
the reliability of the results. Specifically, we use the ANOVA analysis (see Table VI) and linear
regression (see Table VII) to check the consistency of the conclusion drawn from previous PLS
analysis (see Figure 2). Besides, the other reason of this post hoc analysis is to get deeper
insights from the data collected. For example, based on the multiple comparison analysis, we
can further tell which level (high, middle or low) of heuristic–systematic cues is the effective
choice for achieving user popularity in the mobile social media context.
Post hoc analysis of heuristic–systematic cues. We conducted both ANOVA and multiple
comparison analyses based on the data concerning the average value of mobile social media
popularity in groups of different levels of content freshness, source credibility and
Content freshness
0.525***
0.531***
0.133*
0.132*
Significant path Insignificant path
0.106 –0.023 –0.033
Mobile social media
popularity
(R
2
=0.372)
Customer acquisition
(R2
=0.292)
Source credibility
Heuristic cues
Type of loan
Control variables
Transaction relevance
Systematic cue
Number of (WeChat)
accounts
Average interest
rate
Notes: *p<0.05; ***p<0.001
Figure 2.
PLS research
model results
ANOVA results Results
Grouping factor F p Comparisons Mean difference SE p
Content freshness 37.014 o0.001 High–Middle 102.005 26.494 o0.001
High–Low 285.736 33.212 o0.001
Middle–Low 183.732 31.666 o0.001
Source credibility 6.664 o0.01 High–Middle 24.596 30.614 0.423
High–Low 145.858 39.992 o0.001
Middle–Low 121.262 43.496 o0.01
Transaction relevance 4.982 o0.01 High–Middle 66.067 34.611 0.058
High–Low 96.153 31.538 o0.01
Middle–Low 30.086 36.992 0.417
Table VI.
Results of ANOVA
and multiple
comparison analyses
1399
Achieving
mobile social
media
popularity
15. transaction relevance (shown in Table VI). The definitions from Table III are adopted to
define the different groups with respect to their levels (high, middle and low) of source
credibility and transaction relevance. For consistency, we also grouped content freshness
into three levels (according to frequency of posting): the first 33 percent of the cases are
considered high level; the middle 33 percent middle level; and the remainder low level.
The ANOVA analysis confirmed that the average values of mobile social media
popularity showed significant differences in the three levels of content freshness
(F ¼ 37.014, po0.001), source credibility (F ¼ 6.664, po0.01) and transaction relevance
(F ¼ 4.982, po0.01). The results of the ANOVA analysis support the conclusion drawn on
the PLS analysis. The multiple comparison analysis found that increased content freshness
(low to middle (SE ¼ 31.666, po0.001), or middle to high (SE ¼ 26.494, po0.001)) could
bring about positive changes and significantly enhance mobile social media popularity.
Substantial differences in the average values of mobile social media popularity occurred
when comparing the high/middle (SE ¼ 39.992, po0.001; SE ¼ 43.496, po0.01) source
credibility and low source credibility. However, we found no significant difference between
high and middle source credibility (SE ¼ 30.614, p ¼ 0.423). Significant differences in the
average mobile social media popularity values occurred only when comparing high and low
transaction relevance (SE ¼ 31.538, po0.01).
Post hoc analysis of customer acquisition. It is useful for firms to assess whether their
customer diversity is associated with the popularity of mobile social media. We conducted a
regression analysis to correlate mobile social media popularity with number of investors
and number of borrowers. In addition, to assess the exact “conversion value” of popularity,
one other variable (i.e. firm’s turnover, retrieved from www.wdzj.com) was also considered
for regression analysis.
The results are shown in Table VII. We found that a firm’s mobile social media popularity is
positively associated with all three variables, the number of investors (B ¼ 0.475, po0.001),
the number of borrowers (B ¼ 0.488, po0.001) and the firm’s turnover (B ¼ 0.368, po0.001).
However, the adjusted-R2
of Model 3 (adjusted-R2
¼ 0.131) is substantially lower than that of
Model 1 (adjusted-R2
¼ 0.224) and Model 2 (adjusted-R2
¼ 0.240), suggesting a relatively
weaker relationship between the firms’ turnover and mobile social media popularity than that
of the number of customers (investors and borrowers). The control variable was not significant.
The insignificant effect of average interest rate suggests that firms’ mobile social media
popularity may outweigh the influence of average interest rate under circumstances. In other
words, it means that in the situation where customers are to choose financial products from
multiple P2P lending firms, average interest rate (which often reflects the expected investment
profit (for investors) and expected interest expense (for borrowers)) is no longer an important
decision-making factor; instead those firms with high mobile social media popularity are likely
to obtain more customers (i.e. investors and borrowers) to select their products. Because of the
Results
Model 1 Model 2 Model 3
Number of investors Number of borrowers Number of turnovers
Variable B t B t B t
Mobile social media popularity 0.475*** 6.996 0.488*** 7.262 0.368*** 5.130
Average interest rate −0.024 −0.357 −0.035 −0.519 −0.024 −0.328
F 27.256*** 29.755*** 14.777***
R2
0.232 0.248 0.141
Adjusted-R2
0.224 0.240 0.131
Note: ***po0.001
Table VII.
Results of
regression analysis
1400
INTR
29,6
16. insignificant influence of average interest rate on number of investors and borrowers, the
relationship between average rate and number of turnover which is related to the number of
investors and borrowers is therefore identically insignificant. When checking back to the data
profile of the P2P lending firms, we found that the average interest rate of those firms with
high mobile social media popularity is merely slightly lower than that of the firms with low
mobile social media popularity. This indicates that the variance of interest rate among these
companies is not significant enough, namely, most average interest rates of the firms are
similar. This further supports that becoming famous is more important than interest rate when
these P2P lending firms are competing for customers.
6. Limitations and future research
The current study has several limitations that affect future research opportunities and
further improvements. First, the study data were gathered within a short period of time and
the current findings must be further corroborated by future relevant longitudinal research.
Second, this research only involved quantitative analysis. An alternative study direction
would be to adopt a qualitative approach to explore cases of the firms’ successful mobile
social media use, which would contribute to an in-depth understanding of what makes a
firm popular on mobile social media. Third, the heuristic–systematic classification of
cognitive cues in this study is, to some extent, arguable because the cognitive efforts spent
by different individuals are diverse and heuristic–systematic cues are two polar sides of the
continuum that are hard to have a definite cut of category. In the further study, an
experiment for confirming the categories of cognitive cues should be scheduled. Finally, the
generalizability of the findings must be enhanced, as the samples were collected from only
one internet finance sector and the sample size was relatively small.
7. Discussion and implications
Based on SET and the HSM, this study explored the business value and mechanism of a
firm’s mobile social media popularity. This study confirmed the positive effect of mobile
social media popularity on customer acquisition by online enterprises such as P2P lending
firms. Furthermore, this study also found that firms’ mobile social media popularity was
influenced by both heuristic and systematic cues. This study extends existing research both
theoretically and practically.
Theoretically speaking, we extend the understanding of SET in the context of mobile
social media and stress the business value of mobile social media popularity. Drawing on
SET (Emerson, 1976), we demonstrate that mobile social media popularity can improve
the customer acquisition of P2P lending firms because such popularity is relevant to social
exchange relationships which are useful in encouraging users to become actual customers
(Shiau and Luo, 2012). The empirical results support the relationship between a firm’s
mobile social media popularity and its customer acquisition (B ¼ 0.531, po0.001). This
finding indicates that mobile social media, as an emerging digital platform, offers a
convenient way for online enterprises like P2P lending firms to establish social exchange
relationships (i.e. follower relationships) with their users. After following firms’ mobile
social media accounts, users are more likely to become actual customers because they can
obtain detailed information about firms and gain positive impressions via various social
exchange activities (e.g. posting articles) with firms. Our findings also support the claim
that sufficient engagement with potential customers (e.g. followers of the firms’ mobile
social media account) is a viable and important way for online enterprises to achieve
business success (Hollebeek et al., 2014). In addition, the post hoc analysis results indicate
that there is a limited effect of mobile social media popularity on the revenue acquisition
(e.g. number of turnovers) of the firms, which means that firms can gain more customers
1401
Achieving
mobile social
media
popularity
17. (e.g. investors and borrowers), rather than money, through achieving high mobile social
media popularity. Therefore, even though mobile social media is a critical means of
accomplishing business success for online enterprises like P2P lending firms through
acquiring additional customers, firms still need further strategies to gain more money
from these customers, namely, increasing the value of per customer transaction. This is a
promising area for future research.
Furthermore, compared with other research on social media popularity (e.g. Du, 2014;
Kim et al., 2017; Zhang, Peng, Zhang, Wang and Zhu, 2014), this study, focusing on the mobile
context, introduces a social cognition approach to the design of mobile websites and calls for
more attention to heuristic–systematic cues embedded in firms’ mobile social media accounts
that may influence user’s decision making. We also found that the previous literature focusing
the effect of heuristic–systematic cues is mostly conducted in the non-mobile context
(e.g. Chen, 2016; Kim et al., 2017; Ruiz-Mafe et al., 2018). Therefore, this study extends the
literature by relating heuristic–systematic cues to the social media popularity of an online
enterprise in the mobile context. In this paper, we select two heuristic cues from the previous
literature and develop a new systematic cue based on the literature. We found that heuristic
cues (i.e. content freshness and source credibility) and the systematic cue (i.e. transaction
relevance) are significantly correlated with the firms’ mobile social media popularity. These
results help the researchers to confirm that the heuristic cues of content freshness and source
credibility are still available and necessary in the mobile context. Apart from this, this study
also verified the effectiveness of the transaction relevance as a new systematic cues raised for
fulfilling the user’s new requirement (i.e. context-aware) in the mobile context. The findings
from transaction relevance also suggest that in the mobile context, apart from the hedonic
quality of social media accounts (Ali-Hassan et al., 2015), the transaction relevance is also a
main user concern. This study confirms that the availability and fit of a trading function as
a systematic cue on the mobile social media account of a P2P lending firm influences its users’
subsequent decision making (e.g. following this account). This implies that mobile social
media is not merely a simple tool for recreation and social relationship shaping. Instead, a
firm’s mobile social media account serves a greater purpose than just socialization (Ou and
Davison, 2011). Therefore, this study suggests that the “pragmatic quality” (Hassenzahl, 2004)
of a mobile social media account should also be considered as a main concern for achieving
high user popularity in the mobile context. In addition, the findings from heuristic–systematic
cues jointly indicate that appropriately setting heuristic–systematic cues in mobile interfaces
helps users judge the quality of a website, and hence helps online enterprises such as P2P
lending firms to achieve high user popularity in the mobile context. This further implies that
the success of firms’ mobile social media accounts may not simply be determined by the
published content or information – which is primarily dependent on the knowledge and
writing skills of author(s)/editor(s). Rather, the media’s capability to nurture mobile social
media popularity through setting heuristic–systematic cues on the mobile interface of
accounts is also very important – these designable aspects are less likely to be limited by
personal knowledge and writing skills.
Moreover, the post hoc analysis results indicate that content freshness is the most
sensitive factor related to firms’ mobile social media popularity. There is no significant
difference between middle and high source credibility in terms of the mean value of mobile
social media popularity, suggesting that moderate information disclosure (i.e. middle
source credibility) is the most efficient. On the contrary, high level transaction relevance is
the only efficient way for firms to achieve high user popularity. Therefore, in the mobile
social media context, user requirements of trustworthiness may be lower than those of
usability. This may be because mobile platform developers (e.g. WeChat developers) have
already proposed enough policies to protect users from losses. Furthermore, the collective
investment trait of P2P lending (Cai et al., 2016) also helps investors share a lower risk,
1402
INTR
29,6
18. which is another factor contributing to the most efficient effect of middle source
credibility. However, user experience is always the main concern for the users (Hoehle and
Venkatesh, 2015). In the mobile social media context, users also expect highly relevant
content or functions that can help them fulfill their goals, which results in the fact that
high transaction relevance becoming the only efficient approach to help firms, such as P2P
lending firms, to achieve mobile social media popularity. These findings further elaborate
the heuristic–systematic cues’ detailed effects in the mobile context.
So far, most studies on the characteristics of (mobile) social media accounts and their
effects on users’ cognitive and decision-making behaviors have been conducted within
laboratories (e.g. Lee and Hong, 2016) or through surveys (e.g. Hew et al., 2016). This study
analyzed 183 mobile social media accounts of P2P lending firms to gain more insights under
an uncontrolled natural setting. This method is highly appropriate for investigating the
characteristics of mobile social media interfaces that may not be obvious from direct
observation. Additionally, using data from various sources in the study enhances the
credibility of the empirical results and reduces the possibility of common method bias.
From the practitioner’s point of view, we prove that achieving mobile social media
popularity is an available way for online enterprises in their initial stage to gain more
customers – the higher a firm’s mobile social media popularity, the more social exchange
relationships it shapes on mobile social media. Therefore, maintaining the best possible
social exchange relationships with their users/followers (e.g. consistently posting quality
articles to encourage users to follow and spend time reading the posts) will help online
enterprises such as P2P lending firms to gain more actual customers. Furthermore, through
adopting the HSM perspective, our study provides a potential success formula for designing
a popular mobile social media account. We found that heuristic–systematic cues on mobile
social media accounts influenced users’ decisions to follow these accounts. This implies that
online enterprises should dedicate greater effort to determining and adopting the best
heuristic–systematic cues for their mobile social media accounts. In addition, the selection of
the heuristic–systematic cues should be sensitive to previous experience and new
knowledge. For the previous experience, this study helps the online enterprises to confirm
that the content freshness and source credibility are still important in term of mobile social
media popularity. For the new knowledge, this study explores the effectiveness of the
transaction relevance in the mobile social media. The findings of transaction relevance also
indicate that online enterprises should try to fulfill the various goals and needs (hedonic and
practical) of their users by offering the most relevant functions and information on mobile
social media accounts. This means that firms should pay more attention to improving the
users’ systematic cognition on usability of their mobile social media accounts.
8. Conclusion
This SET-based research examines the business potential of firms’ mobile social media
popularity with respect to customer acquisition. This finding extends the understanding of
social exchange in the context of mobile social media accounts and enriches the knowledge
on business value of mobile social media popularity. This study also adds to our
understanding of design strategies for firms’ mobile social media accounts and draws
attention to social cognitive factors (HSM) by exploring the effect of heuristic cues
(i.e. content freshness, source credibility) and systematic cues (i.e. transaction relevance) on
firms’ mobile social media popularity. From our empirical tests on the research model,
we conclude that customer acquisition by online enterprises is positively related to their
mobile social media popularity. Content freshness, source credibility and transaction
relevance are all useful for online enterprises, such as P2P lending firms, wishing to achieve
high mobile social media popularity. The post hoc analysis of heuristic–systematic cues
further suggest the best level (not always the highest level) of cognitive cues sufficient for
1403
Achieving
mobile social
media
popularity
19. firms to achieve mobile social media popularity. Moreover, through the post hoc analysis of
customer acquisition, we also find that the conversion value of mobile social media
popularity generally weights more on customer acquisition than on revenue acquisition.
Overall, this research contributes to the literature of mobile social media from the theoretical
perspectives of SET and HSM. The significance of the findings and similar investigations
will increase with the continued growth of mobile social media use for marketing,
communication and information sharing around the world.
Notes
1. Social exchange relationship refers to the relations where “resources” are introduced into exchange
by two actors (either persons or corporate groups) (Emerson 1976, p. 347). In this paper, followers
and account owners are two social actors and the “resources” (e.g., follower’s attention and account
owner’s post) are introduced into such relationship. These resources are reinforcement or reward
for social actors to help maintain such exchange relations.
2. Data retrieved from tech.sina.com.cn/roll/2017-11-12/doc-ifynsait7519132.shtml
References
Agarwal, R. and Venkatesh, V. (2002), “Assessing a firm’s web presence: a heuristic evaluation procedure
for the measurement of usability”, Information Systems Research, Vol. 13 No. 2, pp. 168-186.
Ali-Hassan, H., Nevo, D. and Wade, M. (2015), “Linking dimensions of social media use to job
performance: the role of social capital”, Journal of Strategic Information Systems, Vol. 24 No. 2,
pp. 65-89.
Balaji, M.S., Khong, K.W. and Chong, A.Y.L. (2016), “Determinants of negative word-of-mouth
communication using social networking sites”, Information & Management, Vol. 53 No. 4,
pp. 528-540.
Burtch, G., Ghose, A. and Wattal, S. (2014), “Cultural differences and geography as determinants of
online prosocial lending”, MIS Quarterly, Vol. 38 No. 3, pp. 773-794.
Butler, B.S., Bateman, P.J., Gray, P.H. and Diamant, E.I. (2014), “An attraction-selection-attrition theory
of online community size and resilience”, MIS Quarterly, Vol. 38 No. 3, pp. 699-728.
Cai, S., Lin, X., Xu, D. and Fu, X. (2016), “Judging online peer-to-peer lending behavior: a comparison
of first-time and repeated borrowing requests”, Information & Management, Vol. 53 No. 7,
pp. 857-867.
Chaiken, S. (1980), “Heuristic versus systematic information processing and the use of source versus
message cues in persuasion”, Journal of Personality and Social Psychology, Vol. 39 No. 5,
pp. 752-766.
Chang, M.K., Cheung, W. and Tang, M. (2013), “Building trust online: Interactions among trust building
mechanisms”, Information & Management, Vol. 50 No. 7, pp. 439-445.
Chen, M.Y. (2016), “Can two-sided messages increase the helpfulness of online reviews?”,
Online Information Review, Vol. 40 No. 3, pp. 316-332.
Chen, X., Huang, Q. and Davison, R.M. (2017), “The role of website quality and social capital in building
buyers’ loyalty”, International Journal of Information Management, Vol. 37 No. 1, pp. 1563-1574.
Cheung, C.M., Lee, M.K. and Rabjohn, N. (2008), “The impact of electronic word-of-mouth: the adoption
of online opinions in online customer communities”, Internet Research, Vol. 18 No. 3, pp. 229-247.
Choi, J., Bell, D.R. and Lodish, L.M. (2012), “Traditional and IS-enabled customer acquisition on the
internet”, Management Science, Vol. 58 No. 4, pp. 754-769.
Choi, W. and Stvilia, B. (2015), “Web credibility assessment: conceptualization, operationalization,
variability, and models”, Journal of the Association for Information Science and Technology,
Vol. 66 No. 12, pp. 2399-2414.
1404
INTR
29,6
20. Chu, S.C. and Sung, Y. (2015), “Using a consumer socialization framework to understand electronic
word-of-mouth (eWOM) group membership among brand followers on Twitter”, Electronic
Commerce Research and Applications, Vol. 14 No. 4, pp. 251-260.
Colquitt, J.A., Baer, M.D., Long, D.M. and Halvorsen-Ganepola, M.D. (2014), “Scale indicators of social
exchange relationships: a comparison of relative content validity”, Journal of Applied Psychology,
Vol. 99 No. 4, pp. 599-618.
De Vries, L., Gensler, S. and Leeflang, P.S. (2012), “Popularity of brand posts on brand fan pages: an
investigation of the effects of social media marketing”, Journal of Interactive Marketing, Vol. 26
No. 2, pp. 83-91.
Dong, J.Q. and Wu, W. (2015), “Business value of social media technologies: evidence from online user
innovation communities”, Journal of Strategic Information Systems, Vol. 24 No. 2, pp. 113-127.
Du, H.S. (2014), “The role of media-embedded heuristics in achieving online readership popularity”,
Journal of the Association for Information Science and Technology, Vol. 65 No. 2, pp. 302-312.
Eastin, M.S., Brinson, N.H., Doorey, A. and Wilcox, G. (2016), “Living in a big data world: predicting
mobile commerce activity through privacy concerns”, Computers in Human Behavior, Vol. 58,
pp. 214-220.
Emerson, R.M. (1976), “Social exchange theory”, Annual Review of Sociology, Vol. 2 No. 1, pp. 335-362.
Ferran, C. and Watts, S. (2008), “Videoconferencing in the field: a heuristic processing model”,
Management Science, Vol. 54 No. 9, pp. 1565-1578.
Filieri, R. and McLeay, F. (2014), “E-WOM and accommodation: an analysis of the factors that influence
travelers’ adoption of information from online reviews”, Journal of Travel Research, Vol. 53 No. 1,
pp. 44-57.
Freyne, J., Berkovsky, S., Daly, E.M. and Geyer, W. (2010), “Social networking feeds: recommending
items of interest”, Proceedings of the Fourth ACM Conference on Recommender Systems,
Barcelona, pp. 277-280.
Gefen, D. and Ridings, C.M. (2002), “Implementation team responsiveness and user evaluation of
customer relationship management: a quasi-experimental design study of social exchange
theory”, Journal of Management Information Systems, Vol. 19 No. 1, pp. 47-69.
Gupta, P. and Harris, J. (2010), “How e-WOM recommendations influence product consideration and
quality of choice: a motivation to process information perspective”, Journal of Business Research,
Vol. 63 Nos 9–10, pp. 1041-1049.
Hassenzahl, M. (2004), “The interplay of beauty, goodness, and usability in interactive products”,
Human-Computer Interaction, Vol. 19 No. 4, pp. 319-349.
Hew, J.J., Lee, V.H., Ooi, K.B. and Lin, B. (2016), “Mobile social commerce: the booster for brand
loyalty?”, Computers in Human Behavior, Vol. 59, pp. 142-154.
Hoehle, H. and Venkatesh, V. (2015), “Mobile application usability: conceptualization and instrument
development”, MIS Quarterly, Vol. 39 No. 2, pp. 435-472.
Hoehle, H., Zhang, X. and Venkatesh, V. (2015), “An espoused cultural perspective to understand
continued intention to use mobile applications: a four-country study of mobile social media
application usability”, European Journal of Information Systems, Vol. 24 No. 3, pp. 337-359.
Hollebeek, L.D., Glynn, M.S. and Brodie, R.J. (2014), “Consumer brand engagement in social media:
conceptualization, scale development and validation”, Journal of Interactive Marketing, Vol. 28
No. 2, pp. 149-165.
Hovland, C.I. and Weiss, W. (1951), “The influence of source credibility on communication
effectiveness”, Public Opinion Quarterly, Vol. 15 No. 4, pp. 635-650.
Hsu, C.L. and Lin, J.C.C. (2008), “Acceptance of blog usage: the roles of technology acceptance, social
influence and knowledge sharing motivation”, Information & Management, Vol. 45 No. 1, pp. 65-74.
Hu, T., Kettinger, W.J. and Poston, R.S. (2015), “The effect of online social value on satisfaction
and continued use of social media”, European Journal of Information Systems, Vol. 24 No. 4,
pp. 391-410.
1405
Achieving
mobile social
media
popularity
21. Jang, S. and Lee, J.S. (2016), “On evaluating perceptual quality of online user-generated videos”,
IEEE Transactions on Multimedia, Vol. 18 No. 9, pp. 1808-1818.
Jones, Q., Ravid, G. and Rafaeli, S. (2004), “Information overload and the message dynamics of online
interaction spaces: a theoretical model and empirical exploration”, Information Systems
Research, Vol. 15 No. 2, pp. 194-210.
Jiang, Y., Ho, Y.C., Yan, X. and Tan, Y. (2018), “Investor platform choice: herding, platform attributes,
and regulations”, Journal of Management Information Systems, Vol. 35 No. 1, pp. 86-116.
Kaplan, A.M. and Haenlein, M. (2012), “Social media: back to the roots and back to the future”,
Journal of Systems and Information Technology, Vol. 14 No. 2, pp. 101-104.
Kim, S.E., Lee, K.Y., Shin, S.I. and Yang, S.B. (2017), “Effects of tourism information quality in social
media on destination image formation: the case of Sina Weibo”, Information & Management,
Vol. 54 No. 6, pp. 687-702.
Konovsky, M.A. and Pugh, S.D. (1994), “Citizenship behavior and social exchange”, Academy of
Management Journal, Vol. 37 No. 3, pp. 656-669.
Lahuerta-Otero, E., Cordero-Gutiérrez, R. and De la Prieta-Pintado, F. (2018), “Retweet or like? That is
the question”, Online Information Review, Vol. 42 No. 5, pp. 562-578.
Lee, A.R., Son, S.M. and Kim, K.K. (2016), “Information and communication technology overload and
social networking service fatigue: a stress perspective”, Computers in Human Behavior, Vol. 55,
pp. 51-61.
Lee, J. and Hong, I.B. (2016), “Predicting positive user responses to social media advertising: the roles of
emotional appeal, informativeness, and creativity”, International Journal of Information
Management, Vol. 36 No. 3, pp. 360-373.
Lee, J.M. and Rha, J.Y. (2016), “Personalization–privacy paradox and consumer conflict with the use of
location-based mobile commerce”, Computers in Human Behavior, Vol. 63, pp. 453-462.
Lee, Y.E. and Benbasat, I. (2004), “A framework for the study of customer interface design for mobile
commerce”, International Journal of Electronic Commerce, Vol. 8 No. 3, pp. 79-102.
Leong, L.Y., Jaafar, N.I. and Sulaiman, A. (2017), “Understanding impulse purchase in Facebook
commerce: does big five matter?”, Internet Research, Vol. 27 No. 4, pp. 786-818.
Lewandowski, D. (2008), “A three-year study on the freshness of web search engine databases”,
Journal of Information Science, Vol. 34 No. 6, pp. 817-831.
Lewandowski, D., Wahlig, H. and Meyer-Bautor, G. (2006), “The freshness of web search engine
databases”, Journal of Information Science, Vol. 32 No. 2, pp. 131-148.
Li, W.S., Po, O., Hsiung, W.P., Candan, K.S. and Agrawal, D. (2003), “Freshness-driven adaptive caching
for dynamic content web sites”, Data & Knowledge Engineering, Vol. 47 No. 2, pp. 269-296.
Lin, K.Y. and Lu, H.P. (2015), “Predicting mobile social network acceptance based on mobile value and
social influence”, Internet Research, Vol. 25 No. 1, pp. 107-130.
Lin, K.Y., Wang, Y.T. and Hsu, H.Y.S. (2017), “Why do people switch mobile platforms? The
moderating role of habit”, Internet Research, Vol. 27 No. 5, pp. 1170-1189.
Lin, T.C. and Huang, C.C. (2010), “Withholding effort in knowledge contribution: the role of social
exchange and social cognitive on project teams”, Information & Management, Vol. 47 No. 3,
pp. 188-196.
Lin, X., Li, Y. and Wang, X. (2017), “Social commerce research: definition, research themes and the
trends”, International Journal of Information Management, Vol. 37 No. 3, pp. 190-201.
Liu, D., Brass, D.J., Lu, Y. and Chen, D. (2015), “Friendships in online peer-to-peer lending: pipes, prisms,
and relational herding”, MIS Quarterly, Vol. 39 No. 3, pp. 729-742.
Liu, Z., Liu, L. and Li, H. (2012), “Determinants of information retweeting in microblogging”, Internet
Research, Vol. 22 No. 4, pp. 443-466.
Liu, Z., Min, Q., Zhai, Q. and Smyth, R. (2016), “Self-disclosure in Chinese micro-blogging: a social
exchange theory perspective”, Information & Management, Vol. 53 No. 1, pp. 53-63.
1406
INTR
29,6
22. Lum, W.Y. and Lau, F.C. (2002), “A context-aware decision engine for content adaptation”, IEEE
Pervasive Computing, Vol. 1 No. 3, pp. 41-49.
Lu, B., Fan, W. and Zhou, M. (2016), “Social presence, trust, and social commerce purchase intention: an
empirical research”, Computers in Human Behavior, Vol. 56, pp. 225-237.
McFarland, L.A. and Ployhart, R.E. (2015), “Social media: a contextual framework to guide research
and practice”, Journal of Applied Psychology, Vol. 100 No. 6, pp. 1653-1677.
Maslowska, E., Malthouse, E.C. and Viswanathan, V. (2017), “Do customer reviews drive purchase
decisions? The moderating roles of review exposure and price”, Decision Support Systems,
Vol. 98, pp. 1-9.
Mun, Y.Y., Yoon, J.J., Davis, J.M. and Lee, T. (2013), “Untangling the antecedents of initial trust in web-based
health information: the roles of argument quality, source expertise, and user perceptions of
information quality and risk”, Decision Support Systems, Vol. 55 No. 1, pp. 284-295.
Nilashi, M., Ibrahim, O., Mirabi, V.R., Ebrahimi, L. and Zare, M. (2015), “The role of security, design and
content factors on customer trust in mobile commerce”, Journal of Retailing and Consumer
Services, Vol. 26, pp. 57-69.
Ntoulas, A., Cho, J. and Olston, C. (2004), “What’s new on the web?: the evolution of the web from a
search engine perspective”, Proceedings of the 13th International Conference on World Wide
Web, New York, NY, pp. 1-12.
Oh, S. and Syn, S.Y. (2015), “Motivations for sharing information and social support in social media: a
comparative analysis of Facebook, Twitter, Delicious, YouTube, and Flickr”, Journal of the
Association for Information Science and Technology, Vol. 66 No. 10, pp. 2045-2060.
Ou, C.X. and Davison, R.M. (2011), “Interactive or interruptive? Instant messaging at work”, Decision
Support Systems, Vol. 52 No. 1, pp. 61-72.
Poojary, P., Moharir, S. and Jagannathan, K. (2018), “Caching policies under content freshness
constraints: in communication systems & networks”, 2018 10th International Conference on
Communication Systems & Networks, Bengaluru, pp. 400-402.
Risius, M. and Beck, R. (2015), “Effectiveness of corporate social media activities in increasing
relational outcomes”, Information & Management, Vol. 52 No. 7, pp. 824-839.
Ruiz-Mafe, C., Chatzipanagiotou, K. and Curras-Perez, R. (2018), “The role of emotions and conflicting
online reviews on consumers’ purchase intentions”, Journal of Business Research, Vol. 89,
pp. 336-344.
Saracevic, T. (1996), “Relevance reconsidered”, in Ingwersen, P. and Pors, N.O. (Eds), Information
Science: Integration in Perspective, Royal School of Library and Information Science,
Copenhagen, pp. 201-218.
Self, C.S. (1996), “Credibility”, in Salwen, M. and Stacks, D. (Eds), An Integrated Approach to
Communication Theory and Research, Lawrence Erlbaum Associates, Mahwah, NJ, pp. 421-441.
Shankar, V., Venkatesh, A., Hofacker, C. and Naik, P. (2010), “Mobile marketing in the retailing
environment: current insights and future research avenues”, Journal of Interactive Marketing,
Vol. 24 No. 2, pp. 111-120.
Shiau, W.L. and Luo, M.M. (2012), “Factors affecting online group buying intention and satisfaction: a
social exchange theory perspective”, Computers in Human Behavior, Vol. 28 No. 6,
pp. 2431-2444.
Stahl, F., Heitmann, M., Lehmann, D.R. and Neslin, S.A. (2012), “The impact of brand equity on
customer acquisition, retention, and profit margin”, Journal of Marketing, Vol. 76 No. 4,
pp. 44-63.
Sundar, S.S., Knobloch-Westerwick, S. and Hastall, M.R. (2007), “News cues: information scent and
cognitive heuristics”, Journal of the American Society for Information Science and Technology,
Vol. 58 No. 3, pp. 366-378.
Syn, S.Y. and Oh, S. (2015), “Why do social network site users share information on Facebook and
Twitter?”, Journal of Information Science, Vol. 41 No. 5, pp. 553-569.
1407
Achieving
mobile social
media
popularity
23. Tam, K.Y. and Ho, S.Y. (2005), “Web personalization as a persuasion strategy: an elaboration likelihood
model perspective”, Information Systems Research, Vol. 16 No. 3, pp. 271-291.
Tao, Q., Dong, Y. and Lin, Z. (2017), “Who can get money? Evidence from the Chinese peer-to-peer
lending platform”, Information Systems Frontiers, Vol. 19 No. 3, pp. 425-441.
Tarute, A., Nikou, S. and Gatautis, R. (2017), “Mobile application driven consumer engagement”,
Telematics and Informatics, Vol. 34 No. 4, pp. 145-156.
Todorov, A., Chaiken, S. and Henderson, M. (2002), “The heuristic-systematic model of social
information processing”, in Dillard, J.P. and Pfau, M. (Eds), The Persuasion Handbook:
Developments in Theory and Practice, Sage Publications, Thousand Oaks, CA, pp. 195-212.
Trumbo, C.W. and McComas, K.A. (2003), “The function of credibility in information processing for
risk perception”, Risk Analysis: An International Journal, Vol. 23 No. 2, pp. 343-353.
Uzunoğlu, E. and Kip, S.M. (2014), “Brand communication through digital influencers: leveraging
blogger engagement”, International Journal of Information Management, Vol. 34 No. 5,
pp. 592-602.
Venkatesh, V. and Agarwal, R. (2006), “Turning visitors into customers: a usability-centric perspective
on purchase behavior in electronic channels”, Management Science, Vol. 52 No. 3, pp. 367-382.
Wang, R.Y. and Strong, D.M. (1996), “Beyond accuracy: what data quality means to data consumers”,
Journal of Management Information Systems, Vol. 12 No. 4, pp. 5-33.
Wang, S.M. and Lin, C.C. (2011), “The effect of social influence on the bloggers’ usage intention”,
Online Information Review, Vol. 35 No. 1, pp. 50-65.
Wang, W.T., Wang, Y.S. and Liu, E.R. (2016), “The stickiness intention of group-buying websites: the
integration of the commitment-trust theory and e-commerce success model”, Information &
Management, Vol. 53 No. 5, pp. 625-642.
Want, R., Schilit, B.N., Adams, N.I., Gold, R., Petersen, K., Goldberg, D., Ellis, J.R. and Weiser, M. (1995),
“An overview of the PARCTAB ubiquitous computing experiment”, IEEE Personal
Communications, Vol. 2 No. 6, pp. 28-43.
Watson, R.T., Pitt, L.F., Berthon, P. and Zinkhan, G.M. (2002), “U-commerce: expanding the universe of
marketing”, Journal of the Academy of Marketing Science, Vol. 30 No. 4, pp. 333-347.
Wells, J.D., Parboteeah, V. and Valacich, J.S. (2011), “Online impulse buying: understanding the
interplay between consumer impulsiveness and website quality”, Journal of the Association for
Information Systems, Vol. 12 No. 1, pp. 32-56.
Wu, T.Y. and Lin, C.A. (2017), “Predicting the effects of eWOM and online brand messaging: source
trust, bandwagon effect and innovation adoption factors”, Telematics and Informatics, Vol. 34
No. 2, pp. 470-480.
Yan, Y., Lv, Z. and Hu, B. (2018), “Building investor trust in the P2P lending platform with a focus on
Chinese P2P lending platforms”, Electronic Commerce Research, Vol. 18 No. 2, pp. 203-224.
Yan, Z., Wang, T., Chen, Y. and Zhang, H. (2016), “Knowledge sharing in online health communities: a
social exchange theory perspective”, Information & Management, Vol. 53 No. 5, pp. 643-653.
Yen, B., Hu, P.J.H. and Wang, M. (2007), “Toward an analytical approach for effective web site design: a
framework for modeling, evaluation and enhancement”, Electronic Commerce Research and
Applications, Vol. 6 No. 2, pp. 159-170.
Yu, C.P., Young, M.L. and Ju, B.C. (2015), “Consumer software piracy in virtual communities: an
integrative model of heroism and social exchange”, Internet Research, Vol. 25 No. 2, pp. 317-334.
Zhang, D., Adipat, B. and Mowafi, Y. (2009), “User-centered context-aware mobile applications – the
next generation of personal mobile computing”, Communications of the Association for
Information Systems, Vol. 24 No. 1, pp. 27-46.
Zhang, K.Z., Zhao, S.J., Cheung, C.M. and Lee, M.K. (2014), “Examining the influence of online reviews
on consumers’ decision-making: a heuristic–systematic model”, Decision Support Systems,
Vol. 67, pp. 78-89.
1408
INTR
29,6
24. Zhang, L. and Peng, T.Q. (2015), “Breadth, depth, and speed: diffusion of advertising messages on
microblogging sites”, Internet Research, Vol. 25 No. 3, pp. 453-470.
Zhang, L., Peng, T.Q., Zhang, Y.P., Wang, X.H. and Zhu, J.J. (2014), “Content or context: which matters
more in information processing on microblogging sites”, Computers in Human Behavior, Vol. 31,
pp. 242-249.
Zhang, W. and Watts, S.A. (2008), “Capitalizing on content: information adoption in two online
communities”, Journal of the Association for Information Systems, Vol. 9 No. 2, pp. 73-94.
Zhao, Q., Chen, C.D. and Wang, J.L. (2016), “The effects of psychological ownership and TAM on social
media loyalty: an integrated model”, Telematics and Informatics, Vol. 33 No. 4, pp. 959-972.
Zhou, X., Song, Q., Li, Y.Y., Tan, H. and Zhou, H. (2017), “Examining the influence of online retailers’
micro-blogs on consumers’ purchase intention”, Internet Research, Vol. 27 No. 4, pp. 819-838.
Zou, X., Gonzales, M. and Saeedi, S. (2016), “A context-aware recommendation system using
smartphone sensors”, Proceedings of the 7th Information Technology, Electronics and Mobile
Communication Conference, Vancouver, pp. 1-6.
Further reading
Choi, Y.K., Seo, Y. and Yoon, S. (2017), “E-WOM messaging on social media: social ties, temporal
distance, and message concreteness”, Internet Research, Vol. 27 No. 3, pp. 495-505.
Kaplan, A.M. (2012), “If you love something, let it go mobile: mobile marketing and mobile social media
4 4”, Business Horizons, Vol. 55 No. 2, pp. 129-139.
Appendix. Formulas for WeChat communication index (WCI)
First-layer formula:
WCI ¼ 80% V þ20% L
ð Þ2
10: (A1)
Second-layer formula:
V ¼ 40% ln vþ1
ð Þþ45% ln
v
n
þ1
þ15% ln vmax þ1
ð Þ
L ¼ 40% ln 10 l þ1
ð Þþ45% ln 10
l
n
þ1
þ15% ln 10 lmax þ1
ð Þ: (A2)
Entire formula:
WCI ¼ 80% 40% ln vþ1
ð Þþ45% ln v
nþ1
þ15% ln vmax þ1
ð Þ
n
þ20% 40% ln 10 l þ1
ð Þþ45% ln 10
l
n
þ1
þ15% ln 10 lmax þ1
ð Þ
2
10; (A3)
where n is the total number of articles on an account; V the total number of views of an account’s
articles; Vmax the maximum value of views of an account’s articles; L the total number of “likes” of an
account’s articles; Lmax the maximum value of “likes” of an account’s articles.
Corresponding author
Xiaobo Ke can be contacted at: xiaoboke-c@my.cityu.edu.hk
For instructions on how to order reprints of this article, please visit our website:
www.emeraldgrouppublishing.com/licensing/reprints.htm
Or contact us for further details: permissions@emeraldinsight.com
1409
Achieving
mobile social
media
popularity
![devices facilitate mobile social media to collect more online and offline information of the users
(e.g. real-time location information) than traditional social media, enabling firms to be more
sensitive to their customer needs (Lin, Li and Wang, 2017). Furthermore, ubiquity is another
significant trait differentiating the mobile social media from the traditional social media, by
allowing users to conveniently access the social network anytime and anywhere (Shankar
et al., 2010; Watson et al., 2002). This trait results in the main trend that most of the social
media users constantly visit their profile and other social media accounts via the mobile
devices. In order to take the advantage of the potential benefits of mobile social media, many
firms are struggling to achieve high mobile social media popularity, which is referred to
as the number of users (Butler et al., 2014), views (Jang and Lee, 2016), comments and “likes”
(De Vries et al., 2012) and/or re-tweets (Zhang, Peng, Zhang, Wang and Zhu, 2014) on a mobile
account. Nowadays, an increasing number of firms are allocating more budgets for the design
and development of their mobile social media accounts in order to achieve high user
popularity and ultimately business success.
According to the definition of mobile social media popularity, high user popularity
means a great number of followers and active engagements in a firm’s mobile social media
account. As the followers are potential customers, whether the followers can be transformed
into actual customers is of course the main concern of the firms. Previous research suggests
that potential customers engaging in the social exchange relationships with firms are more
likely to convert into actual customers or to increase loyalty and satisfaction (Shiau and Luo,
2012; Wang et al., 2016). According to Emerson (1976), relationships between the account
followers and account owners can be regarded as the social exchange relationships[1],
meaning that there is a high possibility of the followers becoming actual customers due to
their engagement in the social exchange activities. Thus, high user popularity (i.e. numerous
account followers) may imply high customer acquisition. However, limited empirical
evidences have been found regarding the relationships between mobile social media
popularity and firm’s customer acquisition. The conversion value of mobile social media
popularity is not yet fully understood (Lu et al., 2016). In order to extend the knowledge
boundary of mobile social media popularity and to realize the business value of mobile
social media popularity, this paper attempts to confirm the conversion value of mobile social
media popularity by examining the relationship between firms’ mobile social media
popularity and firms’ customer acquisition.
Furthermore, research has consistently found that it is difficult for firms to achieve high
user popularity (Du, 2014). This situation remains or is even more serious in the mobile
context because of the challenges derived from the unique features of mobile social media.
First, users are forced to cope with information overload (Lee et al., 2016), which are even
more serious in the mobile environment, as the limited screen size of mobile devices restricts
functionality and content availability (Hoehle and Venkatesh, 2015). Second, information
oversharing (Kaplan and Haenlein, 2012) becomes an emerging concern for mobile users
(Lee and Rha, 2016; Eastin et al., 2016) because firms can easily and invisibly access users’
personal information via the mobile applications. Plus, the context-awareness becomes a
new requirement for the mobile social media because the mobile device is increasing
ubiquitous (Lee and Benbasat, 2004). We find that some challenges are bequeathed from
the online social media (e.g. information overload) but some challenges are emerging in the
mobile context (e.g. context-awareness). Confronted with the bequeathed challenges, we do
not know what previous experiences are still useful in the mobile context. Furthermore, the
new knowledge on how to overcome the challenges originated from the mobile context is
also limited. However, the lack of the clarification on the availability of previous experiences
learnt from traditional social media operations and the missing of new guidance for firms to
set up the mobile social media accounts for high popularity are both the key reasons for why
many firms fail to achieve high mobile social media popularity.
1387
Achieving
mobile social
media
popularity](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)