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Modeling USE CONTINUANCE behavior IN Microblogging Services: THE CASE OF TWITTER STUART J. BARNES University of East Anglia Norwich NR9 4DE, United Kingdom

MARTIN BöHRINGER Chemnitz University of Technology 09107 Chemnitz, Germany

ABSTRACT The most popular microblogging service, Twitter, has established a large user base, in spite of numerous criticisms. This study aims to examine why this is the case. In particular, the study develops a model of microblogging use continuance based on theories of continuance, habit and critical mass. The model is then tested via a Web survey of Twitter users and PLS path modeling. The results suggest that continued use intention is strongly determined by perceived usefulness, satisfaction and habit (R2=0.454). The paper rounds off with conclusions and implications for future research and practice in this very new area of inquiry. Keywords: microblogging, microblogs, continuance, critical mass, habit, Twitter.

microblogging and in support provides an original combination of explanatory theory. For this purpose we developed a research model using continuance theory [4] and related theories of habit [26] and critical mass [40]. Data was collected via a Web survey of Twitter users. The data was then analyzed using structural equation modeling via the partial least squares technique [7]. The structure of the paper is as follows. In the next section we provide some background on the nature and growth of microblogs. In the following section we discuss the theoretical development of a research model of continuance in microblogging services. In the fourth section we describe the study’s methodology before presenting the results of the analysis in section five. Finally, we discuss the findings and their implications for research and practice in the conclusions.

INTRODUCTION

BACKGROUND

Striking changes are afoot in how users interact using the World Wide Web. The concept of ‘Web 2.0’ or ‘social software’ have become used to describe revolution-like developments associated with Internet applications like wikis, weblogs and social networking software. Although the term ‘Web 2.0’ was first introduced around 2004, the development process has been a continual one, spawning ever new types of software. One of the newest developments in this area is that of ‘microblogging’. As the technology is so new there are even several opinions about its name. In addition to ‘microblogging’ terms like ‘microsharing’ and ‘activity streaming’ are also typically used. Notwithstanding the strong adoption of microblogging by private users and enterprises there is, as yet, little academic research on the topic, mostly concentrating on descriptive surveys of Twitter. Microblogging in general and the currently most popular microblogging service Twitter (www.twitter.com) in particular are presently among the most discussed Web 2.0 tools in the ‘blogosphere’ and in spite of substantial positive arguments many users complain about a lack of functionality or reliability issues. Considering the fact that Twitter use is nevertheless not declining but sharply rising there must be strong reasons why users continue using the service. Discovering an explanation for this finding could help us to understand the processes in microblogging platforms as well as to provide implications for the successful future implementation of microblogs in enterprise contexts. The rising adoption of Twitter is a perfect setting for research in IS continuance theory. The purpose of our work is to understand the continued use of the microblogging service by its general users. The research question to be answered is “Why do users of the Twitter microblogging service continue to use the platform?” This paper contributes to the understanding of phenomenon of Received: October 4, 2010

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The rise of Twitter Microblogs are a smaller version of weblogs combined with features for social networking and mobility. Users post short updates without a headline or additional information in their microblog. Other members can add them to their social network (i.e. ‘follow’ them). The messages from a member’s social network appear in a chronologically ordered and combined view on their starting page. Most microblogging services limit the number of characters used in a posting to 140 or similar. The goal is to animate users to post short messages often in their microblogs. Access to microblogging services is also possible using mobile text messages, desktop clients or several third party applications. The first and currently the most popular microblogging service is Twitter. Having earned an estimated US$20 million of venture capital [9] the service has seen an exponential growth in its user base since launching in July 2006 to an estimated 100 million users in April 2010 [33]. The geographic adoption of Twitter and therefore the distribution of the microblogging approach is different. Krishnamurthy et al. [23] find American and Japanese users to be the biggest groups on Twitter at the time of their study. Some obvious evidence for the mainstream adoption of Twitter, at least in the US, was its role in the 2008 presidential campaign. Other parts of the world followed later. In Germany, for example, the first broad media coverage of Twitter could be observed during the civil unrest in Iran in the summer of 2009. While compared with other social networking services such as Facebook Twitter still has a small user base, its basic principles are now known by a broad range of people. Around Twitter a whole ecosystem of third party applications have been developed and much of the microblogging vocabulary

Revised: December 3, 2010 Journal of Computer Information Systems

Accepted: January 6, 2011 1

has its origins in this particular service, e.g. the verb “tweet” for the action of posting into a microblog. As Twitter has become perceived as a standard many so-called Twitter-clones have emerged, in part due to the relative ease in imitating the software. Serious competitors such as Pownce, Plurk and Jaiku tried to differentiate from Twitter by offering wider functionality or slightly different approaches. Google’s acquisition of Jaiku (in 2007) and Facebook’s offer for Twitter worth US$500 million in shares (in 2008) demonstrated the strategic importance of microblogs. Eventually, a first rationalization of the market led to the closure of Pownce in December 2008. Notwithstanding, new competitors entered the market in 2008 with open source microblogging software like laconi.ca; different installations of this program communicate with each other and contribute towards building a big peer-to-peer microblogging network. Gartner [16] added microblogging to its hype cycle in 2008 and predicts that the technology will garner a sharp rise in popularity. This was also a consequence of the fast growing debate on using Twitter-like tools in professional contexts. While Twitter

itself can be an interesting channel for companies to develop their brands and improve their customer service, many argue that due to privacy regulations there should be such a tool behind the company firewall. Major companies such as IBM, Oracle and SAP experimented with so-called enterprise microblogs and in the second half of 2008 special enterprise microblogging products like Communote, Presently and Yammer were launched. Research on Twitter In parallel to the growth of internet users, both Twitter and microblogging in general have become the subjects of an emerging stream of research. By virtue of its unique approach, microblogging has attracted research interest from a variety of different academic disciplines. Table 1 presents an overview of existing research on microblogging. To the best of our knowledge these are currently the key contributions to this new topic. Two types of studies can be identified in the existing body of knowledge: (1) understanding microblogging refers to research which

TABLE 1: Previous research into Twitter and microblogging Type

Source

Content

Understanding microblogging

Java et al. [21] Characterizes user types and different usages of Twitter; presents an early status quo of the microblogging service



Krishnamurthy et al. [23] Identification of different user types and statistical status quo with respect to geographical location and number of users

Descriptive and statistical research about Twitter

Honeycutt & Herring [18]

Usage of the ‘@’-operator

Huberman et al. [19]

Social networks on Twitter

Zhao & Rosson [41] In-depth interviews with Twitter users in order to learn about their expectations with respect to enterprise adoption



Boyd et al. [5]

Retweeting as conversational practice



Kwan et al. [24] Study of the twittersphere; found that Twitter differs from known social network characteristics



Jansen et al. [20] Suggests that since Twitter includes word of mouth it is highly relevant for marketing



Erickson [13]

Model-building

Günther et al. [17] Adaption model for enterprise microblogging based on Unified Theory of Acceptance and Use of Technology

Applies Social Translucence theory to microblogging

Microblogging in special use cases

Barnes et al. [3] Case study about a microblogging-like collaboration tool used in a SME since 1998



Riemer et al. [34] Genre analysis of enterprise microblogging content; finds a significant difference from public microblogging

Enterprise Microblogging



Zhao & Rosson [41]



Zhang et al. [42] Case study on enterprise microblogging (EM); main benefits of EM are found to be connections and staying aware about what others are working on



Böhringer & Richter [6]

Case study on enterprise microblogging; main benefit of EM is awareness

Computer Science-oriented research

Passant et al. [32]

Architecture of decentralized semantic microblogging

Assogba & Donath [1]

Visual display of quantitative information in microblogging

Sandler & Wallach [38]

Architecture of decentralized microblogging

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explains the approach using statistical description or that builds theory (or models) for predicting user behavior; and (2) microblogging in special use cases refers predominantly to design science or case study-oriented research that goes beyond Twitter and further develops approaches for supporting, e.g., enterprise information management and e-learning, with microblogging applications. A large part of existing research is based on statistical description of Twitter. In an early paper, Java et al. [21] presented a topology of various uses and usage intentions on Twitter. They further find that Twitter’s users extend the Twitter question, ‘What are you doing?’ by including information sharing (e.g. URLs) and normal conversations on the platform. Further statistical studies of Twitter were presented by Honeycutt and Herring ([18], usage of the ‘@’ operator), Krishnamurthy et al. ([23], identification of different user types), Huberman et al. ([19], networks on Twitter) and others (see Table 1 for a detailed list). Enterprise adoption of microblogging is at a very early stage of development. Therefore, there are not yet many users in the enterprise context, which would be a prerequisite for broader studies of the area. Günther et al. [17] identified relevant factors for enterprise usage including ‘privacy concerns’ and ‘collaborative norms’ as extensions to their base model of UTAUT. Using genre analysis, Riemer et al. [34] found that enterprise microblogging content significantly differs from public microblogging content. Further, several case studies provide an insight into enterprise microblogging characteristics [3] [6]. Kwak et al. [24] in their extensive study of the ‘twittersphere’ found that social patterns observed within Twitter differ from known behavior in social networks. This includes a lower reciprocity of connections and a non-power-law follower distribution. These findings demonstrate the need for further research to explain behavior on Twitter. THEORY AND RESEARCH MODEL In this section we present prior research and concepts that have informed the development of the research model tested in the study. Three main strands of theory have contributed to theoretical development: continuance theory as the basic theory of our investigation complemented with concepts of habit and critical mass. Let us briefly discuss each of these areas in turn. Continuance theory Continuance theory within the discipline of information systems stems from initial research in marketing. Expectation-confirmation theory (ECT) emerged from the consumer behavior and services marketing literature and has proven broadly robust in a number of service contexts [10] [30]. The general thrust of ECT is the assessment of post-purchase intentions, as influenced by initial expectations about a product or service, subsequent adoption and use (consumption) and the formation of perceptions about performance as influenced by the confirmation or not of initial expectations, the latter determining the level of satisfaction with a purchase and subsequent repurchase or use discontinuance. Bhattacherjee [4] was the first to fully formalize the theory into an expost framework that could be applied to the domain of information systems, adapting the theory to be applied post-acceptance and to encapsulate perceived usefulness [11] [12] as a replacement construct for expectations. Perceived usefulness has consistently proven to be an important construct in longitudinal adoption to

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post-adoption behavior [12] [22]. Thus Bhattacherjee’s [4] model relates the constructs of perceived usefulness and satisfaction to the extent of confirmation of a user’s expectations about an IS, whereby expectations that are fulfilled drive greater satisfaction and perceived usefulness. High levels of perceived usefulness are also posited to lead to greater satisfaction with a system. In turn, the outcome variable of continuance intention is determined by the level of satisfaction with an IS and the perceived usefulness of the system. Bhattacherjee’s model has been successfully applied to individual user contexts involving the Web, such as online banking, and the Internet more broadly ([4], [26]). Microblogging services are also distributed systems reliant on Internet technology, and the focus of this study is on individual users; Bhattacherjee’s theory (which appears to have broader application and generalization in any case) is adopted as a suitable core for a model examining continuance behavior in this context. Habit Habit refers to the extent to which behavior has become automatic as a result of prior learning [26]. Previous research has found a strong relationship between habit and continuance behavior. However, the use of habit in research models is more complex. Research has variously examined habit as a moderator between intention and actual behavior, as a direct effect on actual behavior, and as an indirect effect on behavior that primarily determines intentions. Our focus in this research is on use intentions rather than actual behavior, and so naturally we focus on the latter of these formulations. Our focus on intentions is in line with a core body of previous IS literature [25], whilst the focus on indirect habit effects is a view that is held in a number of previous studies that have examined the effects of habit (and the much used proxy construct of experience) on behavioral intentions [1] [25]. In addition to the effect of habit on intention to continue using an IS, we also posit that habit is significantly influenced by satisfaction. Limayem et al. [26] in their comprehensive definition, application and analysis of the habit construct in continuance theory find very strong support for the linkage between satisfaction and habit. Further, they find that frequency of prior usage and comprehensiveness of usage both have positive associations with habit –assertions that we also make for the purposes of model development. The creation of habit requires a stable context conducive to its formation through repetition or practice [31]; we would hold that such a context exists when focusing on individuals’ behavior with respect to a single system such as a microblogging service. This position is in line with that of Limayem et al. [26] in their study of habitual use of the Internet. Critical mass and perceived critical mass Critical mass in technology adoption has been discussed in the literature for many years (e.g. see [28] [37]). Some of the seminal work in this area was that of Rogers [37] as part of his diffusion of innovation theory. Rogers [37] defined critical mass in terms of a ‘tipping point’ whereby a certain minimum number of users have adopted an innovation which then feeds into rapid continued adoption of the new technology, at which juncture further adoption is self-sustaining. Further research into the application of critical mass theory to electronic communication media has

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demonstrated that universal access to a communication medium is a major driver for critical mass; the decision to use a particular interactive media for routine communication relies on others in a communication network having adopted it and an overall social consensus [28]. Social context thus envelops individuals’ choices and “As more and more individuals in a system adopt an interactive communication innovation, the innovation is perceived as increasingly beneficial to both previous and potential adopters” [40]. As a result, positive network externalities ensue as a result of technology use [27] [40]. Van Slyke et al. [40] examined perceived critical mass within the context of instant messaging, which has similarities to microblogging. They applied diffusion of innovation theory and found direct effects on numerous variables, including social norms, perceived relative advantage, perceived compatibility, ease of use and behavioral intention. As part of continuance theory we focus on continuance intention; however, in our habit model we posit that the effects of critical mass will manifest themselves indirectly through automatic behavior. Thus, based on Markus [28], an individual will exhibit greater habitual behavior and choose a technology for interactive communication if there is a greater perception of critical mass. Such a perception will be driven, at least in part, by the size of an individual’s network — the extent of their personal communication network on the microblogging service. Further, the size of a network is posited to play a role in determining the frequency and comprehensiveness of an individual’s behavior: the greater the extent of the network, the greater possibilities there are for communication, coordination and cooperation, and the more activity that will take place.

Further, we extend the model with the construct of habit and the relationship from satisfaction [26]. In contrast to Limayem et al. [26] however, we model it as a direct effect on continuance intention: H2: Satisfaction has a direct effect on habit. H3: Habit has a direct effect on continuance intention. We accept the habit indicators of frequency of past behavior and comprehensiveness of usage introduced by Limayem et al. [26] and extend these constructs with perceived critical mass due to the property of Twitter as a communication tool. H4: Habit in microblogging usage is driven by: (a) perceived critical mass; (b) frequency of past behavior; and, (c) comprehensiveness of usage. As a basic determinant for these three constructs we introduce social network size: H5: The construct of social network size is a driver of: (a) perceived critical mass; (b) frequency of past behavior; and, (c) comprehensiveness of usage. STUDY DESIGN AND METHOD In this section we briefly outline the process of data collection, scale development and data analysis. Let us examine each of these in turn.

Research model and hypotheses Data collection A combination of all of the above theorizations into a single model results in Figure 1. Here we use Bhattacherjee’s [4] continuance theory as basic core of our research model (marked white in Figure 1). This leads to our first basic hypothesis: H1: B  hattacherjee’s continuance theory can be applied to explain Twitter usage.

Twitter is clearly the best known microblogging service on the Web. This is one reason why we chose this platform as the basis for our investigation. Another point is that Twitter has had several downtime and reliability issues during 2008 and most recently additional, smaller competitors have emerged with broader functionality. Even in the face of this, Twitter’s user base has

FIGURE 1: Research model

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experienced exponential growth. It can be supposed that there are strong reasons why existing users continue to use Twitter and new users choose this service over the various alternatives. Finally, there was a practical reason for selecting Twitter. Twitter has an accessible Web API (application programming interface) which enabled us to retrieve data about users and thus gave us the opportunity to measure real behavior for some constructs. For this reason survey participants were asked to give a valid Twitter user name. Data was collected via the survey Web site, QuestionPro. Data collection was promoted mainly in Twitter with a few blog postings outside the platform. Promotion was initiated from one of the author’s Twitter accounts using this network as a starting point for viral marketing and ‘hashtags’ to reach completely different communities of users. Hashtags are usually short terms describing several topics, such as an event (e.g. #smclondon08 for Social Media Camp London 2008). People attending such an event and writing about it on Twitter use this tag in their postings. In searching for this term one is able to get related messages even from people who are not in one’s network. These streams from Web-related events were used to post advertising for the survey. Overall, we received a total of 131 usable survey responses during the 13 days the survey was open (of the 138 originally collected, 7 were excluded due to incorrect user names or closed accounts which prevented us collecting usage data from the Twitter service). Based on the logfiles of QuestionPro, some 26.0% of the participants were from Germany, 18.3% from the US, 16.8% from the UK, 10.2% from the rest of Europe and 6.1% from the rest of the world (with 22.9% unknown). This demonstrates the wide distribution of the survey. In addition, based on the API data, some 45% of the participants registered their specific username on Twitter in the year of the survey (2008) and 47% in the year before (2007). Only 6% were initial adopters in Twitter’s first year of operation (2006). Measurement Scale development was largely based on a strong foundation of scale items from existing literature. The survey items selected and their provenance is demonstrated in Table 2. All but three constructs were measured using traditional 7-point Likert scales. Our constructs were extended with the new construct of social

network size and two other constructs were measured in different ways to the existing literature. First, using Twitter’s API it was possible to get data for both the frequency of past behavior and social network size using a custom software program developed by one of the authors. Frequency of past behavior was defined as the two weeks prior to survey completion. Based on the date the survey was completed by each respondent we were able to measure this accurately based on the number of Twitter updates posted on the service. We used two weeks rather than the four weeks of Limayem et al. [26] because we considered it more appropriate for the nature of this interactive medium. Social network size was based on the complete network of ‘followers’ and ‘following’ connections. Comprehensiveness of usage relied on a new conceptualization of the variety of usages of Twitter based on a simple three-part categorization into communication, coordination and cooperation, with each being activated as one-way and/or two-way (six uses in total). Data analysis Data analysis was performed using a variance maximization approach to structural equation modeling (SEM) and associated statistics for validity and reliability. More specifically, we used the partial least squares (PLS) technique with reflective indicators in Smart-PLS 2.0 [35]. The PLS technique has become increasingly popular in information systems research, marketing and in management research more generally in the last decade or so, influenced by its flexibility; indeed, PLS does not have the same distributional assumptions of normality for data and is able to handle small- to medium-sized samples [7] [8]. RESULTS In this section we discuss the results of model testing, including scale validity and reliability and the results of our partial least squares path modeling analysis. Tests for validity and reliability of the measures Table 3 demonstrates that the scale items exhibit high levels of convergent validity — the extent to which theoretical scale items are empirically related. The loadings of the measures on their

TABLE 2: Sample measures Construct

Source

Perceived Usefulness

Limayem et al. (2007)

Confirmation

Bhattacherjee (2001)

Satisfaction

Bhattacherjee (2001)

Continuance Intention

Bhattacherjee (2001)

Habit

Limayem et al. (2007)

Perceived Critical Mass

Van Slyke et al. (2007)

Comprehensiveness of Usage The general idea came from Limayem et al. (2007). However, this was implemented using 6 kinds of Twitter uses (communication, coordination and cooperation, with each being one-way and/or twoway)



Frequency of Past Behavior

The number of Twitter postings during the preceding 14 days before survey participation

Social Network Size

The user’s number of followers and number of friends (‘following’) in Twitter

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respective constructs in the model range from 0.727 to 0.955, with all being significant at the 0.1% level. Table 3 also demonstrates that all of the constructs fulfill the recommended levels with reference to composite reliability (CR) and average variance extracted (AVE). All items were higher than the cut-off of 0.50 recommended by Fornell and Larcker [15], ranging from 0.695 to 0.891. Similarly, the values for composite reliability are very good, ranging from 0.842 to 0.942, well above the reliability values of 0.70 and 0.80 that are typically advised for building strong measurement constructs [29] [39]. Table 4 examines the extent to which question items measure the construct intended or other related constructs, otherwise known as discriminant validity. Fornell and Larcker’s [15] stan-

dard test for discriminant validity was used, whereby the square root of average variance extracted for each construct is compared with the correlations between it and other constructs; discriminant validity is demonstrated if the square root is higher than the correlations. Table 4 clearly indicates that each construct shares greater variance with its own measurement items that with other constructs with different measurement items, with a good margin of difference. Table 5 provides an additional test for discriminant validity. Here we utilized the cross-loading method of Chin [7]. The method prescribes a requirement for measurement items to load higher on a construct than the scale items for other constructs and for no cross-loading to occur. Item loadings in the relevant construct columns were all higher than the loadings of items designed to

TABLE 3: Psychometric table of measurements Construct

Item

Loading



Confirmation (CONF)

CR = 0.872

CONF1

0.862

AVE = 0.695

CONF2



CONF3



Critical Mass (CM)

CR = 0.842

Mean

St. Error

t-value

5.71

0.103

20.444

0.904

5.63

0.113

51.131

0.724

5.14

0.123

9.636

CM1

0.885

4.33

0.158

21.662

AVE = 0.646

CM2

0.618

5.28

0.101

6.992



CM3

0.880

4.15

0.156

23.852



Habit (HABIT)

CR = 0.919

HABIT1

0.916

5.49

0.130

51.533

AVE = 0.791

HABIT2

0.930

5.50

0.127

70.021



HABIT3

0.819

4.76

0.135

24.200



IS Continuance Intention (CONT)

CR = 0.893

CONT1

0.865

5.10

0.116

24.278

AVE = 0.737

CONT2

0.842

5.93

0.102

26.120



CONT3

0.867

5.05

0.124

23.480



Perceived Usefulness (PU)

CR = 0.935

PU1

0.915

6.02

0.095

40.056

AVE = 0.827

PU2

0.890

5.92

0.096

21.853



PU3

0.923

5.92

0.089

31.792



Satisfaction (SATIS)

CR = 0.934

SATIS1

0.918

5.87

0.092

41.291

AVE = 0.780

SATIS2

0.923

5.81

0.092

53.296



SATIS3

0.842

5.53

0.103

18.985



SATIS4

0.846

5.61

0.100

18.678



Social Network Size (SNS)

CR = 0.942

SNS1 (Followers)

0.933

217.86

24.677

25.364

AVE = 0.891

SNS2 (Following)

0.955

255.53

30.013

71.194

Note 1: CR = Composite Reliability; AVE = Average Variance Extracted Note 2: Usage Comprehensiveness (mean=4.11, standard error=24.68) and Frequency of Past Behavior (mean=121.58, standard ­error=14.33) are single-item constructs

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measure other constructs; similarly, when glancing across the rows the item loadings are considerably higher for their corresponding constructs than for other constructs. Overall, the results of testing for validity and reliability are very positive and provide us with a high degree of confidence in the scale items used in the study. Test of the research model The results of PLS path modeling are shown in Figure 2. For this analysis we utilized the software package, Smart-PLS [35]. The shaded items are those that have been added to Bhattacherjee’s [4] basic continuance model. A power analysis in G*Power 3.0 [14] shows that the sample size (n=131) has good power for explaining medium population effects (f2=0.15; α=0.05; 1-β=0.88), and is thus suitable for the testing of the model under these conditions. All relationships in Bhattacherjee’s [4] original model are strongly supported by the data (H1), with links from confirmation to perceived usefulness (R2=0.445) and satisfaction at the

0.1% and 1% levels of significance respectively. Similarly, perceived usefulness is a strong driver of satisfaction (R2=0.397; p