Roles of alternative and self-oriented perspectives in the context of the continued use of social network sites

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International Journal of Information Management 33 (2013) 496– 511

Contents lists available at SciVerse ScienceDirect

International Journal of Information Management

jou rn al h om epa ge : www.elsev ier .com/ locate / i j in fomgt

oles of alternative and self-oriented perspectives in the context of theontinued use of social network sites

oung Sik Kanga, Jinyoung Minb,∗, Jeoungkun Kimc, Heeseok Leeb

School of Business Administration, Myongji University, Seoul, South KoreaBusiness School, Korea Advanced Institute of Science and Technology, Seoul, South KoreaSchool of Business, Yeungnam University, Gyeongsan-si, South Korea

r t i c l e i n f o

rticle history:vailable online 26 February 2013

eywords:ocial network sites

a b s t r a c t

Individuals with a positive evaluation of a target system are likely to continue using the system, and thissustained use is likely to result in continued use. This target-oriented perspective has served as a majorconceptual framework for understanding users’ behaviors in online contexts. The primary objective ofthis paper is to address two additional perspectives—alternative- and self-oriented perspectives—for a

ontinued IS useS habitelf-image congruityegret

firmer understanding of continued use in the context of social network sites (SNS). A research model isbuilt by employing regret and self-image congruity to represent these two perspectives. The model alsoexamines the condition under which habits are formed and how this automatic mechanism influencesthe dynamics of the nomological network between intentions and behaviors. The analysis results of tworounds of surveys for capturing the actual link between intentions and behaviors indicate that regret and

play
self-image congruity can
. Introduction

Social network sites (SNS) have witnessed sharp increases insers and thus emerged as a promising IT-based business areaBoyd & Ellison, 2008). As of 2012, Facebook had 901 million

onthly users and 525 million daily users. A research firm pre-icted that the worldwide spending on SNS advertising wouldeach $8.3 billion by 2015 (Wasserman, 2011). Given that manyNS are competing against one another for the expansion of theirnline territory, for SNS to survive and secure consistent revenuetreams, they must find effective ways to prevent customer attri-ion and promote repeat visits (Bhattacherjee, 2001b; Thong, Hong,

Tam, 2006).A number of IS studies have suggested that individuals’ online

ehaviors are driven by conscious and automatic mechanismsCheung & Limayem, 2005; Chiu, Hsu, Lai, & Chang, 2012; Kim,

alhotra, & Narasimhan, 2005; Limayem, Hirt, & Cheung, 2007).n particular, intentions and habits are known to represent con-cious and automatic mechanisms, respectively. Previous studies
ave proposed a number of factors as determinants of the mech-nisms underlying individuals’ online behaviors (Bhattacherjee,001b; Kim & Son, 2009; Limayem et al., 2007) and have generally
∗ Corresponding author. Tel.: +82 2 958 3396.E-mail addresses: [email protected] (Y.S. Kang), [email protected]

J. Min), [email protected], [email protected] (J. Kim), [email protected]. Lee).

268-4012/$ – see front matter © 2013 Elsevier Ltd. All rights reserved.ttp://dx.doi.org/10.1016/j.ijinfomgt.2012.12.004

crucial roles in post-adoption phenomena in the context of SNS.© 2013 Elsevier Ltd. All rights reserved.

suggested that an individual’s evaluation of a target system(e.g., satisfaction) plays a critical role in the formation of hisor her intentions and habits. The rationale behind this propo-sition is that individuals with a positive assessment of a targetsystem are likely to continue using the system and that this sus-tained use is likely to lead to routine use (Jasperson, Carter, &Zmud, 2005). This perspective on IT use, which the present studyrefers to as the “target-oriented perspective,” has served as amajor conceptual framework for understanding individuals’ onlinebehaviors.

The target-oriented perspective focuses mainly on an individ-ual’s interaction with a target system and thus pays little attentionto the availability of alternatives. Organizational members are typ-ically given a specific system such as the ERP (enterprise resourceplanning) system to use at work and thus have little flexibilityfor personal selection. Unlike organizational members, individualonline users are not limited to existing services and thus have anumber of other options. Therefore, their consideration of alter-native service providers may be important in the context of SNS.Users may regret their decision to continue using a target onlineservice if a forgone alternative service is perceived to provide bet-ter performance. In fact, along with satisfaction, regret is known tobe a powerful predictor of consumer behavior in marketing con-texts (Inman, Dyer, & Jia, 1997; Tsiros & Mittal, 2000). The above
discussion implies that a paradigm emphasizing a target systemmay largely overlook the importance of available options. There-fore, a better understanding of the use of SNS requires a paradigmbased on an alternative-oriented perspective.
dx.doi.org/10.1016/j.ijinfomgt.2012.12.004

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dx.doi.org/10.1016/j.ijinfomgt.2012.12.004

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Y.S. Kang et al. / International Journal of

In addition, individuals have a strong appetite for self-xpression through SNS (Attrill & Jalil, 2011; Park, Jin, & Jin, 2011),hich can provide users with IT-enabled capabilities for self-

xpression (e.g., an avatar and a profile). A profile is a uniqueage where the user can type oneself into being (Boyd & Ellison,008). The motivation to express one’s own self can often be theriving force that prompts one to engage in certain behaviors inn online environment (Min & Lee, 2011; Schau & Gilly, 2003).o express their online identities, increasing numbers of usersre using avatars, profiles, or other services that allow IT-enabledersonalization (e.g., My Yahoo!, My MSN, and iGoogle) (Boyd &llison, 2008). Previous studies have suggested that individualsrefer IT-based services with images that are congruent with theirelf-image (Kleijnen, de Ruyter, & Andreassen, 2005). This indicateshat a complete description of SNS cannot be achieved without alear understanding of the self-image construct, which transcendsargets or alternative objects. Despite the enormous potential of theelf-oriented perspective, few studies have examined its efficacy inxplaining the use of SNS.

The purpose of this study is to introduce two additional per-pectives that are important but have not been fully explored inS research: alternative- and self-oriented perspectives. To offern overarching theory integrating different perspectives, the studypecifically draws on a reference-based model that posits that indi-iduals’ behaviors are influenced by their evaluations based onomparisons with reference points (e.g., Bell & Bucklin, 1999; Lattin

Bucklin, 1989; Loewenstein, 1988). Previous studies of services,onsumer behavior, and social psychology have identified threeajor reference points for a target object, its alternatives, and its

sers’ own selves (Yim, Chan, & Hung, 2007). In particular, we useegret, a judgment resulting from a comparison of the perceivederformance of a target IS with that of an alternative IS (a referenceoint for an alternative IS), to represent the alternative-orientederspective and self-image congruity, which is formed through aomparison of the image of a target IS with the user’s self-imagea reference point for the user’s own self), to represent the self-riented perspective. In addition, this study examines the conditionnder which habits are formed and how this automatic mecha-ism influences the dynamics of the nomological network between

ntentions and behaviors.Therefore, drawing from previous research on the continued

se of online services, regret, and self-image congruity, this study’sodel hypothesizes that regret and self-image congruity influence

S habit formation, continuance intentions, and continued use in theontext of SNS. This model is expected to provide new avenues foruture research, thereby promoting a better understanding of theontinued use of SNS. In particular, this study contributes to theS literature by demonstrating the differential effects of regret andelf-image congruity on IS habit formation, continuance intentions,nd continued use in the context of SNS. In addition, the results havemportant practical implications for managers wishing to makeetter decisions on the allocation of resources for customer reten-ion and decipher the complex mechanisms underlying consumerehavior.

. Theoretical background and the conceptual framework

This section summarizes the growing body of IS research on theontinued use of online services and presents this study’s concep-ual framework.

.1. Previous research on the continued use of online services

The continued use of online services has received much atten-ion from IS researchers in recent years. The target-oriented

ation Management 33 (2013) 496– 511 497

perspective suggests that an individual’s evaluation of the use ofa target system is a key input that drives his or her online behavior.As summarized in Table 1, continuance intentions are influencedby an individual’s subjective perception of a target online service,which includes his or her beliefs and expectations concerning theuse of the online service, such as confirmation, perceived use-fulness, and perceived service functionality (e.g., Bhattacherjee,2001a; Cenfetelli, Benbasat, & Al-Natour, 2008; Thong et al., 2006;Zhou, 2011). In addition, affective evaluations such as satisfaction,pleasure, and negative affect reflect perceptions specific to a targetsystem (e.g., Chea & Luo, 2008; Kim, Chan, & Chan, 2007; Lin, Wu,& Tsai, 2005).

Over time in stable contexts, the continued use of online ser-vices tends to become increasingly habitualized (automatic), whichmeans that a sequence of well-learned actions may be repeatedwithout conscious intentions (Liao, Palvia, & Lin, 2006; Limayemet al., 2007). To appropriately address this automatic behaviortoward online services, previous IS research on the continued useof online services has made a major theoretical contribution byintroducing the construct of habits based on previous general andsocial psychology research. As summarized in Table 2, some studieshave suggested that habits moderate the effects of intentions on thecontinued use of online services (Cheung & Limayem, 2005; Chiuet al., 2012; Kim et al., 2005; Limayem & Cheung, 2008; Limayemet al., 2007), and others have found that intentions mediate theeffects of habits on the continued use of online services (Barnes,2011; Kim & Malhotra, 2005; Wilson, Mao, & Lankton, 2010). Stillothers have suggested that habits directly influence the continueduse of online services (Chiu et al., 2012; Kim & Malhotra, 2005;Lankton, Wilson, & Mao, 2010; Limayem & Cheung, 2008; Limayem& Hirt, 2003).

To fully appreciate how habits are formed under this automaticmechanism, recent studies have identified their key antecedents(e.g., Lankton et al., 2010; Limayem et al., 2007). Exploring habitsand prior IT use as distinct constructs, several studies have shownthat prior use (frequency) influences habits (Barnes, 2011; Lanktonet al., 2010; Limayem et al., 2007; Wilson et al., 2010). In addition,an individual’s evaluation of the use of a target online service interms of satisfaction, (utilitarian and hedonic) value and familiar-ity, among others has been identified as key antecedents of habits(Barnes, 2011; Chiu et al., 2012; Lankton et al., 2010; Limayem et al.,2007). Although these studies shed some light on the conditionsunder which habits of concerning the use of online services aremore likely to form, they do not explain how they break.

The target-oriented perspective can help sharpen one’s under-standing of the continued use of online services driven by consciousand automatic mechanisms. However, this perspective can provideonly a partial picture. As discussed earlier, unlike organizationalIS, online services do not restrict individual users to current ser-vices, allowing them greater flexibility for switching to competingservices. Therefore, the target-oriented perspective should be com-plemented with an alternative-oriented perspective. In addition,in the context of online services, unlike in the case of organiza-tional (work-related) computing, individuals have a strong appetitefor self-expression (Boyd & Ellison, 2008). This suggests a needfor incorporating the self-oriented perspective into the concep-tual framework of the continued use of online services. However,few theoretical studies have systematically addressed these uniquecharacteristics of online services.

2.2. Reference points and a reference-based model

Reference points are used in evaluating gains and losses andacceptable or reprehensible behaviors (Kahneman, 1992). Here theperformance of certain decisions is compared to reference points,and perceived performance or preferences can change depending

498 Y.S. Kang et al. / International Journal of Information Management 33 (2013) 496– 511

Table 1Previous research on continuance intentions toward online services based on the conscious mechanism.

Base model Study Target onlineservices/samples

Key research variablesrelated to target IS (use)evaluation

Other key researchvariables

Key findings

TRA, TPB, TAMHsu and Chiu (2004) E-tax filing service

with taxpayersPU, PP, SF Interpersonal Influence (II) PP, PU → SF; II → SF

Kim et al. (2007) Mobile Internet servicewith students

PU, Pleasure (PL), Arousal(AR)

PU, PL, AT → CI; PU, PL,AR → AT

Cenfetelli et al. (2008) B2C retailing websitewith customers

PU, SF, Service Quality (SQ),Perceived ServiceFunctionality (PSF)

PSF → SQ, PU, SF; SQ → PU,SF; PU, SF → CI

TPB and ECM Liao, Chen, and Yen(2007)

Online e-learningsystem with universitystudents

Disconfirmation (DF), PU,EOU, SF, CI

Subjective Norm (SN),Perceived BehavioralControl (PBC)

DF → PU, EOU, SF; SN → CI;PBC → EOU, CI

ECM

Bhattacherjee (2001b) Online banking withcustomers

CF, PU, SF PU, SF → CI; CF, PU → SF;CF → PU, SF

Bhattacherjee (2001a) Online brokerage withcustomers

CF, PU, SF PU, SF → CI; CF, PU → SF

Lin et al. (2005) Web portal withundergraduatestudents

CF, PU, PP, SF PP, PU, SF → CI; CF, PP → SF

Thong et al. (2006) Mobile Internet servicewith customers

CF, PU, PE, SF PE, PU, SF → CI; CF, PE,PU → SF

Chea and Luo (2008) E-services withundergraduatestudents

CF, PU, SF, Positive Affect(PA), Negative Affect (NA)

CF → PU, PA, NA, SF;SF → CI

Kang et al. (2009) Social network servicewith undergraduatestudents

CF, PU, PE, SF Regret (RE), Self-imageCongruity (SIC)

SIC → PU, PE; RE, SIC → CI

Zhou (2011) Mobile services withuniversity students

CF, PU, EOU, SF Usage Cost (UC) CF → PU, SF; PU → SF, CI;UC → SF, CI;

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use of online services.

onfirmation (CF), Ease of Use (EOU), Perceived Usefulness (PU), Perceived Enjoyontinuance Intention (CI).

n which reference points are used in the comparison. A number oftudies have suggested that there is more than one reference pointhat individuals use to evaluate the performance of their decisions.or example, Ordónez, Connolly, and Coughlan (2000) used highnd low salaries of two hypothetical graduates as reference pointsor perceiving satisfaction and fairness in the context of salariesf focal MBA graduates. Bazerman, Magliozzi, and Neale (1985)xamined reference points in a union’s wage negotiation and iden-ified the following: the last year’s wage, the management’s initialffer, the union’s estimate of the management’s reservation point,he union’s reservation point, and the union’s publicly announcedargaining position. Lin, Huang, and Zeelenberg (2006) found thaton-investment outcomes (non-actions), expectations (focal), andhe best and worst performance outcomes of stocks (alternatives)re used as influential reference points in stock investigators’ eval-ation of their stock choices.

Previous studies have demonstrated that individuals not onlyse multiple reference points but also employ them to evaluateargets typically based on the following three questions: What arerevious expectations? What are outcomes of alternatives? Do out-omes fit the perception of an evaluator’s own reality? Based onrevious research, the corresponding examples can be summarizeds follows: for previous expectations, stock performance expecta-ions, the management’s initial offer, and the union’s estimate ofhe management’s reservation point; for outcomes of alternatives,alaries of two other graduates and the best and worst perfor-ance outcomes of other stocks; and for the fit between outcomes

nd the perception of an evaluator’s own reality, the last year’sage, the union’s publicly announced bargaining position, and non-

nvestment outcomes. In particular, adopting and applying thesehree questions, previous studies of services, consumer behav-
or, and social psychology have identified three major referenceoints for a target object, its alternatives, and its users’ own selvesYim et al., 2007). A reference-based model based on evaluations
(PE), Perceived Playfulness (PP), Satisfaction (SF), Behavioral Attitude (AT), and

involving comparisons with the three reference points has beenwidely applied to model consumer behavior in a variety of contexts(e.g., Kressmann et al., 2006; Sirgy, Johar, Samli, & Claiborne, 1991;Wang & Wu, 2011; Yim et al., 2007). Given the similarity betweenindividuals’ IS use decisions and consumers’ purchasing decisions(Bhattacherjee, 2001b; Thong et al., 2006), evaluations involvingthese three reference may points play key roles in understandingindividuals’ online behaviors in the context of SNS.

2.3. Conceptual framework

This study uses a reference-based model as the theoretical foun-dation for integrating the three perspectives for the followingreasons. First, based on expectation-confirmation theory (Oliver,1980), which is a theory based on a reference-based model andwidely applied in consumer behavior research for exploring cus-tomer satisfaction and post-purchase behaviors, among others,Bhattacherjee (2001b) proposed a post-adoption model of contin-ued IS use, namely the expectation-confirmation model (ECM). Asnoted in Tables 1 and 2, the ECM has served as a dominant the-oretical basis for the target-oriented perspective. In this regard,the present study adopts a reference-based model to incorpo-rate core findings of previous research on the continued use ofonline services into this study’s conceptual framework. Next, areference-based model can serve as a basis for the concepts ofregret and self-image congruity, which reflect the alternative-and self-oriented perspectives, respectively (Bell, 1982; Loomes &Sugden, 1982; Sirgy, 1986). Therefore, this study assumes that areference-based model can provide a coherent theoretical frame-work encompassing diverse perspectives relevant to the continued

By synthesizing the three perspectives, this paper proposes aconceptual framework that addresses the unique nature of con-tinuous usage patterns in the context of online services. In order

Y.S. Kang et al. / International Journal of Information Management 33 (2013) 496– 511 499

Table 2Previous research on online service continuance behavior focusing on the automatic mechanism.

Base model Study Target online serviceand sample

Key researchvariables

Variable measuringhabit/source

Key findings Relationshipbetween habit andbehaviora

TRA, TPB, TAM,UTAUTb

Cheung and Limayem(2005)

Blackboard learningsystem with students

ContinuanceIntention (CI), InitialUse (IU), ContinuedUse (CU), Habit

IS Habit/Limayem andHirt (2003)

CI, IU → CU;Habit × CI → CU

M

Kim and Malhotra(2005)

Personalized portalwebsite with actualusers

Past Use (PtU), Use,Ease of Use (EU),Usefulness (PU),Intention (IN)

PastUse/Self-development

PtU → Use; PtU → PU,IN

D, I

Kim et al. (2005) Web-based newssites with actual users

Past Use (PtU), Use,Utilitarian Value(UV), Hedonic Value(HV), Intention (IN)

PastUse/Self-development

PtU × IN → Use M

Wilson et al. (2010) A university Internetapplication withstudents

Prior Use, Habit,Behavioral Intention(BI), Continued Use(CU)

HabitStrength/Limayem andHirt (2003)

Prior Use → Habit;Habit → BI, CU

D, I

Barnes (2011) Second Life websitewith actual users

Frequency of PriorUse (FPU), PU,Enjoyment (PE),Habit, CI

IS Habit/Limayem et al.(2007)

FPU, PU, PE → Habit;PU, PE, Habit → CI

I

ECMLimayem et al. (2007) Website with

university studentsSatisfaction (SF),ContinuanceIntention (CI), ISHabit, ContinuanceUsage (CU),Frequency of PastBehavior (FPB),Comprehensivenessof Usage (COU)


SF, FPB, COU → ISHabit; ISHabit × CI → CU

M

Limayem and Cheung(2008)

Internet-basedlearning system withuniversity students

Satisfaction (SF),ContinuanceIntention (CI), ISHabit, ContinuanceUsage (CU)


SF → CU; ISHabit × CI → CU; ISHabit → CU

D, M

Post-adoption ITmodels

Lankton et al. (2010) A university Internetapplication withstudents

Prior Use, SF,Attention:Importance,Attention: TaskComplexity, Habit, CU

IS Habit/Limayem et al.(2007)

Prior Use → CU; PriorUse, SF,Importance → Habit

D

Chiu et al. (2012) Online shopping mallwith customers

Value, SF, Familiarity,Trust, Habit, RepeatPurchaseIntention(RPI)

Habit/Verplanken andOrbell (2003)

Familiarity, Value,SF → Habit;Habit × Trust → RPI;

D, M

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a D: habit exerts a direct effect on behavior; M: habit moderates the relationshntention (indirect effect).

b The unified theory of acceptance and use of technology (UTAUT) proposed by V

o portray the process by which users’ evaluation of the three
erspectives and habits unfold over time, this paper parsimo-iously employs pertinent insights from the literature on theontinued use of online services, regret, and self-image congruity.
Self -image Congruity

Cognitive Process

(+ /

SatisfactionTarget -

Oriented Perspective

Alternative -Oriented

Perspective

Note : Path confirmed , Path partially c

(+ /

Regret

Self -Oriented

Perspective

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Fig. 1. Conceptual framework synt

tween intention and behavior; I: the impact of habit on behavior is mediated by

esh et al. (2003) is a refined version of TAM.

As shown in Fig. 1, the proposed framework posits that the
continued use of online services is driven by satisfaction, regret,and self-image congruity. The research framework makes deepertheoretical strides by introducing alternative- and self-oriented
ContinuedUse

ContinuanceIntention

ISHabit Automatic

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erspectives. Though Kang, Hong, and Lee (2009) explored theffects of self-image congruity and regret, their model providednly a partial picture because it did not address how regret andelf-image congruity influence habit formation and the continuedse of online services. To the best of our knowledge, this paper

s a first attempt to investigate the condition under which habitsoncerning online service use break.

. Research model

Fig. 1 provides an overview of the research model, and Fig. 2hows the specific paths in the model used to address the researchuestions and to control for the effects of perceived usefulness,erceived enjoyment, and gender.

.1. Target-oriented perspective

The ECM posits that a user’s confirmation of expectations isositively related to his or her satisfaction. The user’s confir-ation of expectations implies that he or she receives expected

enefits through his or her use experiences with online services,nd thus the user’s confirmation influences his or her affectivevaluation of those services, such as satisfaction (Bhattacherjee,001b). The stronger the confirmation of the expectations, theore likely the user is to be satisfied. Therefore, because satisfac-

ion is induced by comparing the expected and actual performancef a target system, the reference point for satisfaction is aarget.

In addition, the ECM holds that satisfaction is positively relatedo continuance intentions. Previous studies have suggested thatatisfaction is a key factor influencing consumers’ decision to repur-hase certain products or to use certain services (e.g., Cronin,rady, & Hult, 2000; Liao, Palvia, & Chen, 2009; Szymanski &enard, 2001; Udo, Bagchi, & Kirs, 2010). A similar line of rea-

oning can be considered in the context of the continued use
f online services, in which users’ satisfaction with their usef an online service tends to reinforce their intention to con-inue using that service. In this regard, we propose the followingypothesis:
h model.

Hypothesis 1. Users’ satisfaction with their use of an onlineservice is positively related to their continuance intentions towardthat service.

Like most other related theories, such as the technology accep-tance model (TAM), the ECM clearly implies a relationship betweenintentions and continued use and this relationship has been veri-fied in the context of the continued use of online services (Denga,Lua, Weib, & Zhang, 2010; Limayem et al., 2007). Although previousstudies have verified a nomological network between behavioralintentions and actual behaviors, the predictive power of intentionsover behaviors is limited because of the temporal gap betweenthe two (Davis, Bagozzi, & Warshaw, 1989) and situational imped-iments that may interfere with the relationship (Kim et al., 2005).Therefore, to examine the predictive power of intentions over a tar-get online service use and investigate potential impediments, weexplicitly test this relationship through the following hypothesis.

Hypothesis 2. Users’ continuance intentions toward an onlineservice are positively related to their continued use of that service.

IS habits have been defined as the extent to which individualstend to make automatic use of a target IS because of learning effects(Limayem et al., 2007). This definition helps to reduce the con-ceptual overlap between habits and intentions (Tyre & Orlikowski,1994) and thus can enable habits to provide a more in-depth under-standing of IS use. To provide a fuller understanding the role of IShabits in the context of the continued use of online services, pre-vious studies have paid close attention to their antecedents (e.g.,Barnes, 2011; Chiu et al., 2012; Lankton et al., 2010; Limayemet al., 2007). Users’ satisfactory experiences with their use ofonline services are a key condition for habit formation becausethese experiences increase their tendency to maintain the samecourse of action (Aarts, Paulussen, & Schaalma, 1997). Based on thisline of reasoning, previous studies have established a relationshipbetween satisfaction and IS habits in the context of the continued
use of online services (Limayem et al., 2007). In this regard, wepropose the following hypothesis:
Hypothesis 3. Users’ satisfaction with their use of an onlineservice has a positive effect on the formation of their IS habits.

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using the target IS. To make some sense of the discrepancy andprevent similar discrepancies in the future, users need to deliber-ate on their IS use (Louis & Sutton, 1991; Wood et al., 2002), andthis deliberation is likely to hinder habit formation. In this regard,


When a habit is established, the increasing automaticity of aehavior increasingly suppresses the need to engage in active cog-itive processing (Wood, Quinn, & Kashy, 2002). In an extreme case,his process continues until it reaches a point where intentions noonger have any influence on behavior. In a similar vein, the strengthf a habit has been found to reduce the need for acquiring informa-ion and using it for decisions (Aarts et al., 1997). Therefore, when anndividual’s behavior is driven by a habit, he or she does not have tohink about it to engage in that behavior. For example, many usersheck updates on their SNS profile page whenever they access thenternet. This indicates that IS habits may have a negative effectn the relationship between continuance intentions and continuedse. In this regard, we propose the following hypothesis:

ypothesis 4. IS habits moderate the positive effect of continu-nce intentions on continued use.

Users continue to build their expectations of the use of a tar-et IS as they become more familiar with its use. The original ECMncludes perceived usefulness as a proxy for post-adoption expecta-ions. In addition to this cognitive response, an emotional responseo IS use may be an input for continued IS use or intention formationOrtiz de Guinea & Markus, 2009). Providing support for this argu-

ent, other studies have found perceived enjoyment as anotherroxy for post-adoption expectations to serve as an antecedent ofhe use of online services (Kang et al., 2009; Thong et al., 2006; Vaner Heijden, 2004).

The perceived usefulness and enjoyment of using online servicesan positively determine users’ satisfaction with those servicesBhattacherjee, 2001b; Thong et al., 2006). The new levels of thewo post-adoption expectations can then encourage or discouragesers’ intention to continue their use of online services. Therefore,s shown in Fig. 2, this study controls for the effects of perceivedsefulness and enjoyment to explicitly identify the effects of theain research constructs.

.2. Alternative-oriented perspective

Realizing that an alternative would have led to a better out-ome than the chosen option would be an unpleasant experienceLandman, 1987). Previous studies of such unpleasant experiencesave suggested a group of decision models known as regret theoryBell, 1982; Loomes & Sugden, 1982). According to this theory, deci-ion regret is a result of decision making under uncertainty, whichay arise when it appears that people have made the wrong deci-

ion even when the decision appeared to be the right one when itas made.

In a similar context, when individuals evaluate outcomes, theyompare the outcome before them with what they would haveeceived if they had made a different choice (Tsiros & Mittal, 2000).hey may regret their decisions if a different choice would haveesulted in a better outcome, whereas they may rejoice if the dif-erent choice would have resulted in a worse outcome (Boles &

essick, 1995; Landman, 1987).The concept of alternatives is a necessary and distinguished

lement of regret formation and has been explicitly shown in aumber of studies. For example, Loomes and Sugden (1982) dis-inguished between the value of the chosen outcome and regretor alternatives not chosen. Inman et al. (1997) and Inman andeelenberg (2002) differentiated an individual’s disappointment inhosen outcomes from his or her regret about forgone outcomes.andman (1987) suggested that regret is a concept associated withndoing and thus is related to the construction of an alternative
eality. Zeelenberg (1999) suggested that “regret is a negative,ognitively based emotion that we experience when realizing ormagining that our present situation would have been better, had
e decided differently” (p. 94) and that post-decision regret is


particularly dependent on outcomes of rejected alternatives. Tsirosand Mittal (2000) argued that regret is specifically related to choicesand that for regret to be experienced, knowledge of what could havebeen should be available whether it is real or hypothetical.

Therefore, although regret and satisfaction are both associatedwith an affective response to a comparison, each uses differentreference points in the comparison process. Satisfaction resultsfrom a comparison between the expected and actual performanceof a target object, whereas regret occurs through a compari-son between the performance of the chosen outcome and thatof forgone alternatives (Tsiros & Mittal, 2000). Satisfaction usestwo temporal reference points that are before and after the useof a target object, and regret uses two objects—a target and itsalternatives—as reference points. Therefore, a disappointing out-come of a target object reduces satisfaction when the outcome isassociated with not meeting the internal performance standardsand induces regret when the outcome is perceived to be worsethan forgone outcomes (Inman et al., 1997; Tsiros, 1998). Kang et al.(2009) found that if a rival online service outperforms the chosenone, then users may show intentions to switch to the rival on theirnext occasion even when they are highly satisfied with the cur-rent one, suggesting that satisfaction and regret are served by twodifferent reference structures.

Therefore, any assessment of a target relative to alternativesas well as to past expectations is likely to influence behavioralintentions (Yim et al., 2007). In this regard, both satisfaction andregret have been found to influence behavioral intentions inde-pendently (Tsiros & Mittal, 2000). The online industry providessome anecdotal support for this relationship. For instance, Face-book has opened its platform to third-party developers and allowedthem to create extendable widgets that can be used on their web-sites (Cheng, 2007). These widgets can be used for both functional(e.g., a portal for book reviews) and entertainment (e.g., an embed-ded game) purposes. As such, an increasing number of tools haveenhanced the performance of websites by offering functional andrecreational benefits. With this enhanced performance, a largeportion of MySpace users, for instance, may regret not choosingFacebook in the first place and show some intentions to switch toFacebook. Indeed, Facebook surpassed MySpace in terms of its pageview and rank in November 2007 (Alexa, 2007). In this regard, wepropose the following hypothesis:

Hypothesis 5. Regret has a negative effect on continuance inten-tions toward online services.

Users must often choose between the current online service anda dominant rival. When making this decision, users not only try tomaximize the positive value of the service but also choose an optionthat protects them from experiencing negative emotions (Inman &Zeelenberg, 2002; Simonson, 1992), of which regret has been recog-nized as a main element (Inman et al., 1997; Tsiros & Mittal, 2000).Factors such as negative and unpleasant experiences with IS useare likely to hinder IS habit formation (Limayem et al., 2007). Louisand Sutton (1991) considered cognitive processing and found that asense of regret resulting from a discrepancy between the perceivedperformance of a target IS and that of an alternative IS can inducea mode switch from automatic processing to active thinking while

we propose the following hypothesis:

Hypothesis 6. Regret has a negative effect on IS habit formation.

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.3. Self-oriented perspective

Users bring not only their expectations but also themselveso the production of online services. In addition, more users arexpressing themselves through avatars, profiles, and other IT-nabled personalization services (e.g., My Yahoo!, My MSN, andGoogle) (Attrill & Jalil, 2011; Boyd & Ellison, 2008; Kim & Son,009). Previous studies have shown that consumers often preferroducts, services, and stores with an image that are congruentith their self-image (e.g., Kleijnen et al., 2005; Sirgy, 1985; Sirgy,rewal, & Mangleburg, 2000) and this is also applied in social mediaontext (Schau & Gilly, 2003). In this regard, a deeper understandingf users’ continued use of online services requires the considerationf their self-image as well as their expectations.

Self-image congruity, a key construct of the self-oriented per-pective, has been one of the major themes in consumer behavioresearch (O’Cass & Grace, 2008; Sirgy, 1982; Yim et al., 2007).n individual’s self-concept has typically been viewed as a mul-

idimensional concept mirroring more than just one type ofelf-perspective (Markus & Wurf, 1987), including self-image, idealelf-image, social self-image, and ideal social self-image (Sirgy,985). As discussed earlier, one’s actual self-image refers to howne perceives oneself; one’s ideal self-image refers to how oneould like to perceive oneself as; one’s social self-image refers toow one is seen by others; and one’s ideal social self-image refers toow one would like to be seen by others (Sirgy, 1985). In this regard,elf-image congruity has been dealt with in a multidimensionalanner. The congruity between one’s actual self-image and that of

product/service is referred to as actual self-image congruity; thatetween one’s ideal self-image and the product/service’s image, as

deal self-image congruity; that between one’s social self-imagend the product/service’s image, as social congruity; and thatetween one’s ideal social self-image and the product/service’s

mage, as ideal social congruity (Sirgy, 1982, 1985; Sirgy et al.,000).

Self-image congruity theory has been developed to explain con-umer behavior (Sirgy, 1982, 1985, 1986). According to this theory,onsumers who perceive the image of a product/service to be con-istent with their actual self-image are likely to be motivated tourchase or use that product/service because the greater the con-umer’s self-image congruity with a particular product/service, thereater the likelihood that the product/service satisfies the con-umer’s need for self-consistency. One’s need for self-consistencyas been defined as one’s motivational tendency to engage inehaviors that are consistent with one’s view of oneself. Doing oth-rwise would cause dISonance, resulting in a state of psychologicaliscomfort that threatens the validation of one’s beliefs about one-elf (Sirgy, 1985). Similarly, to satisfy the need for self-consistency,

user is likely to feel motivated to use a target online service contin-ously with a high degree of self-image congruity. Reflecting this,ung, Chou, and Dong (2011) found that highly active SNS usersre more likely to use personalization features for themselves thaneneral users and that users of online services can define, main-ain, and enhance their self-concept in the online space (Zinkhan &ong, 1991). In this regard, we propose the following hypothesis:

ypothesis 7. Self-image congruity is positively related to theontinued use of online services.

As discussed earlier, self-image congruity theory was originallyeveloped to explain consumer behavior. However, it is generallyifficult to measure behaviors directly. For this reason, previous
tudies have explored and verified the direct effects of self-imageongruity on various proxies for consumer behavior, includingurchase intentions (Ericksen, 1997; Sirgy, 1985), consumers’ com-itment (Yim et al., 2007), brand and store loyalty (Kressmann
ation Management 33 (2013) 496– 511

et al., 2006; Sirgy & Samil, 1985), and continuance intentions (Kanget al., 2009). In this regard, we propose the following hypothesis:

Hypothesis 8. Self-image congruity is positively related to con-tinuance intentions toward the use of online services.

Information-processing perspectives, which are relevant to thepresent study, posit that habits emerge from the repetition ofresponses and that they are guided by automatic processing thatoccurs rapidly and requires minimal attention (Ouellette & Wood,1998). Indeed, in line with the first view, the frequency of pastbehavior has been identified as an antecedent of IS habits becausethe more frequent the IS use, the more likely the cognitive pro-cess involved is to be automatic in nature (Limayem et al., 2007).On the other hand, the second view suggests that self-image con-gruity can be an important precondition for the development of IShabits, which can be theoretically explained as follows. An indi-vidual’s self-image associated with his or her self-perception isa cognitive scheme organized at higher levels of the cognitivehierarchy (Markus, 1980). Cognitive schemes that are situated athigh levels of this hierarchy are referred to as abstract schemes,whereas those at low levels, concrete schemes (Anderson, 1980).Abstract schemes are more accessible and easily initiated in highlyfamiliar circumstances and are less likely to require the allocationof cognitive processing or related efforts than concrete schemes(Wyer & Carlston, 1979). This implies that self-image congruityinvolves abstract cognitive schemes whose cognitive processing israpid and effortless at a lower level of consciousness (Sirgy et al.,1991). Therefore, self-image congruity may be a precondition forthe formation of IS habits, and the greater the user’s need for self-consistency, the more automatic his or her use of the target onlineservice is. In this regard, we propose the following hypothesis:

Hypothesis 9. Self-image congruity has a positive effect on theformation of IS habits.

4. Methodology and analysis results

4.1. Survey administration

We conducted a field survey of actual users of Cyworld, a well-known SNS in Korea. Cyworld is similar to MySpace and Facebookand has attracted more than 25 million users since its launch in1999. Its widespread popularity is well demonstrated by the factthat almost 90% of all Koreans in their twenties are its registeredusers. Cyworld users employ the so-called “mini-homepages” toexpress their individuality and present their self-image to others.That is, a mini-homepage is a type of profile. Cyworld now operatesits website in other countries, including the U.S., China, Japan, andTaiwan, which requires it to compete with other international SNSto attract users in each country. In addition, we targeted SNS thatcould potentially generate regret to examine the research model.Managing personal information on Cyworld involves heavy writ-ing and photo management, which makes it difficult for users tocompletely abandon their mini-homepages even if they want to.In addition, the fact that most peer groups use Cyworld can leadto unwanted use. Further, building their relationships and his-tory through posts and comments requires considerable amountsof their effort and time. Therefore, all these aspects of Cyworldindicate that regret may be likely among its users and suggestthat Cyworld is an appropriate IT system for verifying this study’sresearch model.

We conducted self-administered surveys targeting undergrad-
uate students in Korea and asked them to complete a questionnaireconcerning their most recent Cyworld use. Here we employed twosurvey rounds to capture the actual relationship between contin-uance intentions and continued use. During the first round, we


Table 3Demographic characteristics of respondents.

Item Category First survey (n = 349) Second survey (n = 278)

Frequency Percent Frequency Percent

GenderMale 167 47.9 132 47.5Female 182 52.1 146 52.5

Age18–19 95 27.2 76 27.320–25 216 61.9 168 60.426–30 38 10.9 34 12.2

Academic Year

Freshman 104 29.8 81 29.1Sophomore 73 20.9 61 21.9Junior 77 22.1 58 20.9Senior 95 27.2 78 28.1

Less than 3 months 32 9.2 25 9.03–6 months 13 3.7 11 4.0

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easured seven constructs through the research model: perceivedsefulness, perceived enjoyment, self-image congruity, regret, sat-

sfaction, IS habits, and continuance intentions. Four weeks after therst survey, we conducted the second survey to assess the respon-ents’ continued use of Cyworld. We used the last four digits ofhe respondents’ mobile phone numbers to match their answersetween the two rounds. The first survey included a total of 349espondents, and the second one, 278 respondents. In this way, weonsidered a total of 278 respondents whose actual continuous useas verified. Table 3 shows the demographic characteristics of the

espondents.

.2. Instrument development

We assessed all constructs except for satisfaction and contin-ed use by using multiple items based on a seven-point Likert-typecale. We made conscientious efforts to adapt validated measuresrom previous studies for the latent constructs. In addition, welightly modified the items to fit the study context. We measuredhe items for satisfaction by using seven-point semantic differentialcales and those for continued use by using two self-reported items.ne of the items asked about how long the respondents used the

arget IT system on a daily basis since the first survey: “On average,ow much time did you spend a day visiting this website duringhe past month?” (1 = less than 10 min; 2 = 10 min or more but lesshan 20 min; 3 = 20 min or more but less than 30 min; 4 = 30 min or

ore but less than 1 h; 5 = 1 h or more but less than 2 h; 6 = 2 h orore but less than 3 h; and 7 = 3 h or more). The other item was

elated to behavioral frequency: “On average, how often did youisit this website during the past month?” (1 = less than once aonth; 2 = once a month; 3 = a few times a month; 4 = a few times

week; 5 = about once a day; and 6 = several times a day). Table 4hows the conceptual definition and source of each construct. Theppendix shows the specific items.

For the content validity of each construct, we examined the ini-ial measurement scales for content validity with the help of fouroctoral students in the IS field. We asked them to check the survey

nstrument and comment on its format and length as well as theording of each item. After incorporating suggested changes, we

onducted the first pilot test with six MBA students and three addi-ional doctoral students majoring in business administration. Based
n the results of the pilot survey, we made additional modificationso the measurement scale and conducted the second pilot test with1 undergraduate and graduate students in business administra-ion. Cronbach’s for all scales was acceptable, with satisfactionroducing the lowest one (0.80).
5.2 13 4.723.8 66 23.758.2 166 58.6

4.3. Measurement reliability and validity

To assess the validity of the research model, we adopted a two-step structural measurement approach (Chin, 1998) and assessedthe measurement property through partial least squares based onSmartPLS 2.0 M3 (Ringle et al., 2005). We assessed the reliabilityand validity of the measurement model based on internal consis-tency and a confirmatory factor analysis (CFA). Table 5 shows themeasurement properties. Coefficients for both construct reliabil-ity and Cronbach’s all exceeded the recommended threshold of0.70, and the average variance extracted (AVE) for each constructexceeded 0.50 (Fornell & Larcker, 1981).

For convergent validity, we examined the AVE and t-value ofeach item. The AVE measures the percentage of the variance alatent variable component captures from its indicators relative tothe percentage based on the measurement error. The AVE valuesranged from 0.668 to 0.803, which exceeded the cutoff value of 0.5(Fornell & Larcker, 1981). All t-values of the items exceeded 3.29 atp < 0.001. These results indicate sufficient convergent validity forthe measurement scale. Weights are relevant to the verification ofthe convergent validity of formative measures. Unlike in the caseof reflective constructs, previous studies have recommended thatthe weight of an item should be examined to test for constructvalidity (Petter, Straub, & Rai, 2007). The two formative items forcontinued use showed significant weights: 0.242 (t-value = 6.915)and 0.851 (t-value = 27.413) respectively. This implies that the twoitems contributed substantially to their corresponding constructs.

For discriminant validity, the AVE for a construct should exceedthe variance shared between the construct and other constructsin the model. Table 6 shows the square root of the AVE alongthe diagonal. All AVE values exceeded off-diagonal elements inthe corresponding rows and columns, demonstrating sufficient dis-criminant validity (Chin, 1998).

4.4. Common method bias and non-response bias

Common method bias is one of the main reasons behind system-atic measurement errors (Podsakoff, MacKenzie, Lee, & Podsakoff,2003; Podsakoff & Organ, 1986) and can be caused by exclusivereliance on self-reporting. To minimize common method bias, weconsidered several statistical remedies.

First, following Widaman (1985) and Song and Zahedi (2005),
we have compared Chi-square values of three different models.Model 0 addresses no underlying factor. Model 1 assumes a unidi-mensional model which posits that one method factor accounts forthe variance among all the measurement items. Model 2 represent


Table 4Construct definitions and related studies.

Construct Conceptual definition Related studies

Perceived Usefulness Users’ perception of the expected benefits of target IT system use Bhattacherjee and Premkumar (2004)Perceived Enjoyment The extent to which the activity of using target IT system is perceived to be enjoyable in its own right Van der Heijden (2004)Self-image Congruity Users’ perception of the congruity between their actual image and the image of target IT system Sirgy et al. (1997)Regret Users’ feeling sorry about choosing target IT system Tsiros and Mittal (2000)Satisfaction Users’ affect regarding (feelings about) target IT system use Bhattacherjee and Premkumar (2004)IS Habit The extent to which users tend to use target IT system automatically because of the effects of learning Limayem et al. (2007)Continuance Intention Users’ intention to continue using target IT system Bhattacherjee (2001b)Continued Use Users’ utilization of target IT system in the previous period Kim et al. (2005)

Table 5Measurement properties.

Construct Item Factor loading Weight t-Value Item reliability Cronbach’s Construct reliability AVE

Perceived Usefulness(Reflective)

PU1 0.878 – 15.713 0.771 0.924 0.947 0.814PU2 0.918 – 16.655 0.843PU3 0.929 – 18.368 0.863PU4 0.884 – 16.441 0.781

Perceived Enjoyment(Reflective)

PE1 0.933 – 25.726 0.870 0.922 0.951 0.867PE2 0.943 – 25.691 0.889PE3 0.916 – 20.974 0.839

Self-image Congruity(Reflective)

SI1 0.892 – 12.751 0.796 0.887 0.930 0.815SI2 0.883 – 11.699 0.780SI3 0.934 – 15.812 0.872

Regret (Reflective) RE1 0.785 – 6.013 0.616 0.857 0.915 0.783RE2 0.936 – 13.080 0.876RE3 0.925 – 14.781 0.856

Satisfaction(Reflective)

CS2 0.907 – 13.276 0.823 0.924 0.952 0.868CS3 0.949 – 19.263 0.901CS4 0.938 – 17.181 0.880

IS Habit (Reflective) HB1 0.929 – 76.430 0.863 0.870 0.921 0.795HB2 0.832 – 26.462 0.692HB3 0.910 – 62.724 0.828

ContinuanceIntention (Reflective)

CI1 0.901 – 42.080 0.812 0.849 0.909 0.770CI2 0.911 – 53.340 0.830RCI3 0.817 – 24.744 0.667

Continued Use(Formative)

CU1 – 0.242 6.915 – – – –CU2 – 0.851 27.413 –

CS1 was dropped because it could be affected by the common method bias.

Table 6Correlations between latent variables.

Construct PU PE SC RE CS HB CI

Perceived Usefulness (PU) 0.902Perceived Enjoyment (PE) 0.460 0.931Self-image Congruity (SC) 0.410 0.551 0.903Regret (RE) −0.339 −0.318 −0.287 0.885Satisfaction (CS) 0.328 0.422 0.299 −0.214 0.932IS Habit (HB) 0.369 0.514 0.456 −0.314 0.240 0.892

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alues along the diagonal values indicate the square root of the AVEs.

ur measurement model. Chi-square, degrees of freedom, andoodness of fit indices of each model are presented in Table 7.

Straub, Limayem, and Karahanna-Evaristo (1995) suggest the
omputation of delta explains the percentage explained by eachodel of the total variation. Therefore, delta as the goodness of
t index shows that models 1 and 2 explain 21.9% and 90.8% ofhe total variance among the measures respectively implying that

able 7easurement model comparisons.

Model �2 d.f. ��2 �d.f. p-Value

Model 0 5207.71 276

Model 1 4065.70 252

Model 2 476.89 224 3588.81 28 p < 0.001

Recommended

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M0 where M0 is model 0 and Mi is either model 1 or model 2.

6 −0.548 0.339 0.555 0.877

our measurement model is much superior than the model withcommon method factor. Furthermore, comparison of models 1 and2 shows that model 2 has significant less chi-square and better fit
indices, implying that the measurement model is superior to theunidimensional model (Hair, Black, Babin, & Anderson, 2009).
Second, we conducted Harman’s single-factor test by conduct-ing an exploratory factor analysis and examined unrotated factor

Deltaa GFI AGFI NFI CFI RMSEA

0.224 0.157 0.000 0.000 0.2540.219 0.450 0.345 0.756 0.769 0.2340.690 0.875 0.832 0.964 0.979 0.064

≥0.9 ≥0.8 ≥0.9 ≥0.92 ≤0.07


Table 8Harman’s single factor test.

Construct Item Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6

Perceived Usefulness PU1 0.623 −0.342 −0.153 0.461 0.179 −0.033PU2 0.676 −0.352 −0.148 0.451 0.156 0.014PU3 0.660 −0.366 −0.132 0.513 0.101 0.096PU4 0.636 −0.316 −0.072 0.472 0.207 0.051

Perceived Enjoyment PE1 0.753 0.082 0.200 −0.054 −0.166 −0.464PE2 0.719 0.041 0.251 0.030 −0.195 −0.531PE3 0.722 −0.013 0.292 0.006 −0.154 −0.457

Self-image Congruity SI1 0.575 0.132 0.185 0.047 −0.583 0.323SI2 0.620 0.029 0.133 0.158 −0.534 0.287SI3 0.697 0.096 0.195 0.123 −0.495 0.235

Regret RE1 −0.379 0.201 0.547 0.356 0.112 −0.041RE2 −0.545 0.154 0.561 0.371 0.142 0.030RE3 −0.556 0.098 0.561 0.358 0.125 0.040

Satisfaction CS2 0.433 −0.449 0.475 −0.400 0.232 0.158CS3 0.536 −0.432 0.464 −0.394 0.169 0.115CS4 0.523 −0.450 0.454 −0.357 0.194 0.154

IS Habit HB1 0.689 0.522 0.054 −0.040 0.235 0.130HB2 0.625 0.405 0.000 0.038 0.217 −0.023HB3 0.673 0.496 0.004 −0.045 0.217 0.170

Continuance Intention CI1 0.720 0.067 −0.234 −0.084 0.171 −0.001CI2 0.748 0.027 −0.207 −0.056 0.070 0.052

RCI3 0.623 −0.065 −0.335 −0.255 −0.006 −0.053Continued Use CU1 0.471 0.482 0.215 0.010 0.184 0.006

CU2 0.573 0.569 −0.025 −0.047 0.291 0.113Eigen Values 9.323 2.424 2.182 1.867 1.539 1.091

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S1 was dropped because it could be affected by the common method bias.

olutions. If common method variance does exist, either a singleactor will emerge or one general factor will account for the major-ty of the covariance among measures (Podsakoff et al., 2003). Theoor model fit, which can be checked by the goodness of fit indicesf model 1 in Table 7, suggests that common method bias is not theerious concern here. Table 8 also shows the structure of unrotatedactors.

Lastly, in order to explicitly decompose the total variance intoariance explained by constructs and method factor, we included

common method factor in the partial least squares (PLS) modelnd calculated each indicator’s variance substantively explainedy the principal construct and the method factor following Liang,araf, Hu, and Xue (2007). The results indicate that the methodias had a significant effect on the first item of the satisfaction con-truct and that the indicator’s substantive factor loading was lowerhan its method factor loading. Therefore, we dropped the indicatornd reconducted the test in Liang et al. (2007). The results of theecond analysis indicate that all substantive factor loadings wereigher than the method factor loadings. The average substantivelyxplained variance of the indicators was 0.822 whereas the aver-ge method-based variance was 0.003 (Table 9 shows the detailedesults). Therefore, the measurement reliability, validity, commonethod variance tests including the two methods presented earlier

re reconducted and their results are adjusted in from Table 5 toable 8. These results indicate that the systematic error from theethod bias is not a serious concern.A total of 71 respondents (approximately 20%) in the first sur-

ey did not respond to the second survey. To check for possibleystematic errors from non-response bias, we examined the demo-raphic characteristics of the respondents in the two rounds ofata collection. Table 3 shows only marginal demographic differ-nces between the two samples. In addition, we conducted a seriesf �2 difference tests to investigate potential differences betweenhe two samples. The results indicate no significant differences
etween the two samples (gender: �2 = 0.008, df = 1, p = 0.927; age:2 = 0.297, df = 2, p = 0.862; education: �2 = 0.242, df = 3, p = 0.971;
arget-IT experience: �2 = 0.105, df = 4, p = 0.999), implying no sub-tantial non-response bias.

9.093 7.779 6.412 4.54758.039 65.818 72.230 76.777

4.5. Structural model

Before testing the structural model, we conducted a t-test toensure that satisfaction and regret would result from a compari-son based on two separate sets of reference points: expected andactual target performance for satisfaction and outcomes of the tar-get and its alternatives for regret. If they are induced from the samereference point, then they are highly dependent on each other. Forexample, low satisfaction is an outcome of high regret, and lowregret is an outcome of high satisfaction. If this is not the case,then satisfaction and regret operate independently. For this rea-son, we first assigned the respondents to three groups based ontheir scores for satisfaction measures. For more rigorous groupclassification, we excluded the middle group and employed thehigh- and low-satisfaction groups for further analysis. The high-satisfaction group (mean = 5.565, n = 119) and the low-satisfactiongroup (mean = 2.735, n = 33) showed no significant difference intheir scores for regret measures—2.305 and 2.586, respectively(t = 1.284, p = 0.206)—implying that satisfaction and regret wereinduced by two different reference points.

To test the structural model, we used the PLS model becausethe research model contained a formative construct: continueduse. Given that one of the default assumptions of SEM is that mea-surement items are reflective (Chin, 1998), covariance-based SEMsuch as LISREL is less appropriate. Because the PLS model calcu-lates construct scores from explicitly estimated outer weights, itis better suited for modeling formative constructs (Chin, 2010). Inaddition, the PLS model is known to be robust with fewer identifi-cation issues, work with both small and large samples (Hair et al.,2009), and have soft distributional assumptions (Chin, 2010).

Fig. 3 presents a summary of the analysis. Most of the hypoth-esized relationships were significant at p < 0.05. The exceptionswere the effects of satisfaction, which were not significant atp < 0.05. As expected, regret and self-image congruity had signif-
icant effects on continuance intentions (path coefficients = −0.357,0.140), providing support for H5 and H8, respectively. They also hadsignificant effects on IS habits (path coefficients = −0.187, 0.375)and accounted for 21% of the variance in satisfaction, providing


Table 9Common method bias analysis.

Construct Item Substantive factor loading (R1) R12 Method factor loading (R2) R22

Perceived Usefulness PU1 0.890*** 0.792 −0.017 0.000PU2 0.890*** 0.792 0.036 0.001PU3 0.943*** 0.889 −0.019 0.000PU4 0.886*** 0.785 −0.001 0.000

Perceived Enjoyment PE1 0.893*** 0.797 0.046 0.002PE2 0.992*** 0.984 −0.059** 0.003PE3 0.907*** 0.823 0.013 0.000

Self−image Congruity SI1 0.959*** 0.920 −0.091 0.008SI2 0.893*** 0.797 −0.008 0.000SI3 0.863*** 0.745 0.092** 0.008

Regret RE1 0.875*** 0.766 0.106*** 0.011RE2 0.915*** 0.837 −0.026 0.001RE3 0.871*** 0.759 −0.065** 0.004

Satisfaction CS2 0.942*** 0.887 −0.077** 0.006CS3 0.936*** 0.876 0.029 0.001CS4 0.927*** 0.859 0.025 0.001

IS Habit HB1 0.936*** 0.876 −0.013 0.000HB2 0.817*** 0.667 0.033 0.001HB3 0.918*** 0.843 −0.017 0.000

Continuance Intention CI1 0.909*** 0.826 −0.010 0.000CI2 0.859*** 0.738 0.061 0.004RCI3 0.867*** 0.752 −0.056 0.003

Average 0.906 0.822 −0.001 0.003

CS1 was dropped because it could be affected by the common method bias.** p < 0.05.

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upport for H6 and H9, respectively. Continuance intentions andelf-image congruity had significant direct effects on continued usepath coefficient = 0.288, 0.211), providing support for H2 and H7,espectively.

IS habits had a significant moderating effect on the relation-hip between continuance intentions and continued use (pathoefficient = −0.181, p < 0.01). We calculated Cohen’s (1988) effect
ize f2 for the interaction to examine whether IS habits would have
substantial moderating effect on the relationship between con-inuance intentions and continued use (f2 = 0.03, �R2 = 0.025). Thisuggests that IS habits had a significant moderating effect.

results.

5. Discussion and conclusions

We developed a research model to examine the effectsof regret and self-image congruity on IS habit formation,continuance intentions, and continued use in the context ofSNS. The results based on two surveys of actual users of a popularSNS indicate that regret had negative effects on IS habit forma-
tion and continuance intention. In addition, self-image congruityhad positive effects on IS habit formation, continuance inten-tions, and continued use. Overall, this study contributes to theIS literature by demonstrating the differential roles of regret and

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.1. Theoretical implications

.1.1. Utilitarian and hedonic value of SNSThere have been two research streams that differ in their view

f the value of IS: utilitarian vs. hedonic. Previous studies of tradi-ional work-related IS have focused on their utilitarian value (e.g.,avis, 1989; Venkatesh, Morris, Davis, & Davis, 2003), whereas

hose of pleasure-oriented IS such as IS for home, leisure, and gamesave focused on their hedonic value (e.g., Hsu & Lu, 2004; Van dereijden, 2004). Although it appears that the utilitarian value and

he hedonic value are independent of each other and a single IS can-ot have both, some studies have argued that a balanced effect ofhe utilitarian and hedonic value of IS may enhance their use, par-icularly when technology use is private and voluntary in naturee.g. Venkatesh & Brown, 2001). Because current technologies mixrivate and work lives in the same space (e.g., smartphones), draw-

ng IS users may require the hedonic value as well as the utilitarianalue (Arruda-Filho, Cabusas, & Dholakia, 2010; Gerow et al., 2012;im & Forsythe, 2007; Koufaris, 2002). For example, in the virtualnd social online space, individuals engage in online behaviors foroth functional and experiential motives (Zhou, Jin, Vogel, Fang, &hen, 2011), and this it is particularly the case in the context ofNS. SNS, whose online space makes it easier for users to manageheir impression and self-presentation than in reality because of aack of physical presence, have become a useful tool for users to

anage their self-image (Kim & Lee, 2011; Marwick & boyd, 2011)hile enjoying online activities. Therefore, the results provide sup-ort for the argument that SNS provide not only the hedonic valueut also the utilitarian value and that a balanced effect of those twohould be carefully considered in drawing users in this online spaceecause these users are at the center of IS design.

.1.2. Differential role of regretRegret had a negative effect on IS habit formation. This can be a

ajor barrier to the development of SNS users’ automatic behavioroward continued use. Given that the target-oriented perspectiveuggests that a user’s satisfactory experience with the use of a tar-et IS and its hedonic and utilitarian value is a key driver of his orer IS habit formation, this finding has important implications for ISesearch. The use of online services typically accompanies the user’sositive and negative experiences simultaneously, and thereforeositive experiences alone are not sufficient to explain the com-lexity of habit formation. The present study fills this gap in the IS

iterature by shedding some light on the effects of regret on IS habitormation and answering the call for the exploration of the processf IS habit formation (Limayem et al., 2007). The study initially iden-ifies the condition under which habits concerning the use of onlineervices break. Previous studies have suggested that regret has aegative effect on repurchase intentions. Previous studies have sug-ested that regret has a negative effect on repurchase intentionsTsiros & Mittal, 2000). Based on a similar line of reasoning, regretas been found to have a negative effect on continuance intentionsoward online services (Kang et al., 2009). The results of the presenttudy provide support for this finding. Noteworthy is that the effectf regret on continuance intentions was greater than that of thentecedents such as perceived usefulness and enjoyment fromhe target-oriented perspective. Taken together, this differentialole of regret in post-adoption phenomena in the context of SNSemonstrates the efficacy of the alternative-oriented perspective.

.1.3. Differential role of self-image congruityA number of studies considering the continued use of online ser-

ices, like traditional IS research, have taken the simplistic view that


continuance intentions and their interactions with IS habit forma-tion can serve main determinants of this use. However, this study’sresults suggest that self-image congruity can also play an importantrole in explaining continued use in the context of SNS. This findingimplies that continuance intentions and their interactions with IShabit formation alone are not sufficient to explain the complexity ofcontinued use in this context. Although self-image congruity theorysuggests that self-image congruity influences consumer behavior(Sirgy, 1982, 1985, 1986), this study is the first to demonstrate thiseffect in the context of SNS. In addition, the results suggest thatself-image congruity can have considerable influence on both IShabit formation and use behavior, providing a better understand-ing of how IT-enabled self-expression capabilities (e.g., avatars andprofiles) contribute to individuals’ automatic behavior toward thecontinued use of online services. To the authors’ knowledge, nostudy has demonstrated the effects of self-image congruity on IShabit formation. Overall, this differential role of self-image con-gruity suggests the importance of considering the role of onlineusers’ self-image in post-adoption phenomena and demonstratesthe efficacy of the self-oriented perspective.

5.2. Managerial implications

The results have important practical implications for SNS man-agers wishing to encourage continued use among their users. First,website managers should pay close attention to the discrepancybetween the perceived performance of their website and thatof other websites. The results indicate that if this discrepancyreaches a critical mass, then users may switch to rival websitesto prevent a sense of regret. In addition, this regret can motivateeven habitual users to shift from automatic to conscious cogni-tive processing, disrupting habit formation. Typically, websites canbe developed through the following two distinct phases (Piccoli,Brohman, Watson, & Parasuraman, 2004): (i) evolution (when web-sites are improved through a stable design) and (ii) revolution(when major redesigns and new functionalities are introduced).Website managers should assess performance improvements inrival websites after the important revolution phase, and based onthis assessment, they can determine the types of desirable changesto reduce the discrepancy between the perceived performance oftheir websites and that of rival websites. In this way, online firmscan align the allocation of resources for retaining users with themaintenance of their habitual inertia as well as the prevention oftheir attrition.

The results suggest that self-image congruity plays a crucialrole in post-adoption phenomena in the context of SNS. Given theunderstandable reality that online social networking is ubiquitousin personal settings, users are likely to pay close attention to IT-enabled self-expression capabilities (e.g., profiles) of SNS, whichcan help them to define, maintain, and enhance their self-conceptin the online space. Each SNS user is unique, and how SNS usersperceive themselves and desire to be perceived by others varies.Therefore, individual users can choose to show different selvesdepending on the audience. In this regard, SNS providers shouldnot settle for a single feature that tries to accommodate all users’self-images. Instead, SNS should provide a wide range of flexiblefeatures so that individual users’ unique desires and self-image canbe served in different ways. For example, personalized timelinesand profile presentations and more options for users to portraytheir feelings and image may increase the value of SNS and thusdraw more users. In addition, SNS managers should understand thatnot only profiles but also the entire SNS space can become the object
of users’ evaluation of self-congruity. For example, an advertise-ment that is not created by users can easily irritate them if it showsa message far removed from their self-image. This suggests a needfor being cautious and analyzing the history of users’ page views for

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ore effective advertising. To attract and retain more users, onlinerms should capitalize on incorporating users’ self-image into webervices.

.3. Limitations and future research

This study has some limitations. First, we investigated only oneNS used mainly by undergraduate students in their twenties (i.e.,yworld). Given the diversity of SNS, future research should exam-

ne the proposed model by considering a wider range of users asell as use/competitive contexts in various empirical settings toetermine the external validity of this study’s findings.

Second, we employed a self-reported behavioral measure toapture continued use. Although such measures are generallyccepted (e.g., Davis et al., 1989; Kim et al., 2005), this study’sesults should be carefully compared with those based on objectiveata (Straub et al., 1995).

Third, to capture the realized continued use of a target IS, weonsidered a period of only four weeks, which might not haveeen long enough. However, given that most of the respondentsad used the target IS for more than two years before the survey,hose respondents who used the target IS after four weeks of thenitial survey probably prolonged their use even after the survey.herefore, the use of this four-week period was not a serious issue.

Fourth, the overall level of regret may have been loweredecause many respondents had used the target IS for more thanwo years before the survey. However, the statistical test showshe level of regret is formed independently of the level of satisfac-ion. The result suggests that regret indeed sets a reference pointifferent from satisfaction.

Fifth, we tested a limited set of antecedents of IS habit forma-ion to focus on the main constructs for the three perspectives.ther antecedents such as the frequency of prior behaviors and

he comprehensiveness of IS use may contribute to the formationf IS habits (Limayem et al., 2007). Therefore, the effects of self-mage congruity and regret on IS habits may vary according to thentecedent. However, the comprehensiveness of IS use and the fre-uency of prior behaviors in Limayem et al.’s (2007) model areignificant only at the 0.1 level, suggesting their minimal effects onabit formation. In this regard, self-congruity and regret are likelyo play dominant roles in habit formation even with the inclusionf these variables in the research model.

Finally, we tested only the effects of actual self-image congruity.revious studies have treated self-image congruity in a multidi-ensional manner. In this regard, future research should examine

he differential roles of other types of self-image congruity, suchs ideal self-image congruity and ideal social congruity, in post-doption phenomena.

The results suggest some interesting avenues for futureesearch. First, self-image congruity had significant effects on ISabit formation and continued use. It is generally acknowledgedhat an IT artifact (e.g., a profile) can increase the congruity betweenhe image of a target IS and the user’s self-image by offering self-xpression features such as the uploading of the user’s photos. Inhis regard, future research should identify those self-expressioneatures that can serve as powerful environmental cues trigger-ng the automatic and continued use of online services (Markus &ilver, 2008; Ortiz de Guinea & Markus, 2009). If an individual’s pro-le does not correspond to his or her self-image, then he or she maye discouraged from revisiting the website to update the profile.

Second, this study proposes that continued use in the con-ext of SNS is driven by target-oriented, alternative-oriented,
nd self-oriented perspectives. Although fundamental, these threeerspectives in isolation are not sufficient to offer a completenderstanding of continued use. For example, constraint fac-ors such as switching costs can make it difficult for users to

switch to attractive alternatives (Jones, Mothersbaugh, & Beatty,2000). Future research could explore the interaction betweenthe alternative-oriented perspective and constraint factors. Inaddition, behavioral outcomes such as word-of-mouth commu-nication and inattentiveness can be considered as alternatives(Kim & Son, 2009). Future research could extend the conceptualframework of this study by verifying the theoretical relationshipsbetween the two additional perspectives and these behavioraloutcomes.

Third, this study’s conceptual model may not be limited to SNSbut applicable to other contexts in which a target IS is evaluatedby users, competes with alternatives, and offers IT-enabled self-expression capabilities (e.g., online portals and personal digitalservices such as those offered through smartphones). In this regard,future research should employ this study’s conceptual frameworkin such contexts.

5.4. Conclusions

In the context of continued SNS use, knowledge of alternative-and self-oriented perspectives is far more limited than that ofthe target-oriented perspective. A lack of this knowledge canlead to the misallocation of resources for user retention. Toaddress the unique nature of online services, this study presentsa conceptual framework that integrates all three perspectives.This theoretical framework is expected to serve as a usefulconceptual tool for further research on these underexploredperspectives.

Appendix 1. Questionnaire items

Perceived Usefulness (Strongly Agree . . . Strongly Disagree)PU1. Using improves my productivity in managing personal information on my

profile, photos, and social relationships.PU2. Using improves my efficiency in managing my personal information.PU3. Using improves my effectiveness in managing my personal information.PU4. Overall, is useful for managing my personal information.

Perceived Enjoyment (Strongly Agree . . . Strongly Disagree)PE1. Using is enjoyable.PE2. Using is pleasurable.PE3. I have fun using .

Self-image Congruity (Strongly Agree . . . Strongly Disagree)SI1. Visiting helps to maintain my image and character.SI2. Visiting helps to reflect who I am.SI3. Visiting fits my image well.

Regret (Strongly Agree . . . Strongly Disagree)RE1. I feel sorry for choosing .RE2. I regret choosing .RE3. I should have chosen .

SatisfactionHow do you feel about your overall experience in terms of using ?CS1. Very dissatisfied . . . Very satisfied.CS2. Very displeased . . . Very pleased.CS3. Very frustrated . . . Very contented.CS4. Absolutely terrible . . . Absolutely delighted.

IS Habit (Strongly Agree . . . Strongly Disagree)HB1. Using has become automatic for me.HB2. Using is natural for me.HB3. When faced with a particular task, using is an obvious choice for me.

Continuance Intention (Strongly Agree . . . Strongly Disagree)CI1. I intend to continue using rather than discontinue its use.CI2. My intentions are to continue using rather than using any alternative

means.

Continued UseCU1. On average, how frequently have you visited over the past month?CU2. On average, how much time have you spent per day visiting over the past

month?

Information Management 33 (2013) 496– 511 509

A

Regret Satisfaction IS habit Continuanceintention

−0.294 0.270 0.322 0.458−0.345 0.311 0.351 0.483−0.312 0.291 0.328 0.462−0.270 0.310 0.330 0.435−0.333 0.385 0.514 0.540−0.273 0.362 0.466 0.466−0.279 0.432 0.453 0.437−0.249 0.244 0.390 0.340−0.270 0.253 0.371 0.391−0.260 0.306 0.464 0.466

0.785 −0.161 −0.172 −0.3740.936 −0.212 −0.291 −0.5150.925 −0.192 −0.341 −0.540

−0.148 0.907 0.177 0.286−0.235 0.949 0.248 0.322−0.207 0.938 0.238 0.335−0.276 0.237 0.929 0.509−0.262 0.180 0.832 0.469−0.301 0.222 0.911 0.505−0.449 0.294 0.540 0.901−0.450 0.319 0.533 0.911−0.557 0.278 0.375 0.817

R

A

AA

A

A

B

B

B

B

B

B

B

B

B

C

C

CC

C

C


ppendix 2. Matrix of cross loadings

Perceivedusefulness

Perceivedenjoyment

Self-imagecongruity

PU1 0.878 0.413 0.322

PU2 0.918 0.443 0.376

PU3 0.929 0.402 0.427

PU4 0.884 0.401 0.353

PE1 0.409 0.933 0.520

PE2 0.429 0.943 0.511

PE3 0.448 0.916 0.508

SI1 0.280 0.451 0.892

SI2 0.406 0.470 0.883

SI3 0.415 0.562 0.934

RE1 −0.240 −0.172 −0.170

RE2 −0.329 −0.318 −0.283

RE3 −0.321 −0.326 −0.287

CS2 0.253 0.323 0.220

CS3 0.319 0.440 0.307

CS4 0.335 0.404 0.297

HB1 0.323 0.477 0.433

HB2 0.346 0.461 0.357

HB3 0.324 0.441 0.424

CI1 0.470 0.468 0.373

CI2 0.497 0.489 0.456

RCI3 0.365 0.404 0.339

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hou, T. (2011). An empirical examination of users’ post-adoption behaviour ofmobile services. Behaviour & Information Technology, 30(2), 241–250.

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oung Sik Kang is an associate professor of School of Business Administration inyongji University, South Korea. He is a Director of Research in APPS (Advanced

rocess Performance research Society). His current research interests include social
etwork service, process management, and process mining.
inyoung Min is a research professor in the Business School of Korea Advanced Insti-ute of Science and Technology. She received her Ph.D. in management engineeringrom KAIST. Her research interests include social media, knowledge management,


and human computer interaction. Her research articles have been published inComputers in Human Behavior, Expert Systems with Applications, etc.

Jeoungkun Kim is an assistant professor of Business Administration Department inYeungnam University, South Korea. His research interests include business innova-tions with information technology, EC, and social influence on IT use and customerbehaviors in EC. His research articles have been published in International Journal ofHuman Computer Studies, Expert Systems with Applications, Journal of ComputerInformation Systems, etc.

Heeseok Lee is a professor in the Business School at the Korea Advanced Institute ofScience and Technology. He also directs the Knowledge Management Research Cen-ter at KAIST. He received his Ph.D. in Management from the University of Arizona and
was previously on the faculty of the University of Nebraska at Omaha. His researchinterests include IT management and growth strategy. His work has been publishedin leading academic journals including Journal of Management Information Sys-tems, International Journal of Electronic Commerce, Information and Management,and MIS Quarterly.

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Roles of alternative and self-oriented perspectives in the context of the continued use of social network sites