Problematic Instant Messaging Use - Wiley Online Library

Journal of Computer-Mediated Communication

Problematic Instant Messaging Use Rachel L. Neo Nanyang Technological University

Marko M. Skoric University of Michigan

This study is an empirical investigation of problematic instant messaging (IM) use among university students in Singapore. It adapts Caplan’s (2005) theoretical framework of problematic Internet use (PIU) to the context of problematic IM use by linking pre-existing human dispositions to cognitive-behavioral symptoms and negative outcomes of improper IM use. Four new factors—oral communication apprehension, polychronicity, perceived inconvenience of using offline communication means, and trait procrastination—were tested as predictors of problematic IM use. The results provided strong support for Caplan’s theoretical framework of PIU and indicated that oral communication apprehension and perceived inconvenience of using offline means were significant predictors of problematic IM use, whereas polychronicity and trait procrastination were not. The implications of these findings are discussed. doi:10.1111/j.1083-6101.2009.01456.x

Introduction

Instant messaging (IM) is an immensely popular form of online synchronous communication that enables people to communicate real time on a one-to-one basis or with several people at a go (Nardi, Whittaker, & Bradner, 2000). Millions of people around the world are IM users and billions of instant messages are sent on a daily basis (Marketwire, 2006). Furthermore, IM has even surpassed other forms of online synchronous communication applications such as Internet Relay Chat (IRC) in terms of popularity (Jagtiani, 2006). As a whole, online synchronous communication applications such as IM, IRC, MUD, and Internet chat rooms are much more likely than asynchronous online applications to be a predictor of problematic Internet use (PIU) (e.g. Caplan, 2002, 2003, 2005; Davis, Flett, & Besser, 2002; Kubey, Lavin, & Barrows, 2001; MorahanMartin & Schumacher, 2003; Shapira, Goldsmith, Keck, Kholsa, & McElroy, 2000; Young, 1998). And of all the online synchronous applications, IM has emerged as the chief culprit of PIU (Leung, 2004; Yuen & Lavin, 2004). Journal of Computer-Mediated Communication 14 (2009) 627–657 © 2009 International Communication Association

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However, IM is different from other forms of online synchronous communication such as IRC, MUD, and Internet chat rooms as IM is used predominantly to communicate and maintain relationships with known others whom people first get to know offline such as friends, acquaintances, and peers (Grinter & Palen, 2002; Gross, Juvonen, & Gable, 2002; Lee & Perry, 2004). Most studies examining online synchronous communication do not delineate between the various forms of online synchronous communication applications and deal mainly with people who form relationships with unknown others online (e.g. Bonebrake, 2002; Caplan, 2002, 2003, 2005; Davis, 2001; McKenna & Bargh, 1999; Morahan-Martin & Schumacher, 2000, 2003; Niemz, Griffiths, & Banyard, 2005; Tidwell & Walther, 2002; Walther, 1996). University students provide an extremely salient context in which to examine the phenomenon of problematic IM usage. First, university students are extremely heavy IM users. This is evident in both Western and developed Asian societies. In the United States, university students are amongst the heaviest users of IM. Findings from a Pew Internet & American Life survey showed that nearly 75% of university Internet users in the United States have sent instant messages as compared to only half of all Internet users. University students are also twice as likely as the average Internet user to use IM on any given day (Jones et al., 2002). More recently, in a survey conducted amongst 138 U.S. university students, an overwhelming majority (89%) of the respondents indicated that they used IM to communicate (Hu, Wood, Smith, & Westbrook, 2004). Furthermore, a study of 268 Canadian university students showed that 97% of respondents were users of IM (Quan-Haase, 2007). In Hong Kong, 77.8% of the 576 college respondents surveyed by Leung (2001) reported using ICQ–a form of IM software. Second, the free and readily available Internet access around the university campus makes university students the heaviest users of the Internet and other webbased applications and also puts them at great risk for PIU of any kind (Kandell, 1998; Morahan-Martin & Schumacher, 2000; Niemz et al., 2005; Young, 1998; Young, 2004). Singapore is a highly developed Asian society (Saw, 2004) with one of the world’s highest Internet penetration rates (Internet World Stats, 2008). A nationwide representative survey commissioned by Singapore’s Infocomm Development Authority (IDA) showed that people aged between 15 and 29 utilized IM applications the most (Infocomm Development Authority of Singapore, 2006), making this the age bracket in which the majority of university students fall under (‘‘The nation,’’ 2002). With ubiquitous Internet access in Singapore university campuses and the popularity of IM as an online communication tool amongst Singapore university students (Ng, 2008), it is imperative to examine problematic IM use amongst university students in Singapore’s context. Despite this, there have been no studies done in Singapore which focus solely on examining problematic IM use among university students within an empirically tested theoretical framework linking innate human predispositions with aspects of problematic IM use. Caplan’s (2005) theoretical framework clearly outlines the etiology of PIU by postulating that existing 628

Journal of Computer-Mediated Communication 14 (2009) 627–657 © 2009 International Communication Association

human predispositions culminate in cognitive-behavioral symptoms and negative outcomes of dysfunctional Internet use. Thus, this study marks the first of its kind to adapt and significantly expand Caplan’s (2005) conceptual framework of PIU to gauge problematic IM use among university students in Singapore. Literature Review Theoretical Framework of Problematic IM Use

Previous studies on PIU have been criticized for lacking conceptual clarity and not being theoretically driven enough, with scholars contending that more studies needed to be done within a tested theoretical framework explaining the relationship between human predispositions, cognitive-behavioral symptoms and negative outcomes of PIU (e.g. Caplan, 2002, 2003, 2005; Davis, 2001). Davis (2001) argued that PIU was more than simply a behavioral addiction but rather, a complex, multifaceted syndrome consisting of cognitive and behavioral symptoms that result in negative social, academic, or professional consequences of PIU (Caplan, 2002, 2003, 2005; Davis, 2001; Davis, Flett, & Besser, 2002). He proposed a cognitive-behavioral PIU model in which he stressed the importance of maladaptive cognitions associated with Internet use (e.g. ‘‘It is better to socialize online than to have offline social interactions.’’) as a crucial antecedent of the behavioral symptoms of PIU such as compulsive Internet usage which in turn culminate in negative outcomes of Internet use (Davis, 2001). Also, such cognitions and behaviors of PIU were emphasized as key intermediary variables linking the relationship between pre-existing human predispositions and negative outcomes of PIU (Caplan, 2002, 2003; Davis, 2001). Based on Davis’ (2001) cognitive-behavioral model of PIU, Caplan (2002) sought to develop a theory-based measure of PIU by operationalizing the cognitivebehavioral symptoms and negative outcomes of PIU. He termed the maladaptive cognitive symptoms of PIU as ‘a preference for online social interaction’ (Caplan, 2002, 2003, 2005) and defined it as ‘‘a cognitive individual-difference construct characterized by beliefs that one is safer, more efficacious, more confident, and more comfortable with online interpersonal interactions and relationships than with traditional face-to-face social activities’’ (Caplan, 2003, p. 629). His three behavioral symptoms of PIU were (a) Mood Alteration (the extent to which people utilize the Internet when feeling socially isolated or down); (b) Compulsive Internet use (the inability to control, reduce, or stop online behavior, along with feelings of guilt about time spent online); and (c) Excessive Internet Use (the degree to which an individual feels that he or she spends an excessive amount of time online or even loses track of time when using the Internet). Negative outcomes of Internet use were defined as personal, social, and professional problems resulting from one’s Internet use (Caplan, 2002, 2003). Congruent with Davis’ (2001) proposed cognitive-behavioral model of PIU, preliminary findings showed that the cognitive PIU symptoms i.e. ‘a preference Journal of Computer-Mediated Communication 14 (2009) 627–657 © 2009 International Communication Association

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for online social interaction’ were indeed a strong predictor of the PIU behavioral symptoms, particularly mood alteration and compulsive Internet use (Caplan, 2003). Furthermore, amongst all the PIU behavioral symptoms, compulsive Internet use consistently emerged as the strongest behavioral predictor of negative outcomes stemming from Internet usage (Caplan, 2002, 2003). Since PIU has been extensively dubbed as an impulse control disorder that chiefly entails compulsive Internet usage (e.g. Beard & Wolf, 2001; Griffiths, 2000; Shapira et al., 2000), Caplan (2005) decided to focus solely on the compulsive Internet use construct and further elucidate the relationship between a preference for online social interaction, compulsive Internet use, and negative outcomes of Internet usage. Subsequently, he tested a direct effects theoretical model of PIU which postulated that peoples’ preference for online social interactions would lead to over dependence on the Internet, consequently leading to compulsive Internet use (i.e. difficulties faced in stopping, reducing, or controlling Internet use along with feelings of guilt) (Caplan, 2003, 2005). Ultimately, such compulsive Internet use is in turn likely to culminate in negative personal, social, and professional consequences of Internet use (Caplan, 2005). Robust support was found for the direct effects model. Moreover, consistent with previous PIU studies (Caplan, 2003; Davis, 2001), the cognitive PIU symptom (a preference for online social interactions on the Internet) and behavioral symptom (compulsive Internet use) mediated the relationship between existing human predispositions and negative outcomes of Internet use (Caplan, 2005). Among all online synchronous chat applications, IM merits a study on its own because it has emerged as the strongest predictor of PIU (Eijnden, Meerkerk, & Vermulst, 2005; Leung, 2004; Yuen & Lavin, 2004) and is also the most heavily used among university students (Jones et al., 2002). It has been said that research needs to concentrate on specific aspects of the Internet which people are addicted to (Beard & Wolf, 2001; Lee & Perry, 2004; Shaffer, Hall, & Vander Bilt, 2000). Scholars have also contended that more studies need to be done within an empirically testable theoretical framework explaining the relationship between human predispositions, cognitive-behavioral symptoms and negative outcomes of PIU (e.g. Caplan, 2002, 2003, 2005; Davis, 2001; Davis et al., 2002). Hence, this study adapts Caplan’s (2005) theoretical framework of PIU to specifically examine problematic IM use among university students. However, Caplan’s (2003) original conceptual definition of ‘‘a preference for online social interaction’’ is too narrow as it primarily connotes problems to do with one’s sociocommunicative competence (Caplan, 2003, 2005). Thus, in order for other factors that are unrelated to sociocommunicative competence to be tested as predictors of problematic IM use in this study, ‘‘a preference for online social interactions on instant messenger’’ shall be broadly defined as an individual-level construct comprising of cognitions that using IM to socialize is more gratifying than offline social activities. Extrapolating Caplan’s (2005) theoretical framework of PIU to the context of IM use, the following direct effect hypotheses are proposed: 630


H1: A preference for social interactions on IM is positively associated with compulsive IM usage. H2: Compulsive IM is positively associated with negative outcomes of IM use.

Furthermore, since pre-existing human predispositions have been theorized to be necessary distal factors preceding the development of the cognitive-behavioral symptoms and negative outcomes of PIU (Caplan, 2002, 2003, 2005; Davis, 2001), four new factors shall be incorporated as predictors of problematic IM use in this study. By expanding the conceptual boundaries of Caplan’s (2005) original theoretical socialskill account of PIU, these factors encompass one measure of sociocommunicative competency (as gauged by one’s level of oral communication apprehension), as well as three other predictors that do not pertain to one’s sociocommunicative competence namely, polychronicity, the perceived inconvenience of using offline communication means, and trait procrastination. Extant literature (as elaborated in the literature review section below) strongly suggests that these four aforementioned factors could potentially be salient in the specific context of problematic IM use. Hence, by testing these four factors as predictors of the cognitive-behavioral symptoms and negative outcomes of IM use, this study hopes to make a significant contribution to the field of mediated synchronous interpersonal communication. Predictors of Problematic IM Use Oral communication apprehension

Oral communication apprehension (OCA) is described as an individual’s level of fear or anxiety associated with either real or anticipated oral communication with people (McCroskey, 1970; McCroskey, 1977). It is an enduring personality trait that manifests itself in a wide variety of communication contexts (e.g. during meetings, giving public speeches, talking during group discussions and even just with another person) and with a given type of person/group of persons (McCroskey & Richmond, 1987). Also, OCA is considered to largely be an indicator of maladaptive sociocommunicative functioning (Leary, 1983; McCroskey, 1977). As people with OCA are stifled by anxiety and fear in social situations (Rubin, Perse, & Barbato, 1988), such people are consequently less successful at maintaining and forming relationships and are also more likely to receive negative evaluations from their peers (McCroskey, Richmond, Daly, & Cox, 1975). Some scholars even regard OCA as a unique type of social anxiety (Brown, Fuller, & Vician, 2002; Schlenker & Leary, 1982) whereby such people avoid social situations despite their desire to interact with others (Rubin et al., 1988) because of a lack of confidence about their sociocommunication abilities (Allen & Bourhis, 1996). More importantly, recent PIU research has found social anxiety to be a much stronger and crucial predictor of both a preference for online social interactions and negative outcomes of PIU than loneliness (Caplan, 2007). It has been argued that people facing issues with maladaptive interpersonal sociocommunication functioning (e.g. Journal of Computer-Mediated Communication 14 (2009) 627–657 © 2009 International Communication Association

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social anxiety) were particularly susceptible to PIU because they are drawn to the unique sociocommunicative features of CMC (Caplan, 2002, 2003, 2005, 2007). The increased anonymity (Bargh, McKenna, & Fitzsimons, 2002; McKenna & Bargh, 1999) and reduced social risk of CMC (Freiermuth & Jarrell, 2006; MorahanMartin & Schumacher, 2000; Walther, 1996 play crucial roles in helping people with oral communication apprehension to interact socially (Campbell & Neer, 2001; Spitzberg, 2006). Thus, it is plausible that IM might appeal to people who have OCA with friends and acquaintances as it is utilized primarily to communicate with known others (Grinter & Palen, 2002; Lee & Perry, 2004). However as previous research has shown (Caplan, 2007), it is a logical deduction that in the process of communicating with friends and acquaintances, people with OCA might likewise develop maladaptive cognitions associated with a preference for online social interactions for IM and negative outcomes of IM use. We postulate that: H3: Oral communication apprehension is positively associated with a preference for online social interactions on instant messenger. H4: Oral communication apprehension is associated with negative outcomes of IM use.

Polychronicity

Polychronicity is the extent to which people prefer to be engaged in two or more tasks or events simultaneously at a time; and believe their preference is the best way to do things (Bluedorn, Kalliath, Strube, & Martin, 1999; Slocombe & Bluedorn, 1999). Within a particular culture, varying degrees of time use preference may exist (Scarborough & Lindquist, 1999). On one extreme end are people who are termed as ‘‘monochrons’’. They prefer to concentrate on a single task at a time and complete one activity before progressing on to another. On the other end of the continuum are ‘‘polychrons’’- people who hold highly favorable attitudes and beliefs towards multitasking and relish juggling multiple activities at a time (Scarborough & Lindquist, 1999). Online synchronous communication applications like IM allow for multitasking (Cameron & Webster, 2005; Lee, Tan, & Shahiraa, 2005; Lenhart, Rainie, & Lewis, 2001; Shiu & Lenhart, 2004) because there is a time lag of a few minutes for responses to be made (Walther, 1996), enabling people to manage other demands and juggle a myriad of both online or offline activities simultaneously (Grinter & Palen, 2002; Jacobson, 1999; Quan-Haase, Cothrel, & Wellman, 2005; Shiu & Lenhart, 2004). According to survey findings from the Pew Internet and American Life Project, youths who stated that IM was their main way of communicating with their friends cited the ability of IM to allow them to multitask while chatting with their friends (Lenhart et al., 2001). Likewise, the ease of being able to multitask on IM is a major boon for university students as multitasking is an integral part of college culture (Quan-Haase & Collins, 2008). In a study by Quan-Haase and Collins (2008), multitasking emerged as one of the key facets related to university students’ temporal 632


structuring of IM social accessibility. Lee and Perry (2004) have suggested that a polychronic working style preference could result in over dependency on IM as one’s primary mode of communication. Thus, it is possible for polychronicity to serve as an antecedent in the development of a preference for social interactions on IM. Since no existing study has implicated polychronicity as a factor behind problematic IM use, we propose that: H5: Polychronicity is positively associated with a preference for social interactions on IM.

Perceived inconvenience of using offline means to communicate

Perceived inconvenience plays a significant and crucial role in predicting peoples’ intentions to engage in a wide range of behaviors and activities (e.g. Bhatnagar, Misra, & Rao, 2000; Eastin, 2002; Humpel, Marshall, Leslie, Bauman, & Owen, 2004; Reibstein, Lovelock, & Dobson, 1980; Yang, 2005). Yale and Venkatesh (1986) argue that inconvenience should be considered in psycho-behavioral terms, implying that inconvenience is largely a matter of perception rather than actuality. Although there is no fixed meaning for the term ‘perceived inconvenience’ (Carrigan & Szmigin, 2006), one widely acknowledged definition of perceived inconvenience is the amount of time and physical or mental effort that has to be incurred in the process of performing an activity (e.g. Brown, 1989; Brown & McEnally, 1993, as cited in Carrigan & Szmigin, 2006; Kaufman, 1990). This study extrapolates this aforementioned definition of perceived inconvenience to the context of two forms of synchronous offline interpersonal communication channels—faceto-face conversations and mobile phone calls (Wolz, Palme, & Anderson, 1997). Face-to-face (FTF) and mobile phone conversations1 have greater social presence than an IM conversation. Consequently, both FTF and mobile phone conversations require more motivation and effort than an IM conversation (e.g. Lee, Sim, Tan, & Detenber, 2005; Nardi & Whittaker, 2002; Robert & Dennis, 2005). Hence, people who are too busy to devote their time, attention or mental effort to chat with friends face to face or using mobile phones might find it inconvenient to communicate via these two offline communication channels. Costwise, traveling out to meet and have face-to-face conversations could arguably be more expensive than using CMC technologies such as IM (Baltes, Dickson, Sherman, Bauer, & LaGanke, 2002). Mobile phone calls are also considerably more expensive and might consequently be regarded as more inconvenient than IM communication (Bryant, Sanders-Jackson, & Smallwood, 2006; Hu et al., 2004; Jones et al., 2002). Also, the mobile phone has other unique drawbacks as a mode of communication. Mobile phones are a ‘socially intrusive technology’ (e.g. Ling, 1997; Love & Perry, 2004) whereby calls can be made at times that are considered disruptive and distracting for receivers (Leung & Ran, 1999; Ling, 1997; Rainie & Keeter, 2006). Such interruptions and lack of privacy when chatting on the mobile phone are regarded as a major bane (Leung & Ran, 1999; Rainie & Keeter, 2006; Ran & Leung, 1999). In contrast, IM is a ‘‘quiet technology that is easily integrated into the conduct Journal of Computer-Mediated Communication 14 (2009) 627–657 © 2009 International Communication Association

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of other activities’’ (Grinter & Palen, 2002, p. 26). Although it has been argued that pop-up IM message notifications are disruptive (Renneker & Godwin, 2003), IM nonetheless provides a subtler way of gauging the receiver’s availability status than a mobile phone call (Garrett & Danziger, 2007). Unlike mobile phones, IM allows users to indicate their availability status beforehand. Research has demonstrated that people can use such IM status icons to minimize unwanted interruptions by indicating when they are available to have conversations (Dabbish & Kraut, 2003; Quan-Haase & Collins, 2008). Even if users do not actively make use of the status availability icons, they can indicate availability in other ways such as ignoring an instant message as this is generally regarded as a socially appropriate course of action (Garrett & Danziger, 2007). Thus, since IM offers certain relative advantages over face-to-face and mobile phone conversations (Flanagin, 2005), it is plausible that students who find it inconvenient to communicate with their friends using these communication channels might opt to use IM as the primary way of communicating with friends and develop a preference for socializing on IM. Also, the convenience brought about by the Internet and its online applications such as IM is a direct contributor of compulsive Internet use (e.g. Young, 1998; Young, 2004). IM is a reasonable alternative to face-to-face (e.g. Lee & Perry, 2004; Papacharissi & Rubin, 2000) and mobile phone communication (Flanagin, 2005; Madell & Muncer, 2005). Hence, it is likely that university students who deem such offline means of communication too inconvenient will develop compulsive IM use. Thus, this study addresses the dearth of research in this area and postulates that: H6: Perceived inconvenience of using offline means of communication is positively associated with a preference for social interactions on IM. H7: Perceived inconvenience of using offline means of communication is positively associated with compulsive IM usage.

Trait procrastination

Trait procrastination is defined as an individual’s predisposition to ‘‘postpone that which is necessary to reach some goal by putting off acting upon one’s intentions’’ (Schouwenburg & Lay, 1995, p. 481). The Internet and its web-based applications offer plenty of distractions (e.g. Eastin & LaRose, 2001; Leung, 2001; Morris & Ogan, 1996; Papacharissi & Rubin, 2000) which lure people who are prone to procrastinating away from engaging in activities that they rightfully should perform (Lavoie & Pychyl, 2001). Recent studies investigating the relationship between procrastination and Internet usage have also implicated procrastination as a key indicator of PIU (Davis et al., 2002; Nalwa & Anand, 2003; Philips & Reddie, 2007). Although no studies have been done specifically to ascertain the relationship between trait procrastination and problematic IM use, there is compelling evidence to suggest that trait procrastination might be linked to compulsive IM use and negative outcomes of IM use. First, procrastination has been suggested as an inherent personality characteristic of people with compulsive behaviors (Albanese, 1988; DeSarbo & Edwards, 1996; 634


Ferarri & McCown, 1994; Tice & Baumeister, 1997). In the literature of PIU, IM applications have been found to be a significant predictor of compulsive Internet usage behaviors among college students (Eijnden et al., 2005; Quan-Haase, in press). Since procrastinators are prone to be distracted from their responsibilities by such web applications (Davis et al., 2002; Lavoie & Pychyl, 2001), it is highly possible for trait procrastinators to develop compulsive IM use behaviors through an over dependence on IM to detract them from responsibilities much so that they difficulties curtailing one’s IM use. Second, trait procrastination has demonstrated to result in difficulties with personal, social, and occupational functioning (e.g. Ariely & Wertenbroch, 2002; Ferrari, Harriott, Evans, Lecik-Michna, & Wenger, 1997; Ferrari, Johnson, & McCown, 1995; Muszynski & Akamatsu, 1991; Rothblum, Solomon, & Murakami, 1986; ‘‘Time management,’’ 2003; Wolters, 1993). It has even been suggested that people with PIU experience personal, social, and occupational difficulties because of lapses in productivity as a result of procrastination tendencies (Ho & Lee, 2001). Thus, we expect that: H8: Trait procrastination is positively associated with compulsive IM use. H9: Trait procrastination is positively associated with negative outcomes of IM use.

Method Participants

Data for this study came from a paper and pencil survey completed voluntarily by 230 undergraduates enrolled in introductory communication studies courses at a large public university in Singapore. After a data cleanup, 228 usable surveys were obtained. The participants had a mean age of 20 (M = 20.00, SD = 1.36) and 73% of them were female. The percentage of females in this sample roughly corresponds with the actual proportion of females (81%) enrolled in the university’s communication studies program (‘‘Student statistics,’’ 2008). The average amount of time spent logged onto IM in a day was 5.65 hours, SD = 4.90 and the mean amount of time spent per day actually using IM was 2.28 hours, SD = 1.88. Measures IM usage

Respondents were first asked to indicate if they used IM software. Those who did not were instructed to fill in some basic demographic information on the last page. Two questions from Hu et al.’s study (2004) were extracted to gauge IM usage. The first question asked respondents about the number of hours per day in which they logged into IM. The second question asked about the number of hours per day in which they actually used IM. It is important to note the distinction between the amount of time in which respondents logged into IM and the amount of time in which they actually use IM because people may sign into IM and idle there for long Journal of Computer-Mediated Communication 14 (2009) 627–657 © 2009 International Communication Association

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hours without actually making use of the software to communicate (Hu et al., 2004; Quan-Haase, 2007; Quan-Haase & Collins, 2008). Preferences for social interactions on IM

Seven items were modified from Caplan’s previous studies on problematic Internet usage (Caplan, 2002; 2003; 2005) to gauge one’s gratification for social interactions on IM over offline social interactions. The items were measured on a 5-point Likert scale (1= Strongly Disagree, 5 = Strongly Agree) and consisted of statements like ‘‘Generally, I prefer communicating using IM rather than by using offline means.’’ These items formed a highly reliable index with alpha value 0.87 (M = 2.71, SD = .79). Compulsive IM use

Five items using a 5-point Likert-type scale (1 = Strongly Disagree, 5 = Strongly Agree) were used to measure the extent to which individuals experienced difficulties reducing, stopping or controlling their IM use along with feelings of guilt about their IM use. Three of these items were adapted from Caplan (2002) and two were extracted from Lee and Perry (2004), measuring respondents’ agreement with statements like, ‘‘I am unable to reduce the amount of time I spend on IM’’. One item was subsequently dropped, leaving four items that gave a robust alpha of 0.85 for this study (M = 2.26, SD = .93). Negative outcomes of IM use

A total of five items were combined together to form an index to gauge personal, social and occupational difficulties stemming from one’s IM usage. Three items were adapted from Caplan’s (2005) negative outcomes of Internet usage measures and two items were adapted from Lee & Perry’s (2004) study. These items measured respondents agreement with statements like, ‘‘I have missed classes because of my IM usage’’ and ‘‘I have gotten into trouble at work or in school because of my IM usage’’ on a 5-point Likert scale (1 = Strongly Disagree, 5 = Strongly Agree). The items formed a reliable scale with an alpha value of 0.79 (M = 1.65, SD = .61)2 . Oral communication apprehension

A subset of eight items from the PRCA 24-B scale by McCroskey (1986) were extracted to measure university students’ communication apprehension with regards to talking with friends and acquaintances in dyads or small groups contexts. Examples of such items include, ‘‘When conversing with an acquaintance, I am tense and nervous.’’ Respondents indicated their agreement with the scale items on a 5-point Likert scale (1 = Strongly Disagree, 5 = Strongly Agree). The eight items from this scale formed a robust index with an alpha of 0.86 (M = 1.99, SD = .58). Polychronicity

The 10-item Inventory of Polychronic Values (IPV) by Bluedorn et al. (1999) was adapted to assess the university students’ time usage preference. The pronouns 636


in the items were changed from ‘We’ to ‘I’ because this study seeks to examine polychronicity at an individual level. Respondents indicated their attitudes towards polychronic behaviors like ‘‘I like to juggle several activities at the same time’’ on 5-point Likert scales (1 = Strongly Disagree, 5 = Strongly Agree). For this study, the scale attained an alpha of 0.76 (M = 2.70, SD = .54). Perceived inconvenience

13 items regarding perceived inconvenience of using mobile phone and face-to face communication were constructed for this current study. These items were based on the main aspects of perceived inconvenience that were discussed in the literature review on perceived inconvenience, namely 1) time and physical or mental effort incurred; 2) monetary costs incurred; and 3) for mobile phone conversations, items regarding the perceived social intrusiveness of the mobile phone were constructed. The items measured respondents agreement with statements such as, ‘‘I feel that a lot of time is taken up by traveling out to meet and talk with my friends’’ and ‘‘Generally, I find it tiring to hold a mobile phone while chatting’’ on a 5-point Likert scale (1 = Strongly Disagree, 5 = Strongly Agree). One item was reverse coded to guard against response set bias. The scale achieved an alpha of 0.80 in the present study (M = 3.00, SD = .59). Trait procrastination

The 20-item Trait Procrastination Scale (TPS) for students by Lay (1988) was used to gauge procrastination tendencies among university students. On a 5-point Likert scale (1 = Never, 5 = Always), survey respondents were asked to rate the frequency with which they engaged in dilatory behaviors described in statements such as ‘‘I usually have to rush to complete a task on time’’. For this study, a robust alpha value of 0.84 was obtained (M = 3.04, SD = .50) Demographic variables

Lastly, a set of demographic control variables asking for respondents’ age, year of study, gender, nationality, and whether they stayed in a dormitory were included. Results

Zero-order correlations were first run to examine relationships among the variables. Findings (as shown in Table 1) demonstrated that a preference for social interactions on IM was significantly correlated with compulsive IM usage and compulsive IM usage also had a significant positive correlation with negative outcomes of IM usage. In terms of factors associated with problematic IM use, oral communication apprehension was significantly correlated with a preference for online social interaction on IM and negative outcomes of IM usage. Perceived inconvenience had a significant positive relationship with both preference for online social interaction on IM and compulsive IM usage. However, there was no significant correlation between polychronicity and a preference for online social interaction on IM. Trait Journal of Computer-Mediated Communication 14 (2009) 627–657 © 2009 International Communication Association

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Table 1 Zero-Order Correlations Matrix Between Composite Variables (N = 228) POSI COMP NO OCA POLY INCONV PRO

.36*** .25*** .30*** −.002 .36*** .07

COMP — .59*** .08 .14* .21*** .01

NO

OCA

POLY

INCONV

— .17** .11 .09 .04

— −.09 .27*** .21***

— −.03 −.04

— .12

Note. * p < .05, ** p < .01, *** p < .001. POSI- Preference for social interactions on IM, COMP- Compulsive IM usage, NO- Negative outcomes of IM use, OCA- Oral communication apprehension, POLY- Polychronicity, INCONV- Perceived inconvenience, PRO - Trait procrastination.

procrastination also showed no significant association between either compulsive IM usage or negative outcomes of IM usage. To test out the hypotheses, three sets of hierarchical regression analyses were conducted. All the three sets of regressions controlled for both demographic factors and IM time usage. The missing values from each of the predictor variable scales were replaced as recommended by Garson (n.d.) with the linear regression trend value. Preference for social interactions on IM

The overall regression model predicting a preference for online social interaction was significant, altogether accounting for slightly over 20% of the variance. Both oral communication apprehension (β = .24, p < .001) and perceived inconvenience of using offline communication means (β = .32, p < .001) had a significant positive association with a preference for social interactions on IM. Thus, H3 & H6 are supported. However polychronicity had no significant association with a preference for IM social interactions. H5 is not supported (See Table 2). Compulsive IM use

The overall regression equation was significant and accounted for slightly less than 30% of the variance in predicting compulsive IM use. A preference for social interactions on IM (β = .24, p < .001) as well as perceived inconvenience (β = .14, p < .05) had a positive and significant effect on compulsive IM use (see Table 3). H1 and H7 are supported. However, there is no significant effect of trait procrastination on compulsive IM use. Thus, H8 is not supported. Negative outcomes of IM use

Since ‘‘a preference for social interactions on IM’’ showed a significant moderate correlation with negative outcomes of IM use, r(228) = 0.25, p < .001, it was also added into the regression equation. The overall regression equation was significant and accounted for slightly more than 40% of the variance in predicting negative outcomes of IM use. Both compulsive IM use (β = .53, p < .001) and oral 638


Table 2 Standardized Regression Coefficients of the Variables (Oral Communication Apprehension, Perceived Inconvenience & Polychronicity) Hypothesized as Predictors of A Preference for Social Interactions on IM (N = 228) Preference for social interactions on IM Demographic control variables Gender (1 = Male, 2 = Female) Hostelite (0 = Stay in hall, 1 = Don’t stay in hall) Nationality (0 = Singaporean, 1 = Non-Singaporean) Year of study (Higher value = Higher level of study) Age Adjusted R2 IM time usage control variables Amount of time spent on IM Amount of time spent actually using IM Adjusted R2 Predictor Variables Oral communication apprehension Polychronicity Perceived inconvenience of using offline means Total adjusted R2

−.07 .10 .10 −.02 −.11 −.003 .06 .17* .021* .24*** −.003 .32*** .21***

* p < .05, *** p < .001

Table 3 Standardized Regression Coefficients of the Variables (Preference for Social Interactions on IM, Perceived Inconvenience, and Trait Procrastination) Hypothesized as Predictors of Compulsive IM Use (N = 228) Compulsive IM use Demographic control variables Gender (1 = Male, 2 = Female) Hostelite (0 = Stay in hall, 1 = Don’t stay in hall) Nationality (0 = Singaporean, 1 = Non-Singaporean) Year of study (Higher value = Higher level of study) Age Adjusted R2 IM time usage control variables Amount of time spent on IM Amount of time spent actually using IM Adjusted R2 Predictor variables Preference for social interactions on IM Perceived inconvenience of using offline means Trait procrastination Total adjusted R2

.08 −.11 .16* .03 −.01 .06** .13 .25*** .20*** .24*** .14* −.008 .28***

* p < .05, ** p < .01, *** p < .001


639

Table 4 Standardized Regression Coefficients of the Variables (Compulsive IM Use, Oral Communication Apprehension and Trait Procrastination) Hypothesized as Predictors of Negative Outcomes of IM Use (N = 228) Negative outcomes of IM use Demographic control variables Gender (1 = Male, 2 = Female) Hostelite (0 = Stay in hall, 1 = Don’t stay in hall) Nationality (0 = Singaporean, 1 = Non-Singaporean) Year of study (Higher value = Higher level of study) Age Adjusted R2 IM time usage control variables Amount of time spent on IM Amount of time spent actually using IM Adjusted R2 Predictor Variables Preference for social interactions on IM Compulsive IM use Oral communication apprehension Trait procrastination Total adjusted R2

−.10 −.12* .10 .11 −.23* .13*** .015 −.003 .15* −.02 .53*** .14* .04 .40***

* p < .05, *** p < .001

communication apprehension (β = .14, p < .05) had strong significant positive effects on negative outcomes of IM usage (refer to Table 4). H2 and H4 are supported. However, trait procrastination was a nonsignificant predictor of negative outcomes of IM usage. H9 is not supported. Also, a preference for social interactions on IM did not show any significant relationship with negative outcomes of IM use. Also, since both OCA and perceived inconvenience showed significant associations with the two intervening variables–preference for social interactions on IM and compulsive IM use (see Figure 1), mediation analyses were conducted based on Valkenburg and Peter’s (2007) four-step procedure of establishing mediation. In the first set of regression analyses, OCA and perceived inconvenience served as the independent variables (IV), a preference for socializing on IM was treated as the mediating variable (MV), and compulsive IM use served as the dependent variable (DV). OCA yielded a nonsignificant relationship with compulsive IM use in the regression analyses whereas the other IV i.e. perceived inconvenience (β = .13, p < .05) and a preference for socializing on IM (β = .23, p < .001) were significantly associated with compulsive IM use (see Table 5). The Sobel’s (1982) test statistics (refer to Table 6) showed that OCA had a significant indirect relationship with compulsive IM use via preference for socializing on IM as the mediator (z − score = 2.53, p < .01). Although there was a significant direct association between both perceived inconvenience and compulsive IM use, the Sobel’s (1982) 640


Oral communication apprehension

H4 (.18*)

H3 (.25***) Polychronicity

H5 (-.003)

Perceived inconvenience

H1 Preference for social interactions (.25***) on IM

H6 (.33***)

H7 (.15*)

Compulsive IM use

H2 (.50***)

Negative outcomes of IM usage

H8 (-.009)

H9 (.05) Trait procrastination Note. *p < .05, ***p < .001. All path coefficients are partial correlation coefficients. There is no significant association between a preference for social interactions on IM and negative outcomes of IM use.

Figure 1 Composite model showing all research hypotheses. Table 5 Mediation Analyses (N = 228)

First set of mediation analyses DV: Preference for online social interactions on IM IV: Oral communication apprehension IV: Perceived inconvenience DV: Compulsive IM use IV: Oral communication apprehension IV: Perceived inconvenience MV: Preference for online social interactions on IM Second set of mediation analyses DV: Compulsive IM use IV: Perceived inconvenience IV: Preference for online social interactions on IM DV: Negative outcomes of IM use IV: Perceived inconvenience IV: Preference for online social interactions on IM MV: Compulsive IM use

B

SE

.32 .42

.09 .08

.24*** .32***

.05 .20 .27

.10 .10 .08

.03 .13* .23***

.21 .28

.10 .08

.14* .24***

−.005 .03 .34

.06 .05 .04

−.005 .03 .54***

β

Note. Both sets of mediation analyses controlled for demographics and IM time use. However, only the relationships amongst the independent, mediator, and dependent variables are reported above. * p < .05, *** p < .001

test statistic in Table 6 nonetheless showed that a preference for socializing on IM significantly carried the effect of perceived inconvenience to compulsive IM use (z − score = 2.87, p < .01). Thus, a preference for socializing on IM served as a partial mediator of the relationship between OCA and compulsive IM use but completely mediated between perceived inconvenience and compulsive IM use. Journal of Computer-Mediated Communication 14 (2009) 627–657 © 2009 International Communication Association

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According to the second set of regression analyses, both preference for social interactions on IM and perceived inconvenience yielded nonsignificant relationships with negative outcomes of IM use even when controlling for compulsive IM use. As shown by the Sobel’s (1982) test statistics, perceived inconvenience (z − score = 2.06, p < .05) and a preference for socializing on IM (z − score = 3.41, p < .001) had significant indirect relationships with negative outcomes of IM use with compulsive IM use acting as the mediator (refer to Table 6). Thus, compulsive IM use served as a full mediator of the relationship between the two independent variables (i.e. perceived inconvenience and preference for socializing on IM) and negative outcomes of IM use. Discussion

Overall, a preference for social interactions on IM was a strong predictor of compulsive IM use, which in turn was significantly associated with negative outcomes of IM use. Mediation analyses showed that compulsive IM use mediated the relationship between a preference for social interactions on IM and negative outcomes of IM use (z − score = 3.41, p < .001). Also, both a preference for socializing on IM and compulsive IM use served as key intervening variables linking both OCA and perceived inconvenience with negative outcomes of IM use. Taken together, these results provide robust support to Caplan’s (2005) original direct-effects theoretical model of PIU and strongly suggest that cognitive-behavioral PIU symptoms mediate the relationship between distal human dispositions and negative Internet use outcomes. OCA with friends and acquaintances was significantly and positively associated with a preference for social interactions on IM. While IM might provide a more gratifying place than offline communication channels for people with OCA to manage Table 6 Four Indirect Paths Which Were Tested Using Sobel’s (1982) Method z-score First path: OCA→Preference for socializing on IM→Compulsive IM use Second path: Perceived inconvenience→Preference for socializing on IM→Compulsive IM use Third path: Perceived inconvenience→Compulsive IM use→Negative outcomes of IM use Fourth path: Preference for socializing on IM→Compulsive IM use→Negative outcomes of IM use

2.53** 2.87** 2.06* 3.41***

* p < .05, ** p < .01, *** p < .001 642


their relationships with friends and acquaintances, this study’s findings indicate that OCA could potentially lead to a preference for using IM to socialize, ultimately culminating in negative IM use outcomes via compulsive IM use as a mediating variable. Also, OCA had a significant direct association with negative outcomes of IM use. This finding is consistent with suggestions that dysfunctional interpersonal sociocommunicative competency plays an important role in predicting negative outcomes of Internet use (Caplan, 2002). Thus, these findings imply that university students who have problems with sociocommunication functioning might be more vulnerable to problematic IM use. University counselors should pay attention to the well being of these students to ensure that they do not develop symptoms of problematic IM use. The perceived inconvenience variable in this study served as a significant predictor of both a preference for social interactions on IM as well as compulsive IM use (refer to Figure 1, Tables 5 & 6). There are two possible paths linking perceived inconvenience with negative outcomes of IM use. First, people who regard FTF and mobile phone conversations as inconvenient are more predisposed to developing cognitions associated with problematic IM use which could lead to compulsive IM use and this could subsequently result in negative outcomes of IM use. Alternatively, perceived inconvenience could have a significant indirect relationship with negative outcomes of IM use with compulsive IM use acting as a full mediator. Although some studies have shown that IM supplements other communication channels (e.g. Muller, Raven, Kogan, Millen, & Carey, 2003) other studies have shown that IM is used as a substitute for other communication channels (Lee & Perry, 2004). The results from this study seem to indicate the latter. Future PIU studies should be undertaken to compare people who primarily view IM as a supplementary communication mode with those who usually use IM as a substitute for other communication channels to ascertain if these two groups of individuals differ in the extent to which they display problematic IM use tendencies. Furthermore, perceived inconvenience emerged as a stronger predictor of a preference for IM social interactions than OCA. Given the significant correlation between OCA and perceived inconvenience, it is plausible that this study’s perceived inconvenience construct hints at problems with one’s sociocommunicative functioning. However, over and above the significant correlation between the two variables, it implies that it is likely for even psychosocially well adjusted and socially connected people to be vulnerable to developing cognitions associated with problematic IM use, and that this could be an even stronger precursor to the development of problematic IM use cognitions than factors pertaining to maladaptive sociocommunicative functioning. As IM is primarily used to maintain relationships with known others (Grinter & Palen, 2002; Gross et al., 2002; Lee & Perry, 2004), these findings further challenge the notion that PIU only occurs when socially maladjusted people become too self-absorbed in maintaining virtual relationships with strangers online (e.g. Caplan, 2002, 2003, 2005; Davis, 2001; Morahan-Martin & Schumacher, 2000). Journal of Computer-Mediated Communication 14 (2009) 627–657 © 2009 International Communication Association

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No association was found between polychronicity and a preference for social interactions on IM. This could be due to the task and project oriented nature of the Inventory of Polychronic Values (IPV). Future studies attempting to gauge if polychronicity is related to problematic IM use might benefit from assessing polychronicity in terms of their online communication style preference (e.g. satisfaction gained from talking one-to-one on IM versus with a few people simultaneously). Nonsignificant findings were yielded for the relationship between trait procrastination and compulsive IM use. Since procrastinators are particularly prone to engaging in Internet-based activities with the primary aim of being distracted from responsibilities at hand (Davis et al., 2002), it is possible for trait procrastination to be a better predictor of distraction based IM use rather than compulsive IM use. This implies that procrastinators face difficulties curtailing their IM use specifically whenever they have to deal with aversive tasks or responsibilities and hence does not necessarily translate into facing generalized difficulties controlling, reducing, or stopping their IM use. Also, trait procrastination was unable to predict negative outcomes of IM use. Perhaps, trait procrastination is primarily indicative of psychological distress factors such as depression and low self-esteem (Lee, Kelly, & Edwards, 2006) which have failed to show any significant association with negative outcomes of PIU (Caplan, 2002, 2003). However, it is too premature to conclude that there is no association between these two variables. Procrastinatory cognitions are highly predictive of the distraction dimension of Davis et al.’s (2002) Online Cognition Scale which is in turn significantly associated with consequences of improper Internet use (Davis et al., 2002). Future studies can investigate the relationship among these three variables in the context of problematic IM use. Overall, it appears that interpersonal communication-related factors, i.e. OCA and perceived inconvenience, are much better predictors of problematic IM use than the noninterpersonal communication related factors polychronicity and trait procrastination. This substantiates previous suggestions that factors related to interpersonal behavior are the primary triggers of the development of PIU-related symptoms (Caplan, 2002, 2007). However, this study is not without its limitations. The direction of causality cannot be determined because this study used a cross-sectional design instead of a longitudinal or experimental design. Furthermore, since IM has proven to be very popular among other population demographics such as younger teenagers (Lenhart et al., 2001), future problematic IM use studies could focus on those younger cohorts, e.g. high-school students. In addition, it is possible that Singapore campuses differ from foreign campuses in terms of the overall level of campus Internet connectedness which can potentially affect the extent to which university students are predisposed to experiencing problems with their IM use. Factors such as polychronicity are largely shaped by cultural norms (Conte, Rizzuto & Steiner, 1999; Scarborough & Lindquist, 1999) and 644


oral communication apprehension levels have been known to differ between countries (Lee, Detenber, Willnat, Aday, & Graf, 2004). Thus, the theoretical framework from this study can be utilized for a cross-cultural comparison of problematic IM use between different countries. To conclude, it is undeniable that the Internet and its web-based applications bring about great benefits by helping people to communicate with known others (Davis, 2001; Lenhart et al., 2001; Tyler, 2002; Wellman, Quan-Haase, Witte, & Hampton, 2001). However, as Davis (2001) has astutely pointed out, ‘‘Individuals with general PIU are considerably more problematic in that their pathology would likely not even exist in the absence of the Internet. . . The Internet, in its social role, acts as a means of communication to the most extreme degree imaginable’’ (p. 192-193). Online synchronous applications are known for representing a large component of PIU and the findings from this study strongly imply that new synchronous web-based communication technologies could provide yet another risk factor for developing symptoms of problematic Internet use. Notes 1 Although mobile phones can be used to access the Internet, they are regarded as an offline mode of communication in this study because previous research has shown that only a minority of people surf the Internet and other web-related applications on their mobile phones (Harwood & Rainie, 2004; Infocomm Development Authority of Singapore, 2006; Rainie & Keeter, 2006). Since IM is a synchronous form of online communication (e.g. Flanagin, 2005), this study has chosen to focus on gauging if the perceived inconvenience of using offline synchronous modes of communication is a predictor of problematic IM use. Hence, though mobile phones are widely used for text messaging, text messaging is not tested as a predictor of problematic IM use as it is regarded as an offline form of asynchronous communication (Carlsson, Hyvönen, Repo, & Walden, 2005). 2 Although the mean for this measure is admittedly low, the value obtained is similar to that of the negative outcomes measure which was used in Caplan’s previous PIU studies (e.g. Caplan, 2002, 2003, 2005). References Albanese, P. J. (1988). The intimate relations of the consistent consumer: Psychoanalytic object relations’ theory applied to economics. In P. J. Albanese (Ed.), Psychological foundations of economic behavior (pp. 59–79). New York: Praeger. Allen, M., & Bourhis, J. (1996). The relationship of communication apprehension to communication behavior: A meta-analysis. Communication Quarterly, 44(2), 214–226. Ariely, D., & Wertenbroch, K. (2002). Procrastination, deadlines, and performance: Self-control by precommitment. Psychological Science, 13(3), 219–224. Baltes, B. B., Dickson, M. W., Sherman, M. P., Bauer, C. C., & LaGranke, J. S. (2002). Computer mediated communication and group decision making: A meta-analysis. Organizational Behavior and Human Decision Processes, 87(1), 156–179. Journal of Computer-Mediated Communication 14 (2009) 627–657 © 2009 International Communication Association

645

Bargh, J. A., McKenna, K. Y. A., & Fitzsimons, G. M. (2002). Can you see the real me? Activation and expression of the ‘‘true self’’ on the Internet. Journal of Social Issues, 58, 33–48. Beard, K. W., & Wolf, E. M. (2001). Modification in the proposed diagnostic criteria for Internet addiction. CyberPsychology and Behavior, 4, 377–383. Bhatnagar, A., Misra, S., & Rao, H. R. (2000). On risk, convenience, and Internet shopping behaviour. Communications of the ACM, 43(11), 98–105. Bluedorn, A. C., Kalliath, T. J., Strube, M. J., & Martin, G. D. (1999). Polychronicity and the inventory of polychronic values (IPV): The development of an instrument to measure a fundamental dimension of organizational culture. Journal of Managerial Psychology, 14(3/4), 205–230. Bonebrake, K. (2002). College students’ Internet use, relationship formation, and personality correlates. CyberPsychology and Behavior, 5(6), 551–558. Brown, L. G. (1989). The strategic and tactical implications of convenience in consumer product marketing, The Journal of Consumer Marketing, 6(3), 13–19. Brown, S. A., Fuller, R. M., & Vician, C. (2002). Who’s afraid of the virtual world? The role of anxiety in computer-mediated communication usage and satisfaction. Retrieved June 19, 2007, from http://www.indiana.edu/∼isdept/research/papers/tr117-2.doc. Bryant, J. A., Sanders-Jackson, A., & Smallwood, A. M. K. (2006). IMing, text messaging, and adolescent social networks. Journal of Computer-Mediated Communication, 11(2), Retrieved September 5, 2006, from http://jcmc.indiana.edu/vol11/issue2/bryant.html. Cameron, A. F., & Webster, J. (2005). Unintended consequences of emerging communication technologies: Instant messaging in the workplace. Computers in Human Behavior, 21, 85–103. Campbell, S. W., & Neer, M. R. (2001). The relationship of communication apprehension and interaction involvement to perceptions of computer-mediated communication. Communication Research Reports, 18(4), 391–398. Caplan, S. E. (2002). Problematic Internet use and psychosocial well-being: Development of a theory based cognitive-behavioral measurement instrument. Computers in Human Behavior, 18, 553–575. Caplan, S. E. (2003). Preference for online social interaction: A theory of problematic Internet use and psychosocial well-being. Communication Research, 30, 625–648. Caplan, S. E. (2005). A social skill account of problematic Internet use. Journal of Communication, 55, 721–736. Caplan, S. E. (2007). Relations among loneliness, social anxiety, and problematic Internet use. CyberPsychology & Behavior, 10(2), 234–242. Carlsson, C., Hyvönen, K., Repo, P., & Waldén, P. (2005). Asynchronous adoption patterns of mobile services. Proceedings of the 38th Hawaii International Conference on System Sciences. Retrieved July 31, 2008, from http://www.kuluttajatutkimuskeskus.fi/files/4823/hawaiiconference2005.pdf. Carrigan, M., & Szmigin, I. (2006). ‘‘Mothers of invention’’: Maternal empowerment and convenience consumption. European Journal of Marketing, 40, 1122–1142. 646


Conte, J. M., Rizzuto, T. E., & Steiner, D. D. (1999). A construct-oriented analysis of individual-level polychronicity. Journal of Managerial Psychology, 14(3/4), 269–287. Dabbish, L., & Kraut, R. (2003). Coordinating communication: Awareness displays and interruption. In CHI ’03 Extended Abstracts on Human Factors in Computing Systems (pp. 786–787). Fort Lauderdale, FL: ACM Press. Davis, R. A. (2001). A cognitive-behavioral model of pathological Internet use. Computers in Human Behavior, 17, 187–195. Davis, R. A., Flett, G. L., & Besser, A. (2002). Validation of a new scale for measuring problematic Internet use: Implications for pre-employment screening. CyberPsychology and Behavior, 5, 331–345. DeSarbo, W. S., & Edwards, E. A. (1996). Typologies of compulsive buying behavior: A constrained clusterwise regression approach. Journal of Consumer Psychology, 5(3), 231–262. Eastin, M. S. (2002). Diffusion of e-commerce: An analysis of the adoption of four e-commerce activities. Telematics and Informatics, 19, 251–267. Eastin, M. S., & LaRose, R. (2000). Internet self-efficacy and the psychology of the digital divide. Journal of Computer-Mediated Communication, 6(1), Retrieved August 25, 2006, from http://jcmc.indiana.edu/vol6/issue1/eastin.html. Eijnden R., Meerkerk, G.-J., Vermulst, A., et al. (2005). Online communication, compulsive Internet use and psychosocial well-being among adolescents: a longitudinal study. Retrieved August 8, 2008, from http://publishing.eur.nl/ir/repub/asset/10511/070920 _Meerkerk,+Gert-Jan.pdf#page=33. Ferrari, J. R., Harriott, J. S., Evans, L., Lecik-Michna, D. M., & Wenger, J. M. (1997). Exploring the Time Preferences by Procrastinators: Night or Day, Which is the One? European Journal of Personality, 11(3), 187–196. Ferrari, J. R., Johnson, J. L., & McCown, W. G. (1995). Treatment of academic procrastination in college students. In J. R. Ferrari., J. L. Johnson, & W.G. McCown (Eds.), Procrastination and task avoidance: Theory, research, and treatment. (pp. 187–208). New York: Plenum. Ferrari, J. R., & McCown, W. G. (1994). Procrastination tendencies among obsessive-compulsives and their relatives. Journal of Clinical Psychology, 50, 152–157. Flanagin, A. J. (2005). IM Online: Instant messaging use among college students. Communication Research Reports, 22(3), 175–187. Freiermuth, M., & Jarrell, D. (2006). Willingness to communicate: Can online chat help? International Journal of Applied Linguistics, 16(2), 189–212. Garrett, R. K., & Danziger, J. N. (2007). IM=Interruption management? Instant messaging and disruption in the workplace. Journal of Computer-Mediated Communication, 13(1), Retrieved August 7, 2008, from http://jcmc.indiana.edu/vol13/issue1/garrett.html. Garson, G. D. (n.d.). Data imputation for missing values. Retrieved February 13, 2007, from http://www2.chass.ncsu.edu/garson/pa765/missing.htm. Griffiths, M. (2000). Does Internet and computer ‘‘addiction’’ exist? Some case study evidence. CyberPsychology and Behavior, 3, 211–218. Journal of Computer-Mediated Communication 14 (2009) 627–657 © 2009 International Communication Association

647

Grinter, R. E., & Palen, L. (2002). Instant messaging in teen life. In Proceedings of the ACM Conference on Computer-Supported Cooperative Work (CSCW 2002) (pp. 21–30). New York: ACM Press. Gross, E. F., Juvonen, J., & Gable, S. L. (2002). Internet use and well-being in adolescence. Journal of Social Issues, 58(1), 75–90. Harwood, P., & Rainie, L. (2004). Use of the Internet in places other than home or work: A PIP data memo. Pew Internet & American Life Project. Retrieved March 2, 2007, from http://www.pewinternet.org/pdfs/PIP_Other_Places.pdf. Ho, S. M., & Lee, T. M. (2001). Computer usage and its relationship with adolescent lifestyle in Hong Kong. Journal of Adolescent Health, 29, 258–266. Hu, Y., Wood, F. J., Smith, V., & Westbrook, N. (2004). Friendships through IM: Examining the relationship between instant messaging and intimacy. Journal of Computer-Mediated Communication, 10(1), Retrieved July 30, 2006, from http://jcmc.indiana.edu/vol10/ issue1/hu.html. Humpel, N., Marshall, A. L., Leslie, E., Bauman, A., & Owen, N. (2004). Changes in neighborhood walking are related to changes in perceptions of environmental attributes. Annals of Behavioral Medicine, 27(1), 60–67. Infocomm Development Authority of Singapore. (2006). Annual survey on infocomm use in households and by individuals for 2006. Retrieved March 13, 2007, from http://www.ida.gov.sg/doc/Publications/Publications_Level2/20061205092557/hh06_ public_v4.4.pdf. Internet World Stats: Top 47 countries with the highest Internet penetration rate. (2008). Retrieved August 2, 2008, from http://www.internetworldstats.com/top25.htm. Jacobson, D. (1999). Impression formation in cyberspace: Online expectations and offline experiences in text-based virtual communities. Journal of Computer Mediated Communication, 5(1), Retreived August 1, 2006, from http://jcmc.indiana.edu/vol5/ issue1/jacobson.html. Jagtiani, R. (2006, August 27). Mass-senger appeal. The Straits Times, Retrieved March 1, 2006, from the Factiva database. Jones, S., Madden, M., Clarke, L. N., Cornish, S., Gonzales, M., Johnson, C., et al. (2002). The Internet goes to college: How students are living in the future with today’s technology. Pew Internet & American Life Project. Retrieved November 16, 2006, from http://www. pewinternet.org/pdfs/PIP_College_Report.pdf. Kandell, J. J. (1998). Internet addiction on campus: the vulnerability of college students. CyberPsychology and Behavior, 1, 11–17. Kaufman, C. J. (1990). Usage versus ownership: suggestions for refining studies of time saving and wives’ employment. Journal of Consumer Marketing, 7, 23–30. Kubey, R. W., Lavin, M. J., & Barrows, J. R. (2001). Internet use and collegiate academic performance decrements: Early findings. Journal of Communication, 51, 366–382. Lavoie, J.A., & Pychyl, T.A. (2001). Cyberslacking and the procrastination highway: A web-based survey of on-line procrastination, attitudes, and emotion. Social Science Computer Review, 19, 431–444. 648


Lay, C. (1988). The relation of procrastination and optimism to judgments of time to complete an essay and anticipation of setbacks. Journal of Social Behaviour and Personality, 3, 201–214. Retrieved November 7, 2006, from http://www.atkinson.yorku.ca/∼psyctest/prcrasts.pdf. Leary, M. (1983). The conceptual distinctions are important: Another look at communication apprehension and related constructs. Human Communication Research, 10(2), 305–312. Lee, B. H., Sim, L. C., Tan, M. K. T., & Detenber, B. H. (2004). Getting to know you: Exploring the development of relational intimacy in computer-mediated communication. Journal of Computer-Mediated Communication, 9(3). Retrieved March 26, 2007, from http://jcmc.indiana.edu/vol9/issue3/detenber.html. Lee, D., Kelly, K. R., & Edwards, J. K. (2006). A closer look at the relationships among trait procrastination, neuroticism, and conscientiousness. Personality and Individual Differences, 40, 27–37. Lee, K. C., & Perry, S. D. (2004). Student instant message use in a ubiquitous computing environment: Effects of deficient self-regulation. Journal of Broadcasting and Electronic Media, 48(3), 399–420. Lee, W., Detenber, B. H., Willnat, L., Aday, S., & Graf, J. (2004). A cross-cultural test of the spiral of silence theory in Singapore and the United States. Asian Journal of Communication, 14(2), 205–226. Lee, W., Tan, M. K. T., & Shahiraa, S. H. (2005). Polychronicity, the Internet, and the mass media: A Singapore study. Journal of Computer-Mediated Communication, 11(1). Retrieved October 7, 2006, from http://jcmc.indiana.edu/vol11/issue1/wplee.html. Lenhart, A., Rainie, L., & Lewis, O. (2001). Teenage life online: The rise of the instant-message generation and the Internet’s impact on friendships and family relationships. Pew Internet & American Life Project. Retrieved March 1, 2007, from http://www.pewinternet.org/ reports/pdfs/PIP_Teens_Report.pdf. Leung, L. (2001). College student motives for chatting on ICQ. New Media and Society, 3(4), 483–500. Leung, L. (2004). Net generation attributes and seductive properties of the Internet as predictors of online activities and Internet addiction. CyberPsychology and Behavior, 7(3), 333–348. Leung, L., & Ran, W. (1999). Who are the mobile phone have-nots? Influences and consequences. New Media and Society, 1, 209–226. Ling, R. (1997). ‘‘One can talk about common manners!’’: The use of mobile telephones in inappropriate situations. In L. Haddon (Ed.), Themes in mobile telephony: Final report of the COST 248 home and work group. Stockholm: Telia. Retrieved December 20, 2005, from http://www.cost269.org/Cost248/Cost248_FinalReport.htm. Love, S., & Perry, M. (2004, April). Dealing with mobile conversations in public places: Some implications for the design of socially intrusive technologies. Paper presented at the Conference on Human Factors in Computing Systems (CHI), Vienna, Austria. Madell, D., & Muncer, S. (2005). Are Internet and mobile phone activities complementary among young people? A study from the rational actor perspective. Information, Communication and Society, 8(1), 64–80. Journal of Computer-Mediated Communication 14 (2009) 627–657 © 2009 International Communication Association

649

Marketwire. (2006). Corporate IM Use shows strong uptake in 2006, enterprise IM market growing at a rate of 22%. Retrieved August 1, 2008, from http://findarticles.com/p/ articles/mi_pwwi/is_200609/ai_n16730378. McCroskey, J. C. (1970). Measures of communication-bound anxiety. Speech Monographs, 37(4), 269–277. McCroskey, J. C. (1977). Oral communication apprehension: A summary of recent theory and research. Human Communication Research, 4, 78–96. McCroskey, J. C. (1986). An introduction to rhetorical communication (5th ed.). Englewood Cliffs, NJ: Prentice-Hall. McCroskey, J. C., & Richmond, V. P. (1987). Willingness to communicate. In J. C. McCroskey, & J. A. Daly (Eds.), Personality and interpersonal communication (pp. 129–156). Newbury Park, CA: SAGE Publications. McCroskey, J. C., Richmond, V. P., Daly, J. A., & Cox, B. (1975). The effects of communication apprehension on interpersonal attraction. Human Communication Research, 2, 51–65. McKenna, K. Y. A., & Bargh, J. A. (1999). Causes and consequences of social interaction on the Internet: A conceptual framework. Media Psychology, 1, 249–269. Morahan-Martin, J., & Schumacher, P. (2000). Incidence and correlates of pathological Internet use among college students. Computers in Human Behavior, 16, 13–29. Morahan-Martin, J., & Schumacher, P. (2003). Loneliness and social uses of the Internet. Computers in Human Behavior, 19, 659–671. Morris, M., & Ogan, C. (1996). The Internet as mass medium. Journal of Computer-Mediated Communication, 1(4). Retrieved August 19, 2006, from http://jcmc.indiana.edu/vol1/issue4/morris.html. Muller, M. J., Raven, M. E., Kogan, S., Millen, D. R., & Carey, K. (2003). Introducing chat into business organizations: Toward an instant messaging maturity model. In Proceedings of the 2003 International ACM SIGGROUP Conference on Supporting Group Work (pp. 50–57). Sanibel Island, FL: ACM Press. Muszynski, S. Y., & Akamatsu, T. J. (1991). Delay in completion of doctoral dissertations in clinical psychology. Professional Psychology: Research and Practice, 22, 119–123. Nalwa, K., & Anand, A. P. (2003). Internet addiction in students: A cause for concern. CyberPsychology and Behavior, 6(6), 653–656. Nardi, B. A., & Whittaker, S. (2002). The place of face-to-face communication in distributed work. Retrieved March 2, 2007, from http://dis.shef.ac.uk/stevewhittaker/FTF.pdf. Nardi, B. A., Whittaker, S., & Bradner, E. (2000). Interaction and outeraction: Instant messaging in action. In Proceedings of Conference on Computer Supported Cooperative Work (CSCW) (pp. 79–88). New York: ACM Press. Ng, J. ((2008). May 28). Learning goes the way of Facebook and Wikis. The Straits Times, Retrieved August 1, 2006, from the Factiva database. Niemz, K., Griffiths, M., & Banyard, P. (2005). Prevalence of pathological Internet use among university students and correlations with self-esteem, the General Health Questionnaire (GHQ) & disinhibition. CyberPsychology and Behavior, 8(6), 562–570. 650


Papacharissi, Z., & Rubin, A. M. (2000). Predictors of Internet use. Journal of Broadcasting and Electronic Media, 44, 175–196. Philips, J. G., & Reddie, L. (2007). Decisional style and self-reported email use in the workplace. Computers in Human Behavior, 23(5), 2414–2428. Quan-Haase, A. (2007). Instant messaging on campus: Use and integration in students’ everyday communication. The Information Society, 24(2), 105–115. Retrieved October 4, 2008, from http://www.indiana.edu/∼tisj/24/index.html Quan-Haase, A. (in press). Self-regulation in instant messaging (IM): Failures, strategies, and negative outcomes. International Journal of e-Collaboration, 5(2). Quan-Haase, A., & Collins, J. L. (2008). I’m there, but I might not want to talk to you: University students’ social accessibility in Instant Messaging. Information, Communication & Society, 11(4), 526–543. Quan-Haase, A., Cothrel, J., & Wellman, B. (2005). Instant messaging for collaboration: A case study of a high-tech firm. Journal of Computer-Mediated Communication, 10(4). Retrieved February 19, 2007, from http://jcmc.indiana.edu/vol10/issue4/quan-haase. html. Rainie, L., & Keeter, S. (2006). How Americans use their cell phones. Pew Internet & American Life Project. Retrieved March 2, 2007, from http://www.pewinternet.org/pdfs/PIP_Cell_ phone_study.pdf. Ran, W., & Leung, L. (1999). Blurring public and private behaviors in public space: policy challenges in the use and improper use of the cell phone. Telematics and Informatics, 16, 11–26. Reibstein, D, J., Lovelock, C. H., & Dobson, R. C. P. (1980). The direction of causality between perceptions, affect, and behavior: An application to travel behavior. Journal of Consumer Research, 6(4), 370–376. Renneker, J., & Godwin, L. (2003). Theorizing the unintended consequences of instant messaging (IM) for worker productivity. Sprouts: Working Papers on Information Environments, Systems and Organizations, 3(3), 137–168. Robert, L. P., & Dennis, A. R. (2005). Paradox of richness: a cognitive model of media choice. IEEE Transactions on Professional Communication, 48(1), 10–21. Rothblum, E. D., Solomon, L. J., & Murakami, J. (1986). Affective, cognitive, and behavioral differences between high and low procrastinators. Journal of Counseling Psychology, 33, 387–394. Rubin, R. B., Perse, E. M., & Barbato, C. A. (1988). Conceptualization and measurement of interpersonal communication motives. Human Communication Research, 14, 602–628. Saw, A. (2004, August 7). Singapore lays down its chips. Asia Times Online, Retrieved August 1, 2008, from http://www.atimes.com/atimes/Southeast_Asia/FH07Ae01.html. Scarborough, K., & Lindquist, J. D. (1999). Time management and polychronicity: Comparisons, contrasts and insights for the workplace. Journal of Managerial Psychology, 14(3/4), 288–312. Schlenker, B. R., & Leary, M. R. (1982). Social anxiety and communication about the self. Journal of Language and Social Psychology, 45, 1347–1354. Journal of Computer-Mediated Communication 14 (2009) 627–657 © 2009 International Communication Association

651

Schouwenburg, H. C., & Lay, C. H. (1995). Trait procrastination and the big-five factors of personality. Personality and Individual Differences, 18, 481–490. Shaffer, H. J., Hall, M. N., & Vander Bilt, J. (2000). ‘‘Computer addiction’’: a critical consideration. American Journal of Orthopsychiatry, 70, 162–168. Shapira, N. A., Goldsmith, T. D., Keck, P. E., Jr., Khosla, U. M., & McElroy, S. L. (2000). Psychiatric features of individuals with problematic Internet use. Journal of Affective Disorders, 57, 267–272. Shiu, E., & Lenhart, A. (2004). How Americans use instant messaging. Pew Internet & American Life Project. Retrieved March 1, 2007, from http://www.pewinternet.org/pdfs/ PIP_Instantmessage_Report.pdf. Slocombe, T. E., & Bluedorn, A. C. (1999). Organizational behavior implications of the congruence between preferred polychronicity and experienced work-unit polychronicity. Journal of Organizational Behavior, 20, 75–99. Sobel, M. E. (1982). Asymptotic intervals for indirect effects in structural equations models. In S. Leinhart (Ed.), Sociological Methodology (pp. 290–312). San Francisco: Jossey-Bass. Spitzberg, B. H. (2006). Preliminary development of a model and measure of computer-mediated communication (CMC) competence. Journal of Computer-Mediated Communication, 11(2), 629–666. Student statistics: AY 06/07 Full-time undergraduate enrolment by programme & gender. (2008). Retrieved August 3, 2008, from http://www3.ntu.edu.sg/oas2/AnnualRep/2007/ statistics.htm. The nation. (2002, August 30). The Chronicle of Higher Education, 49, 12–34. Tice, D. M., & Baumeister, R. F. (1997). Longitudinal study of procrastination, performance, stress and health: The costs and benefits of dawdling. Psychological Science, 8(6), 454–458. Tidwell, L. C., & Walther, J. B. (2002). Computer-mediated communication effects on disclosure, impressions, and interpersonal evaluations: Getting to know one another a bit at a time. Human Communication Research, 28, 317–348. ‘‘Time management.’’ (2003). Retrieved March 4, 2007, from http://www.staffs.ac.uk/ services/careers/sponte_staff/site_files/defining_skills/time-management_one.htm. Tyler, T. R. (2002). Is the Internet changing social life? It seems the more things change, the more they stay the same. Journal of Social Issues, 58(1), 195–205. Valkenburg, P. M., & Peter, J. (2007). Online communication and adolescent well-being: Testing the stimulation versus the displacement hypothesis. Journal of Computer-Mediated Communication, 12(4). Retrieved August 2, 2008, from http://jcmc.indiana.edu/vol12/issue4/valkenburg.html. Walther, J. B. (1996). Computer-mediated communication: Impersonal, interpersonal, and hyperpersonal interaction. Communication Research, 23, 3–43. Wellman, B., Quan-Haase, A., Witte, J., & Hampton, K. N. (2001). Does the Internet increase, decrease, or supplement social capital? Social networks, participation, and community commitment. American Behavioral Scientist, 45(3), 437–456. Wolters, C. (2003). Understanding procrastination from a self-regulated learning perspective. Journal of Educational Psychology, 95, 179–187. 652


Wolz, U., Palme, J. and Anderson, P. (1997). Computer-mediated communication in collaborative educational settings: Report of the ITiCSE’97 working group on CMC in collaborative educational settings. ACM SIGCUE Outlook, 25(4), 51–56. Yale, L., & Venkatesh, A. (1986). Toward the construct of convenience in consumer research. Advances in Consumer Research, 13, 403–408. Yang, C. C. (2005). Exploring factors affecting the adoption of mobile commerce in Singapore. Telematics and Informatics, 22, 257–277. Young, K. S. (1998). Caught in the Net: How to recognize the signs of Internet addiction and a winning strategy for recovery. New York: John Wiley. Young, K. S. (2004). Internet addiction: A new clinical phenomenon and its consequences. American Behavioral Scientist, 48(4), 402–415. Yuen, N. C., & Lavin, M. J. (2004). Internet dependence in the collegiate population: The role of shyness. CyberPsychology and Behavior, 7(4), 379–383.

About the Authors

Rachel L. Neo is currently a Masters by Research student at the Wee Kim Wee School of Communication & Information, Nanyang Technological University, Singapore. Her current research interests focus on new communication technologies, and video gaming addiction-engagement amongst elementary school students. Address: Wee Kim Wee School of Communication & Information, 31 Nanyang Link, Singapore 637718. Marko M. Skoric (Ph.D., University of Michigan) is an assistant professor at the Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore. His research broadly focuses on new media and social change, with particular emphasis on civic and political implications of new communication technologies. Address: WKW School of Communication and Information, 31 Nanyang Link, Singapore 637718. Appendix

Information Communication Technology Usage Survey Do you use any Instant Messaging (IM) software (e.g. AOL Instant Messenger [AIM], Google chat, MSN Messenger, ICQ, YAHOO chat etc.)? Yes / No a. If yes, continue with the next question. b. If no, please proceed to page 6 and fill in questions 69–73. On average, how many hours per day do you sign into IM on the computer? (0-24) _____ hours On average, how many hours per day do you actually use IM? (0-24) _____ hours Journal of Computer-Mediated Communication 14 (2009) 627–657 © 2009 International Communication Association

653

Part 1 In this section, please rate the following statements on a scale from 1 = Never to 5 = Always. Circle the option that best represents your choice.

Example: How often do you participate in forum discussions?

Never 1

2

3

4

Never

654

Always 5 Always

1. I often find myself performing tasks that I had intended to do days before.

1

2

3

4

5

2. I do not do assignments until just before they are to be handed in.

1

2

3

4

5

3. When I am finished with a library book, I return it right away regardless of the date it is due.

1

2

3

4

5

4. When it is time to get up in the morning, I most often get right out of bed.

1

2

3

4

5

5. A letter may sit for days after I write it before mailing it.

1

2

3

4

5

6. Even with jobs that require little else except sitting down and doing them I find they seldom get done.

1

2

3

4

5

7. I generally delay before starting on work I have to do.

1

2

3

4

5

8. When preparing to go out, I am seldom caught having to do something at the last minute.

1

2

3

4

5

9. In preparing for some deadline, I often waste time by doing other things.

1

2

3

4

5

10. I usually start an assignment shortly after it is assigned.

1

2

3

4

5

11. I always seem to end up shopping for birthday or Christmas gifts at the last minute.

1

2

3

4

5

12. I usually buy even an essential item at the last minute.

1

2

3

4

5

13. I usually accomplish all the things I plan to do in a day.

1

2

3

4

5

14. I usually take care of all the tasks I have to do before I settle down and relax for the evening.

1

2

3

4

5

15. I generally return phone calls promptly.

1

2

3

4

5

16. I usually make decisions as soon as possible.

1

2

3

4

5

17. I usually have to rush to complete a task on time.

1

2

3

4

5

18. I prefer to leave early for an appointment.

1

2

3

4

5

19. I often have a task finished sooner than necessary.

1

2

3

4

5

20. I am continually saying “I’ll do it tomorrow.”

1

2

3

4

5


Part II In this section of the survey, please rate your areement or disagreement with the following statements on a scale from 1 = Strongly Disagree to 5 = Strongly Agree. Circle the option that best represents your choice. Strongly Disagree Example: I like to play online games.

21. When talking in a small group of acquaintances, I am tense and nervous.

1

Strongly Agree 2

Strongly Disagree 1 2

3

4

5

3

Strongly Agree 4 5

22. When conversing with a friend, I am calm and relaxed.

1

2

3

4

5

23. When conversing with an acquaintance, I am calm and relaxed.

1

2

3

4

5

24. When talking in a small group of friends, I am calm and relaxed.

1

2

3

4

5

25. When conversing with a friend, I am tense and nervous.

1

2

3

4

5

26. When talking in a small group of acquaintances, I am calm and relaxed.

1

2

3

4

5

27. When conversing with an acquaintance, I am tense and nervous.

1

2

3

4

5

28. When talking in a small group of friends, I am tense and nervous.

1

2

3

4

5

29. I like to juggle several activities at the same time.

1

2

3

4

5

30. I would rather complete an entire project every day than complete parts of several projects.

1

2

3

4

5

31. I believe people should try to do many things at once.

1

2

3

4

5

32. When I work by myself, I usually work on one project at a time.

1

2

3

4

5

33. I prefer to do one thing at a time.

1

2

3

4

5

34. I believe people do their best work when they have many tasks to complete.

1

2

3

4

5

35. I believe it is best to complete one task before beginning another.

1

2

3

4

5

36. I believe it is best for people to be given several tasks and assignments to perform.

1

2

3

4

5

37. I seldom like to work on more than a single task or assignment at the same time.

1

2

3

4

5


655

656

38. I would rather complete parts of several projects every day than complete an entire project.

1

2

3

4

5

39. Generally, I find it tiring to hold a mobile phone while chatting.

1

2

3

4

5

40. I am too busy to find time to chat on my mobile phone.

1

2

3

4

5

41. I feel that it is too expensive to chat using a mobile phone.

1

2

3

4

5

42. The ringing of a mobile phone irritates me a lot.

1

2

3

4

5

43. I get very bothered if people around me take notice whenever I am chatting on my mobile phone.

1

2

3

4

5

44. It is distracting when people around me are chatting on their mobile phones.

1

2

3

4

5

45. I usually get very annoyed if I get mobile phone calls when I am busy.

1

2

3

4

5

46. There are times when admittedly, I feel too lazy to meet and talk face-to-face with my friends.

1

2

3

4

5

47. I often have difficulties finding time to meet and talk with my friends in person.

1

2

3

4

5

48. Generally, it is costly to travel out on a regular basis to meet up and talk with my friends.

1

2

3

4

5

49. I feel that it is tiring to travel out to meet and talk with my friends.

1

2

3

4

5

50. I feel that a lot of time is taken up by traveling out to meet and talk with my friends.

1

2

3

4

5

51. I find that it is generally easy to travel to meet and chat with my friends.

1

2

3

4

5

52. Generally, I prefer communicating using IM rather than by using *offline means (i.e. mobile phone or face-to-face means).

1

2

3

4

5

53. I am more comfortable having IM conversations than having conversations using offline means.

1

2

3

4

5

54. Generally, meeting and talking is better when done on IM rather than by using offline means.

1

2

3

4

5

55. I feel like I have more control over IM conversations than I do in offline conversations.

1

2

3

4

5

56. I feel more confident socializing on IM than offline.

1

2

3

4

5

57. I feel safer relating to others using IM rather than by using offline means.

1

2

3

4

5

58. I am happier chatting on IM than chatting offline.

1

2

3

4

5


59. I have made unsuccessful attempts to control my IM use.

1

2

3

4

5

60. I am unable to reduce the amount of time I spend on IM.

1

2

3

4

5

61. I have tried to stop using IM for long periods of time but can’t seem to do it.

1

2

3

4

5

62. I feel guilty about the amount of time I spend on IM.

1

2

3

4

5

63. It would be difficult for me to go for a day without using IM.

1

2

3

4

5

64. I have missed social gatherings because of my IM usage.

1

2

3

4

5

65. I have missed classes or work because of my IM usage.

1

2

3

4

5

66. I have gotten into trouble at school or work because of my IM usage.

1

2

3

4

5

67. I have missed club/CCA meetings because of my IM usage.

1

2

3

4

5

68. My sleep has suffered because of my IM usage.

1

2

3

4

5

Basic information 69. Gender: Male / Female 70. Do you stay in hall? Yes / No 71. Nationality: Singaporean/ Singapore PR/ Foreigner 72. Year of study: 1 / 2 / 3 / 4 73. Age:


657

Problematic Instant Messaging Use - Wiley Online Library

Problematic Instant Messaging Use - Wiley Online Library

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