Individual differences and relative advantage: the case of GSS

Decision Support Systems 32 (2002) 327 – 341 www.elsevier.com/locate/dsw

Individual differences and relative advantage: the case of GSS Elena Karahanna a,*, Manju Ahuja b, Mark Srite c, John Galvin d a MIS Department, Terry College of Business, The University of Georgia, Athens, GA 30602, USA Accounting and Information Systems Department, Kelley School of Business, Indiana University, 1309 East Tenth St.,Bloomington, IN, USA c School of Business Administration, University of Wisconsin-Milwaukee, P.O. Box 742, Milwaukee, WI 53201, USA d Kelley School of Business, Indiana University, 801 W. Michigan St., Indianapolis, IN 46202-5151, USA

b

Received 1 June 2000; received in revised form 1 April 2001; accepted 1 May 2001

Abstract Studies of the effect of individual differences on usage of information systems have yielded mixed results. This study examines the effect of individual differences on the perceived relative advantage (a concept akin to perceived usefulness) of using Group Support Systems (GSS) over traditional face-to-face meetings. Specifically, the current field study investigates the effect of oral and writing communication apprehension, computer anxiety, and personal innovativeness on perceptions of relative advantage of a GSS. Results provide empirical support for the relationships explored and explain about 40% of variance in relative advantage of a GSS meeting vis-a`-vis a traditional face-to-face meeting. D 2002 Elsevier Science B.V. All rights reserved. Keywords: Relative advantage; Group support systems; Oral communication apprehension; Writing communication apprehension; Computer anxiety; Personal innovativeness

1. Introduction Information systems use is a central construct in IS research since it is a necessary, albeit not sufficient, condition for obtaining the many benefits information systems are touted to render. Consequently, significant research effort has been devoted to identifying antecedents of system use. These antecedents include, but are not limited to, organizational characteristics, task characteristics, environmental characteristics, and personality characteristics. In general, studies concerning the effect of individual differences (cognitive style, personality traits, dem*

Corresponding author. E-mail addresses: [email protected] (E. Karahanna), [email protected] (M. Ahuja), [email protected] (M. Srite), [email protected] (J. Galvin).

ographics, and situational variables) on usage of information systems (IS) have yielded mixed results. A possible explanation for the mixed empirical evidence may be that the relationship between individual differences and behavior has been conceptualized in overly simplistic terms. For example, many IS studies have posited a direct path between individual differences and behavior. However, theory and empirical evidence in the field of social psychology [32] suggest that the influence of individual differences on behavior is mediated by beliefs and attitudes. It is upon this assumption that the current study relies. Relative advantage, i.e., the degree to which a new system is perceived as being better than the alternative it supercedes [74], has consistently been found to be an important predictor of usage in many IS studies. In this study, we examined the effect of individual differences on the perceived relative advantage of using Group

0167-9236/02/$ - see front matter D 2002 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 7 - 9 2 3 6 ( 0 1 ) 0 0 1 2 4 - 5

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Support Systems (GSS) over traditional face-to-face meetings. More specifically, the current study examines the effect of oral and writing communication apprehension, computer anxiety, and personal innovativeness on the perceptions of the relative advantage of a GSS. These individual difference variables fall within the personality traits category of individual differences and have been of interest in prior IS research. A field study was conducted to test the proposed relationships. Results provided empirical support for the relationships explaining about 40% of variance in relative advantage of a GSS meeting vis-a`-vis a traditional faceto-face meeting. Specifically, both oral and writing communication apprehension were positively related to perceptions of relative advantage, whereas, surprisingly, personal innovativeness exhibited a significant negative effect on perceptions of relative advantage. Interestingly, the relationship between computer anxiety and relative advantage was non-significant. Taken together, these empirical results lend some credence to the theoretical assertion found in social psychology research that individual differences, at least in GSS settings, may influence behavior via their effect on beliefs. The remainder of the paper is organized as follows. First, the theoretical model underlying the study is presented. Next, the research hypotheses are developed, followed by a description of the field study conducted to test the hypotheses. The paper concludes with results of the data analysis, discussion of the results, and implications for research and practice.

2. Conceptual background Individual difference variables relevant for IS usage research have been broadly grouped into cognitive style, personality, demographic, and situational variables [6,88]. Cognitive style refers to an individual’s characteristic way of perceiving and thinking and includes dimensions such as systematic/heuristic and field dependent/field independent. Personality refers to the cognitive and affective structures maintained by individuals to facilitate adjustments to events, people, and situations [37]. It includes traits such as dogmatism, locus of control, need for achievement, anxiety, and innovativeness. Demographic variables include a range of personal characteristics such as age, gender,

education, and tenure. Finally, situational variables include, among others, training, experience, and user involvement [6,41]. Although many IS studies examining the impact of individual differences on system use exist [2,27,40, 55,69,88], empirical evidence is mixed. For example, in a meta-analysis of the effect of individual differences on DSS success (operationalized as usage and/or attitudes), Alavi and Joachimsthaler [6] report that the effect size pertaining to the relationship between personality traits and DSS success ranged from 0.000 to 1.64. More recent research in IS suggests that the inconclusive empirical results may be attributed to our inadequate understanding of the processes via which individual differences influence system use [2]. Attitude theories in social psychology such as the Theory of Reasoned Action (TRA) [32] explicitly delineate the mechanisms via which individual differences influence behavior. According to these theories, individual differences influence behavior indirectly through their effect on beliefs about the consequences of performing a behavior. Attitude towards performing a behavior, in turn, mediates the effect of these beliefs on behavior. Within the domain of attitude theories, the technology acceptance model (TAM) [23] is probably the most widely used theoretical framework on acceptance and usage of information technology. TAM provides a widely accepted yet parsimonious explanation of beliefs leading to system use and has received considerable empirical support in the IS literature [1,22, 23,62,79,80,81]. TAM posits that a user’s perceptions of a system’s usefulness in achieving instrumental outcomes (perceived usefulness) and of the system’s ease of use will determine one’s attitude towards using the system and ultimately one’s level of usage. Consequently, based on TRA, which forms the theoretical underpinnings of TAM, individual difference variables should influence usage behavior through their effect on perceived usefulness (PU) and preceived ease of use (PEOU). Agarwal and Prasad [2] make a similar argument and empirically examine whether usefulness and ease of use beliefs fully mediate the influence of demographic (such as tenure in work force and level of education) and situational variables (such as role with regard to technology, participation in training, and prior similar experiences) on attitude and behavioral intention to use a system. Their results provide

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credence to the claim that PU and PEOU fully mediate the influence of individual differences on behavior. The current study extends this stream of research by examining personality trait antecedents of relative advantage, a concept akin to perceived usefulness. Even though both the relative advantage and PU constructs tap at the user’s perceptions of advantages afforded by the system, the current study uses the broader construct of relative advantage since it includes a comparison between a new system (i.e., the GSS) and its precursor means of performing a task (i.e., a traditional face-to-face meeting). The positive and highly significant relationship between relative advantage/PU and usage has been well established in the literature [1,22,23,51,62,68,74,79,80,81,82]. In fact, Tornatzky and Klein’s [82] meta-analysis of adoption studies shows a consistently strong empirical relationship between perceptions of relative advantage and adoption and usage. However, the IS literature is predominantly silent on positing antecedents of relative advantage/PU. Since, according to TRA, individual differences influence behavior via beliefs, the current study will attempt to determine antecedents of relative advantage by examining personality traits of interest to IS researchers and positing relationships between these traits and relative advantage. Fig. 1 shows the conceptual model of the study.

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3. Research model and research hypotheses The objective of the current study is to determine personality trait antecedents of user perceptions of relative advantage in the context of a Group Support System. Fig. 2 presents the research model for the study. Relative advantage is defined as the degree to which the innovation is perceived as being better than the alternative it supercedes [74]. As indicated earlier, relative advantage and perceived usefulness are conceptually similar constructs. Indeed, Moore and Benbasat [68] use the perceived usefulness scales to measure relative advantage. However, there exists an important distinction between the two. Relative advantage explicitly contains a comparison between the innovation and its precursor, while this comparison is not an integral part of perceived usefulness. PU refers to the extent to which an innovation enhances one’s effectiveness on the job. As such, the comparison between alternative innovations is at best implied. In the present study, we are interested in understanding user perceptions of the advantages of a GSS meeting over the more traditional face-to-face meeting. Consequently, relative advantage rather than perceived usefulness is a better fit with our theoretical definition of the concept.

Fig. 1. Conceptual model.

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Fig. 2. Research model.

The current research posits that personality traits are significant antecedents of perceptions of relative advantage. Specifically, we suggest that writing and oral communication apprehension, computer anxiety, and personal innovativeness significantly influence the formation of usefulness beliefs about GSS. These specific personality traits were strategically selected because they are reflective of the ‘‘fit’’ between the GSS and aspects of one’s personality. The rationale for the hypothesized relationships as well as specific hypotheses are presented next. Communication apprehension (CA) has been a pivotal focus in the study of communication avoidance since the 1970s [64]. Initially viewed as ‘‘a broadly based anxiety related to oral communication’’ [65], the construct has been extended to include writing apprehension as well [21]. The relevance of this construct to information technology is readily apparent when communication is an integral part of a new (to the individual) technology [15,73,76]. This article will not attempt to review the multitude of research concerning CA since that can be found in the many meta-analyses of this area [8,14,59] but will argue that oral and writing communication apprehension should be considered as important factors when studying an individual’s use of communication technologies. Writing apprehension has been defined as ‘‘a situation and subject-specific individual difference concerned with people’s general tendencies to approach or avoid writing’’ [20, p. 11]. Daly’s work, and that of others [20,21], has shown how writing apprehension affects many personal outcomes such as career choices,

enrollment in writing classes, and message quality. Individuals with writing apprehension tend to avoid situations that require them to write and if that is not possible, they will become anxious and have low writing productivity [60]. This is similar to the behaviors associated with high oral communication apprehension when faced with verbal situations. A few studies have attempted to link writing apprehension and use of text-based communication technologies, such as e-mail, but without conclusive results. For instance, while Hartman et al. [42] suggested that writing apprehension was negatively associated with teacher –student interactions using electronic means, Scott and Rockwell [76] found no support for a relationship between writing apprehension and use of textbased technologies, such as e-mail, electronic discussion groups, and word processing. However, prior empirical research has shown a clear and consistent negative relationship between a person’s writing communication apprehension and his or her writing competency [20,21]. Given that GSS replaces oral with writing communication, and that writing communication apprehensives are likely to be less skillful with and/or more fearful of writing, we would expect them to view GSS as being less attractive as a meeting medium than a face-to-face meeting. H1. Writing communication apprehension will be negatively related to perceptions of relative advantage of a GSS. Conversely, individuals apprehensive of communicating orally are likely to appreciate being given an alternative medium to voice their opinions, and thus, are more likely to view a GSS as being more advantageous compared to a traditional meeting. Oral communication apprehension, or speech anxiety, is defined as an individual’s level of fear or anxiety associated with either real or anticipated oral communication with other people [63]. Most studies have considered oral communication apprehension to be an individual trait characteristic similar to constructs like shyness and reticence [63]. Over time, the research has broadened to encompass both trait and situational views [10,11]. Since the focus of this research is on the individual and their communication with others, we will consider oral communication from a trait or personality perspec-

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tive. Thus, as defined by McCrosky oral communication apprehension is ‘‘an individual’s level of fear or anxiety associated with either real or anticipated communication with another person or persons’’ [63, p. 215]. In our study, the context of interest is that of the individual communicating with groups rather than oneon-one. This is one of McCrosky’s four varieties of communication settings in which oral communication apprehension occurs [64]. The underlying assumption throughout the communication apprehension literature has been that individuals exhibiting high anxiety would not perform communication activities well. Empirical support for this has been provided by two meta-analyses by Allen and Bourhis [8] and Bourhis and Allen [14]. They found that as oral communication apprehension increased, the quantity and quality of communication behavior diminished and that there was a negative correlation between oral communication apprehension and cognitive performance. Furthermore, Scott and Rockwell [76] found that oral communication apprehension is a predictor of an individual’s future use of new communication technologies—specifically phone-based technologies such as cellular phones, pagers, and advanced phone options. Other empirical research [13] has shown that oral communication apprehensives frequently do not ask questions, seek or give feedback, or participate in discussions. In turn, in many settings, this may be a serious impediment to achievement and performance. Given that GSS eliminates the need for oral communication, individuals apprehensive of communicating orally are likely to appreciate being given an alternative medium to voice their opinions, and thus, are more likely to view a GSS as being more advantageous as compared to a traditional meeting. H2. Oral communication apprehension will be positively related to perceptions of relative advantage of a GSS. In addition to changing the communication mode from oral to written, a GSS also requires that communication occur through the use of a computer. This can be problematic for users who face computer anxiety. Computer anxiety refers to generalized anxiety or fear about computers [47] or about learning to use computers [58]. It can be defined as ‘‘fear of impending

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interaction with a computer that is disproportionate to the actual threat presented by the computer’’ [45, p. 630]. People with computer anxiety often choose not to use a computer when given the opportunity [34,72]. Empirical research examining the consequents of computer anxiety has shown that computer anxiety is associated with avoidance of using computers or reduced usage [44,47], with less positive attitude toward computers [16,47], lower self-efficacy and lower perceptions of computer usefulness [16], and lower end user satisfaction [46]. Therefore, since use of a GSS necessitates computer use, we posit that individuals high on computer anxiety are likely to view the GSS as less useful than a traditional meeting. H3. Computer anxiety will be negatively related to perceptions of relative advantage of a GSS. Finally, personal innovativeness in the domain of information systems, defined as the willingness of an individual to try out any new information technology [3], is likely to influence participants’ perceptions of the relative advantage of a GSS. Agarwal and Prasad [3] propose that personal innovativeness with respect to information technology is an important individual trait variable influencing the acceptance of computer technology. Various theoretical and operational definitions of innovativeness at the individual adopter level have been proposed. These include measuring innovativeness by identifying behaviors characteristic of innovators [4,49,52,56], using network analysis to identify innovators since innovators are more interconnected in their social networks [7,9,75,84,85], identifying innovators based on their time of adoption relative to other members of a social system [74], and using extent of implementation of the innovation [28,82]. None of these definitions and measures, however, views innovativeness as a personality trait variable. In the current study, our focus lies in the definition that views innovativeness as a personality trait. This stream of research defines innovativeness by identifying attitudes or other individual properties that distinguish more or less innovative individuals. Definitions of innovativeness in this category include ‘‘innovation orientation’’ [54], ‘‘readiness for change’’ [39,83], ‘‘values favorable or unfavorable to change’’ [38], ‘‘receptivity to new ideas’’ [67], and ‘‘attitude toward

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being innovative’’ [30]. These definitions include an element of predisposition toward new ideas and change. Agarwal and Prasad’s [3] definition of personal innovativeness, which will be used in this research, as the ‘‘willingness of an individual to try out any new information technology’’ (p. 205) also falls within this category. Some empirical evidence suggests that adopter innovativeness significantly affects perceptions and behavior toward the innovation. For instance, Agarwal and Prasad [3] argue that personal innovativeness can contribute to our understanding of how perceptions (such as perceptions of relative advantage, compatibility, and ease of use) and usage intentions are formed. Their study empirically tested for a moderating relationship between relative advantage and behavioral intention. Even though their empirical results did not support a moderating effect (they did not test for a direct relationship), Karahanna et al.’s [50] study has provided support for a direct relationship between innovativeness and relative advantage: more innovative individuals had more positive perceptions of the relative advantage of a specific IT innovation. Consequently, we posit that the level of personal innovativeness will positively influence the formation of relative advantage perceptions. H4. Personal innovativeness will be positively related to perceptions of relative advantage of a GSS. Finally, perceptions of the relative advantage of a GSS vis-a`-vis a traditional meeting may be colored by factors other than individual differences. One such construct that has consistently shown a significant effect on group outcomes is group cohesion, referring to the willingness of groups to stick together [78]. In a similar vein, Festinger [31] defines cohesion as the result of all forces acting on the members to remain within the group, while Janis [48] defines cohesion as ‘‘a high degree of amiability and esprit de corps among the members are manifestations of the high degree to which the members value their membership in a group and want to continue to be affiliated’’ (p. 245). In general, empirical evidence suggests that greater levels of group cohesion lead to greater member-interaction, goal attainment, compliance to group norms, satisfaction with groups, enjoyment of the decision making process [29,35,53,70,77], and may even gen-

erate a sense of elation [48]. In contrast, groups with low levels of cohesion will cooperate less and be less effective [44]. Individual members of highly cohesive GSS groups may then ascribe some of this positive experience to the GSS tool they are using to conduct the meeting and this may subsequently influence their perceptions of the relative advantage of the GSS. Consequently, since group cohesion can influence satisfaction with the decision making process and thus, by extension, may influence perceptions of the efficacy of the GSS meeting, group cohesion is included in our model as a control variable.

4. Methodology A field study was conducted with 46 employees of a large state university in the Southeast. Employees from a wide range of departments in the University met in ad-hoc groups of 6 to 12 to brainstorm on an issue of concern. Specifically, the Assistant Director of Human Resources was interested in generating ideas to determine the characteristics of an effective supervisor. This specific issue was selected because employee satisfaction survey results indicated that ‘‘management skill’’ was seen as an important success factor by the employees. The assistant director, rather than use the traditional face-to-face forum to generate ideas, chose to use the GSS for these meetings. Individuals were invited to participate rather than instructed to participate. Thus, all participants of the study had an interest in the subject and wanted to contribute their opinion. A number of meetings were scheduled, and each participant could sign up for only one meeting. To provide for ‘‘discussion’’ synergy among participants, the groups were divided into academic participant groups and non-academic participant groups. In addition, these groups were further subdivided by organizational level such that each group consisted of either (a) all senior administrative managers, (b) all supervisors, or (c) all non-managerial staff. The senior administrative managers consisted of either directors or associate directors in Finance and Administration who were solicited by the Vice President of Human Resources. The supervisory personnel (e.g., Supervisor, Senior Engineer, Assistant Director, Associate Director) were solicited from a list of participants of the Basic Supervisory Practical Training Program that is required

E. Karahanna et al. / Decision Support Systems 32 (2002) 327–341 Table 1 Sample characteristics

Age Computer experience Gender Male Female

Mean (standard deviation)

Missing

40.7 (9.2) 10.8 (6.5)

3 1 1

17 28

Age and computer experience are number of years.

of all supervisors at the university. These supervisors then identified one or more of their employees to participate in the non-managerial staff meetings (e.g., Administrative Assistant, Program Assistant, Training Specialist). This process resulted in groups ranging in size from 6 to 12 participants. The unequal group size has the potential to bias results of the study. Even though the average amount of participation in electronically supported groups appears to be unaffected by group size [25,86], for brainstorming tasks such as this, benefits of a GSS are greater for larger groups. This is because the technology reduces production blocking more in large than in small groups [25,26,86,87]. Consequently, we included group size as a control variable in the research model tested. As expected, its effect was non-significant (path coefficient = 0.108, T-statistic = 0.65), and we subsequently dropped it from our analysis. The groups used the GSS software VisionQuest for brainstorming. A brief hands-on training session on VisionQuest was provided to all participants prior to the meeting and a consent form was signed by all. The assistant director started each meeting by explaining that the purpose of the meeting was to identify characteristics of effective supervisors and then asked the participants to brainstorm the issue. Ideas were anonymously contributed and displayed on a public screen using the VisionQuest software. To reduce the number of ideas to a manageable number each group member selected his or her top two choices from the list of ideas generated by the group. This resulted in a reduced set of ideas (typically 6 to 10), consisting of any idea selected by at least one group member. Finally, each member of the group ordered the reduced list to generate a ranked list of the group’s ideas. Subsequently, the group’s top three ideas were used to generate more specific items for each. These ideas were later consolidated by the

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assistant director of human resources to form the basis of a supervisor training and evaluation course. Data on the constructs of the study were collected using questionnaires completed at the end of the meetings. Appendix A shows the questionnaire items used to measure the constructs of the study. 4.1. Operationalization of research variables All research variables were measured using multiitem scales (see Appendix A). Existing validated scales were used to measure all constructs except relative advantage. Scales for oral communication apprehension were adapted from McCroskey [66], writing communication apprehension from Daly and Miller [21], computer anxiety from the Computer Anxiety Rating Scale [43], cohesion from Chin et al. [19], and personal innovativeness from Agarwal and Prasad [3]. Since review of extant research revealed no scales for relative advantage of GSS vis-a`-vis face-to-face meetings, three items were constructed drawing upon empirical evidence on GSS outcomes. Sample characteristics are shown in Table 1 and descriptive statistics for the research constructs are presented in Table 2. On average, the sample included individuals who have a reasonable level of experience with computing technology. Nearly two-thirds of the sample were female, which is representative of the male/female ratio of the non-academic employees at the University. None of the participants had any prior experience with a GSS tool such as VisionQuest. First, the psychometric properties of all scales were assessed through confirmatory factor analysis in Par-

Table 2 Descriptive statistics Construct

Mean

S.D.

Reliabilitya (number of items)

Relative advantage Oral communication apprehension Writing communication apprehension Computer anxiety Personal innovativeness Cohesion

4.59 2.64

1.46 1.13

0.90 (3) 0.75 (3)

2.82

1.27

0.77 (4)

1.89 5.22 4.09

1.22 0.96 1.49

0.92 (6) 0.63 (3) 0.90 (6)

All constructs are seven-point scales. a Cronbach’s alpha.

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Table 3 Confirmatory factor analysis results from PLS

tial Least Squares (PLS) and subsequently the structural relationships in the research model were tested in PLS. 4.1.1. Measurement model The psychometric properties of the scales are assessed in terms of item loadings, discriminant validity, and internal consistency (reliability). Item loadings and internal consistencies greater than 0.70 are considered acceptable [33,71]. As can be seen from the confirmatory factor analysis results in Table 3, with two exceptions, all other items load very well on their corresponding factors.1 The two exceptions are one item in cohesion (COH1) and one item in personal innovativeness (PI3). We decided to retain these items in the final analysis since (a) they were part of existing 1 To perform CFA in PLS the following procedure was followed: PLS provides the loadings for the construct’s own indicators. To calculate cross-loadings, factor scores for constructs (provided by PLS) were correlated with all other indicators to calculate cross-loadings of other indicators on the construct.

scales and (b) they did not cross-load on other factors. The internal consistency scores shown in Table 2, with the exception of personal innovativeness, all exceed the 0.70 criterion (scores range from 0.75 to 0.92). In our study, personal innovativeness showed a marginally adequate reliability score of 0.63.2 To assess discriminant validity [18], (a) indicators should load more strongly on their corresponding construct than on other constructs in the model (i.e., loadings should be higher than cross-loadings), and (b) the square root of the average variance extracted (AVE) should be larger than the inter-construct correlations (i.e., the average variance shared between the construct and its indicators should be larger than the variance shared between the construct and other constructs). As shown in Table 3, all indicators load more highly on their own construct than on other constructs. Furthermore, examination of the inter-construct correlations 2 However, the study [3] that originally validated it had found it to be a reliable scale showing a reliability score of 0.84.

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and square root of AVE (shaded leading diagonal) in Table 4 reveals that all constructs share considerably more variance with their indicators than with other constructs. Collectively, these results suggest that our scales exhibit good psychometric properties. 4.1.2. The structural model Following scale validation, which indicated that the scales used had acceptable psychometric properties, data were analyzed using Partial Least Squares (PLS). PLS, a latent structural equations modeling technique, uses a component-based approach to estimation. Because of this, it places minimal demands on sample size and residual distributions [17,18,33,57]. Sample size considerations precluded using individual items in the structural model. To do so, PLS requires approximately 10 observations times the number of items for the construct with the largest number of indicators. In the current study, this would require approximately 60 observations (since computer anxiety and cohesion each have six indicators). Thus, the study’s sample size of 46 precludes this approach. To test the structural model with construct scores, PLS requires approximately 10 times the maximum number of paths to any given construct [18]. In the current study, this implies a sample size of approximately 50. Consequently, construct scores were computed for each construct using the factor scores from the confirmatory analysis. These construct scores were then used in the structural model in PLS to test the theoretical model for the study. The path coefficients and explained variances for the theoretical model of the study are shown in Fig. 3. Path coefficients in PLS can be interpreted as stand-

Fig. 3. PLS results.

ardized beta weights in regression analysis. Results of model testing provide empirical support for the relationships posited in the model explaining about 40% of variance in relative advantage of a GSS meeting vis-a`vis a traditional face-to-face meeting. Specifically, as hypothesized oral communication apprehension was positively related to relative advantage perceptions lending support to Hypothesis 2. However, contrary to expectations (H1) of a negative relationship, writing communication apprehension was also positively related to perceptions of relative advantage. Surprisingly, personal innovativeness exhibited a significant negative (rather than the hypothesized positive (H4)) effect on perceptions of relative advantage. Interestingly, the relationship between computer anxiety and relative advantage (H3) was non-significant, suggesting that a user’s level of anxiety with respect to using computers

Table 4 Inter-construct correlations

The shaded numbers on the leading diagonal are the square root of the variance shared between the constructs and their measures. Off diagonal elements are the correlations among constructs. For discriminant validity, diagonal elements should be larger than off-diagonal elements.

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does not inhibit the formation of positive perceptions on the advantages of using a GSS to conduct a meeting.

The strength of our study lies in the realism of the sample and study context. However, this is also a source of certain limitations in our study. Unlike many GSS studies, this was a field study where employees in the organization used the GSS to brainstorm on an issue of real concern. The task was not artificial and participation in these meetings was seen as part of the participants’ jobs. Thus, the main weakness of our study lies in the sample size limitation, an outcome of the context of our study. The less than ideal sample size, although adequate for the research objectives, might have reduced the statistical power to detect some additional relationships. Nonetheless, the size of the sample should not detract from the effects that were found to be statistically significant. Furthermore, participants for the study were not randomly selected and this may have introduced some bias to our results. However, this is a tradeoff that field studies often have to make. Also, even though the scale used to test personal innovativeness has been satisfactorily validated previously, its reliability in the present study is less than ideal. Since this inflates error variance, it reduces the ability to statistically detect significant effects. However, it does not detract from any significant effects that the study did find. Finally, perceptions of relative advantage of the GSS may be inflated because of ‘‘illusion of control’’ effects. Illusion of control represents ‘‘a person’s expectation of success on a task that is inappropriately higher than objective circumstances warrant’’ [24, p. 58]. Results of prior research on decision making at the individual level indicate that while decisions reached with a Decision Support System (DSS) were not much better than decisions reached without using a DSS, individuals using the DSS perceived their decisions to be better due to the higher level of active participation. This effect can be minimized when the DSS has performance-based reward mechanisms and when the DSS user is charged for their system usage [61]. To the extent that illusion of control effects may generalize to group decision-making and use of a GSS, then this may inflate perceptions of relative advantage. To alleviate this concern, measures of relative advantage in this study have not focused on quality of decision. Rather, relative advantage reflects the extent

to which using the GSS allows one to participate more, contribute more ideas, and be more productive. Nonetheless, since the users were not charged for their use of the system nor were they rewarded based on their performance during the meeting,3 illusion of control effects may still have inflated perceptions of relative advantage. This provides an interesting direction for future research.

5. Discussion and conclusions The main objective of the study was to examine the relationship between personality trait variables and relative advantage perceptions. In general, empirical results are encouraging and provided support for the main objective of the study. We found that individual characteristics influenced the perceptions of relative advantage and imply that individual differences, at least in GSS settings, may influence behavior via their effect on beliefs. Results showed that both oral and writing communication apprehension, as well as personal innovativeness, significantly influenced the formation of relative advantage perceptions. Contrary to our hypotheses, the relationship between writing communication apprehension and relative advantage was positive, and between personal innovativeness and relative advantage was negative. The negative relationship between innovativeness and relative advantage contradicts prior empirical evidence [3], which found a positive relationship. However, the study by Agarwal and Prasad was not in a GSS context and it focused on cutting-edge technology. It is possible that perceptions of relative advantage in general are anchored to an individual’s perceptions of state-of-the-art computing capabilities. Innovative individuals are likely to be aware of cutting-edge technology. Since the GSS tool used in the current study was not state-of-the-art, it is possible that these individuals were disappointed in the features and capabilities afforded by the tool. Furthermore, the anonymity afforded by a GSS may play a role in the negative relationship between innovativeness and relative advantage. Anonymous contexts do 3 Given the anonymity feature of the GSS, individual performance-based rewards are not possible.

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not allow innovative individual to stand out by demonstrating their innovativeness as generators of ideas or as ‘‘first movers’’ to the technology. Thus, they are likely to view the GSS as being less advantageous as compared to a face-to-face meeting that affords them the recognition for their innovative ideas. The anonymous nature of GSS also provides a likely explanation for the positive relationship between writing communication apprehension and relative advantage. Anonymity allows participants to share their writing comments without fear of being embarrassed due to poor grammar, spelling errors, or inability to adequately express themselves. Consequently, anonymity makes the GSS an attractive medium for writing communication apprehensives. This seeming contradiction may represent an interesting avenue for future research. Furthermore, if, in the absence of anonymity, writing apprehension negatively affects an individual’s use of text-based communication technologies in the way that oral communication apprehension affects verbal communication-based technologies, then recognizing this may lead to interventions that can improve the effectiveness of these technologies. In today’s work environment, new communication technologies such as e-mail, internet activity, group decision support systems, and on-line discussion groups are available that replace oral interaction with text-based or writing interactions. Since the empirical research has not been conclusive, the continued exploration of this factor, its relationship with technology use, and its interplay with anonymity is warranted. Computer anxiety was not significantly related to relative advantage. Even though it is possible that high computer anxiety does not inhibit the formation of relative advantage beliefs, it is more likely that this finding is an artifact of our study. Individuals in our sample were, on average, experienced users of per-

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sonal computers with an average 10.8 years of computer experience and an average computer anxiety score of 1.89 (on a seven-point scale). It is, therefore, likely that computer anxiety is not a significant consideration in their evaluation of the GSS advantages. To assess the generalizability of this finding, future research should examine this relationship with a sample that is less comfortable and less experienced with computers. Finally, group cohesion, the control variable of the study, was positively related to perceptions of relative advantage. This may imply that, at least for an initial encounter with a new group technology, group cohesiveness may greatly influence perceptions of the usefulness of the technology. This may suggest that managers can influence the adoption of new group technologies by planning initial try-outs in highly cohesive groups. Further research is needed to examine whether this effect persists during routine use of the GSS when individuals gain a deeper understanding of the technology’s features and capabilities. Results of the study have important implications for both research and practice. In terms of research, we have built upon prior knowledge that suggests that beliefs and attitudes mediate the relationship between individual differences and behavior [5,2]. Extending this research, we have identified personality trait variables instrumental in explaining a large proportion of variance in relative advantage, a belief consistently shown to be central in technology acceptance. These personality trait variables were strategically selected based on the concept of ‘‘fit’’ [36] between an individual’s personality and the technology on hand. We examined advantages afforded by the technology and identified specific personality traits that could benefit from specific capabilities provided by the technology. A fruitful direction for future research

Appendix A. Questionnaire items Relative advantage (RA) RA1 I was able to contribute more ideas in this electronic meeting that in a traditional meeting. RA2 I was more productive in this electronic meeting than I would have been in a traditional meeting. RA3 Using this computer facility, I believe that I was able to participate to a greater extent than in a regular meeting.

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Oral communication apprehension (OCA) OCA1 I like to get involved in group discussions. OCA2 I am tense while participating in group discussions. OCA3 I dislike participating in group discussions. Writing WCA1 WCA2 WCA3 WCA4

communication apprehension (WCA) I do not like my writing to be read by others. I am nervous about writing. I am not able to clearly write down my ideas. I think I write as well as most people.

Computer anxiety (CA) CA1 Computers make me feel uncomfortable. CA2 Working with computers makes me nervous. CA3 I hesitate to use a computer for fear of making mistakes that I cannot correct. CA4 I get a sinking feeling when I think of trying to use a computer. CA5 I feel apprehensive about using computers. CA6 Computers scare me. Personal innovativeness (PI) PI1 I leave it to others to work out the ‘‘bugs’’ in the new computer tools before I will consider them. PI2 I use only computer tools that have a proven track record. PI3 Among my colleagues and coworkers, I tend to be among the first to try new computer tools. Group cohesion (COH) COH1 I was content to be part of this group. COH2 I saw myself as part of this group. COH3 I felt that I was a member of this group. COH4 I felt that I belonged to this group. COH5 I was happy to be part of this group. COH6 This group was one of the best anywhere.

may be to use this concept of ‘‘fit’’ to identify additional individual difference variables or other external variables [2] as antecedents of beliefs. In terms of practice, we have identified a profile of individuals who are likely to find use of a GSS advantageous. These communication apprehensive individuals often represent an untapped pool of talent in contemporary organizations. These same individuals are more likely to find GSS useful and are, therefore, more likely to accept and use it thus affording organizations the opportunity to harness these resources. Furthermore, identifying individuals likely to adopt and gaining their support can facilitate widespread acceptance of the technology since they can serve as opinion leaders and help create

expectations and the social norms conducive to innovation.

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Elena Karahanna is an associate professor of MIS at the Terry College of Business, University of Georgia. Her current research interests include the adoption, mandatory adoption, implementation, use, and infusion of information technologies, the effect of media choice and use on individuals and organizations, and cross-cultural issues. Her work has been published in Management Science, Organization Science, MIS Quarterly, Data Base, the Journal of Organizational Computing and Electronic Commerce, Information and Management, and elsewhere.

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Manju Ahuja is an Assistant Professor of MIS at Indiana University. Her publications have appeared in journals such as Organization Science, Communications of the ACM, Decision Support Systems, and the Journal of Computer-Mediated Communications. She is actively involved in research on issues related to knowledge integration in virtual groups, media choices in virtual teams, trust and distrust on cooperation in project teams, effects of distance in virtual teams, role of network-based coordination mechanisms in organizational design, the effects of mentoring and work-family conflict on IT workers’ careers, and effects of task environment and gender on intention to explore technology. Mark Srite is an assistant professor of MIS at the University of Wisconsin-Milwaukee. His current research interests include the acceptance, adoption, and use of information technologies, crosscultural IT issues, and group decision-making. John Galvin is an assistant professor in the Kelley School of Business at Indiana University. His areas of interest include technologies used to support group work and interactions, the impacts of technology implementation on individual behaviors, and new organization forms enabled through IT.