Opening up the black box in GSS research: explaining group decision outcome with group process

Computers in Human Behavior Computers in Human Behavior 23 (2007) 58–78 www.elsevier.com/locate/comphumbeh

Opening up the black box in GSS research: explaining group decision outcome with group process Wayne Huang

a,*

, D. Li

b

a

b

Department of MIS, College of Business, Ohio University, OH 45701, USA MIS Department, Guanghua School of Management, Peking University, PR China Available online 16 April 2004

Abstract Group process, a central element of group interaction, has been frequently treated as a black box in many prior Group Support Systems (GSS) studies. Most prior GSS research focused on group outcome and efforts to study group process were relatively limited. As a result, effects of group process on group outcome in GSS use are not fully understood. This study focuses on group process and intends to explain variations in group decision outcome from group influence process. Group process variables investigated were informational influence, normative influence, and influence equality. Group decision outcome variable studied was group consensus. The role of task type was also examined. The research findings indicated that GSS had a complex impact on group decision outcome for two reasons. First, this impact was mediated by group influence process variables. Second, this impact was moderated by task type. The research findings provide an additional clue on why prior GSS empirical studies in the literature yielded inconsistent research findings. The implications of the research findings to GSS researchers and users are finally discussed. Ó 2004 Elsevier Ltd. All rights reserved. Keywords: Group support systems; Group interaction process; Group decision outcome; Task type

*

Corresponding author. Tel.: +1 740 593 1801; fax: +1 740 597 1676. E-mail address: [email protected] (W. Huang).

0747-5632/$ - see front matter Ó 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.chb.2004.03.036

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1. Introduction Group Support Systems (GSS), equipped with a variety of tools/functions, aim to increase group working effectiveness and efficiency (DeSanctis & Gallupe, 1987). Some previous empirical studies reported that GSS improved group performance (e.g., Dennis & Garfield, 2003; Gallupe et al., 1992; Gallupe, DeSanctis, & Dickson, 1988; Genuchten, Cornelissen, & Dijk Cor, 19971998; Huang, 2003; Huang, Wei, Watson, & Tan, 2003; Nunamaker, Dennis, Valacich, Vogel, & George, 1991) while some other studies reported that group performance was not enhanced and sometimes even decreased by GSS (e.g., Batenburg & Bongers, 2001; Clapper, McLean, & Watson, 1998; Dennis, Hilmer, & Taylor, 19971998; Gallupe & McKeen, 1990; George, Easton, Nunamaker, & Northcraft, 1990; Huang, Wei, & Tan, 1999; Jarvenpaa, Rao, & Huber, 1988; Watson, DeSanctis, & Poole, 1988). As a result, inconsistent research results exist in GSS research literature, which has become one of the main concerns for GSS research (e.g., Gopal & Prasad, 2000; Hollingshead & McGrath, 1995; McGrath & Hollingshead, 1994). Researchers provided a few explanations to the inconsistent research findings (e.g., Benbasat, DeSanctis, & Nault, 1993; Dennis, Nunamaker, & Vogel, 1991; Dennis, Wixom, & Vandenberg, 2001). Many previous GSS research examined the effects of GSS on group outcomes (e.g., Fjermestad & Hiltz, 1999; Kraemer & Pinsonneault, 1990; Olson et al., 1993), frequently treating group process as a black box (Gutek, 1990; Huang & Wei, 2000; Sambamurthy & Poole, 1992; Zigurs, Poole, & DeSanctis, 1988). On the other hand, group outcome generally depends on group process (e.g., Dennis et al., 2001; Nunamaker et al., 1991; Zigurs et al., 1988), which is the central element of a group (McGrath, 1984). The real power of GSS use may lie in how GSS can transform group work process (Dennis & Gallupe, 1993; Tan, Wei, Huang, & Ng, 2000), and research on group process can help know more about how and why a GSS works well (or not well) under various conditions. Relatively limited research with a focus on group process in GSS research literature could be one of the main reasons that the conditions under which the use of GSS is appropriate and beneficial have not been fully understood (Benbasat & Lim, 1993; Briggs, Nunamaker, & Sprague, 1997–1998). Hence, this study aims to explore group process and explain the linkage between group process and group outcome in GSS use. Further, previous GSS research reported that task type was an important factor for group outcome (e.g., Dennis & Wixom, 2001; Huang & Wei, 1997), even accounting for up to 50% variations of group outcome (e.g., Poole, Seibold, & McPhee, 1985). It is therefore important to study how GSS and task type can jointly influence group processes that in turn influence group outcomes. In summary, the current research studies the joint effects of GSS and task type on group process and outcome, with a focus on group process. The next section briefly reviews related research literature, and hypothesizes GSS effects on group process and the linkage between group process and group outcome. Section 3 describes research methodology. Sections 4 and 5 report research results and discuss the implications of research findings. 2. Literature review and research hypotheses 2.1. Group influence process Group influence had been a research issue for a long time in social psychology (e.g., Cartwright, 1973; Deutsch & Gerard, 1955; Jenness, 1932; Putnam, 1981) and GSS

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(e.g., Lim, Raman, & Wei, 1994; Zigurs et al., 1988). In this research, group process is studied from an influence perspective so that the research findings could be compared to those relevant research results in GSS literature. A literature review indicates that two theories of informational influence and normative influence have become dominant in group influence process in social psychology. Informational influence is based on the acceptance of information from others as the evidence about reality whereas normative influence is based on the desire to conform to the expectations of other group members (Deutsch & Gerard, 1955; Kaplan & Miller, 1987). The two types of influences have been used to explain dynamic and complicated group behaviors in social psychology such as group polarizations and choice shifts (e.g., Isenberg, 1986; Myers & Lamm, 1976). These two influences are therefore examined in this study. The characteristics of the two types of influences summarized from the literature review, are shown in Table 1. As shown in Table 1, informational and normative influence are being manifested in an intellective and a preference task, respectively, these two tasks are chosen for this research. Prior studies reported that without computer support, informational influence was a dominant influence mode for intellective tasks whereas normative influence was a dominant influence mode for preference tasks (Kaplan & Martin, 1992; Kaplan & Miller, 1987; Kaplan, Schaefer, & Zinkiewicz, 1994). 2.2. GSS effects on group influence process GSS effects on group influence process can be analyzed using the Theory of Time, Interaction, and Performance (TIP Theory) (McGrath, 1990, 1991). The theory postulates that a group engages in three major functions of production, member support, and group wellbeing; groups carry out these functions in general four modes – inception of a project, technical issue, resolution of conflict, and the execution of the project; and group activity path in reaching a group’s decision is dependent on task and other factors. Group performance depends on these four group activity modes. 2.2.1. Effects of GSS on informational influence In an intellective task, a correct answer exists and the task solution depends on acquiring facts and exchanging more factual information (McGrath, 1984). A group activity path including inception, technical issue, and execution is normally adopted by groups (Valacich, Mennecke, Wachter, & Wheeler, 1994), and the technical issue is the key group activity mode for this task type (McGrath, 1991). Along this group activity path, members are likely to share factual information, argue about task or technical issues, and provide Table 1 Characteristics of informational and normative influence Characteristics

Informational influence

Normative influence

1. Manifestation

Facts, rationales, arguments (Lamm & Myers, 1978) Factual, intellective (Kaplan & Miller, 1987) Information (Kaplan, 1977), factual arguments, task messages (Kaplan, 1989)

Values, preferences, norms (Lamm & Myers, 1978) Value-laden, preference (Kaplan & Miller, 1987) Majority power, dominance, persuasiveness, status (Clapper et al., 1991)

2. Type of issue (task) 3. Sources of influence

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rationales/justifications, which mainly reflect informational influence (as shown in ‘‘Manifestation’’ and ‘‘Sources of influences’’ of Table 1). This is also in line with the research finding that informational influence dominates in intellective task groups (e.g., Kaplan & Miller, 1987; Kaplan et al., 1994). GSS can support the key group activity mode of technical issue by enhancing factual information exchange. First, with GSS’ anonymity feature, group members can exchange more factual information with less fear of disapproval of others, anxiety about oral communication skills, and sensitivity to the presence of high status members (Jessup, Connoly, & Galegher, 1990; Rao & Jarvenpaa, 1991). Second, GSS provide an electronic communication channel that is equally and openly accessible to every group member. All these can enhance factual information exchange, actually regardless of task type. Previous research also supports this argument that groups using GSS generate more task-focused comments (informational influence), compared to face-to-face (FtF) groups (e.g., Chidambaram, Bostrom, & Wynne, 1991; Kraemer & Pinsonneault, 1990). Therefore, GSS could enhance informational influence for the both task types used in this research. H1a. In an intellective task, there will be more informational influence in GSS groups than in FtF groups. H1b. In a preference task, there will be more informational influence in GSS groups than in FtF groups. 2.2.2. Effects of GSS on normative influence In a preference task, group activities will involve a complicated path of inception, conflict resolution, and execution (Valacich et al., 1994) and the conflict resolution is the key group activity mode (McGrath, 1991). The task solution is based on exchanging different preferences and values among members (McGrath, 1984), and the key group mode of conflict resolution would require more exchange of different preferences and values as well. Therefore, in group activities along this path, members are likely to express their own preferences and values and try to persuade others to accept them. Because no correct answer exists for a preference task (McGrath, 1984), possible forms of influences in the group activities would be reflected as persuasion, dominance in verbal expression, majority power, hierarchy status, and so on (Clapper, McLean, & Watson, 1991), which are classified as normative influence (as shown in ‘‘Manifestation’’ and ‘‘Sources of influences’’ of Table 1). This is in line with the research finding that normative influence dominates in preference task groups (e.g., Kaplan & Miller, 1987; Kaplan et al., 1994). GSS may hinder the key group mode of the conflict resolution by hindering the exchange of preferences and values. Members’ personal preferences and values can be better expressed and exchanged with multiple social cues in an FtF talk, such as the cues of the tone and speed of spoken language. But such social cues can be dampened by GSS (Dubrovsky, Kiesler, & Sethna, 1991; Huang, Watson, & Wei, 1998; Klein, Clark, & Herskovitz, 2003; Siegel, Dubrovsky, Kiesler, & McGuire, 1986; Turoff & Hiltz, 1978). Hence, GSS could dampen normative influence, regardless of task type. H2a. In an intellective task, there will be less normative influence in GSS groups than in FtF groups.

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H2b. In a preference task, there will be less normative influence in GSS groups than in FtF groups. 2.2.3. Effects of GSS on influence equality Influence equality is used to measure the evenness of influence in group meetings (Zigurs et al., 1988), which is an important factor in group decision making processes (DeSanctis & Gallupe, 1987; Huber, 1984). There is a correct answer for an intellective task. Asymmetrical task information likely exists in this task (Valacich et al., 1994), i.e., some group members may be more knowledgeable about the correct answer than others. Consequently, these more knowledgeable members could contribute more to the task solution than others. In GSS groups, they may contribute further more, because first, GSS would enhance the factual information exchange (H1a); and second, it provides an open and equal communication channel to all group members, and hence, the more knowledgeable members are not restricted by the problem of air-time allocation of speaking as they are in FtF meetings (Nunamaker et al., 1991). As a result, more contribution from these more knowledgeable members to the task solution would lead to a less even influence distribution among members in a group. H3a. In an intellective task, influence equality will be lower in GSS groups than in FtF groups. In a preference task, GSS’ anonymous communication can encourage those members who are reluctant to openly express their views (preferences and values) to participate, leading to a greater evenness of influence in a group (DeSanctis & Gallupe, 1987). Previous research (Lim et al., 1994) found that a GSS use resulted in a greater evenness of influence in a preference task. H3b. In a preference task, influence equality will be greater in GSS groups than in FtF groups. 2.3. Linking group process with group outcome The outcome variable chosen for this study was group consensus. There are many outcome variables in the GSS research literature; the choice of group consensus was by no means exhaustive, but merely intended to illustrate how group process can be linked with group outcome in this initial effort to open up the black box. Further, group consensus has been commonly used in many previous GSS research (e.g., Benbasat & Lim, 1993; Fjermestad & Hiltz, 1999) and it is typically used to measure group outcome or performance (Zigurs et al., 1988) and a desired feature for group decision making (Benbasat & Lim, 1993). 2.3.1. Correlation between information influence and group consensus (intellective task) In an intellective task, as discussed earlier, the key group activity mode is the resolution of technical issue, which largely depends upon more change of factual information (informational influence). A group needs to resolve the technical issue before it can reach a consensus on the task solution. In other words, group’s final consensus on the task solution

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(H1a) + GSS

(H2a) (H3a) -

Informational Influence Normative Influence Influence Equality

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+ (H4a) (H5a) Consensus + (H6a)

Fig. 1. Predicted impacts of GSS on group consensus, mediated by group process variables (intellective task).

would largely rely on the resolution of technical issue, which in turn depends upon more exchange of factual information among group members. Therefore, we posit that: H4a. In an intellective task, informational influence will be positively correlated with group consensus. 2.3.2. Correlation between normative influence and group consensus (intellective task) On the other hand, more informational influence, rather than normative influence, is more desirable for solving an intellective task (McGrath, 1984). Hence, given the same amount of time for group interactions, more exchange of preferences and values (normative influence) would actually distract group members from exchanging factual information (informational influence). Because informational influence would positively correlate with group consensus (H4a), we posit that: H5a. In an intellective task, normative influence will be negatively correlated with group consensus. 2.3.3. Correlation between influence equality and group consensus (intellective task) Greater influence equality could mean more members voicing out their views and opinions in group interactions (Kraemer & Pinsonneault, 1990). Because the correct answer exists for an intellective task (McGrath, 1984), more voices and views expressed may help members more effectively discover the existed correct answer, thereby more effectively reaching group consensus. Therefore, H6a. In an intellective task, influence equality will be positively correlated with group consensus. Fig. 1 summarizes the above-discussed relationships between GSS and the group process variables, and between group process variables and group outcome variable. The model, format, and denotations1 of the figure are adapted from the Management Science Paper published by Rao and Jarvenpaa (1991).

1

Note: The arrows in Fig. 1 indicate the direction of causality; the direction of causality between influence process variable and outcome variable is based on the theoretical assumption that group outcome depends on group process (e.g., Nunamaker et al., 1991; Zigurs et al., 1988); the ‘‘+’’ sign denotes a positive correlation; the sign of ‘‘’’ denotes a negative correlation.

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2.3.4. Effects of GSS on group consensus, mediated by process variables (intellective task) As shown in Fig. 1, first, GSS appear to increase informational influence (H1a) that would positively correlate with group consensus (H4a), leading to reach a group consensus more easily. Second, GSS tend to reduce normative influence (H2a) that would negatively correlate with group consensus (H5a), likely promoting group consensus. Third, GSS may decrease influence equality (H3a) that would positively correlate with group consensus (H6a), resulting in more difficult to reach group consensus. As a result, the net effect of GSS on group consensus is difficult to predicte. A meta-analysis (e.g., Benbasat & Lim, 1993) reports that GSS tend to decrease group consensus. Hence, we posit that: H7a. In an intellective task, group consensus will be greater in FtF groups than in GSS groups. 2.3.5. Correlation between normative influence and group consensus (normative task) In a preference task, as discussed earlier, the key group activity mode is the conflict resolution, which largely depends upon more exchange of individual preferences and values (normative influence). Group conflicts in preferences and values should be resolved before a group can reach a consensus on the task solution. In other words, group’s final consensus on the task solution would largely rely on the resolution of group conflicts, which in turn depends upon more exchange of individual preferences and values. Therefore, we posit that: H4b. In a preference task, normative influence will be positively correlated with group consensus. 2.3.6. Correlation between informational influence and group consensus (normative task) On the other hand, normative influence, rather than informational influence, is more desirable for solving a preference task (McGrath, 1984). Hence, given the same amount of time for group interactions, more factual information exchange (informational influence) would actually distract group members from exchanging more preferences and values (normative influence). Because normative influence would positively correlate with group consensus (H4b), we posit that: H5b. In a preference task, informational influence will be negatively correlated with group consensus. 2.3.7. Correlation between influence equality and group consensus (normative task) Greater influence equality may indicate that more people would voice out their opinions/views and try to have their views supported by others (Kraemer & Pinsonneault, 1990). But because there is no correct answer existing for a preference task, more different voices and views would result in more difficult to reach group consensus. Hence, we posit that: H6b. In a preference task, influence equality will be negatively correlated with group consensus. Fig. 2 summarizes the above-discussed relationships.

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2.3.8. Effects of GSS on group consensus, mediated by process variables (normative task) As shown in Fig. 2, first, GSS tend to increase informational influence (H1b) that would negatively correlate with group consensus (H5b), likely reducing group consensus. Second, GSS may decrease normative influence in a preference task (H2b) that would positively correlate with group consensus (H4b), likely decreasing group consensus. Third, GSS could increase influence equality (H3b) that would negatively correlate with group consensus (H6b), resulting in more difficult to reach a group consensus. Hence, GSS may decrease group consensus, which is in line with the meta-analysis result (e.g., Benbasat & Lim, 1993). Hence, H7b. In a preference task, group consensus will be greater in FtF groups than in GSS groups.

3. Research methodology 3.1. Experimental design and task type The research adopted a two-by-two factorial design. Technology support and task type were two independent variables. Under the condition of the non-GSS support, groups had normal FtF group discussions. GSS groups were provided with SAMM system to communicate, a GSS system developed by the University of Minnesota, USA. Group size was five. All meeting sessions were recorded using video cameras. One hundred and sixty first year students from a large university participated in the experiment. Every participant was given the course credit for participating in the experiment. All subjects were assigned to groups randomly. The experimental steps for both tasks were: (1) for both tasks, group members were asked to do warm-up tasks; (2) for intellective task groups, individual members were asked to learn task criteria; (3) for both tasks, individual members were asked to perform the tasks before the meeting; (4) for both tasks, members in GSS groups were asked to learn the operations of the SAMM system; (5) for both tasks, groups were asked to perform the tasks; (6) all groups filled in postmeeting questionnaires. No time limit was imposed on each meeting and all meetings were finished in two and half hours. The intellective task was adopted from Zigurs et al. (1988). This task of international studies program asked group members to score a list of competing applicants based on the four personality measures of the applicants. The four personality measures were

GSS

(H1b) +

Informational Influence

(H2b) -

Normative Influence

(H3b) +

- (H5b) (H4b) +

Consensus

- (H6b) Influence Equality

Fig. 2. Predicted impacts of GSS on group consensus, mediated by group process variables (preference task).

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expectations for social success, self-concept, expectations for independence, and attitudes about pre-marital sex. Subjects were told that the information of these four personality measures was indicative of success in the program. Each of these four personality measures was more or less linearly related to the program success, although the slope of that linear function varied for each measure. Hence, the causal knowledge of the components of success in the program was certain, but its particular nature should be discovered or learned by the subjects in training session. The preference task was adopted from Watson et al. (1988). This task of personal trust foundation asked group members to allocate funds to a list of competing projects based on their personal preferences and values. The projects were based on a personality components scheme with six basic interests or motives in personality: theoretical, economic, aesthetic, social, political, and religious. 3.2. The method for coding group influence process This study, to some extent, can be regarded as extending Kaplan and Miller (1987) from an unsupported FtF experimental setting into a GSS supported experimental setting. As a result, Kaplan and Miller (1987) was adapted to measure informational and normative influence. The five categories of the coding method are: task facts, inferences from task facts, values/norms, personal preferences, and others. The first two categories measure informational influence and the third and fourth category measure normative influence. Each act committed by a group member was coded by viewing the videotapes and examining the computer log files. The total informational influence and total normative influence in a group were the sum of five group members’ respective coding scores. Two coders worked together initially on coding the recorded tapes until they achieved reliability. Thereafter, they completed the remaining tapes separately. The overall inter-rater reliability was 90. A sample of the coding can be found in Appendix A and more detailed coding procedure please refers to Kaplan and Miller (1987). Because the research results in this study showed that GSS groups had significantly longer meeting time than FtF groups, the coded scores were proportioned and divided by the score sum of information and normative influence to correct for unequal session lengths (Poole, Holmes, & DeSanctis, 1991). The higher the scores of informational and normative influence, the more influence exerted by group members. The whole influence mainly consists of informational and normative influence (Deutsch & Gerard, 1955; Kaplan & Miller, 1987). Hence, by using the above coding category, the whole influence was the sum of informational influence, normative influence, and others. Influence equality was measured by calculating the variance of the whole influence in a group (Zigurs et al., 1988). The greater the variance, the lesser the influence equality. Group consensus was computed based on the fuzzy set theory (Spillman, Spillman, & Bezdek, 1980). The bigger the value of the fuzzy computation, the greater the group consensus. Recent GSS research indicated some weaknesses of measuring subjects’ perceptions, and GSS researchers were appealed to use more objective measures in their studies (e.g., Kinney & Dennis, 1994). In this research, informational and normative influence, influence equality, and group consensus can be regarded as objective measures. The formula and respective questionnaires of the dependent variables are attached in Appendix A.

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3.3. Manipulation check Two manipulations were conducted for the support and task type. The support was varied in GSS versus non-GSS support. These two settings were physically different and thus the manipulation was considered as satisfactory. The task type was varied in an intellective versus a preference task. The manipulation check was conducted in terms of task-influence mode pattern. ANOVA test showed that informational influence dominated in an intellective task (t = 17.61, p < 0.01*) and normative influence dominated in a preference task (t = 54.07, p < 0.01*). Based on the prior research findings (Kaplan & Martin, 1992; Kaplan & Miller, 1987; Kaplan et al., 1994), the manipulation in this study was successful. 4. Research analysis ANOVA was used to detect main and interaction effects, and regression analysis was used to detect the correlation between two variables. For those supported hypotheses in the regression analysis, the value of R square was reported that quantifies the proportion of variation in each variable accounted for by fitting the regression line. The test results are summarized in Table 2. Further, the tests reported additional findings that were not hypothesised in Section 2.2: A1a. In an intellective task, informational influence was negatively correlated with influence equality (F = 18.03, p < 0.01*, R2 = 0.56). A2a. In an intellective task, normative influence was positively correlated with influence equality (F = 8.88, p = 0.01*, R2 = 0.39). A3a. In an intellective task, normative influence was negatively correlated with informational influence (F = 130.11, p < 0.01*, R2 = 0.90). Table 2 Results of statistical tests Hypothesis

Analysis

t=

p

H1a (I-task) H1b (P-task) H2a (I-task) H2b (P-task) H3a (I-task) H3b (P-task) H4a (I-task) H4b (P-task) H5a (I-task) H5b (P-task) H6a (I-task) H6b (P-task) H7a (I-task) H7b (P-task)

ANOVA ANOVA ANOVA ANOVA ANOVA ANOVA Regression Regression Regression Regression Regression Regression ANOVA ANOVA

11.32 11.62 10.12 23.55 2.52 8.20 0.41 4.31 1.75 6.21 0.21 6.13 1.09 1.74

0.01* 0.01* 0.01* 0.01* 0.02* 0.01* 0.53 0.06** 0.21 0.03* 0.66 0.03* 0.29 0.10**

I-task: intellective task; P-task: preference task. * p < 0.05. ** p < 0.10.

R2

0.31 0.31

Outcome Support Support Support Support Support Support No support Weak support No support Support No support Support No support Weak support

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A1b. In a preference task, informational influence was positively correlated with influence equality (F = 37.63, p < 0.01*, R2 = 0.73). A2b. In a preference task, normative influence was negatively correlated with influence equality (F = 84.51, p < 0.01*, R2 = 0.86). A3b. In a preference task, informational influence was negatively correlated with normative influence (F = 193.46, p < 0.01*, R2 = 0.93). These results (from A1a to A3b) presented the complicated correlation among the three group process variables in the two tasks. 5. Discussion and implications By opening up the black box of group process and linking group process with outcome, this study is able to present a graphical view describing the conceptual relationships among GSS, group process, and group outcome, as shown in Fig. 3, which is summarized from Figs. 1 and 2, and revised using the tested results (note: the straight line in Fig. 3 only denotes correlation because the direction of causality is theoretically unknown for the three influence variables. All supported hypotheses are presented in Fig. 3, the unsupported hypotheses are excluded). 5.1. The effects of GSS on group process and the moderating effects of task type The research results show that GSS had significant but complicated impacts on group influence process. In an intellective task, GSS increased informational influence (H1a was supported), decreased normative influence (H2a supported) and influence equality (H3a supported). In a preference task, GSS enhanced informational influence (H1b supported), decreased normative influence (H2b supported), and increased influence equality (H3b supported). The findings also confirm prior studies that task acts as a moderator in GSS use (e.g., Dennis & Wixom, 2001; Hollingshead, McGrath, & O’Connor, 1993; Huang & Wei, 1997). The moderating effects of task on group process and outcome are demonstrated by the following research results: (1) In an intellective task, informational influence negatively correlated with influence equality (A1a supported); whereas in a preference task, informational influence positively correlated with influence equality (A1b supported). Nomative Influence

(H2b) -

GSS

+ ( H4b at 10%)

(A3b) (H1b) +

Informational (A1b) Influence + (H3b) +

(H5b) -

- (A2b)

Consensus (H6b)

Influence Equality

Fig. 3. Actual impacts of GSS on group consensus, mediated by group process variables (preference task).

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(2) In an intellective task, normative influence positively correlated with influence equality (A2a supported); whereas in a preference task, the correlation was negative(A2b supported). (3) In a preference task, GSS decreased group consensus (H7b supported at. 10 level); whereas in an intellective task, GSS failed to decrease group consensus (H7a not supported). These findings indicate that the complexity of group influence process in GSS use could be increased by the moderating effects of task type. Group work normally occurs in a variety of tasks (Galegher & Kraut, 1990). GSS research on task dimension, however, has been inadequate (McGrath & Hollingshead, 1994). More research on interactive effects of GSS and task type on groups is therefore still needed in the future. 5.2. The group influence process as a mediator Group influence process may act as a mediator for the effects of GSS on group outcome. In a preference task, the mediating effects of group influence process on group consensus are demonstrated by the following eight influence routes (Fig. 3): (1) GSS increased influence equality that was negatively correlated with group consensus, likely leading to a lower level of group consensus (see the route of ‘‘GSS ! influence equality ! consensus’’). (2) GSS enhanced informational influence, likely leading to a lower level of group consensus (see the route of ‘‘GSS ! informational influence ! consensus’’). (3) GSS dampened normative influence, likely leading to a lower level of group consensus (the route of ‘‘GSS ! normative influence ! consensus’’). (4) The dampened normative influence by GSS could increased influence equality, likely leading to a lower level of group consensus (the route of ‘‘GSS ! normative influence ! influence equality ! consensus’’). (5) The increased informational influence by GSS would increase influence equality, likely leading to a lower level of group consensus (the route of ‘‘GSS ! informational influence ! influence equality ! consensus’’). (6) The increased informational influence by GSS could decrease normative influence, likely leading to a lower level of group consensus (see the route of ‘‘GSS ! informational influence ! normative influence ! consensus’’). (7) The reduced normative influence by GSS could enhance informational influence, likely leading to a lower level of group consensus (see the route of ‘‘GSS ! normative influence ! informational influence ! consensus’’). (8) The reduced normative influence by GSS could enhance informational influence that in turn increased influence equality, likely leading to a lower level of group consensus (see the route of ‘‘GSS ! normative influence ! informational influence ! influence equality ! consensus’’). In sum, all these eight routes (acting as the mediating paths) likely resulted in a lower level of consensus in GSS groups. Therefore, GSS could decrease group consensus (H7b), which is consistent with the findings in GSS research literature (e.g., Benbasat & Lim, 1993; McGrath & Hollingshead, 1994).

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It should be noted that first, the eight influence routes only illustrate the complexity of the mediating effects of group process on group consensus in GSS groups, and they are not exhaustive. Second, the causal directions in the above routes are theoretically unknown and not specifically tested in the research literature, which is not the research purpose for this study as well. These routes with the directions merely indicate possible influence paths and serve for illustrative purpose (to illustrate the complexity). Third, although only a little difference exists among the eight routes, it may result in a big difference in group outcome because prior GSS studies indicate that even apparently subtle differences in GSS use may have significant impacts on group outcome (e.g., Briggs et al., 1997–1998; Dennis & Gallupe, 1993; Huber, 1990; Nunamaker et al., 1991). In sum, the mediating effects of group influence process in GSS use could be very complex. 5.3. An effort to reconcile the inconsistent research results H7a for group consensus in an intellective task was not supported (see Table 2). There are at least two explanations for the result. First, the unsupported hypothesis H7a may be attributed to the complexity of the mediating effects of group process. In an intellective task, although GSS had significant impacts on the three influence process variables (see Fig. 3, H1a, H2a, and H3a supported) and the three variables co-influenced each other (A1a, A2a, and A3a supported), the variations of group consensus were not fully determined by the three influence variables (H4a, H5a, and H6a not supported). The R2 values for H4a, H5a, and H6a were 0.03, 0.11, and 0.01, respectively (very low). As a result, group consensus might be affected by some other exogenous factors as well. One of such exogenous factors could be group cohesion. In general, members in a cohesive group would be more likely to reach group consensus (and be more satisfied with decision process) (e.g., Larson & LaFasto, 1989). Consequently, these exogenous factors (unknown and not specifically studied in this research) would further increase complexity in the mediating effects of group process. This increased complexity (due to the unknown exogenous factors) might result in the failure of GSS to decrease group consensus as expected (H7a not supported), because even subtle change or difference in GSS use may have significant impacts on group outcome (e.g., Briggs et al., 1997–1998). Future research should therefore study group process from other perspectives other than group influence process, such as from the perspective of group well-being (McGrath, 1990, 1991) or group cohesion to further examine the mediating effects of group process in GSS use. Second, the unsupported result may be resulted from the complexity of moderating effects of task type. Because task type, acting as a moderator, may affect the production of group interaction and group decision differently for different tasks (Poole et al., 1985). For example, in this study, the mediating effects of the three influence process variables on group consensus were significant in a preference task (H4b, H5b, and H6b supported) but not in an intellective task (H4a, H5a, and H6a not supported). Similarly, GSS effects on group consensus, moderated by the task type, was significant in a preference task (H7b supported) but not in an intellective task (H7a not supported). 5.4. Implications to GSS researchers The group process complexity of GSS use reported in this study is likely to exist in other previous GSS empirical and field studies as well. Consequently, even if GSS tools, task

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type, and group size were the same in two prior GSS studies, there could be many different influencing process routes existing over there that may result in different group outcomes, thereby leading to inconsistent research findings in GSS research literature. As a result, the complexity of group process provides an additional clue and perspective explaining inconsistent research findings in GSS research literature. Further, the whole picture of the group process complexity of GSS use, of which the graphical view of group influence process presented in this study is a part, has not been completed yet. For example, what are other graphical views of group processes other than the one reported in this study? What are the causal relationships among the three influence process variables as shown in Fig. 3? What are possible theoretical explanations to this group process complexity of GSS use? These questions are still not answered, which should be studied in the future. 5.5. Implications to GSS practice For users of GSS, due to the group process complexity in GSS use, they may need to carefully consider what task to be assigned/performed, what GSS tools to be used, and what possible group influence routes could exist during group interactions before they use a GSS. More specifically, if group consensus is the main group outcome goal when a group performs an preference task, our research findings suggest (see Fig. 3) that GSS use will be likely to decrease normative influence during group interaction processes, which may increase group consensus. At the same time, the GSS use could increase information influence that is likely to decrease group consensus. As a result, it may be difficult to achieve the expected group outcome of increasing group consensus through the use of GSS. One possible solution is that at the stage of problem formulation of group meetings, where factual information (i.e., the informational influence) would be largely exchanged among group members (Dennis et al., 2001; McGrath, 1984), GSS may not be used so that informational influence would not be enhanced by GSS, which could prevent GSS from decreasing group consensus. Further, at the stage of decision alternatives being compared and chosen, where the convergence of suggestions and opinions is expected (Dennis et al., 2001), GSS can be used so that normative influence could be decreased, which may in turn increase group consensus. In this way, the GSS use may result in group consensus being increased as expected. Further, for GSS system designers, specific functions/tools should be designed in the future to support informational influence, normative influence, and influence equality separately. Currently, no such GSS tool/function exists to only support, for example, informational influence while other two factors could be controlled. In this way, the group process complexity could be largely reduced because the other two influence process factors are controlled in group processes at the same time. Therefore, with such system tool/function being designed, it would be easier to use GSS to increase group consensus in the future. 6. Research limitation The effective size of the experiment could be one of the concerns in this study. For example, H7b was not supported at 0.05 level but supported at 0.10 level. A power analysis (Cohen, 1976) reported that the power of the statistical test was only 0.37 and the least significant number (the least number of observations that would have adequate statistical power to

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detect the significant finding) was 23. The number of observations in this study was merely 16. Hence, the failure of detecting significant finding of H7b at 0.05 level may be due to the small sample size in this research. Future research should increase sample size to confirm the finding of H7b at 0.05 level as well as other research findings reported in this study. This study was conducted in a controlled GSS experimental laboratory to explore the mediating effects of group process on group outcome. Like any other controlled experimental studies, it would have relatively low external validity even though its internal validity is relatively high. However, for tasks involving human information processing and decision-making (as in this study), researchers have suggested that the use of student subjects would not invalidate the results (Ashton & Kramer, 1980; Garland & Newport, 1991). Nevertheless, it is useful to validate the findings of this study in the context of real managers in future research. 7. Conclusion Group outcome is relatively adequately addressed and studied in GSS research literature but group process is not. This study adopted a perspective of group influence process to explore the effects of GSS on group process and outcome with the focus on group process. The research findings presented a small but complicated view of group process of GSS use. The group process complexity of GSS use was mainly demonstrated by the mediating effects of group process and moderating effects of task type. Future research on group process is needed to study more why- and how-issues of the complicated group process of GSS use. In this way, GSS appropriation support (Dennis et al., 2001) can be better understood and GSS use can help achieve better group performance. Appendix A. Kaplan and Miller (1987) coding scheme Categories 1 and 2 measure informational influence, categories 3 and 4 measure normative influence: 1. Task facts: Statements citing facts that were provided in the task description. Example: ‘‘We are asked to choose a set of applicants whose diversity represent our country’’. Example: ‘‘We are asked to allocate funds to six projects according to our own preferences’’. 2. Inferences from task facts: Statements of facts not given in the task description but that were inferred from those given. Example: ‘‘If (the value of) PRE is high, then (the value of) SEL is also high’’. 3. Values or norms: Statements of personal values or social norms of right or wrong in actions and outcomes, or statements of appropriateness. Example: ‘‘It is wrong to spend more money buying information systems than helping poor families’’. Example: ‘‘It is incorrect to consider male applicants superior to female applicants’’. 4. Personal preferences: Statements alluding to one’s preference, including both simple declarations and normative pressure to reach a specific consensus. Example: ‘‘I think (that) IND should be more important then SOC’’. Example: ‘‘Homeless people should be allocated more money’’.

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5. Others: Statements not included in the above categories and irrelevant to the task. Example: ‘‘Let’s finish fast, then we can go for lunch’’.

Appendix B. Group consensus Consensus level of a group is computed using a method based on fuzzy set theory (Spillman et al., 1980). Determine the preference relation: Let A be the set of n alternatives considered by the group A ¼ fa½1; a½2; . . . ; a½ng; where a½z ¼ the alternative z. Let R be the fuzzy preference relation defined over all pairs of alternatives R½i; j ¼ 1:0 if there is a definite preference for a½i over a½j; 0:5 < R½i; j < 1:0 if there is a slight preference for a½i over a½j; R½i; j ¼ 0:5

if there is no preference between a½i and a½j;

0:0 < R½i; j < 0:5 if there is a slight preference for a½j over a½i; R½i; j ¼ 0:0 if there is a definite preference for a½j over a½i; where a½z ¼ the alternative z. For the intellective task R½i; j ¼ 0:5

if f ½i ¼ f ½j ¼ 0;

R½i; j ¼ 0:0 if i ¼ j; R½i; j ¼ ðf ½i  8Þ=ðf ½i þ f ½j  16Þ otherwise; where f ½z ¼ the score given to applicant z. For the preference task R½i; j ¼ 0:5

if f ½i ¼ f ½j ¼ 0;

R½i; j ¼ 0:0

if i ¼ j;

R½i; j ¼ f ½i=ðf ½i þ f ½jÞ

otherwise;

where f ½z ¼ the fund allocated to project z. Determine the threshold preference relation: Let Rt be the threshold preference relation for R. Rt is a transformation of R, obtained by thresholding the values of R at the level t where 0 6 t 6 1 Rt½i; j ¼ 1

if R½i; j P t;

Rt½i; j ¼ 0

if R½i; j < t;

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where R½i; j ¼ the fuzzy preference relation; t ¼ the threshold level. Rt indicates which alternatives are preferred at the level t. It is used to determine the consensus level between group members. Let R[1] and R[2] be two group members. If R[1]t = R[2]t, then R[1] and R[2] are in total consensus at the level t. Determine the consensus level: Let C[R[1]t, R[2]t] be the consensus level between two group members, R[1] and R[2], on all pairs of alternatives at the level t C½R½1t; R½2t ¼ ðR½1t  R½20 tÞ=ððR½1txR½10 tÞ þ ðR½2t  R½20 tÞ þ ðR½1t  R½20 tÞÞ; where R½1t ¼ the threshold preference relation for R½1; R½2t ¼ the threshold preference relation for R½2; 0

R½1 t ¼ the transpose of R½1t; 0

R½2 t ¼ the transpose of R½2t. C½R½1t; R½2t ¼ 1

if R½1 and R½2 are in total consensus;

C½R½1t; R½2t ¼ 0

if R½1 and R½2 are never in consensus;

0 < C½R½1t; R½2t < 1 if R½1 and R½2 are in partial consensus. Let R be the set of m members in the group R ¼ fR½1; R½2; . . . ; R½mg; where R½z ¼ the group member z. Let Ct be the consensus relation defined over all pairs of group members at the level t Ct½i; j ¼ 0

if i ¼ j;

Ct½i; j ¼ C½R½it; R½jt

otherwise;

where C½R½it; R½jt ¼ the consensus level between two group members; R½i and R½j; at the level t. Let Lt be the consensus level of the group on all pairs of alternatives at the level t Lt ¼ ð2  ðCt  C 0 tÞÞ=ðm  ðm  1ÞÞ; where Ct ¼ the consensus relation of the group at the level t; C 0 t ¼ the transpose of Ct; m ¼ the number of members in the group.

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Lt ¼ 1

if the group are in total consensus;

Lt ¼ 0 0 < Lt < 1

if the group are never in consensus; if the group are in partial consensus.

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Let L be the consensus level of the group on all pairs of alternatives Z 1 L¼ Lt dt; 0

where Lt ¼ the consensus level of the group at the level t. In practice, L can be computed using numerical methods such as the trapezoidal rule. References Ashton, R. H., & Kramer, S. S. (1980). Students as surrogates in behavioral accounting research: Some evidence. Journal of Accounting Research, 18(1), 1–15. Batenburg, R. S., & Bongers, F. J. (2001). The role of GSS in participatory policy analysis – a field experiment. Information & Management, 39(1), 15–30. Benbasat, I., DeSanctis, G., & Nault, B. R. (1993). Empirical research in managerial support systems: A review and assessment. In Recent development in decision support systems. In C. Holsapple & A. Whinston (Eds.). NATO ASI Series (Vol. 101, pp. 383–437). Berlin: Springer. Benbasat, I., & Lim, L. (1993). The effects of group, task, context, and technology variables on the usefulness of group support systems: A meta-analysis of experimental studies. Small Group Research, 24(4), 430–462. Briggs, R. O., Nunamaker, J. F, Jr., & Sprague, R. H. Jr., (1997–1998). 1001 unanswered research questions in GSS. Journal of Management Information Systems, 14(3), 3–22. Cartwright, D. (1973). Determinants of scientific progress: The case of the risky shift. American Psychologist, 28, 222–231. Chidambaram, L., Bostrom, R. P., & Wynne, B. E. (1991). A longitudinal study of the impact of group decision support systems on group development. Journal of Management Information Systems, 7(3), 7–25. Clapper, D. L., McLean, E. R., & Watson, R. T. (1991). An experimental investigation of the effect of a group decision support system on normative influence in small groups. In Proceedings of the twelfth international conference on information systems (pp. 273–282). New York. Clapper, D. L., McLean, E. R., & Watson, R. T. (1998). Mediating group influence with a group support system: An experimental investigation. Journal of Organizational Computing and Electronic Commerce USA, 8(2), 109–126. Cohen, J. (1976). Statistical power analysis for the behavioral sciences. New York: Academic Press. Dennis, A. R., & Gallupe, R. B. (1993). A history of group support systems empirical research: Lessons learned and future directions. In L. M. Jessup & J. S. Valacich (Eds.), Group support systems: New perspectives (pp. 59–77). New York: Macmillan. Dennis, A., & Garfield, M. J. (2003). The adoption and use of GSS in project teams: Toward more participative processes and outcomes. MIS Quarterly, 27(2), 289–323. Dennis, A. R., Hilmer, K. M., & Taylor, N. J. (1997–1998). Information exchange and use in GSS and verbal group decision making: Effects of minority influence. Journal of Management Information Systems, 14(3), 61–88. Dennis, A. R., Nunamaker, J. F, Jr., & Vogel, D. R. (1991). A comparison of laboratory and field research in the study of electronic meeting systems. Journal of Management Information Systems, 7(2), 107–135. Dennis, A. R., & Wixom, B. H. (2001). Investigating the moderators of the group support systems use with metaanalysis. Journal of MIS, 8(3), 235–257. Dennis, A. R., Wixom, B. H., & Vandenberg, R. J. (2001). Understanding fit and appropriation effects in group support systems via meta-analysis. MIS Quarterly, 25(2), 167–197. DeSanctis, G., & Gallupe, R. B. (1987). A foundation for the study of group decision support systems. Management Science, 33(5), 589–609.

76

W. Huang, D. Li / Computers in Human Behavior 23 (2007) 58–78

Deutsch, M., & Gerard, H. B. (1955). A study of normative and informational social influences upon individual judgment. Journal of Abnormal and Social Psychology, 51, 629–636. Dubrovsky, V., Kiesler, S., & Sethna, B. N. (1991). The equalization phenomenon: Status effects in computermediated and face-to-face decision-making groups. Human–Computer Interaction, 6(2), 119–146. Fjermestad, Jerry, & Hiltz, S. R. (1999). An assessment of group support systems experimental research: Methodology and results. Journal of Management Information Systems, 15(3), 7–150. Galegher, J., & Kraut, R. E. (1990). Technology for intellectual teamwork: Perspectives on research and design. In J. Galegher, R. E. Kraut, & C. Egido (Eds.), Intellectual teamwork: Social and technological foundations of cooperative work (pp. 1–20). USA: Erlbaum. Gallupe, R. B., Dennis, A. R., Cooper, W. H., Valacich, J. S., Bastianutti, L. M., & Nunamaker, J. F. (1992). Electronic brainstorming and group size. Academy of Management Journal, 35(2), 350–369. Gallupe, R. B., DeSanctis, G., & Dickson, G. W. (1988). Computer-based support for group problem-solving: An experimental investigation. MIS Quarterly, 12(2), 277–296. Gallupe, R. B., & McKeen, J. G. (1990). Enhancing computer-mediated communication: An experimental investigation into the use of a group decision support system for face-to-face versus remote meetings. Information and Management, 18(1), 1–13. Garland, H., & Newport, S. (1991). Effects of absolute and relative sunk costs on the decision to persist with a course of action. Organization Behavior and Human Decision Processes, 48(1), 55–69. Genuchten, M. V., Cornelissen, W., & Dijk Cor, V. (1997–1998). Supporting inspections with an electronic meeting system. Journal of Management Information Systems, 14(3), 165–178. George, J. F., Easton, G. K., Nunamaker, J. F., & Northcraft, G. B. (1990). A study of collaborative work with and without computer-based support. Information Systems Research, 1(4), 394–415. Gopal, A., & Prasad, P. (2000). Understanding GDSS in symbolic context: Shifting the focus from technology to interaction. MIS Quarterly, 24(3), 509–546. Gutek, B. A. (1990). Work group structure and information technology: A structural contingency approach. In J. Galegher, R. E. Kraut, & C. Egido (Eds.), Intellectual teamwork: Social and technological foundations of cooperative work (pp. 63–78). Hillsdale, NJ: Erlbaum. Hollingshead, A. B., & McGrath, J. E. (1995). Computer-assisted groups: A critical review of the empirical research. In R. A. Guzzo & E. Salas (Eds.), Team effectiveness and decision making in organizations (pp. 46–78). San Francisco: Jossey-Bass Publishers. Hollingshead, A. B., McGrath, J. E., & O’Connor, K. M. (1993). Group task performance and communication technology: A longitudinal study of computer-mediated versus face-to-face work groups. Small Group Research, 24(3), 303–333. Huang, W. (2003). Impacts of GSS generic structures and task types on group communication process and outcome: Some expected and unexpected research findings. Behavior & Information Technology, 22(1), 17–29. Huang, W., & Wei, K. K. (1997). Task as a moderator for the effects of group support systems on group influence processes. European Journal of Information Systems December(December 6), 208–217, UK. Huang, W., & Wei, K. K. (2000). An empirical investigation of effects of GSS and task type on social interactions from an influence perspective. Journal of Management Information Systems (JMIS) Fall, 17(2), 181–206, USA. Huang, W., Wei, K. K., & Tan, C. Y. (1999). Compensating effects of GSS on group performance. Information & Management, 35, 195–202, USA. Huang, W., Watson, R. T., & Wei, K. K. (1998). Can a lean email medium used for rich communication: A psychological perspective. European Journal of Information Systems, 7, 269–274, UK. Huang, W., Wei, K. K., Watson, R. T., & Tan, C. Y. (2003). Supporting virtual team-building with a GSS: An empirical investigation. Decision Support Systems, 34(4), 359–367. Huber, G. P. (1984). Issues in the design of group decision support systems. MIS Quarterly, 8(3), 195–204. Huber, G. P. (1990). A theory of the effects of advanced information design, intelligence, and decision making. Academy of Management Review, 15(1), 47–71. Isenberg, J. (1986). Group polarization: A critical review and meta-analysis. Journal of Personality and Social Psychology, 50(6), 1141–1151. Jarvenpaa, S. L., Rao, V. S., & Huber, G. P. (1988). Computer support for meetings of groups working on structured problems: A field experiment. MIS Quarterly, 12(4), 645–666. Jessup, L. M., Connoly, T., & Galegher, J. (1990). The effects of anonymity on group process in an ideagenerating task. MIS Quarterly, 14(3), 313–321.

W. Huang, D. Li / Computers in Human Behavior 23 (2007) 58–78

77

Jenness, A. (1932). The role of discussion in changing opinion regarding a matter of fact. Journal of Abnormal and Social Psychology, 27, 279–296. Kaplan, M. F. (1977). Discussion polarization effects in a modified jury decision paradigm: Information influences. Sociometry, 40(3), 262–271. Kaplan, M. F. (1989). Task, situational, and personal determinants of influence processes in group decision making. In E. J. Lawler & B. Markovsky (Eds.). Advances in group processes (Vol. 6, pp. 87–105). Greenwich, CT: JAI. Kaplan, M. F., & Martin, A. (1992). Relative impact of expert and non-expert group members: Implications for mixed (European) juries. Paper presented at the meeting of the European Association of Experimental Social Psychology and the Society of Experimental Social Psychologists. Leuven, Belgium. Kaplan, M. F., & Miller, C. E. (1987). Group decision making and normative versus informational influence effects of type of issue and assigned decision rule. Journal of Personality and Social Psychology, 53(2), 306–313. Kaplan, M. F., Schaefer, E. G., & Zinkiewicz, L. (1994). Member preferences for discussion content in anticipated group decisions: Effects of type of issue and group interactive goal. Basic and Applied Social Psychology, 15(4), 489–508. Kinney, S. T., & Dennis, A. (1994). Re-evaluating media richness theory: The effect of cues, feedback, and equivocality. In Proceedings of twenty-seventh annual Hawaii international conference on system sciences (pp. 21–30). Klein, E. E., Clark, C. C., & Herskovitz, P. J. (2003). Philosophical dimensions of anonymity in group support systems: Ethical implications of social psychological consequences. Computers in Human Behavior, 19(3), 355–382. Kraemer, K. L., & Pinsonneault, A. (1990). Technology and groups: Assessment of the empirical research. In J. Galegher, R. E. Kraut, & C. Egido (Eds.), Intellectual teamwork: Social and technological foundations of cooperative work (pp. 375–405). USA: Erlbaum. Lamm, H., & Myers, D. G. (1978). Group-induced polarization of attitudes and behavior. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 11, pp. 145–195). Larson, C. E., & LaFasto, F. M. J. (1989). Teamwork: What Must Go Right/What Can Go Wrong. (5th printing) USA: Sage Publications Inc. Lim, L. H., Raman, K. S., & Wei, K. K. (1994). Interacting effects of GDSS and leadership. Decision Support Systems, 12, 199–211. McGrath, J. E. (1984). Groups: Interaction and performance. Englewood Cliffs, NJ: Prentice-Hall. McGrath, J. E. (1990). Time matters in groups. In J. Galegher, R. Kraut, & C. Edigo (Eds.), Intellectual teamwork: Social and technological foundations of cooperative work (pp. 23–78). Hillsdale, NJ: Erlbaum. McGrath, J. E. (1991). Time, interaction, and performance (TIP): A theory of groups. Small Group Research, 22(2), 147–174. McGrath, J. E., & Hollingshead, A. B. (1994). Groups interacting with technology: Ideas, evidence, issues, and an agenda. USA: Sage Publications Inc.. Myers, D. G., & Lamm, H. (1976). The group polarization phenomenon. Psychological Bulletin, 83(4), 602–627. Nunamaker, J. F., Dennis, A. R., Valacich, J. S., Vogel, D. R., & George, J. F. (1991). Electronic meeting systems to support group work. Communication of the ACM, 34(7), 40–61. Olson, J. S., Card, S. K., Landaruer, T. K., Olson, G. M., Malone, T., & Leggettt, J. (1993). Computer-supported co-operative work: Research issues for the 90s. Behaviour and Information Technology, 12(2), 115–129. Poole, M. S., Holmes, M., & DeSanctis, G. (1991). Conflict management in a computer-supported meeting environment. Management Science, 37(8), 926–953. Poole, M. S., Seibold, D. R., & McPhee, R. D. (1985). Group decision-making as a structurational process. Quarterly Journal of Speech, 71(1), 74–102. Putnam, L. L. (1981). Procedural message and small group work climates: A lag sequential analysis,. In M. Burgoon (Ed.), Communication yearbook 5 (pp. 331–350). New Brunswick, NJ: Transaction Books. Rao, V. S., & Jarvenpaa, S. L. (1991). Computer support of groups: Theory-based models for GDSS research. Management Science, 37(10). Sambamurthy, V., & Poole, M. S. (1992). The effects of variations in capabilities of gdss designs on management of cognitive conflict in groups. Information Systems Research, 3(3), 224–251. Siegel, J., Dubrovsky, V., Kiesler, S., & McGuire, T. W. (1986). Group processes in computer-mediated communication. Organizational Behavior and Human Performance, 37(2), 157–187.

78

W. Huang, D. Li / Computers in Human Behavior 23 (2007) 58–78

Spillman, B., Spillman, R., & Bezdek, J. (1980). A fuzzy analysis of consensus in small groups. In P. P. Wang & S. K. Chang (Eds.), Fuzzy sets: Theory and application to policy analysis and information systems (pp. 291–308). New York: Plenum Press. Tan, B., Wei, K. K., Huang, W., & Ng, G. N. (2000). A dialogue technical to enhance electronic communication in virtual teams. IEEE Transactions on Professional Communication, 43(2), 153–165, USA. Turoff, M., & Hiltz, S. R. (1978). The network nation: Human communication via computer. Reading, MA: Addison-Wesley. Valacich, J. S., Mennecke, B. E., Wachter, R. M., & Wheeler, B. C. (1994). Extensions to media richness theory: A test for the task-media fit hypothesis. In Proceedings of the twenty-seventh annual Hawaii international conference on systems science (pp. 11–20). Watson, R. T., DeSanctis, G., & Poole, M. S. (1988). Using a GDSS to facilitate group consensus: Some intended and unintended consequences. MIS Quarterly, 12(3), 463–478. Zigurs, I., Poole, M. S., & DeSanctis, G. (1988). A Study of influence in computer-mediated group decision making. MIS Quarterly, 12(4), 625–644. Dr. Wayne W. HUANG is Associate Professor at the Department of Management Information Systems, College of Business, Ohio University, USA. He was a visiting scholar in University of Georgia, USA, and a faculty in University of New South Wales, Australia and Chinese University of Hong Kong, Hong Kong. His main research interests include Group Support Systems (GSS), electronic commerce, eEducation, knowledge management, and software engineering. He has published more than 50 papers in academic journals and international conference proceedings, including some leading information systems journals like Journal of Management Information Systems (JMIS), IEEE Transactions on Systems, Man, and Cybernetics; Information & Management (I&M); IEEE Transactions on Professional Communication; Decision Support Systems (DSS); International Journal of Information Management (IJIM); Journal of Global Information Management (JGIM); and European Journal of Information Systems (EJIS). He is on the Editorial Boards of Information & Management (I&M), International Journal of Global Information Management (JGIM), International Journal of Internet and Enterprise Management, and Journal of Data Management (JDM). Dr. D. Li is Professor at the Department of Management Information Systems, Guanghua School of Management, Peking University, China. He was a visiting scholar in Kellogg Graduate School of Management, Northwestern University, and a foreign professor in Kyoto University, Japan. His research interests include Management Information Systems, Decision Support Systems and e-Business. He has published more than 10 papers in international conferences and journals.