References to Extended Literature Review and Rationale

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Phase Maps for Two Jury Deliberations

These are the phase maps of the jury deliberations referenced in the article "Exploring Conflict Management Processes In Jury Deliberations Through Interaction Analysis" published in Small Group Researchunpublishe

Behavioral Correlates of Flow in Internet Browsing

This study explores the relationship between flow experience and browsing behavior of online shoppers. Behavioral patterns were identified and extracted from screen recording. Analysis both positive and negative correlates of flow experience shows support of perception-based flow measures

Will Flow Experience Lead to Better Outcomes in Online Shopping?

This paper reports the results of a study examining users’ perceptions of flow and outcomes in online shopping. Three characteristics of flow activities and six dimensions of flow are predicted to affect outcomes as measured by perceived usefulness, pleasure, and behavior intentions. Results show that flow affects all outcome measures. Implications for future research and practice are discussed

Building a Facilitated Design Collaboration Environment

Complex design problems require members of project teams go beyond individualistic work to engage in joint activity of generating new insights, new ideas, and new artifacts. Excellence in the design of GUIs requires creative problem solving. It is widely accepted that creative problem solving is most effective when individuals or groups employ a cyclic process of divergence-convergence consisting of three phases. In this paper we propose a collaborative environment for GUI design and evaluation, at the core of which is a process-based Agent Facilitator (AF). The advantage of this AF lies in its 1) focus on process of managing group dynamics rather than the content of the discussion, 2) democratic and non-obtrusive facilitation style, 3) strong feedback mechanism and 4) transparent collaboration and consensus making process. We discuss the system architecture and the implementation of a prototype extending the online chat mechanism. Although our initial effort has focused on the domain of Graphic User Interface (GUI) design, this framework is applicable to other design domains

USING A GDSS TO FACILITATE GROUP CONSENSUS: SOME INTENDED AND UNIMTENDED CONSEQUENCES

RATIONALE AND PURPOSE OF THE STUDY The empirical research examining group decision support systems suggests that many of the hopes for GDSS can be realized. For example, Lewis (1982) and, more recently, Gallupe (1985) both found that groups supported by a GDSS made higher quality decisions than groups without GDSS support. Applegate (1986) and Steeb and Johnston (1981) have demonstrated the viability of GDSS in live planning situations. Positive effects of a GDSS on groups have also been reported by Gray et al. (1981), Turoff and Hiltz (1982), and Siegel et al. (1986). Computer support has been shown to foster a democratic approach to the decision process, with more equality of participation among members (Siegel et at. 1986), to improve satisfaction with the decision process (Applegate 1986), and to result in a greater shift away from initial individual preferences (Siegel et al. 1986). These intended effects of the technology have been demonstrated for a limited number of task types. To date, positive effects of GDSS have been observed for idea generation (Applegate 1986; Lewis 1982), problem finding (Gallupe 1985), intellective choice (i.e., selection of a correct answer among a given set of alternatives) (Hiltz and Turoff 1982), and planning tasks (Applegate 1986; Steeb and Johnston 1981). In two of these studies, group members were dispersed and interacted with one another via a communication network (Hiltz and Turoff 1982; Siegel et al. 1986), while in the other studies group members met in a face-to-face (i.e., conference room) setting. In all cases, each member had direct interaction with the GDSS, and in most of the studies the performance of the group was compared to an objective measure of decision quality. Of course, many organizational meetings occur without prior or post knowledge of the correct outcome of a group meeting. For this reason, the current study aimed to build on the available knowledge of GDSS impacts by examining the usefulness of the technology in situations where a group must resolve competing personal preferences and maximize agreement on a solution to a problem. In such situations, achieving high decision quality is not the primary goal of the group meeting. The theory of GDSS would argue that the technology should be as useful in achieving consensus as in identifying correct solutions. In either situation, the GDSS should foster more even participation in the decision and a more systematic, or structured, group decision process (DeSanctis and Gallupe 1987; Huber 1984a). For the most part GDSS research is being conducted in laboratory settings where the organizational context and other factors can be controlled so that the impact of the technology on group outcomes can be carefully assessed. The current study aimed to build on the available GDSS research by systematically comparing groups supported with a GDSS with groups that had either no support whatsoever ( baseline groups) or a paper-and-pencil ( manual ) support system, that contained the same decision structure as the GDSS (cf. Lewis 1982). The purpose of having two control groups was to determine whether increments or decrements in outcomes were due to the GDSS or simply due to imposing a problem-solving structure on the group. Three major hypotheses were investigated: HYPOTHESES Hl. The degree of post-meeting consensus will vary as a function of the type of support given to the group. Hla. Post-meeting consensus will be higher in the GDSS groups than in the manual support or baseline groups, controlling for initial level of conflict. Hlb. Post-meeting consensus will be higher in manual support groups than in the baseline groups, controlling for initial level of conflict. H2. The equality of influence will vary as a function of the type of support given to the group. H2a. Influence will be more even in the GDSS groups than in the manual support groups. H2b. Influence will be more even in the manual support groups than in the baseline groups. H3. Attitudes toward the group process will be different in the GDSS groups than in the manual system and baseline groups. METHOD Forty-four three-person and 38 four-person groups participated in the study. Group size in this study was similar to that in previous research (Lewis 1982; Gallupe 1985; Siegel et al. 1986). The groups were made up of undergraduate and graduate students enrolled in introductory MIS classes. Many of the students were employed full-time in business settings, and most were working at least parttime. On average, the participants were 24 years of age with slightly more than two-and-a-half years of work experience in a business or related setting. All of the groups were live groups in that they were actively working together as teams on class assignments. In this way, the initial socialization that occurs early in group formation could be avoided during the data collection. THE GDSS The GDSS, called Computer Assisted Meeting (CAM), was designed, coded, and tested by a research team at the University of Minnesota. The system is described in DeSanctis and Dickson (1987) and is being used for several related studies of group DSS (Poole and DeSanctis 1987; Watson 1987; Zigurs 1987). Basically, the system incorporates a rational problem-solving agenda (Dewey 1910). The software is similar to that used by Lewis (1982) and Gallupe (1985) in that it performs the basic functions of recording, storing, and displaying problem definitions, criteria for evaluating solutions, alternative solutions, and a final group decision. Group members can enter relative weights for solution criteria, and the system will aggregate and display average group weightings. In addition, the system will cumulate and display ratings, rankings, and votes associated with one or more alternative solutions to a problem. These features have been identified as appropriate for supporting the communication needs of groups (Huber 1984b; DeSanctis and Gallupe 1987; Joyner and Tunstall 1970). Experimental Task and Procedure The research task required subjects to allocate a given sum of money among six competing projects that have requested funds from a philanthropic foundation. Conflict arises because the team members have varying preference structures that result in different allocation patterns. The projects that subjects can fund are based upon the personality components scheme described by Spranger (1928), who asserts that there are six basic interests or motives in personality: theoretical, economic, aesthetic, social, political, and religious. The six projects that can be funded correspond to Spranger\u27s six personality traits. Correlation analysis based on the 300 experimental subjects was used to check that the amount allocated to a project by an individual was highly correlated with that person\u27s values as measured by the Study of Values instrument (Allport et al. 1970). The strengths of the task are twofold. First, it produces conflict in a group. Second, the source of the conflict is identifiable; it is based upon different preference structures arising from varying personality traits. The task and its validation are further described in Watson (1987). The experimental procedure was as follows: 1. Subjects listened to a standard introductory script read by the administrator of the experiment, and then read a background statement. 2. Subjects completed a consent form, a background questionnaire, and the Study of Values instrument. 3. Subjects individually allocated funds to the six projects requesting support from the philanthropic trust (these measures were used to calculate pre-meeting consensus). Subjects also allocated funds to five other sets of six projects each in order to give them practice and to help stabilize their reasoning processes. 4. Groups allocated funds to the six projects requesting support from the philanthropic trust. 5. Subjects completed a post-meeting questionnaire for measuring an individual\u27s perception of the group\u27s decision-making process, and individually allocated funds to the six projects requesting support from the philanthropic trust (these were used to calculate post-meeting consensus). 6. The administrator conducted a debriefing of the subjects. During section step 4 of the experiment, the group decision-making phase, teams were given one of the three treatments discussed previously. In the case of the manual groups, subjects were provided with a eleven-page handout outlining the same agenda that was on the GDSS. Each page of the handout explained an agenda item, giving details on how to accomplish the item parallel to those in the submenus of the GDSS. Manual groups were given a flip chart to display ideas publicly. Every effort was made to ensure that manual groups had the same structural aids as the GDSS groups, the only difference being that the manual groups operated without computer support. GDSS groups were provided with a 20-minute training session on use of the system, manual groups were also trained in how to use the meeting structure. Baseline groups were given no structure, flip chart, or training. They were told to operate with their own resources. FINDINGS This investigation identified some intended and unintended effects of using a decision support system for groups. Overall, the results on consensus and equality of influence for the GDSS and manual conditions tended to be similar, showing different patterns than the results for the baseline condition. As intended, the presence of a suggested structure for the group meeting improved the degree of post-meeting consensus. Also, in contrast to the baseline and manual system group meetings, users of the GDSS reported more input into the group\u27s solution and were less likely to perceive that there was a leader in the group. The relationship between pre-meeting and post-meeting consensus was similar in GDSS and manual groups, but post-meeting consensus was not significantly higher in the GDSS groups than in the baseline or manual groups. Although the structure provided in the GDSS and manual conditions reduced the variance across groups on their equality of influence, use of the GDSS did not result in more equal influence of group members on the final solution. The most surprising unintended effect was that GDSS users, compared to the other experimental groups, perceived the issues discussed in the group meeting to be more trivial and the group\u27s problem solving process to be less understandable. Other observations of the study were that use of the GDSS tended to reduce face-to-face interpersonal communication in the group; use of the GDSS presented a challenge to the groups, thus making their meeting task more difficult than groups without the GDSS; and groups using the GDSS appeared to become very procedure-oriented, rather than issue-oriented, in their discussions. In the future, GDSS research should press further to sort out what Kiesler calls intended technological effects (faster processing, fewer errors, more equal participation), unintended social effects (heightened conflict), and transient effects (effects that will diminish with group experience with the system) of the technology on groups. ACKNOWLEDGEMENT This project was funded by NCR Corporation, the MIS Research Center, and the Graduate School of the University of Minnesota

The Minnesota GDSS Research Project: Group Support Systems, Group Processes, and Outcomes

The Minnesota GDSS Research Project is a 20-year program of interdisciplinary research that has generated more than 80 articles, chapters, dissertations, and proceedings publications and has influenced other researchers who developed their own niches. Grounded in Adaptive Structuration Theory, which emerged and evolved as the research unfolded, the project studied the impact of technology characteristics (level of support, restrictiveness) and other support (training, heuristics, facilitation) on group processes and outcomes for a range of tasks (problem definition, decision making, planning). The project entailed a complex tapestry of a series of laboratory experiments and two major field studies. The basic theoretical framework, experimental strategy and design, field study design, and results are summarized, along with a discussion of the significance and implications of the project for contemporary theory and practice

COMPREHENSIVENESS AND RESTRICTIVENESS IN GROUP DECISION HEURISTICS: EFFECTS OF COMPUTER SUPPORT ON CONSENSUS DECISION MAKING

The application of heuristic devices has been proposed as one approach to improving consensus decision making. The heuristics are intended to provide problem structuring and, more broadly, to improve the process of interpersonal collaboration in work settings. This study drew from research on group decision making (e.g., Shaw 1971; Poole 1983), problem structuring (e.g., Abualsamh, Carlin and McDaniel in press; Cats-Baril and Huber 1987), computer-mediated communication (e.g., Kiesler, Siegel and McGuire 1987), and technology adoption (e.g., Poole and DeSanctis 1989) to compare alternative approaches to delivery of decision heuristics for a task requiring resolution of competing values and preferences. Based on the arguments of adaptive structuration theory and social judgment theory, we hypothesized that the addition of a general heuristic to a specific, computer-based heuristic would improve group consensus; that is, the greater the comprehensiveness of the heuristic, the greater the gain in consensus. We further anticipated that combining general and specific heuristics in an integrated, interactive form would bring additional gains in group consensus. Greater restrictiveness in how the groups could execute the heuristic devices was also expected to improve group consensus, especially in cases where the specific heuristic was not coupled with the general heuristic. The results supported some of these predictions. By comparing heuristics in terms of their comprehensiveness and restrictiveness, we developed some understanding of how decision heuristics might be implemented within a computer-supported meeting environment

A tutorial for modeling the evolution of network dynamics for multiple groups

Researchers have been increasingly taking advantage of the stochastic actor-oriented modeling framework as a method to analyze the evolution of network ties. Although the framework has proven to be a useful method to model longitudinal network data, it is designed to analyze a sample of one bounded network. For group and team researchers, this can be a significant limitation because such researchers often collect data on more than one team. This paper presents a nontechnical and hands-on introduction for a meta-level technique for stochastic actor-oriented models in RSIENA where researchers can simultaneously analyze network drivers from multiple samples of teams and groups. Moreover, we follow up with a multilevel Bayesian version of the model when it is appropriate. We also provide a framework for researchers to understand what types of research questions and theories could be examined and tested