Coordination Structure and System Restrictiveness in Distributed ...

Coordination Structure and System Restrictiveness in Distributed Group Support Systems: An Experiment on Coordination Mode and Leadership Youngjin Kim Long Island University Brookville, NY

Abstract This study examines coordination processes in Distributed Group Support Systems. A 2x2 factorial design was used to investigate coordination mode and group leadership in an asynchronous environment, as a means of exploring the impact of restrictiveness of coordination structures on group performance. Objective decision quality was equal for both parallel and sequential coordination groups, but was significantly better in groups with a leader. Perceived decision quality was better in parallel coordination groups than sequential groups. Satisfaction with a decision process was higher in parallel coordination groups and groups with a leader. The study concludes that less restrictive coordination structures are more appropriate to support asynchronously interacting distributed groups.

Starr Roxanne Hiltz and Murray Turoff New Jersey Institute of Technology Newark, NJ

interaction, it is very challenging to provide an effective coordination structure for asynchronous interaction [2,8,31,32]. Major challenges include the necessity to support larger decision groups, the increased likelihood of uncooperative subgroups, dealing with “critical mass activity” phenomena, the need for better meta-models to incorporate both individual and group problem-solving, software support for leadership roles, and the need to integrate general capabilities such as hypertext and database structures [32]. The purpose of this research is to study the impact of variations in restrictiveness of coordination structures provided through Distributed Group Support Systems (DGSS) [32] on group performance. In the following sections, related literature is reviewed, the conceptual model developed, and the experimental methodology and results briefly reported.

2. Literature Review 1. Introduction Group decision-making in an asynchronous (anytimeanywhere) interaction mode requires coordination of a series of individual problem-solving and on-line sessions. Each group member performs individual problem-solving and interacts with others on-line. During an on-line session, the results of individual problem-solving are communicated, feedback for others generated, and the output from others collected for the next round of individual problem-solving. Therefore, the effectiveness of group decision-making is contingent upon a group’s ability to coordinate activities such as setting up decision strategies, assigning tasks, allocating resources, synchronizing, integrating and refining individual efforts [22]. This coordination process consists of a particular arrangement of rules and resources which is called the “structure” of group interaction [26]. Though it is relatively easy to coordinate face-to-face

Research on the coordination structure of Group Support Systems (GSS) primarily falls into two areas [38]. One area examines differences in coordination between computer-mediated and non-mediated communication channels [4,18,21], and the other focuses on media richness with research results suggesting that the richness of a medium should match the equivocality and the uncertainty of a task on hand [3,30]. A recently emerging body of research concerns the issue of system restrictiveness of GSS coordination structures [2,5,7,24,25]. DeSanctis, et al. [5] define system restrictiveness as the extent to which a process intervention limits or channels the group’s use of the activities and sequences. Silver [28] conceptualizes it as the degree to which and the manner in which Decision Support Systems (DSS) limit their users’ decision-making processes to a subset of all possible processes. McLeod and Liker [24] explain two perspectives on the impact of a coordination structure imposed by a

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technology. One perspective is the deterministic view that sees a structure as an external, formal system of rules and regulations that determines or strongly constrains the behavior of individuals and groups. Another perspective characterizes a coordination structure as an emergent pattern created in the process of action and interaction. GSS constructed with the deterministic view provide a highly structured, and, therefore, more restrictive, coordination process that defines and limits roles and responsibilities of group interaction. GSS designed with the emergent pattern view, however, define only the boundary of coordination within which a group may develop a flexible and efficient coordination structure. Which perspective should be adopted in building DGSS coordination structures? Groups will not always use GSS designed with the deterministic view in ways intended by system designers, but, rather, actively adapt a technology to their own end [6]. In doing so, groups may lose group cohesiveness and the synergistic benefits of group interaction. When this happens, a group may simply produce the mere sum of individual works, which is one of the characteristics of poorly-coordinated groups [16]. In studying synchronous GSS, McLeod and Liker [24] report that groups with a less restrictive structure exert more influence over the technology than the technology exerts over groups. DeSanctis, et al. [5] observe that excessive restrictiveness may cause groups to lose their sense of ownership and control over the technology and, thus, may reduce consensus. Wheeler, et al. [37] find that groups with a restrictive coordination structure develop a sequential group process pattern while groups with a less restrictive coordination structure follow a cyclical interaction pattern with better performance. Dickson, et al. [7] also confirm the negative impact of being too restrictive. All these studies of synchronous GSS suggest the need to support a group with interaction flexibility by providing a less restrictive structure to help groups develop their own decision strategies and interaction patterns. High structure GSS may reduce the uncertainty and coordination needs, but may not produce optimal group performance. Low structure GSS, on the other hand, do not enforce any pre-determined interaction procedures, but may result in better group productivity by allowing groups to choose the most effective decisionmaking strategy. Because asynchronous group decisionmaking is, in essence, the coordination process of individual problem-solving [29], DGSS coordination structures should be flexible enough to maximize individual’s freedom to concentrate on those aspects of a problem to which each individual can best contribute [32]. To date, however, most GSS studies have examined the impact of highly restrictive coordination modes with

face-to-face synchronous groups. Little research has examined the impact of system restrictiveness of coordination structures on the performance of distributed asynchronous groups supported with DGSS.

3. Conceptual Framework for Coordination Support: Coordination-Cognitive Fit Model The challenge in designing computer-mediated DGSS is to provide a group with a flexible coordination structure that helps individual problem-solving while maintaining a group as a cohesive unit. A coordination structure for distributed groups should be designed to help groups discover how to work and develop strategies for combining and coordinating the utilization of a technology and people skills. To accomplish this design goal of DGSS, Hiltz and Turoff [15] assert the need to consider the complexity of a task, the validation approach that determines what is valid, and the regulatory process by which a group coordinates its examination of a task. If DGSS provide restrictive structures, some individuals may resist or break these structures or even leave groups because of the difference between an individual preference for a particular validity approach and group norms for synchronizing individual work. Therefore, by providing a flexible arrangement of tools and procedures, DGSS should allow a group to tailor the most effective and efficient interaction structure through an adaptive process. This will enable a group to determine the coordination structure that best fits into its needs. The Coordination-Cognitive Fit Model in Figure 1 describes the determinants of restrictiveness of a coordination structure. The model hypothesizes that task complexity and validity approach determine coordination requirements, and the degree of restrictiveness of a coordination structure must match the characteristics of task complexity and validity approach. Therefore, differences in group performance will be explained by the compatibility between the inherent coordination requirements of a task and technological features of (Distributed) Group Support Systems. Also included in this model is the paradigm of cognitive fit [11,27,35]. When a group process is compatible with an individual’s preferred decisionmaking process, he or she can contribute better to overall group processes and outcomes. The Coordination-Cognitive Fit Model incorporates the need to coordinate individual problem-solving as one of the factors that determine the degree of coordination required in group problem-solving. Individual problemsolving is coordinated through the group memory of (Distributed) Group Support Systems. Any individual problem-solving activities should start with reviewing the

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Figure 1 Research Framework for the Coordination-Cognitive Fit Model Group Problem Soving

Evidence

Coordination

Coordination

Validation

Requirements

Technology Fit (Distributed) Group Support Systems:

Task Complexity

Group

Group

Adaptation

Process &

Process

Outcome

Group Memory Group Coordination Technology

Individual

Decision

Cognitive

Preference

Strategy

Fit

Individual Problem Solving

group memory [34] to understand the current status of a problem. As a result of individual problem-solving, the group memory is collectively updated, the number of searches to reach a decision reduced, and the effectiveness of search strategies increased. Therefore, (Distributed) Group Support Systems should synchronize individual problem-solving for group collaboration by providing a group memory and group coordination structures. A highly structured, and thus more restrictive, coordination structure for (Distributed) Group Support Systems has a high probability of being incongruent with some decision makers’ cognitive preferences. When this is the case, the inertia of adapting an individual preference to a group strategy becomes one of the sources of productivity loss. A coordination structure should be compatible not only with the complexity of a task and the group’s validation approach, but also with the cognitive preference of each individual. Therefore, a coordination structure should be less restrictive so that a group can tailor DGSS features to be compatible with the group’s contingent factors. In a less restrictive structure, coordination requirements, technology, and cognitive preference will adapt to each other to eventually determine the best coordination structure for group interaction.

4. Hypothesis Development Independent Variables Coordination Mode: Sequential vs. Parallel: In a sequential coordination mode, groups are asked to discuss one topic at a time and not allowed to discuss previous topics once a new topic is introduced. In a parallel coordination mode, however, all topics are open from the very beginning until a decision is made. Groups with a sequential coordination structure just react to what is required by rules and procedures. A parallel coordination structure, on the other hand, promotes proactive decisionmaking by providing group members with the flexibility of adapting a coordination structure to the way that they can best contribute. In Computer-Mediated Communication Systems (CMCS) where interaction is already limited due to the lack of social cues and context information, a sequential coordination mode will further restrict group interaction. This may lead to increased decision-making cost by reducing efforts and contributions of each participant. Therefore, the coordination mode for asynchronously interacting groups should be flexible enough to allow each participant to customize his or her preferred decision strategy. To date, most GSS research was conducted using a sequential

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coordination mode with face-to-face groups. Very little research has studied the impact of parallel coordination of group interaction. Presence of a Group Leader: With a Leader vs. Without a Leader: Leadership is another independent variable chosen for this study because leadership is basically a coordination process [17]. GSS research indicates diminished emphasis on a leadership variable [7]. Turoff and Hiltz [32] suggest that CMCS may actually undermine effective leadership by filtering out interpersonal cues. Hiltz, et al. [13] find that there is no overall significant difference in decision quality between groups with a designated human leader and statistical feedback that might be considered as a ”computer leader.” Rather, group composition and leader’s knowledge are strong determinants of decision outcomes. Yet, understanding of the impact of a group leader on group performance in DGSS environments is limited [32], and no GSS research has studied leadership as a coordination process variable. Expected Restrictiveness of Independent Variables: McLeod and Liker [24] argue that restrictiveness of a GSS structure is determined by the probability of changing a group’s previously developed structure. Restrictive GSS are designed to change a group’s preexisting structure, while less restrictive GSS help groups create structures for themselves, using tools provided by the system. Putnam [27] summarizes the characteristics of a restrictive structure as planned and sequential patterns, concern for time management, emphasis on regular and predictable procedures, and emphasis on clarifying group procedures. A less restrictive structure is characterized by cyclical procedural patterns, flexibility in establishing and changing plans, oblivious to time constraints, and having a tendency to vacillate between task and socioemotional needs of a group. Therefore, because of its pre-defined and inflexible interaction pattern, a sequential coordination mode is expected to be more restrictive than a parallel mode in which an individual group member is allowed to customize his or her interaction pattern in a cyclical manner. In regard to the group leader variable, a group with a leader is considered less restrictive, or more flexible, because a group leader is allowed to modify a given coordination procedures, and impose new rules and procedures. With this freedom, a group leader is given the ability to flexibly manage time and interaction patterns. A group without a leader, however, does not have ability to modify or implement new procedures, and are asked to restrictively respond to procedures imposed by GSS.

Research Hypotheses Decision Quality: In regard to the impact of restrictiveness of a coordination structure of synchronous GSS, Wheeler, et al. [37] find that a less restrictive coordination structure generates better decision quality. McLeod and Liker [24] report that a less restrictive structure results in better decision quality with a planning task, but has a negative impact on a creative task. Because these prior studies were conducted with face-toface groups in a decision room setting, findings cannot be generalized for asynchronous group interaction. Another shortcoming of these studies is that decision quality was measured only subjectively, either by a panel of experts or by a questionnaire, on the ground that no difference is expected between objective and subjective decision measures [12]. In this regard, the following hypotheses were considered for objective and perceived decision quality: H1a:

H1b: H2a:

H2b:

Parallel coordination groups will make objectively better decisions than sequential coordination groups. Groups with a leader will make objectively better decisions than groups without a leader. Parallel coordination groups will perceive their decisions to be better than sequential coordination groups. Groups with a leader will perceive their decisions to be better than groups without a leader.

Satisfaction Measures: Research findings on the level of satisfaction with a decision and a decision process in GSS-supported groups are mixed. Some research results report that GSS-supported groups are more satisfied with their decisions and decision processes than unsupported groups [10]. However, more research results find either no difference or lower satisfaction in GSS-supported groups [2,9,12,36]. Findings on satisfaction with a decision process are also inconsistent. While one study argues that groups are more satisfied with a decision process with low structure GSS [37], another study reports no difference at all [24]. The proposition of this study, that a less restrictive coordination structure allows individuals to focus on the aspects they can contribute best without changing their preferred decision strategies, leads to hypotheses on satisfaction as follows: H3a:

Satisfaction with a decision will be higher in parallel coordination groups than in sequential coordination groups.

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H3b:

H4a:

H4b:

Satisfaction with a decision will be higher in groups with a leader than in groups without a leader. Satisfaction with a decision process will be higher in parallel coordination groups than in sequential coordination groups. Satisfaction with a decision process will be higher in groups with a leader than in groups without a leader.

5. Research Methodology Research Design: The 2 x 2 Factorial Design A controlled laboratory experiment with a 2 x 2 factorial design is depicted in Figure 2, along with the number of groups per condition. A laboratory experiment was used because of its strength in control and manipulation of research variables.

Figure 2 2 x 2 Factorial Design

Coordination Mode Sequential

Parallel

With

12 groups

12 groups

Without

12 groups

11 groups

Group Leader

Experimental Task The task developed for this study is the Investment Club Task [20]. The task is to agree on at least one, but no more than three stocks from fifteen candidate stocks, to be held for at least six months. The Investment Club Task is a semi-structured task [19], and has the characteristics of both Intellective and Decision-Making task types [23]. It shows the characteristics of a decisionmaking task because when a decision is made at the end of the experiment, there is no way to know objective decision quality. On the other hand, after the decision horizon is reached - at least six months after the experiment, objective decision quality can be evaluated by measuring actual changes in stock prices.

Subjects There were 212 subjects in 47 groups in this experiment, recruited from Rutgers University, New Jersey Institute of Technology (NJIT), and Fairleigh Dickinson University (FDU). Some subjects were distance learning students enrolled in NJIT’s distance learning classes. A class credit was given for all participants. Participation was not mandatory, and an alternative assignment was given to whomever chose not to participate in the experiment. During a recruiting session, the subjects filled out pre-experiment questionnaires, and were asked to schedule an one-hour face-to-face training session. For the distance learning subjects, upon their commitment to the experiment, all pre-questionnaires were mailed, and the subjects were asked to mail them back to the experimenter. The subjects were randomly assigned to groups of size five, but in a few groups, the experiment was conducted with three or four subjects due to dropouts.

Technology and Training Electronic Information Exchange System 2 (EIES 2) developed at NJIT was used for this experiment, and modified with the experiment-specific procedures and rules. A week-long asynchronous on-line training session with EIES 2 was given to all subjects, beginning with an one-hour face-to-face session. After an one-hour face-toface session, training went on on-line for a week. A week-long training was given to minimize the experimental problems associated with students subjects such as the novelty effect of a new technology and the impact of no previous working relationship [1,36]. To take advantage of week-long work experience through training, all subjects from the same training group were assigned to the same experimental group. During training, all subjects were introduced to the system’s features and the experimental format. A group leader was selected in groups that would be assigned to groups with a group leader condition in the experiment. Another purpose of on-line training was to resolve any connectivity problems, since remote access to the system was one of the crucial factors for the study of DGSS.

Administration of the Experimental Conditions Coordination Mode: Parallel vs. Sequential: In parallel coordination groups, complete discussion topics were presented to a group from the very beginning of the experiment. Each group member was allowed to discuss any topic at any time, and the number of visits to each topic was unlimited. Discussion of all topics continued

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concurrently. The presentation of discussion topics to parallel coordination groups was made deliberately different from the order of topics presented to sequential coordination groups. In sequential coordination groups, discussion topics were opened sequentially one at a time. Once a group moved to the next discussion topic, revisiting previously discussed topics was not allowed. Coordination by a Group Leader: Group leaders were selected during a training session, and given freedom to impose any coordination rules and procedures as necessary. Therefore, groups with a group leader was given a flexible procedure in a sense that a group leader was allowed to modify the procedure as necessary. Groups without a leader, however, are asked to simply respond to system-defined interaction procedures. For example, in sequential coordination groups with a leader, the leader made a summary of a current discussion topic, and then called for the next discussion topic. He/she was also allowed to add or eliminate discuss topics. In sequential coordination groups without a leader, in the beginning of the experiment, the discussion deadline for each discussion topic was announced to the group, and discussion topics were introduced following the announced timetable.

6. Research Results Because of the unequal number of subjects and groups for each experimental condition, the General Linear Model was chosen for hypothesis testing instead of the ANOVA. The significance was tested at the α = 0.05 level. When a test result was significant, the Duncan’s Multiple Range Test was conducted to examine the mean difference between two conditions of an independent variable. When an interaction effect was significant, the Fisher’s Least Significant Difference (LSD) Test was used for pairwise comparison among experimental conditions. All statistical procedures were conducted with the SAS System for Windows 3.1 Release 6.08. The results of statistical analysis are summarized in Table 1.

Decision Quality Objective decision quality was measured twice, six months and one year after the experiment, by calculating changes in portfolio value. Though the coordination mode did not have a significant difference, the presence of a group leader did in objective decision quality. Objective decision quality measured six months after the experiment was better in groups with a leader than groups without a leader (F=4.69 and p=0.0360). Objective decision quality measured one year after the experiment, however, was marginally better in groups with a leader

only at the α= 0.10 level (F=3.24 and p=0.0789). Perceived decision quality was measured immediately after the experiment by a composite variable of eight questionnaire items (Chronbach’s Coefficient α=0.81). Though objective decision quality was tested insignificant for the coordination mode, perceived decision quality of parallel coordination groups was significantly better than that of sequential coordination groups (F=5.26 and p=0.0268). There was, however, no significant difference between groups with and without a leader.

Table 1 Summary of Statistical Analysis Hypothesis Objective Decision Quality (H1) After Six Months: Coordination Mode Group Leader* After One Year: Coordination Mode Group Leader** Perceived Decision Quality (H2) Coordination Mode* Group Leader Satisfaction with Decision (H3) Coordination Mode Group Leader Satisfaction with Decision Process (H4) Coordination Mode* Group Leader*

SS

F

Pr > F

1,290,644 66,330,159

0.094 4.690

0.7461 0.0360

1,290,661 55,524,840

0.070 3.240

0.7918 0.0789

36.160 2.766

5.260 0.400

0.0268 0.5293

10.250 4.447

1.890 0.830

0.1761 0.3684

10.368 12.367

5.350 6.400

0.0255 0.0152

* Significant at α=0.05 ** Marginally significant at α=0.10

Satisfaction Measures A composite variable of five questionnaire items was used to test the hypotheses on satisfaction with a decision (Chronbach’s Coefficient α=0.89). There were no significant differences in either the coordination mode or the group leader variable. Satisfaction with a decision process was also measured by a composite variable of five questionnaire items (Chronbach’s Coefficient α=0.96). Statistical tests indicate that parallel coordination groups were more satisfied with the decision process than sequential coordination groups (F=5.35 and p=0.03). Also, groups with a leader showed significantly higher satisfaction with the decision process than groups without a leader (F=6.40 and p=0.02). Because satisfaction with a decision process was significant for both the coordination mode and the

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group leader variables, the interaction effect was tested, and found to be significant (F=6.41 and p=0.0151). The Fisher’s LSD indicates a significant difference in satisfaction with a decision process between sequential groups with a leader (t sw = 8.64) and sequential groups without a leader (t so = 6.58) at the α=0.05 ( | t sw - t so | = 1.88 > Fisher’s LSD=1.321). Sequential groups without a leader reported the lowest level of satisfaction with a decision process. However, there were no significant differences among sequential coordination groups with a leader, parallel coordination groups with a leader, and parallel coordination groups without a leader.

7. Discussion and Conclusion The major research question of this study was to investigate the impact of coordination structures with different degrees of system restrictiveness on group performance. As summarized in Table 2, less restrictive coordination structures - the parallel coordination mode and the presence of a group leader – were tested better than restrictive ones. Though a sequential coordination mode was expected to be better because of a novelty effect of a parallel coordination mode [7,36], perceived decision quality, communication effectiveness and satisfaction with a decision process were significantly better with the parallel coordination mode. The important point is that a sequential coordination mode with which most subjects are familiar, did not generate better objective decision quality and higher satisfaction with a decision than a parallel coordination mode. It appears that group members in sequential groups, while simply responding to a very restrictive interaction procedure, may have had no ability to tailor the system’s features to their preference. This may have led to the feeling of no group cohesiveness and collaboration. In fact, sequential groups without a group leader - the most restrictive arrangement by definition - scored the lowest in all dependent variables measured. Findings of hypothesis testing for the group leader variable also indicate the same conclusion. In most cases, a less restrictive coordination arrangement - the presence of a group leader - generated better results. The simple presence of a formal leader would not make much difference. What is more important is whether group leaders performed leadership roles. To investigate this aspect, the performance of a group leader was investigated by examining whether a leader had generated 50% or more than the average number of lines generated by a group [14]. In 14 out of 24 groups with a leader, group leaders generated 50% more lines than the group average. To further investigate group leaders’ performance, the contents of group leaders’ comments

were examined. Each comment by a group leader was classified either as a task- or a leadership-related comment according to the coding scheme developed by Kim [20]. The results show that 46.4% of comments were leadership-related, such as defining objectives, providing structures, facilitating interaction, and maintaining the group.

Table 2 Significance of Hypotheses Testing Hypotheses Objective Decision Quality (H1) After Six Month After One Year Perceived Decision Quality (H2) Satisfaction with Decision (H3) Satisfaction with Decision Process (H4)

Coordination Mode

Group Leader

-

W>O W > OM

P>S

-

-

-

P>S

W>O

Coordination Mode: P - Parallel Group Leader: W - With a Leader M

S - Sequential O - withOut a Leader

- Marginally significant at α = 0.10

Timing of group leaders’ leadership-related comments is worthy of mention. Group leaders tended to generate more comments on defining group objectives and providing interaction structure in the early stage of the experiment, while comments facilitating interaction and maintaining the group began about the middle of the experiment and continued through the later stages. It appears that in the early stage of asynchronous group interaction, it is important for a group leader to make clear a decision strategy by which group interaction is coordinated. As group members came to understand the coordination requirements of a decision strategy, the role of a group leader tends to change to that of a facilitator to maintain group cohesiveness. This facilitation encourages uncooperative members to improve their participation to increase group cohesiveness and to deal with “critical mass activity” phenomena associated with negative feedback if the participation rate is too low [32]. Group leaders, indeed, appropriately directed the focus of group interaction by timely exercise of their ability to impose new rules and procedures as necessary. Findings of this study consistently indicate that a coordination structure for asynchronous interaction groups should be designed in a less restrictive way. Though the term ‘system restrictiveness’ is used in most studies, the term ‘system flexibility’ or ‘system

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tailorability’ [31] explains better the conclusion of this study: DGSS should be designed in such a way that a group is allowed to “tailor” DGSS features to individual and group preferences and contingent factors. One interesting finding of this study is the difference between objective and perceived decision quality. The presence of a group leader did make a significant difference in objective decision quality, but not in perceived decision quality. The coordination mode, however, made a significant difference in perceived decision quality, but not in objective decision quality. Pearson’s Correlation Coefficient Rho between perceived and objective decision qualities was -0.0604, indicating no correlation. Actually, perceived decision quality was highly correlated with satisfaction with a decision (ρ=0.61793) and a decision process (ρ=0.7718). The significance of this finding is that perceived decision quality cannot be used as a surrogate measure for objective decision quality in all situations. Since many field studies of GSS depend upon subjective measurement of group performance, this is a worrisome result. We need better ways than subjective reports to measure the quality of group performance for problems that have no unique optimal solution. Depending upon contingent factors, perceived decision quality may be as good as objective decision quality in some situations. Any studies that measure only perceived decision quality, however, must make clear why objective decision quality cannot be measured and why perceived decision quality can be used as a surrogate measure for objective decision quality. Otherwise, findings of studies will be misleading. Failure to clearly distinguish between perceived and objective decision quality may have contributed to inconsistent findings on decision quality in previous GSS research

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