An Interaction Meta-Model for Groupware Theory and Research Raquel Benbunan-Fich Graduate School of Management Rutgers University, Newark, NJ 07102, USA E-mail:
[email protected]
Abstract This paper reviews and integrates some of the theoretical and empirical models that have been proposed to study groups using Group Decision Support Systems (GDSS). An interaction meta-model based on contingency theory will be developed to integrate different theories. The meta-model proposes that the outcomes of group interaction are contingent upon three main contextual factors (Technology, Groups and Tasks), and their interaction. This paper presents a new insight into the dynamic interplay of the factors influencing group processes and outcomes and derives the implications for researchers.
1. Introduction An interaction meta-model is developed to integrate different theories and models that have been proposed to study groups using Group Decision Support Systems (GDSS), both synchronously (in decision rooms) and asynchronously (Distributed GDSS). The meta-model presents a new insight into the dynamic interplay of the factors influencing group processes and outcomes and provides a useful research guide for designing empirical studies in this area. One of the most important schools of thought in the field of GDSS contends that the outcomes of group interaction are contingent upon three main contextual elements: Technology, Groups and Tasks. The contingency can be explained not only in terms of these factors but also in terms of their interaction. By grouping in pairs these contextual elements, three types of interaction factors can be identified: group-technology adaptation, task-technology fit and group-task coordination. Interestingly, each one of these interaction factors has been the core of one or several theories proposed in the groupware area: Group-Technology adaptation is the core of the Adaptive Structuration Theory [22]; Task-
Technology fit is the focus of Media Richness [2] and Task-Technology Interaction [23]; and Group-Task coordination is the essence of the Coordination Theory [12] and [13]. Each of these theories tries to explain the interaction of the three contextual elements (Technology, Groups and Tasks) by focusing on a particular dyad. The analysis of the interaction of these contextual factors has important implications for the design of empirical studies in this area.
2. Interaction meta-model Differences in groups using Group Decision Support Systems (GDSS) and Distributed GDSS, can be explained through differences in contextual factors (Technology, Groups and Task), and differences in the interaction of these factors. To represent this complex interplay, a novel contingency model is developed, building on the theoretical frameworks proposed by [4] and [10]. The contextual factors (technology, groups and tasks) are represented in the main circles of the meta-model, and the interaction factors in the overlapping zones of those circles (See Figure 1). Group-Technology Adaptation
Groups
Technology
Task-Technology Fit
Tasks
Group-Task Coordination
Figure 1. Interaction Meta-Model
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effects of many members trying to solve the same problem.
2.1. Contextual factors The "contingency perspective" in [4] seeks to use contextual variables such as group size, member proximity and task type to predict group outcomes. But this model tries to generalize such contextual variables by looking at group, task and technology as multidimensional constructs (sets of variables). Several models agree on this broader conception of contextual factors ([3], [7], [10], [11], [19], [21]). 2.1.1. Groups As a contextual factor, "group" includes both the group attributes or characteristics and its process variables. Group characteristics include size, composition, homogeneity, history, initial consensus, and motivation. Group process variables include anonymity, degree of structure, leadership, level of conflict and information feedback [10]. Historically, one of the main attractions of research at the group level has been the argument that a group can produce better results than any of its members acting alone. In terms of research, however, the comparison between groups and individuals has not been fully explored. A group is a complex system functioning at a higher level than the mere aggregation of member abilities and characteristics [15]. This systemic notion has been explained through several process gains that can result from group interaction. Examples of such gains are: synergy, the ability to consider more information, objective evaluation, cognitive stimulation and member capacity to learn from other members [20]. However, patterns of group interaction are vulnerable to several obstacles that can hinder group performance. Examples of such obstacles are: inhibitory effects, created by being surrounded by other people (production blocking); duplication effects, created by public expression of ideas which could cause other group members to think along the same lines (cognitive inertia); reluctance to contribute because of fear of negative evaluation; reluctance to criticize because of fear of reprisals (conformance pressure); free riding, information overload and coordination problems [20]. Group interaction and performance will depend upon the balance of these gains and losses [20]. Gains and losses are also related to the intrinsic characteristics of the group. Attributes such as group size, group composition and group capabilities bound the levels of potential group productivity. For example, in very large groups, coordination problems can neutralize the synergistic
2.1.2. Task Task has been proposed in every framework in the groupware literature as a contextual factor ([3], [4], [21]). Several task categories and taxonomies have been suggested in the literature (e.g. [9], [15], [25]), but McGrath's task circumplex [14] has been the most widely used for groupware theory and research. McGrath's task classification involves four main categories, each itself containing two task types: generate (ideas or plans), choose (a correct answer or a preferred solution), negotiate (conflicting views or conflicting interests) and execute. These categories along with their task types are related to one another within a bidimensional space, which has the attributes of a circumplex [4]. An important limitation of this task circumplex is the presumption of mutually exclusive categories. In practice, groups confronted with complex problems must generate alternatives, and then decide or reach consensus on a preferred solution. This situation cuts across two quadrants of McGrath's circumplex: "generate" and "choose" [23]. Typically, a group project may involve generating ideas, evaluating advantages and disadvantages of each idea, and finally, choosing one idea to implement. Therefore, group work often encompasses carrying out tasks from different quadrants of McGrath's circumplex. Since the circumplex is a theoretical model, it does not provide tools for objectively measuring the degree to which tasks differ in each category [18], or additional dimensions for distinguishing tasks that belong to the same category. The circumplex also fails to include other important task dimensions such as task complexity or level of difficulty, analyzability, and equivocality and uncertainty [2]. In the proposed meta-model, task is understood as a multidimensional construct which includes not only task type but also other task dimensions such as analyzability, complexity, level of difficulty, equivocality, and uncertainty. 2.1.3. Technology As a contextual factor, technology refers to the medium (i.e. basically hardware and software) that supports group interaction, given the different modes in which this interaction can occur (synchronously or asynchronously, and proximate or dispersed). In general, the technology to support group work varies depending on spatial dispersion and temporal patterning
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of group meetings. Spatial dispersion refers to the degree of member proximity in which the interaction takes place. It can range from same place (in a decision room) to dispersed in different physical spaces. Temporal patterning refers to the classical distinction between synchronous (same time) and asynchronous (dispersed in time) meetings. These categories are not mutually exclusive. There are particular circumstances in which group members are in the same physical space but they can not communicate orally with each other, or mixed modes in which participants meet face-to-face and via an electronic medium [16]. Face-to-face meetings are the most "natural" form of team communication, but they are often costly in both time and travel expenses. The typical groupware tools for face-to-face meetings are decision rooms, in which the participants use workstations to communicate with each other. When team members face time and place constraints that prevent them from meeting face-to-face, they can use E-mail and computer conferencing to coordinate their work. In this case, the orderly flow of communication and the time required to complete a communication cycle are potentially affected. Ideally, in face-to-face communication, all members receive messages in the same order, and the flow of conversation is smooth and synchronized. In asynchronous mode, the natural order of messages is potentially disrupted because different people receive and respond to messages in different sequences. Asynchronous/dispersed systems are designed to enable group members to log-on to the system from diverse locations at any time during the day or night. The objective is to provide a sort of "virtual meeting room" where all of the communications and decision support functions available in a decision room with GDSS support are provided, but each team member can choose his or her place and time to participate [6]. Technology to support group work depends upon time and space characteristics in which group interaction takes place. In DeSanctis and Gallupe's [4] framework, technology is classified in terms of the levels of intervention in group processes. The degree of technological support provided by a GDSS can be categorized in three levels. At level 1, GDSSs provide technical support aimed at removing common communication barriers, to facilitate information exchange among group members and improve the decision process. At level 2, GDSSs provide decision modeling and group decision techniques aimed at reducing uncertainty and "noise" that occur in group decision process.
Finally at level 3, GDSSs are characterized by machineinduced group communication patterns and can include expert advice in the selecting and arranging rules to be applied during a meeting. At this level, deliberate communication patterns are imposed on the group by the technology.
2.2. Interaction factors The paired interaction of the contextual elements (Technology, Groups and Task) produces three interaction factors that need to be considered when analyzing previous research or designing new studies in this area. Graphically, these factors correspond to the overlapping areas of two different contextual factors in the metamodel. They are: Group-Technology adaptation, TaskTechnology fit and Group-Task coordination. Group-Technology Adaptation: Adaptive Structuration Theory (AST)
Groups
Technology
Group- Task Coordination: Coordination Theory Task- Technology Fit: Media Richness and Task-Technology Interaction
Tasks
Figure 2. Interaction factors 2.2.1. Group-Technology adaptation This factor refers to the interaction between technological support and group characteristics; in particular, how the group goes about using the technology. The theory of Adaptive Structuration [22] tries to explain how the technology is used by a group and its resultant effects on group processes and outcomes. According to this theory, each group actively adapts the technology to its own ends, rather than passively receiving it. Since there are many different modes of "appropriation" of the same technology, there will be different effects of such technology on group decision making and other outcomes. Adaptive Structuration Theory (AST) focuses on the interplay between how the group modifies the technologyas-offered, in order to make it work within the group, and how that process modifies the group itself [16]. Each group defines the particular features of the technology it will employ in its practices. Appropriation is the process whereby a group selects and gives meaning to the features
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of a social technology. It is the mode or fashion in which a structure is used, adapted and reproduced [22]. Appropriation is not a passive process. Each group of users gives meaning to, and adapts for its use, particular features of the structure. A technology in use should be conceived as a set of social practices that emerge and evolve over time. In this continuous process, users produce and reproduce the structure. This process is called adaptive structuration. Faithful appropriations are those in which structures are used as designers intended. Stable appropriations are those which occur and are repeated over time. To promote faithful and stable appropriations and limit the misuse of the system, the recommended strategies are training, leadership, or the addition of structures for interaction [22]. Therefore, the interaction between Groups and Technology can be affected through training and facilitation. These are the key elements that can be manipulated to improve the interaction between Groups and Technology, when research studies are designed. Training is meant to familiarize subjects with the technology and with other team members. Adequate training and practice time are necessary in order for groups to be able to use a new technology successfully [6], and to promote stable, faithful and productive appropriations of the technology. Thus, to introduce the features of the new technology adequate training sessions must be scheduled. Facilitation is provided to ease the group into the use of unfamiliar technology by supporting the group in the process of using the system. There are different types of facilitators: (1) Technical facilitators direct the group members as to what GDSS features to use, when to use them and how to use them. This influences how the group uses the technology [5]. The facilitator may also act as a social process agent or external leader, whose function is to ensure that all group members are able to participate in the process and that the session is not dominated by any member or minority of group members [24]. (2) Another mode of facilitative support is called chauffeur-driven. In this approach, an individual external to the group implements features of the GDSS system, at the direction of group members. This mode is similar to the facilitator-driven because it involves an external agent working with the technology. The main difference between facilitators and chauffeurs is that the facilitator intervenes in the group process, in how the group uses the technology to perform the task, while the chauffeur does not attempt to affect the group process [5], although it may affect such a process.
The decision of whether a facilitator should be used and what type of facilitation is necessary, is also a research decision aimed at improving the interaction between Group and Technology. 2.2.2. Task-Technology fit Empirical research has demonstrated that there is an interaction effect between task and technology. GDSS technology seems to be very successful in tasks requiring divergent thinking in groups (creativity tasks such as brainstorming and idea generation tasks). But it seems less appropriate in tasks requiring convergent thinking or consensus among group members [24] [26]. Several meta-analyses have confirmed the interaction between task and technology. McLeod [17] concluded that GDSS lead to increased task focus, increased equality of participation, higher decision quality, longer time to decision and lower consensus. Benbasat and Lim’s [1] meta-analysis concluded that groups working on lower complexity tasks (single component tasks such as generating or choosing tasks) benefit more by the use of GDSS than those working on higher complexity tasks (that is, tasks spanning more than one quadrant in McGrath’s circumplex). The interaction between task and technology can be explained through the “media richness” hypothesis (derived from [2]). According to this hypothesis, it is likely that computer-mediated groups will yield effective task performance on idea generation (cognitivecollaborative task in McGrath's circumplex) when using a lean communication medium such as a GDSS, but will tend to be more ineffective for negotiation and other conflict resolution tasks for which face-to-face interaction would be the most appropriate communication medium [16]. The main proposition of the media richness hypothesis suggest an optimum fit between Task and Technology. Specific features of the technology intend to support certain types of tasks. This is the essence of the TaskTechnology Interaction (TTI) model proposed by [23], which identifies appropriate modes of technological support given several task dimensions. In fact, specific task characteristics or task types call for the use of particular tools. For example, idea generation tasks require electronic brainstorming tools; decision making or preference tasks call for voting, polling or ranking modules. Most of these tools (or “task supports" in [19]) are already embedded in typical GDSSs to facilitate task performance. Table 1 describes the most appropriate types of tools by task type. The researcher must decide which tools (if any) are to be used to improve the Task-Technology Fit.
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Task Type Generate Choose
Negotiate
Table 1. Task-Tool map Support Tool Electronic Brainstorming Tools Idea Evaluation (Weighting, Rating, Ranking and Voting Tools) and Decision Models Negotiation Protocols, Voting, Polling
Source: Adapted from [16], page 69.
2.2.3. Group-Task coordination This refers to the explicit or implicit, imposed or emergent, approach that the group uses to manage the flow of work. According to [12] and [13], coordination is the act of managing interdependencies between activities performed to achieve a goal. Managing the flow of work and making choices regarding what, when, where to do something, and who should be responsible for it, are crucial for the successful completion of a group endeavor [23]. Malone and Crowston [12] identify different types of dependencies (e.g. shared resources, simultaneity constraints and task-subtask interdependency), and discuss basic coordination processes to manage each type of dependency. For example, to manage shared resources a variety of coordination processes such as "first come first served", priority order or budgets may be used. To manage simultaneity constraints, scheduling and synchronization processes are required. To manage tasksubtask interdependencies, top-down goal decomposition or bottom-up goal idenfication may be necessary. In the meta-model, the Group-Task interaction factor includes specific procedures or coordination mechanisms to structure group interaction. In [19], this factor would refer to "process structure" which is intended to organize in steps the group process. According to [6], structuration of group interaction can be achieved either through written instructions such as an agenda or through instructions given by a human leader or facilitator. In both cases, they are explicit coordination mechanisms imposed on the group. In some empirical studies, however, these mechanisms are not predefined, rather they emerge from group interaction. Besides being explicit or implicit, coordination procedures can be manual or automatic. Some systems include group decision process techniques such as Nominal Group Techniques, Delphi and other level 2 GDSS structuration features proposed by [4], which help to structure the group process. Dialectical Inquiry is another approach to structure group interaction for a strategic decision making task [7]. According to this approach, the group is divided into two
sub-groups called "Plan" and "CPlan". The Plan group has to develop a master Plan following the assumptions of the situation. The CPlan group negates the assumptions of the problem and develops a counter plan. Then, both subgroups meet to discuss and develop a single group recommendation. In summary, Group-Task coordination features can be implicit or explicit, imposed or emergent. They encompass a wide range of possibilities that can be employed in order to manage the work flow in a group endeavor.
2.3. Group-Task-Technology (GTT) interaction Besides the dyadic interaction of the contextual factors, there is a three-way interaction among the main factors of the model and their respective dyads. This will be referred to as the GTT interaction factor (Figure 3). Group-Technology Adaptation GTT Interaction Factor
Groups
Technology
Group-Task Coordination Task-Technology Fit
Tasks
Figure 3. GTT Interaction Factor The GTT factor results from the interaction among group, technology and task plus training, facilitation, coordination and adaptation elements. In other words, it refers to the interaction of the single and the dyadic factors among themselves. The interaction meta-model proposes that group outcomes are contingent upon the interplay of contextual factors (Group, Technology and Task) and their dyads. The three-way interaction factor (GTT Interaction Factor), represents the joint interplay of contextual factors and interaction factors. It embodies questions such as: what is the best combination of training, facilitation, tools and coordination mechanisms given the task, technology and group characteristics? The GTT Interaction Factor is essentially dynamic because it emerges from the interaction of many variables. It includes the emergent structuring of the group interaction, given the conditions created from the context in which group processes take place and the adaptation of the group to the technology and the task [8]. The joint
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interaction (GTT Interaction Factor) highlights the fact that contextual factors or dyadic factors in isolation do not account for the results obtained in groupware research. These outcomes are produced by the complex and dynamic interplay of contextual and dyadic factors among themselves.
3. Implications for researchers The meta-model has helped to relate groupware theories to paired interactions of contextual elements. For example, Adaptive Structuration Theory [22] focuses on the Group-Technology dyad; Media Richness [2] and Task-Technology Interaction [23] are concerned with the pair Task-Technology; and Coordination Theory [13] emphasizes the Group-Task dyad. Table 2 relates each theory to its corresponding dyad. Table 2. Groupware theories and dyads Theory Dyad Adaptive Structuration Theory Group-Technology [22] Media Richness [2] and Task- Task-Technology Technology Interaction or TTI [23] Coordination Theory [12] [13] Group-Task Each dyad of the meta-model points to specific research design decisions (Table 3). Group-Technology interaction points to mechanisms to ease group adoption and use of the technology (e.g. training, facilitation). TaskTechnology poses the question: what level of support or what type of tools does the technology provide to improve task performance? (e.g. level 1, 2 or 3 of support or specific tools such as electronic brainstorming). Finally, the Group-Task dyad is related to the emergent or imposed procedures (e.g. agenda) that the group can employ to coordinate its task-related activities and reduce process losses. Table 3. Dyads and research decisions Dyad Research Decisions Group-Technology Training?, Facilitation? Adaptation Task-Technology Fit Tools? (voting, polling, etc.) Level of support? Group-Task Emergent or Imposed (e.g. Coordination agenda)? Therefore, the meta-model provides a useful research guide to make decisions when designing GDSS studies.
However, in practice, it is very difficult to tie these parameters to only two contextual variables and their interaction. For example, some tools (e.g. agenda) can help not only in the performance of the task at hand but also in the coordination of the activities. Hence, electronic tools are related not only to the Task-Technology dyad but also to the Group-Technology and Group-Task dyads. Similarly, training is generally given with practice tasks which closely resemble the actual or experimental tasks. Hence, training is not only related to the GroupTechnology dyad but also related to the task at hand. The bidimensional context of the dyads can help identify some research decisions, but their effects will span to more than one dyad. Therefore, researchers should pay close attention to the joint interaction of these elements and try to answer questions such as what is the best mix of facilitation support, electronic tools and coordination mechanisms, given the characteristics of the contextual factors (Technology, Groups and Task)?
4. Conclusions The meta-model presented in this paper, describes the nature of the contextual elements (Technology, Groups and Task) and their interaction factors, which ultimately account for differences in group processes and outcomes. The proposed interaction meta-model helps to review and integrate several theories, to explain the contingent nature of the outcomes of group work from a broader perspective, and to provide a useful research guide for designing empirical studies in this area. Theory integration and implications for researchers are based upon the interaction of the contextual factors. Paired interactions or dyads helped to identify several research design questions such as type of facilitation, duration and type of training, specific tools or technology features, etc. However, it was shown that these elements (training, facilitation, tools, coordination mechanisms, etc.) could affect more than one dyad. Therefore, the analysis of each contextual factor or dyadic factor in isolation is not enough to account for the results obtained in groupware research. The contingent nature of groupware outcomes must be explained from a broader perspective, which takes into account not only contextual factors and dyadic factors, but also the interactions among them. This higher level interaction is represented by the GTT interaction factor, which points to the conditions (or combination of factors) under which the outcomes of group interaction are better. Two broad implications are derived from this study. First, to organize the existing research in this field, more meta-analyses are needed to identify the more favorable
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mixture of conditions for group interaction. Second, to advance the research agenda in this field, new empirical studies should focus on unexplored combinations of single and dyadic factors. Acknowledgments Partial funding for this research was provided by a National Science Foundation Grant NSF-IRI-9408805. The opinions in this paper are solely those of the author, and not necessarily those of the sponsors of this research. The author thanks Roxanne Hiltz, Murray Turoff, Ajaz Rana and Ron Rice, as well as Alan Dennis and the anonymous referees for their useful comments and suggestions.
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