Paul Gray

Claremont Graduate University

Munir Mandviwalla

Temple University

During the last two decades, GDSS research became a cottage industry within MIS groups at various business schools. Much has been learned. Table 1 (as well as Gray and Nunamaker 1996) summarize this conventional wisdom.

TABLE 1

THE CONVENTIONAL WISDOM OBTAINED FROM GDSS RESEARCH

• The new electronic forum changes how groups behave.
• Analysis of the available data shows that using decision rooms can improve group work in many situations because they:
• Enable all participants to work simultaneously (human parallel processing), thereby promoting broader input into the meeting process and reducing dominance of the meeting by a few people.
• Provide equal opportunity for participation (through anonymity).
• Enable larger group meetings which can effectively bring more information, knowledge, and skills to bear on the task.
• Provide process structure to help focus the group on key issues and discourages irrelevant digressions and nonproductive behaviors.
• Support the development of an organizational memory from meeting to meeting.
• Advantage of the use of anonymity (e.g., reducing group think and fear of retribution) outweigh disadvantages (e.g., loss of information from body language and voice inflection, increased flaming)
• Facility design should provide comfortable, familiar setting for executives; that is, an executive look-and-feel to the room. Lighting and physical organization of the facility can affect outcome.
• Human factors considerations imply that The maximum allowable wait time for a screen is 1-2 seconds. Training time should be minimal (e.g., 5 minutes for a new tool). Color, overlays, windows, consistent interface, multiple screens, and on-line help are desirable.
• GDSS provides organizational memory and an expanding knowledge base over time
• Individual satisfaction increases with group size. For small groups (3-4 people) the overhead of using the system eats up the gains from the system.

The conventional wisdom leads to the following directions in which progress (and hence research) is needed:

1. What do groups do

2. The effects of GDSS on group work

3. The effect of GDSS on organizations

4. Effects of hardware performance on GDSS

5. Effects of software performance on GDSS

6. Cultural effects in GDSS

7. Training people to use GDSS

These were, in fact the findings of a task force on GDSS several years ago (Gray et. al 1992). In reviewing the findings presented, we have come to the conclusion that the task force was not bold enough. The studies recommended tended to be typical of the PhD dissertations being ground out routinely. The net effect is that following this line of thinking would only lead to incremental improvements in understanding and to refining the conventional wisdom.

In our opinion, major rethinking is needed if GDSS is to move forward:

1. Although the computers are faster and the color is more vibrant, the hardware configurations is basically unchanged in over a decade: PC's connected on a network with access to a public screen.
2. With the exception of human parallel processing, little new has been designed. The applications are still mechanizations of the way groups have always worked. The focus is on text-oriented tasks.
3. The software developed for GDSS focuses principally on assisting brainstorming and mechanizing voting, two of the rarer events in business meetings.
4. The history of GDSS in real organizations is not encouraging. The inability of systems to survive beyond the whims of an individual champion may imply that they do not do anything that is sufficiently important to an organization to maintain its investment.
5. The existing body of GDSS experimental research uses the social science paradigm and focuses principally on behavioral aspects. It is tied to the particular characteristics of individual prototype systems. Such research can provide insights but it is difficult to use the findings to generalize and predict. In most social science research, the objective is to examine what exists societally. It deals with phenomena that are relatively static. However, GDSS is a field in which hardware and software are continually upgrading and (if the field follows the recommendations below) innovative approaches to group meetings are being developed.

If we examine these symptoms, we see that we are at a crossroads in GDSS. We have reached the stage of mechanizing word-oriented problems in group meetings. We have created top down designs each of which enforces a particular view of meetings. As best we can tell, with a few exceptions, much he current work in the field involves refining what has been done. We can either follow the road of refining what we have and reach a dead end or move down new roads that expand the range of our capabilities.

Mandviwalla and Gray (1998) concluded that current research falls short in 5 areas:

• Theoretical basis - theories developed internally by IS researchers such as adaptive structuration are promising but the use and adoption of other relevant theories from referent disciplines is slow.
• Task studied - the tasks do not correspond well with the complexity, experience, and political situations faced by people in organizations.
• Methodology employed - the methodologies are narrowly focused while other promising methodologies such as systems development are ignored
• Usage - there are relatively few organizational users of the systems that are studied most often
• Tools and Features -- the feature set that is studied and available only corresponds to a small part of the collaborative life of the organizational user

One way to understand the problems in the field is to apply a philosophy of science perspective. For example, applying a Kuhnian interpretation suggests that GSS is in the "normal science" mode and as a result of our concerns and the concerns of others is moving into a crisis. The current GSS paradigm includes the following elements:

• Technology is a black box that rarely changes
• There is a problem in meetings that can be corrected by engineering
• Technology can directly change human behavior
• Valid and applicable results can be obtained from college student subjects solving pre-defined tasks using static technology

The classic scenario of why GSS is needed is the image of a meeting that goes on for too long, involves 5 to 10 people, is monopolized by a few with most of the other participants contributing little, and does not achieve anything. In this scenario we visualize that we can insert technology and improve meetings. According to Kuhn (1970), precisely identifying paradigms is not a simple task since they might include values, beliefs, "symbolic generalizations", and exemplars (such as the one above). However, we feel that the above exemplar and lists of assumptions does identify a significant portion of the paradigm.

Kuhn's image of science has been criticized as being inappropriate for IS research. For example, Banville and Landry (1989) suggest that Kuhn's approach is:

• too restrictive in its application to bring valuable contribution to MIS
• the advent of a paradigm does not necessarily guarantee progress
• provides a narrow definition of the concepts of scientific progress
• unclear whether progress occurs most during periods of normal science or during periods of crisis-revolution

We believe that Kuhn's approach is still valuable as it allows a level of historical and time-based analysis that is not possible with Banville and Landry's alternative approach. They propose that fields in general and MIS in specific can be understood based on the following categorization scheme:

• Degree of strategic dependence (politics - researchers need to persuade colleagues of significance and importance to achieve a high reputation)
• Degree of functional dependence (standardization of research procedures -- researchers need to use specific ideas, results, and procedures of fellow specialists in order to construct knowledge claims which are considered valid),
• Strategic task uncertainty (schools of thought -- it is low when members of a field agree on a hierarchy of research problems -- high when they don't -- they are loosely coupled schools of thought).

In Banville and Landry’s view, MIS is a fragmented adhocracy meaning that is has low strategic dependence, high strategic task uncertainty, and low functional dependence. They forecast that MIS will move to a partitioned bureaucracy -- where the degree of strategic dependence is high, strategic task uncertainty is low, and functional dependence remains the same.

A similar analysis can be applied to GSS. We believe that to the researcher who "buys into" the paradigm outlined above GSS exhibits (or should exhibit) the following characteristics:

• High functional dependence
• Low strategic uncertainty
• High strategic dependence

In other words, what Banville and Landry call the "conceptually integrated bureaucracy." They also term this classification as being closest to Kuhn's view of normal science which supports our analysis. However, we believe that GSS is (or should be):

• Low functional dependence
• High strategic uncertainty
• Low strategic dependence

That is, GSS is the partitioned adhocracy that Banville and Landry attributed to MIS in 1989. The next section assumes a partitioned adhocracy, and presents suggestions for how can we bootstrap ourselves into such a mode.

We recognize that our analysis is controversial. It will be hard to objectively prove one interpretation over another as being applicable to GDSS. Nonetheless, we believe the ideas presented provide a basis for ongoing discussion.

The old saw that necessity is the mother of invention applies to GDSS. We have reached the point where we need to expand what we can do with GDSS. Growth can come in:

• capabilities – increasing within group support so that more group activities are supported during a meeting
• range - support more organizational tasks
• effectiveness - improve the effectiveness of groups

To do so, we will need to invent, invent, invent rather than test, test, test what exists.

Increasing the Capabilities Available to Groups So That They Match All Aspects of Meetings. As indicated earlier, most of the existing GDSS software focuses only the verbal and text aspects of meetings, and particularly on recording thoughts from people's heads as in brainstorming sessions. The need is to invent new ways of using software to support all parts of a meeting. Some efforts are going on in this direction. For example, Mandviwalla and Olfman (1994) analyze the basic characteristics of groups and identify several attributes of group work that are ignored by current systems. A second example (Mandviwalla, Gray, Olfman 1997) is a prototype GDSS environment that allows use of existing microcomputer software to support all phases of a meeting. Among other capabilities, it makes it possible for people to use the same software and data in a meeting that they use on their desk, and it allows people to work asynchronously from the meeting. That is, since in a meeting with a varied agenda individuals are only involved in some of the items being discussed, it should be possible for them to work on other parts of the meeting rather than daydream during dull moments. Work is also being done to support visual decision making (such as logos and packaging), the use of multimedia (such as bringing video and other images into the meeting), using video conferencing to bring remotely located specialists into the meeting for their advice.

Increasing the Range of Applications. Table 2 lists some "innovative" applications of GDSS. Most of these applications are innovative only in the sense that relatively little has been done thus far to use GDSS for these purposes. However, some (such as electronically mediated debates, temporary organizations, adding an expert system to the meeting) become possible because of the technology.

TABLE 2

SOME INNOVATIVE USES OF GDSS

• Eliciting system design requirements from users and from management

Increasing the range of applications has many benefits, besides improving meetings involving the applications. As Huber (1984) pointed out, systems that are not used fully will not survive. Increasing the number of applications also increases the number of times specific individuals use the facilities. They become better trained and become owners of the systems.

Furthermore, innovation begets innovation. As we use GDSS for more and more purposes, we will inevitably start inventing additional applications to which GDSS applies.

Improving the Effectiveness of Groups. Much still remains to be learned about groups. Most of the existing data is based on small groups meeting in non-computer environments. GDSS results thus far have found that human parallel processing improves the efficiency of groups. Good evidence exists that the anonymity possible with GDSS broadens the number of people who provide input to a meeting and that people are more willing to voice opinions. Yet, groups are still mostly working in the same modes as they did 20 or more years ago when nominal group and Delphi methods were developed. Part of our inventiveness needs to address the group process. How do we achieve more productive groups? How do we achieve more group effectiveness? How can our technological support help the group be more productive, more effective? Are the new technologies such that new group configurations can be implemented?

Group Decision Support Systems is the quintessential information processing problem, involving the intersection of computer science, behavioral science, and management science. In recent years, GDSS has focused on using the behavioral sciences to explore a relatively narrow range of group support applications. It is our contention that continuing along this single dimension will not lead much further. We need to apply all three disciplines in inventive new ways if we are to move GSS to new triumphs. We can do so as a profession because:

• we have the philosophical tools for thinking about the problem
• we have the research experience that gives us the needed insights
• we have inventive, trained researchers skilled in computing, management, and behavior.

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