Zigurs (1998) a Theory of Task-Technology Fit and Group Support Systems Effectiveness

A Theory of Task/Technology Fit and Group Support Systems EffectivenessAuthor(s): Ilze Zigurs and Bonnie K. BucklandReviewed work(s):Source: MIS Quarterly, Vol. 22, No. 3 (Sep., 1998), pp. 313-334Published by: Management Information Systems Research Center, University of MinnesotaStable URL: http://www.jstor.org/stable/249668 .Accessed: 07/11/2011 17:00

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GSS Task/Technology Fit

A Theory of

Task/Technology Fit and Group Support Systems Effectiveness1

By: lize Zigurs University of Colorado, Boulder College of Business

and Administration Campus Box 419 Boulder, CO 80309-0419 U.S.A. [email protected]

Bonnie K. Buckland Great Plains Regional Medical Center P.O. Box 1167 North Platte, NE 69103-1167 U.S.A. buckland @ ns.nque.com

Abstract

The characteristics of a group's task have been shown to account for more than half the variation in group interaction. In the context of group support systems (GSS), the importance of task has been underscored by the recom- mendation that achieving a fit between task and technology should be a principle for effective GSS use. Although the body of group support systems research has grown in recent years, and experience with different tasks and technologies now exists, no generally accepted theory of task/technology fit has emerged. This paper develops a theory of task/technology fit in GSS environments based on attributes

1Robert Zmud was the accepting senior editor for this paper.

of task complexity and their relationship to relevant dimensions of GSS technology. Propositions to guide further research are developed from the theory.

Keywords: Group support systems, electronic meeting systems, group tasks, task complexity

ISRL Categories: AA09, AC0402, DB05, DC02, HA0301, HA1101, IB03

Introduction The study of task has a long history, both in the organizational literature (Drazin and Van de Ven 1985; Perrow 1967; Thompson 1967) and from a group process perspective (Hackman 1968; McGrath 1984). Many scholars of group behavior have argued that the nature of the task plays an important role in a group's interaction process and performance (Poole et al. 1985; Shaw 1981). The advent of computer-based group support systems (GSS) provides a new environment in which to study the role and nature of task. Specifically, the interaction of group tasks with GSS technology is a potent area of study that has possibilities for enhancing group performance.

In the GSS literature, early conceptual papers emphasized the importance of tasks (DeSanctis and Gallupe 1987; Huber 1984) and brought into the GSS mainstream a task classification scheme that has been widely used (Hollingshead and McGrath 1995; McGrath 1984). McGrath's task circumplex has had a significant impact on the GSS field, and subsequent frameworks for GSS research have included task as an important variable (e.g., Benbasat and Lim 1993; Nunamaker et al. 1991). Recent theoretical developments have gone beyond the frameworks and tried to account for complexities such as change and adaptation over time (e.g., DeSanctis and Poole 1994; McGrath and Hollingshead 1994) and task plays an important part in these theories as well.

MIS Quarterly/September 1998 313


But what do we really know about the relationship between group tasks and GSS technology? Can we specify particular combinations of task and GSS technology that will enhance group performance? Recent reviews and meta-analyses of GSS research have addressed these issues to some extent. In some cases, GSS was found to be more appropriate for complex rather than simple tasks (Dennis and Gallupe 1993; Dennis et al.1990b), while in others, GSS was more appropriate for less complex tasks and single- solution tasks (Benbasat and Lim 1993). For idea-generation tasks, GSS-supported groups performed better or no worse than non-supported groups (Dennis and Gallupe 1993; Dennis et al. 1990b; Hollingshead and McGrath 1995). For negotiation and correct- answer tasks, however, GSS groups performed worse (Hollingshead and McGrath 1995). These findings are equivocal and reveal that a considerable opportunity exists for enhancing our knowledge of the task-related circumstances under which GSS can be used most effectively.

The opportunity lies in several areas. First, even though McGrath's circumplex is both useful and widely-used, it is only one of several different ways to define task. Alternate definitions or categorizations need to be examined to determine whether they provide greater leverage for matching task to technology. Second, of the many characteristics by which task has been described, complexity appears often in GSS research yet has rarely been defined much less consistently used. A more in-depth examination of complexity's role in task and GSS technology issues may provide new insights. Third, the concept of fit itself is not well developed in GSS research and more often implied than defined. Since the conceptualization of fit is related to subsequent data analyses (Venkatraman 1989), it is important to define fit explicitly in GSS contexts. Finally, although general frameworks for the study of groups and GSS have made a useful contribu- tion by cataloguing characteristics of both tasks and technology, a detailed scheme for measuring and applying those characteristics is still missing. Such a scheme could provide a consistent structure within which to conduct

and compare studies, as well as a foundation for future evolution of technology design and use.

Given these opportunities, the purpose of this paper is to develop a theory of task/technology fit in GSS environments that serves to enhance and supplement current knowledge. The theory fills a current gap by first separately defining task, GSS technology, and fit. Task/ technology fit is then defined as ideal profiles of task/technology alignment, and propositions are presented for predicting GSS effectiveness and therefore enhanced group performance in a GSS environment. The theory is determinis- tic to the extent that it prescribes expectations about performance, based on characteristics of task and technology. However, the theory is seen as being embedded in the larger context of human actors, technology, and institutional properties, where technology is only one of many elements of social context that influence patterns of action (Barley 1986; McGrath et al. 1993; Orlikowski 1992). Thus the theory co- exists usefully with the "soft determinism" of social construction perspectives that view technology as both an objective and emergent phenomenon (Barley 1986). Ideal profiles of task/technology fit can be viewed as consistent with the perspective of a "taxonomy of scripts" that describe episodes of structuring behavior leading to differences in technological effects. Those episodes are larger in scope than the profiles in this paper since the episodes account for the strategic role of human actors, but the theory presented here is a useful starting point that can be viewed as an "inner layer" of the complex totality of human and technological interaction during group work.

Task

Defining task

The theory is not intended to encompass all tasks, but to focus on the kinds of tasks that are typically encountered in organizational decision making groups. Table 1 summarizes

314 MIS Quarterly/September 1998


a select group of relevant task classifications that have been put forward since 1950.

These classification schemes distinguish among tasks in various ways. These perspectives are summarized as belonging to one of

four conceptualizations of task: (1) task as behavior description, (2) task as ability requirements, (3) task qua task, and (4) task as behavior requirements (Hackman 1969). The argument, accepted here, is that the first and second perspectives are not helpful for

Table 1. Examples of Task Classifications

Author(s) Classification Basis Task Categories Carter, Haythorn, and Group activities Clerical, discussion, intellectual Howell (1950) construction, mechanical assembly,

motor coordination, reasoning Shaw (1954) Complexity Simple vs. complex Bass, Pryer, Gaier, and Difficulty Easy vs. difficult Flint (1958) Hackman (1968) Behavior requirements of Production, discussion, problem

intellective tasks that yield solving written products

O'Neill and Alexander Behavior requirements of Discussion, decision, performance (1971) intellective tasks Steiner (1972) Unitary tasks ultimately classified Unitary vs. divisible, maximizing vs.

into one of four types on basis optimizing, prescribed process vs. of how member contributions permitted process (disjunctive, are permittedto be combined conjunctive, additive, discretionary) to produce the single final product

Shaw (1973) Six non-exclusive continua Difficulty, solution multiplicity, intrinsic interest, cooperation requirements, population familiarity, intellectual- manipulative requirements

Davis, Laughlin, Group member relationships and Among cooperative groups: and Komorita (1976) task performance processes intellective vs. decision; among

competitive or mixed-motive groups: Laughlin (1980) two-person, two-choice tasks vs.

bargaining and negotiation vs. coalition formation

Poole (1978) Dimensions of tasks at the work- Difficulty, variability, interdependence unit level

McGrath (1984) What the group or individual Generate (planning vs. creativity); is to do; presented as a choose (intellective vs. decision "circumplex," a circle divided making); negotiate (cognitive conflict into four quadrants with two vs. mixed motive); execute (contests/ categories in each quadrant battles vs. performances/

psychomotor) Campbell (1988) Various combinations of four Simple, decision, judgment, problem,

basic complexity attributes fuzzy



advancing research about group tasks. Task as behavior description is not an instructive approach because task is defined by whatever the group members actually do. In other words, this approach defines the independent variable (task) in terms of the dependent variable (group performance) rather than in terms of properties of the independent variable itself. Task as ability requirements similarly fails to define a task in terms of its own properties. Instead, this perspective uses "relatively enduring aspects of the performer" (Hackman, 1969, p. 111) to describe tasks. The two remaining approaches to defining task, however, deserve further attention.

The task qua task approach focuses on aspects of the actual task materials that are presented to the group (Hackman 1969). In the literature on task qua task, many studies have focused on task complexity as the characteristic of greatest interest, for instance, differentia- tion between analyzable and unanalyzable problems based on their complexity (Perrow 1967). Task complexity has been defined as consisting of three components: (1) coordina- tive complexity (the number of non-linear sequences between components and task products), (2) component complexity (the number of distinct acts and the number of distinct information cues involved in the task), and (3) dynamic complexity (the stability of the relationships between inputs and the product) (Wood 1986). Other research focuses on task complexity and builds on these earlier ideas by defining complexity in terms of solution paths and their relationship to outcomes (Campbell 1988). That research relates task complexity "directly to the task attributes that increase information load, diversity, or rate of change" (Campbell 1988, p. 43) and further states that this allows complexity to be determined independent of the person performing the task.

The final view-of task as behavior requirements-also provides fruitful ground for group research. Because required behaviors vary from task to task, it is argued that "behavior requirements can legitimately be viewed as characteristics of tasks (rather than characteristics of the performer)" (Hackman 1969, p. 111). McGrath's task circumplex is based on

task as behavior requirements to the extent that each task is categorized by its objective, i.e., what the group members are supposed to do to accomplish the task, for example, a creativity task requires generation of ideas. However, behavior requirements for a task include not only what must be accomplished to meet stated goals, but how those goals should be accomplished, i.e., the processes by which the task should be carried out (Hackman 1969). The stimulus set for any given task (whether experimental or not, and whether explicit or not) includes physical materials and instructions, with the instructions having "the function of eliciting the mediating processes for problem solving" (Gagne 1966, p. 135).

The final view of task complexity integrates these two conceptualizations of task: the task qua task approach and the task as behavior requirements approach (Campbell 1988). While it focuses on characteristics of the task as presented to the group, it simultaneously acknowledges that those characteristics define both what is to be accomplished and how it is to be accomplished. Thus, a group task is defined here as the behavior requirements for accomplishing stated goals, via some process, using given information.

This definition of group task focuses on stated goals, that is, on the problem as it is presented to the group. If a group member has hidden goals and tries to redefine the task according to some private agenda, or if a group support system imposes structure on the task to the extent that the task is modified to fit the tools or agenda enforced by the GSS, it is possible that the assigned task may not be the one actually performed by the group. Indeed, the potentially changing nature of the task is a key part of structurational perspectives (Barley 1986; DeSanctis and Poole 1994). This focus on the assigned task is an initial step that per- mits reliable description of tasks in objective terms that allow comparison within and across studies. Such a view is not a barrier to assessing tasks over time as they are redefined through group interaction.



Defining unique task environments

The task definition presented in this paper is an extension and refinement of earlier descrip- tions (Campbell 1988; Wood 1986) and focuses on the central importance of task complexity. Complexity is the one characteristic of task that has been studied the most. Many prior efforts at task classification have either focused directly on complexity or included it as one of several important task characteristics (e.g., Bass et al. 1958; Campbell 1988; Herold 1978; Perrow 1967; Poole 1978; Shaw 1954, 1973). Task complexity has also been an important part of analyses of the GSS literature (Dennis and Gallupe 1993; Dennis et al. 1990b; Gray et al.1990; Pinsonneault and Kraemer 1989). Finally, complexity relates to both process and outcomes of task performance. Thus, complexity plays a key role in differentiating distinct task environments.

As noted earlier, task complexity is related "directly to the task attributes that increase information load, diversity, or rate of change" (Campbell 1988, p. 43). In addition, the levels of these three information processing factors indicate the level of the cognitive demands placed on the person performing the task. A complex task, then, places high cognitive demands on the task performer.

The complexity level of a task has been defined via four dimensions (Campbell 1988), each of which is important in defining unique task environments. The first dimension is outcome multiplicity. Outcome multiplicity means that there is more than one desired outcome of a task. This is important because it increases information load and information diversity. Each outcome requires a separate information processing stream and is essentially a criterion against which a potential solution is evaluated. An example of a task with outcome multiplicity is one where there is more than one stakeholder and each stakeholder has different explicit expectations about what the objectives of the task are, e.g., selecting a family home from a set of alternatives based on price, size, specific features (for example, accommodation of a handicapped family member), proximity to work, proximity to good schools, etc. Note that

it does not matter who is completing this task; its outcome multiplicity is unaffected by the task performers.

The second dimension can be called solution scheme multiplicity. Solution scheme multiplicity means that there is more than one possible course of action to attain a goal. This dimension of the task increases information load. The term "solution path" has been used for this idea (Campbell 1988). The term used in this paper has the same meaning but is clearer in distinguishing a solution path from a process path. If, for example, there are alternative ways to reach a goal, and those alternatives can be specified using a decision tree, this shows the presence of multiple solution schemes in that many configurations of a final solution are possible depending on which branches of the decision tree are chosen. The idea of having to configure a set of elements in reaching a decision also helps to clarify the meaning of multiple solution schemes. If a task involves the explicit consideration of implementation issues, then it becomes more complex in terms of configuring various elements; such a task has solution scheme multiplicity. Solution scheme multiplicity increases information load because one must consider multiple elements and their best configuration. In con- trast, simply choosing one item from among a given set of alternatives is an example of a single solution scheme. A game of chess, a jig- saw puzzle, and employee scheduling are all examples of tasks with solution scheme multiplicity. Again, it does not matter who is completing the task, as the existence of multiple solution schemes is inherent in the task.

The third dimension is conflicting interdependence. Conflicting interdependence may exist among solution schemes where adopting one scheme conflicts with adopting another possible solution scheme. In this case, the adoption of any one scheme substantially alters the situation such that the decision makers cannot simply change their minds, undo that adoption, and return to essentially the same conditions presented in the original task to make a new decision. Conflicting interdependence also exists in cases where outcomes are in conflict with one another, e.g., the classic quantity



versus quality problem. Finally, it may exist where information is in conflict, for example, in intelligence reports from various sources.

The fourth and final dimension can be called solution scheme/outcome uncertainty and is defined as the extent to which there is uncertainty about whether a given solution scheme will lead to a desired outcome. Solution scheme/outcome uncertainty can range from low to high, where high means that the relationship between a solution scheme and the desired outcome is uncertain or highly proba- bilistic (Campbell 1988). Many factors can enter into a determination of the level of solution scheme/outcome uncertainty. Uncertainty is increased in tasks where outcomes are not explicit, where the scope of the problem is large, where there is little useful historical information about solving a particular problem, or where outcomes are difficult to measure. Uncertainty is decreased, for example, in tasks where the task instructions do not require the task performer to explicitly consider the implications of implementation.

It has been noted that other characteristics associated with complexity, such as lack of structure, ambiguity, and difficulty, can be seen as consequences of the four basic complexity attributes outlined above (Campbell 1988). If a task has one or more of the four basic attributes, it is also likely to possess one or more of the associated characteristics (i.e., it is likely to be ill-structured, ambiguous, and/or difficult). On the other hand, a task may be experienced as ill-structured, ambiguous, or

difficult "for reasons that are independent of the basic characteristics of the task itself" (Campbell 1988, p. 45). For example, writing a piece of software to solve a problem may be easy for veteran programmers but difficult for novices, even though the basic characteristics of the task are unchanged.

Different combinations of the four basic dimensions of complexity would result in 16 distinct task environments, which have been aggregated into five task categories based on similari- ties in the presence or absence of the four basic complexity attributes (Campbell 1988). These five task categories are shown in Table 2. Each category is defined in terms of primary attributes that contribute to its complexity. Simple tasks are primarily characterized by a single outcome and solution scheme, problem tasks by solution scheme multiplicity, decision tasks by outcome multiplicity, judgment tasks by conflicting interdependence or uncertainty, and fuzzy tasks primarily by the joint presence of outcome multiplicity and solution scheme multiplicity.

GSS Technology

Defining GSS technology

Group support systems have been defined as systems that combine communication, computer, and decision technologies to support problem formulation and solution in group

Table 2. Aggregated Task Categories (Adapted From Campbell 1988)

Simple Problem Decision Judgment Fuzzy Tasks Tasks Tasks Tasks Tasks

Outcome Multiplicity no no yes no yes Solution Scheme no yes no no yes Multiplicity Conflicting no yes or no yes or no yes or no yes or no Interdependence Solution Scheme/ not low to low to low to low to

I Outcome Uncertainty applicable high high high high



meetings (DeSanctis and Gallupe 1987); as information-based environments that support group meetings and include but are not limited to distributed facilities, computer hardware and software, audio and video technology, proce- dures, methodologies, facilitation, and group data (Dennis et al.1988); and as the collective of computer-assisted technologies used to aid groups in identifying and addressing problems, opportunities, and issues (Huber et al. 1993). This is only a sample of definitions of GSS, but common elements are evident, and the definitions tend to be all-inclusive rather than restrictive.

Classification schemes for group support systems are as abundant as definitions of the technology. Table 3 shows examples of GSS technology classifications. Some authors name explicit categories of systems (e.g., McGrath and Hollingshead 1994), but the majority of authors describe the technology in terms of dimensions, each of which typically is defined by more detailed attributes (e.g., Fjermestad, forthcoming).

As Table 3 shows, the technology can be characterized from many different perspectives, ranging from relatively broad functional categories, to more detailed tool-based descrip- tions, to configurations defined by time and space. However, three common themes are evident: support for communication, for process structuring, and for information processing. Regardless of the hardware or physical configuration of a system, these three themes are the distinguishing characteristics of GSS that are used the most often in the classifications shown in Table 3 and that have been studied the most in prior research. They are also consistent with the major properties of advanced technologies identified in prior theory: communication, decisional, and informational properties (Huber 1990). Therefore, group support systems technology is defined as a set of communication, structuring, and information processing tools that are designed to work together to support the accomplish- ment of group tasks. Communication support relates to communication needs of the group (e.g., input, feedback, and display capabilities, as well as time and space configuration);

structuring support relates to setting up a process by which groups interact (e.g., providing or enforcing agendas); and information processing relates to manipulating and structuring information (e.g., aggregating, evaluating, or structuring information).

As with task, the potential gap between designer intentions for GSS and how it is actually used is recognized. The duality of technology means that it is enacted by human actors as well as institutionalized in structure (Orlikowski 1992). Research has shown how GSS technology can be used in ways that differ from the original spirit of the designers (DeSanctis and Poole 1994). Again, however, it is only the intended system features and tools that can be reliably described objectively, and hence the focus is on the intended system. However, as the technology is adapted through group use, its nature can be reassessed via the characterization scheme.

Defining unique GSS technology environments

Each of the three dimensions from the definition of GSS can be defined further. Communication support can be defined as any aspect of the technology that supports, enhances, or defines the capability of group members to communicate with each other. It includes such elements as simultaneous input, anonymous input, input feedback, and a group display. Group and individual messaging can be considered part of input and display. The communication support dimension also includes the physical configuration of communication channels, since that configuration defines how group members can communicate.

Process structuring is any aspect of the technology that supports, enhances, or defines the process by which groups interact, including capabilities for agenda setting, agenda enforcement, facilitation, and creating a complete record of group interaction (via storing the agenda, all the input, the votes, and so on). Information processing is the capability to



Table 3. Examples of GSS Technology Classifications

Author(s) Classification Basis GSS Technology Categories DeSanctis and Group size and proximity; Decision room, legislative session, local Gallupe (1987) patterns of information decision network, computer-mediated

exchange conference; level 1, level 2, level 3

Pinsonneault and Type of aid provided by Group decision support systems, group Kraemer (1989) the system communication support systems Dennis, George, Group process and Overall categories are not defined; each element Jessup, outcomes, methods, and of classification basis is further described Nunamaker, and environment (e.g., environment includes group size, Vogel (1988) participant location, and timing of meeting) Nunamaker, Process support, process Overall categories are not defined; each element Dennis, structure, task support, of classification basis is further described Valacich, Vogel, and task structure (e.g., process support includes parallel and George (1991) communication, group memory, and anonymity) Johansen (1992) Time and space Same-time/same-place, same- time/

configuration different-place, different-time/ different-place, different-time/same-place

DeSanctis and Structural features and Overall categories are not defined; each Poole (1994) spirit of technology element of classification basis is

characterized by scalable dimensions (e.g., spirit consists of decision process, leadership, efficiency, etc.)

DeSanctis, Interface, functionality, Overall categories are not defined; each

Snyder, and Poole and holistic attributes element of classification basis is further

(1994) described (e.g., functionality includes public display, anonymous voting, private messaging, etc.)

McGrath and Primary function of system Group internal communication support system, Hollingshead (communication, group information support system, (1994) information, or task group external communication support

support) system, group performance support system

Vickers (1994) Extent to which end users Closed, closed/open, open/closed, open can control and/or modify system

Rana, Turoff, and Component parts of system, Individual support, group process support, Hiltz (1997) i.e., presence or absence of meta process support, group model support

generic support features

Fjermestad Communication mode, Overall categories are not defined; each

(forthcoming) design, process structures, element of classification basis is further and task support described (e.g., communication mode

might be voice, text, video, etc.)



gather, share, aggregate, structure, or evaluate information, including specialized templates such as stakeholder analysis or multiattribute utility analysis. It should be noted that templates that structure the problem may shape solutions as well, since people may view possible solutions in terms of how they structured or viewed the problem.

Table 4 shows these three dimensions and examples of elements of each dimension, drawn from what is commonly provided in existing group support systems. An actual GSS implementation typically combines several elements and dimensions together, although a specific tool within that GSS may focus on a single element or dimension. For example, both Topic Commenter and the electronic brainstorming tool in GroupSystems (Nunamaker et al. 1991) would be characterized as communication support tools, while GroupSystems' voting tool would focus on information processing support. The information processing dimension in particular might be implemented via a variety of tools. For instance, the element of structure information could be provided via stakeholder analysis,

multiattribute utility analysis, a decision tree, and so on. In addition, where this element is present, it typically includes the other elements of gathering, aggregating, and evaluating information. A multiattribute utility tool, for instance, will typically include the other elerents as part of the steps for carrying out the analysis.

The second generation of SAMM (Sambamurthy and DeSanctis 1990) is a good example of how a specific GSS combines various tools, each of which support different dimensions. SAMM 2 includes modules for allocation, stakeholder analysis, and multiattribute utility analysis, as well as the ability to gather, aggregate and evaluate information, thus rating high on the information processing dimension. SAMM 2 also includes simultaneous input, anonymous input, input feedback, and group display, thus rating high on communication support. However, this particular GSS is very flexible as to agenda configuration and facilitation options, thus rating relatively low on process structuring. This example shows the importance of looking at a GSS's different tools and how they are used in order to assess the technology's dimensions.

Table 4. Examples of Elements for the Dimensions of GSS Technology

Dimension Examples of Elements Communication Support * Simultaneous input

* Anonymous input * Input feedback * Group display * Physical configuration of communication channels (e.g., synchronous

or asynchronous, proximate or dispersed) Process Structuring * Agenda setting

* Agenda enforcement e Facilitation * Complete record of group interaction

Information Processing * Gather information * Aggregate information * Evaluate information * Structure information (e.g., allocation, stakeholder analysis, multiat-

tribute utility analysis, cross-impact analysis)



Fit

Defining fit

Although the term fit is widely used in a variety of models that deal with contingencies among variables, its precise nature and meaning are rarely stated (Joyce et al. 1982). One excep- tion to this lack of clarity is the strategic management literature, where fit (typically between strategy and structure) has been examined in some detail. Different definitions of fit in three distinct approaches to structural contingency

theory have been identified: fit as congruence, fit as interaction, and fit as internal consistency (Drazin and Van de Ven 1985). These ideas were extended to identify six unique perspectives on fit in the strategy literature: fit as moderation, as mediation, as matching, as gestalts, as profile deviation, and as covariation (Venkatraman 1989). These perspectives vary in their degree of specificity of the theoretical relationship between variables, in the number of variables in the fit relationships, and in whether the concept of fit is anchored to a particular criterion variable. Table 5 summarizes the six perspectives on fit.

Table 5. Perspectives on Fit (From Venkatraman 1989)

Underlying Example Proposition Perspective Conceptualization Description (From Venkatraman) 1. Fit as Matching A match between two The match between

matching theoretically related variables is strategy and structure defined, without reference to a enhances administrative criterion variable. efficiency.

2. Fit as Internal A pattern of covariation or The degree of internal covariation consistency internal consistency among a set consistency in resource

of underlying theoretically related allocations has a variables is defined, without significant effect on reference to a criterion variable. performance.

3. Fit as Internal Gestalts are defined in terms The nature of internal gestalts congruence of the degree of internal coher- congruence among a set

ence among a set of theoretical of strategic variables attributes, involving many differs across high and variables, but not specified with . low performing firms. reference to a criterion variable

4. Fit as Interaction The impact that a predictor The interactive effects of moderation variable has on a criterion vari- strategy and managerial

able is dependent on the level characteristics have of a third variable, which is implications for the moderator. performance.

5. Fit as Intervention A significant intervening Market share is a key mediation mechanism (i.e., an indirect intervening variable

effect) exists between an between strategy and antecedent variable and the performance. consequent variable.

6. Fit as profile Adherence A profile of theoretically related The degree of adherence deviation to a variables is specified and to a specified profile has

specified related to a criterion variable. a significant effect on profile performance.



The first three conceptualizations of fit are criterion-free (Venkatraman 1989), i.e., they have universal applicability and are not anchored to any particular dependent variable, such as effectiveness. Since the desire in this paper is to tie task technology fit to effective performance of groups, these three conceptualizations are not suitable (Nidumolu 1996). The fourth and fifth conceptualizations are limited in the number of variables considered, that is, they are typically used in an association between a single predictor variable, a single moderating or intervening variable, and a single dependent variable. The most promising perspective for task/technology fit in a GSS context is the idea of fit as an ideal profile. This view is consistent with approach to contingency theory presented earlier (Drazin and Van de Ven 1985), allowing for a holistic approach to examining the complexity inherent in organizations. From this perspective, fit is viewed as feasible sets of equally effective alternative designs. Each design should be internally consistent and matched to a configuration of contingencies that face the organization. This is the idea of fit as an adherence to an externally specified profile (Venkatraman 1989). Testing this conceptualization in an organizational strategy context would involve identifying distinct environments, specifying ideal resource deployments for each environment, and testing the performance effects of environment/strategy coalignments (Venkatraman and Prescott 1990).

This conceptualization of fit translates well into a GSS environment. Task/technology fit can be defined as ideal profiles composed of an internally consistent set of task contingencies and GSS elements that affect group performance. The greater the degree of adherence to an ideal profile, the better the performance of the group. Ideal profiles can be operationalized as viable alignments of task and technology. As suggested (Venkatraman and Prescott 1990), a test of task technology fit would require three steps: (1) identifying distinct task environments, (2) specifying ideal technological support for each task environment, and (3) testing the performance effects of task/technology alignments.

Fit in existing GSS research

Achieving a fit between a group's task and GSS technology was suggested as a principle for effective use of group support systems in a seminal paper (DeSanctis and Gallupe 1987). McGrath's circumplex of task types was rec- ommended as a classification scheme, and prescriptions for appropriate GSS technology were provided for three main task types, e.g., choose type tasks should be used with a GSS that aids in selection of a solution. The majority of GSS researchers have used this circumplex to classify their tasks, but DeSanctis and Gallupe's fit prescriptions have not been sys- tematically tested. There is one problem with how researchers have applied the circumplex, based on the fact that tasks typically have elements of both creativity (generate type) and problem-solving (choose type). In empirical work, even if authors classified a task in multiple categories of McGrath's circumplex according to the task's different phases, the subsequent analysis and discussion of results were typically combined across the entire group session. This practice makes it difficult to unravel task/technology fit issues.

A theory of task/technology interaction (TTI) that defines task by the dimensions of complexity, validation, and coordination and technology by the dimensions of individual support, process support, and meta-process support has been proposed (Rana et al. 1997). Each dimension is further defined by more detailed attributes; for instance, the complexity dimension is defined by the attributes of structure, specificity of relationships, and task uncertainty. Each task dimension has a prescribed, best-fit technology dimension associated with it. However, was noted in the study, the propositions are quite broad and a characterization of expected differences in group interaction based upon their task distinctions has yet to be developed.

Two other recent theoretical developments are notable with respect to their attempt to account for the complexity and dynamic nature of group interaction. Adaptive structuration theory includes task as a key source of structure that combines with other sources, such as



technology and the group's internal system, to affect social interaction (DeSanctis and Poole 1994). In the model of the flow of effects for computer-aided work groups (Hollingshead and McGrath 1995), task is a key input variable that combines with other input variables, such as technology and group member attributes, to set up conditions that result in differing patterns of group interaction. These two models are very similar in the role that task plays, namely as a key source of variation in the interaction process. Detailed characteriza- tions of task and technology remain unspeci- fied, however, as does the combination of characteristics that should lead to optimal performance.

Only a few studies have explicitly compared different tasks in GSS environments in an attempt to test fit empirically. One compared an intellective with a decision-making task in manual versus face-to-face GSS versus dispersed GSS groups (Clapper et al. (1991). That study argued that task type would determine the primary kind of influence that occurred during interaction, and different GSS environments would enhance different types of influence. Results showed no main effect for task type, although manual groups working on the judgment task reached consensus signifi- cantly faster than dispersed groups on the intellective task. A task/technology interaction on influence was suggested, but no clear conclusions about task/technology fit could be drawn. A second study also examined two different tasks (generate and choose) with two different technologies (Gopal et al. 1992-93). Task did not account for any differences in the dependent variables, but the study speculated this was because a consistent procedure across experimental conditions might have reduced their dissimilarity.

A different approach was taken in a third study, where two different GSS technologies were used for the same task (Easton et al. 1990). The task was classified as a combination creativity and intellective task and predict- ed different types of outcomes based on the type of technology. Although not all expectations were borne out, creativity was enhanced with one type of technology, while decision

quality was enhanced with another. These results were consistent with the prescriptions in the study for how different characteristics of the technologies should match to different elements of the task. Electronic conversation provided the focus needed in an intellective task, while the divergence of ideas provided by electronic brainstorming supported the creative component of the task.

Perhaps the most extensive comparison of tasks with different GSS environments has been the work of colleagues (e.g., Sia et al. 1996; Tan et al. 1994), who have compared a specific intellective with a decision-making task under a variety of conditions. Although results tend to be mixed, there is some support for task-related differences under varying conditions of the technological environment. For instance, intellective tasks are best supported by decision aids that enhance reasoning and promote informational influence, while decision-making tasks require support for consensus and the support of normative influence (Tan et al. 1994).

Several conclusions can be drawn from the existing conceptual and empirical work related to tasks and GSS technology. First, McGrath's circumplex is the dominant task classification scheme, but the prescriptions based on it have not been tested in a systematic way across different types of GSS technologies. It is not known whether this approach provides the best leverage in a GSS context. Second, the few theories of task/technology fit that have been presented address relatively broad concepts that make it difficult to formulate specific fit prescriptions. For instance, the study by Rana et al. notes that levels of sophistica- tion that define the three dimensions of technology support still need to be defined and the functions for each dimension need to be characterized. Third, the few studies that explicitly compare task and technology reveal that there are differences, but to best understand their effects, a consistent theoretical model of fit is needed. More work needs to be done on how best to confirm existing findings and extend them to a broader set of tasks and technologies.



The theory of task/technology fit presented here builds on and extends this prior work. Existing fit prescriptions and empirical research provided a starting point for assessing key constructs and their potential relationships. The detailed, operational definitions of task, technology, and fit in this paper provide a level of detail that goes beyond the relatively broad nature of prior work. The next section presents the specifics of the theory and a set of propositions derived from it.

The Theory of Task/ Technology Fit

An appropriate task/technology fit should result in higher performing groups; in other words, the criterion variable is group performance. Group performance is a multifaceted construct that has been defined by such concepts as efficiency, outcome quality, process quality, satisfaction, and consensus (Fjermestad and Hiltz 1997; Hollingshead and McGrath 1995). For a given task and group, the relevant performance measure may vary, whether this be in real-life organizational tasks or in experi- mentally controlled situations. Therefore, it is recognized that group performance will be operationalized in different ways, and in the discussion of ideal task/technology alignments, the term "group performance" is used generi- cally. Figure 1 shows the general model of task/technology fit.

Table 6 summarizes the prescriptions of the task/technology fit theory, i.e., the table shows the fit profiles that are expected to enhance group performance. The terms low, medium, and high have been used as an approximation for the level of support prescribed. The discussion that follows reveals that several of the individual elements of Campbell's task categories (Table 2) imply particular technology support, i.e., outcome multiplicity implies a need for processing support; solution scheme multiplicity implies information processing support; and conflicting interdependence implies communication support. However, as will be seen, where these elements occur in combination, the implication is that the GSS must also provide multiple dimensions in varying configurations.

The remainder of this section provides justifi- cation for and propositions about task/technology fit for each of the five task types. Following each proposition, selected examples are presented from existing research to support the proposition. The analysis of the research is by no means comprehensive and focuses on specific examples that relate directly to the type of task being discussed. In some cases, there is very little, if any, prior research that is relevant to the fit propositions.

Simple tasks

Simple tasks have a single desired outcome, a single solution scheme, and no conflicting interdependence or solution scheme/outcome

Task Fit Group Profile Performance

GSS

Technology

Figure 1. General Model of Task/Technology Fit



Table 6. Fit Profiles of Task Categories and Technology Dimensions

Communication Process Structuring Information Support Dimension Dimension Processing Dimension

Simple Tasks High Low Low

Problem Tasks Low Low High Decision Tasks Low High High

Judgment Tasks High Low High

Fuzzy Tasks High Medium High

uncertainty. Therefore, a GSS should provide come of generating the greatest number of primarily communication support so that group ideas. A series of studies that used the elec- members can communicate their ideas about tronic brainstorming module of GroupSystems the solution to one another. The importance of found that the GSS-supported groups per- communication elements for tasks requiring formed better on a variety of measures than information exchange and greater examination non-supported groups (Gallupe et al. 1990, of alternative solutions has been argued (Rao 1991, 1992). These studies support and Jarvenpaa (1991). Too much structure or Proposition 1 because the GSS emphasized focus on information processing could interfere communication support via providing simulta- with simple communication needs and detract neous input, anonymous input, input feedback, from group performance and/or satisfaction and group display of ideas. Figure 2 shows

(Gallupe et al. 1988). Therefore, the focus for what the general model might look like for a

simple tasks should be on the communication simple task; a similar model could be drawn for

support dimension. the other task types.

P1: Simple tasks should result in the best group performance (as defined for the Prblm task specific task) when done using a GSSroem configuration that emphasizes communication support. For problem tasks, the main focus is on finding

the best solution scheme from among multiple Good examples of simple tasks in existing possible schemes, which satisfies a single, GSS research are the so-called brainstorming well-defined desired outcome. The presence tasks, where the focus is on the single out- of multiple solution schemes increases the

High Communication

Simple Task Low Process Structure Group Low Info. Processing Performance

High Communication GSS Tool

Figure 2. Model of Task/Technology Fit for Simple Task



information processing requirements for the task (Campbell 1988). Group members have to be able to configure the problem in various ways in order to achieve the best outcome. Increased information load can be handled by a GSS with elements of information processing. For example, human information processing theory was used to argue that a GSS with computational support would be more effective for tasks with a high proportion of externaliz- able information processing activities (Rao and Jarvenpaa 1991), which would be expected in problem tasks. It has been shown that a task that required consolidating different solution proposals and deciding on the best one had the highest performance with a GSS configuration that emphasized information processing within a single tool (Easton et al. 1990). Information processing is the key support needed for this type of task, to help group members deal with the increased information load. Communication support should be low- sufficient to provide minimal capabilities for communicating information essential to the problem while avoiding communication overload. Process structuring should also be low, since problem tasks do not imply extensive steps that might need agenda support or enforcement and too much structure could dis- tract from analyzing information. Therefore, the focus for problem tasks should be on the information processing dimension.

P2: Problem tasks should result in the best group performance (as defined for the specific task) when done using a GSS configuration that emphasizes information processing.

A research and development project planning task was used with the Capture Lab GSS in two studies that compared GSS-supported groups with non-supported groups (Losada et al. 1990; McLeod and Liker 1992). The task required group members to reach consensus on the appropriate sequence of management activities for the project, thus the task meets this study's definition of a problem task. No significant differences were found between groups in the first study, a finding consistent with the prediction, since the GSS was very low on the information processing dimension.

For the second study, an information structuring tool was added (an outlining tool) that was specifically designed for structuring information in that task, and GSS groups performed better than non-supported groups, again consistent with the prediction.

Decision tasks

The focus for decision tasks is on crafting a solution that best satisfies multiple (and potentially conflicting) outcomes. Each desired outcome involves a separate information processing stream (Campbell 1988), implying both high information load and high information diversity. Each desired outcome is a criterion against which the proposed solution is evaluated. This type of task requires heavy information processing support and, in particular, support for evaluating information. Process structuring is also important to ensure that the group carries out all the steps of criteria identi- fication and evaluation against alternatives. Multicriteria utility analysis is a classic template for structuring this type of problem. Communication support should be low to avoid communication overload for this type of task. The focus for decision tasks should be on the two dimensions of information processing and process structuring.

P3: Decision tasks should result in the best group performance (as defined for the specific task) when done using a GSS configuration that emphasizes information processing and process structuring.

A decision task was used with a GSS that was relatively high on information processing but quite low on process structuring (Zigurs et al. 1988). No positive effect on outcome quality was found for the GSS, as would be expected since there was insufficient process structure to help devise the best solution from among multiple outcomes.



Judgment tasks

For judgment tasks, the emphasis is on resolv- ing the conflict and uncertainty in information associated with the task. Thus, again, information processing is important, but communication support is important as well. Where conflict and uncertainty are paramount, rich communication is needed (Daft and Lengel 1986). Studies of preference-type tasks, which involve conflicts of values, have shown that elements of the communication support dimension help to promote consensus (Sia et al. 1996; Tan et al. 1994). But communication support alone is not enough, as shown by studies of conferencing systems that provide only communication support and no information processing. For instance, two different tasks with groups using a conferencing system were compared, and greater dissatisfaction and inefficiency were found where the task dealt with attitudes and values (Straus and McGrath 1994). Although this comparison was with face-to-face groups and not different kinds of technologies, the study nevertheless sug- gests that communication elements alone were insufficient for dealing with information diversity. The process structuring dimension for this type of task should be low, to avoid an overemphasis on agenda setting and enforcement, which might tend to limit the kind of free exchange of information that is important. Therefore, the focus for judgment tasks should be on the communication support and information processing dimensions.

P4: Judgment tasks should result in the best group performance (as defined for the specific task) when done using a GSS configuration that emphasizes communication support and information processing.

An earlier study used a task that required group members to generate potential solutions for a sales territory problem, a task that exhibits the conflict and uncertainty associated with a judgment task (Dennis et al. 1990a). The GSS tools emphasized communication support, but were low on information processing. The comparison was of ad hoc versus established groups and the expected differ-

ences in quality of outcome did not occur. It may be that the lack of fit in task and technology masked the expected differences, although this is speculative since the comparison was not directly of GSS with non-GSS groups.

Fuzzy tasks

Fuzzy tasks have very little focus, and group members expend most of their effort on understanding and structuring the problem. Information load, information diversity, conflict, and uncertainty are all part of fuzzy tasks (Campbell 1988). Where complexity is high, enhanced information processing is important (Rana et al. 1997). The greatest emphasis, therefore, should be on communication among group members, gathering of information rela- tive to the problem, and using problem structuring templates to understand the problem. Process structuring is also important to the extent that it helps groups structure the process by which they accomplish their work. The structure, however, should be flexible and adaptable to group needs; a strictly enforced agenda, for instance, could lead to biasing the problem definition in the direction provided by the structure rather than permitting the discovery of the "true" problem definition free of imposed structure (Gopal et al. 1992-93). Therefore, the focus for fuzzy tasks should be on communication support, information processing, and on a medium level of process structuring.

P5: Fuzzy tasks should result in the best group performance (as defined for the specific task) when done using a GSS configuration that emphasizes communication support and information processing, and includes some process structuring.

A widely used fuzzy task in the experimental GSS literature is the Foundation task, in which group members allocate funds for a philan- thropic foundation to competing organizations that reflect conflicting values and goals (Watson et al. 1988). The Foundation task exhibits the characteristics of information load, information diversity, conflict, and uncertainty. Several studies paired the Foundation task with



a GSS that had good communication support but low information processing, and these studies found no positive effect for GSS (Ho and Raman 1991; Poole et al. 1991; Watson et al. 1988). A later version of the GSS implemented more information processing and found positive results on outcome quality (Sambamurthy and DeSanctis 1990; Sambamurthy and Poole 1992). These examples lend support to the proposition on fuzzy tasks.

Conclusions and Directions for Future Research The task/technology fit theory defined in this paper has several advantages. First, the theory is based on an explicit conceptualization of task complexity-a fundamentally important aspect of task that is particularly relevant in a GSS environment and that takes into account an important set of task attributes. Second, the characterization of technology is based on key dimensions from prior work and is specifically linked to task demands. Third, the concept of fit has been explicitly defined and linked to the criterion variable of group performance. Fourth, the definitions of task types and technology dimensions lend themselves to being operationalized via coding instruments that can be used to compare and accumulate findings. Finally, the theory can be embedded as one part of a larger complex of contextual variables, such as social and cultural issues, as well as group development and system use and adaptation over time.

The theory provides ideal profiles of group tasks and GSS technology. The next step for testing the theory is to operationalize and test the profiles. This requires that measures be developed for specific task types and technology dimensions. A coding scheme for task types would include an operationalization of each of the dimensions in Table 2 (outcome multiplicity, solution scheme multiplicity, and so on). A task could be rated "yes" or "no" on each dimension and thus assigned to the appropriate task type. Similarly, each dimension of GSS technology would be operational-

ized and rated; a suggestion for rating the dimensions as "low," "medium," or "high" has been presented (Table 6). Existing tasks and GSS technologies can be coded on these dimensions and tested for the extent to which prescribed patterns correlate with positive group performance. Such an analysis is con- ceptually based and follows from the theoretical expectations. Another approach is to derive the profile empirically by using a calibration sample of high performing groups (Sabherwal and Kirs 1994; Venkatraman 1989; Venkatraman and Prescott 1990). In either case, measures of the dimensions can be developed and validated.

A problem with earlier research was the lack of dealing with multipart tasks in a meaningful way. To some extent, the task dimensions presented here alleviate this problem because they are not tied to outcomes from subtasks that would constitute phases of an overall task, e.g., the "idea" from the idea generation subtask, or the "choice" from the choose subtask. Instead, the task dimensions and typology rec- ommended here are based on the underlying complexity dimensions of the task as a whole. But, just as the McGrath circumplex can be used on subtasks, so can this typology, although researchers need to take that step. A usable coding scheme for task types will make it easier to do so.

Future research could address a variety of issues, ranging from testing specific fit prescriptions to extending the theory to a broader organizational context. With respect to specific prescriptions, one opportunity is to test the role of different elements of GSS dimensions. For instance, both problem tasks and fuzzy tasks are expected to benefit from appropriate problem structuring templates (in the information processing dimension), and existing research confirms this is true in isolated cases. But in most cases, only a single template has been imposed on the task, which itself is often matched to that specific template. What is the best way to provide groups with a choice of problem structuring templates that they can explore and use in the most appropriate way? Similarly, it is important to test the prescription that process structuring be flexible in some



cases (fuzzy tasks) and more rigidly enforced in others (decision tasks). How should the prescribed flexibility in process structuring for fuzzy tasks be designed into the technology to make it maximally available to and useful for the groups?

Another opportunity is to examine the extent and type of flexibility that a GSS configuration should have in order to make it manageable for groups. The propositions were stated in a general and positive way, namely that positive effects are expected from prescribed levels of different dimensions of support. But to what extent is performance affected where unneed- ed functionality is present? For instance, too much structure or information processing was expected to interfere with communication needs in simple tasks (Gallupe et al. 1988), but this has yet to be tested empirically.

The question of flexibility is particularly important for users, facilitators, and designers of GSS in organizations. Group support systems are part of the general trend toward development environments and extensible toolkits that allow customization and integration of a variety of capabilities. But there are barriers to wider use of GSS technology in organizations, due to a lack of understanding of the circumstances when the technology should be used, as well as the inappropriate use of a "one size fits all" approach. Even where different tools are carefully chosen for different parts of a meeting, it is important to choose wisely how those tools are combined. For instance, low or high levels of process structuring should be chosen to match the group's task. Groups can chafe under too rigid an agenda (an inappropriately high level of process structuring for a fuzzy task), or lose focus under too loose an agenda (an inappropriately low level of process structuring for a decision task). With the ability to classify tasks and GSS technologies on the dimensions developed in this paper, facilitators can choose appropriate GSS tools for specific tasks. Continuing field research in a variety of settings will also help to evolve the theory, e.g., adding contextual factors that might impact fit issues.

The group performance criterion is also fertile ground for further elaboration. As a starting point, the theory presented here specified group performance inclusively and as an aggregate concept, recognizing that performance is likely to be defined differently in different situations. How might the theory be different if other (or more differentiated) outcome variables were considered, e.g., group cohesiveness or satisfaction? Next to performance-for tasks where performance can be reasonably defined-satisfaction is probably the most-studied outcome variable. Yet results are mixed as to whether GSS groups are more satisfied than traditional groups (Dennis and Gallupe 1993). Satisfaction might be increased where GSS technology is matched to task, since a good fit is the most efficient way of dealing with the cognitive load of a given task. Group climate factors such as cohesiveness and conflict resolution might also be enhanced in situations of good task/technology fit. For instance, the effective resolution of judgment tasks requires groups to deal with the conflict and uncertainty inherent in information about the task. If a GSS does not provide the requi- site communication support, group members are unlikely to be able to resolve task issues, which may lead to a negative group climate. Of course these are speculations, but they provide ideas for further elaboration of the dependent variable of group performance.

This theory was introduced with the assertion that it is a starting point and only one layer of the larger totality of organizational context. The fit prescriptions need to be studied in varying contexts and over time. To what extent, in what ways, and in what kinds of organizational environments do the different dimensions of GSS technology presented here exhibit "interpretive flexibility" (Orlikowski 1992)? How does this interpretive flexibility interact with task performance? What is the role of human intervention where goals are other than task performance oriented? These are questions that can be appropriately addressed with field studies of longer duration than the typical experiment. Not surprisingly, multiple approaches will be needed.



The issues discussed here illustrate some of the challenges associated with developing GSS technologies that are flexible enough to provide communication support, process structuring, and information processing support at appropriate times for appropriate tasks. The theory provides a framework within which some of these issues can be investigated, and guidance can be provided for more effective design and use of the next generation of group support systems.

Acknowledgments

We thank Professors Gerardine DeSanctis and Kenneth Kozar for helpful comments on earlier versions of this paper, and the MIS Quarterly editors and reviewers for supporting and insightful comments during the revision process. This research was partially funded by the Graduate School of Business and Administration, University of Colorado, Boulder.

References

Barley, S. R. 'Technology as an Occasion for Structuring: Evidence from Observations of CT Scanners and the Social Order of Radiology Departments," Administrative Science Quarterly (31:1), 1986, pp. 78-108.

Bass, B. M., Pryer, M. W., Gaier, E. L., and Flint, A. W. "Interacting Effects of Control Motivation, Group Practice and Problems Difficulty on Attempted Leadership," Journal of Abnormal and Social Psychology (56:5), 1958, pp. 352-358.

Benbasat, I., and Lim, L. "The Effects of Group, Task, Context, and Technology Variables on the Usefulness of Group Support Systems: A Meta-analysis of Experimental Studies," Small Group Research (24:4), 1993, pp. 430-462.

Campbell, D. J. "Task Complexity: A Review and Analysis," Academy of Management Review (13:1), 1988, pp. 40-52.

Carter, L. F., Haythorn, W. W., and Howell, M. A. "A Further Investigation of the Criteria of

Leadership," Journal of Abnormal and Social Psychology (46:6), 1950, pp. 589-595.

Clapper, D. L., McLean, E. R., and Watson, R. T. "An Experimental Investigation of the Effect of a Group Decision Support System on Normative Influence in Small Groups," in Proceedings of the Twelfth International Conference on Information Systems, J. I. DeGross, I. Benbasat, G. DeSanctis, and C. M. Beath (eds.), New York, 1991, pp. 273-282.

Daft, R. L., and Lengel, R. H. "Organizational Information Requirements, Media Richness and Structural Design," Management Science (32:5), 1986, pp. 554-571.

Davis, J. H., Laughlin, P. R., and Komorita, S. S. 'The Social Psychology of Small Groups: Cooperative and Mixed-motive Interaction," Annual Review of Psychology (27), 1976, pp. 501-541.

Dennis, A. R., and Gallupe, R. B. "A History of Group Support Systems Empirical Research: Lessons Learned and Future Directions," in Group Support Systems: New Perspectives, L. M. Jessup and J. S. Valacich (eds.), Macmillan Publishing Company, New York, 1993, pp. 59-77.

Dennis, A. R., George, J. F., Jessup, L. M., Nunamaker, Jr., J. F., and Vogel, D. R. "Information Technology to Support Electronic Meetings," MIS Quarterly (12:4), 1988, pp. 591-624.

Dennis, A. R., Easton, A. C., Easton, G. K., George, J. F., and Nunamaker, Jr., J. F. "Ad Hoc Versus Established Groups in an Electronic Meeting System Environment," in Proceedings of the Twenty-Third Annual Hawaii International Conference on System Sciences, Volume III, J. F. Nunamaker, Jr. (ed.), IEEE Computer Society Press, Los Alamitos, CA, 1990a, pp. 23-29.

Dennis, A. R., Nunamaker, J., and Vogel, D. "A Comparison of Laboratory and Field Research in the Study of Electronic Meeting Systems," Journal of Management Information Systems (7:3), 1990b, pp. 107-135.

DeSanctis, G., and Gallupe, R. B. "A Foundation for the Study of Group Decision Support Systems," Management Science (33:5), 1987, pp. 589-609.



DeSanctis, G., and Poole, M. S. "Capturing the Complexity in Advanced Technology Use: Adaptive Structuration Theory," Organization Science (5:2), -1994, pp. 121-147.

DeSanctis, G., Snyder, J. R., and Poole, M. S. 'The Meaning of the Interface: A Functional and Holistic Evaluation of a Meeting Software System," Decision Support Systems (11:4), 1994, pp. 319-335.

Drazin, R., and Van de Ven, A. H. "Alternative Forms of Fit in Contingency Theory," Administrative Science Quarterly (30:4), 1985, pp. 514-539.

Easton, G. K., George, J. F., Nunamaker, Jr., J. F., and Pendergast, M. O. "Using Two Different Electronic Meeting System Tools for the Same Task: An Experimental Comparison," Journal of Management Information Systems (7:1), 1990, pp. 85-100.

Fjermestad, J. "An Integrated Framework for Group Support Systems," Journal of Organizational Computing and Electronic Commerce, forthcoming.

Fjermestad, J., and Hiltz, S. R. "Experimental Studies of Group Decision Support Systems: An Assessment of Variables Studied and Methodology," Proceedings of the Thirtieth Annual Hawaii International Conference on System Sciences, Volume II, 1997, pp. 45-65.

Gagne, R. M. "Human Problem Solving: Internal and External Events," in Problem Solving: Research, Method and Theory, B. Kleinmuntz (ed.), Wiley, New York, 1966.

Gallupe, R. B., DeSanctis, G., and Dickson, G. "Computer-based Support for Group Problem-finding: An Experimental Investigation," MIS Quarterly (12:2), 1988, pp. 277-296.

Gallupe, R. B., Cooper, W., and Bastianutti, L. "Why is Electronic Brainstorming More Productive Than Traditional Brain- storming?" Proceedings of the 1990 Administrative Sciences Association of Canada Conference, Whistler, BC, Canada, 1990, pp. 82-92.

Gallupe, R. B., Bastianutti, L. M., and Cooper, W. H. "Unblocking Brainstorms," Journal of Applied Psychology (76:1), 1991, pp. 137-142.

Gallupe, B., Dennis, A. R., Cooper, W. H., Valacich, J. S., Bastianutti, L. M., and Nunamaker, J. F. "Electronic Brainstorming and Group Size," Academy of Management Journal, (35:2), 1992, pp. 350-369.

Gopal, A., Bostrom, R. P., and Chin, W. W. "Applying Adaptive Structuration Theory to Investigate the Process of Group Support System Use," Journal of Management Information Systems (9:3), 1992-93, pp. 45-69.

Gray, P., Vogel, D., and Beauclair, R. "Assessing GDSS Empirical Research," European Journal of Operational Research (46:2), 1990, pp. 162-176.

Hackman, J. R. "Effects of Task Char- acteristics on Group Products," Journal of Experimental Social Psychology (4:2), 1968, pp. 162-187.

Hackman, J. R. "Toward Understanding the Role of Tasks in Behavioral Research," Acta Psychologica (31:2), 1969, pp. 97-128.

Herold, D. M. "Improving the Performance Effectiveness of Groups Through a Task- contingent Selection of Intervention Strategies," Academy of Management Review(3:2), 1978, pp. 315-325.

Ho, T. H., and Raman, K. S. "The Effect of GDSS and Elected Leadership on Small Group Meetings," Journal of Management Information Systems (8:2), 1991, pp. 109-133.

Hollingshead, A. B., and McGrath, J. E. "Computer-assisted Groups: A Critical Review of the Empirical Research," in Team Effectiveness and Decision Making in Organizations, R. A. Guzzo, E. Salas and Associates (eds.), Jossey-Bass, San Francisco, 1995, pp. 46-78.

Huber, G. P. "Issues in the Design of Group Decision Support Systems," MIS Quarterly (8:3), 1984, pp. 195-204.

Huber, G. P. "A Theory of the Effects of Advanced Information Technologies on Organizational Design, Intelligence, and Decision Making," Academy of Management Review (15:1), 1990, pp. 47-71.

Huber, G. P., Valacich, J. S., and Jessup, L. M. "A Theory of the Effects of Group Support Systems on an Organization's



Nature and Decisions," in Group Support Systems: New Perspectives, L. M. Jessup and J. S. Valacich (eds.), Macmillan Publishing Co., London, 1993, pp. 255-269.

Johansen, R. "An Introduction to Computer- augmented Teamwork," in Computer- Augmented Teamwork: A Guided Tour, R. P. Bostrom, R. T. Watson, and S. T. Kinney (eds.), Van Nostrand Reinhold, New York, 1992, pp. 5-15.

Joyce, W., Slocum, Jr., J. W., and Glinow, M. "Person-situation Interaction: Competing Models of Fit," Journal of Occupational Behaviour (3:4), 1982, pp. 265-280.

Laughlin, P. R. "Social Combination Processes of Cooperative Problem-solving Groups on Verbal Intellective Tasks," in Progress in Social Psychology, Volume 1, M. Fishbein (ed.), Lawrence Erlbaum Associates, Hillsdale, NJ, 1980, pp. 127-155.

Losada, M., Sanchez, P., and Nobel, E. E. "Collaborative Technology and Group Process Feedback. Their Impact on Interactive Sequences in Meetings," in Proceedings of the Conference on Computer Supported Cooperative Work, D. Tatar (ed.), Association for Computing Machinery, Baltimore, MD, 1990, pp. 53-64.

McGrath, J. E. Groups: Interaction and Performance, Prentice-Hall, Englewood Cliffs, NJ, 1984.

McGrath, J. E., and Hollingshead, A. B. Groups Interacting with Technology: Ideas, Evidence, Issues, and an Agenda, Sage Publications, Thousand Oaks, CA, 1994.

McGrath, J. E., Arrow, H., Gruenfeld, D. H., Hollingshead, A. B., and O'Connor, K. M. "Groups, Tasks, and Technology: The Effects of Experience and Change," Small Group Research (24:3), 1993, pp. 406-420.

McLeod, P. L., and Liker, J. K. "Electronic Meeting Systems: Evidence from a Low Structure Environment," Information Systems Research (3:3), 1992, pp. 195-223.

Nidumolu, S. R. "A Comparison of the Structural Contingency and Risk-based Perspectives on Coordination in Software- development Projects," Journal of

Management Information Systems (13:2), 1996, pp. 77-113.

Nunamaker, J. F., Dennis, A. R., Valacich, J. S., Vogel, D. R., and George, J. "Electronic Meeting Systems to Support Group Work," Communications of the ACM (34:7), 1991, pp. 40-61.

O'Neill, M. S., and Alexander, J. F. "Family Interaction Patterns as a Function of Task Characteristics," Journal of Applied Social Psychology(1:2), 1971, pp. 163-172.

Orlikowski, W. J. 'The Duality of Technology: Rethinking the Concept of Technology in Organizations," Organization Science (3:3), 1992, pp. 398-427.

Perrow, C. "A Framework for the Comparative Analysis of Organizations," American Sociological Review (32:2), 1967, pp. 194-168.

Pinsonneault, A., and Kraemer, K. 'The Impact of Technological Support on Groups: An Assessment of the Empirical Research," Decision Support Systems (5:2), 1989, pp. 197-216.

Poole, M. S. "An Information-task Approach to Organizational Communication," Academy of Management Review (3:4), 1978, pp. 493-504.

Poole, M. S., Siebold, D. R., and McPhee, R. D. "Group Decision-making as a Structurational Process," Quarterly Journal of Speech (71:1), 1985, pp. 74-102.

Poole, M. S., Holmes, M., and DeSanctis, G. "Conflict Management in a Computer-supported Meeting Environment," Management Science (37:8), 1991, pp. 926-953.

Rana, A.., Turoff, M., and Hiltz, S. R. "Task and Technology Interaction (TTI): A Theory of Technological Support for Group Tasks," Proceedings of the Thirtieth Annual Hawaii International Conference on System Sciences, Volume II, 1997, pp. 66-76.

Rao, V. S., and Jarvenpaa, S. L. "Computer Support of Groups: Theory-based Models for GDSS Research," Management Science (37:10), 1991, pp. 1347-1362.

Sabherwal, R., and Kirs. P. "The Alignment Between Organizational Critical Success Factors and Information Technology Capability in Academic Institutions," Decision Sciences (25:2), 1994, pp. 301-330.



Sambamurthy, V., and DeSanctis, G. "An Experimental Evaluation of GDSS Effects on Group Performance During Stakeholder Analysis," in Proceedings of the Twenty- Third Annual Hawaii International Conference on System Sciences, Volume III, J. F. Nunamaker Jr. (ed.), IEEE Computer Society Press, Los Alamitos, CA, 1990, pp. 79-88.

Sambamurthy, V., and Poole, M. S. "The Effects of Variations in Capabilities of GDSS Designs on Management of Cognitive Conflict in Groups," Information Systems Research, (3:3), 1992, pp. 224-251.

Shaw, M. E. "Some Effects of Problem Complexity upon Problem Solution Efficiency in Different Communication Nets," Journal of Experimental Psychology (48:3), 1954, pp. 211-217.

Shaw, M. E. Scaling Group Tasks: A Method for Dimensional Analysis," JSAS Catalog of Selected Documents in Psychology (3), 1973, p. 8.

Shaw, M. E. Group Dynamics: The Psychology of Small Group Behavior (3rd ed.), McGraw-Hill Book Company, New York, 1981.

Sia, C., Tan, B. C. Y., and Wei, K. "Exploring the Effects of Some Display and Task Factors on GSS User Groups," Information & Management (30:1), 1996, pp. 35-41.

Steiner, I. D. Group Process and Productivity, Academic Press, New York, 1972.

Straus, S. G., and McGrath, J. E. "Does the Medium Matter? The Interaction of Task Type and Technology on Group Performance and Member Reactions," Journal of Applied Psychology (79:1), 1994, pp. 87-97.

Tan, B. C. Y., Raman, K. S., and Wei, K. "An Empirical Study of the Task Dimension of Group Support System," IEEE Transactions on Systems, Man, and Cybernetics (24:7), 1994, pp. 1054-1060.

Thompson, J. D. Organizations in Action, McGraw-Hill Book Company, New York 1967.

Venkatraman, N. "The Concept of Fit in Strategy Research: Toward Verbal and Statistical Correspondence," Academy of

Management Review (14:3), 1989, pp. 423-444.

Venkatraman, N., and Prescott, J. E. "Environment-strategy Coalignment: An Empirical Test of Its Performance Implications," Strategic Management Journal(11:1), 1990, pp. 1-23.

Vickers, B. "Designing Layered Functionality Within Group Decision Support Systems," Decision Support Systems (11:1), 1994, pp. 83-99.

Watson, R., DeSanctis, G., and Poole, M. S. "Using a GDSS to Facilitate Group Consensus: Some Intended and Unintended Consequences," MIS Quarterly (12:3), 1988, pp. 463-478.

Wood, R. E. "Task Complexity: Definition of the Construct," Organizational Behavior and Human Decision Processes (37:1), 1986, pp. 60-82.

Zigurs, I., Poole, M. S., and DeSanctis, G. "A Study of Influence in Computer-mediated Group Decision Making," MIS Quarterly (12:4), 1988, pp. 625-644.

About the Authors

lize Zigurs is an associate professor of information systems in the College of Business and Administration at the University of Colorado, Boulder. Her research interests focus on the

design and impacts of computer-based systems for supporting collaborative work. Professor Zigurs' work has been published in such journals as the MIS Quarterly, Journal of

Management Information Systems, and Decision Support Systems.

Bonnie K. Buckland earned her Ph.D. in information systems from the University of Colorado, Boulder. Her research interests are in the small business use of information tech-

nology, the use of technology in education, and the social impacts of information technolo-

gy. She is currently the chief information officer at Great Plains Regional Medical Center in North Platte, Nebraska.


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