Creative Problem Solving in GSS Groups: Do Creative ... - Springer Link

Group Decis Negot (2013) 22:1129–1157 DOI 10.1007/s10726-012-9309-3

Creative Problem Solving in GSS Groups: Do Creative Styles Matter? Deepa K. Ray · Nicholas C. Romano Jr.

Published online: 26 August 2012 © Springer Science+Business Media B.V. 2012

Abstract Creative groups drive innovation and organizational change and collaborative systems can be used to pool creative team members across the globe. How individual creative preference impacts the group’s creative performance across different creative problem solving phases in a GSS environment is not well understood. The objective of this exploratory study was to understand if there are differences in group performance when groups with varying member creative styles interact solely via GSS. We conducted a quasi-experimental study that compared the performance of groups with two alternate member styles interacting only via group support systems during a creative problem solving process. Ideator and Evaluator groups were compared on their divergent and convergent phase performance. Significant differences were found between the Ideator groups and Evaluator groups on idea fluency, idea flexibility, idea novelty, idea elaboration and solution cost-effectiveness. No significant differences were found between the performance of the two groups on solution feasibility and novelty. Results indicate that member creative styles play an important role in determining the performance of technology-supported groups. These results aid researchers and practitioners by improving their understanding of the performance of creative teams interacting solely via collaborative support systems for creative problem solving tasks.

D. K. Ray (B) S. P. Jain Institute of Management and Research, Munshi Nagar, Dadabhai Road, Andheri West, Mumbai 400058, India e-mail: [email protected] N. C. Romano Jr. School of Information Management, Faculty of Commerce and Administration, Wellingotn Room 525, Rutherford House 23 Lambton Quay, Pipitea Campus, Wellington 6140, New Zealand e-mail: [email protected]

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Keywords GSS

D. K. Ray, N. C. Romano Jr.

Creative style · Electronic brainstorming · Creative problem solving ·

1 Introduction Rapidly changing business scenarios and highly competitive markets have forced companies to recognize the need to innovate and be creative. The most successful organizations will have an environment where creativity and innovation are occurring consistently at all levels of the organization, and in all functions—(Vicenzi 2000). Any organization’s ability to be innovative or creative depends on its employees and their creative potential. They are the ones who pioneer new technologies giving rise to powerful economic growth (Florida et al. 2005). Thus, one can reason that an individual’s creative potential forms the base for an organization’s creative capital. Creativity exhibited by individuals within the organization is often in response to finding solutions to organizational problems. This manifestation of creativity which takes the process view to solve problems has been explored in the creativity literature and is known as Creative Problem Solving (CPS) process. According to the CPS literature, the creative problem solving process almost always reflects the steps of (a) looking at the facts, (b) formulating the problem, (c) generating ideas, (d) evaluating and selecting the solution and finally (e) aiming for acceptance (Basadur et al. 1982; Isaksen and Treffinger 1985; Mumford et al. 1991; Osborn 1957; Parnes et al. 1977). In view of the fact that creativity in the organizational context often refers to solving problems for the organization, examining individual creativity will be fruitful if it is also in the same context. This will give a better understanding of how individual creativity benefits the organization. Individual creativity has been examined by looking at the relationship between creative styles exhibited by individuals and how they contribute to the creative problem solving process (Puccio et al. 2005). Individual creative styles are often manifestations of individual preferences or predisposition towards certain creative processes. However, individual creativity is not the only input to an organization’s creative problem solving process. Most employees work as a part of various organizational groups. So, it becomes important to understand how an individual employee’s creativity impacts the creativity of the group. Creativity researchers need to expand the scope of individual level personality traits and cognitive skills to examine team creativity (Kurtzberg and Amabile 2000). Basadur et al. (2000) examined how a group’s composition contributed to its productivity and found that individual creative styles do impact group performance. They called for research to understand if technology could be used to facilitate such teams. They emphasized the importance of understanding if a Group Support System (GSS) could be used to facilitate interaction and improve understanding among team members. This parallels multiple other calls for research on individual creative styles as well as group composition in technology supported groups (Nagasundaram and Bostrom 1995; Valacich et al. 2006). Call for such kind of research reflects the fact that technol-

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ogy has become a major driver of change in today’s context. Advancements in technology are enabling organizations to access expertise, talent and resources irrespective of their physical location (Boh et al. 2007; Robert et al. 2009). Global competition is forcing organizations to become innovative in their efforts. As a result organizational structures are changing and becoming more flexible (Nemiro 2002). Due to this, technology supported teams are more a norm than the exception. Thus, even though it is known that technology contributes to organizational change and effectiveness (Amabile 1996; Woodman et al. 1993), understanding the role of technology in the productivity of virtual creative groups becomes equally important. Creative teams within the organization can collaborate electronically and work together even though they are physically separated (Ocker 2005). Under such conditions, research needs to inform practitioners on what can be done to maximize the distributed group’s productivity. Research on technology supported creative groups has primarily been dominated by studies on idea generation and on the use of specific kinds of GSS for facilitating group brainstorming process (namely electronic brainstorming [EBS]) (Barki and Pinsonneault 2001; Fjermestad 1998). Phases in the creative process that come later and deal with evaluation and selection have not been in the forefront of creativity research (Mumford 2001). This is true of creativity research within the technologysupported group domain as well. Isolating idea generation from the creative process is one of the main issues with most brainstorming research (Faure 2004) because idea generation is necessary but not completely sufficient to bring about innovation (Nijstad and De Dreu 2002). Even if the problem is known or formulated, all ideas generated as possible solutions may not be feasible. Ideas generated will have to be evaluated, selected and refined in order to be implemented (Mullen et al. 1991) within the company. In order to find innovative solutions, organizations will need to complete multiple phases in the CPS process. Prior research examining multiple phases in the CPS process for technology supported groups has been limited (Kerr and Murthy 2004) and needs to be examined more closely. Research on GSS-supported complex tasks which contain multiple phases have indicated that there are differences in group outcomes such as satisfaction and consensus depending on how the features of GSS were used to implement the process (Martz and Shepherd 2004). Also, research has shown that individual characteristics have a significant impact on a technology-supported group’s brainstorming performance (Jung et al. 2012; Valacich et al. 2006). Individuals have different creative styles and it plays a significant role in their creative output because certain creative styles generate more novel ideas as compared to others (Garfield et al. 2001). Thus, it becomes important to understand if the impact of these creative styles extends its effect on the creative performance of a group as well. Similarly, it is also important to understand if the introduction of GSS changes the performance of groups during different phases of the CPS process. This study examines the performance of groups with individuals belonging to specific styles, while engaged in a creative problem solving process and interacting only via technology. Specifically, it contrasts and compares the performance of GSS-supported groups with alternate member creative styles to see if there are any differences in performance and if these differences exist at specific phases in the CPS process. In

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this research paper, we primarily focus on the CPS phases of “alternatives generation” and “alternatives evaluation”. In the next section we review some of the important theories as well as concepts in the areas of individual creative style, creative problem solving process and technology-mediated groups that are relevant to our study and define the theoretical model and hypotheses.

2 Literature Review In this section we review theories on creative problem solving process and how individual preferences for steps within the process can impact the group’s performance. We also try to understand the process gains and losses introduced by technology mediated communication and how it may influence the performance of the creative teams.

2.1 Creative Problem Solving Process Creativity, when studied from the process perspective, often refers to the process that individuals go through to come up with novel solutions to problems. Study of creativity as a process started with Wallas’s Model of creative thinking (Wallas 1926) where he introduced creative thinking as a process with stages of preparation, incubation, illumination and verification in the creativity process. Drawing from Wallas’s model, Osborn (1957) conceptualized the role of creativity in the problem solving process as the Creative Problem Solving (CPS) process. It consisted of 7 steps that included (1) Orientation, (2) Preparation, (3) Analysis, (4) Hypothesis, (5) Incubation, (6) Synthesis and (7) Verification. This was then further modified into a 3-step model: (1) Fact Finding (2) Idea Finding and (3) Solution Finding and then a five step model with (1) Fact Finding (2) Problem Finding (3) Idea Finding (4) Solution Finding and (5) Acceptance Finding (Parnes 1967; Parnes et al. 1977). This model was also known as the Osborn–Parnes 5-stage CPS model (Noller et al. 1976). A review of the process based models for creative problem solving led to the identification of critical processes involved in creative thinking and this was subsequently presented in a new model called the creative process model (Mumford et al. 1991). The processes represented in this model were problem definition, information gathering, information organization, conceptual combination, idea generation, idea evaluation, implementation planning and solution monitoring. Many conceptually similar models have been formulated for CPS, including the SIMPLEXTM model (Basadur et al. 1982) as well as the CPS: Thinking Skills model (Puccio et al. 2005). However, the basic premise of each model remains the same. One has to identify the problem that needs to be solved, formulate the problem, generate multiple alternatives as possible solutions and then evaluate the solution space to pick the most feasible solution. The next steps then deal with actual implementation of the solution. In their review of the cognitive activities associated with CPS efforts, Mumford et al. (2012) have argued that creative problem solving depends extensively on how effectively the processes identified are actually executed. In that case, it becomes important to identify if there are any individual level abilities that impact the execution and subsequently, the performance during the

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various processes within the CPS task. Hence, it is important to understand how individual creativity contributes to the performance at different phases of the CPS process. 2.2 Individual Creativity The most widely cited model for understanding individual creativity is Amabile’s Componential Model of Creativity (Amabile 1996). It states that, in addition to task motivation and domain relevant skills, one of the important components of creative performance are the individual’s creativity relevant skills. This includes cognitive abilities and personality traits that lead to creative ideas. Creativity relevant skills are often measured in terms of cognitive style and are used to understand the individual level differences among members of the groups. In fact, a recent study found that there are significant differences between creative outputs of groups of individuals belonging to two alternate cognitive styles (Sagiv et al. 2010). Individuals can have cognitive styles that favor a specific step within the CPS model and a significant part of individual creativity research has focused on measurement of these individual styles or preference when solving problems creatively. For example, Kirton’s Adaption Innovation Inventory (KAI) measures an individual’s creative preference or style on a continuum while solving problems (Kirton 1976). It suggests that most individuals prefer to either adapt (do things better) or innovate (do things differently) and this impacts their creative productivity. Similarly, cognitive style has also been theorized in multiple ways, including the most common ones like Field Dependence–Independence theory (Witkin and Goodenough 1981), Reflectivity–Impulsivity (Kagan et al. 1964), Assimilators and Explorers (Kaufmann 1979) etc. Along similar lines, Basadur and Gelade (2005) modeled applied creativity as a cognitive process. According to them, applying creativity is multi-step activity that can be understood in terms of two distinct cognitive processes: 1. Apprehension: Acquisition of knowledge or how individuals gain knowledge which consists of two opposite ways of gaining knowledge. One is via direct, concrete experience while the other is via abstract thinking 2. Utilization: Application of the knowledge or how individuals use knowledge which consists of knowledge use for ideation at one end and knowledge use for evaluation at the other end. The two cognitive processes operate in two modes, resulting in four cognitive orientations that outline the conceptual space of creative activity. This model of applied creativity is useful as it maps itself concretely to the different steps in the creative problem solving process. Understanding individual level preferences for the two cognitive process involved can help identify an individual’s pre-disposed strengths towards specific phases in the CPS process. Basadur et al. (1990) asserted that each individual can be characterized as having a unique set of preferences on these two knowledge processing dimensions (experiencing-thinking and ideation-evaluation). These two dimensions can be used to form four quadrants where each quadrant is a combination of the two dimensions of knowledge apprehension and knowledge utilization. These quadrants and profiles are indicated in Fig. 1.

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Fig. 1 Creative problem solving dimensions

A quadrant 1 orientation individual is called a Generator. When in the dominant generator mode, individuals prefer to gain knowledge via concrete experience and prefer to use their knowledge for ideation. They try to imagine possibilities and see relevance in everything by seeing different points of view. A quadrant 2 orientation individual in the creative problem solving process is called a Conceptualizer. This individual prefers to gain knowledge via abstract thinking and use this knowledge for ideation purposes. They like to see the big picture and extract the essence of ideas to solve the problem. A quadrant 3 orientation individual is called an Optimizer. This individual prefers to gain knowledge via abstract thinking while using the knowledge for evaluation purposes, like thinking about criteria to assess alternatives. They are more oriented toward problem solving than the other three types. A quadrant 4 individual is called an Implementor. This individual gains knowledge via direct and concrete experience and uses this knowledge for evaluation purposes. They show a preference for working towards the implementation of solutions to make sure they work and adapt methods to solve problems. Looking at the four profiles it can be seen that preference for using knowledge as well as gaining knowledge in a specific way may be more favorable at some stages of the creative problem solving process than others. Until now, all preferences were discussed at the individual level. How does this individual preference translate to group participation and impact group productivity? What are the different factors that impact the productivity of groups when individuals within the group have preferences for a

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specific phase in the problem solving process? To answer these questions, we first examine the benefits and pitfalls of group interaction by reviewing the research done on creative groups. This will also help us explain why technology supported groups found prominence in the first place and their current role in the CPS process. Then, we theorize how an individual level preference might impact a group’s creative problem solving process.

2.3 Group Creativity Groups are composed of individuals and much of the group’s creative output is influenced by its member’s characteristics. As Woodman et al. (1993) noted, “Individual Creativity, in turn contributes to creativity in groups”. For example, a research study recently concluded that priming impacted individual cognitive performance and this directly impacted the group’s productivity (Dennis et al. 2012). Individual creativity and group creativity are strongly linked because the group creativity process starts with individuals conceptualizing ideas, problems or solutions and then deciding on whether to share them with the team or not (Gilson and Shalley 2004). In addition to individual creativity, group’s creativity will also be influenced by many other factors including group composition, group processes and others (Tagger 2001; Woodman et al. 1993). Creative synergy is based on the premise that a group of people can generate something new together due to new cognitive inputs as well as the interaction process (Kurtzberg and Amabile 2000). For example, some ideas or comments that are shared might stimulate group members to elaborate and suggest additional novel or different ideas (Paulus 2000; Paulus and Dzindolet 1993). Since shared ideas will lead to cognitive stimulation (idea triggering), activating related concepts and categories will result in generation of more ideas (Paulus 2000; Tagger 2001). If group members can be directed to focus on the diverse information (maybe sometimes less relevant) and integrate that into the idea generation process, the quality of ideas generated can be considerably higher (Mumford et al. 1997; Reiter-Palmon and Illies 2004). This could result in suggestion of ideas that are remote and unique. Considering, that the pool of ideas for cross-fertilization will be higher due to contribution from all its members, groups will generate more ideas. This will lead to increased sharing of ideas by individual members and result in higher group productivity because higher member participation enhances group productivity (Gilson and Shalley 2004).This is in agreement with the belief that member ability primarily determines group performance for tasks with a low level of social processes, like idea-generation (Hackman and Morris 1975). Although, this concept is related to the alternatives generation stage, it can be easily extended to other stages of the CPS process as well. Contribution by individual members will lead to the entire group getting multiple perspectives for what may seem like one problem. This will help not only in identification of the problems but also in the problem definition phase as well. The alternatives evaluation and selection process depends on the quality of the pool of ideas as well as the quality of the selection process (Rietzschel et al. 2006). Availability of a pool of good ideas is a prerequisite to selecting a good quality idea (Diehl and Stroebe 1987). As a result, the benefits

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of group interaction will also carry into the idea evaluation phase, as groups may be able to pick from a wider range of ideas. Individual group members will also bring a greater variety of unique knowledge to the group (Shiflett 1979; Steiner 1972) which can assist in better evaluation and selection of ideas. When evaluating the generated ideas, groups will have the benefit of multiple individuals who will use their unique perspectives and knowledge. This can help encourage the groups to consider all relevant information and available options. As a result, the solution may be more balanced and have greater appeal. It has been found that higher participation in decision making by individual members also leads to less resistance and greater acceptance when the solution is implemented (West 2002). However, research on this phase using face-toface groups has had mixed results. Most studies have concluded that although groups do have higher availability of ideas for selection, the evaluation and selection process has not been effective, which has led to sub-optimal results (Faure 2004; Putman and Paulus 2009; Rietzschel et al. 2006). Thus, group creativity has failed to deliver on its promise especially on tasks that are structured similar to brainstorming tasks. Despite the group interaction benefits, many researchers have found that the nominal groups (in which individuals brainstormed alone) perform better than the face-to-face groups (in which individuals brainstormed together) (Diehl and Stroebe 1987, 1991; Lamm and Trommsdorf 1973; Mullen et al. 1991). Productivity losses arising from group interactions have been blamed for this disparity in performance. In the next section, we review some common process losses responsible for decline in performance of face-to-face groups and how technology-mediated communication has been offered as a solution to these issues.

2.4 Creativity in Technology-Mediated Groups One of the widely cited reasons for lower productivity of face-to-face groups is that during the process of idea sharing, individuals have to wait for their turn to share their opinions because someone else is talking. Waiting for one’s turn might result in an individual forgetting their ideas before they had a chance to share them. This process loss is known as production blocking (Diehl and Stroebe 1987; Nijstad et al. 2003). Sometimes, group members may be apprehensive about sharing their ideas. They might be afraid that their ideas or solutions are not good enough and that the other group members might ridicule them. This process loss is known as evaluation apprehension and can result in lower idea generation (Diehl and Stroebe 1987; Lamm and Trommsdorf 1973). Another process loss known as social loafing (Diehl and Stroebe 1987; Paulus and Dzindolet 1993), refers to the tendency of an individual to ride on ideas of others and not put in much effort since the group results are pooled and evaluated. Since then research has identified and reviewed many such process losses and gains associated with face-to-face(Barki and Pinsonneault 2001). However, even though many process losses have been identified, researchers demonstrated that production blocking (inability to share ideas because someone else is talking) is the largest process loss associated with face-to-face groups (Diehl and Stroebe 1987, 1991).

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Using technology as a mediator for group communication, especially on creative tasks such as idea generation a.k.a electronic brainstorming (Nunamaker et al. 1991) was first suggested as a remedy for some of the process losses. This technology was known as Group Support Systems (GSS). In GSS-supported teams, individuals pooled their ideas anonymously and synchronously using computers connected by a network. GSS use resulted in several benefits including increased efficiency, effectiveness and greater satisfaction with the process when compared to face-to-face groups (Gallupe et al. 1992; Nunamaker et al. 1991) because it mitigated some of the losses associate with face-to-face groups. When using GSS, individuals could contribute their ideas simultaneously and no one had to wait for their turn to speak. As a result, the process loss of production blocking (waiting for one’s turn to share, because someone else is talking) could be greatly minimized (Shepherd et al. 1995). This resulted in greater contribution of ideas. The electronic medium could be used to alleviate production blocking associated with face-to-face groups and yet provide a medium for interaction between individuals to add process gains (Barki and Pinsonneault 2001). Evaluation apprehension was reduced through anonymity, because ideas were not identifiable with individuals. This helped individuals contribute all ideas, even poorly developed or risky ideas without worrying about negative repercussions from peers or superiors (Connolly et al. 1990; Dennis et al. 1991). As a result, team members could use the wider pool of ideas to improvise ill-defined ideas and generate more refined and better quality ideas. It was found that using technology mediated brainstorming (a.k.a electronic brainstorming) also led to higher levels of interaction among participants (Santanen et al. 2004). Social loafing would still be an issue, but it was hoped that the benefits of lower evaluation apprehension would overcome the losses due to social loafing. Also, various techniques were suggested so that GSS groups could control this process loss (Michinov and Primois 2005; Shepherd et al. 1995). GSS-supported brainstorming has been found to be superior to other nominal techniques when used to identify fraud risks (Smith et al. 2012). Since then, GSS evolved from supporting just brainstorming activities to include support and structure for other CPS activities as well (Briggs and De Vreede 1997). Research on GSS-supported creative teams has shown that individual level personality traits are related to idea generation performance (Jung et al. 2012). Does this hold true for other individual level variables such as creative styles? Also, does the individual level difference impact the idea evaluation phase performance as well? In the next section, we will integrate the theories of individual creative styles as well as GSS-supported group’s process gains and losses. This will help us determine the propositions and hypotheses for the creative performance during different phases of the CPS process and answer some of the questions asked above.

3 Propositions and Hypotheses Creative problem solving process consists of multiple phases, each important in its own way and groups with styles suited to specific phases will have better performance in those phases. In order to focus mainly on understanding how individual’s knowledge use preference impacts the two phases of generating alternatives and evaluating

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them, the four quadrants outlined in the literature review by Basadur et al. (1990) can be combined into two groups based on the knowledge utilization dimension. Groups formed on the basis of dominant preference for knowledge use (ideation or evaluation) during the creative problem solving process are as follows: 1. Ideators : Consisting of Generators and Conceptualizers who prefer using knowledge for ideation; and 2. Evaluators: Consisting of Optimizers and Implementors who prefer using knowledge for evaluation For the purpose of focus, this study looked at the two phases of alternatives generation and alternatives evaluation (including selection) in the CPS process. 3.1 Alternatives (Idea) Generation Phase During the alternatives generation phase, individual group member styles will contribute to the group’s performance. As a result, groups with individuals whose styles are more suited to the idea generation task will have higher contribution during this phase. Much of the group’s output is determined by its individual member ability (Hackman and Morris 1975) and individuals who prefer to generate alternatives will do better at this phase than individuals who prefer to evaluate alternatives (Basadur et al. 1990). Thus, it can be assumed that Ideator groups will perform better than Evaluator groups during the idea generation phase. GSS will provide the benefits of group interaction without the losses associated with face-to-face groups. Hence the performance during the idea generation phase will primarily be determined by the group member’s creative style and will be improved due to GSS process gains. This leads us to our first proposition. P1: Knowledge preference for Ideation will have a positive effect on idea generation phase performance One of the widely used performance measures for “alternatives generation” or “idea generation” has been idea fluency. Idea fluency refers to the quantity of non-duplicate ideas generated by groups and is the most widely used indicator of idea generation performance. However, it is equally important to understand alternatives generation performance in terms of the variety and of ideas generated. For example, examining how many categories of ideas were generated, how original were the ideas as compared to the other groups and how detailed were the ideas are equally important because they can be good indicators of idea generation phase performance. In order to do this, we can derive measures from Guilford’s work (1967) who used measures of idea fluency, flexibility, originality as well as elaboration to understand idea generation performance ability in individuals. This concept can easily be extended to group performance during the idea generation phase and use it to develop the hypotheses for the idea generation phase use these four measures. Ideator groups will be assisted by their individual member style and will generate more ideas. The ideas suggested might stimulate group members to piggy-back and suggest additional novel or different ideas (Paulus 2000; Paulus and Dzindolet 1993).

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These ideas will lead to cognitive stimulation (idea triggering) activating related concepts and categories resulting in more ideas generated (Paulus 2000; Tagger 2001). Individual styles combined with wide pool of shared ideas will lead to higher participation and increased group productivity (Gilson and Shalley 2004). Evaluator group members will have a natural tendency to evaluate the ideas because they prefer to use knowledge for evaluation purposes. Individual members may simultaneously evaluate the ideas they are generating and not contribute to the current group goal of generating more alternative. They may think that their idea is not good enough. They might provide criticism or feedback, which may cause other members in the group to not share their ideas. GSS will provide anonymity and lead to lower process losses for both Ideator and Evaluator groups. GSS will also display the pool of ideas as they are being generated. This will provide a medium for seeing and recalling the ideas shared without much cognitive effort. It will also help individuals to piggy-back on group member’s ideas without the group process losses. GSS will thus benefit both groups. As a result, GSS-supported Ideator groups will have greater number of ideas as compared to the GSS-supported Evaluator groups. This leads us to our first hypothesis. H1a: Ideator Groups will have greater idea fluency than Evaluator groups The process of idea combination, where individuals can build on other ideas using the process of association has been explored and found to be beneficial in generating much wider range of ideas (Kohn et al. 2011). As reviewed earlier, individual level task supported style will result in Ideator groups generating a higher number of ideas. The larger number of ideas will also provide a wider base for idea combination. This will allow for more diverse as well as higher category spanning ideas resulting from the combination. This is in line with the cognitive network model (CNM) used to explain the creative process. According to CNM, creative solutions are generated when individuals can bring together diverse knowledge in context of the solving a problem (Santanen et al. 2004). Wider range of ideas will provide greater base of ideas for association and activation of related concepts. Thus, multiple ideas suggested in a group environment will lead to greater possibility of diverse associations among the knowledge and ideas within the group. This will also lead to ideas spanning greater number of categories. GSS support will provide anonymity as well as parallel entry thus enabling individuals within the group to generate and share ideas as they think of it. Increased individual group member performance in Ideator groups will positively impact the entire group’s productivity synergistically as compared to Evaluators. Exposure to a larger number of ideas will cause idea triggering which will result in activating related categories. As a result Ideator group members will come up with a greater variety of ideas as compared to Evaluator groups. Even though GSS mediated communication will provide a platform for idea sharing, Evaluator group members will evaluate ideas as they are generated causing lower participation in Evaluator groups. This will result in lesser number of ideas. Fewer ideas will not span as many categories and so Evaluator groups will be exposed to lesser variety of ideas. As a result, GSS-supported Ideator groups will have greater idea flexibility as compared to GSS-supported Evaluator groups. H1b: Ideator Groups will have greater idea flexibility than Evaluator groups

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Assuming Ideator groups will have more number of ideas and cover more categories of ideas; idea triggering will also cause them to find highly unusual associations between ideas and categories, resulting in highly original and rare ideas. Availability of rare and less known categories as well as ideas will lead to higher chances of activating remote categories. This will cause more original (less frequently thought) ideas to be generated. Higher member participation and more in-sync member creative style for Ideator groups will result in enhanced group performance as compared to Evaluator groups. Even though GSS-mediated communication will provide a low-loss medium for sharing ideas, lower participation by Evaluator group members will result in lower number of novel ideas for Evaluators. H1c: Ideator groups will generate more original ideas than Evaluator groups GSS will help group members build on other member’s ideas. Group members will be able to look at ideas generated and develop greater details to make the idea viable. Elaborating on the generated ideas will lead to more number of ideas with finer details. Ideator groups having generated more ideas will have a greater number of ideas to build on. They will use the displayed ideas to refine ideas by providing greater details. As a result, ideas generated by such groups will have greater detail as compared to the Evaluator groups. H1d: Ideator groups will have greater idea elaboration than Evaluator groups

3.2 Alternatives (Idea) Evaluation Phase During the alternatives evaluation phase the focus is to evaluate current set of generated ideas so that few ideas emerge as good creative solutions to the problem. The purpose of idea evaluation is to identify viable ideas and separate poor ideas that have low chances of success (Dailey and Mumford 2006). To evaluate the ideas, most group members will need to use some kind of criteria to assess the quality of the solutions generated. Ideas are most commonly evaluated on criteria such as originality, feasibility, effectiveness etc. During the idea evaluation phase, individual level preferences for idea evaluation will help Evaluator group members more than Ideator group members. Ideators will prefer to generate more ideas and converging to a narrow set of good solutions will be harder for them as compared to the Evaluator groups. Evaluator group members will perform better during the evaluation and selection phase of a creative problem solving process due to their natural style. In addition, GSS will help structure interaction during the evaluation and selection phase of the creative problem solving process for groups by providing tools that enable voting and selecting of ideas. The structured interaction and GSS tools provided will help both Ideator and Evaluator groups. Hence we posit that, idea evaluation phase performance will also be primarily determined by the group member’s creative style. Thus, lower ideation preference (and higher evaluation preference) of individual team members will positively impact the team interaction during this phase, leading us to our second proposition:

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P2: Ideation preference will have a negative effect on alternatives evaluation phase performance After an extensive review of the idea quality literature, Dean and his colleagues suggested that ideas or alternatives should be evaluated on multiple dimensions (Dean et al. 2006). According to the article, creativity can be measured in terms of novelty as well as idea quality, where idea quality refers to non-novelty attributes such as workability, relevance and specificity. Different organizations may have different criteria and qualities they want in the solution to their problem. Some organizations may emphasize cost while others might emphasize implementation ease. This makes solution quality harder to judge and measure. Generally speaking, the best solution should be one that is not only novel but also easy to implement. The solution chosen should be feasible and effective. This is in agreement with MacCrimmon and Wagner’s definition of a creative solution as being a novel, effective and implementable idea (MacCrimmon and Wagner 1994) and has been used before (Rietzschel et al. 2010). Using these measures of solution quality, the hypotheses for the evaluation phase can be outlined as below. Due to their natural preference, Evaluators will do better at evaluating the alternatives or options generated to select ideas. Individual member style will help them rate and evaluate ideas as per the laid down criteria. They will more efficiently separate ideas that are more practical and can be implemented from ones that are not. GSS will also help Evaluator groups efficiently assess the solution space by providing voting tools. Once the evaluation is done, GSS will help by providing tools to aggregate the scores on specific measures to give an overall score to each of the ideas suggested. This will help Evaluator groups see the top ideas on the criteria listed and pick a more novel, feasible as well as more effective solution as compared to Ideator groups. H2a: Evaluator groups will solution with higher novelty as compared to Ideator groups H2b: Evaluator groups will pick a solution with higher feasibility as compared to Ideator groups H2c: Evaluator groups will pick a solution with higher effectiveness as compared to Ideator groups Figure 2 visually depicts the hypotheses outlined so far. In the following section we describe the methodology followed in this study.

4 Methods This section describes in detail the experimental design, participants, the process followed, the task, the instruments used and the analyses that were used to test the hypotheses presented in the previous section.

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Fig. 2 Measurement model for the study

4.1 Independent Variable The independent variable for the study was the ideation preference of members within a group and had two levels: Ideator and Evaluator. To operationalize this, homogeneous groups were formed based on individual member ideation preference. In order to measure participant’s ideation preference, Basadur’s Creative Problem Solving Inventory (CPSI) was used. All participants took the CPSI test online and the scores for all individuals were delivered as an excel sheet. All participants had scores in each of the four quadrants. Using the excel sheet, profile plots for individuals were plotted. Following the example of previous studies by Basadur and his colleagues (Basadur and Head 2001; Basadur et al. 1990), dominant quadrants were found for every individual. An example of a profile plot for one of the participants is illustrated in Fig. 3. Figure 3 represents the profile of an individual with “Generator” as their dominant quadrant. The four quadrants according to the two dimensions were combined into two groups depending on the dominant quadrant’s position on the knowledge utilization

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Fig. 3 Profile plot for a “Generator”

dimension. Groups of 4 were formed using the dominant quadrant as the classifier. Thus the Generator groups, which consisted of 4 individuals with “Generator” as their dominant quadrant. Similarly, Conceptualizer, Implementors and Optimizer groups of 4 were formed. “Generator” and “Conceptualizer” groups consisted of members who preferred to use knowledge for ideation. Hence, these groups were classified as Ideator groups. Similarly, the “Implementor” and “Optimizer” groups consisted of members who preferred to use knowledge for evaluation. Hence these groups were classified as the “Evaluator” groups. After attributing for individuals who dropped out, the usable groups in our study consisted of a total of 12 Ideator groups (9 Generator, 3 Conceptualizer groups) and 27 Evaluator groups (9 Optimizer, 18 Implementor groups). 4.2 Demographics and Profile Distribution in the Sample The participants in this experiment were undergraduate students enrolled in business courses at 3000–4000 level at a southwestern university campus. All IRB regulations were followed and no identifiable information was collected from the participants. As many students enrolled on this campus are non-traditional students, completing their education while working full time, the average age for this sample was 27.8 years with a range of 20–51 years. Work experience values ranged from 0 to 31 years, with an average of 8.7 years. The gender distribution in the sample was 53.29 % females and 46.71 % males. Of a total of 250 students who participated in the study and took the survey online, 60 students were found to be Ideators, 150 were classified as evaluators and 40 fell under the unknown category (individuals with equal preference or scores for ideation

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and evaluation). Basadur et al. (1990) found significant results in their study supporting their claims that the distribution of Evaluators (“Implementors and Optimizers”) were higher than the Ideators (“Generator and Conceptualizer”) in their business undergraduate sample. In their sample there were 69.7 % Evaluators and only 30.3 % Ideators. Hence the distribution in the sample for this study was typical of those found in other studies. 4.3 Task Specifics The experimental task was a creative problem solving task which involved generating options and then evaluating the ideas generated. Although evaluation does not always mean converging to one solution, for the sake of simplicity, the idea with the highest aggregated rating on solution criteria was picked as the chosen solution. We used the University Coffee shop Problem, which has been used in previous studies in the management literature (Goncalo and Staw 2006; Goncalo 2004). This task was primarily used due to its independence from the need for domain level knowledge as well as the fact that it was highly relevant to students on this particular campus. The coffee shop on campus had recently closed and no new business had been set up in the empty space. As a result, the students could relate to the task and it made them feel that the task had real implications. Relevancy of task to the subjects is an important issue, since it promotes greater involvement and also helps individuals draw on their personal knowledge and experience (Connolly et al. 1990). Also, the task could easily be mapped to the two stages the creative problem solving process: 1. Alternatives Generation Phase: Group members generated as many as possible alternatives for businesses that could be opened up in an empty university space. 2. Alternatives Evaluation (and Selection) Phase: Group members rated the ideas generated on novelty, feasibility and effectiveness. The idea with the highest aggregate average rating was selected and the group was asked to elaborate on the steps needed to implement the solution. Thus, even though selection was not actually done by the group members by discussion, it was structured and implemented using GSS. 4.4 Experimental Procedure The aim of the study was to simulate a virtual environment, where group members could only interact using the GSS during the task. Hence, the participants were randomly seated such that all team members were seated away from their group members. A script was followed for the purpose of ensuring fairness in the delivery of instructions and content for all groups. The students were given a short training lesson lasting about 10 minutes on how to use the GSS software “Think-Tank”. It was followed by a practice task similar to the actual task to familiarize them with the software. The participants were guided through a mock idea generation and a mock idea evaluation process using the software, where they rated ideas on predefined criteria. The practice task was done in the exact order in which the actual task was going to be done and lasted

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about 10 minutes. The GSS interface was very intuitive and familiarizing participants with the software was achieved easily. The actual task was started only after ensuring that all participants were comfortable using the software. After task was completed, all participants were debriefed and thanked for their participation. 4.5 Dependent Variables The dependent variables for the alternatives generation phase in the study included: Idea Fluency (quantity of ideas); Idea Flexibility; Idea Originality; and Idea elaboration (quantity of comments on ideas) and were based on Guilford’s definition and measure of creativity (Guilford 1950). The dependent variables were operationalized and measured as follows: 1. Idea Fluency was measured by counting the total number of ideas generated by the groups, after eliminating redundant or duplicate ideas if any. Thus, idea fluency in this study basically refers to the count of total number of non-redundant ideas for each group. This was consistent with the previous studies in the brainstorming research area (Gallupe et al. 1992; Garfield et al. 2001). 2. Idea Flexibility was scored by looking at the number of unique idea categories suggested. For initial categorization of the ideas, the entire list of ideas was analyzed with great details a list of broad categories were outlined. These categories were then given to an independent coder along with the final list of 186 ideas. 3. Idea Originality was measured as the infrequency of the particular idea generated as compared to the range of ideas generated. So if a particular idea was mentioned by only one group, it was to be rated as highly original whereas an idea mentioned by many groups was rated low on originality. Previous studies have used this measure by coding the frequency of idea occurrences and using this as the originality measure (Dennis and Valacich 1999; Faure 2004; Garfield et al. 2001). The average idea originality for the sample was 3.2. So only those ideas were selected as highly original which were mentioned a maximum of two times. This frequency measure has been suggested by other researchers as well (Dennis et al. 1997; Diehl and Stroebe 1987). Idea Originality for a group was then determined by adding the total number of highly original ideas that was suggested by each group. 4. Idea Elaboration was measured by looking and coding the number of comments that were generated to elaborate on a previous idea. Ideas that added details to a previous idea were counted. Irrelevant comments not related to any ideas were eliminated from the solution. Operationalization of this construct was based directly on Guilford’s definition of the measurement of elaboration. A similar score called the elaboration coefficient has been used previously (Santanen et al. 2004; de Vreede et al. 2010). Table 1 shows a snapshot of ideas generated by one of the experimental groups. For this group, the idea selected for solution elaboration phase would be the idea with the highest aggregated average on all three criteria of novelty, feasibility and cost-effectiveness. Outputs like these were used for each group that participated in the experiment and their creative output was measured in terms of the dependent variables.

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Table 1 Snapshot of group output at the end of voting phase Evaluate all options Criteria

Novelty

Feasibility

Cost effective

Average

Ballot items Day care

4.5

4.2

3.8

4.2

Study hall

2.0

4.0

4.8

3.6

Oxygen bar

3.8

3.8

3.2

3.6

Relaxation room with water fountain

4.0

3.5

3.0

3.5

Internet cafe

3.0

3.5

3.8

3.4

AAA

3.8

3.2

3.2

3.4

Star bucks

3.5

3.2

3.2

3.3

Panera bread company

3.2

3.5

3.2

3.3

Used book exchange room

2.5

3.5

4.0

3.3

A lounge for sleeping students

3.5

2.8

3.2

3.2

During the evaluation phase of the creative process, it was important to assess the quality of the final solution in terms of its novelty as well as quality. Idea quality was operationalized as different from idea novelty and judged in terms of feasibility and effectiveness. Research has shown that idea evaluation and selection is greatly benefited when selection criteria is specified (Rietzschel et al. 2010). So, for the sake of uniform understanding of the judgment criteria, it was clarified that effectiveness was actually a cost-effectiveness measure. In addition to this, the two other measures used to measure performance during the idea evaluation and selection phase were solution novelty as well as feasibility. One of the most widely used techniques of creativity assessment is Amabile’s consensual assessment technique (Amabile 1982) which involves a panel of judges that rate the creativity of the output. In this study, the consensual assessment technique employed faculty judges with knowledge and background in the creativity literature as well as student judges familiar with the campus and the relevant problem, to judge the creative output of groups. This is consistent with other studies in the IS literature (Connolly et al. 1990; Faure 2004; Valacich et al. 2006). The judges rated the novelty, cost-effectiveness as well as feasibility of the selected solution for each group. Thus, the following variables were measured for each group in the evaluation and selection phase: 1. Solution Feasibility was measured by averaging the aggregated panel of judges’ rating for feasibility of the selected idea in lieu of the current problem context, for each of the groups. 2. Solution Novelty was measured by averaging the aggregated panel of judges’ rating on novelty for the selected idea for each of the groups. 3. Solution Cost-effectiveness was measured by averaging the aggregated panel judges’ rating for cost-effectiveness for the selected idea for each of the groups.

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4.6 Analysis During the alternatives generation phase of the creative problem solving process multiple output variables were measured which were significantly correlated with each other. Hence, Multiple Analysis of Variance (MANOVA) was used to assess whether or not significant differences existed between the two groups. Assumptions of MANOVA include normality of variables, homogeneity of error variances across groups, equality of variance-covariance matrix for both groups and independence of error terms. All these assumptions were checked and transformations were performed if violations existed to correct it. Analysis was performed only after all assumptions had been met satisfactorily. Multiple dependent variables were measured during the alternatives evaluation and selection phase of the creative problem solving process as well. However, none of the 3 dependent variables measured during the evaluation and selection phase were correlated with each other. Hence, separate ANOVA tests were performed for each dependent variable to check for significant differences between the two groups. Analysis was performed only after meeting all assumptions for the ANOVA test.

5 Findings and Discussion 5.1 Results of Alternatives Generation Phase MANOVA Using MANOVA, we compared the mean number of ideas (H1a), mean idea flexibility (H1b), mean idea originality (H1c) and mean idea elaboration (H1d) of Ideators with Evaluators. The MANOVA statistic for the idea generation phase is outlined in Table 2 and showed a statistically significant difference at the p < 0.05 level for the two groups. The test of between subjects (individual ANOVAs) for idea fluency, idea flexibility, idea originality as well as idea elaboration was also found to be statistically significant at p < 0.05 with F and p values as displayed in Table 3.

Table 2 MANOVA results for idea generation phase Multivariate test Effect

Group Pillai’s Trace

Value

F

Hyp df

Error df

Partial η2

Obs. power

Sig.

0.439

60.65

4

34

0.000 0.439

0.983

Wilks’ Lambda

0.561

60.65

4

34

0.000 0.439

0.983

Hotelling’s Trace

0.783

60.65

4

34

0.000 0.439

0.983

Roy’s largest root 0.783

60.65

4

34

0.000 0.439

0.983

Computed using alpha = 0.05 Design: Intercept + GrpType

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Table 3 Individual ANOVAS for idea generation phase Tests of between-subjects effects Source

Dependent variable

Group type Idea fluency Idea Flexibility

Type III SS df Mean square

F

Sig.

Partial η2

Obs. power

7250.13

1

7250.13

70.26

0.01 0.164

0.75

320.01

1

320.01

50.33

0.03 0.126

0.61

Idea Elaboration 6160.03

1

6160.02

240.10 0.00 0.394

0.99

Idea Originality

1

280.80

70.75

0.77

280.80

0.01 0.173

Tested at Alpha = 0.05 Table 4 ANOVA results for idea evaluation phase Sum of squares

df

Mean square

F

Sig.

10.70

0.20

2.23

0.14

6.49

0.02

Solution novelty Between groups

0.75

1

0.75

Within groups

16.41

37

0.44

Total

17.17

38

Solution feasibility Between groups

1.82

1

1.82

Within groups

30.08

37

0.81

Total

31.90

38

Solution cost-effectiveness Between groups

4.91

1

4.91

Within Groups

27.99

37

0.76

Total

32.90

38

5.2 Results of Evaluation and Selection Phase ANOVAs Using three separate ANOVAs we compared the average solution novelty (H2a), average solution feasibility (H2b) as well as the average solution cost-effectiveness (H2c) of Ideator and Evaluator groups. The ANOVAs for the idea evaluation phase showed statistically significant difference at the p < 0.05 level only for the measure of solution effectiveness for the two groups. The results of the 3 individual ANOVAs with the F and p values are given in Table 4.

5.3 Overview of Results for Both Phases Results of the hypothesis testing for both phases are indicated in Tables 5 and 6. Table 5 indicates that all the idea generation phase hypotheses were supported and also gives the F and p values associated with each hypothesis. H1a, which hypothesized that GSS-supported Ideator groups would generate greater number of ideas (idea fluency) than Evaluator groups was supported. H1b which stated that GSS-sup-

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Table 5 Results for idea generation phase hypothesis testing Dependent variable

Group type

Mean

Hypothesis

Supported

F value

p value

Idea fluency

Ideators

39.75

H1a: Ideator groups will have greater idea fluency than Evaluator groups

Yes

7.26

0.01

Evaluators

30.41

Ideators

12.67

H1b: Ideator groups will have greater idea flexibility than Evaluator groups

Yes

5.33

0.03

5.33

0.03

H1c: Ideator groups will have greater idea originality than Evaluator groups

Yes

7.75

0.01

H1d: Ideator groups will have greater idea elaboration than Evaluator groups

Yes

24.10

0.00

Idea flexibility

Idea originality

Idea elaboration

Evaluators

10.7

Ideators

6.99

Evaluators

5.13

Ideators

24.5

Evaluator

15.89

Table 6 Results for idea evaluation phase hypothesis testing Dependent variable

Group type Mean Hypothesis

Solution novelty

Ideators

Evaluators Solution feasibility

Ideators

Evaluators Solution cost-effectiveness Ideators

Evaluators

Supported F value p value

2.54 H2a: Evaluator groups No will have higher solution novelty than Ideator groups 2.24

1.70

0.20

No

2.23

0.14

2.75 H2c: Evaluator groups Yes will have higher solution cost-effectiveness than Ideator groups 3.51

6.49

0.02

3.45 H2b: Evaluator groups will have higher solution feasibility than Ideator groups 3.92

ported Ideator groups would have greater number of categories (idea flexibility) than Evaluator groups was supported. H1c which stated that GSS-supported Ideator groups would have greater number of rare ideas (greater idea originality) was supported. H1d which stated that GSS-supported Ideator groups would have greater number of details in their ideas (idea elaboration) as compared to the Evaluator groups was supported.

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Table 6 indicates that for the evaluation and selection phase, only one of the 3 hypotheses was supported and also lists the corresponding F and p values for each hypothesis. As seen from Table 6, there were no significant differences between GSS-supported Ideator groups and Evaluator groups on 2 of the three measure of idea evaluation performance. H2a, which stated that Ideators groups would have lower solution novelty as compared to Evaluator groups, was not supported. H2b, which stated that GSSsupported Ideator groups would have lower solution feasibility, was not supported. However, H2c which stated that GSS-supported Ideator groups would have lower solution cost-effectiveness was supported. Ideators had significantly lower rating of solution cost-effectiveness than the Evaluator groups.

5.4 Discussion The study found that the GSS-supported Ideator groups performed significantly better than their Evaluator counterparts on all measures of performance for the idea generation phase. Ideator groups generated a greater number of ideas and a greater variety of categories of ideas than the Evaluator groups. The ideas generated by the Ideator groups were also more original (less frequently mentioned) and were more detailed (elaborate) as compared to their Evaluator counterparts. However, in the idea evaluation phase, Ideator and Evaluator groups did not perform differently from each other on all the measured variables. Performance of the groups was measured on the novelty of the solution selected, cost-effectiveness and the feasibility of the solution. The two groups differed significantly only on the costeffectiveness of the solution chosen. As predicted, GSS-supported Evaluator groups picked solutions that were more cost-effective than the Ideators. However, there were no significant differences among the performance of the two groups on rest of the two dependent variables. This means that Ideators and Evaluators picked solutions that were equally novel and feasible. The results for the two phases indicate that group member creative styles play an important role in determining the groups productivity as well as certain qualities of the solution they pick and should be taken into account (measured or controlled) when researching GSS performance. Although comparison between face-to-face groups and GSS groups would have given this study the empirical evidence to argue for GSS interaction with member styles, the results during the evaluation stage for this study do indicate a possible effect of GSS interaction on team output across two groups. Here is why. Based on theory and creative style literature, Ideator groups should perform better than Evaluator groups during the idea generation stage and Evaluator groups should perform better that Ideator groups during the evaluation stage due to member preferred style. Even though Ideator groups performed better than Evaluator counterparts during idea generation phase, idea evaluation phase results proved that there were no significant differences between the two groups on 2 out of the 3 dependent variables. Using the GSS to pick the final solution aggregated on individual members rating of the ideas on three important qualities, might have resulted in equalizing the advantage Evaluator groups had for this phase over certain measures of solution quality. GSS-supported

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interaction during the evaluation phase of the CPS process may have helped both groups achieve similar levels of solution quality with respect to novelty and feasibility as compared to generation phase. Since the medium of interaction was only GSS, it can be logically argued that maybe GSS had a differential impact across the two groups. One of the reasons for equal performance for the two groups during the evaluation and selection phase could have been how GSS structured interaction between group members. During the idea generation phase, Ideator and Evaluator groups had a screen which basically displayed the ideas that they could type in a chat type window. So while Ideator groups were busy generating ideas, some Evaluator group members wrote comments that evaluated some of the ideas generated. Analyzing the group transcript showed comments like “That is not possible” or “The University will never allow that” and so on. This might have led to reduction in ideas generated and subsequently poorer performance by the Evaluator group. On the contrary, during the evaluation phase, all group members could only see the voting window with ideas listed and the option to rate it. There was no place where individuals could write their thoughts or continue to generate more ideas. As a result, even though Ideator group members would have preferred to continue to generate ideas, the way GSS structured interaction during this phase forced them to focus on evaluation. Hence, we can argue that GSS support may have enhanced the performance of the Ideator groups by forcing them to focus and perform the current task at hand instead of what they preferred to do. Due to this, Ideator and Evaluator groups may have picked equally novel and feasible ideas. This potentially strengthens our argument that the GSS mediated interaction may have impacted the results. Another reason may be that the evaluation and idea selection phase was implemented such that the idea with the highest aggregated average of group member ratings was selected as the final solution. This may have impacted the results of the solution implementation phase, since only one idea was selected based on a voting mechanism. Most often in real world scenarios, groups might consider a selection of few ideas and evaluate them on the final solution criteria before selecting one. Hence, one can argue that the process of idea selection used in the study could have impacted the final results of the evaluation phase. In the following sections, we discuss the contributions of this study, implications for researchers and practitioners as well as direction for future research. We conclude by examining the limitations of the study.

6 Contribution, Implications for Research and Practice 6.1 Contributions of the Study The contributions of this research are multi-fold. First, it enhances the understanding of how groups of members with a particular creative style perform when facilitated solely by GSS during the creative problem solving process. The study findings revealed that Ideator groups performed significantly better than Evaluator groups on the alternatives generation phase of the creative problem solving process, when facilitated by GSS. Even though it makes intuitive sense that Ideators will do better at ideation and Evaluators will do better at evaluation, it is still important to see if this held true in

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the GSS setting. Consequently, it was interesting to find that no significant differences were found between the two groups on the novelty and feasibility of the solution they picked even though theory suggests that Ideators would perform worse than Evaluators during this phase. Although GSS facilitation was used in both the phases of the creative problem solving process, we can argue that the way the process was structured using GSS, might have helped the Ideator groups in achieving similar efficiencies as the Evaluator groups during the evaluation and selection phase. The GSS used, not only provided an anonymous, apprehension-free environment for the idea generation process, but also helped the Ideator groups with their preference for generating ideas by focusing their interaction on related task during the evaluation phase. This is a very important contribution since it enhances our understanding of how GSS can support and structure the interaction of creative group members, thus maybe impacting the final creative productivity of groups. This study also informs GSS designers and developers on how different features of the GSS can be used to support individuals during each phase. For example, if we know that there are some Evaluators in a group who will need help in focusing on the generation aspect of the task at hand, then maybe we can emphasize on individual contribution by turning off the anonymity feature. This may discourage the Evaluators from giving negative feedback and instead make them focus on generating ideas. Similarly, during the evaluation phase, if we know of Ideators in the group and we want them to continue to generate new ideas even while they are evaluating the current set, we will want to provide a separate mechanism for this. This lends support to the notion that we need flexible and customizable interfaces because they can affect productivity as well as support changes in the process. Another contribution of this research study is that it measures brainstorming output in terms of idea flexibility, idea originality and idea elaboration. Research has shown that nominal groups perform on par or better than the GSS-supported group. The traditional measure of idea fluency has been unable to capture the interaction effects which may impact the idea quality. Using additional measures such as idea flexibility and idea elaboration help extend the traditional productivity measures of quantity by analyzing the “variety” and “quality” aspect of the idea generation productivity. These additional measures can help understand if group interaction without group losses (as provided by GSS support) results in a wider range of ideas. Using a more holistic set of performance measures may also help explain some of the contradicting results in the EBS literature on group performance in comparison to nominal groups. One important contribution of this study is that it examined the performance of GSS-supported groups on the creative process as a whole, incorporating multiple phases in the creative problem solving cycle. Most research in the GSS literature for creativity has studied only at the alternatives generation phase. It has been argued that looking at just alternatives generation phase may give an incomplete picture of group performance because evaluating ideas is an equally important step towards the solution selection and implementation phase. Thus, an important contribution of this research is that it went beyond the idea generation phase and considered selection aspects of those ideas as well. The findings of this study suggest that GSS-based interaction may enhance the performance of some creative problem solving styles more than others during specific

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phases of the creative problem solving process. This has multiple implications for researchers as well as practitioners. 6.2 Implications for Research For researchers, the findings suggest that composition of groups is an important factor in a creative problem solving process. Creative style or preference is an important variable that cannot be ignored and should be taken into consideration when performances of GSS-supported groups are studied. It should be measured and either controlled for or included as a factor when examining technology-supported group performance. The results of the study also suggest that it is important to consider how the GSS structures and impacts different tasks within the CPS framework. GSS features and interface may enhance or impair performances of certain styles during an entire CPS cycle. Also, using multiple indicators of performance is important as it may help point at which aspect of idea generation or evaluation performance is being impacted. When measured by researchers, it will also give a more holistic view of a group’s performance. This will help researchers to easily compare and consolidate findings across studies and build a cumulative research tradition. 6.3 Implications for Practice For practitioners, the study gives insight into performance of groups that are supported only via GSS and do not meet face-to-face. Managers need to understand the differences in individual group member cognitive styles within the organization and how it can impact the performance of a GSS-supported group. Managers could potentially use these measures and form groups based on these styles to best support their task on hand. For example, managers can look at profiles for various employees and form groups that contain individuals with preference for ideation during the first phase of real world problem scenario. Another group could then be formed consisting of individuals with preference for evaluation during the solution selection and implementation phase. This can help in two ways. First, it can help capitalize on individual member skills and preferences for specific task phases and second, it would involve multiple employees in the problem solving process. This might lead to a greater acceptance of the selected solution, due to a feeling of greater involvement in the decision process for the employees within the organization. Also, this study helps inform managers of the differential influence of GSS and how it can be used to structure interaction to support the groups through different phases in the CPS process. Managers need to be cognizant of different levels of impact during the different phases when GSS is used to support groups during the creative problem solving process. 7 Conclusions This study looked at the performance of homogeneous groups formed using individuals with similar knowledge utilization preferences. Both the phases of the creative

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problem solving phase were studied. The Ideator groups performed significantly better than their Evaluator counterparts on the alternatives generation phase of the creative problem solving process. However, no significant differences were found among the two of the three main indicators of performance of the two groups on the evaluation and selection phase of the task. GSS-supported Ideator groups performed better or on par with GSS-supported Evaluator groups during the alternatives generation phase of the CPS process and picked equally novel and feasible solutions for implementation. However, the Ideators picked less cost-effective solution as compared to Evaluator groups. The features of the GSS used in the study may have structured interaction in a way that helped the Ideator groups achieve similar or greater levels of efficiency as compared to the Evaluators with respect to group performance on the various phases of the creative problem solving process. One of the most important limitations of the study was the small sample size. The number of Ideator groups was only 12 while the number of Evaluator groups was 27 after accounting for the dropped groups. As discussed previously, the ratio of Ideators to Evaluators in previous studies were around 1:3. Our ratio of nearer to 1:2 was better, still small. It may be possible that insufficient or small sample size may have contributed to some part of the results for evaluation and selection phase. A meta-analysis (Ma 2009) of creativity studies found that the effect size of problem solving creativity on creativity measures were large (0.86) as per Cohen’s guidelines. Considering this and the fact that we detected other similar size effects in our sample, we are hopeful that our non-significant results were probably not just the product of small sample size. The task used for the purpose of this study was selected on the basis of its relevance to the participants. However, participants knew they were not the final decision makers on this problem. As a result, they might have not worked as hard on the problem at hand. Also, accountability for the quality of the chosen solution was not enforced. Hence, this result cannot be generalized to individuals in the organizational setting, where groups are held accountable for their decisions and performance. This study used specific GSS software (Think-Tank) that was easily available for the purpose of study. Hence, it limits the generalization of the study results to GSS technologies with similar interface design and features. The results from this study point towards many potentially interesting and exciting avenues of research. Logically, the next step in this research would be to investigate the performance of heterogeneous groups (groups with members from each of the four quadrants on the creative problem solving profile) on the two phases of the creative problem solving process when assisted by GSS or similar kind of technology. This study only considered individual preference for using their knowledge. Hence, it might be interesting to see how the other dimension of gaining knowledge (experiencing-thinking) plays into the entire creative process. It may be valuable to see the performance of teams with members on the creative problem solving process from specific quadrants without aggregating across specific dimensions. Another possible interesting study would be to see how nominal and face-to-face groups formed using the above preferences perform as compared to the GSS-supported groups. It will be useful to see if GSS support shows significant impact on the multiple measures of performance used in this study as compared to the other two

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groups. For example, it might be useful to see how nominal, face-to-face groups and GSS-supported groups perform on idea fluency, idea flexibility, idea originality and idea elaboration. This will provide a more detailed and stronger evidence of how GSS impacts the group performance. It might be interesting to see the performance of groups if top three or four ideas could be selected from the initial phase of idea generation. Then the groups could be given an opportunity to discuss and maybe vote among the top three or four ideas. For example, maybe an evaluation process which uses more voting features as well as discussion oriented environment may be more effective Considering the experimental lab setting of this study, it might be worthwhile to investigate how such GSS support impacts the performance of groups in the organizational setting, where the stakes for the decision maker are very realistic. From the above ideas, it is seen that this study opens up a richer stream of research with multiple avenues for creativity, GSS and creative problem solving researchers.

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