Question Avg. n=19,
(scale 1 to 5) I put a lot of effort to this course / I participated actively in this course. 3.8
Participation and studying in this course was interesting. 3.6 The working methods fitted well for the course and the methods supported my learning. 3.9 I can exploit and apply the skills and knowledge which I obtained from this course. 3.8
Overall grade for the course. 3.6
Table 3. Course feedback survey questions and average answers
Most of the students who responded the open-ended question, “What did you think about the GSS exercises?” listed very positive comments, such as ”the GSS exercises were the best part of the course”; ”the GSS exercise concept was good”; ” the exercises were pleasant and I learned from them better than from the lectures and seminars”; ”the exercises deepened my understanding of the theories in the lectures”. These answers give some evidence that the GSS concepts used in the exercises were perceived as interesting and useful by the students. It also seems that these GSS cases enhanced the learning by showing the students practical means and methods to solve complex problem and planning situations. The overall student satisfaction in the whole course seems also better than average, which could indicate that the GSS exercises were found to be a functional part of the course and suitable for learning purposes.
5.1 Idea generation in the GDSS laboratory
In idea creation cases, the group size is generally 6-10. The session begins with an introduction to the day’s agenda. After the introduction, idea creation begins with electronic brainstorming. The basic rules of brainstorming are applied to the idea generation session; freewheeling is welcome, quantity is wanted, criticism is forbidden, and combination and improvement are sought (Osborn, 1953). Brainstorming is followed by categorizing the ideas and priorization by voting. A typical session lasts approximately 90 minutes, during which time many common phenomena can be witnessed, such as M-curve and group memory. M-curve is an illustration of the volume of ideas per minute generated over a given amount of time; the illustration often looks like the two arches of the letter M (Haman, 1996). Breakthrough ideas are supposed to materialize more likely in the second arch of the M-curve, so it is justified to stretch the idea generation time. There are also some generally noticed problems in traditional brainstorming, such as social loafing, evaluation apprehension and waiting for a chance to speak, which can be seen relatively easily in normal brainstorming.
Phase I
The findings of the student cases are in line with similar cases done with industry settings (Elfvengren et al., 2005).
The students showed increased participation and found the system easy to use. As an educational tool, the GSS offers opportunities for teachers. Students can compare the traditional brainstorming method and electronic brainstorming in the laboratory sessions. This way the use of the GSS enables the presentation of the mentioned group dynamical phenomena found in brainstorming and the benefits of supporting the process in a very concrete way to the students.
Then as the students are able to reflect these experiences in a control session, the usefulness of the GSS in reducing these problems can be easily demonstrated, as described. The phenomena include group memory, which enables ‘piggy backing’ on others’ contribution as the processing is parallel and the input is commonly viewable, and displaying the accumulation of ideas as a function of session time in turn demonstrates the M-curve effect. Considering the learning aspect, this case allows the students to reflect their work practices and teamwork skills, as well as functions as an introduction to the benefits of the GSS.
5.2 Scenario planning in the GDSS-laboratory
In examining the scenario process, the possibility of using the GSS to make the process more efficient and effective became apparent. The basic concept in developing scenarios is bringing a group of experts together and synthesizing their understanding of the future to a set of plausible scenarios. For this, the scenario process was examined further and the result is illustrated in the following figure.
In the scenario planning case, the team size was 10-20 people, and the students either worked as a pair on a single computer or by themselves, depending on how many participants were present. The session followed the process presented in Figure 4 and it took 100-150 minutes to complete. In practice, the sessions went from a short presentation to identifying the drivers, which were discussed briefly and printed to every participant. After that, the group was asked to identify concrete events which would be triggered by the drivers in the given time span. After the events were once again discussed, they were voted for probability of occurring and impact on the target organization. Out of these events, three sets were grouped to form a scenario each, which form the base of a scenario set. After the session, the events and the drivers were formed to concept maps, which were presented to a select audience and corrected according to comments on the spot using a smart board.
Phase I
Figure 4. A GSS-supported scenario process (adapted from Bergman, 2005; van der Heijden et al. 2002; Schoemaker, 1991)
Like in the idea creation case, in the scenario planning the GSS showed positive effects on group member participation.
In the scenario process, the group tries to build common understanding of the situation by analyzing the problem together. In the case of scenarios, the problem is the future state of e.g. a particular industry or technology. The students felt that the procedure gave the session a systematic backbone. However, there were also perceived inconsistencies in the process, which lowered the trust in the results. In a more motivated business case, the results show increase in confidence on the results. As a learning tool, GSS offers a method for interactive learning. The combination of GSS and scenarios offers lucrative options especially when a group needs to build understanding of the development of e.g. an industry by identifying the forces and trends that influence the development.
5.3 Business intelligence exercise
Generally, cluster analysis, or clustering, comprises a wide array of mathematical methods and algorithms for grouping similar items in a sample to create classifications and hierarchies. The clustering methods are used widely in empirical and social sciences to classify and group observations to comprehensible and representative groups. Without going into too much detail, clustering may answer questions like “how can we group and classify this dataset?” “to which class does this particular observation belong to?” (Witten and Frank, 2005; Everitt et al. 2001)
In a practical industrial management framework, clustering methods can be used for example in classifying a customer database to internally homogenous user groups for e.g. marketing purposes. Two cases undertaken at LUT are examples of experimental use of clustering in the scenario process (see the previous subchapter) to group the identified event to scenario sets, and educational use of clustering customer information for business intelligence exercises.
In the business intelligence case, the group consisted of 10-20 people who were divided into pairs that worked separately.
The basis for the case was a demonstration of the Weka machine learning software to the whole group in the beginning of the session, after which each pair conducted their own work with a generated customer database dataset to form customer segments to support marketing decisions. The casework was done through the trial-error procedure and the tutor was present to answer questions and help the students in their assignment. The session lasted about 90 minutes.
Phase I
The overall results were good judged as individual achievements toward the session goals. All the pairs were able to complete the assignment and got valid conclusions. However, findings concerning the relative advantage of the GSS facility and learning showed that the situation differed little, or not at all, from a teaching situation in a traditional computer laboratory. Thus these findings suggest that the GSS as a pure technology or the laboratory as an environment has little to offer for learning purposes by itself. These findings offer some support to the findings presented in earlier cases, as the results imply that the procedures and routines are increasingly important over technology if efficient learning is to be achieved in the GSS environment.
5.4 Supporting complex selection decisions
One of the main purposes of the GDSS laboratory is to use it in complex selection decisions that require group work and consensus. The authors have found out that an effective tool for selection tasks is to use the GSS for generating selection criteria and to evaluate the alternatives with the Analytic Hierarchy Process (AHP). The AHP put forward by Saaty (1980) is a technique for supporting selection decisions in a complex environment. The AHP is a objective, multi-criteria decision-making approach that employs a method of multiple paired comparisons to rank alternative solutions to a problem formulated in hierarchical terms (Ramanujam and Saaty, 1981). The AHP is based on three principles:
decomposition, comparative judgments, and the synthesis of priorities. The AHP structures a complex problem into a hierarchy (see figure 4). The criteria and the relevant factors are decomposed hierarchically, corresponding to the decision makers’ understanding of the situation (Poh et al., 2001; Korpela et al., 2001).
Choosing
Figure 6. Illustration of the structure of an AHP hierarchy
According to Dyer and Forman (1992), the AHP is well suited to group decision-making, offering numerous benefits as a synthesizing mechanism in group decisions. Group decisions involving participants with common interests are typical of many organizational decisions. With the AHP hierarchy model, the discussion centres on objectives rather than on
alternatives. After structuring the AHP hierarchy, the group would provide the judgments using a hierarchy. The debate, which usually occurs in a group priority setting, can be reduced by a questionnaire. If consensus is difficult to achieve, the approach is to have each group member make individual judgments and then combine the results. One way is to use voting and to take an average of the judgments to the model (Sierilä and Tuominen, 1991). In this case, the GSS was used to synthesize the weighting for the AHP hierarchy, as the abilities of the GSS seemed advantageous for the task. On the practical level, Expert Choice software has formed the pivot point for operationalising the use of AHP-techniques at LUT. The weights of the criteria derived from the voting averages of the GSS session are entered directly with the data option in Expert Choice. This approach allows more fluid use of the model for the decision maker, while effectively utilizing the group’s collective knowledge, instead of one expert opinion.
Phase I
The findings in the student cases suggest that the GSS helps the student group to generate a needed set of selection criteria for the given problem. Each member can propose criteria freely and the group can discuss the proposed criteria openly and evaluate the importance of each criterion. It seems that the collaborative way to generate the AHP hierarchy tree helps the group to understand the complex decision problem better than without any formal group work method.
In addition, the AHP hierarchy helps students to evaluate the decision alternatives in-depth, compared to unstructured decisions. The sensitivity analysis feature of Expert Choice offers a graphic tool for the analysis of the decision. The exercise as a whole helps to understand how the used selection criteria and the weights of the criteria affect the outcome of the AHP-model, and also allows the participates to examine their decision making practices and rationality compared to the relative strictness of the AHP-method. With respect to the GSS itself, the results are in line with the cases presented above.