Archives of Design Research

The problem-solving approach represents which factors different teams concentrated on during the process of solving problems while accomplishing a design task. Jin and Kim (2006) categorized various problem-solving approaches. They developed categories that are appropriate for product sketching tasks by referencing the information categories of Suwa and Tversky (1997) Architecture Design task.

<Table 1> (Suwa and Tversky, 1997; Jin and Kim, 2006) illustrates the comparison between the information categories of Suwa and Tversky Architecture Design and the information categories of Jin and Kim Design Problem Solving Approach. Suwa and Tversky subdivided emergent properties, spatial relations and functional relations. On the other hand, Jin and Kim subdivided human elements into physical and mental elements and types of background knowledge into external and internal knowledge, and examined them more closely.

The group activity represents the processes through which a group approached problem-solving, and it is categorized depending on the design process. In their on-site study of group activity, Olson et al. (1992) made 10 observations of software design problem-solving meetings that took place in the course of a small group project. They categorized design meeting activities into three —Design, Review and Summary, and Coordination— categories, and again divided these categories into 11 sub-categories in their subsequent study, as shown in <Table 2>.

Ocker and Fjermestad (2008) further modified the task used by Olson et al. (1992) and utilized it to analyze the process by which Virtual teams (VT) designed the Computerized Post Office (CPO) by reaching a consensus through collaborative work via computer-mediated on-line communication systems. They sorted group activities into ‘Design, Summary, Coordination, and Other’ categories depending on the design process, and into ‘Debate and Supportive’ categories depending on the team climate, and subsequently employed these categories as the basis for analyzing the conversations of business graduate students and for comparing the quality of output (Functionality, Interface layout, Coherence of these ideas) and creativity.

We conducted a preliminary experiment (1st experiment on November 16, 2011) in order to confirm what multidisciplinary design education seeks to achieve: namely, whether members from various knowledge backgrounds working together indeed influence a group’s problem-solving skills. We first divided our participants into two teams, one team with students of the same major (design team), and the other with students of various majors (multidisciplinary design team), and compared the creativity of the two teams based on the variety of the members’ study concentrations. Through the actual experiment (2nd experiment on August 12, 2013) with a different participant pool, we aimed to find significant results by dividing students with different knowledge backgrounds into two groups. We gave multidisciplinary design education to one group and only traditional design education to the other group, and compared the task accomplishment processes of each team.

All the participants of this study were students of Sungshin Women’s University. Sungshin Women’s University has implemented multidisciplinary design education after being selected to participate in the multidisciplinary design school development project. It was selected as the first-year base university for the multidisciplinary design school development project in 2011, along with Seoul National University and KAIST, and was subsequently awarded for its excellent operations, as evidenced by the outstanding performance results of its multidisciplinary design education program.

The 1st experiment participants comprised of 12 junior design majors from the multidisciplinary design education program, 12 junior design majors from the traditional design education program, and 12 sophomores to seniors from other departments, for a total of 36 students. They were grouped into 12 teams of three. Of these 12 teams, 6 teams were heterogeneous with each group consisting of 1 design major and 2 non-design majors, while the remaining 6 teams were homogeneous with each group made up of traditional design majors only, or of participants in the multidisciplinary education program only.

The results of the first experiment showed that the difference in creativity was only present among heterogeneous teams. In order to further examine the creativity of heterogeneous teams, we conducted a second experiment with different participants and divided the teams into a multidisciplinary design major team and a design-only major team. Both teams comprised members of various study concentrations. A total of 36 students participated in the second experiment, including 7 design major juniors and 2 design major seniors from the multidisciplinary design education program, and 2 design major juniors, 7 design major seniors, and 18 non-design major students ranging from freshmen to seniors, all from the traditional design education program.

In both experiments, each team was composed of 3 members. Cast et al. (2012) stated that a 3-5 member team is the optimal size, suited for stable conflicts of opinions that lead to effective problem solving. A larger team would have longer decision-making processes and would require a competent leader in order to carry out consistent and efficient outcomes. On the other hand, a smaller team would show higher levels of concentration and would be fitted for ideational conflicts that can prevent hastily settling on a single idea.

The multidisciplinary design students in the first and second experiments took multidisciplinary education courses for 2-3 years, with experiences in 2.5-11 month-long corporate projects. In addition to taking an industrial design course, the multidisciplinary students were required to take ‘Industry- Academia Team-Teaching Capstone Design 1,2’ courses. We varied the two heterogeneous groups by enrolling one group in the multidisciplinary design education program and the other group in the traditional design education program. Besides this variation, however, we ensured that all the participants had similar GPAs, and non-design major members of both groups were selectively chosen so that both group members had similar majors and a similar proportion of freshmen to seniors. Furthermore, to ensure similar levels of interest in design among non-design major students, we only drew our participants from students taking design courses as electives that had voluntarily signed up to participate in a design experiment.

We sought to examine the difference between the creativity of the output produced by a team of design and non-design major students who had received multidisciplinary design education and a team with similar design and non-design major students who had only received traditional design education. We also sought to examine their design processes. The procedure was as follows.

The creativity produced by a team during the brainstorming stages is different from that produced during the in-depth discussion stages (Gong, Suteu, and Shen, 2009; Bechtoldt, De Dreu, Nijstad, and Choi, 2010). We therefore divided the design process into the brainstorming stage and the in-depth discussion stage. The time allotted to perform the design task was divided into experiment A and experiment B. Experiment A (brainstorming) required sketching or writing the description of the ideas produced in the group, without limitations and consideration of implementation methods. Experiment B (in-depth discussions) required choosing an idea from the prior session (Experiment A) or a new idea and further developing it based on a specific method for its realization and then writing and submitting its design background, design concept, and design solution image.

In the 1st experiment, each team was allotted a time limit for each task (Experiment A: 20 minutes, Experiment B: 50 minutes). In the 2nd experiment, each team was allotted a total of 70 minutes for both tasks so that we could examine how both the heterogeneous multidisciplinary team and heterogeneous design team distributed their time between brainstorming and idea development. This was on the basis of other studies that had found that creative teams allocate less time to the brainstorming process. Ocker and Fjermestad (2008) divided participants into a high-performance team and a low-performance team based on the measurements of creativity and the quality of their outcomes. The analysis of communication among team members showed that high-performing teams spent less time on brainstorming and rather carried out more detailed and rigorous discussions. In addition, Cho and Jung (2006) found that after the initial collection of ideas, the longer participants invest in executing their idea and the more active their discussion, the greater the originality of their output. Thus the study showed that although the initial concept is indeed important, the originality of the output is strongly dependent on the creativity of the discussion and idea realization process.

Each team was given 10 sheets of B4 size paper, pencils, erasers, and one computer. The entire process of the experiment was filmed with two cameras for each team. Each team was given a computer for research, with a 10-minute and 15-minute limit in experiments A and B respectively. The time cap was lifted in the 2nd experiment.

The topics given in the design task were as follows: ‘It is 7 PM in the evening. How can we make people feel better after work?’ (1st experiment) and ‘How can we make a family’s weekend outdoor activities more enjoyable?’ (2nd experiment).For our present study examining design creativity, we selected a task that could reveal integrated problem-solving ability. The task was also one on which a design major had little impact and which was easily accessible to non-design majors in everyday life. The design task for creativity evaluation needs to be challenging, realistic, appropriate for the subjects, not too large, feasible in the time available and within the sphere of knowledge of the researchers, as well as centered around a problem that is typical as far as industrial design practice is concerned in that it calls for the integration of a variety of aspects (Dorst and Cross, 2001). Moreover, La (2012) stated that the more conceptual the topic of a project is, the more important it is for a team to have members from various study concentrations. Thus our choice of topic, which was quite conceptual, was appropriate for the task of team design.

In order to verify whether an individual’s creativity affected group creativity, individual creativity tests were given. The participants each performed three actions: receiving one TTCT Figure A test sheet and following the test giver’s instructions, completing the figure and attaching the name during the given time.

The results of the individual creativity test showed that the average creativity of the multidisciplinary team students and design team students were 53.1 and 56.4 respectively, and the average creativity of the design major students with multidisciplinary design education and design major students with traditional design education were 55.8 and 53.8 respectively. However, because the significance of probability (p-value) for the homogeneous groups in the 1st experiment was 0.725 and that for the heterogeneous groups in the 1st and 2nd experiment was 0.456, the difference of aver-age creativity among members of the multidisciplinary team and of the design team was statistically insignificant. Likewise, there was no significant difference in individual creativity among the design major students with multidisciplinary design education and those without. In other words, it was confirmed that there was no significant difference in creativity among individual team members in the multidisciplinary team and in the design team, and the same was true for the design major students with multidisciplinary design education and those without.

Design output creativity was judged by three experts based on prepared evaluation sheets. The judges consisted of two professors and a doctoral candidate in design. The evaluation sheets were drawn up according to the Korean creative product evaluation tool (Kim and Lee, 2004; Lee, Kim, and Choi, 2007) developed by modifying Besemer’s (1999) Creative Product Analysis Model (CPAM) and Creative Product Semantic Scale (CPSS) to reflect the cultural peculiarities of Korea for the purpose of assessing the degree of product creativity.

According to CPAM, the evaluation dimension comprises of 3 factors: Novelty, Resolution, and Elaboration and Synthesis (Style) (Besemer, 1998). Novelty represents the newness of materials, process, concept, and methods to produce output; Resolution represents a product’s usefulness or the extent to which it solves a problem; Elaboration and Synthesis (sometimes called “Style”) contains stylistic elements of the output (Besemer and O’Quin, 1999; Kim and Lee, 2004).

The Korean creative product evaluation tool was developed through methods that involved exploring synonyms and performing content analysis on words that depict the properties of “creative output”. The final evaluation tool differed from the original three-component structure. Among the nine sub-components of the original structure, the organic component under the elaboration dimension was excluded and only eight sub-components were included in the final tool. Moreover, this evaluation tool was confirmed to be effective on evaluations of outcomes in different areas and in Korea.

Figure 2 Creative Product Analysis Metrix(Besemer, 1998)

In this study, the evaluation tool is structured with three components and eight sub-components - novelty (surprising, original), resolution (logical, useful, valuable), style/elaboration and synthesis (organic, well-crafted, elegant). The responses to these questions were evaluated on a 7-point scale and the average of the three components was taken as the ‘creativity’ measure. Separately from this, a single ‘score’ question was established to assess the overall creativity of the design. It asked, “On a scale of 100, how many points would you give to this design?”

The output evaluation results showed the Chronbach’s coefficient value, which signifies the degree of congruity among the 3 judges, to be 0.6 or higher for all evaluation items of the 1st and 2nd experiment, indicating high measurement reliability.

In the 1st experiment, for the homogeneous groups that consisted of students with the same majors, there was no significant difference in the creativity scores and ratings of design output between the students who received multidisciplinary design education and those who did not. Hence in order to examine the effect of multidisciplinary design education, we conducted a 2nd experiment targeting the heterogeneous groups that had shown a difference in creativity.

We conducted the 2nd experiment with a different participant pool and tasks from the 1st experiment. There was no significant difference in creativity between the heterogeneous multidisciplinary team (EMT) and the heterogeneous design team (EDT) during the brainstorming task (experiment A), but a difference in creativity was found during the in-depth discussion (experiment B). This further strengthened our results that revealed a significant difference in creativity during the idea development processes between the heterogeneous multidisciplinary team (EMT) and the heterogeneous design team (EDT).

We therefore examined the reason for these differences shown between the design-only teams and multidisciplinary teams in the heterogeneous groups of experiment B (in-depth discussion) by performing a transcript analysis of the creative process. To do so, we transcribed the recorded conversations of the participants and then performed an in-depth analysis by using Nvivo9, a tool for qualitative study of the problem-solving approach and group activity process.