approaches for improving m method based on …

Journal of the Science of Design Vol. xx No. x 20xx 1

APPROACHES FOR IMPROVING M METHOD BASED ON REQUIREMENTS FOR IDEA GENERATION METHODS

Daisuke ISEKI*, Hayata TANAKA*, Shota AMABE* Takeo KATO**, Yoshiyuki MATSUOKA**

* Graduate School of Keio University Hiyoshi, 3-14-1, Kohoku-ku, Yokohama, Kanagawa 223-8522, Japan ** Keio University Hiyoshi, 3-14-1, Kohoku-ku, Yokohama, Kanagawa 223-8522, Japan

Abstract: Design artifacts have become massive and more complex. With that, the elements required for designing them are increasing steadily. To cope with this situation, both “rational thinking” and “unrestricted thinking” are necessary. The M method—a generic name for design methods using the Multispace design model—enables the two types of thinking. This study aims to evaluate the usefulness of the M-BAR, a type of the M method, and propose ways to improve it. We do this by first establishing requirements for idea generation methods, then conducting a design workshop to design products under two conditions: using the M-BAR and not using the M-BAR. Participants are subsequently asked to evaluate the process using the requirements. The results reveal that five out of seven teams extracted more design elements when using the M method. Based on the findings, we suggest approaches for improving the M-BAR. Keywords: Multispace Design Model, M Method, M-BAR, Idea Generation

1. Introduction 1. 1. Background

In recent years, the factors to be considered in designing has increased with the enlarging scale and the complexity of artifacts and the diversification of users and environments. Examination of enormous design elements require designing using both top-down "rational thinking", which organizes design elements holistically while facilitating designing, and bottom-up "unrestricted thinking", which considers various designing to eliminate the constraints of enormous design elements [1]. The Multispace design model is a model which expresses designing from a comprehensive viewpoint, and the M method has been proposed as a design methodology with the concept of the model [2]. The M method is a design methodology that allows the model to be applied to design practice. The M-BAR is a M method which is the introduction of the M model perspective to both the bottom-up generation and top-down analysis design developments, and mainly supports the idea generation in the conceptual design process. In fact, the M-BAR has

already been applied to various designs and has shown its effectiveness [3-5]. However, since it has been required to design various artifacts accurately and efficiently in recent years, idea generation methods are considered to need to evolve [6-7]. Therefore, it is necessary to consider the need for improvement and the approaches for improving the M method. 1. 2. Research Objectives and Methods

The purpose of this study is to evaluate the usefulness of the M-BAR, which is one of the M method, which is a generic name for design methods with the concept of the Multispace design model to support the idea generation in the concept design process. At the same time, this study aims to present approaches for improving the M-BAR.

First, we set requirements for high quality idea generation methods. We established the requirements on the basis of (1) previous studies evaluating idea generation methods and (2) our own questionnaire survey on idea generation methods conducted with industrial designers and engineers. Next, using the requirements derived from the results, we conducted an experiment through a workshop to

Original paper

Journal of the Science of Design Vol. 4 No. 1 2020 39

Original ArticlesReceived September 29, 2019; Accepted December 16, 2019

2 Journal of the Science of Design Vol. xx No. x 20xx

evaluate the M method. We then present the usefulness of the method and future improvement approaches.

The structure of this paper is described below. Section 2 outlines the M method and its underlying model, the Multispace design model. Section 3 describes how we set requirements for evaluating idea generation methods. Section 4 details the M method evaluation experiment and presents improvement approaches for the M method on the basis of the results. Lastly, Section 5 summarizes the results of this research and considers future research. 2. Outline of M method based on Multispace design model 2. 1. Multispace design model

The Multispace design model is a model which expresses designing in various fields with different knowledge and methods from a comprehensive perspective. This model expresses the design object in the many spaces of thinking and knowledge, and expresses modeling

manipulation in the inner space and the interval between spaces. The model is made up of the Thinking Space, in which users manipulate design elements and relationships between design elements, and the Knowledge Space, which the thinking is based on [8-10]. Note that, design elements are matter that users consider when designing, and are expressed by keywords, photographs, sketches, and so on. 2. 2. M method

The M method is a general term for design methods based on the Multispace design model that can comprehensively handle various designing. The M-BAR is a M method which is the introduction of the M model perspective to both the bottom-up generation and the top-down analysis design developments. This study targets the M-BAR, in which derives design solutions by continuously repeating the process of sampling, classifying, and associating the design elements (Figure 1). The elements are distributed into four spaces and circumstances: value, meaning, state and attribute spaces, and

Figure 1. M-BAR procedure and definition of each space used

Journal of the Science of Design Vol. 4 No. 1 202040


circumstances defined in the Multispace design model. Specifically, analysis and idea generations are performed while repeating the following four steps.

Step 1 “Sampling of design elements”: Keywords, pictures, sketches, etc. are used to extract design elements, dividing them into various spaces

Step 2 “Classification of design elements”: Extracted design elements are grouped together

Step 3 “Structuration of design elements”: Correlations are drawn among grouped design elements.

Step 4 “Breakdown and addition of design elements”: Correlations are re-examined; design elements are broken down and added to.

The M-BAR allows for the iteration of Step 1 through 4 as required by the user. The design process is progressed by creating embodiments, such as sketches and mock-ups, at each iterated stage. 3. Evaluating idea generation methods 3. 1. Previous research evaluating idea generation methods

In his classic work, Young [11] proposes two principles of idea generation. First, ideas are generated by combining existing elements in a new way; and second, the ability to find new combinations depends largely on the ability to notice connections between things. He believes that idea generation follows clear methods. Matsui [12] similarly states that the idea generation method is “a method to create ideas”, where the ability to generate ideas and solve a creative problem “can be enhanced by using appropriate methods, not talent.” Here, creative problem solving is known to contain two main processes: divergent thinking and convergent thinking [13]. Studies have evaluated the productivity of each process.

Divergent thinking is often evaluated on the basis of fluency, or the amount of ideas [14, 15], allowing methods like brainstorming [16] to be appropriately assessed. Takahashi et al. pointed out that it is not enough to measure amount of ideas alone; flexibility and uniqueness were also important [17]. Convergent thinking is often evaluated on the basis of fluency, diversity, and originality [18, 19]. KJ method [20] is said to be appropriately evaluated by evaluation factors. The factors are defined below. Fluency: The total number of ideas that came out; the more, the better. Diversity (flexibility): Whether ideas can be applied to various areas. The purpose is to investigate the breadth of

ideas and the number of thinking viewpoints. Originality (uniqueness): Uniqueness of an idea, or ideas that no one else has put out on the same theme.

These three factors are also included in Guildford's measures of creativity [21] and the Torrance Tests of Creative Thinking (TTCT) [22], demonstrating their usefulness in evaluating idea generation. 3. 2. Questionnaire survey to evaluate idea generation methods

As mentioned in the previous section, idea generation methods have been evaluated by factors such as “fluency”, “diversity” and “creativity”. However, as design objects in recent years have become larger and more complicated, requirements for idea generation methods are considered to have changed. For example, when designing large-scale objects, the process of concurrently designing two different objects at the same time, such as hardware and software, total system and subsystem, product creation and product usage, is also considered important [6-7]. Therefore, in order to construct requirements for idea generation methods to evaluate the M-BAR, we will extract the advantages and disadvantages of idea generation methods and organize them. We will then conduct a questionnaire survey for industrial designers and engineers who often use idea generation methods.

The survey respondents consisted of 15 industrial designers and 15 engineers. Their ages varied from 20s, 30s and over 40, and there were five respondents in each group. In the survey, respondents answered the following four open-ended questions. -What kinds of idea generation methods do you usually use? -What are the good points of the commonly used idea generation methods? -What are the bad points of the commonly used idea generation methods? -What kind of new idea generation methods would you like to see?

There were differences between designers and engineers in the answers obtained as commonly used idea generation methods. Compared to engineers, industrial designers tended to answer idea generation methods for expressing images such as "Sketch" and "Mood board"(see “Appendix” for more information). First, we grouped the answers (127 items) into 20 themes using the affinity diagram (see Table 1). Then, we identified bad points of idea generation methods and turned them into evaluation items.



Next, we performed a pairwise comparison for similarity among the items organized earlier. We analyzed the results using multidimensional scaling and cluster analysis. When using cluster analysis, we employed Ward’s method—known to have high classification sensitivity and high stable solutions—to clarify the characteristics of each item as a way to define the distance between clusters [23]. We set the degree of similarity between the requirements to range from 1 (“very similar”) to 7 (“not very similar.”) We used the average value of the evaluation from a total of four industrial designers and four engineers.

Figure 2(a) shows a two-dimensional scatter plot based on the multidimensional scaling method, and Figure 2(b) shows a dendrogram from cluster analysis. The circles in Figure 2(a) relate the groups of requirements to idea generation methods obtained by cluster analysis. 3. 3. Derivation process of requirements for idea generation method

We now describe how the requirements for idea generation are derived from the themes in Table 1.

In Group 1, respondents wish to connect, order, and analyze a large amount of design elements. See No. 20—“Able to generate ideas by arranging a large amount of information.” Requirement 1 is thus set as “Enables clarification of relationships between design elements.”

In Group 2, respondents wish to compare multiple ideas and distinguish between superior and inferior idea. See No. 17—“Able to distinguish the merits and demerits of ideas.” Requirement 2 is thus set as “Enables distinction between differences in ideas.”

In Group 3, respondents wish to organize complex thinking processes that generate ideas and refer to them in a clean state. See No. 4—“Able to organize the idea generation process,” and No. 6—“Able to refer to the idea thinking process.” Requirement 3 is thus set as “Enables clarification of the thinking process.”

In Group 4, respondents wish to apply idea generation

Table 1. Items arranged using the affinity diagram method

b Results of cluster analysis

a Results of multidimensional scaling

Figure 2. Results of multidimensional scaling

and cluster analysis



methods in diverse specialized domains. See No. 12—“Able to generate ideas that are socially and economically useful,” No. 13—“Able to generate ideas from people in different domains,” and No. 14—“Able to generate ideas for ‘intangible things’ (other than ‘tangible things’).” Requirement 4 is thus set as “Applicable to diverse domains.”

In Group 5, respondents need the place, tool, and time for generating ideas to be convenient and accessible. See No. 15—“Able to generate ideas regardless of location,” No. 16—“Able to generate ideas with minimal tools”, No. 18—“Able to easily generate ideas”, No.19—“Able to generate ideas quickly.” Requirement 5 is thus set as “Applicable to individual design approach.”

In Group 6, respondents wish to collaborate with various levels of knowledge and ability. See No. 1—“Able to generate ideas regardless of users' knowledge and abilities”, No. 8—“Able to generate ideas even with a small number of people”, and No. 11—“Able to generate ideas with a large number of people.” Requirement 6 is thus set as “Applicable to multi-person collaborations.”

In Group 7, respondents wish to come up with many new ideas which are more novel and valuable than before; this validates the existing factors of “originality”. See No.2—“Able to generate innovative ideas” and No.3—“Able to generate many ideas.” Requirement 7 is thus set as “Enables idea generation to create new values”

In Group 8, respondents wish to create and utilize various viewpoints such as the environment, users, usage, society and global environment. See No. 10—“Able to generate ideas from diverse perspectives.” Requirement 8 is thus set as “Enables idea generation appropriate to/ to create new circumstances.”

In Group 9, respondents wish to generate ideas efficiently without taking time and effort by combining and utilizing various technology seeds. See No. 5—“Able to generate ideas automatically,” No. 7—“Able to combine ideas with different tools,” and No. 9—“Able to generate ideas together with different methods.” Requirement 9 is thus set as “Enables idea generation using innovation seeds.”

Table 2 summarizes the requirements described above. Additionally, we consider the following. Group A: Requirements 1, 4, and 5 state that idea generation methods must be able to clarify the relationships of design elements, be used in diverse domains and be applied to individual design approaches. These items relate to “originality.”

Group B: Requirements 6, 7, and 8 state that idea generation methods must enable multi-person collaborations, creation of new values, and must be appropriate to new circumstances or their creation. These items relate to “diversity.” Group C: Requirements 2, 3, and 9 state that idea generation methods must enable distinction between differences in ideas, clarification of the thinking process, and the use of seeds. These items relate to the “concurrent property” required for large-scale and complicated design objects today. 4. M-BAR evaluation experiment and improvement approaches 4. 1. Experimental method

To validate the usefulness of the M-BAR, we conducted a workshop to design products under two conditions: using the M-BAR (condition 1) and not using the M-BAR (condition 2).

Figure 3 shows how the experiment appears at the workshop. Under any condition, each team has four types of 7.5cm x 7.5cm sticky notes, magic pens, A1 size paper, 36 copic color markers, 24 color pencils, and A4 and A3 size sketch paper (Figure 4). Before the design workshop, we explained the overview and the procedure of the M-BAR to the participants for 45 minutes. A total of 62 students attended the workshop, 46 students majoring in industrial design and 16 students majoring in engineering design. They were divided into teams of six or seven people conducting product design. Design targets were “stapler”, “table tap” and “table lamp”; they were given to reduce the differences due to participants’ knowledge. In addition, since students who majored in industrial design and engineering design jointly participated in the design workshop, these design objects composed of a combination of a design part and a mechanical part were considered suitable. After the workshop, we asked all the students to evaluate the process on a five-point scale, where 1 is “I

Table 2. Requirements for idea generation methods



4. 2. 2. Questionnaire survey results and discussion Figure 6 shows an evaluation of overall effectiveness

under condition 1 (using the M-BAR) vis-à-vis condition 2 (not using the M-BAR) using box plots. In the figure, the horizontal line in the box shows the average value, the top and bottom edges of the box show the average value plus the standard deviation, and the difference between the maximum value and the minimum value ** and * indicate significant differences at significance levels of 1% and 5% by t-test respectively. Requirements 1, 3, and 9 are significantly more effective under condition 1 than condition 2.

Requirement 1 “Enables clarification of the relationships between design elements”: Requirement 1 is significantly more effective under condition 1 than condition 2. A possible reason for this is that the M-BAR enables users to conduct rational thinking, as shown in the open-ended comments saying that the M-BAR enables users to “visualize the relationship” and “organize thinking.”

Requirement 2 “Enables distinction between differences in ideas”: Although there is no significant difference, Requirement 2 under condition 1 is less effective than under condition 2. Issues encountered include “I cannot generate multiple ideas” and “It is difficult to compare final deliverables because one element relationship diagram is written for each product”. We reflect that we must make it easier to produce and compare ideas.

Requirement 3 “Enables clarification of the thinking process”: Requirement 3 is significantly more effective under condition 1 than condition 2. A possible reason for this is that the M-BAR enables users to conduct rational thinking, as shown in the open-ended comments saying that the M-BAR enables users to “easily explain ideas to others” and “follow the order of idea generation.”

Requirement 4 “Applicable to diverse domains”: Requirement 4 is significantly less effective under condition 1 than condition 2. Issues encountered include “Some elements cannot be divided into spaces” and “It is difficult to organize if there are many design elements (ideas)”. We reflect that we must make it easy to classify elements into space and to handle large amounts of ideas in large-scale design objects.

Requirement 5 “Applicable to individual design approach”: Requirement 5 is less effective under condition 1 than condition 2. One respondent justifies it as “M method has a way” and “I am not used to it.” It is therefore necessary to explain how to use the Multispace design model and the M-BAR, and to make the method accessible and freely usable in a short time.

Requirement 6 “Applicable to multi-person collaborations”: Although there is no significant difference, Requirement 6 is more effective under condition 1 than condition 2. Users explain that the M-BAR enables them to “think through trial and error” and “understand other people's ideas.”

Requirement 7 “Enables idea generation to create new values”: Although there is no significant difference, Requirement 7 is more effective under condition 1 than condition 2. A possible reason for this is that the M-BAR enables users to conduct unrestricted thinking, as shown in the open-ended comments saying that the M-BAR enables users to “be conscious of the value of the idea” and “generate ideas (with value) in stages.”

Table 6. Number of linkages and groupings

used by each team

Table 5. Breakdown of extracted design elements

Figure 5. Ratio of the number of design elements

classified for each space under each condition

Condition 1: Using M-BAR Condition 2: Not using M-BAR


don't think at all” and 5 is “I think very much”, and the reason referencing the requirements set in Table 2 and the overall effectiveness of conditions 1 and 2. 4. 2. Results and discussion 4. 2. 1. Verification of fluency

To recap, Table 1 shows that respondents want to generate many ideas (see No.3, No.18, No.19). Therefore, we compared the number of ideas generated between the two conditions. Table 3 summarizes the number of design elements (ideas) extracted by each team in the workshop for condition 1 (using the M-BAR). After the workshop, experts who normally use the M-BAR arranged the design elements derived in condition 2 (not using the M-BAR) in each space. Table 4 shows the number of design elements in each space under the two conditions.

In this table, all teams except team 5 and 7 extracted more design elements in condition 1 than in condition 2. Since many design elements were derived using the M-BAR, it is assumed that more ideas can be conceived using the M-BAR. We obtained open-ended comments on idea generation under condition 1, such as “it is easy to organize ideas and easy to understand the thoughts” and “it is easy to come up with ideas in order.” The M-BAR demonstrated its advantage in allowing users to extract many more design elements of value by classifying ideas using the four spaces of value, meaning, state, and attribute, and by organizing their relationships; all teams extracted

more design elements under condition 1 than under condition 2.

Figure 5 shows the ratio of the number of design elements classified for each space under each condition. From the figure, we consider that when sampling design elements using the M-BAR in the workshop, participants were able to use value and meaning spaces (representing psychological design elements), and the state and attribute spaces (representing physical design elements) relatively evenly. Therefore, it seems that industrial design and engineering design students were able to derive design elements using their respective spaces.

Table 5 shows the breakdown of keywords and sketches of design elements for each condition. Sketches with keywords were counted as sketches. In this table, most of the design elements extracted in this workshop are keywords. The reason why there are few sketches is that it was difficult to write sketches on small sticky notes.

Table 6 also shows the number of groupings and number of association lines in each team. The extracted design elements are structured and the relationships between the extracted design elements organized by all teams other than team 1 and 6. However, as shown in Table 6, the number of groupings and the association lines vary from team to team. Therefore, when generating ideas with the M-BAR, we can extract more design elements by making groupings and association easier.

Figure 3. Experimental initiatives at

the design workshop

Figure 4. Experimental condition

Table 4. Number of design elements per space used

by each team

Table 3. Number of design elements conceived

by each team



4. 2. 2. Questionnaire survey results and discussion Figure 6 shows an evaluation of overall effectiveness

under condition 1 (using the M-BAR) vis-à-vis condition 2 (not using the M-BAR) using box plots. In the figure, the horizontal line in the box shows the average value, the top and bottom edges of the box show the average value plus the standard deviation, and the difference between the maximum value and the minimum value ** and * indicate significant differences at significance levels of 1% and 5% by t-test respectively. Requirements 1, 3, and 9 are significantly more effective under condition 1 than condition 2.

Requirement 1 “Enables clarification of the relationships between design elements”: Requirement 1 is significantly more effective under condition 1 than condition 2. A possible reason for this is that the M-BAR enables users to conduct rational thinking, as shown in the open-ended comments saying that the M-BAR enables users to “visualize the relationship” and “organize thinking.”

Requirement 2 “Enables distinction between differences in ideas”: Although there is no significant difference, Requirement 2 under condition 1 is less effective than under condition 2. Issues encountered include “I cannot generate multiple ideas” and “It is difficult to compare final deliverables because one element relationship diagram is written for each product”. We reflect that we must make it easier to produce and compare ideas.

Requirement 3 “Enables clarification of the thinking process”: Requirement 3 is significantly more effective under condition 1 than condition 2. A possible reason for this is that the M-BAR enables users to conduct rational thinking, as shown in the open-ended comments saying that the M-BAR enables users to “easily explain ideas to others” and “follow the order of idea generation.”

Requirement 4 “Applicable to diverse domains”: Requirement 4 is significantly less effective under condition 1 than condition 2. Issues encountered include “Some elements cannot be divided into spaces” and “It is difficult to organize if there are many design elements (ideas)”. We reflect that we must make it easy to classify elements into space and to handle large amounts of ideas in large-scale design objects.

Requirement 5 “Applicable to individual design approach”: Requirement 5 is less effective under condition 1 than condition 2. One respondent justifies it as “M method has a way” and “I am not used to it.” It is therefore necessary to explain how to use the Multispace design model and the M-BAR, and to make the method accessible and freely usable in a short time.

Requirement 6 “Applicable to multi-person collaborations”: Although there is no significant difference, Requirement 6 is more effective under condition 1 than condition 2. Users explain that the M-BAR enables them to “think through trial and error” and “understand other people's ideas.”

Requirement 7 “Enables idea generation to create new values”: Although there is no significant difference, Requirement 7 is more effective under condition 1 than condition 2. A possible reason for this is that the M-BAR enables users to conduct unrestricted thinking, as shown in the open-ended comments saying that the M-BAR enables users to “be conscious of the value of the idea” and “generate ideas (with value) in stages.”

Table 6. Number of linkages and groupings

used by each team

Table 5. Breakdown of extracted design elements

Figure 5. Ratio of the number of design elements

classified for each space under each condition




Requirement 8 “Enables idea generation appropriate to new circumstances and their creation”: Although there is no significant difference, Requirement 8 is

more effective under condition 1 than condition 2. A possible reason for this is that the M-BAR enables users to conduct rational thinking, as shown in the open-ended comments saying that the M-BAR enables users to "set" and "organize" ideas related to circumstances.

Requirement 9 “Enables idea generation using innovation seeds”: This requirement is significantly more effective under condition 1 than condition 2. Users explain that the M-BAR enables them to “extract the innovation seeds in space.”

4 .3. Suggestions for improvement of M-BAR Finally, we suggest approaches for improving the

M-BAR. Improvement approach 1: Make it easier to group and

associate ideas in order to conduct rational thinking (from the verification of fluency)

Improvement approach 2: Make it easier to generate various ideas and compare them in order to conduct rational thinking (from Requirement 2).

Improvement approach 3: Make it easier to classify various ideas into multiple spaces and to handle a large amount of ideas in large-scale designs in order to conduct rational thinking (from Requirement 4).

Improvement approach 4: By Explaining how to use the Multispace design model and the M-BAR in an easy-to-understand manner so that people can start using the M-BAR in a short time in order to conduct unrestricted thinking (from Requirement 5).

5. Conclusion

In this study, we investigated the usefulness of the M-BAR and presented areas for improvement. First, we set requirements for idea generation methods. We established the requirements by analyzing previous studies on idea generation methods and conducting our own questionnaire survey with industrial designers and engineers. We organized survey results into 20 themes using the affinity diagram. After that, we performed multidimensional scaling and cluster analysis on the grouped items, thus arriving at requirements for evaluating idea generation methods. Next, using the requirements derived from the results, we performed an experiment to evaluate the M-BAR. In the experiment, we conducted a workshop under condition 1 (using the M-BAR) and condition 2 (not using the M-BAR), asking participants to evaluate the process on the basis of the requirements. It was found that the number of design elements extracted in the experiment was larger in the


Figure 6. Results of t test



condition 1, hence it was assumed that more high quality ideas could be conceived using the M-BAR. Significant differences were found under condition 1 for Requirement 1, 3, and 9 (p <0.05), and although no significant differences were observed in Requirements 6, 7, and 8, they were found to be more effective under condition 1 than condition 2, further validating the advantages of the M-BAR in idea generation (p < 0.05). Finally, on the basis of the discussion so far, we presented improvement approaches for the use of the M-BAR. In future research, we plan to further improve the M-BAR on the basis of these approaches, apply it to actual projects, and again, verify the usefulness of the M-BAR. Appendix Table 7 presents idea generation methods commonly used by the respondents, as mentioned in section 3. 2. Brainstorming is most commonly used, followed by the KJ method. References [1] Matsuoka, Y.(Eds.): Design Science, Maruzen, 2-26,

2010

[2] Matsuoka, Y.(Eds.): M Method Design Thinking on Multispace, Kindai-Kagaku-Sha, 34-143, 2013

[3] Kato, T.(Eds.): Introduction to Design Science Theory and Practice of Multispace Design Model, Keio University Press, 33-45, 2018 (in Japanese)

[4] Kanazawa, S., Sakae, Y., et al., Application of M method and Consideration of Measures to Adapt for Diverse Users. Proceedings of UMTIK 2014 International Conference on Machine Design and Production (2014)

[5] Arita, M., Kikuchi, et al., Timeaxis Design of Health Monitoring Seat System Using M method and SysML (Procedia Manufacturing). Proceedings of AHFE-2015 6th International Conference on Applied Human Factors and Ergonomics (2015), 5435-5442

[6] Matsuoka, Y.: Design Science for Product Creation × Product Usage: 10 Guidelines for Creating New Values Toward Business Strategy, Kindai-Kagaku-Sha, 130-143, 2017

[7] Matsuoka, Y., Concurrent design method for impression and function, International Journal of Entrepreneurship and Innovation Management, 3(4), 340-348, 2003

[8] Matsuoka, Y.(Eds.): Concept of Emergent Design, Kyouritsu Shuppan, 48-51, 2013 (in Japanese)

[9] Matsuoka, Y., Multispace Design Model as Framework for Design Science towards Integration of Design. Proceedings of International Conference on Design Engineering and Science 2010 (2010), 48-51

[10] Matsuoka, Y. and Sato, K., Design Science; Multispace Design Model. Proceedings of UMTIK 2012 International Conference on Machine Design and Production (2012)

[11] James, W.: A Technique for Producing Ideas, CCC Media House, 32-42, 1998

[12] Matsui, K., Idea Generation Method, Journal of the Society of Instrument and Control Engineers, 46(2), 292-297, 2007 (in Japanese)

[13] Guilford, J, P.: The nature of human intelligence, McGraw-Hill, 138-184, 1967

[14] Isaksen, S, G., A Review of Brainstorming Research: Six Critical Issues for Inquiry, Creative Research Unit Creative Problem Solving Group-Buffalo, 1-28, 1998

[15] Takahashi, M.: The Bible of Creativity, Mode-Gakuen-Shuppankyoku, 254-256, 1993 (in Japanese)

[16] Alex F, O.: Applied Imagination—Principles and Procedures of Creative Writing, Scribner, 229-258, 1953

Table 7. Commonly used idea generation methods


1

VISUAL IMPRESSION OF BAMBOO STICKS AND ACRYLIC RODS COMBINATION

Based on Evaluation of Indonesian Perspective

Hari NUGRAHA*, Sim Teck CENG**, Koichiro SATO***, Fumio TERAUCHI***

* Graduate School of Chiba University 1-33, Yayoicho, Inageku, Chiba-shi, Chiba 2638522, Japan ** Junior College of Aizu 1-1, Itsukimachi ooaza yahata kadota, Aizuwakamatsu-shi, Hukushima 9650003, Japan

*** Chiba University 1-33, Yayoicho, Inageku, Chiba-shi, Chiba 2638522, Japan

Abstract: Traditionally, bamboo plants are used as primary materials for various needs and for making products. These materials can be combined with synthetic materials such as acrylic rods. The results of the combinations of these materials can produce a visual appearance namely the moiré effect that can present certain visual impressions on the surface of the materials. The purpose of the study is to make clear the visual impressions of woven bamboo by combining it with acrylic rods materials. The process for combining these materials, called cross-weave, is taken from the Numazu fence style pattern. The Correspondence Analysis method was used to evaluate the visual impressions of combining materials and then the cluster analysis method was utilised to classify the samples of materials based on the visual impressions they generated. The visual impression results obtained are Active, Passive, Traditional, Futuristic, Organic, Non-organic; and the samples of materials are divided into five groups based on the types of visual appearance they produce. The results of this study can be used to improve the use of bamboo materials for various purposes. Keywords: visual impression, bamboo stick, acrylic rod, material, moiré effect, woven pattern

1. Introduction Bamboo materials have a long history use in daily

life [1]. Bamboo is a typical plant that grows widely in tropical and sub-tropical climates in Africa, Asia, and Central and South America. In Asia, bamboo plants can be found in countries such as Japan and Indonesia. Bamboo can grow in different environments and soil conditions, and the plants are self-sustainable [2]. Bamboo materials can be used for a variety of purposes. Bamboo materials are often used for the making of woven products such as bamboo baskets and mats [3]. Bamboo can also be processed using low-level technology [4]. The advanced use of bamboo materials in household products has been extended to industrial applications [5].

In several villages in Indonesia, these bamboo plants are famous as the materials for making traditional woven products and play significant roles in the villages’ economy and income [6]. Meanwhile, in Japan, there has been a reduction in the use of bamboo materials because

farmers are no longer motivated to maintain bamboo forests. Bamboo materials have low prices, and production costs are more expensive than the benefits. Under these conditions, most bamboo forests in Japan have been negligently managed, and bamboo plantations that are not managed properly have crept into the surrounding lands [7].

In Indonesia as well, bamboo materials that are commonly traditionally processed to become woven products are perceived as visually unappealing because lacking of visual impressions, cheap as they have low product values, and complicated as it requires additional skills to handle the materials [8].

The traditional process of weaving bamboo to produce visual impressions on the surface usually applies the conventional method by creating various combinations of weaving patterns [9] and by combining with other materials such as rattan or other fibre materials. The colouring methods are also usually used to improve the visual impressions on the surface of the

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[17] Takahashi, M., Research on Brainstorming (1), Journal of Japan Creativity Society 2, 94-122, 1998 (in Japanese)

[18] Yuizono, T. and Munemori, J., Performance of GUNGEN Idea Generation Support Groupware: Lessons from Over A Few Hundred Trial Sessions, The Japanese Society for Artficial Intelligence, 19(2), 105-112, 2004 (in Japanese)

[19] Munemori, J. and Yuizono, T.: Idea Generation Methods and Collaboration Technologies, Kyouritsu Shuppan, 94-106, 2014 (in Japanese)

[20] Kawakita, J.: Idea generation method, Chuokoron-Shinsha, 66-114, 1967 (in Japanese)

[21] Guilford, J, P.: Creativity and its Cultivation, Harper & Brothers Publishers, 142-161, 1959

[22] Torrance, E, P.: Guiding Creative Talent, Englewood Chiefs, 7-8, 44-64, 1994

[23] Ueda, S.: Cluster Analysis, Asakura Shoten, 1-23, 2003 (in Japanese)


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