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Article

CBDHS: A Case-Based Design Heuristics Tool to Support Product Design Students in Idea Generation

by
Xin Cao
1,2,*,
Yen Hsu
1 and
Honglei Lu
1
1
The Graduate Institute of Design Science, Tatung University, Taipei 104, Taiwan
2
College of Art and Design, Fuzhou University of International Studies and Trade, Fuzhou 350202, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(23), 16011; https://doi.org/10.3390/su142316011
Submission received: 29 July 2022 / Revised: 14 November 2022 / Accepted: 28 November 2022 / Published: 30 November 2022
(This article belongs to the Special Issue Sustainable Design Education and Implementation)
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Abstract

:
Previous research have demonstrated the efficiency of card-based design heuristics in product design courses, but the product case sources selected for these design heuristics have been too homogeneous and have targeted design goals, making it difficult for product design students to use them quickly and accurately in the classroom. As new products continue to iterate, previous design heuristics are no longer fully meeting the requirements of product design education. There is no process for developing design heuristics for course-targeted products that would allow design school teachers and students to develop new design heuristics on their own, based on course objectives. This study proposes case-based design heuristics (CBDHS) to support product design students in idea generation and a step-by-step process for constructing CBDHS. In addition, this study develops an eco-friendly product packaging example to validate the applicability of CBDHS in product design courses using an empirical evaluation. A total of 38 product design students participated in this quasi-experiment and were asked to generate as many ideas as possible in 45 min, with the experimental group (19 participants) generating ideas using CBDHS and the control group (19 participants) generating ideas using the brainstorming method. This quasi-experiment evaluated the ideas generated by each participant using five evaluation metrics (quantity, novelty, quality, number of good ideas, and level of design fixation). The results of the experiment show that (1) in terms of the number of ideas, the experimental group (M = 10.95, SD = 4.14) produced fewer ideas per participant on average than the control group (M = 13.68, SD = 4.44), t(36) = 1.966, p = 0.057; (2) regarding the novelty of ideas, there is no statistically significant difference between the control group (M = 4.00, SD = 1.47) and the experimental group (M = 4.48, SD = 1.56), t(149) = −1.928, p = 0.056; (3) with respect to the quality of ideas, there is no statistically significant difference between the control group (M = 4.19, SD = 1.96) and the experimental group (M = 4.40, SD = 2.05), t(149) = −0.648, p = 0.518; and (4) concerning the number of good ideas, there is a significant difference in the value of the proportion of the control group (9.5%) versus the experimental group (31.3%), x2(1, n = 151) = 11.44, p = 0.001. (5) There is no statistically significant difference between the experimental and control groups in terms of the level of design fixation. CBDHS can support product design students in generating ideas for the targeted products of the course, and the integration of CBDHS into the product design curriculum can help teachers to impart innovative ideas to students, ultimately leading to an improvement in teaching quality.

1. Introduction

Producing new ideas in the early stages of design can have a major impact on the success and innovation potential of a product [1]. Krippendorf (2005) proposed design discourses that connect designers with effective guidance in a refined form from past solutions [2]. This comprehensive knowledge of design is called design heuristics [3]. Although researchers have proven the importance of design heuristics in the product concept design phase [4,5,6], product design students often have difficulty generating ideas quickly and accurately [7]. Product design educators lack technical guidance to support idea generation, and teaching strategies for innovative ideas may pose challenges for them.
Several universities already offer courses related to product design (e.g., Tsinghua University, Tongji University, Soochow University, etc.). Similar to other design courses, product design teaching is practice-based and problem-solving oriented [8]. Practical teaching, on the other hand, revolves around specific design propositions, and students gradually deepen their understanding of product design by applying their learned theoretical knowledge in practical design activities. In the practical phase, courses adopt a seminar-based teaching method with student-directed learning. Teachers act as question answerers and transfer knowledge to students while participating in discussions.
This teaching pattern can effectively build students’ knowledge of product design and help them master the basic processes, methods, and tools of product design, but it still has limitations with regard to teaching product design expertise to students. On one hand, theoretical teaching content is mainly about the basics of product design but rarely involves experience-related design strategies, and the product design methods and tools that are taught are mainly focused on analysis and sorting, which do not compensate for novice designers’ lack of experience [9]. On the other hand, in practical teaching, teachers usually transform their personal experience (design expertise) into concrete advice to inform students, and students have difficulty acquiring domain-specific knowledge from this implicit form of knowledge transfer [10].
It can be challenging for product design students to further develop their initial ideas into alternative ideas [11]. Thinking tools that provide strategies and tips to assist product design students to break through thought barriers, such as design fixation [12,13,14], facilitating the creation of new ideas or improving on previous ones. The characteristics and focus of the design tools used to create ideas vary [15,16], so using different tools may generate different ideas.
Currently, product design students rely primarily on their existing knowledge to generate ideas, and brainstorming methods seem to be the most commonly used design method [17,18,19]. Although they have design specialist knowledge, these students usually do not have a lot of hands-on practical experience in solving the design issues targeted by courses. With the wide range of materials and techniques used in packaging, students who lack extensive design experience often have difficulty generating good ideas during courses.
Despite the improvements and perfections of various design approaches, product design education in universities still faces many challenges, among which is the absence of design tools for generating ideas. Previously, several researchers have proposed relevant design heuristics [16,20,21,22,23,24,25]. However, the product cases used in these previous design heuristics were too general and the cards too cumbersome, while the product design course was centered on specific objectives. Students had to perform experimental tests on each design heuristic separately without knowing the problem they were targeting, thus reducing the efficiency of using the design heuristics to generate ideas. Design heuristics are developed mainly in the fields of mechanical design and product design. Evidence-based research suggests they are useful to students and practices in certain product disciplines in developing diverse, innovative, and functional ideas that contribute to the design of final programs [5,21,26,27,28,29,30,31]. Nevertheless, little research has examined the generation of ideas in a curriculum-targeted product design environment. There is an absence of a process for developing product design heuristics for course objectives.
The Kawakita Jiro (KJ) approach was created to generate novel ideas and helps researchers to organize vast quantities of disordered material, and discover underlying content and patterns in the material, through inductive reasoning. Applying the KJ approach in the process of extracting design heuristics increases the credibility and accuracy of the extracted design heuristics, allowing design school teachers and students to be able to develop new design heuristics on their own based on course objectives. Brainstorming is one of the most popular creative techniques used in groups [32], and numerous studies have shown that nominal groups (i.e., groups that work independently but are present with each other) outperform interactive groups (i.e., groups that generate ideas from face-to-face discussion structures) in terms of the quantity and quality of ideas generated in brainstorming sessions [33,34]. These effects may be due to a variety of social and character factors, such as assessment fears, social fears, blocked production, or downward comparisons [35,36]. Group members may feel inhibited from expressing their ideas in the group because of social anxiety [37], or may have difficulty expressing their ideas effectively when they have to wait for others to express their ideas and then formulate them when it is their second turn [38]. Therefore, the brainstorming method for nominal groups was used by the control group participants in the quasi-experiment of this study.
With the above research background and motivation, this study proposes case-based design heuristics (CBDHS) to support product design students in generating ideas. To assess the effectiveness of CBDHS, this study conducts a quasi-experiment with 38 design students who were asked to generate as many ideas as possible in 45 min, with the experimental group (19 participants) using CBDHS to generate ideas and the control group (19 participants) using the brainstorming method to generate ideas. The difference between CBDHS and the brainstorming method in terms of the effectiveness of the students’ idea generation is also assessed by five evaluation metrics (quantity, novelty, quality, quantity, number of good ideas, and level of design fixation) that have been widely used in previous studies [22,39,40]. As indicated in Figure 1, the particular questions examined in this research are:
(1)
Can the use of CBDHS significantly improve the number of ideas created by students compared with the brainstorming method?
(2)
Can the use of CBDHS significantly improve the novelty of ideas created by students compared with the brainstorming method?
(3)
Can the use of CBDHS significantly improve the quality of ideas created by students compared with the brainstorming method?
(4)
Can the use of CBDHS significantly improve the number of good ideas created by students compared with the brainstorming method?
(5)
Does the use of CBDHS increase students’ level of design fixation compared with brainstorming?
This study is structured as follows: Section 1 describes the current dilemma of product design students in generating ideas and presents the research objectives of this study. Section 2 reviews previous research on ideation tools and outlines the definition and development status regarding design heuristics and the KJ method. Section 3 presents a step-by-step process for developing case-based design heuristics. Section 4 lets teachers create a set of customized design heuristics for use in a particular course (eco-friendly packaging design). Section 5 presents the case in which students used these design heuristics to generate ideas and evaluate these ideas. Finally, a summary and recommendations are made in Section 6.

2. Literature Review

2.1. Tools for Idea Generation

There are many ideation tools available to support the generation of ideas across disciplines [4,41]. For example, brainwriting [42], analogical thinking [43], brainstorming [44], conceptual combination [45], Synectics [46], TRIZ (Teoriya Resheniya Izobreatatelskikh Zadatch) [20,47], SCAMPER (substitute, combine, adapt, put to other uses, eliminate, reverse) [16], and Design Heuristics [4,41,48]. Conceptualization tools facilitate innovation and variety in ideas, as well as conceptualization and transformation [49,50].
Conceptualization tools are likely to assist students throughout the design process in developing concepts. Nevertheless, the applicability of these models can differ in terms of how they are used to originally generate and transform ideas. For instance, brainstorming offers some universal principles, such as “suggest lots of ideas” and “don’t evaluate ideas” but gives little guidance on exactly how to create ideas. SCAMPER provides tips on how to form new ideas by “combining” or “modifying” existing ideas, which might be better suited to applying to current ideas. TRIZ is a different conceptualization method that suggests applying strategies to existing ideas that have been sufficiently developed so that contradictions can be identified [20]. Synectics and TRIZ tools need widespread practice and education to make them proficient [51]. In contrast, analogical thinking focuses only on supporting the generation of the initial idea.
Design experience is often difficult to put into words, and most conceptual tools are generated by learning about the corresponding field, but this tacit knowledge can be found in relevant cases. For example, TRIZ was developed by analyzing nearly 2.5 million advanced invention publications around the world. A total of 3450 relevant cases were used in the development of 77 design heuristics. Case-based design heuristics (CBDHS) is therefore also developed by collecting a large number of relevant cases, aggregating them, and extracting design heuristics.

2.2. Design Heuristics

Design heuristics are principles of idea generation that aim to stimulate the discovery of ideas [25]. The purpose of design heuristics is that they aid designers in engineering to stimulate creativity, guiding them toward apparent ideas and helping them to produce additional ideas that differ from each other [27]. Several designers working with the identical design heuristic can come up with diverse ideas, as design heuristic cards do not offer one uniquely correct resolution, but instead provide a reference to a potentially correct solution. The design heuristic has its origins is in cognitive psychology, which provides “best estimates” and is a shared feature of specialist achievement [52].
Design heuristics are developed through studies of experts, senior designers, and product designers, along with analysis of prize-winning projects [4,41]. The 77 design heuristics with descriptions on a deck of cards were identified for teaching purposes. These 77 design heuristics incorporate data from four evidence-based research on design processes and results, which include winning prizes, several ideas designed by experienced industrial designers for a single project, and a collection of ideas from 48 designers for individual design issues. A compendium in excess of 3450 design procedure results was collated to pull out the variations in ideas apparent in design issues and resolutions. The results produced a set of models, presented as cards in 77 design heuristics, which helps designers generate more candid ideas in the early stages of design. Every design heuristic is shown separately on a corresponding card, providing a graphic and textual description on the front and graphic illustrations of its application to established products on the back.
Design heuristics were developed mainly in the fields of mechanical design and product design. Evidence-based research suggests they are useful to students and practices in certain product disciplines in developing diverse, innovative, and functional ideas that contribute to the design of final programs [5,21,26,27,28,29,30,31]. Nevertheless, little research has examined the generation of ideas in a curriculum-targeted product design environment. A recurring theme at global conferences and design heuristics workshops at universities since 2012, which have encompassed design pedagogues from a variety of product disciplines, is the degree to which design tools are metastable outside the fields of product design [4,21,48,49,53,54]. The 77 design heuristics have proven to be useful design tools in several studies; for example, a group of US product design students was set the assignment of designing mobile photovoltaic cooking equipment based on 12 of the 77 cards, and the findings indicated that the ideas created by the students using the cards developed better. Leahy et al. (2018) also obtained the same findings from a study of 185 students. The 77 design heuristics can be effective in helping students generate innovative design concepts and facilitate the design collaborative process while collaborating in a team; however, novel designers or inexperienced students may need more guidance to use them effectively. Therefore, this study uses the 77 design heuristics as the basis for developing a card tool that is more suitable for use by product design students.
Students’ understanding of the tools and their educational background may influence their effectiveness in using design heuristics applied to the field of product design [27]. Some design items in product design may have aspects similar to those of mechanical engineering design, such as the components of a product. However, the training of product design students may limit the effectiveness of their use of design heuristics. For example, the design heuristics that have been developed are universal only and do not address course target products, which makes it difficult for students in product design courses to generate ideas in relation to course objectives. Therefore, this research develops design heuristics in course objectives, making them more targeted and easier to be used by students in the innovative design process.

2.3. Kawakita Jiro Method

The Kawakita Jiro (KJ) was developed to generate new ideas to help researchers organize massive volumes of unorganized databases, and to discover potential content and patterns in the databases through inductive reasoning [55]. Kawakita attempted to use the KJ method to analyze the results of ethnographic data that could be applied to complex, unmeasurable, specific, non-duplicative, characterized data gathered in the domain [56,57]. One of the most common questions about the KJ relates to its general availability. Similar to other methods of the Japanese solution to problems, the KJ method stresses a team or group focus, rather than a personalistic focus, to reach a solution. Kawakita’s proposed KJ method promotes group solidarity and can be used outside of a world-oriented social context. The KJ method has been broadly embraced in the Japanese commercial sector because it aids in reaching consensus or forming settlements. Kawakita’s effective use of the KJ method for development programmers in Nepal earned him the Ramon Magsaysay Award in 1984. Many companies have used the KJ method in diverse development programs. Lately, the KJ method has been established as an element of the ongoing management reform in the USA to deliver Total Quality Management (TQM) [58]. The KJ method can be employed individually, but usually in a group. The KJ method allows group members to completely grasp how the outcomes were achieved, meaning that each member understands the issues at nearly an identical level. Previous studies on card-based design heuristics did not explicitly propose specific steps for extracting design heuristics but operated only through individuals with subjective experience, which may lead to different levels of understanding and arguments among group members. Therefore, this study applies the KJ method to the process of extracting design heuristics to increase the credibility and accuracy of the extracted design heuristics.

3. The CBDHS Tool Based on the KJ Method

To address the aforementioned lack of design expertise among product design students, this research combines outstanding past product cases by exploring keywords and libraries, extracting and summarizing their design heuristics using the KJ method, and proposing case-based design heuristics (CBDHS). CBDHS are design heuristics that bring collective design knowledge that may stimulate product design students creatively, helping them conceptualize ideas for products that meet course objectives. CBDHS is derived from the experience of analyzing past cases of products that meet the requirements of course objectives [23,24,59,60]. CBDHS can create design heuristics for different course objective products and making them available to students can significantly relieve the above knowledge deficit. Hwang and Park [22] proposed a design heuristic to aid in the product concept generation for objective X, on the basis of which this study builds on it by proposing a stepwise procedure for building CBDHS for the course’s objective products, divided into the following four steps.

3.1. Requirement Analysis and Functional Transformation

In product design courses, teachers usually link course objectives to design requirements. Chen and Zhang et al. [51] proposed that design requirements are problematic conceptual environments that people transform into ideal conceptual environments. Since these conceptual environments usually exist in the subjective world, students cannot directly relate to these subjective environments to design objects in the objective world. Students need to find a more objective description of needs that translates the conceptual environment of the subjective world into functions in the objective world. Chen and Zhao et al. [61] summarized the various definitions of functions that should deal with changes in the objective environment, while the subject of needs is only the subjective environment of the person. Users’ requirements define the intrinsic properties of a product, which are reflected in the functionality of that product. Therefore, users’ requirements are matched with corresponding features to determine the main functions and constraints of the target product, and functional configurations are established through case studies. This study uses the Function–Behavior–Structure (FBS) tool suggested by Gero and Kannengiesser [62] for the functional transformation of requirements. Function refers to the intent or objective of the design, behavior pertains to how the purpose is accomplished, and structure relates to the elements and their relationships. This description in turn provides the standard format for the subsequent search of keywords and database operations.

3.2. Search of Keywords and Databases

After defining the requirements and functions, the keyword search phase begins to identify the products and patents that meet the aforementioned requirements and functions. The selection of the keywords dictates the search results and the quality of the extracted design heuristics.
This research uses three types of keywords for the case search. The first type includes adjectives describing product characteristics, such as “sustainable”, “eco-friendly”, and “minimalist”; the second type includes verbs describing actions, such as “telescope”, “rotate”, and “flip”; and the third type includes nouns, such as “product”, “device”, and “case”, as well as their synonymous singular and compound forms.
The formula for searching associates keywords from each type with the Boolean operator OR, and then associates each combined keyword with the operator AND.
In addition to searching for keywords, it is also important to have appropriate libraries or PC aids, such as ProCAPD [63] and ENVOPExpert [64]. It is also useful to use the Google research tool to look for related product-specific examples and to search for the winners of prestigious competitions, such as the International Design Excellence Awards (IDEA), the Red-Dot Product Design Awards, and the iF Product Design Awards.

3.3. Database Acquisition and Pre-Processing

Following the selection of keywords and databases, the relevant product cases are explored. The cases are then filtered using predefined criteria that are related to products or patents that match the design requirements. After filtering, cases with duplicate or very similar functions are merged.
There are different formats for describing products and patents. Therefore, this study standardizes their formats as follows: (1) The issue addressed by the product and (2) how the product resolves the issue. The procedure of format standardization can help designers to learn the invented product [65], and aids in the subsequent data integration process and discovery of design heuristics.

3.4. Identifying Design Heuristics with the Kawakita Jiro Method

Following the design heuristics summarized from the products gathered in the above phase, three or more teachers or experienced students jointly conducted an integrated analysis with the Kawakita Jiro (KJ) method. The KJ method allows designers to organize many unordered databases and find the potential meanings through inductive data. This study uses the same process proposed by Hwang and Park [22], and the analysis procedure is the following:
(1)
Develop a small card for each case that describes how it resolves a design issue that meets the course objectives, using the criteria format defined in the above phase, with the requirement that each card description is brief and easy to read. The cards are then arranged in a large work area where they can all be seen by all members involved.
(2)
The participating members work separately and remain silent. They look for cards similar to one another based on the design questions of the course objectives and place the cards close to each other on the table until there are no differences. If one card may be part of more than one group, it may be duplicated in different groups.
(3)
Participants talk about the grouping pattern of the cards in the work area. Minor modifications can be applied to the grouping outcomes if necessary. Participants examine the features of each card according to their design for similarities between them. Participants then describe the similarities in the content of each group’s cards using design heuristics.
(4)
Participants combine design heuristics summarized by the groups into large groups. Participants examine the likeness of the design heuristics within each large group and summarize their similarities using higher-level design heuristics. The large groups that are similar to each other are then grouped nearer together to create supergroups until they cannot be grouped further. This grouping forms a tree hierarchy diagram of design heuristics, where the minimum-to-maximum design heuristic levels are shown. The highest level of design heuristics is the first-level design heuristic, followed by the next level as the second-level design heuristic, and so on down the hierarchy. The CBDHS development process is shown in Figure 2, which provides various levels of design heuristics. Figure 3 presents a group of case studies illustrating the extraction of design heuristics derived from a group of cases.
The CBDHS presented in this research provides the final collated design heuristics in the format of cards for use by product design students as a reference, with each card describing each design heuristic in the format of an example with text. CBDHS can assist in the conceptual design phase of products targeted by courses, as well as in conjunction with other design tools to promote product innovation. In the concept design stage, product design students can rapidly develop design ideas and produce additional ideas from them. The brainstorming method does not usually need to use any design tools, instead relying on students’ personal design knowledge, and the CBDHS method may be used during this phase to promote the creation of design ideas.

4. CBDHS Applied to Eco-Friendly Product Packaging Design

The cases for this study are chosen to support product design students in generating ideas from a proposition in a basic course in product design at XX University: Eco-friendly product packaging design. The concept of eco-friendly in this study refers only to the physical product packaging, excluding other intangible concepts such as service design, and applies only to the conceptual design phase of product development. According to the above description of the CBDHS development process, CBDHS searches for cases using three types of keywords: The first type of keywords (e.g., eco-friendly OR sustainable OR low-carbon, etc.) AND the second type of keywords (stretch OR fold, etc.) AND the third type of keywords (bag OR box, etc.). The search formula is then imported into the Google search engine database to look for relevant product cases.
A total of 158 product cases resulted from the keyword search. Subsequently, among the 158 cases, 120 cases met the mentioned criterion and were chosen and reserved, and each case was transformed into a standard form. Lastly, the KJ method (as displayed in Figure 1) was applied to group discussions.
As Table 1 shows, the results identify 15 design heuristics and their descriptions in detail. The 15 design heuristics in the CBDHS approach have a range of 3–14 examples, with noticeable differences between the number of cases. As shown in Figure 3, three levels of design heuristics are determined by further grouping.
Figure 4 presents an illustration of extracting design heuristics from a group of similar cases, illustrating 5 product cases in a similar set using the KJ method. The cases are designed to address the design goal of “eco-friendly product packaging”, which are similar because they can be transformed for use as attachments to the packaging as well as functioning as packaging.
Table 1 shows the design heuristics that help product design students generate ideas for eco-friendly packaging. This study creates cards for use as design tools, with each containing a design heuristic corresponding to a product case and its description. Figure 5 and Figure 6 illustrate some of the cards to demonstrate the form of the design heuristics in CBDHS.

5. Empirical Evaluation of CBDHS

This study was conducted in a one-course educational environment and empirically evaluated CBDHS in terms of its ability to support product design students in generating ideas. This empirical evaluation compares the quasi-experiment results of two groups: An experimental group (using CBDHS) and a control group (using the brainstorming method).

5.1. Participants

A total of 44 product design students from a university in southeastern China were involved in the empirical assessment of this quasi-experiment, with six of them declining to participate in the study. Ultimately, 38 students were involved in the quasi-experiment. All participants undertook a product design course and have experience using the brainstorming method to generate ideas.

5.2. Quasi-Experimental Design

In order to ensure the validity of the quasi-experiment, they were each given a 45-min session on idea generation prior to the experiment as follows:
(1)
Using the FBS model to transform the design questions of the course objectives into functions: 15 min.
(2)
Using the brainstorming method to produce ideas and iterate on the generated ideas (control group only): 30 min.
(3)
Using CBDHS to produce ideas and iterate on the generated ideas (experimental group only): 30 min.
The participants were randomly divided into 2 groups of 19 participants each. One group was the control group, in which participants used brainstorming methods to generate ideas without any ideation tools. The other group was the experimental group, in which participants could use the CBDHS cards for ideation.
As shown in Figure 7, the CBDHS method proposed in this study can be used in combination with the brainstorming method in the following process [41]:
(1)
Drawing the initial idea.
(2)
Transforming and modifying the initial idea using the CBDHS cards and recording all the design heuristics used; multiple different design heuristics can be used in the same idea.
(3)
Defining the final idea through lateral transformations (built on the previous idea) and vertical transformations (a more detailed version).
The brainstorming method is among the most prevalent creation techniques used in groups [32]. Numerous researchers have demonstrated that nominal groups (i.e., groups that work independently but are present with each other) generate a better quantity and quality of ideas in brainstorming meetings than interactive groups (i.e., groups that generate ideas through in-person meetings) [33,34]. To compare the effects of the experimental groups, the grouping format of the brainstorming method used in the control group in this quasi-experiment was nominal groups (i.e., individual brainstorming). Each member of the control group worked independently on design ideas in the same space and was encouraged to freely use their imagination to produce as many ideas as they could, with the ideas of each member eventually brought together.
The quasi-experiment also invited two auditors as assessment experts. Both auditors had master’s degrees in packaging design-related disciplines, and they had also carried out many packaging development activities, which include several environmental packaging design projects.

5.3. Quasi-Experimental Tasks

The quasi-experimental task was to generate as many ideas as possible for a given eco-friendly packaging theme in a limited time frame, with each participant working individually on creative ideas. The design theme of the quasi-experiment was eco-friendly beverage packaging design, and the problem was described as follows: “there is a need to develop eco-friendly beverage packaging to address the problems of waste and environmental pollution in current beverage packaging”.

5.4. Quasi-Experimental Procedures

This study used the quasi-experimental protocol proposed by Hwang and Park [22], as shown in Figure 8, in which participants in the quasi-experiment used sketches and definitions to express their ideas, generating as much as possible within a 45 min time period. The participants in the control group generated ideas without using any design tools, while the participants in the experimental group were free to refer to the design heuristics and the corresponding product cases on the CBDHS cards. The number of ideas proposed by the total number of ideas in the group was recorded separately. The auditors collated the ideas presented by each participant individually, and by removing duplicate ideas, the final ideas obtained were used for subsequent idea evaluation and analysis.

5.5. Quasi-Experimental Assessment Metrics

This quasi-experiment evaluated the ideas of each participating group using five assessment metrics (quantity, quality, novelty, number of good ideas, and level of design fixation). These metrics have been used extensively in prior research to assess design ideas [22,39,40,67,68,69]. Novelty refers to the fact that an idea is more unusual than others. Quality relates to the viability of an idea as well as its proximity to the desired design goals. The quality and novelty of ideas were quantified through the subjective scores of the auditors. For novelty evaluation, this quasi-experiment used the rareness criteria proposed by Viswanathan and Linsey [70], as shown in Equation (1), in addition to subjective scoring by the auditors. For the quantitative evaluation, the total number of diverse ideas produced by the individuals in each group and the number of different ideas produced by each participant is calculated separately. Among them, the novelty and quality evaluation metrics are only for individual ideas.
Novelty = 1 − F r e q u e n c y = 1 − N u m b e r   o f   i d e a s   i n   a   b i n t o t a l   n u m b e r   o f   i d e a s
Two auditors subjectively evaluated each idea with the novelty and quality assessment criteria, which used a 7-point scale (1 = extremely low, 7 = extremely high) to identify the novelty and quality score for each idea, with a score of 1 indicating the lowest score and a score of 7 indicating the highest score, as shown in Figure 9. For each criterion, the mean of the two reviewers’ scores was calculated and used in the ensuing analysis. In this case, the two reviewers were not aware of the process and protocol of this experiment.
This quasi-experiment used Cohen’s kappa coefficients and the proportion of absolute agreement to gauge the agreement between the two auditors. After data measurement, Cohen’s kappa coefficients expressed a novelty of 0.846 and a quality of 0.795, both of which are above the acceptable standards of agreement [71]. These kappa coefficients for novelty and quality indicate that the two auditors are in agreement [72]. If the difference between the two reviewers’ scores is no more than 1 point, it means that the two auditors agree [73]. The agreement between the two reviewers for the novelty score is 84.25% and the agreement for the quality score is 87.39%, and both percentages meet the acceptable agreement criteria [74,75].
In addition to the metrics evaluated above, two other metrics are used in this study to gauge the number of good ideas produced by both groups. For the purpose of this research, good ideas are identified as ideas with strong novelty and quality scores [73,76,77]. In order to compare the variability of good ideas, this study compared the number of good ideas and the percentage of good ideas. The number of good ideas is determined by the number of both novelty and quality scores above a predetermined threshold based on the auditors’ subjective evaluations, with the threshold set at 4 in the 7-point scoring criteria shown in Figure 7. The percentage of good ideas is defined by dividing the number of good ideas by the total number of ideas. The measure of the number and proportion of good ideas can be viewed as a measure of group idea performance since the eventual objective of generating ideas is to produce good ideas that are both strong in novelty and strong in quality.
This research assesses the level of design fixation in each participant’s idea generation [12,78]. This is carried out to test whether the use of CBDHS helps to solve the problem of design fixation compared with the brainstorming method. As shown in Equation (2), this study used the design fixation evaluation criteria proposed by Moreno [79], in which the scores are for each idea generated by the participants:
Fixation = T o t a l   #   o f   r e p e a t e d   i d e a s T o t a l   #   g e n e r a t e d   i d e a s = Q R Q T o t a l
Both groups are compared using each of the above criteria: Novelty score based on subjective evaluation by the auditors, novelty score as suggested by Viswanathan and Linsey [69], quality score due to subjective evaluation by the auditors, total number of participants’ ideas, total number of participants different ideas, number of ideas per participant, number of good ideas, and design fixation score. This research carried out a series of t-tests to statistically compare the averages of three metrics between the two groups: Novelty score, quality score, and design fixation score. In addition, t-tests were carried out to compare the mean of the number of ideas for each participant in the two groups. In addition, the chi-square test was used in this study to analyze the percentage of good ideas in the two groups as a comparison.

5.6. Quasi-Experimental Results

To illustrate the experimental process more clearly, Table 2 provides six example ideas randomly generated by participants from the quasi-experiment, showing the design heuristics used as well as the draft drawing and definition of the ideas provided by the participants.

5.6.1. Quantity of Ideas

The control group generated a total of 272 ideas, and after filtering to remove similar ideas, a total of 84 different ideas were generated. In the experimental group, 216 ideas were generated, and 67 different ideas were generated after filtering to remove similar ideas. In addition, this study compared the mean number of individual ideas in the two groups using the t-test. The t-test results indicate that the experimental group (M = 10.95, SD = 4.14) generated fewer ideas per participant on average than the control group (M = 13.68, SD = 4.44), t(36) = 1.966, p = 0.057.

5.6.2. Novelty and Quality of Ideas

Furthermore, this study compared the novelty and quality scores of the two auditors for the ideas generated by each group using a t-test, as shown in Table 3. The findings of the t-test regarding novelty show no statistically significant differences between the control group (M = 4.00, SD = 1.47) and the experimental group (M = 4.48, SD = 1.56), t(149) = −1.928, p = 0.056. The results of the t-test with regard to quality show no statistically significant difference between the control group (M = 4.19, SD = 1.96) and the experimental group (M = 4.40, SD = 2.05), t(149) = −0.648, p = 0.518.
In addition, the t-test for the mean novelty of the ideas of the two groups was conducted according to the novelty measure proposed by Viswanathan and Linsey [69], and statistically significant differences were found between the two groups. The experimental group (M = 0.93, SD = 0.025) produced more novel ideas than the control group (M = 0.89, SD = 0.022), t(36) = −3.825, p < 0.001.
As shown in Figure 10, the distribution of novelty scores for the ideas generated in the control and experimental groups is represented by a bar graph, while Figure 11 shows the distribution of quality scores for ideas in the same way for both groups. The CBDHS group generated more ideas in terms of high novelty (novelty score > 4) and high quality (quality score > 4) than the brainstorming group.

5.6.3. Number of Good Ideas

As shown in Figure 12, the number of good ideas produced by the two groups is represented by a frequency dot graph with 8 and 21 for the control and experimental groups, respectively. The percentage of good ideas was 9.5% for the control group and 31.3% for the experimental group, with a significant difference in the percentage between the two groups: x2(1, n = 151) = 11.44, p = 0.001. The chi-square test results indicate that good ideas are unevenly spread between the two groups and are statistically significantly different.

5.6.4. Level of Design Fixation of Ideas

This study used a t-test to test for differences in the level of design fixation of the two groups of participants who generated ideas, and the results show no statistical difference between the two groups: t(36) = −0.375, p = 0.085.

6. Discussion

This study proposes a case-based design heuristics (CBDHS) tool that can support product design students in generating ideas for course objectives. CBDHS uses experience derived from the analysis of past product cases and can provide useful assistance and guidance in the conceptual design stage of a product design course. As illustrated in Figure 2, this research presents a stepwise procedure for CBDHS. The development procedure of CBDHS uses the KJ method to derive the relevant design heuristics from the illustrative examples in an abductive approach, which is then organized in a hierarchical approach in order to produce a multi-layered presentation of the design heuristics.
To further verify the usefulness of CBDHS, the theme of eco-friendly packaging in the product design course at XX University in China was chosen to illustrate this study. In Table 1, there are 15 design heuristics identified by analyzing 158 packaging product cases found from the keyword research. Three layers of design heuristics are then determined through group assessment by the KJ method, which is shown in Figure 3.
This research also conducted a quasi-experiment to verify the practicality of CBDHS, with the quasi-experiment topic being eco-friendly beverage packaging design. Participants in the quasi-experiment were separated into two groups, a control group and an experimental group, to generate ideas for eco-friendly beverage packaging designs. The participants in the control group were limited to using the brainstorming method during the ideation period, while the participants in the experimental group were able to access the CBDHS cards. The ideas generated by the two groups were compared with regard to the following aspects: Quantity, novelty, quality, number of good ideas, and level of design fixation. The main results are as follows:
(1)
The use of CBDHS did not significantly improve the number of ideas produced by the students compared with the brainstorming method. The control group (84 different ideas) generated more ideas than the experimental group (67 different ideas). In terms of average ideas per student, more ideas were generated by students in the control group (13.68 ideas) than in the experimental group (10.95 ideas). Students generated more ideas using the brainstorming method compared with the individual ideas based on the design heuristics. This is comparable to the results of other research where the time for ideation is limited, and more time is required for reading and applying design tools than for direct ideation methods [27,50,80].
(2)
The use of CBDHS significantly increased the novelty of the ideas generated by the students compared with those produced using the brainstorming method. The outcomes of an empirical assessment based on auditors’ scores show that there was no significant difference in the mean novelty scores of the ideas generated by the control group and those generated by the experimental group. However, the test results of the rarefaction criteria proposed by Viswanathan and Linsey [70] indicate that the mean novelty score of the ideas generated by the experimental group is significantly greater than that of the control group, indicating the advantage of using CBDHS. In addition, the experimental group generated more ideas with a high novelty score (novelty score > 4) than the control group.
(3)
Using CBDHS did not significantly improve the quality of the ideas generated by the students compared with those produced using the brainstorming method. With the auditors’ empirical evaluation, there is no significant difference in the quality scores of the ideas produced by the control group and those generated by the experimental group. Nevertheless, the experimental group generated more high-quality (quality score > 4) ideas than the control group.
(4)
The use of CBDHS increased the number of good ideas generated by students compared with those produced using the brainstorming method. Based on the auditors’ empirical evaluation, the experimental group generated more good ideas (novelty and quality scores > 4) than the control group. In addition, there is a statistically significant difference in the percentage of good ideas between the experimental group (31.3%) and the control group (9.5%).
(5)
Using CBDHS did not improve the level of design fixation of students compared with that of the students using the brainstorming method. According to the metric proposed by Moreno [79] to evaluate the level of design fixation, the ideas generated by the experimental and control groups are not significantly different in terms of design fixation.
This study also compared CBDHS with three previously proposed universal design heuristics. The 15 design heuristics in CBDHS were found to be different from the three previously proposed design heuristics in terms of content. Only a few design heuristics in SCAMPER, TRIZ, and the 77 design heuristics have similar meanings to CBDHS. SCAMPER, TRIZ, and the 77 design heuristics are all universal design tools and do not have products targeted to design courses. As a result, students must experiment with each design heuristic if they do not know which universal design heuristic to use. CBDHS is independent of other universal design heuristics and can be targeted at the conceptual design phase of a course-targeted product to creatively inspire product design students and help them conceptualize ideas that fit the course-targeted product. The low development investment of CBDHS allows it to be implemented in stand-alone classrooms without the need for extensive course instructions for students. In order for CBDHS to have a real impact, it needs to be developed to include a variety of course-targeted product categories, which may require the combined efforts of a large number of people.

7. Conclusions

Overall, according to Hwang and Park [22], the quasi-experimental results show that CBDHS can support product design students in generating ideas for course-targeted products and that CBDHS increases the total number of ideas, the novelty level, the quality level, and the total number of good ideas produced by students during conceptualization. According to Youmans’ [78] proposed level of design fixation, the use of CBDHS does not improve the level of design fixation compared with the brainstorming method. The findings of this study are consistent with those of Leahy et al. [81], and CBDHS can be integrated into product design courses to help them teach innovative ideas to students, and it can be used as an aid to guide students in concept development in addition to brainstorming [27]. Furthermore, product design textbooks can integrate CBDHS as an ideation method. Through a step-by-step method combining theory with practice, students can be guided to understand and master the concept and use of CBDHS from the superficial to the deepest level and can be helped to implicitly transform CBDHS into their own design experience, ultimately improving the quality of teaching. The contributions of this study are summarized as follows:
(1)
This study proposes a case-based design heuristics tool that uses the KJ method for case summarization, uses the FBS model to identify and categorize usable cases and develops CBDHS in the format of a hierarchical structure that can be developed by teachers or experienced students, who can develop their own design heuristics based on course objectives and introduce them into university product design courses.
(2)
This study develops CBDHS for use in an eco-friendly product packaging example and includes 15 design heuristics that are expected to be effective in transferring the personal experience of the teacher to the students. CBDHS has not been developed to replace existing design heuristics, but rather to address different design requirements in the design course.
(3)
This study uses five assessment metrics (quantity, novelty, quality, number of good ideas, and level of design fixation) and validates their effectiveness in supporting product design students in generating ideas through an empirical assessment approach. The results indicate that the CBDHS can effectively support product design students in generating ideas.
This study also has some limitations and future research directions: (1) CBDHS is in the process of continuous enrichment due to its own large information-carrying capacity, and although product design students can provide feedback in the short term, they will still need to learn it in the long term. (2) The current study only provides empirical evaluation methods to validate CBDHS and only develops design heuristics for eco-friendly packaging in design courses. To further generalize the effectiveness of CBDHS in supporting idea generation for product design students, more empirical research on other course-targeted products is needed. (3) The high cost of using the KJ method for constructing CBDHS, which is both time-consuming and labor-intensive, will likely limit the subsequent development of CBDHS, and a more automatic procedure for building CBDHS needs to be created, digitized, and made available online to product design students from around the world. (4) The CBDHS proposed in the current study is only applicable to the conceptual design phase of a product, and it doe not allow for other phases of the product design procedure, such as problem identification, problem definition, and detailed design phases. To overcome this limitation, further integration of CBDHS with existing product innovation design methods or computer-aided design methods is needed [82,83,84]. For example, when designing a product concept, handling some of the repetitive tasks in a computer-aided design can assist designers in the execution of the product concept design procedure more efficiently than through manual design only.

Author Contributions

Conceptualization, X.C. and Y.H.; methodology, X.C.; software, X.C.; validation, X.C. and Y.H.; formal analysis, X.C.; investigation, X.C. and Y.H.; resources, X.C. and Y.H.; data curation, X.C. and Y.H.; writing—original draft preparation, X.C.; writing—review and editing, X.C. and Y.H.; visualization, X.C. and Y.H.; supervision, X.C. and Y.H.; project administration, H.L.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Youth Project of Anhui Provincial Philosophy and Social Science Planning Project, grant number AHSKQ2021D127.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The case-based design heuristics (CBDHS) process framework.
Figure 1. The case-based design heuristics (CBDHS) process framework.
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Figure 2. CBDHS development process.
Figure 2. CBDHS development process.
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Figure 3. A hierarchical structure of the design heuristics for eco-friendly product packaging (adapted with permission from [66].
Figure 3. A hierarchical structure of the design heuristics for eco-friendly product packaging (adapted with permission from [66].
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Figure 4. Five illustrative examples of design heuristics drawn from a set of similar cases. Adapted with permission from [66].
Figure 4. Five illustrative examples of design heuristics drawn from a set of similar cases. Adapted with permission from [66].
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Figure 5. The design heuristic, “Reduce the amount of packaging material”, and the corresponding cases described in the card. Adapted with permission from [66].
Figure 5. The design heuristic, “Reduce the amount of packaging material”, and the corresponding cases described in the card. Adapted with permission from [66].
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Figure 6. The design heuristic, “Use waste packaging materials”, and the corresponding cases described in the card. Adapted with permission from [66].
Figure 6. The design heuristic, “Use waste packaging materials”, and the corresponding cases described in the card. Adapted with permission from [66].
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Figure 7. CBDHS transformation idea process.
Figure 7. CBDHS transformation idea process.
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Figure 8. The quasi-experimental procedures.
Figure 8. The quasi-experimental procedures.
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Figure 9. The 7-point scale for idea novelty and quality assessment.
Figure 9. The 7-point scale for idea novelty and quality assessment.
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Figure 10. Bar graph of idea quality scores for the control and experimental groups.
Figure 10. Bar graph of idea quality scores for the control and experimental groups.
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Figure 11. Bar graph of idea novelty scores for the control and experimental groups.
Figure 11. Bar graph of idea novelty scores for the control and experimental groups.
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Figure 12. Frequency dot graph presenting the distribution of good ideas (novelty score > 4 and quality score > 4) for the control and experimental groups.
Figure 12. Frequency dot graph presenting the distribution of good ideas (novelty score > 4 and quality score > 4) for the control and experimental groups.
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Table
Table 1. Fifteen design heuristics for eco-friendly product packaging. Adapted with permission from [66].
Table 1. Fifteen design heuristics for eco-friendly product packaging. Adapted with permission from [66].
No.Design HeuristicsExplanationNo. of Cases
1Combine several products into oneEco-friendly product packaging can combine several products into one.5
2Reduce the number of packaging materialEco-friendly product packaging can reduce the number of packaging material.13
3Use recyclable packaging materialsEco-friendly product packaging can use recyclable packaging materials.12
4Use reusable packaging materialsEco-friendly product packaging can use reusable packaging materials.9
5Use waste packaging materialsEco-friendly product packaging can use waste packaging materials.10
6Use naturally degradable packaging materialsEco-friendly product packaging can use naturally degradable packaging materials.8
7Use low-cost packaging materialsEco-friendly product packaging use low-cost packaging materials.12
8Integrate the packaging with the productEco-friendly product packaging can integrate the packaging with the product.4
9Convert into accessories for the productEco-friendly product packaging can convert into accessories for the product.5
10Convert into secondary packaging for productsEco-friendly product packaging can convert into secondary packaging for products.8
11Be available for different sizes of productsEco-friendly product packaging can be available for different sizes of products.5
12Reduce extra packaging between product packagesEco-friendly product packaging can reduce extra packaging between product packages.4
13Use green patterns and colorsEco-friendly product packaging can use green patterns and colors.14
14Use modular architectureEco-friendly product packaging can use modular architecture.5
15Convert packaging functionsEco-friendly product packaging can convert packaging functions.6
Table
Table 2. Illustrative examples of ideas that have developed from conceptual experiments.
Table 2. Illustrative examples of ideas that have developed from conceptual experiments.
Design Heuristic(s)Draft DrawingDefinition
Be available for different sizes of products.Sustainability 14 16011 i001The beverage packaging can be freely retracted to fit different sized bottles.
Reduce the amount of packaging material.Sustainability 14 16011 i002By reducing the amount of packaging material for beverages.
Reduce extra packaging between product packages.Sustainability 14 16011 i003Decrease the number of packaging materials by linking bottles together in a package.
Use naturally degradable packaging materials.Sustainability 14 16011 i004Use rattan as a beverage packaging material.
Use modular architecture.Sustainability 14 16011 i005Combining packaging and bottles to form a modular structure.
Convert into secondary packaging for products.Sustainability 14 16011 i006Beverage packaging can continue to be used after use.
Table
Table 3. Results of the t-tests comparing the two groups for average novelty and quality scores.
Table 3. Results of the t-tests comparing the two groups for average novelty and quality scores.
Effectiveness CriteriaGroupNMeanSDtp
NoveltyC844.001.47−1.9280.056
E674.481.56
QualityC844.191.96−0.6480.518
E674.402.05
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Cao, X.; Hsu, Y.; Lu, H. CBDHS: A Case-Based Design Heuristics Tool to Support Product Design Students in Idea Generation. Sustainability 2022, 14, 16011. https://doi.org/10.3390/su142316011

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Cao X, Hsu Y, Lu H. CBDHS: A Case-Based Design Heuristics Tool to Support Product Design Students in Idea Generation. Sustainability. 2022; 14(23):16011. https://doi.org/10.3390/su142316011

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Cao, Xin, Yen Hsu, and Honglei Lu. 2022. "CBDHS: A Case-Based Design Heuristics Tool to Support Product Design Students in Idea Generation" Sustainability 14, no. 23: 16011. https://doi.org/10.3390/su142316011

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