The investigation into the influence of the features of furniture product design on consumers' perceived value by fuzzy semantics

Lee, A. S. Article The investigation into the influence of the features of furniture product design on consumers' perceived value by fuzzy semantics South African Journal of Business Management Provided in Cooperation with: University of Stellenbosch Business School (USB), Bellville, South Africa Suggested Citation: Lee, A. S. (2014) : The investigation into the influence of the features of furniture product design on consumers' perceived value by fuzzy semantics, South African Journal of Business Management, ISSN 2078-5976, African Online Scientific Information Systems (AOSIS), Cape Town, Vol. 45, Iss. 1, pp. 79-93, https://doi.org/10.4102/sajbm.v45i1.119 This Version is available at: https://hdl.handle.net/10419/218536 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ S.Afr.J.Bus.Manage.2014,45(1) 79 The investigation into the influence of the features of furniture product design on consumers’ perceived value by fuzzy semantics A.S. Lee * Associate Professor, Department of Wood-Based Materials and Design, National Chiayi University, No.300 Syuefu Rd., Chiayi City, Taiwan 60004, R.O.C. [email protected] This study employed fuzzy theory to investigate the influence of the features of furniture product design on the fuzzy semantics of Taiwanese consumers’ perceived value in new product design and development in hopes of providing product designers or developers with a reference to new product design and development. Furniture designers or manufacturers can use u(x), the fuzzy membership function value of the model for investigating the fuzzy semantic relation between the features of furniture product design and consumers’ perceived value, to evaluate the fuzzy semantics of consumers’ perceived value, and they can also employ the fuzzy semantic mean equation, namely =∑(+2+)  /4, to transform the research data into fuzzy semantics. In this study, all the consumers agreed that the features of furniture product design were significantly correlated to the fuzzy semantics of consumers’ perceived value. In terms of the fuzzy semantics of the perceived value, the consumers with different genders, ages, education backgrounds, and incomes had different opinions about the features of furniture product design, and there were significant differences between the variables. In short, this study is helpful to understand consumers’ demand for products as well as the development of consumer-oriented products, enhance their purchase intention, and improve corporate performance and market competitiveness. *To whom all correspondence should be addressed. Research background and motives Due to consumers rapidly changed demand in the environment full of drastic competition, so consumers’ demand and the value of furniture products were very important to enterprises when they were performing furniture product design, development, and marketing. Adner and Levinthal (2001) mentioned that product development becomes a way of maintaining product value. Zedtwitz and Gassmannb (2002) stated that products enterprises with an important R&D department are either in a strong dominant design position in its main technologies or their principal market is helpful to fit consumer’s demand and satisfaction. They also pointed out that technologyoriented R&D product development departments will be employed to develop new products. Product market and consumer’s demand will influence research, technology, and product development. Hence, Zedtwitz’s and Gassmannb’s study is to focus on how technology-oriented R&D product development departments to transform marketing-oriented in order to fit consumer’s demand and consumers’ perceived value. Therefore, contemporary enterprises should change concepts of their technology-oriented product development concepts into marketing-oriented product development concepts. Enterprises should understand consumers’ preference and demand for furniture products as well as the methods for customer-based and market-based product development. Since new product development and design are closely related to consumers’ behavior, enterprises should increase consumers’ purchase intention by enhancing their perceived value of products in order to achieve the corporate management goals and advance their market competitiveness. Janikow (1998) motioned that most studies on the framework of this fuzzy semantic theory to existing methodologies has concentrated only on emerging fields such as artificial neural networks (ANNs) and genetic algorithms( GAs). Janikow’s study is to combine artificial neural networks (ANNs) and genetic algorithms (GAs) into fuzzy semantics, with its close reasoning capabilities, and symbolic decision trees while preserving advantages of both: uncertainty handling and gradual processing of the former with the popularity and comprehensibility. Hence, fuzzy semantics is more advantage, advancement, and reasoning capabilities to examine the influence of the features of furniture product design on consumers’ perceived value in new product design development than artificial neural networks (ANNs) and genetic algorithms (GAs) in this study. Aiming at the fuzzy data in the decision-making process of design, Goumas and Lygerou (2000) brought up the fuzzy decision expansion models of PROMETHEE, or Preference Ranking Organization Method for Enrichment Evaluation, for sorting design decisionmaking projects. Fuzzy semantic evaluations were applied to all of those integration decision-making models, and the obtained fuzzy semantic results were provided for furniture product designers as references in order to establish furniture product design which satisfies consumers’ demand. 80 S.Afr.J.Bus.Manage.2014,45(1) Therefore, from product strategies and concepts to development, the key points of product design should cater to consumers’ preference, which is an important competitive advantage and strategy for enterprises in product differentiation. In this study, fuzzy theory was employed to conduct an integration investigation on the influence of the features of furniture product design on the fuzzy semantics of Taiwanese consumers’ perceived value in new product design and development in hopes of providing product designers or developers with references to new product design and development and introducing the correlation between the features of furniture product design and the fuzzy semantics of consumers’ perceived value into the planning and marketing stages of product design. In addition, fuzzy theory was applied to the features of furniture product design to investigate the fuzzy semantic models of Taiwanese consumers’ perceived value and provide enterprises with the basis for new furniture product design and development in order to advance the performances of enterprises in new product development and design. Research purposes This study was aimed to examine the influence of the features of furniture product design on the fuzzy semantics of Taiwanese consumers’ perceived value in new product design and development as well as the differences caused by demographic variables between the features of furniture product design and the fuzzy semantics of consumers’ perceived value in hopes of providing the research result for product designers as a reference. Designers are the bridge between products and users, and the matching role that they play is helpful for product designers or developers to understand the messages for demand, which customers would like to convey, to further satisfy the consumers’ demand and enhance their purchase intention. Enterprises will thus create and convey the product value. Based on the aforementioned research motives and background, the research purposes are listed as follows: • Investigating the relationship between the features of product design and the fuzzy semantics of consumers’ perceived value by using fuzzy theory. • Investigating the influence of the different backgrounds of consumers on the features of product design and the fuzzy semantics of the consumers’ perceived value. Research limitation This study was designed to explore the fuzzy semantics of Taiwanese consumers’ perceived value on the features of product design mainly from the aspect of using furniture products. The product design was focused on furniture products in this study, which was also one of the limits of this study. Furthermore, consumers in the warehouse stores in Taiwan were randomly sampled in this study. Literature review Product design features Design indicates that consumers’ demand for the overall features of a product is expressed in the appearance, functions, characteristics, and problem solutions of the product. When design is conducted, the functions, appearance, materials, technology, specifications, and quality of a product should be considered. Nussbaum (1988) addressed that product design is a creative strategy which helps enterprises gain predominance in market competition. Hsiao and Chen (2010) proposed nine features of product design that include color, delicacy, biomimetics, association, unreasonable combinations, narrative, symbolic symbols, operation procedures, and shaping and operation. The nine features of product design influence judgment on pleasant images, which are further divided into three categories, respectively shaping elements (color, delicacy, and biomimetics), emotional elements (association, unreasonable combinations, narrative, and symbolic symbols), and operational elements (operation procedures, shaping and operation). Chen (2010) argued that the compositional and measuring elements of product features include (1) distinction: the size, shape, color, and texture of a product, (2) integration: the arrangement of keys and the combination of colors, and (3) interaction: responses to users’ feedback and intuitive operation. If enterprises could employ the trend of consumers’ demand and preferences in new product design and development when they design and develop them, they will not only save development cost but also extend the product life cycle, or PLC (Tsai, Chang & Wang, 2003). Therefore, product design is a critical element for deciding market success, which not only attracts consumers’ attention and clearly communicates with them but also enhances product value and makes consumers perceive positive value from all products. The fuzzy semantics of consumers’ perceived value Through trades, consumers conduct an overall effectiveness assessment of perceived sacrifice and the obtainment of perceived benefits. The aforementioned viewpoint is to explain the fuzzy semantics of consumers’ perceived value (Dodds & Monroe, 1985). Bowen, Lai, and Bahler (1992) stated that as in classical logic, the truth of a sentence in fuzzy logic and fuzzy semantics is based on the interpretation of consumer’s perceived value in the sentence of the survey for exploring the features of product design by consumers’ using product experience. In relevant research, such as by Kacprzyk, Fedrizzi and Nurmi (1992), problems related to fuzzy semantic measurement were focused on fuzzy theory introduced to investigate the fuzzy semantics of consumers’ perceived value. Engel, Blackwell and Miniard (2001) considered that perceived value indicates the difference between the time, money, physical strength, and other resources that consumers pay for a product as well as the benefits that the consumers obtain. Chen and Dubinsky S.Afr.J.Bus.Manage.2014,45(1) 81 (2003) addressed that perceived value results from the sum of trade cost as well as the value of expected benefits or loss. According to Ravald and Gronroos (1996), the concept of consumer value has become a differentiation tool and one of the critical factors to maintain corporate predominance in competition. In addition, Woodruff (1997) argued that the fuzzy semantics of consumers’ perceived value is actually one type of consumers’ perceived evaluation of product preferences, product attributes, attributive performance, and goal achievement; it is consumers’ feeling of value for a product before they purchase the product. Zeithaml (1988) considered that consumers’ perceived value is the value perceived by customers as well as their overall evaluation for the effectiveness of a product after pay and gain, and when the gain is greater than the pay, a product will provide customers with higher value. Hence, product designers should feel consumers’ preferences and demand for product attributes in order to design a product that consumers love and increase consumers’ perceived value, and consumers will thus be willing to purchase the product. The correlation between consumers’ perceived value and the demand for the features of product design among the consuming behavior models is thus clearly understood through the aforementioned research. Fuzzy theory Zadeh (1965), a professor emeritus of computer science at the University of California, Berkeley in U.S.A., brought up the theory of fuzzy sets, which expands the relationship between elements and sets in the classical set theory. In fuzzy theory, membership functions are used to express the relationships between elements and sets. A great number of practical problems are full of uncertainty and imprecision, so fuzzy concepts are quantified by the theory of fuzzy sets brought up by Zadeh (1965) mainly to deal with some research objects’ mental feelings which are fuzzy and cannot be dealt with by the “either this or that” binary logic. The “either this or that” relationship does not certainly exist in some phenomena, so elements’ degree of membership to sets is expanded to any value in a single interval (0, 1). Relevant literature on the application of fuzzy theory in product design included the researches that Wu (2010) employed fuzzy theory to explore product design and multichoice goal planning on marketing behavior; Wei (2003) utilized fuzzy theory for decision making in early stage of product design in order to increase market share; Tang (2009) used fuzzy theory to design concept of creative product development so as to fit consumers’ needs and behavior; and Su (2007) employed fuzzy theory to explore product design to influence consumers’ perceived value from consumers’ behavior in the market. Fuzzy semantics Chen and Hwang (1992) mentioned eight common fuzzy semantic variables, which are great contribution to following related research. Costs, Maranon, and Cabrera (1994) as well as Hesketh, Pryor, Gleitzman and Hesketh (1988) designed fuzzy scales as the basis of attitude measurement and developed applicable measurement tools. Chen, Wang and Chiu (2000) brought up a method for calculating the membership values of fuzzy sets, which is effective in estimation and efficient in cost-saving. Voxman (2001) classified the canonical representations of discrete fuzzy values into two categories and respectively proposed the computing methods. Matarazzo and Munda (2001) brought up integration methods for calculating fuzzy values since traditional research on semantic decisions was all confined to triangular fuzzy numbers. Herrera, Lopez, Mendana, and Rodriguez (2001) investigated solutions for linguistic decision models and brought up the genetic method for linguistic biobjective fitness functions. Carlsson and Fuller (2000) explored the key to weighted aggregations and established a feasible solution for the process of weighted messages of importance. The fuzzy numbers commonly used in research include: triangular fuzzy number, trapezoidal fuzzy number, and normal fuzzy number. Triangular fuzzy numbers with a semantic scale of five levels and fifty equal portions were used to display index weights in this study. Zadeh (1965) pointed out that the whole fuzzy semantic scale and fuzzy numbers can be used to explain the relationship between potential features and the membership of semantic terms from the feelings of the participants. Fuzzy numbers (from 0 to 5) could easily calculate feelings of the participants from their demand and the value of furniture products of furniture products. Goldberg (1989) stated that genetic algorithms (GAs) need spend more time making binary code for performing the parameter optimization techniques on the issues of ill-behaved problem and highly non-linear spaces. Fant and Pamela (1992) pointed out that artificial neural networks (ANNs) consist of a collection of simple nonlinear computing elements whose inputs and outputs are tied together to form a network. Recently, due to increase of the computational speed, artificial neural networks (ANNs) have also been administered in many fields, e.g., image processing, forecasting, and control. Hence, the study employed fuzzy semantics to get survey data for examining the influence of the features of furniture product design on the fuzzy semantics of Taiwanese consumers’ perceived value in new product design and development. In other words, fuzzy semantics is more suitable and convenient than genetic algorithms (GAs) and artificial neural networks (ANNs) to analyze survey data for exploring the influence of the features of furniture product design on the fuzzy semantics of Taiwanese consumers’ perceived value in new product design and development in the study. Based on the aforementioned fuzzy semantic aspects, the fuzzy scale and the method of writing fuzzy numbers were designed to obtain the feelings of the participants. In this study, fuzzy theory was employed to investigate the relationship between the features of furniture product design and the fuzzy semantics of consumers’ perceived value. 82 S.Afr.J.Bus.Manage.2014,45(1) The meaning of fuzzy theory and fuzzy numbers Fuzzy theory was brought up by Zadeh (1965). When it is applied to semantic measurement, the methods are usually represented by fuzzy numbers. Excluding the fuzziness of research subjects is presupposed in traditional sciences whereas the fuzziness of research subjects and the fuzzy semantics of participants on products are recognized in fuzzy theory. Several are commonly used. The membership functions and the functional figures are illustrated as follows: Triangular Fuzzy Number As for the entire fuzzy semantic scale, fuzzy numbers can be used to describe the relationship between potential features and the membership of semantic terms. The relationship of the fuzzy semantic figure is illustrated as follows, as shown in Figure 1&2: Figure 1: Triangular fuzzy number and the membership function figure Figure 2: Using fuzzy numbers to describe the relationships between potential features and the membership of semantic terms Questionnaire data processing and fuzzy semantic statistics The fuzzy semantics of Taiwanese consumers’ perceived value resulting from the features of furniture product design in this study belong to human psychological cognition, which is fuzzy, subjective, and uncertain. Hence, it is difficult to reasonably describe the differences and fuzziness expressed by human semantics by using a five-point Likert scale, namely using 5 to represent “strongly agree,” 4 for “more agree,” 3 for “agree,” 2 for “disagree,” and 1 for “strongly disagree,” to represent the semantic terms answered by the participants. Therefore, the fuzzy semantic scale for the questionnaire in this study was divided into five levels. Fuzzy theory was first employed to process the fuzzy semantic data of Taiwanese consumers’ perceived value resulting from the features of furniture product design, and relevant research analyses were then conducted. According to Klir and Yuan’s (1995) complete framework of fuzzy system, the entire fuzzy process includes four steps: (1) fuzzification mechanism (data input); (2) fuzzy rules (data processing); (3) fuzzy inference engine (fuzzy inference); (4) defuzzification mechanism (data output). The steps of the fuzzy process are detailed as follows: Step 1 (fuzzification mechanism): Triangular fuzzy numbers were used to represented the semantic variables of expectations and actual feelings in the fuzzy semantics of Taiwanese consumers’ perceived value resulting from the features of furniture product design. A great number of researchers and scholars, such as Chien and Tsai (2000), Wu, Hsiao and Kuo (2004), and Hsu and Lin (2005), employed triangular fuzzy numbers to represent semantic variables, so they were used in this study accordingly. Step 2 (fuzzy rules): Triangular fuzzy numbers were respectively attached to the five semantic terms, including “strongly agree,” “more agree,” “agree,” “disagree,” and “strongly disagree,” which were the semantic variables of expectations. “A” represented a triangular fuzzy number while X = (0, 0, 1), (0, 1, 2), (1, 2, 3), (2, 3, 4), and (3, 4, 5) represent the five semantic terms respectively. The membership function figure is shown in Figure 2. Furthermore, triangular fuzzy numbers were also respectively provided to the five semantic terms of actual feelings, including “strongly agree,” “more agree,” “agree,” “disagree,” and “strongly disagree.” Step 3 (defuzzification): The statistics resulting from fuzzy computation were defuzzified to obtain specific values for further research. The defuzzification was conducted by the method of Center of Area, or CoA, used by Kaufmann and Gupta (1991), Chien and Tsai (2000), and Hsu and Lin (2005). The triangular fuzzy numbers are detailed in Figure 3: The defuzzification formula of X ~is as follows: Suppose =(,,), =(+2+)/4, From the fuzzy descriptive statistic equation for the second part of the fuzzy semantic questionnaire, the following equation was deduced: (1): =(,,), and = (+2+)/4 was transformed into =∑(+2+)  /4, N=723 (the number of the participants). (1) From the fuzzy descriptive statistic equation for the third part of the fuzzy semantic questionnaire, the following equation was deduced: (2): =(,,), and =(+2+)/4 was transformed into () xu ()          ∞∈ ∈− = ∈− ∈ = ],[,0 ),(, ,1 ),(, ],0[,0 cx cbxxc bx baxax ax xu a b c x 1 S.Afr.J.Bus.Manage.2014,45(1) 83 =∑ (+2+)  /4, N=723 (the number of the participants) (2) The fuzzy semantic value of the questionnaire was calculated through Equations (1) and (2). Figure 3: The membership function figure of five-level semantic variables Semantic terms To measure the potential characteristics of the participants, the words of options with various degrees of responses were applied to the questions of the scale to express the perception of the question. The words of options are called semantic terms, which usually consist of adverbs and adjectives and are used to express human mental perception. For example, the five fuzzy semantic terms in the five-level and fifty-equal-part scale of this study were “strongly agree,” “more agree,” “agree,” “disagree,” and “strongly disagree.” Fuzzy semantic scales and Likert’s scales were researched and analyzed (Lin, 2003; 2004), but there was not any comparison between the survey conformity of the interviewees’ perceptions of survey questions as well as the application of the primary survey data to decision improvement. For each evaluation item, an appropriate statement of judgment was selected. Fuzzy evaluation methods were used, and fuzzy modes were calculated, so that the opinions of the majority were expressed. If there are difficulties among the evaluation results, fuzzy evaluation can be used to calculate the mean of the fuzzy semantics of consumers’ perceived value, which can provided for furniture designers and manufactures as a reference to new product development. Research methodology Research design Research framework This study was aimed to investigate the relational model of the “product design features” of furniture products and the “fuzzy semantics of consumers’ perceived value” and the differences between the fuzzy semantics of the perceived values of the consumers with different backgrounds in terms of product design features, the research framework as shown in Figure 4. Figure 4: The relational model of the “product design features” of furniture products and the “fuzzy semantics of consumers’ perceived value” Hypothesis H1: The fuzzy semantics of consumers’ perceived value are significantly influenced by different features of furniture product design. H1-1: The fuzzy semantics of consumers’ perceived value is significantly influenced by “distinction.” H1-2: The fuzzy semantics of consumers’ perceived value is significantly influenced by “integration.” H1-3: The fuzzy semantics of consumers’ perceived value is significantly influenced by “interaction.” H2: The features of furniture product design cause differences among the fuzzy semantics of the perceived values of different consumers. H2-1: The features of furniture product design cause differences among the fuzzy semantics of the perceived values of consumers with different genders. H2-2: The features of furniture product design cause differences among the fuzzy semantics of the perceived values of consumers with different ages. H2-3: The features of furniture product design cause differences among the fuzzy semantics of the perceived values of consumers with different education backgrounds. H2-4: The features of furniture product design cause differences among the fuzzy semantics of the perceived values of consumers with different incomes The operational definitions and measurements of research variables a. Product design The relationships between the features of furniture product design and the fuzzy semantics of consumers’ perceived value were investigated in this study. To summarize the aforementioned literature, Chen’s (2010) three product Distinction Integration Interaction The fuzzy semantics of consumers’ perceived value Features of furniture product design Size, appearance, color, texture Arrangement, color combination, eases of use, and simple structure Feedback, intuitive operation The computation model of fuzzy semantic statistics sequations Independent Variables Dependent Variable 84 S.Afr.J.Bus.Manage.2014,45(1) design features, respectively distinction, integration, and interaction, were used as the major research dimensions in this study. In addition, “ease of use” and “simple structure” were added to measure furniture products. The operational definitions and measurement are detailed as follows: • Distinction: the size, appearance, color, and texture of a product. • Integration: arrangement, color combination, eases of use, and simple structure. • Interaction: feedback for users and intuitive operation. b. The fuzzy semantics of consumers’ perceived value Lin (2011) addressed that consumers’ perceived value is an important factor for customers to decide whether to purchase a product or not. After the aforementioned literature was summarized, Zeithaml’s (1988) opinion was adopted in this study, in which the fuzzy semantics of consumers’ perceived value is the value perceived by consumers as well as consumers’ overall evaluation of product efficacy after pay and gain. The “overall evaluation” indicates the difference between the pay and gain that customers perceive. When the gain is greater than the pay, the value provided by a product for customers is higher. This was employed as the operational definition and measurement of the fuzzy semantics of consumers’ perceived value in this study. c. Demographic variables Korgaonkar, Lund, and Price (1985) brought up the causeeffect structural model and argued that demographic variables, such as age, income, and race, influence consumers’ purchase behavior. Kotler (1998) classified demographic variables into ten categories, respectively age, education, the number of family, occupation, religion, family life cycle, income, nationality, and ethnic origin. Since this study was focused on the influence of product design features and the fuzzy semantics of consumers’ perceived value in furniture products, the demographic variables were divided into four variables, namely gender, age, education background, and income. Research object Furniture products and consumers’ lives are closely related. Hence, the features of furniture product design were regarded as the research scope. Moreover, consumers in the warehouse stores were randomly sampled in this study. In total, 723 copies of questionnaire were analysed. Questionnaire design and execution In this study, fuzzy theory was applied to the questionnaire survey, and a fuzzy semantic scale was used to replace Likert’s scale to understand the fuzzy questions resulted from the mental feelings of the participants. Relevant literature was reviewed and summarized for the operational definitions of the variables and the rules of questionnaire measurement in this study. The expert panel was interviewed in order to confirm the questionnaire semantics. In addition, 102 copies of the questionnaire were pre-tested, and the questionnaire was then appropriately modified and adjusted. According to the standard that Nunnally (1995) suggested, when Cronbach’s α is higher than 0.7, the reliability of a research questionnaire is regarded as good. Consequently, the questionnaire was formally delivered. The questionnaire included three parts. The first part was about personal background and information, including the gender, age, education background, and income of each participant. The second part was about the participants’ opinions and evaluation on the features of furniture product design, including distinction, integration, and interaction. The third part was about the fuzzy semantics of consumers’ perceived value, namely the participants’ evaluation after purchasing and using a furniture product, in which a Likert five-level and fifty-equal-part scale was transformed into a fuzzy scale (strong disagree: 0.1-1; disagree: 1.1-2; neither agree nor disagree: 2.1-3; agree: 3.1-4; strongly agree: 4.1- 5) to measure consumers’ recognition of and opinions about product design features as well as the fuzzy semantics of the consumers’ perceived value. Questionnaire survey design Based on Parasuraman, Zeithaml, and Berry’s (1988) SERVQUAL scale, the questionnaire was designed in this study, as shown in Tables 1 and 2. The dimensions for the fuzzy semantics of consumers’ perceived value on the features of furniture product design were respectively “strongly agree,” “more agree,” “agree,” “disagree,” and “strongly disagree.” The first part of the questionnaire draft was of basic demographic statistics, including gender, age, education background, occupation, and monthly income. The second part (Table 1) was for the opinions and evaluation on product design features, respectively distinction, integration, and interaction. The third part (Table 2) was the evaluation questions about the participants’ actual experience in purchasing and using furniture products. Research result statistics and analysis Descriptive statistic results The structure of the samples in this study is displayed in Table 3. All of the 723 participants from different areas in Taiwan recognized the features of furniture product design. In terms of gender, male participants accounted for 45.2% whereas female participants accounted for 54.8%. In terms of age, “20 to 29 years old” accounted for 23.4%, which was the highest, whereas “50 to 60 years old” accounted for 10.1%, which was the lowest. As for education background, “college/university” accounted for 39.8%, which was the highest, while “postgraduate institute or higher” accounted for 7.7%, which was the lowest. In terms of income, NT$30,000 to 39,999” accounted for 18.8%, which was the highest, whereas “NT$ 60,000 or higher” accounted for 11.1%, which was the lowest. S.Afr.J.Bus.Manage.2014,45(1) 85 Table 1: Opinions and evaluation on product design features, including distinction, integration, and interaction The second part of the questionnaire: This part is for your opinions and evaluation on product design features, including distinction, integration, and interaction. Please answer the following questions according to your subjective feelings. “Strongly Agree” is Level 5, and the score ranges from 4.1 to 5, totally 10 equal portions. “More Agree” is Level 4, and the score ranges from 3.1 to 4, totally 10 equal portions. “Agree” is Level 3, and the score ranges from 2.1 to 3, totally 10 equal portions, “Disagree” is Level 2, and the score ranges from 1.1 to 2, totally 10 equal portions. “Strongly Disagree” is Level 1, and the score ranges from 0.1 to 1, totally 10 equal portions. In total, there are 50 equal portions. Please score the level you select according to your opinions and evaluation. In the display of fuzzy semantic scale, 4.8 and 4.4, for instance, both belong to Level 5, but they differentiate the score more delicately. Questionnaire Items on Product Design Features L5 L 4 L 3 L 2 L 1 item 11. I pay attention to the size of a furniture product. item 12. A beautiful furniture product will increase my purchase intention. item 13. The color of a furniture product attracts me first. item 14. I always consider the texture of a furniture product, namely if it feels good when I touch it. item 15. The color combination of a furniture product makes me happy when I use it. item 16. The design of a furniture product should provide users with the ease of use. item 17. I like to use a furniture product with a simple structure but multiple functions. item 18. When I use a furniture product, it will bring me comfort and enable me to enjoy wonderful responses and interaction. item 19. When using a furniture product, I can operate it easily with my intuition, and I do not need any additional explanation. Item 20. I will consider the ergonomic design of a furniture product in order to meet my demand. Table 2: The evaluation items for the fuzzy semantics of the consumers’ perceived value based on your actual experience in purchasing and actually using a furniture product. The third part of the questionnaire: This part is to evaluate the fuzzy semantics of consumers’ perceived value. Please answer the questions subjectively according to your actual experience in purchasing and using furniture products. “Strongly Agree” is Level 5, and the score ranges from 4.1 to 5, totally 10 equal portions. “More Agree” is Level 4, and the score ranges from 3.1 to 4, totally 10 equal portions. “Agree” is Level 3, and the score ranges from 2.1 to 3, totally 10 equal portions, “Disagree” is Level 2, and the score ranges from 1.1 to 2, totally 10 equal portions. “Strongly Disagree” is Level 1, and the score ranges from 0.1 to 1, totally 10 equal portions. In total, there are 50 equal portions. Please score the level you select according to your opinions and evaluation. In the display of fuzzy semantic scale, 4.8 and 4.4, for instance, both belong to Level 5, but they differentiate the score more delicately. Questionnaire Items for the Fuzzy Semantics of Consumers’ Perceived Value L5 L 4 L 3 L 2 L 1 item 21. I feel a furniture product is more worth buying when its size is bigger, and the price is lower. item 22. I fell a furniture product is more valuable when I like the appearance and shape. item 23. I feel a furniture product is not worth purchasing when I don’t like the color. item 24. I feel a furniture product is not worth purchasing when the materials do not feel good. item 25. I will feel a furniture product is worth purchasing if the color combination makes me happy when I use it. item 26. I regret purchasing a furniture product when the design does not bring me any ease of use. item 27. I like to use a furniture product with a simple structure, and I feel it is worth purchasing. item 28. I feel a furniture product is worth purchasing when, intuitively, it is easy to use. item 29. I feel more like purchasing an ergonomically designed furniture product which meets my demand. 86 S.Afr.J.Bus.Manage.2014,45(1) Table 3: The background statistics of the samples Demographic Variables Samples (n=723) Total Sample Percentage Gender Male 327 45.2 Female 396 54.8 Age Under 20 years old 143 19.8 20-29 years old 169 23.4 30-39 years old 90 12.4 40-49 years old 160 22.1 50-60 years old 73 10.1 Above 60 years old 88 12.2 Education Background Junior high school or lower 97 13.4 Senior/vocational high schools 161 22.3 Junior college 121 16.7 College/university 288 39.8 Postgraduate institute or higher 56 7.7 Income Under NT$20,000 134 18.5 NT$20,000- 29,999 121 16.7 NT$30,000- 39,999 136 18.8 NT$40,000- 49,999 114 15.8 NT$50,000- 59,999 138 19.1 NT$60,000 or higher 80 11.1 Reliability and validity The second part of the questionnaire was about the participants’ opinions and evaluation on the features of furniture product design, respectively distinction, integration, and interaction. It is shown in Table 4 that the Cronbach’s α value was 0.942, higher than 0.7, indicating that there was a certain degree of content validity. The third part of the questionnaire was about the participants’ purchase and post-use evaluation on furniture products. The Cronbach’s α value was 0.927, higher than 0.7, indicating that there was a certain degree of content validity. Table 4: Questionnaire reliability analysis Item Cronbach's α Value The features of furniture product design .942 The fuzzy semantics of consumers’ perceived value .927 The descriptive statistics of the fuzzy semantic questionnaire The second part of the questionnaire was about the influence of the features of furniture product design, respectively distinction, integration, and interaction, on consumers’ fuzzy semantics. As shown in Table 5, item 17 “I like to use a furniture product with a simple structure but multiple functions.” was the most significant, and the fuzzy semantic mean was 4.4205. Meanwhile, item 11 “I pay attention to the size of a furniture product.” was the least significant, and the fuzzy semantic mean was 3.8562. Table 5: The descriptive statistics of the second part of the fuzzy semantic questionnaire N Fuzzy Semantic Mean Std. Deviation item11 723 3.8562 .79736 item12 723 4.0553 .80836 item13 723 4.1245 .83284 item14 723 4.0858 .80052 item15 723 4.1452 .69997 item16 723 4.2254 .72952 item17 723 4.4205 .73701 item18 723 4.3430 .77614 item19 723 4.1411 .73648 item110 723 4.2282 .72102 The third part of the questionnaire was about the influence of the features of furniture product design, respectively distinction, integration, and interaction, on consumers’ fuzzy semantics, as shown in Table 6. The fuzzy semantics of item 17 “I like to use a furniture product with a simple structure but multiple functions.” was the highest, and the fuzzy semantic mean was 4.4205, as shown in Figure 5. The fuzzy membership function was () xu : 1= (4.42-4.0) : (5-4) and the obtained fuzzy membership function was that () xu =0.42. It was thus known that the questionnaire value was between “Strongly Agree” and “More Agree,” and the fuzziness was .42, indicating that the participants more agreed but did not strongly agree on that consumers like to use a furniture product with a simple structure but multiple functions. As shown by Figure 6, the fuzzy semantics of item 11 “I pay attention to the size of a furniture product.” was the lowest, and the fuzzy semantic mean was 3.8562. The fuzzy membership function was () xu =.8562, as indicated by Figure 6. It is thus known that the questionnaire value was between “Agree” and “More Agree,” and the fuzziness was.8562, indicating that the participants tended to more agree on that consumers pay attention to the size of a furniture product. 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