Practical Applications and Psychometric Evaluation of a Computerised Fuzzy Graphic Rating Scale

Hesketh, Beryl; Pryor, Robert; Gleitzman, Melanie; Hesketh, T.

doi:10.1016/s0166-4115(08)60493-8

Cited by 51 publications

(38 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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.…”

Section: Fuzzy Semanticsmentioning

confidence: 99%

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

Lee

2014

SAJBM

View full text Add to dashboard Cite

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 motivesDue 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 produc...

show abstract

Section: Fuzzy Semanticsmentioning

confidence: 99%

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

Lee

2014

SAJBM

View full text Add to dashboard Cite

show abstract

“…Although insights into the ambiguous nature of social judgment were identified early in the development of measurement of social judgment, the subsequent methods used failed to capture this ambiguity, no doubt because traditional mathematics was not well developed for dealing with vagueness of judgment (Hesketh et al,1988).…”

Section: Fuzzy Rating Data and Fuzzy Setmentioning

confidence: 99%

“…In order to measure the vagueness of human judgment, the fuzzy rating method has recently been proposed and developed (Hesketh et al,1988;Takemura,1996). In the fuzzy rating method, respondents select a representative rating point on a scale and indicate lower or upper rating points if they wish depending upon the relative vagueness of their judgment (see Figure 2).…”

Section: Fuzzy Rating Data and Fuzzy Setmentioning

confidence: 99%

“…The membership function has a value of 0 when the properties of the fuzzy set are not at all satisfied, and 1 when the properties of fuzzy set are completely satisfied. Hesketh et al(1988) pointed out that fuzzy rating data can be represented as fuzzy sets by making certain implifying assumptions, which are not uncommon within fuzzy set theory. According to Hesketh et al(1988), those assumptions are:…”

Section: Fuzzy Rating Data and Fuzzy Setmentioning

confidence: 99%

“…This rating scale allows for asymmetries and overcomes the problem, identified by Smithson (1987), of researchers arbitrarily deciding the most representative value from a range of scores. By making certain simplifying assumptions (which is not uncommon in fuzzy set theory), the rating can be viewed as an L-R fuzzy number, thereby making the use of fuzzy set theoretical operations possible (Hesketh et al, 1988;Takemura, 2000). Lastly, numerical illustrations of psychological experiments are provided to examine the ambiguous comparative judgment model (Takemura, 2007) using the proposed data analysis.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations