Affective engineering is being increasingly used to describe a systematic approach to the analysis of consumer reactions to candidate designs. It has evolved from Kansei engineering, which has reported improvements in products such as cars, electronics, and food. The method includes a semantic differential experiment rating candidate designs against bipolar adjectives (e.g., attractive–not attractive, traditional–not traditional). The results are statistically analyzed to identify correlations between design features and consumer reactions to inform future product developments. A number of key challenges emerge from this process. Clearly, suitable designs must be available to cover all design possibilities. However, it is also paramount that the best adjectives are used to reflect the judgments that participants might want to make. The current adjective selection process is unsystematic, and could potentially miss key concepts. Poor adjective choices can result in problems such as misinterpretation of an experimental question, clustering of results around a particular response, and participants' confusion from unfamiliar adjectives that can be difficult to consider in the required context (e.g., is this wristwatch “oppressive”?). This paper describes an artificial intelligence supported process that ensures adjectives with appropriate levels of precision and recall are developed and presented to participants (and thus addressing problems above) in an affective engineering study in the context of branded consumer goods. We illustrate our description of the entire concept expansion and reduction process by means of an industrial case study in which participants were asked to evaluate different designs of packaging for a laundry product. The paper concludes by describing the important advantages that can be gained by the new approach in comparison with previous approaches to the selection of consumer focused adjectives.
Purpose -The purpose of this paper is to describe the Kansei engineering toolkit that has been developed to provide a set of tools and techniques to support better packaging design. Design/methodology/approach -The toolkit has its foundations in Kansei engineering but the work has extended the scope and increased reliability of results by: including structured linkages to designers; replacing "highest level Kansei" from Kansei type 1 with brand values; introducing a more structured process for the elicitation of type 2 selection of pack physical properties; reducing the complexity of the semantic differential survey used to elicit consumer perceptions; and structuring a process for selection of the Kansei words. Findings -The work has shown that the proposed toolkit is able to support the design of packaging by illustrating the process with industrial case studies. Research limitations/implications -Kansei engineering and the techniques presented in this toolkit are inevitably simplifications of the real situation, since many more variables affect the consumers purchase decision than is tested in this process. There is still a need to test the insights gained by the toolkit into a wider investigation. Practical implications -This paper offers the packaging industry a robust and repeatable method to develop better packaging. Originality/value -The paper presents an overall description of the Kansei engineering toolkit for packaging design and is a structured process that provides quantitative results for the relationship between branding, consumer perception and design variables.
Understanding the affective appeal of consumer products is important for today's consumer-oriented markets. However, very few tools and techniques are available that support affective decision making within a product development process in a way to enhance rather than replace current operations. This paper reports on a project to develop an Affective Toolkit for incorporating decision-making within product development processes. The tools have their roots in Kansei Engineering and we have refined this methodology for use with branded consumer goods. We have worked on case studies with several companies which offered a unique perspective on how affective product appeal can be incorporated within design decision making. This paper reports on two of these studies and discusses issues experienced when trying to implement such affective decision making in an industrial context. The analysis of this insight is reported as a research agenda for affective engineering and highlights a number of opportunities for future work.
Introduction This study examined if a diffused fragrance used at home before bedtime would contribute to sleep improvement in a sample of healthy females. Existing evidence regarding the sleep-promoting properties of fragrances often has been anecdotal or based on clinical research, thereby limiting the generalizability and ecological validity of results. Methods 26 women with self-reported interest in air care to support healthy sleep participated in a 9-week field study. A within-subjects, counterbalanced intervention design was implemented, comparing 3 weeks of nightly product use to 3 weeks without using the product after a baseline period. Intervention consisted of the use of a fragrance diffuser and fragrance by Reckitt’s Scientific Platform Fragrance Research for at least an hour at the participants’ preferred settings in the room where they spent the most time before going to bed. Sleep was measured objectively with SleepScore Max every night. Self-report data were collected at bedtime, in the morning, and after each measurement period. Multilevel regression and paired t-tests were used to test for statistical significance. Results Across all participants there were 835 nights of tracked sleep. Participants (100% female, age 21 to 55, average 36 years old) showed improvement in both objective and perceived sleep during the intervention. Participants got more deep sleep, spent a greater proportion of the night in deep sleep, and had an improved BodyScore, a measure of deep sleep (ps<.05). Additional objective improvements were related to sleep consistency: fewer awakenings during the night, less time awake during the night, and better sleep maintenance (ps<.05). Self-report results complemented the objective findings. Participants felt sleepier at bedtime, felt they woke up less often and spent less time awake after initially falling asleep, reported better sleep quality, and experienced better mood both at bedtime and in the morning (ps<.05). No significant negative impacts were seen on sleep in the objective and self-report measures. Conclusion Using the fragrance diffuser before bed may contribute to improvement of many aspects of sleep within this study population of females without underlying sleep conditions. Objectively improved sleep outcomes were supported by self-report, showing multifaceted benefits of the diffused fragrance on sleep. Support (If Any) Reckitt
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