Purchasing Decision Making among Filipino Middle Income Household Heads: How Green is it?

- This study examines the extent by which urban middle income household heads in Cebu City, Philippines, incorporate environment impact into their purchasing decisions. The objectives of the study are to find out: (1) the degree of importance the participants give to environmental impact as a purchase consideration; (2) the interest of participants’ to purchase an environment-friendly product when made available; and, (3) whether environmental impact as a purchase consideration is related to the participants’ interest in purchasing an environment friendly product. The results of the face-to-face structured survey participated by 500 household heads from the top ten most populated barangays in Cebu City show that environmental impact ranked third in order of importance among five purchase criteria, trailing behind price and product effectiveness. Sixty eight percent (68%) expressed their interest to purchase an environment friendly laundry product if made available in the market and if marketed as “environment friendly.” The correlation analyses show a moderately positive significant relationship between environment impact as a purchase criterion and the level of interest in purchasing an environment friendly laundry product. Implications to policy makers, marketers and environmental advocates in terms of promoting purchasing and consumption behaviors that care for the environment are presented. Keywords - green purchasing, green consumption, eco-friendly products, consumer environmentalism

Linguistic support for concept selection decisions

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.