Consumer insights that drive value creation opportunities in the Indonesian domestic apple supply chain

Abstract

Agrifood chains that are based on the values defined by end consumers are able to secure a competitive edge over other chains, but securing that competitive edge requires a thorough understanding of the consumers and their value preferences. This study was conducted with a view to understanding domestic apple consumers in Malang, Indonesia, and using that information to guide the opportunities for domestic apple supply chain development in Indonesia. Data were collected through an intercept survey of 200 apple consumers. The survey was designed to identify the respondents' consumption and buying patterns, as well as their attribute preferences for domestic apple compared to imported apple. Respondents were segmented based on their demographic profile and their value preferences for the domestic apple. Three significantly different clusters of domestic apple consumers were identified: the 'indifferent consumers' disregarded the importance of most attributes of domestic apples; the 'pro-sensory consumers' valued sensory attributes more than search attributes; and the 'value seeking consumers' rated most of the value attributes as important in making a purchase. This study identifies the need for value chain members to adopt practices along the chain that enhance particular attributes of domestic apples in order to increase value for each consumer segment.

Application of check‐all‐that‐apply and non‐metric partial least squares regression to evaluate attribute's perception and consumer liking of apples

“…To improve the consumer test assessment, 1–5‐point scales such as Just‐about‐right (JAR) scales are widely applied to evaluate an attribute's perception (Ares et al, 2014; Estiaga, 2015; Hampson & Quamme, 2000; Robertson, Meredith, Senter, Okie, & Norton, 1992; Zamzami & Ariyawardana, 2019). JAR became a very popular tool to investigate the optimum intensity of sensory attributes (Estiaga, 2015) and is often combined with the Penalty analysis in order to obtain mean drop estimates of liking caused by suboptimal attributes (Pagès, Berthelo, Brossier, & Gourret, 2014).…”