Relationship between users' perceptions of coolness and intention to use digital products: a user-centered approach

Abstract

PurposeThe purpose of this study is to employ a user-centered approach to identify subgroups of people with similar profiles based on their perceptions of multiple dimensions of digital products' coolness and to test the differences across subgroups in the intention to use these products.Design/methodology/approachIn Study 1, 1,161 adults rated a virtual digital product on four dimensions of coolness. In Study 2, 660 college students made similar ratings and also reported their intention to use a real digital product. Participants' ratings were analyzed using a user-centered approach, namely latent profile analysis.FindingsStudy 1 identified groups of participants who had similar profiles of product ratings on the four dimensions of coolness: niche cool, mass cool, uncertain cool and uncool. Study 2 replicated the findings of Study 1 and in addition showed that these groups varied in their intention to use the product and in whether a specific dimension increased or decreased this intention.Research limitations/implicationsThe cross-sectional data preclude inferences about causality, calling for experimental or longitudinal research. Additionally, future research should explore whether the results generalize to other product categories and other age groups.Practical implicationsManagers should design digital products, segment the market and develop flexible strategies based on combined responses to dimensions of coolness perception.Originality/valueThe present research employed a user-centered approach to identify groups of people who share similar patterns of coolness perception. This study provides new insight that was not available in variable-centered research.

Research on personalized user-centered product improvement based on sentiment mining of online reviews and competitor analysis

“…Online reviews contain a wealth of valuable information about product performance and user sentiment, which can effectively assist manufacturers to make production decisions (Geng et al, 2020;J. Li et al, 2022).…”

“…Conducting user-centered product improvement to enhance consumer satisfaction, improve customer loyalty and expand brand influence, has proved promising for manufacturers (Geng et al, 2020; J. Li et al, 2022). Traditional market survey methods, e.g., product trials (Schultz et al, 2009), telephone interviews (Yao et al, 2022) and questionnaires (Mohanty et al, 2022), which are resource-intensive and have a long feedback loop, cannot meet the requirements of enterprises to obtain information in time.…”

How to earn a premium price: the effect of green marketing and brand coolness

“…Often employed as an indicator of success of a brand (Swaminathan et al , 2020), the cool factor is frequently associated with product/brand adoption, as described in literature dedicated to consumer-brands relationships (e.g. Li et al ., 2022; Warren et al ., 2019). Empirical studies confirmed that brand coolness is significantly associated with the popularity of luxury fashion goods (Loureiro et al ., 2020b), city destinations (Kock, 2021) and museums (Loureiro and Blanco, 2021), suggesting that the downstream consequences can shape the WTP and intention to visit/buy of customers (Warren et al ., 2019).…”

A Study on the Relationship Between Consumer Motivations and Subjective Well-Being: A Latent Profile Analysis

“…By contrast, LPA is a more flexible and model-based classification method by allowing the estimation of alternative models and accordingly relaxing the restrictive conditions of cluster analysis ( Howard et al, 2016 ). Importantly, LPA can provide more objective classification indicators for selecting the optimal number of profiles, with reasonable subtype identification criteria and lower classification errors ( Wang and Hanges, 2010 ; Li et al, 2021 ). The profiles identified by LPA are typical in nature and referred to as potential.…”

“…However, these studies focus exclusively on hedonic and utilitarian aspects of consumer motivation without considering normative aspects (i.e., ethics and social acceptance). They used cluster analysis, and it is more advisable to use LPA than cluster analysis in studying taxonomy ( Howard et al, 2016 ; Li et al, 2021 ).…”