Articlegold2023

Understanding consumers’ intentions to purchase smart clothing using PLS-SEM and fsQCA

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Technology Adoption and User Behaviour Consumer Behavior and Marketing Influence Consumer Perception and Purchasing Behavior

Abstract

With the advancement of artificial intelligence (AI) and the Internet of Things (IoT), smart clothing, which has enormous growth potential, has developed to suit consumers' individualized demands in various areas. This paper aims to construct a model that integrates that technology acceptance model (TAM) and functionality-expressiveness-aesthetics (FEA) model to explore the key factors influencing consumers' smart clothing purchase intentions (PIs). Partial least squares structural equation modeling (PLS-SEM) was employed to analyze the data, complemented by fuzzy-set qualitative comparative analysis (fsQCA). The PLS-SEM results identified that the characteristics of functionality (FUN), expressiveness (EXP), and aesthetics (AES) positively and significantly affect perceived ease of use (PEOU), and only EXP affects perceived usefulness (PU). PU and PEOU positively impact consumers' attitudes (ATTs). Subsequently, PU and consumers' ATTs positively influence PIs. fsQCA revealed the nonlinear and complex interaction effects of the factors influencing consumers' smart clothing purchase behaviors and uncovered five necessary and six sufficient conditions for consumers' PIs. This paper furthers theoretical understanding by integrating the FEA model into the TAM. Additionally, on a practical level, it provides significant insights into consumers' intentions to purchase smart clothing. These findings serve as valuable tools for corporations and designers in strategizing the design and promotion of smart clothing. The results validate theoretical conceptions about smart clothing PIs and provide useful insights and marketing suggestions for smart clothing implementation and development. Moreover, this study is the first to explain smart clothing PIs using symmetric (PLS-SEM) and asymmetric (fsQCA) methods.

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References

1981Evaluating Structural Equation Models with Unobservable Variables and Measurement Error 2003Common method biases in behavioral research: A critical review of the literature and recommended remedies.1989Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology 2014A new criterion for assessing discriminant validity in variance-based structural equation modeling 2018When to use and how to report the results of PLS-SEM 2011PLS-SEM: Indeed a Silver Bullet 2016Using PLS path modeling in new technology research: updated guidelines 2011Building Better Causal Theories: A Fuzzy Set Approach to Typologies in Organization Research

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