Business strategic insight: understanding the impact of perceived value and impulsiveness on consumer response in global mobile shopping

Yeneneh Tamirat Negash, Liao Pei Jyun,Ali Tarhini,Shafique Ur Rehman

REVIEW OF INTERNATIONAL BUSINESS AND STRATEGY(2024)

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摘要
PurposeThis study aims to contribute to the International Business literature by investigating the marketing stimuli that drive impulsiveness and perceived value in mobile shopping (MS) platforms and their impact on consumer response.Design/methodology/approachThis study uses a sample of 891 MS platform users and applies structural equation modeling based on the stimulus-organism-response and the consumption value theory.FindingsThe empirical finding revealed that rewards, recognition, reviews and ratings are the most influential factors driving perceived value. In addition, the results indicated that customized offerings and visually appealing experiences were the most critical factors affecting the state of impulsiveness. This study also highlights the negative impact of the ubiquitous nature of MS on impulse buying behavior, emphasizing the importance of providing consumers with tools to make informed decisions. This study demonstrates a significant positive relationship between perceived value and impulsiveness, influencing MS.Practical implicationsThis study reveals generational differences in the impact of reviews and ratings on perceived value, which can inform businesses' MS strategies. The results have implications for managers of international firms seeking to optimize their business strategies.Originality/valueTo the best of the authors' knowledge, using structural equation modeling, this study is the first to conduct a comprehensive examination of marketing stimuli, impulsiveness and perceived value in MS platforms. It offers businesses strategic insights by identifying rewards, recognition and customized offerings as the key determinants of consumer behavior.
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关键词
Global mobile shopping,Impulsive buying,Perceived values,Stimulus-organism-response,Generational cohorts,Structural equation modeling
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