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<p>Organizations are increasingly leveraging artificial intelligence to support consumer decision-making. However, consumer adoption of artificial intelligence varies significantly, necessitating organizations to adopt flexible approaches to target different segments effectively. Therefore, the study aims to segment e-commerce consumers based on their decision-making styles, forming distinct clusters that represent the likelihood of adopting artificial intelligence in their decision-making processes. Using an integrated research framework, data from 276 e-commerce shoppers were analyzed through cluster analysis, multiple discriminant analysis, and Chi-square tests to identify behavioral and socioeconomic patterns. The results reveal three distinct consumer segments: resistors, indifferents, and adopters, each reflecting varying levels of acceptance and reliance on artificial intelligence. For instance, while adopters (e.g., consumers high on impulsiveness, price consciousness, and brand loyalty) actively use artificial intelligence for decision-making, resistors (e.g., consumers high on perfectionism and shopping consciousness) prefer independent information searches. Organizations targeting resistors should provide multiple related links on their websites to allow customers to search for information themselves. For other customers, organizations should use artificial intelligence-based personalized recommendations. Thus, the study offers novel insights into consumer behavior and underscores the organizational necessity of customizing artificial intelligence strategies to meet diverse consumer preferences.</p>

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Organizational Flexibility in the Era of Artificial Intelligence: Leveraging Decision-Making Styles for Market Segmentation

  • 期刊论文

  • 发表日期：

    2025年8月22日

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  • Hitesh Sharma,
  • Dheeraj Sharma,
  • Tanya Singh

摘要

Organizations are increasingly leveraging artificial intelligence to support consumer decision-making. However, consumer adoption of artificial intelligence varies significantly, necessitating organizations to adopt flexible approaches to target different segments effectively. Therefore, the study aims to segment e-commerce consumers based on their decision-making styles, forming distinct clusters that represent the likelihood of adopting artificial intelligence in their decision-making processes. Using an integrated research framework, data from 276 e-commerce shoppers were analyzed through cluster analysis, multiple discriminant analysis, and Chi-square tests to identify behavioral and socioeconomic patterns. The results reveal three distinct consumer segments: resistors, indifferents, and adopters, each reflecting varying levels of acceptance and reliance on artificial intelligence. For instance, while adopters (e.g., consumers high on impulsiveness, price consciousness, and brand loyalty) actively use artificial intelligence for decision-making, resistors (e.g., consumers high on perfectionism and shopping consciousness) prefer independent information searches. Organizations targeting resistors should provide multiple related links on their websites to allow customers to search for information themselves. For other customers, organizations should use artificial intelligence-based personalized recommendations. Thus, the study offers novel insights into consumer behavior and underscores the organizational necessity of customizing artificial intelligence strategies to meet diverse consumer preferences.