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Exploring Effectiveness of Relationship Marketing on Artificial Intelligence Adopting Intention

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  • Cheng-Feng Cheng
  • Chien-Che Huang
  • Ming-Chang Lin
  • Ta-Cheng Chen

Abstract

The major contribution of present study is to revisit how consumer obtain high adoption intention of artificial intelligence (AI)’s production/service based on symmetric and asymmetric thinking in data analysis. In recent years, technology involving AI has become key technology for success worldwide and has received prominence among academics and practitioners. Accordingly, it is necessary to identify the relationships among relationship marketing, AI perceived usefulness, perceived ease of use, perceived risk, and adoption intention. The main contribution of the symmetric approach (i.e., SEM, structure equation modeling) is to test the research hypothesis to determine the net effect between the variables, and asymmetric approach (i.e., fsQCA, fuzzy set qualitative comparative analysis) contributes to the identification of sufficient conditions leading to high level adoption intention in the concept of fuzzy sets. With symmetric approach, results of path analysis of SEM indicate that impacts of trust are greater than that of commitment. Similarly, perceived usefulness of AI has a greater impact on the adoption intention. Furthermore, AI perceived risk is negative associate with adoption intention. With asymmetric approach, intermediate solutions from fsQCA show that there are three sufficient conditions for high adoption intention of AI. For instance, one of configurations or sufficient conditions is trust, commitment, and AI perceived usefulness present but perceived ease of use absent.

Suggested Citation

  • Cheng-Feng Cheng & Chien-Che Huang & Ming-Chang Lin & Ta-Cheng Chen, 2023. "Exploring Effectiveness of Relationship Marketing on Artificial Intelligence Adopting Intention," SAGE Open, , vol. 13(4), pages 21582440231, December.
    • Handle: RePEc:sae:sagope:v:13:y:2023:i:4:p:21582440231222760
    DOI: 10.1177/21582440231222760
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