How to build trust: Evidence from Thai customers in the latex glove industry

  • Received November 16, 2021;
    Accepted December 16, 2021;
    Published December 21, 2021
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/im.17(4).2021.11
  • Article Info
    Volume 17 2021, Issue #4, pp. 120-131
  • TO CITE АНОТАЦІЯ
  • Cited by
    13 articles
  • 729 Views
  • 151 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

This paper examined the impact of product quality, perceived risk, and perceived value on customer trust in the latex glove industry of Thailand. It used a structural equation model (SEM) to analyze the association between two or more variables. Data collection was conducted in Thailand during the pandemic of COVID-19. Five hundred people looking for glove protection were invited to join the survey; however, only 384 provided responses were valid enough for the data analysis. According to the empirical results of this study, product quality demonstrated significant and positive impacts on perceived value and trust. In addition, perceived value acted not only as a significant and positive predictor of customer trust but also as a partial mediator between product quality and customer trust. On the other hand, the current results demonstrated that perceived risk had little impact on perceived value and trust, while product quality was the primary benefit for increasing perceived value and trust among customers. Thus, ambiguity among customers was unlikely to demonstrate any serious concern for customer value and trust. Overall, customer trust relied significantly on perceived value through increased product quality.

Acknowledgment
This study was supported by Internal Research Grant Funding of Academic year 2021, Hatyai University and Postdoctoral Fellowship, Prince of Songkla University.

view full abstract hide full abstract
    • Figure 1. Customer trust model
    • Figure 2. SEM results
    • Table 1. Confirmatory factor analysis and model fit
    • Table 2. SEM model measurement
    • Investigation
      Long Kim, Pattarawadee Maijan
    • Validation
      Long Kim, Wanamina Bostan Ali
    • Funding acquisition
      Long Kim, Pattarawadee Maijan, Teerasak Jindabot
    • Writing – original draft
      Long Kim
    • Data curation
      Pattarawadee Maijan
    • Resources
      Pattarawadee Maijan, Teerasak Jindabot, Wanamina Bostan Ali
    • Writing – review & editing
      Pattarawadee Maijan
    • Conceptualization
      Teerasak Jindabot
    • Methodology
      Teerasak Jindabot
    • Supervision
      Teerasak Jindabot, Wanamina Bostan Ali
    • Project administration
      Wanamina Bostan Ali
    • Visualization
      Wanamina Bostan Ali