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Leveraging Technology and Visitor Feedback for Enhancing Tea Tourism: An Empirical Analysis

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  • , Ashokkumar Palaniyandi

    (PSG COLLEGE OF ARTS & SCIENCE)

  • P, Sangeetha C

Abstract

This study examines the impact of technology integration and systematic visitor feedback on improving tea tourism outcomes. Drawing on survey responses from 106 tea tourism stakeholders, we developed a composite measure of Tea Tourism Improvement (encompassing operational efficiency, service quality, and visitor satisfaction) and assessed its relationship with two key constructs: positive digital practices (e.g., enhanced online marketing, streamlined booking systems, and effective feedback utilization) and perceived barriers (e.g., limited internet access and the absence of a formal feedback process). An Exploratory Factor Analysis (EFA) validated a two-factor structure with a high sampling adequacy (KMO = 0.893) and significant Bartlett’s Test (p < 0.001), while the two factors collectively explained over 89% of the variance in tea tourism improvement. Subsequently, multiple regression analysis demonstrated that positive digital practices (Factor 1) have a strong positive influence (β = 0.929, p < 0.001) on tourism improvement, whereas the barrier construct (Factor 2) is significantly negatively related (β = –0.171, p < 0.001) to the same outcome. Residual diagnostics confirmed that the model assumptions of normality and homoscedasticity were adequately met. The results underscore the critical role of digital transformation and structured feedback in enhancing tea tourism, while also highlighting the importance of addressing infrastructural and procedural barriers. Limitations include the cross-sectional design and reliance on self-reported measures. Future research should consider longitudinal approaches and incorporate objective performance metrics.

Suggested Citation

  • , Ashokkumar Palaniyandi & P, Sangeetha C, 2025. "Leveraging Technology and Visitor Feedback for Enhancing Tea Tourism: An Empirical Analysis," SocArXiv xq9dz_v1, Center for Open Science.
  • Handle: RePEc:osf:socarx:xq9dz_v1
    DOI: 10.31219/osf.io/xq9dz_v1
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    1. Zhixue Liao & Xinyu Gou & Qiang Wei & Zhibin Xing, 2024. "Forecasting tourism demand with helpful online reviews," Nankai Business Review International, Emerald Group Publishing Limited, vol. 15(4), pages 703-726, March.
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