Exploring fintech adoption drivers among tourism-supported culinary SMES

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Fintech adoption drivers are relevant for tourism-supported culinary SMEs for a number of reasons, including sustainability, economic growth, and technological advancements. This study aims to confirm the fintech adoption drivers among tourism-supported culinary SMEs in West Sumatra, Indonesia. The study uses primary data collected through a survey. Forty-four experts from various relevant academic backgrounds were respondents to this study. Data were analyzed in multiple stages. First, data were analyzed using the univariate test by applying the Mann-Whitney U and Kruskal-Wallis tests. Second, data analysis proceeds to exploratory factor analysis to separate the drivers into several factors. Finally, confirmatory factor analysis was employed using a second-order structural equation model. The result shows that five of the thirteen drivers identified in the literature were deleted due to no expert agreement. Based on exploratory factor analysis, it was found that two factors were created as fintech adoption drivers: time reduction process and new customer attraction factor (factor 1), and ease of use, security, and cost reduction factor (factor 2). The third analysis using second-order smart_PLS indicates that the two factors were confirmed. It can be concluded that two factors drive fintech adoption: (i) time reduction process and new customer attraction factor, and (ii) ease of use, security, and cost reduction factor.

Acknowledgment
This research was funded by the Ministry of Education, Culture, Research and Technology, Republic of Indonesia (No. 186/E5/PG.02.00.PT/2023).
We acknowledge the directorate of higher education, the Ministry of Education, Culture, Research and Technology, Republic of Indonesia, for research funding (No. 186/E5/PG.02.00.PT/2023). Our thanks are also directed to the Rector of Universitas Bung Hatta and anonymous reviewers of this article for constructive suggestions.

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    • Figure 1. Scree plot
    • Figure 2. Measurement model
    • Table 1. Drivers of fintech adoption
    • Table 2. Agreement analysis: univariate test
    • Table 3. Sampling adequacy test
    • Table 4. Exploratory factor analysis results
    • Table 5. Convergent validity
    • Table 6. Discriminant validity: Cross-loading
    • Conceptualization
      Neva Novianti, Zaitul Zaitul
    • Data curation
      Neva Novianti, Desi Ilona, Zaitul Zaitul
    • Funding acquisition
      Neva Novianti, Zaitul Zaitul
    • Methodology
      Neva Novianti, Zaitul Zaitul
    • Supervision
      Neva Novianti
    • Writing – original draft
      Neva Novianti, Desi Ilona, Yeasy Darmayanti, Zaitul Zaitul
    • Writing – review & editing
      Neva Novianti, Desi Ilona, Yeasy Darmayanti, Zaitul Zaitul
    • Formal Analysis
      Desi Ilona, Yeasy Darmayanti
    • Investigation
      Desi Ilona, Yeasy Darmayanti
    • Project administration
      Desi Ilona
    • Validation
      Desi Ilona
    • Resources
      Yeasy Darmayanti
    • Software
      Yeasy Darmayanti