Document Type : Original Article
Authors
1 Department of Management, Faculty of Management and Accounting, Imam Reza International University
2 Department of Management, Faculty of Management and Accounting, Imam Reza International University, Mashhad, Iran
3 Department of Iranian Medicine, School of Traditional and Complementary Medicine,, Mashhad University of Medical Sciences, Mashhad, Iran
Abstract
Given the importance of value creation in business and the increasing role of big data, the adoption of big data-driven value creation models has been emphasized over the past decade. Accordingly, this study aims to assess big data capabilities preparedness with a focus on the successful implementation of these models in Medical tourism.
Methods: This study is applied in terms of purpose and descriptive-correlational in design, conducted using a cross-sectional approach in 2024. The statistical population consisted of managers of medical tourism healthcare centers in Mashhad, Iran, who were included through a census sampling method. Data were collected using a researcher-developed questionnaire distributed among the center managers. The instrument’s validity was confirmed through confirmatory factor analysis, and its reliability was established using Cronbach’s alpha and composite reliability coefficients. Descriptive statistics were employed for data summarization, while structural equation modeling (SEM) was used to test the research hypotheses. Due to the limited sample size, the non-parametric Partial Least Squares (PLS) approach was implemented using SmartPLS software.
Results: T The overall readiness of big data capabilities (mean = 2.28)—along with analytical capabilities (mean = 2.12), managerial capabilities (mean = 2.14), and infrastructure (mean = 2.49)—was below the theoretical midpoint of 3, indicating a low level of preparedness. Infrastructure, analytical capabilities, and managerial capabilities exerted the strongest influence on overall readiness, with path coefficients of 0.540, 0.300, and 0.192, respectively.
Conclusion: Improving the readiness level of all capabilities—considering their respective impacts on overall readiness—should be prioritized by first enhancing big data infrastructure, followed by analytical and managerial capabilities. This can be achieved through the implementation and integration of diverse data sources, adoption of advanced analytical capabilities, and fostering a stronger data-driven organizational culture.
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