Assessment of factors influencing digital transformation in Vietnamese Pharmaceutical Enterprises
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Tóm tắt
Objective: This study aimed to assess the degree of influence of key factors on influencing the digital transformation (DT) maturity level among Vietnamese pharmaceutical enterprises. Subject and method: A cross-sectional study was conducted on 150 pharmaceutical enterprises. Data was collected using a validated survey instrument administered to key informants (e.g., C-level executives, department heads). The survey measured eight factor groups (C1–C8) on a five-point Likert scale. Data analysis employed Cronbach’s alpha, descriptive statistics, one-way ANOVA by enterprise size, and multivariate regression. Result: The measurement scales demonstrated high reliability (Cronbach’s alpha > 0.87). A notable finding was that large enterprises (≥ 200 employees) achieved the highest DT maturity, whereas medium-sized enterprises (100-199 employees) reported the lowest. Regression analysis identified three primary drivers of DT maturity: C1 - Strategic focus on business model innovation (β = 0.246), C2 - Corporate digital strategy (β = 0.178), and C5 - Technological infrastructure (β = 0.168). Interestingly, C4 - The perceived impact of DT on current business performance was not a statistically significant predictor, suggesting that transformation is driven more by strategic foresight than by realized short-term gains. Conclusion: Business model innovation, a clear digital strategy, and robust technological infrastructure are the three foundational pillars for advancing DT maturity in Vietnam's pharmaceutical industry. The findings suggest that current DT initiatives are largely vision-led rather than performance-driven. Policymakers should prioritize support for medium-sized enterprises in finance, policy, and workforce training to help them overcome unique barriers and bridge the maturity gap.
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2. Bamakan SMH, Moghaddam SG, Manshadi SD (2021) Blockchain-enabled pharmaceutical cold chain: Applications, key challenges, and future trends. J Clean Prod 302: 127021. doi: 10.1016/j.jclepro.2021.127021.
3. Al-Kahtani NS (2022) Factors influencing digital transformation in healthcare: Evidence from Saudi Arabia. A cross-sectional analysis using Healthcare Information and Management Systems Society' digital health indicators. Digit Health 175: 121366. doi: 10.1177/20552076221117742.
4. Arief NN, Gustomo A, Roestan MR, Putri ANA, Islamiaty M (2022) Pharma 4.0: Analysis on core competence and digital levelling implementation in pharmaceutical industry in Indonesia. Heliyon 8(8): 10347. DOI: 10.1016/j.heliyon.2022.e10347
5. Dal Mas F, Massaro M (2023) The challenges of digital transformation in healthcare: An interdisciplinary literature review, framework, and future research agenda. Technovation. 123: 102716. doi:10.1016/j.technovation.2022.102716.
6. Ho MT, Understanding the acceptance of emotional artificial intelligence in Japanese healthcare system: A cross-sectional survey of clinic visitors’ attitude. Technology in Society, Elsevier 72(C). https://www.sciencedirect.com/science/article/pii/S0160791X22003074
7. Miozza M (2024) Digital transformation of the pharmaceutical industry: A future research agenda for management studies. Technol Forecast Soc Change. 207: 123580. doi: 10.1016/j.techfore.2024.123580.
8. Khanh VC, General Methods For Assessing Digital Transformation Within The Pharmacy Industry. Vietnam Journal of Community Medicine, 66(Special Issue CĐ8–Scientific Research). https://doi.org/10.52163/yhc.v66iCD8.2574.
ISSN: 1859 - 2872