Influence of digital transformation and strategic learning on agility and performance of Thai hotels

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This study investigates the influence of digital transformation capability and strategic learning capability on strategic agility as well as competitive and innovative performance of Thai hotels. The sample consisted of 303 4–5-star hotels, selected through simple random sampling from a total population of 2,079 hotels nationwide. Data were collected through questionnaires and analyzed using structural equation modeling. The results revealed that digital transformation capability significantly influences strategic agility (β = 0.249, t = 5.182). Strategic learning capability, which encompasses strategic knowledge generation (β = 0.309, t = 5.759), strategic knowledge interpretation (β = 0.118, t = 2.325), strategic knowledge implementation (β = 0.266, t = 5.561), and strategic knowledge database (β = 0.106, t = 3.395), also has a positive impact on strategic agility. Furthermore, strategic agility positively affects competitive and innovative performance (β = 0.313, t = 4.984). It also serves as a mediator in the relationships between digital transformation capability (β = 0.078, t = 3.834), strategic knowledge generation (β = 0.097, t = 3.671), strategic knowledge interpretation (β = 0.037, t = 2.015), strategic knowledge implementation (β = 0.083, t = 3.704), and strategic knowledge database (β = 0.033, t = 2.651) on competitive and innovative performance. The findings offer hotel operators valuable insights into the importance of developing digital transformation capabilities and strategic learning capabilities to enhance strategic agility, which in turn leads to improved competitive and innovative performance, particularly within a volatile and uncertain business environment.

Acknowledgments
The authors declared that this study complied with ethical guidelines by the Institutional Review Board of the Human Research Ethics Committee of Walailak University (WUEC-23-264-01), Thailand. Dr. Kanokwan Meesook is also a co-first author of this article.

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    • Figure 1. Conceptual framework
    • Figure 2. Structural model
    • Table 1. Population and sample size
    • Table 2. Characteristics of the sample
    • Table 3. Reliability and validity values
    • Table 4. Fornell–Larcker criterion
    • Table 5. Heterotrait-monotrait (HTMT) ratio
    • Table 6. Direct effects analysis
    • Table 7. Mediation effect analysis
    • Conceptualization
      Sirinthra Sungthong, Kanokwan Meesook, Charoenchai Agmapisarn, Somnuk Aujirapongpan
    • Formal Analysis
      Sirinthra Sungthong, Charoenchai Agmapisarn, Somnuk Aujirapongpan
    • Methodology
      Sirinthra Sungthong, Kanokwan Meesook, Charoenchai Agmapisarn, Somnuk Aujirapongpan
    • Resources
      Sirinthra Sungthong, Narinthon Imjai
    • Software
      Sirinthra Sungthong
    • Visualization
      Sirinthra Sungthong, Abror Abror
    • Data curation
      Kanokwan Meesook, Somnuk Aujirapongpan
    • Investigation
      Kanokwan Meesook, Charoenchai Agmapisarn, Abror Abror, Narinthon Imjai, Somnuk Aujirapongpan
    • Supervision
      Kanokwan Meesook, Somnuk Aujirapongpan
    • Writing – original draft
      Kanokwan Meesook, Charoenchai Agmapisarn, Narinthon Imjai, Somnuk Aujirapongpan
    • Writing – review & editing
      Kanokwan Meesook, Charoenchai Agmapisarn, Abror Abror, Somnuk Aujirapongpan
    • Validation
      Charoenchai Agmapisarn, Abror Abror
    • Project administration
      Narinthon Imjai