Why readiness is not enough: Routinized digital use and MSME competitiveness

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Type of the article: Research Article

Abstract
Digital transformation has become a strategic priority for micro, small, and medium-sized enterprises (MSMEs), yet its business effects remain uneven because digital adoption does not always translate into sustained organizational gains. This study examines whether digital readiness conditions and technology acceptance beliefs help Indonesian MSMEs convert digitalization into organizational performance and competitiveness through routinized digital technology use. The study uses primary survey data collected in Indonesia between January and June 2025 from 404 MSME owners and managers, and analyzes these data using partial least squares structural equation modeling (PLS-SEM). The results show that organizational readiness positively influences routinized digital technology use (β = 0.425, p < 0.001), while perceived usefulness is the strongest direct predictor of routinized use (β = 0.807, p < 0.001). Perceived ease of use significantly increases perceived usefulness (β = 0.688, p < 0.001). By contrast, technological readiness (β = 0.051, p = 0.095) and environmental readiness (β = –0.014, p = 0.649) do not have significant direct effects on routinized use. Routinized digital technology use positively affects organizational performance (β = 0.680, p < 0.001) and competitiveness (β = 0.672, p < 0.001), while organizational performance also strengthens competitiveness (β = 0.133, p = 0.023). These findings indicate that MSME digital transformation creates greater business value when digital tools become embedded in recurring work routines rather than remaining at the level of access or initial adoption.

Acknowledgment
This article was funded by the Indonesian Endowment Fund for Education (LPDP), Ministry of Finance of the Republic of Indonesia, through the EQUITY WCU (Enhancing Quality Education for International University Recognition – World Class University) program, Ministry of Higher Education, Science and Technology, administered by Universitas Terbuka. We also thank all participating MSME respondents for their time and insights. Ethical approval was obtained from the relevant institutional committee, and all participants provided informed consent. We gratefully acknowledge the use of ChatGPT (OpenAI, 2025 version) to support language refinement and grammar improvement during manuscript preparation. The tool was used exclusively for copy-editing to enhance linguistic clarity and readability. No part of the research design, data collection, data analysis, interpretation of results, or substantive content was generated by AI. We take full responsibility for the accuracy, integrity, and originality of the work. 

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    • Figure 1. Conceptual framework
    • Table 1. Respondents’ profile
    • Table 2. Descriptive statistics
    • Table 3. Quality criteria
    • Table 4. Discriminant validity
    • Table 5. Structural collinearity
    • Table 6. Direct effects and hypothesis testing
    • Table A1. Constructs and measurement items
    • Conceptualization
      Meirani Harsasi, Isbandriyati Mutmainah
    • Formal Analysis
      Meirani Harsasi
    • Methodology
      Meirani Harsasi, Isbandriyati Mutmainah
    • Project administration
      Meirani Harsasi
    • Software
      Meirani Harsasi
    • Supervision
      Meirani Harsasi, Isbandriyati Mutmainah
    • Writing – original draft
      Meirani Harsasi
    • Writing – review & editing
      Meirani Harsasi, Isbandriyati Mutmainah, Aprihatiningrum Hidayati, Erni Ernawati
    • Validation
      Isbandriyati Mutmainah
    • Data curation
      Aprihatiningrum Hidayati, Erni Ernawati
    • Investigation
      Aprihatiningrum Hidayati, Erni Ernawati
    • Resources
      Aprihatiningrum Hidayati, Erni Ernawati
    • Visualization
      Aprihatiningrum Hidayati