Needs Assessment and Clustering of Technological Infrastructure for the Transition to Industry 4.0 and Industry 5.0 in Small and Medium-Sized Enterprises: A Systematic Literature Review and Computational Text Analysis Approach

Maryam Hadipour; Seyed Haidar Mirfakhradini; Habib Zare Ahmadabadi; Seyyed Habibollah Mirghafoori

Authors

Maryam Hadipour Ph.D. Student, Department of Management, Economic and Accounting, Faculty of Humanities and Social Sciences, Yazd University, Yazd, Iran Author https://orcid.org/0000-0001-7518-1078
Seyed Haidar Mirfakhradini Professor, Department of Management, Economic and Accounting, Faculty of Humanities and Social Sciences, Yazd University, Yazd, Iran Corresponding author https://orcid.org/0000-0002-8150-0415
Habib Zare Ahmadabadi Associate Professor, Department of Management, Economic and Accounting, Faculty of Humanities and Social Sciences, Yazd University, Yazd, Iran Author https://orcid.org/0000-0003-3662-5953
Seyyed Habibollah Mirghafoori Professor, Department of Management, Economic and Accounting, Faculty of Humanities and Social Sciences, Yazd University, Yazd, Iran Author https://orcid.org/0000-0001-5362-6590

Keywords:

Industry 4, Industry 5, small and medium, sized enterprises (SMEs), systematic literature review, computational text analysis

Abstract

In the current global business landscape, organizations face increasing pressure to align with technological advancements. Small and medium-sized enterprises (SMEs), as key drivers of economic growth, must enhance their absorptive capacities to maintain competitiveness within digital ecosystems. However, while Industry 4.0 primarily focuses on industrial optimization, Industry 5.0 introduces a new paradigm emphasizing social values and human-centricity. Many SMEs encounter significant challenges in this transition due to the complexities of adopting smart manufacturing and the lack of appropriate maturity assessment frameworks. To address existing gaps in the literature, the present study aims to identify and cluster the infrastructural dimensions and criteria required for transitioning to Industry 4.0 and Industry 5.0. This study employs an integrated methodological approach combining a systematic literature review (SLR) with computational text analysis. The research process involves systematic database searches, multistage screening, and data extraction based on a transparent protocol. Subsequently, to refine the extracted data and identify latent patterns, natural language processing and computational techniques were employed, including Sentence-BERT (SBERT) for semantic representation, k-nearest neighbors (k-NN) for neighborhood analysis, the Leiden algorithm for clustering, and PaCMAP for visualization. The findings indicate that the initial dataset, comprising 572 features, was ultimately reduced to 49 key indicators classified into seven final clusters. These indicators provide a practical framework for assessing technological readiness and facilitating the strategic transition of SMEs toward emerging industrial paradigms.

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