A Multianalytical Approach to Identifying Influential Factors in Data Sharing among International Construction Enterprises

With the advent of the data era, academia and industry have gradually recognized the pivotal role of cross-organizational data sharing in the construction sector. However, given the nature of international construction enterprises (ICEs), data sharing is often complicated by cross-border data flows, diverse standardized formats, and varying regulatory environments. Besides, existing studies have yet to investigate the factors affecting data sharing at a firm level; most identified factors are isolated and do not consider the inner relations or priorities of these elements. To bridge this gap, this study aims to construct a holistic model consisting of the influential factors in data sharing while considering their interactions and prioritization. The model derives from data collected through a literature review and semistructured interviews with 15 experts. This paper uses a multianalytical approach comprising a two-pronged grounded theory (manpower and ChatGPT-4o), fuzzy interpretive structural modeling-matrix of cross-impact multiplications (FISM-MICMAC), and analytic network process (ANP). The analysis demonstrates that 21 factors are organized into a seven-tier hierarchy in the FISM model. Based on the FISM-ANP analysis, the most significant factors include corporate scale, data-sharing technical maturity, compliance with cross-border data flow, financial cost, corporate profit, and corporate operation structure. Finally, a three-stage data-sharing promotion strategy is proposed. These findings pave the way for ICE managers and policymakers to formulate strategies when sharing data and release the value of data flow to align with the sustainability paradigm within the construction sector.