Publication Type

PhD Dissertation

Version

publishedVersion

Publication Date

4-2025

Abstract

Digital economic industrial parks serve as significant vehicles for the development of the digital economy, as highlighted in the national 14th FiveYear Plan for Digital Economy Development (hereinafter referred to as "the Plan"). The Plan identifies the digital transformation of industrial parks and clusters as a key task in the broader industrial digital transformation. Intelligent parks represent the future trend of park development. Current data reveals that there are over 80,000 industrial parks across China, with revenues of the top 100 parks in 2023 reaching RMB 37.9 trillion. In 2022 alone, investment in intelligent parks exceeded RMB 300 billion, a figure indicating a vast market for digital and intelligent transformation in parks.

During the digital transformation of industrial parks, big data technology plays a crucial role. Practical cases demonstrate that the adoption of big data technology has improved the operational performance of these parks. This study aims to reveal the mechanisms by which big data drives performance enhancement in industrial parks and is structured into three parts. First, by reviewing existing literature and considering the context of big data technology proliferation, it reconstructs the theoretical framework of resource-based view (RBV). This framework encompasses three dimensions—organization, resources, and environment—and identifies six pathways: organizational effectiveness enhancement, strategic content enrichment, resource allocation optimization, data-based decision-making, environmental alignment improvement, and dynamic capability improvement. Second, based on this framework, antecedent conditions and the outcome variable are defined. This study designs a questionnaire through interviews and gathers data, utilizing a configuration analysis method that combines necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA) for empirical research. Finally, the study analyzes and discusses representative cases based on the empirical findings, offering both theoretical and practical insights.

This study draws three conclusions based on theoretical research and empirical analysis. First, big data-driven dual performance enhancement in industrial parks exhibits multi-configuration pathways. NCA reveals that there is no single pathway leading to enhanced dual performance of industrial parks; instead, six established pathways reflect a complex interplay of mutually reinforcing relationships within the theoretical analysis framework. Second, big data-driven dual performance enhancement in industrial parks presents both pathway variances and effectiveness thresholds. The fsQCA method identifies four configuration pathways leading to high dual performance: (1) conventional resource mobilization supported by organizational and strategic optimization; (2) agile resource mobilization where organizational optimization substitutes for data-based decision-making; (3) agile resource mobilization in which databased decision-making replaces organizational optimization; and (4) strategic dynamic orientation where organizational optimization substitutes for databased decision-making. These pathways exhibit differences. Core condition distribution indicates that optimizing resource allocation through big data is the most fundamental condition and represents the threshold for big data-driven dual performance enhancement in industrial parks. Third, the configuration patterns for financial and non-financial performance driven by big data in industrial parks are identical. The fsQCA analysis indicates the same pathways that lead to high dual performance, financial performance, and non-financial performance. The analysis also suggests four similar configuration pathways in terms of financial performance and non-financial performance. These findings imply no clear boundaries in the management of financial performance and nonfinancial performance at present. Despite the integration of emerging technologies such as big data, the management practices in industrial parks remain rooted in traditional performance management models. This situation is corroborated by the core condition of optimizing resource allocation.

This study presents two primary innovations: First, it develops a theoretical research framework for big data-driven dual performance of industrial parks based on strategic management theory. Current research on combination of big data and strategic management theory primarily focuses on the national, governmental, and corporate levels; however, there has been no study focused on big data-driven dual performance of industrial parks. This study establishes an analytical framework based on the strategic management theory and constructs a theoretical research framework that analyzes big datadriven dual performance enhancement in industrial parks. These frameworks provide theoretical reference and foundation for related studies. Second, this study reveals the mechanisms underlying big data-driven dual performance enhancement in industrial parks from a configuration analysis perspective. To date, no studies have systematically combined theoretical frameworks with empirical research to unveil how big data drives performance enhancement in industrial parks. Existing research on big data and performance evaluation often focuses on the influence of a single pathway on performance evaluation output. This study employs a directed questionnaire to collect data and utilizes fsQCA to elucidate the configuration pathways underlying big data-driven dual performance of industrial parks, thus offering new insights for related research.

Keywords

industrial park, big data, resource-based view, dual performance, fuzzy-set qualitative comparative analysis

Degree Awarded

Doctor of Business Administration (Accounting and Finance)

Discipline

Accounting

Supervisor(s)

CHEN, Xia

First Page

1

Last Page

134

Publisher

Singapore Management University

City or Country

Singapore

Copyright Owner and License

Author

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