| Author: | Zhang, Yi |
| Title: | Conducting research on big data cooperative assets from a time value perspective |
| Advisors: | Fong, S. W. Patrick (BRE) Shen, Jeff (BRE) |
| Degree: | Ph.D. |
| Year: | 2025 |
| Department: | Department of Building and Real Estate |
| Pages: | xvi, 134 pages : color illustrations |
| Language: | English |
| Abstract: | Digital transformation has attracted the attention of a growing number of scholars. Increasing turbulence and pressures from the external environment, such as advances in next-generation technologies, changes in consumer group behavior, and the entry of heterogeneous competitors into the market, have led to a series of changes in the business ecosystem of companies and even entire industries (Hienorth et al., 2014; Kavadias et al., 2016; Atlur et al., 2018). Digital transformation is one way for companies to gain lasting competitive advantages (Govindarajan & Immelt, 2019). Firms can achieve digital transformation by meeting the needs of their users (Porter & Heppelmann, 2014; Wulf et al., 2017). In addition, value co-creation between enterprises and users using digital technologies is an adaptive digital transformation approach (Hansen & Sia, 2015; Saldanha et al., 2017; Yeow et al., 2020). The construction industry is one of the important pillar industries of the economy, and its development maintains a close relationship with the scale of social investment in fixed assets. In China, for example, the proportion of the value added of the construction industry to the gross domestic product (GDP) has remained at a high level over the years. For example, the added value of the construction industry reached 7.2% of GDP in 2020, and the added value of the construction industry reached 8.0138 trillion yuan in 2021, accounting for 7% of GDP, which fully highlights its role in promoting economic development. Therefore, the digital transformation of the construction industry has received attention. Digital transformation can promote the upgrading of the construction industry and reshape its core values. Through digital transformation, enterprises are able to utilize resources more efficiently, improve core competitiveness and management level, while promoting the improvement of project management and operation level, and enhancing eco-collaboration ability, thus creating greater value. Digital transformation has become a must for the high-quality development of the construction industry. Construction companies are able to realize digital transformation by using digital technology to create value with users. For example, using digital technology, construction companies can collect and analyze users' needs and preferences, so as to provide more personalized building design and services. Through virtual reality (VR) and augmented reality (AR) technologies, users can participate in the design in the early stages of the project, viewing and adjusting the design in real time to ensure that the final building meets the expectations of users. Using big data as a digital transformation scenario, some studies have proposed that big data cooperative assets are the product of value co-creation and have identified the characteristics of the value that can be generated in firm-user collaborations (e.g., Xie et al., 2016). Nevertheless, there is a scarcity of research on the value realization mechanism of big data cooperative assets within the framework of digital transformation. This study explores the value creation path of big data cooperative assets in the context of adaptive digital transformation in firms. First, from a time value perspective (i.e., immediate and potential value), this study analyzes the value characteristics, and realization mechanisms of big data cooperative assets in the context of enterprise-user interactions, and the cases of service industry and construction enterprises are especially cited to support the theoretical explanation. Second, based on users' revenue preferences, this study constructs a game-theoretic model of the time value realization mechanism of big data cooperative assets under conditions of information asymmetry, and reveals the optimal mechanism selection strategy for enterprises. Finally, the study conducts empirical analyses on whether selecting strategies to match user preferences can help firms realize the different time values of big data cooperative assets. The main findings of this research are as follows. First, from a time perspective, big data cooperative assets can generate immediate value and potential value. Specifically, big data cooperative assets with immediate value represent value-transforming big data cooperative assets, while big data cooperative assets with potential value represent value-accumulating big data cooperative assets. Furthermore, in practice, in the contexts of enterprise- and user-led interactions, value-transforming big data cooperative assets mainly involve enterprises' value-transforming big data cooperative assets and users' value-transforming big data cooperative assets. In this case, the value realization mechanism is a data link mechanism that creates close-loop data links to strengthen accurate and timely delivery of user data, capture user demand in real time to achieve instant matching between supply and demand, and emphasize data integration, agile analysis, and rapid feedback. Value-accumulating big data cooperative assets mainly involve enterprises' value-accumulating big data cooperative assets and users' value-accumulating big data cooperative assets. In this case, the value realization mechanism is a data insight mechanism that focuses on integrating multi-dimensional data to explore potential user needs and promote user participation in value co-creation through multi-source fusion, innovative insights, and in-depth development. Second, under conditions of information asymmetry, the game-theoretic model suggests that users with short-term revenue preferences transmits the correct signal of their preferences, and enterprises select the data link mechanism, which enables both enterprises and users to obtain short-term revenue. In addition, users with long-term revenue preferences accurately reveal their preferences, and enterprises use the data insight mechanism, which enables both enterprises and users to maximize their long-term value. Third, empirical analyses confirm that when users have different revenue preferences, enterprises choose to match their short-term revenue preferences to increase the immediate value of big data cooperative assets through the data link mechanism and choose to match users' long-term revenue preferences to increase the potential value of big data cooperative assets through the data insight mechanism. This study provides multiple contributions to the existing literatures. First, this study is the first to develop a classification framework for big data cooperative assets from a time perspective. Specifically, this study defines big data cooperative assets with immediate value as value-transforming big data cooperative assets, and big data cooperative assets with potential value as value-accumulating big data cooperative assets. Furthermore, based on specific interaction situations, the study explores the value realization mechanism of value-transforming and value-accumulating big data cooperative assets as data links and data insights, respectively. Therefore, it provides a theoretical foundation for subsequent research on how to measure the value of big data cooperative assets. Second, this study enhances the previous research on the value realization of big data cooperative assets by developing a game-theoretic model that incorporates information asymmetry into the value co-creation mechanism of big data cooperative assets. This study is the first to reveal that to improve their net utility and reputation, users correctly signal their revenue preferences to enterprises, which enables enterprises to choose the relevant mechanism (i.e., data link or data insight mechanism) to realize the value of big data cooperative assets. Finally, the study conducts empirical analyses on matching the mechanisms and value of big data cooperative assets using 5,364 listed enterprises and 42,420 firm-year observations. The results validate the correlation between the time value of big data cooperative assets and the compatibility of users' revenue preferences. Unlike traditional case studies and other qualitative methods, this study is the first to empirically test the value of big data cooperative assets using a large sample of firms and industries. The study also has practical implications. First, the classification framework for big data cooperative assets and the value realization mechanism proposed in this study can effectively help enterprises to improve their big data utilization efficiency, especially in terms of data acquisition, integration, processing, application, service, and promotion. Second, this study assesses the impact of users' revenue preferences on the value of enterprises' big data cooperative assets under conditions of information asymmetry. The findings provide insights for enterprises to encourage data-enabled user engagement and data sharing. Overall, the findings of this study can help enterprises better realize the short-term and long-term value of big data cooperative assets. |
| Rights: | All rights reserved |
| Access: | open access |
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