This Is AuburnElectronic Theses and Dissertations

A Three-essay Dissertation on Big Data Analytics Value Creation for Organizations and Their Supply Chains




Zhu, Suning

Type of Degree

PhD Dissertation


Systems and Technology


This dissertation consists of three essays. The first essay conceptualizes BDA capability in supply chain context based on two aspects: the level of analytics and the operational functions of supply chain. Content analysis technique was adopted to analyze existing academic and practical articles concerning BDA in supply chain management (SCM). A rigorous inductive approach was employed to synthesize the 129 articles and develop the data structure of BDA capability in SCM. The proposed data structure includes four aggregate BDA capability dimensions, twenty-two BDA capability constructs, and the measures of each construct. The findings of this study expand the current view of BDA in supply chain context and ground new empirical research in this field. Following the construct development and validation procedures proposed by Mckenzie, Podsakoff, and Podsakoff (2011), the second essay focuses on developing and validating a comprehensive instrument for measuring BDA capability in the supply chain domain. Building on the results from the first essay, BDA capability in SCM was developed into 22 first-order constructs that formed 4 second-order constructs. Measurement items were created to measure each first-order construct. After conducting face validity and content validity check, a set of data (n=137) was collected from supply chain practitioners to evaluate scale properties and refine measurement items. This study provides a comprehensive and detailed conceptualization of an instrument for BDA capability in SCM that can serve as a springboard for future empirical research to understand the antecedents and impacts of BDA capability on supply chains. Industry practitioners may adopt this instrument to evaluate their BDA capabilities and identify the capabilities they lack. The third essay is a longitudinal study on how organizations’ business analytics initiatives influence operational efficiency and business growth. Drawing upon dynamic capability and contingency theory, I conceptualize organizational BDA initiatives as a dynamic information processing capability which will bring competitive advantage to organizations. Additionally, industry factors (i.e. dynamism, munificence, and complexity) will moderate the relationship between BA initiatives and organizational performance. To test the research model, I collected secondary data from Lexis/Nexus and COMPUSTAT databases and constructed two dynamic panel data models. Using system generalized method of moments (system GMM), I found that: organizational BDA initiatives enhance operational efficiency and facilitate business growth; at lower level of industry dynamism and munificence, BDA initiatives have a greater impact on operational efficiency; at higher level of industry dynamism and complexity, BDA initiatives are associated with greater increase in business growth. These findings provide a theory-based understanding about the economic benefits of BDA and also offer guidance regarding what practitioners can expect from BDA initiatives and how firms can realize value from BDA given the characteristics of industries they are operating in.