Leveraging Big Data to Develop Supply Chain Management Theory: The Case of Panel Data

Abstract

I ncreased data availability is poised to shape both business practice and supply chain management (SCM) research. This article addresses an issue that can arise when trying to use big data to answer academic research questions. This issue is that distilled data often have a panel structure whereby repeated measurements are available on one or more variables for a substantial number of subjects. Thus, to fully leverage the richness of big data for academic research, SCM scholars need an understanding regarding the different types of research questions answerable with panel data. In this article, we devise a framework detailing different types of research questions SCM scholars can answer with panel data. This framework provides a basis to categorize how SCM scholars have examined the services supply chain setting of health care with public data regarding hospital-level patient satisfaction. We extend prior research by testing a series of three questions not yet examined in this area by fitting a series of structured latent curve models to seven years of hospital-level patient satisfaction for nearly 4,000 hospitals. The discussion highlights theoretical and methodological challenges SCM scholars are likely to encounter as they use the panel data in their research.

Electronic Logging Device Compliance of Small and Medium Size Motor Carriers Prior to the December 18, 2017, Mandate

“…Using our approach measure to capture the passage of time to serve as a proxy for the strength of mechanisms (Astbury and Leeuw ) that are theorized to bring about empirical relations aligns with Miller et al. 's (, p. 194) statement, “the passage of time should not be viewed as a causal process per se. Rather, time's passage provides a tableau or fabric upon which processes (i.e., mechanisms) can operate to bring about the hypothesized relationships.”…”

“…This work also responds to Miller et al. 's () call for temporally focused theorizing that explores how firms’ actions and behaviors evolve over time.…”

Theorizing Supply Chains with Qualitative Big Data and Topic Modeling

“…Shifting the research lens from supply chains as static networks of organizations to a complex adaptive system is both theoretically and empirically daunting. Most supply chain researchers see supply chains as well‐defined bounded structures and focus on a single level of analysis at a single point in time and on specific organizations, products, or services (Miller et al, 2018). For the most part, supply chain researchers have modeled average, linear, causal behavior of “small causes lead to small effects” (Nair & Reed‐Tsochas, 2019: 89).…”

“…The advent of digital technologies has opened up opportunities for researchers to gather large volumes of data, often called Big Data, to theorize supply chains. Quantitative Big Data can inform supply chain theory because the large volume of granular data can describe all aspects of supply chains, including customer Web site visits, customer sentiments from social media, varying service levels, or evolving contractual ties and material flows in a supply chain (Miller, Ganster & Griffis, 2018; Mishra et al, 2018; Park, Bellamy & Basole, 2018).…”

Sustainability in Practice

“…In Miller et al. (), the researchers take a look at the potential for panel data to inform supply chain research. They examine data from nearly 4,000 hospitals to demonstrate both the promise and pitfalls to repeated measurement studies.…”