The Event Study Method in Logistics Research: Overview and a Critical Analysis

The Event Study Method in Logistics Research: Overview and a Critical Analysis

Lincoln C. Wood, Jason X. Wang
Copyright: © 2018 |Pages: 23
DOI: 10.4018/IJAL.2018010104
(Individual Articles)
No Current Special Offers


Logistics researchers often want to understand how particular management changes or external factors influence a firm. While this can be accomplished using operational or survey data, the authors outline an alternative approach using the event study method where inferences are made with the estimated magnitude and direction of abnormal returns. The calculated abnormal returns can be used as a dependent variable in a cross-sectional regression to understand which managerial decisions may affect these outcomes. As the method remains little used by logistics researchers, the authors outline key assumptions and design considerations. They review recent articles and provide suggestions for logistics researchers improve the rigor of their research designs. This article aims to provide an overview of the method for logistics and supply chain researchers with a focus on developing the capability to design an effective study and to evaluate research articles to assess methodological weaknesses that may lead to untrustworthy results.
Article Preview

1. Introduction

The event study method is a valuable and powerful technique that has helped logistics researchers to better understand the impact of changes from different logistics management approaches. It is an analysis of the impact of a given event. The method allows researchers to determine whether or not there is an abnormal change in the stock return, above and beyond a change that is otherwise expected, associated with the event; that is, whether it is believed (by stock market investors) that the event will make a substantial difference to the fortunes of the firm. Examining these abnormal returns allows researchers to infer whether or not the event was useful or valuable for the firm, based or the magnitude, direction, and overall significance of the event. From this, a logistics manager would be able to understand costs associated with a negative event – this may help them to invest in preventative measures. Alternatively, they can more clearly understand the positive returns that may accrue from taking particular management actions (e.g., implementing new technologies or changing business models).

The method has been used extensively in accounting and finance research. In management literature, however, the approach has been more widely used to examine a range of different scenarios including how firms becoming sustainable (Cheung, 2011), the impact of new executives joining the firm (Hendricks, Hora, & Singhal, 2014), the impact and management of recalls (Chen, Ganesan, & Liu, 2009; Ni, Flynn, & Jacobs, 2014; Wood, Wang, Olesen, & Reiners, 2017b), research into sustainable practices in construction (Kajander, Sivunen, Vimpari, Pulkka, & Junnila, 2012), the response to food safety issues (Dai, Kong, & Wang, 2013; Hammoudi, Hoffmann, & Surry, 2009; Mazzocchi, Ragona, & Fritz, 2009), or the impact of outsourcing business processes (Duan, Grover, Roberts, & Balakrishnan, 2014).

For logistics management researchers, a major focus is determining whether or not a logistics technique or management approach is capable of providing a substantial benefit to a firm. While a small number of firms, as highlighted in case studies, can successfully make use of an approach does not mean that, overall, it is capable of providing a benefit when generalized to other firms and circumstances. While logistics research is sometimes conducted with surveys, it can prove challenging to extract objective and reliable data using surveys. In contrast, finance researchers have found it valuable to use the event study analysis to determine the impact of an event. While a finance specialist may be more interested in the impact of a stock split, an operations manager might focus on the impact of a quality improvement program.

Complete Article List

Search this Journal:
Volume 14: 1 Issue (2024): Forthcoming, Available for Pre-Order
Volume 13: 1 Issue (2023)
Volume 12: 2 Issues (2022): 1 Released, 1 Forthcoming
Volume 11: 2 Issues (2021)
Volume 10: 2 Issues (2020)
Volume 9: 2 Issues (2019)
Volume 8: 2 Issues (2018)
Volume 7: 2 Issues (2017)
Volume 6: 2 Issues (2016)
Volume 5: 2 Issues (2014)
Volume 4: 4 Issues (2013)
Volume 3: 4 Issues (2012)
Volume 2: 4 Issues (2011)
Volume 1: 4 Issues (2010)
View Complete Journal Contents Listing