Addressing endogeneity in operations management research: Recent developments, common problems, and directions for future research

• Author: Howard Hao‐Chun Chuang,Xin (David) Ding,Guanyi Lu,David Xiaosong Peng
• Date: 01 November 2018
• DOI: http://doi.org/10.1016/j.jom.2018.10.001
• Published date: 01 November 2018

Contents lists available at ScienceDirect

Journal of Operations Management

journal homepage: www.elsevier.com/locate/jom

Addressing endogeneity in operations management research: Recent

developments, common problems, and directions for future research

Guanyi Lu

a

, Xin (David) Ding

b,∗

, David Xiaosong Peng

c

, Howard Hao-Chun Chuang

d

a

College of Business, Florida State University, Tallahassee, FL, 32306, USA

b

Department of Supply Chain Management, Rutgers Business School of Newark and New Brunswick, Newark, NJ, 07102, USA

c

C.T. Bauer College of Business, University of Houston, Houston, TX, 77204, USA

d

College of Commerce, National Chengchi University, Taipei, 11605, Taiwan

ARTICLE INFO

This paper has been recommended for

acceptance by T Browning

Keywords:

Endogeneity

Literature review

Empirical research

Healthcare

Instrumental variable regression

ABSTRACT

Addressing endogeneity can be a challenging task given the different sources of endogeneity and their impacts

on empirical results. While premier business journals typically expect authors to rigorously address endogeneity,

this expectation is relatively new to many Operations Management (OM) scholars, as exemplified by a recent

editorial in Journal of Operations Management that calls for more rigorous treatment for endogeneity. This study

serves two purposes. First, we summarize recent OM literature with respect to the treatment for endogeneity by

reviewing studies published in leading OM journals between 2012 and 2017. The review provides evidence that

endogeneity problems have received increasing attention from OM scholars. However, we also find some

common problems that may render the chosen techniques for addressing endogeneity less effective and po-

tentially lead to biased analysis results. Second, since instrumental variable regression is the most prevalent

technique for dealing with endogeneity in the OM literature according to our review, we provide an empirical

illustration tailored to OM researchers for using instrumental variable regression in the post-design (data ana-

lysis) phase. Using variables from a publicly available healthcare dataset, our analysis sheds light on the im-

portance of examining instruments' quality and triangulating results based on more than one test/estimator.

1. Introduction

Regression modeling is one of the most widely used techniques in

empirical research. However, the validity of regression analysis results

is threatened by endogeneity on many occasions. In the context of re-

gression modeling, endogeneity refers to the problem where an ex-

planatory variable is correlated with the error term (Cameron and

Trivedi, 2005, p. 92), or disturbance term.

1

Endogeneity has become an

editorial concern in premier business journals. “Most rigorous academic

journals require, more or less as part of their editorial policy, that en-

dogeneity must be addressed somehow”(Ketokivi and Mclntosh, 2017,

p. 3). While addressing endogeneity has become a common practice in

empirical research in many fields, this practice is relatively new to

many Operations Management (OM) scholars. Addressing endogeneity

is a non-trivial task given the different sources of endogeneity and their

varying impacts on analysis results. The need for addressing en-

dogeneity has been highlighted in several leading business journals

(Larcker and Rusticus, 2010;Rossi, 2014), and recently in an editorial

published by Journal of Operations Management (JOM)(Guide and

Ketokivi, 2015).

To respond to the need for more rigorous endogeneity treatments in

OM research, we conduct this study with the following purposes. First,

we summarize recent OM literature with respect to the treatment for

endogeneity. In particular, we are interested in whether the recent call

for addressing endogeneity problems promotes more rigorous treatment

for endogeneity in empirical OM research. We review studies published

in leading OM journals between January 2012 and July 2017. The re-

view provides evidence that OM scholars are becoming more con-

scientious about potential endogeneity problems and are increasingly

addressing endogeneity with theoretical arguments and/or empirical

techniques. The review also identifies common issues related to the

choice of instruments in addressing endogeneity, among others.

Second, we provide an empirical illustration tailored to OM researchers

for using instrumental variable regression to address endogeneity.

Specifically, we demonstrate the application of a set of statistical tests

for detecting and addressing endogeneity using a hospital performance

https://doi.org/10.1016/j.jom.2018.10.001

Received 1 December 2017; Received in revised form 7 October 2018; Accepted 9 October 2018

∗

Corresponding author.

E-mail address: xding@business.rutgers.edu (X.D. Ding).

1

In the remainder of this paper, we use the label “disturbance term”since it is more appropriate than “error term”(Ketokivi and McIntosh, 2017).

Journal of Operations Management 64 (2018) 53–64

Available online 26 October 2018

0272-6963/ © 2018 Elsevier B.V. All rights reserved.

T