Notes from the Editors: Redefining some methodological criteria for the journal⋆

• Date: 01 July 2015
• DOI: http://doi.org/10.1016/S0272-6963(15)00056-X
• Author: V. Daniel R. Guide,Mikko Ketokivi
• Published date: 01 July 2015

Journal of Operations Management 37 (2015) v–viii

Contents lists available at ScienceDirect

Journal of Operations Management

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

Editorial

Notes from the Editors: Redefining some methodological criteria for

the journal夽

There are many things in which Operations Management (OM)

researchers can take pride. Since the inception of empirical OM, we

have rigorously incorporated measurement reliability and valid-

ity into our analyses. In many respects, the OM literature is a few

steps ahead of its sister disciplines — incorporating measurement

error into analyses is perhaps the best example. We have also made

considerable progress in terms of theory development, whether by

way of case research or purely conceptual and theoretical anal-

ysis. Finally, recent developments in the area of problem solving

and design science demonstrate that OM scholars are genuinely

interested in solving actual managerial problems and remaining

practically relevant. These are all reasons to celebrate the progress

in empirical OM.

But there are a number of blind spots, many of which continue to

be reasons for rejections in the manuscript review process. The pur-

pose of this editorial is to describe some of these issues. Specifically,

there are a number of misunderstandings about some of the key

methods used in manuscripts submitted to us. There are also some

outdated practices that we want to discourage authors from using

in their manuscripts. These issues are discussed in this editorial, in

a roughly descending order of importance.

1. It is time to take causality seriously

We all know correlation does not establish causality. It is high

time we do something about this. We constantly get manuscripts —

based on cross-sectional surveys in particular — where the authors

make causal claims. We no longer send to the review process

manuscripts that uncritically interpret a cross-sectional correla-

tion of X and Y as support of a causal claim, or more mildly, that

the variance of X is driving the variance of Y. This applies to both

econometric and structural equation models.

The problem with assuming that the variance of X drives the

variance of Y is well documented. Ignoring the problem often

results in over-permissive tests of substantive hypotheses: we see

evidence for our hypotheses even when there is not any.

We now require all authors to take steps — theoretical or empir-

ical, preferably both — to address the problem of endogeneity. This

夽We thank John Antonakis, Mikko Rönkkö, Aleda Roth, Fabrizio Salvador, and

Suzanne de Treville for useful comments on an earlier draft of this editorial. All

errors and omissions are obviously our own.

is now a standard practice in most top-tier management journals,

and it is time for JOM, as a premier operations management journal,

to follow suit. The literature on endogeneity is massive, going back

almost a hundred years. Roberts and Whited (2013) offer a com-

prehensive summary of the key issues in the context of corporate

finance research. All the issues discussed are directly applicable to

OM research as well.

In a nutshell, the problem of endogeneity is this: when a

researcher is using non-experimental data to test the hypothesis

that X has an effect on Y, it is possible that the variance of X is not

exogenous but endogenous to the model. The end result is that the

model is misspecified. This in fact applies not just to cross-sectional

but even longitudinal research. Even if X is measured at t-1 and Y

at t, there could be an unobserved variable Z that affects X and t-1

andYatt.

In a recent manuscript submitted to us, authors hypothesized

that organizational integration drives employee commitment. Inte-

gration was assumed exogenous to commitment. This is a very

problematic assumption, because we have many reasons to believe

commitment could easily drive integration, making the variance of

organizational integration indeed endogenous to the model. The

consequence of endogeneity is asymptotic bias in parameter esti-

mation.

We must come to terms with the fact that plausible claims about

the direction and magnitude of an effect cannot rest on an analy-

sis that completely ignores endogeneity. If our inferences are to be

biased, they need to be biased toward being conservative. The prob-

lem of endogeneity often has just the opposite effect, it inflates our

results. We are not aware of any scientific principles that warrant

the use of over-permissive inference.

1.1. What can you do about this?

Examination of endogeneity starts with a simple question: What

is the source of the variance in the exogenous variables in my

model? So far JOM authors have been allowed simply to declare

that these sources are exogenous to the model. Authors must take

steps toward either demonstrating exogeneity or correcting for endo-

geneity. Both approaches have the common denominator: they call

for addressing assumptions that have thus far gone untested.

Endogeneity can probably never be completely eliminated from

empirical analysis, and it is well known that many “solutions” cre-

ate more problems than they solve (Murray, 2006). But there are no

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