aula-3-2009-operations-management-research-2008

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Operations management
research: evolution and
alternative future paths
Christopher W. Craighead
Department of Supply Chain and Information Systems,
Smeal College of Business, The Pennsylvania State University, University Park,
Pennsylvania, USA, and
Jack Meredith
Babcock Graduate School of Management, Wake Forest University,
Winston-Salem, North Carolina, USA
Abstract
Purpose – This paper aims to investigate the evolution of operations management (OM) research
along two major dimensions from 1977 to 2003 and discusses possible paths for research progression
in the future.
Design/methodology/approach – To identify OM research papers, a careful definition of OM
research was constructed based initially on earlier work and then more precisely extended through
empirical analysis. The research on OM builds on a previous study that took snapshots of OM
research in 1977 and 1987. It then extends and updates it through a content analysis of 593 articles
published in 1995 and 2003 in five journals recognized for publishing OM research.
Findings – The overall results illustrate that OM has evolved from heavily rationalistic, axiomatic
analyses based on artificial reconstructions of reality toward more interpretive analyses based on
natural observations of reality.
Research limitations/implications – As the OM field continues to evolve, it is important to
monitor and reassess published research to discern its changing dimensions. While this effort is not an
exhaustive review of all OM research and does not consider all relevant journals and years, it does
offer the “big picture” perspective needed for analyzing changing research approaches in the field.
Practical implications – The research provides an analysis of the evolution of knowledge creation
within the field and possible paths for its future development. The practical implications are that as
research becomes more interpretive and observation-based, the findings will have more relevance for
managers and the problems they face.
Originality/value – While several authors have analyzed the OM field relative to select research
methods and journals, this paper provides a broader and more encompassing view of OM research
along two important research dimensions: the researcher’s framework and the source of the data.
Keywords Research methods, Operations management, Function evaluation
Paper type Literature review
Introduction
Research in operations management (OM) has changed dramatically over the years.
Originally concerned with industrial management methods and procedures for
improving processes (Buffa, 1980, p. 1), the field moved through relatively simple
mathematical techniques for independent process improvements such as assembly
line balancing and job shop scheduling to more sophisticated management science
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0144-3577.htm
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Received February 2007
Revised March 2008
Accepted April 2008
International Journal of Operations &
Production Management
Vol. 28 No. 8, 2008
pp. 710-726
q Emerald Group Publishing Limited
0144-3577
DOI 10.1108/01443570810888625
techniques for optimizing flows, blends, and resource allocations. More recently, there
has been a movement toward a more diverse set of empirical (i.e. based on direct sense
experiences or observations) and even interpretive frameworks based on
surveys/questionnaires, case/field studies, and interviews. Moreover, we are also
seeing changes in the means of data collection such as the use of postal, e-mail, and
internet communication, direct personal contact with managers, and even personal
observation of the unit of analysis such as when a plant or manufacturing cell is
involved.
The purposes of this paper are to first track the evolution of OM research as
published in several top journals and then discuss various alternative paths that may
define the future evolution of research in our field. We are particularly interested in two
patterns of OM research:
(1) the rationalistic versus interpretive orientation of the researcher; and
(2) whether the researcher desires observational or artificial data for conducting
the research.
We will elaborate on these two patterns shortly but we wish to emphasize that we are
not talking about research methods, though methods naturally embody these two
patterns of interest.
Background
In terms of research in OM, 1980 marked an epoch in the field. Two new journals
devoted to research solely in OM started publication: International Journal of
Operations & Production Management (IJOPM) in Europe and Journal of Operations
Management ( JOM ) in America. Included in that first issue of JOM were two seminal
articles written by recognized leaders in the field, Elwood Buffa and Richard Chase.
They summarized the past history of both research content and process in the field,
and both called for major changes in research approaches. Buffa (1980) forecast that
future OM research would move away from mathematical optimization, which we
would characterize as being a heavily rationalistic treatment of artificial
reconstructions of the situation of interest. Likewise, Chase (1980) noted the
prevalence of mathematical/computer modeling, the unsophisticated research designs,
and the paucity of “macro-oriented” research. The narrowness of OM research (“micro”
in the terms of Chase, 1980) was reemphasized by Miller et al. (1981).
In 1989, Meredith et al. (1989) defined a “Research Matrix” (Meredith et al., 1989,
Figure 3, p. 309) intended to more explicitly describe the character of research
approaches other than just listing various research methods. The axes of the matrix
consisted of two separate dimensions:
(1) the researcher’s framework ranging from rational (i.e. highly deductive,
axiomatic) to existential (inductive, interpretive); and
(2) the source of the data ranging from natural (i.e. empirical, directly observed) to
artificial (typically hypothetical reconstruction).
These two dimensions better identify research movement in a field because selected
research methods (e.g. surveys, mathematical modeling, interviews) inherently
embody both the researcher’s framework as well as how the researcher obtains data for
analysis. For example, the researcher’s framework may be closer to rational than, say,
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interpretive and thus be at the logical positivist/empiricist level, favoring, for example,
experimentation. But to obtain data, the researcher can experiment either through
direct observation of human subjects during laboratory experiments, or by artificial
reconstruction using computer simulation. Similarly, a researcher may be dedicated to
artificial reconstruction for obtaining data, but could use mathematical modeling if she
or he held a logical positivist/empiricist’s framework or conceptual modeling with an
interpretivist’s framework.
The Meredith et al. (1989) paper identified a series of alternative research
approaches in OM and plotted the trends of three OM research journals – JOM,
Management Science (MS), and Decision Sciences (DS) – during the years 1977 and
1987 on the axes of the Research Matrix. Almost two-thirds of the papers
were classified in the highly rational (axiomatic) category, almost one-third were in the
adjacent logical positivist/empiricist category, and 9 percent were in the interpretive
category. There seemed to be a slight shift toward the existential end of the scale over
the decade. The great majority (93 percent) of the papers fell in the artificial end of the
natural-artificial continuum and there was no significant change over the decade.
In 1993, Neely (1993) used a modification of Chase’s categorization to examine all
the articles published in IJOPM during the decade of the 1980s. His objective was to see
if the research content and research processes of European (primarily) OM publications
had changed substantially over the decade. He found that while the content had
changedfrom a focus on small, hard issues to larger (“macro,” in Chase’s terminology),
softer issues, there had been no discernable change in the research processes. He
speculated that the reason may have been because research content is often driven by
the P/OM community and environment, whereas the research process (framework and
data source) is selected by the individual doing the investigation and reflects personal
preference and situational needs.
The previously discussed literature, along with many other analyses
(Amoako-Gyampah and Meredith, 1989; Scudder and Hill, 1998; Pannirselvam et al.,
1999; Rungtusanatham et al., 2003; Chopra et al., 2004), has provided valuable insights
into selected aspects of OM research. However, most of the analyses have been
narrowly focused in one way or another, such as those that have looked at a particular
topic (Voss, 2005), a select research method (Malhotra and Grover, 1998; McCutcheon
and Meredith, 1993), institutional productivity (Malhotra and Kher, 1996; Young et al.,
1996), or particular journals (Chopra et al., 2004; Pilkington and Fitzgerald, 2006).
Although all of these have been informative, they have typically centered on topics,
methods, journals, researchers, schools, or some other limited aspect of the field. There
is a need to examine OM research in a larger, more philosophical framework along
multiple dimensions, such as those selected in the Meredith et al. (1989) study: the
perspective of the researcher and the data source. The purpose of this study is to
provide such a broader, more all-encompassing analysis of research in the field.
Research study
As noted earlier, the purpose of this research is to track the evolution of OM research with
a focus on two dimensions of the research from the past to the present. It might be noted
that many other business fields have conducted the same type of analysis during their
evolution. For example, Information Systems has had a long history of attempting to
define its boundaries of research (Agarwal and Lucas, 2005; Benbasat and Zmud, 2004;
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Benbasat and Weber, 1996; Orlikowski and Iacono, 2001; Whinston and Geng, 2004),
even though it is an extremely young field.
Journal selection
We wished to expand the journals included in the study beyond the three in the
Meredith et al. (1989) paper. We used Barman et al. (2001) which captured journal
perceptions from US scholars and Soteriou et al. (1999) which captured European
perceptions. Specifically, we took the top journals from Table 5 in both Barman et al.
(2001, p. 376) and Soteriou et al. (1999, p. 232). We felt that by including journals that
are highly regarded by both US and European scholars, our research would be less
biased toward any single view of acceptable OM research outlets. The final list of
journals included the original three – MS, DS, and JOM – as well as two other
well-recognized journals: IJOPM, and Production and Operations Management (POM).
It should be noted that by selecting highly-regarded (i.e. by established scholars)
outlets, we are potentially biasing our results against new approaches (i.e. the choice
and use of various research methods). Hence, our results about the evolution of OM
research could fall on the more conservative side of change.
Article inclusion
Owing to the journals selected (i.e. DS and MS are interdisciplinary), it was necessary
to extract articles that were primarily OM rather than another discipline. Our approach
was to start with the definitions of OM research employed by other researchers
(Young et al., 1996; Malhotra and Kher, 1996) as our initial set of criteria for article
inclusion. The initial criteria involved lengthy discussion and underwent several
revisions based on the results of several pilot rounds.
We first decided that each article had to contribute to only OM research, thus
eliminating introductions to special issues, articles that focused on teaching or
curriculum issues (including research on teaching), and articles that focused on the
discipline of the OM field itself such as publication productivity rankings and journal
rankings. To be included in our study, an article’s focus must either be on an OM topic
(see A, below) or on OM research itself (B):
A. Similar to Malhotra and Kher (1996), the article’s major emphasis had to fall within the
Operations Management Body of Knowledge (OMBOK), as defined (e.g. Young et al., 1996) by
the major topics in OM textbooks, rather than within management science, engineering,
economics, or other allied field. “Major emphasis” means the primary focus of the paper,
rather than the strict number of pages, title, intent of the work, types of references, or some
other mechanical characteristic of the paper. In those often difficult cases where a
quantitative model was a substantial portion of the paper, the article would be considered an
Operations Management paper if either substantial insights were provided into the OMBOK
in terms of better understanding the relationships and concepts that form the foundations of
Operations Management, or substantial guidance was provided for OM managers based on
the outcome of the research.
B. The article had to analyze and contribute to the research being conducted in OM, though
not necessarily restricted to a specific topic in the OMBOK. This would thus include analyses
of the literature and research methodologies as long as the analysis culminated in substantial
recommendations for improving OM research.
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OM research evolution dimensions
To track the evolution of OM research, we chose to update and extend the information
contained in Meredith et al. (1989) which took “snapshots” of OM research in 1977 and
1987. As the four authors did in their research in 1989, we classified the OM research
papers into the cells of their Research Matrix whose axes identify the degree to which
the papers are rationally or existentially oriented (henceforth referred to as the
axiomatic/interpretive dimension), and whether they employ direct observations of the
object reality or artificial reconstructions (henceforth referred to as the natural/artificial
dimension). However, as in the 1989 study, we did not find any papers that could be
classified as “critical theory” so, in the interest of parsimony, we have deleted that row
on the matrix. This framework was originally devised to be a simple descriptive
measure of research rather than prescriptive. Other than the assumption that research
cycles through the process of description ! explanation ! testing (see original
paper), there was no indication that research should be balanced across the two axes,
nor should a field’s research evolve in any specific direction along either of the axes.
We summarize the definitions for each of the dimensions here; for further
discussion, see Meredith et al., 1989, pp. 305-8. Relative to the axiomatic/interpretive
dimension, we categorized each article as either:
. Axiomatic. The axiomatic perspective represents the theorem-proof world of research,
as well as reasoning and logic models. Also, normative (e.g. mathematical
programming) and descriptive (e.g. queuing) models tend to fall in this category.
. Logical positivist/empiricist. This perspective assumes that the phenomenon
under study can be isolated from the context in which it occurs. The research tends
to be deductive and objective with the findings directly based on the data collected.
. Interpretive. Interpretive researchers focus on people, context, and concepts
rather than objects, with an emphasis on meanings and interpretations rather
than behavior. Interpretive researchers explain by placing behaviors in a broader
context in which the behaviors make sense. This differs from the positivist/
empiricist approach in that the research tends to be more inductive and
subjective, the purpose being to understand how others construe, conceptualize,
and understand events and concepts.
Relative to the natural/artificial dimension,we categorized each article as either:
. Object reality. This refers to direct observation by the researcher of the
phenomenon and thus assumes that there is an objective reality and that human
senses can detect it. The observations may be subjected to formal structured
analysis, as with experimentation, or to interpretation as with action research.
. People’s perceptions of object reality. This orientation relates to research
conducted “through somebody else’s eyes,” as with surveys, historical accounts,
or even introspective reflection. The primary concern is with the accurate
representation of the perceptions of individuals exposed to the phenomenon.
. Artificial reconstruction of object reality. This approach is used in those modeling
approaches where the object reality is recast, based on the researcher’s own
belief, into another form that is more appropriate for testing and
experimentation, such as analytical, descriptive, or physical models, computer
and laboratory simulations, or information constructs.
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To give a sense of the trend in these two research dimensions in the field, our research
extends the original analysis over even lengths of time, every eight years, from 1987 to
1995 to 2003. We chose an eight-year increment to allow for a fairly consistent timeline
with the Meredith et al. (1989) analysis, but also to enable electronic access to all
articles in all journals. We also wanted to have: at least two additional data points;
equally sized increments; and a relatively recent complete year electronically indexed
as the final snapshot.
Method
As discussed, the intent of the paper was to track the evolution of OM research by
using the research matrix framework. In general, the method of the study involved
using two author-coders to classify the OM research articles of the 1995 and 2003
issues of the five journals (specified above) into the framework. During those two
years, there were a total of 593 articles contained in the five journals. Both the
determination of article inclusion and of the “location” of the article in the framework
(3 £ 3 matrix) was accomplished via content analysis of all 593 articles.
The process for conducting content analysis, which was adapted from Weber (1990)
and Neuendorf (2002), is shown in Figure 1. Owing to the importance of reliable results,
content analysis was designed to guard against threats to reliability (Neuendorf, 2002).
Phase 1 involved variable definition, pilot testing and coder training (Figure 1).
We used articles published in 1993 (year arbitrarily chosen) from all five journals
during this phase of the study. In phase 2, all articles of the 1995 issues of the five
journals were independently classified as either OM research or not. Upon completion,
agreements and disagreements were tabulated and the intercoder crude agreement
values (percent of agreement) and intercoder reliabilities using Cohen’s k (Cohen, 1960)
were calculated. Disagreements were then resolved and all of the 1995 articles
(n ¼ 319) were classified as OM research or not. Each of the OM research articles was
then independently classified along both dimensions of the research matrix. Once
again, agreements and disagreements were tabulated, intercoder crude agreement
(percent of agreement), and intercoder reliability using Cohen’s k (Cohen, 1960) were
calculated. Disagreements were then resolved and the 1995 OM research articles
were classified along the two Research Matrix dimensions. We then repeated the entire
phase 2 process on the 2003 articles (n ¼ 274).
Results
Intercoder agreement and reliability
Table I contains the results relative to crude intercoder agreement (percent agreement)
and intercoder reliability using Cohen’s k (Cohen, 1960). As mandated by Neuendorf
(2002), intercoder agreement and reliability are reported separately for each variable
rather than aggregating the results to a single measure. Also, the reliability measures
were calculated prior to discussing disagreements, as advised by Weber (1990). Our
results relative to both crude agreement and Cohen’s k appear to be acceptable based
on the standards set forth by Popping (1988), Riffe et al. (1998), Banerjee et al. (1999)
and Neuendorf (2002). Additionally, we investigated the face validity of our selection
criteria by distributing our article inclusion criteria to several of our colleagues. All felt
that it was appropriate given the intent of this study. To provide evidence of the
robustness of the matrix classification criteria, we also ran a completely separate
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Coding
Number
of categories
Crude intercoder
agreement (percent)
Intercoder
reliability (Cohen’s k)
Operations management – Y/N 2 97.98 0.96
Axiomatic/interpretive dimension 4 94.06 0.90
Natural/artificial dimension 3 93.73 0.89
Table I.
Intercoder performance
Figure 1.
Overview of content
analysis
Conceptualize/Define
Variables
Create Codebook & Coding
Form
Pilot Variable Definitions &
Train Coders
Are Pilot Results
Satisfactory?
Assess Pilot Reliabilities
Code Articles:
OM Research
Yes/No
Code OM Research
Articles: Dimensions
Calculate Results
Discuss Disagreements &
Results
Calculate Intercoder
Reliability
Resolve OM Research
Yes/No Disagreements
Calculate Intercoder
Reliability
Resolve Dimensions
Disagreements
No
Yes
Phase 2: OM
Research Coding
Phase 1: Iterative
Variable
Definition/Pilot
Testing and Coder
Training
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analysis on articles in a different discipline using one of the co-authors of this study
and another colleague. Specifically, a small sample of articles was extracted from three
Logistics journals and classified along the two dimensions of the matrix. The crude
agreements were in the 80-83 percent range, which is in line with the first pilot round of
this study.
Articles extracted
Table II shows the results relative to the research content (primarily OM versus
another field) of each journal for 1995 and 2003. The table shows both the frequency of
OM research articles and the percentage of the total articles represented. Overall out of
the 319 articles in 1995, 155 (48.59 percent) of them were classified as OM research.
Similar results were found for 2003, where 148 out of 274 articles (54.10 percent) were
classified as OM research.
Research dimensions
We now take a look at the results relative to the research matrix and then take a closer
look along each of the matrix’s dimensions. To demonstrate the significance (or lack
thereof) of the changes, we ran a series of x 2-tests. There is a debate among
statisticians concerning the expected frequencies of the cells. As Levine et al. (2000)
note, some researchers argue that no more than 20 percent of the cells should contain
expected frequencies below 5 and none should be below 1 (Dixon and Massey, 1983).
Others have found that the test provides accurate results as long as the expected
frequency for each cell is greater than, or equal to, 0.5 (Lewontin and Felsenstein, 1965).
We utilized a “compromise” between these two extremes as prescribed by Levine et al.
(2000) that all expected cell counts be at least 1.
Table III shows both the 1977 and 1987 results reported in Meredith et al. (1989) and
the results of our content analysis of 1995 and 2003 relative to the research matrix. The
results of a x 2-test indicated that there were significant ( p , 0.01) differences relative
to the distribution of the articles across the four selected years. The numbers within
each cell represent the percentage of OM research articles published in the journal for
that given year. For example, 82 percent of the MS OM research articles published in
2003 was classified as axiomatic-artificial reconstruction. The total (shown as “Tot”) is
1995 2003
Journal OM Other Total OM Other Total
Management Science 21 (14.38) 125 (85.62) 146 17 (15.32) 94 (84.68) 111
Decision Sciences 9 (25.71) 26 (74.29) 35 3 (10.34) 26 (89.66) 29
Journal of Operations Management 18 (90.00) 2 (10.00) 20 33(97.06) 1 (2.94) 34
International Journal of Operations &
Production Management 91 (96.81) 3 (3.19) 94 66 (95.65) 3 (4.35) 69
Production and Operations
Management 16 (66.67) 8 (33.33) 24 29 (93.55) 2 (6.45) 32
Total 155 (48.59) 164 (51.41) 319 148 (54.10) 126 (45.99) 274
Note: Numbers given in parentheses represent percents
Table II.
Articles by journal
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a weighted percentage (based on number of OM articles) average across all five
journals. Thus, 14 percent of all of the OM research articles published in the focal
journals during 2003 were classified as axiomatic-artificial reconstruction. The
measure “Orig” represents a weighted average of the articles in JOM, MS, and DS.
Although we sought a broader list of journals than that used in the original article,
we thought it would be useful to isolate the results of those three original journals
(MS, DS, and JOM) to allow an “apples to apples” comparison over the years. For ease
of discussion, each cell will be referred to by the first two letters of each category
(Table III). Thus, axiomatic-artificial reconstruction will be referred to as Ax-AR,
logical positivist-people’s perceptions as LP-PP, and interpretive-direct observation as
In-DO.
In terms of the totals, OM research is, in general, moving out of the Ax-AR cell
(69 percent in 1977 versus only 14 percent in 2003) into more “natural” and logical
positivist or interpretive-based approaches (shown both by “Tot” and “Orig” in Ax-AR).
Direct observation
of object reality
People’s perceptions
of object reality
Artificial reconstruction
of object reality
Axiomatic Ax-AR
JRN 77 87 95 03
MS 60 70 57 82
DS 82 58 11 33
JOM – 33 0 0
IJOPM – – 10 0
POM – – 19 17
Tot 69 57 16 14
Orig 69 57 27 16
Logical positivist/ LP-DO LP-PP LP-AR
empiricist JRN 77 87 95 03 JRN 77 87 95 03 JRN 77 87 95 03
MS 0 0 0 0 MS 0 4 19 0 MS 28 15 14 12
DS 0 0 0 0 DS 0 0 44 33 DS 18 42 33 0
JOM – 0 0 6 JOM – 0 28 58 JOM – 47 61 21
IJOPM – – 0 0 IJOPM – – 16 30 IJOPM – – 38 12
POM – – 19 0 POM – – 13 21 POM – – 25 41
Tot 0 0 2 1 Tot 0 2 19 31 Tot 24 30 36 20
Orig 0 0 0 5 Orig 0 2 27 47 Orig 24 30 35 18
Interpretive In-DO In-PP In-AR
JRN 77 87 95 03 JRN 77 87 95 03 JRN 77 87 95 03
MS 8 7 0 0 MS 4 0 0 0 MS 0 4 10 6
DS 0 0 11 0 DS 0 0 0 0 DS 0 0 0 33
JOM – 7 6 3 JOM – 0 0 0 JOM – 13 6 12
IJOPM – – 8 35 IJOPM – – 3 3 IJOPM – – 24 20
POM – – 0 3 POM – – 0 0 POM – – 25 17
Tot 5 5 6 17 Tot 2 0 2 1 Tot 0 6 19 16
Orig 5 5 4 2 Orig 2 0 0 0 Orig 0 6 6 13
Notes: All numbers represent percent of year’s articles. For a given year, the percentages may not add
to 100 percent due to rounding; p , 0.01
Table III.
Evolution of OM research
within the matrix
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Likewise, the numbers in 2003 reflect a decrease in LP-AR oriented research (shown both
by “Tot” at 20 percent and “Orig” in LP-AR at 18 percent). The LP-PP cell, primarily
represented by survey research, has evolved to be the most dominant approach to OM
research as shown by the 31 percent for all five journals and 47 percent based on the
original set of journals (MS, DS, JOM). The In-AR cell illustrates an increase over the
years caused by an increasing number of literature review-type and conceptual
development-type of articles. The In-DO cell (where case research typically resides)
results are somewhat surprising. Although the total of all five journals reflects an
increase over time, this is mainly due to the receptivity of IJOPM to these forms of
research. When the original set of journals (MS, DS, JOM) is isolated, the percentage of
In-DO types of research is actually decreasing. This is surprising given the many calls
and editorials for more case-based research throughout the 1990s, particularly in JOM,
but even back in 1981 in DS (Miller et al., 1981, pp. 568-9). Perhaps there is more
case-based research in the research pipeline, but the overall results also show very little
OM research activity in the LP-DO (field studies and experiments) and In-PP (historical,
panels, etc.) cells.
The evolution of each journal’s results over the years displays some interesting
trends also. While overall, OM research is moving out of the Ax-AR cell, MS appears to
be gravitating towards this type of research. It might be noted that our 1995 data for
MS included a special issue on quality that contained quite a few papers that were
perhaps not consistent with the “norm” in MS and therefore probably skewed the
results for that year; yet, even excluding 1995, the percentage of Ax-AR for MS is still
increasing substantially. In contrast, DS appears to being moving out of the Ax-AR cell
into more empirically-based approaches. Likewise JOM has gravitated towards the
LP-PP cell (primarily due to the abundance of survey research) where almost 60 percent
of its research was classified in 2003. IJOPM has moved furthest in this evolution; for
example, along the Natural-Artificial dimension, 72 percent of its papers in 1995
were artificial reconstructions but only 32 percent in 2003. And along the
Axiomatic-Interpretive dimension, 35 percent of its papers in 1995 were interpretive
but had grown to 58 percent by 2003, with the In-DO cell itself representing the most
frequent classification in 2003 at 35 percent. Last, POM remains primarily in the
artificial column but has moved toward the LP-AR cell with 41 percent of its papers in
this classification in 2003.
In general, when looking at the results within the matrix, OM research appears to be
evolving out of the more artificial and axiomatic types of research that dominated in
1977 and 1987. We now take a closer look at each of these dimensions of the matrix.
Table IV represents the OM research over the selected years with a focus on the
Axiomatic/Interpretive dimension. The results of a x 2-test indicated that there were
significant ( p , .01) differences relative to the overall distribution of the articles across
the four selected years along this dimension. In addition to the overall test, we
performed a x 2-test comparing one selected year to the next (e.g. 1977 compared to
1987) along this dimension – the results of these tests are contained in the bottom row
of Table IV.
When the results are isolated, much of what was observed in the matrix of Table III
is highlighted. Overall, OM research appears to be evolving in the direction of more
Interpretive types of research. The percentage of Axiomatic has declined from
69 percent in 1977 to about 14 percent in 2003, whereas Interpretive rose from 7 percent
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to about 34 percent during those same years. Although most scholars (most recently,
Marshall Fisher, 2005) envision the nature of research as generally moving from
description (through case studies, for example) to explanation of principles to testing of
hypotheses to validating relationships (through surveys and interviews, possibly) to
implementation and optimization, the evolution of OM research seems to be going in
the opposite direction: from optimization to empirical positivism and possibly on
toward interpretivism. This is discussed further below.
Table V represents the OM research with a focus on the natural/artificial dimension.
The results of a x 2-test indicated that there were significant ( p , 0.01) differences
relative to the overall distribution of the articles across the years along this dimension.
In addition to the overall test, we performed a chi-square test comparing one selected
year to the next (e.g. 2003 compared to 1995) along this dimension – the results of these
tests are contained in the bottom row of Table V.
When the results are isolated, much of what was observed in the matrix of Table III
is again highlighted. Overall, OM research appears to be evolving towards more
Natural types of research. In 1977 and 1987, approximately 92 percent of the OM
research fell into the artificial category. These percentages dropped to 71 percent in
1995 and to 49 percent in 2003. However, it is perhaps premature to say that OM
Year [numbersshow count (percentage) for each year]
Dimension 1977 1987 1995 2003
Axiomatic 29 (69.05) 30 (56.60) 25 (16.13) 20 (13.51)
Logical positivist/empiricist 10 (23.81) 17 (32.08) 89 (57.42) 77 (52.03)
Interpretive 3 (7.14) 6 (11.32) 41 (26.45) 51 (34.46)
Total 42 (100) 53 (100) 155 (100) 148 (100)
Difference from previous
snapshot along this dimension
– Not significant
from 1977
Significant
( p , 0.01) from
1987
Not significant
from 1995
Notes: p , 0.01; numbers given in parentheses represent percents
Table IV.
Evolution along the
axiomatic/interpretive
dimension
Year [numbers show count (percentage) for each year]
Dimension 1977 1987 1995 2003
Artificial reconstruction of
object reality 39 (92.86) 49 (92.45) 110 (70.97) 73 (49.32)
People’s perception of object
reality 1 (2.38) 1 (1.89) 33 (21.29) 48 (32.43)
Direct observation of object
reality 2 (4.76) 3 (5.66) 12 (7.74) 27 (18.24)
Total 42 (100) 53 (100) 155 (100) 148 (100)
Difference from previous
snapshot along this dimension
– Not significant
from 1977
Significant
( p , 0.01) from
1987
Significant
( p , 0.01) from
1995
Notes: p , 0.01; numbers given in parentheses represent percents
Table V.
Evolution along the
natural/artificial
dimension
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research has evolved away from the artificial end of the dimension, given that more
OM research fell in the artificial category in 2003 than any other category.
Discussion
Our findings in the previous section indicate that as a field, OM has been moving
substantially away from both axiomatic and artificial research approaches since
around 1987. The “anti-axiomatic” trend is impressive: in 1987, 56 percent of the
research was axiomatic while by 1995 only 16 percent was. As a result, our
methodologies moved from 32 percent logical positivist/empiricist and 6 percent
interpretive in 1987 to 57 percent logical positivist/empiricist and 26 percent
interpretive in 1995, a statistically significant change. Although the logical
positivist/empiricist percentage did not change much between 1995 and 2003, the
interpretive percentage increased again, to 34 percent; however, the distribution as a
whole was not significantly different in 2003 compared to 1995. We can thus conclude
that the field has definitely moved to a greater focus on logical positivism/empiricism,
but whether it will continue to move on toward interpretivism, which still appears to be
growing, is difficult to say. In summary, axiomatic work appears to be stabilizing at
about a tenth of OM research and logical positivist/empiricist appears to be stabilizing
at about half, yet interpretive seems to still be growing, currently exceeding a third of
OM research.
Similarly, the field has also had an “anti-artificial” trend since 1987, but rather than
stabilizing, this trend appears to be continuing, with artificial methodologies falling
from 92 percent in 1987 to 71 percent in 1995 to 49 percent in 2003. Taking its place is
people’s perceptions, moving from 2 percent in 1987 to 32 percent in 2003, and direct
observation, moving from 6 percent in 1987 to 18 percent in 2003. It would appear that
research in OM is clearly moving toward a more balanced distribution across both
dimensions of the research matrix. As a result, we conclude that research in OM is still
dynamically evolving. Next we discuss some possible impacts of this evolution on
journals, industry, and research.
Outlets for operations management research
Many discussions have occurred throughout the years concerning the editorial
philosophies of the journals. Of special interest was whether it was best for a journal to
“specialize” in a particular methodology or aspect of research, or to embrace all forms
of research, or even publications (i.e. non-research: teaching papers, book reviews,
literature reviews, comments to the editor, etc.). The Academy of Management offers a
good example of the alternate route of specialization. When they recognized the need to
publish conceptual research papers, they created a new journal – Academy of
Management Review – which after a few years itself became academically respected as
a top quality journal. And when they later decided they needed a journal to speak to
executives, and one for papers on teaching, they again created new journals, rather
than defocusing their existing high-quality journals. Thus, it would appear that focus
on some aspect of research, rather than balance across areas, is key to attaining a
reputation for a top quality journal. How then do the five journals compare at this
point?
Tables II and III illustrate how both the trends in the publication of OM papers as
well as the types of papers has changed in the five journals studied here. It was noted in
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the previous section that as of 2003, JOM appears to be focusing on empirical research
(85 percent of all papers), IJOPM on interpretive research (58 percent), and POM on
artificial reconstruction research (75 percent). It was also noted that MS appears to have
become even more focused on the axiomatic-artificial reconstruction cell (82 percent,
but 100 percent in artificial reconstruction) although the majority of its papers have
always been published there. And DS also appears to be moving even further toward
artificial reconstruction, though this is not yet clear. In summary, it does appear that the
journals are tending to focus, some on the rational-existential dimension, some on the
natural-artificial dimension, and some on particular cells in both dimensions.
Moreover, we can probably also expect the continuing emergence and growth of
new focused journals in the field, such as the recent journals Manufacturing and
Service Operations Management and Journal of Supply Chain Management. Where the
functional fields of finance and marketing have dozens of journals devoted to their
research, OM still has only a handful. Although journals focusing on specific, popular
topics (supply chain, six sigma, enterprise resource management) are emerging, we
could well use a few more mainstream OM journals that are open to all OM topics.
Industry and managers
Both industry and academia are attempting to “tear down” some of their functional
boundaries, as evidenced by increasing emphasis on initiatives such as supply chain
management and enterprise resource planning where seamless flows are critical. To
the extent that OM research is moving more toward managers’ perceptions and direct
observation of these interfunctional problems with more real-world empirical data and
interpretation of that data, it would seem that OM research is more likely to have a
positive effect on industry and managers in the institutions we purport to be trying
to help.
Kurt Lewin’s adage that “There is nothing so practical as a good theory.” might well
turn out to be true for operations managers. Lundberg (2004, p. 8) maintains
that managers also use theories, but employ a conceptual frame that informs them on
what to do, how to do it, and what to do if it does not work. Scientists use a different
conceptual frame – one that helps us understand what reality is like, how it works, and
how to improve the conceptual frame. Lundberg concludes (p. 14) that the more
accurate, focused, and verified the frame; the more useful it is to both managers and
academics.
Early OM research was largely devoted to the description of factory (or wartime)
procedures. Principles were developed but hypotheses were not, research moving
directly to mathematical modeling and solution of the problem (for those problems
where mathematical modeling was appropriate). However, for many problems,
mathematical models were insufficient and now we find we must go back to observe
and collect data on the phenomenon/problem, perhaps hypothesize some relationships
to better understand it, and then perhaps in the future try to mathematically model it
for implementation and optimization. Hence, we are not moving backwards in our
research, but returning to our roots to address more difficultproblems.
Research opportunities
It seems apparent that future research in OM will continue its evolution. In that regard,
it will probably continue its anti-artificial and anti-axiomatic trends but how much
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further it will go toward direct observation and interpretivist approaches is unclear.
Still, it does not seem unlikely that, as noted above, the field will continue moving
toward its origins of direct observation, at least for a significant portion of research. In
fact, there are voices even now calling for more theory generation research (Handfield,
2006) and a movement away from the survey/questionnaire methodology
(Rungtusanatham, 2006).
From the data presented earlier, artificial reconstructions of reality and people’s
perceptions of reality (primarily through surveys) account for 84 percent of OM
research efforts published in 2003. This, in essence, may be interpreted to mean that
OM scholars are still “not leaving their offices” as they develop their research.
However, it is becoming more important that we, as scholars, directly observe the
reality that we wish to study, especially for developing rather than testing theory. As an
applied discipline, OM scholars cannot fully capture the complexity of these
phenomena through “remote” methods such as artificial reconstruction and/or surveys.
Our results do show movement toward more direct observation of the phenomenon
being studied, but we need to expand these efforts through such research methods as
case and field-based studies, action research, and experiments.
We are taught early in our academic careers that the “research question should
drive the research method.” But are we really letting the critical and relevant research
questions drive the method or have we only changed from an axiomatic hammer to a
survey hammer and now we are looking for “survey nails.” Based on our results, it
appears that OM is expanding its approaches, but there are still many methods that are
seldom used in OM such as focus groups, historical analysis, field experiments,
ethnography, action research, and many others, which might provide unique insights
into the OM body of knowledge. In addition, by expanding the domain of methods that
we use we should be able to address more pertinent OM research questions.
This study has brought some interesting insights to light and we have tried to
identify their implications. However, these implications need to be qualified by
reiterating that our results are based on only a selection of journals and years by only
two researchers. That is, not all OM research that has been published over the years
has been studied, nor all the journals that publish OM research. For example, we have
ignored books, monographs, and other outlets that may have had an important
influence on the trend in OM research. Nevertheless, we believe that a selection of
representative journals that are known for publishing OM research should be sufficient
for detecting trends in the field. Of more concern is the selection of “snapshot” years to
study, rather than looking at all the years, which would have required an excessive
amount of work and time. By selecting only particular years, the results could be
biased by the inclusion of special issues of the journals (as mentioned earlier), or even
random fluctuations in the nature of the papers published in the journals, particularly
the less frequently published journals (e.g. POM). Still, although the percentages may
be slightly off, we believe the overall trends are correct.
Another limitation is that our most recent year is 2003, a reflection of our desire to
have even increments of time spans (eight years), the inherent delays of electronic
indexing, study analysis and write-up time, and the unavoidable delays in publishing.
Another potential weakness was the use of only two coders, though this was countered
by close adherence to potential threats to validity and the high intercoder agreement
results, as explained earlier. And finally, we chose to use a quantitative approach for
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inferring the trends in OM research due to our desire to extend an earlier quantitative
study, but a qualitative study might have concluded with different trends, insights,
and implications. For example, we evaluated each article as being worth the same
“weight” in our analysis but a qualitative study could give different weights to more
“important” articles. There are many other interesting questions that arise relative to
qualitative approaches. What types of qualitative studies would yield the most insight
into OM research trends, and how might they differ? How might a qualitative study
weight individual articles according to their importance? Creative OM researchers will
probably see even more interesting questions worthy of study.
Some issues that were not explored here would be interesting for further study. For
example, are the new methods being applied to new issues and problems, or are they
being used across the realm of the OM body of knowledge? If the former, then we might
have one set of researchers applying their tools to one set of problems and others
applying their tools to different problems. This immediately raises another question:
are researchers applying the most appropriate tools to the questions they are studying,
or would we make more progress by applying some of the newer methods to some of
the older problems, and some of the older methods to some of the newer issues?
Concluding thoughts
The field of OM has seemed to many to be fractured, with one group holding on to
axiomatic research methods and another embracing empirical methods. We saw from
our analysis that the movement away from axiomatic approaches began around 1987
or thereabouts, so this “split” has been going on for at least 20 years. However, the
research here also shows that many other research methods have also been embraced,
even surging in popularity, though they have not become mainstream yet (and may
never). Perhaps, in some ways, OM is returning to its original basis of direct interaction
with the phenomenon under study, but bringing a new set of research tools to better
understand, and thereby improve, the processes being investigated. As a result, it
seems to us that the evolution of the field is not actually a split but rather an expanding
of research approaches and philosophies, and that this evolution will continue until
some fair balance is reached where, as many have preached, the question should
dictate the method. We believe that this is a sign of a healthy and maturing discipline!
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Corresponding author
Jack Meredith can be contacted at: jack.meredith@mba.wfu.edu
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