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International Journal of Operations & Production Management
Emerald Article: The importance of national culture in operations
management research
Mark Pagell, Jeffrey P. Katz, Chwen Sheu
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The importance of national
culture in operations management
research
Mark Pagell
College of Business, Oregon State University, Corvallis, Oregon, USA
Jeffrey P. Katz and Chwen Sheu
Department of Management, Kansas State University, Manhattan,
Kansas, USA
Abstract
Purpose – The purpose of this study is to test the validity of national culture as an explanatory
construct for international operations management decision-making.
Design/methodology/approach – National culture is multi-dimensional thereby allowing for
much finer grained comparisons than are possible when examining differences based purely on
geography or the level of industrialization. This proposition is examined from the theoretical
standpoint then empirically investigated using an existing database.
Findings – This article finds that national culture significantly explains international operations
management behaviors among similar manufacturing plants in the same industry located in different
cultures.
Originality/value – This study represents a first attempt at using national culture to explain
differences of operations decision-making.
Keywords Globalization, Culture, Operations management
Paper type Research paper
1. Introduction
One of the most important trends facing managers is the ever-increasing emphasis on
globalization. In the past, many organizations competed mainly within their home
country or region. Today firms purchase materials and services, develop alliances, and
sell their products all over the world. It is an unusual company that is not directly
impacted by globalization, and a rare manager who does not have to make decisions in
a global context.
There have been numerous calls for operations management research to take a more
global focus in order to remain relevant to the changes impacting industry
(Chakravarty et al., 1997; Frohlich and Dixon, 2001). In this paper we argue one of the
ways to ensure the relevance of the field and move to a more international research
perspective is to expand the constructs used to explain international differences in
operational decision-making. Specifically, we argue that operations management
research that has examined international issues has primarily focused on differences
between countries or regions (for example, Pagell and Sheu, 2001) while generally
over-looking the importance of national culture as a means to explain and predict
operations management in a global context (Govindarajan and Gupta, 2001). We argue,
consistent with Burgess (1995), that national culture is an equally relevant lens through
The Emerald Research Register for this journal is available at The current issue and full text archive of this journal is available at
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The importance
of national
culture
371
International Journal of Operations &
Production Management
Vol. 25 No. 4, 2005
pp. 371-394
qEmerald Group Publishing Limited
0144-3577
DOI 10.1108/01443570510585552
which the viewing of these systematic differences makes sense to advance the field of
operations management and related research.
This research attempts to increase our understanding of the systematic differences
that national culture may create in regards to operational decision making. The
purpose of this study is to test the validity of national culture as an explanatory
construct for international operations management decision-making. Such
understanding is of paramount importance as firms globalize, because senior
managers need to better understand plant managers’ preferences for certain
operational structures and infrastructures. These preferences may be based on the
culture the plant manager is operating in, which may differ significantly from the
culture of senior management (see for example, Hofstede et al., 1993). Therefore, in the
present study we link dominant models of national culture to an existing database and
provide evidence regarding how national culture affects operational decision-making
at the plant level.
Our paper is organized as follows. First, we review the way that international
research has traditionally been conducted in the field of operations management.
Second, we examine conducting research through the lens of cultural differences as
opposed to examining differences between countries or regions. Third, we introduce an
existing measure of culture that has a long history of use in other organizational fields.
Fourth, we examine an existing database to examine the macro influence of culture on
operational decisions. Finally, we discuss the importance of cultural distinctions to
operations management research.
2. Traditional international research in operations management
The amount of research in international operations management has increased
dramatically in the last decade. The purpose of this section is to identify and discuss
research designs used in the majority of international operations management
research. In the following paragraphs we draw from recent research for two reasons.
First, Prasad and Babbar (2000) provide a comprehensive review of the content of
international operations management research published between 1986 and 1997.
Therefore, more recent work may be less familiar to the reader. Second, and of greater
import, our aim is the further development of the field and recent publications give the
clearest indication of the present state of development.
Based on the work of Prasad and Babbar (2000), and our own review of the
literature, the majority of international operations management research falls into one
of three categories; single country studies, country comparisons, and regional
comparisons. Implicit in many single country designs is the assumption that research
is “international” if it investigates entities outside of North America. For instance the
Journal of Operations Management uses the keywords, “international issues,” to
describe Song and Parry’s (1999) study concerned with product development in Japan.
Similarly, articles by Badri et al. (2000) and Ward et al. (1995) are regarded as
international studies because they tested the same theory using data collected in the
United Arab Emirates and Singapore, respectively. In general, the purpose of such
single country studies is to test the generalizability of a theory in a new or different
setting, with an underlying supposition that there are likely to be differences between
countries.
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Country comparisons are an explicit attempt to test the generalizability of a theory
by applying the same survey or protocol in multiple settings. The majority of such
studies make comparisons between two countries; often the USA and another
industrialized country. For instance, Klassen and Angell (1998) compare
environmental management in the USA and Germany and conclude that regulatory
differences between countries influence the ability of manufacturing flexibility to
support environmental management. Ichniowski and Shaw (1999) examine human
resource practices in American and Japanese steel plants, with an explicit intent to test
the supposition that cultural differences make it difficult to import Japanese human
resource policies into American plants. Sheu and Wacker (2001) study the relationship
between planning and control systems and manufacturing competitiveness in the USA
and Japan. Overall, country comparisons and single country studies share the same
generalizability goal. But they also share a supposition that crossing borders
automatically means a substantive change in business practices, decision-making
strategies, and outcomes.
Regional comparisons serve the same purpose as country comparisons, but are
often done on a larger scale. Regional comparisons propose that the proximity of
geographical locations can be a key explanatory factor in operations management
decisions. For instance, Whybark (1997) suggests that North American firms are very
different from European firms regarding patterns of international expansions. Ettlie
(1997) reports regional differences (European, Asia, North America and South
America) in the performance of quality management. Many studies focusing on
regional differences were the result of few large-scale survey efforts conducted through
the Manufacturing Future Projects, Global Manufacturing Research Group (GMRG),
World Class Manufacturing Project, and Vision in World Class Manufacturing Project
(Roth et al., 1997). For example, Pagell and Sheu (2001) use GMRG data to examine
differences in supply chain practices and performance, splitting the globe into three
regions; North America, Europe, and Asia.
Comparing differences between regions of the world tends to have similar
limitations to conducting comparisons between countries, with two additional
important limitations. First, such comparisons assume that all countries in a region are
somehow similar in terms of culture and level of industrialization, an assumption that
is difficult to support in most regions of the world. Second, the identification of regions
tends to be judgmental, and therefore somewhat subjective. For instance, the GMRG
data set contains a number of Eastern European countries such as Poland and Hungry.
Pagell and Sheu (2001) considered these plants part of the European region, but they
could just as easily have been categorized into their own Eastern European region.
Similarly categorizing a country like Australia, geographically close to Asia but with
cultural roots in Europe, will be problematic in a study assessing regional differences.
These works, whether addressing one country outside the US or a number of global
regions, attempt to generalize theory from a single country to the entire world.
However, all of these international operations management studies failed to consider
international differences from the cultural perspective, a perspective that has been
studied in other areas of international business (see for example, Fernandez et al., 1997;
Kogut and Singh, 1988). The importance of culture in operational decision-making has
been noted but not empirically validated (Burgess, 1995; Starr, 1997). The following
section advances our key argument that comparisons of international operations
The importance
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373
management should consider cultural differences in addition to geographic
boundaries.
3. Cultural differences
Simply describing differences in the behavior of firms by country of origin suggests
that each individual country maintains a unique set of characteristics that will affect
decisions made within the firm. While this may be true in some situations, many
countries and their workers also share common factors such as language, religion,
customs, borders, beliefs, rules, and ethnic heritage. Therefore, calculating the number
of miles between countries, or manufacturing plants, to infer the magnitude of their
differences would be a critical error in logic that fails to systematically consider that
perceptions and actions of managers in each country differ (Bonvillian and Nowlin,
1994; Ronen and Shenkar, 1985).
For senior-level managers responsible for global manufacturing operations, the
important question to ask is: why might resident managers based in different countries
pursue significantly different approaches when faced with the same set of decisional
factors? While neo-classical economists would prefer that we assume all managers are
motivated by maximizing profits, international business researchers have suggested a
variety of possibilities beyond the classic profit-maximization that includes interest in
the protection of national culture, exploitation of unique products or resources, unique
approaches to internationalization, or development of national infrastructures
(Anderson and Coughlan, 1987; Hofstede, 1980; Keegan, 1984; Porter, 1990). That is,
over the past 20 years international management research has successfully linked
national culture to the bases for many of the cross-national differences in business
behaviors (Trompenaars and Hampden-Turner, 1998). More recently, the GLOBE
project has again focused attention on the role of culture and decision-making in terms
of nine dimensions with particular attention on leadership (House et al., 2002; Javidan
and House, 2001).
The expanding base of the related literature on national culture offers a number of
explanations for the differences that extend beyond the existence of borders. These
studies have sought to examine corporate strategies, such as international expansion,
business strategies, such as product low cost leadership versus differentiation, and
functional strategies, such as differences in the way the firm chooses to structure its
workers, their compensation, and the firm’s finances. For example, some of the earliest
studies suggested that differences in how managers make decisions based on their
locations was due to a combination of the home country of the company and the
physical location of the manager (Dunning and Pearce, 1982). Kogut and Singh (1988)
argued the way firms expand their base of operations is related to “national culture.”
Porter (1990) suggested that in addition to culture, the location of plants and
resource-based advantages related to the industry help explain patterns of global
competition within specific industries. And finally, Sekely and Collins (1988) posited
that cultural differences influence the capital structure of firms. All of these researchers
and their studies seem to agree that the decision-making of managers varies from
culture to culture as opposed to being based simply on geographical boundaries.
Several studies suggest the importance of cultural values in explaining the
differences in overall performance of the firm (Hofstede, 1980; Shane, 1993; Tse et al.,
1988). However, it has been suggested that performance differences at the firm level are
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partially due to different cultures defining desirable corporate performance in different
ways. For example, the business culture in the USA tends to focus on short-term
profitability, while Japanese firms have been more focused on building market share
over the long term. As partial evidence of different cultures defining performance
differently it has been empirically demonstrated that cultural differences explain more
than half of the cross-national difference in corporate growth patterns (Franke et al.,
1991).
More recent research has suggested that examining the underlying functional
decisions made within the firm will provide greater understanding of how national
culture affects the way managers make specific decisions intended to implement the
goals of the firm (Katz et al., 1999). For example, Nakata and Sivakumar (1996), in their
review of the literature regarding the role of national culture on new product
development, called for more empirical research examining the effects of national
culture on functional business strategies, such as operations. The authors cited the lack
of adequate research to properly assess the role of national culture on the decisions
made by managers in their respective operational functions.
Following this call for the initiation of research in the area of operations and related
decisions, the current study is an exploratory investigation of the link between national
culture and operations management. We focus on providing evidence that expanding
international research in operations management to include a cultural perspective will
increase our understanding of operations management decisions in a more systematic
global context. We argue that national culture may be a more effective way to assess
operational decisions than considering cross-border or regional differences based on
geographical boundaries.
The following section describes the research documenting differences in national
culture with a particular focus on how national culture is measured. The strong
construct and predictive validities of measures of national culture, combined with data
on plant-level decisions, provide an excellent opportunity to begin assessing the impact
of national culture on the field of operations management.
4. National culture: measures and models
Hofstede (1980) defines national culture as the collective mental programming of the
people in a national context. Through an empirical study examining more than 10,000
managers in over 50 countries, Hofstede developed a quantitative classification scheme
for measuring differences and similarities between national cultures. Hofstede
conducted a factor-analysis of responses to questions relating to work-place issues and
proposed that attitudes, beliefs, and behaviors could be categorized into four
dimensions. Each dimension provides a numerical score, generally between 0 and 100.
The four cultural dimensions that emerged from his study are
individualism-collectivism, masculinity-femininity, power distance, and uncertainty
avoidance.
Recent research has reconfirmed the construct validity and workplace relevance of
Hofstede’s four dimensions of national culture. For example, Merritt (2000) reports the
results of a survey of 9,400 workers in 19 countries confirming the predictive validity
of the measures. The author concludes that the successful replication confirms that
national culture exerts a significant influence on senior-level workers within a specific
industry that impacts their behavior beyond the level of professional factors that
The importance
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culture
375
would typically affect their behavior in the workplace. Specific descriptions of the
dimensions follow.
4.1 Power distance
The power distance dimension as defined by Hofstede (1980) reflects human inequality
in the areas of prestige, influence, wealth, and status in each culture. According to
Hofstede, the extent to which people accept unequal power is culture-based. Power
distance reflects the desirability or undesirability of inequality and of dependence
versus interdependence in society. Although both low and high power distance
countries have hierarchical power relationships, they can be interpreted differently.
For instance, in high power distance societies, such as India and France, power needs
less legitimization than in low power distance societies, such as Israel and Denmark.
Moreover, high power distance societies tend to use more coercive and referent power,
while low power distance societies use legitimate power more through reward systems
based on expertise. Conversely in low power distance cultures, a latent conflict and
basic mistrust between the powerful and powerless seems to prevail, whereas high
power distance cultures have relationships with higher levels of internal harmony
(Hofstede, 1980).
4.2 Uncertainty avoidance
Coping with the inevitable uncertainties in life is partly a non-rational process that
different individuals, organizations and societies resolve in different ways, including
the application of law, religion, rituals, rules, and uses of technology. All these cultural
inventions can make life seem more predictable or less uncertain. Uncertainty
avoidance measures the extent to which countries deem the pursuit of certainty
important. We expect cultures with high uncertainty avoidance scores to reveal a
preference for long-term predictability of rules, work arrangements, and relationships
as well as an avoidance of risk taking. On the other hand, a low uncertainty avoidance
score suggests a higher tolerance for uncertainty and, therefore, an acceptance of more
informal actions such as ad hoc negotiation for the settlement of disputes (Hofstede,
1980; Trompenaars, 1994), and a willingness to take risks. High uncertainty avoidance
cultures include Germany, Japan, and Spain while low uncertainty avoidance cultures
include Denmark and the UK.
4.3 Individualism
Individualism describes the relationship between the individual and the collective.
This relationship is not only a matter of ways of living together, such as in nuclear or
extended families, but it is closely linked with societal norms. Thus, individualism will
affect not only the functioning of families, but also education, religion and politics. A
given society’s norm for individualism versus collectivism will strongly affect the
nature of the relationship between a person and the organization to which they belong.
More collectivist societies, such as Japan, demand greater emotional dependence from
members, while organizations in more individualistic societies, such as the USA and
Canada, assume employees have broad responsibility for individual actions and, are
rewarded accordingly. Furthermore, the level of individualism or collectivism will
affect members’ reasons for complying with organizational requirements, as well as
affect the type of people admitted into positions of special influence.
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4.4 Masculinity
Different societies cope in different ways with differences in gender roles. A higher
level of masculinity is the dominant role pattern in the majority of societies. Although
biological differences between men and women are the same for all societies, the actual
division of labor between women and men varies widely. This gender pattern is
transferred to each society through families, schools, peer groups, and the media. The
predominant socialization pattern is for men to appear autonomous, aggressive, and
dominant. Conversely, it is common in many societies for females to appear nurturing,
helpful, humble, and affiliating. These social patterns seem to be projected onto
cultural perceptions of organizations. Business organizations are oftentimes perceived
to be more “masculine” while organizations, such as hospitals, tend to be perceived as
more “feminine.” In countries with lower masculinity (higher levels femininity) indices,
such as Norway or Sweden, life satisfaction of workers tends to take precedence over
job success. The opposite is true in countries such as the US that score higher on the
masculinity index. It is not surprising that cultures having lower levels of masculinity
(higher levels of femininity) encountered affiliated work activities in the form of work
teams earlier than countries with cultures displaying higher levels of masculinity. For
example, in the automobile manufacturing industry, the first modern use of
autonomous work teams occurred in Sweden while the USA favors primarily assembly
line production methods.
However, recent criticisms of Hofstede’s dimensions suggest that they have lost
relevance because the data they are based on was collected over 20 years ago
(Fernandez et al., 1997). Work by Verbeke (2000) and others (Spector et al., 2001) have
called into question the modern-day validity of Hofstede’s (1980) original work. Despite
the debate over whether Hofstede’s original work continues to remain valid, many
recent studies continue to cite the value of the original four dimensions of national
culture in decision-making research (see for example, Merritt, 2000; Dugan et al., 1998)
4.5 Complementary models of national culture
The work of two additional research groups should also be acknowledged because they
support the notion that national cultures, or clusters of countries with similar cultural
tendencies, are more appropriate for understanding business behaviors than merely
considering individual country differences. Ronen and Shenkar (1985) integrated the
literature on differences in how people of a country hold beliefs and values. The
authors suggested that eight country clusters, rather than individual country
differences, exist. The clusters are not created through geographic proximity but rather
they are derived based on similarities with regard to cultural factors such as religion,
language, legal systems, and historical contact. As the authors note:
As multinational companies increase their direct investment overseas, especially in less
developed and consequently less studied areas, they will require more information
concerning their local employees in order to implement effective types of interactions between
the organization and the host country (Ronen and Shenkar, 1985, p. 452).
A more recent examination of national culture was accomplished by Trompenaars
(1994) reporting a ten-year study examining the responses of over 15,000 managers
from 23 countries. His research approach, similar to Hofstede (1980), identified five
relationship orientations that address how people in different cultures relate in the
work place. These polar dimensions are:
The importance
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culture
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.
universalism vs particularism;
.
individualism vs communitarianism;
.
neutral vs emotionalism;
.
specific vs diffuse; and
.
achievement vs ascription.
Trompenaars and Hampden-Turner (1998) also examined the relative cultural
differences between societal views regarding time, and perceived control over the
environment.
In addition, the GLOBE project has begun citing results of a wide-scale study by
more than 150 researchers collecting information on more than 18,000 middle
managers in 62 countries (Javidan and House, 2001). The global leadership and
organizational behavior effectiveness study focuses on leadership behaviors and the
role of national culture.
Regardless of the operationalization, all of the research efforts reach similar
conclusions. Specifically, culture is multi-dimensional and can explain some of the
variance in managerial behaviors and decision-making.
5. Empirical evidence of national culture impact on operations
management decisions
The following analysis is provided to demonstrate the impact of national culture on
operational decisions at the plant level of analysis. We are interested in testing a
macro-level theory related to the influence of culture on operational decision making.
The literature is clear that culture matters in other settings. Our goal is to determine if
culture is also an important explanatory construct for operations management
research. This research effort is not a definitive test of the specific role culture plays in
individual operational decisions. Rather our aim is to use an existing data set that
includes a significant number of countries from various cultures to demonstrate that
culture can explain a significant portion of the variance in items that reflect operational
decision areas. That is, culture can be used to help explain some of the decisions made
by managers in a global context.
Toward this aim we used a sub-set of the data collected for the Global
Manufacturing Research Group (GMRG) manufacturing practices survey. GMRG is a
multi-national community of researchers dedicated to the study of international
operations management. Its primary goal is to promote an understanding of
differences in manufacturing practices across international boundaries through joint
research efforts. The GMRG data were primarily collected from two industries:
non-fashion textiles and machine tools. Survey questions cover key categories of
manufacturing activities such as sales forecasting, production planning and
scheduling, shop floor control, purchasing and materials management, and
manufacturing performance. The survey questionnaire has been previously
validated in other studies. Full details about its development and the administration
of the survey are available in Whybark and Vastag (1993) and Whybark (1997).
We examine the responses from778 plant-level managers in the GMRG database for
whom industry data existed. These 778 plants represent 21 different countries in most
regions of the world. In addition, both developed and developing nations are
represented. Finally, some of the plants are state owned. Table I details the breakdown
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of the sample, as well as the scores each country received on Hofstede’s cultural
dimensions. In order to test the ability of culture to explain a significant amount of
variance in operations decisions we examine the role that national culture plays on:
.
decisions to export products;
.
sales forecasts;
.
the number of outsource relationships; and
.
the purchase of productions inputs rather than the manufacture of them.
Our discussion of the constructs examined follows.
5.1 Constructs examined
As noted above, our aim was to show that culture is an important construct in
operational decision-making. If culture explains a significant portion of the variance in
operational decisions, then future studies of international operational decision-making
could be expanded from country and regional comparisons, to explicitly consider
culture.
Therefore, we selected a subset of items from the GMRG database to examine the
impact of culture on operational decision-making. Two other criteria influenced the
choice of items used in the analysis as well. First, all items are objective measures,
rather then perceptual measures as reported by plant managers, which reduces some
concerns with validity and measurement bias. Second, the items chosen were those
likely to result in consistent interpretations by respondents. For example, we did not
Country
Number of
plants
Uncertainty
avoidance
Power
distance Masculinity Individualism
Australia 24 51 36 61 90
Bulgaria 32 N/A N/A N/A N/A
Canada 92 48 39 52 80
China 17 N/A N/A N/A N/A
Ireland 8 35 28 68 70
Germany 18 65 35 66 67
Hungary 73 N/A N/A N/A N/A
Israel 4 81 13 47 54
Japan 91 92 54 95 46
Mexico 52 82 81 69 30
New Zealand 18 49 22 58 79
Northern Ireland 7 35 28 68 70
Poland 29 N/A N/A N/A N/A
Portugal 27 104 63 31 27
Russia 93 N/A N/A N/A N/A
Spain 20 86 57 42 51
Sweden 18 29 31 5 71
Taiwan 10 69 58 45 17
US 120 46 40 62 91
Wales 7 35 35 66 89
UK 18 35 35 66 89
Note: N/A = not available due to non-inclusion in original Hofstede (1980) study
Table I.
Measures of national
culture and number of
plants in the sample
The importance
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select frequency of forecast modification as a variable of interest since respondents
could potentially interpret the meaning of the item differently. Table II describes the
four constructs that were examined, as well as the manner in which they were
measured in the GMRG database. The hypothesis we are testing is:
H0. The elements of culture are not significant predictors of operational decisions
in a global sample.
Rejection of H0 would indicate that the elements of culture might be able to explain
differences in decision-making patterns globally. This aim is obviously limited to
testing the macro level theory that culture can predict a significant amount of variance
in operational decisions. There is no attempt to test more specific theory surrounding
the role of culture in individual operational decisions because such theory first needs
empirical evidence that culture matters in general.
If the effect of national culture is confirmed, future research certainly should
incorporate cultural variables to measure operational decision making more
thoroughly. The first two constructs examined, suppliers per part and percentage of
purchasing cost, are important to supply chain decisions. Many authors have noted
that the field of operations management has expanded from examining decisions
within a single function at a single firm, to trying to simultaneously optimize across an
entire supply chain (e.g. Frolich and Westbrook, 2001). By examining the influence of
culture on a pair of important supply chain decisions we are really attempting to
combine two trends in the field: an increased emphasis on globalization and an
increased emphasis on managing the entire supply chain. Evidence that one or more of
the elements of culture influenced either of these supply chain decisions would indicate
that as research in supply chains (especially global supply chains) moves forward,
more variance will be explained if culture is explicitly considered in research efforts.
Forecasting has long been a fundamental activity for operations managers hence it
has a long history of study within the field of operations management (e.g. Winters,
1960). Forecasts are fundamental to all planning activities. Forecast horizons influence
the forecasting tools used, as well as the types of planning that can be done. Because of
the importance of forecasts to various planning functions, evidence that culture
influenced forecast horizons would suggest that culture could have either a direct or
indirect influence on many other planning decisions from the timing of production to
the types of inventory systems used.
Finally, we examined the affect of culture on the level of exports because this
decision is fundamental to international operations. The decision to sell in other
Construct Measure/item
Suppliers per part About how many suppliers does the company have, on average,
per part (objective measure)
Purchased as percent of costs What percent of the company’s total manufacturing cost is for
purchased material (objective in percentage terms)
Horizon How far into the future does the company’s sales forecast extend
(objective measured in months)
Exports Export sales as a percentage of total sales (objective in
percentage terms)
Table II.
Constructs examined
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countries, as well as the decision regarding the amount of sales coming from global
customers, will influence operations in a number of different ways including the
potential for more part and product variety, and increased logistical complexity.
Overall, the four constructs used in our study underpin important operational
decisions. While there is evidence indicating the differences of their practices across
various countries (Whybark and Vastag, 1993), the relationship between those four
constructs and culture has never been studied.
5.2 Analysis
To analyze the impact of culture on each of these constructs we used hierarchical
regression with industry and employment entered as controls in the first step, and the
cultural measures entered in the second step (Pedhazur and Schmelkin, 1991).
Hierarchical regression was chosen for two main reasons. First, it is a straightforward
method that focuses on control variables. Second, and of greater importance,
hierarchical regression provides a clear picture of the additional explanatory power
created by adding elements of culture to a base model.
All of the tests were performed using Hofstede’s dimensions of national culture.
Hofstede’s dimensions were chosen for the initial tests based on their widespread use in
other fields. As noted in Table I some of the countries which we had data for were not
included in Hofstede’s study. Therefore our effective sample size for the analysis
conducted using these measures was 556 plants in 16 countries.
Our aim is to demonstrate that culture is a valid explanatory construct in operations
management research, not that any one set of cultural measures is preferred. Therefore,
we also performed tests using Trompenaar’s (1994) dimensions of culture as a
secondary analysis. While not as widely cited in academic research, these dimensions
provide the dual benefits of having been derived from more recent data and of
providing greater coverage, especially of Eastern Europe and China. The primary
results using Hofstede are discussed in the following sections while the results of the
analysis using Trompenaar’s dimensions (n ¼ 778) are presented in Appendix 1. The
rest of this section presents the primary statistical results of the effect of culture on four
operational decisions. More detailed discussion of the results is presented in the next
section.
6. Statistical results
6.1 Suppliers per part
Much of the recent literature in supply chain management, especially the literature
with a focus on purchasing processes, has noted that in order to create integrated
supply chains a company needs to optimize the supply base (Monczka et al., 2002).
Optimization usually includes a reduction in the number of overall suppliers, as well as
a move to use single sources for the majority of inputs, especially strategic inputs
(Venkatesan, 1992). However, the relationships created between buyers and suppliers
in single sourcing environments differ greatly from traditional arms-length
relationships. Therefore, culture may play a key role in determining a company’s
willingness and ability to move to strategic supply chain management.
The results in Table III suggest that culture plays a significant role in decisions
surrounding the number of suppliers per input, and by extension the types of
relationships companies have with their suppliers. Adding culture to the regression
The importance
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culture
381
model increased R-squared from 0.002 (p ¼ 0:173) to 0.022 (p ¼ 0:009). More
importantly given our hypothesis, Uncertainty avoidance and individuality are
significant predictors of suppliers per input, even when controlling for industry and
firm size. Thus, there is evidence to reject HO. Culture does explain some of the
variance in this decision.
6.2 Purchasing percentage
A key operational decision is the make-buy decision. Theory suggests that firms
should focus the majority of their resources of their own core competencies and
outsource processes that do not make use of these competencies (Venkatesan, 1992).
However, there may be cultural elements that will make a company more or less likely
to allow other companies to be responsible for some of the value created.
The results in Table IV suggest that culture does play a role in deciding how much
outsourcing occurs. Adding culture to the regression increases R-squared from 0.058
Step Construct
Coefficient
(standardized) F/T value P Adjusted R
2
1 Model 1.760 0.173 0.002
Constant 4.179 0.000*
Industry 0.018 0.483 0.629
Employment 0.067 1.789 0.074
2 Model 2.870 0.009* 0.022
Constant 3.591 0.000*
Industry 20.025 20.515 0.607
Employment 20.009 20.189 0.850
Uncertainty avoidance 20.429 23.741 0.000*
Power distance 0.020 0.254 0.8
Masculinity 0.094 1.694 0.091
Individuality 20.338 22.779 0.006*
Note:
*
P , 0.05
Table III.
Average number of
suppliers per input
Step Construct
Coefficient
(standardized) F/T value P Adjusted R
2
1 Model 22.873 0.000* 0.058
Constant 42.618 0.000*
Industry 20.245 26.702 0.000*
Employment 0.045 1.234 0.218
2 Model 14.073 0.000* 0.138
Constant 2.167 0.031*
Industry 20.293 26.508 0.000*
Employment 0.059 1.402 0.162
Uncertainty avoidance 20.039 20.375 0.708
Power distance 0.270 3.605 0.000*
Masculinity 20.106 22.042 0.042*
Individuality 0.203 1.870 0.062
Note:
*
P , 0.05
Table IV.
Purchasing percentage
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(p ¼ 0:000) to 0.138 (p ¼ 0:000). In addition, power distance and masculinity are
significant predictors of outsourcing, once more providing empirical evidence to reject
H0.
6.3 Exports
While the decision to sell products in other countries may be a strategic one, it has
significant implications for operations and the overall supply chain. Selling in other
countries increases a plant’s potential market, but selling across borders also entails
greater risk, an increase in logistical complexity, and often a need for customizing
products for specific markets (Ferdows, 1997). Therefore, it is possible that some or all
elements of culture would impact the decision to export, as well as the amount of
exporting actually done. We would also suggest that companies that have plants
located in certain cultures might find it harder to create products for export or to use
foreign sources.
The results in Table V indicate that culture does play a major role in decisions
surrounding the level of exports. Adding culture to the regression increases the
adjusted R-squared from 0.005 (p ¼ 0:075) to 0.19 (p ¼ 0:000). And all four of the
cultural dimensions are significant predictors of exports, once more providing evidence
to reject H0.
6.4 Forecasting horizon
Forecasting is one of the most fundamental operational activities. All other planning
activities and the majority of operational decisions are based on the forecast.
Companies with operations in multiple cultures, or using suppliers from other cultures
will have difficulty planning if they blindly assume that time horizons for forecasts are
stable across all cultures. The results in Table VI suggest that culture does indeed
influence the time horizon of forecasts within our sample. Adding culture to the
regression model increases R-squared from 0.000 (p ¼ 0:402) to 0.092 (p ¼ 0:000). In
addition, masculinity and individualism are significant predictors of forecasting
horizon, once more providing evidence to reject H0.
Step Construct
Coefficient
(standardized) F/T value P Adjusted R
2
1 Model 2.606 0.075 0.005
Constant 11.319 0.000*
Industry 0.087 2.280 0.023*
Employment 20.009 20.225 0.822
2 Model 19.758 0.000* 0.190
Constant 0.191 8.427 0.000*
Industry 0.144 3.252 0.001*
Employment 0.096 2.328 0.02*
Uncertainty avoidance 20.595 25.158 0.000*
Power distance 20.298 24.079 0.000*
Masculinity 20.147 22.822 0.005*
Individuality 20.721 25.989 0.000*
Note:
*
P , 0.05
Table V.
Exports – as a percent of
overall sales
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383
6.5 Alternative operationalization of culture
The purpose of this study is to test the validity of culture as an explanatory construct.
Therefore, we performed the same analysis detailed in sections 6.1-6.4 using
Trompenaar’s dimensions of culture. As previously noted these dimensions were
derived using more recent data and include more countries in the study.
In this secondary analysis using Trompenaar’s dimensions we find significant
results for all of the constructs examined (see Appendix 1). There is, once again,
overwhelming evidence that each of the individual dimensions of culture has an effect
on one or more of the constructs examined. The secondary analysis demonstrates that
cultural dimensions are significant predictors of operational decisions. This is
additional empirical evidence to reject H0 and supports our claim that the cultural
construct is important to operations management research.
7. Discussion
The purpose of our analysis is to demonstrate the validity of culture as an explanatory
variable in operations management research. The tests are far from comprehensive. In
the interest of parsimony we did not examine the entire GMRG database. Rather, we
examined a sub-sample that seemed to include a broad range of operational topics at a
number of different levels of analysis.
The results are unambiguous: culture is an important explanatory variable for
operations management research. Both sets of analysis provide clear empirical
evidence that national culture is a predictor of the operations management variables
chosen. More importantly, individual dimensions of national culture are significant
predictors of the variables examined.
7.1 Cultural differences compared to regional differences
The results demonstrate that culture can explain a significant amount of the variance
in the operational decisions we examined, leading us to reject H0. However, an
additional important contribution would be to demonstrate that national culture can
provide information not available if the analysis had been performed in a more
traditional manner. Therefore, we split the sample into four (arbitrary) regions; Europe,
Step Construct
Coefficient
(standardized) F/T value P Adjusted R
2
1 Model 0.912 0.402 0.000
Constant 18.3 0.000*
Industry 0.044 1.188 0.235
Employment 20.026 20.692 0.489
2 Model 9.618 0.000* 0.092
Constant 1.865 0.063
Industry 20.024 20.541 0.589
Employment 0.061 1.437 0.151
Uncertainty avoidance 20.01 20.095 0.924
Power distance 20.069 20.926 0.355
Masculinity 20.101 21.931 0.054
Individuality 0.208 1.920 0.055
Note:
*
P , 0.05
Table VI.
Forecast horizon
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Asia Pacific, Asia, and North America and performed some additional analysis to
expand the discussion. Table VII shows how the countries were grouped, as well as the
country scores along Hofstede’s dimensions.
Table VII reinforces the limitations of any type of regional grouping. Beyond the
arbitrariness of our groupings, it is also clear that countries within the same region
differ greatly on some or all of the cultural elements. In the North American region,
Mexico differs greatly from the USA and Canada along uncertainty avoidance, power
distance and individuality. However, Mexico is closer to the USA than Canada is in
terms of the masculinity dimension. Europe is equally inconsistent: Sweden is very
similar to the Anglo countries across the majority of the dimensions, with the exception
of masculinity – where Sweden is very different. Within Asia, Japan and Taiwan are
both low on individuality, but Taiwan scores much lower than Japan. Even countries
such as Spain and Portugal, that share borders and levels of industrialization, differ
along some of the dimensions. Table VII strongly suggests that any type of regional
grouping will ignore cultural differences within a region. Finally, Table VII makes it
clear that some countries not located near each other will share some or all of the
cultural elements.
The data also indicates that when firms are grouped via region, sample composition
effects will probably have a great influence on the results (see Table VIII). For instance
the North American composite score on globalization is 18 percent. But the average
scores on globalization for Canada, Mexico, and the USA are 33 percent, 6 percent, and
11 percent respectively. If the sample had been more weighted to plants from the USA
Country
Number
of plants
Uncertainty
avoidance
Power
distance Masculinity Individuality
Europe 123
Ireland 8 35 28 68 70
Germany 18 65 35 66 67
Northern Ireland 7 35 28 68 70
Portugal 27 104 63 31 27
Spain 20 86 57 42 51
Sweden 18 29 31 5 71
Wales 7 35 35 66 89
UK 18 35 35 66 89
Asia Pacific 42
Australia 24 51 36 61 90
New Zealand 18 49 22 58 79
Asia 101
Japan 91 92 54 95 46
Taiwan 10 69 58 45 17
North America 264
Canada 92 48 39 52 80
Mexico 52 82 81 69 30
USA 120 46 40 62 91
Table VII.
Culture by region
The importance
of national
culture
385
or Mexico the regional average would have dropped, while an increase in the Canadian
composition of the sample would have increased the regional average.
To move beyond the hypothetical discussion raised by Table VII we conducted
regression analysis by entering region, as opposed to dimension of culture, as the
second step of the hierarchical regression. Appendix 2 details the analysis using region
as a predictor. The test performed using Hofstede’s cultural dimensions explained
more variance than region for three of the four constructs examined (see Table IX). In
the case of suppliers per part, the regional model is not significant, while Hofstede’s
cultural model is significant. Trompenaars’s cultural dimensions explain more
variance than region for all four of the constructs examined. Finally, the model for
number of suppliers per part is significant for culture even though it is not for region.
The cultural models also give us greater insight.
The regional model of horizon indicates that European countries’ forecast horizons
are not significantly different from those of the North American countries. A typical
regional study trying to explain this result would probably discuss similar cultures
and levels of industrialization as an explanation for this result. In our study we are able
to be much more specific. The cultural dimension that these two regions are most
similar on (in aggregate) is masculinity – which based on the results presented in
Table IV – is a significant predictor of forecast horizon. The use of cultural variables
allows us to empirically demonstrate what aspect of culture is most responsible for this
similarity. It also allows us to suggest that countries such as Sweden would not follow
the European norm.
The results of a cultural examination are then useful because of the level of
specificity they engender. Our work strongly supports the assertions by Hofstede
(1980), Ronen and Shenkar (1985) and Trompenaars (1994) that national culture
provides researchers the opportunity to explain and predict how managers in different
parts of the world will make their operational decisions based on their perceptions,
Construct R
2
control R
2
Hofstede R
2
Trompenaars R
2
region
Exports 0.005 0.19
**
0.307
**
0.185
**
Horizon 0.000 0.092
**
0.117
**
0.07
**
Suppliers 0.002 0.022
**
0.017* 0.001
Percentage 0.058 0.138
**
0.189
**
0.183
**
Notes: All R
2
are adjusted to account for differences in the number of dimensions;
*
p , 0.05;
**
p , 0.01
Table IX.
Comparisons
Region
Exports
(as percentage
of total sales)
Forecasting horizon
(in months)
Purchased as
percentage of costs Suppliers per part
Europe 41 12.18 37 5.6
Asia Pacific 12 16.17 47 3.6
Asia 7 6.48 26 4.9
North America 18 11.43 40 5.5
Table VIII.
Regional means
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values, goals, and attitudes. Our examination of national culture and plant-level
outcomes provides a strong link between the literatures on national culture and the
emerging field of international operations management.
8. Conclusions
Traditionally, the OM field has examined international issues by making comparisons
between countries or regions. The purpose of this research was to show that national
culture is a valid way to explain differences in international operational
decision-making. Country comparisons may be of value if the countries are truly
different. But, culture is not a uni-dimensional construct. Countries may differ on one
element of culture but not on all. Blanket comparisons obscure a level of specificity
making it harder to explain and generalize results.
Considering entire regions as the basis for analysis can also be misleading. The
culture of the UK is much closer to the culture of the USA than the culture of its
European neighbors such as France. Yet most studies include a European region that
matches the UK with France. Similarly, the manufacturing practices in Hong Kong are
very different from Taiwan or even China even though they are all located in the same
geographical region. Other studies also indicated the distinct cultural differences
between US and Mexico, which are located just across the border (Plenert, 2002).
Cultural differences affect the way managers communicate, which in turn affect
their decisions of new product introduction, forecasting and scheduling, and quality
management (Flaherty, 1996). In other words, managers manage their resources and
production processes in a manner that reflects the culture of the country. This is why in
many international management studies, Ireland, Northern Ireland, New Zealand,
Wales, and England are combined because they exhibit the same cultural tendencies
(Ronen and Shenkar, 1985).
To successfully manage an international supply chain, it takes more than knowing
that operations decisions are made differently in different countries or regions.
Managers must realize what issues make international operations management
different and challenging. Specifically, managers must understand how and what
dimensions of national culture influence operations decisions. Such understanding will
better prepare multi-national companies to more effectively manage the global supply
chain. Based on the cultural dimensions suggested by Hofstede (1980) and
Trompenaars (1994), this study shows that culture allows us to understand
decision-making in manner which is not possible with regional comparisons.
This study represents a first attempt at using national culture to explain differences
of operations decision-making. While the results have clearly indicated the significant
relationship between cultural dimensions and operational decisions, several data and
research limitations need to be reconciled in future research to confirm our findings.
First, we assume that national culture is the same for all plants within the country. It is
possible that managers of foreign-owned plants could make different decisions than their
colleagues in strictly domestic operations, although no significant confounding effects of
plant ownership has been identified in the GMRG data (Whybark and Vastag, 1993).
A second issue revolves around testing the influence of country as opposed to
region. We did not compare the predictive power of country to the predictive power of
culture for three main reasons. First, our purpose was to show that culture was a valid
predictor, not to show its superiority. To properly compare culture to country and
The importance
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culture
387
region would require a data collection effort specifically structured for such a purpose.
Second, a proper test that included all of the countries in the sample would have
created power issues. Related to the second issue is one of sub-sample size. Most of the
countries are represented by a fairly low number of plants making meaningful tests
impossible. Given this limitation future studies need to compare the predictive ability
of cultural variables to country-by-country comparisons.
Finally and most importantly, future studies need to move beyond the macro level
theory that focuses on whether culture matters, to the more micro level question
focusing on how culture matters. Our initial analyses indicate that culture influences
all four operational decision areas we examined. Theory must now be proposed and
tested to examine how and why culture influences individual operational activities. For
instance, Chikan and Whybark (1990) identified many operational activities such as
production planning, inventory management, and shop floor control practices that
varied from country to country. It would be interesting to investigate how cultural
dimension influence those operational activities.
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Appendix 1. Results using Trompenaars’s dimensions
Step Construct
Coefficient
(standardized) F/T value P Adjusted R
2
1 Model 2.606 0.075 0.005
Constant 11.319 0.000
Industry 0.087 2.280 0.023
Employment 20.009 20.225 0.822
2 Model 24.071 0.000 0.307
Constant 21.888 0.060
Industry 0.093 2.179 0.030
Employment 0.006 0.151 0.880
Universalism 0.721 4.023 0.000
Individualism 20.501 25.938 0.000
Emotionalism 20.454 26.639 0.000
Specific 0.488 6.925 0.000
Achievement 0.641 6.385 0.000
Time horizon 20.085 21.106 0.269
Control 21.164 25.971 0.000
Table AI.
Exports – as a percent of
overall sales
Step Construct
Coefficient
(standardized) F/T value P Adjusted R
2
1 Model 0.912 0.402 0.000
Constant 18.3 0.000
Industry 0.044 1.188 0.235
Employment 20.026 20.692 0.489
2 Model 8.343 0.000 0.117
Constant 2.056 0.040
Industry 0.017 0.376 0.707
Employment 20.009 20.212 0.832
Universalism 0.742 3.538 0.000
Individualism 0.107 1.169 0.243
Emotionalism 20.029 20.387 0.699
Specific 20.006 20.087 0.931
Achievement 20.157 21.336 0.182
Time horizon 20.162 21.959 0.051
Control 20.373 21.729 0.084
Notes: Universalism represents universalism vs particularism; individualism represents
individualism vs communitarianism; emotionalism represents neutral vs emotionalism; specific
represents specific vs diffuse; achievement represents achievement vs ascription; time horizon
represents societal views; and control represents perceived control over the environment
Table AII.
Forecast horizon
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391
Appendix 2. Regional comparisons
Regional comparisons made using dummy variable regression with North America as the
reference group. A significant beta for one of the regional variables indicates that this region
differs significantly from the North American reference group.
Step Construct
Coefficient
(standardized) F/T value P Adjusted R
2
1 Model 1.760 0.173 0.002
Constant 4.179 0.000
Industry 0.018 0.483 0.629
Employment 0.067 1.789 0.074
2 Model 1.934 0.045 0.017
Constant 0.455 0.650
Industry 0.013 0.268 0.789
Employment 20.025 20.514 0.607
Universalism 20.209 20.964 0.336
Individualism 20.083 20.832 0.406
Emotionalism 20.135 21.697 0.090
Specific 0.116 1.442 0.150
Achievement 0.329 2.671 0.008
Time horizon 20.167 21.876 0.061
Control 20.245 21.068 0.286
Table AIII.
Average number of
suppliers per input
Step Construct
Coefficient
(standardized) F/T value P Adjusted R
2
1 Model 22.873 0.000 0.058
Constant 42.618 0.000
Industry 20.245 26.702 0.000
Employment 0.045 1.234 0.218
2 Model 13.132 0.000 0.189
Constant 3.083 0.002
Industry 20.239 25.193 0.000
Employment 20.027 20.624 0.533
Universalism 0.572 2.863 0.004
Individualism 0.367 4.051 0.000
Emotionalism 20.039 20.532 0.595
Specific 20.128 21.745 0.082
Achievement 20.012 20.107 0.915
Time horizon 20.168 22.058 0.041
Control 20.725 23.413 0.001
Table AIV.
Purchasing percentage
IJOPM
25,4
392
Step Construct
Coefficient
(standardized) F/T value P Adjusted R
2
1 Model 2.606 0.075 0.005
Constant 11.319 0.000
Industry 0.087 2.280 0.023
Employment 20.009 20.225 0.822
2 Model 22.58 0.000 0.185
Constant 6.4 0.000
Industry 0.118 2.575 0.01
Employment 0.087 2.086 0.037
Europe 0.330 7.45 0.000
Asia Pacific 20.029 20.653 0.514
Asia 20.178 23.875 0.000
Table AV.
Exports – as a percent of
overall sales
Step Construct
Coefficient
(standardized) F/T value P Adjusted R
2
1 Model 0.912 0.402 0.000
Constant 18.3 0.000
Industry 0.044 1.188 0.235
Employment 20.026 20.692 0.489
2 Model 8.579 0.000 0.07
Constant 14.63 0.000
Industry 0.072 1.53 0.126
Employment 0.069 1.59 0.113
Europe 20.009 20.201 0.841
Asia Pacific 0.152 3.342 0.001
Asia 20.232 24.91 0.000
Table AVI.
Forecast horizon
Step Construct
Coefficient
(standardized) F/T value P Adjusted R
2
1 Model 1.760 0.173 0.002
Constant 4.179 0.000
Industry 0.018 0.483 0.629
Employment 0.067 1.789 0.074
2 Model 1.11 0.355 0.001
Constant 8.016 0.000
Industry 20.103 22.045 0.041
Employment 20.013 20.283 0.777
Europe 0.001 0.017 0.986
Asia Pacific 20.076 21.576 0.116
Asia 0.008 0.160 0.873
Table AVII.
Average number of
suppliers per input
The importance
of national
culture
393
Step Construct
Coefficient
(standardized) F/T value P Adjusted R
2
1 Model 22.873 0.000 0.058
Constant 42.618 0.000
Industry 20.245 26.702 0.000
Employment 0.045 1.234 0.218
2 Model 22.932 0.000 0.183
Constant 33.036 0.000
Industry 20.264 25.914 0.000
Employment 0.062 1.507 0.133
Europe 20.110 22.514 0.012
Asia Pacific 20.029 20.658 0.511
Asia 20.289 26.447 0.000
Table AVIII.
Purchasing percentage
IJOPM
25,4
394