marketing metric use around the world: competing resource and cultural...
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Marketing Metric Use around the World:
Competing Resource and Cultural Perspectives
Ofer Mintz
Jan-Benedict E.M. Steenkamp
Imran S. Currim *
December 2015;
Working paper; Please do not circulate without permission
* Ofer Mintz (e-mail: [email protected]) is Assistant Professor of Marketing, E. J. Ourso College
of Business, Louisiana State University, Baton Rouge, LA 70803. Jan-Benedict E.M. Steenkamp
is C. Knox Massey Distinguished Professor of Marketing and Marketing Area Chair, University
of North Carolina at Chapel Hill (e-mail: [email protected]). Imran S. Currim (e-mail:
[email protected]) is Chancellor’s Professor and Director, Don Beall Center for Innovation and
Entrepreneurship at the Paul Merage School of Business, University of California, Irvine, CA
92697. The authors would like to thank Mike Hanssens (UCLA), Donna Hoffman (George
Washington), and Ivan Jeliazkov, Robin Keller, and Connie Pechmann (all of UCI) for their
support and feedback throughout the paper’s development. In addition, the authors thank
participants at the 2012 Theory + Practice in Marketing Conference at the Harvard Business
School and the 2013 Marketing Science Conference in Istanbul for their comments on a previous
version of the manuscript. This research was supported by grants from the Marketing Science
Institute (MSI), the Beall Center for Innovation and Entrepreneurship, Paul Merage School of
Business, University of California Irvine, the Global Business Center, Kenan-Flagler College of
Business, University of North Carolina at Chapel Hill, and the Dean’s office at the Paul Merage
School of Business and at the Kenan-Flagler College of Business.
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Marketing Metric Use around the World:
Competing Resource and Cultural Perspectives
ABSTRACT
This paper analyzes metric use around the world. Global competition is consistently
increasing, many of the world’s largest firms are based abroad, and more U.S. firms have a
global presence. Several marketing initiatives discuss the importance of managers employing
metrics in marketing decisions (e.g., MSI Research Priorities 1998-2016, ISBM B-to-B Trends
2008-2014) and some previous research has shown that total metric use affects performance.
However, currently there is lack of understanding of what drives metrics use in a global setting,
which is both a practical and theoretical limitation. This work employs two of the most dominant
paradigms in international research, resources and culture, to propose dual-competing conceptual
models of global metric use in marketing decisions. Testing the models on 4,103 managerial
decisions from firms in 15 countries that account for 80% of the world’s GDP, results
demonstrate that national, organizational, and managerial resources exhibit main and interaction
effects on metric use, while national and organizational culture only exhibit interaction effects
based on appropriate congruencies. The theoretical and managerial implications of these results
are further discussed.
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INTRODUCTION
There has been considerable concern about marketing’s decreasing influence in the firm (Rust et
al. 2004), in the boardroom (Webster et al. 2005), and at the corporate strategy level (McGovern
et al. 2004). Marketing is increasingly viewed as a cost and not as an investment (Morgan and
Rego 2009). Strategically important aspects of marketing have moved to other functions in the
organization (Sheth and Sisodia 2005). Roles of financial managers have become more important
than marketing managers (Nath and Mahajan 2008), and the tenure of chief marketing officers
only averages 22.9 months (Hyde et al. 2004). One main reason identified for this decline in
marketing’s influence is its lack of accountability (Verhoef and Leeflang 2009). Further, global
competition, recession, and stock market pressures have only increased the demands for
marketing accountability (Lehmann and Reibstein 2006).
In response, marketing scholars have developed metrics for a variety of marketing mix
decisions (Ambler 2003, Farris et al. 2010) and linked marketing mix efforts and assets to
financial metrics (Abramson, Currim, and Sarin 2005, Srinivasan and Hanssens 2009). Despite
valuable efforts, there is no understanding of what drives managers residing in various countries
to use metrics in their marketing mix decisions, outside of the U.S., which was studied by Mintz
and Currim (2013). Lack of global insights on drivers of metric use is an important practical or
managerial limitation as U.S. firms increasingly depend on international markets while many of
the world’s largest firms reside in overseas countries. It is by now well-established that
organizational and managerial behavior are affected by the availability of resources (Chan et al.
2008) and the culture of the country in which they operate (Hofstede et al. 2010). For example,
Calantone et al. (1996) find that Chinese firms possess fewer marketing related resources and
skills than U.S. firms, which makes marketing capabilities more critical to new product success
in China than in the U.S.; while Deleersnyder et al. (2009) find that firms’ response to business
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cycles through advertising efforts is systematically moderated by the national culture in which
they operate. Lack of global insights on drivers of metric use is also an important scientific or
theoretical limitation. Marketing scholars have urged their colleagues to investigate substantive
marketing problems on an international basis to arrive at global insights, empirical
generalizations, and boundary conditions (Burgess and Steenkamp 2006, Farley and Lehmann
1994, Grewal et al. 2008).
This study deals with the antecedents of marketing metric use in a cross-national context.
What distinguishes between companies that make heavy use of marketing metrics versus
companies that make little use of marketing metrics? We focus our approach on the influence of
two of the most dominant paradigms in international research, resources and culture, and propose
dual-competing perspectives of cross-national managerial metric use. In marketing, management,
operations research, and information systems there exists a large number of studies that discuss
the importance of external and internal to the organization resources for firms to achieve
sustainable competitive advantages (Barney 1991, Calantone et al. 1996, Hult et al. 2006,
Montealegre 1999, Wernerfelt 1984). Resources can stem from any internal, external, economic,
organizational behavior, and managerial knowledge strategic factors that provide valuable and
exploitable information for the organization (Powell 1995). Therefore, in order to propose an
integrated framework of how resources impact metric use, we follow previous studies in the
international management and marketing literature that emphasize national (Calantone et al.
1996), firm (Lee and Grewal 2004), and managerial (Hitt et al. 2001) resources as key drivers of
organizational practices.
An equally large stream of literature in marketing, management, organizational behavior,
as well as psychology and sociology have documented the importance and usefulness of studying
behavioral phenomena through a cultural lens (Deshpandé et al. 1993, Hofstede et al. 2010,
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Steenkamp et al. 1999, Steenkamp and de Jong 2010). To fully understand the impact of this
complex phenomenon on metric use, a detailed framework is needed that incorporates cultural
antecedents at different levels of abstraction. In particular, this study also postulates important
effects of national culture (Inglehart and Welzel 2005) and organizational culture (Cameron and
Quinn 2011, Deshpandé et al. 1993).
The contribution of this study is fourfold. First, we develop dueling conceptual models
that incorporate resource and cultural antecedents from two levels of abstraction, national and
organizational levels (Figure 1). It has been repeatedly observed that a fuller understanding of
managerial and organizational behavior requires the investigation of both micro-organizational
and macro-country antecedents (Hofstede et al. 2010; cf. Erbring and Young 1979). Managers
and organizations operate in a particular country, which will affect their behavior (Lachman et al.
1994, Triandis 1989). Thus, we develop specific expectations of main effects of resource and
cultural variables on marketing metric use. Second, by proposing separate conceptual drivers of
metric use by managers around the world based on resource and cultural characteristics, we offer
empirical comparisons of the value of two of the dominant theoretical paradigms of the
international management field. Specifically, we compare which theory is more appropriate in
our cross-national setting and offer an integrated post-hoc model based on such results, which
can help guide future research.
Third, we address the false dichotomy that according to Farley and Lehmann (1994, p.
112) has plagued much of marketing research and practice, namely the polarization of views
between ―everything is the same‖ versus ―everything is different.‖ We develop expectations
concerning the environmental conditions (national resources or culture) in which particular
organizational variables will have a greater or smaller effect on marketing metric use. Fourth, the
hypotheses are tested on managers in 15 countries from 5 continents, including three of the major
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groupings of economic and political importance – G7, BRIC, and MIST – and involve 4,103
decisions. Our set of countries account for over 80% of the world’s total GDP, which provides a
strong basis for deriving global empirical generalizations concerning the antecedents of
marketing metric use.
WHY AND HOW RESOURCES AND CULTURE IMPACT METRIC USE
Metrics quantify trends or characteristics in order to explain phenomena, understand
relationships, and decision results of future actions (Farris et al. 2010). Metrics are employed in
diagnostic, coordinating, benchmarking, and monitoring roles in order to assist managerial
decision making (Shugan and Mitra 2009). Thus, Hauser and Katz (1998) suggest that every
metric employed matters to performance and Mintz and Currim (2013) find the more metrics
employed, the better the marketing mix performance. Consequently, understanding drivers of
total use of metrics is important for managerial practice.
In this study, we choose to focus on resource and cultural drivers of metric use by
managers around the world for the following reasons. First, resources are ―tangible and
intangible assets firms use to conceive of and implement its strategies‖ (Barney and Arikan
2001, p. 138), and comprise of organizational processes, managerial skillsets, information,
knowledge, and environment economic attributes, etc. that firms possess as assets and employ as
competitive capabilities (Song et al. 2005). Across the international marketing and management
literature, it has been persuasively argued that such resources influence organizational and
managerial behavior across nations (e.g., Lachman et al. 1994). In terms of metric use, in the
managerial accounting literature, Henri (2006b) suggests that resources are a primary link
between successful organizational strategy, management metric control systems, and decision
performance. Second, due to the nature of the data and the subsequent skills needed to
comprehend and employ such data in marketing decisions, the ability of managers to employ
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metrics is often dependent on organizational, technological, and managerial resources (Ambler
2003, Hanssens et al. 2003). For example, Morgan et al. (2005) find that human and
technological resources often impact the firm’s ability to employ customer satisfaction data, and
Petersen et al. (2009) suggest that certain managerial, firm, and environmental resources are
needed in order to enable accurate forecasting of metrics, which would then affect their use.
Third, in contrast to resources, culture is a pattern of shared values and beliefs across
organizations or individuals in a nation (Deshpandé et al. 1993, Hofstede et al. 2010). As
opposed to resource-based theory which suggests that national, firm, and managerial resources
drive organizational behavior, cultural theory posits that control, flexibility, formalization,
rewards systems, uncertainty avoidance, etc. are determinants of how managers will act within a
firm (Henri 2006a). The central argument in this vast literature in culture is that managers
working in specific organizations and residing in certain countries share similar goals and beliefs
regarding specific situations or behavioral domains, which will lead them to behave differently
than managers working in other organizations and countries (e.g., Newman and Nollen 1996,
Tan et al. 1998). Fourth, the implementation of how firms value and adopt management
performance and decision support systems is often a function of culture (Ambler et al. 2004). For
example, Barwise and Farley (2004) in a purely descriptive work, report differences in how often
six metrics are reported to the board in five countries. They suggest that both national and
organizational culture may explain variation in their results, but recommend future research to
expand on their work to include more theoretical development and to test data from more
countries, firms, metrics, and types of businesses.
In summary, resources and culture are two of the most dominant theoretical paradigms in
international management research, and are expected to be two of the prominent reasons for why
managers employ metrics in marketing decisions. Resources-based theory emphasizes that
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differences between assets and capabilities across countries, firms, and managers lead to
differences in organizational and managerial behavior, while culture emphasizes shared values
and similarity within firms and nations drives organizational and managerial behavior.
Therefore, in this work we propose a dual-competing resource and cultural based framework
allow us to empirically tests differences between the paradigms (Figure 1). This conceptual
development should provide managerial and theoretical insights.
We proceed by first discussing our resources-based conceptual framework and
expectations, followed by our cultural-based conceptual framework and expectations.
Subsequently, we describe our data, econometric models, and results. Last, we discuss
theoretical and managerial implications of our study.
CONCEPTUAL FRAMEWORK
Overview of How National, Firm, and Managerial Resources Impact Metric Use
Our resources-based proposed conceptual model follows resource-based theory (RBT), which is
a dominant paradigm across international business research (e.g., Ahuja and Katila 2004, Capron
and Hulland 1999, Lampel and Giachetti 2013). RBT integrates multiple and often dissimilar
resources into one theoretical framework (e.g., Kozlenkova et al. 2014) by emphasizing that such
resources are central to understanding sustainable firm performance (Amit and Schoemaker
1993). RBT assumes that firms (i) possess different bundles of such resources and (ii) these
differences may persist over time (Barney 1991). Both tangible and intangible assets and
capabilities are considered as resources (e.g., Wernerfelt 1984), and can be obtained from
strategic factors such as market information, economic environment, organizational attributes
and processes, and managerial skills, training, and backgrounds as long as they are valuable,
rare, imperfectly imitable, and exploitable by the organization (Lee and Grewal 2004). Firm
decisions regarding the accumulation and selection of resources used to execute its strategies are
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shaped by internal and external to the firm attributes and characterized as economically rational
but bounded by constraints of available information and managerial biases (Oliver 1997).
In a global context, resource availability within a country influences the overall
productivity of the economy and of specific firms (Bahadir et al. 2015, Röller and Waverman
2001). National institutions, technological advances, and market imperfections lead to
heterogeneity in resources that are rare, valuable, and sources of competitive advantage for firms
(Cheung et al. 2010). Firm practices are typically reliant and a function of such national
resources, skills, and existing technology (e.g., Montealegre 1999) as they reflect the norms of
the countries in which they operate (Tractinsky and Jarvenpaa 1995). For example, idiosyncratic
national environments can impact the development and availability of technology resources
(Ahuja and Katila 2004), the development of higher level of education and professional
management can help firms capitalize on more advanced business practices (Chan et al. 2008),
and advanced marketing-related capabilities can impact the resources available or unavailable for
firms (Song et al. 2005). Thus, firms with such location specific advantages are able to capitalize
on better developed resources in their institutional practices (Porter 1985), which should impact
the ability of mangers to employ metrics in decisions.
RBT further argues that organizational attributes and managerial processes account for
differences in the exploitation and application of resources (Kozlenkova et al. 2014). At the firm
level, organizational characteristics such as firm size and CMO presence often account for
differences in resources and the ability to employ such resources in marketing decisions (Mintz
and Currim 2013); while, other organizational characteristics such as level of market orientation
can also lead to differences in the way managers process information (Kohli and Jaworski 1990).
Further, organizational characteristics and business processes often determine the development,
allocation, and efficacy of resources (Lee and Grewal 2004), with capabilities and the
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effectiveness of firm strategies a function of such resources (Cheung et al. 2010). Consequently,
from an RBT view point, organizational characteristics are expected to impact metric use.
At the managerial level, resources should provide competitive advantages for firms since
they enable managers to exploit market-based assets, information, and know-how in decisions
(Wernerfelt 1984). However, RBT suggests that assets and capabilities are only valuable and will
lead to competitive advantages to the degree to which managers within the firm exploit/employ
such resources in their decisions (Amit and Schoemaker 1993). For example, managers may lack
incentives and skillsets to maximize available resources (e.g., Hunt and Morgan 1995) or may
vary in their tacit knowledge based on their experience which would influence the amount of
resources needed and the ability to exploit such resources (Hitt et al. 2001). Thus, prior research
(e.g., Day and Nedungadi 1994) has suggested that managers use differing levels of resources in
terms of searching for information, understanding which information is relevant, and
consequently knowing which information to employ in their decisions. Consequently, it is
expected that managerial characteristics such as managerial experience, metric training, and
monetary incentives to employ such information will impact metric use.
Application of RBT on Metric Use around the World for Marketing Decisions
Main Effects of National Level Resources
In the context of impacting metric use, at the national level, we build on prior work by Calantone
et al. (1996), Day (2011), Hitt et al. (2006), Lachman et al. (1994), and Song et al. (2005) to
investigate three characteristics studied extensively in the RBT literature: (a) management
quality, or human capital, defined as the level of competent senior management and the quality
of management education in meeting the needs of the business school impact the human-based
resources of the firm in the country; (b) digital emphasis, defined as the percent of overall
internet users, adverting spending on digital mediums, and shoppers purchasing online in the
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country; and (c) customer orientation, defined as the extent of marketing, buyer sophistication,
and customer orientation in the country.
We expect these three national level characteristics to influence managerial metric use for
the following reasons. First, managerial competence and their formal education represent a
unique organizational resource as these traits vary across countries and are hard to imitate
(Walsh et al. 2008). Greater formal education provides managers higher levels of knowledge
acquisition (Chan et al. 2008) and articulable knowledge in the field (Hitt et al. 2001), and
greater competence influences managerial decision making strategies (Day 1994) and the value
of market intelligence across the firm (Li and Calantone 1998). Thus, with greater country-level
management quality, firms can provide greater resources to obtain additional market research
information (Homburg et al. 2014), deploy such information and higher-quality resources across
the firm (Montealegre 1999), and hence understand and employ more complex information in
decisions (Pennings et al. 1998) which should lead to an increase in the use of metrics in
marketing decisions. Second, firms in countries with increasing levels of digital emphasis are
increasingly shifting towards more knowledge-based decision processes (van Knippenberg et al.
2015), which should also increase their use of metrics in marketing decisions. Digital technology
and the emphasis to use such technology has magnified the amount of information or resources
available to assist managers and firms in their decisions (Day 2011). Consequently, differences
in digital emphasis between countries lead to differences in resource capabilities for market
research (Lee and Grewal 2004), which may lead to differences in metric use for marketing
decisions. Third, from an RBT point of view, resources help drive the firm’s strategy and
decision making process (Hitt et al. 2006), and with increasing country-wide levels of marketing
and consumer complexity, it becomes more difficult for firms to develop market knowledge
resources and infer the causal relationships necessary to compute additional metrics (Homburg et
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al. 2012). Hence, even though market intelligence is an important component of consumer
orientation (e.g., Day 1994), in line with RBT, we expect this lack of resources to negatively
impact managers’ ability to employ metrics in their marketing decisions.
Main Effects of Firm and Managerial Level Resources
At the firm resource level, we examine three firm attributes often studied in the RBT literature
(e.g., Boyd et al. 2010, Kohli and Jaworski 1990, Macher and Mayo 2015) that should directly
impact metric use: (a) CMO presence, defined as whether the firm employs a chief marketing
officer (CMO); (b) market orientation, defined as the extent to which a firm measures, monitors,
and communicates customer needs and experiences throughout the firm and whether the firm’s
strategy is based on this information; and (c) firm size, defined as the number full-time
employees in the firm.
In the marketing and management literature, previous research posits that characteristics
of the top management team will affect the importance of organizational functions and their
provided resources (e.g., Hambrick and Mason 1984, Nath and Mahajan 2011). Therefore, the
inclusion of a CMO in the firm should help attract greater resources for marketers (Germann et
al. 2015), which we expect would lead managers to use a greater variety of metrics in their
decisions. Further, CMO’s are tasked internally to gather, analyze, and disseminate market
information (Homburg et al. 2014), and ensure the voice and wants and needs of the customers
are heard (Boyd et al. 2010). Thus, managers in such firms are also expected to focus on similar
tasks that are mandated to their highest level executive, which should compel these managers to
employ more metrics in their marketing decisions. Next, we discuss market orientation. Market
oriented firms often have a strong ideology and take firm performance as a matter of ―faith‖
(Deshpandé et al. 1993), and because of their greater levels of customer and market knowledge
(Kohli and Jaworski 1990), they experience lower levels of uncertainty when making their
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decisions (Morgan et al. 2005). In contrast, managers in non-market oriented firms possess less
customer and market knowledge so they will need to employ greater resources in their marketing
decisions to help reduce the uncertainty of their decisions. Further, managers in non-market
oriented firms often must employ more metrics in their marketing decisions to assist in the
justification of their decisions to superiors who on average will be less familiar with marketing
(Mintz and Currim 2015). Hence, we expect less market oriented firms to employ more metrics
in their marketing decisions. Third, we discuss firm size. Larger firms possess a greater
knowledge base internally (Boyd et al. 2010), more access to external market information
(Harmancioglu et al. 2010), and superior resources overall (Macher and Mayo 2015).
Consequently, we expect managers to employ more metrics in such firms.
Finally, at the managerial level, we investigate three characteristics, (a) metric training,
(b) metric compensation, and (c) managerial experience, that previous research has suggested
impacts managerial motivation and ability to deploy resources (e.g., Hitt et al. 2001, Li and
Calantone 1998, Oliver 1997). Metric training is defined as a manager’s level of training on the
use of metric; metric compensation is defined as the importance of metrics in a manager’s
compensation package; and managerial experience is defined as a manager’s experience in
number of years as a manager, at the firm, and in the current position.
For resources to be valuable, managers must possess, be capable, and actually use such
resources in their decisions (Amit and Schoemaker 1993). RBT argues that for this to transpire,
managers need to possess tacit knowledge, which is knowledge focused on routines and
operational skills (Homburg et al. 2014) that managers often learn through on-the-job training
and experience (Hitt et al. 2001). By providing metric training, firms are trying to increase
managerial tacit knowledge and knowledge competence for decision-specific resources to
strengthen decision resource capabilities (e.g., Li and Calantone 1998), which should adjust
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managerial behavior (Hult et al. 2006) to improve their use of metrics. By providing monetary
incentives based on the results of their decisions, firms are more forcefully directing managers to
employ their tacit knowledge in decisions towards to the measure that is being incentivized
(Oliver 1997). Consequently, when firms provide greater metric based compensation, they intend
for managers to utilize such decision resources in their marketing decisions and employ a greater
number of metrics. And when managers possess greater work experience, they typically have
acquired greater tacit knowledge from their experiences, and as a result have a greater
understanding of how to apply resource assets and capabilities in their decisions (Hitt et al.
2001). Therefore, even though greater managerial experience can also lead to greater managerial
hubris (Boyd et al. 2010) which would associate with managers being less reliant on resources
and metrics, we expect more experienced managers to utilize their greater tacit knowledge to
employ more metrics in their marketing decisions.
Interactions between National Level and Firm and Managerial Level Resources
For firms to take advantage of national, firm, and managerial resources in order to increase firm
performance, RBT suggests that firms need enhanced capabilities to maximize these different
leveled assets. Thus, in this section, we propose four cross-level interactions between national
and firm and managerial level resources that are expected to impact metric use.
National Management Quality x Managerial Metric Training. To achieve maximum
resource capabilities, managers need to possess a mixture of both articulable, educational
knowledge and tacit on-the-job learning (Hitt et al. 2001). Better quality managers typically
possess greater articulable knowledge, but this does not necessarily mean that they have obtained
tacit knowledge. Metric training can provide such tacit learning, and since these better quality
managers typically value market intelligence (Li and Calantone 1998), we expect they will take
advantage of this training and employ more metrics in their marketing decisions.
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National Management Quality x Managerial Metric Compensation. In general, as better
quality managers feel more comfortable when making marketing decisions due to their
articulable knowledge, we expect them to focus on what they perceive to be the best metrics for
their situation to improve marketing performance, and be less susceptible to just employ metrics
related to firm compensation schemes (e.g., Anderson and Oliver 1987). Thus, we expect a
negative interaction.
National Digital Emphasis x Firm Size. With greater digital emphasis in a country, larger
firms can devote even more resources towards digital avenues in order to track consumers and
the results of their various marketing activities. While smaller firms will also prosper from
greater digital technology capabilities, they do not possess the same level of assets to collect the
equivalent level of information (van Knippenberg et al. 2015) or the capabilities to integrate the
various digital and non-digital touchpoints as larger firms (Day 2011). Consequently, we expect
a positive interaction between national digital emphasis and firm size.
National Customer Orientation x CMO Presence. Regardless of the level of customer
orientation in the firm, CMOs are often employed to reduce the complexity of the marketing
function for the top management team (Nath and Mahajan 2008). However, when firms are more
customer oriented, CMOs typically possess greater power within the organization (Germann et
al. 2015). Consequently, in countries with greater customer orientation, CMOs will more likely
have greater authority to delegate and coerce managers making marketing decisions to ensure
they are employing significant amount of metrics so that the CMO can justify and explain
marketing decision processes and goals to others in top management.
Overview of How National and Organizational Culture Impact Metric Use
National Culture
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National culture can be defined as ―the pattern of shared values and beliefs that help individuals
understand societal functioning and thus provide them with the norms for behavior in society.‖
National-culture priorities reflect the basic issues and problems that societies must confront in
order to regulate human activity (Schwartz 1994). The shared cultural priorities in society help to
shape the social and economic reward contingencies to which companies must adapt in the
institutions in which they spend most of their time (families, schools, businesses, etc.) in order to
function smoothly and effectively (Smith and Schwartz 1997). National-cultural priorities will
encourage the activation of organizational behaviors that are in line with these priorities and
conducive in their preservation, while organizational choices that run counter to these cultural
priorities are discouraged (Deleersnyder et al. 2009, Hofstede 2001).
Our view that mangers’ behavior is affected by the cultural context in which managers
operate is widely shared by cultural theorists (Hofstede 2001, Roberts and Greenwood 1997,
Schneider and Barsoux 2003). Hofstede (1994, p. 4) put it as follows: ―... the culture of the
human environment in which an organization operates affects the management processes.‖
Relatedly, Roberts and Greenwood (1997, p. 361) maintained that ―[firms] face pressures to
adopt designs that are within the subset of socio-politically legitimated designs.‖
Multiple national culture typologies have been proposed, with the most influential and
best-known ones proposed by Hofstede et al. (2010), Inglehart (Inglehart and Baker 2000,
Inglehart and Welzel 2005), and Schwartz (1994). For the purposes of the present study,
Inglehart’s framework is the most relevant for two reasons. First, the central focus of Inglehart’s
framework is that a country’s level of socioeconomic development is linked with that society’s
decision practices since it influences availability of resources for such decisions. Consequently,
using Inglehart’s framework allows us a more direct comparison with our competing resource
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perspective. Second, in our empirical sample, Hofstede’s five dimensions exhibit extremely high
correlation while Inglehart’s two dimensions do not.
Inglehart classifies socioeconomic development by two dimensions, traditional/secular-
rational and survival/self-expression. The traditional/secular-rational dimension is linked with
modernization and industrialization, with firms in countries that are more traditional
characterized by greater centralization, deference to authority, and bureaucracies. In contrast, the
survival/self-expression dimension is linked with postmodernization and postindustrialization,
with managers in countries that have greater self-expression characterized by more individual
autonomy and interpersonal trust.
Organizational Culture
Deshpande and Webster (1989, p. 4) define organizational culture as ―the pattern of shared
values and beliefs that help individuals understand organizational functioning and thus provide
them with the norms for behavior in the organization.‖ Like for national culture, multiple
organizational culture typologies have been proposed (see Zohar and Hofmann 2012 for an
overview), the most influential being Cameron and Quinn's (2011) Competing Values
Framework (Figure 2).
The Competing Values Framework specified two fundamental dimensions of
organizational effectiveness. One dimension describes a continuum ranging from organic to
mechanistic processes. It differentiates effectiveness criteria that emphasize flexibility,
discretion, and dynamism from criteria that emphasize stability, order, and control. That is, some
organizations are viewed as effective if they are changing, adaptable, and organic; neither the
product mix nor the organizational form stays in place very long. Other organizations are viewed
as effective if they are stable, predictable, and mechanistic. They are characterized by longevity
and staying power in both design and outputs (Cameron and Quinn 2011).
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The second dimension describes a continuum ranging from organizational cohesion,
integration, unity, and consonance on the one end (internal maintenance) to organizational
separation, differentiation, and rivalry (external positioning) on the other. That is, some
organizations are viewed as effective if they have harmonious internal characteristics; for
example, IBM has traditionally been recognized for a consistent ―IBM way.‖ Others are judged
to be effective if they are focused on interacting or competing with others outside their
boundaries; for example, companies that have adopted the ―think globally, act locally‖ mantra
have units adopt the attributes of the local environment rather than follow a centrally prescribed
approach (Cameron and Quinn 2011).
According to Deshpandé et al. (1993), these key dimensions represent a merging of two
major theoretical traditions from the organizational behavior literature, the systems-structural
perspective and the transaction cost perspective. Together, these two dimensions form four
quadrants, each representing a distinct organizational culture type. Because the quadrants
represent opposite poles of the underlying dimensions, they identify competing assumptions and
values. The hierarchy (―control‖) culture is characterized by an internal orientation and a stable
organization. The market (―compete‖) culture combines an external focus with stability. The clan
(―collaborate‖) culture is internally focused and flexible, while the adhocracy (―create‖) culture
is externally focused and flexible. Since these different cultural types are assumed to compete
one with the other, organizations will have a certain level of each culture. Organizational
effectiveness will result from different patterns of cultures that are congruent with environmental
demands (Cameron and Quinn 2011). Figure 2 provides more information about the
characteristics of each organizational culture type.
Application of Culture’s Effect on Metric Use around the World for Marketing Decisions
Main Effects of National Culture
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In comparison to secular societies, traditional societies emphasize deference to authority,
standards, and formal hierarchies, which implies that final decisions are centralized and made by
top executives (Inglehart and Welzel 2005). Thus, managers who are responsible for marketing
decisions could face increasingly bureaucratic environments which require greater amounts of
approval from top executives who necessitate greater justification of decisions (Hofstede et al.
2010). Metrics not only serve as decision aids, i.e., for considering, benchmarking, and
monitoring marketing mix decisions, but can also be employed to justify such decisions (Pauwels
et al. 2009). Hence, we expect managers in traditional (secular) societies to employ more (less)
metrics in their decisions.
In survival-based societies, organizations place social and cognitive constraints on
managers (Steenkamp and de Jong 2010). Evaluation of managers are more often tied to success,
achievements, and equity, and judged via control systems designed by their firms to evaluate
such results (Newman and Nollen 1996). Metrics allow one to track the progress of reaching
such goals within a control system, which should encourage managers in survival-based societies
to employ more metrics in their decisions in order to perform better. In contrast, in self-
expression societies, individuals have greater choice and autonomy to make decisions, and less
predication to conform to expectations of others (Steenkamp and Maydeu-Olivares 2015).
Consequently, managers in such societies are less likely to have to justify their marketing
decisions via use of additional metrics. As a result, we expect managers in survival (self-
expression) cultures to employ more (less) metrics in their marketing decisions.
Main Effects of Organizational Culture
Underlying the Deshpandé et al. (1993) framework are two fundamental dimensions: organic
versus mechanistic processes and internal maintenance versus external positioning. We expect
that organizational cultures higher on mechanistic processes (Hierarchy, Market) with more
19
formal information utilization and evaluation procedures make greater use of metrics in
marketing decisions than organizational cultures that emphasize organic processes (Clan,
Adhocracy). Further, we expect organizational cultures that emphasize external positioning
(Adhocracy, Market) who have better developed information acquisition processes make greater
use of metrics than organizational cultures that emphasize internal maintenance (Clan,
Hierarchy). Combining these two insights suggests that the stronger the market culture is in a
company, the greater its use of metrics, while the reverse is true to clan culture. Whether
adhocracy and hierarchy have a positive or negative effect on metric use in marketing decisions
depends on the relative strength of the two underlying dimensions. We argue that the
mechanistic versus organic dimension, associated with control, order, and stability versus
flexibility and spontaneity, is more pertinent for use of metrics, which allow for greater
organizational control. Hence, we expect that the extent to which the culture in the company can
be characterized as a hierarchy (adhocracy) is positively (negatively) related to metrics use.
Interactions between National and Organizational Culture
The shared cultural priorities in society help shape the social and economic reward contingencies
to which managers must adapt in order to function smoothly and effectively (Smith and Schwartz
1997). Managers may thus experience compatibilities and conflicts between their own
organizational culture and national-cultural priorities (Hofstede et al. 2010). These positive or
negative social reinforcement mechanisms – which operate between the two types of constructs –
give rise to interactions between organization-level and national-level culture variables.
Organization culture types that are congruent with national-cultural priorities will be encouraged,
while the expression of organization culture types that are incongruent with national priorities
are discouraged. This basic mechanism of congruity vs. incongruity between the micro
(manager) and macro (country) drivers is widely supported in the psychological literature
20
(McCrae 2000, Schwartz 1994, Triandis 1989), as well as in marketing (Steenkamp et al. 1999,
Steenkamp and de Jong 2010).
We develop contingency hypotheses using this fundamental notion of cultural
congruence. If a particular organizational culture construct and a particular national-culture
dimension are aligned, they mutually reinforce each other in their effect on organizational
behavior. Cohen (2003, p. 285) labels this as synergistic or enhancing interactions. On the other
hand, if the two constructs work against each other, we can expect negative synergies with the
national-culture construct in question weakening the effect of the organizational culture variable
involved. Cohen (2003, p. 285) calls this a buffering interaction. We posit four interactions
involving the moderating influence of organizational culture on the relationship between national
culture and marketing metrics use, based on the relative congruence or incongruence between a
specific organizational culture type and a particular national-cultural dimension.
National Traditional vs. Secular Culture x Market Organizational Culture. Market
organizational cultures emphasize competitiveness, goal achievement, and superiority,
characteristics that promote information utilization (Moorman 1995). Further, they rely more on
formal communication systems and less on interpersonal communication (White et al. 2003),
which would necessitate managers to employ metrics as a means of conveying information. This
organizational orientation is congruent with traditionally focused national cultures which stress
formality and bureaucracy, and should lead to greater metric use in marketing decisions. Hence,
we expect a positive (negative) relationship between traditional (secular) national culture and
market organizational culture.
National Survival vs. Self-Expression Culture x Clan Organizational Culture.
Organizations with a strong clan culture emphasize cohesiveness, teamwork, and a sense of
family (Moorman 1995). This organizational culture is highly compatible with national-cultural
21
self-expression characteristics of interdependence, relationships, and trust (Markus and Kitayama
1991, Triandis 1989). Thus, there is a congruence for managers that reside in more self-
expression national cultures who work in clan organizational cultures that should enable them to
obtain greater cooperation and commitment from others in the firm, and lead them to possess
greater resources for their decisions (White et al. 2003). Consequently, we expect a positive
interaction between self-expression national and clan organizational cultures.
National Survival vs. Self-Expression Culture x Adhocracy Organizational Culture.
There is a basic congruence between adhocracy organizational and national self-expression
culture. Adhocracy cultures emphasize adaptability, individual initiative, and market
responsiveness (White et al. 2003); organizational priorities that are consistent with national
cultures high on self-expression that are more comfortable with individual choice and authority
(Steenkamp and de Jong 2010). Thus, following the congruence-encouragement principle
established in previous research in which congruency between organizational and national
cultures will lead to superior performance, we expect a positive interaction between clan and
self-expression.
National Survival vs. Self-Expression Culture x Hierarchical Organizational Culture.
Managers in countries with greater self-expression cultures are more likely to be empowered to
make decisions independently because they are individually held responsible for the results of
their decisions. With hierarchical organizational cultures emphasizing order, rules, and top-down
processes and regulations, managers in self-expression cultures could face greater objections
from their superiors (Tan et al. 1998), which would require managers to demonstrate the results
of their decisions (Newman and Nollen 1996) through metric use. Hence, we expect a positive
interaction between self-expression national and hierarchical organizational cultures.
EMPIRICAL DATA
22
Data Collection
To establish a global sample of managers, we initially targeted the 16 countries included in the
G7 (Canada, France, Germany, Italy, Japan, U.K., and the U.S.), BRICS (Brazil, Russia, India,
China, and South Africa), and MIST (Mexico, Indonesia, South Korea, and Turkey)
classifications. We selected these countries because they account for over 80% of the world’s
total GDP, and are classified into these respective categories based on their economic and
political importance. After being unable to obtain enough quality responses for South Africa, it
was replaced by Australia. Information on digital emphasis, which will be described later, was
unavailable for Turkey, so analysis on that country was also dropped. Primary data on managers,
firms, and metric use was collected in collaboration with a Survata, a market research panelist
company, while secondary data was used for country-level constructs.
Primary Data Questionnaire
Our questionnaire is based on Mintz and Currim (2013), using identical measures and items, with
the addition of the organizational culture construct. To quickly recap the format of the
questionnaire, it consisted of two sections. First, managers were asked to indicate which of 10
decisions they recently undertook, with the clarification following Menon et al. (1999, p. 28) that
they were to select decisions that ―(1) were not so recent that performance evaluation is
premature and (2) not so long ago that memory about the decision and performance is fuzzy.‖
For each decision managers indicated they recently undertook, they were tasked to indicate
which of 12 general and 3 specific marketing metrics and 12 general and 3 specific financial
metrics. The metrics listed were based on Ambler (2003), Ambler et al. (2004), Barwise and
Farley (2004), Du et al. (2007), Farris et al. (2010), Hoffman and Fodor (2010), Lehmann and
Reibstein (2006), Mintz and Currim (2013), Pauwels et al. (2009), and Srinivasan et al. (2010).
In Online Appendix A we provide the recap of metrics. Second, managers answered questions on
23
the remaining organizational and managerial drivers of metric use, in addition to controls related
to manager, firm, and industry characteristics. As mentioned previously, all constructs except
organizational culture are identical to Mintz and Currim (2013), and for the readers convenience,
we provide summaries of their literature sources, definitions, items, and descriptive statistics in
Table 1.
For organizational culture, the measures are based on Cameron and Quinn (2011)’s
highly utilized Organizational Culture Assessment Instrument (OCAI). Six content dimensions
serve as the basis for the OCAI:
1. The dominant characteristics of the organization, or what the overall organization is like.
2. The leadership style and approach that permeate the organization.
3. The management of employees or the style that characterizes how employees are treated
and what the working environment is like.
4. The organizational glue or bonding mechanisms that hold the organization together.
5. The strategic emphases that define what areas of emphasis drive the organization’s
strategy.
6. The criteria of success that determine how victory is defined and what gets rewarded and
celebrated.
Four alternatives are provided for each content dimension, reflective of each of the four
organizational culture types. As advocated by Cameron and Quinn (2011), we employ the
constant-sum scale version in which managers are asked to allocate 100 points among items
representative of the four organizational culture variables for each of the six content dimensions
described above (see Online Appendix B). A second primary reason for employing this OCAI
version is because it forces managers to identify tradeoffs that actually occur within their
organization, even though we acknowledge that its construct reliability is sometimes worse than
Likert-scale based measures. To avoid linear dependency in our modeling that exists because of
24
the ipsative, constant sum measure, we randomly ignore one item per each of the four
organizational constructs.1
The questionnaire was first developed in English, and following previous international
research (e.g., Steenkamp and Geyskens 2006), we had two native speakers from each country
requiring translations (Brazil, France, Germany, India, Indonesia, Italy, Japan, Mexico, Russia,
and South Korea) back-translate the questionnaire into the respective language. Our market
research collaborating firm, Survata, was tasked with finding representative and qualified
managers, and offering proper monetary incentives to obtain accurate responses to our online
survey. Screenings based on managerial qualifications, attention checks, and post-hoc analysis
before, during, and after managers interacted with the survey were designed and implemented to
help control for the quality of respondents. Overall, our final primary dataset consists of 4,103
decisions by 1,546 firms/managers (each firm had a manager answer the questionnaire) in 15
countries with an average of 103 firms describing 274 decisions per country.
Secondary Data
For country-level data, we obtained data from several external secondary datasets. National
management is based on the country’s level of competent senior management and management
education as scored in the IMD World Talent Report (2014). Digital emphasis is calculated
based on the country’s three-year average percent of all advertising spending on digital mediums,
percent of population who purchased a product online, and two-year average percent of the
population online, with each measure calculated as relative to the average of the 15 countries
included in our dataset, as found in eMarketer (2015). Consumer orientation is the country’s
degree of customer orientation, buyer sophistication, and extent of marketing based on data from
1 We also tried 6 other variations of the OCAI measures, including Likert-scale based, but results largely hold
regardless of how we calculate the construct. 2 Note this latest version of the World Values Survey did not provide scores for Italy, so we were forced to use a
25
the World Economic Forum’s World Executive Survey Global Competitiveness Report (2015).
National culture variables are directly taken from the World Values Survey (2014)2. Table 1
recaps the specific items taken from each data source to develop our constructs.
Testing for Collinearity, Reliability, and Non-Response, Self-Selection, and Common Method
Biases
In terms of managerial, firm, and industry variables, almost all 424 of 435 (97%) of the pairwise
correlations calculated in the Online Appendix C are below 0.4 (Leeflang et al. 2000). Country
variables, as expected, exhibit some correlation especially regarding the consumer orientation
resource construct. Therefore, we ran additional analysis for the RBT conceptual model that
excludes management quality or digital emphasis from the analysis, and obtained similar results
as specifying all three national-based resource variables. Variance inflation factor scores
calculated for all constructs are all well below 6, indicating no multicollinearity problems for
estimation of the models (Hair et al. 1998).
All computed coefficient alphas were greater than .7, other than the constant-sum
construct for organizational culture, which is line with previous research (e.g., Deshpandé et al.
1993), and market turbulence which has reverse coded items. We do not detect non-response bias
in our sample based on the Armstrong and Overton (1977) test in which late and early
respondents scores are compared on the included constructs (p>.05). To help mitigate a priori
common method and self-selection biases (i.e., where managers only participate or will only
report decisions in which they employ large amounts of metrics), we adapted the Fredrickson
and Mitchell (1984) instructions and stated in our recruitment letter and in the introduction to the
questionnaire that we were interested in responses from managers who do and do not employ
2 Note this latest version of the World Values Survey did not provide scores for Italy, so we were forced to use a
prior version’s scores.
26
metrics in their decisions and that their answers would remain anonymous (Chang, van
Witteloostuign and Eden 2010). Out of the 4,103 marketing mix decisions reported in the total
sample, 595 (15%) involved managers who only employed zero to three metrics, evidence that
managers were not reluctant to describe decisions in which no metrics or a very small number of
metrics were involved. Lastly, we do not find evidence of common-method bias based on
Harman’s one-factor test and Lindell and Whitney (2001) post hoc tests. We employed White's
(1980) test to check for heteroscedasticity and found that the null hypothesis on the variance of
residuals being homogenous cannot be rejected in any of our models, indicating no
heteroscedasticity.
Descriptive Statistics
Table 1 provides descriptive mean, standard deviation, and Cronbach alpha’s for each of our
measures. The data exhibits a good variety between metric-based training, compensation, work
experience, CMO presence, market orientation, and firm size. In Table 2 we present statistics
regarding metric use by country, and provide the percentages that general metrics were employed
by firms in these different countries in order of overall use. Overall, managers employed 9.1
metrics in their marketing decisions, with South Koreans (11.7) and Chinese (11.1) managers
reporting the greatest use of metrics, and Japanese (4.3) and French (5.8) reporting the least use.
In terms of individual metrics, satisfaction, awareness, and ROI were the three metrics most
employed by our sample but it is interesting to note differences between the countries, such as
Indians employing awareness in 71% of their decisions, but Japanese only employing it for 27%
of their decisions. The data in Table 2 should help provide researchers and managers benchmarks
for which metrics are employed by managers in the 15 countries listed.
ANALYSIS
Econometric Specification
27
Our conceptual framework of the antecedents of managerial use of marketing metrics involves
variables at two levels of aggregation: the organization and managerial level, and the country
level (see Figure 1). Such data are designated as multi-level data (Bryk and Raudenbush 1992)
since these levels are hierarchical in that respondents are nested within countries. OLS regression
applied to the individual-level data pooled across all countries yields biased estimates of the
parameters and the estimated standard errors of the effects are too small. Multilevel modeling
(MLM; Bryk and Raudenbush 1992) has been specifically developed to deal with multi-level
data. It enables the simultaneous estimation of relationships of variables at two (or more) levels.
It borrows strength from all the data in each of the countries, and makes it possible to estimate
cross-level effects, thus enabling one to test hypotheses on how variables measured at the
country level affect relations occurring at the individual level. In addition, in the multi-level
model, the coefficients of individual-level effects may be treated as random, partially explained
by country-level variables. This enables the investigation of interactive effects of individual-level
and country-level variables. We will use MLM to test our research hypotheses.
For our resources-based conceptual framework, the data will be analyzed using the
following multi-level model:
Level 1 ( )
∑
Level 2 ( ) ( )
( )
( )
( )
( ) ( )
( )
28
For our cultural-based conceptual framework, the specification is analogous, with cultural
variables replacing the resource-based variables for main effects, and appropriate cross-level
interactions included in the level 2.
Level 1 ( )
∑
Level 2 ( )
( )
( ) ( )
( )
( )
For all models, total number of metrics employed in an individual decision is specified as
the dependent variable. Controls are all other significant results from Mintz and Currim (2013),
which includes type of decisions (9 types of marketing mix decisions each relative to
PR/Sponsorships), and other managerial (manager level, functional area, and quantitative
background), firm (organizational involvement in decision, recent performance, B2B vs. B2C,
and goods vs. service oriented), and industry characteristics (concentration and turbulence).3 The
random effects ( ) are multivariate normally distributed over countries. To control
for potential cross-national differences in scale usage (Baumgartner and Steenkamp 2001), we
grand-mean centered all country level variables and group-mean centered all firm and
managerial level variables by country.
Results
3 Note we exclude private vs. public ownership as a control variable since the countries listed in our sample have
different requirements, regulations, and obstacles to be publicly listed (e.g., La Porta et al. 2006). In addition, we
exclude strategic orientation due to its conceptual and empirical similarity to organizational culture.
29
To assess and produce robust empirical results, we estimate five variations of the resource-based
(cultural-based) models. R1 (C1) focuses solely on organizational, firm, and control variables;
i.e., it ignores national level resources (culture) and sets their respective ’s ( ’s) in equations 2,
3, 5, 6, and 7 (11-15) to zero. R2 (C2) focuses on national level variables while ignoring
organizational and managerial level resources by setting their respective to zero. R3 (C3)
ignores controls variables. R4 (C4) examines only main effects, so the difference from R1 (C1) is
in equation 2, ( ) are no longer set to zero. R5 (C5) is the full model
that includes cross-level interaction-effects without any parameters set to zero.
In Table 3, we provide results of R1-R5 which test our resource-based conceptual
framework. For national resource variables, we find management quality (p<.01) and digital
emphasis (p<.05) positively impact metric use across all models except for R3, while consumer
orientation has a negative impact in all models (p<.01). We note that not including our control
variables in R3 may have led to omitted variable bias and spurious findings as national
management quality and digital emphasis are no longer found to be significant in this model,
which demonstrates the importance of including these controls in our analysis. For firm and
managerial resources, we find CMO presence, firm size, metric training, and metric
compensation all positively associate with metric use in each model (each p<.01), but do not find
support of a relationship for either market orientation or work experience and metric use in any
model. In addition, in line with our expectations, we find support of positive interactions
between (a) national management quality and metric training (p<.05) and (b) national digital
emphasis and firm size (p<.01), and (c) a negative interaction between national management
quality and metric compensation (p<.01). Overall, we find robust results across all models that
national and firm and managerial resources impact metric use.
30
In Table 4 we provide results of C1-C5 which test our cultural-based conceptual
framework. For national culture variables, we find partial results across the models
demonstrating that survival vs. self-expression negatively associate with metric use (p<.05 in C2
and C5), indicating that firms employ more metrics in countries whose societies are more
survival focused, but do not find significant support in any of the models of a relationship
between traditional vs. secular societies and metric use. Similarly, we do not find support of any
main effect of organizational culture on metric use in any model other than clans are found to
employ more metrics in C3. This result is surprising given the extensive prior literature that
suggests organizational culture is often a primary driver of firm and managerial behavior, but
mirrors Moorman's (1995) findings that organizational cultural factors were less important to
predicting the mere presence of organizational information processes. Finally, in line with our
expectations, we find positive interactions between (a) national survival vs. self-expression
culture and clan organizational culture, (b) adhocracy organizational culture, and (c) hierarchical
organizational culture (each p<.01), and (d) a negative interaction between national traditional
vs. secular culture and market organizational culture (p<.05). Thus, our results demonstrate that
while national culture only partially effects and organizational culture primarily does not effect
metric use, national and organizational culture instead act only in congruence or interacting with
each other to increase or decrease metric use in certain societies.
Post-Hoc Integration of How Resources and Cultures Impact Metric Use
Our earlier results show that national and organizational resources exhibit main and interaction
effects in their impact of managerial metric use, but national and organizational cultural variables
only exhibit interaction effects. In this section, we try to synthesize these findings by conducting
an exploratory analysis in order to integrate resources and culture into one framework. We begin
by proposing a model, I1, which includes all significant effects from our main models, R5 and
31
C5, into a single MLM framework. Results in Table 5 demonstrate the results of our earlier
models hold, except for the interaction between traditional vs. secular national culture and
market organizational culture is no longer found to be significant.
Next, we exclude this interaction term and extend I1 to account for six possible other
additional congruencies between national and organizational level variables. Specifically, in I2,
we include interactions between:
National management quality and hierarchy organizational culture: greater quality in
management is imposing top-down processes, which should encourage superior managerial
practices like metric use;
National digital emphasis and clan organizational cultures: digital resources should alter
communication from more traditional face-to-face clans culture towards technology-based
more formal communication systems;
National consumer orientation and adhocracy organizational cultures: consumer orientation
would structure adhocracies, or more uncertain avoidant organizations, to focus more on
consumers;
National digital emphasis and market organizational cultures: greater technological resources
would allow market cultures to rely even more on control systems to evaluate employees;
National survival vs. self-expression cultures and metric training resources: managers
partaking in metric training in societies that have less individual autonomy will likely feel
more forced to utilize the decision aids organizations provided in training;
National traditional vs. secular cultures and CMO presence: CMO’s should have greater
power and authority to coerce managers to employ metrics within societies that rely more on
formalization and bureaucracies.
Results in Table 5 show positive interaction effects for (a) national management quality and
hierarchy organizational culture, (b) national digital emphasis and clan organizational cultures,
and (c) national digital emphasis and market organizational cultures, and (d) a negative
32
interaction effect for national survival vs. self-expression cultures and metric training resources.4
Consequently, the results of this integrated resources and cultural model of drivers of metric use
around the world are a good starting point for interested marketing and international business
researchers.
CONCLUSION
The Marketing Science Institute (MSI) and the Institute for the Study of Business Markets
(ISBM) have consistently designated research on metrics as priority (e.g., MSI Research
Priorities 1998-2016, ISBM B-to-B Trends 2008-2014). While global competition has only
increased marketers need to be accountable through the use of metrics in their marketing
decisions (Lehmann and Reibstein 2006), little prior research has investigated what drives metric
use in global settings, limiting prior research’s practical implications. Further, the lack of insights
on metric use by managers in a more global setting has hindered marketers from establishing
boundary conditions and empirical generalizations, which are theoretical limitations. Therefore,
this work investigates both micro-organizational and macro-country antecedents by proposing a
dual-competing resource-based and cultural-based theory of drivers of metric use which accounts
for national, organizational, and managerial characteristics, while also controlling for the type of
decision, manager, and firm who partook in the questionnaire. Based on initial results of the
dual-competing conceptual frameworks, an integrated model involving cross-theory interactions
is also proposed. We now discuss theoretical and managerial contributions in addition to
limitations and future avenues of this work.
This works utilizes RBT and cultural theories to develop a conceptual model of metric
use by managers around the world. It extends prior research on RBT by providing an integrated
4 Note, in this integrated framework some of our original effects are no longer found to be significant, most likely
due to collinearity between so many interaction terms.
33
framework based on resources to describe managerial decision making. Knowledge and
information use can contribute as a strategic resource for firms (Hult et al. 2006) with previous
research demonstrating that increasing metric use associates with superior marketing
performance (Mintz and Currim 2013), therefore understanding the resource-based drivers of this
strategic research is important for managerial practice. Results indicate that national,
organizational, and managerial resources each influence metric use. In addition, beneficial
congruencies in order to increase metric use are proposed and identified.
Prior research also suggests that the shared cultural values of organizations and
individuals should influence managerial practices. This work extends prior research on cultural-
theory by investigating both the effects of national and organizational culture on managerial
practices, instead of just looking at one or the other or assuming that everything is the same or
everything is different. Unlike the RBT-based conceptual framework, national and organizational
factors have a less direct effect on metric use, but instead influence it based on proper or
improper social reinforcement mechanisms through a society’s and organization’s values.
Overall, we find the RBT conceptual model does a better job explaining metric use than
the cultural based model, however the results of both models offer the following managerial
contributions. First, we find that resources matter in order for metrics to be employed; and
resources need to be in place from each of a national, organizational, and managerial level. Our
results show that at the national level, better quality management and superior digital emphasis
provide more articulable knowledge and knowledge-based resources for firms to enact superior
decision techniques; at the organizational level, CMO presence and greater firm size provide
marketers better resources for marketing decisions; and at the managerial level, metric training
and compensation provide tacit knowledge and motivation to employ such resources.
Congruencies between national management quality and metric training, and national digital
34
emphasis and firm size national digital emphasis, are proposed and identified to increase metric
use even further.
Second, we find that neither national nor organizational culture generally influence
managerial metric use around the world. Instead, metric use is impacted by the congruence
between the micro-organizational and macro-country types of cultures. Identifying congruencies
between such types of cultures is important to managerial practice as without such identification,
managers may believe culture has little or no effect on metric use. For example, we find the
congruence between managers residing in traditional (vs. secular) societies and working in
market organizational cultures associates with greater use of metrics in marketing decisions; yet,
without identifying such congruencies we would not have found that either culture influences
metric use. In addition, we find that managers residing in societies that allow for greater self-
expression employ metrics less overall, however, we also find that structuring the firm to follow
a more clan, adhocracy, or hierarchical organizational culture can help increase metric use in
such societies.
Third, in our post-hoc analysis, we integrate synergies between resources and cultures.
For example, to increase managerial use of metrics, we find superior managerial talent in a
country provide a useful resource when firms have a more hierarchical organizational culture. In
addition, while providing metric training to managers is found to be beneficial towards metric
use for all firms, we find that it is less useful in more self-expression cultures where managers
have greater individual choice and autonomy and may be less likely to utilize such training in
their decisions. Consequently, firms operating in such societies may want to provide more direct
motivation to managers such as increasing their metric-based compensation in order to
incentivize them to employ additional metrics. Finally, we find that as a nation’s emphasis on
digital technology increases, managers are utilizing an increasing amount of metrics in their
35
decisions, even in clan based organizational cultures which are often described as reliant on more
informal face-to-face communication; results that provide an example of how increasing digital
technology is shifting managerial decisions towards knowledge-based decision processes.
Limitations of this research are that we were forced to conduct primary research on self-
reported data on managerial metric use, and organizational and managerial characteristics. In
addition, as is common with survey methodologies, the data is cross-sectional and may not be
fully representative of national samples. It would be preferable to obtain objective, behavioral
data from larger samples, but this data was unavailable to the authors. A strength of this study is
that we were able to obtain data on a wide variety of firms, industries, and types of marketing
mix decisions. However, its broad scope also limits detailed insights per type of industry or type
of decision. This study considers 84 different metrics across 10 types of marketing mix decisions
based on 10 different published studies, but it may have excluded several relevant metrics from
the analysis. Managers were given the option to write-in any excluded metrics, but few did so.
Future research can investigate how individual metrics impact marketing mix decision
performance to propose ―right metrics‖ for managers to employ for their situation. In addition, it
would be interesting to examine how firm’s learn from metrics, and how they adapt their use for
different circumstances.
36
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Figure 1. Conceptual Framework
National Drivers
Firm and Managerial Drivers
Resources
Management Quality
Market Orientation
Digital Use/Emphasis
Culture
Traditional vs. Secular
Survival vs. Self-Expression
Total Metric
Use
Organizational Culture
Clan
Adhocracy
Hierarchy
Market-Like
Theoretical Drivers
Managerial Resources
Metric Training
Metric Compensation
Work Experience
Firm Resources
Market Orientation
CMO Presence
Firm Size
Controls Type of Decision
Manager Chr.
Firm Chr.
Environment Chr.
Total Metric
Use
Controls Type of Decision
Manager Chr.
Firm Chr.
Environment Chr.
Resource-Based
Cultural-Based
45
Figure 2. A Model of Organizational Culture Types
Source: Deshpande, Farley, and Webster (1993)
46
Table 1. Literature Sources, Measures, and Descriptive Statistics of Variables Construct Basis Definition and Operational Measures α Mean St. Dev.
National Variables
Resources
National Management
Quality
(IMD World Talent
Report 2014)
From a scale of 1-6 please rate the following (note IMD then converts average
country score to a 1-10 scale):
Competent Senior Management (1=not readily available, 6=are readily available)
Management Education (1=does not meet the needs of the business community,
6=meets the needs of the business community)
.95 5.61 1.07
National Digital
Emphasis (eMarketer
2015)
Average of the following 3 country-wide statistics, each item was subsequently
computed as relative to the 15 country level mean:
3-year Average of the Percent of all Ad Spending on Digital Ads (average of
eMarketer, GroupM, and ZenithOptimedia estimates; α =.97)
Percent of Population who purchased a Product Online (average of eMarketer
yearly and We are Social monthly estimates; α =.92)
2-year Average of the Percent of Population on the Internet (eMarketer)
.92 0.97 0.38
National Consumer
Orientation
(World Economic
Forum’s World
Executive Survey
Global Competitiveness
Report 2015)
From a scale of 1-7 please rate the following (note WEF weighs the respondents’
answers to provide a national average):
Degree of Customer Orientation: In your country, how well do companies treat
customers? (1=poorly – mostly indifferent to customer satisfaction; 7=extremely
well – highly responsive to customers and seek customer retention)
Extent of Marketing: In your country, how successful are companies in using
marketing to differentiate their products and services? (1=not successful at all; 7=
extremely successful)
Buyer Sophistication: In your country, on what basis do buyers make purchasing
decisions? (1=based solely on the lowest price; 7=based on sophisticated
performance attributes)
.86 4.65 0.45
Culture
National Traditional vs.
Secular
(Inglehart & Welzel
2005)
Data taken from the 2010-2014 (Wave 6) World Values Survey
--- 0.15 0.88
National Survival vs.
Self-Expression
(Inglehart & Welzel
2005)
Data taken from the 2010-2014 (Wave 6) World Values Survey
--- 0.40 1.00
Firm and Managerial
Level Variables
Resources
CMO Presence Does your firm employ a Chief Marketing Officer (CMO)? --- 0.67 0.47
Market Orientation
(Deshpande & Farley
1998; Kohli & Jaworski
1990; Verhoef &
Leeflang 2009)
How strongly do you agree or disagree with each of the following statements: (1 =
strongly disagree, 7 = strongly agree)
Our business objectives are driven primarily by customer satisfaction
We constantly monitor our level of commitment and orientation to serving
customer needs
We freely communicate information about our successful and unsuccessful
customer experiences throughout all business functions
Our strategy for competitive advantage is based on our understanding of customer
needs
We measure customer satisfaction systematically and frequently
We have routine or regular measures for customer service
We are more customer focused than our competitors
I believe this business exists primarily to serve customers
.86 5.59 0.78
Firm Size Approximately how many full-time employees does your firm have?
---
9476
(median=
500)
130351
Metric-based Training
(Mintz and Currim
2013)
Please indicate your level of training with metrics (can be through work or
educational experiences): (1= much less than average amount of training, 7 = much
more than average amount of training)
Overall Metrics
Marketing Metrics
Financial Metrics
.82 5.20 0.95
Metric-based
Compensation
Please indicate how important each metric type is related to your compensation
package: (1= not at all important, 7 = extremely important) .83 5.61 0.90
47
(Mintz and Currim
2013)
Overall Metrics
Marketing Metrics
Financial Metrics
Managerial Experience
(Mintz and Currim
2013)
How many years of managerial experience do you have?
How many years have you been working for this company?
How many years have you been working at your current position?
.82 8.01 4.96
Culture
Clan-Like (Cameron
and Quinn 2011)
Please distribute 100 points among the four descriptions depending on
how similar the description is to your business (Note see Online Appendix B for other
items in question managers were required to trade-off among)
My organization is a very personal place. It is like an extended family. People
seem to share a lot of themselves.
The leadership in my organization is generally considered to exemplify
mentoring, facilitating, or nurturing.
The management style in my organization is characterized by teamwork,
consensus, and participation.
My organization is held together by loyalty and mutual trust. Commitment to
this organization runs high.
My organization emphasizes human development. High trust, openness, and
participation persists.
My organization defines success on the basis of the development of human
resources, teamwork, employee commitment, and concern for people.
.67 24.37 5.45
Adhocracy-Like
(Cameron and Quinn
2011)
Please distribute 100 points among the four descriptions depending on
how similar the description is to your business (Note see Online Appendix B for other
items in question managers were required to trade-off among)
My organization is a very dynamic and entrepreneurial place. People are willing
to stick their necks out and take risks.
The leadership in my organization is generally considered to exemplify
entrepreneurship, innovating, or risk taking.
The management style in my organization is characterized by individual risk-
taking, innovation, freedom, and uniqueness.
My organization is held together by commitment to innovation and
development. There is an emphasis on being on the cutting edge.
My organization emphasizes acquiring new resources and creating new
challenges. Trying new things and prospecting for opportunities are valued.
My organization defines success on the basis of having the most unique or the
newest products. It is a product leader and innovator.
.55 24.79 5.54
Hierarchy-Like
(Cameron and Quinn
2011)
Please distribute 100 points among the four descriptions depending on
how similar the description is to your business (Note see Online Appendix B for other
items in question managers were required to trade-off among)
My organization is very results oriented. A major concern is with getting the job
done. People are very competitive and achievement oriented.
The leadership in my organization is generally considered to exemplify an
aggressive, results-oriented, no-nonsense focus.
The management style in my organization is characterized by hard-driving
competitiveness, high demands, and achievement.
My organization is held together by the emphasis on achievement and goal
accomplishment. Aggressiveness and winning are common themes.
My organization emphasizes competitive actions and achievement. Hitting
stretch targets and winning in the marketplace are dominant.
My organization defines success on the basis of winning in the marketplace and
outpacing the competition. Competitive market leadership is key.
.55 25.47 6.09
Market (Cameron and
Quinn 2011)
Please distribute 100 points among the four descriptions depending on
how similar the description is to your business (Note see Online Appendix A for
other items in question managers were required to trade-off among)
My organization is a very controlled and structured place. Formal procedures
generally govern what people do.
The leadership in my organization is generally considered to exemplify
coordinating, organizing, or smooth-running efficiency.
The management style in my organization is characterized by security of
employment, conformity, predictability, and stability in relationships.
My organization is held together by formal rules and policies. Maintaining a
smooth-running organization is important.
My organization emphasizes permanence and stability. Efficiency, control and
smooth operations are important.
.60 25.40 6.17
48
My organization defines success on the basis of efficiency. Dependable delivery,
smooth scheduling, and low cost production are critical.
Control Variables
Marketing-mix
Decision (Menon et al.
1999)
Please indicate which types of major marketing decisions you have undertaken (or
implemented) that (1) were not so recent that performance evaluation is premature
and (2) not so long ago that memory about the decision and performance is fuzzy:
Traditional Advertising (i.e., TV, Magazine, Radio, etc.)
Internet Advertising (i.e., Banner Ads, Display Ads, SEO, etc.)
Direct to Consumer (i.e., Emails, CRM, Direct mail, etc.) Social Media (i.e., Twitter, Facebook, MySpace, etc.)
Price Promotions Pricing
New Product Development
Sales Force
Distribution
PR/Sponsorships
---
0.12
---
0.17
0.13
0.15
0.06
0.09
0.10
0.08
0.04
0.06
Functional Area
(Finkelstein, Hambrick,
& Cannella 2009)
Please indicate your job title (whether a manager works in the marketing department
or not):
CEO/Owner, CMO, C-Level (Other than Marketing), SVP/VP of Marketing, SVP/VP
Sales, SVP/VP (Other than Marketing and Sales), Director of Marketing, Director of
Sales, Brand Manager, Marketing Manager, Product Manager, Sales Manager, Other
(Please list)
--- 0.61 ---
Managerial Level
(Finkelstein, Hambrick,
& Cannella 2009)
Please indicate your job title (whether a manager is (a) VP-level or higher (e.g., SVP,
C-level or Owner) or (b) lower than VP-level (e.g., Director, Manager):
CEO/Owner, CMO, C-Level (Other than Marketing), SVP/VP of Marketing, SVP/VP
Sales, SVP/VP (Other than Marketing and Sales), Director of Marketing, Director of
Sales, Brand Manager, Marketing Manager, Product Manager, Sales Manager, Other
(Please list)
--- 0.43 ---
Quantitative
Background
Please rate your qualitative/quantitative background: (1 = entirely qualitative, 7 =
entirely quantitative)
Overall orientation
Educational Background
Work Experience Background
.85 4.56 1.30
Organizational
Involvement
(Noble & Mokwa 1999)
How strongly do you agree or disagree with each of the following statements:
(1 = strongly disagree, 7 = strongly agree)
This marketing action was a real company-wide effort
People from all over the organization were involved in this marketing action
A wide range of departments or functions in the company got involved in this
marketing action
.84 5.12 1.15
Recent Business
Performance
(Jaworski & Kohli
1993)
To what extent did the overall performance of the business unit meet expectations last
year: (1= poor, 7=excellent)
To what extent did the overall performance of your business unit relative to your
major competitors meet expectations last year: (1= poor, 7=excellent)
.79 5.51 0.96
B2C (vs. B2B)
(Verhoef & Leeflang
2009)
Please indicate the extent to which your sales come from B2B or B2C markets: (1 =
mostly B2B, 7 = mostly B2C) --- 4.30 1.70
Services (vs. Goods)
(Verhoef & Leeflang
2009)
Please indicate the extent to which your sales come from goods or services markets:
(1 = mostly goods, 7 = mostly services) --- 3.89 1.93
Industry Concentration
(Kuester, Homburg, &
Robertson 1999)
Approximately what percentage of sales does the largest 4 competing businesses in
your market control?
0-50%, 51-100%
--- 0.28 ---
Market Turbulence
(Miller, Burke, & Glick
1998)
How strongly do you agree or disagree with each of the following statements
(1 = strongly disagree, 7 = strongly agree): ® = reverse scored
Products/services become obsolete very slowly in your firm’s principal industry
®
Your firm seldom needs to change its marketing practices to keep up with
competitors ®
Consumer demand and preferences are very easy to forecast in your firm’s
principal industry ®
Your firm must frequently change its production/service technology to keep up
with competitors and/or consumer preferences
.64 4.33 1.14
49
Table 2. Use of General Metrics across Countries
O
ver
all
Au
stra
lia
Bra
zil
Can
ada
Ch
ina
Fra
nce
Ger
man
y
Ind
ia
Ind
on
esia
Ital
y
Jap
an
Mex
ico
Ru
ssia
So
uth
Ko
rea
UK
US
Number of
Decisions 4103 295 280 239 322 158 333 333 281 372 160 322 260 244 282 222
Number of
Managers/Firms 1546 97 101 95 105 79 123 86 126 111 88 118 107 107 108 95
Total Metrics
Employed 9.1 10.1 8.9 8.5 11.1 5.8 8.9 10.7 8.5 8.4 4.3 9.7 10.2 11.7 8.0 7.4
Satisfaction 52% 58% 64% 50% 60% 47% 56% 66% 40% 48% 21% 55% 45% 64% 51% 36%
Awareness 45% 38% 35% 47% 51% 27% 46% 71% 42% 40% 27% 49% 48% 46% 46% 45%
ROI 43% 48% 46% 50% 49% 27% 50% 59% 33% 52% 18% 30% 34% 49% 45% 37%
Likeability 41% 36% 20% 38% 42% 20% 41% 53% 37% 48% 17% 46% 59% 52% 39% 36%
Net Profit 40% 43% 43% 37% 48% 32% 25% 36% 57% 44% 24% 53% 52% 32% 34% 28%
ROS 38% 50% 41% 32% 50% 25% 33% 58% 25% 40% 14% 26% 45% 46% 40% 29%
Target Volume 38% 35% 34% 38% 61% 27% 32% 30% 61% 40% 24% 38% 39% 43% 24% 30%
Total
Customers 38% 32% 41% 44% 49% 35% 36% 27% 53% 42% 23% 46% 41% 32% 25% 28%
Market Share 36% 45% 29% 31% 51% 28% 42% 30% 44% 39% 24% 37% 33% 45% 24% 26%
Loyalty 35% 41% 37% 37% 40% 20% 35% 29% 41% 38% 15% 40% 36% 37% 26% 27%
Preference 34% 33% 45% 26% 34% 20% 29% 46% 27% 31% 6% 45% 42% 54% 28% 24%
Cust. Segment
Profitability 31% 35% 38% 28% 31% 19% 46% 29% 32% 26% 24% 40% 32% 30% 28% 18%
ROMI 31% 34% 29% 21% 38% 18% 45% 55% 16% 30% 10% 20% 29% 46% 26% 25%
Willingness to
Recommend 28% 32% 24% 25% 37% 13% 42% 28% 24% 24% 13% 27% 38% 38% 23% 26%
CLV 27% 35% 28% 22% 29% 12% 32% 53% 17% 26% 9% 17% 16% 34% 26% 27%
Marketing Exp.
on Brand
Building
27% 22% 26% 14% 47% 7% 44% 31% 19% 24% 15% 27% 28% 41% 18% 18%
Share of Voice 26% 37% 24% 23% 44% 9% 20% 55% 17% 10% 3% 19% 30% 32% 27% 23%
NPV 25% 29% 23% 23% 28% 19% 33% 38% 17% 22% 9% 28% 17% 39% 19% 18%
Perceived
Product Quality 25% 18% 41% 27% 36% 28% 13% 14% 13% 24% 11% 35% 43% 38% 22% 10%
EVA 24% 25% 21% 23% 31% 14% 26% 25% 22% 30% 9% 26% 12% 48% 22% 19%
Share of
Customer
Wallet
23% 39% 26% 19% 10% 13% 29% 26% 17% 22% 19% 30% 15% 29% 23% 15%
Stock Prices /
Stock Returns 14% 15% 15% 17% 10% 4% 5% 9% 23% 11% 3% 26% 16% 20% 13% 12%
Consideration
Set 8% 7% 10% 4% 16% 5% 2% 5% 7% 3% 7% 7% 13% 21% 7% 5%
Tobin’s q 4% 8% 3% 6% 5% 1% 2% 2% 1% 1% 3% 3% 2% 14% 6% 1%
Note: Use of specific metrics across countries not shown for space reasons
50
Table 3. Results of Resource-based Drivers of Metric Use
R1: No
National
Resource
Variables
R2: No
Org/Mgr
Resource
Variables
R3: No
Control
Variables
R4: All Main
Effects
R5: Full
Model
Intercept 5.97 *** 5.75 ** 9.00 *** 6.13 ** 6.32 **
National Variables
National Mgmt. Quality 0.61 *** 0.29 1.03 *** 0.85 ***
National Digital Emphasis 1.83 ** 1.27 2.98 *** 3.42 ***
National Consumer Orientation -4.66 *** -3.08 *** -5.31 *** -5.41 ***
Firm and Individual Variables
Firm Resources
CMO Presence 0.87 *** 1.01 *** 0.84 *** 0.82 ***
Market Orientation -0.18 -0.07 -0.16 -0.20
FirmSize(LN) 0.31 *** 0.36 *** 0.32 *** 0.32 ***
Managerial Resources
Metric Training 0.65 *** 0.75 *** 0.68 *** 0.65 ***
Metric Compensation 1.04 *** 1.18 *** 1.02 *** 1.18 ***
Work Experience -0.04 * -0.05 -0.04 * -0.03
Resource Cross-Level Interactions
National Mgmt. Quality * Metric Training
0.25 **
National Mgmt. Quality * Metric Comp.
-0.69 ***
National Consumer Orientation * CMO Pres.
0.17
National Digital Emphasis * FirmSize(LN)
0.40 ***
Controls
Traditional Advertising Decision1 -1.24 *** -1.25 *** -1.24 *** -1.20 ***
Internet Advertising Decision1 -0.81 ** -0.86 ** -0.83 ** -0.83 **
Direct to Consumer Decision1 -0.53 -0.65 * -0.52 -0.47
Social Media Decision1 -0.41 -0.56 -0.53 -0.53
Sales Force Decision1 0.21 0.15 0.26 0.30
Price Promotions Decision1 -0.84 * -1.06 *** -1.06 *** -1.04 ***
Pricing Decision1 -0.49 -0.53 -0.43 -0.40
New Product Development Decision1 -0.09 -0.15 -0.02 0.00
Distribution Decision1 0.26 0.35 0.31 0.33
Functional Area (Marketing) 0.19 0.60 *** 0.21 0.28
Managerial Level (TMT) 0.57 *** 0.60 *** 0.60 *** 0.63 ***
Quantitative Background -0.09 0.14 -0.10 -0.12
Organizational Involvement 0.40 *** 0.77 *** 0.40 *** 0.40 ***
Recent Business Performance 0.04 0.60 *** 0.02 0.02
B2B (vs. B2C) -0.15 *** -0.16 *** -0.14 ** -0.12 **
Services (vs. Goods) -0.11 ** -0.12 *** -0.10 ** -0.09 **
Market Concentration (Concentrated) 0.36 ** 0.42 ** 0.35 ** 0.35 **
Market Turbulent (More Turbulent) -0.38 *** -0.37 *** -0.40 *** -0.41 ***
Model Diagnostics
-2 Residual Log Likelihood 24615
24954
24633
24579
24554
Notes: 1 = relative to PR/Sponsorship decision; *p<.1, **p<.05, ***p<.01
51
Table 4. Results of Cultural-based Drivers of Metric Use
C1: No
National
Cultural
Variables
C2: No Org.
Cultural
Variables
C3: No
Control
Variables
C4: All Main
Effects
C5: Full
Model
Intercept 5.71 ** 5.24 ** 8.82 *** 5.83 ** 5.75 **
National Variables
National Traditional vs. Secular
-0.64 -0.92 -0.20
-0.60 *
National Survival vs. Self-Expression
-0.73 ** -0.53 -0.50 * -0.82 **
Firm and Individual Variables
Organizational Culture
Clan 0.00 0.11 *** -0.20 0.06
Adhocracy -0.20 0.03
-0.50 0.00
Hierarchy -0.16 0.02
-0.20 0.01
MarketLike -0.20 0.04
-0.50 0.02
Cultural Cross-Level Interactions
National Traditional vs. Secular * Market
-0.07 **
National Surv. vs. Self-Express. * Clan
0.05 ***
National Surv. vs. Self-Express. * Adhocracy
0.04 **
National Surv. vs. Self-Express. * Hierarchy
0.09 ***
Controls
Traditional Advertising Decision1 -1.22 *** -1.21 *** -1.12 *** -1.11 ***
Internet Advertising Decision1 -0.77 ** -0.72 * -0.64
-0.63
Direct to Consumer Decision1 -0.54 -0.57
-0.45
-0.45
Social Media Decision1 -0.42 -0.56
-0.47
-0.51
Sales Force Decision1 0.15 -0.16
-0.14
-0.16
Price Promotions Decision1 -1.02 *** -1.04 ** -0.94 ** -0.96 **
Pricing Decision1 -0.54 -0.54
-0.38
-0.38
New Product Development Decision1 -0.21 -0.25
-0.15
-0.14
Distribution Decision1 0.26 0.23
0.22
0.18
Functional Area (Marketing) 0.59 *** 0.54
0.51
0.49
Managerial Level (TMT) 0.66 *** 0.72 ** 0.70 ** 0.71 **
Quantitative Background 0.14 * 0.15
0.20
0.20
Organizational Involvement 0.75 *** 0.86 *** 0.84 *** 0.84 ***
Recent Business Performance 0.58 *** 0.62 ** 0.57 ** 0.57 **
B2B (vs. B2C) -0.14 *** -0.19
-0.17
-0.17
Services (vs. Goods) -0.14 *** -0.14 * -0.14 * -0.14 *
Market Concentration (Concentrated) 0.43 ** 0.47
0.50 * 0.51 *
Market Turbulent (More Turbulent) -0.38 *** -0.35 * -0.39 ** -0.40 **
Model Diagnostics
-2 Residual Log Likelihood 24948
24772
25721
24669
24677
Notes: 1 = relative to PR/Sponsorship decision; *p<.1, **p<.05, ***p<.01
52
Table 5. Results of Integrated Resources and Cultural-based Drivers of Metric Use
I1: All Significant
Coef. from
R5 and C5
I2: Integrated
Model
Intercept 6.25 ** 6.35 **
National Variables
National Resources
National Mgmt. Quality 1.21 *** 1.20 ***
National Digital Emphasis 3.76 *** 3.45 ***
National Consumer Orientation -5.75 *** -5.80 ***
National Culture
National Survival vs. Self-Expression -0.50 ** -0.37
Firm and Individual Variables
Firm Resources
CMO Presence 0.89 *** 0.87 ***
FirmSize(LN) 0.31 *** 0.31 ***
Managerial Resources
Metric Training 0.61 *** 0.60 ***
Metric Compensation 1.14 *** 1.15 ***
Cross-Level Interactions
Resources
National Mgmt. Quality * Metric Training 0.23 ** 0.32 ***
National Mgmt. Quality * Metric Comp. -0.69 *** -0.72 ***
National Digital Orientation * FirmSize(LN) 0.40 *** 0.46 ***
Culture
National Traditional vs. Secular * Market 0.05 ***
National Surv. vs. Self-Express. * Clan 0.01 0.04 **
National Surv. vs. Self-Express. * Adhocracy 0.04 *** 0.05 ***
National Surv. vs. Self-Express. * Hierarchy 0.05 *** 0.04 ***
Cross-Theory Interactions
National Mgmt. Quality * Hierarchy 0.03 ***
National Digital Emphasis * Clan 0.18 ***
National Consumer Orientation * Adhocracy
0.06
National Digital Emphasis * Market 0.12 ***
National Survival vs. Self-Expression * Metric Training -0.32 ***
National Traditional vs. Secular * CMO Presence
-0.23
Model Diagnostics
-2 Residual Log Likelihood 24584
25233
Notes: Controls not shown for space reasons; *p<.1, **p<.05, ***p<.01
53
Online Appendix A. Table of Metrics
Marketing
Mix Activity
Marketing Metrics Financial Metrics
General Metrics
• Market Share (Units or Dollars)
• Awareness (Product or Brand)
• Satisfaction (Product or Brand)
• Likeability (Product or Brand)
• Preference (Product or Brand)
• Willingness to Recommend (Product or
Brand)
• Loyalty (Product or Brand)
• Perceived Product Quality
• Consideration Set
• Total Customers
• Share of Customer Wallet
• Share of Voice
• Net Profit
• Return on Investment (ROI)
• Return on Sales (ROS)
• Return on Marketing Investment (ROMI)
• Net Present Value (NPV)
• Economic Value Added (EVA)
• Marketing Expenditures (% specifically on
Brand Building Activities)
• Stock Prices / Stock Returns
• Tobin’s q
• Target Volume (Units or Sales)
• Customer Segment Profitability
• Customer Lifetime Value (CLV)
Traditional
Advertising
• Impressions
• Reach
• Recall
• Cost per Customer Acquired / Cost per
Thousand Impressions (CPM)
• Lead Generation
• Internal Rate of Return (IRR)
Internet
Advertising
• Impressions
• Hits/Visits/Page Views
• Click-through Rate
• Cost per Click
• Conversion Rate
• Internal Rate of Return (IRR)
Direct to
Consumer
• Reach
• Number of Responses by Campaign
• New Customer Retention Rate
• Cost per Customer Acquired
• Conversion Rate
• Lead Generation
Social Media • Hits/Visits/Page Views
• Number of Followers / Tags
• Volume of Coverage by Media
• Lead Generation
• Cost per Exposure
• Total Costs
Price
Promotions
• Impressions
• Reach
• Trial / Repeat Volume (or Ratio)
• Promotional Sales / Incremental Lift
• Redemption Rates (coupons, etc.)
• Internal Rate of Return (IRR)
Pricing • Price Premium
• Reservation Price
• Relative Price
• Unit Margin / Margin %
• Price Elasticity
• Optimal Price
New Product
Development
• Belief in New Product Concept
• Attitude toward Product / Brand
• Expected Annual Growth Rate
• Expected Margin %
• Level of Cannibalization / Cannibalization
Rate
• Internal Rate of Return (IRR)
Sales Force • Reach
• Number of Responses by Campaign
• New Customer Retention Rate
• Sales Potential Forecast
• Sales Force Productivity
• Sales Funnel / Sales Pipeline
Distribution • Out of Stock % / Availability
• Strength of Channel Relationships
• Product Category Volume (PCV)
• Total Inventory / Total Distributors
• Channel Margins
• Sales per Store / Stock-keeping units
(SKUS)
PR /
Sponsorship
• Volume of Coverage by Media
• Reach
• Recall
• Lead Generation
• Cost per Exposure
• Total Costs
Source: Mintz and Currim (2013)
54
Online Appendix B. Organizational Culture Values
The next six questions relate to what your operation is like. Each question contains four
descriptions of organizations. Please distribute 100 points among the four descriptions depending
on how similar the description is to your business. None of the descriptions is any better than any
other; they are just different. For each question, please use all 100 points. You may divide the
points in any way you wish. Most businesses will be some mixture of those described.
DOMINANT CHARACTERISTICS:
My organization is a very personal place. It is like an extended family. People seem to share a lot
of themselves.
My organization is a very dynamic and entrepreneurial place. People are willing to stick their
necks out and take risks.
My organization is very results oriented. A major concern is with getting the job done. People
are very competitive and achievement oriented.
My organization is a very controlled and structured place. Formal procedures generally govern
what people do.
ORGANIZATIONAL LEADERSHIP:
The leadership in my organization is generally considered to exemplify mentoring, facilitating,
or nurturing.
The leadership in my organization is generally considered to exemplify entrepreneurship,
innovating, or risk taking.
The leadership in my organization is generally considered to exemplify an aggressive, results-
oriented, no-nonsense focus.
The leadership in my organization is generally considered to exemplify coordinating, organizing,
or smooth-running efficiency.
MANAGEMENT OF EMPLOYEES:
The management style in my organization is characterized by teamwork, consensus, and
participation.
The management style in my organization is characterized by individual risk-taking, innovation,
freedom, and uniqueness.
The management style in my organization is characterized by hard-driving competitiveness, high
demands, and achievement.
The management style in my organization is characterized by security of employment,
conformity, predictability, and stability in relationships.
ORGANIZATIONAL COHESIVENESS:
My organization is held together by loyalty and mutual trust. Commitment to this organization
runs high.
My organization is held together by commitment to innovation and development. There is an
emphasis on being on the cutting edge.
My organization is held together by the emphasis on achievement and goal accomplishment.
Aggressiveness and winning are common themes.
My organization is held together by formal rules and policies. Maintaining a smooth-running
organization is important.
55
STRATEGIC EMPHASIS:
My organization emphasizes human development. High trust, openness, and participation
persists.
My organization emphasizes acquiring new resources and creating new challenges. Trying new
things and prospecting for opportunities are valued.
My organization emphasizes competitive actions and achievement. Hitting stretch targets and
winning in the marketplace are dominant.
My organization emphasizes permanence and stability. Efficiency, control and smooth
operations are important.
CRITERIA OF SUCCESS:
My organization defines success on the basis of the development of human resources, teamwork,
employee commitment, and concern for people.
My organization defines success on the basis of having the most unique or the newest products.
It is a product leader and innovator.
My organization defines success on the basis of winning in the marketplace and outpacing the
competition. Competitive market leadership is key.
My organization defines success on the basis of efficiency. Dependable delivery, smooth
scheduling, and low cost production are critical.
Source: Cameron and Quinn (2011)
Clan = average of results from 1st question from 5 of the 6 items, with the 1 excluded item
randomly skipped; adhocracy = average of results from 2nd
question from 5 of the 6 items,
with the 1 excluded item randomly skipped; hierarchy = average of results from 3rd
question from 5 of the 6 items, with the 1 excluded item randomly skipped; market =
average of results from 4th
question from 5 of the 6 items, with the 1 excluded item
randomly skipped;
56
Online Appendix C. Correlation Matrix 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
1 TotalMetrics 1
2 NatMgmtQual -.04 1
3 NatDigitalUse -.06 .39 1
4 NatConsOr -.17 .55 .77 1
5 NatTradRat .01 .00 .41 .28 1
6 NatSurvSelf -.08 .35 .33 .29 -.27 1
7 CMO .17 .00 .00 .00 .00 .00 1
8 MarkOr .14 .00 .00 .00 .00 .00 .19 1
9 FirmSizeLN .20 .00 .00 .00 .00 .00 .30 .12 1
10 MetTrain .23 .00 .00 .00 .00 .00 .25 .44 .21 1
11 MetComp .23 .00 .00 .00 .00 .00 .24 .55 .16 .53 1
12 WorkExp .00 .00 .00 .00 .00 .00 -.03 .13 .09 .03 .02 1
13 Clan .10 .00 .00 .00 .00 .00 .08 .06 .00 .09 .00 -.08 1
14 Adhocracy .00 .00 .00 .00 .00 .00 -.01 -.12 .07 -.01 .00 -.02 -.07 1
15 Hierarchy -.06 .00 .00 .00 .00 .00 -.03 -.07 .05 -.05 .01 .07 -.55 -.20 1
16 Market -.02 .00 .00 .00 .00 .00 -.04 .12 -.12 -.01 -.01 .02 -.23 -.55 -.24 1
17 TrAd -.04 -.05 .04 .00 -.02 .02 .00 -.02 .02 .01 .00 .02 -.01 .00 .02 .00 1
18 Int .00 .02 .00 -.05 .07 -.05 .03 .00 -.02 .02 .01 -.05 .02 -.02 .01 .00 -.17 1
19 D2C .00 .16 .06 .09 .05 .05 -.01 .02 -.01 .00 .00 .03 .00 .00 .00 .01 -.15 -.15 1
20 SM -.01 .12 .03 .04 -.12 .07 -.03 -.02 -.02 -.02 -.02 -.06 .00 .01 -.01 .00 -.15 -.18 -.14 1
21 SF .05 -.04 .04 .01 .02 -.04 .00 .02 -.01 .00 .00 .04 -.01 .03 -.01 -.01 -.09 -.12 -.11 -.11 1
22 PP -.02 -.11 -.03 -.08 -.03 -.06 -.02 -.02 .00 .00 .01 .00 -.01 .04 .00 -.03 -.12 -.14 -.14 -.13 -.07 1
23 Pri -.02 -.07 -.04 .02 -.01 .01 .00 .00 -.01 -.02 .01 .02 -.02 -.01 .01 .01 -.12 -.16 -.13 -.15 -.07 -.11 1
24 NPD .01 -.06 -.02 .00 .02 -.01 .00 .00 .05 -.01 -.02 .01 .02 -.02 .00 .00 -.11 -.14 -.12 -.13 -.07 -.09 -.09 1
25 Dist .01 -.07 -.07 -.01 .01 -.05 .01 .00 .01 .00 .01 .02 -.03 -.01 .02 .01 -.07 -.10 -.09 -.10 -.05 -.06 -.05 -.05 1
26 PR .04 .04 -.05 -.04 .02 .02 .02 .03 .01 .02 .02 .00 .03 -.01 -.04 .02 -.10 -.11 -.09 -.11 -.07 -.09 -.07 -.08 -.06 1
27 Marketing .05 .00 .00 .00 .00 .00 .12 .02 .13 .11 .09 -.09 .08 -.02 -.02 -.05 .03 .02 .01 .00 -.03 -.01 .00 -.01 -.02 .00 1
28 TMT .04 .00 .00 .00 .00 .00 .04 .03 .01 .03 -.01 .06 .00 .01 .00 .00 .00 .00 .00 .01 -.01 .00 .00 .00 .01 -.01 -.44 1
29 Quant .09 .00 .00 .00 .00 .00 .18 .21 .16 .32 .28 .02 .04 .06 -.02 -.08 .03 -.01 .00 -.02 .01 -.01 .02 -.01 .00 .00 .07 .08 1
30 OrgScope .18 .00 .00 .00 .00 .00 .24 .44 .18 .36 .44 .03 .12 -.06 -.07 .01 -.01 .00 -.01 -.03 .00 .00 .00 .02 .01 .02 .05 .08 .26 1
31 RecBusPerf .15 .00 .00 .00 .00 .00 .22 .52 .20 .44 .42 .05 .10 -.03 -.09 .04 -.01 -.01 .02 -.01 .00 -.02 -.01 .01 .00 .04 .05 .06 .28 .36 1
32 B2C -.03 .00 .00 .00 .00 .00 .02 .06 .02 .03 .07 -.01 -.07 -.02 .08 .00 .01 .01 -.01 .02 .01 .03 -.02 -.01 -.01 -.04 .01 .04 .09 .08 .04 1
33 Services -.04 .00 .00 .00 .00 .00 .00 -.04 -.03 -.07 -.01 -.07 -.06 -.05 -.02 .11 .02 .02 .02 .01 -.01 -.03 -.02 -.02 -.02 .01 -.01 -.02 -.05 .00 -.01 .10 1
34 MarkConc .03 .00 .00 .00 .00 .00 -.02 .03 -.02 .04 .03 .04 .01 -.02 .01 -.01 .01 -.01 .00 .00 .02 .01 -.02 .01 .00 .00 -.08 .02 -.04 -.02 .04 .01 .03 1
35 MarkTurb -.04 .00 .00 .00 .00 .00 .04 .07 -.01 .03 .05 -.05 .05 .09 -.04 -.07 .00 .01 .01 .02 -.01 -.02 -.02 -.01 .01 -.02 -.05 .04 .01 .04 .02 -.01 -.08 .01 1
57