British Journal of Management, Vol. 0, 1–22 (2022)
DOI: 10.1111/1467-8551.12659
How Does Selling Capability Impact Firm
Value? The Moderating Roles of Relative
Strategic Emphasis, Market Volatility, and
Technological Volatility
Mahabubur Rahman ,1 Seongsoo Jang2 and Shaker Ahmed3
1Department of Marketing, Rennes School of Business, 2 Rue Robert d’Arbrissel, Rennes, 35065, France,
2Cardiff Business School, Cardiff University, Aberconway Building, Colum Drive, Cardiff, CF10 3EU, Cardiff ,
UK, and 3School of Accounting and Finance, University of Vaasa, P.O. Box 700, Vaasa, FI-65101, Finland
Corresponding author email: mahabubur.rahman@rennes-sb.com
Firms develop and deploy selling capability to create and sustain a competitive
advantage. Previous studies have focused predominantly on static, input-based selling ca-
pability, paying little attention to dynamic, efficiency-focused selling capability. This trea-
tise reconceptualizes selling capability from a dynamic and efficiency (input–output) per-
spective and investigates the effect of selling capability on firm value with the contingent
role of internal [i.e. relative strategic emphasis (SE)] and external (i.e. market volatil-
ity and technological volatility) factors. Using data from 341 US-based manufacturing
and service firms over the period 2014–2020 and an endogeneity-robust dynamic estima-
tion technique, the authors find that selling capability positively affects firm value, and
firms with a relative SE on value appropriation (i.e. advertising) as opposed to value cre-
ation (i.e. R&D) reap more rewards from selling capability. That is, relative SE positively
moderates the nexus between selling capability and firm value. Furthermore, the results
demonstrate that the interactive effect of selling capability and relative SE is weaker when
an industry experiences higher market volatility but stronger when technological volatil-
ity is higher. Overall, this study demonstrates that a firm’s selling capability should be
managed dynamically in light of its (internal) relative SE and (external) environmental
conditions. The results are robust to several additional sensitivity analyses.
Introduction
An organization’s portfolio of capabilities is
viewed as a catalyst for enhancing and sustaining
competitive advantage (Barney, 1991; Rahman
et al., 2022). Consequently, firms marshal and
deploy resources to develop and bolster distinct
value-creating capabilities, including selling ca-
pability (Rangarajan et al., 2020; Schaarschmidt,
Walsh and Evanschitzky, 2022). In fact, on av-
erage, firms expend 10–40% of their revenue to
develop efficient sales systems (Mantrala et al.,
2010). A capability represents a firm’s ability to
efficiently combine various resources (inputs)
to attain certain objectives (outputs) (Amit and
Schoemaker, 1993; Dutta, Narasimhan and Rajiv,
2005). Albeit the literature on firm capabilities,
such as marketing capability and innovation
capability, has adopted an input–output approach
(Rahman et al., 2022), sales management re-
searchers have hitherto focused on input-related
selling capability and have defined selling capa-
bility as a firm’s capacity to configure and deploy
scant firm resources, salespeople’s knowledge, sell-
ing skills, and control systems (Krush et al., 2013;
Schaarschmidt et al., 2022). Also, the majority
of earlier studies adopted a micro-perspective,
investigating mainly individual- or group-level
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of Management.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distri-
bution and reproduction in any medium, provided the original work is properly cited.
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2 M. Rahman et al.
selling capability (Hughes and Ahearne, 2010;
Hughes and Ogilvie, 2020).
Research on organization-level selling capabil-
ity warrants in-depth theoretical and empirical
investigation because sales is a cross-functional
process that is developed and implemented bymul-
tiple divisions within an organization (Storbacka,
Polsa and Sääksjärvi, 2011). Hence, selling capa-
bility should be regarded as a transformational
ability to utilize fewer resources (i.e. selling inputs)
to attain maximum outcomes (i.e. selling outputs).
Further, a focal organization’s selling capability
needs to be judged and measured relative to those
of its competitors (Dutta et al., 2005). Although
scholars have adopted a relative perspective and
employed an input–output approach to concep-
tualize and measure other strategic capabilities,
such as marketing capability (Feng et al., 2017;
Nath, Nachiappan andRamanathan, 2010), no re-
search has applied this approach to selling capabil-
ity, despite its usefulness for capturing the concept
effectively.
A handful of studies have explored the effect
of selling capability on organizational perfor-
mance (e.g. Guenzi, Sajtos and Troilo, 2016;
Schaarschmidt et al., 2022), but these studies
adopted a static view and ignored germane in-
ternal and external boundary conditions. The
resource-based view (RBV) suggests that organi-
zational performance is heterogeneous owing to
the ownership of resources that have differential
productivity (Makadok, 2001). Because a firm’s
capability pertains to its capacity to deploy fewer
resources (inputs) to achieve maximum outcomes
(outputs) (Dutta et al., 1999), it is imperative
to use an input–output (efficiency) framework
to conceptualize and measure selling capability.
Furthermore, regarding the need to maintain an
optimal level of efficiency over time, dynamic
capabilities enable firms to reconfigure resource al-
location strategy from one time period to another,
in keeping with the marketplace dynamism (Teece,
Pisano and Shuen, 1997). Hence, the efficiency
aspect of an organization’s selling capability may
change over time, meaning that selling capability
should be viewed as one type of dynamic capa-
bility. Also, marketplace dynamism may induce a
capability gap between a firm’s existing configu-
ration and its value-maximizing configuration in
a changing environment (Wilden and Gudergan,
2015). In response to such a capability gap, a firm is
likely to deploy its dynamic capabilities to identify
an optimal configuration of the value-maximizing
selling capability and relative strategic emphasis
(SE). However, few studies have considered the
contingency roles of a firm’s other complementary
strategies, such as relative SE on value creation
(e.g. R&D) and value appropriation (e.g. advertis-
ing) activities (Han, Mittal and Zhang, 2017).
To fill these gaps, this study attempts to use a
positivist approach and empirically address the
questions of (1) whether organization-level selling
capability affects firm value, and (2) how comple-
mentary firm strategy—particularly relative SE
(value creation vs. value appropriation)—and two
environmental factors (market volatility and tech-
nological volatility) separately and jointly moder-
ate the association between selling capability and
firm value. This research used the operational and
financial data of 341 US firms in manufacturing
and service industries over 7 years (2014–2020)
and an endogeneity-robust, instrumental variable
estimation technique (the generalized method of
moments, or GMM) to examine the direct and in-
teractive effects (two- and three-way interactions)
of selling capability, relative SE, market volatility,
and technological volatility on firm value. We also
employed the data envelopment analysis (DEA)
technique to measure selling capability, which is
purported to be the most apposite technique to
measure a firm’s capabilities (Chen, Delmas and
Lieberman, 2015). The findings show that selling
capability increases firm value and that the posi-
tive link between the focal variables is moderated
by internal and external factors. Specifically, a
firm’s relative SE on value appropriation (vs. value
creation) amplifies the positive effect of selling
capability on firm value. Furthermore, the inter-
active effect of selling capability and relative SE
is negatively moderated by market volatility but
positively moderated by technological volatility.
This treatise contributes to the extant literature
in four significant ways. Firstly, combining propo-
sitions from the RBV and dynamic capabilities,
we extend the extant stock of knowledge to better
understand and measure selling capability. Unlike
previous studies that examined input-oriented
selling capability based on mostly soft (percep-
tual) data, the current study used hard (actual
company) data to measure selling capability from
an input–output perspective, which transforms
sales support resources to achieve sales manage-
ment goals. Secondly, our analysis documents the
value relevance of selling capability: our results
© 2022 The Authors. British Journal of Management published by John Wiley & Sons Ltd on behalf of British
Academy of Management.
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How Does Selling Capability Impact Firm Value? 3
Table 1. Contribution of the present research relative to earlier research
Author Explanatory variable Moderator Outcome variable
Schaarschmidt
et al. (2022)
Hybrid offering sales
capability
None Relative firm performance (survey data)
Vadakkepatt et al.
(2021)
Marketing and R&D capital Environmental munificence and
dynamism
Sales leadership maintenance
Homburg et al.
(2020)
Multichannel sales system Governance mechanism Earnings before interest and taxes
Panagopoulos
et al. (2018)
Sales Force Downsizing Product market fluidity,
advertising intensity, accruals
management, and CEO
external focus
Idiosyncratic risk
Guenzi et al.
(2016)
Sales force structuring
capability, personal selling
capability
None Profitability, customer-based performance,
and market-based performance
Panagopoulos and
Avlonitis (2010)
Sales strategy Transformational leadership,
demand uncertainty, and
customer solution orientation
Sales revenue and EBIT
This study Selling capability Relative strategic emphasis,
market volatility and
technological volatility
Tobin’s Q, Total Q, market value and
market-to-book ratio
demonstrate that selling capability positively af-
fects a firm’s value. Thirdly, this study shows that
more of a firm’s internal resources should be allo-
cated to value appropriation (i.e. advertising) than
to value creation (i.e. R&D) activities to maximize
the impact of selling capability on firm value. This
finding confirms not only the trade-off between
two fundamental strategic processes (Mizik and
Jacobson, 2003), but also the importance of con-
sidering the association of selling capability and
relative SE (value creation vs. value appropria-
tion) with firm value (Han et al., 2017). Finally,
this study incorporates two external boundary
conditions, market volatility and technological
volatility, which moderate the interactive effect
of selling capability and the relative SE on firm
value. Previous studies investigating the financial
implications of selling capability ignored the mod-
erating role of internal and external contextual
factors and consequently failed to capture the ex-
tent to which contingency factors may accentuate
or attenuate the effectiveness of selling capability.
We have summarized some related studies (Ta-
ble 1) to show the gap in the literature as well as to
demonstrate the relative contribution of this study.
Theoretical background and hypotheses
According to the RBV, firms possess specific, het-
erogeneous resources that enable them to execute
value-creating strategies, which in turn lead to dif-
ferences in inter-firm performance (Barney, 1991).
The RBV suggests that firm resources and capabil-
ities engender competitive advantage and lead to
superior performance (Barney, 1991; Dubey et al.,
2019). In the same vein, firms can attain a sustain-
able competitive advantage by developing and de-
ploying a selling capability that satisfies customer
needs in ways that competitors are unable to repli-
cate (Barney et al., 2011; Schaarschmidt et al.,
2022). TheRBV also acknowledges either the com-
plementary or the substitutive effect of one specific
capability when it is co-deployed with other ca-
pabilities (Feng et al., 2017). However, the RBV
adopts a static view of a firm’s resource allocation
strategy and does not incorporate the notion of re-
source reconfiguration to sustain competitive ad-
vantage over time in keepingwith the externalmar-
ketplace dynamism (Aragón-Correa and Sharma,
2003).
Dynamic capabilities—defined as a firm’s ability
to build, configure, and reconfigure firm-specific
resources (Teece et al., 1997)—allow firms to
create value by (re)designing appropriate strate-
gies (Teece, 2018). They also enable firms to
(re)configure and (re)allocate their existing re-
source base in keeping with the external environ-
ment (Easterby-Smith et al., 2009; Eisenhardt and
Martin, 2000; Rahman, Rodríguez-Serrano and
Hughes, 2021). That is, a firm’s capabilities should
be sufficiently dynamic to enable it to implement
novel strategies that reflect marketplace dynamism
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Academy of Management.
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4 M. Rahman et al.
(Morgan, 2012; Rahman et al., 2021). Hence, the
combined perspectives of the RBV and dynamic
capabilities suggest that firms should transform in-
ternal resources and strategies into realized value
offerings in a volatile marketplace (Morgan, 2012;
Teece, 2018).
It is imperative for firms to develop and deploy
capabilities in those areas that are critical to com-
petition because, to a great extent, their success
hinges upon their idiosyncratic capabilities (Day,
1994). A capability has been broadly defined as a
set of processes and routines used to marshal and
deploy a firm’s resources to create value (Vandaie
and Zaheer, 2014). Previous studies on conceptu-
alizing and measuring selling capability have fo-
cused mainly on the intra-firm sales support re-
sources and capacities that contribute to the sales
process (Jaakkola, Frösén and Tikkanen, 2015;
Krush et al., 2013; Schaarschmidt et al., 2022).
Also, existing conceptualizations adopt a static ap-
proach without a simultaneous consideration of
selling outputs over time, such as the efficiency
and effectiveness of sales-related resources. For ex-
ample, marketing scholars contend that market-
ing capability is an integrative process in which a
firm uses its resources to achieve, maximize, and
sustain its market-related business goals over time
(Vorhies and Morgan, 2005). As such, selling ca-
pability should also be considered as a dynamic
input–output framework (Narsimhan, Rajiv and
Dutta, 2006; Nath et al., 2010).
In view of the aforementioned argument, this
study reconceptualizes selling capability as a dy-
namic capability of a firm that requires the devel-
opment of internal processes and routines, which
in turn enable it to configure and reconfigure its
sales-related resources from one time period to an-
other, in keeping with the marketplace dynamism,
and thereby attain maximum possible selling out-
puts (i.e. sales growth) using minimum possible
selling inputs (i.e. size of sales force). Our con-
ceptualization acknowledges that firms with su-
perior selling capability are able to minimize sell-
ing inputs and maximize selling outputs, which is
in keeping with the fundamental principle of the
RBV—efficiency. Further, this conceptualization
accommodates the notion of relative efficiency by
considering whether a focal firm allocates selling
inputs optimally to outperform its competitors—
competitive advantage (Chen et al., 2015). How
a firm deploys its rare resources (selling inputs)
and complements its existing (selling) capability
infrastructure to achieve its objective can engen-
der inimitability in the formation of (selling) capa-
bility (Song, Di Benedetto and Nason, 2007). In
sum, deviating from prior studies that investigate
the effectiveness of individual and absolute sell-
ing capability, this study incorporates three types
of elements—longitudinal (over multiple periods),
efficiency (inputs and outputs), and relative (focal
and other firms)—in defining organization-level
selling capability.
In keeping with the tenets of the RBV and
dynamic capabilities, this research combines in-
ternal (capabilities co-deployment) and external
(environmental volatility) views to investigate
the impact of selling capability on firm value.
Specifically, we explore the moderating role of
internal strategy and external volatility because
sales organizations amalgamate and use knowl-
edge and expertise from various divisions to
align internal processes with external conditions
(Peterson et al., 2021). From an internal perspec-
tive, this study incorporates a firm’s relative SE
between value-creation activities (e.g. R&D) and
value-appropriation activities (e.g. advertising)
as the moderator in the selling capability–firm
risk link (Han et al., 2017). Firms may search
and explore opportunities across markets and
technologies and reconfigure their capabilities in
response to changes in environmental conditions
(Teece, 2007). In that sense, a firm’s relative SE
(between R&D and advertising) can change over
time through sensing and reconfiguring processes.
Hence, relative SE should also be regarded as a dy-
namic process. As previous studies have reported
mixed findings regarding the effectiveness of two
strategic processes (e.g. Luo and Bhattacharya,
2009; McAlister, Srinivasan and Kim, 2007), it is
necessary to address themoderating role of a firm’s
relative SE on value creation versus value appro-
priation when examining the impact of selling ca-
pability from the dynamic capabilities perspective.
From an external perspective, this study exam-
ines how the interactive effect of selling capability
and relative SE on firm value may be further
moderated by environmental conditions. This
notion is based on the dynamic capabilities
perspective, whereby firms acquire and deploy
resources to match resource dispersion with en-
vironmental conditions, which in turn explains
performance variance across firms (e.g. Mor-
gan, 2012; Eisenhardt and Martin, 2000; Teece
et al., 1997). We focus on market volatility and
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How Does Selling Capability Impact Firm Value? 5
Figure 1. Research model of the study
technological volatility as key environmental
conditions that affect the selling capability–firm
value relationship, because environmental volatil-
ity emanates from dynamism in both the market
environment and the technological environment
(Carson, Madhok and Wu, 2006). Market volatil-
ity refers to the unpredictability and variability
of customer preferences, whereas technological
volatility denotes the uncertainty of product and
process technologies in an industry in which a firm
is active (Glazer and Weiss, 1993). By examining
how the nexus between selling capability and rela-
tive SE works under volatile conditions, we seek to
gain deeper insights into how firms should manage
their selling capability and whether they should in-
vest more in value-creation or value-appropriation
activities for maximum business impact.
Figure 1 illustrates our research model, which
investigates (1) the direct effect of selling capabil-
ity on firm value, (2) the two-way interaction of
selling capability and relative SE, and (3) the mod-
erating role of market volatility and technological
volatility on the interactive effect between selling
capability and relative SE on firm value.
Selling capability and firm value
RBV theorists suggest that the strategic capabil-
ities of a firm, such as selling capability, that
meet the criteria of being valuable, rare, inim-
itable, and non-substitutable (VRIN) enable it
not only to attain but also to sustain competi-
tive advantage (Peteraf, 1993; Rahman et al., 2018;
Schaarschmidt et al., 2022). The valuable and rare
attributes of selling capability serve as an ex-ante
limit to competition, and the inimitable and non-
substitutable attributes serve as an ex-post limit to
competition (Peteraf, 1993; Schaarschmidt et al.,
2022). Put simply, valuable and rare attributes
help firms to attain competitive advantage, while
inimitable and non-substitutable attributes assist
firms in sustaining their competitive advantage
(Guenzi et al., 2016; Peteraf, 1993). In keeping
with RBV theory, we argue that selling capa-
bility should be regarded as the level of effi-
ciency with which a firm uses the inputs avail-
able to it (e.g. sales support resources) and con-
verts them into desired outputs (e.g. sales rev-
enue) (Dutta et al., 2005; Rahman et al., 2020).
The efficient utilization of sales resources en-
ables a firm to reduce its cost burden while at-
taining optimal sales (Chen et al., 2015; Guenzi
et al., 2016; Rahman et al., 2020). Hence, a firm’s
idiosyncratic practices embedded in its internal
routines and processes for the deployment of its
limited resources to attain the desired goals can
engender valuable, rare and inimitable attributes
(e.g. intangible assets) in the selling resource-
capability framework (Song et al., 2007).
From the dynamic capabilities perspective,
selling capability not only involves complex coor-
dinated mechanisms of sales skills and knowledge
that become embedded as organization-level
routines over time (Grant, 1996), but also is dis-
tinguished from other organizational processes by
being performed well relative to competitors in
the focal industry (Bingham, Eisenhardt and Furr,
2007). Hence, it is imperative to conceptualize
and measure the firm-specific and relative selling
capability that leads to competitive advantage
in a particular industry. Furthermore, a firm’s
selling capability needs to be understood based on
some reference points in order to draw inferences
about a firm’s relative selling capability, including
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Academy of Management.
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6 M. Rahman et al.
self-comparison (its current capability com-
pared with its past capability), social comparison
(capability levels of comparable firms), and
both self-comparison and social comparison (a
weighted average of self-reference and social ref-
erence points (Han et al., 2017). Consequently,
the dynamic nature of selling capability enables
a firm to sense and reconfigure its selling-related
inputs in response to environmental conditions
while attempting to achieve its selling goals.
The productive deployment of a firm’s selling
capability can contribute to both revenue ad-
vantage and cost advantage, which in turn will
positively affect the firm value. From the revenue
advantage perspective, firms with stronger selling
capability can bolster market-based performance
by building and strengthening market-based as-
sets, such as relational and intellectual assets
(Guenzi et al., 2016). Furthermore, firms with
better selling capability understand their cus-
tomers’ needs better than their competitors do,
which improves customer satisfaction and loyalty
and thus positively affects their current revenue
stream (Krush et al., 2013; Panagopoulos et al.,
2018; Schaarschmidt et al., 2022). From the cost
advantage perspective, as firms with greater selling
capability are able to use sales resources more
efficiently, this ability to utilize sales-related inputs
productively brings forth significant cost reduc-
tion, thereby positively affecting forward-looking
stock market-based performance (Guenzi et al.,
2016; Patil and Syam, 2018). In fact, prior stud-
ies have shown that firms with greater strategic
capabilities perform better based on stock market-
based performance measures and have a higher
firm value (Angulo-Ruiz et al., 2018). In essence,
a firm’s selling capability that incorporates three
elements—longitudinal (over multiple periods),
efficiency (input and output), and relative (focal
and other firms)—can be seen as a reliable deter-
minant of firm value. That is, a firm’s superior
selling capability that is dynamically managed
over time through recurrent reconfiguration fulfils
the VRIN criteria, which assists the focal firm
in sustaining its competitive advantage (Peteraf,
1993; Guenzi et al., 2016)
In view of the arguments outlined above, we
posit that having a stronger selling capability is
likely to increase a firm’s value. Thus,
H1: There is a positive relationship between selling
capability and firm value.
The interactive effect of selling capability and
relative SE
Firms dynamically deploy their limited resources
into two broad business processes, value creation
and value appropriation, which are fundamental
to achieving a sustained competitive advantage
(Fang, Palmatier and Grewal, 2011). The process
of value creation involves creating customer value
through research and development initiatives, such
as new products or innovative processes, whereas
value appropriation focuses on extracting value
through investment in branding and advertising
(Mizik and Jacobson, 2003). Scholars have ex-
amined the efficacy of value creation and value
appropriation both separately (e.g. Fang et al.,
2011) and jointly (e.g. Josephson, Johnson and
Mariadoss, 2016). For instance, McAlister et al.
(2007) demonstrated that R&D and advertising
investments are positively related to a firm’s fi-
nancial performance, whereas Osinga et al. (2011)
showed that advertising is negatively related to
shareholder returns. Luo and Bhattacharya (2009)
demonstrated that advertising increases the impact
of corporate social strategies on a firm’s finan-
cial performance, but the simultaneous pursuit of
advertising and R&D decreases its impact. These
mixed findings suggest that it is crucial to investi-
gate the impacts of value creation and value appro-
priation jointly—in other words, employing rela-
tive SE (Mizik and Jacobson, 2003)—rather than
separately (Han et al., 2017).
Relative SE was used as a moderator to evaluate
the relationship between a firm’s selling capability
and financial performance in this study because
a firm’s capability–performance link varies de-
pending on the firm’s strategic type (Feng et al.,
2017). The nexus between selling capability and
firm value is also expected to be accentuated by
the complementary effect of a firm’s relative SE
(Feng et al., 2017; Mizik and Jacobson, 2003;
Schaarschmidt et al., 2022). Specifically, because
selling capability and a relative SE on value appro-
priation are dynamically deployed with a common
strategic objective (i.e. value extraction), a syner-
gistic effect between them is envisaged (Feng et al.,
2017; Mizik and Jacobson, 2003; Schaarschmidt
et al., 2022). When a firm develops its selling
capability, or when its selling capability is already
advanced, the relative emphasis on value appro-
priation (e.g. advertising) compared with value
creation (e.g. R&D) will enable it to build and
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Academy of Management.
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How Does Selling Capability Impact Firm Value? 7
sustain customer relationships because both of
these strategies emphasize extracting value from
customers. In other words, the co-deployment of
these complimentary mechanisms will help firms
to satisfy the needs of existing customers better
than their competitors. In sum, we theorize that
the dynamic co-deployment of selling capability
and a relative SE on value appropriation will have
a synergistic effect on a firm’s value. Thus,
H2: The positive association between selling capa-
bility and firm value is stronger when firms place
a greater relative strategic emphasis on value ap-
propriation than they do on value creation.
The moderating effect of market volatility and
technological volatility
Numerous studies have emphasized the impor-
tance of how a firm’s external environment af-
fects its capabilities (Feng et al., 2017; Rahman
et al., 2021). Dynamic capability theory suggests
that firms need to combine and reconfigure their
intangible and tangible assets in novel ways to
neutralize threats and exploit emergent opportuni-
ties in an ever-changingmarketplace (Teece, 2007).
Firms deploy and co-deploy a range of capabilities
to best fit the external conditions they face and to
deal with future opportunities and threats (Mor-
gan, 2012; Porter, 1985). Researchers emphasize
that firm capabilities have a greater effectiveness
when (re)deployed in ways that are consistent with
the external environment (Moorman and Slote-
graaf, 1999). That is, different environmental con-
ditions imply that different capabilities have differ-
ing degrees of importance and impact on a firm’s
value in different ways (Feng et al., 2017), suggest-
ing that the impact of selling capability and a firm’s
relative SE are contingent upon external environ-
mental conditions.
In this study, we consider two types of external
environments—market volatility and technolog-
ical volatility (Carson et al., 2006; Hanvanich,
Sivakumar and Hult, 2006; Snyder and Glueck,
1982)—as moderating factors that affect the in-
terplay among selling capability, relative SE, and
firm value. Firms operating in an industry with
high market volatility must satisfy the needs of
new customers, which are heterogeneous com-
pared with those of their existing customers, as
well as the frequently changing needs of existing
customers (Hanvanich et al., 2006). To survive in a
volatile market environment, a firm must become
responsive to the changing preferences of existing
customers as well as to the preferences of new
customers, because the firm’s existing value
propositions are unlikely to satisfy customers’
requirements (Kohli and Jaworski, 1990). During
times of high market volatility, changing customer
demands require that firms develop innovative
strategies, which is particularly critical for satisfy-
ing the evolving needs of customers (Atuahene-
Gima, Li and De Luca, 2006; Santos-Vijande and
Alvarez-Gonzalez, 2007). When faced with a high
degree of market volatility, firms require greater
innovativeness to engage in value-creating activi-
ties (e.g. new product development) and perform
well (Hult, Hurley and Knight, 2004). Also, value-
extraction opportunity in a volatile market is lim-
ited and challenging owing to constantly changing
customer needs (Snyder andGlueck, 1982; Santos-
Vijande and Alvarez-Gonzalez, 2007). As a result,
the precarity that persists in a highly volatile
market renders value-extraction capabilities
(selling capability and advertising) less effective
(Snyder and Glueck, 1982; Mizik and Jacobson,
2003). Consequently, we posit that the positive
interaction effect of selling capability and the rel-
ative SE on value appropriation will be attenuated
when market volatility is high. Thus,
H3: Market volatility moderates the joint effect of
selling capability and relative strategic emphasis
such that the joint effect of selling capability and
relative strategic emphasis on firm value is weaker
when market volatility is high.
Firms operating in industries with high tech-
nological volatility compete more on the basis
of product and process technologies (Snyder and
Glueck, 1982). When technological volatility is
high, choosing the right technologies is difficult
owing to the uncertainty and ambiguity endemic in
such environments (Daft and Weick, 1984). Some
firms may take a risk by adopting a highly spec-
ulative technology with a low commercial success
rate, while others may consider low-risk existing
technologies (Ross, 2014). Furthermore, techno-
logical volatility may invalidate successful innova-
tion experiences, rendering them irrelevant for fu-
ture practices (Zhang and Duan, 2010).
High technological volatility forces firms to
continuously use resources and actively develop
or buy fluctuating product and process technolo-
gies to generate new value propositions. Further,
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8 M. Rahman et al.
a rapidly changing technological environment
is characterized by the shortening of product
lifecycles and the fast obsolescence of existing
technologies (Atuahene-Gima and Li, 2004),
which means that technological uncertainty leads
to high product failure rates (Cunha et al., 2014).
As a result, a higher emphasis on value creation
(e.g. R&D) compared with value appropriation
(e.g. advertising) during times of technological
turbulence may lead to a lower return on techno-
logical investments, which harms a firm’s value. In
technologically turbulent environments, a firm’s
success often hinges on its ability to better deliver
value to customers through more effective supply-
side operations and cost management (Jaworski,
Kohli and Sahay, 2000). Hence, rather than focus-
ing on value creation activities in environments
where technology changes rapidly, firms should
invest relatively more in value-appropriation ac-
tivities and develop a few customer-need-centric
appropriate technologies to satisfy customers’ ex-
isting needs. That is, rather than adopting a future-
oriented approach for innovation, firms should
limit their innovation initiatives. Such a strategic
choice will strengthen and stabilize firm perfor-
mance. In essence, in a technologically volatile
industry, even a highly innovative technology
obtained through the investment of large R&D
budget becomes obsolete in a short span of time
(Snyder and Glueck, 1982). Consequently, firms
fail to reap the reward from such innovations,
which eventually hurts the financial wherewithal
of the firm. Therefore, we propose that firms
gain more by focusing on value-extraction activ-
ities rather than on value-creation initiatives in
technologically volatile industries. Thus,
H4: Technological volatility moderates the joint ef-
fect of selling capability and relative strategic em-
phasis such that the joint effect of selling capabil-
ity and relative strategic emphasis on firm value
is stronger when technological volatility is high.
Methodology
Data sources and sample
The sampling frame of this study is the an-
nual ‘Selling Power 500: The Largest Sales Forces
in America’ list (www.sellingpower.com). Selling
Power ranks the top 500 US-based firms in
terms of the size of their sales force. Selling
Power lists have been used in previous studies
(e.g. Panagopoulos et al., 2018). This study worked
with the most recent data. Specifically, the sam-
ple period of this study was from 2014 to 2020
(7 years). Our initial sample included all manufac-
turing and service firms (a total of 577 firms). Pri-
vate firms were discarded owing to the unavailabil-
ity of data; firms for which data were not available
in Compustat were also discarded, leaving a final
study sample of 341 firms (341 firms × 7 years).
However, data for some firms for some years were
still missing, so the final dataset used in this study
was unbalanced. The total number of observations
was a maximum of 2207 (for details, see Table 4).
The sample consisted of firms belonging to 48 in-
dustries (according to two-digit SIC codes). The
mean and the standard deviation of the size of the
sales force of the sample firms were 3062 and 5030,
respectively. Data relating to the measurement of
the outcome variables, explanatory variable, mod-
erating variables, and control variables were col-
lected from Wharton Research Data Services’ Fi-
nancial Ratios Suite and Compustat.
Dependent variable: Firm value
This study used two measures of firm value. We
used Tobin’s Q (see Table 2), the ratio of a firm’s
market value to the current replacement costs of
its assets (Germann, Ebbes and Grewal, 2015), as
the first measure of firm value. While still being
used across diverse disciplines, Tobin’s Q appropri-
ateness as a measure of firm value has been ques-
tioned recently (Edeling, Srinivasan, & Hanssens,
2021). Hence, we used Total Q (Peters and
Taylor, 2017) as the second measure of firm value
(Table 2).
Independent and moderating variables
Selling capability. Selling capability was mea-
sured using data envelopment analysis (DEA; see
online Appendix A for details). We used DEA
window analysis to measure the selling capability
of each firm for each year. We used an input-
oriented, variable return-to-sale DEA model to
measure selling capability, because firms havemore
control over sales inputs than over sales outputs.
Furthermore, sales outputs do not always increase
proportionally when sales inputs are ramped up.
Consequently, a variable return-to-scale model is
appropriate. This study used three selling inputs:
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Academy of Management.
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How Does Selling Capability Impact Firm Value? 9
Table 2. Operationalization of variables and data sources
Variable Measurement Data source
Selling capability See text for details Selling power and
Compustat
Tobin’s Q Tobin’s Q = (MV + LPS + DEBT)/BTA, where MV is the market value of equity,
LPS is the liquidating value of the firm’s preferred stock, DEBT is [(short-term
liabilities — short-term assets) + (long-term debt)], and BTA is the book value of
the total asset
Compustat
Total Q Qtotit = VitKphyit +Kintit
, Compustat
where Vit is the firm’s market value measured as the sum of outstanding equity and
book value of debt, minus the current asset; Kphyit is the book value of property,
plant and equipment; and Kintit is the aggregate of externally purchased and
internally created intangible capital. The externally purchased intangible capital is
measured by the balance sheet item Intangible Assets. In contrast, the proxies for
the two components of internal intangible capital – knowledge and organizational
capital, are calculated using the perpetual inventory method accounting for the
accumulated capital from past investments
Relative strategic
emphasis
See text for details Compustat
Market volatility Compustat√∑x
i=1
(yi−y¯)2
x
y¯ +···+
√∑x
i=1
(y′i−y¯′ )
2
x
y¯′
z ,
where x is the number of years (for the purposes of this study, this time was set as 4
years, so x = 4 years); y is the sales revenue of firm y in each of the 4 years; y¯ is the
average sales revenue of firm y over 4 years; y′ is the sales revenue of firm y′ in each
of the 4 years; y¯′ is the average sales revenue of firm y′ over 4 years; and z is the
number of firms in the industry (represented by a four-digit SIC code)
Technological
volatility
Compustat∑x
i=1
ai+bi
ci
x +···+
∑x
i=1
a′ i+b′ i
c′ i
x
z ,
where x is the number of years (for the purposes of this study, this time was set as 4
years, so x = 4); a is the R&D expenditure of firm y in each of the 4 years; b is the
capital expenditures of firm Y in each of the 4 years; c is the total assets of firm y in
each of the 4 years; a′ is the R&D expenditure of firm y′ in each of the 4 years; b′ is
the capital expenditure of firm y′ in each of the 4 years; c′ is the total assets of firm
y′ in each of the 4 years; and z is the number of firms in the industry (four-digit SIC
code)
Firm size Log of a firm’s total assets Compustat
Leverage Long-term debt divided by the total assets Compustat
Employee
productivity
Sales revenue divided by the total number of employees Compustat
Capital intensity Invested capital divided by the number of employees Compustat
Financial slack Working capital divided by total assets Compustat
Financial constraint See text for details Compustat
ROA (profitability)
growth
Yearly growth of return on asset Compustat
Industry
differentiation
Industry advertising expenditure divided by industry sales Financial ratio
(WRDS)
Industry financial
soundness
(cashflow margin)
Income before extraordinary items and depreciation as a fraction of sales Financial ratio
(WRDS)
Market growth Market growth was calculated as the annual percentage growth in industry (four-digit
SIC) sales revenues
Compustat
the size of the sales force, operationalized as
the total number of salespeople; selling and pro-
motional expenditure, operationalized as selling,
general, and administrative expenditure; and cus-
tomer relationship commitment, operationalized
as the dollar amount of account receivables.
Two outputs were used: sales revenue and sales
growth.
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Academy of Management.
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10 M. Rahman et al.
Relative SE. We followed extant studies (Mizik
and Jacobson, 2003) to measure the effect of rel-
ative SE on value appropriation compared with
value creation as follows:
SE = Advertising expenditureit − R&D expenditureit
Total assetsit
,
where i is the firm and t is the time (year). A pos-
itive value of relative SE denotes a firm’s relative
SE on value appropriation as opposed to value cre-
ation, and a negative value signifies a firm’s rel-
ative SE on value creation as opposed to value
appropriation.
Market volatility and technological volatility. We
followed prior studies to measure market volatil-
ity (Ghosh and Olsen, 2009; Habib, Hossain and
Jiang, 2011; Snyder and Glueck, 1982) and tech-
nological volatility (Snyder andGlueck, 1982). See
Table 2 for details.
Control variables
This study incorporated a set of firm- and
industry-specific variables guided by theory and
prior studies (Table 2).
Firm-specific controls. Prior studies have shown
that a firm’s financial gain varies depending on
size, because firm size affects economies of scope
(Feng, Morgan and Rego, 2017). Therefore, this
study controlled for firm size. Firm value is in-
fluenced by firm leverage (Bayer et al., 2020) and
was thus also controlled for. Prior studies have
shown that performance hinges upon employee
productivity because employee productivity posi-
tively affects a firm’s revenue streams (Shan, Fu
and Zheng, 2017). Therefore, employee productiv-
ity was incorporated as a control variable. Capital
intensity was also controlled for (Rahman et al.,
2021). Previous studies have documented that fi-
nancial slack impacts firm performance (Tang,
Hull and Rothenberg, 2012), so this variable was
incorporated to control for its effect. Similarly,
studies have shown that the extent of financial
constraint also affects performance (Zhang, 2020),
because a lack of funds prevents financially con-
strained firms from embarking upon gainful in-
vestment projects. Accordingly, this was controlled
for as the effect of financial constraints. This study
used the KZ index, which has been used in previ-
ous studies (Cheng, Ioannou and Serafeim, 2014),
to measure financial constraints, as follows:
KZ Index = −1.002CFit/Ait−1 − 39.368DIVit/
Ait−1 − 1.315Cit/Ait−1 + 3.139LEVit + 0.283Qit,
where CFit/Ait-1 is cash flow over lagged assets,
DIVit/Ait-1 is cash dividends over lagged assets,
Cit/Ait-1 is cash balances over assets, LEVit is lever-
age, and Qit is the market value of equity (price
times shares outstanding plus assets minus the
book value of equity over assets). This study also
controlled for return on asset (ROA) (profitabil-
ity) growth, as it is expected to affect firm value
(Bayer et al., 2020; Feng et al., 2017). We first mea-
sured the yearly ROA as net income divided by
total assets and then calculated the yearly ROA
growth. Finally, we included two periods’ lag val-
ues of the firm value variable as control variables
(Marino et al., 2015; Rahman et al., 2021). The ra-
tionale for this decision is explained in the model
estimation method section.
Industry-specific controls. As the sample firms
used were drawn from multiple industries, this
study incorporated a set of industry control vari-
ables, because firm value can be influenced by
industry-specific attributes. Industry differentia-
tion was controlled for because industry advertis-
ing intensity affects industry performance (Hull
and Rothenberg, 2008; Vadakkepatt, Shankar and
Varadarajan, 2021). Industry financial soundness
(cashflow margin) was controlled for because per-
formance across industries varies. We also con-
trolled for market growth, as it varies from one in-
dustry to another (Vadakkepatt et al., 2021).
Other unobserved factors. Even though this study
used a set of relevant firm- and industry-specific
variables, there are other time-invariant unob-
served firm factors, such as firm culture, that can
affect firm value. Hence, this study controlled for
firm-specific unobserved time-invariant factors us-
ing the appropriate estimation technique, as dis-
cussed below. Firm value may also be affected
by various time-invariant, unobserved, industry-
specific factors, so industry dummies were in-
cluded to control for their effect. Finally, because
this study is longitudinal in nature, there could be
time-specific exogenous shocks, the effect of which
has to be controlled for, so we controlled for time-
specific factors.
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How Does Selling Capability Impact Firm Value? 11
Table 3. Results of diagnostic tests
Diagnostic test Purpose and results
Unobserved
firm-specific
effects
This study tested for unobserved fixed firm-specific effects using the Hausman test (χ2 = 360.08, p =
0.0000). As explained below, this study used system-GMM to estimate the model, which includes
firm-fixed effects, to account for unobserved heterogeneities across sample firms (Marino et al., 2015)
Serial correlation One widespread challenge in the panel data structure is the autocorrelation in the idiosyncratic error
term, which biases the standard errors. The results are less efficient in the presence of autocorrelation
(Drukker, 2003). We conducted a Woolridge test using Stata’s xtserial module, the results of which (F
= 33.073, p = 0.0000) confirmed the presence of serial correlation in the idiosyncratic error term of
the model. As autocorrelation was detected in the model, an appropriate model estimation method
that can produce robust results must be used, which will be explained below
Endogeneity test We conducted a Durbin–Wu–Hausman test using the lag values as instrumental variables (internal
instruments) to check for endogeneity in the key explanatory variable of theoretical interest (selling
capability). The results confirmed that selling capability is exogenous (χ2 = 1.62025, p = 0.203). Also,
we conducted a Durbin–Wu–Hausman test for the moderating variables. Our analysis showed that
relative strategic emphasis (χ2 = 4.74510, p = 0.029), market volatility (χ2 = 7.01812, p = 0.008),
and technological volatility (χ2 = 4.91890, p = 0.027) are endogenous
Number of lags of
dependent
variable
Because a firm’s value in the current period can be associated with previous periods’ value (Marino,
2015; Rahman et al., 2021), this study controlled for persistence in firm value by incorporating the
lagged firm value (autoregressive model). Specifically, lagged firm values for the two periods were
included based on two criteria. First, we ran a regression with a 1-year lag of firm value and increased
the number of lags by one until the additional lag of firm value was found to be statistically
insignificant (Duru, Iyengar and Zampelli, 2016). Second, we calculated the Akaike information
criterion (AIC) and the Bayesian information criterion (BIC) to determine the number of lags
(Steenkamp and Fang, 2011). The values of AIC and BIC for the first lag were 1691.704 and 1874.57,
respectively, and the values of AIC and BIC for the first and second lags were 1386.529 and 1562.236,
respectively. The lower values for both AIC and BIC suggest that two period lags are preferable to
one period lag, so we included the lagged values of the dependent variable in the final analysis
Model specification
The dynamic model used to explore the associa-
tion between selling capability and firm value is as
follows:
Firm valueit
= β + α0F irm valueit−1 + α1F irm valueit−2
+α2Sell ingcapabil ityit + α3Relative strategic emphasisit
+α4Market volatil ityit + α5Technological volatil ityit
+α6 Sell ingcapabil ityit × Relative strategic emphasisit
+α7 Sell ingcapabil ityit × Market volatil ityit
+α8Sell ingcapabil ityit × Technological volatil ityit
+α9Relative strategic emphasisit × Technological volatil ityit
+α10 Relative strategic emphasisit ×Market volatil ityit
+α11 Sell ingcapabil ityit × Relative strategic emphasisit
×Market volatil ityit + α12 Sell ingcapabil ityit
×Relative strategic emphasisit × Technological volatil ityit
+Covariates+ Time f ixed e f f ects
+ Industry f ixed e f f ects+ ηi + εit (1)
where i and t represent the firm and year, respec-
tively, ηi is the possible firm-specific component of
the error term, and εit is the error term.
Diagnostic tests and model estimation method
We conducted a number of tests (Table 3) on our
panel data because they pose an array of econo-
metric challenges, such as serial correlation, unob-
served heterogeneity, and endogeneity.
Model estimation method. This study used a two-
step system-GMM estimation technique incorpo-
rating Stata’s xtabond2 module (Roodman, 2009)
for several reasons. Firstly, as indicated above, this
study used two-period lagged values of the depen-
dent variable on the right-hand side of the equa-
tion (autoregressive model). The system-GMM is
particularly designed to estimate such autoregres-
sive models because it can generate reliable co-
efficient estimates by accounting for the dynamic
panel bias stemming from the inclusion of the
lagged values of the dependent variable (Marino
et al., 2015; Roodman, 2009). Secondly, as shown
above, the Hausman test confirmed the use of the
fixed-effect model, and the GMM incorporated
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12 M. Rahman et al.
Table 4. Descriptive statistics
Variable Observations Mean Standard deviation VIF
Tobin’s Q 2199 1.545 1.397
Total Q 1914 1.482 1.452
Selling capability 1773 0.541 0.293 1.243
Relative strategic emphasis 2199 −0.016 0.047 1.381
Market volatility 2211 0.211 0.135 2.896
Technological volatility 2211 0.164 0.33 1.797
Leverage 2199 0.257 0.172 1.287
Capital intensity 2122 552.656 634.268 1.764
Profitability growth 2183 0.465 9.859 1.003
Employee productivity 2122 539.112 505.667 1.42
Firm size 2199 9.462 1.733 1.8
Industry differentiation 2048 0.006 0.011 1.178
Industry financial soundness 2048 −0.042 0.525 2.396
Firm financial constraint 2187 0.349 1.124 1.363
Market growth rate 2207 0.014 0.195 1.037
Firm financial slack 2199 0.122 0.153 1.342
firm fixed effects and accounted for unobserved
heterogeneities across sample firms. Thirdly, the
Woolridge test confirmed the presence of serial
correlation in our dataset, for which GMM is
an appropriate estimation method (Steigenberger
and Wilhelm, 2018). Fourthly, our Durbin-Wu-
Hausman tests confirmed that some of the key
explanatory variable were endogenous, and the
GMM produced endogeneity-robust results in the
presence of endogenous regressors (Roodman,
2009). Finally, GMM allows for the use of lagged
values of endogenous variables as internal in-
struments to generate endogeneity-robust results
(Marino et al., 2015; Steigenberger and Wilhelm,
2018).
As the Durbin-Wu-Hausman confirmed above,
we employed relative SE, market volatility, and
technological volatility as endogenous variables—
that is, they were modelled as GMM-style vari-
ables and their lagged values were used as in-
struments. The remaining firm-specific control
variables were also treated as endogenous and in-
corporated asGMM-style variables. Conversely, as
the Durbin-Wu-Hausman test confirmed that sell-
ing capability was an exogenous variable, it was
incorporated as an IV-style variable (standard in-
strument) along with year-dummies and industry-
dummies. The remaining industry variables were
also employed as standard instruments. As it is rec-
ommended that the number of instruments should
not exceed the number of firms (Roodman, 2009),
we controlled for the proliferation of instruments
in two ways. Firstly, we used the collapse option to
limit the number of instruments. Secondly, we used
the laglimits option and the nearest lagged val-
ues of the endogenous variables. Finally, to mini-
mize data loss (andmaximize sample size), we used
orthogonal deviation instead of first differencing
(Roodman, 2009).
Descriptive statistics and correlations matrix
The low variance inflation factor (VIF) (Table 4)
confirms that multicollinearity is not an issue.
We winsorized variables at 1% and 99% to deal
with the outliers. Correlations are reported in
Table 5.
Main findings
The findings of the two-step system-GMM esti-
mation are reported in Table 6. Models 1 and 2
report the results for Tobin’sQ andTotalQ, respec-
tively. The lagged values of the dependent vari-
ables are significant in bothmodels, confirming the
benefit of using the system-GMM. We discuss the
results reported in the two models concurrently.
The first hypothesis (H1) predicted a positive im-
pact of selling capability on firm value. The coef-
ficient for selling capability is positive and signifi-
cant for Tobin’s Q (p < 0.05) as well as for Total
Q (p < 0.05), confirmingH1. The second hypothe-
sis (H2) predicted that the positive effect of selling
capability on firm value would be greater if firms
placed relatively greater SE on value appropriation
as opposed to value creation. The coefficient for
© 2022 The Authors. British Journal of Management published by John Wiley & Sons Ltd on behalf of British
Academy of Management.
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How Does Selling Capability Impact Firm Value? 13
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© 2022 The Authors. British Journal of Management published by John Wiley & Sons Ltd on behalf of British
Academy of Management.
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14 M. Rahman et al.
Table 6. Regression results using a two-step system GMM
Model 1 Model 2
Variable DV: Tobin’s Q DV: Total Q
Tobin’s Q (Lag 1) 0.556***
(0.0814)
Tobin’s Q (Lag 2) 0.276***
(0.0676)
Total Q (Lag 1) 0.635***
(0.0765)
Total Q (Lag 2) 0.197***
(0.0762)
Main effect (Hypothesis 1)
Selling capability 0.907** 0.727**
(0.371) (0.362)
Two-way interaction effect (Hypothesis 2)
Selling capability × Relative strategic emphasis 27.08*** 25.98**
(9.841) (10.09)
Three-way interaction effect (Hypothesis 3)
Selling capability × Market volatility × Relative strategic emphasis −138.4*** −146.5***
(32.74) (37.69)
Three-way interaction effect (Hypothesis 4)
Selling capability × Technological volatility × Relative strategic emphasis 61.29*** 59.22***
(15.76) (15.96)
Technological volatility × Relative strategic emphasis −43.23*** −38.72***
(12.13) (12.84)
Market volatility × Relative strategic emphasis 92.07*** 83.88***
(21.60) (23.19)
Selling capability × Market volatility −6.211*** −5.903***
(2.067) (2.112)
Selling capability × Technological volatility 2.756*** 2.536***
(0.996) (0.968)
Relative strategic emphasis −23.82*** −21.42***
(5.966) (6.337)
Market volatility 4.329*** 3.453***
(1.258) (1.261)
Technological volatility −2.036*** −1.827**
(0.748) (0.733)
Leverage 0.259 −0.119
(0.212) (0.229)
Capital intensity −8.93e-05 −4.37e-05
(6.00e-05) (7.02e-05)
Profitability growth −0.00673 −0.00563
(0.00802) (0.00753)
Employee productivity 0.000139** 6.95e-05
(6.18e-05) (6.40e-05)
Firm size −0.0175 0.0116
(0.0223) (0.0221)
Industry differentiation 21.09*** 12.63*
(7.560) (7.378)
Industry financial soundness 0.178* 0.194***
(0.0939) (0.0747)
Firm financial constraint 0.0180 0.0345
(0.0403) (0.0336)
Market growth 0.192* 0.346***
(0.0987) (0.0960)
Firm financial slack 0.177 0.147
(0.227) (0.205)
Year fixed effects YES YES
© 2022 The Authors. British Journal of Management published by John Wiley & Sons Ltd on behalf of British
Academy of Management.
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How Does Selling Capability Impact Firm Value? 15
Table 6. (Continued)
Model 1 Model 2
Variable DV: Tobin’s Q DV: Total Q
Industry fixed effects YES YES
Constant −0.771** −0.516
(0.373) (0.362)
Wald (χ2) 1650.08*** 1676.67***
Number of instruments 76 76
AR (1) −2.91 −2.25
p value 0.004 0.024
AR (2) −0.75 −0.86
p value 0.453 0.392
Hansen J test 41.00 40.56
p value 0.299 0.316
Number of observations 1119 981
Number of firms 333 294
Standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.
the interaction term between selling capability and
relative SE is positive and significant in both mod-
els (Model 1, p < 0.01; Model 2, p < 0.05), con-
firming H2. The third hypothesis (H3) predicted
that market volatility would negatively moderate
the joint effect between selling capability and rela-
tive SE on firm value. The three-way interaction
term among selling capability, market volatility,
and relative SE is negative and statistically signif-
icant in both models (Model 1, p < 0.01; Model
2, p< 0.01), confirmingH3. The fourth hypothesis
(H4) predicted that technological volatility would
positively moderate the interaction effect between
selling capability and the relative SE on firm value.
The three-way interaction among selling capabil-
ity, technological volatility, and relative SE is posi-
tive and statistically significant (Model 1, p< 0.01;
Model 2, p < 0.01), confirming our prediction.
Robustness check
We conducted additional analyses (Table 7) to
check the robustness of our findings. We used the
market-to-book ratio and log of market value as
alternative measures of firm value (Model 3 and
Model 5). Also, we included additional control
variables (Model 4 and Model 5). Specifically, we
included a dummy variable for service versus man-
ufacturing firms because the sample in the study
includes both types. It could be argued that the sell-
ing capabilities of service and of manufacturing
firms might have different impacts (Wang, Zhao
and Voss, 2016). In addition, recent research in
corporate finance has confirmed that the coeffi-
cients of the independent variables other than firm
size often change in sign and significance when
different operationalizations of firm size are used
(Dang, Li and Yang, 2018). Accordingly, we used
an alternative measure of firm size based on the
number of individuals employed by a firm. As can
be seen inModel 3, using the market-to-book ratio
to measure the dependent variable indicated that
all four hypotheses are supported, thus confirming
the robustness of the findings reported in the pre-
ceding section.Models 4 and 5, using an additional
control variable as well as an alternative measure
of firm size, also confirmed that our results were
robust.
Discussion and conclusion
Proponents of the RBV and dynamic capabilities
theory contend that firm capabilities, including
selling capability, should be relative compared
with other firms and dynamic when they interact
with internal resources in ways that match the ex-
ternal environment in determining firm outcomes
(Barney, 1991, Teece et al., 1997). This research im-
proves our understanding of how to conceptualize
and measure selling capability in terms of organi-
zation level (vs. individual level) and relativity (vs.
absolute capability) and its effect on firm value.
Also, this treatise identifies the internal boundary
condition—relative SE (value creation vs. value
appropriation)—and the external boundary con-
ditions (i.e. market and technological volatility)
under which firms must manage their selling
© 2022 The Authors. British Journal of Management published by John Wiley & Sons Ltd on behalf of British
Academy of Management.
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16 M. Rahman et al.
Table 7. Regression results using a two-step system GMM
Variable Model 4 Model 5 Model 6
DV:
Market-to-book
ratio
DV:
Market-to-book
ratio
DV: Log of
market value
Market-to-book ratio (Lag1) 0.622*** 0.535***
(0.0875) (0.114)
Market-to-book ratio (Lag2) 0.218*** 0.314***
(0.0695) (0.0986)
Log of market value (Lag1) 0.681***
(0.120)
Log of market value (Lag2) 0.331***
(0.118)
Main effect (Hypothesis 1)
Selling capability 0.780** 0.915*** 0.494*
(0.345) (0.343) (0.284)
Two-way interaction effect (Hypothesis 2)
Selling capability × Relative strategic emphasis 27.00*** 23.56*** 14.89**
(9.018) (8.348) (7.506)
Three-way interaction effect (Hypothesis 3)
Selling capability × Market volatility × Relative strategic emphasis −138.2*** −134.1*** −71.18**
(31.58) (33.55) (29.07)
Three-way interaction effect (Hypothesis 4)
Selling capability × Technological volatility × Relative strategic emphasis 60.24*** 56.22*** 24.79**
(15.00) (15.90) (12.11)
Technological volatility × Relative strategic emphasis −43.05*** −39.05*** −22.15**
(11.58) (12.43) (9.982)
Market volatility × Relative strategic emphasis 89.31*** 81.40*** 51.33***
(21.05) (22.02) (19.71)
Selling capability × Market volatility −5.234*** −5.685*** −2.260
(1.957) (2.042) (1.524)
Selling capability × Technological volatility 2.412** 2.129** −0.107
(0.939) (0.983) (0.655)
Relative strategic emphasis −22.78*** −19.08*** −11.95**
(5.521) (5.160) (4.846)
Market volatility 3.807*** 3.911*** 1.410
(1.200) (1.294) (0.906)
Technological volatility −1.898*** −1.627** −0.213
(0.690) (0.742) (0.488)
Leverage 0.0504 0.0173 0.0782
(0.189) (0.192) (0.118)
Capital intensity −5.44e-05 −5.91e-05 −1.90e-05
(5.69e-05) (4.90e-05) (4.83e-05)
Profitability growth −0.00534 −0.00274 −0.0170**
(0.00764) (0.00799) (0.00813)
Employee productivity 0.000104* 4.08e-05 −2.95e-05
(6.18e-05) (7.43e-05) (5.58e-05)
Firm size (employees) −0.000439 −0.00115
(0.000342) (0.000929)
Industry differentiation 16.48** 8.775 0.279
(7.065) (7.035) (5.915)
Industry financial soundness 0.187** 0.165* 0.0426
(0.0894) (0.0880) (0.0671)
Firm financial constraint 0.0365 0.0379 0.0361**
(0.0395) (0.0329) (0.0173)
Market growth 0.173* 0.229** 0.102
(0.102) (0.104) (0.0847)
© 2022 The Authors. British Journal of Management published by John Wiley & Sons Ltd on behalf of British
Academy of Management.
 14678551, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/1467-8551.12659 by University Of Vaasa, Wiley Online Library on [18/10/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
How Does Selling Capability Impact Firm Value? 17
Table 7. (Continued)
Variable Model 4 Model 5 Model 6
DV:
Market-to-book
ratio
DV:
Market-to-book
ratio
DV: Log of
market value
Firm financial slack 0.271 0.0951 −0.0908
(0.231) (0.228) (0.188)
Service/Manufacturing dummy −0.0793 −0.153
(0.290) (0.198)
Firm size (log of Assets) −0.0167
(0.0213)
Year fixed effects YES YES YES
Industry fixed effects YES YES YES
Constant −0.620* −0.520 −0.119
(0.351) (0.490) (0.485)
Wald (χ2) 2135.97*** 1984.67*** 26943.11***
Number of instruments 76 76 76
AR (1) −3.45 −2.54 −2.96
p value 0.001 0.011 0.003
AR (2) −0.57 −1.22 −0.86
p value 0.568 0.221 0.387
Hansen J test 38.75 31.91 49.04
p value 0.391 0.663 0.072
Number of observations 1119 1119 1015
Number of firms 333 333 303
Standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.
capability to maximize firm value. Our results
show that organization-level selling capability is
positively associated with firm value. Although
the association of capability with firm value is
positive and robust across different industries
(manufacturing and service) and different firm
attributes (e.g. firm size), it is still moderated
by internal and external factors. Specifically, the
relative SE on value appropriation as opposed to
value creation strengthens the positive association
of selling capability with firm value, while the
interactive effect of selling capability and relative
SE is moderated negatively by market volatility
but positively by technological volatility. Our find-
ings provide scholars and managers with strategic
guidance on how to deploy and measure selling
capability and internal resources under external
conditions to maximize firm value.
Theoretical implications
This study contributes to the literature on mar-
keting and sales strategies in three ways. Firstly,
this study contributes to the selling capability
literature by providing a more precise conceptu-
alization and measurement of selling capability.
The integration of RBV and dynamic capabilities
theory assists the reconceptualization by avoiding
some of the limitations that have been ascribed
to the capability view, such as a static orientation
and subjectivity. The extant literature on selling
capability is dominated by the individual- or
group-level selling inputs based on perceptual
data for the measurement of selling capability (e.g.
Guenzi et al., 2016; Jaakkola et al., 2015; Krush
et al., 2013; Schaarschmidt et al., 2022). Past
studies relied on the static and non-contingent
RBV to conceptualize selling capability from the
input-oriented and absolute perspective; these
studies consequently failed to measure the rela-
tive efficiency of a firm’s selling capability, which
transforms resource inputs into outputs over time
compared with other firms’ selling capability. Our
reconceptualization, grounded in both the RBV
and dynamic capabilities, provides amore accurate
definition of a firm’s selling capability because a
firm uses its resources to achieve its desired objec-
tive in changing environments (Dutta et al., 1999;
Vorhies and Morgan, 2005). The reconceptualized
selling capability, which is a dynamic capability,
fulfils the VRIN criteria because the longitudi-
nal, integrative processes of transforming selling
© 2022 The Authors. British Journal of Management published by John Wiley & Sons Ltd on behalf of British
Academy of Management.
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18 M. Rahman et al.
inputs into selling outputs are firm-specific assets
(Song et al., 2007; Teece et al., 1997).
Secondly, this study adds to the literature that
describes how firms should configure their inter-
nal resources (value creation and value appropria-
tion) and capabilities (selling capability) over time
and dynamically adapt the capability-driven firm
value framework accordingly. The empirical evi-
dence on the relationship between selling capabil-
ity and firm value extends our knowledge about
how the RBV and dynamic capabilities can be ap-
plied to operations management (Hitt, Xu and
Carnes, 2016). Our findings reveal that if a firm
allocates more resources to value appropriation,
its managers generate market knowledge and in-
sights pertaining to the predictability and stability
of future revenue-generating activities (Luo and
Bhattacharya, 2009), and the association of SE on
value appropriation with selling capability leads to
an increase in firm value. Although value-creation
activities can help a firm to stay ahead of competi-
tors through impactful innovation (e.g. Vorhies,
Orr and Bush, 2011), a firm’s relative emphasis
on or shift towards value creation may jeopar-
dize the extraction of economic rents from the
current market opportunities (e.g. Mizik and Ja-
cobson, 2003). A balanced alignment of the two
processes may allow a firm to satisfy current mar-
ketplace demands while simultaneously attending
to its long-term position, as a firm tends to empha-
size one process more strongly than the other (He
and Wong, 2004). Our results suggest the impor-
tance of a relative SE on value appropriation (vs.
value creation) in strengthening the positive im-
pact of selling capability on firm value. Hence, this
study adds to the literature on dynamic capability
theory extensions to the RBV in that (selling) ca-
pability and (internal) resources interact with one
another and further explain interfirm performance
variations (Teece et al., 1997).
Finally, this study contributes to the dynamic
capabilities literature by demonstrating that exter-
nal volatility may generate a selling capability gap
and the deployment of dynamic capabilities en-
genders reconfiguration of internal resources and
capabilities that outline how to close the capabil-
ity gap. As the RBV has traditionally focused on
the competitive implications of internal resources
and capabilities, the application of dynamic capa-
bilities theory to the selling capability–firm value
link can better capture the efficiency and effective-
ness of selling capability, depending on the source
of volatility. This study identified two sources of
volatility, namely market volatility and technologi-
cal volatility, and showed their divergent impact on
the nexus among selling capability, relative SE, and
firm value. Prior studies investigating the financial
implications of selling capability have ignored the
moderating role of pertinent internal factors and
external conditions, which may not capture the ex-
tent to which contingency factors accentuate or
attenuate the effect of selling capability on firm
value. However, our results show that when a mar-
ket is volatile, firms should explore new knowledge
through R&D activities to better serve changing
customer needs (Atuahene-Gima et al., 2006). In
contrast, when technology is volatile, firms must
focus more on efficiency and refine their exist-
ing value-extraction capabilities to optimize their
short-term results owing to the difficulty of choos-
ing the right technologies (Daft and Weick, 1984).
To date, most studies on selling capability have
ignored the effects of various moderating factors
(i.e. internal resource allocation and external envi-
ronmental turbulence) in examining the outcomes
of selling capability. The results of our boundary
conditions, obtained by capturing two- and three-
way interaction effects, provide the basis for future
research in marketing and sales management.
Managerial implications
This study has several useful implications for mar-
keting and sales managers. Sales managers should
adopt a dynamic and an input–output approach to
manage the selling capability of a firm efficiently.
That is, for the successful deployment of selling ca-
pability, managers should consider three elements
simultaneously: efficiency (i.e. sales-related inputs
and outputs), relativity (i.e. comparison with other
firms), and longitudinal (i.e. over time). In ad-
dition, they must be aware of their firm’s inter-
nal SE. When the selling capability of a firm is
strong, managers should shift their SE towards
value-appropriation activities (i.e. advertising) to
maximize firm value. Successful alignment of sell-
ing capability with that of relative SE necessitates
a well-coordinated effort across several divisions,
including sales, marketing, and R&D.
Furthermore, this study highlights that man-
agers should identify the sources of environ-
mental volatility (market or technological), be-
cause selling capability and relative SE (i.e. value
creation and value appropriation) fit external
© 2022 The Authors. British Journal of Management published by John Wiley & Sons Ltd on behalf of British
Academy of Management.
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How Does Selling Capability Impact Firm Value? 19
conditions differently. Put differently, managers
should dynamically manage internal resources
(i.e. R&D and advertising) and capabilities (i.e.
selling capability) in line with external contex-
tual factors. In a highly volatile market where
customer needs change rapidly, marketing man-
agers should increase investment in value-creation
activities to unearth new opportunities. Con-
versely, when product and process technologies
are volatile, managers should enhance exploitative
marketing actions, such as selling capability and
advertising. In essence, our findings suggest that
managers should develop and deploy selling ca-
pability dynamically while considering the inter-
nal resource allocation process and external con-
ditions.
Limitations and future research
Our study has a few limitations. Firstly, the sample
firms in the current study are mostly large firms
that can attain economies of scale in sales oper-
ations more easily than can smaller firms. Hence,
future studies should incorporate the scale effect
of sales operations to further expand our un-
derstanding of the effect of selling capability on
firm value. Secondly, this study explored a single
internal strategy (relative SE), so future studies
should examine how the impact of selling capa-
bility varies when they interact with multiple firm
strategies/capabilities. Thirdly, this study focused
only on US-based firms, so future studies should
draw samples from multiple countries to enhance
generalizability. Finally, future studies should ex-
amine the non-linear moderating effect of external
contextual factors.
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22 M. Rahman et al.
Dr. Mahabubur Rahman is an associate professor of Marketing at Rennes School of Business in
France. His current research focus is on the nexus between marketing strategy and firm performance.
He has published in leading journals, such as British Journal of Management, Journal of Business Re-
search, Industrial Marketing Management, Journal of Advertising Research, International Marketing
Review, and Journal of BrandManagement, among others. One of his papers won the best paper award
from the Journal of Advertising Research in 2017. He has academic and industry experience in France,
Sweden, Ireland, Vietnam, and Bangladesh. He also taught as a visiting professor in Sri Lanka, Sin-
gapore, and Morocco
Dr. Seongsoo Jang is a senior lecturer in Marketing at Cardiff Business School, Cardiff University, in
the UK. His current research focus is on digital marketing, sustainability/resilience, and spatial analyt-
ics in retail, tourism, and hospitality settings. He has published in leading journals, such as Journal of
Product Innovation Management, Journal of Public Policy & Marketing, Journal of Business Research,
Annals of Tourism Research, and Journal of Travel Research, among others. He has academic experi-
ence in the UK, France, Turkey, and South Korea.
Shaker Ahmed is a doctoral candidate in Finance at the University of Vaasa in Finland. His main
research interests are in the influence of managerial characteristics on corporate decisions and out-
comes. He has published articles in peer-reviewed academic outlets such as the European Financial
Management, Quarterly Review of Economics and Finance, Personality and Individual Differences, and
Finance Research Letters. He also acknowledges the full-time doctoral study grants from theOPGroup
Research Foundation.
Supporting Information
Additional supporting information can be found online in the Supporting Information section at the end
of the article.
© 2022 The Authors. British Journal of Management published by John Wiley & Sons Ltd on behalf of British
Academy of Management.
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