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Article
Revenue and Profit Implications
of Industrial Service Strategies
Andreas Eggert
1,2
, Jens Hogreve
3
, Wolfgang Ulaga
4
, and
Eva Muenkhoff
1
Abstract
In many business markets, manufacturers seek service-led growth to secure their existing positions and continue to grow in
increasingly competitive environments. Using longitudinal data from 513 German mechanical engineering companies and latent
growth curve modeling, this study offers a fine-grained view of the financial performance implications of industrial service stra-
tegies. By disentangling the revenue and profit implications of industrial service strategies, findings reveal that such strategies
increase both the level and the growth of manufacturing firms’ revenue streams. In contrast, they reduce the level but improve
the growth of manufacturers’ profits. Results further suggest that services supporting the clients’ actions (SSC) and services sup-
porting the supplier’s product (SSP) affect performance outcomes in different ways. SSCs directly affect revenue and profit
streams. In turn, SSPs display only indirect effects on financial performance mediated through SSCs. A moderator analysis iden-
tifies two organizational contingencies that facilitate service business success: Only companies with decentralized decision-making
processes and a high share of loyal customers can expect favorable financial results from industrial service strategies. In summary,
this research provides significant insights and managerial guidance for turning service strategies into financial successes.
Keywords
industrial service strategies, financial performance, services supporting the supplier’s products (SSPs), services supporting the
clients’ actions (SSCs), latent growth curve modeling
Manufacturing companies increasingly seek service-led growth
to secure existing positions and expand in business markets
(Ostrom et al. 2010). Many suppliers indeed rely heavily on
an installed base and may derive substantial revenues and profits
from providing services over their products’ life cycles (Potts
1988). Service revenues frequently offer healthy profit margins
that help compensate for the declining revenues and profitability
in equipment sales (Reinartz and Ulaga 2008). Moreover, ser-
vices might stabilize cash flows and provide increased visibility
in revenue streams, a key benefit in economic slumps (Fang,
Palmatier, and Steenkamp 2008; Oliva and Kallenberg 2003).
Extending the service business thus promises greater firm reven-
ues and profits (Wise and Baumgartner 1999).
Yet increasing anecdotal evidence indicates mixed results at
best. For example, according to a Bain & Co. study, only 21%
of companies succeed with service strategies (Baveja, Gilbert,
and Ledingham 2004). Goods-centric companies that enter
service markets often cannot outperform their pure product
counterparts in terms of revenue growth, profit margins, or
return on equity. Stanley and Wojcik (2005) find that half of all
solution providers realize only modest benefits, and 25% actu-
ally lose money with their value-added services. Neely (2008)
provides evidence that manufacturing firms offering industrial
services enjoy higher revenues than traditional manufacturing
firms, but they also generate lower profits.
Take the example of Michelin, a leading manufacturer of
truck tires. When the company launched its innovative offer
Michelin Fleet Solutions (MFS), that is, performance-based con-
tracts aimed at selling kilometers instead of tires, the goods-
centric company moved into the new world of services, which
provided Michelin with a chance to differentiate itself in the
mature tire industry. However, initial sales were disappointing,
and costs exceeded projections, seriously jeopardizing the sus-
tainability of the company’s innovative go-to-market approach.
It took Michelin several years to understand the root cause of its
financial difficulties: To be profitable, MFS required a different
business model, changes to the organizational structure, and a
This manuscript was accepted under the editorship of Dr. Katherine Lemon.
1
Marketing Department, University of Paderborn, Paderborn, Germany
2
Newcastle University Business School, Newcastle upon Tyne, UK
3
Catholic University of Eichsta
¨
tt-Ingolstadt, Ingolstadt School of Management,
Ingolstadt, Germany
4
IMD, Lausanne, Switzerland
Corresponding Author:
Andreas Eggert, University of Paderborn, Marketing Department, Warburger
Strasse 100, 33098 Paderborn, Germany and Newcastle University Business
School, 5 Barrack Road, Newcastle upon Tyne, NE1 4SE, United Kingdom.
Email: andreas.eggert@notes.upb.de
Journal of Service Research
2014, Vol 17(1) 23-39
ª The Author(s) 2013
Reprints and permission:
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DOI: 10.1177/1094670513485823
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careful selection of targeted customers to generate satisfactory
revenue while keeping service provision costs in check (Renault,
Dalsace, and Ulaga 2010).
Empirical research on the financial implications of industrial
service strategies is still at an early stage. Most studies emphasize
positive outcomes and focus on single measures of financial suc-
cess, such as revenues, profits, or firm value (Antioco et al. 2008;
Fang, Palmatier, and Steenkamp 2008; Gebauer 2007; Homburg,
Fassnacht, and Guenther 2003). Yet, for a more fine-grained
understanding of the financial implications of industrial service
offerings, we need empirical research that specifically sheds light
on both its revenue and profit effects in a single analytical frame-
work. Such a framework needs to acknowledge two simultaneous
challenges. First, manufacturing firms must create customers’
willingness to pay for their service offerings. Second, they must
manage the costs of the service provision to turn service revenues
into profits. Revenues and profits are interdependent, but they do
not necessarily evolve in the same direction. Therefore, by disen-
tangling the revenue and profit implications of industrial service
strategies, we can capture implicitlythe costs of service provision.
A longitudinal perspective is needed to comprehensively
explore the financial consequences of industrial service offerings.
Moving into services typically represents an investment, which
initially might lead to lower profitability levels for companies
focusing on services. Over time though, industrial services might
increase the rate at which profits grow, causing companies with
extensive industrial service offerings to realize financial advan-
tages. To date, empirical research has not yet separated these
financial implications. Understanding the effects of industrial ser-
vices on the development of financial performance thus helps
industrial suppliers better prepare for and effectively manage their
service business.
Furthermore, with a few exceptions, empirical investiga-
tions treat industrial services as homogeneous. Yet evidence
suggests a need for a differentiated view of how various service
categories relate to firm performance (Antioco et al. 2008;
Mathieu 2001; Ulaga and Reinartz 2011). We distinguish
between services supporting the supplier’s products (SSPs) and
services supporting the clients’ actions (SSCs) as one tactic to
investigate how different types of services affect manufactur-
ing companies’ revenue and profit trajectories.
Finally, moderator variables, such as industry and service
business characteristics, may affect the financial outcomes of
industrial services (Antioco et al. 2008; Fang, Palmatier, and
Steenkamp 2008). We build on this stream of research by shed-
ding light on the moderating role of two organizational design
characteristics, decentralization of decision-making authority
and share of loyal customers.
We contribute to service research and practice in three ways.
First, by employing longitudinal data from the German mechan-
ical engineering industry and a latent growth curve modeling
(LGCM) approach, we provide a detailed view of the effects of
industrial service strategies on the level and the growth rate of
manufacturing companies’ revenues as well as profits. Although
manufacturers with a broad industrial service portfolio possess
higher levels and growth rates of revenue streams, they have
lower levels but still enjoy higher growth rates of profit streams.
Second, our results underscore the need to distinguish industrial
service types. For example, SSCs directly affect manufacturers’
revenue and profit streams, whereas SSPs only display an indirect
impact. Third, we contribute to a better understanding of the con-
ditions in which industrial service strategies result in revenue and
profit growth. According to our moderation analysis, the positive
effect of SSCs on revenues and profit growth is strongest when
manufacturers also exhibit significant decentralization of
decision-making authority and possess a high share of loyal
customers.
In the remainder of our article, we first review extant litera-
ture on the link between industrial service strategies and firm
performance and provide a rationale for our distinction between
SSPs and SSCs in industrial markets. Drawing on a resource-
based perspective, we then develop our hypotheses, present the
methodology, specify our model, and present our empirical find-
ings. Finally, we discuss implications for practitioners and scho-
lars and conclude with limitations and research directions.
Literature Review
Industrial Service Strategies and Firm Performance
Industrial service strategies have become an important topic in
both the business-to-business (B2B) and service marketing fields
(Jacob and Ulaga 2008; Kunz and Hogreve 2011; Ostrom et al.
2010). However, empirical research is still nascent and focused
on two issues: (1) the effect of industrial service strategies on firm
performance and (2) the identification of variables that moderate
the effect of industrial service strategies on firm performance.
Regarding the effect of industrial service strategies, empirical
studies tend to emphasize positive outcomes (see Table 1), such
as the positive effects on relative product sales (e.g., Antioco
et al. 2008), service profitability (Homburg, Fassnacht, and
Guenther 2003), and overall profitability (Gebauer 2007). How-
ever, in their analysis of the link between service revenue share
and firm value, Fang, Palmatier, and Steenkamp (2008) confirm
that industrial service strategies can also have negative effects,
such as decreasing firm value at the beginning of the service tran-
sition. These authors also show that the effect becomes positive
once the firm’s service share exceeds 20–30% of its total sales.
In addition, several empirical studies test the assumption that
performance outcomes depend on the alignment between the
industrial service strategy and contextual variables. We find two
types of moderating contextual variables in prior studies: industry
or service business characteristics. Fang, Palmatier, and Steen-
kamp (2008) demonstrate that industry growth has a negative
effect on the service-performance link, but industry turbulence
has a positive effect. With regard to service business characteris-
tics, they identify service relatedness (the extent to which services
are related to the firm’s core product business) and the availability
of slack resources as positive moderators of the service-
performance link. In addition, the service orientation of human
resource management and corporate culture have attracted scho-
lars’ attention (Antioco et al. 2008; Homburg, Fassnacht, and
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Table 1. Empirical Research on Financial Outcomes of Industrial Services.
Authors (Year) Sample, method
Financial performance measure
Moderating effectsRevenue Profit Firm value
Antioco et al. (2008) 137 manufacturing firms located in
Belgium, the Netherlands, and
Denmark, SEM
þ (relative
product sales)
Service training
Cross-functional communication of
service employees
Service technology
Fang, Palmatier, and Steenkamp
(2008)
477 U.S.-based, publicly traded
manufacturing firms, fixed-effects panel
regression
–/þ(U) (Tobin’s q)
Service relatedness
Resource slack
Industry growth
Industry turbulence
Gebauer (2007) 212 German and Swiss machinery and
equipment manufacturing companies,
SEM
þ (overall profitability)
Top management recognition of the
potential of customer support
services
Service orientation of corporate
culture
Service orientation of human
resource management
Gebauer, Edvardsson, and Bjurko
(2010)
302 German and Swiss machinery and
equipment manufacturing companies,
SEM
þ (average return on
sales)
Type of organizational structure of
the service business (integrated vs.
separated service organization)
Homburg, Fassnacht, and
Guenther (2003)
271 German companies from three
industrial sectors: electrical
engineering, mechanical engineering,
and metalworking, SEM
þ(direct service
profitability, overall
profitability)
This research 513 German companies from the
mechanical engineering industry,
LGCM
þ(revenue) –/þ (profit)
Decentralization
Share of loyal customers
Note. SEM ¼ structural equation modeling; LGCM: latent growth curve modeling.
25
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Guenther 2003). Both variables positively moderate the link
between industrial services and firm performance. Management
commitment to the service business and cross-functional commu-
nication between the service division and other departments fos-
ter positive effects of industrial service offerings too (Antioco
et al. 2008), as we summarize in Table 1.
This review reveals two main gaps. First, previous research
has not separated the impact of industrial service strategies on
the level versus the growth of firms’ revenue and profit trajec-
tories. Doing so would provide a fine-grained view of the differ-
ential financial implications of industrial service strategies.
Second, we need to gain a better understanding of the conditions
under which industrial services contribute to financial success.
In particular, previous research has not explored whether overall
company characteristics (e.g., level of decentralization and share
of loyal customers) support or hinder service success. Such
insights could provide managers with actionable recommenda-
tions about how they should integrate services into their business
logic to achieve overall revenue and profit growth.
Industrial Service Types
With the term industrial services, we refer to all services pro-
vided by manufacturing companies to organizational customers,
irrespective of whether that service is independent or combined
with the companies’ goods (Homburg and Garbe 1999). Such
services are not homogeneous: They differ substantially in their
level of risk, level of competition, and potential to create com-
petitive advantages (Oliva and Kallenberg 2003). Traditionally,
industrial services have been classified according to stages of
the industrial purchasing process (Samli, Jacobs, and Wills
1992). Frambach, Wels-Lips, and Guendlach (1997) suggest
that product services can be classified as either transaction or
relationship related. Boyt and Harvey (1997) instead call for a
more fine-grained classification that distinguishes between
elementary, intermediate, and intricate services according to six
service characteristics: replacement rate, essentiality, risk level,
complexity, personal delivery, and credence properties.
Although their classification seems straightforward and plausi-
ble, Boyt and Harvey (1997) provide no rationale for using these
criteria as a basis for categorizing industrial services.
Mathieu’s (2001) service classification scheme distinguishes
between SSPs and SSCs. The former support the installation and
use of the supplier’s core products and ensure they are properly
functioning (M athieu 2001). Thus, SSPs typically include ser-
vices, such as installation, product inspections, equipment repair
or maintenance. The latter service category instead refers to the
client’s action in relation to the supplier’s product. These ser-
vices typically include offerings, such as process optimization,
research and development, business consultancy, or the opera-
tion of entire processes on the client’s behalf.
A number of qualitative and survey-based studies have
relied on this classification scheme. For example, Antioco
et al. (2008) distinguish between SSPs and SSCs to capture
manufacturers’ industrial service strategies and their antece-
dents and outcomes. In the same vein, Ulaga and Reinartz
(2011) differentiate SSPs and SSCs in a qualitative study of
manufacturing companies that move from goods-centric to
service-centric business models. These authors show that SSPs
and SSCs draw on different critical resources and capabilities.
In addition, they explain how distinctive capabilities in each of
these categories lead to different positional advantages, that is,
differentiation and cost leadership. Collectively, these prior
studies suggest that it is reasonable to distinguish between SSPs
and SSCs, because the distinction not only is grounded in man-
agerial practice but also helps explain differences in antece-
dents and outcomes of industrial service strategies.
Hypotheses Development
We draw on the resource-based view (RBV) to analyze the rev-
enue and profit implications of industrial service strategies.
The fundamental proposition of the RBV is that firms consti-
tute complex bundles of idiosyncratic resources and capabil-
ities (Barney 1991) that support strategic actions designed to
enhance firms’ competitive positions (Lee, Naylor, and Chen
2011). To the extent that these resources and capabilities are
valuable, rare, inimitable, and nonsubstitutable (VRIN), they
ensure the sustainability of competitive advantage and promote
long-term financial success (Peteraf 1993). In addition to
explaining how resources and capabilities generate sustainable
competitive advantages, the RBV addresses the formation and
development of resources and capabilities (Grant 1996).
Effects of Industrial Service Strategies on Revenue and
Profit Trajectories
Manufacturers’ industrial service strategies can be character-
ized by the breadth (i.e., scope) and depth (i.e., focus) of the
service portfolio (Fang, Palmatier, and Grewal 2010; Prabhu,
Chandy, and Ellis 2005). The breadth of a service portfolio cap-
tures the number of services offered; the depth refers to the
emphasis placed on each service, that is, the degree to which
the manufacturer proactively offers each service to its custom-
ers (Challagalla, Venkatesh, and Kohli 2009). We focus specif-
ically on the breadth of the service portfolio and distinguish
between SSPs and SSCs. This approach is in line with previous
research that has shown that the breadth of a (product) portfolio
constitutes an important strategic dimension (Miller 1987;
Varadarajan and Clark 1994).
Implementing an industrial service strategy demands major
organizational learning and change processes (Gebauer et al.
2010). Traditionally, manufacturers have operated in a goods-
dominant mode, and they must develop additional resources and
capabilities to compete on services. When offering SSPs, firms
can resort primarily to existing competencies in the product
domain and must develop only a few service-specific capabil-
ities (Kowalkowski, Brehmer, and Kindstro¨m 2009). In contrast,
when offering SSCs, they must institute extensive organizational
changes and investments to establish a corresponding resource
base (Kowalkowski, Brehmer, and Kindstro¨m 2009). Because
of the greater similarity between manufacturers’ established
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resources and the required resources for SSPs, manufacturers
likely enter the service business by offering SSPs (Helfat and
Lieberman 2002; Raddats and Easingwood 2010). By offering
multiple SSPs, manufacturing companies gain the basic reso-
urces and capabilities needed to participate in a service business
(Matthyssens and Vandenbempt 2008). In particular, SSPs
should help manufacturers learn how to manage services and
build a reputation as a service provider (Ulaga and Reinartz
2011). The resources and capabilities thus acquired in turn func-
tion as stepping stones to the further development of more dis-
tant resources and capabilities that enable the provision of
SSCs (Wernerfelt 1984). Both organizational learning from the
experiences with basic services and the development of a repu-
tation as a service provider make it easier for manufacturers to
offer advanced, knowledge-intensive SSCs. Therefore, we
expect firms with a broader SSP portfolio to have a broader SSC
portfolio compared to firms with a narrower SSP portfolio.
Hypothesis 1: The breadth of the SSP portfolio positively
influences the breadth of the SSC portfolio.
SSCs require resources and capabilities that are highly service
specific, have a strong tacit dimension, and are often socially
complex. Consequently, the underlyingresources and capabilities
satisfy the VRIN requirements for a sustainable competitive
advantage (Peteraf 1993). Hence, SSCs provide a basis for sus-
tainable competitive advantages, leading to increased revenues
(Reed and DeFillippi 1990). With a broader scope of resources
and capabilities, a broad SSC portfolio also can better ensure a
sustained competitive advantage, compared with a narrow SSC
portfolio (Grant 1996). We therefore expect manufacturing
companies with a broader SSC portfolio to have a higher level
and growth rate of revenue streams compared to firms with a
narrower SSC portfolio.
Hypothesis 2: The breadth of the SSC portfolio positively
influences the (a) level and (b) growth of firm revenues.
The development of service-specific resources and capabil-
ities demands substantial investments (Reinartz and Ulaga
2008). In manufacturing firms, existing resources and capabil-
ities typically are geared toward the product domain (Raddats
and Easingwood 2010); the RBV depicts these resources as
committed. Committed resources tend to slow down change
processes and increase the costs of change (Kraatz and Zajac
2001). As service initiatives are investments, the costs of the
service business will initially outweigh revenue enhancements,
leading to lower initial profit levels for firms offering industrial
services (Fang, Palmatier, and Steenkamp 2008). These nega-
tive effects should be especially high for companies with a
broad SSC portfolio. Over time though, organizational learning
likely decreases the costs of resource accumulation and leads to
growth in firm profitability. Because organizational learning
tends to be greater with the repeated use of resources and cap-
abilities (Hamel and Prahalad 1993), we expect stronger profit
growth for firms with a broader SSC portfolio. Consequently,
firms with a broader SSC portfolio will possess lower levels but
higher growth rates in profits.
Hypothesis 3: The breadth of the SSC portfolio (a) nega-
tively influences the level but (b) positively influences the
growth of firm profits.
Compared with SSCs, SSPs are less firm specific, less custo-
mized, and less knowledge intensive (Antioco et al. 2008). Con-
sequently, we expect the tacitness, complexity, and specificity of
the underlying resources and capabilities of SSPs to be relatively
low (Reed and DeFillippi 1990). Therefore, it will be difficult to
maintain barriers to imitation for SSPs (Antioco et al. 2008).
Furthermore, SSPs mainly encompass basic services that manu-
facturers must possess to meet customer demands. In this sense,
SSPs represent necessary requirements to participate in the mar-
ket, rather than sources of differentiation. Because SSPs nor-
mally do not satisfy the VRIN requirements, we predict that
they also do not provide sustainable sources of competitive
advantage and fail to exert a direct influence on manufacturers’
financial outcomes, on average. However, we expect an indirect
influence of SSPs, operating through SSCs. We thus extend our
claim of a positive effect of the breadth of the SSP portfolio on
the breadth of the SSC portfolio (Hypothesis 1) to propose that
SSCs mediate the impact of SSPs on firms’ revenue and profit
trajectories.
Hypothesis 4: The breadth of the SSC portfolio mediates the
impact of the breadth of the SSP portfolio on the firm’s rev-
enue and profit trajectories.
Moderating Effects of Decentralization and Share of
Loyal Customers
Previous research indicates that industrial services’ success
depends on the presence of a supportive organizational design
(Bowen, Siehl, and Schneider 1989; Neu and Brown 2005).
The organizational design encompasses all ‘‘decisions about
how firms organize’’ (Homburg, Workman, and Jensen 2000,
p. 460) and can be characterized by its structural and nonstruc-
tural dimensions (Workman, Homburg, and Gruner 1998). We
focus on two dimensions that likely influence a manufacturer’s
success in the service domain: decentralization (a structural
dimension) and the firm’s share of loyal customers (a nonstruc-
tural dimension).
Decentralization. Decentralization reflects the dispersion of
decision-making authority in an organization; it demands sig-
nificant participation by organizational members at lower hier-
archical levels (see Jaworski and Kohli 1993). Centralized
firms enjoy greater efficiency because they engage in stream-
lined information processing and decision making (Auh and
Menguc 2007). They are well adapted to environments with
stable and simple market demands (Ruekert, Walker, and Roer-
ing 1985) and achieve the efficient provision of highly standar-
dized market offerings. In contrast, a high level of customer
Eggert et al. 27
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orientation, which is needed to provide complex services such
as SSCs, demands a decentralized organization (Auh and Men-
guc 2007; Gebauer et al. 2010), because such organizations can
deal adequately with diverse, rich resources incorporated
through their human capital (Auh and Menguc 2007). For
example, employees with greater decision-making authority
can react faster to changing customer needs that should
increase the firm’s service profits over time (Gebauer, Edvards-
son, and Bjurko 2010; Grizzle et al. 2009). Decentralized firms
thus should be better prepared to turn SSC-based service stra-
tegies into financial results, and we hypothesize:
Hypothesis 5: The degree of decentralization positively
moderates the relationship between the breadth of the SSC
portfolio and firms’ (a) revenue growth and (b) profit growth.
Share of Loyal Customers. Existing relationships with loyal cus-
tomers provide an important resource for manufacturing com-
panies (Matthyssens and Vandenbempt 1998; Raddats and
Easingwood 2010) that might moderate the revenue and profit
growth implications of industrial service strategies. Regarding
revenue growth, we find arguments for both positive and neg-
ative effects. On the one hand, a high share of loyal customers
should make it easier for firms to market their services success-
fully and create additional revenues. Long-term relationships
with loyal customers can help overcome purchase uncertainty
(Patterson, Johnson, and Spreng 1997) because they establish
an existing foundation of trust. Loyal customers are more likely
to source services from a trusted manufacturer (Raddats and
Easingwood 2010), which implies that a higher share of loyal
customers could strengthen the positive impact of SSCs on rev-
enue growth. On the other hand, long-term customers tend to
have higher expectations (Pressey and Tzokas 2004) and may
demand extra activities for free. They also pay lower prices
than short-term customers (Reinartz and Kumar 2000). Conse-
quently, a high share of loyal customers could negatively affect
the relationship between industrial service offerings and reve-
nue growth. These opposing effects might cancel each other
out, so we do not expect a significant net effect of the share
of loyal customers on the link between SSCs and revenue
growth.
Regarding profit growth though, we hypothesize a signifi-
cant moderating effect, driven by cost reductions. Deep cus-
tomer insights are required to enhance the efficiency of
service provision, and manufacturers with more loyal custom-
ers should have better insights into their customers’ experi-
ences, capabilities, and needs (Dietz, Pugh, and Wiley 2004).
This information helps companies improve their production
and deployment of SSCs and thereby achieve cost reductions
over time. Firms with a higher share of loyal customers there-
fore should be more successful in turning service revenues into
profits:
Hypothesis 6: The share of loyal customers positively mod-
erates the relationship between the breadth of the SSC port-
folio and firms’ profit growth.
Method
Sample
We rely on panel data from the German mechanical engineer-
ing industry (Beck and Walgenbach 2005). With roughly a mil-
lion employees and overall sales of €201 billion in 2011, the
mechanical engineering industry represents the largest indus-
trial sector in Germany (German Engineering Federation
2012). The data for this panel were gathered in annual, national
mail surveys sent to a population of approximately 5,500 man-
ufacturing companies with more than 20 employees. In the first
measurement wave, one questionnaire was mailed to the top
management of each firm. In the cover letter, managers were
asked to fill out the questionnaire themselves or pass it on to
someone who had the in-depth knowledge to answer the sur-
vey. The respondents thus mainly represented top decision
makers in their companies, such as chief executive officers,
chief operating officers, and heads of administration. Respon-
dents also indicated their name on the questionnaire. In the fol-
lowing waves, given that a specific contact person was known
from the previous wave, the questionnaires were directly
addressed to that person; therefore, the same persons answered
the annual surveys. Only 4.9% of all cases experienced a
change in the respondent between Periods 1 and 2.
We analyzed data from three consecutive, annual survey
waves. The first wave contained 1,342 usable questionnaires.
Annual attrition rates in the next two periods were 35.0% (t
1
to t
2
) and 41.2% (t
2
to t
3
). We thus ended up with 513 cases that
provided data across all three measurement waves; we limited
our analysis to these cases because more lenient inclusion cri-
teria would have resulted in large amounts of missing values
(Jaramillo and Grisaffe 2009).
We conducted several analyses to test for biases due to initial
nonresponse and attrition. First, for biases due to initial nonre-
sponse, we compared the overall sample (all companies that
responded at least once) with the total population of the German
mechanical engineering industry. We used data that we gathered
from the German Federal Statistical Office to determine overall
revenues and number of employees. Goodness-of-fit tests indi-
cated a similar composition of both groups in terms of revenue,
w
2
emp.
¼ 6.92, w
2
tab.
(3; .95) ¼ 7.81, and number of employees,
w
2
emp.
¼ 7.33, w
2
tab.
(3; .95) ¼ 7.81. Therefore, nonresponse bias
does not appear to represent a serious problem for our study.
Second, we tested whether our focus on cases that responded
in all three measurement waves caused an attrition bias
(Bentein et al. 2005) by comparing those cases included in the
analysis with excluded cases of firms that participated only
once or twice. To find any differences between included and
excluded cases, we conducted t-tests on the number of employ-
ees, overall revenue, SSPs, and SSCs. None of these analyses
yielded significant results (p .10).
Measures
Data about industrial service strategies were collected in the
first measurement wave. The questionnaire asked respondents
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to characterize their SSP and SSC portfolios. Specifically, each
respondent selected, from two lists of core SSPs and SSCs, the
services that his or her company offered. The list of SSPs
encompassed product-related services, such as product repair,
spare part delivery, product documentation, maintenance, and
product recycling and dismantling; the list of SSCs featured
customer process-related services, such as consulting, training,
financing services, and research and development. To measure
the extent of SSP and SSC provision, we counted the number of
services in the respective category, which represented the
breadth of the SSP and SSC portfolios. Similarly, Gebauer and
colleagues (2010, p. 205) note that the number of services
offered reflects the ‘‘scope of the service strategy in terms of
strategic marketing offering.’’ The scale ranges from 0 to 5 for
SSPs and 0 to 4 for SSCs. The substantial ranges of SSPs and
SSCs we observed, as well as the variety of combinations of
SSPs and SSCs in our sample, indicate meaningful variance
in the composition of the overall service portfolio.
To measure financial outcomes, respondents indicated, in
monetary terms, the overall annual revenue of their respective
firms. To correct for nonnormality in this measure, we applied
a natural logarithmic transformation. Respondents also assessed
the profit situation of their company on a 5-point Likert-type
scale (1 ¼ poor to 5 ¼ excellent). The revenue and perceived
profit data were gathered in all three measurement waves.
For the moderator variables, we used data taken during the
second measurement wave. With regard to decentralization, the
respondents rated, on a 5-point Likert-type scale (1 ¼ very low
to 5 ¼ very high), the extent to which decision-making author-
ity in their firms was delegated to lower hierarchical levels
(Jaworski and Kohli 1993). The operationalization of the share
of loyal customers relied on respondents’ indications of the per-
centage of revenue earned from repeat customers.
We included company size as a control variable captured as
the number of employees. We estimated the natural logarithm
for this measure to correct for nonnormality. The company size
data came from the first measurement wave. We summarize all
these measurement scales and items in the Appendix. Table 2
provides an overview of descriptive statistics and correlations
between variables.
Using a longitudinal study design and the documented mea-
sures, we attempted to minimize common method effects in two
ways. First, we measured the predictors (i.e., SSPs and SSCs)
and outcomes (i.e., revenue and profit growth) at different points
in time. This temporal separation should prevent respondents
from remembering responses they gave previously (Rindfleisch
et al. 2008). Second, we measured predictors and outcomes
using different formats and scales. This measurement separation
helped reduce response biases, such as halo effects, consistency
motifs, acquiescence, and implicit theories and illusory correla-
tions, that otherwise could result in common method variance
(Podsakoff et al. 2003).
Data Analysis
We employed LGCM for data analysis. LGCM is an advanced
application of structural equation modeling that analyzes long-
itudinal change (Chan 1998). Using measures observed across
multiple points in time that capture the level of a variable each
year, LGCM calculates the latent intercept (i.e., level) and
latent slope (i.e., growth) of the underlying developmental tra-
jectory (Jaramillo and Grisaffe 2009).
Compared with more traditional longitudinal data analysis
approaches (e.g., difference scores, repeated measures, panel
regression), LGCM provides several advantages for change
research (Chan 1998; Lance, Meade, and Williamson 2000).
First, because LGCM is based on an analysis of means and cov-
ariances, it captures within- and between-subject changes in the
same analytical framework (Byrne, Lam, and Fielding 2008).
Thus, it applies not only to analyses of the average parameters
of a growth curve but also accounts for the variance of these
parameters in the sample, such as interindividual or firm differ-
ences (Byrne, Lam, and Fielding 2008).
Second, using item-level information, LGCM accounts for
measurement error in the estimation process (Ployhart and
Vandenberg 2010). Because the parameters of the growth curve
Table 2. Descriptive Statistics and Correlations.
Variable 1 234567891011
1. ln REV t
1
2. ln REV t
2
.98
**
3. ln REV t
3
.96
**
.97
**
4. Profit t
1
.03 .07 .07
5. Profit t
2
.03 .07 .09
*
.66
**
6. Profit t
3
.05 .07 .10
*
.50
**
.60
**
7. SSP .23
**
.24
**
.22
**
.04 .01 .02
8. SSC .39
**
.40
**
.40
**
.10
*
.02 .01 .53
**
9. lnCS .92
**
.92
**
.90
**
.03 .01 .01 .19
**
.35
**
10. DEC .11
*
.12
**
.13
**
.05 .05 .06 .05 .09 .12
**
11. SLC .06 .06 .05 .04 .00 .00 .14
**
.18
**
.04 .00
M 2.695 2.719 2.764 2.524 2.69 2.914 2.673 1.667 4.282 3.185 72.98
SD 1.068 1.082 1.11 1.062 1.06 1.053 1.28 1.036 .875 .876 21.283
Note. REV ¼ revenue, CS ¼ company size, DEC ¼ decentralization, SLC ¼ share of loyal customers; M ¼ mean; SD ¼ standard deviation.
*p < .05. **p < .01.
Eggert et al. 29
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(i.e., intercept and slope) are also modeled as latent variables,
the estimated growth curve represents the true nature of the
change pattern (Chan 1998).
Third, LGCM can handle violations of the basic assumptions
(e.g., independence of residuals, homogeneity in residuals) that
underlie general linear models (Ployhart and Vandenberg
2010). Specifically, it can compare the model fit of alternative
growth curve specifications with independent or dependent resi-
duals, as well as homoscedastic or heteroscedastic residual struc-
tures (Bollen and Curran 2006; Chan 1998).
Fourth, the flexibility of LGCM enables researchers to model
complex multivariate change models (Ployhart and Vandenberg
2010). Within these models, the latent intercept and slope vari-
ables can each serve as independent and dependent variables
(Lance, Vandenberg, and Self 2000; Ployhart and Vandenberg
2010). Consequently, compared with traditional methodologies,
LGCM is applicable to tests of interrelationships between the
changes in two variables, that is, to tests of concomitant change
(Ployhart and Vandenberg 2010).
Fifth, no precise representation of the functional form of the
growth trajectory is possible with most methodologies, but
LGCM can identify and compare different functional forms of
trajectories (Chan 1998; Lance, Meade, and Williamson 2000).
Each of the traditional longitudinal data analysis approaches
offers some of these advantages, but LGCM is the only method
that can fulfill all possible requirements for analyzing longitudi-
nal change (Chan 1998; Lance, Meade, and Williamson 2000).
Model Specification
We conducted all analyses using AMOS 19.0 (Arbuckle 2010).
Similar to the successive specification of measurement models
and structural models in SEM, we adopted a two-step process
to build our LGCM (Bollen and Curran 2006). First, we tested
two unconditional LGCMs for the revenue and profit outcome
variables separately. We thus specified two models that ade-
quately and parsimoniously described the respective growth
trajectories of revenues and profits. In this within-individual
step, the intercept and slope ‘‘constructs’’ were fit with the
repeatedly measured variable to model intraindividual change
(Jaramillo and Grisaffe 2009). It is also possible to determine
interindividual differences in change, because we modeled the
intercept and slope as random effects (Byrne, Lam, and Field-
ing 2008). Second, as the unconditional models demonstrated
adequate model fit and significant variability in their intercept
and slope, we merged the two unconditional models and built a
conditional latent growth model (Bollen and Curran 2006). In
this between-individual stage, we focused on explaining inter-
individual differences in the latent intercept and latent slope of
the revenue and profit growth curves by implementing explana-
tory variables (Lance, Vandenberg, and Self 2000).
Unconditional Latent Growth Models
For the first step, we specified and tested two separate uncondi-
tional LGCMs for both dependent variables. The specification of
an unconditional LGCM is similar to a factor analysis. The cor-
responding equations are shown in the Appendix. For each vari-
able, we compared alternative LGCMs that varied in their
functional form (no growth, linear growth) and the residual
structure (homoscedastic and heteroscedastic) of the growth
curve (Lance, Vandenberg, and Self 2000). Allowing heterosce-
dasticity among the errors of the repeatedly measured revenue
and profit variables, we account for the common observation that
the precision with which an attribute is measured is not identical
across time periods (Willett and Sayer 1994). To select the opti-
mal model specification, we relied on nested model comparisons
and inspections of the fit indices (Bentein et al. 2005).
The results of the model specification and nested model
comparisons for revenue appear in Table 3, panel A. The nested
model comparisons indicated that the linear model with hetero-
scedastic error structure most adequately describes the data.
1
For our research period, revenue shows a linear change over
time, and the final model for revenue provides good model fit
statistics, w
2
¼ 1.98, df ¼ 2; normed fit index (NFI) ¼ .999,
comparative fit index (CFI) ¼ 1.000, and root mean square
error of approximation (RMSEA) ¼ .000.
Table 3. Model Specification and Nested Model Comparison for the Two Unconditional Growth Curves.
Model specification w
2
df Model comparison Dw
2
Ddf NFI CFI RMSEA
A: Revenue trajectory
Model M0 (no growth; homoscedastic residual structure) 159.51 6 .950 .976 .224
Model M1 (linear growth; homoscedastic residual structure) 35.89 3 M0 vs. M1 123.62
**
3 .993 .993 .115
Model M2 (linear growth; heteroscedastic residual structure) 1.91 1 M1 vs. M2 33.98
**
2 .999 1.000 .042
Model M3 (linear growth; heteroscedastic residual structure, no covariance) 1.98 2 .999 1.000 .000
B: Profit trajectory
Model M0(no growth; homoscedastic residual structure) 109.59 6 .796 .806 .184
Model M1 (linear growth; homoscedastic residual structure) 6.89 3 M0 vs. M1 102.70
**
3 .987 .993 .050
Model M2 (linear growth; heteroscedastic residual structure) .79 1 M1 vs. M2 6.10
*
2 .999 1.000 .000
Note. NFI ¼ normed fit index; CFI ¼ comparative fit index; RMSEA ¼ root mean square error of approximation.
*p < .05. **p < .01.
30 Journal of Service Research 17(1)
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LGCM estimates firm-specific growth trajectories for every
firm, which can vary in their intercepts and slopes. To charac-
terize the final unconditional growth model for revenue, we
inspect the means and variances of the random intercept and
slope parameters. The means of intercept and slope reveal a
positive average development in revenue across firms. The
average level for revenue is 2.69 (p < .01) and the average slope
is 0.03 (p < .01). Because we applied a natural logarithmic
transformation for revenue, we can interpret the slope as the
average percentage increase in revenue per year. Moreover,
the significant intercept (1.13, p < .01) and slope variances
(0.01, p < .01) indicate important interindividual or firm differ-
ences in both the level of revenue and its rate of change. Such
evidence provides strong justification for incorporating predic-
tor variables into the subsequent conditional LGCM.
In Table 3, panel B, we show the model specification and
nested model comparisons for profit. The homoscedastic linear
growth model and the heteroscedastic linear growth model pro-
duce good fits with the data. We chose the homoscedastic
linear growth model as our final model, because it adequately
and parsimoniously represents changes in profit, w
2
¼ 6.89,
df ¼ 3; NFI ¼ .987, CFI ¼ .993, RMSEA ¼ .050.
To characterize the final unconditional growth model for
profit, we again inspect the means and variances of the random
intercept and slope parameters. We find that the average level of
profit in year 1 is 2.52 (p < .01), and profit then increases by 0.19
per year (p < .01). Both parameters are significant, so our results
confirm a joint development of profit across firms. Significant
variability in the intercept (.81, p < .01) and slope (.09, p <
.01) indicate important interfirm differences in both parameters,
which can be analyzed with a conditional latent growth model.
Conditional Latent Growth Model
In the second analytical step, we built a conditional growth model
to analyze the relationships between our predictor variables and
the latent parameters of the revenue and profit trajectories. To
do so, we incorporated the two unconditional growth models for
revenue and profit into a multivariate LGCM and included all
possible cross-domain correlations between the parameters of the
two growth curves. Next, we added predictors to the model that
might explain variation in the revenue and profit trajectories.
Specifically, we introduced SSPs and SSCs as potential antece-
dents of the latent intercept and the latent slope of the revenue and
profit trajectories and controlled for company size (see Figure 1).
The results for our conditional LGCM indicate an adequate fit to
the data, w
2
¼ 26.51, df ¼ 19; NFI ¼ .995, CFI ¼ .999, RMSEA ¼
.028, as we summarize in Table 4.
Hypotheses Tests
Effects of Industrial Service Strategies on Revenue and
Profit Trajectories
In Hypothesis 1, we predict that firms with a broader SSP port-
folio will have a broader SSC portfolio too. In line with this, we
Ci
Unconditional latent growth model for
revenue
Revenue
level
ln revenue t
1
ln revenue t
2
C
ompany size
1
1
0
Revenue
growth
2
ln revenue t
3
SSPs
2
1
1
Profit
Profit t
1
SSC
1
0
Profit
level
Profit
th
Profit t
2
SSC
s
1
0
1
1
growth
Profit t
3
hypothesized effect
control effect
2
Unconditional latent growth model for
profit
Figure 1. Conditional latent growth model.
Eggert et al. 31
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find a significantly positive relationship between SSPs and
SSCs (b ¼ .484, p < .01).
Also in support of Hypotheses 2a and 2b, we find significant,
positive paths between SSCs and both revenue level (b ¼ .08, p <
.01) and revenue growth (b ¼ .15, p ¼ .05). In Figure 2, panel A,
we illustrate exemplary revenue trajectories for firms with a nar-
row (mean 1 SD) and a broad (mean þ 1 SD) SSC portfolio.
Consistent with our hypotheses, firms with a broader SSC portfo-
lio possess a higher level of revenue and greater percentage
increase in revenue each year. Particularly, the average revenue
increase per year is 4.65% for companies with a broad SSC port-
folio and only 1.75% for companies with a narrow SSC portfolio.
Furthermore, we uncover a significant negative path between
SSCs and profit level (b ¼.13, p < .05) and a significant pos-
itive path between SSCs and profit growth (b ¼ .20, p <.05),in
support of both Hypotheses 3a and 3b. The profit trajectories for
firms with a narrow and a broad SSC portfolio (mean + 1 SD)
appear in Figure 2, panel B. Although companies with a broad
SSC portfolio start from a relatively low level of profit, they
slightly outperform companies with a narrow SSC portfolio at
the end of the study period, that is, after 3 years.
In Hypothesis 4, we hypothesize that the breadth of the SSC
portfolio mediates the effects of the breadth of the SSP portfo-
lio on both revenue and profit trajectories. Although all of the
direct effects of SSPs on the revenue and profit trajectories
are insignificant, we find significant indirect effects (.000 <
p < .075) on the outcome variables through SSCs when we
apply the bootstrap procedure recommended by Preacher and
Hayes (2008). We thus confirm that the effects of SSPs on rev-
enue and profit trajectories are fully mediated by SSCs.
Moderating Effects of Decentralization and Share of
Loyal Customers
To test our moderating hypotheses, we estimated multiple-
group LGCM. Multiple-group analysis is a well-established,
widely accepted method for detecting moderating effects in
structural equation models (e.g., Homburg, Droll, and Totzek
2008; Palmatier, Scheer, and Steenkamp 2007).
2
For each
moderator, we used a median split to divide the sample into two
subgroups, then tested whether the hypothesized path is mod-
erated through a comparison of the two models. In the first
model, we allowed the hypothesized path to vary freely across
groups; in the second, we constrained the path to be equal
across groups. To compare these models, we used a chi-
square difference test. We summarize the results of the
multiple-group analyses in Table 5.
3
The moderation test reveals that firms with low and high
decentralization differ significantly in terms of the impact of
SSCs on their revenue growth, Dw
2
(1) ¼ 4.83, p < .05, and
profit growth, Dw
2
(1) ¼ 6.28, p < .05. In particular, SSCs have
a significantly positive effect on both revenue and profit
growth for companies with high decentralization (b ¼ .40,
p < .01; b ¼ .47, p < .01, respectively). Both effects become
insignificant for firms with low decentralization (b ¼ .01,
p >.10;b ¼ .01, p>.10, respectively). Accordingly, Figure 2,
panels C and D, reveals that the highest growth of the reve-
nue and profit trajectories occurs for companies with a broad
SSC portfolio and high decentralization. Thus, we find sup-
port for Hypotheses 5a and 5b.
Finally, in support of Hypothesis 6, the share of loyal cus-
tomers positively moderates the effect of the breadth of the
SSC portfolio on firms’ profit growth, Dw
2
(1) ¼ 3.84, p <
.05. When firms have a low share of loyal customers, SSCs
exert no significant effect on profit growth (b ¼ .03, p >
.10), but for firms with a high share of loyal customers, SSCs
have a significant and positive effect (b ¼ .36, p < .01). As
we depict in Figure 2, panel E, when companies have a high
share of loyal customers, profit growth is highest if they also
have a broad SSC portfolio. Although their profit trajectory
starts from the lowest level, these companies achieve the high-
est level of profit at the end of the 3-year period. In contrast, for
Table 4. Conditional Latent Growth Model: Test for Direct Effects.
Path tested
Standardized path
coefficient b p value Hypothesis Hypothesis direction Result
SSP ! SSC .484
***
.000 Hypothesis 1 Positive Supported
SSP ! revenue level .022 .267 – – –
SSP ! revenue growth .066 .397 – – –
SSP ! profit level .028 .644 – – –
SSP ! profit growth .010 .915 – – –
SSC ! revenue level .075
***
.000 Hypothesis 2a Positive Supported
SSC ! revenue growth .151
*
.054 Hypothesis 2b Positive Supported
SSC ! profit level .125
**
.041 Hypothesis 3a Negative Supported
SSC ! profit growth .198
**
.027 Hypothesis 3b Positive Supported
Company size ! SSP .193
***
.000 – – –
Company size ! SSC .252
***
.000 – – –
Company size ! revenue level .898
***
.000 – – –
Note. SSP ¼ services supporting the supplier’s product; SSC ¼ services supporting the clients’ actions. Double-dashed line indicates no relationship was
hypothesized.
*p < .10 (two tailed). **p < .05 (two tailed). ***p < .01 (two tailed).
32 Journal of Service Research 17(1)
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companies with a low share of loyal customers, profit growth
does not differ, regardless of SSC portfolio breadth.
Discussion
A growing consensus among managers and scholars suggests that
goods-dominant companies should seek service-led growth to
secure their existing positions in business markets and continue
to grow (Ostrom et al. 2010). Yet rather than just a general agree-
ment about why manufacturers move toward services, we need a
better understanding of how they can ensure that this move proves
profitable over time (Reinartz and Ulaga 2008). Both anecdotal
evidence (Renault, Dalsace, and Ulaga 2010; Stanley and Wojcik
2005) and emerging academic results (Fang, Palmatier, and
Steenkamp 2008) suggest a need for further research on how ser-
vice strategies contribute to firms’ success and the conditions in
which these initiatives lead to revenue and profit growth.
The present study makes several important contributions to
the academic literature and managerial practice. First, we specif-
ically disentangle the effects of industrial service strategies on
different performance indicators. Prior studies have focused pre-
dominantly on single outcome variables, such as revenue (Anti-
oco et al. 2008), profitability (Gebauer, Edvardsson, and Bjurko
2010), or firm value (Fang, Palmatier, and Steenkamp 2008),
without differentiating among key levers of firm performance.
We analyze the effects of industrial service strategies on manu-
facturers’ revenue and profit trajectories. Revenues and profits
received from service strategies do not necessarily evolve in the
same direction. Against this backdrop, we find that firms with a
broader service portfolio display higher levels and percentage
increases in revenues but not per se more satisfying profit
streams. Instead, firms with a broader service portfolio suffer
from lower profit levels at the beginning of our study period.
As we estimated the effects of industrial services on revenues
and perceived profits simultaneously—while controlling for
their correlations—we can ascribe this negative effect to higher
costs at these firms. Higher costs can result, for example, from
investments in required resources and capabilities or
organizational and managerial difficulties with managing ser-
vices. Over time though, manufacturing companies with a
broader service portfolio can compensate for these costs and
attain a profit advantage compared to firms with a narrower SSC
portfolio.
From a managerial standpoint, our findings suggest that
industrial service strategies of goods-dominant firms take time
to pay off; that is, they lead to lower profitability levels before
firms can realize profit growth and compensate for initial
losses. As the Michelin example illustrated, companies need
experience to learn how to sell services (Renault, Dalsace, and
Ulaga 2010). Our results indicate that firms with a broader ser-
vice portfolio can more easily move along the learning curve
and improve the rate at which revenues and profits grow over
time. Companies that fail to continuously invest in their service
business, for example, by not sufficiently broadening their ser-
vice portfolio, take the risk of missing an opportunity to
increase revenues and profits and compensate for possible ini-
tial profit losses. In addition, revenue effects are as straightfor-
ward as we might expect, but a problem arises on the cost side.
Hence, our results underscore that it is important for firms to
specifically account for the costs of services in their control
systems, monitor costs in service production and deployment,
and align incentive systems to ensure that both revenues and
costs remain on target during their pursuit of service strategies.
As a second contribution, our study underscores the different
roles of industrial service categories. Previous research has high-
lighted the need to account for different types of service offer-
ings (Ulaga and Reinartz 2011). By distinguishing the effects
of SSPs and SSCs, we find that the latter directly affect the com-
pany’s revenue and profit streams, whereas the former display
only an indirect impact. SSCs further appear to mediate the
effects of SSPs on both revenue and profit trajectories. By iden-
tifying this key mediating role of SSCs for both revenue and
profit trajectories, we can confirm SSPs’ status as a starting point
for a successful service business. Therefore, manufacturers
should concentrate on developing and consolidating their SSP
portfolio first. A broad SSP portfolio can help them gain
Table 5. Multiple-Group LGCM: Test for Moderating Effects.
Path tested
Standardized path coefficient b
Dw
2
(Ddf ¼ 1) Hypothesis Result
Decentralization
Low High
SSC ! revenue growth .01 .40
***
4.83
**
Hypothesis 5a Supported
SSC ! profit growth .01 .47
***
6.28
**
Hypothesis 5b Supported
Share of Loyal Customers
Low High
SSC ! profit growth .03 .36
***
3.84
**
Hypothesis 6 Supported
Note. SSC ¼ services supporting the clients’ actions. b represents the standardized path coefficient for that group; Dw
2
represents the difference in w
2
between the
restricted and the general model for the path being tested.
*p < .10 (two tailed). **p < .05 (two tailed). ***p < .01 (two tailed).
Eggert et al. 33
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Main Effects of SSCs
Moderating Effects of Decentralization
Moderating Effect of Share of Loyal Customers
2.8
3.0
3.2
e (ln)
A: Effect o
f
SSCs on Manufacturer’s Revenue Trajectory
2.8
3.0
3.2
e (ln)
broad SSC portfolio,
high DEC
broad SSC portfolio,
C: Moderating Effect on Manufacturer’s Revenue Trajectory E: Moderating Effect on Manufacturer’s Profit Trajectory
2.6
2.8
3.0
it
broad SSC portfolio,
high SLC
broad SSC portfolio,
2.2
2.4
2.6
t1 t2 t3
Revenue
broad SSC
portfolio
narrow SSC
portfolio
2.2
2.4
2.6
t1 t2 t3
Revenue
low DEC
narrow SSC portfolio,
high DEC
narrow SSC portfolio,
low DEC
2.0
2.2
2.4
t1 t2 t3
Profi
low SLC
narrow SSC portfolio,
high SLC
narrow SSC portfolio,
low SLC
30
3.2
Time
B: Effect of SSCs on Manufacturer’s Profit Trajectory
Time
30
3.2
D: Moderating Effect on Manufacturer’s Profit Trajectory
Time
2.4
2.6
2.8
3
.0
Profit
broad SSC
portfolio
narrow SSC
portfolio
2.4
2.6
2.8
3
.0
Profit
broad SSC portfolio,
high DEC
broad SSC portfolio,
low DEC
narrow SSC portfolio,
high DEC
narrow SSC portfolio,
low DEC
2.2
t1 t2 t3
Time
2.2
t1 t2 t3
Time
DEC
Figure 2. Growth curves under different conditions. DEC ¼ decentralization; SLC ¼ share of loyal customers.
34 Journal of Service Research 17(1)
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substantial insights into their service business, build fundamental
service competences, and lay a foundation for more complex ser-
vices. By systematically developing SSCs beyond SSPs,
manufacturers, can then reap the financial benefits of industrial
service strategies. Specifically, our results confirm that firms
with a broader SSC portfolio have higher percentage increases
in revenue and higher growth rates for profit. By offering a broad
SSC portfolio, companies satisfy broader customer needs and
ensure future revenue growth; at the same time, they can take
advantage of complementarities between services offerings and
consecutive learning effects that facilitate future cost savings.
As a third contribution, our findings highlight the need for
manufacturers to implement an appropriate organizational
design across their entire business before they can reap the finan-
cial benefits of service offerings. To thrive, companies should
decentralize their decision-making authority to lower levels in
the hierarchy as they move from services designed around prod-
ucts toward services designed around customers’ processes. The
moderating role of decentralization shows that industrial service
strategies entail essential foundations of a company’s organiza-
tional structure.
Next, our findings highlight the critical role of a loyal cus-
tomer base for growing the firm’s industrial service business
profitably. We find a particularly strong positive effect of SSCs
on profit growth for companies with a high share of loyal cus-
tomers. Loyal customers help companies manage the costs of
service provision and thus increase their profit growth. Because
manufacturers have more experience working with loyal cus-
tomers, they bear lower costs in serving them, compared with
less loyal customers (Kalwani and Narayandas 1995). Rather
than rolling out SSCs across their entire customer base, manu-
facturers might target loyal, core customers in their efforts to
grow toward SSCs.
The final contribution of our study refers to the methodology
we used. We show that LGCM provides advantages over tradi-
tional approaches when measuring longitudinal change. Among
other things, LGCM allows researchers to model latent intercept
and slope constructs of developmental trajectories, distinguish
intra- and interindividual/firm change, test complex structural
relationships, and account for measurement error. Despite its
promise for marketing and service research, we find few previous
applications of LGCM (e.g., Eggert et al. 2011; Jaramillo and
Grisaffe 2009; Koehler et al. 2011). To promote this methodology
in the service domain, we provide a step-by-step description of its
specification; we hope this effort grants researchers greater famil-
iarity with this promising methodology.
Limitations and Research Directions
Our research design is subject to several limitations, some of
which offer fruitful avenues for research. First, we relied on
self-reported measures to analyze profit trajectories. These sub-
jective performance measures ensure some comparability across
different types of companies and situations with varying stan-
dards for acceptable performance (Pelham and Wilson 1996). But
when company representatives provide subjective performance
ratings, they may overstate them (Haugland, Myrtveit, and
Nygaard 2007). However, the longitudinal design of our study
should reduce the likelihood of self-reported method effects
(Podsakoff and Organ 1986) because response biases caused by
performance overstatements affect only the level of profit, not its
changes. Hence, the relationships among SSPs, SSCs, and profit
growth should not be biased. Nonetheless, further research could
test the relationship between industrial services and financial per-
formance using objective performance measures or multiple data
collection methods to determine the stability of our results.
Second, by focusing on the mechanical engineering industry
in Germany, we reduced the risk of uncontrollable factors that
create noise in cross-industry studies (Haugland, Myrtveit, and
Nygaard 2007). It is worth mentioning that this industry repre-
sents a heterogeneous group of organizations that span different
subsectors (e.g., machine manufacturers, precision instruments,
optical instruments, medical equipment manufacturers). Our
model fits this heterogeneous sample well, which implies that
the effects are robust. However, the results are not necessarily
generalizable to all industrial sectors.
Third, we operationalize companies’ industrial service strate-
gies by measuring the number of different services they offer
within SSC and SSP categories. Two issues may be related to
this measurement approach. First, although the breadth of a
portfolio represents a major strategic dimension (Miller 1987;
Varadarajan and Clark 1994), previous research has highlighted
the importance of both breadth (i.e., scope) and depth (i.e.,
focus) in marketing activities (Fang, Palmatier, and Grewal
2010). Therefore, further research should attempt to capture the
role of focus in manufacturers’ industrial service strategies. For
example, it could be interesting to investigate the extent to which
manufacturers proactively contact customers to sell industrial
services (Challagalla, Venkatesh, and Kohli 2009). Second, we
did not intend to create an exhaustive directory but to compile
a list of common industrial services representing SSPs and SSCs.
Future research might provide a more detailed view by adding
additional services to the measures.
Fourth, we were able to show the revenue and profit changes
over a period of three consecutive waves (i.e., years). Thus, our
data provides a first glimpse into the effects of industrial ser-
vice strategies on manufacturing companies’ revenue and
profit development over time. To better understand the success
of industrial service strategies, a longer observation period
would be insightful. Extending the time frame could further
enrich our findings and probe into the robustness of our results.
Last, our research referred to one organizational design ele-
ment, that is, decentralization of decision-making authority. Yet,
we can think of other organizational contingencies, such as a
firm’s service culture or the presence of a process management,
which might also facilitate service growth. By analyzing their
moderating role, researchers could further enhance our under-
standing of the consequences of industrial service strategies.
These limitations must be kept in mind when considering our
results and implications, yet our findings provide new insights that
we hope stimulate further research in this important, underre-
searched domain.
Eggert et al. 35
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Appendix
Measurement Scales and Items
Model Equations and Definitions
We can represent the unconditional growth curve model for a
repeatedly measured variable y as follows:
1) y
it
¼ Z
a
i
þ l
t
Z
b
i
þ e
it
Level 1 (y measurement) model
2) Z
a
i
¼ m
a
þ z
a
i
Level 2 (structural) model for the inter-
cept factor
3) Z
b
i
¼ m
b
þ z
b
i
Level 2 (structural) model for the slope
factor
y
it
: observed score on measure y (e.g., revenue or
profit) for individual i at time t
Z
a
i
: intercept of the trajectory for individual i
Z
b
i
: slope of the trajectory for individual i
l
t
: factor loading at time t (i.e., value of time)
e
it
: individual- and time-specific residual
m
a
: mean intercept
m
b
: mean slope
z
a
i
: individual-specific deviation from mean intercept
z
b
i
: individual-specific deviation from mean slope
Acknowledgment:
The authors thank the participants of the Frontiers Pre-Conference on
Service and Solution Innovation 2010 (Karlstad, Sweden), the partici-
pants of the ISBM Academic Conference 2010 (Boston, MA), and the
participants of the AMA Winter Marketing Educators’ Conference
2011 (Austin, TX) for their helpful comments on previous versions
of this article. The authors also thank Robert W. Palmatier for his valu-
able comments on this research project.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to
the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, author-
ship, and/or publication of this article.
Notes
1. For parsimony, we fix the insignificant covariance between the
intercept and slope to equal 0.
2. In latent growth curve modeling (LGCM), multiple-group analysis
is typically used to detect moderating effects. First, this is due to
model complexity restrictions of the LGCM approach. In many
cases, adding multiple interaction terms to LGCM leads to estima-
tion problems because LGCM uses many degrees of freedom (Li,
Duncan, and Acock 2000). Second, multiple-group analysis pro-
vides advantages for interpreting the results. In LGCM, the interac-
tion effect of two predictor variables on the slope of a growth curve
technically needs to be interpreted as a three-way interaction
(Curran, Bauer, and Willoughby 2004), because the interaction
effect itself is influenced by time.
3. We performed post hoc moderation tests to check for the stability
of our results. First, we conducted multiple-group LGCM in which
we dichotomized our moderator variables with the top and bottom
one third of the sample, instead of a median split. Second, we
employed a regression analysis as a ‘‘sanity check,’’ in which we
calculated the differences in revenue and profit between Time 3
and Time 1 as dependent variables. The moderator variables were
modeled as continuous variables and interaction terms were built
with SSCs. The results remain consistent across all analyses.
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Author Biographies
Andreas Eggert is a chaired professor of marketing at the University
of Paderborn, Germany. His research interests focus on strategies for
the creation and appropriation of value in business relationships.
Eggert’s work has appeared in the Journal of Marketing,theJournal
of Service Research,theJournal of Supply Chain Management, the
Journal of Business Research,theEuropean Journal of Marketing, the
Journal of Marketing Theory and Practice, Industrial Marketing Man-
agement,theJournal of Business-to-Business Marketing, and the
Journal of Business and Industrial Marketing, among others.
Jens Hogreve is a professor of service management at the Catholic
University of Eichstaett-Ingolstadt, Germany. He received his doc-
toral degree from the University of Hagen, Germany. His research
focuses on service issues such as service recovery and service guaran-
tees, service innovation, industrial services, and customer co-creation.
The results of his work appear in the Industrial Marketing Manage-
ment, the International Journal of Research in Marketing, Journal
of Service Research, Journal of Retailing, German-language journals,
and edited book chapters.
Wolfgang Ulaga is a professor of B2B marketing at IMD Lausanne,
Switzerland. His research focuses on how firms understand, (co-)cre-
ate, communicate, deliver, and capture value in B2B markets. His cur-
rent research focuses on how goods-centric firms grow beyond their
traditional core and investigates how industry leaders successfully
implement service-growth strategies in industrial markets. His work
has appeared in Journal of Marketing, Harvard Business Review,
Journal of Business Research, Industrial Marketing Management,
European Journal of Marketing, Journal of Services Marketing, Jour-
nal of Business-to-Business Marketing, and Journal of Business and
Industrial Marketing, among others.
Eva Muenkhoff is an assistant professor of marketing at the Univer-
sity of Paderborn, Germany. She received her PhD from the University
of Paderborn. Her research interests are in business-to-business and
services marketing. Current projects focus on the profitability of
industrial services and the transition of manufacturing companies
toward service and solution providers. Her work has been published
in Industrial Marketing Management and several conference proceed-
ings. For her work, she received the best overall conference paper
award 2011 from the American Marketing Association.
Eggert et al. 39
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