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Smart Manufacturing for
EU growth and prosperity
Servitization, R&D intensity and performance
Oscar Bustinza, University of Granada, [email protected]
Ferran Vendrell-Herrero, University of Birmingham, [email protected]
Emanuel Gomes, Universidade Nova, [email protected]
Tim Baines, Aston University, [email protected]
Abstract
This study is positioned at the intersection of R&D intensity and servitization literatures. It explores
empirically whether R&D intensity help to unpack the complex relation between product-service
innovation (servitization) and performance. We contend that manufacturing firms in R&D-intensive
industries are more likely to benefit from implementing service provision than those firms operating
in other sectors because of industry dynamics and reduction of customer uncertainty. The study uses a
primary survey sample of multinational companies. Its results reinforce the importance of context for
successful product-service innovation.
Key words: Servitization, performance, R&D intensity
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1. Introduction
Manufacturing firms’ capability to provide customer-specific or industry-specific product-service
“solutions” (Cusumano et al., 2015) through close relationships between product manufacturers and
clients (Neely, 2008) has been referred to as servitization (Tukker, 2004; Baines and Lightfoot, 2013;
Vendrell-Herrero et al., 2017). Servitization is a source of product differentiation and a useful
competitive tool to lock out competitors and lock in customers (Vandermerwe and Rada, 1989).
Nevertheless, servitization as a complex process for capturing value blurs the link between
implementation of services and firm performance (Cusumano et al., 2015). Some authors state that
the relationship between servitization and performance is non-linear (Kohtamäki et al., 2013; Suarez
et al., 2013; Visjnic Kastalli and Van Looy, 2013), while others argue that the relationship depends on
internal variables such as organizational structure or external variables such as value chain position
(Bustinza et al., 2015). A gap thus exists in analysis of this relationship and the importance of
contingent variables. To fill this gap, our paper aims to better understand the link between service
implementation and firm performance in manufacturing firms. An important contribution of this
study is its analysis of this relationship in light of one key moderating variables: R&D industrial
intensity.
Previous studies suggest that successful implementation of services is contingent on industry
characteristics (Bustinza et al., 2015; Vendrell-Herrero et al., 2016; Bigdeli et al., 2017). A
contribution of this study is thus its exploration of the idiosyncratic effect of servitization in
manufacturing firms operating in industries with high levels of R&D intensity, as such firms are
more exposed to technological disruptions (Christensen et al., 2003). This observation is particularly
important for our study because, as argued by Weber et al. (2015), successful implementation of
innovative changes in collaborative agreements tends to vary substantially between high-tech and
more traditional industries.
In addition to the previous theoretical contributions, this study provides an empirical
contribution by using a unique and robust survey dataset composed of data collected from senior
executives from large manufacturing firms. All firms in the sample are undergoing a process of
product-service innovation (servitization) through both in-house development and external
collaborative agreements with Knowledge-intensive Business Services (KIBS). The model is tested
with Structural Equation Modelling (SEM), and the dependent and independent variables are
framed through development of second-order constructs.
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This paper is structured as follows. Following this introduction, we conceptualise the context
further by reflecting on the literature on product-service innovation and R&D intensity. The
methodology is then described in full, followed by an analysis of the data collected from the survey.
The last section discusses the key findings, followed by implications for research and practice,
limitations and suggestions for future studies.
2. Theoretical framework and hypotheses
2.1. Product-service innovation
Product innovation is introduced to meet market needs following a predictable model (Utterback and
Abernathy, 1975) that emphasises different stages: a) product performance (performance
maximising), b) product variety (sales maximising), and c) product standardization and costs (cost
minimising). Most studies analysing the product-service continuum are not strictly linked to this
framework, but to analysing the evolution from pure product to pure service along a continuous
line. For instance, Oliva and Kallenberg (2003) consider the firm’s total number of products under
use as the product-installed base (IB), where IB services represent the increasing range of related
services over the useful life of a product. The transition then follows three stages: a) consolidating
product-related service offering, b) entering the IB service market, and c) expanding to
relationship-based services. Tukker (2004) also proposes three stages: a) product-oriented services,
b) use-oriented services, and c) result-oriented services. Some authors suggest that product-service
can be positioned on a continuum from products with services as an “add-on” to services with
tangible goods (Gebauer and Friedli, 2005; Neu and Brown, 2005; Baines et al., 2009).
Along these lines, Baines and Lightfoot (2013) propose that the product-service continuum
follow three stages: a) base services (outcome based on product provision), b) intermediate services
(outcome focused on product condition) and c) advanced services (outcome focused on capability).
Rather than linking product-service to a continuum, we analyse product innovation following
Utterback and Abernathy’s (1975) framework, in which product variety through intermediate and
advanced services—product-service innovation—is related to sales maximising. We also take into
account the work of Visjnic et al. (2016), which shows that a firm’s product-service business models
result in a significantly higher profit margin than product-only business models. In light of our
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research objectives, we configure product-service innovation (servitization) in two dimensions:
product-service development (Product innovation and Updated product lifecycle) and customer
engagement (Product-service alignment and Service feedback & analytics).
Updated product lifecycle guarantees continuous improvement of the product while ensuring
proper functioning and extending product lifecycle. It is considered a core service, since it is
necessary to achieve economies of scale through standardization (Ulaga and Reinartz, 2011). It is
also part of the total service experience that links products and services during the entire product
lifecycle (Wise and Baumgartner, 1999) and is connected to the concept of value-in-use (Vargo and
Lusch, 2008). Some authors indicate the need for further analysis of the role of updating services in
product-service supply chain reconfiguration throughout the entire lifecycle (Johnson and Mena,
2008).
Product-service alignment is required to manage an integrated offering or respond to customer’s
requests effectively (Martinez et al., 2010; Mauger et al., 2013). It is one of the important capabilities
enabling firms to compete through product-service offerings (Matthyssens and Vandenbempt,
2008). The importance of alignment has been proven to be related to competitive competencies in
manufacturing firms, as it leads to more efficient use of resources (Birou et al., 1998). For
manufacturing firms, the primary focus of alignment is on product and service enhancement
processes because they decrease the cost of designing new products and services, reduce
time-to-market for new products and services, enhance product-service quality, and support
product/service innovation (Tallon, 2007).
Service feedback & analytics facilitate leveraging knowledge, skills and resources between
providers and customers to maximise value creation (Vargo and Lusch, 2008). These tools are
useful for analysing the preferences and behaviour of individual customers (Baines et al., 2017).
Moreover, these tools capture and assess information required to support key decisions in the
product-service’s lifecycle (McFarlane and Cuthbert, 2012).
2.1.1. Product-service innovation and performance
The link between product-service innovation (servitization) and firm performance is blurred
(Cusumano et al., 2015), and there have been various attempts to clarify the
servitization-performance link quantitatively. Suarez et al. (2013) design a longitudinal analysis of
464 firms from the US Software industry for the period 1990-2006. Their model takes the percentage
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of service revenue as a measure of the service business model, which is related to profit margin as a
measure of firm performance. They find a U-shaped relationship, in which minimum profit occurs
when service revenues are 56% of total revenue. Visjnic-Kastalli and Van Looy (2013) design an
intra-firm analysis by constructing a panel dataset comprising the operations of the 44 subsidiaries
of Atlas Copco for the period 2001-2007. Their measures of firm performance and service
implementation are the ratio of subsidiary profits over subsidiary sales and total subsidiary sales in
service, respectively. The researchers find a cubic relationship. While initial increments of service
sales have a positive impact on subsidiary performance, this effect gradually diminishes with the
growth of service sales. When service sales are relatively large, again, the positive effect gradually
increases. Kohtamäki et al. (2013) design a cross-country survey with 91 Finnish machine
equipment manufacturing firms. Survey data allow for construction of a scale for the degree of
service within the business model, and performance is captured in data on sales growth between
2008 and 2011. Consistent with the work of Suarez et al. (2013), they also find a U-shaped
relationship. The declining performance found in these studies, at least for a period of time, may be
partly explained by the fact that firms in the process of servitization are more likely to incur higher
product-service development costs as a result of having to adopt new innovative processes and
sometimes even invest in R&D.
Some studies have attempted to unpack the complex relationship between product-service
innovation and performance by exploring the effects of combinative variables. For instance, Visjnic
et al. (2016) propose coupling service business models with product innovation processes as a way
to enhance profit margin. Similarly, Utterback and Abernathy (1975) find that sales are maximised
through product innovation and product variety; that variety indicates the firm’s product market
strategy; and that it is related to the breadth and depth of different product offerings (MacDuffie et
al., 1996). Servitization increases product range, as produces are combined with varying bundles of
services (Baines et al., 2009). Degree of product variety increases with competitiveness of the market
(Lancaster, 1990). Servitization is related to competitive differentiation (Bustinza et al., 2015),
defined as obtaining services linked to greater market value and organizational and business
performance (Bustinza et al., 2010; Sparrow and Cooper, 2014). Therefore, we hypothesise that:
Hypothesis 1: Product-service innovation is positively associated with the performance of
manufacturing firms.
2.2. Industrial R&D intensity and product innovation outcomes
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Recent research suggests that the processes and success of service innovation in manufacturing
companies depend on the characteristics of the industry in which the company operates. Two causes
have been highlighted in the literature, namely, product lifecycle (Cusumano et al., 2015) and
proximity to end consumers in the supply chain (Bustinza et al., 2015).
The work of Cusumano et al. (2015) examines the different types of services provided by
manufacturers during the lifecycle of the product. Their study suggests that both incumbents and new
entrants in high-tech industries with high investments in R&D focus more on product innovation than
do other industries with a lower level of R&D and are thus more prone to technological disruptions
(Christensen et al., 2003).
Significant technological disruptions are likely to change the way firms compete in the
marketplace and re-establish the power relations between incumbent and new entrants (Roy and
Cohen, 2015). This dynamism in industries with high investment in R&D tends to produce
continuous rivalry for technological superiority between incumbents and new entrants, periodically
reigniting rivalry. Mobile phone, aircraft, pharmaceutical and medical equipment are all industries in
which completion of a new project/product does not stop innovation processes, which are continuous
activity. Consequently, according to Suarez (2004), R&D intensive industries are prone to experience
the “battle for dominance” between two or more rival technologies.
Another important characteristic of industries in which firms invest large amounts in R&D is that
frequent technological disruptions re-establish a new ferment phase in the lifecycle of the product and
thus raise uncertainty, product variation and investment in product innovation (Anderson and
Tushman, 1990; Grodal et al., 2015). Implementation of services can help to alleviate customer
uncertainty with respect to each new generation of technology, as services can be used to help and
support customers who do not yet have the confidence to engage with product complexities and
therefore are reluctant to purchase the latest generation of the product (Cusumano et al., 2015).
What then is the position of the firm in the supply chain, and how does this position help the firm
to face consumer uncertainty? Bustinza et al. (2015) find that service innovation processes are
different for industries with a B2B and B2C orientation. It is not the same to deliver services to end
consumers as it is to deliver them to other firms. Firms must deal differently with end consumer
uncertainty when selling new pharmaceutical or electronic products than, for example, aircraft
manufacturers do with their customers (i.e. flight operators). Still, regardless of proximity to the
customer, implementation of services in product firms reduces customer uncertainty. Provision of
optimization services through software and big data technologies, for example, gives product firms
valuable feedback about product use and customer needs (Opresnik and Taisch, 2015). This
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information feeds into new product development and supports product innovation (Visnjic et al.,
2016).
In sum, service innovation in manufacturing potentially provides more economic value to
industries with high R&D intensity than to industries with low or medium degrees of R&D intensity.
In essence, industries with high R&D intensity continuously develop new and complex products and
the related service help to reduce customer uncertainty. Therefore, firms operating in industries with
high R&D intensity have more opportunities to capture economic value through implementation of
services. From this argument, we construct the following hypothesis:
Hypothesis 2: Industrial R&D intensity positively moderates the relationship between
product-service innovation and firm performance.
Figure 1 presents the hypotheses formulated in a relationship model. The next section presents
methodology used in this study.
Figure 1. Model of relationships
3. Methodology and results
3.1. Sampling procedures
An empirical investigation is performed to verify the study hypotheses. The data included in the
sample are analysed through the statistical software SPSS 22.0 and EQS 6.1. The survey questioned
services executives in manufacturing multinationals. The selected survey sample targeted
PRODUCT-
SERVICE INNOVATION
(SERVITIZATION)
NEW PRODUCTS &
SERVICES
PRODUCT
LIFECYCLE
UPDATED
PRODUCT-SERVICE
ALIGNMENT
SERVICE FEEDBACK &
ANALYTICS
OVERALL
PERFORMANCE
HIGHER CUSTOMER
SATISFACTION
INCREASED PROFITABILITY
COMPETITIVE DIFFERENTIATION
PROFIT LEVEL
PROFIT MARGIN CHANGE
BUSINESS PERFORMANCE
ORGANIZATIONAL PERFORMANCE
PRODUCT-SERVICE
DEVELOPMENT
CUSTOMER ENGAGEMENT
HIGH R&D INDUSTRY INTENSITY
(VS. MEDIUM-HIGH)
H1
H2
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companies located in North America, Europe and Asia that were validated by an advisory board
prior to administration.
3.2. Main scales
3.2.1. Product-Service innovation (Servitization)
This scale is composed of items included in a questionnaire using a 5-point Likert scale (1= Total
disagreement, 5 = Total agreement) to assess the distinctive indicators of the two components for
product-service innovation explained in the theoretical section above: a) Product innovation and
Updated product lifecycle for Product-Service development, and b) Product-service alignment, and
Service feedback & analytics for Customer engagement. Principal component analysis (Hair et al.,
2001) with Varimax rotation—Kaiser-Meyer-Olkin test confirms the existence of two components.
An index of discrimination is produced, as all the firms offer product-service innovations. A subset
of criterion-referenced tests (those firms in extreme positions of the service continuum) enables
analysis of the scale’s internal consistency, showing a Cronbach’s alpha value higher than 0.700
(Cronbach, 1951). The scale reliability measures are also higher than threshold levels. Values
demonstrate the internal consistency and reliability needed to validate this novel scale.
3.2.2. Overall business performance
As for the scale analysed above, a 5-point Likert scale (from 1= Total disagreement, 5 = Total
agreement) is developed to collect the main performance indicators, including organisational and
business performance measures (Bustinza et al., 2010). Principal components analysis showed these
two dimensions. To analyse the scale’s internal consistency—again applying the index of
discrimination explained above—the Cronbach’s alpha value was calculated. The scale’s reliability
was analysed through Composite Reliability and Average Variance Extracted and produced values
that show internal consistency and reliability. The next section analyses the relationship between
this variable and product-service innovation.
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3.2. Results and discussion
Structural Equations Modelling (SEM) was used to test the model hypotheses, following Robust Maximum
Likelihood estimation. The analysis provided a value for the parameters of Hypothesis 1, the relationship
between product-service innovation and overall performance (β1=0.402; p-value<0.001). The role of R&D
intensity as moderator of the relationship between product-service innovation and performance was also
analysed following SEM procedures. To do so, the sample was divided through median multi-group
analysis. The same process was used to analyse both moderators. Firstly, parameters were restricted and
the goodness-of-fit of the model estimated (χ2 Satorra-Bentler of 56.453). Secondly, the parameters were
restricted to equality in the different subsamples, resulting in a change of χ2 Satorra-Bentler to 64.889. A χ2
difference test shows significant differences between the models, verifying the role of R&D intensity as
moderator of the relationship between product-service innovation and overall performance. In estimating
the model for subsamples with R&D intensity (H2), the parameter increased from β1=0.402 to β1=0.425
(βMod=+0.023, p-value<0.001) for R&D intensity. These results suggest that the effect of product-service
innovation on performance is significantly higher when firms are in high-R&D-industry-intensity
environments, supporting Hypotheses H2.
4. Discussion
The purpose of this study is to examine some of the crucial variables that affect the relationship between
product-service innovation and performance. Previous studies analyse this topic through a different
methodological approach (Kohtamäki et al., 2013; Visjnic-Kastalli and Van Looy, 2013; Cusumano et al.,
2015; or Visjnic et al., 2016), and our study pioneers in applying second-order construct and path analysis
(Bustinza et al., 2010). Results on the relationship between product-service innovation and performance
are in line with Vijnic et al. (2016), who propose coupling servitization with product innovation processes to
enhance long-term profitability. Our results also support Bustinza et al. (2015), which argues the
relationship between servitization and competitive satisfaction. This is thus the first study to demonstrate a
relationship between servitization and complex overall performance measures that encompasses business
performance and organizational performance measures. Our approach thus relates servitization to
appropriate performance indicators, as the study incorporates different and increasingly complex
performance outcomes (Sparrow and Cooper, 2014).
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The study validates a novel scale for measuring product-service innovation that shows the importance
of analysing servitization throughout the entire product lifecycle (Neely, 2008). The basics of servitization
suggest that new business models from the entire value chain will maximise profit capture (Wise and
Baumgartner, 1999). They also demonstrate the role played by product-service alignment in
product-service configuration. This alignment is responsible for providing synergies and adding value for
both providers and customers (Mauger et al., 2013). It also promotes product-service offering integration
and provides a useful tool for fulfilling customers’ requests (Martinez et al., 2010). In analysing customers
as part of product-service innovation processes, the results support the importance of customers’
engagement in successful addition of services in product firms (Vargo and Lusch, 2008).
Finally, the results add clarity to the moderating role of R&D industry intensity. Industries with
high R&D intensity are more prone to disruptive technologies (Christensen, 2003) and hence to
return to the ferment phase in the product lifecycle (Anderson and Tushman, 1990; Grodal et al.,
2015). Including services in the offerings of firms that sell products in the ferment phase may help to
reduce consumer uncertainty with respect to unknown technology, facilitating customer engagement
and value capture (Bustinza et al., 2015; Cusumano et al., 2015). Our results empirically validate this
theoretical argumentation by showing that high R&D industry intensity increases the positive impact
of product-service innovation on performance.
5. Conclusions and future research avenues
5.1. Academic implications
This article contributes to theory by reinforcing some of the assumptions about the relationship between
servitization and performance, and by providing a new perspective through analysis of the role of R&D
intensity. The first academic contribution is construction of a novel construct for product-service innovation
(servitization). This construct is composed of different elements. Service innovation favours customer
engagement and constitutes a strategic tool for lock-in customers (Vandermerwe and Rada, 1989), a
strategic benefit related to performance outcomes such as customer satisfaction (Bustinza et al., 2015).
Servitization opens a continuous dialogue with customers, creating the appropriate link channels to boost
value-in use contexts (Vargo and Lusch, 2008). Customer engagement and service feedback analytics
leverage resources and knowledge, maximising value creation (Vargo and Lusch, 2008). Finally,
product-service alignment is needed to maximise this process of value creation (Martinez et al., 2010). This
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study shows how such alignment constitutes an organisational capability that influences superior
performance.
Another important academic contribution to the body of knowledge on servitization is our study’s
response to increasing interest in the academic community in understanding the industry-specific factors
that enhance or diminish returns obtained from product-service strategies (Bustinza et al., 2015; Cusumano
et al., 2015). Our study examines different levels of industrial R&D intensity more specifically and concludes
that, the higher the level of R&D intensity, the higher the potential for profits for manufacturers
implementing service business models. This novel finding is consistent with previous arguments suggesting
that in changing business environments product-service innovation reduces consumer uncertainty in
dealing with new technologies (Christensen, 2003: Suarez, 2004), and informs product developers (Visjnic
et al., 2016).
5.2. Managerial implications and future research lines
Our results have important practical implications for firms and their managers. First, they provide evidence
that manufacturing firms that are not servitized can enhance their organizational and strategic
performance through development of product-service innovation. Our factor analysis demonstrates that
product-service innovation is composed of two different constructs: technical development of products and
services, and engagement with customers. Accurate implementation of service business models is thus not
only a matter of designing and delivering a novel and distinctive service. It also requires good
comprehension of consumers and the capacity to react quickly to their demands.
Another significant aspect of this research is its industry-level analysis of the role of R&D
intensity. We adopted this approach in response to recent calls to explore empirically the
heterogeneities of service implementation at industry level (Cusumano et al., 2015). Our evidence
suggests that there are different competitive dynamics in sectors with high R&D intensity.
However, our sample is silent on R&D investment and product lifecycle at firm level. Future
research should explore how these variables influence the relation between service implementation
and firm performance.
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MAKERS - Smart Manufacturing for EU growth and prosperity is a project funded by the Horizon 2020 Research and Innovation Staff Exchange Programme under the Marie Sklodowska-Curie Actions - Grant agreement number 691192.
