Week 6 - Assignment Rate Methods of HR and Technology Practices f.docx

Week 6 - Assignment: Rate Methods of HR and Technology
Practices for Developing Sustainable Innovation
Assignment
Top of Form
Due December 8 at 11:59 PM
Bottom of Form
For this week’s assignment, you will create a video presentation
by using the Kaltura CaptureSpace tool located in NCUOne. To
access the video capturing tool, follow the tutorial found in
your Books and Resources for this Week.
For this assignment, you are asked to read the story about
Progressive Insurance (Megson & Hammer, 2004) as a
foundation for your presentation. Your task is to act like a
business reporter covering a story for a business news network.
You are expected to provide a summary of the human resource,
technology, and process improvement efforts explained by
Megson and Hammer, and then, provide a grade of A-F on the
company performance. You are expected to give a grade on each
of the summary elements and then an overall grade of the
company’s performance. Your news story and grading should be
no more than 5 minutes. You are expected to submit a transcript
of your video. Feel free to be creative with your video as this is
your news story to tell. Please keep in mind that while you are
not expected to note your sources in your video presentation,
you are expected to cite them in your transcript. You should
reference at least 4 resources for this assignment using sources
from the Library.
Length: Your video should be no more than 5 minutes.
References: You may reference any of the other resources
provided in your reading this week.
Your video presentation should demonstrate thoughtful
consideration of the ideas and concepts presented in the course
and provide new thoughts and insights relating directly to this

topic. Your response should reflect scholarly writing and
current APA standards.
Reference
Megson, L., & Hammer, M. (2004). Deep change: How
operational innovation can transform your company. Harvard
Business Review, 82(7/8), 182–183.
Week 6
Print
Leading and Managing Sustainable Innovation
Perhaps there is no important rule in business than
understanding that there is no one-size-fits-all approach to
creating innovation. Even if you can create a spark of
innovation, there is no guarantee that it can be maintained if
there is no culture to maintain it. Simply stated, organizational
leaders have to build an environment where innovation can
occur and where it can be maintained. For innovation to happen,
many conditions must be met. However, the conditions are not
formulas for perfect innovations but simple elements that a
manager must mix in the proper proportions for their respective
organization. These elements include, but are not limited to,
employees being encouraged to participate in process
improvement; managers being willing to allow for failures with
innovation; and finally, risk management must occur and must
become socially ingrained. Keep in mind this is not risk
avoidance; it is risk management of the inherent risks of
seeking to be an innovator. Other elements may include
multitasking, job rotation, and modification of responsibilities.
Sustainable innovation is the continued application of all the
previously mentioned ideas and a developed culture that starts
with the hiring process and continues with the development of
incumbent employees and mid-level managers. The willingness
and acceptance of change as an ally towards growth versus
seeing change as an enemy is essential. Executive leaders, mid-
level managers, and even employees must understand the
mission and capture the vision of the organization and the push

for continued perfection of the process, product or service must
be the always sought-after goal for the organization. To that,
each member of the organization must see their functional role
as contributing towards those stated objectives.
Be sure to review this week's resources carefully. You are
expected to apply the information from these resources when
you prepare your assignments.
Full Terms & Conditions of access and use can be found at
https://www.tandfonline.com/action/journalInformation?journal
Code=ciai20
Industry and Innovation
ISSN: 1366-2716 (Print) 1469-8390 (Online) Journal homepage:
https://www.tandfonline.com/loi/ciai20
Driving business performance: innovation
complementarities and persistence patterns
Eleonora Bartoloni & Maurizio Baussola
To cite this article: Eleonora Bartoloni & Maurizio Baussola
(2018) Driving business performance:
innovation complementarities and persistence patterns, Industry
and Innovation, 25:5, 505-525,
DOI: 10.1080/13662716.2017.1327843
To link to this article:
https://doi.org/10.1080/13662716.2017.1327843
Published online: 05 Jun 2017.

Submit your article to this journal
Article views: 459
View related articles
View Crossmark data
Citing articles: 1 View citing articles
https://www.tandfonline.com/action/journalInformation?journal
Code=ciai20
https://www.tandfonline.com/loi/ciai20
https://www.tandfonline.com/action/showCitFormats?doi=10.10
80/13662716.2017.1327843
https://doi.org/10.1080/13662716.2017.1327843
https://www.tandfonline.com/action/authorSubmission?journalC
ode=ciai20&show=instructions
https://www.tandfonline.com/action/authorSubmission?journalC
ode=ciai20&show=instructions
https://www.tandfonline.com/doi/mlt/10.1080/13662716.2017.1
327843
https://www.tandfonline.com/doi/mlt/10.1080/13662716.2017.1
327843
http://crossmark.crossref.org/dialog/?doi=10.1080/13662716.20
17.1327843&domain=pdf&date_stamp=2017-06-05
17.1327843&domain=pdf&date_stamp=2017-06-05
https://www.tandfonline.com/doi/citedby/10.1080/13662716.201
7.1327843#tabModule
https://www.tandfonline.com/doi/citedby/10.1080/13662716.201
7.1327843#tabModule
INDUSTRY AND INNOVATION, 2018
VOL. 25, NO. 5, 505–525

https://doi.org/10.1080/13662716.2017.1327843
Driving business performance: innovation complementarities
and persistence patterns
Eleonora Bartolonia,b and Maurizio Baussolac
aISTAT, Italian National Institute of Statistics, Milano, Italy;
bDipartimento di Scienze Economiche e Aziendali,
Università di Parma, Parma, Italy; cDipartimento di Scienze
Economiche e Sociali, UCSC, Università Cattolica
del Sacro Cuore, Piacenza, Italy
ABSTRACT
Complementarities between technological and non-technological
innovation are crucial determinants of firm performance.
Although
innovation complementarity has been extensively tested in the
empirical literature, it has not been analysed in conjunction
with
innovation persistence. This fact is mainly due to the lack of
data sets
able to provide adequate longitudinal information. The
capacities to
developmarket-
orientedbehaviourandintroduceneworganisational
innovations, together with technological innovation, are the
drivers
of a firm’s productivity and profitability. We find that these
activities
complement technological innovation and that their impact is
greater
when they persist over time, thus introducing a more general
concept
of innovation persistence. We present an empirical model based

on a
large new panel of Italian manufacturing firms covering the
period
2000–2012 which enables us to determine the precise impacts of
a firm’s innovative attitude, in a broad definition that
incorporates
non-technological innovation and persistence, on its
productivity and
profitability.
KEYWORDS
Technological and
non-technological
innovation;
complementarities;
European community
innovation survey;
profitability; productivity;
unbalanced panel data
JEL CLASSIFICATIONS
L25; 030; 032; 033
1. Introduction
The relationship between innovation and firms’ performance has
long been debated within
the economic and managerial literature. The former has focused
on both macro- and
microeconomic implications underlining, on the one hand, the
role of innovation inputs
(e.g.R&Dactivity)indetermininglong-
runeconomicgrowth.Thisapproachcharacterised
the early R&D endogenous growth models (Romer 1990; Aghion
and Howitt 1992; Jones
1995).

On the other hand, the microeconomic approach has focused
particularly on the
empirical estimation of the impact of innovation on firms’
productivity (Geroski 1989;
Crépon, Duguet, and Mairessec 1998; Lööf and Heshmati 2002),
thus emphasising the
methodological issues underlying such empirical investigations.
On the managerial side, particular emphasis has been devoted to
the impact of a
firm’s attitude of being an innovator (product and/or process)
and, simultaneously, to
its ability to be market-oriented (Narver and Slater 1990). This
approach embraces a more
comprehensive definition of an innovative attitude, which
typically brings about other
CONTACT Eleonora Bartoloni [email protected]
© 2017 Informa UK Limited, trading as Taylor & Francis Group
http://www.tandfonline.com
17.1327843&domain=pdf
506 E. BARTOLONI AND M. BAUSSOLA
forms of non-technological innovations, i.e. organisational and
marketing innovations.
Indeed, these forms of innovation play a crucial role in
affecting firms’ performance
in terms of productivity and even profitability, in that the
innovation process affects
the internal allocation and use of resources, thus enabling
innovating firms to be more

responsive to changing market conditions (Geroski, Machin, and
Van Reenen 1993).
All of these issues imply that for innovation to be effective, it
should be persistent,
thus enabling those continuously innovating firms to gain a
premium with respect to
peers that do not act accordingly. This view is also supported on
theoretical grounds
by theories addressing (i) the existence of sunk costs in
innovation activities (e.g. R&D
expenditures) (Stiglitz 1987; Mañez et al. 2009); (ii) the
positive correlation with past
successful innovations (success-breeds-success), which implies
a positive impact on firms’
profitability and thus on their future ability to finance more
innovative activities (Carpenter
and Petersen 2002; Le Bas and Latham 2006); and (iii) the
dynamic accumulation of
knowledge or, in other words, the dynamic process of
innovation that enables a firm to
learn and adapt its innovation strategy (David 1992; Geroski,
Machin, and Van Reenen
1993; Geroski, Van Reenen, and Walters 1997).
Innovation persistence provides a firm with the ability to
exploit competitive advantages
withrespecttocompetitorsandthustoearnprofitsthataresystematica
llyhigherthanthose
gained by non-innovating or at least only occasional innovating
firms (Mueller 1992; Cefis
2003; Cefis and Ciccarelli 2005; Bartoloni and Baussola 2009).
However, the role of non-technological innovation has not been
completely consid-
ered in this framework. Indeed, non-technological innovation is

crucially associated with
technological innovation (e.g. product or process innovation)
and generates technological
activities related to new organisational and marketing activities,
which affect the success
of such new technological practices. In particular, process
innovation and organisation
innovation may be closely linked to one another, whereas
product innovation may be
more effectively related (although not exclusively) to marketing
innovation.
Percival and Cozzarin (2008) and Evangelista and Vezzani
(2010) show how com-
plementarities between organisational and technological
innovation affect firms’ perfor-
mance; in a more recent study Bartoloni and Baussola (2016)
underline how emphasis
on technological innovation alone is misleading, and that the
ability to adopt marketing
innovation positively affects firms’ profits.
We propose an empirical investigation in which we explicitly
consider the role of
persistenttechnologicalandnon-
technologicalinnovationsinaffectingfirms’performance
in terms of productivity and profitability. We use a panel of
Italian manufacturing firms
over the period 1998–2012 derived from the Community
Innovation Survey and matched
with administrative data that enabled us to obtain information
on firms’ balance sheets.
The paper is therefore structured as follows. In Section 2, we
provide the interpretative
framework used to develop the empirical analysis. In Section 3,

we describe the charac-
teristics of the data-set, we present the empirical model in
Section 4, and the results are
discussed in Section 5. Section 6 concludes the paper.
INDUSTRY AND INNOVATION 507
2. The interpretative framework
The debate on the persistence of innovation has typically
analysed the role of persistent
activities as measured by R&D (input) or patents (output) and,
to a lesser extent, by
technology adoption without considering the role of non-
technological innovation.
Cefis (2003) and Cefis and Ciccarelli (2005) analyse the impact
of a pattern of persis-
tent innovation on firms’ profitability by using patent data, and
suggest that persistent
behaviour brings about higher profits compared with those
achieved by companies that
are non-persistent innovators. Johansson and Lööf (2010) adopt
an input measure of
persistent innovation, referring to the impact on sales,
productivity and exports of firms’
long-term R&D strategy. Persistent innovation affects economic
performance, and this
impact is significantly higher compared with a strategy of
occasional innovation.
Raymond et al. (2010) attempt to test whether innovation
output, endogenously
determined by the decision to undertake R&D, is affected by

previous values, thus verifying
whether persistence may be partially spurious. In addition, as
their data-set is a balanced
panel of Dutch firms covering three consecutive Community
Innovation Surveys during
the 1990s, they are able to take into account initial conditions
and adopt a dynamic
specification of the model. Using this approach, they
disentangle the persistence effect
and verify whether a spurious persistent effect does exist. Their
results suggest that this
is indeed the case, in that persistent innovation activity (either
process and/or product
innovation) exists only when initial conditions are taken as
exogenous. Once endogenised
and unobserved individual effects have been taken into account,
the persistence hypothesis
is rejected.
However, a milder persistence effect is observed concerning
innovation output, as the
past share of innovative sales does indeed affect the current
share. It is worth noting,
however, that when they use an innovation input measure, i.e.
R&D and the share of
R&D expenditures as a ratio of total sales, persistence is
observed, thus confirming other
evidence for manufacturing and service companies in other
economies (Peters 2009).
The impact of innovation on firms’ performance may be
analysed with respect to both
the input and output of the innovation process. Typically, the
former is considered by
using R&D expenditure as a proxy for knowledge capital, which
therefore contributes,

akin to other production inputs, to output growth. Innovation
input is also considered,
focusing on the adoption of new process technology, which
implies the use of new and
more efficient capital goods.
This approach has been particularly developed within the
endogenous growth theoret-
ical setting (Romer 1990), in which an R&D sector interacts
with a manufacturing sector
producing new capital goods and final output. The model
implies an equilibrium growth
path crucially depending on the resources allocated to R&D.
Innovation output is considered the key variable for increasing
productivity in the
seminal study by Crépon, Duguet, and Mairessec (1998). By
adopting a Cobb–Douglas
production function framework, the authors derive a
simultaneous equation model that
links productivity, innovation output and R&D spending among
a cross section of indus-
trial firms. They find that innovation output, as proxied either
by the share of innovative
sales or by patent counts, is positively and strongly affected by
R&D. This model, which
has inspired an increasing number of studies based on the same
methodological approach,
focuses on the empirical tools required to overcome the bias
related to information being
available only for innovative firms when using innovation
surveys.

Lööf and Heshmati (2002) use such an approach to develop an
empirical analysis of
knowledge capital and productivity at the firm level for a
sample of Swedish firms par-
ticipating in the national Community Innovation Survey. They
emphasise how intangible
assets are crucial in affecting the results, thus underlining the
implicit relevance of their
measurement issue.
Another branch of the literature has focused, instead, on panel
data investigations to
address causality issues (Rouvinen 2002; Frantzen 2003;
Battisti, Mourani, and Stoneman
2010), finding support for a causal link running from R&D to
productivity.
In our empirical specification, we focus on the relationship
between productivity – as
measured by real value added per worker – and production
inputs while also accounting
for the effect of persistently adopted technological and non-
technological innovations.
The inclusion of non-technological innovation draws on the
Schumpeterian view that
new management methods represent another form of innovation
(Schumpeter 1934). This
dimension has been explored in the current empirical debate
dealing with innovation
complementarities. In particular, it has been argued that
marketing and organisation
activities may crucially affect firms’ performance, with the link
between technological
and non-technological (i.e. managerial and organisational)

innovation being analysed by
using micro-data typically derived from innovation surveys (e.g.
the CIS) (Bartoloni and
Baussola 2016; Battisti and Stoneman 2010; Schubert 2010).
Hollenstein (2003) achieves results that are consistent with
these findings although he
refers to a large sample of Swiss service companies and uses a
methodological approach
based on cluster analysis. Labour productivity or sales growth
are crucially affected by dif-
ferent innovation modes, implying the different structural and
organisational properties of
firms. However, such performance indicators are additionally
and significantly influenced
by human capital and knowledge capital, thus underlining the
need to provide a better
understanding of the accumulation of knowledge within and
between firms.1
This stream of investigation falls within the debate on
innovation complementarities
originated by the seminal paper by Milgrom and Roberts (1990).
In particular, Mohnen
and Röller (2005) set-up an empirical framework in which
different complementarity
hypotheses are tested for. Following this line of investigation,
many authors proposed
different tests for complementarities, in particular concerning
organisational strategies
and technological innovation.
Cozzarin and Percival (2006) tested the impact of various
organisational strategies on
labour productivity and profitability, also controlling for
industry effects and firm size. The

results suggest that engaging in organisational strategies
involving hiring skilled people and
promoting the firm and product reputation, or stimulating R&D
and focusing on market
or reputation, are pairwise complements and thus have a
significant and positive impact
either on profits or on productivity.
Also, they find that focusing on hiring highly qualified
personnel together with at-
tempting to produce a world-first innovation may reduce profits.
This fact may depend
on the simultaneous combination of the relatively high hiring
and innovation costs.
However, complementarities vary significantly by firm size and
sector once the analysis is
concentrated specifically on a subsample of firms by industry
and firm size (Percival and
Cozzarin 2008).
1This issue, which is beyond the scope of our investigation, is
however relevant for identifying possible sources of economic
growth that are not accounted for (Arrighetti, Landini, and
Lasagni 2015; Montresor and Vezzani 2016).
Product, process and organisational innovation are jointly
investigated by Evangelista
and Vezzani (2010) using the fourth wave of the Italian
Community Innovation Survey
(CIS). Complementarities in product, process and organisation
innovation enable firms to
gain a competitive advantage as measured by the impact on the

turnover growth rate, also
suggesting that the impact is stronger in manufacturing
compared with services. However,
the purely cross-sectional nature of the data-set does not allow
for testing the robustness
of this finding over time.
Complementarities between human resource management and
innovation activities
may have an impact on firms’ innovativeness and productivity,
as highlighted by studies
in diverse contexts.
Laursen and Foss (2003) and Antonioli, Mazzanti, and Pini
(2010) test this hypothesis
for a large sample of Danish and Italian firms, and suggest that
firms’ innovativeness is
positively affected by human resource management in which
personnel training is a key
element of a firm’s innovative success and, in the empirical test
proposed by Antonioli,
Mazzanti, and Pini (2010), productivity.
Profitability is the other aspect of performance. Its relationship
with innovation has
received less attention compared with the analysis of the
determinants of productivity,
particularly in recent years. The traditional approach to
analysing firms’ profitability is
based on the structure–conduct–performance (SCP) paradigm
(Bain 1956), in that a firm’s
performance is determined by structural characteristics of the
industry. In contrast to this
approach, the so-called firm efficiency view (Demsetz 1973;
Peltzman 1977) emphasises the
role of firms’ characteristics in determining their profits.

However, empirical studies have
generated controversial results, which crucially depend on the
characteristics of the data-
set used to implement such tests. Slade (2004), Allen (1983),
and Delorme et al. (2002) find
support for the SCP approach, whereas Roberts (1999, 2001)
and Hawawini, Subramanian,
and Verdin (2003) recognise the role of managerial capabilities
in determining profitability.
Bartoloni and Baussola (2009) emphasise that the traditional
SCP effect, although it was
verified in a large panel of Italian manufacturing firms in the
1990s, had a very mild effect
on profitability and its persistence, whereas firms’ innovative
behaviour was more relevant
in this respect.
The impact of innovation on profitability has also been analysed
in the framework of
technology adoption. Geroski, Machin, and Van Reenen (1993)
emphasise not only the role
of adoption per se but also that such a decision implies a full
process that involves other
choices and actions within a firm (e.g. organisational changes)
that determine different
internal allocations of resources.
Mueller and Cubbin (2005) emphasise how technological
adoption provides a com-
petitive advantage to innovating firms, thus enabling them to
increase their profitabil-
ity. Technology adoption and profitability are considered in a
dynamic perspective by
Stoneman and Kwon (1996). They emphasise that multiple
adoption may occur, and firms

may thus introduce new technologies at different points in time.
Profitability – as in the
case of technological adoption – should be considered along the
diffusion path together
with the distinction between older and more recent innovations,
as the former are more
exposed to greater competition, thus affecting profitability.
Within this interpretative framework, our aim is therefore to
conduct an empirical
analysis in which the main factors discussed are considered as
determining a firm’s
performance, and then to test whether: (a) persistent
technological and non-technological
innovation enables firms to experience a significant increase in
productivity compared
with firms that do not innovate persistently; (b) joint occasional
technological and non-
technological innovation enables firms to experience an
increase in productivity which is,
however, lower than that achieved by persistent joint
innovators. These hypotheses also
implytestingforcomplementaritybetweentechnologicalandnon-
technologicalbehaviour
in both the persistent and occasional modes.
3. Panel data description
Our main data source is the Micro-Manu dataset,2 an
unbalanced panel of Italian manu-
facturing firms linking consecutive waves of the Italian

Community Innovation Survey –
which forms part of the EU science and technology statistics
and is conducted every
two years – with the ASIA archive (Statistical Register of
Active Businesses)3 and an
administrative data source providing balance sheets and income
statements for those
firms included in the CIS samples of respondents. The richness
of this data-set allows
one to enlarge the set of economic indicators typically explored
in the innovation survey
micro-data and to derive a set of financial and efficiency ratios
that are not included
in the CIS questionnaire. In accordance with international
standards (OECD-Eurostat
2005), firms are classified by their type of innovation activity
(technological and non-
technological). Information on non-technological aspects of
innovation (new marketing
and/or organisational methods) allows one to consider
comprehensive innovative activities
by focusing on the reciprocal interactions between different
aspects of innovation.
To analyse firms’ innovative pattern in a longitudinal context,
we select an unbalanced
panel of firms from the original data-set responding to at least
two consecutive non-
overlapped4 CIS waves (CIS1, years 1998–2000; CIS2, years
2002–2004; CIS4 years 2006–
2008; and CIS6, years 2010–2012). We have more than 3000
firms, corresponding to nearly
8000 observations over the whole period 1998–2012.
A strictly technological innovating firm is defined as one that
has implemented an

innovation only in the technological domain (i.e. a product
and/or process innovation,
with the exclusion of other non-technological forms of
innovation) during the observed
period. A complementary innovating firm is defined as one that
has innovated in all the
technological and non-technological domains (product and
process and organisation and
marketing). We distinguish between persistent and occasional
innovative profiles in both
the technological and complementary domains by defining (i) a
persistent innovator as one
that has innovated in at least two consecutive CIS periods
(pers_tech and pers_tech_ntech)
and (ii) an occasional innovator as one that has innovated at
least once during the entire
time span but never in two consecutive periods (tech and
tech_ntech).
It is worth noting that the specific nature of the CIS’s sampling
design gives rise to
potential selection bias when using a longitudinal framework.
Indeed, whereas large firms
with more than 250 employees are selected on a census basis,
small firms are randomly
selected, and this sampling mechanism may negatively affect
the probability of a firm
2The Micro-Manu dataset is a result of collaboration between
the Italian National Institute of Statistics (ISTAT, Regional
office for Lombardy) and the Catholic University of the Sacred
Hearth.
3This archive is the most relevant administrative register used
by ISTAT as the basis for many sample surveys and even
census investigations.

4A characteristic that merits attention is that the measurement
of the degree of innovation persistence may be over-
estimated when two consecutive waves are partially overlapped.
Table 1. Unbalanced panel of manufacturing firms with non-
missing accounting information (CIS1,
1998–2000; CIS2, 2002–2004; CIS4, 2006–2008; CIS6, 2010–
2012).
Patterns of presence Obs. No. of firms (average) Size (no. of
employees, median)
0011 725 363 115
0110 577 289 74
0111 574 191 281
1011 287 96 450
1100 3331 1666 37
1101 633 211 73
1110 747 249 116
1111 1049 262 365
Total 7923 3326 79
Firms by innovative behaviour (sample proportion)
Patterns of presence tech_ntech pers_tech_ntech tech pers_tech
non_inn
0011 0.16 0.34 0.10 0.03 0.26
0110 0.15 0.23 0.12 0.04 0.33
0111 0.12 0.44 0.08 0.07 0.21
1011 0.19 0.48 0.07 0.05 0.11

1100 0.16 0.16 0.09 0.02 0.33
1101 0.20 0.24 0.09 0.02 0.27
1110 0.13 0.36 0.07 0.03 0.22
1111 0.06 0.62 0.03 0.11 0.11
Total 0.14 0.30 0.08 0.04 0.26
Notes: The patterns of inclusion indicate absence (0) or
presence (1), during the four consecutive innovation surveys.
Innovative behaviour: tech – the firm has innovated
occasionally only in the technological domain; pers_tech – the
firm
has innovated persistently only in the technological domain;
tech_ntech – the firm has innovated occasionally in both
the technological and non-technological domains;
pers_tech_ntech – the firm has innovated persistently in both
the
technological and non-technological domains; non_inn – the
firm has never innovated during the observed time span.
being selected in consecutive surveys. Table 1 reports
descriptive statistics for each ‘feasible’
pattern of inclusion5 relative to the relevant outcomes of a
firm’s innovative activity. Hence,
we can observe, for example, that the mean size of firms that
are present only in the first two
waves is 37 employees, but the size increases to 365 employees
when the balanced sample
of firms present in all four waves is considered. If we decided
to retain this restricted
group, we could define a persistent innovator in a more
stringent way (i.e. as one that
has continuously innovated during a four-period time span).
However, by following this
approach, we would probably confine our analysis to those
firms with higher innovative
propensity, with possible bias as a result. On the basis of this

consideration, we decided
to base our empirical investigation on the full set firms
appearing in the unbalanced
panel.
It is worth emphasising that balance sheet information for the
period 1998–2012 is
provided on a yearly basis, whereas the qualitative variables
derived from the CIS survey
are defined on a three-year basis. To address the problem of
different information timing,
we averaged accounting information over a three-year period;
thus, the economic and
financial indexes are provided as average values over the
reference CIS time span. One
should note that the full samples of firms from the CIS surveys
also include small individual
firms for which balance sheet information is not available from
the Italian public register;
thus, our analysis excludes these firms. We have compared the
final sample of firms for
which there is complete accounting information to the initial
CIS samples in the ‘feasible’
5According to the methodology proposed by Raymond et al.
(2009), a pattern is ‘feasible’ when the dynamics of innovation
are potentially observable. This implies that a firm must be
present in at least two consecutive CIS waves.
panel and then concluded that the loss of sampling units due to
the use of out-of-sample
information is negligible.6 The variables used in the empirical

model are described in
greater detail below.
Economic performance. We use a measure of operating
profitability, return on sales
(ros), that is appropriate for investigating the profitability
generated by the core business
of a manufacturing firm and a measure of labour productivity
(Y), which is given by the
value added per employee ratio and may be considered an
intermediate measure of a firm’s
innovation success.7
Financial efficiency indexes. Financial efficiency can be
considered by using a measure
of a firm’s exposure to external financing sources (lev), which
is given by the ratio of
shareholders’ funds to total debt, thus reflecting the extent to
which a firm uses internal
resources instead of borrowing to finance its activity.
Capital deepening. The role of physical capital is captured by
considering the capital-to-
labour ratio (K, tangible fixed assets per employee). It measures
the extent of capital
deepening in fostering productivity. Typically, the impact of
this variable on labour
productivity may be derived from growth accounting exercises,
together with the impact
that may be exerted by Total Factor Productivity (TFP). Instead,
we test its impact by
using an econometric approach, which enables us to consider
other possible determinants
related, in particular, to a firm’s innovative effort. One should
note that capital deepening
may also incorporate process innovation; this latter determinant

typically implies the
acquisition of new machinery.8
Innovation input. As noted above, together with physical
capital, a firm’s innovative
effort should be considered when describing the core
determinants of labour productivity.
The proxy that we use, R&D activities, may also be considered
a proxy for knowledge
capital, which can contribute directly to labour productivity
growth and exert a positive
influence through TFP growth. Because we refer to the entire
sample of innovative and
non-innovative firms, the aforementioned information is not
available for this latter group
of firms, given the characteristics of the CIS survey. Therefore,
we use a dummy variable
indicating whether a firm has undertaken R&D activity in at
least two consecutive periods
(pers_R&D).9 Thus, the impact of R&D may be considered a
shifting parameter in the
adopted specification (see the following Section 4).10
Innovation output. The aim of our investigation is to explore the
complementary role
of technological and non-technological aspects of innovation in
determining a firm’s
performance relative to innovation that is strictly technological.
We consider marketing and organisational innovation jointly, as
these two innova-
tive behaviours interact almost simultaneously. As suggested by
the market orientation
6Considering the entire period, the manufacturing firms
included in the selected CIS waves with balance sheet

information
are on average almost 80% of the total number of respondents.
The Micro-Manu dataset includes more than 90% of the
total number of manufacturing limited companies.
7We are aware that the relationship between innovation and
productivity produces diverse empirical results. However,
followingMohnenandHall(2013),
innovationleadstoanincreaseinproductivity,althoughit
isnotpossibletodisentangle
the price and output effects on growth, given the characteristics
of the available data sets.
8This argument is also considered in Hall, Lotti, and Mairesse
(2009), who estimate a productivity equation that depends on
product and process innovation together with fixed investment.
9Otherwise, a different modelling strategy would have been
applied, i.e. focusing only on innovative firms or using a Tobit
model with a selection equation. This approach, however, is
beyond the scope of our investigation, the aim of which is to
specify the different behaviour and performance of innovative
and non-innovative firms.
10As it is clarified in the next Section, the inclusion of a
persistent R&D dummy variable, which excludes occasional
R&D,
also depends on the adopted empirical specification.
literature (Slater and Narver 1995), this implies that the
creation of superior customer value
entails an organisational commitment to learning, information

gathering and coordination
of consumers’ needs. In other words, market orientation
involves a redefinition and easing
oftheadministrativeprocesswithinacompany,andthusultimatelyin
volvesorganisational
change.11
We aim to reveal the presence of possible performance gains
that may be earned by
firms developing innovation continuously over time compared
with occasional innovators.
Thus, we consider the four different proxies for a firm’s attitude
towards innovation that
are described above. As in the case of R&D, these variables
enter the productivity equation
as factors that shift the production function (shifting
parameters).
Other firm-specific characteristics. Firms’ age (years, log
values)12 may positively affect
their growth if older companies experience better access to
external financing, higher
capitalisation and more qualified workforce. Haltiwanger, Lane,
and Spletzer (1999) find
that age is positively associated with a firm’s productivity level,
thus exerting an indirect
effect on profitability. However, empirical results are
controversial, as suggested by Coad,
Segarra, and Teruel (2013), in that this clear-cut relationship is
not observed within a large
longitudinal sample of Spanish manufacturing companies.
Another two variables – available from the CIS survey – reflect
a firm’s ownership
structure and its propensity to internationalise. Thus, we use
two dummy variables: the

first indicates whether a firm belongs to a corporate group (gp),
and the second indicates
whether a firm sells its products in the international market
(intern). The first variable
may affect a firm’s efficiency, whereas the latter is closely
related to the ability to expand
internationally and thus increase turnover.
Sectoral structure and localisation. Industry-specific
characteristics are accounted for
by considering two sectoral dummies that, in line with the
Pavitt taxonomy, identify the
high and medium-high-technology sectors (pavitt_mh) and the
low and medium-low-
technology sectors (pavitt_ml). Geographical characteristics are
captured by four regional
dummies (nwest, neast, centre, south), reflecting a firm’s
location in the north-west, north-
east, central or southern regions of Italy.
Additionally, we consider the cr5 ratio to capture the SCP
mechanism described in
Section 2 and the ratio of the sectoral number of technological
innovating firms to the
total number of firms in that sector (sect_inntech). Descriptive
statistics on the full set of
variables are reported in Appendix 1.
4. The empirical model
Wemodelproductivityandprofitabilityusinganempiricalspecificat
ionthatcanbederived
from an augmented production function and a profit function.
In particular, productivity, which is defined in terms of real
value added per employee,

may be derived from Equation (1), assuming constant returns to
scale.13
11Also, it is worth noting that disentangling product, process,
organisation and marketing innovation over four successive
CIS surveys may imply the loss of a significant number of
observations because companies may not persist in innovation
in the same disaggregated way. We therefore prefer to maintain
a wider definition, enabling us to preserve an appropriate
longitudinal data set, which is however consistent with the
interpretative framework we have described.
12This variable is available from the Statistical Register of
Active Businesses (ASIA).
13One can specify this equation without imposing constant
return to scale. We also estimated such a specification, which
provides, however, similar results in terms of capital and
shifting factor parameters. A Wald test for constant returns to
scale is rejected, but returns to scale are only slightly
increasing. Given these issues, we prefer a specification that
enables
yit = ait + βkit + uit (1)
where y is the log of per capita real value added of firm i, k is
the log of physical capital per
employee, and ait is a shifting factor that depends on a firm’s
attitude towards technological
and non-technological innovation and R&D effort. This latter
factor also depends on other
firms’ characteristics that may be relevant in shifting
productivity. uit is a one-way error

component:
uit = μi + �it (2)
where:
μi ∼ IID(0, σ 2u ) and �it ∼ IID(0, σ 2� ) (3)
are independent of each other and themselves. In addition, the
error term �it is assumed
to be white noise, that is:
E(�it, �is) = 0 for t �= s (4)
We account for the persistent innovative attitude of a firm by
adopting the definition
described in the previous section, i.e. a firm is considered a
persistent innovator – from both
thestricttechnologicalandcomplementaryperspectives(thusinclud
ingnon-technological
innovation) – if it has adopted such innovations in at least two
consecutive innovation
surveys. The persistent R&D effort may be described in the
same way, thus defining a
persistent R&D firm as one that has undertaken R&D activities
over at least two consecutive
surveys.Wecanthereforeusetwodifferentdummyvariablestorepres
entafirm’spersistent
innovative attitude from both an innovation input and output
perspective.14
In addition, ait depends on a firm’s specific characteristics, i.e.
age, being part of a group,
sectoral innovative characteristics and location. Thus, we can
define ait as follows:
ait = γ0 + γ1 I it + γ2 Xit (5)

where Iit represents a firm’s innovation attitude and Xit is a
vector of firms’ additional
characteristics that may affect productivity.
The profitability equation is derived while accounting for both
traditional SCP effects
and firm efficiency view considerations. Additionally, we
account for the role of innovation
by considering its effect on productivity and, through the latter,
on profitability.
Thus, the empirical specification may be represented as follows:
yit = γ0 + γ1 I it + γ2 Xit + βkit + τTt + uit (6)
rosit = α0 + α1yit + α2cr5it + α3levit + α4internit +
α5sect_inntechit + vit (7)
where Tt is a time dummy common to every firm and refers to a
three-year time span and
vit is a one-way error component.
The time variable we consider refers to a three-year time span,
i.e. the time interval of
the CIS survey, as discussed in Section 3. The estimates
therefore refer to contemporaneous
relationships over a three-year time span. We are aware of a
possible endogeneity issue
us to explicitly consider the capital deepening factor – which
may include a firm’s innovative attitude – as a determinant of
productivity. Otherwise, we would have had to consider capital
and labour separately, thus losing such an interpretation.
14See the variable description in Section 3.

related to the innovative variables; however, given such a time
interval, we can also specify
a model in which the innovative variables are treated as
predetermined, i.e. they may
be thought of as independent of current disturbances uit. In
other words, we can also
introduce a calendar time lag between innovation and balance
sheet information, in that
the former precedes the latter. Thus, the innovation variables
refer to the conventional
time t associated with the three-year time span of the CIS
Survey, whereas the economic
performance variables refer to the time averages covering the
three years after the CIS
Survey. Given a firm’s innovative behaviour at time t, we can
estimate its effect on
productivity and profitability at a later calendar time.
In addition, we are aware of possible correlation between the
innovation variables
and the individual error component, and so we also estimate
Equation (2) by using the
predicted outcomes of the innovation variables derived from
logit models that explain
innovation propensities in terms of firm and sectoral
characteristics These estimations
follow previous studies in which such determinants have been
successfully used to derive
a firm’s innovative behaviour (Bartoloni 2012), and are reported
in the Appendix 2.
From Equations (6) and (7), it appears that the model may be
thought of as a recursive

system because the matrix of endogenous variables is triangular.
Productivity does affect
profitability and not vice-versa. In this case, OLS estimates are
appropriate, provided that
the model is also diagonal recursive, i.e. stochastic disturbances
are not correlated.15
Specifically, the productivity equation includes the following
explanatory variables:
• a dummy variable reflecting a firm’s attitude towards
persistent (occasional) inno-
vation (pers_tech, pers_tech_ntech, tech, tech_ntech, depending
on the specific case),
which is included in the I vector of variables in Equation (7);
• another dummy variable that is also included in the I vector,
reflecting whether a firm
has persistently undertaken R&D activities (pers_r&d). This
variable also reflects a
firm’s absorptive capacity, as discussed in Cohen and Levinthal
(1990), and its attitude
towards sustaining this capability over time;
• physical capital deepening (k);
• sectoral innovation characteristics (pavitt_mh and pavitt_ml);
• localisation (nwest, neast, centre and south) and other firm-
specific characteristics
(age and gp).
All variables except for physical capital may be considered as
shifting factors for a firm’s
production function, as we have previously discussed.16
The explanatory variables in the profitability equation
represent, on the one hand, the

SCP mechanism (industry concentration) and, on the other,
firms’ characteristics related
to subjective efficiency (leverage), the ability to sell products
on international markets and
productivity.Thislattervariablealsoreflectsafirm’sabilitytocompe
tethroughinnovation,
as productivity is crucially affected – as shown in Equation (6)
– by a firm’s innovative
attitude.17
15We also estimated a SURE model to account for such a
correlation. The results are very similar to the OLS estimates,
thus
suggesting that such a correlation is feeble and that the use of
OLS is therefore appropriate.
16Firm size is not considered because – when included – the
R&D variable becomes insignificant, as these variables are
strictly related in our sample of manufacturing firms. We
decided to use the pers_r&d dummy variable because it enters
our empirical specification as a shifting factor of the
productivity equation and it reflects a firm’s long-term
commitment
to invest in innovation activities.
17We have not included an innovative dummy reflecting a
firm’s innovative attitude in the adopted profitability
specification, as it was not significant in regressions in which it
was included. Indeed, the productivity variable does
We also include a sectoral variable to reflect the possible
effects on profitability related

to the number of innovative firms in each industry. This is a
proxy for new technological
opportunities brought about by the increase in an industry’s
technological knowledge. In
this framework, two different mechanisms are operational. On
the one hand, we can have a
positive effect as an increasing number of sectoral innovators
increases a firm’s probability
of introducing an innovation (epidemic effect) (Mansfield
1968). This fact may have a
positive effect on profitability. On the other hand, this
information effect may be offset
by a competitive mechanism that implies that the number of
competitors in an industry
increases, thus squeezing the profits of firms operating in the
same market (stock effect)
(Karshenas and Stoneman 1993). Thus, the explanatory
variables entering the profitability
equation are the following:
• market structure (cr5);
• financial efficiency (lev);
• ability to sell products on international markets (intern);
• productivity (y);
• technological spill-over (sect_inntech).
5. Results
Table 2 presents the estimates over the entire period, taking
previous considerations into
account; thus model (1) refers to the base specification, model
(2) refers to the specification
in which the innovative variables are treated as predetermined,
and model (3) specifies
these variables as endogenous and so predicted outcomes are
endogenised.

The estimates are derived by applying random effect (RE)
estimation techniques to the
system of Equations (6) and (7).18
With reference to the results, the productivity equation shows
that persistent technolog-
ical and non-technological innovation increases productivity
with an impact that ranges
from 13.4% (model 2) to 6.2 % (model 3) compared with non-
innovative firms, which
form the reference group. Firms that use only persistent
technological innovation do not
experience a significant increase in productivity in models (1)
and (3).
Joint but non-persistent innovation has a positive and
significant impact on productiv-
ity, although milder compared with persistent innovation. This
evidence holds in models
(1) and (2) but not in (3), where the impact is not significant.
This result depends crucially
on the fact that model (3) uses predicted outcomes derived from
a logit regression which
is less satisfactory in modelling the occasional innovative
behaviour. However, the impact
of persistent technological and non-technological innovation is
significantly higher with
respect to occasional innovation, as shown by coefficient values
and Wald tests (Table 3).
We also test for complementarity, considering both persistent
and non-persistent
innovation, in Table 3. These tests suggest that complementarity
is confirmed when
considering persistent innovation; in other words, persistent

technological and non tech-
incorporate a firm’s innovative attitude, which therefore
determines the non-significant effect of such an innovative
dummy variable.
18The choice of the RE specification depends on the need to
control for the effect of time-invariant variables such as
regional
localisation and industrial sector, and also the persistent
innovative variable. In addition, when the target population is
large, as in our case, and the selected sample may not be fully
representative regarding all the characteristics under
investigation, it may be preferable to adopt a random effect
model as this permits generalisation of the inferences
beyond the sample used in the model.
Table 2. Firms’ economic performance – period 2000–2012.
Productivity RE
Variables 1 – Base model 2 – Predetermined innovation 3 –
Endogenous innovation Profitability RE
pers_tech_ntech 0.122*** 0.134*** 0.0617***
[0.0207] [0.0266] [0.0185]
pers_tech 0.0222 0.0781** 0.0253
[0.0373] [0.0392] [0.0186]
tech_ntech 0.0362*** 0.0594*** −0.0053
[0.0114] [0.0176] [0.0159]

tech 0.0524*** 0.0572*** −0.0012
[0.0144] [0.0216] [0.0307]
y 0.0964***
[0.00336]
cr5 0.000366***
[7.83e−05]
sect_inntech −0.00112***
[6.57e−05]
lev 0.0109***
[0.00145]
intern −0.0123***
[0.00191]
k 0.178*** 0.162*** 0.176***
[0.00750] [0.00727] [0.00755]
pavitt_ma 0.118*** 0.107*** 0.122***
[0.0144] [0.0163] [0.0152]
age 0.0485*** 0.0445*** 0.0478***
[0.0104] [0.0126] [0.00937]
pers_r&d 0.0512*** 0.0407* 0.0282***
[0.0195] [0.0237] [0.0201]
gp 0.0914*** 0.116*** 0.0908***
[0.0109] [0.0137] [0.0104]
nwest 0.244*** 0.204*** 0.252***
[0.0235] [0.0265] [0.0210]

neast 0.207*** 0.179*** 0.212***
[0.0230] [0.0252] [0.0219]
centre 0.197*** 0.151*** 0.203***
[0.0274] [0.0303] [0.0248]
d2000 −0.0107 0.136*** −0.0028
[0.0109] [0.0159] [0.0121]
d2004 −0.0220** 0.0968*** −0.0181*
[0.00987] [0.0147] [0.01000]
d2012 −0.0355*** 0.116*** −0.0370***
[0.0113] [0.0150] [0.0103]
Constant 8.593*** 8.655*** 8.653*** −0.894***
[0.0847] [0.0869] [0.0840] [0.0351]
Observations 7923 7923 7923 7923
R2 0.323 0.238 0.319 0.295
within 0.040 0.031 0.039 0.416
between 0.364 0.284 0.361 0.273
ρ 0.682 0.564 0.682 0.665
σμ 0.348 0.377 0.347 0.053
Notes: The variables y, k and age are in log values. In order to
perform complementarity tests, two additional dummy
variables indicating whether a firm has innovated occasionally
or persistently in the non-technological domain have been
added in the productivity regressions. In models 1 and 2 for
productivity and in the profitability model robust standard
errors
are reported in brackets. In model 3 for productivity we use
predicted events for pers_tech_ntech, pers_tech, tech_ntech
and tech derived from logistic regressions as shown in

Appendix 2. Bootstrapped standard errors in brackets. ***p <
0.01,
**p < 0.05, *p < 0.1. ρ is an estimation of the contribution of
unobserved heterogeneity to the total unexplained variance.
σμ is the estimated standard error of the random effect
component μi .
Table 3. Wald tests for innovation complementarity and equality
between coefficients.
Complementarity tests: C11 ≥ C10 + C01
1 – Base model
pers_tech_ntech vs. pers_tech 7.95*** > 0 (p = 0.997)
tech_ntech vs. tech 2.49a > 0 (p = 0.942)
2 – Predetermined innovation
pers_tech_ntech vs. pers_tech 2.29c > 0 (p = 0.934)
tech_ntech vs. tech 0.28 > 0 (p = 0.701)
3 – Endogenous innovation
pers_tech_ntech vs. pers_tech 8.08*** > 0 (p = 0.997)
tech_ntech vs. tech 0.02 –
Test for equality between the coefficients of the innovation
variables
1 – Base model 2 – Predetermined innovation 3 – Endogenous
innovation
pers_tech_ntech vs. pers_tech 7.26*** 2.09c 20.46***
pers_tech_ntech vs. tech 9.97*** 7.85*** 17.61***
pers_tech_ntech vs. tech_ntech 17.66*** 8.65*** 17.60***
Notes: Following Mohnen and Röller (2005) the
complementarity test is based on the following null hypothesis:

C11 ≥ C10
+ C01 where: C11 indicates a joint technological and non-
technological innovation; C10 and C01 indicate the introduction
of, respectively, a technological and a non-technological
innovation in isolation. A Wald χ2 one-sided test is run in two
steps. The first step tests the null hypothesis of equality. If the
null is rejected, then the second step tests the null of
submodularity vs. supermodularity (i.e. complementarity). Thus,
a significant Wald χ2 test in the second step reveals the
existence of complementarity since the test indicates that
introducing only technological innovation has a lower effect on
a
firm’s productivity than introducing jointly technological and
non-technological innovation. Since we are testing one linear
restriction at a time, the χ2 distribution has one degree of
freedom.
***p < 0.01, **p < 0.05, *p < 0.1. ap = 0.11; bp = 0.13; cp =
0.14.
nological innovation is more effective compared with a strategy
that implies technological
adoption alone. This is confirmed in all model specifications,
although in model (2) the
significance level is 0.13. When considering non-persistent
innovation, the results of
the complementarity test are not clear-cut. Weak
complementarity is observed only in
model (1).
A positive effect of a firm’s persistent innovating attitude is
provided by the impact of the
R&D variable, which implies that a firm has invested in R&D in
two consecutive surveys.
The premium in terms of the productivity gain is between 5.1
and 4.0% in specifications
(1) and (2), whereas in (3) the impact is milder (2.8%).

Given these findings concerning the persistent innovation
premium, we can discuss
the other results in detail (Table 4). The capital-to-labour ratio
(k) implies an elasticity
of almost 0.18 in model (1) and (3) and 0.16 in model (2). This
estimate is consistent
with estimates presented in other empirical studies (Mairesse
and Sassenou 1991; Crépon,
Duguet, and Mairessec 1998).
We have not estimated the return on knowledge capital, as our
choice has been to
estimate an equation in which we show the impact on
productivity of a persistent techno-
logical and non-technological attitude, on the one hand, and of
positive and persistent R&D
expenditures, on the other hand, conditional on a set of firm-
specific control variables and
the capital-to-labour ratio. However, these estimates provide an
indirect measure of the
impact of knowledge capital, which implies, on the whole, a
significant and non-negligible
productivity premium comparable with the impact of the capital
deepening variable (k).
Another significant impact reflecting technological
opportunities available at the indus-
try level is captured by the dummy variable representing an
industry’s technological level
Table 4. Marginal effects on performance (selected variables).

Effects on profitability
y (+10%) +0.9 p. p.
lev (+10 p. point) +0.1 p. p.
Effects on productivity 1 – Base model (%) 2 – Predetermined
innovation (%) 3 – Endogenous innovation
pers_tech_ntech (=1) +12.2 +13.4 +6.2%
tech_ntech (=1) +3.6 +5.9 n.s.
pers_r&d (=1) +5.1 +4.0 +2.8%
k (+1%) +0.18 +0.16 +0.18%
pavitt_mh (=1) +11.8 +10.7 +12.2%
gp (=1) +9.1 +11.6 +9.1%
age (+1) +4.8 +4.4 +4.8%
nwest (=1) +24.4 +20.4 +25.2%
neast (=1) +20.7 +17.9 +21.2%
centre (=1) +19.7 +15.1 +20.3%
Notes: Recall that profitability (ros) is a ratio, whereas
productivity (y) is expressed in log values and thus impacts are
calculated accordingly.
(pavitt_mh). Its impact is significant and relevant because it
implies a productivity gain of
about 12 % for those firms operating in medium-high-tech
sectors according to the Pavitt
taxonomy.
The age and group dummy variables show a positive and
significant effect, suggesting
that older firms have a productivity premium of approximately
5% and that those firms
which belong to a group experience a positive impact on their
productivity of more than

9% (model 1 and 3) and 12% (model 2).
Regional differentials are significant and reflect the
disadvantage of the South, in that
North and Centre Italy exhibit a gain in productivity that is, on
average, more than 20%.
Regarding profitability, we can argue that the effect of the
variable reflecting the SCP
mechanism (cr5) – although significant – is mild, whereas the
other variables reflecting
firms’ efficiency condition are significant and show non-
negligible impacts.
The leverage variable (lev) is significant and positive. A 10%
increase brings about a 0.1
p.p. increase in profitability, thus signalling that internal
resources are crucial in affecting
a firm’s ability to finance its activity and then earn profits. In
other words, as the cost of
borrowing increases – in particular because of an increasing
economy-wide risk caused by
the financial crisis – internal resources play a significant role in
affecting firms’ investment
decisions, as suggested by the pecking order theory (Myers and
Majluf 1984).
A negative sign, i.e. a condition in which highly indebted firms
earn higher profits, is
plausible but prevailing in financial market conditions in which
risk is relatively low and a
firm’s external debt may amplify the potential gain from
investment.
The intern dummy variable represents a proxy for a firm’s
internationalisation propen-

sity.Itsimpactisnegativeandsignificantbutverylimited(0.01p.p.).T
hisevidencesuggests
that firms that sell products on international markets earn
profits slightly lower than
those earned by firms that do not internationalise. This
observation may be controversial,
as one would expect the opposite result, i.e. a positive sign on
the coefficient of this
dummy variable. However, one can argue that operating on
international markets implies
additional costs that may be not fully compensated by the
potential increase in revenues
that the internationalisation process generates.
The sect_inntech variable shows a very mild and negative
impact on profitability,
thus signalling that the previously mentioned technological
competitive mechanism may
prevail, although its effect is feeble.
Productivity, which reflects both a firm’s efficiency
characteristics and a technological
attitude, enters the profitability equation positively. Highly
productive firms receive a profit
premium corresponding to 0.9 p.p. when productivity increases
by 10%.19
In the adopted specification we have not included, a dummy
variable reflecting the
persistent attitude of firms in introducing technological and
non-technological innovation,
as this variable is not significant when included. It does

significantly affect productivity,
and through this route it indirectly affects profitability.
6. Conclusions
We have presented an empirical model of the determinants of a
firm’s productivity and
profitability which has enabled us to ascertain the role of
factors related to technological
and non-technological innovations. In addition, we have
underlined how such activities,
if undertaken persistently, provide a significant additional
increase in a firm’s productivity
and profitability. Formal tests suggest that in this framework
non-technological innovation
is complementary to technological innovation.
Occasional technological innovation either combined with non-
technological innova-
tion or alone, does have a significant effect on firms’
performance in model specifications
in which technology adoption enters the productivity equations
as an exogenous or
predetermined variable.
We find support to our initial hypotheses thus emphasising the
relevance of the
innovation process, in that learning, organisational adjustments
and market orientation –
together with technological innovation – determine a firm’s
superior performance.
In addition, we also use an input measure of innovative
knowledge, related to a firm’s
R&D effort. The underlying productivity premium is
significant, with an impact that ranges

from 5.1 to 2.8%.
Capital deepening, i.e. the capital–labour ratio, exhibits a
positive and significant impact
that implies an elasticity of almost 0.18. This finding underlines
the role of physical capital
accumulation, although a direct comparison with the impact of
knowledge capital (R&D
activities) cannot be derived, as we proxy for this effect by
using a dummy variable.
Additional firm characteristics are taken into account,
suggesting that older firms
experience a significant and non-negligible productivity
premium, which is also acquired
by those firms that are part of a group.
Sectoral characteristics related to innovative criteria (Pavitt
taxonomy) suggest an
increasing relationship between productivity and technological
levels.
We also analysed firms’ profitability by estimating a profit
function that summarises
different mechanisms affecting profits. Thus, we have
considered the traditional SCP and
efficiency view mechanisms, together with the role played by a
firm’s innovative attitude.
The effect of the SCP mechanism (proxied by a concentration
index) is negligible,
although positive and significant, whereas other firm-level
efficiency variables (leverage
and the ability to sell products on international markets) show a
negative mild impact.

19At sample mean the difference between profits of persistent
joint innovators and persistent technological innovators is
on average about 2 p.p. over the entire period.
This latter effect in particular – although negative – is feeble,
suggesting that possible gains
from internationalisations may be offset by the increased fixed
costs associated with it,
particularly for small- and medium-sized enterprises.
According to the specified empirical model, productivity
reflects a firm’s efficiency
variable that also incorporates the impact of innovative
advances – considered in their
extensive definition – on profitability. Its impact on
profitability is much larger than that
represented by the traditional SCP mechanism, thus underlining
the relevance of a firm’s
innovative attitude in driving its profitability.
Acknowledgements
The authors would like to thank seminar participants at the
EARIE 2016 Conference, Lisbon
(Portugal), the Institute of Economics, Scuola Superiore
Sant’Anna, Pisa and the Dipartimento
di Scienze Economiche e Aziendali, Università di Parma, Italy.
Comments received from two
anonymous referees on an earlier version of the manuscript have
significantly contributed to
improving the paper. Needless to say, the usual disclaimer
applies.

Disclosure statement
No potential conflict of interest was reported by the authors.
References
Aghion, P., and P. Howitt. 1992. “A Model of Growth through
Creative Destruction.” Econometrica
60 (2): 323–351.
Allen, R. F. 1983. “Efficiency, Market Power, and Profitability
in American Manufacturing.” Southern
Economic Journal 49: 933–940.
Antonioli, D., M. Mazzanti, and P. Pini. 2010. “Productivity,
Innovation Strategies and Industrial
Relations in SMEs. Empirical Evidence for a Local Production
System in Northern Italy.”
International Review of Applied Economics 24 (4): 453–482.
Arrighetti, A., F. Landini, and A. Lasagni. 2015. “Intangible
Asset Dynamics and Firm Behaviour.”
Industry and Innovation 22 (5): 402–422.
Bain, J. S. 1956. Barriers to New Competition: Their Character
and Consequences in Manufacturing
Industries, vol. 3. Cambridge, MA: Harvard University Press.
Bartoloni, E. 2012. “The Persistence of Innovation: A Panel
Data Investigation on Manufacturing
Firms.” International Review of Applied Economics 26 (6):
787–810.
Bartoloni, E., and M. Baussola. 2009. “The Persistence of
Profits, Sectoral Heterogeneity and Firms’

Characteristics.” International Journal of the Economics of
Business 16 (1): 87–111.
Bartoloni, E., and M. Baussola. 2016. “Does Technological
Innovation undertaken Alone have a
Real Pivotal Role? Product and Marketing Innovation in
Manufacturing Firms.” Economics of
Innovation and New Technology 25 (2): 91–113.
Battisti, G., A.-G. Mourani, and P. Stoneman. 2010. “Causality
and a Firm-level Innovation
Scoreboard.” Economics of Innovation and New Technology 19
(1): 7–26.
Battisti, G., and P. Stoneman. 2010. “How Innovative are UK
Firms? Evidence from the Fourth
UK Community Innovation Survey on Synergies between
Technological and Organizational
Innovations.” British Journal of Management 21 (1): 187–206.
Carpenter, R. E., and B. C. Petersen. 2002. “Capital Market
Imperfections, High-tech Investment,
and New Equity Financing.” The Economic Journal 112 (477):
F54–F72.
Cefis, E. 2003. “Persistence in Innovation and Profitability.”
Rivista Internazionale di Scienze Sociali
111: 19–37.
Cefis, E., and M. Ciccarelli. 2005. “Profit Differentials and
Innovation.” Economics of Innovation and
New Technology 14 (1–2): 43–61.

Coad, A., A. Segarra, and M. Teruel. 2013. “Like Milk or Wine:
Does Firm Performance Improve
with Age?” Structural Change and Economic Dynamics 24:
173–189.
Cohen, W. M., and D. A. Levinthal. 1990. “Absorptive
Capacity: A New Perspective on Learning
and Innovation.” Administrative Science Quarterly 35: 128–152.
Cozzarin, B. P., and J. C. Percival. 2006. “Complementarities
between Organisational Strategies and
Innovation.” Economics of Innovation and New Technology 15
(03): 195–217.
Crépon, B., E. Duguet, and J. Mairessec. 1998. “Research,
Innovation and Productivity: An
Econometric Analysis at the Firm Level.” Economics of
Innovation and New Technology 7 (2):
115–158.
David, P. A. 1992. “Knowledge, Property, and the System
Dynamics of Technological Change.” The
World Bank Economic Review 6 (Suppl 1): 215–248.
Delorme Jr., C. D., D. R. Kamerschen, P. G. Klein, and L. F.
Voeks. 2002. “Structure, Conduct and
Performance: A Simultaneous Equations Approach.” Applied
Economics 34 (17): 2135–2141.
Demsetz, H. 1973. “Industry Structure, Market Rivalry, and
Public Policy.” Journal of Law and
Economics 16: 1–9.
Evangelista, R., and A. Vezzani. 2010. “The Economic Impact
of Technological and Organizational

Innovations. A Firm-level Analysis.” Research Policy 39 (10):
1253–1263.
Frantzen, D. 2003. “The Causality between R&D and
Productivity in Manufacturing: An
International Disaggregate Panel Data Study.” International
Review of Applied Economics 17 (2):
125–146.
Geroski, P. A. 1989. “Entry, Innovation and Productivity
Growth.” The Review of Economics and
Statistics 71: 572–578.
Geroski, P. A., S. Machin, and J. Van Reenen. 1993. “The
Profitability of Innovative Firms.” RAND
Journal of Economics 24: 198–211.
Geroski, P. A., J. Van Reenen, and C. F. Walters. 1997. “How
Persistently Do Firms Innovate?”
Research Policy 26 (1): 33–48.
Hall, B. H., F. Lotti, and J. Mairesse. 2009. “Innovation and
Productivity in SMEs: Empirical Evidence
for Italy.” Small Business Economics 33 (1): 13–33.
Haltiwanger, J. C., J. I. Lane, and J. R. Spletzer. 1999.
“Productivity Differences across Employers:
The Roles of Employer Size, Age, and Human Capital.” The
American Economic Review 89 (2):
94–98.
Hawawini, G., V. Subramanian, and P. Verdin. 2003. “Is
Performance Driven by Industry-or Firm-
specific Factors? A New Look at the Evidence.” Strategic
Management Journal 24 (1): 1–16.

Hollenstein, H. 2003. “Innovation Modes in the Swiss Service
Sector: A Cluster Analysis Based on
Firm-level Data.” Research Policy 32 (5): 845–863.
Johansson, B., and H. Lööf. 2010. Innovation Strategy and Firm
Performance: What is the Long-run
Impact of Persistent R&D? Stockholm: CESIS, KTH, Royal
Institute of Technology.
Jones, C. I. 1995. “R&D-based Models of Economic Growth.”
Journal of Political Economy 103:
759–784.
Karshenas, M., and P. L. Stoneman. 1993. “Rank, Stock, Order,
and Epidemic Effects in the Diffusion
of New Process Technologies: An Empirical Model.” The
RAND Journal of Economics 24 (4): 503–
528.
Laursen, K., and N. J. Foss. 2003. “New Human Resource
Management Practices, Complementarities
and the Impact on Innovation Performance.” Cambridge Journal
of Economics 27 (2): 243–263.
Le Bas, C., and W. Latham. 2006. The Economics of Persistent
Innovation. Berlin: Springer.
Lööf, H., and A. Heshmati. 2002. “Knowledge Capital and
Performance Heterogeneity: A Firm-level
Innovation Study.” International Journal of Production
Economics 76 (1): 61–85.
Mairesse, J., and M. Sassenou. 1991. R&D Productivity: A
Survey of Econometric Studies at the Firm
Level. Working Paper 3666, National Bureau of Economic
Research.

Mañez, J. A., M. E. Rochina-Barrachina, A. Sanchis, and J. A.
Sanchis. 2009. “The Role of Sunk Costs
in the Decision to Invest in R&D.” The Journal of Industrial
Economics 57 (4): 712–735.
Mansfield, E. 1968. Industrial Research and Technological
Innovation: An Economic Analysis. New
York: Norton.
Milgrom, P., and J. Roberts. 1990. “The Economics of Modern
Manufacturing: Technology, Strategy,
and Organization.” The American Economic Review 80: 511–
528.
Mohnen, P., and B. H. Hall. 2013. “Innovation and Productivity:
An Update.” Eurasian Business
Review 3 (1): 47–65.
Mohnen, P., and L.-H. Röller. 2005. “Complementarities in
Innovation Policy.” European Economic
Review 49 (6): 1431–1450.
Montresor, S., and A. Vezzani. 2016. “Intangible Investments
and Innovation Propensity: Evidence
from the Innobarometer 2013.” Industry and Innovation 23 (4):
331–352.
Mueller, D. C. 1992. “The Persistence of Profits.” In Profits,
Deficits and Instability, edited by
D. B. Papadimitriou, 82–102. Heidelberg: Springer.
Mueller, D. C., and J. Cubbin. 2005. The Dynamics of Company

Profits. Cambridge: Cambridge
University Press.
Myers, S. C., and N. S. Majluf. 1984. “Corporate Financing and
Investment Decisions When Firms
have Information that Investors Do Not Have.” Journal of
financial economics 13 (2): 187–221.
Narver, J. C., and S. F. Slater. 1990. “The Effect of a Market
Orientation on Business Profitability.”
The Journal of Marketing 54 (4): 20–35.
Peltzman, S. 1977. “The Gains an Losses from Industrial
Concentration.” Journal of Law and
Economics 20 (2): 229–263.
Percival, J. C., and B. P. Cozzarin. 2008. “Complementarities
Affecting the Returns to Innovation.”
Industry and Innovation 15 (4): 371–392.
Peters, B. 2009. “Persistence of Innovation: Stylised Facts and
Panel Data Evidence.” The Journal of
Technology Transfer 34 (2): 226–243.
Raymond, W., P. Mohnen, F. C. Palm, V. der Loeff, and S.
Schim. 2009. Innovative Sales, R&D and
Total Innovation Expenditures: Panel Evidence on their
Dynamics. CESifo Working Paper Series.
Raymond, W., P. Mohnen, F. Palm, and S. S. Van Der Loeff.
2010. “Persistence of Innovation in
Dutch Manufacturing: Is it Spurious?” The Review of
Economics and Statistics 92 (3): 495–504.
Roberts, P. W. 1999. “Product Innovation, Product-market
Competition and Persistent Profitability

in the US Pharmaceutical Industry.” Strategic Management
Journal 20 (7): 655–670.
Roberts, P. W. 2001. “Innovation and Firm-level Persistent
Profitability: A Schumpeterian
Framework.” Managerial and Decision Economics 22 (4–5):
239–250.
Romer, P. M. 1990. “Endogenous Technological Change.”
Journal of Political Economy 98 (5): S71–
S102.
Rouvinen, P. 2002. “R&D-productivity Dynamics: Causality,
Lags, and ‘Dry Holes.” Journal of
Applied Economics 5 (1): 123–156.
Schubert, T. 2010. “Marketing and Organisational Innovations
in Entrepreneurial Innovation
Processes and their Relation to Market Structure and Firm
Characteristics.” Review of Industrial
Organization 36 (2): 189–212.
Schumpeter, J. A. 1934. The Theory of Economic Development:
An Inquiry into Profits, Capital,
Credit, Interest, and the Business Cycle, Vol. 55. Transaction
Publishers.
Slade, M. E. 2004. “Competing Models of Firm Profitability.”
International Journal of Industrial
Organization 22 (3): 289–308.
Slater, S. F., and J. C. Narver. 1995. “Market Orientation and
the Learning Organization.” The Journal
of Marketing 59: 63–74.
Stiglitz, J. E. 1987. “Learning to Learn, Localized Learning and

Technological Progress.” In
Economic Policy and Technological Performance, edited by P.
Dasgupta and P. Stoneman, 125–153.
Cambridge: Cambridge University Press.
Stoneman, P., and M. J. Kwon. 1996. “Technology Adoption
and Firm Profitability.” The Economic
Journal 106 (437): 952–962.
Appendix 1. Descriptive statistics
Period 1998–2000 2002–2004 2006–2008 2010–2012 Tot.
No. of observations 2462 2855 1471 1135 7923
Variable name Type Variable description
tech_ntech 0/1 1 if the firm has occasionally
introduced a technological inno-
vationinconjunctionwithanon-
technological innovation
0.22 0.21 0.28 0.30 0.24
pers_tech_ntech 0/1 1 if the firm has persistently
vationinconjunctionwithanon-
technological innovation
0.07 0.06 0.12 0.13 0.08
tech 0/1 1 if the firm has occasionally

vation
0.21 0.22 0.25 0.25 0.22
pers_tech 0/1 1 if the firm has persistently
innovated in the technological
domain
0.03 0.03 0.05 0.05 0.04
ros c Return on sales. The ratio
between gross operating profits
and sales. An index of operating
profitability.
0.12 0.11 0.10 0.09 0.11
Y c Value added per employee
(thousands euros)
59.4 60.1 71.3 74.2 64.0
K c Tangible fixed assets per em-
ployee (thousands euros)
53.1 54.5 69.7 82.8 60.9
lev c The ratio of shareholders’
funding to total debts
0.71 0.76 0.83 1.03 0.80
cr5 c Concentration index (Pavitt sec-
tors)

0.33 0.35 0.26 0.29 0.30
sect_inntech c Share of sectoral technological
innovators %
50.1 46.8 66.0 66.2 54.2
intern 0/1 1 if the firm sells its products in
the international market
0.71 0.67 0.80 0.86 0.73
pavitt_mb 0/1 1 if in the low and medium-low
technology sectors
0.66 0.66 0.63 0.61 0.65
pavitt_ma 0/1 1 if in the high and medium-high
technology sectors
0.34 0.34 0.37 0.39 0.36
AGE c Firm’s age (years) 23 26 30 33 27
r&d 0/1 1 if the firm has undertaken R&D
investments
0.31 0.37 0.53 0.50 0.40
pers_r&d 0/1 1 if the firm has persistently
undertaken R&D investments
0.26 0.26 0.42 0.43 0.32
gp 0/1 1 if the firm belongs to an
industrial group

0.30 0.35 0.62 0.76 0.44
nwest 0/1 1 if the firm is localised in the
North-West
0.35 0.36 0.39 0.36 0.36
neast 0/1 1 if the firm is localised in the
North-East
0.34 0.34 0.35 0.36 0.34
centre 0/1 1 if the firm is localised in the
Centre
0.16 0.16 0.13 0.14 0.15
south 0/1 1 if the firm is localised in the
South
0.15 0.15 0.13 0.14 0.14
Notes: Y and K have been deflated using sectoral deflators
(base year 2010). ‘Persistently’ means in at least two
consecutive
periods. ‘Occasionally’ means at least one time but never in two
consecutive periods.
Appendix 2. RE Logistic regressions for the innovative and
R&D binary vari-
ables – period 2000–2012

Variables pers_tech_ntech tech_ntech pers_tech tech pers_r&d
size 0.544*** −0.0237 −0.0133 −0.152*** 0.601***
[0.0266] [0.0308] [0.0531] [0.0391] [0.0282]
cr5 −0.00924*** 0.00216 0.00733 0.0121*** 0.000276
[0.00250] [0.00263] [0.00493] [0.00336] [0.00250]
sect_inntech 0.0448*** 0.0240** 0.0161 −0.0304* 0.0374***
[0.0101] [0.0106] [0.0220] [0.0183] [0.0104]
lev −0.0275 −0.0700** 0.0692 0.0475 0.0129
[0.0267] [0.0353] [0.0453] [0.0374] [0.0277]
intern 0.910*** 0.208** 0.360** 0.285*** 1.075***
[0.0850] [0.0849] [0.172] [0.107] [0.0892]
k 0.110*** 0.0453 0.155*** 0.0178 0.101***
[0.0272] [0.0317] [0.0518] [0.0396] [0.0275]
pavitt_ma −0.0912 −0.553** −0.282 0.655** 0.374*
[0.190] [0.216] [0.420] [0.322] [0.194]
gp 0.0336 0.0268 0.0306 −0.123 0.111
[0.0692] [0.0861] [0.158] [0.109] [0.0709]
nwest 0.326*** −0.00099 0.795*** 0.261* 0.781***
[0.104] [0.108] [0.234] [0.140] [0.113]
neast 0.598*** −0.0128 0.616*** 0.141 0.814***
[0.103] [0.108] [0.236] [0.142] [0.112]
centre 0.0469 0.129 0.587** 0.154 0.399***
[0.120] [0.121] [0.261] [0.161] [0.130]
d2000 0.542*** 0.943*** −0.207 −1.494*** 0.379**

[0.172] [0.187] [0.373] [0.326] [0.178]
d2004 0.727*** 0.0302 −0.0975 −0.481 0.557***
[0.205] [0.223] [0.432] [0.363] [0.212]
d2012 0.0327 0.168 −0.0562 −0.333** −0.0914
[0.0896] [0.113] [0.180] [0.148] [0.0945]
Constant −8.271*** −3.834*** −6.660*** −0.646 −8.816***
[0.679] [0.720] [1.438] [1.200] [0.710]
LR χ2(14) 1769.675 180.51 56.31 151.92 1498.38
pseudo R2 0.18 0.03 0.02 0.03 0.23
Observations 7.923 7.923 7.923 7.923 7.923
Notes: Robust Standard errors in brackets. ***p < 0.01, **p <
0.05, *p < 0.1. Following Bartoloni (2012), it is possible to
estimate a firm’s innovation probability using logit models that
incorporate explanatory variables causing different firms’
innovativebehaviours.Weusethefollowingexplanatoryvariables:fi
rmsize(size,numberofemployees, logvalues),financial
efficiency (lev), physical capital deepening (K), industrial
group membership (gp), ability to sell products on international
markets (intern), market structure (cr5), technological spill-over
(sect_inntech), and regional, sectoral and time dummies.
We derive predicted probabilities that can then be used to
predict the estimated events (pers_tech, pers_tech_ntech, tech,
tech_ntech, and pers_R&D) used in the productivity regression.
1. Introduction2. The interpretative framework3. Panel data
description4. The empirical model5. Results6.
ConclusionsAcknowledgementsDisclosure
statementReferencesAppendix 1. Descriptive statisticsAppendix
2. RE Logistic regressions for the innovative and R&D binary
variables – period 2000–2012

EXECUTIVE DIGEST
The secret to true service innovation
Lance A. Bettencourt a,*, Stephen W. Brown b, Nancy J.
Sirianni c
a Partner, Service 360 Partners LLC, & Distinguished
Marketing Fellow, Texas Christian University, U.S.A.
b W.P. Carey School of Business, Arizona State University,
P.O. Box 874106, Tempe, AZ 85287-4106, U.S.A.
c M.J. Neely School of Business, Texas Christian University,
TCU Box 298530, Fort Worth, TX 76129, U.S.A.
Business Horizons (2013) 56, 13—22
Available online at www.sciencedirect.com
www.elsevier.com/locate/bushor
KEYWORDS
Services;
Service excellence;
Service innovation;
Strategy;
Service research
Abstract The secret to true service innovation lies in shifting
focus away from the
service solution back to the customer. Rather than asking,
‘‘How are we doing?’’
managers must ask, ‘‘How is the customer doing?’’ For far too
many businesses,
service innovation means making incremental improvements to

existing services.
While a focus on improving current services certainly has its
place, we indicate that
this has constrained firms’ innovation capabilities by limiting
new ideas. In order to
truly innovate, firms must expand their focus beyond existing
services and service
capabilities to address the fundamental needs of their
customers, including the jobs
and outcomes those customers are trying to achieve. By further
focusing service
innovation on developing shared solutions with customers, firms
are better able to
create breakthrough service offerings and processes. This will
result in value co-
creation that is both meaningful to customers and uniquely
differentiated from
competitive offerings. To this end, we present a four-step
process for firms to guide
job-centric service innovation.
# 2012 Kelley School of Business, Indiana University.
Published by Elsevier Inc. All
rights reserved.
1. Truly innovative service innovation
As the service economy increasingly dominates glob-
al business, product and service firms are seeking to
advance their service offerings not only to retain
customers, but also to stay ahead of rivals (Jana,
2007). Successful service innovation approaches are
especially relevant in a slow economy, where
* Corresponding author
E-mail addresses: [email protected]
(L.A. Bettencourt), [email protected] (S.W. Brown),
[email protected] (N.J. Sirianni)

0007-6813/$ — see front matter # 2012 Kelley School of
Business, I
http://dx.doi.org/10.1016/j.bushor.2012.09.001
decreased spending leads to a decline in economic
activity and intensified competition among rival
firms. As such, service innovation has become a
major source of competitive advantage for compa-
nies cultivating the ability to use knowledge gleaned
from customers, competitors, and their own capa-
bilities to create meaningful and distinctive
services.
In today’s challenging business environment,
however, it is no longer enough to merely deliver
a quality service to customers in a timely manner.
Instead, companies must find ways to innovate en-
tirely new service offerings that their customers will
find valuable. This type of service innovation is not
ndiana University. Published by Elsevier Inc. All rights
reserved.
http://www.sciencedirect.com/science/journal/00076813
mailto:[email protected]
14 EXECUTIVE DIGEST
Figure 1. Traditional approach: Service as unit of
analysis
easy to achieve, as the intangible nature of service
activity and the active participation of the customer
in producing the offering has led to uncertainty

about how to innovate new services (Chesbrough,
2005). Yet, service innovation is increasingly consid-
ered a vital element of a firm’s competitive strategy
(MacDonough, Zack, Lin, & Berdrow, 2008), and this
strategy will be misguided if the firm’s innovation
approach is too short-sighted to make a real differ-
ence for customers–—or to be truly differentiated in
today’s marketplace.
As firms work to uncover the most effective ways to
innovate, service experts have swarmed into the field
to assist them, offering models for understanding
customers’ expectations, improving companies’ de-
sign processes, and removing variability from the
service development process. While resourceful,
these efforts have mainly led to incremental service
improvements, such as increasing store hours or en-
hancements to loyalty programs (Berry, Shankar,
Parish, Cadwallader, & Dotzel, 2006). A primary rea-
son this is the case is that most methods currently
employed (e.g., service blueprinting, moments-of-
truth, service quality research) propose to help com-
panies improve their service offerings in the design or
implementation stages of service development (Bit-
ner, Ostrom, & Morgan, 2008; Thomke, 2003). While
this can be helpful, in actuality, these approaches
have limitations in the area of identifying and
prioritizing opportunities for ground-breaking service
concepts. Creating revolutionary service requires
genuine innovation that is anchored in true customer
needs, not simply enhancing existing service
offerings.
To truly innovate, firms must expand their short-
sighted focus beyond existing services and service
capabilities to address the fundamental needs of

their customers, including the jobs that customers
are trying to achieve and the outcomes that they use
to measure success (Bettencourt, 2010; Heskett,
1987). Broadening the strategic viewpoint to en-
compass the jobs and outcomes that service offer-
ings must help customers satisfy requires active
engagement in order to fully understand their
needs. Adjusting the firm’s innovation focus away
from the service solution and back to the customer
will result in value co-creation that is both mean-
ingful to customers and uniquely differentiated
from competitive offerings (Gummesson, 1995).
2. How companies get service
innovation wrong
As shown in Figure 1, current approaches to service
improvement constrain innovation by focusing on
service as the unit of analysis, rather than on the
fundamental needs of the customer (Bettencourt,
2010; Christensen, Cook, & Hall, 2005). For exam-
ple, service quality research identifies service im-
provement opportunities through the use of mystery
shoppers, point-of-purchase and trailer satisfaction
surveys, and annual service quality surveys. By de-
sign, these approaches can only assess currently-
offered solutions; that is, they evaluate service
offerings that have already been proposed. The
results from this research can only improve extant
company offerings, not provide a better under-
standing of customer needs or invent new ways of
satisfying them. As far as innovation is concerned,
we find three basic issues with traditional ap-
proaches to service innovation.
2.1. The cart before the horse

First, focusing on a service solution puts the cart
before the horse. When conducting service quality
research, for example, managers must ask them-
selves: ‘‘What’s the point of evaluating a solution
when we’re still not sure what the problem is? What
are our customers trying to achieve?’’ For example,
current approaches to service quality research
might uncover the fact that retail customers value
a knowledgeable sales associate. Aha! This type of
finding leads company managers to believe that they
can improve their service offerings by making sure
sales associates are knowledgeable. This may be
helpful initially, but unless managers learn why
customers value a knowledgeable sales associate–—
particularly what it is that customers expect a knowl-
edgeable sales associate to help them with (Avoiding
mismatching outfits? Keeping current with fashion
trends? Finding particular items quickly?)–—they
will continue to make less-than-optimal decisions
regarding improvements to service offerings.
EXECUTIVE DIGEST 15
2.2. Constraining innovation thinking
Second, focusing on a service solution constrains
innovation thinking. Continuing with the aforemen-
tioned retail example, having discovered that their
customers are dissatisfied with the sales associates’
level of knowledge, retail clothing managers may
feel the only available option is to hire, train, and
reward more knowledgeable sales associates. In
contrast, if they try to find out what problem or
problems their customers are hoping to solve–—or, to

put it another way, if they try to find out what jobs
customers are trying to get done or what outcomes
customers are hoping to achieve–—the field of pos-
sibilities opens up (Ulwick & Bettencourt, 2008).
Suppose, for instance, that company managers dis-
cover the outcome customers are most interested in
achieving is speeding up the process of creating
outfits. Potential solutions might then include not
only knowledgeable sales associates, but also inter-
active kiosks, helpful signage, product arrange-
ments and merchandise displays showing potential
outfits, clothing redesign, clothing labeling, and
many other possibilities.
2.3. Reinforcing the status quo
Third, focusing on a service solution reinforces the
status quo. Customers seek out service for a reason;
they want to accomplish something. One could say
that customers have a job to get done, and they are
hiring the service to help them accomplish it (Chris-
tensen, Anthony, Berstell, & Nitterhouse, 2007;
Ulwick & Bettencourt, 2008). When managers think
of offering services only on already-established pat-
terns, it limits the number of jobs the service can
help customers get done, and it inhibits understand-
ing the limitations of the current service offering for
helping the customer to satisfy their needs. Con-
tinuing with our retail clothing example, some man-
agers tend to think about their customers only in
terms of current encounters within their stores; but
that means they miss other, novel ways of serving
those customers outside of the store. If the company
managers thought about customers’ broader desire
to manage a wardrobe and all that entails, they
might discover opportunities to help customers not

only with figuring out what types of outfits to pur-
chase, but also with what to do with outdated
clothing or putting together outfits from clothing
they already own. Service quality research generally
reinforces the status quo because it asks customers,
‘‘How are we doing?’’ when really what companies
need to ask is, ‘‘How are you doing?’’ By shifting the
question to focus on what the customer is trying to
achieve, managers are better equipped to upset the
status quo by creating value with the customer, and
not just for the customer. In considering the cus-
tomer’s entire experience of building a wardrobe,
and not just shopping for a single outfit at the store,
retail managers in the aforementioned example can
impact the customer’s entire experience with a shift
to their service innovation approach (Prahalad &
Ramaswamy, 2003).
3. A better approach to service
innovation
What companies need is an approach to innovation
that enables them to identify opportunities for
breakthrough service offerings that is not con-
strained by current or proposed service solutions. A
job-centric approach to service innovation does just
that. As the phrasing implies, this approach focuses
not on customers’ evaluations of current offerings,
but on the job that customers are trying to get done.
It looks deeply into why customers presently hire
service solutions and then expands this view to con-
sider related customer jobs and more encompassing
customer processes (Bettencourt & Ulwick, 2008;
Ulwick, 2002). This is the type of innovation that
allows Zipcar customers to easily pre-pay, reserve,
use, and then return fully fueled and insured short-

term, shared cars, alleviating the worry and hassles
of borrowing vehicles from friends and relatives for
shopping, errands, and other brief tasks. Zipcar tran-
scends the rental market’s typical model of longer-
term contracts and add-on fees for liability insurance
and gasoline. By fully understanding its customers’
needs for quick trip transportation and desired has-
sle-free outcomes, Zipcar has been able to transform
these clients’ entire car rental experience and gain
market share against competing entrenched players.
In its simplest form, this approach to service
innovation involves four steps, as shown in
Figure 2. To begin, Step 1 requires active partner-
ship with customers to uncover what jobs they are
trying to accomplish by hiring services in the first
place. Next, Step 2 necessitates that service-pro-
viding firms dig deeper to find out if these jobs are a
part of some larger process that can be tapped into
to create additional customer value. Then, Step 3
entails that firms learn about the opportunities that
currently exist to get these customer jobs done–—
including capturing the right type of customer needs
to ensure that their inputs are not only correct, but
also useful for guiding meaningful service innova-
tion. Finally, Step 4 involves identifying and then
applying resources to create value for both the
service-providing firm and its customers to achieve
genuine service innovation.
16 EXECUTIVE DIGEST
Figure 2. Job-centric approach: Customer job as unit of analysis
To illustrate these steps, we’ll take a detailed

look at how Abbott Medical Optics (AMO) has ap-
proached service innovation with success. AMO,
which operates in more than 25 countries, is a
provider of medical device technologies for vision
improvement. Its ophthalmic product line includes
lenses, insertion systems, laser vision correction
systems, and other devices for both cataract and
refractive surgery procedures. Like many compa-
nies, AMO operates in markets in which product
differentiation is limited and competitors quickly
copy innovative products. In such markets, second-
ary sales, service, and support mechanisms assume
added importance for gaining and retaining custom-
ers. In the medical device market, these secondary
support offerings target not only the physician user,
but also surgical nurses and materials managers who
are responsible for purchasing and replenishment.
Although AMO is pursuing service and support in-
novations that target each of those customer
groups, the materials managers are the ones who
have been most directly impacted by company
services.
In late 2005, AMO decided to apply the same
scientific rigor to service innovation that it had long
applied to the innovation of vision technologies.
Angelo Rago, then-senior vice president of global
customer services, knew that AMO required a unique
approach if it truly sought to differentiate on the
basis of excellent service. He realized that AMO
needed to break the cycle of incremental service
improvements that resulted in the company’s virtu-
ally identical (to its chief rivals) service delivery
mechanisms and support services. Moreover, em-
ployees within the firm recognized that sales were
being lost to competitors due to poor customer
service. In the following sub-sections, we introduce

our four step model and illustrate how it was used to
guide service innovation at Abbott Medical Optics.
3.1. Step 1: Determine what job or jobs
customers are trying to get done by using
current services and support
Step 1 requires research to uncover what job or
jobs customers are trying to accomplish by hiring
EXECUTIVE DIGEST 17
the firm to provide services. It is important to note
that this process goes beyond merely asking cus-
tomers what they want in a service, because–—as
many service innovation managers know–—most
customers have a limited frame of reference
(Ulwick, 2002). Instead, this step involves scan-
ning every customer-firm touch point and asking
the right questions to learn what customers are
trying to accomplish when they hire the services
provided by the company, as well as deeply
probing to reveal underlying reasons provided in
customers’ responses. This type of active investi-
gation and learning helps firms uncover blind spots
in order to develop a company-wide peripheral
vision (Day & Schoemaker, 2005), allowing them to
more fully understand the value their customers
receive from company services.
In executing Step 1, managers should ask ques-
tions, including: What goals are customers hoping to
achieve in working with company representatives
and departments? What are customers seeking to
accomplish when they access the firm’s website?

What problems are customers trying to resolve in
utilizing the firm’s service and support apparatus? By
focusing on the jobs customers need to get accom-
plished, we eliminate worry over neglecting latent
or unarticulated requirements; customers under-
stand the challenges of their own situation and
are able to state their needs in the frame of how
they currently get jobs done–—even before a poten-
tial service innovation exists (Bettencourt, 2009).
For example, a hospital asking these questions of
patients would discover various reasons they are
contacted for support, including determining op-
tions to address a health problem, selecting a
healthcare provider, scheduling healthcare ser-
vices, receiving treatment, and understanding the
impact of a health issue.
In the case of AMO’s cataract business, there are a
number of employees who have service and support
interactions with its customers’ materials manag-
ers. These include account managers, call center
employees, and field service staff. In seeking service
innovation opportunities, initial queries of both
internal AMO staffers and materials managers fo-
cused on why customers utilized a given service
mechanism; that is, the goal was to know what
customers were seeking to accomplish in their in-
teractions with an account manager, a call center
employee, or when they contacted technical field
service. The primary goals included placing orders
and resolving problems relating to product delivery,
invoicing, and returns. The materials managers also
had service encounters with AMO’s ‘materials’ at
other points; for example, when receiving orders
and reviewing invoices.
3.2. Step 2: Determine whether the jobs

for which customers are hiring current
services are part of a larger process
Customers’ jobs are extremely varied in their com-
plexity: they can be as simple as locating a specific
type of information or as complex as developing a
financial investment portfolio. Interestingly, simple
jobs are more often than not steps in larger pro-
cesses or more complex jobs. This fundamental
truth provides the basis for bringing structure and
predictability to the service innovation process. As
processes, the beginning and end points of a cus-
tomer job can be identified, the metrics by which
customers judge how well the job is executed can be
uncovered and measured, and the overall method
can be improved by developing offerings that help
the customer execute the job more effectively and
efficiently.
This insight has important implications for service
innovation because if the jobs for which customers
have hired a company’s service are part of a larger
process, it is likely that there are other elements in
that larger process that have been overlooked by
the company’s managers–—and by their competitors,
too! By considering those other elements and by
broadening the scope of the services it is willing to
offer, a company can surge ahead of the competi-
tion. Consider consumers who turn to banking ser-
vices such as checking accounts and credit cards to
help them with jobs like receiving money, paying
bills, and making purchases. They are really en-
gaged in a broader process of managing day-to-
day cash flow. In speaking with consumers about
the steps in this process, it quickly becomes evident
that banks have overlooked opportunities to help

Week 6 - Assignment Rate Methods of HR and Technology Practices f.docx

Week 6 - Assignment Rate Methods of HR and Technology Practices f.docx

Recommended

Recommended

More Related Content

Similar to Week 6 - Assignment Rate Methods of HR and Technology Practices f.docx

Similar to Week 6 - Assignment Rate Methods of HR and Technology Practices f.docx (20)

More from helzerpatrina

More from helzerpatrina (20)

Recently uploaded

Recently uploaded (20)

Week 6 - Assignment Rate Methods of HR and Technology Practices f.docx