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Organizational cross-cultural differences in the context of innovation-oriented partnerships

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Purpose – The purpose of this paper is to empirically examine organizational cross-culture differences in public-private research-oriented relationships. More precisely, it focusses on the analysis university-industry collaborations partnering for research agreements with the aim of fostering the transfer of knowledge and innovation. It analyzes the key organizational cross-cultural differences that could hinder the successful performance of these agreements from a relationship marketing (RM) perspective. Design/methodology/approach – Based on a comprehensive literature review of organizational culture and RM, a quantitative study was carried out and a structural equation model was proposed and tested. Findings – Cross-cultural organizational differences in private-public sectors are proved to negatively influence relationship performance. Market orientation difference appears as the most significant barrier to relationship performance, followed by time orientation difference and to a lesser extent flexibility difference. Originality/value – By integrating organizational culture and RM literatures, the main contribution of this paper is the cross-cultural analysis of private-public relationships (in this case university-industry relationships) from the perspective of RM. Hence, this research will inform management seeking to develop successful public-private collaborations by enhancing their understanding of cross-cultural factors underlying relationship success and failure.
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Cross Cultural & Strategic Management
Organizational cross-cultural differences in the context of innovation-oriented
partnerships
Pervez Ghauri Veronica Rosendo-Rios
Article information:
To cite this document:
Pervez Ghauri Veronica Rosendo-Rios , (2016),"Organizational cross-cultural differences in the
context of innovation-oriented partnerships", Cross Cultural & Strategic Management, Vol. 23 Iss 1
pp. 128 - 157
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http://dx.doi.org/10.1108/CCSM-06-2014-0059
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Organizational cross-cultural
differences in the context of
innovation-oriented partnerships
Pervez Ghauri
Department of International Business, Kings College London,
London, UK, and
Veronica Rosendo-Rios
Department of Business and Marketing,
Colegio Universitario de Estudios Financieros, Madrid, Spain
Abstract
Purpose The purpose of this paper is to empirically examine organizational cross-culture
differences in public-private research-oriented relationships. More precisely, it focusses on the analysis
university-industry collaborations partnering for research agreements with the aim of fostering the
transfer of knowledge and innovation. It analyzes the key organizational cross-cultural differences
that could hinder the successful performance of these agreements from a relationship marketing
(RM) perspective.
Design/methodology/approach Based on a comprehensive literature review of organizational culture
and RM, a quantitative study was carried out and a structural equation model was proposed and tested.
Findings Cross-cultural organizational differences in private-public sectors are proved to negatively
influence relationship performance. Market orientation difference appears as the most significant barrier to
relationship performance, followed by time orientation difference and to a lesser extent flexibility difference.
Originality/value By integrating organizational culture and RM literatures, the main contribution of
this paper is the cross-cultural analysis of private-public relationships (in this case university-industry
relationships) from the perspective of RM. Hence, this research will inform management seeking to
develop successful public-private collaborations by enhancing their understanding of cross-cultural
factors underlying relationship success and failure.
Keywords Innovation, Cross-cultural differences, Relationship marketing, Market orientation,
Technology transfer
Paper type Research paper
1. Introduction
The speed of innovation and rapid technological change (Palmer, 2002; Monreal-Perez
et al., 2012) have forced private and public sector organizations to join together in order
to encourage the transfer and diffusion of knowledge and to create innovation-oriented
linkages (Zilian et al., 2014; Brewer, 2008; Organization for Economic Co-operation and
Development (OECD), 2002). University-industry relationships (UIRs) constitute a fine
example of these linkages. UIRs are considered to be innovation oriented since they
facilitate the transfer of knowledge and technological diffusion through the licensing to
industry of inventions and/or intellectual properties resulting from university research
(Siegel et al., 2003). This transfer of knowledge has been frequently cited as stimulating
for local economic development and innovation (Burnett and Williams, 2014; Siegel
et al., 2003; Cohen et al., 1998).
Thus, this paper bridges a very important gap in innovation by identifying the
differences between the academic and the business communities. As businesses seek more
practice-based approaches to solving innovation problems, universities are challenged to
Cross Cultural & Strategic
Management
Vol. 23 No. 1, 2016
pp. 128-157
© Emerald Group PublishingLimited
2059-5794
DOI 10.1108/CCSM-06-2014-0059
Received 16 June 2014
Revised 23 October 2014
3 February 2015
Accepted 6 February 2015
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/2059-5794.htm
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provide research that is less theoretically focussed and to provide guidelines for managers.
This paper addresses the diversity in these two approaches that is under-researched and
suggests how this gap between the two approaches can be bridged.
While an understanding of business-to-business services has largely been developed
in the literature of management (Leisen et al., 2002; Leo et al., 2005; Cao et al., 2009),
Research & Development (Triulzi et al., 2014; Soh and Subramanian, 2013) and mergers
and acquisitions (Weber and Tarba, 2012; Evans, 2006; Kahn, 2001), it has mainly been
focussed on private sector organizations (Decter et al., 2007). In these areas,
organizational cultural differences (OCD) have been studied either from an individual
level or an organizational level. From an individual level, characteristics such as
ethno-cultural background differences between individualsorganizational values have
been reported to impact those of their organizational culture (Hendel and Kagan, 2012);
cultural differences have also been found to have a direct impact on individualsjob
satisfaction levels (Froese and Xiao, 2013; Lok and Crawford, 2004); likewise, OCD have
been considered to have a negative impact on leadership and ethical decision making
(Kuntz et al., 2013); and age or cultural differences have also been reported to affect
organizational performance (McNamara et al., 2012). From a purely organizational
perspective, M&A literature and strategic alliances literature have mainly focussed on
national clashes between private organizations (Vaara et al., 2012) or the effect of
cultural differences on organizational performance measured mainly in economic
or financial gains (Kroon et al., 2009; Lee, 2000).
However, this prior research has a major limitation. While these preceding studies
highlight important factors for the effective integration of M&As that may be affected by
OCD (Moorman et al., 1992; Weber, 1996; Lee, 2000; Leisen et al., 2002; Kroon et al., 2009;
Vaara et al., 2012), they are focussed on a purely private business-to-business setting. The
effect of cultural differences between public-private organizations in research-oriented
partnerships has scarcely been empirically examined to date. Also, in the specific setting
of UIRs, the effect of cultural differences on the effectiveness of the integration process
and relationship performance have barely been explored from a relationship marketing
(RM) approach ( Jarrat and ONeill, 2002; Plewa et al., 2005, 2013). Therefore,
cross-cultural differences in public-private partnerships constitute an under-researched
topic in RM.
This paper furthers and advances our understanding of the effect of organizational
cross-cultural differences on relationship performance in innovation-oriented
partnerships in two ways: first by studying these linkages in two fundamentally
different sectors: private-public cooperations; and second by applying a RM approach
to this study. This new approach includes additional outcome measures of relational
performance i.e., satisfaction and relationship sustainability- instead of the traditional
financial perspective.
Therefore, in order to address these gaps, the main objective of this paper is to study
the effect of OCD in private-public linkages in the context of research-oriented
university-industry relationships (UIR hereafter) from a RM perspective. This paper
answers previous calls on further understanding the effects of OCD on relationship
performance and success (Van Gelder, 2011; Plewa et al., 2005; Plewa and Rao, 2007;
Plewa, 2009; Hewett et al., 2002; Sarkar et al., 2001; Müller et al., 2009; Stahl and Voigt,
2004). Thus, the main research questions addressed here are the following ones:
RQ1. Do organizational cross-cultural differences prevent the evolvement and
success of innovation-oriented private-public sector relationships?
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RQ2. What are the main factors in OCD that managers working under private-
public settings would have to take into account to prevent relationship failure?
Thereby, the main contribution of this paper is the cross-cultural analysis of private-public
relationships from the perspective of RM. Hence, this research will inform management
seeking to develop successful public-private collaborations by enhancing their
understanding of cross-cultural factors underlying relationship success and failure.
This paper is structured as follows. First, the importance of UIR as a relevant
research context is addressed and a literature review of the concepts of RM,
organizational culture and OCD is presented. Following this, a conceptual model and
hypotheses are developed and tested. Next, the methodology is discussed and the main
results are presented. The paper concludes with some managerial implications,
limitations and guidelines for further research.
2. Theoretical background
2.1 Relevance of the context of analysis
2.1.1 The need for university-industry cooperation. University-industry linkages have
changed considerably during the last few years. In this respect, firms are now under
pressure to speed up the process of innovation through the creation and development of
new products and technologies (Santoro and Chakrabarti, 2002; Zilian et al., 2014;
Monreal-Perez et al., 2012). For its attainment, they are currently looking for new ways
of outsourcing R&D activities (Casper, 2013). On the other hand, universities are also on
the lookout for additional financial resources, due to factors such as the decrease in
government funds, the fact that the potential funding from student fees is limited, and
the increase of competition regarding research (Marzo-Navarro et al., 2008). In view of
these challenges, the commercialization of knowledge i.e, the external knowledge
exploitation (Lichtenthaler, 2005) has grown rapidly over the last few years. This
commercialization would unquestionably constitute an advantage for both parties
and it would definitely foster the innovation process of private companies, as well as
for this knowledge society. According to RedOtri (2010) a public Spanish national
report on R&D the investment gained in Spain by R&D activities with organizations
and other institutions amounted to 617 million Euros in 2009. The larger increase was
reported to be achieved in research activities with third parties that involved financial
support for the Spanish university. Notwithstanding the relevance of these
collaborations, there has been a large number of failures in technology transfer
attempts between these two type of institutions (Lee, 2000) and research carried out in
this context from a RM approach has been very limited. (Few notable exceptions
include the studies reported by the Knowledge Commercialization Australasia, Plewa
et al. (2005), Plewa and Quester (2006a, b, 2007, 2008), Plewa and Rao (2007), and
Plewa (2009)). Furthermore, despite its growing importance, the technology transfer
literature has primarily focussed on one-way transaction rather than relational
exchanges. A research stream on UIRs does not exist and a thorough understanding of
these relationships is still very scarce. Therefore, UIRs constitute a very good context
for the analyses of private-public relationships and their cross-cultural differences.
2.1.2 RM. While a comprehensive understanding of RM is beyond the scope of this
paper, a general understanding of this concept is considered appropriate for the purpose
of this research. RM attempts to involve and integrate customers, suppliers and other
infrastructural partners into a firms developmental and marketing activity (Parvatiyar
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and Sheth, 1994). The purpose of RM is, therefore, to enhance marketing productivity by
achieving efficiency and effectiveness(Sheth and Parvatiyar, 1995, p. 400).
RM was first coined by Berry (1983). However, the conceptual background of RM
can be traced back to the existence of three major lines of research. The Industrial
Marketing and Purchasing Group from the industrial sector (Häkansson and Ford,
2002); the Nordic School of Services (Gummesson, 1998, 2002; Grönroos, 1994) from the
services field and the North American and Anglo-Australian School (Sheth and
Parvatiyar, 1995; Sheth, 2002; Christopher et al., 1991, 2002). Each stream contributed to
the evolution of the RM and all had in common, among other things, the establishment
of long-term relationships between buyers and sellers (Grönroos, 2002, 2004), the
relevance of understanding the perspectives of both sellers and customers and the need
to develop policy actions appropriate to the specific circumstances of the relationship
(Payne et al., 1998). RM emerged as a response to the need for differentiation that
characterized a market which had evolved from being a mass market to become a much
more complex one. This new market has promoted the existence of increasingly
demanding and selective customers, mainly due to a number of structural factors such
as increased competition or hyper-competition globalization, and mega-alliances
between nations, deregulations, technological development, economic growth and
many other factors. In this context, the creation of an added value constitutes a critical
strategic factor.
A lot of research has been carried out in RM for inter-firm relationships. However, in
these relationships both parties belong to the same private setting, and thus, are not so
likely to collide in the long term (Reynolds, 1986). However, we may have different
reasons to clash in a private-public cooperation. Private-public UIRs stem from
fundamentally different sectors that may cause a greater level of friction. This paper
focusses on collaborative research relationships between the university and the
industry. These relationships are characterized by being long-term projects, and
therefore a transactional approach was deemed to be inappropriate to suit the context
of this study. RM, however, focusses on long-term linkages, and the creation of added
values for the customer. Therefore, the application of RM in long-term private-public
relationships is deemed crucial, and has been largely ignored.
Moreover, most M&A and R&D literature has also focussed on private business
settings, and most transfer and UIR literature that has studied OCD has been centered
on the analysis of patent and licensing, or financial gains as outcome measures of UIRs
success. The examination of OCD although highlighted as relevant for UIR has
been mainly under-researched in the literature of RM. Researchers and practitioners
now advocate a relationship approach for UIRs (Mora-Valentín et al., 2004; Plewa and
Quester, 2007). Hence, the approach taken in this study is based on the analysis of UIRs
from a RM approach. This study therefore contributes to further understanding RM
strategies in new settings beyond inter-firm relationships.
2.2 Organizational cross-cultural differences in private-public UIRs
Although there are many conceptualizations (cf. Kroebar and Kluckhohn, 1985) and
general models of cultural differences (Hofstede et al., 1990; Hofstede, 1991; Kanungo,
2006), organizational culture can be generally understood as the pattern of shared
values and beliefs that help individuals understand organizational functioning and
thus provides them with norms for behavior in their organizations (Deshpandé and
Webster, 1989; Wilson, 2001; Kanungo, 2006).
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Following Deshpandé and Webster (1993) there was a clear connection between
corporate culture and innovation adoption in an industrial setting (Deshpandé
and Webster, 1993). Thus, further studies corroborated the link between corporate
culture and technology adoption in business-to-business settings (Kitchell, 1995).
Nevertheless, sometimes academic research is not appropriately or even successfully
transferred to industry, thus preventing the process of knowledge transfer and
innovation (Decter et al., 2007). This may be due to the cross-culture disharmony
between private and public sector institutions (Barnes et al., 2002). Hence, organizations
and institutions have appeared to differ in terms of values, attitudes, practices and
expectations (Plewa et al., 2005; Kanungo, 2006), confirming the concept of OCD as
relevant for this study.
However, much of the Marketing literature has not focussed too much on the
existence of differences in organizational cultures between the partners involved in
the same relationship, as well as the potential effect these differences may have on their
success or failure. First, most studies on cultural differences between the parties are
based on national or geographical relational differences (Avloniti and Filippaios, 2014),
and not organizational ones (Decter et al., 2007). Although there is some research on
organizational compatibility between the companies forming alliances or mergers
(Evans, 2006; Stahl and Voigt, 2004; Bucklin and Sengupta, 1993, Smith and Barclay,
1997; Anderson and Weitz, 1989), and cultural differences in dyadic relationships
between buyers and suppliers (Hewett et al., 2002), the differences in organizational
culture have hardly been considered within the context of RM, with very few notable
exceptions from a dyadic perspective carried out in Australia (Plewa et al., 2005; Plewa
and Rao, 2007; Plewa, 2009). This gap may stem from the fact that inter-firm research
has focussed primarily on relationships within the purely private business sector.
In these sectors, some authors such as Reynolds (1986) have considered that cultural or
social differences are not significant enough to be considered important factors from
the point of view of organizational management. However, in collaborations between
public entities and private companies, the need for a detailed examination of such
differences is evident. The importance of this fact is that, mainly due to the limited
cooperation and the different roles that both institutions have developed in society over
time, different organizational cultures of both the private company and the public
university environment have gradually evolved (Cyert and Goodman, 1997; Santoro
and Chakrabarti, 2002). Therefore, potential conflict arises when both parties approach
in relationships involving a close link between the two, as in the case of partnerships to
conduct knowledge transfer and innovation processes (Tushman and OReilly, 2013).
A comprehensive literature review of the general concepts of organizational culture
differences has been carried out in order to identify the key dimensions in which
universities differ most from industries. Unfortunately, there are very few studies that
have researched UIRs and their cultural differences so far hence the recent call for
further research on the effect of cultural difference on relationship outcomes in these
collaborations (Hewett et al., 2002; Sarkar et al., 2001; Plewa, 2009) and there is
practically no proven framework to identify the main dimensions in which these
cultures differ (Siegel et al., 2003). Therefore, following previous studies (Plewa et al.,
2005; Plewa and Rao, 2007; Plewa, 2009) dimensions for examining differences between
university-industry cross-organizational cultures are based on related areas from
private business-to-business settings (Deshpandé et al., 1993; Siegel et al., 2003), and
previous dyadic research (Plewa and Rao, 2007; Plewa, 2009). We also rely on field
research, that is, a prior qualitative exploratory study with fifteen in-depth interviews
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with experts. We asked for barriers to effective UIRs and a list of possible cultural
differences gathered from related literature served as a starting frame (these included:
time orientation differences, language and communication pattern differences,
differences in motivations and drivers, market orientation differences and corporate
flexibility differences). This list was shortened according to the in-depth interview
results. Results were aligned with prior UIRs literature and models (Plewa et al., 2005;
Plewa and Rao, 2007; Plewa, 2009). Thus, the field research provided the three main
factors identified as key variables, which were: time orientation (Barnes et al., 2002;
Brimble and Doner, 2007; Decter et al., 2007; Siegel et al., 1999, 2003; Davenport et al.,
1999) market orientation (Baaken, 2003; Plewa and Rao, 2007) and flexibility (Brimble
and Doner, 2007; Plewa et al., 2005; Plewa, 2009; Siegel et al., 2003) differences. After
this preliminary qualitative study, a quantitative research study was carried out.
2.2.1 Time orientation. Time orientation can be understood as the totality of the
individualsviews of his psychological future and past existing at a given point in time
(Lewin, 1951, p. 75; Merchant et al., 2014, p. 326). Time perspective influences how firms
and consumers act, think and behave. It influences values, behaviors and the
propensity to take risks (Merchant et al., 2014). Universities and organizations are
reported to differ in their timeframe approaches and their perceptions of time (Barnes
et al., 2002; Brimble and Doner, 2007; Siegel et al., 2003). In this respect, three notable
aspects must be taken into account. First, timeframes appear to be shorter in the
industry than in the university. The launch of a new product on the market at a given
point in time is a key factor for business success, and that is why organizations
normally work with short-term perspectives in their R&D activities (Santoro, 2000).
Universities, however, have a longer temporal timeframe (Cyert and Goodman, 1997)
since, in many cases, they are more focussed on the quality and the publication
opportunities of their research than by the market needs themselves. Second, it is
relevant to emphasize the importance of deadlines. Punctuality or the adherence to
deadlines has also been reported as a fundamental facet of time orientation difference
(Plewa et al., 2005). Failure of academics to meet the set deadlines may be due to the
variety of tasks that faculty members perform simultaneously, as well as the fact that
the prioritization of these tasks is partially beyond their control. Third, another aspect
to be considered in relation to the temporal orientation difference is the rotation of
employees, since this generates the need to finish the projects as soon as possible.
In this sense, the high frequency of employee turnover in some multinational
companies should be seen as a potential barrier to the continuation and evolution of the
university-industry relationship.
2.2.2 Market orientation. Market orientation is one of the most highlighted cultural
differences described in the literature of university-industry cooperations (Fisher and
Klein, 2003). Market orientation could be defined as the application of the marketing
concept at an organizational level. Following Jaworski and Kohli (1993), the market
orientation of an organization reflects the degree to which the marketing concept is
integrated into the business philosophy of a company. The role of market orientation as
a means to achieve a sustainable competitive advantage has widely been recognized in
the marketing literature (Slater and Narver, 1995; Barroso et al., 2005; Elg, 2008). Thus,
companies that adopt a market orientation strategy get better business results (Kirca
et al., 2005). Companies that have managed to achieve better business results through
market orientation, have primarily based their strategies on the creation of a culture or
an organizational environment that focusses on reinforcing a philosophy of
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customer-oriented business marketing (Harris et al., 2005). However, while companies
have always been very market oriented, and therefore, interested in applied research,
universities seem to have opted for a more theoretical knowledge, and have been
primarily focussed on basic research. It has also been argued that the fact that some
public universities may not consider companies as clients can be a symptom of their
lack of market orientation.
2.2.3 Corporate flexibility. Corporate flexibility can be defined as the degree to
which a business unit is adaptable in administrative relations and the authority is
vested in situational expertise. A firm exhibiting low flexibility has rigid administrative
relations and strictly adheres to bureaucratic practices(Bhardwaj and Momaya, 2006,
p. 39). An entrepreneurial firm provides innovation by practicing flexibility and
responsiveness (Guth and Ginsberg, 1990). Moreover, flexibility in organization
structure has been reported to contribute positively toward product success (Saleh and
Wang, 1993). Likewise, a flexible structure promotes communication across the
functional boundaries (Bhardwaj and Momaya, 2006).
A high level of organizational bureaucracy and corporate inflexibility can be
detrimental to any relationship, causing its discontinuation. University environment
does not require the same degree of flexibility or adaptability to change that is
necessary for business survival in todays competitive environment. Thus, while the
incompatibility of structures has been linked to a mismatch between partners in
alliances and mergers from the private sector ( Jordan, 2004), an even greater disparity
seems to exist in relationships characterized by a high cultural difference, as in the case
of university-business partnerships (Rampersad et al., 2003).
2.3 Integration
Much of the organizational literature has examined the main reasons why
organizations build inter-organizational collaborations. In a comprehensive summary
of this literature, Oliver (1990) identified six key generalizable factors that affect the
formation of cooperations: necessity, asymmetry, reciprocity, efficiency, stability and
legitimacy. Each of these causes, therefore, can be pivotal in the formation of such ties.
Also within this literature, Nevin (1995), among other researchers, assumes that the
formation of relationships is based primarily on reciprocity. The motivations for such
reciprocity emphasize cooperation, collaboration, coordination and integration between
the partners as alternative elements to domination or imposition of power, which could
make up the control mechanisms or key factors in an asymmetrical relationship. This
view is in line with RM postulates mentioned above. RM seeks to develop close
interactions with certain customers, suppliers and competitors, to create superior value
through efforts of cooperation and collaboration (Sheth and Parvatiyar, 1995).
Therefore, while the contractual approach might be used in order to minimize
transaction costs and reduce opportunism, which does not necessarily fosters
cooperation between the parties, the RM approach is based on an alternative proposal
where the maximization of the mutual relational value between the parties is based on
trust, balance, cooperation and integration, as well as in the mutual desire that the
relationship persists over time (Gosh and John, 1999; San Martin et al., 2000).
Integration can be conceptualized as the symbiotic interrelationship between two or
more entities that result in the production of net benefits for both parties which exceed
the sum of the net benefits that both sides would produce in a non-symbiotic
relationship (Souder and Chakrabarti, 1978). From this optic, many researches have
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demonstrated both qualitatively and quantitatively the influence that integration has on
the success of research projects (Souder and Chakrabarti, 1978; Lamore et al.,2013).
Research projects between different functional groups -such as public-private UIRs- can
give rise to a large number of conflicts if attention is not paid to the above OCD. In such
circumstances, an effective integration between the two relational parties can be
considered a vital and necessary element to finally obtain the initial mutual objectives or
benefits. Thus, integration is considered to be a key strategic factor for UIRs success.
3. Hypotheses development
Previous research has already shown that companies may differ in their organizational
cultures in the same sector (Wiewiora et al., 2014). However, it has been noted that
organizational cross-cultural differences are strongest when they cross different
sectors. Therefore, the evaluation of OCD in relationships between private entities and
public academic institutions offers a suitable context for analysis.
3.1 Relationship antecedents: organizational culture differences
Universities and organizations differ markedly in their approach to their timeframes
(Decter et al., 2007). Firms are pressurized to make strategic decisions, in this sense, they
tend to seek short-term results to meet the competitive needs of the market. Universities,
however, act on a longer time-span since they may be more worried about basic research
than market needs (Plewa, 2009). In fact, some efforts to develop joint initiatives have
resulted in ambitious projects, which main consequence has been a temporary delay in
the performance of the proposed research (Brimble and Doner, 2007). Thus, the following
hypothesis is proposed:
H1. Differences in time orientation negatively influences integration in UIR.
Market orientation has been conceptualized from both behavioral and cultural
perspectives (Homburg and Pflesser, 2000). Following Kirca et al. (2005), the behavioral
perspective focusses on organizational activities that are linked to the generation and
dissemination of market intelligence and the responsiveness to it (e.g. Jaworski and
Kohli, 1993). The cultural perspective focusses on organizational norms and values that
encourage behaviors that are consistent with market orientation (Deshpandé et al.,
1993; Narver and Slater, 1990). From both perspectives, the marketing literature has
extensively recognized the role of market orientation as a source to achieve a
sustainable competitive advantage (Narver and Slater, 1990; Jaworski and Kohli, 1993;
Matsuno and Mentzer, 2000). Private sector companies, as highlighted frequently in the
marketing literature, are highly driven by market orientation (Deshpandé, 1999;
Deshpandé and Farelly, 1998). In this sense, they tend to create a superior value for the
customer and meet customer needs (Kirca et al., 2005). It has also been proposed in
private business settings, that a firms strategic choice regarding market orientation
may influence the relationship and integration between the parties, and how this
relationship may affect organizational success (Lamore et al., 2013). However,
universities and private sectors organizations do not seem to convey the same
approach to market orientation. While private companies are customer driven, and seek
to maximize profits by delighting customer needs, public universities receive most of
their funding by the government, and therefore do not seem to be so focussed on
creating customer values. Additionally, prior research has identified that universities
and organizations adopt different views of the value creation for customers or partners
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(Plewa et al., 2005), which is likely to create friction in UIRs setting. Therefore, the
following hypothesis is established:
H2. Market orientation difference negatively influences integration.
The cross-cultural disparity in corporate flexibility can cause a great discomfort
between partners working under the same project (Brimble and Doner, 2007; Siegel
et al., 2003).
There are many perspectives that can be taken to define flexibility (Long, 2001),
most of them having central elements in common, such as a certain degree of employee
freedom or the restriction of rigid control methods and bureaucracy in an organization
(Barrett and Weinstein, 1998). Bureaucracy appears to be linked to the control, by
lawyers or third parties, of negotiation processes and the relationship in general. In
addition, bureaucracy appears to result from a functional organizational structure
(Barroso and Martín, 1999), which is prevalent in universities (Plewa and Rao, 2007).
The RM literature has emphasized the importance of cross-functional orientation
processes rather than functional corporate structures for successful relationships
(Gordon, 1998). While functional corporate structures provide relational barriers,
restricting the organizational relationships, cross-functional structures promote
interaction and relational fluidity.
Technology transfer offices and universities have been reported to differ in terms of
flexibility with industrial organizations (Mora-Valentín, 2000). In this sense,
universities have been regarded as having a higher level of bureaucratic culture
(Siegel et al., 2003). However, so far, with a few exceptions (Plewa and Rao, 2007; Plewa,
2009), there is practically no literature that has empirically studied the effects of
corporate flexibility differences between private and public institutions on relationship
outcomes. In line with these postulates, the following hypothesis is stated:
H3. Corporate flexibility differences negatively influence integration.
3.1 Relationship outcomes
Based on previous studies and in line with the RM perspective proposed in this paper,
satisfaction with the relationship and sustainable relationship have been taken as
relevant relationship outcomes.
Long-term sustainable relationship can be defined as the intention to renew the
relationship with the supplier for a long period of time. In the case of UIRs, it can be
understood as a relationship renewal for a second or subsequent research project of
collaborations between the university and the industry after the first research project
between the two relational parties is over. Knowing a priori a customers intention to
form long-term sustainable linkages is extremely important when determining the
long-term business strategy of a firm. This is especially relevant for companies in the
service sector, such as universities (Patterson et al., 1997), where on the one hand
products are characterized for being intangible and subjectively assessed, and on the
other hand, contracts are normally agreed on specific projects timeframes and not with
an indefinite continuity (Gray et al., 2001).
Satisfaction is a complex construct and it falls out of the scope of this paper to
provide a comprehensive approach. Nevertheless, the following lines provide a
conceptualization of this variable to better understand the approach taken in this paper
regarding satisfaction with the relationship. Customer satisfaction with a service
provider, in this case the university, can be defined as a customers related fulfillment
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response regarding a consumption experience with a product or service (Oliver, 1980).
According to many Marketing postulates, the relevance of this concept is mainly based
on the fact that business profitability is a direct consequence of the achievement of
customers satisfaction (Churchill and Suprenant, 1982). Notwithstanding its
significance, there is a lack of general consensus regarding its conceptualization,
delimitation and measurement (Giese and Cote, 2000).
In this respect, customer satisfaction has been generally conceptualized as either an
emotional or a cognitive response, although most recent research has attributed an
emotional response to it (Giese and Cote, 2000). This response has been traditionally
based on the disconfirmation paradigm (Oliver, 1980, 2010), as a feeling coming from a
comparison of customers expectations with a product or service and the perceived
performance of that product or service (Yi, 1990). Hence, whenever performance exceeds
expectations, satisfaction would be increased. At this point, it is also important to clearly
distinguish between transaction-specific satisfaction and overall satisfaction, as well as
between economic and non-economic satisfaction. Prior to Geyskens et al.s(1999)
proposals, and mainly for relationships between channel members, it was generally
understood that satisfaction was a positive affective state resulting from the appraisal of
all aspects of a firms working relationship with another firm (Gaski and Nevin, 1985;
Frazier et al., 1989). In line with this approach, satisfaction was composed of both
economic and non-economic components. Economic satisfaction referred to the economic
reward (e.g. price discounts) that was obtained from the relationship with the exchange
partner. Non-economic satisfaction referred to the positive affective response to
non-economic psychological aspects of any relationship, such as respectfulness, or
goodwill (Geyskens et al., 1999). Geyskens and Steenkamp (2000) proposed that these two
facets of satisfaction should be separated, since the proportion of economic and non-
economic items that had been included in the rating scales proposed to measure
satisfaction varied considerably in the different research available, and therefore, it was
not possible to make an effective comparison across previous studies.
In this regard, and in line with RM postulates, for the purpose of this research,
satisfaction will be considered as a non-economic factor. Regarding the aforementioned
distinction between transaction specific and relational or overall satisfaction,
researchers have traditionally opted for either one of these concepts of satisfaction,
although it is worth mentioning that some researchers (e.g. Tse and Wilton, 1988;
Szymanski and Henard, 2001) have stated that only overall satisfaction can properly
measure this construct. Overall satisfaction has been understood as a global assessment
based on the buying experience of multiple encounters, whereas transaction-specific
satisfaction is more related with a feeling of satisfaction or dissatisfaction regarding a
specific transaction or service (Anderson et al., 1994). Following the objectives presented
here, and having into account that university-industry collaborations form normally a
part of long-term projects, for the purpose of this research satisfaction with the
relationship has been conceptualized as an overall uni-dimensional construct based on
non-economic components.
As conceptualized earlier on, integration has been proposed in previous exploratory
studies within the context of the UIR as one of the explanatory variables more related with
satisfaction (Souder and Chakrabarti, 1978; Mora-Valentín et al., 2004), and long-term
sustainable relationship (Plewa and Quester, 2006a, b; Lamore et al., 2013). Equally, it has
also been proposed as a key variable in the literature related to strategic alliances (Cyert
and Goodman, 1997; Child and Faulkner, 1998; Van de Ven and Walker, 1984; Luo, 2008).
The empirical study of this variable in this specific setting has practically been ignored.
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Therefore, the estimation of this causal path in this paper is a new contribution to the
small existing literature which proposes integration as a key strategic factor for
the application of an appropriate collaborative relationship. The following hypotheses are
then purposed:
H4. Integration positively influences satisfaction.
H5. Integration positively influences sustainable relationship.
Overall, Figure 1 presents different hypotheses and our conceptual model, adapted
from Plewa et al. (2005), Plewa and Rao (2007) and Plewa (2009).
4. Empirical study
4.1 Operationalization of constructs and measurement scales
The measures used in this study were derived from existing scales that had proved to
have sound psychometric properties in previous studies. These measures were slightly
modified to be adapted to the specific context of university-industry links. The final
scales were all seven-point Likert-type ones, anchored by completely disagree (1) to
completely agree (7). Time orientation differences were measured based on the scale
reported by Parkhes (1991). This scale was adapted to the specific context and was
used to determine the extent to which the respondents adhere to timeframes, meet
deadlines and conform to punctuality. Corporate flexibility was measured following
Kitchell (1995), and was employed to assess the degree to which partners are flexible,
adaptable to changes, and welcome ideas. Market orientation was operationalized
using private sector measurements ( Jaworski and Kohli, 1993; Kholi and Jaworski,
1990; Narver and Slater, 1990) and adapting them to the specific context. It tapped the
levels of interaction, dissemination of ideas, coordination and understanding and
caring for customer values.
Regardingsatisfactionwiththerelationship, the scale adapted for this study was based
on the measure reported by Oliver (1980, 1981) and Westbrook and Oliver (1981).
Integration was based on the scale proposed by Dwyer and Oh (1987). This four-item
seven-point Likert scale measured the level of respondentsintegration with the university
research group. Sustainable relationship was weighted on a seven item 0-100 percent Juster
scale, and measured the likelihood that the relationship of collaboration would be sustained
at the end of the present contract (Seymor and Brennan, 1994). All the items were
randomized in the final questionnaire to minimize the impact of order bias.
Time orientation
Market orientation
Corporate flexibility
Integration
H4
H5
H1
H3
H2
Satisfaction
Sustainable
relationship
Figure 1.
Conceptual model
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4.2 Data collection and sampling
The target population for this study consisted of national firms operating all over Spain
that had recently been, or were at the moment of the study, engaged in research and
development projects of collaboration with a major public Spanish university in
Madrid. An initial list of companies was provided by the major public university and
served as a sampling frame. There was no alternative sampling frame available
because there is no public registration database of firms that collaborate with
universities in Spain. The complete list containing about 1,000 collaborating
agreements was assessed and shortened following certain criteria, namely, the
time-span period was limited to collaborations that were current or that had not
finished prior to the last two years, since a larger time-span period could introduce bias
in the sample; and the purpose of the collaboration was of a research nature.
Sponsorships and other forms of collaborations were excluded, since the aim was not
the creation of knowledge or innovation. Following these criteria, the final list
contained 370 collaboration agreements. To examine whether the final sample was
representative of the population under study, besides confidence interval tests (see
Table I) the selected firm profiles were compared to descriptions of industries that
collaborate with universities in research projects provided by previous studies
(Barge-Gil et al., 2007). Overall, the profile of the selected companies matched well with
the overall UIR in terms of project duration, firm characteristics and firm size.
Subsequently, the study followed a two-step approach based on an exploratory
qualitative investigation to determine the main factors that could influence these
relationships in terms of cross-cultural differences, as well as to purify the scales in this
context, and a quantitative study supported with the use of structural equation model.
In the exploratory investigation, a critical validation by a group of 15 experts was
conducted in order to review and edit the items, leading to the final questionnaire
developed for the self-administered electronic survey, using Le Sphinx a statistics
software for online surveys to gather the data. Following a pre-test conducted with
managers similar in profile to the target respondents, in April 2010 the final
questionnaire was e-mailed to 370 ID managers of national firms on the sampling frame
operating all over Spain.
To account for the impact of the low response rate normally associated with
electronic surveys, respondents were offered a summary of the results to encourage
their participation. A letter from the R&D Vice-Chancellor of the university was also
included, encouraging cooperation and reiterating that individual responses would be
strictly confidential. Surveys were returned directly to the researcher to emphasize the
academic control of the information. The response rate for this initial phase was
18 percent. Following Dillmans (2007) TDM procedures, follow-up surveys were sent to
Initial sample 370 R&D collaboration agreements between public universities and private
companies, that were current between January 2007-December 2010
Final sample 183 valid questionnaires
Response rate 49.5%
Survey type Electronic survey (self-administered survey)
Error margin 5.16% for the sample/simple random sampling
Confidence
interval
95% confidence interval, two-tailed, ( p¼q¼50%)
Fieldwork March-May 2010
Table I.
Survey data
specification
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those respondents who had not returned their surveys within a three-week period,
thereby increasing the response rate to 25 percent. Two weeks after the second mailing,
a reminder was sent to respondents asking them to complete the questionnaire. After
extensive mail and telephone follow-up, an effective response rate of 49 percent was
accomplished with a total of 183 completed questionnaires valid for use in this study.
It was considered a very satisfactory rate for this type of survey of business people
where response rate below 15 percent becomes questionable (Malhotra, 1993, 2004). All
183 questionnaires were analyzed.
In order to avoid non-response bias, possible differences in the means obtained for
each one of the factors proposed in this study were analyzed between early and late
respondents. To this end, the Levenes-test and the independent samples t-test were
conducted. Results showed no statistical significant differences in the mean scores.
Having considered non-response bias in sample quality, and assuming non-response is
not the only source of possible sample bias (Blair and Zinkhan, 2006), coverage bias and
selection bias were also checked (Blair and Zinkhan, 2006). Table II shows the survey
data specifications. Common method bias was also tested. Common methods variance
or bias is variance that arises from the method of measurement rather than by what is
being measured (Siemsen et al., 2010) and is regarded as a leading cause of systematic
measurement error ( Johnson et al., 2011). Common method bias can arise from many
sources, such as the type of scale, the general context, response format, social
desirability, acquiescence, leniency effects, desirability or halo effects (Podsakoff et al.,
2003). Although there are different test that can be applied to detect this bias, following
Podsakoff et al. (2003) Harmans one factor test is one of the most widely used, and
therefore was the one selected for this analysis. Therefore, all items were loaded into an
exploratory factor analysis with unrotated factor solutions, showing a single factor
explaining less than 50 percent of the total variance (34.3 percent). Thus, it could be
concluded that common method bias was not a major problem. All these previous steps
suggested that the final sample was adequate for further analysis.
5. Results
5.1 Data analysis
Structural equation modeling (SEM) with EQS 5.0 was used as the main technique for
data analysis. Numerous multivariate methods and techniques are used in the field of
social sciences. However, structural models permit the interplay of variables among
different subsets, allowing the estimation of multiple linear relationships between
different latent variables by means of a correlation or covariance matrix (Hair et al.,
1999). Another advantage of SEM is the ability to incorporate latent constructs and
Cronbachs
α
Composite
reliability
Average variance extracted
(AVE)
Highest shared
variance
Satisfaction 0.936 0.952 0.833 0.579
Integration 0.899 0.9 0.75 0.286
Time
orientation 0.898 0.906 0.763 0.304
Market
orientation 0.91 0.94 0.82 0.307
Flexibility 0.85 0.924 0.754 0.21
Table II.
Reliability and
validity scores
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observable or manifest variables in a single model. Thus, these models integrate a
number of different but interdependent multiple regressions equations that can
perform as dependent and independent variables at the same time (Byrne, 2005).
Furthermore, as opposed to other multivariate techniques where it is assumed that
there are no measurement errors in the variables, these models use multiple indicators
when measuring a latent construct, allowing to calculate or specify the measurement
error in the observable variables. Therefore, SEM was considered to be the most useful
method for the study of multiple linear causal inter-relationships, such as those
proposed in the theoretical model of this study.
The procedure used to evaluate the reliability, dimensionality and validity of the
scales was the following. First, descriptive statistics were analyzed to determine
the distribution of the data and consequently select the most appropriate estimation
methods. Then, exploratory factor analyses using principal components to check the
dimensionality of the constructs were carried out and finally a confirmatory factor
analysis to assess the goodness of fit of the measurement model, reliability and validity
(see Table II), and the overall model fit were developed. Construct reliability was
assessed by the Cronbachsαscores and composite reliability coefficients (Byrne, 2005).
The Cronbachsαand composite reliability scores for the different scales were all above
0.7 (see Table AII for factor loadings), which indicates a very satisfactory internal
consistency and reliability. Construct validity was calculated by means of the average
variance extracted (AVE), with all coefficients over the recommended cut-off value of
0.5, indicating that the final items of each scale contained less than 50 percent error
variance and converged on only one construct (Byrne, 2005), confirming convergent
validity for all constructs (see also Table AII).
Discriminant validity was also tested in two different ways (see Tables II and III).
First, by comparing the square root of the AVE with the correlations among constructs
(Dhanaraj et al., 2004; Hair et al., 1999), and second by comparing the AVE with the
shared variances between the constructs (Byrne, 2006). Table III shows the correlation
coefficients in the off-diagonal elements of the matrix, and the square root of the AVE
along the diagonal. Discriminant validity is confirmed when the diagonal elements (AVE)
are greater than the off-diagonal elements (correlations). As it can be seen in Table III, all
correlations among constructs were below AVE coefficients (Dhanaraj et al.,2004)
and also below 0.9 (Luque-Martínez, 2000), demonstrating an adequate discriminant
validity. Alternatively, all shared variance coefficients (Table II) were lower than
the corresponding AVE scores (Hair et al., 1999), confirming that each construct
shared more variance with its own measures than with the other constructs in the
model, and therefore assessing a good level of discriminant validity. Finally, since data
(1) (2) (3) (4) (5) (6)
(1) Time orientation 0.88
(2) Market orientation 0.085 0.90
(3) Flexibility 0.551 0.554 0.863
(4) Integration 0.535 0.081 0.516 0.866
(5) Satisfaction 0.550 0.024 0.558 0.503 0.913
(6) Sustainable relationship 0.537 0.012 0.551 0.523 0.761 0.935
Notes: The square root of the average variance extracted (AVE) is shown in the main diagonal of the
cross-table. The rest of coefficients are the correlations among constructs
Table III.
Discriminant validity
and correlation table
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was found to be non-normal, (Mardia and Jupp, 2000) with z-values over 5.0 (Byrne, 2006),
the method of maximum likelihood was not considered to be appropriate. EQS offers
the possibility of using a robust version of maximum likelihood for non-normality, as
well as the Satorra-Bentler χ
2
. Hence, these were the methods of analyses chosen to test
the model.
5.2 Results
The conceptual model was tested with EQS v.5.0. As shown in Table III, the robust
goodness of fit statistics show very satisfactory values. Goodness of fit indicators,
namely, normed fit index, non-normed fit index, comparative fit index and incremental
fit index exceed the cut-off values that are considered as acceptable (0.9), confirming
that the proposed model fitted the data satisfactorily. Equally, the χ
2
adjusted by
the degrees of freedom falls within the recommended range (1-3), showing a good fit
(Table IV). Moreover, the root mean-square error of approximation was also used to test
parsimony, showing a value of 0.007, quite below the required 0.05. Finally, since the
diagnosis cannot be completed without analyzing the standardized residuals matrix
(see Table AI), analyses showed that 70 percent of the residues are contained within the
range 0.1 and +0.1, and the maximum number of residues outside the range ±0.258
does not exceed 5 percent (Byrne, 2006). On the basis of all these indexes, it can be
ascertained that this model has a very good fit to the data and therefore the results can
be interpreted.
The results of path analyses with standardized estimates are presented in Figure 2
and Table V. As it can be observed in the table, all paths were found to be significant,
which led to the acceptance of all five hypotheses. Quite as expected, time orientation,
market orientation and corporate flexibility differences were found to negatively
influence integration, therefore accepting H1,H2 and H3.
Robust fit
S-B χ
2
χ
2
/df RMSEA NFI NNFI CFI IFI GFI AGFI
2.346.165 df: 214 p: 0.0451 1.056 0.007 0.921 0.925 0.931 0.934 na na
Table IV.
Goodness of fit
statistics
INTEGRATION
TIME ORIENTATION
DIFFERENCE
MARKET
ORIENTATION
DIFFERENCE
FLEXIBILITY
DIFFERENCE
SATISFACTION
SUSTAINABLE
RELATIONSHIP
–0.278*
–0.416***
–0.209*
0.176*
0.164*
Notes: *p<0.05; ***p<0.001
Figure 2.
Results of
hypothesis testing
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However, while market orientation difference appeared to have a significant negative
impact on integration ( β¼0.416***, po0.001), time orientation ( β¼0.278*, po0.05)
and corporate flexibility ( β¼0.209*, po0.05) showed a comparatively weaker effect.
Integration was also found to have a positive direct effect on satisfaction with the
relationship ( β¼0.176*, po0.05), confirming H4. Likewise, integration was found to be
a positive predictor of sustainable relationship ( β¼0.164*, po0.05), which led to the
acceptance of H5.
6. Conclusions and managerial implications
The main objective of this paper was to examine the impact of OCDs on the relationship
of innovation-oriented collaborations between public university research groups and
private sector institutions. Thus, the main research questions addressed in this paper
were do organizational cross-cultural differences prevent the evolvement and success
of innovation-oriented relationships? What are the main factors in OCD that managers
working under this setting would have to take into account to prevent relationship
failure?
Therefore, this study contributes to the existing literature in several ways. First, the
concept of OCD has scarcely been examined in private-public UIRs context to date from
a RM perspective. Other contexts show a lack of consensus regarding the influence of
OCD on different outcomes. Cultural differences have been described to have a negative
impact on shareholder gains (Plewa and Rao, 2007; Chatterjee et al., 1992), and
satisfaction levels (Froese and Xiao, 2013; Lok and Crawford, 2004), while not
influencing financial performance of a merger (Plewa and Rao, 2007; Weber, 1996).
However, our results support the idea that market orientation, time orientation and,
to a lesser extent, corporate flexibility differences are key elements that can if not
managed properly negatively influence the process of integration between partners
working in different cross-cultural sectors, and thus have a negative impact on
relationship continuation.
First, and in line with previous research (Fisher and Klein, 2003; Barroso et al., 2005;
Plewa, 2009; Plewa and Quester, 2006a), this study confirms market orientation
difference as one of the most important variables that can prevent the correct flow of
communication, information and therefore, knowledge creation and innovation both in
the short (satisfaction) and long (sustainable relationship) terms. Market orientation
was operationalized as tapping interaction, dissemination of ideas, coordination and
understanding and caring for customer values. The application in this setting allows us
Hypotheses Hypothesized path
Standardized
effects
Critical
ratio
Empirical
evidence
H1 Time orientation
difference integration
0.278* 1.969 Yes
H2 Market orientation
difference integration
0.416*** 3.679 Yes
H3 Corporate flexibility
difference integration
0.209* 2.055 Yes
H4 Integration satisfacción 0.176* 2.382 Yes
H5 Integration sustainable relationship 0.164* 2.064 Yes
Notes: *po0.05; ***po0.001
Table V.
Results of
hypotheses testing
and standardized
estimates
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to hence, market orientation negative effects could be counterbalanced if both parties
work at creating more value for their partners. Partners may encourage a better
understanding of their respective market needs, and endeavor to meet each others
requirements as the relationship evolves.
Time orientation difference was also found to have a negative impact on the
relationship (Decter et al., 2007; Santoro, 2000; Plewa et al., 2005), both through
the direct negative effects on integration, and the indirect effects on satisfaction with
the relationship and sustainable relationship. In this sense, punctuality and the
adherence to timeframes for both parties should be considered critical for
the collaborative projects success.
Surprisingly, the impact of corporate flexibility although still significant was
much weaker than expected. This view supports previous studies where learning and
flexibility increased performance in innovation for university-industry cooperations
(Harrison et al., 2008). However, the weaker effect found may suggests that managers
and university faculty members may find it easier to adapt to flexibility requirements
than to time or market orientation differences. Managerial teams are responsible for the
level of bureaucracy and flexibility that is expected in these relationships. Agreements
can be reached and implemented more easily for corporate flexibility than for market
orientation, for instance, since most flexibility requirements are more objectively
assessed. Therefore, managers should primarily focus on improving market orientation
differences and work toward resolving time orientation differences. The level of
bureaucracy and corporate flexibility, although still significant and important, should
be regarded as less determinant.
Another contribution is the examination of the predictive effect of integration for
relationship success. While prior studies have shown that integration might support
short-term relationships, processes and outcomes (Plewa, 2009), it has also been suggested
not to be of any direct consequence for long-term planning in industrial channel
relationships (Anderson and Weitz, 1989; Simon et al., 2014). Our results, however, further
this view. Integration has been shown to significantly affect both short-term satisfaction
with the relationship and long-term sustainable relationship. This may be due to the fact
that the capability of integration to predict long-term sustainable relationships is highly
context specific (Yu et al., 2013). Therefore, more research in different contexts is needed
to confirm these findings. Integration was also found to moderate the negative effect
between cultural differences and relationship performance. This highlights the
importance of integration in this setting as a key element in order to attain successful
agreements of collaboration between universities and private organizations, both in the
short and the long terms. Integration facilitates the communication and therefore,
the process of knowledge transfer and innovation (Lee, 2000).
The issue of what indicators are most appropriate in order to measure the
performance of universities-industry cooperation in knowledge transfer activities and
innovation creation remains relatively under-investigated (Rossi and Rosli, 2014). UIRs
have mainly been studied from a technological point of view, and most outcome
measures have concentrated on financial gains, shareholders gains or patents and
licenses as outcomes measures of success (Siegel et al., 2003). This narrow range of
indicators have limited the ability of universities to accurately represent their
knowledge transfer and innovation performance (Rossi and Rosli, 2014). Therefore, the
use of MR outcome measures of success in UIRs is another contribution of this paper,
which facilitates a further understanding of the knowledge transfer and innovation
success processes.
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6.1 Summary of the main managerial implications
(1) Punctuality and the adherence to timeframes for both parties should be
considered critical elements for the collaborative projects success. Therefore,
managers should establish the appropriate mechanisms to guarantee deadlines
are met. These mechanisms may include the assessment of the level of
involvement necessary to meet timeframes and forecast resource allocations
accordingly (both time and human capital); elaboration of mutual work-plans;
periodic reports to ensure timeframes and tasks are met adequately.
(2) Members of the research team must make an effort to understand the objectives
and expectations of the business group, with the aim of trying to create superior
value for the company and its customers and vice-versa. This may include regular
meetings with management teams to match partnersgoals,ensurethereareno
misunderstandings, and check expectations are achieved according to scheduled.
(3) Managers of the research team should also try to avoid rigid systems of
bureaucracy. They should work toward trying to reduce the load of bureaucratic
procedures, or look for alternative ways to eliminate unnecessary administrative
requirements. This would allow the parties to work with greater efficiency and
productivity, as well as save the costs associated with high levels of
administrative tasks.
(4) The importance of integration between the parties in this setting as a key
element in order to attain successful agreements of collaboration between
universities and private organizations is vital. Hence, managers should try to do
their best to guarantee that both parties have a positive relationship. Managers
should discuss with their teams on a regular and continuous basis to detect and
avoid any possible conflict between the parties involved, as well as to ensure
that mutual expectations are dealt with, and the level of understanding is
adequate. Achieving a proper level of integration would unquestionably favor
both parties. Through a close contact and follow up, university research
managers can discuss and improve the possible intentions regarding renewal
and long-term sustainability of the relationship with future projects.
For company managers, given the low rate of employee turnover that
characterizes the university workforce, businesses are given the opportunity to
develop and sustain successive collaborative projects with the same research
team in future agreements. The main advantage is that once the necessary
time and resources needed for a successful integration of both parties have been
allocated to the relationship, the company reduces the costs commonly
associated with the creation of new work teams in future projects.
(5) Therefore it is crucial that both managers promote group communication
mechanisms that facilitate and promote integration between the relational
parties, such as formal events or informal team meetings.
(6) Also, given the structural weaknesses associated with the innovation market
(uncertainty, risk, opportunism and indivisibility of service) an appropriate level of
integration may allow university researchers foster a climate of trust that enables
companies to be more oriented toward the research and innovation process itself,
and less toward secrecy and patenting the results derived from the projects.
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(7) In general, managers should primarily focus on improving market orientation
differences and work toward resolving time orientation differences. The level of
bureaucracy and corporate flexibility, although still significant and important,
should be regarded as less determinant.
In short, by analyzing effects that OCDs have in these relationships, information
generated from this research could help cross-cultural management teams to establish
successful knowledge transfer collaborations and contribute to foster the creation of
innovations.
Finally, results derived from this study are not limited to public and private sector
UIRs, since they may also apply to other cross-cultural private-public organizational
settings. These can include, for instance, partnerships between public hospitals and
private companies, private foundations or public governmental institutions agreements
with private organizations.
7. Limitations and future research
The added value of this paper relies on the original data set, high response rates and
the idea of studying cross-cultural differences in private-public sector relationships in
this case university-industries relationships from a relational marketing perspective.
However, these results only offer some insight into these private-public collaborations,
and future research should verify these findings.
The range of relationship variables and organizational difference factors included
should also be broadened and further analyzed. Moreover, supplementary
cross-cultural factors in private-public linkages may be included as antecedents,
mediators or moderators.
Furthermore, given the recent call for UIRsstudy (Santoro and Chakrabarti, 2002;
Plewa and Quester, 2007), and the importance that these linkages have for the
development of the new knowledge society, more research is unquestionably necessary
in this setting.
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Appendix
Note: Each “*” represents seven residuals
Table AI.
Standardized
residual matrix
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Factor
loadings α
Composite
reliability AVE
Market orientation (disemination, intelligence generation, and
response design) 0.91 0.949 0.829
We meet with our customers at least once a year to find out
what products or services they will need in the future 0.986
Individuals from the research group interact directly with us to
learn how to serve our customers better 0.940
Data on customer satisfaction is disseminated at all levels in
this firm on a regular basis 0.845
The activities of all staff in this business unit are well
coordinated 0.864
Time orientation 0.89 0.906 0.76
Punctuality is important 0.799
Time is money 0.893
Deadlines are important 0.923
Corporate flexibility 0.85 0.924 0.754
The research group can be described as flexible and continually
adapting to change 0.789
New ideas are always being tried out here 0.819
Top managers in the research group can be described as set in
their ways (reverse scored) 0.897
The research group is always moving toward improved ways
of doing things 0.959
Integration 0.899 0.900 0.750
Our ideas are welcomed by the research group 0.876
We welcome ideas from the research group 0.892
This research group encourages suggestions from us for
improving programs and processes related to our relationship 0.828
Satisfaction 0.936 0.952 0.833
Our choice to work with this research group was a wise one 0.810
We are delighted with this research groups performance in the
relationship 0.918
The relationship with them has been satisfactory 0.985
We think we did the right thing when we decided to work with
this research group 0.929
Sustainable relationship 0.874 0.87
Using the following scale, please indicate the likelihood that the
relationship with this research group will be renewed at the end
of the current contract. Please circle % figure. Zero indicates no
chance that the relationship with this research group will be
renewed at the end of the current contract, and 100% indicates
that this relationship will definitely be renewed at the end of the
current contract 0.935
0 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Table AII.
Construct reliability,
validity and item
factor loadings
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About the authors
Pervez Ghauri is a Professor of International Management at Kings College London and the
Head of the International Business, Marketing and Strategy group. He has taught marketing and
international business in Uppsala University, the Norwegian School of Management (where he
also served as the Dean of Academic Affairs), and Maastricht University in the Netherlands.
Professor Pervez Ghauri is also the founding Editor-in-Chief of International Business Review
since 1992 and the Editor (Europe) for the Journal of World Business, since 2008. He is also
the Editor of the International Business and Management book series by Elsevier Science.
Professor Ghauri has been elected the Vice President for the Academy of International Business
(AIB Worldwide) for the years 2008-2010.
Dr Veronica Rosendo-Rios is an Associate Professor of Marketing at the Colegio Universitario
de Estudios Financieros. She has also taught marketing management and market research in
Rey Juan Carlos University. She is the author of several books and papers. She has many years of
experience in headquarters of international companies, performing European Marketing
Managerial positions at EDS, GTS, Sony UK, Johnson & Johnson UK, etc. She has worked in
international projects funded by the Interior Ministry, Foreign Affairs Ministry and EEC.
Dr Veronica Rosendo-Rios is the corresponding author and can be contacted at: vrosendo@cunef.edu
For instructions on how to order reprints of this article, please visit our website:
www.emeraldgrouppublishing.com/licensing/reprints.htm
Or contact us for further details: permissions@emeraldinsight.com
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The literature reflects remarkably little effort to develop a framework for understanding the implementation of the marketing concept. The authors synthesize extant knowledge on the subject and provide a foundation for future research by clarifying the construct's domain, developing research propositions, and constructing an integrating framework that includes antecedents and consequences of a market orientation. They draw on the occasional writings on the subject over the last 35 years in the marketing literature, work in related disciplines, and 62 field interviews with managers in diverse functions and organizations. Managerial implications of this research are discussed.