reconsidering the affective character of relationships
TRANSCRIPT
1
Reconsidering the affective character of relationships: strong ties and innovation
in supplier-customer relations 1
Cristina Boari
Università di Bologna (Italy)
F. Xavier Molina-Morales
Universitat Jaume I de Castelló (Spain)
Manuela Presutti
Università di Bologna (Italy)
Abstract:
Scholars have found contradictory effects of strong ties in interfirm relations. They are
considered both beneficial and detrimental for knowledge acquisition and innovation. In
consequence, the debate on this paradox is flourishing.
We wish to contribute to the discussion using a construct –relational bonding- originally
developed in studies on vertical interfirm relationships in business-to-business markets. The
approach distinguishes between structural bonding and social bonding, which allows us to
consider the two different components of tie strength put forward in Granovetter’s seminal
work: respectively the behavioral -frequency of social interaction and reciprocal services
characterizing the relationship - and the affective ones -the emotional intensity and the
intimacy that characterize the tie.
1 Submitted to Technovation
2
By explicitly measuring and appreciating also the more neglected affective component of
tie strength, we show that strong ties with key customers - in their behavioral and affective
components- positively impact on innovation of SMEs located in an high tech cluster in
central Italy, with knowledge acquisition moderating the relation.
Key words: strong ties; innovation; knowledge acquisition; key customers
3
INTRODUCTION
Over the last decades there is a considerable increase of attention on the impact of inter-
firm relationships on innovation of the individual organizations (Inkpen, 1998; Sammarra
and Biggero 2008). Supplier-customer relationships are recognized as strategic by
organizations for their impact on knowledge acquisition, innovation capability and
competitive advantage (Dyer and Singh 1998; Lorenzoni and Lipparini, 1999; Dyer and
Nobeoka 2000, Meschita et al, 2008).
In this study we particularly focus on the strength of the dyadic relationship between
supplier and customer, investigating its impact on organization’s knowledge acquisition
and innovation. Considering the seminal definition, tie strength is a combination of
affective components – the emotional intensity and the intimacy that characterize the tie-
and behavioral components - frequency of social interaction, and reciprocal services
characterizing the relationship (Granovetter, 1973, 1974). Strong ties are considered both
beneficial and detrimental for organizations: the same process that enables the firm to
develop innovation might restrict the identification of new opportunities and their
exploitation (Danneels 2003, Fredberg and Piller 2011).
Although previous research made a curvilinear prediction, empirical evidences are not
homogeneous both in innovation studies and in social capital studies (Uzzi 1996; Lowik et
Al, 2012, Rost, 2011; Yli-Renko et al 2001, Presutti et al 2007; Martines-Canas et al 2012).
More recently authors have addressed the controversy by conditioning the effect of strength
on organization to some specific conditions such as: the kind of innovation (Bonner and
Walker 2004) and of the relationship (Tomlinson and Fai 2016), the degree of customers
knowledge similarity (Bonner and Walker 2004) and the active or passive customer’s
involvement in the innovation process (Fredberg and Piller 2011).
We intend to contribute to this debate going back to the seminal definition of tie strength by
Granovetter and explicitly consider not only the behavioral components, but also the
affective ones, which are more often disregarded in empirical studies (Krackhardt 2003). In
fact, only recently scholars have started to appreciate the role of the affective components
4
of tie strength and related it to individual actors’ change resistance (Battilana and Casciaro
2013).
The affective component of tie strength, originally defined at the interpersonal level, was
rarely measured and applied at the inter-firm level. We found an interesting exception in
the literature on supplier- customer relationships in business-to-business markets where the
construct “relational bonding” was originally developed and used to explain the stability
and success of the relationship (Wilson, 1995). We consider this approach particularly
suggestive because it explicitly considers and measures both the objective and behavioral
elements of strong ties, defined as structural bonding, and the affective elements, defined as
social bonding.
Using this conceptual approach we investigate both direct and indirect effects of the tie
strength with key customers (in their different components) on supplier innovation, also
considering the mediating role of knowledge acquisition. If the relationship among tie
strength, knowledge acquisition and innovation has already been explored, our aim is to go
further considering and disentangling the contributions of the two different components of
tie strength.
Our empirical setting is based on high-tech SMEs belonging to a geographical cluster
located in one of the most economically important areas in central Italy, the Tiburtina
Valley. We focus on vertical strategic partnerships between these firms and their key
customers located both within and outside the cluster. To identify these relationships we
follow previous studies addressing similar research questions (Yli Renko, Autio, Sapienza,
2001; Presutti, Boari & Fratocchi, 2007, 2016).
We expect and confirm a positive impact of tie strength on innovation - both in terms of
affective and behavioral components- with the mediating role of knowledge acquisition.
The paper has been structured as follows: first, we review previous research on the topic
and propose hypotheses. Then we describe the empirical setting and results. Finally,
findings are discussed, including implication for theory and practitioners, limitations and
future research.
5
THEORETICAL FRAMEWORK
Innovation emerges through interactions (Ahuja, 2000) mainly based on external social
relationships (Phelps, Heidl, Wadhwa, 2012). The relationships of organizations with other
external actors have attracted a lot of attention as they might positively impact on firms’
knowledge acquisition and innovation (Powel et al 1996).
In particular, research on vertical relationships have shown how investing on and managing
these relationships benefit organizations in different ways, including innovation (Von
Hippel 1998, Antonelli and Fassio 2015), knowledge acquisition (Kotabe et al 2003;
Meschita et al 2008; Pirolo and Presutti, 2010) and competitive advantage (Dyer and Singh,
1998, Langfield-Smith and Greenwood, 1998; Lorenzoni and Lipparini, 1999; Dyer and
Nobeoka 2000, Meschita et al, 2008).
In this paper we focus on the strength of ties in explaining knowledge acquisition and
innovation by suppliers involved in downstream relationships with key customers.
Granovetter (1973, p.1361) defined tie strength, initially intended for interpersonal
relationships, as a “combination of the amount of time, the emotional intensity, the
intimacy (mutual confiding) and the reciprocal services which characterize the tie”. His
approach greatly influenced innovation studies where the role of tie strength is evaluated in
terms of knowledge acquisition and innovation of individuals and organizations, as it was
widely adapted to the inter-firm context
Strong ties enable the development of trust between buyer and supplier, benefit firms from
lowered transaction costs, greater commitment, collaboration, improves efficiency in the
relationship (Villena et al, 2016). Strong ties can favor the exchange of complex and tacit
knowledge, fine-grained information transfer and joint problem solving arrangements (Uzzi
1996). These advantages encourage companies to continue existing relationship (Kim, Oh,
& Swaminathan , 2006; Li, Madhok, Plaschka, & Verma, 2006).
However, these ongoing relationships can be counterproductive in many respects
transforming strong ties from assets to liabilities. In other words, there is a dark side in
strong ties. Sometimes, trust could cover or hide the supplier's misconduct. Alternatively,
6
the buyer may fear a disruption due to its reputational consequences (Gulati, 1995). In
addition, the buyer/seller can tolerate the opportunism of suppliers/buyer if it is a less costly
response in comparison to the negative consequences of the breakdown of relations
(Seggie, Griffith, & Jap, 2013). Contrary to what happens for weak ties, strong ties might
restrict actions outside of the relation and constrain the access to other resources and
opportunities. Accordingly the risk to limit firm’s knowledge–base is also increased and the
innovation restrained. (Marsden & Campbell, 1984; Brown and Konrad, 2001).
With strong ties considered both as assets and as liabilities, some scholars found a
curvilinear relation with knowledge acquisition by firms (Uzzi 1996; McFadyen and
Cannella, 2004) theorizing the benefits of a dual network structure combining both strong
and weak ties (Capaldo, 2007; Tiwana, 2008). Other authors found evidences of a persistent
positive relationship between strong ties, at the dyad level, and knowledge acquisition, even
within networks rich of weak ties (Lowik et Al, 2012, Rost, 2011). Similar contrasting
results can also be found in studies based on the social capital approach to innovation
where Granovetter’s seminal contribution on tie strength was deeply assimilated (Yli-
renko et al 2001; Presutti et al 2007; Martinez-Canas et al 2012).
The paradox concerning both the beneficial and the detrimental impact of strong ties on
knowledge acquisition and innovation (Danneels 2003) has been addressed considering the
interactive role of different factors, such as the kind of innovation (radical and incremental)
(Bonner and Walker 2004), the relationship being downstream or upstream (Tomlinson and
Fai 2016), the customer knowledge similarity (Bonner and Walker 2004) and the active or
passive customer’s involvement in the innovation process (Fredberg and Piller 2011).
We intend to contribute to this debate going back to Granovetter’s definition of strong ties
also considering the more affective elements of strong ties and incorporating it in previous
studies. Two of the characteristics of strong ties, emotional intensity and intimacy, are
considered by Granovetter (1973) as strongly motivating actors to invest time and resources
in sharing any kind of knowledge. This makes organizational scholars recognize that the
informational and knowledge benefits of strong ties are a consequence of the affective link
between them. Krackhardt (2003, p. 217) asks for studies which appreciate both the
behavioral and the affective components of tie strength claiming that Granovetter’s theory
7
draws on the psychological theory of balance but “we seldom see the affective dimensions
in the operationalization of strong ties”. There is a growing interest for the role of the
emotional intensity of strong ties. For example the affective component of strong ties is
found enabling actors to overcome change resistance (Battilana and Casciaro 2013).
With the affective component of tie strength originally developed at the interpersonal level,
we rarely find it applied to the inter-organizational level. For the context of our research we
propose a construct originally developed in the literature on supplier- customer
relationships in business to business markets and use it to analyze the impact of strong ties
on knowledge acquisition and innovation (Wilson, 1995) Accordingly with this perspective
the starting point for the development of a relationship between two firms is the
interdependence between them (Hakansson, 1982). Interdependence is based on the need to
access resources, knowledge and capabilities of other organizations, delivered in the form
of products or services (Pfeffer and Salancik, 1978). Within this framework, the
development of supplier-customer relationships can be seen as an evolutionary process in
terms of increasing experience, reduction of uncertainty, perceived commitment, formal
and informal adaptations and investments (Ford, 1980).
Incremental investments and adaptation activities may create relational bonding, between
supplier and customer (Han, 1991; Rodriguez and Wilson 2002). What is defined as
relational bonding describes the strength of a relationship (Easton and Araujo, 1986) and
can be considered as the degree of resistance of that relationship to disruption. Relational
bonding can thus be considered an expression of tie strength between collaborating
partners. Relationships based on high level of relational bonding are more likely to be
successful (Sarkar et al., 1998) and become more stable (Wilson and Mummalaneni, 1986).
We claim that relational bonding might also enhance the access and acquisition of
knowledge from collaborating partners.
We find this perspective particularly attractive because it clearly considers all the
components of strong ties. On the one hand two more objective and behavioral elements–
frequency of interaction and reciprocal services- defined as structural bonding and on the
8
other one, two more affective elements –emotional intensity and intimacy- defined as social
bonding.
Although the two components are highly interrelated they may vary in different ways, and
it is therefore interesting to analyze them separately. This is particularly relevant in
business to business markets where interfaces (digital and/or personal) with customers play
a critical role in managing the relationship and, we claim, in allowing the creation and
maintenance of proper conditions for knowledge exchange and innovative performance.
We will use the relational bonding construct– in terms of structural and social bonding- to
rise separated hypotheses on the role of a firm’s tie strength with customer on its
knowledge acquisition and innovation.
Relational bonding and knowledge acquisition
Relational bonding develops at two different levels: the structural and the social levels.
Particularly, structural bonding is based on task interdependences between supplier and
customer and reflects investments, contractual conditions, obligations and expectations
(Han 1991). It represents the degree to which certain ties link and hold a customer and a
supplier in an economic, strategic, and organizational sense, regardless of personal or
emotional matters. Structural bonding in a supplier-customer relationship can come with
and from economic effects for that relationship. These effects are recognized to increased
efficiency and effectiveness in operations and value-creation shared by both partners
(Rodríguez and Wilson 2002).
We claim that a main performance implication for the involved organizations refers to the
knowledge access and acquisition, for two main reasons. Structural bonding may motivate
and enable an organization to access and acquire external knowledge. Structural bonding
with a customer motivates the supplier to address customer’s needs and therefore to
identify and access from that customer all the needed information and knowledge (Dyer
and Nobeoka 2000; Rowley, Behrens, and Krackhardt 2000). Moreover, as the firm
becomes highly interdependent with a customer, it also develops knowledge-enhancing
practices that favor the transfer, recombination and creation of knowledge. This was found
9
true for different firms, both at the dyad and at the network level (Lipparini et al 2014). We
expect that the more structural bonding a firm develops with its customer, the more likely it
is to acquire knowledge from it. We express this in a formal way:
Hypothesis 1: The greater the level of structural bonding between a firm and its key
customer, the greater the knowledge the firm acquires from it.
On the other hand social bonding refers to the degree to which a relation between a buyer
and a seller is linked closely in a personal (emotional) sense through friendship,
familiarity, social support, staying in touch, self-disclosure and any other interpersonal
interactions (Han, 1991; Rodriguez and Wilson, 2002). In supplier-customer relationships
personal contacts occur between various individuals, groups and at different hierarchical
levels. Through these personal contacts information is exchanged, negotiations are
performed, adaptations are settled, crises are overcome and relational bonding develops and
so on.
Competitiveness has been found, to some extent, depending on how this interface with
customers is managed and how the human resources involved are distributed and
coordinated among different types of customers and among different members of their
purchasing decision-making unit. Buyers and sellers who have a strong personal
relationships are more committed to maintaining the relationship than less socially bonded
partners (Mummalaneni and Wilson,1991; Kachra and White, 2008). At the intra-
organizational level it was found that frequent contacts and emotional closeness among
product development team members enhance the amount of complex knowledge transferred
among team members (Hansen 1999).
Moreover, research on other intra-organizational settings shows that information and
knowledge exchange is highly dependent on the degree of emotional closeness among
social actors (Krackhardt, 1992). Commitment and trust may arise and may lower fears of
opportunism (Rindfleisch and Moorman 2001), expectation of reciprocity emerges (Kachra
and White 2008), and transfer of complex, sensitive and even tacit knowledge can easily
10
occur (Hansen 1999; Reagans and McEvily 2003). Therefore we extend these reasonings at
the interorganizational level and expect that social bonding in a supplier-customer
relationship enables the knowledge access and acquisition from the customer. We can
express this proposition as follows:
Hypothesis 2: The grater the level of social bonding between a firm and its key customer,
the greater the knowledge the firm acquires from it.
Knowledge acquisition and innovation
According to previous research, external knowledge acquisition provides opportunities for
integration with a firm’s already existing knowledge, thus creating new knowledge (Yli-
Renko et al. 2001), and increasing the relevant knowledge base in the firm (Galunic and
Rodan 1998). Thus, knowledge acquisition favors identification and assimilation of
relevant knowledge for firms, particularly new ideas that improve the ability to create
future innovations and to exploit them in a more effective and efficient way (Cohen and
Levinthal 1990). In fact, a number of recent studies have supported the positive effect of
external knowledge acquisition on innovation performance (for instance, Chen and Huang,
2009), particularly in terms of product innovation (Yli-Renko et al. 2001). Companies may
combine external knowledge with already existing knowledge with positive impact on
innovation (Dyer and Singh, 1998; Lane and Lubatkin, 1998; Larsson et al., 1998).
The positive implication of the acquisition of knowledge on innovative performance has
already been exhaustively proved in the literature (among others, Ahuja and Katila 2001;
Chen and Huang 2009; Yli-Renko et al., 2001). Although, we consider this causal relation
as a base-line hypothesis, it is important to propose it in order to complete our conceptual
framework. We express the positive implication of the external knowledge as follows:
Hypothesis 3: The knowledge acquired from key customer is positively associated with
firm’s innovative performance.
11
The mediating effect of knowledge acquisition
According to our hypotheses 1 and 2 we are expecting that relational bonding in its two
components facilitates the exchange of knowledge between different partners of a
relationship However, relational bonding might not be sufficient condition to enhance the
innovation. In fact, bonding can be considered as a prerequisite for the innovation through
knowledge acquisition (Capaldo, 2007). Firms vary in terms of their ability to understand
and exploit the advantages of relational bonding. and exhibit various capacities to acquire
and learn from the valuable knowledge in the relationship. For instance, Fredberg and Piller
(2011) in a longitudinal study on customer relationships for innovation, argued that a strong
tie does not automatically lead to better innovation: it depends on the firm’s co-creation
capabilities.). Thus, we consider that acquired knowledge is a basic explanatory factor that
links relational bonding to innovative performance. We are expecting that relational
bonding has in fact an indirect effect on innovation through the acquisition of knowledge
from key customer.
This effect has been operationalized as the mediator effect (Baron and Kenny, 1986).
Accordingly, we formulate the following hypotheses for innovative performance and for
the two dimensions of the relational bonding, that is structural and social bonding.
Hypothesis 4: The knowledge that a firm acquires from it key customer positively mediates
the relationship between the level of structural bonding with the customer and its
innovative performance.
Hypothesis 5: The knowledge that a firm acquires from it key customer positively mediates
the relationship between the level of social bonding with the customer and its innovative
performance.
The Table 1 formalizes and summarizes the research’s hypotheses and Figure 1 describes
the hypothesized model
12
Table 1 Research’s hypotheses
Hypothesis Expected effect
Direct effects
Structural bonding on knowledge
acquisition
H1 +
Social bonding on knowledge acquisition H2 +
Knowledge acquisition on innovative
performance
H3 +
Indirect effects
Structural bonding on innovative
performance through knowledge
acquisition
H4 +
Social bonding on innovative performance
through knowledge acquisition
H5 +
13
Figure 1: The hypothesized model
METHODS
Sample and data
The field setting of this research consists of a geographical cluster of small and medium-
sized high-tech firms located in one of the most important economical areas in central Italy,
the Tiburtina Valley, which is approximately 14 kilometres from the centre of Rome. As in
Silicon Valley in the US, the high-tech firms located in this restricted urban context have
Structural bonding
Knowledge acquisition Bonding Social
Bonding
Innovative performance
14
created a homogeneous agglomeration that currently represents the most typical example of
a high-tech metropolitan cluster in Italy (Majocchi, Odorici, Presutti, 2016). This cluster is
also known for its high export rates also due to the presence of several important foreign
multinationals. The high number of specialised firms located in this small area has led to
the development of new businesses and the rapid diffusion of knowledge among local
actors.
At the beginning of our data collection (June 2015), the sectors represented in this area
were 1) electronics (370 firms); 2) media (250 firms); and 3) new economy (e.g.,
manufacturers of new hardware, firms in the information services industry, Internet access
providers, and telecommunication network managers; 350 firms). We focused on the
electronics sector, which, according to the definition of the National Federation of
Electronics Firms, consists of the computer industry, electronics in the strictest sense, and
telecommunications.
To ensure that all electronic firms in our sample were involved in innovation activities, we
checked their business descriptions in the source database provided by the Chamber of
Commerce of Rome. Electronic firms with no internal R&D activity or who only offered
non-technical services were excluded from the study. This process left a total of 275 firms,
130 of which accepted our request for a personal interview to complete the questionnaire
(47% response rate). A comparison of differences in the mean values of the responding and
non-responding firms revealed no statistically significant response bias in terms of their 3-
year average sales revenues, firm age, or number of employees. Our empirical research
focuses on the strategic vertical relationships between each high-tech SME located in the
cluster and its main customers. In this respect, we implemented Yli-Renko et al.’s (2001)
suggestion to give entrepreneurs the freedom to list their main customers, imposing a
maximum of 10 partners per company. The final relational map of the adopted sample (130
companies) is composed of 511 main customers spontaneously listed by the interviewed
entrepreneurs (3,9 customers on average).
The firms in our sample belong to the “specialized suppliers” category according to the
Pavitt classification in that they primarily produce and offer technology and services to
their industrial customers. In this case, customers also access technology through the
15
acquisition of products from suppliers (Yanez et al., 2010). Thus, for both customers and
suppliers, firm knowledge acquisition and innovation processes depend to a great extent on
interactive learning between them. This situation encourages local firms to become
increasingly specialised, resulting in strong differentiation in their internal knowledge
bases. The R&D activity and the scope and extent of new products launched from these
firms are strongly conditioned by knowledge acquired from their customers because new
products need to be in line with customers’ specific requirements (Dyer and Sing 1998).
The data to test our hypotheses come from a direct survey using a specially designed
questionnaire. Our key informant was the entrepreneur. Firms were contacted by telephone
to obtain the names of potential respondents (the entrepreneurs) and to determine whether
they would agree to complete the survey through face-to-face interviews. Questionnaire
was pre-tested on five randomly selected sample firms. The data collection process strated
at the beginning of 2016 and lasted approximately five months. As the questionnaire
constitutes the primary data source for this research, in some cases, we even conducted a
second interview when necessary to complete the questionnaire.
Overall, the firms in the sample developed an average of 8 new products per year (range 1-
23). The average sales of the respondent firms amounted to €810,000, with an average age
of 18 years. In addition, the average R&D expenditure in relation to total sales was 3.5%
(range 0.01-15%). The key customers are similar in age to their suppliers (M = 18.16 years)
but were on average smaller (Msales = €510.15). Table 2 reports the descriptive statistics
for the untransformed variables.
Table 2. Descriptive statistics of the untransformed variables
Average S.D. Min Max
Innovative
performance
8 7.2 1 23
Firm Size 810.10 1108.95 100 10,000
16
CustSize 510.15 542-34 121 3,000
CustAge 18.01 12.86 2 90
FirmAge 18 14.44 6 70
R&D expenditure
Structural bonding
Social bonding
3.5
5.5
5
3.04
4.9
4.5
0.08
1
1
0.15
7
7
7
Measures
The individual measurement items for the study’s dependent, independent, and control
variables are listed in Table 4; the construction of the measures is explained in the
following section. All statement-style items were measured on a scale from 1 = do not
agree to 7 = completely agree.
Dependent variable
Innovative performance
We measured innovative performance following the work of Rindfleisch and Moorman
(2001). Specifically 4 items assessed the firms’ opinion about the innovativeness of their
products developed as a result of the key customer relationships. The entrepreneurs were
asked to evaluate the extent to which the new products developed as a result of their key
customer relationships developed in the past three years were novel to the industry and
offered new ideas.
17
Independent variable
Knowledge acquisition in the relationship
We measured knowledge acquisition with two statements reflecting the technological and
market knowledge that a firm may acquire from the key customer. The items were based on
Nooteboom et al. (1997) and Von Hippel (1988) and have previously been used effectively
by several authors (i.e. Simonin, 1997; Zander and Kogut, 1995; Zahra et al., 2000; Yli-
Renko and Autio, 1998).
Structural bonding
We adopted the Han’s (1991) definition of structural bonding as the degree to which certain
ties link and hold a customer and supplier in an economic, strategic, and organizational
sense, regardless of personal [emotional] matters. Structural bonding was assessed through
a two-item 7-point Likert scale according to Han (1991) and Koenig and Van Wijk (1991)
Social bonding
In this research we defined social bonding as the degree to which certain ties link and hold
a customer and supplier together closely in a personal [emotional] sense according to Han
(1991). Social bonding was assessed through a two-item 7-point Likert scale according to
Han (1991) and Sullivan and Peterson (1982).
Control variable
We included some control variables to isolate the effect of the independent variables in the
model. First, an important factor influencing innovation activity is the size of the involved
partners (Acs and Audretsch, 1991; Tsai and Ghoshal, 1998), in line with the idea that
larger firms may invest more resources in R&D activities. As several studies suggest (e.g.,
Kogut and Zander, 1992), both superior resources and economies of scale allow larger
firms to exploit external knowledge successfully for their innovation process. We controlled
18
for the effects of size by including the total sales of both the firms (Size) and their
customers (CustSize).
Moreover, we included two additional control variables to evaluate the age of the firm
(FirmAge) and that of its customers (CustAge). We assume that these elements will have an
influence on the firm’s learning ability and its knowledge exploitation process (Zahra et al.,
1999). We computed the age of the firms and of the customers as the number of years since
their foundation. We used the log of these values to account for declining effects at higher
values. Finally, in the analysis we use R&D spending as a control variable for a firm’s
willingness to invest in absorptive capacities useful to its knowledge acquisition from its
key customer. Following other recent research on measuring absorptive capacity (e.g.,
Cohen and Levinthal, 1990; Meeus et al., 2001), this parameter was measured by
considering the log value of the average ratio between R&D expenditures and total sales for
the previous three years.
Table 3 reports the descriptive statistics of the variables and their correlations.
Table 2. Descriptive statistics and correlations
Variables 1 2 3 4 5 6 7 8
1. Firm size
2. Customer size 0.21
3. Firm age 0.40*
0.18
4. Customer age 0.15 0.15*
0.07
5. R&D spending 0.31*
0.04 0.14 0.11
6 Structural bonding 0.24 0.22 0.18 0.17 0.23
7. Social bonding 0.16 0.13 0.30 0.24 0.20 0.33*
8.Knowledge acquisition 0.15 0.08 0.12 0.03 0.28*
0.40**
0.27*
19
9. Number of new products developed
0.11 0.13 0,01 0,03 0,03 0.28*
0.31*
0.45**
10. Innovative performance
0.01 0.03 0.01 0.22 0.13 0.31**
0.29*
0.40**
* p-value at the 10% level. ** p-value at the 5% level. *** p-value at the 1% level.
Reliability, validity and endogeneity
We took several steps to ensure data validity and reliability. First, we pretested the survey
with 5 entrepreneurs and asked them to closely review the survey. We then revised any
potentially confusing items following the received suggestions. Second, we used previously
validated measurement items wherever possible to help ensure the validity of our measures.
Third, multiple-item measures were used for most constructs to enhance content coverage.
We measured these items on the questionnaire using 7-point Likert scales. As a first step of
measure validation, to assess the unidimensionality of the research constructs (Churchill,
1979), we factor analysed the final scales using the principal axis method, positing a single
factor (exploratory factor analysis). After exploring the factor structure of the data, we
submitted the data to confirmatory factor analysis. The results of this analysis verify that
the measurement model performed well because the selected constructs demonstrate good
internal consistency and reliability: the standardised factors are all above the recommended
minimum of 0.40, and the average variances extracted are all above the recommended
minimum of 0.50 (range 0.55-0.69). All of our multiple-item constructs achieved Cronbach
alphas of 0.71 or higher, indicating strong internal consistency (Table 4).
Finally, we consider in this study also the endogeneity problem. In particular, to identify
the endogeneity of the explanatory variables, we use the Durbin–Wu–Hausman test
(Wooldridge, 2012). Table 5 illustrates the test results, which reveal that the two selected
explanatory variables do not suffer from an endogeneity problem when both knowledge
acquisition and product innovativeness are used as the dependent variables (Table 5).
20
Table 4. Measurement model
Factor name Measurement
item
Standardised
loading
Cronbach’s
alpha
Average
variance
extracted
Structural bonding Since time your company needs
to have close relationship with
this customer, regardless of your
personal relationships?
In an economic sense, to what
extent does your company need
to have a close relationship with
this customer?
0.65**
0.70**
0.85
0.59
Social bonding On a personal sense, how close
are your relationships with this
customer?
I have excellent social
relationships with this customer
0.78***
0.71**
0.81 0.63
Knowledge
acquisition
Since we supply to this customer
we are able to obtain a
tremendous amount of market
and technical knowledge
We get most of our valuable
information on customer needs
and trends from this customer
0.76**
0.59*
0.85 0.69
Innovative
performance
The new product is very novel in
our industry
The new product offers new
ideas in our industry
This new product is creative
0.81***
0.61*
0.55*
0.78 0.55
21
This new product is capable of
generating ideas for other
products
0.70**
∗∗∗p >0.001
Table 5. Results of Durbin–Wu–Hausman test.
Knowledge
acquisition
Innovative
performance
Structural bonding 183.5 125.6
Social bonding 160.1 164.2
Chi-square values are present in this table.
RESULTS
The hypotheses are tested using structural equation model. As the hypotheses involves the
testing of mediation effects, a series of nested models were compared to select the one with
the best fit. Nested model tests help internally validate a hypothesized model by comparing
the chi-squares of models that differ in the number of paths hypothesized; nested models
can be derived by adding or deleting paths. A significant difference in chi-square indicates
that the more complex model provides a better fit with the data. The three nested models
are (1) the direct effect model which consists of only the direct relationships among
variables; (2) the indirect model which considers only the indirect effects of social and
structural bonding through knowledge acquisition; 3) the hypothesized mediation model,
which includes both the direct effect of social, structural bonding on the dependent variable
and indirect effects through knowledge acquisition. The fit indices are shown in Table 6.
Based on the fit indices, all of the models showed reasonably good fit with the data. Model
22
3 had the highest goodness of fit indices in general (Chi2 =423.28; Chi2 /df= 1.36, p<0.01;
RMSEA=0.06; CFI=0.94; IFI=0.94; TLI=0.92). Akaike’s information criterion revealed a
relatively better fit with the data for Model 3. A Chi-square difference test also suggested
that Model 3 had a significantly better fit than the direct effect model (Model 2) (∆Chi2 =
38.18 (2), p<0.01) and Model 1 ((∆Chi2 =12.13 (4), p<0.05). Therefore the results of
Model 3 are reported in Table 7 to test our hypotheses.
Table 6. Nested model testing sequence and difference tests
Models Chi2
(df)
IFI TLI CFI RMSEA AIC ∆Chi2
Model 1 461.45 0.901 0.881 0.911 0.069 581.32 38.18
(2)
Model 2 435.41 0.910 0.911 0.910 0.062 562.40 12.13
(4)
Model 3 423.28 0.941 0.921 0.941 0.060 553.13 -
Note: ∆Chi2 statistics are based on the comparing model 3 with the other two models.
Models 1 and 2 demonstrated significantly poorer fit than Model 3 (p<0.01).
Hypotheses 1 and 2 propose that structural and social bonding have a significantly positive
association with knowledge acquisition. The results indicate that the standardized
regression weights between structural and social bonding and knowledge acquisition were
significant and positive with coefficients of 0.58 (bias-corrected p<0.01) and 0.54 (bias-
corrected p<0.01) respectively (Table 7). Thus the two hypotheses are confirmed.
Hypothesis 3 is concerned with the relationship between knowledge acquisition and
product innovativeness. The results indicate that the standardized regression weights
23
between knowledge acquisition and product innovativeness were significant and positive
with coefficient of 0.49 (bias-corrected p<0.01) . Therefore hypothesis 3 is supported.
H4 suggests significant indirect effects of structural bonding on product innovativeness. As
predicted, the indirect paths through knowledge acquisition from structural bonding to
product innovativeness (β=0.399, bias-corrected p<0.01) were significant. As no direct
effects of structural bonding on product innovativeness were found, full mediation of
knowledge acquisition in the relationships was in evidence, in support of hypothesis 4.
In a similar vein, the results disclosed no significant direct effect of social bonding on
product innovativeness, but social bonding had indirect significant effects through
knowledge acquisition on product innovativeness (β=0.31, bias-corrected p<0.01). Thus,
Hypothesis 5 was supported.
Finally, the only significant relationship between a control variable and our mediating
variable is the positive association between R&D and knowledge acquisition (Table 7). In
terms of innovation, we may observe that product innovativeness is positively related to
both R&D spending and firm size. The not significant effect of age variable can be
probably explained by the evidence that there are not differences of age among customers
and suppliers; moreover the evidence that all customers are quite smaller than their
suppliers can explain why the size variable is not significant in our model. The Figure 2
show the results of the model.
24
Figure 2: Results of the model
0.19
0.25 0.49
0.29
0.49
Social bonding
Knowledge acquisition
Structural bonding
Innovative performance
0.58
0.54
0.31
0.39
25
Table 7 Structural equation modelling results
Standardized estimate
P value Hypothesis
Direct effects
Structural bonding on innovative performance
0.17 n.s.
Social bonding on innovative performance
0.29 n.s.
Structural bonding on knowledge acquisition
0.58 <0.01 H1
Social bonding on knowledge acquisition
0.54 < 0.01 H2
Knowledge acquisition on innovative performance
0.9 < 0.01 H3
Indirect effects
Structural bonding on innovative performance via knowledge acquisition
0.39 <0.01 H4
Social bonding on innovative performance via knowledge acquisition
0.31 <0.01 H5
Control variables
Firm size on knowledge acquisition 0.19
Firm size on innovative performance
0.28 <0.01
Customer size on knowledge 0.33 n.s.
26
acquisition
Customer size on innovative performance
0.19 n.s.
Firm age on knowledge acquisition 0.19 n.s.
Firm age on innovative performance
0.28 n.s.
Customer age on knowledge acquisition
0.21 n.s.
Customer age on product innovativeness
0.015 n.s.
R&D on knowledge acquisition 0.39 <0.01
R&D on innovative performance 0.5 <0.01
DISCUSSION
The strength of tie in supplier-customer relationships, and particularly its role on focal
firm’s knowledge acquisition and innovation has attracted a lot of attention in the last
decades. Although the seminal definition of strong ties contains a combination of different
elements (Granovetter 1973), the majority of later papers tend to overlook the affective
component (Krackhardt, 2003). We claim that by addressing this shortcoming we can
contribute to the debate about the role of strong ties on knowledge acquisition and
innovation in vertical relations. With this aim, we benefit from the literature on supplier-
customer relationships in business-to-business markets where the construct “relational
bonding” was originally developed and used. This approach distinguishes between
structural bonding -the more objective and behavioral elements of strong ties - and social
bonding –the more affective one.
27
In this study we focused on the direct/indirect effects of structural and social bonding on
innovation by considering the mediating role of knowledge acquisition. We have
empirically analyzed the vertical partnerships between SMEs and their key customers.
Our results indicate that both structural and social bonding are positively related to
knowledge acquisition. We also find support for the mediating role of knowledge
acquisition between structural/social bonding and knowledge exploitation in terms of
innovation.
Our theory and findings advance the research on the role of tie strength on knowledge
acquisition and innovation. The positive correlation between structural bonding and
knowledge acquisition, sustains the role of interdependence between focal firm and its key
customers in motivating the supplier to fulfill customers’ need and develop useful routines
for learning. The positive association between social bonding and knowledge acquisition is
consistent with the assumptions that learning between different business partners is aided
by personal and affective relationships built through interpersonal exchange. With
reference to the relation with key customers we find that what was proved true at the
interpersonal level is also true at the inter-organizational level (Tsai and Ghoshal, 1998).
Both elements of tie strength -the behavioral and the affective- seem supporting knowledge
acquisition of focal firms involved in relationships with their key customers. This
contributes to address the recent request for more studies on how different level of
embeddedness between supplier and customers impact on knowledge and innovation
(Lipparini et al 2014).
In addition, our results also complement the more recent research that addressed the so-
called paradox of strong ties suggesting different factors (such as kind of innovation, of
relationship and of involvement in the innovation process and customer knowledge
similarity) that might interact with tie strength and produce different results in terms of
innovation (Bonner and Walker 2004; Fredberg and Piller 2011; Tomlinson and Fai 2016).
We suggest that the impact of tie strength can also depend on how we measure it. With
reference to key customers our study shows that strong ties, both in their behavioral and
affective components, represent for focal firms an opportunity in terms of knowledge
acquisition and innovation and the paradox fades away.
28
We also show that knowledge acquisition mediates the relationship between
social/structural bonding and knowledge exploitation in terms of innovation, confirming the
essential role of knowledge acquisition in transforming the potential benefits of strong ties
into concrete innovation outcomes. While structural and social bonding inside vertical
relationships are unable to directly influence the innovation activity, our study highlights
the essential role of knowledge as a key mechanism by which the relationship with key
customers is leveraged for the development of new products. Accordingly, these results
confirm that knowledge is the most strategically significant resource of a SME for its
innovation activity, justifying a growing interest on how organizations can acquire
knowledge from their partners (Burgelman and Hitt, 2007; Davidsson, 2005; Smith and
Cao, 2007; Woolley and Rottner, 2008). Moreover we find a support to the idea that strong
ties with key customers represent an enabling environment for innovation, but routines and
other mechanisms have to be created to enable knowledge exchange and acquisition and
sustain the co-creation of innovation (Fredberg and Piller 2011)
The paper also contributes to social network studies, considering a larger set of tie strength
components, in particular operationalizing the affective element. We participate with the
idea that it is “not simply a question for the methodological curious. It is an important part
of the theory itself, since the theory itself makes a curvilinear prediction” and we need to
understand how different components count toward tie strength (Krackhardt (2003, p.216-
217). The fact that we did not find evidences of curvilinear relation could be ascribed, in
our opinion, to a combination of contingencies: the downstream relationships we observe
(Tomlinson and Fai 2016) and their role to sustain the incremental innovation that mainly
characterizes our firms (Bonner and Walker 2004).
We also provide new evidences of the beneficial effect of the affective component of strong
ties for knowledge search and acquisition at the inter-organizational level. This
complements evidences in social network studies on the informational benefits of strong
ties in affective bonding at the intra-organizational level (Hansen 1999). Finally, we
contribute to business-to-business studies showing that relational bonding between supplier
and key customers can have a positive impact not only on the efficiency and stability of the
relationship, but also in term of innovation.
29
MANAGERIAL IMPLICATIONS, LIMITATIONS AND FUTURE RESEARCH
Apart from the theory contributions, in our opinion, a set of managerial implications arise
from this study. First, as relationships with key customers give access to resources and
knowledge not otherwise available for innovation, this implies a need of competence
coordinate and utilize a set of interrelated profitable relationships, that is, to derive value
from a differentiated set of customer relationships As key customer relationships are
specific and differ from one another, a supplier firm may have several relationships,
providing various experience, resources and skills, but the strength of the relationship is
likely to determine whether they are able to share each other’s resources and knowledge.
Furthermore, knowledge-exchanging relationships based on strength ties can form the basis
for profitable relationships that may eventually lead to even greater innovation
opportunities This requires management based on professional competences able to develop
networks characterized by strength ties with a significant number of key customers. Hence,
the ability of managers to leverage external strength networks can be considered as a core
competence
In summary, with reference to vertical relations in BtoB industrial markets, our study
suggests to suppliers to invest in strong ties with key customers, developing both task
interdependence and affective bonds with them to foster innovation. To do that,
organizations could establish and sustain ongoing interpersonal relationships with key
customers, if necessary involving different organizational levels.
Finally, our research also suggests that firms need to invest in routines and mechanisms to
enable the exchange and acquisition of knowledge with key customers to transform the
strong tie with them in a source of opportunities and resources for innovation.
Some limitations of our study must be discussed to pinpoint opportunities for further
research. First, our results refer to high-tech firms that are “specialized suppliers”. What we
find could not be generalizable to other industries and to less specialized firms. Second, our
definition of innovative performance is limited to the typology of products and services
developed by these firms, regardless of the drivers of innovation (customer, process,
design, etc.) and with no distinction between different types of innovation (i.e., disruptive
30
and incremental). Third, we focused on the role of strong ties with key customers. We
cannot generalize our results for relationships involving less strategic partners.
In consequence, further research should try to overcome these limitations. Researchers
could also combine in their models other significant boundary conditions that might
influence the importance of strong ties- in their behavioral and affective components- for
knowledge acquisition and innovation.
References
Acs, Z. J., and Audretsch, D. B. (1991). “Innovation and technological change: an
overview”. In Innovation and Technological Change: An International Comparison, Ed.
Z.J.
Adler, P. S., & Kwon, S. W. (2002). Social capital: Prospects for a new concept. Academy
of Management review, 27(1), 17-40.
Ahuja, G. (2000). “Collaboration Networks, Structural Holes, and Innovation: A
Longitudinal Study”, Administrative Science Quarterly, 45, 425–455
Ahuja, G. and Katila, R. (2001). "Technological acquisitions and the innovation
performance of acquiring firms: A longitudinal study." Strategic Management
Journal 22, 3: 197-220.
Antonelli, Cristiano, and Claudio Fassio. "The role of external knowledge (s) in the
introduction of product and process innovations." R&D Management 46.S3 (2016): 979-
991.
Baron, R. and Kenny, D. (1986) "The moderator–mediator variable distinction in social
psychological research: Conceptual, strategic, and statistical considerations." Journal of
personality and social psychology 51,6, 1173-‐1182.
Battilana, Julie, and Tiziana Casciaro. "Change agents, networks, and institutions: A
contingency theory of organizational change." Academy of Management Journal 55.2
(2012): 381-398.
31
Baum, J.A.C., McEvily, B. and Rowley, T.J. (2012), “Better with age? Tie longevity and
the performance implications of bridging and closure”, Organization Science, Vol. 23
No. 2, pp. 529-546.
Berry, L.L. (1995); Relationship marketing of services growing interest, emerging
perspectives'', Journal of the Academy of Marketing Science, 23, 4, 236-45.
Bonner, Joseph M., and Orville C. Walker. "Selecting influential business-‐to-‐business
customers in new product development: relational embeddedness and knowledge
heterogeneity considerations." Journal of Product Innovation Management 21.3 (2004):
155-169.
Brown, D. W., & Konrad, A. M. (2001). Granovetter was right the importance of weak ties
to a contemporary job search. Group & Organization Management, 26(4), 434-462.
Burgelman, R. A., and M.A. Hitt, (2007). “Entrepreneurial Action, Innovation and
Approbiability”, Strategic Entrepreneurship Journal, (1), 349–352.
Burt, R.S., (1992). Structural Holes: the Social Structure of Competition. Harvard Business
Burt, R.S., (1997). The contingent value of social capital. Administrative Science Quarterly
Callaghan, M., McPhail, J. & Yau, O.H.M. (1995), ``Dimensions of a relationship
marketing orientation: an empirical exposition'', Proceedings of the Seventh Biannual
World Marketing Congress Vol. VII-II, Melbourne, July, 10-65.
Caloghirou, Y. Kastelli, I. & Tsakanikas, A.. (2004) "Internal capabilities and external
knowledge sources: complements or substitutes for innovative performance?.
Technovation 24,1: 29-39.
Campbell, T. T., John, D. N., & Phillip, J. K. (2006). “The importance of social bonding
and loyalty: an empirical investigation within UK private health clubs”. Journal of
Hospitality & Leisure Marketing, 14, 1, 49–73.
Capaldo, A. (2007), “Network structure and innovation: the leveraging of a dual network as
a distinctive relational capability”, Strategic Management Journal, Vol. 28 No. 6, pp.
585-608.
32
Chen, C. J., Huang, J. W. (2009). Strategic human resource practices and innovation
performance--The mediating role of knowledge management capacity. Journal of
Business Research, 62(1), 104-114.
Churchill G.A., Jr., (1979). A paradigm for developing better measures of marketing
constructs. Journal Marketing Research 16, 64–73.
Cohen, W.M., and Daniel A. Levinthal, D.C. (1990). "Absorptive capacity: A new
perspective on learning and innovation." Administrative science quarterly: 128-152.
Coleman, J., 1988. Social capital in the creation of human capital. American Journal
Coleman, J., 1990. Foundations of Social Theory. Harvard University Press, Cambridge.
Dahlander, L. and Gann, D.M. (2010). 'How open is innovation?'. Research Policy, 39,
699– 709.
Danneels, Erwin. "Tight–loose coupling with customers: the enactment of customer
orientation." Strategic Management Journal 24.6 (2003): 559-576.
Davidsson, P., 2005. Researching Entrepreneurship. New York: Springer.
Dyer, J. H., and H. Singh (1998). “The relational view: Cooperative strategy and sources of
interorganizational competitive advantage”, Academy of Management Review, 23, 4,
660–679.
Dyer, Jeffrey H., and Harbir Singh. "The relational view: Cooperative strategy and sources
of interorganizational competitive advantage." Academy of management review 23.4
(1998): 660-679.
Dyer, Jeffrey H., and Kentaro Nobeoka. "Creating and managing a high-performance
knowledge-sharing network: the Toyota case." Strategic management journal (2000):
345-367.
1986). Networks, bonding and relationships in industrial markets. Ind ustrial Marketing and
Purchasing 1, 8–25.
Felzensztein, C. and Gimmon, E. and Carter, S.L. (2010) Geographical colocation, social
networks and inter-firm marketing co-operation : the case of the salmon industry. Long
Range Planning, 43 (5-6). 675–690.
33
Ford, 1980).
Fredberg, Tobias, and Frank T. Piller. "The paradox of tie strength in customer
relationships for innovation: a longitudinal case study in the sports industry." R&D
Management 41.5 (2011): 470-484.
Galunic, D. C. and Rodan, S. (1998) "Research notes and communications: resource
recombinations in the firm: knowledge structures and the potential for Schumpeterian
innovation." Strategic Management Journal 19.12: 1193-1201.
Granovetter, M. S. (1973). “The strength of weak ties”. American journal of sociology,
78(6), 1360-1380
Granovetter, M. (1974) “Strength of Weak Ties – Reply” American Journal of Sociology
80(2):527-529.
Gulati, R. (1995). “Social structure and alliance formation patterns: A longitudinal
analysis” Administrative Science Quarterly, 619-652.
Hakansson, H. (Ed.) (1982), International Marketing and Purchasing of Industrial Goods:
An Interaction Approach , John Wiley, Chichester.
Han and Wilson (1993) "Buyer-supplier relationships today." Industrial Marketing
Management 22.4: 331-338.
Han, S. (1991).”Antecedents of Buyer-Seller Long-Term Relationships: An Exploratory
Model of Structural Bonding and Social Bonding”, Unpublished Doctoral Dissertation.
Department of Marketing. The Pennsylvania State University.
Hansen, Morten T. "The search-transfer problem: The role of weak ties in sharing
knowledge across organization subunits." Administrative science quarterly 44.1 (1999):
82-111.
Inkpen, A.C., Tsang, E.W., 2005. Social capital, networks, and knowledge transfer.,
Academy of Management Journal 30, 146–165.
Inkpen, Andrew C. "Learning and knowledge acquisition through international strategic
alliances." The Academy of Management Executive 12.4 (1998): 69-80.
34
Kachra, A. and White, R.E. (2008). 'Know-‐ how transfer: The role of social,
economic/competitive, and firm boundary factors'. Strategic Management Journal, 29,
425–445.
Kim, T. Y., Oh, H., and Swaminathan, A. (2006). “Framing interorganizational network
change: A network inertia perspective”. Academy of Management Review, 31(3), 704-
720.
Koenig, C., and van Wijk, G. (1991). “Inter-firm Alliances: The Role of Trust.” In
Microeconomic Contributions to Strategic Management, eds. J. Thepot and R. -A.
Thietart. 169-83. Amsterdam: North Holland Publishing.
Kogut, B., and U. Zander (1992). “Knowledge of the firm, combinative capabilities, and the
replication of technology”, Organization Science, 3, 3, 383–397.
Kotabe M, Martin X, Domoto H. 2003. Gaining from vertical partnerships: knowledge
transfer, relationship duration, and supplier performance improvement in the U.S. and
Japanese automotive industries. Strategic Management Journal 24(4): 293–316.
Krackhardt, D. (1992). "The strength of strong ties. The importance of philos" In N.
Nohria, and B. Eccles. Networks and Organizations: Structure, form, and action (pp.
216-239) Boston: Harvard Business School Press.
Lane, P. J. and Michael Lubatkin, M. (1998). "Relative absorptive capacity and
interorganizational learning." Strategic Management Journal 19,5, 461-477.
Langfield-‐Smith, K. and Greenwood, M.R. (1998). "Developing Co-‐operative Buyer–
Supplier Relationships: A Case Study of Toyota." Journal of Management Studies 35, 3,
331-353.
Larson, A. (1992). "Network dyads in entrepreneurial settings: A study of the governance
of exchange relationships." Administrative science quarterly, 76-104.
Larsson, R., Bengtsson, L., Henriksson, K., & Sparks, J. (1998). “The interorganizational
learning dilemma: Collective knowledge development in strategic alliances”
Organization Science, 9,3, 285-305..
35
Levin, D.Z., Walter, J. and Murnighan, W. (2011), “Dormant ties: the value of
reconnecting”, Organization Science, Vol. 22 No. 4, pp. 923-939
Li, L., Jiang, F., Pei, Y., & Jiang, N. (2017). "Entrepreneurial orientation and strategic
alliance success: The contingency role of relational factors." Journal of Business Research
72: 46-56.
Li, S., Madhok, A., Plaschka, G., and Verma, R. (2006). “Supplier-‐switching inertia and
competitive asymmetry: a demand-‐side perspective”. Decision Sciences, 37(4), 547-576.
Lin, P., Weng, J. C. M., & Hsieh, Y. (2003). “Relational bonds and customer's trust and
commitment- a study on the moderating effects of web site usage”. The Services
Industries Journal, 23(3), 103–127.
Lipparini, A., Lorenzoni, G., and Ferriani, S. (2014). “From core to periphery and back: A
study on the deliberate shaping of knowledge flows in interfirm dyads and networks”.
Strategic Management Journal, 35(4), 578-595.
Lorenzoni, G. and Lipparini, A. (1999) "The leveraging of interfirm relationships as a
distinctive organizational capability: a longitudinal study." Strategic Management
Journal: 317-338.
Lowik, S., van Rossum, D., Kraaijenbrink, J., & Groen, A. (2012). Strong ties as sources of
new knowledge: How small firms innovate through bridging capabilities. Journal of
Small Business Management, 50(2), 239-256.
Majocchi, A., Bacchiocchi E., & Mayrhofer, U. (2005):. "Firm size, business experience
and export intensity in SMEs: A longitudinal approach to complex relationships".
International Business Review 14,6 719-738.
Majocchi, A., Odorici, V., Presutti, M. (2016) - Firms' ownership and internationalisation:
is it context that really matters?, European Journal of International Management, 10, 2,
202-220.
Marsden, P. V., & Campbell, K. E. (1984). Measuring tie strength. Social Forces,63, 482.
36
Martínez-Cañas, R., Sáez-Martínez, F. J., and Ruiz-Palomino, P. (2012). “Knowledge
acquisition's mediation of social capital-firm innovation”. Journal of Knowledge
Management, 16(1), 61-76.
Mayer R.C., Davis J.H., & Schoorman F.D., 1995. “An integrative model of organizational
trust”. Academy Management Review 20, 3, 709–734.
McCall, G. J. (1970). “The Social Organization of Relationships.” In Social Relationshins.
Ed. G. J. McCall et al. Chicago: Aldine Publishing Company.
McFadyen, M. A., and Cannella, A. A. (2004). “Social capital and knowledge creation:
Diminishing returns of the number and strength of exchange relationships” Academy of
management Journal, 47(5), 735-746.
McFadyen, M.A., Semadeni, M., Cannella, A.A., 2009. Value of strong ties to
disconnected others: examining knowledge creation in biomedicine. Organization
Science, 20(3), 552-564. DOI: 10.1287/orsc.1080.0388
Meeus, M., Oerlemans, L. & Hage, J. (2001). “Patterns of interactive learning in a high-
tech region”. Organization Studies, 22, 145–174.
Mesquita, LF. Anand, J. and Brush, TH. (2008). “Comparing the resource-based and
relational views: knowledge transfer and spillover in vertical alliance”. Strategic
Management Journal 29(9): 913–941.
Mollona, E., Presutti, M. (2016) The dynamics of inward foreign investments inside an
industrial district: using computer simulation to guide empirical research, in Unfolding
cluster Evolution, a cura di Belussi, F., Oliver, J.L.H., ISBN 9781138123687, Routledge
Taylor&Francis Group, 117-141
Mowery, D.C., Oxley, J.O. and Silverman, BS. (1996) "Strategic alliances and interfirm
knowledge transfer" Strategic management journal, 17, 2, 77-91.
Mummalaneni, V. and David T. Wilson, D.T. (1991). “The Influence of a Close Personal
Relationship Between a Buyer and Seller on the Continued Stability of Their Role
Relationship.” Working Paper 4- 199 1,
37
Nooteboom, B., Berger, H.and Noorderhaven, N.G (1997). "Effects of trust and governance
on relational risk." Academy of management Journal 40, 2, 308-338.
Odorici, V, and Presutti, M. (2013). The entrepreneurial experience and strategic
orientation of high-tech born global Start-ups: An analysis of novice and habitual
entrepreneurs, Journal of International Entrepreneurship, 11, 3, 268-291.
Pfeffer, J., and Salancik, G. R. (1978). “The external control of organizations: A resource
dependence approach” NY: Harper and Row Publishers.
Phelps, C., Heidl, R. and Wadhwa, A. (2012). 'Knowledge, networks, and knowledge
networks A review and research agenda'. Journal of Management, 38, 1115–1166
Pirolo, L., Presutti, M., 2010. “The impact of social capital on the start-ups’ performance
growth”. Journal of Small Business Management 48 (2), 197–227.
Powell WW, Koput KW, Smith-Doerr L. 1996. Interor- ganizational collaboration and the
locus of innovation: networks of learning in biotechnology. Administrative Science
Quarterly 41(1): 116–145.
Presutti, M., Boari, C., Fratocchi, L. (2016) The evolution of inter-organisational social
capital with foreign customers: its direct and interactive effects on SMEs' foreign
performance, Journal of World Business, 51 (2016) 760–773doi 10.1016/j
Presutti, M., Boari, C., Majocchi, A. (2011) “The importance of proximity for the start-ups’
knowledge Acquisition and exploitation”, Journal of Small Business Management, 49, 3,
pp. 361-389.
Presutti, M., Boari, C., Majocchi, A. (2013) Inter-organizational geographical proximity
and local start-ups' knowledge acquisition: a contingency approach, Entrepreneurship &
Regional Development: An International Journal, 25, 5-6, 446-467, ISSN: 0898-5626
Presutti, M., Boari, C., Majocchi, A., F.X Xavier Molina, (2017) Distance to customers,
absorptive capacity and innovation in high-tech firms: the dark face of geographical
proximity, Journal of Small Business Management
38
Presutti, Manuela, Cristina Boari, and Luciano Fratocchi. "Knowledge acquisition and the
foreign development of high-tech start-ups: A social capital approach." International
Business Review 16.1 (2007): 23-46.
Reagans, Ray, and Bill McEvily. "Network structure and knowledge transfer: The effects of
cohesion and range." Administrative science quarterly 48.2 (2003): 240-267.
Rindfleisch, Aric, and Christine Moorman. "The acquisition and utilization of information
in new product alliances: A strength-of-ties perspective." Journal of marketing 65.2
(2001): 1-18.
Ring P.S., (1996). “Fragile and resilient trust and their roles in economic Exchange”.
Business and Society 35 (2) 148–175.
Rodriguez, C.M. (2002) Relationships bonding and trust as a foundation for commitment in
US-Mexican strategic alliances: a structural equation model approach. Journal of
International Marketing, 4, 53-76.
Rost, Katja. "The strength of strong ties in the creation of innovation." Research policy 40.4
(2011): 588-604.
Rowley, Tim, Dean Behrens, and David Krackhardt. "Redundant governance structures: An
analysis of structural and relational embeddedness in the steel and semiconductor
industries." Strategic management journal (2000): 369-386.
Sammarra, Alessia, and Lucio Biggiero. "Heterogeneity and specificity of Inter-‐Firm
knowledge flows in innovation networks." Journal of Management Studies 45.4 (2008):
800-829.
Sarkar, M. B., Aulakh, P. A.and Cavusgil, S.T. (1998). "The strategic role of relational
bonding in interorganizational collaborations: An empirical study of the global
construction industry".Journal of international management, 4, 2 85-107.
Seggie, S. H., Griffith, D. A., and Jap, S. D. (2013). “Passive and Active Opportunism in
Interorganizational Exchange,” Journal of Marketing, 77 (November), 73–90.
39
Shammout, A. B. and Algharabat, R. S. (2013) "An Investigation into the Determinant of
Jordanian Customer's Loyalty towards Travel Agencies". International Journal of
Marketing Studies, 5, 6 122.
Shan, W., Walker, G., Kogu, B. (1994). Interfirm cooperation and startup innovation in the
biotechnology industry. Strategic Management Journal, 15, 5, 387-394.
Sheth, J. N., and Parvatiyar, A. (1995). Relationship marketing in consumer market:
Antecedents and consequences. Journal of the Academy of Marketing Science, 23(4),
255–271.
Simonin, B. L. (1997) "The importance of collaborative know-how: An empirical test of
the learning organization." Academy of management Journal 40,5, 1150-1174.
Simonin, B. L. (1999) "Ambiguity and the process of knowledge transfer in strategic
alliances." Strategic Management Journal, 20,7, 595-623.
Slotte-Kock, S., and N. Coviello (2010). “Entrepreneurship Research on Network
Processes:A Review and Ways Forward”, Entrepreneurship Theory and Practice, 34,
31–57.
Smith, K., and Q. Cao (2007). “An Entrepreneurial Perspective on the Firm-Environment
Relationship”, Strategic Entrepreneurship Journal, 1, 316–329.
Squire, B., Cousins, P. D., Lawson, B., & Brown, S. (2009). "The effect of supplier
manufacturing capabilities on buyer responsiveness: the role of
collaboration".International Journal of Operations & Production Management, 29.8,
766-788.
Stanko, Michael A., Joseph M. Bonner, and Roger J. Calantone. "Building commitment in
buyer–seller relationships: A tie strength perspective." Industrial Marketing
Management 36.8 (2007): 1094-1103.
Subramaniam, M., Youndt, M.A., 2005. The influence of intellectual capital on the types
of innovative capabilities. Academy of Management Journal 48, 450–463.
Sullivan, J. and Peterson, R.B. (1982). ``Factors associated with trust in Japanese-American
joint ventures'', Management International Review, 22, 2, 30-40.
40
Tiwana, A. (2008). "Do bridging ties complement strong ties? An empirical examination of
alliance ambidexterity." Strategic Management Journal 29.3: 251-272.
Tomlinson, P. R. and Fai, F. M. (2016). “The impact of deep vertical supply chain
relationships upon focal-‐firm innovation performance”. R&D Management, 46(S1), 277-
290.
Tortoriello, M., Reagans, R. and McEvily, B. (2012), “Bridging the knowledge gap: the
influence of strong ties, network cohesion, and network range on the transfer of
knowledge between organizational units”, Organization Science, Vol. 23 No. 4, pp.
1024-1039
Tsai, W. P., and S. Ghoshal (1998). “Social capital and value creation: The role of intrafirm
networks”, Academy of Management Journal, 41, 4, 464–476.
Turner, R. H. (1970). Family Interaction. New York: John Wiley & Sons.
Uzzi, B. (1996). “The sources and consequences of embeddedness for the economic
performance of organizations: The network effect”. American sociological review, 674-
698.
Villena, V. H., Choi, T. Y., and Revilla, E. (2016). Revisiting interorganizational trust: is
more always better or could more be worse?. Journal of Management,
0149206316680031.
Von Hippel, E. (1977). "Successful and failing internal corporate ventures: An empirical
analysis."Industrial Marketing Management, 6,3, 163-174.
Von Hippel, E. (1988) “Sources of Innovation”. New York: Oxford University Press.
Weber, B. and Weber, C. (2007) "Corporate venture capital as a means of radical
innovation: Relational fit, social capital, and knowledge transfer". Journal of
Engineering and Technology Management, 24,1, 11-35.
Wilson, D. T., and Mummalaneni, V. (1986). Bonding and commitment in buyer-seller
relationships: a preliminary conceptualisation. Industrial Marketing and
Purchasing, 1(3), 44-58.
41
Wilson D.T., (1995). An integrated model of buyer-seller relationships. Journal of the
Academy of Marketing Science 23 (4), 335–345.
Wooldridge, J.M., 2012. Introductory Econometrics: A Modern Approach.
CengageLearning, Mason, OH.
Woolley, J. L., and R.N. Rottner (2008). “Innovation Policy and Nanotechnology
Entrepreneurship”, Entrepreneurship Theory and Practice, 32, 791–811.
Yanez, M.; T. M. Khalil & S. T. Walsh (2010). IAMOT and Education: Defining a
Technology and Innovation Management (TIM) Body-of-Knowledge (BoK) for
graduate education (TIM BoK), Technovation, Volume 30, (7–8), 389-400.
Yli-Renko, H. and Autio, E. (1998). "The network embeddedness of new, technology-based
firms: developing a systemic evolution model." Small Business Economics 11, 3, 253-
267.
Yli-Renko, H., Autio, E., and H.J. Sapienza (2001). “Social capital, knowledge acquisition,
and knowledge exploitation in young technology-based firms”, Strategic Management
Journal, 22, 6–7, 587–613.
Zahra, S. A., Nielsen, A. P., and Bogner, W. C. (1999). Corporate entrepreneurship,
knowledge, and competence development. Entrepreneurship: Theory and
Practice, 23(3), 169-169.
Zahra, S.A., Ireland, D.R., & Hitt, M.A. (2000). International expansion by new venture
firms: international diversity, mode of market entry, technological learning and
performance. Academy of Management Journal, 43, 925–950.
Zander, U.and Kogut, B. (1995), "Knowledge and the speed of the transfer and imitation of
organizational capabilities: An empirical test. Organization Science, 6,1,76-92.
Zheng, W., (2010). “A social capital perspective of innovation from individuals to Nations:
Where Is Empirical Literature Directing Us?” International Journal of Management
Reviews 12(2):151 - 183