Influence of Trust and Knowledge Sharing on e-Business Adoption:
A Field Study in Two Italian Footwear Districts
Abstract
Market globalization represents an ongoing challenge for firms operating in industrial
districts. It poses a need for continuously innovating existing products and production
processes and heavily relying on new technologies to reach target customers. While firms
operating in advanced technological sectors may be prone to employ new technologies and ebusiness solutions, those operating in mature industrial sectors (e.g. footwear and agri-food)
often experience difficulties in adopting these technologies and exploiting their potentiality.
To understand which conditions facilitate e-business adoption by traditional district firms, we
addressed two constructs that, to date, have received scarce attention in the field of district
studies: trust and knowledge sharing. We carried out an empirical research aimed at
examining the influence of such constructs on e-business adoption, that is the use of
technologies in order to conduct business over Internet (Amit and Zott, 2001), by district
firms. Specifically, we surveyed a sample of firms operating in two footwear districts located
in the Apulia Region (Southern Italy). Results from a Structural Equation Modeling analysis
indicate that knowledge sharing mediates the relation between a specific dimension of trust
(i.e., “cognitive trust”) and peculiar aspects of e-business adoption concerning the
technological and organizational contexts of the surveyed firms. Implications for practitioners
and policy-makers interested in supporting mature industrial districts are discussed.
Keywords: Industrial districts, Trust, Knowledge sharing, e-Business adoption
Introduction
In the current economic scenario, characterized by declining prospects of economic growth
(especially for developed countries), the revitalization of agglomerations of firms operating in
a same productive sector – so-called industrial districts – is gaining momentum among
national and international authorities as well as the academia. For several decades, industrial
districts represented a crucial source of wealth for national economies and, due to their
potentiality to generate new products and employment opportunities, they represented a
possible solution to the drawbacks determined by the current economic crisis (Becattini,
Bellandi and De Propris, 2009). To exploit such a potential, national governments in Europe
and other developed Countries pursue the adoption of new technologies by district firms as a
primary goal of their policies, to increase the firms’ capacity to face competition of emerging
Countries. It is reputed that the adoption of e-business solutions and technology platforms
may help district firms be more efficient in terms of communication, data exchange and
sharing. At the same time, e-business solutions may improve the way these firms interact with
partners and customers (Margherita and Petti, 2009; Passiante, Elia and Massari, 2000). Here
we define e-business (solutions) adoption as the use of Internet technologies able to improve,
enhance, transform or invent a business process and/or system, in order to create a superior value
for customers, suppliers, business partners, and employees, using at least one of the following: (a)
e-commerce websites, (b) customer-service websites, (c) intranets and enterprise information
portals, (d) extranets and supply chains, and (e) electronic data interchange” (Sawhney and Zabin,
2001; Wu, Mahajan and Balasubramanian, 2003). However, it is important here to note that,
despite the deployment of appropriate measures to encourage the adoption of such
technologies, several districts continue to experience managerial and organizational
difficulties that hinder the introduction of these technologies, thus precluding them from
exploiting new market opportunities (Wang, Heng and Chau, 2010).
Existing research (e.g., Becattini, 1979; Farrell and Knight, 2003) has emphasized a major
strength of industrial districts, that is, the complex set of relationships that links together the
firms located in these productive systems. The development of such relations is facilitated by
the fact that these firms operate in the same geographical area (Ganesan, Malter and
Rindfleisch, 2005). Besides geographical proximity, however, a crucial factor determining the
establishment of these linkages in the district is the perception of a sense of trust in other
firms’ capacities and strategic decisions. The more firms trust other firms located in the same
district, the more they may be willing to cooperate with these firms. In turn, cooperation
involves the exchange of knowledge flows and triggers learning processes that help district
firms to adapt to changing market dynamics (Amighini and Rabellotti, 2006; Barabel, Huault
and Meier, 2007; Morgan and Hunt, 1994; Niu, Miles and Lee, 2008). Knowledge sharing, in
particular, may reduce firms' uncertainties about the effectiveness of new technologies and, by
facilitating their adoption, is likely to increase firms’ competitive capacity (cf. Bell, Tracey
and Heide, 2009; Randelli and Boschma, 2012; Schmitz and Nadvi, 1999).
In this research we propose that both trust and knowledge sharing facilitate the adoption of
e-business solutions by district firms. We also posit that e-business adoption is likely to
positively affect the economic performance of district firms and, therefore, can be considered
as a valid tool overwhelming the negative effects of international economic shocks. To
appraise the validity of our argumentations, we carried out a field study in two footwear
districts located in Southern Italy and sought to find empirical evidence for our conclusions.
The reminder of the study is organized as follows: in the next section we shall provide
some insights on industrial districts. Then we shall introduce the mains constructs of this
research (i.e., trust, knowledge sharing and e-business adoption) and its conceptual
framework. Next, we shall present the research methodology and illustrate the results of the
field studies. Finally, we shall discuss the theoretical and managerial implications results and
provide some suggestions for future research.
The industrial district model: basic features and recent strategic orientations
The geographical concentration of firms involved in a peculiar economic sector is a
relevant source of wealth for regional economies (Cortright, 2006). Firms’ tendency to colocate in the same geographical area is basically determined by the possibility to benefit from
labor market pooling, supplier specialization, and knowledge spillovers, that is exchanges of
knowledge among organizations settled in close proximity to each other (Becattini, 1979;
Breschi and Lissoni, 2001; Brusco, 1982; Piore and Sabel, 1984).
Existing research on industrial districts (e.g., Becattini, 1979; Carbonara, 2002; Guido,
1999; Molina-Morales, 2005; Piscitello and Sgobbi, 2004; Piore and Sabel, 1984; Rabellotti,
1995) identifies other relevant characteristics of these peculiar production systems, that is, the
presence of: i) Small and Medium Enterprises (SMEs) specialized in peculiar production
2
processes; ii) strong cooperative links based on reciprocal trust among firms; iii) relations
with local institutions and firms located outside the district; iv) leading firms conferring an
international outlook to the district; v) high labor mobility. Two further characteristics are
important for the development of an industrial district: knowledge flows among district firms,
and innovation fostering. Mobility of human resources allows, in particular, the diffusion of
tacit knowledge throughout the district and, hence, determines an ongoing collective learning
process (cf. Passiante, Elia and Massari, 2000).
The model of economic development based on industrial districts is typical of the Italian
economy as, in this Country, it found suitable conditions for its diffusion. For several decades,
it has represented the prevailing organizational model that permitted the rise of the so-called
Made-in-Italy industry – which mainly encompasses the Textile, Clothing and Footwear
(TCF) sectors – and played a key-role in the development of the Italian economy. Till the end
of the last century, this industry experienced a significant growth and positive results in terms
of sales, exports, labor employment and competitiveness in general (Dei Ottati, 2003). The
footwear sector, in particular – which developed in several Italian regions during the '60s –
allowed Italy to become a leading exporter in Europe and the second largest footwear exporter
in the world (Rabellotti, 1995). However, starting from the beginning of this century, both the
globalization and the increasing competitive pressure from low-cost producers located both in
Eastern Europe (e.g., Albania, Romania, Hungary) and Asia (e.g., Thailand, Malaysia,
Bangladesh) determined an inversion of this tendency. Sales of footwear products
manufactured in Italy decreased sharply and many producers had to revise their marketing
approaches. Some of them moved from a low-cost strategy centered on price competitiveness
to the targeting of new customer segments: they focused on product quality, design, and brand
image, and pushed sales by means of thorough marketing actions. Some other footwear
producers, instead, continued approaching the market through low-cost strategies and
outsourced productive processes characterized by low added-value to Eastern European and
Asian suppliers (Tattara, 2008). In the majority of cases, the former succeeded in positioning
their products on the markets, and the latter firms went out of market (Mariotti, Mutinelli and
Piscitello, 2008). This underlined the need for the deployment of alternative marketing
approaches that, grounding on the exploitation of technology potential, may allow them to
generate value and hence survive and prosper.
Trust: its meaning and relevance for district firms
Collaboration among firms characterizes the majority of economic sectors (Grant and
Baden-Fuller, 2004). It grounds on trust, which is considered one the most relevant
preconditions for the establishment of both interpersonal and interorganizational relationships
(Gargiulo and Ertug, 2006; Gulati, 1995). Scholarly research (McAllister, 1995) started
analyzing trust in the organizational field and found that it can be considered as the synthesis
of two dimensions: cognitive trust and affective trust. Cognitive trust grounds on rational
judgments and appreciation of technical competencies, whereas affective trust is based on
emotional judgments and concerns the social side of a relationship among individuals, rather
than the mere professional one. This second facet of trust is deemed to tighten up connections
which are based only on knowledge and previous interaction experiences (Johnson and
Grayson, 2005). Both forms of trust, however, have been found to foster interactions among
3
individuals and firms and shape the willingness to engage in cooperative behaviors (Becerra,
Lunnan and Huemer, 2008). In addition, high levels of trust are related to higher willingness
to share knowledge and experiences, thus enhancing organizational learning process (Swift
and Hwang, 2013).
Research on this topic (e.g., Welter, 2012) suggests that trust has a positive effect on
alliance formation and boosts cooperation among firms (Welter and Smallbone, 2006).
Indeed, firms prefer to collaborate with trusted partners and this is particularly true in the case
of firms belonging to a same industrial district (Sengun, 2010). In reference to such
productive systems, trust can be defined as a firm’s belief that other partners will not engage
in possible opportunistic behaviors (Gulati, 1995; Zaheer, McEvily, and Perrone, 1998). This
belief develops over time and is generally enhanced by face-to-face interactions occurring in a
context of spatial proximity. Spatial proximity fosters a sense of “familiarity” among firms
located in the same geographical area and hence favors the establishment of trusting
relationships (Ganzaroli, 2000). They are facilitated by the presence of the so-called
“boundary spanners”, i.e., employees or managers capable of linking their firms to other
institutions and playing a crucial role in engendering trust among different organizations
(Gulati and Sytch, 2008). Existing studies (e.g., Passiante and Secundo, 2002) have also
noticed that the pervasive diffusion of Information and Communication Technologies (ICTs)
has significantly increased collaboration opportunities among firms which are not located in a
same geographical area. This has favored the raise of the so-called virtual districts, which are
productive systems consisting of organizations that, although dispersed in different places,
interact and cooperate by means of an intensive use of ICT tools (from the Internet, to the
internal organizational networks, such as Intranets, social media, etc.). These tools have
deeply increased communication efficiency and favored the exchange of knowledge among
firms. Details about the characteristics of knowledge exchange and its benefits for district
firms are clarified in the following section.
Knowledge sharing in industrial districts
Interaction among different individuals as well organization generally involves an
exchange of knowledge, which may be categorized as “explicit” or “tacit” knowledge
(Polanyi 1966). Explicit knowledge can be codified into documents, datasets, or other sources
of information by using words, numbers, codes, etc. (Nonaka, 1994). In contrast, tacit
knowledge cannot be formalized and is embedded in individuals’ skills and expertise. It is
highly subjective and is basically transferred through personal contacts with other people. As
it cannot be easily articulated into words, people capture tacit knowledge by interacting with
other persons or also by simply observing their behaviors (Haldin-Herrgard, 2000; Nonaka
and Takeuchi, 1995). It has been also specified that tacit knowledge may be acquired through
either formal interactions with other individuals (for example, by attending training programs
or taking part to conferences) or informal interactions (for example, occasional meetings
occurring in a given workplace) (Marquardt, 1996).
Knowledge sharing is considered as a crucial determinant of innovation and a factor
capable of enhancing firms’ competitiveness (Porter, 2000). By exchanging knowledge, firms
learn from each other and this facilitates the development of new products and production
processes (Whittington, Owen-Smith, and Powell, 2009). This is because, by enabling the
4
share of existing knowledge, collaborative relations simultaneously determine the creation of
new knowledge (Hardy, Phillips and Lawrence, 2003). These processes of knowledge
exchange and generation of new knowledge are an intrinsic characteristic of industrial
districts. Indeed, due to their peculiar organizational structure (consisting of firms of different
sizes specialized in specific production activities), industrial districts represent a suitable
setting for the exchange of knowledge flows (Becattini and Rullani, 1993). However, the
mechanisms through which the process of knowledge exchange occurs in a district may have
different nature (Brusco 1990): they may range from the simple exchange of information
among suppliers and customer firms to the exchanges of labor force among different
organizations or the creation of new independent joint ventures by individuals working in a
pre-existing organization (so-called spin offs). It should be also noted that firms located in
industrial districts may share knowledge with other district firms as well as with firms located
outside the district and that the latter situation is the one that increases the most likelihood of
accessing new knowledge.
We finally observe that, within a district, new knowledge may be acquired by means of
exploitation and exploration processes (Gustafsson and Autio, 2011). Knowledge exploitation
concerns a set of activities by which firms employ existing knowledge to create economic
value (March, 1991). They basically regard the expansion and reinforcement of existing
relations among firms located in the district. In the majority of cases, firms adopt such an
approach to cope with market uncertainty and reduce their productive risks. Knowledge
exploration, instead, refers to those activities which are aimed at increasing firms’ stock of
knowledge (March, 1991). Such activities may involve the establishment of new relations and
alliances, especially with firms located outside the district. However, due to the uncertainty
connected with the establishment of new relations, it is reputed that knowledge exploration
processes are more rarely deployed (Baum, et al., 2005).
E-Business adoption in industrial districts
In the last decades, the efficacious management of value creating processes has become a
key challenge for firms, especially for those operating in mature economic sectors, such as
Textile-Clothing-Footwear (Amit and Zott, 2001; Kim, Nam and Stimpert, 2004; Porter,
2001). ICT diffusion has determined profound changes in the ways firms interact with
suppliers, competitors and customers and has created new opportunities to generate economic
value (Guido et al., in press). The adoption of e-business solutions, which regard the use of
Internet technologies (such as e-Supply Chain Management Systems, Web-services, etc.), has
radically altered firms’ value propositions and has favored the development of new business
models (Rao, Metts and Monge, 2003). In many cases, the adoption of e-business solutions
has allowed firms to deliver value to customer in a more effective way than in the past, thus
improving their competitive positioning (Liao, 2003). Indeed, e-business solutions yield both
organizational and economic advantages for firms: on the one hand, they permit to reduce
transaction costs and speed up communication processes; on the other hand, they reduce
operational costs, thus increasing the added value generated by production processes. It is
commonly argued that their usage may turn to be beneficial especially for SMEs, as they may
help them close the gap with respect to larger competitor firms.
5
Despite the advantages connected with their employment, e-business solutions are diffused
only to a limited extent among SMEs, especially those operating in footwear sector (Guido,
2001ab, 2002, 2003). It has been found, in particular, that larger firms have a higher
proneness to adopt e-business solutions than SMEs (Burke, 2005; Charles, Icis and Leduc,
2002; Davis and Vladica, 2006). One of the reasons underlying the scarce adoption of ebusiness solutions by SMEs is the lack of technical and business skills, which prevents these
firms from adopting these solutions and fully exploiting their potentialities (Gottardi, 2003;
Guido, Marcati and Peluso, 2011; Marcati, Guido and Peluso, 2008; Piscitello and Sgobbi,
2004). However, research on this topic is far from being conclusive as a multiplicity of factors
may negatively – or, conversely, positively – affect firms’ intention to adopt e-business
solutions.
In this study, we tried to investigate the conditions that favor the adoption of e-business
solutions by firms operating in footwear districts. We addressed e-business adoption by
referring to the so-called Technology-Organization-Environment (TOE) framework. This
framework was developed by Tornatzky and Fleischer (1990), who established that e-business
adoption stems from the interplay of factors relating to the technological, organizational, and
environmental contexts wherein individuals operate. According to this framework, two
specific factors relate to the “technological context”, namely, technology readiness –
regarding the technological infrastructure individuals have at their disposal as well as their
technological competences – and technology integration, intended as the adoption of
analogous technological tools and applications by different individuals. Factors relative to the
“organizational context” concern the perceived benefits and perceived obstacles connected
with the adoption of e-business solutions. Factors relative to the “environmental context”
refers to the level of Internet penetration in a given place as well as the diffusion of Internetbased technologies among individuals living in the place at issue. We hypothesized that ebusiness adoption is affected by interpersonal trust and knowledge sharing and, to appraise
the validity of such argumentation, an empirical research was carried out. Its objectives are
clarified in the following section.
Research objectives
This study addressed the relations among trust, knowledge sharing, and e-business
adoption. We argued that both trust and knowledge sharing affect firms’ willingness to adopt
e-business solutions (see Figure 1). More specifically, we hypothesized that knowledge
sharing mediates the relationship between trust and e-business adoption, with trust directly
affecting knowledge sharing (Politis, 2003), and knowledge sharing, in turn, being a key
factor for e-business adoption (Romano, Passiante and Elia, 2001). To shed further light on
the relations among these constructs, we also considered the dimensions at the basis of trust –
namely, cognitive trust and affective trust – as well those at the basis of e-business adoption –
namely, the technological, organization and environmental factors. We sought, in particular,
to understand which dimension of trust affects knowledge sharing, and to identify the factors
of e-business adoption that are mostly influenced by knowledge sharing.
To achieve both these goals, we carried out a field study on firms located in two footwear
districts. The research methodology is illustrated in the following section.
6
Figure 1: The conceptual model
Trust
e-Business Adoption
Cognitive Trust
Technological
Context
Knowledge
Sharing
Affective Trust
Organizational
Context
Environmental
Context
Methodology
Research setting
This study focused on two footwear districts located in the Apulia Region (Southern Italy):
the Barletta District and the Casarano District. The former consists of firms located in 9
major municipalities in the Province of Bari, whereas the latter consists of firms located in the
Province of Lecce, in an area including 29 municipalities (Guido, 2003). They share a similar
internal structure, which, as for the prototypical industrial district, consists of a network of
small and medium-sized enterprises (SMEs) operating in competitive-cooperative networks.
However, they differ with respect to the final product and the organization of production
processes. Firms in the Barletta district produce sports shoes, and, more recently, safety shoes.
All the production processes are performed within this district, in the majority of cases by
individual firms. Whereas firms in the Casarano district produce casual or fashion shoes (in
particular men’s shoes). In this case, production processes are dispersed among several
production units (Guido, 2001ab, 2002).
Firms of these two districts have adopted two different kinds of strategies to face the
negative downturn of the footwear industry and the international economic crisis. Some firms
in both districts (Barletta district more than Casarano) have chosen to move towards new
target markets with the conversion of their production processes from a low-cost strategy to
high-quality strategy. In particular, firms in the Barletta district started concentrating their
production on the high-quality safety shoes market segment (at the beginning of new
millennium just few firms produced safety shoes), while Casarano district firms started
concentrating their production on high-quality fashion (more women than men) shoes. Such
strategy, combined with the pursuit of innovation, guaranteed the survival of the firms. On the
other hand, most of the firms in both districts (Casarano district firms more than Barletta)
have opted for outsourcing their production processes in the Eastern Europe and Asian
countries, where labor cost is lower than in Italy. Hence, they continued pursuing a low cost
strategy, which, however, did not guarantee the survival of the district.
7
Sampling procedure
Data were collected through a survey on a sample of 138 firms, 63 of which were located in
the Barletta district, and 75 in the Casarano district. These firms were randomly selected
among those present in the official list of the firms registered in the Chambers of Commerce
of Lecce and Bari. Then, the selected firms were contacted by phone and invited to fill in an
electronic questionnaire available on the Internet. Some firms were visited and invited to fill
the questionnaire face to face. Data collection lasted approximately four months.
Measures
The questionnaire was structured in two parts. The first part included questions regarding
the economic and productive characteristics of the surveyed firms. Specifically, it consisted of
seven questions. The first question asked respondents to indicate their position in the surveyed
firm by choosing among the following options: “Owner of the company”; “Member of the
company”; “Chief Executive Officer (CEO) of the company”; “Director of the company”;
“Employee”; “Other”. The second question asked respondents to indicate the number of
individuals employed in the firms by choosing among the following options: “Less than 10
employees”; “From 10 to 50 employees”; “From 50 to 120 employees”; “More than 500
employees”. The third question asked respondents to indicate the type of activity performed
by the firm, choosing among the following options: “Footwear producer performing all
phases of the production process”; “Subcontractor, that is, a company performing productive
processes for another company (e.g., leather cutting, production of upper footwear materials);
“Producer of accessory parts and semi-finished products (e.g., insoles, heels, etc.); “Firm
performing complementary processes (e.g., production of boxes, labels, retailers of footwear
products, etc.). The fourth question asked respondents to indicate the specific activity
performed by their firm, choosing among: “Footwear producer”; “Producer of upper footwear
materials”; “Leather cutting company”; “Leather edging manufacturer”; “Footwear soles
manufacturer”; “Footwear insoles manufacturer”; “Producer of accessories and packaging
materials”; “Shoe store”; “Other”. The lasts three questions concerned the firm’s turnover of
the last three years, precisely 2009, 2010 and 2011.
The second part of the questionnaire consisted of three sets of scales aimed to measure ebusiness adoption, willingness to share tacit knowledge, and interpersonal trust, respectively.
E-business adoption was measured according to Tornatzky and Fleischer’s (1990) TOE
framework, which assesses the characteristics of the technological, organizational, and
environmental contexts relative to the examined firms. The analysis of the technological
context concerned an assessment of the level of technology readiness and integration of
firms. As regards the first aspect, respondents were asked the following question: “To what
extent each of the following technologies describe the one used in your firm?”. Responses
were gathered using six items, such as “Sum of the following technologies: Internet, intranet,
web-site”, “Sum of the following ICT skills: our firm currently employs ICT practitioners;
our firm regularly send employees to ICT training programs”. As regards the second aspect,
respondents were asked the following question: “Does your firm use any of the following
systems or applications for managing information in the firm?”. Answers to this question
were gathered by means of four items, such as “A Supply Chain Management (SCM)
8
system”, “An Enterprise Document Management system”, “An Enterprise Resource Planning
system”, “A Knowledge Management software”. Responses relative to both technology
readiness and technology integration were measured on a 7-point scale ranging from 1 =
“Definitely not” to 7 = “Definitely”.
As regards the assessment of the organizational context, we measured both the expected
benefits and the possible obstacles to e-business adoption. Benefits were measured by asking
respondents the following question: “Which are the benefits that your firm evaluate in order
to engage in e-business activities?”. Answers were collected using six items, such as
“Because our customers expected if from us”, “Because our competitors also engage in ebusiness”. Obstacles were measured by asking respondents to indicate their level of
agreement with a series of statements concerning possible obstacles to e-business adoption,
such as “My company is too small to benefit from any e-business activities”, “E-business
technologies are too expensive to implement”. Answers were measured on a 7-point scale
ranging from 1 = “Completely disagree” to 7 = “Completely agree”.
The section devoted to the analysis of the environmental context was designed to measure
the level of Internet penetration in the surveyed areas using six items, such as “The majority
of the individuals in the Province of Lecce/Bari have a computer”, “The majority of firms in
the Province of Lecce/Bari use a broadband connection to the Internet”, “The majority of
firms located in the province of Lecce shop online”. Also in this case, answers were measured
on a 7-point scale ranging from 1 = “Completely disagree” to 7 = “Completely agree”.
Willingness to share knowledge was measured using Holste and Fields' (2010) scale, which
asked respondents to express their agreement with the following statements: i) “If requested
to do so, I would allow the personnel of a firm partner to spend significant time observing and
collaborating with the personnel of this company in order for them to better understand and
learn from our work”; ii) “I would willingly share with the personnel of a firm partner rules of
thumb, tricks of the trade, and other insights into the activities of my company and that of the
organization I have learned”; iii) “I would willingly share my new ideas with a firm partner”;
and iv) “I would willingly share with a firm partner the latest organizational rumors, if
significant”. Answers were measured on a 7-point scale ranging from 1 = “Definitely not” to
7 = “Definitely”.
Trust was measured by tapping both the cognitive and affective components of the
construct, according to McAllister’s (1995) framework. Specifically, cognitive trust was
measured by asking respondents to express some judgments about a firm with which
they maintained a close relationship. To this end, they were asked to express
their level of agreements with items, such as “This firm approaches its job with
professionalism and dedication”, “Other work associates of mine who might interact with this
firm consider them to be trustworthy”; whereas affective trust was measured using five items,
such as “We have a sharing relationship”; “We can both freely share our ideas, feelings, and
hopes”, “If I shared my problems with this firms, I know they would respond constructively
and caringly”. Also in this case, answers were measured on a 7-point scale ranging from 1 =
“Definitely not” to 7 = “Definitely”.
9
Results
The sample was preliminarily analyzed by means of a series of descriptive statistics
concerning the position of respondents in their companies, the number of employees of the
surveyed firms, and the main activity performed by each one. We also computed descriptive
statistics relative to the productive characteristics of each firm. As for respondents’ positions,
52% of respondents were owners; 23% were co-owners of these companies; and 25% were
CEOs. All sample firms were SMEs, with the following structural compositions: concerning
the number of employees, 69% of the investigated firms employed less than 10 individuals,
whereas 26% employed a number of individuals ranging from 10 to 50, and 5% employed
individuals between 50 and 120. We also ascertained that 39% of the sample consisted of
firms that perform all the production processes; 29% of them were subcontractors; 19% were
producers of accessory parts and semi-finished products; 13% were firms performing
complementary production processes. As for the activities performed by these firms, 39% of
them were footwear producers performing all the production processes, 10% were produced
upper footwear material; 10% were involved in leather cutting, while others in the allied
industries.
We subsequently focused on the economic performance of the surveyed firms and found that
the majority of them experienced a negative economic performance between 2009 and 2011.
Specifically, we established that 57% of the examined firms (79 out of 138) registered
negative economic results, whereas the remaining 43% (59 firms of 138) registered positive
economic results. This second group of 59 firms is composed of 43 firms located in the
Barletta district, and only 16 located in the Casarano district.
To achieve our research goals, we took into account the whole group of firms that registered
a positive economic performance between 2009 and 2011 and built a causal model using the
structural equation modeling technique. This analysis was performed through a three-step
process. In Step 1, the model related trust to adoption of e-business solutions via knowledge
sharing, which thus served as mediator. The path analysis returned good fit statistics (2(1) =
.812, p = .368; 2/d.f. = .812; GFI = .991; CFI = 1.000; NFI = .976; SRMR = .036). Structural
estimates showed a positive effect of trust on knowledge sharing (γ = .50, p < .001), which in
turn positively impacts e-business adoption (β = .48, p < .001). Results showed a nonsignificant direct effect of trust on e-business adoption (p > .10). Thus, to establish full
mediation, the bootstrap method was adopted. This technique revealed an indirect effect of
trust on e-business adoption that is positive and significant (indirect effect: .50 × .48 = .24, p
= .001).
To better understand the role of trust in stimulating e-business adoption, in Step 2, the
antecedent variable of trust was split into two major components concerning, respectively, the
cognitive and affective dimensions of the construct. The outcome variable of e-business
adoption was also split into its three dimensions according to the TOE framework, namely,
the technological context, the organizational context, and the environmental context. (see
Figure 1, supra). A structural model was then built as displayed in Figure 2 (infra). Constructs
in the model were treated as observed variables, without measurement errors (structural errors
were not displayed in Figure 2 for clarity).
10
Figure 2: The structural model
Technological
Context
Cognitive
Trust
Organizational
Context
Knowledge
Sharing
Affective
Trust
Environmental
Context
All variables showed acceptable levels of convergent validity (α  .50), considered the
limited sample size. Furthermore, the examined variables also showed adequate levels of
discriminant validity. Inter-constructs correlations were in fact less than 1.0 by an amount
greater than twice the corresponding standard errors (cf. Bagozzi and Warshaw, 1990), as
shown in Table 1.
Table 1: Inter-construct correlations
Variable
1. Cognitive Trust
1.
2.
3.
4.
5.
6.
1
.36*
1
(.11)
.62*
.25
1
3. Knowledge Sharing
(.10)
(.13)
4. Technological
.16
.18
.46*
1
Context
(.14)
(.11)
(.11)
5. Organizational
.10
-.04
.41*
.59*
Context
(.11)
(.11)
(.12)
(.10)
6. Environmental
.25
-.07
.10
.11
Context
(.10)
(.14)
(.13)
(.13)
Note: n = 59. * = p < .01. Standard errors are reported in parentheses.
2. Affective Trust
1
-.07
(.15)
1
The new analysis yielded acceptable fit statistics (2(8) = 15.110, p = .057; 2/d.f. = 1.889;
GFI = .928; CFI = .909; NFI = .838; SRMR = .076), yet structural estimates showed nonsignificant relationships from affective trust to knowledge sharing (p > .10), and from
knowledge sharing to the environmental component of e-business adoption (p > .10). Then,
the structural model was revised by removing these non-significant paths. In Step 3, such a
revised model was estimated, thus obtaining better fit statistics (2(2) = 3.151, p = .207;
2/d.f. = 1.576; GFI = .974; CFI = .983; NFI = .956; SRMR = .063). Structural estimates
reported in Table 2 (infra) show a positive effect of cognitive trust on knowledge sharing (γ =
11
.62, p < .001), which positively impacts both technological context (β = .46, p < .001) and
organizational context (β = .41, p < .001). Direct effects of cognitive trust on the two
dimensions of e-business adoption are non-significant (p > .05), while the indirect effects of
cognitive trust on technological context (indirect effect: .62 × .46 = .28, p = .001) and
organizational context (indirect effect: .62 × .41 = .25, p = .004) are positive and significant,
thus confirming the mediating role of knowledge sharing in the model.
Table 2: Starndardized structural estimates
Causal Path
R2
Cognitive Trust  Knowledge Sharing
.38
Standardized
Estimate (γ, β)
.62*
Knowledge Sharing  Technological Context
.21
.46*
.17
.41*
Knowledge Sharing  Organizational Context
Note: n = 59. * = p < .001. Fit statistics:  (2) = 3.151, p > .10;  /d.f. = 1.576; GFI = .974; CFI =
.983; NFI = .956; SRMR = .063.
2
2
General discussion and conclusions
Starting from the end of the last millennium, Italian industrial districts have experienced
profound changes mostly determined by market globalization (Dei Ottati, 2009). After a
period in which the majority of production processes have been outsourced to manufactures
settled in developing Countries (in order to contain production costs), some district firms have
started re-internalizing the production processes that mostly determine the quality of final
products, and have moved towards the high-quality production, thanks also to the adoption of
innovation technology and to the adoption of Internet based technologies (Amighini and
Rabellotti, 2006; Randelli and Boschma, 2012). With the globalization, another reason of the
loss of industrial districts competitiveness has been identified in their low predisposition to
apply new technologies and innovations, mainly for the manufacturing firms (Daveri, 2006;
Larch, 2004; Piispanen and Kajanus, 2012). E-business solutions, for example, may create
new opportunities of development for small businesses, allowing them to reduce transaction
costs and effectively placing their products on the international markets (Chiarvesio, Di Maria
and Micelli, 2004, 2010). However, only a limited number of these firms appear capable of
exploiting such opportunities. To understand the reasons of this phenomenon, we
concentrated on some peculiar factors likely to affect adoption of new technologies and ebusiness solutions, in particular: trust and knowledge sharing.
The present study focused on the relations among trust, knowledge sharing and e-business
adoption in industrial districts. We investigated such relations by building a model that
considered both trust and knowledge sharing as determinants of e-business adoption.
Particularly, we hypothesized that trust shapes knowledge sharing, which, in turn, positively
affects e-business adoption. These relationships were tested by implementing a structural
equation model which returned significant results. This model indicated that trust indirectly
affects e-business adoption as the relation between these two constructs is fully mediated by
knowledge sharing. This result indicates that the more district firms trust each other, the more
they may be willing to share their knowledge. The same result also suggests that the higher
12
the level of knowledge sharing among firms, the higher is the likelihood that firms will adopt
e-business solutions. As trust seems to multiply knowledge flows among district firms (and
ultimately to facilitate e-business adoption), the development of close relations among district
firms may turn to be an efficacious way to sustain the productivity of the whole district and
enhance its capacity to cope with external competition (cf. Becattini and Rullani, 1996).
These conclusions may hold a considerable importance for the formulation of policies aimed
at boosting the competitiveness of mature industrial districts. Policy-makers interested in
boosting the adoption of e-business solutions in such productive systems could, in fact, target
the relations among district firms and seek to foster mutual trust among them. In this way,
they could boost the internal cohesiveness of the district and induce firms to share knowledge.
Circulation of knowledge among firms will presumably reduce difficulties in adopting ebusiness solutions and ultimately increase the efficiency and competitiveness of the whole
district (Sanders, 2007).
In a second step of our analysis, we split trust and e-business adoption into the respective
sub-dimensions in order to deepen our comprehension of the interactions among these
constructs. This step allowed us to establish that: i) only cognitive trust influences knowledge
sharing, thus affecting the willingness of firms to share their knowledge as well as their work
abilities; ii) knowledge sharing is capable of influencing specific aspects of e-business
adoption (the technological and organizational contexts, in particular), but does not exert any
effect on the environmental context, which is a variable not directly controllable by firms.
Hence, the first result suggests that only if district firms trust each other on the basis of
cognitive factors they may be willing to share knowledge. Moreover, relations based on
affective trust may not be equally efficacious in inducing district firms to share knowledge, as
affective trust is directly related to emotional aspects of relationships, which do not always
guarantee efficient relationships (Zaheer, McEvily and Perrone, 1998). The second result
suggests that knowledge sharing can positively affect the technological and organizational
contexts of district firms, thereby increasing the likelihood that these firms will adopt of ebusiness solutions. No relationship emerged between knowledge sharing and the
environmental context, probably because the environmental context (i.e., Internet penetration
in the area object of the analysis) is a variable more related to institutional and public actors,
as State and Regions, than to private actors as firms. Both these relationships – that between
trust and knowledge sharing and that between knowledge sharing and e-business adoption derive from literature. In particular, trust in organizations supports and enables collaboration
and also knowledge sharing, with the goal to acquire and then create new knowledge (Polities,
2003, Niu et al., 2012). Moreover, ICT and new technologies based on Internet give a support
to organizations in their knowledge sharing process with other firms (Warkentin, Sugumaran
and Bapna, 2001). In addition, the social environment of a typical industrial district is
characterized by some elements such as: a common culture, trust among members and
frequent behaviors of competition-collaboration. So, in this environment, tacit knowledge
represents the main resource upon which industrial district’s competitive advantage is
founded and through which the innovation technology process is guaranteed (Albino,
Garavelli and Schiuma, 1999).
We finally observe that the obtained results concern only a part of the surveyed sample of
firms: namely, those that registered positive economic performances between 2009 and 2011.
13
We also tested the proposed model on the sub-sample of firms that registered a negative
economic performance in the same temporal interval, but we did not obtain any significant
result. This might mean that e-business adoption, for the firms of our sample, could improve
the competitiveness of district firms. Yet, to generalize this conclusion, further investigations
are necessary. Future research could, for example, expand our model by also considering the
impact of e-business adoption on the economic performance of district firms, and assess the
value of any significant relation between these two constructs.
Another limitation of the present research concerns the fact that the time dimension has not
been considered in the evaluation of the effects that the two variables, trust and knowledge
sharing, have on e-business adoption. Obviously in the inter-firm relationships the time
dimension has its relevance, especially with respect to the trust variable. Some Authors (e.g.,
Coulter and Coulter, 2002; Schoorman, Mayer and Davis, 2007) have found in fact that time
favorably affects the development of the trust. In contrast, Eriki (2013) found that the length
of relationships appears to be negatively correlated with the level of trust. While, other studies
have shown that the two dimensions here considered, i.e., affective and cognitive trust,
develop separately and that often firm’s trust, as well as personal trust, move from the
affective to the cognitive dimension and vice versa, along time (Huang and Wilkinson, 2013;
Webber, 2008).
The length of relationships is closely related also with the knowledge sharing variable as it
fosters firms’ willingness to share knowledge, as well as information and other resources (Li,
Veliyath and Tan, 2013). Considering that this research is still at an exploratory stage, the role
of time has not been considered. Future research, however, is suggested to also take into
account the length of relationships in order to evaluate its effect on the analyzed variables.
Aside from the above-mentioned limitations, however, this research provides valuable
indications for managers of district firms operating in mature sectors and local policy-makers.
It reveals that the creation of trusting relations based on cognitive factors – rather than
affective ones – may represent a valid strategy to overwhelm possible obstacles to the
technological evolution of firms that, for cultural reasons, are not particularly inclined to
adopt new technologies or innovate their products and production processes (cf. HirschKreinsen, 2008). Inducing these firms to trust each other by gaining awareness of the
reciprocal capacities and share their knowledge may favor the spread of e-business solutions
in the technological and organizational contexts of such firms. The adoption of e-business
solutions can help these firms deal with the turbulence of modern international markets, as
such solutions can provide them with a further collaboration power, thus allowing them to
easier face the fierce competition of emerging economies (Belussi and Sammarra, 2010;
Giuliani and Bell, 2008).
In this respect, we believe that local policy-makers and development agencies may
significantly contribute to the creation of a cooperative climate among district firms, for
example, by incentivizing them to take part to common projects or by multiplying the
opportunities of interaction among the same firms, such as through workshops, conventions,
etc. (Biggerio, 2006; Cainelli and De Liso, 2003)..
In this scenario, firms providing Knowledge Intensive Business Services (KIBS) have a
crucial role for districts, as these firms may enhance the internal integration of mature
industrial districts (Bettiol, Di Maria, Grandinetti, 2012). The services supplied by KIBS
14
firms differ from the outputs of other service providers, as they incorporate high level of
knowledge. For these reasons, KIBS providers can play an important role in stimulating the
innovative capacity of district firms and territories, by giving them relevant features to
compete in an international environment (Camuffo and Grandinetti, 2006). Grandinetti (2011,
p. 310) asserts that “KIBS act as cognitive interfaces between the industrial district where
they are located and the surrounding competitive environment”, so cognitive trust is essential
for the success of the relationships created with the scope to share knowledge and innovate.
By supporting their customer-firms through the provision of tailored services, KIBS firms
can gain awareness of their peculiar difficulties or deficiencies and provide common solutions
to these drawbacks. In this way, these firms create the conditions that induce district firms to
interact and trust each other. By acting as a sort of “cognitive interface” among district firms,
KIBS providers can foster the creation of collaborative relations among them and the pursuit
of common development goals.
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