the dark side of trust: the benefits, costs and optimal levels of trust for innovation performance

Long Range Planning 44 (2011) 118e133 http://www.elsevier.com/locate/lrp

The Dark Side of Trust: TheBenefits, Costs and OptimalLevels of Trust for InnovationPerformance

F. Xavier Molina-Moralesa, M. Teresa Mart�ınez-Fern�andeza

and Vanina Jasmine Torl�oa

This article explores the positive and negative impact of trust on the innovation perfor-mance of firms in industrial districts. A recent explosion of interest in trust has generateda rapidly expanding body of literature demonstrating the importance of trust to economiclife, but several authors have noted that the subject has been largely underappreciated inmanagement literature. Discussing trust as an integral part of the strategy formulationprocess, this article finds that trust is good, but a conditional good. Some level of trust isbeneficial because it enables transfer of tacit knowledge and risk taking, but firms that overinvest in trust, trust too much, or invest in trusting relationships that have little value forthe firm, may be misallocating precious resources and/or taking unnecessary risks thatcould have substantial negative effects on their innovation performance. Drawing ona sample of 156 manufacturing firms from different industrial districts in Valencia we find,that beyond an optimum threshold level, additional increases of trust bring diminishingbenefits and may even decrease innovation returns for the firm involved. By exploring therelationship between trust and firm innovation, this study presents innovative results withimplications for both research and practice.� 2011 Elsevier Ltd. All rights reserved.

IntroductionThe recent explosion of interest in trust has generated a considerable and rapidly expanding body ofliterature, demonstrating its importance as a factor in economic life.1 Trust helps facilitate

a The three authors contributed equally to this work.

0024-6301/$ - see front matter � 2011 Elsevier Ltd. All rights reserved.

doi:10.1016/j.lrp.2011.01.001

http://dx.doi.org/10.1016/j.lrp.2011.01.001


http://www.elsevier.com/locate/lrp


cooperation, lowers agency and transaction costs, promotes smooth and efficient market exchanges,and improves firms’ ability to adapt to complexity and change. This stream of research holds thatfirms can find a wealth of benefits from trust, including cost savings and enhanced organizationalcapacities, but several works highlight how the subject has been largely under-appreciated in man-agement literature.2 What is also evident is that whether firms create mutually trusting relationshipsis a matter of their strategic choice e that is, they can determine the levels of trust they place in theirvarious relationships. We discuss trust as an integral part of the strategy formulation process, a focusthat is important because it highlights that trust is good, but a conditional good. Wicks et al. warnthat overinvesting in trust is undesirable from a strategic point of view: while some trust is bene-ficial in enabling transfer of tacit knowledge and risk taking, firms can trust too much or invest intrusting relationships that have little value for the firm, and thus misallocate precious resourcesand/or take unnecessary risks, with the potential for substantial negative effects on their innovationperformance. While such speculation has received wide attention, little empirical work has beendone to support the idea that trust is not necessarily always good, and none in the context of in-dustrial districts, although de Man and Roijakkers rehearse an interesting debate about the tradeoffbetween trust and control, while Lin et al. find that internet work interactions can enhance therejuvenation of a declining industrial network.3

there is a tipping-point beyond which increases in trust may bring

diminishing benefits, and can even decrease innovation returns

This study aims to fill this gap and explore the positive and negative impacts of trust on firminnovation in the industrial district context. Our empirical findings show that there is a tipping-point beyond which additional increases in trust may bring diminishing benefits, and can even de-crease innovation returns for the firms involved. The industrial district context provides a valuablesetting in which to observe the positive and negative impacts of trust on firms’ innovation, bothbecause the characteristic geographical proximity of firms in industrial districts provides consider-able opportunities for cooperation and for frequent, repeated, non-marked and informal ties, all ofwhich can foster mutually trusting relationships, and because innovation represents a key factor forthe success of firms in such environments.

The contribution of this study extends beyond the counterintuitive conclusion that trust can beharmful for organizations to suggest that the most beneficial strategy for innovation in networkedsettings is one that pursues an optimal level of trust in its relationships, one where the benefits interms of knowledge and capabilities for innovation outstrip the costs and risks required for theirdevelopment and maintenance. Beside the lesson that some level of monitoring is always neededbetween cooperating organizations if process loss and coordination errors are to be avoided, thepractical implication of these findings is not that trust should be avoided; rather, it is that firmsneed to steer clear of the propensity to stick only with familiar partners, which is likely to leadthem to a cognitive ‘lock-in’ that will tend to isolate them from the outside world. They wouldbe better advised to aim for a balanced portfolio of relationships that combines trusted and familiarties with new ones that can offer them a richer pool of alternatives in accessing new ideas, infor-mation and knowledge exchange. The successful experience of firms in the industrial districtssettings analysed here often seems to be associated with this mix of ties, providing both richnessin content and safe cooperation (through trusted ties) and access to novel and varied knowledge(through new ties). An interesting case is the Tau Ceramic company (located in Valencia’s ceramictile district), which has developed a smart and exclusive ceramic floor with many new features (in-cluding anti-slip characteristics for those with mobility difficulties) for a range of applications. Thisnew product grew out of Tau Ceramic’s dual portfolio structure, that illustrates this beneficialalliance strategy for innovation, where a mix of both trust-based and new relationships e includingwith both supplier and customer firms, and with supporting organisations such as trade and

Long Range Planning, vol 44 2011 119

professional associations, government agencies and research institutions e gives the firm the advan-tages of both trust-based cooperation and of novel and varied knowledge flows.4

The article is organized as follows: the next section provides an overview of the relationship betweentrust and innovation, after which we describe our analytical representation and provide basic informa-tion on our research design and study setting. After presenting our empirical results, we conclude witha discussion of our research contribution and its implications for managers and policy makers.

Trust and innovation

The positive effect of trust on firm innovationWe define trust as themutual confidence between parties to an exchange that none of themwill engagein opportunistic behaviour thatwould exploit any others’ vulnerabilities, and thereby violate the values,principles and standards of behaviour they have internalised as part of the exchange. Conditions of trustarise when parties have something at risk, and it is an important element becausee although such ex-changes carry the potential for opportunisme they promise to create preferred economic outcomes forfirms, such as lower transaction or agency costs. In that respect, trust can act as a substitute for formalcontrol mechanisms, facilitating dispute resolution, and allowing greater flexibility.5

Trust dissolves the boundaries between organisations and helps foster common interests, allowingfirms to increase the depth, breadth and efficiency of their mutual exchange of knowledge, thus help-ing them obtain access to a wider or deeper range of information and to more valuable resources. Thepositive association between trust and knowledge acquisition is consistent with the assumption thatlearning - particularly that involving difficult-to-transfer information and tacit knowledge e isenhanced by intensive and repeated interactions. Trust acts as a governance mechanism in embeddedrelationships between organisations, and facilitates the exchange of confidential information by di-minishing the risk that one party will opportunistically exploit it to another’s disadvantage. It alsofacilitates social exchange by reducing the need for time-consuming and costly monitoring, allowingpeople and organisations to devote more of their time to beneficial actions and endeavours. Ring andVan de Ven find that trust induces and supports joint efforts, increasing the likelihood of partnersgaining the help they need to attain their goals, while Nahapiet and Ghoshal note that participantsin trusting relationships are more willing to engage in social exchanges in general and cooperative in-teractions in particular. Moreover, trust promotes the exchange of a range of resources that are dif-ficult to put a price on, but which strengthen an organisation’s ability to solve problems and tocompete. A free flow of information depends on an atmosphere of trust, particularly when partnersoperate through resources that are produced cooperatively and which result from interorganisationalagreements, such as when a firm develops a project in collaboration with a research institution.6

With regard to the specific relationship between trust and innovation, previous researchers haveshown that trust in inter-organisational settings can create collaborative climates that foster inno-vation by increasing both the quantity and quality of information flows and the sharing of relevantknowledge and resources. Powell et al. have argued that, since individual organization may haveinsufficient information to compete on their own, innovation often depends on broadly dispersedinterorganisational relationships, while Ahuja has associated both direct and indirect ties with thegeneration of innovation. An important feature of trust is that firms that have to spend less cost andeffort on monitoring possible malfeasance by partners and suppliers can devote more energy to in-novation in new products or processes. Different forms of linkages between information exchangeand innovation have also been found by Daft and Becker; Tsai and Ghoshal; Utterback, and others.7

firms that don’t invest in trust may miss out on cost savings, or

developing organizational capabilities vital to their innovation objectives

120 The Dark Side of Trust

Taken together, these arguments suggest that firms that do not invest in trust may miss out onopportunities to create cost savings, or to develop those organizational capabilities vital for realizingtheir innovation objectives. We can expect that trust will enhance firms’ ability to exchange relevantinformation and tacit knowledge, as well as their opportunities for valuable combinations of re-sources, so exhibiting a positive impact on their innovation performance. Hence:

Proposition 1: There is a positive relationship between trust and innovation.

The negative effect of ‘too much’ trust on firms’ innovationMoral philosophers have argued that people can trust foolishly, that excessive trusting can be cul-pable, and that ‘saintly trust’ (i.e., trust without suspicion) can be dangerous and invite or exacer-bate abuse. The dangers of ‘trusting too much’ have been widely noted in fiction e take, forinstance, Kazuo Ishiguru’s novel The Remains of the Day where the butler (Stevens’) blind trustin his employer, Lord Darlington, leads him to becoming an accomplice in the latter’s unwittingassistance in the Nazi war effort.8

In business terms, too, trust clearly has value: but its benefits for firms’ ability to innovate do notpersist indefinitely. Firms that overinvest in truste trust too much or invest in trusting relationshipsthat have little value for the firmemay bemisallocating precious resources and/or taking unnecessaryrisks that could have substantial negative effects on their innovative abilities or performance. Clearlymany forces are at work that may not always be easy to disentangle: but we can pick out a few key ar-guments to represent the general tendency. Firmsmight devote toomuch time and effort tomaintain-ing trusting relationships, and this can affect the firm itself in a negativemanner. The higher the level oftrust, the more likely there is to be a ‘boomerang’ effect for the focal firm, which can take differentforms. First, we can expect an inverse relationship between trust and monitoring to operate, wherehigh levels of inter-organization trust are associated with low levels of monitoring, and low levels oftrust with high levels of monitoring. Firms that trust too much e and thus monitor inadequately ecan allow opportunists to steal from them with relative impunity: in combination with other factors,this insufficient monitoring can lead in turn to lower performance. The more organizations are awareof each others’ activities, the better they can coordinate their work, but some monitoring is also nec-essary to avoid potential coordination and process losses. Taken together, these factors suggest thata certain level of monitoring is necessary, no matter how high inter organizational trust becomes.9

Second, long-running trusting relationships are likely to arrive at the point where the organisa-tions involved no longer provide each other with new information and knowledge, and furtherexchanges of information become progressively more redundant. Last, but not least, the cost ofmaintaining their existing cooperative and trusting relationships may leave firms without the space(in terms of cash, time or effort) to cultivate promising new relationships. Portes and Sesenbrennerhave shown that firms’ obligations to their current partners (in terms of trust and other consider-ations), and the difficulties they encounter in freeing themselves from those obligations, can limittheir subsequent ability to pursue new opportunities.10 People in a firm need to spend time on fre-quent visits and meetings with contacts in other firms to cultivate mutually trusting relationships,and process the information they gain from these direct meetings. Thus existing obligations can beconsidered as a form of cost, the extent of which may mean firms can rarely afford to maintainmany such relationships, and therefore tend to focus on only a few ‘trusted’ firms, reducing theflow of new ideas into the group, resulting in lock-in and inertia. Thus ‘crony capitalism’ (whichis prevalent in Southeast Asia, particularly Indonesia) involves high levels of trust among networksof friends and family, but can impede the creation of economically viable institutions in both publicand private sectors. Since a firm’s relationships with its partners are, to some degree, interchange-able, a firm might be better off broadening its network by establishing ties with other partnerswhere information flows are less likely to be redundant, rather than investing time and resourcesin maintaining and reinforcing its existing ties with trusted partners.

Taken together, these arguments suggest that firms that invest too much in trusting relationshipsare less likely to have access to novel and varied knowledge thereby, over time, inhibiting their novel


recombination of knowledge. So we can argue that overinvesting in trusting relationships is unde-sirable from a strategic point of view because it leads to insufficient monitoring, and inhibits firms’access to diverse knowledge and novel flows of alternatives, both of which will impact negatively ontheir rates of innovation.

overinvesting in trusting relationships is strategically undesirable . it

leads to insufficient monitoring, and inhibits firms’ access to diverse

knowledge and novel flows of alternatives

In this study, the normative and institutionalized environments provided by industrial districtsare very likely to lead to the kind of situation outlined above, where longstanding partners feelingcompelled to maintain relationships that are not monitored adequately or that are no longer advan-tageous. A supplier, for instance, investing in creating and sustaining a high-trust relationship withthe management of a given customer and acting on that basis may face severe penalties, includinghaving its trust-based agreements renegotiated (e.g. for lower cost and/or greater quality specifica-tions than originally agreed to) or cancelled. Too high a trust level can create a suboptimal hedgeagainst opportunism (i.e., where there are no longer sufficient incentives to deter it) e if the rela-tionship founders in the end, the resources which both organizations have invested in creating orsustaining the trust will have been misused, risking negative impacts on both their innovationperformances.

Considerations about how much to trust recalls Aristotle’s advice that one should have a stableand ongoing commitment to the notion of trust, but that judgments about trusting others shouldbe made prudently and realistically: in other words, one ought to know whom to trust, how much totrust them, and with respect to what matters. All these arguments bring us back to Wicks et al.concept of ‘optimal trust’, according to which trust is good, but only a conditional good, and whichwe use here to mean levels of trust that meet either (or both) of two criteria:� They provide the focal firm with essential capacities that enable it to effectively implement and

maintain the firm strategies and processes it needs to achieve high rates of innovation; and� They provide it with benefits (e.g., cost savings, reduced risk, and/or strategic advantage) that

outstrip the costs and risks required to develop and maintain the trust involved.11

This study suggests that additional increases of trust beyond a certain point - the level of ‘optimaltrust’ e will bring diminishing benefits, and may even decrease innovation returns for the firm in-volved, by reducing their efforts towards gaining fresh knowledge. Hence:

Proposition 2: The impact of trust on firm innovation has a non-linear (inverted U) shape).

Research methodology

The empirical settingOur empirical research draws on a sample of Spanish industrial firms located in the Valenciaregion, the industrial structure of which is based on a number of districts specialising in such sec-tors as ceramic tiles, shoes, furniture, toys, etc. Most firms are SMEs e official statistics show 48%of the region’s firms have less than six employees, and only 1% more than 100. We use a conceptof ‘the district’ that adds a social component to the geographical or spatial agglomeration of firms,meaning a district is a population of firms and a community of people in a bounded geographicalarea: and this social aspect is important in understanding the role played by trust in interfirm re-lationships.12 Tables 1 and 2 display some relevant data about Valencia.


Table 1. Valencia region: descriptive statistics

GNP at market prices (in €m) 102,403 CV/SP 9,8%

Population 5,094,675 inhabitants

Land area 23,255 sq kilometres

Exports (in €m) 18,554.89

Imports (in €m) 19,767

Coverage rate % 82

Source: Instituto Valenciano de Estad�ıstica (IVE), Valencian Institute of Statistics (2008).

Valencia is an appropriate research site for several reasons. First, trust requires some of theenvironmental conditions which characterise districts and territorial agglomerations (of which Va-lencia provides many examples), and which are analyzed in our research. Second, the region hasa predominance of SMEs, where innovation depends to a greater extent on external sources ofknowledge and transmission mechanisms than it does in larger organizations, and where proximitybetween firms favours these mechanisms, allowing trust and other shared values and norms to func-tion most effectively. Finally, our interest was attracted to this particular site because of the chal-lenges it is currently undergoing resulting from changes in global markets: of course, the factthat two of the authors had strong local knowledge of the area also facilitating our understandingof these challenges in context, as well as our ability to collect data.

We consider the Valencian region as representative of other Spanish regions, and also of manyother manufacturing clusters in developed countries. It has traditionally been considered one ofSpain’s most vibrant regions, with (compared to others) an important industrial sector and a strongexporting focus. But most of Valencia’s industries are suffering significant difficulties as a conse-quence of changes in international markets, globalisation and the impact of emergent countries’manufacturing activities. In fact, the current ongoing economic crisis has affected this regionmore than others because of its specific economic structure, with the preponderance of its buildingsector (where both productivity and technological levels are low), and its industrial specializationon home equipment products (ceramic tiles, furniture, textiles, etc.) exposing it to some significantthreats.

Valencia’s two main trade associations (Confederaci�on Empresarial Valenciana (CEV) andAsociaci�on Valenciana de Empresarios (AVE)) consider it needs to become more diversified, whichthey suggest could involve extending the activity range of traditional industries by detecting activ-ities and technologies directly or indirectly related to them, whether already existent, in early de-velopment, or new creations. They also suggest firms in existing sectors should diversify byadding new product/service lines or by enlarging their supply range, or by undertaking radicallynew activities in emergent fields (biotechnology, new raw materials, ergonomics, health and lifequality, nanotechnologies, etc.). The position of established regional industrial districts in interna-tional markets has been widely threatened by competition from emergent regions and countries,particularly China. The worldwide availability of technology, the liberalization of some traditionalproduct markets (such as textiles and footwear) and many other globalisation factors have increased

Table 2. Valencia region: structure of GDP (%)

Agriculture, livestock and fisheries 2.3

Energy 2.0

Industry 16.4

Construction 13.0

Services 66.5

Source: Instituto Valenciano de Estad�ıstica (IVE), Valencian Institute of Statistics (2008).


competitive pressures in many existing industrial clusters, forcing them to make decisions abouthow to redesign their networks to remain competitive. Thus e like many other European regions -Valencia is undergoing a period of challenge, and innovation is vital if it is to counteract thisnew competitive pressure: but the trade-off for many firms is a choice between maintaining theirstrong and trust-based ties within their native territory and building new ties with globalperspectives.

the trade-off is between maintaining strong trust-based ties within

firms, native territory and building new ties with global perspectives

Ybarra has used quantitative criteria to identify 11 different industrial districts or clusters (foot-wear, leather products, toys, textile industry, ceramic tiles, food, wooden furniture, natural stone,glass, carpets, and foodstuffs), which are typical of mature or traditional sectors.13 The SpanishMinistry of Industry (2005) report identified 54 subdistricts in Valencia (as shown in Figure 1),accounting for 38.3 % of the region’s total manufacturing output. Overall, these industries havebeen relatively successful, with important presences in international markets, and similaritieswith the ‘Italian model’ are frequently mentioned e even including the range of goods produced.

Data collection and validityThe fieldwork was carried out during the autumn and winter of 2002. The firms selected for ourempirical research were intended to be representative of Valencian manufacturers, and includedfirms from eight different sectors (food, textiles, furniture, ceramics, leather, chemical products,machinery and paper), and were identified from their addresses and allocated four-digit StandardIndustrial Classifications (SICs) from public databases like ARDAN.14 A questionnaire addressed tothe general managers of sample firms yielded complete data for 156 firms, whose basic character-istics (descriptive statistics, Cronbach’s alpha for multiple-item variables) are shown in Table 3. Weused a pilot questionnaire and controlled for non-respondent bias, asking participants to answer ona five-point Likert scale for the sake of simplicity.

The table shows that our sample was fairly well spread across the industrial segments, ranging fromceramic tiles andmachinery (18.8% each), leather (14.9%), chemical products (13.6%), food (12.3%),textiles (9.7%), furniture (6.5%) to paper (5.2%), and (as noted above), confirms that most of ourmanufacturing firms were small: 73.4% had 11e50 employees, and only 0.6% more than 250. We

Figure 1. Map of the industrial districts in the Region of Valencia


Table 3. Sample firms: data & descriptive statistics, mean, standard deviation, Cronbach’s Alpha

Variables INNOV SIZE INDUST TRUST AGE

Mean 3.87 1.26 e 3.57 33.54

S.D. .19 .47 e .96 26.62

a .64 e e .82 e

Industrial

districts

Food Textile Furniture Ceramic

tiles

Leather Chemical

products

Machinery Paper

% 12.3 9.7 6.5 18.8 14.9 13.6 18.8 5.2

Size 11e50 employees 51e250 employees þ250 employees

% 73.4 26.0 0.6

N¼ 156, a¼ Cronbach’s alpha for all multiple-item variables.

also included control variables such as industrial district and size (collected through ARDAN) in ouranalyses, as well as testing for construct validity, convergent validity, discriminant validity, and validityof the subjective assessments of single respondents (see Appendix for further details).

Research designThe approach used to build our scale for measuring the variables involved a number of items widelyused in empirical research by previous authors: however, since we did not found a precise or de-finitive set of items, we adapted them to our particular case in some instances.

Independent variable: TrustAlthough a large number of antecedents exist, we were not able to find any really specific indicatorof trust for our research, and so collected a number of indicators applied in different fields or levelsof analysis and adapted them to our specific case and sample characteristics, resulting in the follow-ing statements to measure our independent variable (TRUST)15:

(1) Other firms can rely on your company without fearing you will take advantage of them, even ifthe opportunity arises;

(2) In general, your company always keeps the promises it makes to others;(3) Suppose your company is seeking to be a business partner in a joint project: you are confident

your company would do what is required in the agreement (i.e. what partners believe youshould do), even without a written contract that clearly specifies your obligations;

(4) You consider that other firms feel a special duty to stand behind you in times of trouble, andthat it is only fair that your company should give such support in return;

(5) Generally speaking, there is a trusting climate among suppliers and customers in the localarea, a feeling that most people can be trusted, or that you can deal with people easily; and

(6) You have confidence in a variety of organisations or institutions, such as the legal system, thegovernment and major companies.

Dependent variable: InnovationWith respect to innovation, we used particular references to show how authors have associated itwith social networks. Prior research has used many indicators of innovation, measuring it by com-bining several dimensions related to the levels of technology activities in and output generated bya given firm, and suggesting self-reported data as a valid indicator.16 Our questionnaire includedtwo sections to measure this variable (INNOV), asking respondents to report the number of prod-uct and process innovations they had introduced in their field of activity over the last three years,a measure of innovation based on Tsai and Ghoshal, and Meeus et al.17 although, once again, weadapted the questions to address the specific characteristics of our different case firms. Difficulties


arose because the smaller firms had few (or no) patenting activities or other legal protection fortheir innovations, and also small (if any) R&D departments. We tried to phrase our measurementquestions so as to capture all new elements or improvements across all phases of the productionprocess, including organisational improvements, asking about:

(1) Number of developments or introductions of new materials;(2) Number of developments or introductions of new intermediate products;(3) Number of developments or introductions of new components;(4) Number of developments or introductions of new product attributes;(5) New developments or introductions of new equipment;(6) Improvements in levels of automation;(7) Number of new methods of organising the productive activities; and(8) Use of new energy sources.

Control variables and analysis methodWe also included three main control variables; size, industry and age. Although many innovationstudies have used a great number of factors and control variables, we have also found supportfor our approach of focusing on a selected number of control variables. Assuming that the sizeof a firm can affect its innovation capacity, we controlled for firm level economies and disecon-omies of scale through a variable (SIZE), specified as the number of employees. Large organisationstend to have more resources, which can enhance their innovation and performance,18 as well asusually being more powerful, giving them some advantages in gaining support for their businessoperations and innovation activities from other organisations, especially public institutions. Asinnovation processes can vary from one district to another, we used each firm’s sectoral locationas a second control variable (INDUST), assigning different scores to firms from different industrialdistricts. In effect, this was also a spatial control, since firms in our sample devoted to the same kindof activity tend to be geographically concentrated. Our theoretical propositions indicate that be-longing to a particular industry and location could have a direct effect on innovation. To definethe sample, we employed a random stratified process to select firms with proportional assignationaccording to size and product segment across our eight different industries. Firm age was also in-cluded as a control variable (AGE), since some authors have suggested that the time-evolution offirms in industrial districts affects their performance.19 In our analysis we first calculated the de-scriptive statistics (means and standard deviations) and then the Cronbach’s alpha for all multi-ple-item variables to prove the validity of the aggregation. We used Pearson’s correlation matrixto analyse the correlation of all pairs of variables, and tested our propositions by running a non-linear, inverted U-shaped (quadratic) regression analysis for the independent variable against thedependent variable.

ResultsTable 4 shows Pearson’s correlations for all pairs of variables, showing that trust is indeed associ-ated with innovation, as hypothesised. However, we found the control variables SIZE and INDUSTwere associated with neither the dependent nor independent variables, although AGE was nega-tively associated with innovation.

Table 5 shows the results of a non-linear (inverted U-shaped) regression analysis carried out toanalyse the impact of trust on firms’ innovation, and the significant correlations obtained againsupport our research proposition P1. The regression for the quadratic term was significant andthe coefficient negative for the trust variable, indicating the inverted-U shape of the function, aspredicted by the research proposition P2 and illustrated in Figure 2. Moreover, the value of theF-statistic was 6.530 and the fit of the global model was acceptable, since it was significant andhad a Cronbach’s alpha below 0.01. These results demonstrate that increased trust is positively as-sociated with greater firm innovation up to a certain ‘optimal trust’ point, beyond which furtherincreases of trust seem to be associated with diminishing benefits, and even with decreasing


Table 4. Bivariate correlation for all pairs of variables.

Variables INNOV SIZE INDUST TRUST AGE

Innvon 1.000

Size .067 1.000

Indust e.002 e.038 1.000

Trust .291)) e.023 .039 1.000

Age e.115) .030 e.173)) .003 1.000

N¼ 156 Pearson’s correlation is significant at levels: )p < .10; and ))p < .05.

innovation returns for the firm involved: in other words our results confirm our basic thesis thattrust is good, but too much trust is not.

Discussion and conclusionThis study provides a better understanding of the impact of trust on firm innovation in industrialdistricts: exploring this relationship leads to valuable results with implications both for research andfor practice.

Research contributionA large body of literature has demonstrated that industrial districts tend to act as innovation-enabling network structures. But, in contrast to the traditional perspective of trust as an enablerof productive relationships, this article highlights a ‘dark side’ that can make the establishedtrust-based relationships typical of such environments detrimental to innovation. This study sug-gests that some level of inter-organizational trust is essential for innovation in network-based con-texts (such as industrial districts), since it enhances firms’ ability to exchange relevant informationand tacit knowledge, and thus increases their opportunities for accessing new valuable combina-tions of resources, a conventional view supported by empirical evidence showing that informalnetworks help explain the relationship between geographical proximity and cooperation.20 How-ever, we extend this argument to suggest that, in fact, too much trust is undesirable from a strategicpoint of view, since it may eventually inhibit firms’ access to diverse knowledge and novel flows ofalternatives e thus having a negative impact on their rates of innovation e and can also lead firmsto employing insufficient monitoring, risking opportunistic actions by partners having negativeaffects on firm performance. In support of this theory, this study’s findings demonstrates this‘dark’ side to firms’ embeddedness in their district, where the over-entrenched nature of inter-or-ganizational ties mean that (beyond a threshold level) higher levels of trust are associated withdiminishing innovation returns for the firms involved. These results have important implicationsfor the normative ideal for innovation in networked settings, suggesting that the most beneficialstrategy for innovation is the one that pursues an optimal level of trust e i.e., that provides

Table 5. Results of inverted U-shaped function analysis: quadratic method.

Innovation

Variable Inverted U-shaped R2 Adjusted F Statistic

Trust .899))) (.375) .079 6.530)))Trust))2 e.109)) (.059)

Constant 1.063) (.590)

N¼ 156, )p < .10; ))p < .05; and )))p < .01. Non-standardized regression coefficients (errors in brackets).


Innovation

Trust

Trus

t

6543210

4.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

.5

Quadratic

Observed

Figure 2. Inverted U-Shaped Function Analysis: Trust and Innovation.

benefits in terms of innovation capabilities only where these outstrip the costs and risks involvedin their development.

the most beneficial strategy for innovation is the one that pursues an

optimal level of trust where benefits in terms of innovation capabilities

outstrip the costs and risks involved

These findings takes us back to (and are in line with) traditional organizational learning wisdom,that emphasizes the need to balance the competing goals of exploiting existing information to im-prove efficiency and returns from present strategies, competencies and procedures to strengthenfirm success, and exploring to develop new knowledge and find emerging innovations to contributeto future profits.21 Our research suggests the need for firms to achieve a similar sort of ‘balancingact’ between exploiting their existing ‘trusted’ ties and exploring new ones. This study also recallsthe empirical research supporting the theoretical argument of over-embeddedness,22 arguing thatunder certain conditions embeddedness turns into a liability as firm networks become ossifiedand out of step with the changing demands of their environments, leading ultimately to decline.Our findings also align with the wide body of existing research linking industrial districts with in-novation that has supported the role of relational capital as an important driver of firms’ innovativecapacity, in particular, finding support in the contingent view of the effects of social capital on firmsand their innovation processes.23

Yet, while being in line with previous empirical research, our findings also contribute to theexisting literature on trust and innovation in considering the negative effects of high trust levelson performance, which have not previously been empirically explored, despite several researchers’suggestions that such a downside could exist.24 Our findings suggest that the relationship betweentrust and innovation must be treated with caution, because the two do not necessarily increase pro-portionally to each other and, in fact e beyond an optimal point e increases in the amount a firminvests in trust may be associated with decreases in its innovatory capacity. The contribution ofthese findings extends beyond the simple counterintuitive conclusion that trust can be harmful


for organizations to address some limitations of previous studies and raise some questions as to theaccepted wisdom about the relationship between trust and innovation.

Suggestions for managers and policy makersThe practical implication of our findings is not that trust among cooperating organizations shouldbe avoided: rather, it is that a firm’s investments in certain relationships can reach a point wherethey produce no further benefits, and it would be better to invest the time and energy in developingnew contacts so as to create novel opportunities and a richer pool of alternatives. Another impor-tant implication is that some monitoring between organizations is needed if process loss and co-ordination errors are to be avoided, and this appears to be particularly important in industrialdistricts, where high levels of embedded inter-organizational trust are especially likely to make or-ganizations reluctant to monitor one another. In other words, too little monitoring is na€ıve, regard-less of the level of trust: a little scepticism never hurt anyone e or any organization. In terms ofincreasing understanding of the strategic value of trust in general, we also support calls for the in-clusion of trust in performance measurement systems.25

This is also particularly relevant in the broader current situation, when globalisation pressuresrepresent an additional challenge for firms whose local ‘territories’ are no longer self-contained,and who must therefore seek to establish contact with a wider group of global actors to sustain theircapacity for innovation. Our main suggestion is that network structures focused only on trustingrelationships will (eventually) decrease firms’ ability to innovate, so they should develop structuresbased on a portfolio of ties that balances ‘safe’ cooperation with relationships designed to help themsearch out and explore a wider range of knowledge.

What lessons can be learnt from the Valencia region’s experience? A number of illustrative casesfeaturing both industrial districts and individual firms support our thesis. Various differentcourses of actions have been undertaken and solutions proposed in the region recently to addressthe new competitive challenges faced by its districts and their firms. Those industrial districts char-acterised as being focused only on a limited number of trusted ties (and therefore at risk of beinglocked-in to them) e such as the furniture district around the regional capital and the footweardistrict centred on the southern town of Alacant e have lost much of their previous competitive-ness. Although there are some individual exceptions, it seems that firms and local policy makers inthese districts have generally failed to introduce new knowledge and technologies to face currentchallenges, and have seen falling growth rates for some years. In the furniture case, the district hasoften been slow to understand the importance of implementing the new marketing concepts in-troduced by such competitors as IKEA or Habitat to move focus from a single product to a widerset, or adopting more modern distribution systems. The difficulties and poor innovation perfor-mance of the footwear district contrasts with other regions’ successful experiences in the same sec-tor, for example that of the Montebelluna (Sportsystem) district in the north of Italy, which hasachieved outstanding technology and design performance. The main difference between the twoexperiences has been ascribed to the Italian district’s better connections with ‘distant’ ties, allowingit to respond to competition more quickly by increasing efficiency and collectively relocating pro-duction activities.

On the other hand, Valencia has seen some interesting and successful experiences. The toy dis-trict located in Ibi (Alacant) has undergone a dramatic restructuring process, with some of its mainsuppliers assuming new leadership roles, and crossing their ‘natural’ boundaries to use their existingtechnologies to enter new markets (automobile, home, plastics, etc.). Other examples can be foundin the textile industry, where a number of firms have moved towards a new product segment - theso-called ‘technical textile’ e and now serve markets far away from their traditional ones. An evenmore interesting case concerns the ceramic tile district, which has very long-lasting and trusted linkswith Italian ceramic tile manufacturers from Sassuolo; interactions between the two districts havecreated models that, while very different, are open to each other and exchange technology, designand marketing innovation knowledge. The district’s dynamic response to global competition hasled to it being rated as the region’s best performer over the past several years.


Taken together with our findings, these experiences provide food for thought for both regionalplanners and individual entrepreneurs. Regional planners must map all relevant intermediary agents,local institutions and supporting organisations, in order to balance some monitoring with safe coop-eration and access to novel information and knowledge. Individual entrepreneurs could benefit fromour findings as a guide to allocating their time and effort in building portfolios where ties providingboth trust-based cooperation and novel and varied knowledge coexist. In terms of exploiting the socialcapital available to them, we suggest managers should map their firm’s social capital as a visual rep-resentation, with themselves at the centre and identify the links to all their independent knowledgeproviders, so as to allow them to assess the extent of their diversification, and spot any obvious‘gaps’. This representation would also allow managers to examine and compare their links with thoseof their competitors, with a view to optimising their firms’ innovation potential.

entrepreneurs . should . build portfolios where ties providing both

trust-based cooperation and novel and varied knowledge coexist

Limitations and future researchThis article has three main limitations, which all point towards possible directions for future research.First, we haven’t studied the fine-grained process throughwhich network structures are created andmod-ified (as, for instance, Dyer andNobeoka have analysed howToyota built the dense supplier network thatrepresents an important driver of its success26): this could constitute an interesting and important area forfuture research. In this regard, it would be interesting to study the process through which firms plan andimplement their future network strategies. In the same vein, this study lacks the longitudinal analysis nec-essary for a dynamic study: using cross-sectional data precludes considerations of some interesting ques-tions about the directionof causality in the relationship between trust and innovation. Another promisingarea of inquiry would be analysing the dynamics of the evolution of firms’ network structures in districtsfacing external challenges and opportunities, and identifying the extent to which inertia constrains theirability to reconfigure their patterns of network ties. Finally, this study does not account for the influence ofsocial capital dimensions other than trust, which might contribute stronger or weaker impacts to firms’innovation performance - future theoretical and empirical research should include these lines of inquirywhen investigating the relationship between the two factors.

AcknowledgementsThe authors would like to thank the outgoing Editor-in-Chief, Professor Charles Baden-Fuller andtwo anonymous reviewers for their encouragement and valuable suggestions in the preparation ofthis article. This research was financially supported by the Spanish Ministerio de Ciencia yTecnolog�ıa, Plan Nacional de IþDþi research project number ECO2008-04708/ECON. All threeauthors have contributed equally to this article.

Appendix

ValiditySince a measure of a construct is only valid to the extent that it actually measures what it purportsto measure, we controlled for several different factors to ensure construct validity:

� The logic of construct validity suggests that multiple indicators of the same theoretical constructshould be positively and strongly related. In particular, Phillips notes that convergent validity re-fers to ‘the degree to which multiple attempts to measure the same concept by different methods are


in agreement’.27 We have included Cronbach’s alpha here as a test of the reliability of the mea-surement. For district firms, the least favourable Cronbach’s alpha value corresponded to themultiple-item innovation scale, but its score of 0.64 was still within the limits of tolerance sug-gested in previous literature.

� The degree to which two theoretical constructs differ from each other is termed discriminant val-idity. We have added the correlation matrix since the discriminant validity of two constructs canbe assessed by demonstrating that the correlation between a pair of constructs is significantlydifferent.

� Finally, the validity of single respondents’ subjective assessments has been questioned. In general,construct data depends on respondents’ ability to assess ties accurately. There is a potential samplingbias due to the fact that the survey relied only on the responses of companymanagers, and thus someinformal exchanges (of which they might have been unaware) have been excluded from this study.We found similar concerns noted in previous research, and adopted similar solutions to control forthem, evaluating the validity of answers against data from a knowledgeable second respondent whowas able to provide an accurate report. A second sample of 25 randomly selected respondent firmswere asked to complete our survey to check the accuracy of main sample managers’ responses anda second executive agreed to provide the information independently of the previous assessment(similar forms of control can be found in Baron and Markman). The comparison of means tech-nique was used to analyse possible second respondent bias, but no significant mean differenceswere found on analysis, confirming our conclusion that the validity of our measures was acceptable,and that our self-reporting measure provided a reasonable valid proxy of original responses.28

References
1. D. Gambetta (ed.), Trust: Making And Breaking Cooperative Relations, Blackwell, New York (1988); B. A.
Misztal, Trust In Modern Societies: The Search For The Bases Of Social Order, MA Polity Press, Cambridge(1996); D. M. Rousseau, S. B. Sitkin, R. S. Burt and C. Camerer, Not so different after all: a cross-disci-pline view of trust, Academy of Management Review 23(3), 393e404 (1998); K. Smith, S. Carroll andS. Ashford, Intra- and interor-ganizational cooperation: toward a research agenda, Academy of Manage-ment Journal 38, 7e23 (1995).

2. R. C. Mayer, J. H. Davis and F. D. Schoorman, An integration model of organizational trust, Academy ofManagement Review 20(3), 709e734 (1995); Smith K, Carroll S, Ashford S. (1995) ibid.; R. H. Frank, Pas-sions Within Reason: The Strategic Role of Emotions, Norton, New York (1988); T. M. Jones, Instrumentalstakeholder theory: a synthesis of ethics and economics, The Academy of Management Review 20(2),404e437 (1995); K. J. Arrow, The Limits of Organization, Norton, New York (1974); A. Smith, An Inquiryinto the Nature and Causes of the Wealth of Nations, Liberty Classics, Indianapolis (1981); M. Korsgaard,D. Schweiger and H. Sapienza, Building commitment, attachment, and trust in strategic decision-makingteams: the role of procedural justice, Academy of Management Journal 38(1), 60e84 (1995);D. J. McAllister, Affect- and cognition-based trust as foundations for interpersonal co-operation in orga-nizations, Academy of Management Journal 38(1), 24e59 (1995).

3. See also A. C. Wicks, S. L. Berman and T. M. Jones, The structure of optimal trust: moral and strategicimplications, The Academy of Management Review 24(1), 99e116 (1999); A.-P. de Man and N. Roijakkers,Alliance governance: balancing control and trust in dealingwith risk,LongRange Planning 42(1), 75e95 (2009)who deal with trust paradoxes directly; H.-M. Lin, H. Chen, P. J. Sher and H.-C. Mei, Inter-networkco-evolution: reversing the fortunes of declining industrial networks, Long Range Planning 43(5/6),611e638 (2010).

4. G. Padula, Enhancing the innovation performance of firms by balancing cohesiveness and bridging ties,Long Range Planning 41(4), 395e419 (2008).

5. A. C. Wicks, S. L. Berman, T. M. Jones. (1999) op. cit. at Ref. 3; J. B. Barney and M. H. Hansen, Trust-worthiness as a source of competitive advantage, Strategic Management Journal 15, 175e216 (1994); G. R.Jones and J. M. George, The experience and evolution of trust: implications for cooperation and team-work, Academy of Management Review 23, 531e546 (1998).

6. P. S. Ring and A. H. Van de Ven, Developmental processes of cooperative interorganizational relation-ships, Academy of Management Review 19, 90e118 (1994); J. Nahapiet and S. Ghoshal, Social capital,


intellectual capital, and the organizational advantage, Academy of Management Review 23(2), 242e266(1998).

7. S. Knack and P. Keefer, Does social capital have an economic payoff? A cross country investigation, TheQuarterly Journal of Economics 112(4), 1252e1288 (1997); C. P. Ruppel and S. J. Harrington, Sharing knowl-edge through intranets: a study of organizational culture and intranet implementation, IEEE Transactions onProfessional Communication 44(1), 37e52 (2001); Special Issue On Communication as a Social Constructwithin an Information Society.W. W. Powell, K. W. Koput and L. Smith-Doerr, Interorganizational collab-oration and the locus of innovation: networks of learning in biotechnology, Administrative Science Quarterly41(1), 116e145 (1996); G. Ahuja, Collaboration networks, structural holes, and innovation: a longitudinalstudy, Administrative Science Quarterly 45, 425e455 (2000); R. L. Daft and S. W. Becker, The InnovativeOrganization, Elsevier, New York (1978); W. Tsai and S. Ghoshal, Social capital and value creation: therole of intrafirm networks, Academy of Management Journal 41(4), 464e474 (1998); J. M. Utterback,Mastering the Dynamics of Innovation, Harvard Business School Press, Boston (1994).

8. L. C. Becker, Trust as noncognitive security about motives, Ethics 107(1), 43e61 (1996); F. Flores andR. C. Solomon, Creating trust, Business Ethics Quarterly 8(2), 205e232 (1998); R. Hardin, Trustworthi-ness, Ethics 107, 26e42 (1996); K. Ishiguru, The Remains of the Day, Vintage books, New York (1989).

9. C. W. Langfred, Too much of a good thing? Negative effects of high trust and individual autonomy in self-managing teams, Academy of Management Journal 47(3), 385e399 (2004); J. D. Orton and K. E. Weick,Loosely coupled systems: a recoceptualization, Academy of Management Review 15, 203e223 (1990).

10. A. Portes and J. Sesenbrenner, Embeddedness and immigration: notes on the social determinants ofeconomic action, American Journal of Sociology 98, 1320e1350 (1993).

11. Aristotle, Nicomachean ethics, translated by M. Ostwald, , MacMillan, New York (1986); Wicks A. C, Ber-man S. L, Jones T. M. (1999) op. cit. at Ref. 3.

12. At that point we follow the so called Marshallian district as defined and developed by the Italian econo-mist Giacomo Becattini see for instance G. Becattini, Alcune considerazioni Sull’unit�a di Indagine inEconomia Industriale, Revista di Economia e Politica Industriale (1), 1e8 (1979) Dal Settore Industrialeal Distretto Industriale.

13. J. A. Ybarra, Industrial Districts and the Valencian Community, OIT, Discussion Papers DP/44, New Indus-trial Organisation Programme, Geneva (1991).

14. The ARDAN is a public database published by IMPIVA, a regional industrial policy agency, that providesproduction and financial information about all manufacturing firms with annual revenues above €240,000ARDAN, Comunidad Valenciana 2000, Directorio e informe econ�omico-financiero, Consorcio de la ZonaFranca de Vigo, Vigo (2000).

15. See S. Knack, P. Keefer. (1997) and W. Tsai, S. Ghoshal. (1998), both op. cit. at Ref. 7; B. F. Blumberg,Cooperation contracts between embedded firms, Organization Studies 22(5), 825e852 (2001); M. Dakhliand D. de Clercq, Human capital, social capital, and innovation: a multicountry study, Entrepreneurship &Regional Development 16, 107e128 (2004).

16. D. Keeble, Small firms, innovation and regional development in Britain in the 1990s, Regional Studies 31,281e293 (1997).

17. W. Tsai and S. Ghoshal (1998) op. cit. at Ref. 7.; M. T. H. Meeus, L. A. G. Oerlemans and J. Hage, Patternsof interactive learning in a high-tech region, Organization Studies 22(1), 145e172 (2001).

18. W. Tsai, Knowledge transfer in intraorganizational networks: effects of network position and absorptivecapacity on business unit innovation and performance, Academy of Management Journal 44, 996e1004(2001).

19. A. Glasmeier, Technological discontinuities and flexible production networks: the case of switzerland andthe world watch industry, Research Policy 20, 469e485 (1991); R. Pouder and C. St. John, Hot spots andblind spots: geographic clusters of firms and innovation, Academy of Management Review 21(4),1192e1225 (1996).

20. C. Felzensztein, E. Gimmon and S. Carter, Geographical co-location, social networks and inter-firmmarketing co-operation: the case of the salmon industry, Long Range Planning 43(5/6), 675e690 (2010).

21. D. A. Levinthal and J. G. March, The myopia of learning, Strategic Management Journal 14, 95e112.(1993); J. M. Hite and W. S. Hersterly, The influence of the firm life cycle on the evolution of entrepre-neurial dyadic network ties, Strategic Management Journal, (1999) Special Issue Conference on StrategicNetworks, Northwestern University.

22. B. Uzzi, Social structure and competition in interfirm networks, The paradox of embeddeness, Adminis-trative Science Quarterly 42, 35e67 (1997).


23. B. McEvily and A. Zaheer, Bridging ties: a source of firm heterogeneity in competitive capabilities, Stra-tegic Management Journal 20, 1133e1156 (1999); R. A. Capello, Spatial transfer of knowledge in high-technology milieux: learning versus collective learning processes, Regional Studies 33(4), 353e368(1999a); R. A. Capello, Measurement of collective learning effects in Italian high-tech milieux, R�eveud’Economie R�egionale et Urbaine 3, 449e468 (1999b); T. Rowley, D. Behrens and D. Krackhardt, Redun-dant governance structures: an analysis of structural and relational embeddedness in the steel and semi-conductor industries, Strategic Management Journal 21, 369e386 (2000); M. Woolcock, Social capital andeconomic development: toward a theoretical synthesis and policy framework, Theory and Society 27,151e208 (1998).

24. R. M. Kramer, Trust and distrust in organizations: emerging perspectives, enduring question, inJ. T. Spence, J. M. Darley and D. J. Foss (eds.), Annual Review of Psychology vol. 50, PaloAlto CA, Annual Reviews, 569e598 (1999).

25. K. Kolehmainen, Dynamic strategic performance measurement systems: balancing empowerment andalignment, Long Range Planning 43(4), 527e554 (2010).

26. J. H. Dyer and K. Nobeoka, Creating and managing a high performance knowledge sharing network: thecase of Toyota, Strategic Management Journal 21, 345e367, (2000) Special Issue.

27. L. W. Phillips, Assessing measurement error in key informant reports: a methodological note on organi-zational analysis in marketing, Journal of Marketing Research 18, 395e415 (1981).

28. W. Tsai, Social capital, strategic relatedness and the formation of interorganizational linkages, StrategicManagement Journal 21, 925e939 (2000); R. A. Baron and G. D. Markman, Beyond social capital: therole of entrepreneurs’ social competence in their financial success, Journal of Business Venturing 18(1),41e60 (2003).

BiographiesF. Xavier Molina-Morales is a Full Professor at the Universitat Jaume I in Castellon, Spain. He graduated in

Economics at Valencia University in Spain, took an MBA at the Maastricht School of Management in Holland and

holds a PhD in Management from the Universitat Jaume I. His research focuses on Industrial Districts, and he has

published numerous works and papers in the fields of strategy and regional science, including Management and

Development Review, Journal of International Management, European Planning Studies, Environment & Planning A,

Research Policy, Entrepreneurship and Regional Development, Strategic Management Journal. Department of Business

Administration & Marketing, AERT Research group (CSIC-Associate Unit), Campus Riu Sec, Universitat Jaume I,

12080 Castell�on, Spain. tel: þ34 964 387117 fax: þ34 964 728629 e-mail: [email protected].

M. Teresa Mart�ınez-Fern�andez is an Associate Professor at the Universitat Jaume I. She graduated in Business Ad-

ministration at Valencia University in Spain and holds a PhD in Management from the Universitat Jaume I. Her

research focuses on interorganisational relationships in Industrial Districts and on the ceramic industry in par-

ticularly. She has published numerous works and papers in the field of strategy, including Environment & Planning

A, European Planning Studies, Research Policy, International Journal of Human Resources and Management, Entre-

preneurship and Regional Development, Strategic Management Journal. Department of Business Administration &

Marketing, AERT Research group (CSIC-Associate Unit), Campus Riu Sec, Universitat Jaume I, 12080 Castell�on,

Spain. tel: þ34 964 387117 fax: þ34 964 728629 e-mail: [email protected].

Vanina Jasmine Torl�o is an EU Research Fellow and a visiting lecturer in Business Strategy at Cass Business School

(London). She graduated in Management at Luiss Business School in Italy and holds a PhD in Business Admin-

istration from the University of Bologna. Her research is focused on the co-evolution of social networks, and

individual and firm attributes (such as attitudes and performance), with a particular focus on the mutual de-

pendence between influence and selection processes that shape the relation between social networks and perfor-

mance. She has published numerous works and book chapters in the field of strategy, organisational behaviour and

social networks, and was awarded Best Paper at the 2007 Academy of Management Proceedings. Department of

Management, Cass Business School, 106 Bunhill Row, London EC1Y 8TZ (UK). tel: þ44 2070408630 fax: þ44

2070408328 e-mail: [email protected].


mailto:[email protected]



the dark side of trust: the benefits, costs and optimal levels of trust for innovation performance

Documents