why reputation is not always beneficial: tolerance and opportunism in business networks
TRANSCRIPT
The Journal of Socio-Economics 38 (2009) 908–915
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Why reputation is not always beneficial: Tolerance and opportunism inbusiness networks
Martin Abraham ∗
School of Business and Economics, University of Erlangen-Nürnberg, Findelgasse 7/9, 90402 Nuremberg, Germany
a r t i c l e i n f o
Article history:Received 28 December 2008Received in revised form 19 April 2009Accepted 3 May 2009
JEL classification:A13D21D83L14L22
a b s t r a c t
Many researchers in economics as well as sociology have stressed the important role of business net-works for cooperation, trust, and performance. This claim is based on solid theoretical arguments as wellas empirical findings. However, neither theory nor the selective empirical results support the view thatnetworks are always beneficial for economic transactions. This paper begins with an observation that forthe purchase of IT products, network embeddedness leads to even more problems for the customer. Inorder to explain this effect, possible reasons for this phenomenon are discussed using theory as well asempirics. The most promising explanation for this special case is the effect of uncertainty and incom-plete information ex post. In order to reduce this uncertainty, the buyer forms beliefs on the basis of theopinions existing in a shared network or group. However, if the network members have the same prob-lem of uncertainty, suppliers have an incentive to reduce their performance because such behavior will
Keywords:ReputationBU
not be detected and sanctioned. An analysis of the customer’s tolerance to a supplier’s behavior in busi-ness transactions yields support for this argument. Even if problems in a transaction are kept constant,customers give suppliers more reputational credit if they share a common network.
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. Introduction
During the last two decades, the concept of networks hasecome a key idea in economic sociology. Based on the pioneeringork of Granovetter, Burt, Powell and others (Granovetter, 1973,
985; Burt, 1992; Powell et al., 1996), the idea of markets as struc-ures of social interactions is now a well established theory notnly in sociology (see for an overview Nohria and Eccles, 1992;wedberg, 1993; Powell and Smith-Doerr, 1994), but for economistss well (see e.g. Casson and Cox, 1997; Economides, 1996; Csorba,008; Fudenberg and Tirole, 2000). At the core of this theoreticaloncept, we find the idea that the social embeddedness of economicransactions is able to foster trust and cooperation between part-ers with partially conflicting interests. A large number of empiricaltudies in different fields provide convincing evidence for this linef reasoning.
However, it is often overlooked that networks are complex struc-ures that may also yield unintended consequences for its members.
etworks may restrict people’s choices by norms, information bar-iers, or power. Of course, as the results from a vast literature tells, networks may help to solve trust problems in social and eco-omic exchange, but sometimes they may instead turn out to have
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© 2009 Elsevier Inc. All rights reserved.
the opposite effect. Consequently, we should try to find out underwhich circumstances networks are beneficial and when they willfail to enhance cooperation.
In this paper, I present an example of unintended consequencesof network embeddedness for economic transactions. In a sam-ple of German firms, network embeddedness turns out to yieldmore problems in a transaction with a supplier of IT products. Afterproviding relevant evidence, I will also discuss and test differentexplanations for this observation.
2. The problem of trust and opportunism in economictransactions
In social and economic theory it is a well-known fact that anyexchange situation is principally jeopardized by incentives to defecton the exchange partner. This is because people do not have com-plete information about the partner’s abilities and intentions. In ourexample, a firm – the customer – wants to buy information tech-nology (let’s say a computer) from a supplier. The customer doesnot know in advance if the product has the quality promised orif the supplier will deliver in time. Although contracts allow one
to specify these points and to establish sanctions in case of fraud,there are considerable problems to this kind to safeguard againstopportunism. First, the exchange partners will not be able to specifyall contingencies ex ante, and second, there are considerable coststo enforce a contract by courts. As it is well known in sociological
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M. Abraham / The Journal of S
see Durkheim, 1973[1893]: book I, Ch. 7) as well as economic the-ry (Williamson, 1985: 56–60), contracts are not able to solve theroblem of opportunism in exchange relations completely. Hence,ven if a contract exists, placing the order requires a certain amountf trust on the side of the customer.
The theoretically possible solutions to this problem are wellnown. Besides the assignment of enforceable contracts (ofteneferred to as institutional embeddedness, e.g. Weesie and Raub,996), temporal embeddedness (Buskens and Weesie, 2000) can besed to solve problems of trust. First, opportunistic behavior maye avoided if the partners have a chance for future exchange. Inhis case, the threat of future sanctions by the exchange partner
ay yield cooperative behavior.1 Some studies provide empiricalvidence for this mechanism. Buskens and Weesie show on theasis of a vignette study that the expectation of future business
ncreases trust (Buskens and Weesie, 2000). Heide and Stump1995) present empirical evidence for the hypothesis that futureusiness is especially important in situations of uncertainty. How-ver, this so-called shadow of future (Axelrod, 1984) is not alwaysong enough to establish cooperation in real world transactions.n particular, the exchange of goods and services, which are notought regularly and often, will not provide the possibilities forngoing business relationships. Second, a vast literature has shownhat a shared history of business transactions may also fosterooperation between the exchange partners. This ‘shadow of theast’ may deter opportunistic behavior because it enables us to
earn about our business partner’s preferences and his reliabilityBuskens and Raub, 2002). Moreover, people tend to developommitments over time which stabilize the exchange relationshipnd foster cooperation (Lawler and Yoon, 1996; Yamagishi et al.,998). Although there is clear empirical evidence that successfulxchanges in the past leads to trust and cooperation in the futureGulati, 1995; Gautschi, 2000; Rooks et al., 2006), this mechanismas only limited practical consequences. Obviously, most firmsave to do business with new suppliers or customers in order tourvive on dynamic markets. Taken together, temporal embed-edness may reduce opportunistic behavior in a transaction, butrms will not be able to restrict themselves to transactions withell-known and stable exchange partners.
Due to the limited usefulness of institutional and temporal coop-ration mechanisms, network theorists claim that the structuralmbeddedness (Granovetter, 1985) is the key mechanism whichnables cooperation and trust between exchange partners. At theore of this theoretical argument is the idea that opportunisticehavior may be punished by third parties that are part of aommon network structure. The information mechanism in net-orks enables people to obtain information about the past behavior
f others and to act on the basis of this information. Exchangeartners showing opportunistic behavior can be identified andvoided or punished in future transactions. The vast literature onndirect reciprocity (e.g. Alexander, 1987; Nowak and Sigmund,998, 2005) provides evidence that people make use of such infor-ation even if it does not yield direct benefits. An extensive
xperimental literature demonstrates that people are more will-ng to help somebody who had helped others (Engelmann andischbacher, 2002; Greiner and Levati, 2005; Seinen and Schram,
006). On the other side, people who are not involved in anxchange are willing to punish opportunistic actors who cheatheir partners, even if this leads to costs for the punisher (Fehrnd Fischbacher, 2004). However, the possibility of observing other1 This mechanism can be modelled using game theoretical tools. Especially, thenalysis of iterated games offers the possibility to reveal the conditions for this kindf solution (see therefore e.g. Aumann, 1981; Fudenberg and Tirole, 1993; Axelrod,984).
conomics 38 (2009) 908–915 909
people’s exchanges is limited because of the restricted number ofdirectly connected network members and the private characterof most exchange situations. Consequently, the reputation mech-anism plays a central role for this line of reasoning (see also Kreps,1990). In most cases, we do not observe the exchange behaviordirectly, but those who do will tell others about the result. Thisway, network members acquire a reputation which enables othersto avoid those with a bad reputation (Allen, 1984; Buskens, 1999;Fombrun and Shanley, 1990; Raub and Weesie, 1990). Moreover,the research on indirect reciprocity provides evidence that punish-ment of opportunistic behavior is not restricted to exclusion fromexchange.
Taken together, an actor’s reputation in a social system may pro-vide an incentive to behave cooperatively in economic exchangesituations. The logic of this idea is straightforward and we find manystudies providing empirical support for this kind of mechanism.For example, McMillan and Woodruff present evidence that net-work embeddedness of business partners in Vietnam leads to morecredit for the supplier (McMillan and Woodruff, 1999). Powell et al.show that networks foster innovation because they enable trust andcooperation in strategic alliances (Powell et al., 1996). In view of ourempirical focus, Rooks et al. analyzed the effect of network embed-dedness and other characteristics of the transaction on problemswith IT purchases (Rooks et al., 2006). Using a Dutch sample, theyfind that various indicators for network embeddedness enhancedthe supplier’s performance. Based on this kind of evidence, manynetwork theorists implicitly argued that networks will have a (over-all) positive effect on economic transactions. Within this view,networks of firms and individuals are the basic grease making thecogwheels of markets work. However, as some studies show, thisconclusion may be too rash. For example, Uzzi showed that, fora sample of New York dress apparel firms, the effect of networkembeddedness on the firms’ performance is inverted U-shaped. Upto a certain degree, networks increase the chance of firm survival.However, when network embeddedness becomes too high, a firm’schance of survival decreases again because the entrepreneur is notable to find new business partners (Uzzi, 1996, 1997). A similarresult can be found in a paper of Powell et al., who demonstratethat networks have decreasing returns with respect to firm per-formance (Powell et al., 1999). These results show that networkembeddedness may have a “dark side” and – dependent on the sit-uation and the network characteristics – network members do notalways benefit from a high level of embeddedness. Similarly, Burtand Knez found an ambiguous effect of intra-organizational net-works on trust between managers. In this case, networks providedan interaction structure which reinforced already existing beliefsabout another person in the network. In this way, both distrust andtrust could develop independently of a person’s actual behavior ina network (Burt and Knez, 1996, 1995). Similar results come fromexperimental research on indirect reciprocity. In an experimentalstudy, Sommerfeld and his collaborators found that gossip infor-mation about others will influence people’s decisions even if theyhad contradicting experiences (Sommerfeld et al., 2007).
Another line of research focuses on restrictions resulting fromnetworks. Networks may impose norms on the members or leadto power inequalities. Many do not recognize that Burt’s structuralhole argument implies an unequal distribution of power. Accordingto Burt, actors occupying positions in structural holes have advan-tages due to local monopolies of information or goods. However,a situation beneficial to the network broker occupying a structuralhole may not be beneficial to the group or to the network in gen-
eral. Moreover, as Krackhardt shows, being close to a structural holemay impose restrictions like norms or expectations about grouployalty and reciprocity (Krackhardt, 1999). Friedkin gives evidencethat power based on certain network traits influences the formationof opinions in a network (Friedkin, 1993).
9 ocio-Economics 38 (2009) 908–915
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Table 1Description of variables.
N Min Max Mean Std
Problems in transaction 1011 1 4.42 1.35 0.60Problems solved (1 = no) 399 0 1 0.32 0.47Payments delayed (1 = yes) 999 0 1 0.10 0.31Satisfaction with supplier 1014 1 6 4.91 0.82
Search via network 1019 0 1 0.35 0.48Network embeddedness 1019 0 6 0.71 0.89Search intensity 1019 0 7 1.36 1.45Product price (German Marks) 973 100 5,000,000 51,809 237,485Importance of contract 1011 1 5 3.02 1.07Diversity of products 1011 1 17 3.14 2.56Shadow of the past 1011 0 1 0.65 0.48Shadow of the future 1010 1 5 2.72 0.87Product complexity 1014 0 1 0.26 0.44Supplier size 999 0 1 0.71 0.45Customer size 1016 0 1 0.56 0.50
10 M. Abraham / The Journal of S
These findings are not so surprising if we take into considera-ion that networks are complex structures which are usually notesigned by its members. As with any social structure, a networkomprises possibilities as well as restrictions for people’s actions,nd whether the advantages outweigh the disadvantages is mostften an empirical question. Based on these considerations, I willow present an empirical example of the (at least partially) nega-ive effects of structural embeddedness for economic transactions.s we will see, in this case, network embeddedness leads to moreroblems within a business relationship. In the next step, I williscuss and test different possible explanations for this empiricalnding.
. Disadvantages of structural embeddedness: buying ITroducts
Although the following results are based on a large random sam-le of firms, it is nevertheless a case study restricted to a certainegment of market. In this study, firms were asked for informationbout one specific transaction with a supplier for IT products likeardware (e.g. computers, monitors), software, or IT-related ser-ices (such as training). The sample was drawn from two regions inermany around the cities Munich and Leipzig. For the first contact,
he firms were asked for the name of the manager responsible forhis kind of purchase. A personal interview was carried out with thiserson which was based on a standardized questionnaire (see Vosst al., 2000 for details). The interviews were completed betweenarch and November 2000 and resulted in answers from 831 firms
net response rate of 49 percent). Because some firms agreed toive information about a second purchase, the data contains 1019ransactions which are the basis of the analyses in this paper.2
Network embeddedness was operationalized by two indicators:rst, managers were asked how they found the supplier of the trans-ction in question. The resulting variable search via network wasoded with 1 if the supplier was found via contacts with other firms,ommercial associations, or informal relationships with colleaguesr friends, and 0 otherwise. Network embeddedness was measuredy the question “Are you acquainted with business partners ofour supplier?” Here, six types of different business relationshipsere listed (other buyers, the supplier’s supplier, banks, consul-
ants, other business partners of the supplier, and other contacts).or network embeddedness, a simple sum score was built rangingrom zero to six types of contacts. In addition to these network mea-ures, the respondents were asked for the address and the postalode of their supplier. Based on this information, a dummy vari-ble “regional embeddedness” was recorded, indicating whetherhe buyer and supplier are located in the same state (Bundesland).his variable has the disadvantage that only 77 percent of the firmsave the information on their supplier’s location. Due to the reducedample size, this variable is only included if there are significantffects for a dependent variable.
Concerning problems with the transaction, the questionnairerovided three measures that will serve as dependent variables inhis paper. First, the respondents were shown a list of 12 different
ypes of problems which could arise (e.g. low quality, late delivery,ad service). The seriousness of every category had to be estimatedn a five-point scale. For our index, the mean of all 12 scales wasomputed, with the minimum 1 indicating no problems at all. About2 This means that some firms (about 374) are included twice in our data,lthough with information about two different suppliers. Consequently, the statis-ical assumption of independence is no longer valid which may lead to theoreticallyiased estimates in regression analyses. Hence I corrected the standard errors ofll regression models by applying the so-called Huber/White estimation for robusttandard errors (implemented as “cluster”-option within STATA, see Rogers, 1993).
Positive reputation 583 0 1 0.35 0.48Negative reputation 583 0 1 0.03 0.17Supplier in the same region 786 0 1 0.44 0.50
56 percent of all customers reported that the transaction was com-pleted without any problems at all. Second, if at least one problemwas reported, the respondents were asked, on an overall scale, howthese problems were solved by the supplier. The scale ranged from 1(not solved) to 5 (always solved). The dummy used in the analysisbelow is coded as 1 if the problems were solved often or always, 0otherwise. Third, we asked the buyer if he paid the supplier in time.Although a late payment can be interpreted either as a punishmentfor bad service or as opportunism by the supplier (Abraham, 2001),it is in any case a signal for a problematic relationship betweenbusiness partners. Approximately 90 percent of the suppliers werepaid on time, while only 10 percent received their money later thanexpected.
As can be seen in Table 1, there are some theoretically impor-tant control variables. For search intensity, the respondents wereasked how they found their supplier (e.g. other firms, internet, atfairs, phone books, etc.). Up to 10 different sources of informationwere possible; however, on average, only 1.36 sources were used.The average order placed by the customer had a volume of roughly52,000 German Marks (equivalent to about 25,000 US$ for the year2000). Importance of contract for the customer was based on fourscales measuring different types of problems in case the productdid not worked. For a diversity of products, the number of differentkinds of goods and services within the transaction (e.g. computers,monitors, training, etc.) were counted. The average contract com-prised three different types of products. Shadow of the past indicatesthat both firms had a business relationship before they agreed onthe contract in question (which was the case 65 percent of the timein the sample). Shadow of the future is an index based on two ques-tions: first, “Did you expect to do business with the supplier in thefuture?” and second, “Have you been an interesting customer dueto your demand for IT products?” The index is simply the weightedsum of both five-point Likert scales, thus ranging from 1 to 5 as well.Product complexity was measured by the question “How difficultwas it for your firm to evaluate different offers for the product?”Approximately 74 percent reported that it was difficult or even verydifficult. Because the firm size of the supplier as well as the cus-tomer was heavily skewed, both variables were dichotomized byfirms with less than 10 employees and those with more. Twenty-nine percent of the suppliers and 46 percent of the customers weresmall sized enterprises below this threshold.
If there was more than one possible supplier to choose from,the reputation both of the chosen supplier and its competitors wasmeasured. Hence, a measure of reputation exists only for 583 sup-pliers. The dummy variables tell us whether the customer received
M. Abraham / The Journal of Socio-Economics 38 (2009) 908–915 911
Table 2Regression models for various problem indicators.
Problems(model 1),Tobit, ˇ
Problem solved?(model 2), Logit,Exp(B)
Payment in time(model 3), Logit,Exp(B)
Search via network 0.23* 0.36*** 0.71Network embeddedness 0.14** 1.03 0.79*
Search intensity 0.13*** 1.14 0.89Product price (ln) 0.13*** 1.09 0.77**
Importance of contract 0.00 0.67** 0.93Diversity of products 0.05** 1.00 0.94Shadow of the past −0.21* 1.60+ 0.98Shadow of the future −0.10+ 0.91 0.93Product complexity 0.44*** 0.39*** 0.60*
Supplier size −0.02 0.53* 1.20Customer size −0.11 1.37 0.93Constant −0.62**
N 935 362 924Chi2 211.65*** 41.16*** 59.65***
*** Sign. <0.001, adjustment for within-cluster correlation (firm level) by robuststandard errors.
** Sign. <0.01, adjustment for within-cluster correlation (firm level) by robust stan-dard errors.
* Sign. <0.05, adjustment for within-cluster correlation (firm level) by robust stan-d
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Table 3Logistic regression on network search.
Supplier found via network search,Logit (model 4), Exp(B)
Network embeddedness 1.59***
Regional embeddedness 1.42+
N other suppliers 1.06*
IT competence of customer 0.92Product price (ln) 1.11Importance of contract 0.84+
Diversity of products 1.17***
Shadow of the future 0.27***
Product complexity 0.87Supplier size 0.62*
Customer size 0.96
N 736Chi2 113.33
*** Sign. <0.001, adjustment for within-cluster correlation (firm level) by robuststandard errors.
** Sign. <0.01, adjustment for within-cluster correlation (firm level) by robust stan-dard errors.
Within the data set there are some possibilities to evaluate this
ard errors.+ Sign. <0.1, adjustment for within-cluster correlation (firm level) by robust stan-
ard errors.
ositive or negative information about the supplier from a thirdarty. Finally, 44 percent reported that the supplier in question is
ocated in the same region.With these data, we can now evaluate whether network
mbeddedness yields fewer problems concerning the economicransaction between supplier and buyer of IT products. Table 2isplays the results of multivariate analysis with the three indi-ators of a problematic relationship – problems, problem solving,nd delayed payment – as dependent variables. Due to differentypes of dependent variables, two kinds of regression models arepplied. The indicator for the extent of problems is a metric variablehich allows standard regression analysis in principal. However,
he variable is skewed due to the fact that 56 percent did not reportny problems. In order to deal with this problem, we should beware that the information on problems is not an “objective” itemut rather a subjective one. People may not report minor problemshich fall below a certain threshold because they believe them to
e “normal” or not worth reporting for some reason. Consequently,his variable is treated as a left-censored variable in this analysis.or this kind of dependent variable, Tobit analysis is the model ofhoice which corrects for the bias on the basis of the variable’sssumed distribution (e.g. Breen, 1996; Long, 1997). The other twoependent variables are dichotomous; hence we can apply a simple
ogistic regression model. For those, the odd ratios are displayedhich indicate a positive relationship if the coefficient is greater
han one, and a negative correlation if it is smaller than one.The results do not correspond with the general assumption that
etwork embeddedness has positive effects on economic transac-ions. Network search, search intensity, and general embeddednessll contribute significantly to more problems in the transaction.
ithin model 2 (problem solving) and model 3 (payment), onlywo variables have a significant effect. Network search reduces therobability of problem solving, and network embeddedness has aegative effect on the odds for payment in time. None of the rel-vant nine correlations between embeddedness and the problemndicators in the transaction support the view that embeddedness
eads to fewer problems in a business transaction. The other con-rol variables have the expected signs. Expensive, more complex,nd highly diverse products go along with more problems becausehe chance of failure rises due to complexity of the transaction. The* Sign. <0.05, adjustment for within-cluster correlation (firm level) by robust stan-dard errors.
+ Sign. <0.1, adjustment for within-cluster correlation (firm level) by robust stan-dard errors.
shadow of the past and the expectation of future transactions eachreduce problems.
Although these results do not seem to support the assumption ofadvantageous network effects, the story may be more complex thana simple correlation between network embeddedness and prob-lems in the transaction can tell us. Hence, we will take a closer lookat the theoretical and empirical relationship between networks andthe seller’s performance.
First, there may be a selection effect such that a buyer whoexpects a problematic transaction will invest more in search. Thiswill lead to more search activities via networks too. In this case,networks may indeed reduce opportunistic behavior, but due tothe negative selection of embedded transactions we are not able toobserve this effect. However, we can test this kind of selection byanalyzing the determinants which are responsible for the assign-ment of network search. It is plausible to assume that productcomplexity, price, and diversity of the transaction are key factorsof a more risky transaction. Moreover, customers with less expe-rience with IT products should make more use of networks thanmore competent customers. Table 3 displays the results of a logis-tic regression on the dependent variable of whether the supplierwas found due to search activities (coded 1) or if no search activitywas necessary (coded 0). As can be seen, only diversity of products ispositively correlated with network matching of the business part-ners whereas price, product complexity and competence are notcorrelated with more network matching. This can be interpretedas an indicator that the selection effects in question are not thatimportant.
For the second refinement of this reasoning, we assume thatfirms are not completely informed about their network’s perfor-mance. From this point of view, not every network is able to reduceproblems. In order to find a “good” supplier, networks must pro-vide valid information on a sufficient number of business partners.If firms assign themselves to networks without being aware thatthese networks are not really suitable to solve their matching prob-lems, we may have an explanation for the positive correlation ofnetwork embeddedness and problems in a transaction.
kind of argument. First, we can conclude that searching via net-works should be especially effective if there is a sufficiently largenetwork which can provide reputational information about the sup-plier. That can be tested by integrating an interaction effect between
912 M. Abraham / The Journal of Socio-E
Table 4Tobit regression models on problems in a transaction.
Problems
Tobit (model5), ˇ
Tobit (model6), ˇ
Tobit (model7), ˇ
Positive reputation −0.08Negative reputation 0.60*
Search via network 0.23* 0.46** 0.42**
Network embeddedness 0.14*** 0.25*** 0.19**
IA network × embeddedness −0.27** −0.24*
Search intensity 0.13*** 0.14*** 0.07+
Product price (ln) 0.13*** 0.12** 0.13**
Importance of contract 0.01 0.02 0.07Diversity of products 0.05** 0.05** 0.03Shadow of the past −0.22* −0.21* −0.22*
Shadow of the future −0.10+ −0.09 −0.06Product complexity 0.44*** 0.44*** 0.41***
Supplier size −0.02 −0.01 −0.05Customer size −0.11 −0.12 −0.16Constant −0.62** −0.66** −0.58+
N 935 935 537Chi2 211.65*** 220.85*** 0.10***
*** Sign. <0.001, adjustment for within-cluster correlation (firm level) by robuststandard errors.
** Sign. <0.01, adjustment for within-cluster correlation (firm level) by robust stan-dard errors.
* Sign. <0.05, adjustment for within-cluster correlation (firm level) by robust stan-d
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ard errors.+ Sign. <0.1, adjustment for within-cluster correlation (firm level) by robust stan-
ard errors.
etwork size and network search in the analysis of transactionalroblems. As can be seen in model 6 (Table 4), this interaction effect
s significant and has a negative sign. That means that an intensiveetwork search in combination with higher embeddedness doesield fewer problems than the rest of the sample. However, theain effects of the network variables become even stronger indi-
ating that overall the network variables still yield more problems.ery good network embeddedness can dampen the negative effectf network search on problems within the transaction, but it can-ot explain why there is still an overall positive effect of networkearch on problems.
Another possibility of exploring reputation effects involvessing the questions on reputational information that the buyer hadefore choosing the supplier. Perhaps the actors try to search viaetworks in any case, but only those with “good” reputational net-orks actually get useful information about the supplier ex ante.
ncluding whether the buyer had such information should reducehe effect of the network variables considerably. As seen in Table 4,he reputational information in model 7 does not play a key role inxplaining transactional performance; a negative reputation goeslong with more problems, but a positive reputation does not havesignificant effect. Moreover, the effects of the network variableso not decrease considerably and still have a strong positive effectn problems.
To sum up this first round of analysis, we can conclude thatetwork embeddedness and network search lead to more prob-
ems in the economic transaction between suppliers and buyers ofT products. That does not change if we take interaction effects ofetwork structure and search activities or actual reputational infor-ation about the supplier into consideration. This result challenges
he general assumption of network embeddedness as a governanceechanism which enables and enhances economic action. How-
ver, this does not necessarily mean that embeddedness never does
he trick, but obviously there are situations where networks haveevere disadvantages for embedded transactions. Consequently, theext step will be to explain why embeddedness failed to reduceroblems in this special field. I will discuss two possible expla-conomics 38 (2009) 908–915
nations and try to provide empirical evidence to support whetherthose mechanisms are at work in our case.
4. Why may embeddedness fail?
To identify situations that lead to disadvantages of networkembeddedness for economic action I will focus on two possiblemechanisms: power and uncertainty.
Power in the sense used in this paper means that an economicactor is restricted in his or her possible choices due to some directenforcement or punishment by other economic actors. In our exam-ple, a firm may not pick any possible supplier in the market becausethe network members force it to choose among a restricted sam-ple. Although cartel agreements and other “closed shop” situationscome first to our mind here, the typical case may not be so drastic.We can think of a highly integrated vertical business relationshiplike a “just in time delivery” between a firm and several suppli-ers. Here, it is absolutely necessary that the business partners aretightly connected by appropriate software and IT tools. Moreover,this software may be optimized for certain hardware. The supplier’sdecision for choosing an IT supplier may be narrowed down to oneor two IT specialists who provide the business network with com-patible products. Even if there may be a technological equivalenton the market, the business network may enforce the choice for the“in network” solution.
Of course that situation is a special case, and power situations inbusiness networks may arise from many different reasons (e.g. cap-ital interlocking). However, in order to explain the negative effectsof network embeddedness, it is not important to know where thepower comes from. All we do have to know is whether networkembeddedness leads to a restricted choice in business partners. Forthis, I use an indirect test assuming that a restricted choice due topower should result in fewer search activities. A firm should notengage in a costly search for a business partner if its network forcesthe firm to choose among a given set of suppliers. To gain somepreliminary evidence for this kind of argument, we can look backat Table 3. Overall, higher network embeddedness leads to a higherchance of finding the supplier by their own search activities. Thisis interpreted as an indicator that power is not the driving forcebehind the problem-enhancing effect of networks.
An alternative explanation for the network effects in questionhere is based on the assumption that the buyer is uncertain aboutthe performance of the supplier. As principal agent theory tellsus, nearly every buyer–seller relationship is to some extent anasymmetric situation (Pratt and Zeckhauser, 1985; Rees, 1985). Thesupplier usually has private information about the quality of goodshe is providing and his level of motivation. Principal agent modelsusually focus on the kind of institutional and contractual incentivesthe buyer has to give in order to prevent opportunistic behavior bythe supplier. Therefore, it is assumed that the buyer is at least ableto evaluate the supplier’s performance ex ante, otherwise it wouldnot be possible to decide on incentives like profit shares or penaltiesto be imposed on the supplier.
However, empirically the situation is somewhat more compli-cated because the buyer may not even be able to evaluate the levelof performance he received from the supplier. Consider again thecase of a more or less complex IT order like specialized softwarein combination with hardware components. For the buyer, it maybe difficult to decide on the quality of these products because helacks experience and expertise in this field. Even if the IT systemis working it is unknown whether it can be successfully adapted
not work properly, the supplier may lay the blame on the hardwareproducers or other contingencies he is not responsible for.
Hence it seems plausible to assume that buyers are to someextent always uncertain about the supplier’s performance after they
ocio-Economics 38 (2009) 908–915 913
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Table 5OLS-regression on customer’s satisfaction.
Customer’s satisfaction
OLS, model 8, b OLS, model 9, b OLS, model 10, b
Positive reputation 0.12+
Negative reputation −0.49Search via network −0.26** −0.08 −0.02Network embeddedness 0.06+ 0.09** 0.10*
Regional embeddedness 0.11* 0.05 0.07Search intensity 0.01 0.03 0.01Product price (ln) −0.03 0.00 0.01Importance of contract −0.05* −0.06* −0.06+
Diversity of products 0.01 0.02+ 0.01Shadow of the past 0.18* 0.08 0.12Shadow of the future 0.01 0.01 0.12Product complexity −0.30*** −0.06 −0.09Supplier size −0.04 −0.02 0.00Customer size 0.02 −0.01 0.02Problems −0.70*** −0.81***
Problems not solved −0.42** −0.23Constant 5.34*** 5.87*** 5.69***
N 741 740 404R2 0.11*** 0.43*** 0.48***
*** Sign. <0.001, adjustment for within-cluster correlation (firm level) by robuststandard errors.
** Sign. <0.01, adjustment for within-cluster correlation (firm level) by robust stan-dard errors.
* Sign. <0.05, adjustment for within-cluster correlation (firm level) by robust stan-
M. Abraham / The Journal of S
ot the goods or services. Nevertheless, the buyer needs to evalu-te the transaction because he has to decide on further actions likehether to sue the business partner or to do business with him
gain. As the vast literature in social psychology tells us, peopleend to form opinions in such situations of uncertainty by adapt-ng to the opinions of others (Knudsen, 2007; Economides, 1996;elley, 1968; Hyman, 1953; Stafford, 1966; Bearden and Etzel, 1982;artrell, 1987). In our case, the network will be used to form anpinion about the supplier in question (see e.g. Shane and Cable,002 for a similar reasoning). If the supplier and the customer shareetwork contacts, the customer may be more willing to believehe supplier is credible if other network members recommendedhem or did business with the supplier themselves. In this situa-ion, one may be more tolerant towards the business partner evenf the performance fell below the expected level.3 This process maylso be fueled by the mechanism of indirect reciprocity (Nowak andigmund, 2005). If a customer observes that a supplier had been fairo another buyer in the network, indirect reciprocity may dictate toe fair to this supplier, especially if the customer is uncertain abouthe performance and the source of his problems with the product.
However, this fact alone would not be able to explain why net-orks yield more problems in the business transactions on hand.
or this, it is important to realize that this kind of uncertainty “pro-uces” an incentive for opportunistic behavior for the supplier. It islausible to assume that he is aware of (1) the customer’s inabilityo evaluate his performance correctly and (2) the shared networkontacts. Usually, theories of reputation assume at this point thatetwork embeddedness will lead to better performance becausehe supplier is interested in a good reputation. However, this willnly work if the information about a bad performance is spreadmong the network members. If the customers are uncertain abouthe performance, it may be sufficient for the supplier to gain aood reputation when he enters the network. If he is successful,system of self-enforcing opinion forming is possible: all may
elieve a business partner is a high performer because the otheretwork members confirm that belief on the basis of the opinionsf all others. Burt and Knez described a similar process for gossipnd friendship ties in an organization (Burt and Knez, 1996). Theyhowed that trust or distrust in relationships is amplified by denseetworks. Gossip within those networks serves as a mechanism toeinforce opinions on an interaction partner by providing selectivenformation. Although Burt and Knez do not stress this point explic-tly, they assume that the employees are uncertain whether theyan trust the colleagues with respect to future cooperation. In theirnalysis, actors deal with this uncertainty by a combination of usingheir first brief personal experience and network information. Sim-larly, Sommerfeld and others demonstrated in their experimentaltudy that gossip serves as a powerful source of information wheneople are uncertain about the intentions and preferences of pos-ible exchange partners (Sommerfeld et al., 2007). This mechanismay even work when actors do have their own experiences which
ontradict the gossip information. The same kind of process shoulde possible in business networks between firms if the uncertaintybout the performance is high enough. In our example, IT productsertainly do produce such a situation of uncertainty. The averageser has not only limited knowledge how these systems work, and
as probably most of us have experienced – most often IT expertsre reduced to a “trial and error” strategy in case of a system failure.oreover, there is a rapid technological development in this fieldhich leads to a fast depreciation of knowledge.
3 This argument corresponds with research results on the effect of social distancend anonymity in economic transactions. It can be shown that lifting the anonymityf an exchange partner in ultimatum or dictator games leads to higher cooperation
n experimental settings (e.g. Bohnet and Frey, 1999).
dard errors.+ Sign. <0.1, adjustment for within-cluster correlation (firm level) by robust stan-
dard errors.
As a consequence, suppliers in such network systems mayreduce their performance to some degree without being punishedby a loss of reputation. It is difficult to test this implication becausewe would need data on different types of transactions or differenttypes of networks, which varies by the level of uncertainty aboutthe supplier’s performance. We do not have that, but it is possi-ble to indirectly test this theory. If incomplete information anduncertainty are at work here, the customers should be more sat-isfied with a supplier if a shared network exists—independentlyof the problems we observe in a transaction. Table 5 displays thiskind of analysis. The dependent variable is an indicator of the cus-tomer’s satisfaction including satisfaction with the product as wellas with the supplier’s behavior. In model 8, I simply reproduce theanalysis of problems. Because the amount of problems and satis-faction are highly correlated, we get more or less the same resultsof Table 2. However, in contrast to the analysis of problems, thenetwork embeddedness has a positive correlation, meaning thatcustomers of embedded transactions are more satisfied despite thefact that this leads to more problems (see therefore Table 2).
For the next step of our analysis, the amount of problems in thetransaction as well as problem solving are controlled for; that is,we compare transactions with the same level of problems (Table 5,model 9). If only the problems and their solution are relevant for thecostumer’s satisfaction no other variable should yield a significanteffect. However, we do find significant effects, especially for thevariable indicating network embeddedness. Given the same levelof problems in a transaction people tend to have a higher satis-faction with transactions with an embedded supplier. This can beinterpreted as evidence of a higher tolerance in network settings;the supplier is given more credit when a shared network exists. Thisresult is in accordance with our theoretical argument of the opinionforming in networks. Uncertainty is an important determinant for
evaluation of performance, and networks help to reduce this kindof uncertainty by increasing tolerance. The same seems to be truefor our direct measures of reputation (model 10). A bad reputationreduces tolerance (albeit not significantly), whereas the variable for
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14 M. Abraham / The Journal of S
good reputation has a positive sign. This is an additional indicatorhat what the customer believes about the services performed goeseyond the actual performance. Finally, the higher the importancef a transaction the less tolerant customers tend to be.
. Summary and discussion
Many researchers in economics as well as sociology havetressed the important role of business networks for cooperation,rust and performance. This claim has been based on solid theoret-cal arguments as well as empirical findings. However, neither theheory nor the selective findings support the view that networkshould always be beneficial for economic transactions. Networksre complex social structures in the first place, and such structuresay provide opportunities as well as restrictions for successful eco-
omic action. There is still a considerable lack of knowledge aso when network structures are beneficial and when they fail torovide or even jeopardize cooperative mechanisms.
This analysis can be seen as a small contribution to filling inhis lack of knowledge. It has been shown for a certain type ofransaction – the purchase of IT products – that networks mayot have a beneficial effect on the quality of results. Furthermore,etwork embeddedness even leads to more problems for the cus-omer. In order to explain this effect, some possible reasons forhis phenomenon were discussed, some theoretical, others empir-cal. Although selection effects, differences in network structure,nd power in networks are possible explanations, little empiricalvidence was found for these theoretical mechanisms. The mostromising explanation for this special case seems to be the effectf uncertainty and incomplete information. In addition to the stan-ard economic explanations, we note that customers do not onlyave incomplete information about the performance of a supplierx ante but also ex post, especially for complex products whosesefulness cannot be determined immediately after the purchase.ustomers usually have difficulties evaluating the quality of prod-cts and the performance of the supplier. In order to reduce thisncertainty, they form beliefs on the basis of the opinions existing
n a shared network or group. However, if the network membersave the same problem of uncertainty this information may not beery reliable. As has been described for other types of relationships,nvalid reputational information in such systems may be made sta-le by mutual reinforcement of shared beliefs (Burt and Knez, 1995,996). In such a situation, suppliers have an incentive to reduce theirerformance because such a behavior will not be detected and sanc-ioned by the business partner himself. Empirical support for thisrgument comes from an analysis of the customer’s tolerance inusiness transactions. Even if problems in a transaction are keptonstant, customers give suppliers more reputational credit if theyhare a common network.
These results shed new light on the theoretical and empiricalesearch on learning and imitation in markets. Within this researchine, it is usually argued that information about the competitors’ayoffs can be used to imitate and learn about better market strate-ies und thus enhance market competition (see e.g. Huck et al.,000; Offerman et al., 2002; Altavilla et al., 2006). However, withinhis literature it is always assumed that the information about thether actor’s behavior is correct and empirical evidence for this isased on well-controlled experimental designs. Empirically, thisssumption may be problematic. Especially in the case of high exost uncertainty and dynamic markets, this reputational informa-ion may be biased and – due to quickly changing market conditions
not improving in the long run. Another theoretical approachonnected with this study is the concept of indirect reciprocity.ere it is assumed that the observation of an actor’s fair behavior
owards other persons will trigger a fair behavior by us towards thisctor. As has been acknowledged by other researchers in this field,
conomics 38 (2009) 908–915
it may be problematic to decide whether the observed behaviortowards other persons has been fair indeed: uncooperative actionmay be due to uncooperative behavior of the third person in the past(Sugden, 1986). The results of this paper suggest that the mech-anism of indirect reciprocity becomes even more complicated. Ifthere is a kind of “natural” tendency to behave reciprocally in sucha way (as there seems to be, see Engelmann and Fischbacher, 2002),tolerance towards low performers may be induced by the belief thatthose low performers had been fair to others in the past. If we areuncertain about the reasons for the low performance, our tolerancemay be interpreted as a kind of help for a business partner who had“bad luck” but behaved fair to others in the past. This way, indirectreciprocity may be an incentive for opportunistic behavior.
Of course our analysis has some limitations and open questions.First, the empirical analysis provides only an indirect test of the the-oretical argument. We do not know what kind of information on acertain supplier is really flowing within the network. Here Sommer-feld and others could show that more information from differentsources in a social system is better for establishing a valid reputation(Sommerfeld et al., 2008). In an experiment, groups with a higheramount of gossip produced a better fit of an actor’s reputation withhis actual behavior. Since the average network embeddedness in thesample on IT purchases used in our study is pretty low (less than oneout of six possible contact types were reported), a possible explana-tion for this paper’s findings may be that, because networks in thistype of business may be small and loosely connected, there is notenough gossip to balance out bad information. Hence, future stud-ies will have to focus on the amount of sources as well as contentsof information in a network. Second, although we have a large sam-ple of firms on a national level, the analysis is restricted to a certaintype of transaction, the purchase of IT products. However, uncer-tainty about the performance should vary with the complexity andcharacteristics of the product. Consequently, we would expect pos-itive or zero effects of network embeddedness for goods or serviceswhich are easy to evaluate. Moreover, the result may be biased bythe “institutional embeddedness” of the national sample. Since Ger-many has relatively well functioning institutions, there may be noneed for economic actors to rely on their networks for governingtheir relationships. In order to sort this out, we need more studiesfrom societies with a weak institutional setting (like the Vietnamstudy of McMillan and Woodruff, 1999). Nevertheless, it is still puz-zling why actors make use of networks for finding business partnerswhen there are disadvantages in doing so. Moreover, there is con-vincing evidence that people do not use institutional mechanismseven if those are easily available (Ellickson, 1986, 1991).
Hence more research on the unintended and unwanted con-sequences of network embeddedness seems necessary. Crucial tothis research will be the necessity to compare social systems withrespect to the “produced” amount and content of reputational infor-mation. Two lines of inquiry seem to be especially promising: First,laboratory experiments should be especially appropriate to manip-ulate and compare different social systems. Second, we need morefield studies on different markets in different institutional settings.
Acknowledgements
Financial support by the German Research Foundation (DFG) isgratefully acknowledged. I would like to thank Thomas Voss and ananonymous reviewer for helpful comments.
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