competence in mutual dependence

COMPETENCE IN MUTUAL DEPENDENCE

Paper for the DRUID summer conference

“Competence, Governance and Entrepreneurship”

Denmark, June 18-20, 1998.

by

Bart Nooteboom

Gjalt de Jong

Robert W. Vossen

University of Groningen, the Netherlands

Susan Helper

Case Western Reserve University, the United States

Mari Sako

Oxford University, the United Kingdom

FIRST DRAFT, March 1998, DO NOT QUOTE

Abstract

This paper builds on previous theoretical and empirical research by the authors. It is based on an

integration of transaction cost economics with the resource (competence, capabilities) view and a

social exchange view, from a dynamic perspective: how do competencies develop in interaction

between firms? The social exchange view brings in trust as an important dimension of

governance. The research question is how risks of mutual dependence between firms can be

mitigated without either hierarchical or legal control. Five hypotheses concerning such

mechanisms of mutual dependence are tested on data from the car industry.

Corresponding author: Bart Nooteboom, University of Groningen, PO Box 800, 9700 AV

Groningen, the Netherlands, phone + 31 50 3633852, e-mail [email protected].

2

Nooteboom, De Jong, Vossen, Helper, and Sako

1. Introduction

According to the resource (or competence or capability) view, the firm is made up from a number

of resources, which to a greater or lesser extent are specific to the firm, i.e. cannot be immediately

copied by others (Penrose 1959, Foss and Knudsen 1996). Resources can be classified into assets,

competencies and positional advantages (Stoelhorst 1997). Assets are subject to legal ownership

and contracts. Competencies and positional advantages are not easily subject to property rights.

Resources cannot all be instantly copied by other firms because they are to some extent inscruta-

ble or subject to ‘causal ambiguity’ (Lippman and Rumelt 1982): even if a would-be imitator can

observe activities, that does not yet imply that he can understand and implement them. Resources,

and especially competencies, can be difficult to understand and imitate, because the knowledge

involved is to a greater or lesser extent tacit (not documented) and embodied in the heads and

hands of people, in teams, organizational structure and procedures, and organizational culture. It is

particularly such unique capabilities of firms, in addition to market structure (concentration, price

elasticity, entry barriers), that yield a profit.

Competencies reside on the personal level, in the form of knowledge and skill, but we

would also include motivation, and ‘morality’. Motivation entails entrepreneurial drive and the

ability to make a commitment. Morality includes norms and values of conduct that the individual

holds, his degree of commitment to them and susceptibility to ethical appeals (concerning loyalty,

justice, truthfulness). On the aggregate, interpersonal level of ‘communities of practice’ (Brown

and Duguid 1991) within an organization, entire organizations and even networks of organizations

there are assets and positional advantages, but also organizational competencies. Assets include

organizational structure (of the team, organization or network). Organizational competencies

include routines embedded in organizational structure (Nelson and Winter 1982), patterns of

knowledge exchange and transformation (Nonaka and Takeuchi 1995), institutions and relational

competencies. Institutions are defined as environments and arrangements which limit and guide

conduct (North 1990, North and Thomas 1973). They include practices, procedures, rules, techni-

cal standards as well as cultural entities such as prevailing norms and values of conduct, goals,

role models, rituals. These stimulate and consolidate motivational and moral competencies on the

level of individuals. Relational competencies enhance the development or acquisition of

resources, and they produce positional advantages. The latter include product-technology-market

combinations, access to materials, distribution channels, political acceptance, brand loyalty, repu-

tation, position in networks of organizations.

Chiles and McMakin (1996) distinguished two perspectives in transaction cost economics

(TCE). The first is a long-term evolutionary perspective, where objective transaction costs

determine the survival of the fittest governance forms. The second is a short term managerial

choice perspective, where managers act on subjective costs which are based on varying

3

Competence in Mutual Dependence

perceptions and evaluations of risk. The latter explains why firms in similar circumstances may

make different make-or-buy trade-offs. Here, we take the latter perspective.

The present paper focuses on relational competencies. They include the ability of an

organization to employ modes of governance of inter-firm relations. As will be discussed in more

detail later, governance includes four basic classes of instruments:

1. hierarchical fiat and control;

2. contracts and monitoring;

3. motivation on the basis of self-interest, including mutual dependence, posting of hostages,

reputation mechanisms;

4. trust based motivation and loyalty.

The first three are part of established TCE, as developed by Williamson (1975, 1985).

While in his earlier work Williamson (1975) recognized the role of ‘atmosphere’, which seems

close to the notion of trust, this was not developed in his later work. Williamson (1993) explicitly

asked the question whether trust had anything to offer beyond the calculative self-interest that was

already included in the theory, and concluded that if it did it would imply blind trust, which is ill-

advised and would militate against survival. Therefore, in his opinion, trust does not yield

anything meaningful and viable, and should be discarded. Others have argued that trust is

indispensable in any workable relation, and that it can go beyond calculative self-interest without

being blind (see e.g. Helper 1987, 1990, 1991, Sako 1992, Nooteboom 1996, 1998a,b, Nooteboom

et. al. 1997). One can have trust, which reduces transaction costs by allowing for limited

safeguards, while it is not blind in the sense that one may beware of conditions which go beyond

the partner’s resistance to temptations of opportunism. Trust may be based on contractual

constraints, self-interest, prevailing ethics, in the institutional environment (such as an inclination

to exercise ‘voice’ rather than ‘exit’, cf. Helper 1987, 1990, 1991), or bonds of friendship or

kinship, the development of shared norms or routinization of behaviour (Sako 1992, Nooteboom

1998).

The issues adressed by TCE, and especially its core concept of specific assets, are still of

crucial importance. If anything, its importance has increased. Increasing prosperity yields more

differentiation of demand, and thus creates a ‘pull’ of product differentiation, and increasing

competition pushes firms to evade pure price competition by product differentiation. Product

differentiation entails transaction specific assets, unless technology is flexible. And specific assets

create the problems of dependence and ‘hold-up’ that TCE has analyzed (Williamson 1985).

4


2. Innovation, learning and entrepreneurship

The implications of the competence view fully emerge in a dynamic perspective of innovation and

learning. Individual and organizational cognition (perception, interpretation and evaluation) are

enabled but also constrained by categories (or ‘mental models’) that are formed on the basis of

experience. As a result, ways of viewing the world are idiosyncratic and path-dependent, and one

needs complementary cognitive competence from partners to appreciate opportunities and threats

that one could not have appreciated oneself. Nooteboom (1992) called this ‘external economy of

cognitive scope’. This yields a cognitive dimension to inter-firm relations; another reason to

engage in relations, apart from the traditional reasons of market incentives and economy of scale

in production. While TCE predicts that at greater levels of uncertainty firms will integrate

activities under a unified hierarchy, to control hazards of opportunism, this line of thinking yields

the opposite prediction that under greater uncertainty, in the sense of complexity and variability of

technology and markets, there is a greater need for firms to seek relations with outside sources to

compensate for their cognitive constraints.

Organizational competencies in the form of ‘patterns of knowledge exchange and

conversion’ refer to the way in which knowledge is converted from tacit to documented

knowledge, absorbed from documented into tacit knowledge, transmitted, pooled, shared and

recombined in novel combinations (cf. Nonaka and Takeuchi 1995). Nooteboom (1998c)

reconstructs organizational routines and patterns of knowledge exchange and learning in terms of

organizational scripts, which serve to connect the level of individuals to the level of the

organization, and to model the interaction between exploitation of current competencies and

exploration of new ones (March 1991), in a stage theory of organizational learning adopted from

Piaget.

The resource perspective has implications for the notion of entrepreneurship. According

to Schumpeter’s theory an entrepreneur builds a firm on an innovation, which consists of ‘novel

combinations’, causing ‘creative destruction’ of existing practices, and thus exerts a force away

from equilibrium between supply and demand. According to Walras’ theory, an entrepreneur

performs ‘arbitration’ between supply and demand: by filling ‘holes’ in the market he exerts a

force towards equilibrium (which does not imply that equilibrium will actually be reached). The

resource perspective suggests that the role of an entrepreneur is to establish a configuration of

resources that are to some extent specific to the firm, and thereby earn a rent. This is not so far

from the Schumpeterian notion. According to a theory of learning which connects exploitation and

exploration (Nooteboom 1998c) the two types of entrepreneurship are no longer separate:

Walrasian entrepreneurship then is related to exploitation, which is equilibrating in the sense that

the potential of an innovation is fully realized, but during this process experience accumulates

which provides the incentives, materials and hints for exploration, leading up to a novel

5


Schumpeterian breakthrough.

Schumpeter recognized innovations not only in technology but also in finding or

developing new sources of materials, new markets and new forms of organization. According to

the resource perspective entrepreneurship would include, for example, relational competencies,

especially when there is no adequate institutional basis for markets: no adequate legal system to

support contractual governance and no well-developed capital markets. Then entrepreneurs have

to develop networks of contacts to gain access to resources and to manage them.

Birley, Cromie and Myers (1991) found that in Italy entrepreneurs spend significantly

more time on the set-up and maintenance of networks of personal contacts than in Sweden, Nor-

thern Ireland and the US.

The importance of relational competence has increased. Due to pressures of globalization,

increasing complexity of input- and output markets and increasing speed of technological change,

firms need to ‘focus on core competencies’ and utilize complementary resources from other firms.

This is needed for the static efficiency of current production, typically because other, specialized

firms can make products at a greater scale and therefore more efficiently (Williamson 1975). But

as indicated it is especially needed for dynamic efficiency: the development of resources and lear-

ning, by utilizing firm-specific competencies of development that are complementary to one’s own

(Mody 1993); to exploit ‘external economy of cognitive scope’.

3. Governance

Firms engage in relations to obtain access to complementary resources that they do not have and

could not or would not want to have, in their effort to focus on core competencies. The value of

the partner, in terms of resources offered, can have many dimensions, as illustrated in figure 1

below (Nooteboom 1998).

Figure 1 Value of Y for X

techn.cap. Y

integrcap. Y

reliabY

knowlY

innovcap. Y

network

internat Y

continY

%volY

flex Y

+ + + + + + + / - + + +

value of Y for X (VYX)

positionaladvantages

assetscompetencies

1

But relations also entail risks, as indicated by TCE (Williamson 1975, 1985). Nooteboom

(1996) developed an extended scheme for the analysis and design of relations, which includes a

6


dimension of trust next to calculative self-interest (see figure 2 below).

Figure 2 Relational Risk of X and Y

sw itc h in g c o sts X (S W X )

v a lu e o f Y to X (V Y X )

+ +

c a p tiv en e ss X (C A X )

v a lu e o f X to Y (V X Y )

sw itc h in g c o sts Y (S W Y )

+ +

c a p tiv en e ss Y (C A Y )

ro o m o p p .Y (R O Y )

in ten t o p p .Y (IO Y )

+ +

re la tio n a l r isk X (R R X )

in ten t o p p .X (IO X )

ro o m o p p .X (R O X )

+ +

re la tio n a l r isk Y (R R Y )

+ +

+ +

_ _

_ _ _+

_

X Y

2

Nooteboom et. al. (1997) extended the scheme to distinguish two dimensions of relational

risk: the size of the damage if something goes wrong – i.e. if the relation breaks or hold-up occurs

– and the probability that this will occur. It is reproduced in figure 3.

Figure 3 Determinants of Size and Probability of Loss

switching costs: SW partner value: VA

captivenessego

incentives opp.partner

perceiveddependencepartner: VE

+ +

+

_

propensityopp.: HI

opportunitiesopp.: ROA

size (SLE) andprob. (PLE) of loss

+

+ +

7


Partner value indicates relative value, i.e. value in excess of the next best alternative

(which might include not having a partner at all and generating the resource within the firm). This

value can become negative for two reasons: the partner detracts more value than he adds, e.g. as a

result of hold-up or spill-over, so that value becomes negative regardless of alternatives, or a more

attractive partner appears on the scene, so that relative value becomes negative. In either case one

would be inclined to exit from the relation, but may be withheld by switching costs, which include

one’s ownership in specific assets, loss of hostages or further spill-over. One may also refrain

from exit in the expectation that by deliberation and cooperation (‘voice’, cf. Helper 1987, 1990,

1991) value can be improved again. The sum of relative value and switching costs yields what is

called ‘captiveness’. It is what one can lose if the relation breaks, and thereby also constitutes the

maximum to which one can be held up: the size dimension of relational risk. The probability

dimension of risk is determined by opportunities that the partner has for opportunism and his

inclination to employ those opportunities. Opportunities are determined by the loopholes left by

contracts and lack of monitoring due to assymmetric information. Inclination to employ the

opportunities has two dimensions: incentives from self-interest and inclinations towards

opportunism as a function of ‘intentional trust’, which may be based on morality, ethics, bonds of

friend- or kinship, habituation or the development of shared norms of conduct. In an empirical

study, Nooteboom et. al. (1997) tested and confirmed these effects.

Figure 2 shows alternative paths to influence relational risk. One can try to reduce the size

of the damage that can occur or the probability that it will occur, as follows:

S1: Reduce relative value of the partner, by maintaining alternatives. A drawback of this is that

it multiplies set-up costs of relational governance and presents one’s partners with an

increased risk of spill-over, since one engages in contacts with alternative partners which

are likely to be competitors to each other.

S2: Evade specific assets, or demand shared ownership of them, or safeguards that guarantee

that their value will be recouped (by a guaranteed duration of transactions or a severance

pay in case of termination). The first action inhibits specific assets, which limits opportuni-

ties for product differentiation (to the extent that technology is so inflexible that diffe-

rentiated products require specific investments, cf. Nooteboom 1993).

Alternatively, one can try to reduce the probability that damage will occur, as follows:

8


P1: Impose narrower contractual constraints and improve conditions of monitoring, in order to

narrow down opportunities for opportunism. This may not be possible in view of

uncertainty and related restrictions on contingent contracting. Furthermore, the draw-back

of this is that contracting can be costly, and can impose inflexibilities that are undesirable

especially when the objective of collaboration is innovation: when the outcome or even the

aim of collaboration cannot be specified one cannot specify detailed rights and obligations.

Also, detailed contracting can stimulate mutual suspicion, which provides a bad basis for

building trust and can lead to a vicious circle of contractual constraints.

P2: Limit incentives to opportunism on the basis of self-interest: make the partner at least as

dependent as one is oneself, or mobilize reputation mechanisms. Partner’s dependence can

be increased by increasing one’s value to him, either by investing in the resources that one

offers or by creating a longer time-perspective, in an open-ended, ongoing relationship, and

thereby mobilize the ‘shadow of the future’. For this reason figure 2 includes a line from

‘captiveness X’ to ‘value X for Y’: to the extent that X accepts a high stake in the

relationship, this indicates a long term perspective to Y. This effect is familiar from the

study of repeated games: when the partner expects ongoing business he will be less prone to

opportunism, which would jeopardize future returns. If the purpose of the relation is joint

development, shirking may be prevented if absorption of knowledge by the partner requires

him to uphold his effort in order to maintain ‘absorptive capacity’ (Cohen and Levinthal

1990). Shirking may also be limited by giving side-payments for knowledge received. It is

especially in this mode of governance that the role of building complementary

competencies, including cognitive competencies comes up. Here mutual adjustments to

build appropriate absorptive capacity and learn to produce knowledge together constitute

specific investments and take time to develop.

P3: Limit inclinations towards opportunism by building trust (Sako 1992). Trust can be

enhanced ex ante by selecting partners from an environment of trust: family, friends, social

or ethnic community. Trust can be developed in ‘process-trust’, on the basis of ongoing

success, the practice of loyalty and voice (Helper 1987), the building of shared norms or

habituation (Gulati 1995, Nooteboom et.al. 1997).

These effects were confirmed in previous empirical research on 80 supply relations of a

producer of photo-copying machines (Berger et.al. 1995) and 100 customer relations of firms

supplying intermediate products in the elctronic industry (Nooteboom et.al. 1997).

9


4. Mechanisms of mutual dependence

The perspective taken here is that most specific investments will generally lie on the side of the

producer in the relationship, i.e. the supplier. Of course, the customer may also have to make

specific investments in the form of adjustments necessary to receive the suppliers input or

contribute to its development, but generally these will be less than investments on the side of the

producer.

Some interventions by X, in figure 2, impact not on his own side, but on the side of Y.

Thus X’s action of investing in his value to Y increases Y’s captiveness and thereby his relational

risk, which will limit his incentives for opportunism. Now the most salient way to improve one’s

value to the a partner is make specific investments, and thereby improve the partner’s perspective

for product differentiation. But now specific investments have two contrary effects: on the one

hand they increase one’s switching costs and thus one’s captiveness and the size of damage in

case of hold-up, but on the other hand it increases value to the partner, making him more captive

and thereby reducing the probability of hold-up. In other words: the net effect of specific invest-

ments on expected loss – i.e. size times probability of loss – is ambiguous. The purpose of the

present paper is to investigate mechanisms of mutual dependence in more detail.

Our conclusion is that in order to study inter-firm relations we should take into account

causal loops such as the one indicated above. This idea has been developed, in an application of a

theory of transactions extended with the competence view and a social exchange perspective

including trust, by de Jong et. al. (1998). This research employed LISREL modelling, which is the

appropriate method to deal with causal loops. This method was applied to data on the car industry

collected by Helper and Sako. Details on the method are given in appendix A, on construction of

variables in appendix B, and on the correlation matrix in appendix C. The results are reproduced

in figure 4. As illustrated there the model includes the following loops:

L1: Self-interested customer commitment. This loop of mutual dependence appeals to self-

interest (mode of governance P2) on the basis of commitment, as follows. Specific invest-

ments increase the supplier’s dependence, but they also increase his value to the customer,

which makes him also more dependent and thereby increases his commitment to the

relation, which reduces behavioral uncertainty and thereby reduces the need for risk

avoidance, which allows for more specific investments, which increases dependence. This

is the loop that seems to be intended in ‘classical’ TCE, as developed by Williamson

(1985). By means of LISREL it was tested whether all the causal links in this loop are

confirmed by the data, and this turns out to be the case: see figure 5, which highlights the

causal path involved.

10


L2: Supplier dedication to a valued partner. This loop is indicated in figure 6. The supplier is

faced with a customer of great value, which is enhanced by the latter’s commitment to

support the supplier. In view of this, the supplier is willing to be open and to engage in

specific investments. Openness is needed to receive the assistance that the customer is

willing to supply, and allows knowledge transfer from the supplier to the customer. Both

enhance his value to the customer, which in turn make him dependent. The logic of this

loop is close to that of the previous one, but not the same: it does not run via behavioral

uncertainty.

L3: Habituation. This loop of mutual dependence is based on trust that grows from habituation

(mode of governance P3). Past duration yields habituation which creates bilateral

dependence and acceptance of it. An ongoing, successful relation (if it were not successful

it would not be ongoing), breeds trust in both the partner’s competence and his resistance to

opportunism. This is based on the development of shared norms of good conduct, habits or

routines which lower attention to both opportunities and incentives for opportunism, and

possibly bonds of friendship. This loop also was confirmed: see figure 7.

Originally, we expected that these hypotheses would exhaust the mechanisms of mutual

dependence. But as a check we allowed the LISREL model to indicate inductively additional

loops that would increase the explanatory value of the model, and one of the loops added was a

direct loop from customer dependence to supplier dependence. What this seemed to be telling us

was that there might be missing variables or missing links; that in addition to the mechanisms of

mutual dependence allowed in the model there were more. The goal of this paper is to further

investigate this issue.

4. Extensions

As a first possibility, the apparent direct link between dependencies may be due to the fact that

legal governance is nowhere included in the model. The causality here would be as follows:

L4: Legal governance. In this mode of governance (P1) legal contracting and monitoring would

yield bilateral dependence.

However, there are more possibilities, as follows:

L5: Controllable guarantees. The customer is willing to reduce supplier switching costs (mode

of governance S2) due to specific assets by sharing their ownership or supplying guarantees

11


for their full utilization. A well-known complication here is that the customer would want

counter-guarantees that the investment is indeed specific and is not used for other custo-

mers. This requires openness on the part of the supplier for the customer to exercise such

control. The causal loop runs as follows: supplier’s openness allows for customer

commitment, in the form of guarantees, which enables the supplier to engage in specific

investments, which increase both his own dependence and that of the customer.

L6: Exclusiveness. A mechanism of mutual dependence can also arise from lack of alternatives

(mode of governance S1). If one side is dependent ('captive'), and thus faces high costs of

switching, then the other side will be more prepared to accept exclusiveness; to maintain

fewer alternative partners for the same activity, and thereby becomes more dependent

himself. By the same logic, this will encourage the partner to accept more exclusiveness.

And so on. For each side lack of alternatives creates dependence, which is accepted in view

of the partner’s dependence due to lack of alternatives.

The following experiment is now conducted: L5 and L6 are added to the model, and again

we test whether the links in the loops are confirmed and whether after inclusion of these loops the

model inductively still indicates a direct remaining link of bilateral dependence.

5. Results and discussion

Unfortunately, the data do not contain measures of legal governance. It is remarkable that so much

is explained without it. This confirms the widespread idea that contracts play only a subsidiary

role in many inter-firm relations. However, we would have liked to test explicitly whether the

inclusion of legal governance (P1) might eliminate the direct link between dependence of the two

partners that the LISREL model indicated inductively.

Figure 8 illustrates the outcome of adding L5: controllable guarantees. The causal arrow

from supplier's openness to customer commitment is not significant, and carries the wrong sign.

The arrow from customer commitment is significant but carries the wrong sign. Thus this

experiment has failed. The inductively generated direct connection from customer dependence tot

supplier dependence is undiminished in size and significance. The evidence is not quite

conslusive, however, because the variable 'commitment' acts as a proxy; it does not explicitly refer

to guarantees provided by the customer to safeguard supplier's specific assets.

Figure 9 illustrates the outcome of adding L6: exclusiveness. On both sides, the causal

arrows from dependence to alternatives available to the partner carries the correct sign but is not

significant. The inductively generated direct connection from customer dependence tot supplier

dependence is again undiminished. Thus this experiment also has failed.

12


Our conclusion is that either of the two following possibilities obtains. One is that legal

governance, in the form of contracts, is the missing variable. The hypothesis is that when this is

included the direct link from dependence to dependence will disappear, but this hypothesis cannot

be tested with the present data. The second possibility is that there exists a direct link from

dependence to dependence, which does not operate through any of the mechanisms of mutual

dependence L1 to L6. Not through the reduction of behavioral uncertainty due to commitment; not

through the dedication of a supplier to a valued partner which drives him to make himself

indispensable to that partner; not through habituation in a long-lived relation that generates

routines and bonds as a basis for trust and leads partners to take the relation for granted; not

through legal safeguards; not through other guarantees to safeguard specific assets; not through

mutual exclusiveness. Then what could this be? Could there be a direct link in the sense that

regardless of all those mechanisms dependence breeds dependence by itself, by imitation or some

psychological mechanism?

Further research might be designed for further investigation of this issue. In the present

research only data for the US car industry were used. Data are also avialable for Europe and

Japan. We will test whether the same phenomena obtain there. We will investigate the possibility

of including legal governance.

13


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Figure 4 Causal Loops in Long-Term Supply Relationships

supplier’suncertaintyavoidance

supplier’sfutureperspectives

customer’scommitment

supplier’sdedicatedinvestments

supplier’svalue to thecustomer

number ofalternativesto customer

supplier’sopenness

number ofalternativesto supplier

customer’svalue to thesupplier

customer’sdependence

pastduration

supplier’senvironmentaluncertainty

supplier’sdependence

habituationbetween thepartners

***.11

.09**

.25***

***.12

**--.08

***.22

***.21

***.33

***.19

.10***

.07**.05*

--.01.26***

.15***

.02

***.27

***--.23

.15***

.15***

***.18

***.16

.15***

supplier’sbehavioraluncertainty

***.30

.25***

***--.44

Notes: (1) * p<.10; ** p<.05; *** p<.01 (2) Source: De Jong et.al. (1998)

16


Figure 5 Self-Interested Customer Commitment











pastduration




***.11

.09**

.25***

***.12

**--.08

***.22

***.21

***.33

***.19

.10***

.07**.05*

--.01.26***

.15***

.02

***.27

***--.23

.15***

.15***

***.18

***.16

.15***


***.30

.25***

***--.44


17


Figure 6 Supplier Dedication to a Valued Partner











pastduration




***.11

.09**

.25***

***.12

**--.08

***.22

***.21

***.33

***.19

.10***

.07** .05*

--.01.26***

.15***

.02

***.27

***--.23

.15***

.15***

***.18

***.16

.15***


***.30

.25***

***--.44


18


Figure 7 Habituation











pastduration




***.11

.09**

.25***

***.12

**--.08

***.22

***.21

***.33

***.19

.10***

.07**.05*

--.01.26***

.15***

.02

***.27

***--.23

.15***

.15***

***.18

***.16

.15***


***.30

.25***

***--.44


19


Figure 8 Controllable Guarantees











pastduration




***.12

.10***

.25***

***.12

**--.08

***.22

***.20

***.34

***.21

.12***

.07** .06**

--.01.16***

.15***

.02

***.27

***--.24

.15***

.15***

***.18

***.16

.15***


.25***

***--.44

--.05

*--.07

***.30

Notes: (1) * p<.10; ** p<.05; *** p<.01

20


Figure 9 Exclusiveness (no alternatives)











pastduration




***.11

.09**

.25***

***.12

**--.06

***.22

***.21

***.33

***.19

.10***

.07**.05*

--.01

.26***

.15***

.02

***.27

***--.23

.15***

.15***

***.18

***.16

.15***


.25***

***--.44

--.05

--.00

***.30

Notes: (1) * p<.10; ** p<.05; *** p<.01

21


Appendix A Methods

A.1 Data Collection

This paper applies data on supply relationships which were collected via a survey in the United

States by professor Susan Helper. The survey was part of and financially sponsored by the

International Motor Vehicle Program of the Massachusetts Institute of Technology (Cambridge,

the United States). In spring 1993, the survey was mailed to every automotive supplier and

automaker division mentioned in the Elm guide to Automotive Sourcing. This guide lists the

major first-tier suppliers – both domestic and foreign owned – to manufacturers of cars and light

trucks in the United States and Canada. The target respondents were the divisional directors of

marketing at independent firms and the divisional business managers or directors of strategic

planning at car manufacturer components divisions. These respondents were selected on the

grounds that they would have the broadest knowledge about both customer relationships as well as

their firms’ products and processes. Because many companies supply their customer with several

different types of products, and their relationship with their customer differ by product, the

respondents were asked to answer the survey for their most important or significant customer

regarding one product which was typical of their company’s output. The respondents had a wealth

of experience. On average they had worked for more than 18 years in the auto industry and more

than 11 years in their companies. The response rate of the survey was 55 per cent after accounting

for those firms which were unreachable, i.e., mail sent to them was returned undelivered, and

those which were not eligible to answer the survey, i.e., they were not first-tier automotive

suppliers or they specialized in supplying for heavy trucks and buses. It provided detailed

information about 665 supply relationships.

A.2 Measurement Models: Factor Analyses

The theoretical model of this paper incorporates nine latent variables which were each

measured by more than one item. The remaining six items were measured by one indicator and

therefore did not have to be analyzed with a factor analysis (see appendix B). An exploratory

and a confirmatory factor analysis (henceforward EFA and CFA) were performed for each of

the nine latent variables. EFA – principal component analysis with varimax rotation calculated

with SPSS 7.5 – helped identifying whether or not items clustered on a factor with a factor-

loading of .30 as the usual cut-off point. Further the standardized values of the Cronbach’s

alpha were computed for which .50 is used as the threshold value. Afterwards, PRELIS 2.14

and LISREL 8.14 were used for the CFA (Jöreskog and Sörbom, 1993; 1996). The former

calculated the (polychoric) correlation matrix for each of the nine measurement models. The

latter offered maximum likelihood estimates of the relationship between one indicator and the

22


latent construct, λij , and the significance of λij , for which the threshold value is that the t-values

must be at least 2.

The selected items for seven latent variables – supplier’s dependence, customer’s dependence,

supplier’s uncertainty avoidance, supplier’s environmental uncertainty, customer’s value,

habituation, and customer’s commitment – exceeded the various criteria for the EFA and CFA.

For the constructs supplier’s behavioral uncertainty and supplier’s value to the customer, the

EFA resulted in more than one dimension. Table A1 presents the EFA-results for supplier’s

behavioral uncertainty.

Table A.1 EFA for Supplier’s Behavioral UncertaintyFactor 1

OpportunismFactor 2

Competences

SBU1 .28 .62SBU2 .28 .39SBU3 .08 .86SBU4 .07 .86SBU5 .87 .13SBU6 .72 .15SBU7 .83 .12

Alpha .77 .70

The first factor is operationalized by the items SBU5 through SBU7 and measures the

supplier’s perception of the customer’s opportunism. This aligns with Williamson’s (1985)

interpretation of behavioral uncertainty. The Cronbach’s alpha of .77 for these three items is

high. The second factor is operationalized by the items SBU1 through SBU4 and measures the

customer’s unpredictability in terms of his competences. The Cronbach’s alpha of .70 for these

four items is very satisfactory. This outcome provides a clear confirmation of the distinction

between competence trust and goodwill trust. The results of the EFA were used for a second-

order CFA which showed that the two latent constructs ‘opportunism’ and ‘competences’ were

highly significantly related to the original construct supplier’s behavioral uncertainty.

Table A2 below presents the EFA-results for the supplier’s value to the customer. These results

show three factors which have a clear interpretation namely the supplier’s value in terms of the

supplier’s relative skills (factor 1), innovative capabilities (factor 2), and technical competences

(factor 3).

Table A2 EFA for Supplier’s ValueFactor 1 Factor 2 Factor3

23


RelativeSkills

InnovativeCapabilities

TechnicalCompetences

SV1 .72 .32 -.03SV2 .84 -.05 .12SV3 .87 .05 .06SV4 .01 .09 .62SV5 .12 .04 .75SV6 .02 .01 .69SV7 -.04 .78 .02SV8 .21 .56 .10SV9 .06 .75 .06

Alpha .76 .52 .46

The items SV4, SV5, and SV6 have a Cronbach’s alpha of .46 which is below the threshold value

of .50. Nevertheless, the factor-loadings of these items are far above the cut-off point of .30 and

therefore we continued the analyses with these items. Again, a second-order CFA was performed

following the results of the EFA which showed that the three separate dimensions were highly

significantly related to the original construct ‘supplier’s value to the customer’.

A.3 Structural Model: Testing the Hypotheses

A LISREL application is restricted by the size of the sample. Lawley and Maxwell (1971)

recommend a minimum sample of 50 cases greater than half the number of measured variables

times one plus the number of variables – k (k+1) / 2 + 50 – for an analysis using maximum

likelihood estimation. Others have suggested to use at least 20 observations for every observed

variable included in the model with a minimum sample size of 100 cases. Since this study

measured 43 variables roughly 950 to 1000 cases would have been desirable. For this, the

sample size of 665 cases was not large enough. As a solution to this problem, we measured

each of the nine latent variables by the additive scale of the specific items. The remaining six

variables were measured by a single indicator. This resulted in a structural model with 15

measured variables and a required sample size of about 200 cases.

PRELIS 2.14 was used to calculate the appropriate product-moment input matrix of the 15

variables (Jöreskog and Sörbom, 1993; 1996). Many but not all of the 15 variables had a ratio

measurement scale. Therefore, a covariance matrix could not be calculated. The correlation

matrix presented in appendix C contains Pearson as well as polychoric and polyserial

correlation coefficients. These correlations served as the input matrix for LISREL 8.14 and the

parameter-estimates were obtained with the maximum likelihood procedure. A hypothesis is

confirmed if it equals the sign of the parameter-estimate and if it is significant. One-tailed

significance levels are used because the hypotheses formulate the explicit predictions of the

effect of a variable. A t-value larger than 1.2821 aligns with p<.10 (weakly significant), a t-

24


value larger than 1.6449 with p<.05 (moderately significant) and a t-value larger than 2.3296

with p<.01 (strongly significant).

PRELIS 2.14 treats any variable with 15 categories or more as a ratio-scaled variable. This is a

default setting which can be changed. We used this option to calculate a Pearson correlation

matrix and a covariance matrix of the 15 variables. Subsequently, these two matrices were used

as a sensitivity analysis with LISREL 8.14 ML-estimates. With very minor deviations the

resulting parameter-estimates, t-values, and global-fit values were identical to the ones

presented in the paper.

The global model fit was assessed by four indicators. The chi-square indicates the probability that

the measurement matrix of the form implied by the model. It is a test statistic and a p-value greater

than .05 is generally considered to be acceptable. The goodness-of-fit index (GFI) measures the

relative amounts of variance and covariance in a sample covariance matrix that the model predicts.

The adjusted goodness-of-fit index (AGFI) is an extension of the GFI, i.e., adjusted for the

degrees of freedom of the model relative to the number of variables. For both, a value greater than

.90 is considered an indication of good fit (Mathieu et.al., 1992). The root mean square error of

approximation index (RMSEA) takes into account the error of approximation in the population

and the precision of the fit measure itself. Browne and Cudeck (1993) suggest that values lower

than .05 indicate a ‘very good’ fit; a value from .05 to .08 indicates a ‘fair to mediocre’ fit; a value

from .08 to .10 indicates a ‘poor’ fit; and values greater than .10 a ‘very bad’ fit.

Table A3 Global Model-FitChi-square RMSEA GFI AGFI

M0: Benchmark Model 259.3 .062 .953 .922

M1: Controllable Guarantees 255.4 .063 .953 .921

M2: Exclusiveness 278.5 .063 .949 .920

25


Appendix B Constructs, Items, and Scales

01 Supplier’s dedicated investments SUPINVPlease estimate the total amount of your business unit’s investment in equipment to make this product over thelast four years.

02 Supplier’s dependence SUPDEPSD1 If you were to stop getting these orders from this customer, approximately how much of your investment for this

product in plant, equipment, and training would you be unlikely to find alternative uses for and have to write off?Scale: 1 = 10% or less; 2 = 11-33%; 3 = 34-66%; 4 = 67-89%; 5 = 90-100%.

SD2 Please estimate the technical complexity involved in manufacturing the product in 1992.Scale: 1 = fairly simple; 5 = highly complex.

SD3 Please check the appropriate range for the average piece price of the product in 1992. Scale: for the United States 1 = <$1; 2 = $1-10; 3 = $11-50; 4 = $51-100; 5 = > $100.SD4 Does your business unit have any of the following?

A marketing office near your customer; a design office near your customer; a facility near your customer toconsolidate shipments of your parts for ‘Just-in-Time’ (JIT) delivery; an engineers resident at your customer'sfacility.Scale: one point for each.

03 Customer’s Dependence CUSDEPCD1 Please estimate the number of months it would take your customer to replace your business unit with another

supplier. Consider the time required to locate, qualify, train, make investments, test, and develop a workingrelationship with another firm. Please exclude legal considerations such as the existence of long-term contracts.Scale: 1 = 0; 2 = 1-3; 3 = 4-12; 4 = 13-24; 5 = 25-48; 6 = > 48.

CD2 What percent of your business unit’s sales ends up as original equipment for cars or light trucks?Scale: 1 = 0-10; 2 = 11-25; 3 = 26-40; 4 = 41-65; 5 = 66-80; 6 = 81-100.

04 Supplier’s uncertainty avoidance SUPUNCSUA1 If our customer had given us less assurance of continued business for this product, we would definitely have

invested less in plant, equipment, and training which could be used to serve only this customer.Scale: 1 = strongly disagree; 5 = strongly agree.

SUA2 If our customer had given us less assurance of continued business for this product, we would definitely haveinvested less in plant, equipment, and training which could be used to serve either this customer or othercustomers.Scale: 1 = strongly disagree; 5=strongly agree.

05 Supplier’s environmental uncertainty SUPENVSEU1 In the production of this product, how much certainty is there regarding your production costs over 4 years.

Scale: 1 = fairly certain; 5 = completely unpredictable.SEU2 In the production of this product, how much certainty is there regarding the production technology for this

product over 4 years.Scale: 1 = fairly certain; 5 = completely unpredictable.

06 Supplier’s behavioral uncertainty BEHUNCSBU1 In the production of this product, how much certainty is there regarding the customer’s production schedule 2

weeks ahead.Scale: 1 = fairly certain; 5 = completely unpredictable.

SBU2 In the production of this product, how much certainty is there regarding the customer’s production schedule 1year ahead.Scale: 1 = fairly certain; 5 = completely unpredictable.

SBU3 In the production of this product, how much certainty is there regarding the customer’s final productspecifications before job 1.Scale: 1 = fairly certain; 5 = completely unpredictable.

SBU4 In the production of this product, how much certainty is there regarding the customer’s final productspecifications after job 1.Scale: 1 = fairly certain; 5 = completely unpredictable.

SBU5 Given the chance, our customer might try to take unfair advantage of our business unit.Scale: 1 = strongly disagree; 5 = strongly agree.

26


Appendix B (continued)

06 Supplier’s behavioral uncertainty (cont.)SBU6 We feel that our customer often uses the information we give to check up on us, rather than to solve problems.

Scale: 1 = strongly agree; 5 = strongly disagree.SBU7 Please circle the number which best describes your belief that your customer will treat you fairly.

Scale: 1 = customer always treats us fairly; 5 = can’t depend on customer to treat us fairly.

07 Supplier’s value to the customer SUPVALSV1 For design engineering. Currently, how would you rate your business unit's skills at making modifications to

products or processes? Please compare yourself to other firms in your industry throughout the world.Scale: 1 = significantly below average; 5 = significantly above average.

SV2 For making incremental process improvements. Currently, how would you rate your business unit's skills atmaking modifications to products or processes? Please compare yourself to other firms in your industrythroughout the world.Scale: 1 = significantly below average; 5 = significantly above average.

SV3 For implementing entirely new processes. Currently, how would you rate your business unit's skills at makingmodifications to products or processes? Please compare yourself to other firms in your industry throughout theworld.Scale: 1 = significantly below average; 5 = significantly above average.

SV4 Of the metal cutting machines currently in use at the plant which makes this product, about what percent areCNC?Scale: 1 = 0%; 2 = 1-25%; 3 = 26-50%; 4 = 51-75%; 5 = 76-100%.

SV5 Of the other machines currently in use at the plant which make this product, about what percent have PLC?Scale: 1 = 0%; 2 = 1-25%; 3 = 26-50%; 4 = 51-75%; 5 = 76-100%.

SV6 About how many robots (programmable machines with at least three axes of movement) are in use at the plant?Scale: 1 = 0; 2 = 1-2; 3 = 3-5; 4 = 6-10; 5 = >10.

SV7 Approximately what percent of the contacts with your customer regarding this product were for ‘your businessunit providing technical assistance to customer’?Scale: 1 = 0-19; 2 = 20-39; 3 = 40-59; 4 = 60-79; 5 = 80-100.

SV8 Which range best describes your business unit’s R&D as a percent of sales?Scale: 1 = 0%; 2 = 0.1-1%; 3 = 1.1-2%; 4 = 2.1-4%; 5 = >4%.

SV9 Please check the descriptions which apply to the product development process for your company’s product.Scale: 1 = customer took entire responsibility; 2 = customer provided majority of engineering hours; yourbusiness unit provided the rest; 3 = customer and your business unit contributed equally to the design; 4 = yourbusiness unit provided majority of engineering hours; 5 = your business unit took entire responsibility.

08 Alternatives to the supplier SUPALT.Please indicate the number of automakers of each nationality of ownership to whom you supply this product fromthis plant. Scale: the number of firms.

09 Customer’s value to the supplier CUSVALCV1 Over the last four years, what sorts of technical assistance have you received from your customer?

Provided personnel who visited your site to aid in implementing improved procedures for zero or a nominalcharge; for a fee; did not provide. Arranged for training of your personnel at their site for zero or a nominalcharge; for a fee; did not provide. Provided personnel who worked two weeks or more on your shop floor toimprove your processes for zero or a nominal charge; for a fee; did not provide.Scale: one point for each.

CV2 Approximately what percent of the contacts with your customer regarding this product were for ‘customerproviding technical assistance to your business unit’?Scale: 1 = 0; 2 = 1-10; 3 = 11-20; 4 = 21-30; 5 = 31-100.

CV3 The advice our customer gives us is not always helpful.Scale: 1 = strongly agree; 5 = strongly disagree.

CV4 In dealing with this customer, we have learned much that will help us with other customers.Scale: 1 = strongly disagree; 5 = strongly agree.

10 Alternatives to the customer CUSALTPlease indicate the appropriate number of other firms supplying the same product to same automaker or otherfirms potentially able to supply similar product (without major investment).Scale: the number of firms.

27


Appendix B (continued)

11 Habituation HABITHAB1 For face to face contact. In each year, approximately how often did someone from your business unit have a

substantive discussion with your customer? (Please include discussions about issues such as design changes andquality problems, but exclude routine delivery notifications and contacts by resident engineers).Scale: 1 = every 6 months or less often; 2 = monthly; 3 = weekly; 4 = daily; 5 = more than once a day.

HAB2 For phone contact. In each year, approximately how often did someone from your business unit have asubstantive discussion with your customer? (Please include discussions about issues such as design changes andquality problems, but exclude routine delivery notifications and contacts by resident engineers).Scale: 1 = every 6 months or less often; 2 = monthly; 3 = weekly; 4 = daily; 5 = more than once a day.

HAB3 For fax contact. In each year, approximately how often did someone from your business unit have a substantivediscussion with your customer? (Please include discussions about issues such as design changes and qualityproblems, but exclude routine delivery notifications and contacts by resident engineers).Scale: 1 = every 6 months or less often; 2 = monthly; 3 = weekly; 4 = daily; 5 = more than once a day.

12 Customer’s Commitment CUSCOMCC1 How would your customer react if one of your competitors offered a lower price for a product of equal

quality?Scale: 1 = switch to competitor as soon as technical feasible; 2 = switch at end of contract; 3 = reduce yourmarket share; 5 = help you match your competitors’ efforts.

CC2 How would your customer react if your material suppliers raised their prices?Scale: 1 = reduce your business unit’s market share or switch to another supplier at end of contract; 2 = holdyou to your original price; 3 = allow partial pass-through of your business unit’s cost increases; 4 = allow fullpass-through of your business unit’s increases in out-of-pocket costs; 5 = provide significant help for yourbusiness unit to reduce costs.

CC3 Suppose your business unit had an idea that would allow you to reduce your costs, but would require yourcustomer to make a slight modification in its procedures. How would your customer react?Scale: 1 = customer does not welcome suggestions that would require modifications in its procedures; 2 =customer would adopt the suggestion, but would seek to capture most of the savings; 3 = customer wouldadopt the suggestion, but would seek to capture some of the savings; 5 = customer would eagerly solicits suchsuggestions.

CC4 We can rely on our customer to help us in ways not required by our agreement with them.Scale: 1 = strongly disagree; 5 = strongly agree.

13 Supplier’s openness SUPOPNWhat types of information does your business unit provide to your customer about the process you use to makethe product you listed above?Detailed breakdown of process steps; cost of each process step; financial information not publicly available;production scheduling information; type of equipment used; your sources of supply; detailed informationregarding materials you use.Scale: one point for each.

14 Supplier’s future perspectives SUPFUTFor how long do you think there is a high probability that your business unit will be supplying this or a similaritem to your customer (in years)?

Scale: the number of years.

15 Past duration of the relationship PASTApproximately how long has your firm sold products in this product line to this customer (in years)?Scale: the number of years.

28


Appendix C Correlation Matrix

No Construct Abbrev. Supinv Supdep Cusdep Supunc Supbeh01 Supplier's Dedicated Investments Supinv 1.00002 Supplier's Dependence Supdep 0.330 1.00003 Customer's Dependence Cusdep 0.211 0.398 1.00004 Supplier's Uncertainty Avoidance Supunc -0.189 0.083 0.014 1.00005 Supplier's Behavioral Uncertainty Supbeh -0.004 -0.010 -0.061 0.153 1.00006 Supplier's Value Supval 0.288 0.341 0.281 -0.024 -0.03207 Customer's Value Cusval 0.090 0.099 0.105 0.066 -0.29508 Habituation Habit 0.205 0.296 0.293 0.037 0.02009 Customer's Commitment Cuscom 0.007 0.040 0.140 -0.065 -0.46710 Supplier's Openness Supopn 0.114 0.223 0.172 0.079 0.02311 Supplier's Future Perspectives Supfut 0.071 0.097 0.156 -0.027 -0.17312 Supplier's Environmental Uncert. Supenv 0.069 -0.042 -0.029 0.039 0.30113 Supplier's Alternatives Supalt 0.086 0.012 0.002 -0.023 -0.02014 Customer's Alternatives Cusalt -0.038 -0.066 0.101 0.045 0.07815 Past Duration Past -0.036 -0.017 -0.016 0.029 0.085

No Abbrev. Supval Cusval Habit Cuscom Supopn Supfut Supenv Supalt Cusalt Past06 Supval 1.00007 Cusval 0.077 1.00008 Habit 0.207 0.173 1.00009 Cuscom 0.034 0.331 0.119 1.00010 Supopn 0.184 0.231 0.240 0.063 1.00011 Supfut 0.088 0.062 0.177 0.194 0.001 1.00012 Supenv -0.055 -0.068 -0.022 -0.121 -0.121 -0.077 1.00013 Supalt 0.087 0.062 0.010 0.002 0.016 0.076 -0.019 1.00014 Cusalt -0.145 -0.051 0.025 -0.048 0.032 -0.004 0.044 -0.031 1.00015 Past 0.038 -0.039 0.120 -0.065 -0.031 0.139 -0.048 0.022 0.161 1.000

competence in mutual dependence

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