An Empirical Examination of the Relation between Performance Measurement, Collaborative Contracting, and Asset Ownership Ranjani Krishnan Michigan State University & Harvard Business School Fabienne Miller Worcester Polytechnic Institute Karen Sedatole Michigan State University June 20, 2022
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An Empirical Examination of the Relation between Performance Measurement, Collaborative Contracting, and Asset Ownership
Ranjani KrishnanMichigan State University
&Harvard Business School
Fabienne MillerWorcester Polytechnic Institute
Karen SedatoleMichigan State University
April 12, 2023
____________________________________We thank Ella Mae Matsumara, Vallabh Sambamurthy, Geoff Sprinkle, participants of the 2008 Management Accounting Research Conference of the American Accounting Association, and workshop participants at Indiana University, and Michigan State University. This research was supported by the Center for the Leadership of the Digital Enterprise (CLODE) of the Broad School of Business, Michigan State University. We also thank our research partners for their assistance.
An Empirical Examination of the Relation between Performance Measurement, Collaborative Contracting, and Asset Ownership
ABSTRACT:
This study uses insights from transaction cost economics and agency theory to posit that
uncertainty in inter-firm relations increases the difficulty in measuring contractual performance
thereby leading to contractual incompleteness. To protect from the resultant contractual
opportunism, firms are more likely to use collaborative contracting. Results using data from 87
contracts with customers of a leading provider of electronics manufacturing services indicate that
services such as supply chain management have higher uncertainty than production services. In
addition, performance measurement difficulty due to high uncertainty is associated with greater
use of collaborative contracting. Empirical results also show that suppliers are more likely to
invest in relation-specific assets when there is a greater reliance on collaborative contracting. Our
study suggests that uncertainty has implications for inter-firm contracting in the presence of
Data availability: A confidentiality agreement with the firm precludes us from publicly sharing
the data.
An Empirical Examination of the Relation between Performance Measurement, Collaborative Contracting, and Asset Ownership
I. INTRODUCTION
Most inter-firm relationships impose risks to both the supplier and the customer (Das and
Teng 1996, 2001). Contractual safeguards can be employed to limit such risks if the contracting
parties can achieve ex ante consensus about which mechanisms can be used to measure and
monitor performance and determine the extent to which each party is meeting its contractual
obligations. For example, in the manufacturing of routine components that have pre-determined
standards, supplier performance can be assessed by the extent to which the specifications of
supplied components fall within tolerance limits or deliveries are made as scheduled (Anderson
and Dekker 2005). Prior literature suggests that contracts are one of the most frequently used
mechanisms parties use to protect themselves against potential trade opportunism (Tirole 1988;
Joskow 1985).
Under certain circumstances, however, contracting parties are unable to find or ex ante
agree on which performance measures will be used for assessing contractual performance. One
such circumstance is when there is uncertainty about the factors that influence performance. The
contracting parties are exposed to the risk of ex-post hazards that may arise subsequent to the
initial contractual agreement. While the supplier may be able to negotiate a favorable initial
contract, transaction cost economics (hereinafter TCE) suggests that this contract will likely be
incomplete, and, therefore, unable to take into account all the contingencies that may arise in the
future (Williamson 1985, 1991).
Prior TCE research shows that when transactional hazards are high, collaborative
relationships are more likely than arm's-length transactions (Artz and Brush 2000; Jap and
1
Anderson 2003; Judge and Dooley 2006). Milgrom and Roberts (1992) define a collaborative
contract (which they refer to as a “relational contract”) as one that “does not attempt the
impossible task of complete contracting but instead settles for an agreement that frames the
relationship” (p. 131, emphasis added) and relies on “unarticulated but (presumably) shared
expectations that the parties have concerning the relationship” (p. 132).1 A collaborative relation
entails sharing not only information and resources, but also risks and rewards (Kumar 1996).
Indeed, confidence and mutual trust exist between the parties because each expects the other to
cooperate (Das and Teng 1998; Holmstrom and Roberts 1998). Thus, trust and the repeated
exchange associated with collaboration compensate for the lack of adequate performance
measures necessary to enforce contractual provisions. Collaboration, which is also associated
with alignment of strategic objectives and temporal horizons, can therefore facilitate contracting
by increasing trust between the contracting parties. We extend this area of research by first
examining the antecedents of collaboration. Specifically, we explore measurability of contractual
performance as a factor that drives whether a buyer-seller relation will be collaborative.
We next examine the effect of collaborative contracting on relation-specific investments,
i.e., investments in assets that have a low value outside the relationship. Ex-post contractual
risks are especially pronounced when a supply relationship entails relation-specific investments
and uncertainty is high. One example of such a relation-specific investment is an in-process die
used in the automobile industry to shape steel sheets into parts for a specific vehicle (Klein,
Crawford, and Alchian 1978). These dies, which require significant capital investments by the
parts supplier, have little to no value outside the relationship between the automaker and the
parts supplier. Moreover, once the supplier invests in the dies, it is exposed to the “hold-up” 1 Anderson and Dekker (2005) define collaborative arrangements as transactions conducted via close partnership relationships rather than through arm’s-length market transactions. Subramani and Venkatraman (2003) define collaboration as the linkage between supplier and customer that are reflected in communication and information exchange, and the extent to which the supplier orients its resources toward serving the customer's distinctive needs.
2
problem, i.e., the customer may later force unfavorable exchange terms on the supplier. To the
extent the supplier anticipates potential hold-up, it may choose either to not invest in relation-
specific assets at the socially optimal level or to spend resources to protect itself, both of which
lead to inefficiencies (Williamson 1995; Holmstrom and Roberts 1998).2 We empirically
examine whether the collaborative nature of the relationship affects the likelihood of the supplier
making a relation-specific investment. We predict that, because collaboration helps protect firms
from contract incompleteness, collaboration reduces the risk of hold-up by the customer and
thereby increases the supplier's willingness to invest in relation-specific assets (Parkhe 1993).
We use data from 87 customer contracts obtained from a Fortune 500 corporation to
empirically test the above predictions. This corporation is a leading provider of electronics
manufacturing and integrated supply chain services. We analyze and code the contracts to
measure the degree of collaboration, the relation-specific investments made by the supplier and
the customer, and other important contract characteristics. Then, based on field interviews with
managers from the firm, we examine how difficult it would be for customers to evaluate the
supplier's performance based on the types of services the supplier provided, uncertainty, and
difficulty in monitoring performance.
Our results suggest that collaborative contracting is more likely to be used when the
measurement of contractual performance is more ambiguous because of uncertainty. Our results
also show that the supplier is more likely to invest in relation-specific assets when there is
greater reliance on collaborative contracting. These results indicate that when performance
measurement is difficult, firms reduce their risks by making greater use of collaborative
2 Relation-specific investments have the potential to benefit both the supplier and the customer, and thus both parties stand to be worse off if the investment is not made.
3
contracting, presumably because it is not feasible (or too costly) to write contracts that offer
protection from all the contingencies that may arise.
This study makes an important contribution to the accounting literature. Prior contracting
literature in accounting has examined how the properties of performance measures influence
their use in contracting within the firm (Banker and Datar 1989; Feltham and Xie 1994; Datar,
Kulp, and Lambert 2001). We add to this literature by examining the role of performance
measurement in an inter-firm setting. Using a unique data set of actual customer-supplier
contracts,3 our results show that firms that make relation-specific investments protect themselves
by collaborative contracting to mitigate the risks of opportunism posed by incomplete contracts,
as suggested by transaction cost economics. Our results, therefore, imply that the TCE
framework can enrich researchers' insights into inter-firm contracting in the presence of relation-
specific investments. Our results also have practical implications for firms interested in
determining the conditions under which risk management via collaborative contracting is
advantageous; namely, conditions wherein performance measures either are not available or
unable to effectively evaluate contractual performance such as with high uncertainty.
The remainder of this study is organized as follows. Section II discusses the relevant
theory and poses research questions. Section III provides details about the data and empirical
models. Section IV presents the results of our empirical analysis and Section V offers concluding
remarks.
II. THEORY AND RESEARCH QUESTIONS
Performance Measurement and Contracting
3 Our study is one of the few to use actual contract data. Others include Joskow (1985), Crocker and Reynolds (1993), and Saussier (2000).
4
A significant body of research in economics, strategy, and accounting has explored
contracting relationships within and between firms. The two theoretical frameworks most
frequently used to examine these contractual relations are contracting theory using an agency
lens and TCE. 4 From an agency perspective, a contract can be used to assign responsibilities,
determine outcomes and shares of contracting parties, and specify penalties for non-compliance
(Poppo and Zenger 2002). Under certain circumstances contract can be “complete”, i.e., fully
specify the terms of exchange. A complete contract is defined by Baiman and Rajan (2002) as
follows: “there are no restrictions on the feasible set of contracts from which the contracting
parties can choose; all information that will be observed by the contracting parties can be
specified by them at the time of contracting and will be verifiable by a court; and there are no out
of pocket costs associated with writing or enforcing contracts” (p. 214).
An essential feature of complete contracting is the ability to measure supplier
performance, and use such performance information in monitoring and assignment of rewards.
Considerable research in contract design has therefore focused on the choice of appropriate
performance measures and the extent of their use in contracting (e.g., Holmstrom 1979; Banker
and Datar 1989; Feltham and Xie 1994; Datar, Kulp, and Lambert 2001). The general consensus
from these models is that the optimal design of incentives and monitoring mechanisms depends
on the availability of precise, sensitive, and congruent performance measures. Some routine
production services, for example, can often be monitored using performance measures such as
cost or quality, where performance is defined as a cost or quality measure that falls within an
acceptable range of a target level (i.e., is within a “tolerance limit”). To the extent that routine
4 While agency theory has predominantly been used to examine contracting within firms (such as between the board and the CEO), the basic tenets of agency theory are applicable to inter-firm contracting. Agency theory primarily deals with contracting to minimize agency losses arising from hidden information and/or hidden action without explicitly considering the boundaries of the firm. Moreover, Baiman and Rajan (2002) state “there is no consensus on what distinguishes inter-firm from intra-firm transactions” (page 214).
5
production services use established technologies, the variability of the parameters associated
with those services and their corresponding target levels are known. The degree of acceptable
deviation from the target could therefore be agreed upon ex ante by both the supplier and the
customer.
In some instances however, the degree of uncertainty associated with some parts of the
process imposes difficulties in measuring performance. In the case of activities such as
innovation and R&D, even simply defining, let alone measuring performance is difficult.
Additionally, when tasks are nonseparable, i.e., when each party's contribution to the outcome
cannot be easily identified, outcome performance measures cannot be relied upon to assess
contractual performance. Measuring a supplier's performance may also be complicated by
“systems effects,” in which the performance of one supplier depends to some extent on either the
performance of another supplier or of the customer itself. The aforementioned automotive die
illustrates the latter case in that a particular die's performance is defined as the fit of the parts it
produces with the other parts of the same automotive subassembly (Anderson, Glenn, and
Sedatole 2000).
Thus, identifying performance standards and using those standards to evaluate
performance is more difficult for certain services than for others. In such instances, monitoring
the supplier's performance and accordingly, determining whether the supplier has fulfilled the
terms of the contract becomes more difficult. Under such circumstances, contracting between the
parties may be hampered by the supplier's unwillingness to risk expending effort without the
guarantee of some return. At the same time, the customer may be unwilling to guarantee a return
in the absence of measurable outcomes. As a result, formal contractual safeguards cannot be
employed.
6
Transaction cost economics offers useful insights into circumstances in which firms are
unable to find or agree upon performance measures. Originally propounded by Coase (1937) and
developed by Williamson (1975, 1985, 1995, 2002) and Klein et al. (1978), transaction cost
economics addresses the following question: What factors determine whether a firm is better off
vertically integrating with its suppliers and customers or contracting with another firm? As
Shelanski and Klein (1995, 336) point out, the basic insight of transaction cost economics is that
“transactions must be governed as well as designed and carried out, and certain institutional
arrangements effect this governance better than others.”
Transaction cost economics proposes that, in a complex world, contracts are inherently
incomplete. This incompleteness can arise from many factors such as the bounded rationality of
the agents, their inability to anticipate all the changes that may occur after the contract has been
signed, and the unobservability or unverifiability of outcomes (Shelanski and Klein 1995).
Indeed, because of the incomplete nature of contracts, they are often not enforceable in a court of
law. The incomplete nature of contracts gives rise to a number of problems, not the least of
which is how partners can protect themselves from each other's opportunistic behavior.
Transaction cost economics posits that collaborative contracting offers protection to the
contracting parties when the risk of opportunism is high because of incomplete contracting.
Collaborative contracting can provide safeguards against opportunism by both parties through
mechanisms such as trust and repeated transactions (Gulati 1995; Dyer 1997). Importantly, such
collaborative relationships are often associated with increased information sharing and sharing of
risks and benefits (Dekker 2003, Tomkins 2001). A variety of collaborative relationships can be
used such as alliances, joint ventures, and strategic supplier relationships (Anderson and Sedatole
7
2003).5 Drawing on agency and TCE theories, we propose that firms will employ collaborative
contracting to address the challenges associated with measuring outcome performance. Thus,
when an activity has higher uncertainty then performance measures are noisier; they cannot be
incorporated in contracts and contract incompleteness increases.
We also assume that lower monitoring is a proxy for unavailability or the poor-quality of
performance measures and, as a result, similar contracting risks exist when an activity is less
amenable to monitoring. That is, we expect that uncertainty and lack of monitoring proxy for
performance measurement difficulty and consequently we are more likely to observe
collaborative contracting in such instances.
Hypothesis 1: Collaborative relationships are more likely when an activity’s performance
is more difficult to measure.
Investments in Relation-Specific Assets Collaborative Contracting
The risk of opportunistic behavior intensifies in the presence of relation-specific
investments when uncertainty is high and contractual outcomes cannot be specified ex ante.
Williamson (1983) identifies four types of relation-specific investments. These include: (a) site
specificity, where the supplier and the customer are located in a “cheek-by-jowl” relation to
reduce inventory and transportation expenses, (b) physical asset specificity, where one party or
the other must invest in an asset that has no (or significantly less) value outside the relationship
for which the investment was made (e.g., the aforementioned dies required to produce an
automotive component for a specific vehicle), (c) human-asset specificity which arises when
employees acquire knowledge and skills that do not extend beyond the given relation, and (d)
5 Researchers have observed that collaborative customer-supplier relationships fall along a continuum that ranges from arm’s length to complete integration. Indeed, there is a “vast middle ground” of collaborative supplier relationships that includes licensing arrangements, joint ventures, and strategic alliances (Anderson and Sedatole 2003).
8
dedicated assets committed to a particular supply arrangement that, if terminated, would leave
the firm with considerable excess capacity.
To protect themselves from opportunism when investments in relation-specific assets are
made, contracting parties will attempt to employ various safeguards including formal contracts
(Dyer 1997). A comprehensive contract that stipulates the obligations and expected actions of
each party, as well as the ramifications in the event of unexpected environmental conditions,
decreases the risks that a supplier would be exposed to from the relation-specific investments.
Indeed, from an agency perspective, when complete contracting is feasible, asset ownership is
irrelevant because the contract can assign rights associated with asset ownership (Baiman and
Rajan 2002).
However, TCE argues that as the complexity of the contract increases, so does the cost of
contracting for both parties. In extreme cases, contracting costs can increase to such a degree that
they become prohibitive, requiring the contracting parties to explore other options for
safeguarding their relation-specific investments (Dyer 1997). Moreover, some contracting
contingencies, while foreseeable, are ultimately indescribable (Tirole 1999). As a result,
contracting parties need to establish governance procedures whose safeguards against
opportunism are sufficient to increase the supplier’s willingness to invest in relation-specific
assets. While both the supplier and customer stand to gain from the supplier’s investments in
relation-specific assets, the distribution of risk thereafter is uneven (recall that once the supplier
invests in the relation-specific asset, it exposes itself to the risk of hold-up by the customer).
We explore collaborative contracting as a governance safeguard firms use to facilitate
investments in relation-specific assets (Dyer 1997; Bensaou and Anderson 1999; Dekker 2004;
Lee and Cavusgil 2006). Collaborative contracting, which is associated with increased trust
9
between contracting parties, establishes a sense of reciprocity that reduces the probability that the
parties will behave strategically (Ring and Van De Ven 1992; Baiman and Rajan 2002; Jap and
Anderson 2003). We therefore predict:
Hypothesis 2: The supplier is more likely to invest in relation-specific assets when
the relation between the supplier and the customer is collaborative.
In sum, we predict that uncertainty and lack of monitoring proxy for difficulty in
measuring performance, and are associated with collaborative contracting (Figure 1).
Collaborative contracting in turn is associated with higher probability of relation-specific
investments. The next section describes the data and methods.
[Insert Figure 1 here]
III. DATA AND METHODS
Research Setting
We analyze data from a firm (hereinafter, "EMS") that is a leading provider of electronics
manufacturing services.6 With a global supply base and short product life cycles, the electronics
manufacturing services business is very competitive. Firms in this industry have suffered from a
downturn during the high-tech industry slump of 2001, driving margins to all-time lows. In
response, firms have implemented several strategies to adapt to this new environment. Some
firms have increased their emphasis on product design and helping customers to reduce their
R&D costs, while others have focused on supply chain management and providing lean
configuration. Additionally, while some firms have chosen to emphasize quality, others have
aimed to become low-cost providers. Materials account for as much as 80 percent of the costs
incurred, leading all firms to manage their inventories very closely. In addition, electronics
6 Owing to a confidentiality agreement with the firm, we refer to the firm as “EMS” (not its real name) and do not provide any financial or operating data.
10
manufacturing services firms across the board have attempted to shorten time-to-market and
increase manufacturing efficiency. Exclusivity of customer-supplier relationships is rare in this
industry, but most firms have a few large customers that comprise the majority of their revenue.
Finally, most customers do not commit to long-term production schedules and margins are very
low. In sum, the firm we analyze operates in a very competitive environment characterized by a
high degree of uncertainty.
Data
Like most of its competitors, EMS offers services ranging from production and repairs to
supply chain management. Production (i.e., manufacturing of electronics ranging from
components of mobile phones to set-top boxes) constitutes the core of EMS business and is
characterized by a focus on quality, flexibility, and efficiency. EMS also provides its customers
with end-to-end services that encompass production modification (i.e., manufacturing with the
goal of reducing cost), repairs (i.e., aftermarket services such as warranty support and reverse
logistics), and supply chain management (i.e., supply chain optimization). EMS is the market
leader in repairs and aftermarket services. Our research team initially conducted exploratory
interviews with senior EMS executives to gain an understanding of the industry and of the
relationships EMS encounters with its customers. Subsequently, we obtained all of EMS's
contracts with its customers. From these contracts, we selected agreements that were in force and
that included the provision of manufacturing services. In other words, all the contracts we
selected were production contracts. From this group, we identified those contracts that, in
addition to production, provided production modification, repair, or supply chain management
services. We excluded all contracts written in a foreign language, with the exception of those
written in French because a member of our research team was able to translate these contracts
11
into English. Three raters then independently rated the contracts from the 33 largest customers as
well as 54 additional customers. EMS identified its 33 largest customers based on the size of the
revenues each customer generated for the firm. The other 54 contracts were selected at random
from the sample of all manufacturing contracts. In sum, we analyzed 87 of 179 manufacturing
contracts, or 49 percent of the total manufacturing contracts. Importantly, EMS disclosed to our
research team that its ten largest customers account for over 60 percent of its revenues. Thus, we
estimate that our sample (comprising the 33 largest customers plus 54 additional customers)
represents between 80 and 90% of EMS sales revenue.
Two of the raters were researchers associated with the project and a third was a JD/ MBA
student. The data collected from the contracts included a reference to the relevant contract page
for easy verification of the source of the information. When the raters were unable to find the
necessary data after a first read through the contract, they used Adobe Acrobat word searches to
capture the relevant data. Any discrepancies in coding were thoroughly investigated by the raters
by reviewing the contracts' terms and meeting as a group to ensure agreement and consistency.
Furthermore, in addition to the assigned rater, one or both of raters who are members of our
research team also coded the contracts from the 33 largest customers. The JD/MBA student
coded the remainder of the contracts, with random verification provided by one of the
researchers. Any discrepancies underwent another round of investigation and recommendations
for corrections were made to the student orally as well as in writing.
The minimum duration of the contracts we analyzed was twelve months, and some
contracts were open-ended. While most contracts were written by customers, we estimate that
over 20 percent were prepared by EMS. The contracts prepared by EMS were similarly
organized from one customer to the next, although the terms of the contracts varied.
12
Additionally, the contracts prepared by EMS were with smaller customers. The length of the
contracts varied from 5 to 185 pages. Based on theoretical support provided by agency theory
and transaction cost economics, we selected variables to examine from the contracts. These
variables, defined in detail below, included measures of relation-specific investment,
collaboration, type of service, number of services, monitoring, contract duration, customer size,
and uncertainty.
Variable Definitions
This section discusses how we define the variables used in our empirical analyses.
Uncertainty:
As mentioned earlier, we use uncertainty and monitoring as proxies for measurement
difficulty. We examine the contracts to evaluate the extent of the uncertainty the services
provided by EMS are faced with. We believe that EMS is faced with higher uncertainty when
contracts include demand forecast, provisions for order reduction, and responsibility for unsold
inventory. In other words, we propose that the presence of contractual clauses that address
demand forecasts, order reduction, and responsibility for unsold inventory signifies that
uncertainty is high. Conversely, absence of such clauses can be taken as a sign that uncertainty is
low.
We compute a factor score to capture the extent to which the services performed by EMS
are exposed to uncertainty. We use factor analysis of the following variables extracted from the
contract: specification of demand forecast (included or not included), protection from reduction
of orders (i.e., downside) and from cancellation (included or not included), clauses specifying
responsibility of customer for obsolete and excess inventory (specified or not specified). All five
variables load on a single factor with the following factor weights: forecast 0.677, downside
13
0.624, cancellation 0.64, obsolete 0.476, and excess 0.722 (Cronbach alpha = 0.62). This factor
explains 40% of the total variance.
Monitoring
We examine the contracts to identify provisions for auditing and inspections. EMS
customers will only be able to rely on the monitoring of services as a control mechanism if they
are able to define, measure, and agree on the appropriate performance measures with which to
monitor the contractual performance of those services. We thus assume that the presence of
monitoring provisions implies that performance measures are available for use in the contracts;
conversely, we assume that the absence of such provisions suggests that performance is
ambiguous and difficult to define and, therefore, to measure.
We compute a monitoring factor score to capture the extent to which the customer is
allowed to audit and inspect the work and records of EMS. We use factor analysis of the
following variables extracted from the contract: provisions for conducting a formal audit of EMS
records by the customer or its representative (audit allowed or not specified), provisions for
inspecting EMS records (inspection allowed or not specified), and provisions for inspecting the
EMS production facility (inspection allowed or not specified). All three variables load on a
single factor with the following factor weights: audit 0.712, inspection of records 0.802, and
inspection of facility 0.663 (Cronbach alpha = 0.55). This factor explains 53% of the total
variance.
Collaboration: One of the purposes of our study is to examine the drivers of collaboration and
the effect collaborative contracting has on the supplier’s decision to invest in relation-specific
assets. Therefore, it is important that our measure of collaboration accurately captures the
underlying construct. We use two methods to measure collaboration. The first is based on the
14
extent to which the contract language conveys the signal that the customer and the supplier have
a collaborative relationship (i.e., collaborative tone). The second is based on a combination of
collaborative tone and other important determinants of collaboration.
Collaborative Tone of Agreement: We measure a contract's collaborative tone by examining
its language and coding the contract based on the extent of collaborative tone used. Three
raters coded a common set of six contracts and then compared the scores. The raters coded
the tone of the contract agreement on a scale of one to ten, where one implies least
collaborative and ten implies most collaborative. Examples of collaborative language
include:
“collaborate in the development and execution of strategic business plans”
“align business strategies”
“operate based on mutual trust”
“jointly and openly work to reduce costs”
“good faith discussion regarding nature and extent of each party’s contribution”
Examples of non-collaborative tone include:
“Penalties for ___” and substantial use of penalties
“Supplier shall reimburse opportunity costs of lost revenues”
Extensive use of the phrase “supplier will” and relatively small use of the phrase
“customer will”
Substantial use of threatening language such as “Supplier will strictly adhere to these
terms”
Collaboration based on factor analysis: Based on the contracts, we identified a number
of indicators that suggest that the supplier and the customer have a collaborative relation.
15
These include the following: the presence of mutual sharing of information (as opposed
to the contract specifying that only EMS will provide information to the customer), the
extent of information that was shared (none, limited, or extensive), whether EMS was the
customer's preferred vendor, the extent to which EMS had the flexibility to use any
vendor for inputs rather than only those vendors on the customer’s approved vendor list
(AVL), and the tone of the contract as described above. We performed a factor analysis
to examine whether these variables load on one distinct construct or have multiple
constructs. Our factor analysis indicated the presence of one significant factor, and the
following three variables loaded on the single factor: tone (0.756 factor score), mutual
sharing (0.840 factor score), and extensive sharing (0.792 factor score). The other two
variables, i.e., preferred vendor and flexibility in using the customer’s AVL, did not load
on any single factor. We used the three variables that loaded on the single factor (tone,
mutual sharing, and extensive sharing) to construct a weighted factor measure of
collaboration. This factor explained 63.50% of the combined variance.
Supplier Relation-Specific Investment: Relation-specific investments refer to those investments
that, while essential to the success of a particular customer-supplier transaction, have lower or no
value outside of that transaction. The EMS contracts provide information about investments
made specifically for a particular customer-supplier relation. Contracts in which EMS made
investments in assets unique to the customer are coded as one. Contracts in which relation-
specific investments are either made by EMS and reimbursed by the customer or made by the
customer are coded as zero.
Type of Service:
16
The contracts cover four types of services including production, production modification,
repair, and supply chain management. As described in Section 2, the degree of difficulty in
identifying performance standards and the resulting challenge of measuring performance are
likely to vary as a function of the type of service provided. Our discussions with EMS
management and our analyses of their contracts suggest that production and production
modification contracts are similar in that they both focus on product manufacturing and have low
to moderate levels of performance measurement difficulty. Their similarities lead us to combine
production and production modification under the umbrella of manufacturing. As a result, we use
three indicator variables to identify the three possible types of service: Manufacturing, Repair,
and Supply Chain Management. Note that while all contracts include manufacturing services,
they vary in the extent to which they include repair and supply chain management services.
Control Variables
Customer Size: As discussed above, we observed that contracts with smaller customers are more
likely to be prepared by EMS. Additionally, we noted that contracts prepared by EMS seem to
offer EMS better protection against customer opportunism than contracts prepared by customers.
Thus, we control for customer size using the revenue ranking of the customers provided to us by
EMS. Larger customers might have greater negotiation power thus dominating the relationship
and imposing contractual terms onto EMS. EMS identifies its 33 largest customers in terms of
revenue. These 33 customers are coded as one. The other 54 customers are coded as zero. We
include a control for customer size in all the models.
Contract Duration: This variable captures the duration of the contract, measured in months.
Joskow (1987, 169) proposes that “A long-term contract that specifies the terms and conditions
for some set of future transactions ex ante, provides a vehicle for guarding against ex post
17
performance problems.” Thus, by encouraging repeated exchanges, long-term contracts offer
EMS another type of protection against opportunistic behavior by the customer.
Number of Services: This variable captures the number of services that the supplier provides the
customer. The number of services provided is a proxy for the complexity of the relationship
between EMS and its customer. Since services include manufacturing, production modification,
repairs, and supply chain management, Number of Services takes values from 1 to 4.
The Appendix provides examples of variables extracted from the contract.
[Insert Appendix here]
Empirical Models
Hypothesis 1 examines whether collaborative contracts are more likely when the
contracted services are difficult to measure, which is proxied by uncertainty and lack of ability to
monitor performance. Based on the conversations we had in the field with EMS managers, it
appeared that the type of service provided was an important driver of uncertainty. Therefore, in
the first portion of our analysis, we explore whether uncertainty and monitoring are associated
with the type of service provided.
Discussions with EMS management and our own examination of the contracts suggest
that specifying and measuring the contractual performance of supply chain management services
is more complex for several reasons. First, supply chain management services have a longer
lead-time. In other words, effort expended at a particular point in time is associated with returns
at a different point in time. Second, the payoffs for increased effort in one part of the value chain
may accrue at a different part of the value chain. Third, the range of acceptable outcomes is less
clear because the activity is inherently more ambiguous. Fourth, supply chain management
requires coordination from people in different parts of the supply chain as well as in different
18
functional areas such as engineering, marketing, and finance, each of which may have different
measures of performance. Finally, since the outcome of services such as supply chain
management depends on the performance of numerous suppliers as well as on the customer,
attributing responsibility for the outcome can prove difficult.
Hence, we expect performance to be easiest to measure with repair services and hardest
with supply chain management services. In sum, the arguments presented above suggest that
uncertainty and accordingly noise in performance measurement will be greater with supply chain
management service than manufacturing, and that consequently because of difficulty in assessing
performance, monitoring will be lower.
We perform a multivariate analysis to determine whether the type of service is associated
with uncertainty by estimating the following equation:
Panel A: Type of Service and Uncertainty Predictor Coefficient (t value, p value)
Supply Chain Management ServiceRepairs Service
0.571 (2.129, p < 0.04)-0.030 (-0.107, p = 0.915)
Customer Size 0.130 (0.513, p = 0.609)InterceptNAdjusted R2
F-statistic
-0.159 (-1.127, p = 0.263)87
0.031.82 (p<0.07)
Panel B: Type of Service and Monitoring Predictor Coefficient (t value, p value)
Supply Chain ManagementRepairs
-0.116 (-0.463, p = -0.463)0.245 (0.937, p = 0.351)
Customer Size 0.748 (3.166, p < 0.01)InterceptNAdjusted R2
F-statistic
-0.324 (-2.453, p < 0.02)87
0.158.573 (p<0.01)
Notes: Coefficients that are significant at p < 0.10 or better are boldfaced. Manufacturing Services is the omitted indicator variable. Repairs takes the value of 1 if the contract specifies repair services. Supply Chain Management is defined similarly. The contracts included in the Panel A analyses offer either repair or supply chain services (in addition to manufacturing services). Customer Size is an indicator variable that indicates whether the customer is in the top 33 in terms of revenue. In Panel A, the dependent variable is Uncertainty, which is based on a factor analysis of the extent to which demand forecast, order modification, and inventory responsibility are specified. In Panel B, the dependent variable is Monitoring, which is based on a factor analysis of the extent to which the customer is allowed to audit and inspect the work of EMS.
0.183 (0.451, p = 0.654)-0.600 (-1.331, p = 0.187)
Customer Size -0.459 (-1.872, p < 0.07)Number of Services 0.411 (1.449, p = 0.151)Contract Duration -0.004 (-1.065, p = 0.29)InterceptNAdjusted R2
F-statistic
-0.284 (-0.779, p = 0.438)87
0.198 4.024 (p < 0.01)
Notes: Coefficients that are significant at p < 0.10 or better are boldfaced. Manufacturing Services is the omitted indicator variable. In Panel A, Collaborative Tone is coded based on the tone of the contract from 1 to 10, where 1 implies least collaborative and 10 implies most collaborative. In Panel B, Collaboration is constructed based on a factor analysis and is a combination of the following three contract provisions: collaborative tone (1–10), sharing of information (one-sided or mutual), and extent of sharing (limited or extensive). Uncertainty is based on a factor analysis of the extent to which demand forecast, order modification, and inventory responsibility are specified. Monitoring is based on a factor analysis of the extent to which the customer is allowed to audit and inspect the work of EMS. Repairs takes the value of 1 if the contract specifies repair services. Supply Chain Management is defined similarly. The contracts included in the Panel A analyses offer either repair or supply chain services (in addition to manufacturing services). Customer Size is an indicator variable that indicates whether the customer is in the top 33 in terms of revenue. Number of Services indicates the number of services that the supplier provides the customer and takes values from 1 to 4. Duration is contract duration in months.
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TABLE 4Determinants of Supplier Relation-Specific Investment
Panel A: Collaboration Defined as Collaborative TonePredictor Coefficient (t value, p value)
Collaboration 0.240 (Z = 2.186, p < 0.03)Customer Size 0.875 (Z = 2.152, p < 0.04)Duration -0.003 (-0.435, p < 0.664)InterceptN
Adjusted R2*
Chi-square (Pearson)**
-2.872 (3.554, p < 0.01)87
0.09373.124 (0.344)
Panel B: Collaboration Defined Based on Factor AnalysisPredictor Coefficient (t value, p value)Collaboration 0.353 (1.805, p < 0.08)Customer Size 0.920 (2.252, p < 0.03)Duration -0.001 (-0.182, p < 0.856)InterceptN
Adjusted R2*
Chi-square (Pearson)**
-1.434 (-4.374, p < 0.01)87
0.06480.517 (0.162)
Notes: Coefficients that are significant at p < 0.10 or better are boldfaced.Supplier Relation-Specific Investment takes the value of 1 if the supplier owns the relation-specific asset. In Panel A, Collaborative Tone is coded based on the tone of the contract from 1 to 10, where 1 implies least collaborative and 10 implies most collaborative. In Panel B, Collaboration is constructed based on a factor analysis and is a combination of the following three contract provisions: collaborative tone (1–10), sharing of information (one-sided or mutual), and extent of sharing (limited or extensive). Customer Size is an indicator variable that indicates whether the customer is in the top 33 in terms of revenue. Duration is contract duration in months. *Based on results from OLS. OLS results are consistent with the results from the PROBIT.** Pearson goodness-of-fit test. A well-fitting model has an insignificant Chi-square statistic.
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APPENDIXExamples of Variables from Contracts
Type of service
Supply Chain Management
This Exhibit describes Supplier’s contractual requirements for implementing [customer] direct customer fulfillment supply chain initiative solely with respect to business of [customer] anticipated to be conducted with Customer(s), as defined below.
This section documents the extended supply chain planning process which addresses the coordination of [customer] forecasted demand and [customer] related material planning, capacity planning and collaborative supply commitment.
Repair
At Company’s request, Supplier shall repair the Products listed in Schedule 1 and perform all the necessary tests specified by Company in order to verify compliance of the repair services being performed under this Attachment G. Supplier agrees to work with Company in support of Company’s maintenance agreements with its end customers concerning all required tests, ATP replacements and metrics.
This Attachment addresses (a) the repair of Product by Supplier at Company’s request subject to Supplier’s obligations to company as set forth in Article 23 – WARRANTY and Article 24 – REPAIRS NOT COVERED UNDER SUPPLIER’S WARRANTY of the Electronics Manufacturing Services Agreement No. _ made between Company and Supplier (the “Agreement”), of which this Attachment is made a part, and (b) other products manufactured by third parties. The Products subject to this Attachment are set forth in Schedule 1 hereto.
The Supplier will be responsible to perform for the Customer testing, diagnostic and repair works of [customer] equipment [customer] on the territory of [customer] and return it back to the Customer.
Production Modification
1.2.8 provide design for manufacturability and technical support services to [customer] new and existing Product development teams in accordance with [customer] then-current processes and procedures, which current processes and procedures are described in Attachment F (“Assembly/Test Requirements”), Attachment L(“Supplier Quality Requirements”) and Attachment P (“Development and Engineering Change Requirements”), as product ideas evolve into potential new Products or improvements to existing Products;
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[Customer] may contract the development of new product and enhancements to existing Products. EMS shall work with the Product development teams in [customer] to assist in the design of new product and shall have design personnel co-located at [customer] facilities, as mutually agreed upon and set out in the applicable VSHA.
Contract duration
Subject to Clauses 27.2 and 27.3 this Agreement will continue until terminated.
17.1 This Agreement becomes effective on the Effective Date and, unless terminated sooner in the accordance with Section 17.2, 17.3, 17.4, 17.5 or 17.6, shall remain in effect for a period of five (5) years thereafter.
This Agreement shall be effective on the Effective Date and shall remain in force for one (1) year.
Uncertainty
Forecast
[Customer] shall provide EMS monthly with a non-binding 6 months rolling forecast (“Forecast”) for each Product. EMS understands and agrees that the Forecast is an estimation of quantities required. EMS will provide to [customer] the written production plan within 5 (five) working days (after EMS receives materials delivery confirmation from [customer]) after receiving each forecast.
Company will use reasonable commercial efforts to provide to Supplier, at a minimum, a rolling 6-month forecast. The forecast will be updated periodically and tied to Company’s demand planning process for all Products required under this Agreement (the “Forecast”), and Supplier shall acknowledge delivery capability as called for in the forecast documents or firm Orders placed by Company. The parties will mutually agree as to the definition supplied within the Forecast (i.e. weekly or monthly schedules). Except as otherwise provided for in the terms of any Flexible Delivery Agreement agreed to by the parties, all Forecasts, whether for use by Supplier or Supplier’s Material vendors, are for planning purposes only and do not constitute a commitment to purchase by Company except for the liability set forth in Article 10 EXCESS FINISHED GOODS AND WORK IN PROCESS INVENTORY and Article 11 EXCESS UNIQUE MATERIAL INVENTORY.
Downside Purchase orders are subject to a variation of plus or minus 20%
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provided that if [customer] wishes to increase the volumes by more than 20%, Y shall use its best endeavor to meet [customer] requirements. [Customer] may vary the volumes for week 13 and thereafter by plus or minus 100%.
[Customer] ’s sole liability and the sole remedy for Company with respect to a downward variance shall be payment by [customer] as set forth in this Section 9(a).
Cancellation
Company may at any time terminate an Order for Services without cause, in the whole or in part, upon written notice to Supplier. In such case, Company’s liability shall be limited to payment of the amount due for the Services including any materials purchased or which have been ordered by Supplier and are non-cancelable or non-returnable up to and including the date of termination (which amount shall be substantiated with reasonable proof to Company) and no further Services pursuant to such terminated Order will be rendered by Supplier. Such payment by Company shall constitute a full and complete release and discharge of Company’s obligations. In no event shall Company’s liability exceed the price identified in the applicable Order for the Services being terminated.
Company may at any time terminate a Repair Order or Repair Orders in whole or in part, upon written notice to Supplier and no further repair of Products pursuant to such terminated Repair Order or Repair Orders will be rendered by Supplier.
Obsolete Inventory
Any Product that has not been ordered within the past six (6) months and for which there is no forecasted demand in the Market Forecast shall be reviewed during the QBR process to determine the disposition of that Product.
At the end of every calendar quarter, Supplier shall identify to Company, Unique Material in Supplier’s inventory that is in excess of 135 days of supply. Such inventory shall be defined as “Excess Unique Material Inventory” provided that it was the result of either Company’s complete or partial termination without cause of an Order, a change in Specifications, an Engineering Change Order, discontinuance of a Product or a change in Forecast; and was purchased by Supplier consistent with the vendor’s lead-time and the delivery requirements of Company’s Orders and Flexible Delivery Arrangements.
Excess Inventory Exceptional Excess Inventory shall be defined as Inventory that is Unique Inventory to [customer] including Work in Process and Finished Goods, and is on hand or on order but not within Time-
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to-Cancel, as a result of forecast reductions that, by Product Family, exceed 50% over a two month period, in accordance with the methodology set out in Exhibit 20 (“Exceptional Excess Inventory”).
Such FGI, as defined in this article 10.1, shall be considered “Excess FGI” provided that it was the result of Company’s complete or partial termination without cause of an Order, change in Specifications, Engineering Change Order, or change in Forecast and was manufactured consistent with Supplier’s manufacturing cycle times and the delivery requirements of Company’s Orders and Flexible Delivery Arrangements. Three weeks of supply as used in this Article 10.1 will be determined by adding the prior four weeks of dollar shipments by Product family and the next four weeks of forecasted demand and multiplying the total by three eighths (3/8).
Monitoring factor
Facility inspection
Access to assembly Lines used to manufacture Products shall be limited to those Company employees who have a need for access, and no other party shall have access without [customer] prior written consent. [Customer] personnel shall have access to such Assembly Lines, subject to Company’s Standard plant security and safety rules, Confidential Information obligations, and upon reasonable advance notice.
Supplier shall allow Company’s customer(s) to conduct onsite evaluations of Company’s Product, or allow for inspection of Company’s Product by Supplier or Company, given Company’s customer inspection requirements.
[Customer] shall have the right to review EMS facilities, operations, and procedures as they relate to the Products at any reasonable time with adequate prior notice for purposes of determining compliance with the requirements of this Agreement.
Record inspection EMS shall maintain, and provide [customer] request copies of or access to, appropriate records in a manner sufficient for the Parties to ascertain [customer] compliance with the requirements of this Agreement.
[Customer] shall be entitled to review and inspect all relevant manufacturing records (including reasonable backup documentation to substantiate the charges payable under the quoted Bills of Materials and agreed Pricing Models between the
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parties), in order to verify that Manufacturer is in due compliance with all of its contractual and legal obligations under this Agreement.
Audit of records
Upon [customer] request and at [customer] cost, EMS shall cooperate in the audit of its records in the manner set forth below, for the purpose of confirming compliance with this Agreement. [Customer] may retain the services of a major, independent accounting firm, other than the accounting firm(s) employed as primary outside auditors of [customer] .
Company shall, at its cost and expense, have the right exercisable on a semi-annual basis upon reasonable notice to Supplier during Supplier’s normal business hours to have a nationally recognized accounting firm that has executed a non-disclosure agreement reasonably acceptable to Supplier, to examine and audit (“Audit”) the necessary records described in Article 33.1 to confirm conformance to the terms of this Agreement.
Collaboration Factor
Extensive sharing of information
Company and Supplier acknowledge that a strategic relationship is required in order to insure the ongoing continuity of supply and service to Company’s end customers. To that end, both parties agree to establish a Strategic Alliance Team, which will meet quarterly, coinciding with the Quarterly Performance Review Process, as described in Article 27 QUARTERLY PERFORMANCE REVIEW PROCESS. In addition, Supplier shall appoint a senior operations executive and the parties shall agree on a governance model for managing the relationship including accountability metrics that the senior operations executive shall meet for Company and Supplier.
The purpose of the ER is a senior level update of each Party’s corporate developments, roadmaps, strategies, etc; Top level review of Scorecard performance and issues; Highlight of any specific issues that the Parties collectively decide to focus on (including such matters as System Staging, Global Supply Chain, Systems Test Engineering etc). This is an opportunity for the respective Senior Management of the Parties to meet at least twice a year to ensure and maintain overall alignment in the relationship. The QMRs provide much of the input that is reviewed at these executive level reviews.
Mutual sharing of information In addition to all the above, [customer] recommends regular
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executive communications between [customer] Executive Team (typically GAE, and occasionally _, etc), with Y Executive Team, & major customers – through lunches, dinners, conference calls etc. This is a process that needs to be actively managed and encouraged by the GAM, to ensure the right executives stay in touch with each other in both formal and informal environments so as to enhance the overall relationship.
Parties agree to seek best in class supply chain costs through a total cost of ownership business model that includes operating and capital expenses. Furthermore, both parties agree that it will be the responsibility of the Strategic Alliance Team to establish and document detailed process and information flows, procedures and guidelines applicable to the process management required to facilitate timely delivery of Products, Commercially Purchased Items and Services as described in this Agreement.
Supplier relation-specific investment
For the Team of this Agreement, Supplier agrees to maintain in working condition any unique Tooling purchased by Supplier or consigned by Company performing all routine and other maintenance including reasonable calibration as may be required in order to maintain the unique Tooling at the same level of functionality as when Supplier purchased or Company consigned such unique Tooling.
All Reserved Assets in EMS’s custody or control shall be held at EMS risk and be kept insured by EMS at EMS expense in accordance with the provisions of Section 24.2, with loss payable to [customer] and EMS as their interests appear. EMS shall use such Reserved Assets solely in the performance of its obligations hereunder.
For Supplier developed [customer] Tooling, Supplier shall provide complete tool design drawings to [customer] SE for approval prior to construction or [customer] tooling. Tool Approval/First Article Inspection. Supplier shall provide to [customer], for its approval, data obtained form a 100% inspection of all dimensions/specifications of the initial parts produced to evaluate the tooling and set-up.