Contracting for Innovation: Defining an Exchange that Fosters Creativity While Mitigating Opportunism

Kyle J. Mayer

Zhe (Adele) XingPablo Mondal

University of Southern CaliforniaMarshall School of Business

Management & Organization DepartmentBridge Hall 306

Los Angeles, CA 90089-0808

We explore how firms govern inter-firm transactions that require innovation to complete. Designing contracts to foster innovation is challenging and becomes more complex when exchange hazards are present along with the need for innovation. Incorporating insights from social psychology to complement transaction cost economics, we are better able to address how firms should design their contracts to balance the need for innovation and protection against opportunistic behavior. Using a sample of contracts from the information technology services industry, we find that in the presence of exchange hazards, innovation requirement leads to less use of detailed task descriptions and hybrid payment systems, but to more extensive contingency planning.

Dec. 2014

Preliminary draft. Please do not cite or circulate without permission.

INTRODUCTION

Please email to kmayer@marshall.usc.edu for any questions.1

Innovation has been the subject of a rich body of literature in strategy, economics and other

fields. Many studies have examined the role that alliances and other inter-firm transactions

play in innovation outcomes (e.g. Ahuja, 2000; Hoetker, 2005; Shan, Walker, and Kogut,

1994; Stuart, 2000). The focus of this research is typically on whether or not engaging in

various types of inter-firm collaboration makes individual firms more innovative (i.e., effect

of learning in alliances on firm-level innovation) (e.g. Hagedoorn and Schakenraad, 1994;

Sampson, 2007). While most studies in the inter-firm context treat innovation as an output,

the topic of how firms can design and govern inter-firm transactions that require innovation

to complete has received less attention. This is an important omission, as governance has

been shown to influence transaction performance (e.g. Mayer and Nickerson, 2005) and one

aspect of performance (for some transactions) is innovation.

Given the importance of addressing governance issues in any inter-firm contexts, transaction

cost economics (TCE) (Williamson, 1975, 1985) is a framework that provides a solid

foundation to study governance and innovation (Adner and Kapoor, 2010; Argyres and

Bigelow, 2009). The challenge when applying TCE to transactions requiring innovation is

that an implicit assumption of TCE is that maximizing the likelihood of success involves

properly specifying the transaction (i.e., overcome bounded rationality) and preventing

opportunistic behavior. This assumption puts the focus on preventing a negative outcome

(i.e., limiting opportunism and misunderstandings) while innovation is much more about

creating a positive outcome (i.e., fostering an encouraging environment for innovation). We

incorporate perspectives from social psychology to complement TCE to examine how firms

design contracts when the transaction to be governed requires innovation to be successfully

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completed. We contribute to work on contract design by addressing a new governance

problem—designing a contract that both fosters innovation and prevents opportunism.

Using proprietary data on the information technology service contracts of a large IT firm and

141 different buyers, we examine how innovation and exchange hazards influence the use of

three different governance mechanisms. First, we find that the need for innovation leads firms

to rely on more detailed descriptions of tasks in the contract, but this effect is negatively

moderated by the presence of exchange hazards. The need for an innovative outcome can

result in firms using greater amounts of detailed task description in a contract that spells out

the desired functionality of a new product or service; but the presence of exchange hazards

makes framing task descriptions that do not imply distrust or negative expectations (Ghoshal

and Moran, 1996) regarding the transaction more difficult.

Second, our results indicate that contingency planning, contractual terms specifying how to

deal with various issues that might arise during the execution of the transaction, may also

provide a mechanism to address the need for innovation in the presence of exchange hazards.

When exchange hazards are present, firms may address some of the challenges of fostering

innovation by adding more flexibility in dealing with how to adapt to different situations.

We also examine how the need for innovation influences a third aspect of the contract—the

type of payment mechanisms. We find that compared to both fixed fee and time and materials

(T&M) (a variant of cost plus that specifies an hourly or daily wage), the need for innovation

leads to the use of a hybrid of the two that specifies an hourly wage with a price cap. When

exchange hazards are also present, however, the payment cap in a hybrid contract becomes

more difficult to identify, and a hybrid contract may give way to a T&M contract to address

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the increased uncertainty.

We make three contributions to the literature on strategic management and contract design.

First, we explore how contracts can be designed when there is a need for innovation while

still mitigating exchange hazards. Whereas most prior work on alliances and innovation has

examined innovation as an outcome, we examine the need for innovation as a driver of

contract design. Second, we continue the effort to combine social psychology with TCE to

better understand how governance choices, including contract design, are influenced by the

needs of the exchange partners. Economists tend to focus on incentives in driving outcomes,

including innovation, but social psychologists have shown that pecuniary incentives are only

one part of a larger story. We demonstrate that exchange hazards moderate the relationship

between the need for innovation and three aspects of contract design—task description,

contingency planning, and the payment mechanism. Third, critiques of TCE have argued that

one type of exchange hazard is often linked to one term in the contract, and this one-to-one

match does not offer a holistic view of contract design (Reuer and Ariño, 2007). We respond

to this problem by examine how hazards and innovation influence three aspects of contract

design.

THEORY AND HYPOTHESES

Governance and innovation

Addressing the question of creating the right environment for innovation to occur in the

context of an inter-firm transaction involves at least two key literatures. First, transaction cost

economics (TCE) (Williamson, 1975, 1985) is the primary theory used to address issues of

how inter-firm transactions are governed. The focus of TCE is on mitigating exchange

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hazards by overcoming bounded rationality and preventing opportunistic behavior. TCE has

been applied in many areas including vertical integration and contract design (see Macher

and Richman, 2008 for a review), and focuses on crafting governance structures that

minimize governance costs. While TCE facilitates identification and mitigation of exchange

hazards, it is not well equipped to deal with how cognitive, emotional or social factors may

impact the likelihood of successfully completing the transaction (e.g. Ghoshal and Moran,

1996). The primary issue with applying TCE to facilitate innovation in an inter-firm

transaction is that TCE is primarily concerned with ensuring that nothing goes wrong to

derail a transaction (i.e., structure governance to prevent any negative events), while largely

ignoring issues of cognitive biases and emotions in governance that could either improve or

hinder the transaction (Weber and Mayer, 2014). The extensive micro-level literature on how

to meet innovation requirements, however, shows that the processes and context matter in the

generation of innovation from a team (e.g. George, 2007).

This leads to the second relevant research stream, which involves micro level work, mainly

from social psychology, on fostering innovation and creativity. In this area, some research

examines how to use intrinsic motivation to increase creative outcomes (e.g. Amabile, 1985 ;

Amabile and Mueller, 2007). Intrinsic motivation refers to the extent to which an individual

is excited about a work activity and engages in it for the sake of the activity itself (Utman,

1997). In the same vein, cognitive evaluation theory (CET) further developed the contextual

conditions (i.e., informational and controlling) that affect intrinsic motivation (Deci and

Ryan, 1985; Ryan, 1982). More controlling aspects in a context shift an individuals’

interpretation of the locus of causality to external constrains, thus diminishing their belief in

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the value of their own competence, whereas more focus on informational aspects facilitates

an internal perceived locus of causality, leading to enhanced intrinsic motivation and a

perception that the individual’s competence will play a greater role in the outcome (Deci and

Ryan, 1985; Shalley, Zhou, and Oldham, 2004). Thus the salience of different aspects of

context (e.g. feedback, communications, rewards) may influence the level of intrinsic

motivation of an individual, thus affecting the level of creativity, and ultimately innovative

output.

Another explanation for the effects of contract design on innovation could be the construal

people use to interpret the framing of the contract. Construal level theory (CLT) suggested

that people’s construal of psychologically near or distant stimulus is important in

understanding their behavior (e.g. Trope, Liberman, and Wakslak, 2007). Specifically, CLT

states that events that are interpreted as psychologically near are seen as more concrete and

tangible, while events perceived as more psychologically distant as seen as more high level

and abstract. Research drawing on CLT suggests that low level and concrete construal inhibits

creativity, while high level and abstract thinking promotes creativity (e.g. Finke, 1995; Wald,

1995).

Adopting social psychology theories to study governance problems, scholars looked at one

central aspect of the governance of an inter-firm transaction—the contract. The contract plays

a key role in setting the tone and serving as the blueprint for how the parties will interact

(Macneil, 1977). Some scholars have argued that using contracts to mitigate opportunism

creates a negative self-fulfilling prophecy of distrust and damages the relationship of the

firms involved (e.g. Ghoshal and Moran, 1996). However, Weber, Mayer, and Macher (2011)

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incorporated elements of social psychology to complement traditional TCE and argued that

different ways of framing safeguards can help prevent such effect and even foster positive

outcomes such as innovation. We believe that the contract needs to specify a blueprint for the

transaction that does more than define the exchange and overcome opportunism; the contract

also needs to help foster an environment that will be conducive to creativity and innovation.

In many cases, inter-firm transactions involve the execution of well-understood tasks and thus

the supplier merely needs to execute the task once she makes the decision to complete it. In

the case of needing to generate an innovative solution, however, an additional element is

important—fostering an environment in which the supplier can be creative. The environment

in which the transaction takes place is always important and the contract plays a significant

role in creating that environment. Problems in this area often take the form of lessening the

supplier’s motivation to complete the project (i.e., mitigate opportunism); when the project

involves innovation, however, it influences the supplier’s ability to complete the project by

either fostering or hindering the innovative process.

Our goal here is to understand when firms rely more or less on different parts of the contract

in the face of the need to innovatively execute the contract, given the need in these cases to

provide the supplier with an understanding of the transaction, the motivation to complete it

(incentives) and foster an environment to increase the supplier’s ability to do so, and how

these effects are moderated when significant contractual hazards are also present in the

transaction.

Innovation and Task Description

The first important aspect of a contract that we discuss is task description. Suppliers may

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choose to write a more complete and concrete contract by including more extensively

detailed descriptions of the task to be completed. Task descriptions may vary according to the

specific factors of transactions. Descriptions may take the form of processes for the supplier

to follow or they may consist of elaborate specifications of the output the supplier is to

generate (i.e., how to do the job versus what to produce).

For some kinds of transactions, the task to be completed is easily identified, and clearly

interpreted by the suppliers; however, Argyres, Bercovitz, and Mayer (2007) have also found

that in complex, high technology contracts, tasks can be quite involved and firms must decide

how much effort to devote to describing these tasks in their contracts.

The need for an innovative outcome makes inter-firm projects more difficult and complex.

One way to address the additional complexity is through the use of more detailed descriptions

that provide extensive and concrete information to the suppliers, helping them understand the

requirements of the innovation outcome and the needs that the innovation should fulfill. In

such usage, task description may take different forms, including providing detailed steps the

supplier must follow or leaving how the project will be done to the supplier while clearly

specifying the performance goals of the project. Thus, detailed task description serves a

communication role between the parties that is particularly important when innovation is

required to achieve the desired outcome.

Whether the description is about processes or outcome, the need for innovation enhances the

value of clearly communicated what is expected of the supplier. In some cases, the buyer may

not know what steps the supplier needs to follow; they may only care about the end results.

Examples of these cases would include instances when the supplier has proprietary

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technology that they understand better than the buyer, so they buyer will focus more on their

desired outcome than on how the supplier should achieve it. In other cases, the end result may

be harder to predict ex ante, so the contract may focus on what steps the supplier must

undertake to work towards the desired outcome. Examples of these might include consulting

projects to develop new processes in which the contract stipulates the kind of research on the

focal firm that the supplier will do in developing their new process. Thus transactions

requiring innovation will typically have more detailed task descriptions to ensure that both

parties understand what is desired. This is especially important, as monitoring may be more

difficult when innovation is required so misunderstandings over what to do might take longer

to notice than in more standard transactions. Thus, in line with Argyres et al. (2007), we

argue that innovation should lead to more extensive task description in the contract.

Hypothesis 1: The higher the level of innovation required in an inter-firm transaction, the greater the amount of task description used in the contract governing the transaction.

While the main effect may be that innovation leads to more detailed task descriptions that

focus on the construal of goals and means, incorporating the literature on construal level

theory (CLT) and cognitive evaluation theory (CET) also implies that such task descriptions

will only aid in the innovation process if they create the right kind of environment for the

transaction (e.g. by encouraging prosocial behavior or fostering creativity). The contract can

play a role by helping set the frame under which the transaction will be executed. However,

the problem is when one or more exchange hazards are present, whether innovation

requirement still favors large amount of task description.

As the need to innovate does not inherently pose a specific contractual hazard, the parties can

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tailor the type and level of task description to the needs arising from the level of innovation

required to complete the transaction. We do not argue that exchange hazards cannot be

present with innovation, but merely that the need to innovate to complete a transaction does

not itself pose an exchange hazard. For example, if an innovative outcome didn’t involve

proprietary knowledge, interdependence or significant specific investments and the outcome

was easily measured, then uncertainty would be present from the need to innovate to

complete the transaction, but exchange hazards that increase the likelihood of opportunism

would be absent.1 However, if exchange hazards arising from other sources were also present

in the transaction, the impact on the level of task description would be more complex. In this

situation, we need to describe the tasks in a way that safeguards transactional hazards.

While it may be possible to frame safeguards in a promotion manner that fosters innovation

(e.g. Weber et al., 2011), it is not always possible to do so. Framing task descriptions to

ensure that opportunism is prevented while fostering innovation is difficult to do because

opportunism and innovation each deals with different parts of the task—the means and the

goals. In this situation, CLT implies that a contract with detailed process control would lead

to concrete thinking (Dhar and Kim, 2007), which may constrain the supplier from acting

opportunistically, but at the expense of the creativity required for innovation. Fostering

innovation, on the other hand, only involves clearly specifying what is required (the goals), to

facilitate abstract thinking and promote creativity. In the same vein, CET also proposes that

adding controlling aspects (i.e., to mitigate exchange hazards) in a context impairs

individual’s intrinsic motivation and thus impedes creativity (e.g. Shalley et al., 2004). The

1 There would, however, still be agency issues to address involving effort, but the ease of measuring the output would largely alleviate even these concerns.

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issue is that exchange hazards are likely to lead to process controls in a variety of situation,

including when protecting proprietary technology from leakage (to avoid exposing the

technology) and when output is difficult to measure (hard to verify output).

Therefore, adding more details to frame the outcome of a task (i.e., to foster innovation)

while simultaneously including more descriptions on the process of that task (i.e., to mitigate

the exchange hazard) will indeed hurt one or the other. The type of details required to

mitigate hazards is likely to be incompatible with the creativity needed to foster innovation.

Consequently, innovation is stifled if we incorporate too much process control into the task

description to reduce opportunistic behavior when exchange hazards are present. Moreover,

as opportunistic behavior may be perceived more likely in a transaction with exchange

hazards, the supplier may easily misconstrue the informational aspects for controlling aspects

on the description of a task (required by innovation), hurting creativity. In addition, if the firm

puts in only goal oriented detail, then they may open themselves up to failing to recognize

misaligned expectations, shirking or opportunistic behavior. Thus we argue when innovation

is required for a transaction that also involves exchange hazards, it will negatively moderate

the impact of innovation on detail in the task description.

Hypothesis 2: When exchange hazards are high, innovation will lead to less task description than is the case when exchange hazards are low (i.e., innovation will only lead to more task description when exchange hazards are low; this positive relationship will disappear when exchange hazards are high).

Innovation and contingency planning

Given the challenges in crafting effective detailed task descriptions while both innovation and

hazard are present, alternative contractual safeguards may be used. Contingency planning

refers to the provisions addressing contingencies that may arise during execution of the

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transaction that could interfere with its successful completion. Contingency planning clauses

can be defined as “the parts of a contract that are designed to support within-agreement

adjustments by proscribing the ways in which the contractual partners will deal with

problematic contingencies that might arise during the execution of the contract (Argyres et

al., 2007).” In some situations, contingencies can be easily predicted and codified in advance,

so that both parties are fully aware of their tasks and duties when a certain situation happens,

and then they can take actions according to the predefined clauses and procedures to preserve

each party’s interest in the transaction. Without these adjustment clauses, the firms may

abandon the transaction prior to its successful completion due to either honest disagreement

over how to proceed or the perception that one party is trying take advantage of the other.

Contingency planning can be used to facilitate innovation in three ways. First, open-ended

clauses can provide suppliers with a greater level of flexibility. Studies show that it is critical

to inspire people’s creativity by encouraging their participation in decision-making, providing

them with more autonomy and independent decision rights ( Amabile, 1988; Amabile et al.,

2004); in contrast, trying to micro-manage people’s behavior or adding a great amount of

controlling concerns will impair their intrinsic motivation which then reduces creativity

(Shalley et al., 2004). Thus with the open-ended options that contingency planning makes

possible, suppliers can be allowed to retain the ability to self-specify how they will

accomplish the innovation tasks under the looser constraints of processes that focus on

adaptation rather than specific actions.

Second, contingency planning clauses can be relatively generic, specifying processes or

procedures to follow in case any type of contingency occurs. This kind of clause provides

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changes to possible situations, ensuring a protective environment for the suppliers to

complete the task creatively.

Third, contingency planning clauses can also be more specific, identifying specific events

that might occur. For instance, these clauses may clarify which party owns what kind of

rights and takes what kind of responsibilities if a sudden technological change turns the

supplier’s innovation into obsolesce. There may also be clauses specifying additional benefits

if the suppliers create breakthroughs. Thus when innovation is required, contingency

planning not only provides a safe environment for the suppliers, but also leaves them

sufficient freedom and autonomy for creativity.

Hypothesis 3: The higher the level of innovation required in an inter-firm transaction, the greater the amount of contingency planning used in the contract governing the inter-firm transaction.

When a transaction that requires innovation also involves exchange hazards, it posed

challenges for using detailed task description as discussed above. This complex situation,

however, may actually favor the use of additional contingency planning. While Argyres et al.

(2007) found that contingency planning and task description generally complement one other

in mitigating exchange hazards, they may not always be complements and in particular we

argue that contingency planning may substitute for detailed task description in the instance

when a transaction involves both exchange hazards and the need for innovation.

Task description to mitigate opportunism may involve too much process detail to facilitate

innovation, but this issue is unlikely to exist for contingency planning. In fact, the time taken

to identify more open ended processes to deal with adaptation issues may actually be more

valuable when both innovation and exchange hazards are present. The issue of uncertainty

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that may arise increases the value of processes to guide adaptation, including basic types of

adaptation such as engineering change processes to more intricate clauses involving

allocation of decision rights to different actors in various types of situations.

One danger in transactions that require innovation in the presence of exchange hazards is that

issues of oversight or unintentional conflict (i.e., problems of bounded rationality) will be

mistakenly interpreted as having for more strategic and/self-serving motives (i.e., they will be

seen as problems of opportunism). Having procedures in place to facilitate adaptation can

help the parties navigate the uncertainty involved in their exchange in a way that doesn’t

promote distrust nor impose (seemingly) restrictive controls but also avoids the other extreme

of a completely underspecified contract that provides ample room for inconsistent

interpretations and divergent expectations. In part because they can be more process-based

such as in communication and adaptation, areas not seen as controlling, contingency planning

clauses may be easier to frame in a more promotion-oriented manner. We argued above that

the process controls in describing the task could stifle innovation, but the same is not true

when the process is simply a guideline of steps to take to help reach agreement on how to

address the need to adapt. These processes are more easily framed as adaptation mechanisms

that avoid stifling creativity.

These open-ended contingency planning clauses motivate suppliers to innovate with freedom

and autonomy. When hazards are present, contingency planning can help to identify possible

solutions in response to possible changes in the future but does not signal distrust or a desire

to control the supplier, thus offering safeguards while facilitating the supplier completing the

task with as innovative an output as possible.

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Hypothesis 4: Exchange hazards positively moderate the relationship between the level of innovation required in the transaction and the level of contingency planning in the contract that governs the exchange.

Innovation and the payment mechanism

Another element of the contract that is likely to be influenced by the need for innovation is

the payment mechanism. The two typical payment mechanisms in most commercial contracts

are a fixed fee or an hourly wage. A fixed fee is straightforward as the contract specifies

precisely how much the buyer will pay the supplier upon completion of the task. An hourly

wage contract (often referred to as a time and materials or T&M contract) is a variant of a

cost plus contract and relieves the parties of the obligation to specify a total price ex ante and

instead specifies an hourly or daily rate (typically plus expenses) until the task is completed.

While both payment mechanisms are prevalent in a wide variety of industries, they also pose

specific challenges that need to be addressed (Kalnins and Mayer, 2004).

Fixed fee contracts create a shirking hazard as the supplier can retain any money saved that

they can avoid investing in the project. Shirking can lead to cutting corners and finding non-

obvious ways to reduce costs because the supplier gains all the benefits from cost reduction.

This is problematic for a project requiring innovation for three reasons. First, Enzle and Ross

(1978) suggested that task contingent rewards (i.e., given simply for doing the task) was a

controlling factor that reduced intrinsic motivation to innovate. Second it puts the supplier’s

focus on costs and trying to complete the transaction as cheaply as possible so that the

supplier can emerge with the most possible profit. Finally, the supplier may not be able to

predict how much effort will be required to create an innovative solution, so she is likely to

offer a very high fixed fee to ensure that they don’t lose money on the transaction.

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A T&M contract overcomes the challenges that plague fixed fee contracts because there is not

incentive to excessively reduce costs since all costs are passed along to the customer; nor is

there a need to quote a high total price because the only price quoted is an hourly or daily

wage and the buyer pays for the precise amount of work required to complete the transaction.

The problem with a T&M contract is that it creates weak incentives for the supplier to work

hard to either complete the task by a particular date or to be efficient in resource utilization

because they pass all their costs along to the buyer. Such a contract is particularly problematic

when innovation is required because it will be difficult for the buyer to determine if delays

and additional work are the result of the supplier padding their fee or truly a challenge in

coming up with the right innovative solution.

In the presence of the need for innovation, a third option can help the firms overcome the

challenges of each type of payment mechanism. A T&M contract with a cap represents a

hybrid between fixed fee and T&M contracts. A T&M contract with a cap operates just like a

regular T&M contract until the cost of the project hits a certain amount, after which point the

supplier incurs all additional costs. Thus the buyer has protection from the supplier inflating

costs (beyond the cap) and the supplier has some incentives for efficiency but without having

to specify an exact price ex ante. The hybrid payment solution introduces flexibility that

offers the supplier the opportunity to be creative and seek the most innovative solution

possible while also apply some efficiency incentives.

Hypothesis 5: The higher the level of innovation required in an inter-firm transaction, the higher the likelihood that a hybrid payment mechanism will be used in the contract governing the inter-firm transaction.

While a hybrid payment mechanism can enhance innovation, we again raise the question of

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what will happen when both exchange hazards and innovation are present in the same

exchange. We argue that the preference for hybrid contracts as innovation increases will

diminish when the exchange also involves significant exchange hazards.

The first issue that arises is determining the appropriate cap. The benefits of the hybrid

payment scheme rest on having a well-chosen ceiling amount, above which the supplier must

absorb all additional costs. If the ceiling is low such the supplier believe that it is highly

likely their costs will reach that level, then the contract takes on more characteristics of a

fixed fee contract, as the supplier will seek to reduce costs and may be tempted to resort to

shirking. If the ceiling is so high it could never realistically be reached, then the exchange is

effectively simply a time and materials contract in which the supplier charges by the hour or

the day of work. Thus it becomes crucial to specific a viable ceiling amount in order for a

hybrid contract to have values.

The challenges of agreeing on a ceiling amount, above which the supplier will cover all costs,

are exacerbated in the presence of both exchange hazards and the need for innovation. With

issues arising from two very different sources, it becomes increasingly difficult for the two

parties to agree on an appropriate ceiling. The supplier always wants a very high ceiling to

avoid the cost penalty of hitting the cap, while the buyer would like a low enough ceiling that

it effectively begins to feel like a fixed price contract.

When both exchange hazards and the needs for innovation are present, the firms are more

likely to resort to a time and materials contract. The T&M contract frees the parties from

having to specify a fixed price or even a cost ceiling. The challenge in this situation is

ensuring high effort from the supplier, but that may be easier to do in terms of utilizing

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various types of contingency clauses that could address issues of how to deal with varying

rates of progress or abilities to meet various milestones or with monitoring provisions.

Hypothesis 6: Exchange hazards negatively moderate the positive relationship between the level of innovation required and the likelihood that a hybrid payment mechanism is used in the contract governing transaction.

EMPIRICAL METHODS

Data and sample

We test our hypotheses with data from Compustar, a provider of computer-related hardware

and IT services. IT services is a very suitable industry to test these hypotheses as it is a key

sector of the economy and there is significant variability in the extent to which innovation is

required to complete various transactions. The IT services industry involves the storage,

transfer and management of information, typically using mainframes, servers and other

related hardware. IT service firms perform many types of projects for their customers,

including but not limited to designing customized software systems, dealing with network

design and security, and updating and/or maintaining existing systems. IT services are

generally performed on a project basis. Buyers identify an IT project and then secure a

supplier to complete it. Every project is distinct, allowing buyers to engage one supplier for

one transaction and another supplier for the next transaction. Most projects are complex, and

many require innovation to complete while others merely involve executing well-understood

capabilities or tools. As the contract serves a key role in defining the exchange, its design is

particularly important and can play an important role in influencing execution of the deal.

Compustar has produced mainframes and related hardware since the 1970s, and entered the

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platform-independent IT services business in the mid – 1980s.2 By 1997, Compustar’s IT

services division accounted for worldwide revenues of roughly $100 million. Compustar’s

buyers are primarily Fortune 1000 firms, as its core mainframe business naturally coincided

with the needs of larger clients. Compustar provided access to IT services contracts they

fulfilled, as well as corresponding internal documentation and other records in its corporate

contracts library. One of the authors inspected IT services contracts spanning the years 1986-

1998. This sample includes IT services contracts between Compustar and 141 customers, and

represents roughly 25% of the IT service deal in the contract library. An evaluation by

Compustar personnel indicated that this sample was representative along key dimensions

(e.g. customer industries, customer size, number of contracts between Compustar and the

customer, etc.). Several contracts could not be used because of missing data and 11 more

were removed because they involved a unique type of contract payment mechanisms that

Compustar discontinued because it wasn’t working as planned.3 The 385 remaining contracts

in the sample each represent a discrete project for which Compustar supplied IT services. A

typical IT services contract in our sample is about five pages long and is designed to

accomplish a specific task. It contains a project description, including the type of service

required and the responsibilities of each party (in varying degrees of detail). Project duration

can range from one week to over a year, while project dollar values range from

approximately one thousand to several hundred thousand dollars.

Two experienced Compustar engineers familiar with the IT services industry, Compustar, and

2 Platform-independent means that the firm supplies services across a variety of hardware types. These services included network support, programming, data migration, etc.

3 The omitted contract type calls for the supplier to be compensated as a percentage of the money that the supplier saves the buyer. These contracts led to conflicts between Compustar and their customers over the exact amount of realized savings and thus how much Compustar should be compensated.

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contracting coded several variables. To ensure measurement validity, the following coding

process was used. Each engineer first coded the same eighty randomly selected contracts. The

two engineers and one of the authors then examined all eighty contracts and identified

discrepancies (measurement programming, and innovation—variables described below). The

engineers then discussed conflicts and converged on the same criteria to code the remaining

contracts. One of the authors also interviewed a variety of IT professionals, many of them

outside of Compustar to discuss the measures and solicit additional comments and feedback.

Measurement

Dependent variables. The first dependent variable is task description. It was coded by our

two engineers on a one to seven Likert-type scale, where one represents cases in which the

contracts contains very little detail in the description of the task to be accomplished and seven

if very extensive technical description was included. Examples of technical detail include

references to particular types of databases or other software systems on which Compustar

will work, specific responsibilities the customer must fulfill in order for the project to be

completed, or details of what would constitute a completed task.

Our second set of predictions focused on the level of contingency planning in the contracts.

Many of Computar’s contracts made no provision for contingency planning while others

contained clearly identified efforts to plan for future contingencies. Contingency planning

was code one if processes were included to address contingencies and zero if no contingency

was mentioned. 41 percent of our sample contracts contained no contingency planning and

the vast majority of those that did plan for contingencies did so using processes to address

whatever contingencies might arise.

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Our last dependent variable explores the payment mechanism specified in the contracts.

Fixed fee contracts involve Compustar completing a specific task in exchange for a

predetermined total price. Time and materials (T&M) contracts involve Compustar being

compensated based on an hourly or daily rate plus expenses until the task is complete. We

code hybrid as a dichotomous variable that is coded as one if the contract involves a hybrid

payment structure (i.e., T&M with a cap) and zero if payment is a fixed fee or “pure” T&M.

Independent variables. Innovation is an ordinal variable ranging from one for projects that

“require no innovation to complete” to seven for projects that “cannot be completed without a

technological breakthrough.”4 This variable does not merely capture complexity, but instead

measures the requirements to push technology forward for successful project completion.

Hazard is a count variable ranging from zero to three. We identified three types of contractual

hazards present in the IT services industry: measurement cost, proprietary technology, and

interdependency. Hazard denotes the number of exchange hazards each contract contains.

Measurement cost captures the cost of measuring quality after project completion, and is

based primarily on technological factors. Due to the largely subjective nature of measurement

costs, Compustar personnel coded measurement issues as one if quality is difficult to

determine immediately after the project is completed and zero if it is relatively ease to

determine. The coding criterion used was whether a brief, inexpensive test or inspection

could determine the quality of Compustar’s work. Proprietary captures appropriability

concerns and was coded as one if one or more of Compustar’s proprietary technologies is

required for the project. Interdependency captures instances when the buyer is directly

involved in the project in such a way that Compustar depends upon the buyer to complete its

4 There were no projects that were coded by Compustar engineers as a 7. The actual range is from 1 to 6.21

task(s) and is also coded as zero or one. Thus the presence of each hazard is identified by a

dummy variable and hazard is a sum of these three dummy variables.

Control variables. Capabilities may influence what is included in a contract including the

payment mechanism and the level of detail. Compustar has superior internal capabilities

relative to its competitors in servicing hardware that it designed and manufactured.

Compustar hardware is a dummy variable coded as one if the project involves Compustar

hardware, and zero otherwise. Compustar engineers are acknowledged experts at servicing

mainframes from other vendors due to their experience and training in all aspects of

mainframe technology. Mainframe is a dummy variable coded as one if the contract involves

mainframe computers, and zero otherwise. While Compustar has relative strengths in these

areas, the technology used is not proprietary.

Compustar’s capabilities are acknowledged as weaker or at best equivalent to its competitors

in servicing other vendor’s non-mainframe hardware and in programming5. Other hardware is

coded as one if the contract involves hardware from another vendor, and zero otherwise.

Compustar was founded as a hardware firm and has limited experience in programming.

Programming is a dichotomous variable coded as one if the project involves programming.

Other project-level attributes that may affect contract design are also included. Another factor

than can lead to more complete contracts is when failure is very costly. Disrupt is a dummy

variable (coded by Compustar engineers) as one if a project has the potential to shut down a

“significant portion” of a customer’s data center. Accidental data center shutdowns are very

costly for customers and tend to be visible events, thus causing significant reputational

5 Many IT firms service storage devices and other non-mainframe IT hardware, including the firms that originally manufactured this equipment.

22

damage to suppliers such as Compustar. When a project could result in an outage, Compustar

will describe exactly what must be done to minimize the chances of a data center outage. We

also took the current value of the project into account. Dollar value was captured by the total

monetary value of the project, and we used the mean value to fill in missing data. Since the

distribution of dollar value is quite skewed, we used the logarithm of this variable.

We also control for the effect of prior transactions in which this buyer engaged Compustar for

IT services. Prior projects is the number of projects that Compustar has completed for the

buyer prior to the current transaction, and since the distribution of this variable was skewed,

we entered the logarithm of prior projects.

Non-IT services purchases represent other links between the firms. How much business each

customer has completed with Compustar prior to the focal project may influence task

description or the payment mechanism. Compustar was reluctant to provide customer dollar

values, but did develop a Likert variable that captures relationship breadth. Breadth measures

the extent of non-IT services provided by Compustar for each customer, and ranges from one

(no prior ties in other lines of business) to seven (one of the largest customers outside of IT

services). Year dummies were also included to test for time effects.

[Insert Table 1 here]

Methodology

Since task description is a continuous variable, we use Ordinary Least Square regression to

test our first two hypotheses. Probit models were used for contingency planning and hybrid.

To add interactions of innovation and hazard, we adopted a simulation-based approach

proposed by King, Tomz, and Wittenberg (2000) and Zelner (2009) because the coefficient of

23

interaction term in probit models may not represent the true interaction effect (Norton, Wang

and Ai, 2004); Accordingly, we create and interpret graphical representations of the

interaction effect as recommended by Zelner (2009).

RESULTS

Table 2 provides the OLS regressions on task description. Model (1) is the baseline model

including all control variables, and Model (2) and Model (3) add innovation and number of

hazards respectively. Model (4) captures the joint effect of innovation and hazard on task

description. From Model (1) to Model (4), innovation is significantly positively associated

with the degree of task description (p<0.05), providing strong support for Hypothesis 1. The

positive relationship between innovation and task description is significant (p<0.01) when we

add the interaction term of innovation and hazard in Model (5). Our results are consistent

with our hypothesis that exchange hazards negatively moderate the positive relationship

between innovation and task description. We plot this interaction effect in Figure 1 using one

standard deviation below and above the mean of our moderator and predictor, and we can see

that when fewer exchange hazards are present, innovation has a stronger positive impact on

task description. The results in Figure 1 support H2.

[Insert Table 2 and Figure 1 here]

Table 3 and Figure 2 contain the analysis of contingency planning (H3 and H4). Models (1)

through Model (4) in Table 3 show the impact of level of innovation and hazards on

contingency planning, and we do not find a significant main effect of the level of innovation

on contingency planning. In model (5) we add the interaction term and specify 1,000

iterations to estimate our graphical model. The negative and statistically significant

24

coefficient of innovation in Model (5) and those non-significant coefficients through Model

(1) to Model (4) fail to support Hypothesis 3.

To test the interaction effect predicted by Hypothesis 4, we interpret the graphical

presentation of the interaction presented in Figure 2 below. Figure 2(a) plots the predicted

probability of using contingency planning between contracts having a high number of

exchange hazards and those having a low number of exchange hazards, while all other

explanatory variables are held at the mean values estimated by the probit model (5) in Table

3. The bars and scattered dots indicate the 95% confidence intervals of the two predicted

probabilities. The figure shows that innovation has a stronger positive impact on the

likelihood of contingency planning when exchange hazards are high. The y-axis in Figure

2(b) represents the difference in the predicted probability of using contingency planning

between contracts having high number of exchange hazards and those having a low number,

again holding explanatory variables to the mean values estimated in the probit model (5) from

Table 3. The symbols indicate the regions in which the difference of the predicated

probability differs from zero at the 95% level. Figure 2b shows that contingency planning is

used more often when the number of exchange hazards is high as innovation required goes

up, compared to when the number of exchange hazards is low. This relationship is

statistically significant (p<0.05) when the level of innovation required to complete the

exchange is relatively high. In addition, the general increasing trend of the differences in

Figure 2(b) is significant at the 95% level. These results provide strong support for H4.

Additionally, our results suggest that the interaction of the number exchange hazards and the

level of innovation strongly affects the presence of contingency planning and subsequently

25

washes out the main effects that either the number of exchange hazards or the level of

innovation have on their own on contingency planning. Thus, we suggest that this is the

reason why we do not find any statistically significant main effect of innovation in Model (1)

through (4) in Table 3.

[Insert Table 3 and Figure 2 here]

We use probit models to test our last two hypotheses (H5 and H6) on the payment

mechanisms firms specify in the presence of innovation and we present our results in Table 4

and in Figure 36. Our analysis in Model (1) through Model (4) of Table 4 suggests that

increases in the level of innovation as a desired outcome increases firm reliance on the use of

hybrid payment mechanisms, supporting H5.

In Model (5) (of Table 4), consistent with H6, the coefficient of the interaction term is

negative and significant. However, it is not accurate to interpret the interaction effect in a

probit model by simply reading the sign of coefficients Zelner (2009); thus, we rely on

simulation based estimations represented graphically to analyze the interaction between

innovation and hazard. Figure 3(a) plots the predicted probability of using contingency

planning between contracts having a high number of exchange hazards and those having a

low number, and both lines plotted for low and high hazards fall within the 95% significant

level as denoted by the bars (i.e., the solid line plotted within the bars for a low number of

hazards) and scattered dots (i.e., the dashed line within the dotted area). Figure 3(b) shows

that the difference between using a hybrid payment mechanism when then number of

exchange hazards is high versus when the number of exchange hazards is low become

statistically significant as the level of innovation as a desired outcome becomes moderate or

6 We did not use ordered probit analysis because it dose not allow simulations on interaction terms.26

high. The general decreasing trend of the differences in Figure 3(b) is significant at 95%

level, strongly supports H6.

[Insert Table 5 and Figure 3 here]

Our analysis so far suggests that innovation leads to the use of hybrid payment when the

number of exchange hazards is low. Our data also allows us to do some follow up analysis on

what kind of payment mechanisms firms prefer when both the level of innovation required

and the number of exchange hazards are high. To investigate, we split our sample and test the

probability of using hybrid versus T&M payment mechanism when fixed-fee is not included,

and also the probability of using fixed-fee versus hybrid payment mechanism when T&M is

not included. Figures 4(a) and 4(b) show that when level of innovation and number of

exchange hazards are both high, firms are more likely to use a T&M payment mechanism

compared to hybrid payment mechanism. Figures 4(c) and 4(d) do not show a statistically

significant difference between hybrid and fixed-fee payment mechanisms. These results are

consistent with our prior hypotheses about the payment mechanisms that firms specify when

both the level of innovation required and the number of exchange hazards are high; that is,

firms use T&M payment mechanisms to avoid the difficulty of identifying the payment caps

in a hybrid payment mechanism when the number of exchange hazards is high. T&M

mechanisms also provide the supplier firm with more flexibility to be creative in pursuing

innovation outcomes.

[Insert Figure 4 here]

DISCUSSION

The purpose of this paper is to better understand how firms use contracts to govern

27

transactions that require innovation. While there are a variety of components to a contract, we

selected three that we believe are particularly important for innovative projects. A control

variable in prior work (Argyres et al., 2007) suggested relationships between innovation

requirement and both task description and contingency planning, and we have sought to build

on their work. While to our knowledge prior research has not directly addressed the link

between the need for innovation and the payment mechanism, this portion of the contract

plays a huge role in determining overall incentives—especially those related to effort, which

complements creativity and intrinsic motivation in driving innovation.

Facilitating supplier innovation to completely an exchange requires more than just defining

expectations and mitigating hazards; it involves creating an environment that encourages

people to think creatively (e.g. Miron-spektor, Erez, and Naveh, 2011). We know that team

and individual level factors, including how teams are governed and staffed, influence the

innovativeness of team, but to our knowledge this insight has never been applied to the role

contracts play in governing inter-firm transactions, which are also completed by teams of

people striving for innovative solutions.

This paper takes one important step filling the gap by bringing in some of the micro-level

literature on fostering innovation and creativity for an important but under-studied issue—

how to effectively govern transactions requiring innovation. We show that compared to lack

of exchange hazards, innovation requirement leads to less use of detailed task descriptions

and hybrid payment systems, but led to more extensive contingency planning when exchange

hazards are present. Simultaneously mitigating hazards while fostering innovation requires a

creative approach to contract design and may include more reliance on relational governance

28

(e.g. Poppo and Zenger, 2002) and a better understanding of micro-level factors like construal

and framing have the potential to help bridge the gap between those who advocate

minimizing the use of contracts in favor of relational governance (e.g. Ghoshal and Moran,

1996) and TCE scholars who focus on contracts as a purely rational response to exchange

hazards (e.g. Williamson, 1985). More work is needed to explore the governance of

transactions involving innovation, including the role played the relationship between the

firms. In our data, both of our proxies for a relationship between the buyer and supplier (the

number of prior projects that the supplier has completed for the buyer and the number of

product lines the buyer purchases from the supplier) have low but negative correlations with

transactions requiring innovation. Thus it does not appear that relational governance is

playing a significant role in governing the innovative projects in our data, but more work is

needed to explore this possibility.

Years after the exchange between Ghoshal and Moran (1996) and Williamson (1996) in the

Academy of Management Review, we still lack a strong integration of social/emotional issues

with the rational governance approach of TCE (Weber and Mayer, 2011, 2014 being

exceptions). The critique that Ghoshal and Moran (1996) leveled at Williamson and TCE was

that emotions were being ignored, which would lead to problems that would be missed if

people were assumed to operate in a purely rational way. Ghoshal and Moran (1996) pointed

out that the contract could engender a very negative response if people perceive the presence

of distrust and suspicion based on what is put in the contract. TCE has been slow to

incorporate any real effect of emotions on governance. This may be due to the lack of training

most TCE scholars have in social psychology and other areas where these micro-level

29

emotions are studied extensively or, as Williamson has sometimes implied (e.g. Williamson,

1996), to the belief that emotions don’t matter in commercial exchanges. Perhaps a third leg

needs to be added to TCE; in addition to overcoming bounded rationality and mitigating

opportunism, we also need people designing contracts and governing inter-firm relationships

to manage the emotional environment of the transaction (the partner’s emotional response).

Doing this requires insight from social psychology and possibly sociology as well. By

incorporating a more complete understanding of the impact the contract can have on the

exchange, firms will be better positioned to design better contracts and enjoy more successful

inter-firm transactions.

We believe that this paper is just the start of research exploring how inter-firm projects

requiring innovation should be governed. Governance does more than prevent negative

events; it can also foster the creation of positive events. Similar to Lindenberg's (2000)

distinction between creating trust and avoiding mistrust, firms can work to create a positive

as well as avoid a negative. While we don’t explicitly address the issue of trust in this paper,

we cite Lindenberg’s work to highlight distinction between the creating of something positive

and the avoidance of something negative; both are important but current work in TCE tends

to be focused on avoiding a negative (i.e., avoiding opportunism and problems arising from

bounded rationality) rather than creating a positive environment. The assumption has been

that if negative events are presented, then a positive outcome will ensue. In some cases,

however, a positive outcome requires more than just avoiding negative events—it requires

creating a positive environment.

This paper moves us one step closer to understanding how to govern inter-firm transactions

30

that require innovation and the potential for combining the boundedly rational governance

approach of TCE with micro-level research on individual behavior and team processes (e.g.

Grant and Berry, 2011; Miron-spektor et al., 2011). While we focus on fostering innovation,

there are many other areas where better understanding individual and team level factors can

produce a more successful transaction. We hope this is the first of many studies to draw

insights from micro and macro level research to both extend theory and explain important

phenomena.

Limitations & Future Research

As with all studies, potential limitations must be addressed. First, lawyers did not negotiate

the contracts examined in this study—managers and engineers were the primary contract

drafters. Lawyers did conduct a final stage review of the contracts (making few changes), but

the contracts might be different if lawyers were the primary negotiators. How the contracts

might differ based on who negotiates is a promising topic for future research.

Second, while most contracts are based on templates, the templates used here don’t specify

one payment that must be used nor do they specify the details required for any specific

exchange. Thus whether templates were used, and typically Computar’s template was

employed, should not impact the results of this study, as the contractual aspects we examine

were negotiated for each transaction. The role of the contract template and how it influences

both negotiation and execution is potentially productive avenue for future research.

One strength of this study— microanalytic data from a single firm—is also a limitation. Our

rich transaction-level data enables us to offer insights into contractual choices rarely available

with studies of a large number or firms. Some may be concerned that the results only reflect

31

Compustar’s contracting policy, but most of Compustar’s buyers are large companies with

many alternatives for IT service suppliers. Thus we believe that, on average, buyers have

significant leverage that the negotiated contracts do not solely reflect Compustar policy but

also significantly integrate buyer concerns. Critics may also suggest that results from a single

firm do not generalize to other industries and settings. Because a large percentage of contracts

—particularly those in high technology industries—govern project-based exchanges

involving some level of complexity and uncertainty, we suggest that our theory does

generalize to other project based industries (e.g. software, telecomm, and consulting).

Nevertheless, future research to assess the generalizability of this study would be valuable.

The idea that the design of the contract can play an important role in fostering innovation not

just by creating economics incentives but by helping create a particular type of environment

is important and calls for additional research to better understand this effect. We are not

arguing that a well-designed contract is a sufficient condition for a successful exchange, the

parties must still manage the exchange and the individuals involved, but it is an important

first step that can set the exchange off on the right foot and increase the chances of success.

Future research examining the link between contract design and subsequent behavior during

the transaction would be valuable, as would studies that include data on transaction

performance, including innovativeness.

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35

Task

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Num

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1

Yea

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2

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3

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4

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5

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6

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7

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8

Task Description 1.000Contingency Planning 0.211 1.000

Hybrid 0.044 - 1.000

Innovation 0.145 - 0.169 1.000

Hazard - - 0.049 0.302 1.000

Compustar HW - 0.101 - - - 1.000

Mainframe - - 0.081 - 0.052 0.418 1.000

Programming - - 0.130 0.260 0.228 - 0.035 1.000

Other Firm Hardware 0.195 0.091 - - - 0.097 0.068 0.021 1.000

Ability to Disrupt - 0.122 - - - 0.385 0.331 0.035 - 1.000

Breadth 0.119 0.110 - - - 0.059 - - 0.008 - 1.000

Dollar Value 0.429 0.175 0.041 0.211 - 0.040 0.079 0.146 0.225 0.145 0.222 1.000

Number of Prior 0.071 0.313 0.045 - - 0.013 - - - - 0.587 0.189 1.000

Year 1991 0.013 - 0.017 - 0.029 0.092 0.126 0.022 - 0.060 - 0.001 - 1.000

Year 1992 0.045 - - - 0.042 - - 0.152 - - - - - - 1.000

Year 1993 0.023 - - 0.194 - - - 0.033 0.014 - 0.022 0.089 - - - 1.000

Year 1994 0.028 - 0.065 0.119 - - - 0.110 0.090 - 0.036 - 0.054 - - - 1.000

Year 1995 - 0.097 0.036 - 0.011 - 0.077 0.009 0.031 - 0.054 - 0.083 - - - - 1.000

Year 1996 - 0.213 - - 0.003 0.099 - - 0.031 0.050 0.274 0.124 0.238 - - - - - 1.000

Year 1997 - 0.166 - - - 0.078 0.032 - - 0.194 - - 0.167 - - - - - - 1.000

Year 1998 - 0.089 0.017 0.093 - 0.018 0.090 - - 0.224 - 0.030 0.047 - - - - - - - 1.00

Mean 3.402 0.481 0.107 2.521 0.714 0.232 0.262 0.459 0.091 0.472 4.249 10.53 1.023 0.04 0.074 0.067 0.123 0.178 0.185 0.156 0.04

Standard Deviation 1.866 0.5 0.309 1.203 0.708 0.423 0.44 0.499 0.288 0.5 2.766 1.56 1.01 0.195 0.262 0.25 0.329 0.383 0.389 0.363 0.21

2

36

Tabl

es a

nd

Figu

res

Table 2: OLS regression on task description Note: (1) Standard errors in parentheses (2) *** p<0.01, ** p<0.05, * p<0.1 for two-tail test

Table 3: Probit Models on Contingency PlanningContingency PlanningModel Model (2) Model Model Model (5)

Innovation -0.026 -0.034 -0.275**(0.078) (0.080) (0.120)

Hazard 0.048 0.059 -0.695**(0.117) (0.120) (0.303)

Innovation*Hazard 0.283***(0.099)

Compustar HW 0.220 0.202 0.237 0.216 0.107(0.225) (0.232) (0.229) (0.234) (0.238)

Mainframe -0.403* -0.404* -0.417* -0.420* -0.378*(0.216) (0.216) (0.219) (0.219) (0.212)

Programming -0.307* -0.297 -0.321* -0.311* -0.247(0.178) (0.181) (0.182) (0.183) (0.183)

Other Firm Hardware 0.546* 0.531* 0.574* 0.560* 0.498(0.301) (0.304) (0.308) (0.310) (0.326)

Ability to Disrupt 0.207 0.215 0.208 0.220 0.226(0.197) (0.199) (0.197) (0.199) (0.195)

Breadth -0.082** -0.083** -0.082** -0.083** -0.073*(0.039) (0.039) (0.039) (0.039) (0.040)

Dollar Value 0.100* 0.105* 0.101* 0.108* 0.129**(0.056) (0.058) (0.056) (0.058) (0.056)

Number of Prior Projects 0.353*** 0.354*** 0.355*** 0.356*** 0.316***(0.109) (0.109) (0.109) (0.109) (0.109)

Constant - - - - -(0.643) (0.648) (0.655) (0.657) (0.624)

Year Effect YES YES YES YES YESChi2 95.12*** 95.23*** 95.29*** 95.47*** 87.94***Pseudo R-squared 0.226 0.226 0.226 0.227 0.246Observations 304 304 304 304 304

Note: (1) Standard errors in parentheses (2) *** p<0.01, ** p<0.05, * p<0.1 for two-tail test

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Table 1: Summary of Statistics & Correlations Between VariablesTask DescriptionModel (1) Model (2) Model (3) Model (4) Model (5)

Innovation 0.187** 0.195** 0.518***(0.089) (0.092) (0.134)

Hazard 0.016 -0.054 0.947***(0.140) (0.143) (0.339)

Innovation*Hazard -0.374***(0.115)

Compustar HW -0.461* -0.344 -0.456* -0.355 -0.234(0.264) (0.268) (0.267) (0.270) (0.268)

Mainframe 0.049 0.056 0.045 0.070 -0.009(0.250) (0.248) (0.252) (0.251) (0.248)

Programming -0.845*** -0.930*** -0.850*** -0.918*** -1.023***(0.209) (0.212) (0.214) (0.215) (0.214)

Other Firm Hardware 0.755** 0.879** 0.763** 0.856** 0.944***(0.348) (0.351) (0.357) (0.357) (0.352)

Ability to Disrupt 0.079 0.030 0.079 0.027 0.050(0.232) (0.231) (0.232) (0.232) (0.228)

Breadth 0.060 0.067 0.060 0.067 0.054(0.045) (0.045) (0.045) (0.045) (0.044)

Dollar Value 0.529*** 0.498*** 0.529*** 0.496*** 0.472***(0.066) (0.067) (0.066) (0.068) (0.067)

Number of Prior Projects 0.029 0.023 0.029 0.022 0.080(0.126) (0.125) (0.126) (0.125) (0.125)

Constant -1.390** -1.612** -1.407** -1.568** -1.999***(0.692) (0.696) (0.707) (0.707) (0.708)

Year Effect YES YES YES YES YESR-squared 0.268 0.279 0.268 0.279 0.305Adjusted R-squared 0.227 0.236 0.224 0.234 0.259F Statistics 6.547*** 6.492*** 6.142*** 6.120*** 6.549***Observations 303 303 303 303 303

Table 4: Probit Models on Contract TypeContract Type (Hybrid Contract=1)Model (1) Model (2) Model (3) Model (4) Model (5)

Innovation 0.220** 0.249** 0.443***(0.096) (0.101) (0.147)

Hazard -0.038 -0.155 0.774**(0.152) (0.162) (0.377)

Innovation*Hazar -0.305**(0.122)

Compustar HW -0.767** -0.682** -0.773** -0.698** -0.622*(0.335) (0.342) (0.336) (0.342) (0.346)

Mainframe 0.723*** 0.786*** 0.730*** 0.822*** 0.766***(0.270) (0.275) (0.271) (0.278) (0.276)

Programming 0.431* 0.340 0.439* 0.359 0.205(0.223) (0.229) (0.226) (0.230) (0.210)

Other Firm -0.199 -0.015 -0.217 -0.068 -0.076(0.398) (0.407) (0.404) (0.410) (0.398)

Ability to Disrupt -0.263 -0.312 -0.261 -0.306 -0.322(0.253) (0.256) (0.253) (0.256) (0.221)

Breadth -0.047 -0.036 -0.046 -0.034 -0.046(0.048) (0.049) (0.048) (0.049) (0.047)

Dollar Value 0.056 0.019 0.055 0.009 -0.011(0.078) (0.082) (0.078) (0.082) (0.081)

Number of Prior 0.353** 0.355** 0.351** 0.352** 0.325**(0.142) (0.143) (0.142) (0.144) (0.144)

Constant -1.331* -1.637** -1.288 -1.500* -2.031**(0.790) (0.826) (0.808) (0.837) (0.877)

Year Effect YES YES YES YES YESChi2 39.56*** 44.89*** 39.63*** 45.82*** 41.98***Pseudo R-square 0.170 0.193 0.171 0.197 0.183Observations 289 289 289 289 308

Note: (1) Standard errors in parentheses (2) *** p<0.01, ** p<0.05, * p<0.1 for two-tail test

Figure 1: The Moderating Effect of Hazard of Innovation on Task Description

38

.2.4

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Predicted probability when hazard takes value of .674 Predicted probability when hazard takes value of 1.412

-.10

.1.2

.3P

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Figure 2 (a) Figure 2(b)Figure 2: Graphic Presentations of Interaction Effect of Innovation and Hazard on Contingency PlanningNote: (1) Figures 2(a) plot the predicted probability of using contingency planning between contracts having high

hazards and those of low hazards, while all other explanatory variables are held at their mean values in the probit model (5) in Table 3. The bars and scattered dots denote the 95% confidence intervals of the two predicted probabilities.

(2) The y-axis in Figures 2(b) represents the difference in the predicted probability of using contingency planning between contracts having high hazards and those of low hazards, while all other explanatory variables are held at their mean values in the probit model (5) Table 3. The symbols denote the regions in which the difference of the predicated probability differs from zero at the 95% level.

(3) The general increasing trend of the differences in Figure 2(b) is significant at 95% level.

0.2

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Predicted probability when hazard takes value of .668 Predicted probability when hazard takes value of 1.340

-.2-.1

5-.1

-.05

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5P

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ybrid

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1 2 3 4 5 6innovation

Figure 3 (a) Figure 3(b)Figure 3: Graphic Presentations of Interaction Effect of Innovation and Hazard on Contract TypeNote: (1) Figures 3(a) plot the predicted probability of using hybrid contract between contracts having high hazards

and those of low hazards, while all other explanatory variables are held at their mean values in the probit model (5) in Table 4. The bars and scattered dots denote the 95% confidence intervals of the two predicted probabilities.

(2) The y-axis in Figures 3(b) represents the difference in the predicted probability of using hybrid contract between contracts having high hazards and those of low hazards, while all other explanatory variables are held at their mean values in the probit model (5) Table 4. The symbols denote the regions in which the

39

difference of the predicated probability differs from zero at the 95% level. (3) The general decreasing trend of the differences in Figure 3(b) is significant at 95% level.

0.2

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.81

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Predicted probability when hazard takes value of .864 Predicted probability when hazard takes value of 1.594

-.6-.4

-.20

.2.4

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Figure 4(a) Figure 4(b)

0.2

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Predicted probability when hazard takes value of .613 Predicted probability when hazard takes value of 1.320

-.15

-.1-.0

50

.05

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Figure 4(c) Figure 4(d)Figure 4: Graphic Presentations of Interaction Effect of Innovation and Hazard on Contract Type (Fixed-Fee and T&M)Note:(1) Figure 4(a) and 4(b) present the probability of using hybrid payment versus using T&M payment,

while Figure 4(c) and 4(d) examine the probability of using hybrid payment versus using Fixed Fee payment.

(2) Figures 4(a) and 4(c) plot the predicted probability of using hybrid contract between contracts having high hazards and those of low hazards, while all other explanatory variables are held at their mean values. The bars and scattered dots denote the 95% confidence intervals of the two predicted probabilities.

(3) The y-axis in Figures 4(b) and 4(d) represent the difference in the predicted probability of using hybrid contract between contracts having high hazards and those of low hazards, while all other explanatory variables are held at their mean values. The symbols denote the regions in which the difference of the predicated probability differs from zero at the 95% level.

(4) The general decreasing trend of the differences in Figure 4(b) and 4(d) are significant at 95% level.

40