An Empirical Analysis of Contract Structures in IT Outsourcing

Transcription

1 An Empirical Analysis of Contract Structures in IT Outsourcing (Job Market Paper (Essay #1 of Dissertation)) Yuanyuan Chen Goizueta Business School Emory University Atlanta, GA 30322

2 An Empirical Analysis of Contract Structures in IT Outsourcing Abstract Outsourcing of IT services has received much attention in the information systems literature. However, there has been considerably less attention paid to actual contract structures used in IT outsourcing. Examining contract structures yields important insights into how the contracting parties structure the governance provisions and the factors or transaction risks that influence them. Based on insights from prior literature, from practicing legal experts and through in-depth content analysis of actual contracts we develop a comprehensive coding scheme that captures contract provisions across four major dimensions: monitoring, dispute resolution, property rights protection, and contingency provisions. We then develop an empirical dataset describing the contract structures across these distinct dimensions using a sample of 112 ITO contracts from the SEC database in the period Drawing on transaction cost, agency and relational exchange theories we hypothesize the effects of transaction and relational characteristics on the specific contractual provisions as well as on overall contract extensiveness. Further, we examine how these associations vary under conditions of fixed price and time and materials pricing structures. The results provide good support for the main hypotheses of the study and yield interesting insights about contractual governance of IT outsourcing arrangements. Keywords: Outsourcing; IT Outsourcing contract; contract structure; fixed price contracts, time and material contracts 2

3 An Empirical Analysis of Contract Structures in IT Outsourcing 1 Introduction It is essential to establish a solid legal contract with a supplier before entering into any outsourcing deal the contract could be the key to success or failures try to anticipate in the contract, anything that might happen 1 Understanding outsourcing contract structures has been a focus of theoretical and empirical work in law and economics for over twenty years. Transaction cost economics has been widely used to explain how outsourcing governance structure have enabled exchanges by safeguarding against hazards arising from exchange characteristics such as asset specificity and uncertainty (e.g., Ang and Straub 1998, Nam et al. 1996, Lacity and Willcocks 1996). Recent empirical research has examined discrete contract provision such as pricing clauses (Crocker and Reynolds 1993, Kalnins and Mayer 2004), contract duration clauses (Crocker and Masten 1988, Joskow 1987), take-or-pay provisions (DeCanio and Frech 1993, Masten and Crocker 1985), and termination clauses (Arruñada et al. 2001), focusing on both the rationale for and the performance consequences of specific contract terms. Despite the rapid growth in information technology outsourcing (ITO), information systems research has largely focused on issues such as propensity of firms to outsource their IT activities (Aubert et al. 1996, Ang and Straub 1998, Grover et al. 1996), the types of IT functions that are likely to be outsourced (Lacity et al. 1996, Quinn and Hilmer 1994), and variables influencing IT outsourcing performance (Grover et al. 1996, Lacity and Willcocks 1998, Lee et al., 2004). There has been much less emphasis on examining contract structures with a view to understanding the specific provisions that are emphasized in IT outsourcing and the transaction characteristics that affect the choice of contract structures. However, a few scholars have noted the importance of understanding contract structures in ITO (Gopal et al. 2003, Kalnins and Mayer 2004, Kern and Willcocks 2001, Saunder and Hu 1997). For example, Kern and Willcocks (2001) note that a major finding in research has been that, if an organization outsources IT, the outsourcing contract is the only certain way to ensure that expectations are realized. Additionally, some empirical work 1 Cathay Pacific consultant, from the HBS case Cathay Pacific: Doing More with Less, HBS case December 2003 by McFarlan and Young. 3

4 examining discrete contracting provisions such as incentive mechanisms (Gopal et al. 2003, Kalnins and Mayer 2004), and management control mechanisms in IT contracts (Anderson and Dekker 2005, Kern and Willcocks 2000) have begun to pave the way for a more holistic understanding of the entire contract structure. Against this backdrop, the objectives of this study are (a) to conduct a fine-grained investigation of ITO contracts to refine our understanding of the essential dimensions or major contract terms typically found in ITO contracts and (b) to examine how transaction characteristics such as the type of function outsourced, the nature of the outsourcing relationship, etc. affect contract design choices in ITO. We develop our understanding of ITO contracts through a careful examination of contractual provisions using a sample of 112 IT outsourcing contracts culled from the Securities and Exchange Commission (SEC) database. Information contained in the contract clauses provides rich data about the manner in which firms structure the exchange and cope with problems that might arise. The contributions from this study are three-fold. First, our paper is one of the early attempts to examine in detail ITO contract structures based on a comprehensive analysis of actual contracts. This allows us to go beyond the case study and game-theoretic approaches common in much of the literature examining IS outsourcing contract choices (Mahnke et al. 2003). The difficulty of obtaining detailed contract information has limited empirical investigation of contract design choices. Even some recent empirical work on ITO contract design is based on perceptual (survey) data about the contracts used (Anderson and Dekker 2005) and not on the actual contracts. In contrast, our ex post analysis of the ITO contracts spanning a 10 year time period allows us to develop a comprehensive scheme for classifying contract clauses and provides a more granular understanding of the structure of the contracts. Second, our detailed database of ITO contracts provides a unique opportunity to examine the transaction characteristics that affect the structure of the contracts. Using data culled from other external sources about the contracting parties, the type of activities outsourced, and the nature of the outsourcing relationship we provide important insights into when contracting firms are likely to include specific provisions. For example, we find that when partners have contracted with each other in the past they are likely to include more, not less, contractual provisions for monitoring, dispute resolution, and contingency 4

5 planning. The effects of other transaction characteristics such as use of customized technology and process interdependence on the use of specific contract provisions are also systematically examined. Finally, we recognize that especially in ITO a significant portion of risks in contracting is managed through the use of discrete pricing structures, the two prevalent forms being fixed-price (FP) and time-andmaterials (TM) contracts (Gopal et al. 2003). The use of other contractual provisions such as monitoring, dispute resolution, property rights, and contingency planning provide the means for managing the residual risks not fully covered by the pricing structure 2. We therefore examine the transaction characteristics associated with the use of discrete contract provisions within each of the two broad contract types of FP and TM respectively. The sub-group analysis yields interesting results that are masked in the full sample. For example, while asset specificity is associated with more dispute resolution and property rights provisions in the case of FP contracts, dispute resolution and property rights are not increasing with asset specificity in the case of TM contracts, presumably because the vendor s risks are quite adequately covered by the pricing flexibility of TM contracts. The rest of the paper is organized as follows. In the next section, we explain the contract structure and the broad provisions in great detail. Next, we develop the research hypotheses, followed by the research methods, empirical analysis, and results. We conclude the paper with a discussion of implication for research and practice, as well as directions for future work. 2 Contract Structure in IT Outsourcing The theoretical background for much of the work on structure of contracts comes from the management control framework posited by Jensen and Meckling (1992), describing management control systems as policies and practices that govern performance management, rewards and sanctions, and assignment of rights and responsibilities. In the IS literature, although the structure of the contract has been noted as a key determinant of subsequent performance (Fitzgerald and Willcocks 1994, Lacity and Hirschheim 1993), there 2 We thank one of our anonymous reviewers for this insight. 5

6 are only a few studies that provide guidance about the structure of ITO contracts and empirical evidence as to the extent to which different structures are used in real-world settings is relatively rare. The earliest work on the structure of ITO contracts is a case analysis of 5 outsourcing contracts (Whang 1992). Although based on a small sample, Whang (1992) concluded from his content analysis that while some similarities reflecting boilerplate templates exist, there was still considerable diversity across the five contracts in their use of various contractual provisions. The three components that he found in all contracts were product/task definition, intellectual property protection, and payment terms. Ryall and Sampson (2003) in their analysis of the structure of technology development 3 contracts also note a similar diversity in contract specifications and found three broad categories to account for a large portion of the variance across contracts: (a) extent to which parties obligations are precisely specified; (b) the extent of monitoring that was explicitly provided for; and (c) the specification of penalties for noncompliant behavior. Specific sub-clauses across these three broad categories were identified and the heterogeneity in the use of these clauses across the different technology development contracts was tracked and associated with the nature of interactions between the parties engaged in the transaction. The most recent empirical study on the structure of IT contracts (Anderson and Dekker 2005) was based on survey data collected from a sample of IT buyers (clients) representing a broad array of IT products. Although this study is significantly different from ours as it focused on outright IT purchasing contracts, and not on IT outsourcing contracts 4, it nevertheless provides a useful starting point through its mapping of contract clauses into four dimensions of management control: assignment of rights, product and price terms, aftersales service terms 5 and terms of legal recourse. Using exploratory statistical methods, the authors empirically detect the presence of these four dimensions of management control provisions and hypothesize that transaction costs are associated with more extensive (greater use of provisions) contract structures. 3 The contracts used in this study are broad in scope, reflecting all technology development contracts and are not specific to IT outsourcing. 4 IT purchasing generally involves the buying of IT products such as hardware, standard software, telecommunications equipment etc. from the vendor company and to subsequently use/bring it in-house; IT outsourcing generally involves IT services such as software development, application maintenance etc. and represents a decision to have an external vendor perform the service on a regular or on-going basis per the terms of the contract. 5 While after-sales-service terms are common in IT purchase contracts, they are generally absent in ITO contracts. 6

7 Using the aforementioned frameworks of contract structure as a starting point, we followed the approach described in Ryall and Sampson (2003) to develop a comprehensive understanding of contract structures used in the IT outsourcing context. We first chose three detailed and well-specified contracts from our sample and conducted a textual analysis of the contract provisions to comprehend the structure and provide the grounding for a coding scheme to be used subsequently to analyze the broader sample of contracts. Using these three contracts we made an exhaustive list of all the contract terms and provisions contained in these documents. Next we eliminated redundancies by identifying the same or equivalent clauses that were repeated across these contracts and retained only unique terms and clauses. Then we eliminated boiler-plate like terms that were relatively invariant across contracts, such as task/service description. Clauses related to pricing and payment terms were also removed as these were captured separately in our subsequent analysis under fixed-price and time-and-materials contracts. This resulted in a set of 16 provisions that captured fairly exhaustively all the remaining governance provisions used in these contracts. Next, we grouped these 16 terms into the following four broad categories that seemed to represent distinct governance choices relating to: (a) monitoring provisions; (b) dispute resolution; (c) property rights allocation/protection; and (d) contingency provisions. Finally, we ensured that the classifications adopted in the study corresponded to the frameworks identified in prior literature and were validated by an expert panel of three members, comprising two law professors and a practicing lawyer. The experts verified that the textual analysis of the three contracts were comprehensive. A summary of the four dimensions and the underlying clauses in each dimension is provided in Appendix Table A Monitoring provisions. Monitoring is an integral part of the enforcement apparatus of contracts and seeks to reduce contractual risks by specifying procedures designed to oversee the vendor s operations on an on-going basis. Monitoring provisions include, among others, clauses that describe periodic audit and review procedures to be followed including timing and content of reviews, benchmarking of vendor s performance against pre-specified standards to ensure compliance, and adherence to guidelines such as disaster recovery plans in case of failures. In the control, system framework of Jensen and Meckling (1992), monitoring provisions correspond to the performance measurement dimension, establishing the governance framework 7

8 for observing and recording vendor s performance on an on-going basis. While some level of monitoring provisions are likely to be found in many ITO contracts the extent of specification of different monitoring mechanisms have been found to vary across different contracts (Anderson and Dekker 2005). Dispute resolution. While monitoring provisions record the governance framework for ongoing contract management, and are helpful for detecting underperformance or non-compliance of the parties, they provide little guidance for managing disputes between the vendor and the client. The dispute resolution dimension of ITO contracts correlates with the rewards & sanctions dimension of the Jensen and Meckling (1992) framework. ITO contracts can vary in the extent to which specific clauses for resolving disputes are actually recorded in the contract. While some contracting parties may prefer to specify in detail the arbitration processes for dispute resolution, others may prefer to mange such situations through relational mechanisms and therefore leave these clauses out of the contract (Ang and Beath 1993). To the extent to which they are specified, they help establish the processes to be adopted when disputes arise, including provisions for problem escalation, provisions specifying conditions for arbitration, and conditions that constitute grounds for a more formal lawsuit. Property rights. IT outsourcing often involves intensive information exchange and intellectual property sharing between vendors and clients. In the Jensen and Meckling framework, this dimension corresponds to the assignment of rights and responsibilities and specifies the ownership and rights to control and use the intellectual property along with associated provisions for security and confidentiality. These clauses cover ownership or copyright, title and licensing rights, patent protections, data security and confidentiality terms, rights to access the source code (in case of software development)including rights to modify or enhance the product. Significant heterogeneity of ITO contracts with respect to property rights protection have been suggested in prior literature with the diversity being related to the underlying transaction characteristics (Clemons and Hitt 2004, Walden 2005). Contingency provisions. In dynamic environments, outsourcing decisions may need to be continually reevaluated. Responding to unforeseen contingencies necessitates an adaptive capability or flexibility built into the contract. Contingency provisions refer to the built in provisions for variation or modification, 8

9 suggesting that it incorporates both proactive and responsive perspectives (Johnson et al. 2003). Although this dimension does not find a corresponding parallel in the Michael & Jenson framework, or in the structures described by Whang (1992), Ryall and Sampson (2003) and Anderson and Dekker (2005), our content analysis of actual contracts suggested this to be an important dimension along which contracts seemed to vary in their specifications. Against a backdrop of evolving enterprise requirements, industry and technological changes, client companies seem to want to ensure that the contract provides sufficient flexibility to allow for future changes. Equally, a vendor firm would want to ensure that its performance capabilities are not compromised by unforeseen environmental contingencies. The option to terminate the contract for convenience gives one or both parties the rights to terminate the agreement (either in whole or in part) without cause at any time by simply giving written notice to the other party (Holland 2003). The IT marketplace is subject to periodic change, and so are the requirements of outsourced IT functions. These changes could enhance or diminish the parties interest in the outsourcing deal. As such, the flexibility to be able to terminate or exit from the relationship could be an important strategic concern for parties in the contract when the contractual arrangement ceases to meet the needs of the parties (Evans 1991, Minehart and Neeman 1999, Young-Ybarra and Wiersema 1999). Another contingency provision found in some ITO contracts allows the parties to make mutually agreed upon changes to the contract in response to changes in the environment or due to evolving needs of the relationship. The right to modify contract mechanisms is likely to specify generic processes or procedures for proposing and accepting contract amendment for resolving change requirements, including scope and price adjustment provisions (Mulherin 1986). Finally, some ITO contracts also include contingency clauses to deal with more specific issues that might arise between the parties and are sometimes idiosyncratic to the transaction and unlikely to be captured in generic contingency planning templates (Argyres et al. 2007). This kind of clauses specifies the decision-making process and parties obligations in case that a foreseen event happened. Taken together, the four dimensions of contractual provisions yields a measure of contract extensiveness (Anderson and Dekker 2005) as the total number of sub-provisions written into the contract along each of the four major dimensions. The provisions not only cover the most important clauses in ITO contracts, but are also likely to yield significant variance 9

10 across contracts as these are not the standard boiler-plate terms. Next we describe the broader empirical sample of ITO contracts used in this study before we set out the hypotheses positing the relationship between the transaction characteristics and contract structures. Contracts Sample: Coding and Description We gathered a set of 112 IT outsourcing contracts entered into between 1993 and 2003 from public SEC filings. Public firms in the U.S, under the U.S. Securities and Exchange Commission s (SEC) disclosure requirements, are required to file material contracts 6 as exhibits of their 8K, 10K, 10Q and S-1 filings, including substantial or important IT outsourcing contracts. We downloaded SEC filings from the EDGAR database and performed textual searches to filter the IT outsourcing contracts 7 covering a broad array of IT functions, from maintenance type contracts to multi-function IT outsourcing. Our final sample consists of firms drawn from nine industries: food, finance and banking, printing, chemicals, industrial machinery & equipment, petroleum & coal, electronic equipment, computer, and instruments & related products. The average client company in our sample had sales volume of about $17 million per year while the vendor company average was about $5 million per year. Contract value ranged from large contracts of over $1 billion to much smaller contracts of about $50,000. Among the 56 contracts that released contract value, 26 of the contracts were worth more than $10 million. A sample list of contracts is included in Appendix B. We use the contract structure described in the previous section to examine the 112 contracts in our broader sample. Using the dimensions identified above, we developed careful and precise coding criteria to measure the extent to which each contract contained provisions for monitoring, dispute resolution, rights protection, and contingency provisions. To keep the task of contract coding simple and straightforward, we decided that each target provision would be coded simply as present or absent. Thus, the expected values for monitoring provisions can range from 0 to 7 indicating a minimum of 0 monitoring provisions to a maximum 6 A contract is material if there is a substantial likelihood that a reasonable shareholder would consider it important in making an investment decision. (explanation from SEC filings) 7 In many cases, firms filed multiple copies of the same documents with SEC. In these cases, different versions of the documents were reviewed to ensure that information was consistent across the versions. For our analysis, we eliminated contracts where: (1) one of the parties was a non-profit organization; (2) one of the parties has a majority equity stake in the other (i.e. equity alliance); and (3) pure software licensing agreements. 10

11 of 7 monitoring provisions in each contract. Likewise, the ranges for dispute resolution, rights protection and contingency provisioning are 0 to 3 (Please refer to Appendix Table A). To ensure coding consistency and reliability, two coders (one of the authors and another PhD student in engineering with significant software development experience) independently coded all the contract documents. Examples of sample provisions from actual contracts were also provided to the coders. For each contract, the coders independently generated scores for the four major dimensions, representing the extent to which the contract contained the four provisions. A total score for contract extensiveness representing the sum of the four dimensional scores was also computed. We checked the inter-rater reliabilities for all categories and found them to be high (Cohen s k =0.80 or above). We further attempted to ensure test-retest reliability by recoding a small number of contracts after some time had elapsed since the original coding. Throughout the coding process, the results of the recoding were almost identical to the previous results, and the agreement rate ranged from 0.96 to Additionally, two law professors also coded a small sub-sample of the contracts and found good overall agreement with the coders. Overall, we believe that this process resulted in highly reliable coding of the contract data. Table 1 provides the frequencies and cross-frequencies of the contract terms for all the contracts in the sample (Panel A) and for the Fixed Price (Panel B) and Time & Materials (Panel C) contracts. As expected, the contracts exhibited considerable variance in overall contract extensiveness as well as across the different provisions. For example, while 87% of the contracts incorporated a periodic review provision only 27% of the sample contained specific clauses to ensure regular benchmarking of vendor performance. Overall, only about two-thirds of the sample contained any arbitration clauses with only about a fourth specifying additional litigation provisions. A good majority of the contracts included one or more clauses for property rights protections (between 75% & 90%) while only slightly more than a third of the contracts included any contingency provisions for modification or ease of exit from the contractual stipulations and less than 30% of the contracts included any specific contingency stipulations. Panels B and C also reveal some interesting patterns across the FP and TM contracts. In general we find greater reliance on monitoring provisions in the case of TM contracts when compared to FP contracts. Likewise, with the anticipation of greater likelihood of 11

12 disputes, the dispute resolution provisions are also more extensive in the case of TM contracts. In contrast, FP clients may feel that risk of cost escalation is already mitigated by the pricing structure. [Table 1 here] 3 Determinants of Contract Structure Outsourcing contract structures are best understood through an integration of perspectives derived from transaction costs theory (Williamson 1985), agency theory (Achian and Demsetz 1972) and relational exchange theory (Macneil 1978, 1980). Table 2 provides an overview of the various risks associated with contracting identified by the different theories and the corresponding contractual provisions they are most likely to be associated with. Transaction cost economics (TCE) asserts that transaction characteristics such as asset specificity, interdependence of activities, and uncertainty related to the task environment can increase the risks associated with contracting and therefore corresponding safeguard mechanisms in the form of monitoring, rights allocation, and contingency provisions are needed. Agency theoretic perspectives in the literature have emphasized the role of factors such as task complexity, uncertainty of (vendor) behavior and measurement problems in increasing the cost of writing and enforcing contracts. Relational exchange theory posits that contracts are an organizational phenomenon and as such elements of the exchange relationship among parties such as trust and relational norms are expected to play a role in determining the contract structures (Heide and Miner 1992, Levinthal and Fichman 1988, Van de Ven and Walker 1984). Therefore, under this perspective, each contract must be viewed in terms of the history of the relationship between parties (Gulati 1995, Heide 1992). We draw on these theories to formulate more specific hypotheses relating the various transaction risk elements to contract extensiveness in the context of ITO contracts. Asset Specificity Asset specificity defines the degree to which the assets in an exchange are more valuable in their current application than in their next best use (Leiblein and Miller 2003). In the IT context, asset specificity arises due to highly customized solutions or technologies developed for clients, making their use in alternate contexts difficult and sub-optimal (Lacity and Willcocks 1995). Customized technology may involve physical asset specificity, wherein a piece of equipment or component is designed exclusively for a specific client. 12

13 Developing customized solutions can also require human asset specific investments in training and learning. For example, developing a customized CRM system for a firm would require intimate understanding of the firms customer interactions and other customer-centric processes. The greater level of business process specificity also creates an increased requirement for integration and coordination of activities that incur additional relationship specific investments (Wang et al 1997). Thus heightened asset-specificity raises the potential value loss if the contractual relationship were to turn sour and increases switching costs for the clients. Companies will therefore find it beneficial to implement close monitoring processes (e.g., review and auditing procedures) to offset the shirking and hold-up problems caused by such specific assets (Reuer and Ariño 2007, Zaheer and Venkatraman 1995). Hence, H1a: Asset specificity will be positively associated with monitoring provisions In addition to the risks of shirking and hold-up, misappropriation or theft of intellectual property (IP) is another kind of strategic risks prevalent in outsourcing (Aron et al. 2005), since the transfer of intangible asset is irreversible and difficult to be detected (Anand and Khanna 2000, Teece 2000) 8. The problems of IP control are further exacerbated by the weak IP protection for intangible properties such as software code, proprietary information, and know-how (Clemson and Hitt 2004). Therefore, IP owners will try to use contractual provisions to clearly stipulate intellectual property rights and institutionalize stricter IP protection mechanisms aimed at deterring misappropriation and theft behavior. Empirical work demonstrates that asset specificity increases the level and scope of IP control provisions in technology agreements (Brousseau and Coeurderoy 2005). Hence, H1b: Asset specificity will be positively associated with property rights provisions IT outsourcing contracts involving proprietary technology development are generally more idiosyncratic (Niederman et al. 1991) and require intensive sharing of proprietary information and knowledge. 8 A recent software development outsourcing case highlights the risk of IP theft, the vendor company was commissioned by the client to work on development of a client s market leading software. After the vendor got the 13

14 Both parties anticipate greater likelihood of disputes under such situations and are therefore more likely to invest time and energy to specify contractual stipulations for dealing with disputes (Poppo and Zenger 2002). The asymmetric information about the quality of proprietary technologies developed and effort invested in the service delivery processes, typically observable only by the contracting parties and not verifiable by third parties such as courts and mediators, makes dispute resolution in IT outsourcing particularly challenging (Grossman and Hart 1986, Hart 1995). Resolving disputes through legal processes is therefore considered time-consuming and complicated (Thornburg 2000). The involved and often very long trial processes relative to the pace of technology may allow the IP abusers to develop next generation products on the existing technology and ultimately overtake the IP holders as the industry leader 9. Therefore, in an attempt to control litigation costs, many companies seek to employ alternative dispute resolution mechanisms, such as through mediation and arbitration processes to avoid lawsuits. Therefore, H1c: Asset specificity will be positively associated with dispute resolution provisions The association between asset specificity and contingency provisions which generally confer more flexibility in modification and termination of the contract is not very straightforward. Prior literature in marketing and alliance contracts (Heide and John 1992, Parkhe 1993) suggest that firms become committed to making the existing relationship succeed to protect their idiosyncratic investments in the alliance. Hence, firms are more willing to be flexible in terms of modifying the agreement rather than causing it to fail by being unwilling to adjust. However, a more recent study by Young-Ybarra and Wiersema (1999) on IT alliance proposed an opposite association between these two constructs. They found that both intangible and tangible asset specificity have a detrimental impact on the strategic flexibility of the IT alliance relationship. The reason behind that rigidity is that the requirement to create specific or idiosyncratic technologies that cannot be put to productive use in other contexts tend to make the contracting parties less amenable to periodic changes in specifications and especially more resistant to provisions that guarantee termination of contract, it began to develop its own competing software. (see Point Solutions Limited v. Focus Business Solutions Limited.(2005) Case number HC 04C 03870, High Court of Justice Chancer Y Division, U. K.) 9 Overcoming the legal behemoth: The urgent need for a private legal system in electronics, Imparato (1999)

15 the contract at will. Contracting parties therefore are likely to seek protection of their asset specific investments by disallowing such contractual guarantees for modification or exit flexibility. Given the contrasting viewpoints, we leave this open to empirical examination by specifying alternate forms of hypotheses H1d(i): Asset specificity will be positively associated with contingency provisions H1d(ii): Asset specificity will be negatively associated with contingency provisions Given the lack of specificity about the association between asset specificity and contingency provisions, its relationship to overall contract extensiveness is also unclear and we therefore leave it open to empirical assessment. Process interdependence Process interdependence in IT outsourcing occurs when clients need to integrate their business processes and systems with those of the vendor company. For example, if a vendor manages the billing systems for an online company in real time, then the software for bill presentment, verification, and payment have to be integrated with the client s order management systems. This kind of interdependence is well exemplified in the outsourcing relationship between Independent Bank Corp. and Fidelity Information Services, Inc., where Fidelity s proprietary HORIZON Banking system had to be integrated to work with Independent and its affiliates applications to support the full range of loan, deposit, and investment management products and services to businesses and individuals. In general, we expect process interdependencies between the client and the vendor to create more risks for the clients such as risks due to switching costs, and loss of internal control over critical aspects of their operations Process interdependencies also creates risks for the vendors as they are more likely to be associated with specialized investments that may be hard to deploy elsewhere without retraining (in the case of human assets) or reconfiguring (in the case of technological assets). Thus, our expectation is that, similar to task complexity, process interdependence will result in more extensive contracts through increased use of all contractual dimensions to mitigate the risks. Therefore, 15

16 H2[a-d]: Process interdependence will be positively associated with monitoring, property right, dispute resolution and contingency provisions H2e: Process interdependence will be positively associated with overall contract extensiveness Prior Interaction It is generally believed that prior interaction between the parties will lead to better mutual understanding of each others processes and norms, which may in turn encourage further contracting (Heide and Miner 1992). Generally two elements of partner specific interactions have been studied in the literature: trust and learning and both are expected to be positively influenced by successful prior interactions between the vendor and the client. However, the effects of these two factors on the level of contract specifications have been posited to be opposite of each other. It has been argued that prior interactions provide some degree of mutual trust and therefore reduce the need for devoting attention to crafting extensive or highly detailed contracts (Dyer and Singh 1998, Gulati 1995, Ring and Van de Ven 1994). However, more recent theoretical and empirical work in contract structures notes that prior interactions increase the opportunities for contracting parties to become more familiar with the other party s business processes, decision making styles and corporate cultures, and thus improve overall ability to draft more specific contracts (Mayer and Argyres 2004, Poppo and Zenger 2002, Ryall and Sampson 2003). Parties are thus more likely to learn mutual needs and therefore be able to describe the tasks and associated monitoring requirements such as ongoing review, staffing, and benchmark provisions with greater precision. Therefore, H3a: Prior interactions will be positively associated with monitoring provisions A history of prior interactions is also likely to have enabled partners to learn more effective strategies for dispute resolution and therefore evolve built-in provisions for handling such disputes through a streamlined process specified in the contracts, rather than leave it unspecified or be resolved ex post through litigation. The partner specific learning will help firms codify best practices for dispute resolution and arbitration mechanisms and rather than allow these to remain unspecified, firms are more likely to encode these provisions in the contract arrangement. In a similar vein, contracting parties are also likely to learn 16

17 more about issues that might arise due to intellectual property protections, third-party obligations toward data protection and therefore be able to write better contractual safeguards against such risks. Thus, H3b: Prior interactions will be positively associated with property rights provisions H3c: Prior interactions will be positively associated with dispute resolution provisions Contingency planning provisions rely heavily on the contracting parties abilities to foresee situations that might need adjustments and stipulate corresponding actions accordingly. While prior interactions might increase the ability of the parties to better anticipate the need for contingency provisions, it is also plausible that with prior interactions, parties have evolved other informal (non-contractual) mechanisms to resolve unanticipated contingences thereby causing them to actually make fewer stipulations in the contract, given that it is costly to identify contingencies ex ante. We therefore leave this open to empirical examination by specifying alternate forms of the relationship between prior interactions and contingency planning. H3d(i) Prior interactions will be positively associated with contingency provisions H3d(ii): Prior interactions will be negatively associated with contingency provisions Overall, given our preceding arguments the effect of prior interactions on overall contract extensiveness is unclear and we therefore leave it open to empirical assessment. 4 Empirical Analysis We supplemented the data coded from the contracts (please see Section 2) with additional data about the contracting activity and the contracting parties from numerous sources, including 10-K reports, industry reports, industry-specific articles reporting outsourcing deals, Factiva database, and Dow Jones Interactive database. In their 10-K reports to SEC, firms explained the context of their outsourcing decisions and their reasons for choosing a specific vendor. The 10-K reports also disclosed information about prior contractual relationships, if it existed, with the same vendor and the client s perception of the vendor s capability in providing the outsourced functions. We also searched the Factiva and Dow Jones Interactive databases on the company names to collect news and press release related to the outsourcing deal. These documents 17

18 allowed us to code the explanatory (independent) variables for examining contract structures. These included the client s prior interaction with the same vendor, use of customized technology in the outsourcing deal, and the process level interdependence of the outsourced activities. Additionally we collected data on several variables that were introduced as controls in our empirical model. These included firm size of the vendor and client firms measured as the log of average sales over the past 3-year period before contract date respectively, contract duration, a measure based on deviation of earnings forecasts over the past 3-year period before signing the contract as our proxy for environmental dynamism, and four task dummies for measuring complexity of the outsourced tasks,. The sales and earnings data were obtained from the I/B/E/S and COMPUSTAT databases. Table 3 provides detailed descriptions for variables and measurements and Table 4 provides summary statistics and correlation matrix for the variables. About 41% of the contracts were between parties that had prior relationships and 47% incorporated the customized technologies. The mean contract term (duration) was 3 years and 77% of our sample represented contracts signed after [Table 3 and 4 here] Model Estimation and Results We ran separate models for the four dependent variables reflecting the contract dimensions of monitoring, dispute resolution, property rights, and contingency provisions, and an overall model of contract extensiveness. In order to avoid potential problems due to heteroskedasticity, we first ensured that there were no repeating firms (both vendors and clients) in the sample. The explanatory variables in our model were proxies for asset specificity, process interdependence, and prior interactions and the control variables included in the model were environmental dynamism, vendor size, client size, contract duration, and task complexity. Since our sample time frame extended from 1993 to 2003, we also added a time dummy to capture other unaccounted time-period effects by using 1997, the midpoint of our sampling time frame, as a cut-off point. We ran the analysis using the full sample which included an additional dummy to control for pricing terms fixed price or time & materials and also separate sub-sample analysis for these two types of contracts separately. The basic structure of the different models which test the factors associated with the use of the major contractual provisions is as follows: 18

19 Contractual[Provision] = β + β Asset Specificity + β Process Interdependence + β Prior Interaction β Environmental Dynamism + β log(vendor Size) + β log(client Size) β log(contract Duration) + β Task Dummy 1+ β Task Dummy β Task Dummy 3 + β Task Dummy 4 + β Time Dummy + β Pricing Dummy + ε The results of the OLS analysis are captured in Table 5 (panel A = full sample; panel B = fixed price contracts and panel C= time & materials contracts) [Table 5 here] Overall, the results obtained provide very good support for the hypotheses. Across all models for the combined sample, the R-squared values range from a high of 0.76 (Monitoring) to a low of 0.34 (property rights) and 0.74 for overall contract extensiveness. Among the controls, contract duration, the task dummies representing software development, network/telecommunication management and multi-function outsourcing, and price dummy representing T&M contracts were significant in explaining overall contract extensiveness, while client size and time dummy were significant for some discrete provisions. As posited, we find asset specificity to be positively associated with property rights provisions and dispute resolution, and although it is negatively associated to contingency provisions as hypothesized, it is non-significant. Overall, asset specificity has no discernable relationship to contract extensiveness, as it increases the use of some provisions while decreasing others such as contingency planning. Process interdependence was predicted to positively impact all contractual specifications and the data provide very good support for this hypothesis. Finally, in line with our expectations, prior interaction was positively associated with monitoring, dispute resolution, and contract extensiveness. However, we did not find it to be positively associated to property rights. We had made no predictions about its association to contingency provisions, but found it to be positive and significant in the results. The sub-sample analyses revealed some interesting differences between fixed-price and time & materials contracts. Overall, our model was a better predictor of contract extensiveness in TM contracts (R 2 = 0.74) when compared to FP contracts (R 2 = 0.61). While asset specificity showed no relationship to contract extensiveness in the full sample, the sub-sample analysis shows it to be positive and significant for TM but not for FP contracts. 19

20 Robustness Checks Before we discuss the implications of the results obtained, we mention a number of additional robustness checks that were carried out to confirm the validity of the results obtained in the OLS estimations. First, we note that we included several control variables in the model to address potential omitted-variable biases. We also conducted the OLS regressions in a hierarchical manner that included the full sample (which included a control for pricing terms) as well as the FT and TM sub-samples to account for outsourcing risks that are already captured by the pricing arrangements. Thus our models seek to explain the residual risks over and beyond those captured by the pricing arrangement. As our study is built on variables obtained through the coding of contracts, it is conceivable that the results obtained are sensitive to coding standards. First, in our main empirical model, we had coded contract time as a binary measure. It is possible that ITO contract structures may be influenced by the events that dramatically changed the IT industry in these 10 years, e.g., Y2K, the dot com bubble and subsequent burst, etc. Therefore, we used three time dummies, dividing the total period into four different periods of outsourcing activities: , , , and However, none of the three time dummies was significant across the five models. Second, we tested for the sensitivity of the contract provisions since the assignment of clauses to provisions was done manually by the coders. We dropped two items (auditing and periodic review) that appear in more than 80% of the sample and ran the OLS regression. We find that the results still hold thereby validating the robustness of the coding scheme. These results are shown in Appendix Table C & Table D. Next, we addressed the potential concern of using OLS when our dependent variables are all ordinal data. Although we treat the contract dimensions and contract extensiveness as continuous variables, we also tested the model using ordered probit, which is a more robust estimation technique for ordered variables (Crocke and Reynolds 1993). The corresponding results from the ordered probit estimations are provided in Appendix Table E and show remarkable consistency with the OLS results obtained earlier. In the case of constructs that were measured on a less than 5- point scale, (i.e., property rights, dispute resolution, and 20

21 contingency), we also used multinomial logit model to estimate them. The estimation results were once again found to be consistent with the OLS and ordered probit results (Appendix Table F). Another potential concern with the results obtained stem from the possibility of endogeneity. One candidate for endogeneity would appear to be asset specificity since the decision to develop or use customized systems or technologies in the outsourcing arrangement represents a choice variable under the control of the parties. The decision to source a customized technology is made well in advance of the contract negotiations that determine the contract structure. Firms may select to buy customized technologies based on unobserved characteristics associated with the firms or transaction. Failure to address this selection process leads to the possibility that variances of contract structure may be incorrectly attributed to outsourcing asset specificity, when they are in fact due to unobserved factors associated with the asset specificity. To correct for these potential biases we use a two-stage econometric technique suggested by (Heckman 1979, Shaver 1998). In the first step, we split the sample into two subsamples: contracts that specified development or use of customized technology and those that used generic or non-customized technologies. Following Shaver (1998), we estimate a probit model using maximum likelihood to assess the effects of task complexity, process interdependence, prior interactions, as well as other controls, which might be expected to influence the decision of firms to use customized technologies. The estimates from this firststage model were used to calculate a control variable lambda. Endogeniety is corrected for by creating a control variable lambda using estimates obtained from the first stage. Lambda ( λ ), or the inverse Mill s ratio, is calculated as ˆ λ φ( ˆ γ ω ) / Φ( ˆ γ ω ) i = if Asset Specificity =1, and ˆ λ φγω ( ˆ )/( 1 ( ˆ γω) ) i i i Asset Specificity=0, where φ is the standard normal density function, ω i and i i = Φ if i i i i i γˆ i are the vector of independent variables and coefficients from the first stage probit model, and Φ is the standard normal distribution function (Greene 2003). In the second stage, the contractual provisions and contract extensiveness were estimated as a function of their appropriate determinants and via the inclusion of lambda as an additional predictor variable (Shaver 1998). Once again, the OLS results with the inclusion of the instrument variable were consistent with the results obtained earlier. Lambdas are not significant in all four 21