Service Coproduction with Information Stickiness and Incomplete Contracts: Implications for Consulting Services Design

PRODUCTION AND OPERATIONS MANAGEMENTVol. 17, No. 3, May–June 2008, pp. 357–372issn 1059-1478 �eissn 1937-5956 �08 �1703 �0357

POMSdoi 10.3401/poms.1080.0024

©2008 Production and Operations Management Society

Service Coproduction with InformationStickiness and Incomplete Contracts:

Implications for Consulting Services Design

Mei Xue, Joy M. FieldOperations and Strategic Management, Carroll School of Management, Boston College,

Chestnut Hill, Massachusetts 02467 {[email protected], [email protected]}

Drawing from three theoretical bases—“information stickiness” from the knowledge management literature,“service coproduction” from the service operations management literature, and “incomplete contract the-

ory” from the transaction cost economics literature—we discuss a theoretical framework and develop modelsto study the efficiency of the service coproduction process in a knowledge-intensive consulting environment.We apply, refine, and interpret these theories to determine how work should be allocated between the consul-tant and the client and the corresponding pricing under different contractual relationships that occur in thisindustry. We find that, with a pricing schedule that relates the fee adjustment to the self-service level and oneparty’s ownership of the residual right to specify the workload allocation, the client underinvests her effortsin the service coproduction process, whereas the consultant overinvests his efforts, resulting in an inefficientprocess. In addition, to improve overall process efficiency, we show that the more productive party should ownthe residual right to respecify the self-service level when the final service need emerges. Our results, as wellas interview data from experienced consultants, provide insights into the causes of inefficient service deliveryprocesses and offer direction for achieving better efficiency through contract design and pricing schedules.

Key words : information stickiness; service coproduction; incomplete contract theory; consulting services;service process design

History : Received: September 2005; Revised: July 2006 and December 2006; Accepted: December 2006 byRoger Schroeder and Kalyan Singhal.

1. IntroductionKnowledge-intensive services, such as managerialand technology consulting, constitute an increas-ingly significant sector of the U.S. economy, withan estimated 30% of the workforce engaged inknowledge work and growing (Hayes et al. 2005).However, achieving high productivity and efficiencylevels has been viewed as particularly difficult forknowledge-intensive services, because their opera-tions often require high service product customiza-tion, significant customer involvement, and looselystructured service delivery processes (Bettencourtet al. 2002). In fact, Drucker (1999, p. 92) identifiesknowledge-worker productivity as “the biggest of the21st century management challenges.” Consideringthe unique features and challenges of knowledge-intensive services and customers who are demand-ing both cost-effectiveness and high-quality solutions,companies in knowledge-intensive service industriesare in critical need of better management tools toimprove their performance (Bettencourt et al. 2002,Davenport et al. 2002, Drucker 1999).

To date, academic research has generated limitedguidance in this regard. Thus, in the current study,

we focus on the optimal design of the service “copro-duction process” (i.e., the joint efforts of the ser-vice provider and customer to create and deliverthe service product) for consulting, an important andlittle-researched knowledge-intensive service industry(David and Strang 2006), to maximize process effi-ciency. In particular, we focus on the determinationof two key variables: (1) the self-service level, whichcaptures the workload allocation between the consult-ing firm (referred to as “the consultant” hereafter) andthe client company (referred to as “the client” here-after), and (2) pricing, both of which are critical toprocess efficiency, service value, and profit. Moreover,we investigate how these variables are determinedunder two different contractual relationships betweenthe consultant and the client and their implicationsfor managerial decision making.

We draw on three theories—“information sticki-ness” from the knowledge management literature,“service coproduction” from the service operationsmanagement literature, and “incomplete contract the-ory” from the transaction cost economics literature—as a basis for our model. These theories directlyaddress important characteristics of consulting ser-vices and have major implications for the design of

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the coproduction process. First, the primary prod-uct of most consulting services is knowledge, and,thus, the transfer and processing of knowledge canbe considered the main consulting process activities(Sarvary 1999, Skjølsvik et al. 2007). The informationstickiness literature suggests that knowledge trans-fers between two parties can be difficult and costly,i.e., “sticky” (Szulanski 2000; von Hippel 1994, 1998),which directly affects where and by whom workshould be done (i.e., by the consultant or by theclient). In addition, unlike much previous researchthat assumes that knowledge transfers are costless andinstantaneous, Szulanski and Cappetta (2003, p. 527)find that “� � � stickiness is pervasive, � � � stickiness is therule rather than the exception,” indicating that infor-mation stickiness is or should be a key considerationin the design of consulting services.

Second, Bettencourt et al. (2002, p. 101) describeknowledge-intensive services as having a servicedelivery process that is “complex, unstructured, andhighly customized to meet a particular client’s uniqueneeds”; to achieve a successful outcome, clients musttake an active role as coproducers of the knowledge-based solution. Thus, the design of the coproduc-tion process is a particularly important considerationfor such services (Bettencourt et al. 2002, Skjølsviket al. 2007). Karmarkar and Pitbladdo (1995) discussthe importance of understanding how a customer’sengagement in the service delivery process influencesthe design of the process and, in turn, the competitionin the market, and they call for explicit modeling ofservice coproduction, as is done in this study.

Third, in consulting practice, it is difficult to com-pletely specify the terms of the consulting engagementa priori. In particular, consultants and clients mustagree on the “expectations and scope of the consulta-tion, an agreement that is often negotiated and renego-tiated over the life of the project” (Jacobson et al. 2005,p. 315). Thus, the allocation of work in the coproduc-tion process is often left incompletely specified at thetime the relationship between the client and the con-sultant is established or the service contract is signed.Often the allocation decision is made (or renegotiated)after the service need emerges. Incomplete contracttheory addresses the question of the consequences ofhaving an “incomplete” contract, which results fromthe unwillingness or inability to specify certain con-tingencies in the original contract, leaving them opento renegotiation. One of the central issues of man-agerial and economic significance is how the owner-ship of the residual right to later specify an action ordisposition of property would influence a subsequentoutcome, e.g., management efficiency or profit (Gross-man and Hart 1986). Furthermore, the concept of the“Arrow core” in replication strategy, which refers to“the ideal informational endowment for a replicator

of a particular business model,” is arguably a type ofincomplete contract, insomuch as its precise contentscannot be determined in advance and are more fullyunderstood only through learning as the replicationprocess unfolds (Winter and Szulanski 2001, p. 733).Winter and Szulanski (2001) argue that the knowledgecontent of the Arrow core tends to be complex andcausally ambiguous, with knowledge transfers beingcostly and difficult (i.e., sticky). Thus, this work sug-gests that an incomplete contract itself may be a sourceof information stickiness.

The contribution of our study follows from therecognition that the self-service level has substantialconsequences for both the consultant and the client.The consultant’s cost of providing employee serviceversus self-service and the client’s time and effortrequired for self-service versus consultant service dif-fer significantly. These costs involve trade-offs forboth the consultant and client, and we find that theoptimal self-service level depends on the types andrelative magnitudes of information stickiness costsas well as with whom the residual right resides torespecify how the workload will be divided. How-ever, to apply information stickiness theory for thepurposes of this study, we need to further refinethe theory and interpret both information stickinesstheory and incomplete contract theory to be usefulin a service coproduction context. To do this, wefirst review the related literature that forms the the-oretical framework for this study and then discussrefinements and interpretations for application to thequestion of how the coproduction process for a con-sulting service should be designed.

After the literature review, we develop a modelbased on information stickiness theory as applied toa consulting environment to measure the efficiency ofthe service delivery process. We then explore the issueof the optimal residence of the residual right to respec-ify the distribution of the workload between the con-sultant and the customer in an incomplete service con-tract. Within the context of a Stackelberg equilibrium,we study two incomplete contracts in which the resid-ual rights reside with different parties (i.e., the consul-tant or the client) to determine how the choices of self-service level and price will be made and what the dis-tortions due to the ownership of the residual right are.We also investigate how information stickiness influ-ences the determination of the self-service level andpricing under the two contract forms. The managerialimplications of the results are illustrated based on fieldinterviews with experienced consultants.

2. Theoretical BackgroundMany studies in the operations management andmarketing literature have attempted to understandhow and when customer participation should be

Xue and Field: Service Coproduction with Information Stickiness and Incomplete ContractsProduction and Operations Management 17(3), pp. 357–372, © 2008 Production and Operations Management Society 359

incorporated in the design and management of ser-vices. These studies are often motivated by the desireto improve performance along one or more dimen-sions, with efficiency, productivity, quality, and cus-tomer satisfaction being often-cited dimensions ofinterest (Bateson 2002, Bitner et al. 1997, Chase 1981,Goodwin and Radford 1993, Kellogg and Nie 1995,Lengnick-Hall 1996, Metters and Vargas 2000, Xue andHarker 2002). Because of the involvement of both theservice provider and customer in the service deliveryprocess, the service product is the result of a copro-duction or cocreation process (Bettencourt et al. 2002,Bitner et al. 1997, Lengnick-Hall 1996, Mills and Morris1986, Xue and Harker 2002). In such a process, “cus-tomers have essential production roles that, if not ful-filled, will affect the nature of the service outcome”(Bitner et al. 1997, p. 195), with this being particularlysalient to the coproduction process in knowledge-intensive industries (Bettencourt et al. 2002, Skjølsviket al. 2007). In general, we refer to the proportion ofthe service produced by the client as the “self-servicelevel.”

According to the customer contact model, customerparticipation in the service delivery process createsinefficiencies due to variability in customers and ininteractions between the service provider and cus-tomers (Chase 1981, Chase and Tansik 1983, Kelloggand Chase 1995, Mersha 1990). Xue and Harker (2002)propose the concept of “customer efficiency” to cap-ture the most relevant characteristics of a customeras a coproducer, i.e., a customer’s capability and skillin participating in service coproduction, which is inparallel to the concept of employee labor productiv-ity. With the empirical studies of retail and bankingindustries, respectively, Xue and Harker (2002) andXue et al. (2007) show that customer efficiency variessignificantly among customers, and such variationsconstitute a source of differences in the outcomes ofthe service delivery processes and, consequently, vari-ations in customer service demand. Thus, greater cus-tomer participation (i.e., a higher self-service level)makes achieving an efficient service delivery pro-cess more challenging. However, Lovelock and Young(1979) suggest that customers’ participation can helpa firm to increase its productivity with the appro-priate design of self-service interfaces. In this studywe examine how customer participation through self-service is set to achieve an efficient service design.

The service coproduction literature consists mostlyof exploratory and empirical studies, with few ana-lytical models. Yet Karmarkar and Pitbladdo (1995)argue that to simply adapt analytical models built ina manufacturing context to a service environment canbe misleading, because some of the distinctive fea-tures of services, such as the direct involvement and

participation by customers in the service coproduc-tion process, are not incorporated in those models andneed to be modeled directly. The service coproduc-tion model developed in the current paper contributesto filling this analytical modeling gap in the serviceoperations literature. In addition, Cachon and Harker(2002) note that one of the promising directions ofnew research is to study how firm-controlled factorsinfluence a customer’s coproduction cost, e.g., a firm’sdesign effort and the number of tasks customers per-form, which are issues addressed in this study.

In interactions between the service provider andcustomer, and in knowledge-intensive services in par-ticular, key activities in the service delivery processinvolve the acquisition, transfer, and use of informa-tion, suggesting the importance of effectively man-aging information exchanges (Bettencourt et al. 2002,Ferdows 2006, Hoffman and Novak 1996, Mills andTurk 1986, Skjølsvik et al. 2007). However, currentresearch suggests that the conveyance of informationbetween parties is often difficult and costly—in con-trast to some previous studies that assume it is easyand costless (Szulanski 2000). In the context of techni-cal problem solving, von Hippel (1994, p. 429) coinedthe term “sticky information” and defines it as infor-mation that is “costly to acquire, transfer, and use ina new location.” More specifically, von Hippel (1994,1998) and von Hippel and Katz (2002) define the stick-iness of a given unit of information in a given instanceas “the incremental expenditure required to trans-fer that unit of information to a specified locus in aform usable by a given information seeker.” Informa-tion stickiness-related expenditures may result fromdelays in the conveyance or receipt of the informa-tion as well as explicit charges by the informationprovider for access to the information (von Hippel1994). Researchers have searched for the causes ofinformation stickiness and have examined the impli-cations of information stickiness for organizationaldesign and supply chain management (i.e., the allo-cation of activities within and between organizations)in a variety of contexts. For example, for customerproblem-solving work, von Hippel (1994) finds that:(1) when sticky information is held at one site only,the locus of problem solving will shift to that site;(2) problem solving may iterate among multiple siteswhen sticky information is dispersed; (3) with highiteration costs, the task may be subpartitioned amongmultiple sites; and (4) in certain cases, investments toreduce stickiness may be made at some locations. Inaddition, researchers have examined intrafirm impli-cations of information stickiness in the context of prob-lem solving using new production equipment, infor-mation embedded in upstream process outputs, andthe transfer of “best practices” (King 1999, Szulanski1996, Tyre and von Hippel 1997).


Much of the empirical work related to informa-tion stickiness is primarily focused on understandingthe sources of information stickiness or, more broadly,the facilitators of and barriers to knowledge transfers,either considering a project as a whole or throughoutstages of knowledge transfer (Haas and Hansen 2005,Jensen and Szulanski 2004, Ko et al. 2005, Szulanki1996, Szulanski and Cappetta 2003, Szulanski et al.2004). For example, in a study of impediments to theintrafirm transfer of best practices, Szulanski (1996)finds that the three most important sources of infor-mation stickiness are the recipient’s lack of absorptivecapacity, the recipient’s lack of knowledge of or uncer-tainty about cause-and-effect relationships in similarand/or new contexts, and a laborious and/or dis-tant relationship between the source and recipient ofinformation. In a study of enterprise resource plan-ning (ERP) consulting, Ko et al. (2005) obtain simi-lar results, with the addition that motivational factorsfor both the consultant and client strongly influencethe effectiveness of knowledge transfer. Notably, theyfind that more than 58% of the variance in knowl-edge transfer is explained by these sources of infor-mation stickiness (e.g., recipient’s lack of absorptivecapacity, motivational factors), which supports the rel-evance of information stickiness in the consulting pro-cess. In addition, Szulanski (2000) explores predictorsof information stickiness in four successive stages ofinformation transfer, from initiation to integration. Hefinds that, in terms of predicting difficulty in transfer,factors affecting the opportunity to transfer are moreimportant in early stages, whereas factors affectingthe execution of transfer are more important in laterstages. These factors create delays in the conveyanceand/or receipt of information and require additionalexpenditures than would be needed in their absence.

These findings are suggestive of strategies for reduc-ing information stickiness. For example, Bettencourtet al. (2002) discuss seven categories of client roleresponsibilities in knowledge-intensive services thatare essential for effective client coproduction of aknowledge-based service solution. However, Haasand Hansen (2005, 2007) note that the implicit as-sumption behind research that focuses exclusivelyon the impediments to knowledge transfer is thatreduced barriers and increased knowledge flows willbe beneficial for organizations. They find that this isnot necessarily so (e.g., management consulting teamsthat relied on using codified knowledge in their salesproposals actually had a lower probability of winningproject bids because it reduced the distinctivenessof proposals). Thus, they suggest that more researchshould focus on the net effects of knowledge flows onperformance, which is an aim of our study.

For information exchanges between a consultantand client, typically both parties may possess sticky

information, with different costs associated withconsultant-to-client versus client-to-consultant infor-mation stickiness. Although von Hippel (1994, p. 430)acknowledges that information stickiness may stemfrom a variety of causes, and subsequent researchhas confirmed this, he states that “the purpose ofbeing inclusive with respect to causes of informationstickiness in [his] definition is to allow us to focuson the impact of information stickiness independentof cause.” However, Szulanski and Cappetta (2003,p. 528) write that, “when it comes to manage sticki-ness, such distinctions of cause are significant.” Simi-larly, when attempting to understand the implicationsof information stickiness for knowledge-intensive ser-vice design, aggregating the costs associated with dif-ferent causes of information stickiness into one expen-diture neglects any interdependencies and relation-ships among the components of this expenditure stem-ming from different sources of information stickiness.For example, the knowledge transfer literature distin-guishes between the movement or transfer of informa-tion and its application or processing by a recipient(Haas and Hansen 2005, Haines and Goodhue 2003,Ko et al. 2005). Different sources of information sticki-ness, as previously discussed, may incur costs relatedto the information transfer or processing or both, andthese costs can change over time as the transfer pro-cess evolves (Szulanski 2000, Szulanski and Cappetta2003). Furthermore, in ERP implementation consult-ing, the cost to transfer information from the con-sultant to the client may be high (Haines and Good-hue 2003), but “one objective for business clients is toacquire ERP-related knowledge so that they can main-tain and operate the systems independent of the con-sultants” (Ko et al. 2005, p. 77), suggesting that therelationship between transfer and processing costs isimportant. In addition, because consulting involves acoproduction process, iterating between the consul-tant and client or task partitioning, as suggested byvon Hippel (1994), often will not be possible becauseboth assume an ability to decouple the efforts of thetwo parties.

Thus, in our study we contribute to the devel-opment of information stickiness theory and itsrelationship to service operations design in a knowl-edge-intensive coproduction environment as follows:First, we disaggregate the unit information stick-iness expenditure into two components—the costto transfer and the cost to process information—tounderstand the interrelationships between these twocomponents of information stickiness costs. Second,we jointly consider the performance implications ofconsultant-to-client and client-to-consultant informa-tion stickiness. To date, the research on informa-tion stickiness and service design has been primar-ily based on limited cases and is largely exploratory(von Hippel 1994, 1998; von Hippel and Katz 2002).


By disaggregating information stickiness costs anddeveloping a rigorous analytical model, both thestructure of the model itself and the findings from themodel advance information stickiness theory, espe-cially in terms of its performance implications for con-sulting services. In addition, our model also allowsfor reevaluation of the coproduction decision overtime as the transfer and processing costs change due,for example, to subsequent consulting engagementswith the same client decreasing barriers to knowledgetransfer (e.g., tacit knowledge of the client’s busi-ness domain and operating routines through repeatedinteractions) (Ethiraj et al. 2005).

We further contribute to theory development inthe context of a knowledge-intensive coproductionenvironment by simultaneously considering that, asa complex, unstructured, and highly customized ser-vice, the terms of the consulting engagement aredifficult to completely specify upfront and are typi-cally renegotiated over the course of the engagement.Thus, we incorporate incomplete contract theory,a type of transaction cost economics theory (Coase1988, Williamson 1989), to address the question ofwhich party should have the residual right to spec-ify the actions that should be taken or how theresources should be used when an unforeseen con-tingency arises, with the residual (controlling) rightassigned or acknowledged at the time the relation-ship is established or the contract is originally signed(Grossman and Hart 1986, Hart 1995, Hart and Moore1988). More generally, the theory recognizes that con-tracts may be left “incomplete” because in some casesthe cost of fully specifying all contingencies a prioricould exceed the benefits. This may occur because ofthe parties’ unwillingness to incur the high (transac-tion) costs of completing the contract with all contin-gencies specified or the inability to fully specify allcontingencies because of uncertainty. In such cases,an incomplete contract with a residual right to decidehow certain “property” (i.e., resources) should beused when an unforeseen contingency arises is signed(Salanie 1997). Over the course of the relationship, ifsuch an unforeseen contingency arises, renegotiationoccurs. During renegotiation, incomplete contract the-ory states that the party who owns the residual rightoften gets an upper hand in the renegotiation or thedistribution of the income stream coming from theunforeseen contingency (Salanie 1997).

In the context of a consulting contract, one ques-tion of interest is whether the residual right to spec-ify how the workload will be divided between thetwo parties should be left to the client or the con-sultant whenever a specific service request emerges.Incomplete contract theory predicts that the owner-ship of the residual right to certain assets or to spec-ify actions should be decided based on its impact on

productivity or efficiency. For example, in a firm–firmrelationship context, it suggests that Firm 1 shouldpurchase Firm 2 and therefore get the ownership tothe actions of Firm 2 if the increase in productiv-ity under its management exceeds the decrease inproductivity of Firm 2 due to the loss of controlof its assets. In particular, when there is an invest-ment to be made prior to the residual right beingexercised, the ownership of the residual right hasdirect influence on whether the parties will overin-vest or underinvest. In the context of the consultingservices considered in this study, we define “overin-vest” to mean undertaking more information process-ing/application work (which is considered the mostcrucial and value-adding portion of the consultingservice) than what is required by the socially optimalsolution that maximizes the overall process efficiency.That is, if the self-service level, which is defined as theproportion of the information processing tasks com-pleted by the client, is higher than the socially optimallevel, the client overinvests and the consultant under-invests; similarly, if the self-service level is lower thanthe socially optimal level, the client underinvests andthe consultant overinvests.

Our contribution in this respect is the novel appli-cation of incomplete contract theory in the consult-ing environment that features knowledge transfer andservice coproduction, thereby enriching and broaden-ing the theoretical foundations of service operationsmanagement research. The study results lend supportfor the previous literature on the effect of the res-idence of the residual right on outcomes when anunspecified contingency arises and provide findingsuseful for the ongoing discussion about the relation-ship between contract incompleteness and underin-vestment in this area (Salanie 1997, pp. 184–187).

In addition, following the concept of the Arrow corethat relates characteristics of an incomplete contractto information stickiness (i.e., the inability to deter-mine the precise content of the Arrow core in advancedue to the complex and causally ambiguous natureof the associated knowledge, resulting in costly anddifficult knowledge transfers), we identify and modelcontract incompleteness as a relevant and yet unex-plored source of information stickiness (Winter andSzulanski 2001). By doing so, we further extend theapplication of incomplete contract theory to the fieldsof knowledge management and knowledge-intensiveservice operations management, in particular.

3. Conceptual FrameworkBuilt on work in service coproduction from the serviceoperations management literature and in consultingservices from the knowledge management literature,we consider the operational process of a consultingservice as consisting of two types of key processes:information transfer and information processing or


application (Bettencourt et al. 2002, Haas and Hansen2005, Jacobson et al. 2005, Ko et al. 2005, Mills andTurk 1986). The underlying assumption is that boththe client and the consultant possess (i.e., own) a cer-tain amount of information needed for the completionof the service delivery process, which is not sharedcommon knowledge, and thus needs to be transferredfrom one party to the other whenever the informationis to be processed by the party other than the informa-tion owner (Bettencourt et al. 2002, Haines and Good-hue 2003, von Hippel 1994).

Thus, by focusing on information transfer and pro-cessing, we consider a knowledge-intensive servicecoproduction process such as consulting as consist-ing of the following four types of basic activities:(1) client-to-consultant information transfer: the clienttransfers information to the consultant; (2) consultant-to-client information transfer: the consultant trans-fers information to the client; (3) client informa-tion processing: the client processes acquired and/orself-owned information; and (4) consultant process-ing information: the consultant processes acquiredand/or self-owned information. Note that the fourtypes of activities are not necessarily sequential, andsome of them can take place simultaneously. Also, anactual service coproduction process may include mul-tiple rounds of information transfer and processing.The separation of client and consultant activities inthe classification provides the basic structural unitsfor a complex consulting operational process, includ-ing multiple rounds of interactions involving infor-mation transfer and processing by both parties work-ing either separately or together.

In the context of consulting and similar knowledge-intensive services, information processing is usuallydeemed as the core value-adding production activ-ity (e.g., research for and write-up of a market-ing campaign strategy for a new product), whereasinformation transfer is necessary and important forthe completion of the project (e.g., providing back-ground information about the new product, specify-ing desired goals for the campaign, communicating inregard to the implementation of the strategy).

Based on this framework, we define the self-servicelevel �, 0 ≤ � ≤ 1, as the proportion of the informa-tion processing tasks to be completed by the client.The information involved includes both common andprivately owned information by the two parties thatneeds to be processed for the completion of the ser-vice. Accordingly, the proportion of the informationprocessing tasks to be completed by the consultant is(1−�). Once � is determined, the client transfers infor-mation to and/or acquires information from the con-sultant. Likewise, the consultant then transfers infor-mation to and/or acquires information from the client.

Based on the information stickiness literature, firmsincur expenses due to both information transfers andinformation processing (Szulanski 1996, 2000; vonHippel 1994, 1998). Thus, we use the total expensefor information transfer and processing as the mea-sure of process efficiency in the current study basedon the idea that the more efficient a process is, thefewer resources it consumes. In the context of con-sulting, we assume that the expense for informationtransfer mainly includes the cost for information col-lection, preparation, and delivery and is primarilyundertaken by the information provider, whereas theexpense for information processing mainly includesthe cost for information acquisition, absorption, anal-ysis, and implementation and is primarily undertakenby the information receiver (Haas and Hansen 2005,2007; Hansen and Haas 2001).

Thus, assume a client’s expense or resources con-tributed to the service coproduction process to bea function of the self-service level: z�� = zt�� +zp��, where zt�� is the client’s expense for infor-mation transfer and zp�� is the client’s expense forinformation processing. Similarly, we have the con-sultant’s expense/resource function as r�� = rt��+rp��, where rt�� is the consultant’s expense for infor-mation transfer and rp�� is the consultant’s expensefor information processing. Assume zt��, zp��, rt��,and rp�� are continuous functions of �� ∈ 01�.

4. Analytical ModelsThe problem we consider is as follows: a consultantand client sign a contract at time T0, under which theconsultant is obligated to meet a service need uponrequest from the client that emerges at time T , whereT0 ≤ T ≤ T1, for which the client pays the consultant afee for the service. T1 is the end of the period duringwhich the consultant is obligated to provide a unit ofservice to the client upon request. Assume that thepricing policy consists of a linear relationship betweenthe fee adjustment rate and the self-service level:

f ��h�= f0 −h� (1)

where f ��h� is the total fee for the service includingthe base fee, f0, and the adjustment, h�. If h > 0, thenthere is a price discount for doing self-service; if h< 0,then there is additional charge for using the self-serviceoption; and if h = 0, then the price is fixed regard-less of the self-service level. With a self-service levelset, the service task essentially involves informationtransfer between the consultant and client and process-ing by each party, for which the consultant and clientincur information transfer and processing expenses.We define the client’s utility function for the service as

u= v− f ��h�− zt��+ zp�� (2)


where v is the base value of the service for the clientand f ��h� is the fee for the service. The expensefunctions, zt�� and zp��, indicate the client’s infor-mation transfer and processing expenses, respectively,for a given self-service level, �. In the current paperwe assume that expenses are additive. An extensionof the model may consider expenses as multiplicativeto address potential interaction effects. Meanwhile,we define the consultant’s profit function as

� = f ��h�− rt��+ rp�� (3)

where rt�� and rp�� are the consultant’s informationtransfer and processing expenses, respectively, for agiven self-service level, �. Examples of expenses forinformation transfer and processing include tangiblecosts such as material, overhead, and labor costs andintangible costs such as quality loss.

It is worth noting that the self-service level canhave a significant impact on service quality and, con-sequently, the value of the service for both the cus-tomer and the service provider due to variabilityin customers’ ability to conduct self-service and thepotential gap of skills and capability between inex-perienced self-service customers and professionallytrained employees (Bitner et al. 1997, Karmarkar andPitbladdo 1995, Xue and Harker 2002). Any suchimpact of the self-service level on service qualityis embedded within the expenses in our modelingframework in this study. For example, causal ambi-guity and lack of absorptive capacity, two sources ofinformation stickiness, could prevent the client fromprocessing information with a quality level equalto that of the same information processed by theconsultant and would be reflected in higher clientprocessing costs and/or higher consultant-to-clientinformation transfer costs (Szulanski 2000, Szulanskiand Cappetta 2003).

With these assumptions, we study a game in whicha consultant tries to maximize his profit and a clienttries to maximize her utility with regard to a futureservice need with contract incompleteness and infor-mation stickiness. Under the assumption of initialservice need uncertainty that leads to incompletecontracts, we consider two different contractual for-mats (both incomplete) in which the residence ofthe residual right to specify self-service level oncethe service need emerges differs: one with the client(Model 2) and the other with the consultant (Model 3)possessing the residual right. However, as a bench-mark, we first present a model (Model 1) withoutservice need uncertainty, which leads to a completecontract that specifies the self-service level at the timethe contract is signed and before the service needemerges. The optimal self-service level from Model 1is deemed as socially optimal because it minimizes

the total information transfer and processing expensesof both the client and the consultant, which is ameasure of process efficiency. It is important to notethat, in all three models, the objective is to maximizethe consultant’s profits and the client’s utility, but inModel 1 we show that this is equivalent to the mini-mization of total costs due to the lack of service needuncertainty.

In Models 1–3, we assume symmetric informa-tion between the client and the consultant. Ourassumption of symmetric information is based onour interviews with consultants who would like theirrelationships with clients to be open-ended and, ide-ally, long-term (this is also discussed in Starbuck1992). In addition, previous research in the consult-ing industry suggests that open-ended relationshipsand a shared understanding of heuristics and experi-ences between the consultant and client promote sym-metric relationships (Fincham 1999, Ko et al. 2005).This prompts us to assume that the intention is toestablish the basis for a long-term relationship, whichreduces the incentive for either side to “shirk” (e.g.,withhold information or give false information) andmakes the symmetric information assumption reason-able. However, for types of consulting services otherthan those we focus on, it is possible that the partiescan shirk, which would make an asymmetric infor-mation assumption more appropriate. This is left forfuture research.

4.1. Model 1: Socially Optimal ProcessEfficiency Model

Assuming there is no uncertainty about the serviceneed, the expense functions (e.g., form and parame-ters) for information transfer and processing by theclient and consultant can be specified at time T0 whenthe contract is initially signed. In particular, the feeadjustment rate, h, can be estimated at the time thecontract is signed because the service need is known.Thus, it is appropriate to consider h as a prespecifiedparameter in this case. Then, the consultant’s utilitymaximization problem becomes

Max�∈01�

� = f ��− rt��+ rp�� (4)

and the client’s profit maximization problem becomes

Max�∈01�

u= v− f ��− zt��+ zp�� (5)

Assume that the two parties have equal power innegotiation. Then, solving these two optimizationproblems actually solves a global problem:

Max�∈01�

� +u= v− rt��+ rp��+ zt��+ zp�� (6)

which is equivalent to

Min�∈01�

w��= rt��+ rp��+ zt��+ zp�� (7)


Clearly, given the amount and nature of the informa-tion to be transferred and processed, a process thatis more socially efficient is one with lower expensesor fewer resources committed to the process by bothsides in total. Thus, the model above states the opti-mization problem that aims to minimize the totalexpense or maximize the efficiency of the overall pro-cess. Then the optimal self-service level that mini-mizes the total expense is defined as

�∗ = argmin�∈01�

�w�� w��= z��+ r�� (8)

where �∗ is referred to as the “socially optimal self-service level” hereafter. Using the first-order optimalcondition, we have

�∗ ∈{�

∣∣∣∣ �w�� = �z

��+ �r

��= �zt

��+ �zp

��

+ �rt��

+ �rp

��= 00≤ �≤ 1

}� (9)

4.2. Models 2 and 3: Client/Consultant ChoiceModels Under Incomplete Service Contracts

We next address the determination of the self-servicelevel within the context of an incomplete servicecontract and Stackelberg (leader–follower) game. Asdiscussed previously, the knowledge management lit-erature on consulting services has long recognizedthe uncertainty surrounding consulting processes andnotes that consultants and clients often renegotiate theterms of their agreement as the consulting engage-ment unfolds (Jacobson et al. 2005). As a result,consulting contracts are often inherently incomplete(Grossman and Hart 1986, Hart 1995, Hart and Moore1988, Salanie 1997).

In fact, our use of incomplete contract theory tostudy the effect of the contractual format on the issuesof interest is not only guided by the theoretical linksbut also motivated by findings from our field inter-views that indicate that it is common practice in con-sulting to include a “change management clause” inan initial consulting contract, which essentially leavesthe window open for future renegotiation. This sug-gests that the original contract is incomplete in aneconomics sense, and both parties are aware of it.The change management clauses in consulting agree-ments include information on (1) what constitutesa “change” and (2) what steps are to be taken ifthere is a change. Potential changes in the consult-ing engagement can be a source of tremendous con-flict between the consultant and client if there is notan agreement in the original contract about what willhappen if changes are needed. The individuals whohave authority to renegotiate the contract determinewhat change has occurred or will occur and the pric-ing mechanisms associated with making the change.

Although it is typically the case that the consultantis obligated to inform the client ahead of time ofanticipated changes in the engagement (particularly,if additional consultant hours are needed), it is thenthe client’s right to authorize changes. In addition,the client may hold the upper hand in the consultingrelationship if, for example, the client does not carethat much when the work gets done or the consultantneeds the work or is especially motivated to satisfythis client. These situations are aligned with the clientchoice contract in our framework. However, in somecases, the consultant is in a better position to deter-mine the best allocation of tasks or holds the upperhand in the relationship (e.g., if the client needs workdone right away or the consultant is overcommitted),and the actual power lies with the consultant to deter-mine what changes will be made, which is alignedwith the consultant choice contract in our framework.The client choice contract and consultant choice con-tracts are next described.

Consider two service contract formats that differ inthe ownership of the residual right to decide the self-service level. At time T0, the client and the consul-tant sign a service contract, in which the consultant isobligated to provide the client a unit of service uponrequest during the period between T0 and T1, T1 > T0.Under the assumption of service need uncertainty, theexpenses for information transfer and processing forboth sides are also uncertain. Thus, the initial contractdoes not specify the final self-service level or the feeadjustment rate at time T0. Instead, depending on therelationship between the two sides, the service for-mat design, the norms of the industry, etc., one sidegets assigned or is acknowledged to have the residualright to control the “actions” to be taken by both sidesin the service coproduction process once the serviceneed emerges (i.e., the right to specify the workloadallocation between the two sides by setting the self-service level). Meanwhile, the pricing schedule, whichis a function of the self-service level and fee adjust-ment rate, is subject to renegotiation. Essentially, ithas to be accepted by the other side, which allows theother side to demand a pricing schedule that maxi-mizes his or her payoff given the chosen self-servicelevel. That is, if the consultant gets to determine theself-service level, the corresponding fee adjustmentrate (i.e., an extra charge or discount for increasingthe self-service level) has to be accepted by the client;if the client gets to decide the self-service level, sheneeds to accept a fee adjustment rate requested bythe consultant. We designate the contracts as “clientchoice” if the client sets the self-service level once theservice need emerges and as “consultant choice” if theconsultant sets the self-service level.


4.2.1. Model 2: Client Choice Contract Model.In a client choice contract, the client determines theself-service level, �, once the service need emerges,and, as a result, the expenses for information trans-fers with different self-service levels can then be esti-mated. However, the consultant holds the right tomake a related fee adjustment; that is, the consultantdecides the fee schedule, f ��h�= f0 −h�, and essen-tially the fee adjustment rate, h. Assume that eachside decides its strategy based on anticipation of theother’s response and that both sides are rational play-ers and opportunistic but with bounded rationality(Salanie 1997). Thus, under the client choice contract,the two parties play a Stackelberg (leader–follower)game in which the client is the leader who takesaction (choosing the self-service level, �) first, andthe consultant is the follower who responds (choosingthe fee adjustment rate, h) after observing the client’schoice of �. Define

�c = argmax�∈01�

{u �u=v−f ��hc�−zt��+zp��

} (10)

hc = argmaxh∈R

{� �� = f ��ch�− rt��c�+ rp��c��

}� (11)

Then, with f ��h�= f0 −h�, and using the first-orderoptimal condition for the client’s utility maximizationproblem, we have

hc =�zt��

+ �zp

��= �z

�� (12)

Thus, the consultant solves the following problem todetermine �c and then hc that maximizes his profit:

max�∈01�

� = f0 −(�zt��

+ �zp

��

)�− rt��+ rp�� (13)

Therefore,

�c ∈{�

∣∣∣∣(�zt��

+ �zp

��+ �rt

��+ �rp

��

)+[�2zt��2

+ �2zp

��2

]�

= �w

��+ �2z

��2�= 0 0≤ �≤ 1

}� (14)

Then

�c =

−(�w

��

/�2z

��2

) if

�2z

��2�= 0

� ∈{�

∣∣∣∣ �w�� = 0}� otherwise

�c ∈ 01�� (15)

4.2.2. Model 3: Consultant Choice Contract. Aspreviously discussed, in some cases the consultantmay control the determination of the final self-servicelevel because of a better overall understanding of the

types and characteristics of the tasks that need to becompleted and their own capabilities and constraints.We now present a contract format that allows theconsultant to do so: the consultant choice contract.In this contract option, the consultant has the resid-ual right to determine the self-service level after thefinal service need emerges. Assume that the clientwill accept only the price schedule that maximizesher utility. Thus, under the consultant choice con-tract, the two parties again play a Stackelberg (leader–follower) game. However, this time the consultant isthe leader who takes action (determining the self-service level, �) first, and the client is the followerwho responds (accepting the fee adjustment rate, h)after observing the consultant’s choice of �. In theresulting Stackelberg equilibrium, we then have

�f =argmax�∈01�

{� ��=f ��hf �−rt��+rp��

}(16)

hf =argmaxh∈R

{u �u=v−f ��f h�

−zt��f �+zp��f ��}� (17)

Using the first-order optimal condition for the consul-tant’s profit maximization problem, we have

hf =−(�rt��

+ �rp

��

)=− �r

�� (18)

and the client solves the following problem to deter-mine �f and, consequently, hf , which maximizes herutility:

max�∈01�

u=v−f0−(�rt��

+ �rp

��

)�−zt��+zp�� (19)

Thus,

�f ∈{�

∣∣∣∣(�zt��

+ �zp

��+ �rt

��+ �rp

��

)+[�2rt��2

+ �2rp

��2

]�

= �w

��+ �2r

��2�= 0 0≤ �≤ 1

}� (20)

Then

�f =

−(�w

��

/�2r

��2

) if

�2r

��2�= 0

� ∈{�

∣∣∣∣ �w�� = 0}� otherwise

�c ∈ 01�� (21)

4.3. Discussion of the Results

4.3.1. The Loss of Process Efficiency. In this sec-tion, we explore how information stickiness, thecoproduction nature of the consulting service process,and contract incompleteness in Models 2 and 3 arelinked to the loss of efficiency in the consulting ser-vice process.


Proposition 1. �c = �∗ = 0 and �f = �∗ = 0 if �∗ = 0.(The proofs of all of the propositions are straight-forward and thus omitted because of space limita-tions. The results of the sensitivity analyses are shownin the Online Supplement, available at http://www.poms.org/journal/supplements.)

Proposition 1 shows that if having all informationprocessed by the consultant is an optimal solution toModel 1, it will also be an optimal solution to Mod-els 2 and 3. Thus, theoretically this means that havinga zero self-service level is one solution that makes theprocesses in Models 2 and 3 as efficient or sociallyoptimal as the one in Model 1. However, becauseof the coproduction nature of consulting services, itis unlikely that all information can be processed bythe consultant alone with none of it processed by theclient (Bettencourt et al. 2002, Haines and Goodhue2003). That is, consulting as a coproduction serviceprocess prevents � = 0 from being a practically fea-sible solution that would allow Models 2 and 3 toachieve the socially optimal efficiency level. This sug-gests that the coproduction nature of consulting ser-vices contributes to the loss of process efficiency.

In addition, we have previously shown that if thereis no contract incompleteness (i.e., Model 1), the util-ity and profit maximization problems reduce to a pro-cess inefficiency minimization problem that yields asocially optimal outcome. This suggests that contractincompleteness, whether it is due to the uncertaintyof the service need or the high cost to fully specifyeach contingency, contributes to the loss of processefficiency in Models 2 and 3.

4.3.2. Pricing. Recall that if h> 0 the fee decreaseswith an increasing self-service level, and if h < 0 thefee increases with an increasing self-service level. Itis of managerial interest to explore what drives thefee adjustment rate with each of the contracts. Notealso that with the client choice contract, hc = �z/��;i.e., the marginal information transfer and processingexpense of the client drives the fee adjustment rate.With the consultant choice contract, hf = −��r/��;i.e., the negative of the marginal information transferand processing expense of the consultant drives thefee adjustment rate.

This suggests that the residence of the residual rightto specify the self-service level when the service needemerges does play a central role in the determinationof the allocation or distribution of the benefits/payoffsrelated to the unspecified contingency. It shows thatthe ownership of the residual right to specify the self-service level gives the party an edge in setting the finalprice. This is consistent with the incomplete contracttheory literature that has shown that owning the resid-ual right to specify action when the unspecified con-tingency arises typically allows the party to gain more

from the benefits distribution or allocation related tothe contingency (Grossman and Hart 1986, Hart 1995,Hart and Moore 1988, Salanie 1997).

5. Illustrative ModelIn this section, we use more specific expense func-tions to further investigate the issues involved. Thecomplexity of an item of knowledge includes thenumber of elements it contains or, more specifically,the amount of information required to characterizeit (Rivkin 2001). This complexity increases the diffi-culty of knowledge transfer (i.e., information sticki-ness), search costs, and the likelihood that problemswill escalate (Haas and Hansen 2005, Szulanski andCappetta 2003, von Hippel 1994, von Hippel and Katz2002). Prior research has suggested that the complex-ity of knowledge (and, therefore, its stickiness) mayhave a curvilinear relationship with the efficiencyand effectiveness of its use (Haas and Hansen 2005,Rivkin 2001). In a consulting context, in particular,Haas and Hansen (2005) find support for includinga squared term related to complexity to predict per-formance outcomes. Also, by our model assumptions,as � increases, more information will be processedby the client and less by the consultant. This meansthat more client-owned information will be kept at theclient locus and more consultant-owned informationneeds to be transferred to the client. As a result, theclient will commit more resources to information pro-cessing, including acquiring and absorbing informa-tion from the consultant and analyzing both acquiredand self-owned information, with fewer resources forcollection, preparation, and delivery of client-ownedinformation to the consultant. Thus, we expect theclient’s information processing expense to increaseand her information transfer expense to decrease as� increases. Meanwhile, the consultant will commitfewer resources to acquiring and absorbing client-owned information and analyzing all the informationinvolved but will commit more resources to collec-tion, preparation, and delivery of needed consultant-owned information to the client. Accordingly, weexpect the consultant’s information transfer expenseto increase and her information processing expense todecrease as � increases. Based on the related litera-ture and considerations above, the following expensefunctions that are functions of the self-service level, �,are used to advance our discussion:

zt��= lt�1−��2 zp��= lp�2

rt��= ct�2 r��= cp�1−��2

where lt lp ct cp > 0. lt and ct are parameters for theclient and consultant transfer costs, respectively, andlp and cp are the parameters for the client and con-sultant processing costs, respectively. The more costly


it is for a client to process information, the highervalue of lp; the more costly it is for a client to transferinformation, the higher value of lt . Similar relation-ships hold for cp and ct , respectively. The parametersare assumed to be positive because the informationstickiness literature claims that information transferand processing are not costless or effortless (Szulanski2000, Szulanski and Cappetta 2003, von Hippel 1994).

5.1. Optimal ResultsWith these expense functions and solving the corre-sponding optimization problems in Models 1–3, theoptimal solutions are as follows: �∗ = �lt+cp�/�lt+ lp+ct + cp� for Model 1; �c = �lt + cp�/�2�lt + lp�+ ct + cp�and hc = 2lpcp − lt�lt + lp + ct��/�2�lt + lp�+ ct + cp� forModel 2; and �f = �lt+cp�/�2�ct+cp�+ lt+ lp� and hf =2cp�ct + cp + lp�− ctlt�/�2�ct + cp�+ lt + lp� for Model 3.Using these results, we further investigate the issuesof the loss of process efficiency and the pricing mech-anism with incomplete contracts.

5.2. Self-Service Level and Process Efficiency

Proposition 2. �c < �∗, �f < �∗.

Proposition 2 shows that, with an incomplete con-tract, the resulting utility-maximizing and profit-maximizing self-service levels from Model 2 andModel 3, respectively, are lower than the sociallyoptimal self-service level in Model 1, given thepositive value assumption of the cost parameters,lt lp ct cp > 0. This is consistent with Proposition 1,which uses the generalized model assumptions. Thisresult further illustrates the roles of information stick-iness, contract incompleteness, and service coproduc-tion in the loss of process efficiency. For example, �c =�∗ if and only if lt + lp = 0, which holds only if ltor lp can be zero or negative. However, informationstickiness theory states that information transfer andprocessing are not costless (Szulanski 2000, Szulanskiand Cappetta 2003, von Hippel 1994), which rules outa nonpositive lt or lp. In addition, we could have �c =�f = �∗ = 0 if lt = 0 and cp = 0, in which case havingthe consultant process all information is the optimalsolution. However, to have lt = 0 means that informa-tion transfer by the client to the consultant is costless,and to have cp = 0 means that information processingby the consultant is costless, which are both impos-sible based on information stickiness theory and as acoproduction process with the client and consultantboth involved in producing and delivering the con-sulting product.

Moreover, Proposition 2 also highlights the linkbetween information stickiness and contract incom-pleteness. von Hippel (1998) points out that eitherparty or both parties can invest in reducing infor-mation stickiness, but each party may have different

incentives to “unstick” the information. For example,if a supplier needs to transfer the same informationrepeatedly to different customers, the supplier willhave a strong incentive to ‘unstick the information,e.g., to set up a website that has the answers to fre-quently asked questions (FAQs), which reduces theinformation stickiness by facilitating the customer’sinformation receipt and absorption. Our results alsosuggest that other incentives, e.g., a fee adjustment,could play a role in the consultant’s or client’s deci-sion regarding the resources or effort they want toinvest to reduce information stickiness by decidinghow much information to process and how muchinformation to transfer. The decision (i.e., the level ofself-service chosen) has a direct impact on the effi-ciency of the overall process measured by the expensefor information transfer and processing. As shownbefore, the reason a fee adjustment plays a role here isbecause of the uncertainty or difficulty of fully spec-ifying the service need beforehand, which makes thefee adjustment subject to negotiation and a part of thepricing policy. Thus, this shows not only that contractincompleteness is a source of information stickinessbut also that it influences the investment decisions ofthe parties to reduce it.

Proposition 2 also shows that with both incom-plete contracts the client underinvests her contribu-tion to the service coproduction process (e.g., effortsin processing/applying information) compared withthe efficient process, and the consultant overinvests.There has been an ongoing debate on the issue ofunderinvestment with incomplete contracts in the lit-erature (Salanie 1997, pp. 184–188), and our resultsprovide an interesting contribution to the discussion.Earlier work in incomplete contract theory (Grossmanand Hart 1986, Hart and Moore 1988) indicates thatthe party that does not own the residual right under-invests and the party that owns the residual rightoverinvests. Later works in the literature show that,with more than one instrument in the contract, dif-ferent results can be achieved (Noldeke and Schmidt1995; Salanie 1997, p. 186). Our results are consistentwith these later works: we have two instruments (self-service level and a fee, which are decided throughan initial contract and a later bargaining process)instead of one instrument used in the earlier works.As pointed out in Salanie (1997, p. 187), the fact thatthere are the various results in regard to the under-investment issue with incomplete contracts in the lit-erature suggests that the results of the models usingincomplete contract theory are often sensitive to thefine details of modeling.

Proposition 3. �c > �f if and only if lt + lp < ct + cp.

According to Proposition 3, if it is less costly for theclient to transfer and process information than for the


consultant, the self-service level chosen by the clientwill be higher than the one chosen by the consultant.Consequently, �c is closer to the efficient level, �∗,than �f . Otherwise, the self-service level chosen bythe consultant will be higher and therefore closer tothe efficient level, �∗. In other words, when it is lesscostly for the client to process and transfer informa-tion than the consultant, the self-service level underthe client choice contract will be closer to the efficientlevel. However, if these costs are lower for the con-sultant, then the self-service level chosen by the con-sultant under the consultant choice contract will becloser to the efficient level.

This result is consistent with incomplete contracttheory (Grossman and Hart 1986). It shows that theresidual right to specify the self-service level whenthe final service need emerges should reside with theparty who has lower costs for transferring and pro-cessing information. In other words, the more pro-ductive party should be given the residual right toachieve higher efficiency of the overall coproductionprocess. The implications of this result for servicedesign when self-service is used are worthy of furtherexploration. The following sensitivity analysis pro-vides more insights into this issue.

Using sensitivity analysis, we now study how infor-mation stickiness, more specifically, the expense forinformation transfer and processing by either party,influences the resulting self-service level and thusprocess efficiency in the three models.

Proposition 4. ��∗/�lt > 0, ��∗/�lp < 0, ��∗/�ct < 0,��∗/�cp > 0.

In Model 1, as one may expect based on infor-mation stickiness theory (von Hippel 1994, 1998;von Hippel and Katz 2002), when only the totalinformation transfer and processing costs of the ser-vice coproduction process are considered, a higherinformation transfer cost for client-owned informa-tion leads to a higher self-service level, and a higherinformation processing cost for client-owned informa-tion leads to a lower self-service level. The costs forconsultant-owned information have the exact oppo-site effect on the self-service level.

The following two propositions further highlighthow contract incompleteness introduces distortionsthat lead to a loss of process efficiency and is a sourceof information stickiness:

Proposition 5. With the client choice contract,��c/�lt > 0 if lp > �cp−ct�/2; otherwise, ��c/�lt ≤ 0. Also,��c/�lp < 0, ��c/�ct < 0, ��c/�cp > 0.

Proposition 6. With the consultant choice contract,��c/�lt > 0, ��c/�lp < 0, ��c/�ct < 0. Also, ��c/�cp > 0 ifct > �lt − lp�/2; otherwise, ��c/�cp ≤ 0.

The sensitivity analysis results in Propositions 5and 6 suggest that, depending on the ownership ofthe residual right to set the self-service level, distor-tions occur through different mechanisms. Proposi-tion 5 shows that, under the client choice contract, theself-service level chosen by the client that maximizesher utility can decrease, rather then increase, when itactually becomes more costly for the client to trans-fer the information to the consultant. The ownershipof the residual right allows the client to set the self-service level below the efficient level. On the otherhand, Proposition 6 shows that, under the consultantchoice contract, the consultant’s optimal self-servicelevel that maximizes his profit can decrease, ratherthan increase, when it actually becomes more costlyfor the consultant to process information. Overall,Propositions 5 and 6 show that contract incomplete-ness constitutes a source of information stickinessby enabling counterefficient distortions that increaseoverall information transfer and processing expenses.These propositions provide a specific explanation forwhy an incomplete contract can be considered a sourceof information stickiness, as implied in the discussionof the Arrow core by Winter and Szulanski (2001).Because an incomplete contract moves the resultingself-service level away from the socially optimal leveland increases overall information transfer and process-ing costs, information becomes more costly to acquire,transfer, and use in a new location, which is consistentwith the definition of information stickiness.

5.3. PricingIn this section, we study pricing in Models 2 and 3 byexamining the role of information stickiness, contractincompleteness, and service coproduction.

Proposition 7. hc > 0 if and only if cp > �lt/lp��lt +lp + ct� or ct < �lp/lt�cp − �lt + lp�.

Proposition 7 shows that, with the client choice con-tract, in which the consultant sets the fee adjustmentrate, hc, there is a discount for increasing the self-service level if and only if the consultant’s processingcost is above a certain threshold or his informationtransfer cost is below a certain threshold. Otherwise,there will be a surcharge to the client for doing moreself-service. This implies that only when the con-sultant has a sufficiently high cost for informationprocessing and/or information transfer is relativelyinexpensive for the consultant, the consultant is will-ing to give the client a fee discount for doing moreself-service.

Proposition 8. hf < 0 if and only if lt > �cp/ct��ct +cp + lp� or lp < �ct/cp�lt − �ct + cp�.

Proposition 8 shows that with the consultant choicecontract in which the client has to accept the fee


adjustment rate, hf , there is a surcharge for self-service if and only if the client’s information transfercost is above a certain threshold or her informa-tion processing cost is below a certain threshold, lp <�ct/cp�lt − �ct + cp�. This implies that if client-ownedinformation is too costly to transfer and/or the infor-mation processing is very inexpensive for the client,a client will be willing to process the information her-self even if there is a higher charge for the self-serviceoption. This result shows that, using the right to setthe self-service level, the consultant is able to chargea fee because of the client’s willingness to pay.

Despite its being seemingly counterintuitive for acustomer to pay a higher price for self-service thanfor employee service, scenarios do exist under whicha customer is willing to pay more for the self-serviceoption in practice. According to a large survey con-ducted by Atlanta-based Synergistics (Bank TechnologyNews 2004), $0.50 per transaction is identified as theprice that a consumer is willing to pay for using Inter-net banking to conduct transactions online themselveseven though they can conduct the same transactionsthrough the branch tellers for free. Such transactionsare often routine and standard and thus have verylow information processing cost for the customer butrelatively high information transfer cost, e.g., the timeopportunity cost for traveling to the bank branches,waiting in line, and talking to the teller. Other exam-ples from the consulting industry are discussed in §6.

6. Implications for ConsultingServices

In this section, we discuss the managerial implica-tions of the proposed model and results for consult-ing services. The information provided is based onour interviews with senior executives, senior consul-tants, and partners in three different managementand technology consulting firms. The interviews con-sisted of a list of open-ended questions addressingissues raised in our model. Interviews were primarilyconducted face-to-face but also included some phoneinterviews, especially for follow-up. An individualinterview typically included two to three rounds toclarify responses from previous rounds. With theinsights gleaned from our interviews, we next discussthe implications of the major model results for con-sulting practice.

6.1. Inefficient Self-Service LevelOne issue facing consultants that emerged fromour interviews is the surprisingly high amount ofresources the consultant actually commits to thesolution implementation process, in which the con-sultant works with the client to help them to under-stand the solution and to implement it, in many cases.

One senior technology consultant indicated duringthe interview that for many projects approximately a1:10 ratio of the total cost is spent for the researchstage versus the implementation stage. For example,for a technology consulting project with a budget ofapproximately $100,000, only $8,000 was spent on theactual research ($3,000 for lab experiments and $5,000for a literature review), and $92,000 was spent onworking with the client to implement the solution,which primarily included meetings with the clientand other activities needed to “transfer” the knowl-edge to the client. The high cost for the implementa-tion stage often catches even experienced consultantsby surprise, who expect the ratio between researchand implementation to be closer to 1:1, because boththey and the client view the core value of the consult-ing service to be created in the research stage.

Using the results from our model, we explore thefactors that may contribute to the unexpectedly highcosts for solution implementation. From our inter-views, we found that factors such as corporate cultureand politics possibly play roles in the time-consumingimplementation process. Nevertheless, the high costfor the solution implementation stage underscoresone of the major results from our model, i.e., the inef-ficient self-service level as shown in Propositions 1and 2. In other words, a key reason behind thehigh cost for the solution implementation stage is theunderinvestment of efforts or resources by the client,because the client is likely doing less than needed toabsorb and process the knowledge transferred fromthe consultant. This requires the consultant to incuradditional costs to prepare and convey the knowledgeto the client.

According to Proposition 3, the party that is moreproductive in processing and transferring informationshould set the self-service level to achieve a higherefficiency for the overall process. However, in theactual consulting world, it is often the client and notthe consultant who has the final say on the allocationof work. From the consultants’ perspective, they maychoose to comply with whatever the client demands,as long as they can charge for it. So when the exacttiming and nature of the service need and the cor-responding work allocation cannot be prespecified inthe initial contract and a financial incentive or penaltyis present, as shown in Propositions 5 and 6, the con-sultant and client would accept a self-service levelbelow the efficient level, which is consistent with ourresults. Because the consultant often is more produc-tive in processing and transferring information, basedon our results in Proposition 3, the consultant shouldbe the one to set the self-service level to achievehigher efficiency. The fact that it is the client ratherthan the consultant who often decides what shouldbe done by each party contributes to a more costlysolution implementation.


6.2. PricingWe also learned from our interviews that consultantsare often willing to charge the client a lower fee ifthe client relationship involves recurring work. Thisis consistent with Proposition 7, which states that ifthe consultant has a relatively low information trans-fer cost or relatively high information processing cost,then a discount will be offered to the client. For arecurring project, with established relationships andchannels and previously acquired knowledge aboutthe client’s organization and business, the consultantshould have a lower cost for information transfer. Inaddition to the motive to attract returning business,this result financially justifies the discount to a recur-ring client.

Other information from our interviews also sup-ports Proposition 8. It is a common practice for theconsultant to offer clients access to certain databases,finished research reports, and analysis tools, whichare now often made accessible through the Internetfor a fee. Clients who are interested in doing someresearch on their own often use such a self-service for-mat to obtain information and decision-making tools.This is more like the consultant choice contract, as theself-service level is essentially prespecified by the con-sultant and the client has to agree on the fee. Propo-sition 8 states that, with the consultant choice con-tract, a client would be willing to pay for a self-serviceoption if her information processing cost is relativelylow or her information transfer cost is relatively high.For simple projects a client typically has lower totalinformation processing costs and, therefore, based onProposition 8, would be willing to pay to use self-service. For projects that involve much internal infor-mation that is difficult to organize and transfer orcannot be shared with outsiders for legal or other rea-sons (e.g., the transfer cost is very high or approach-ing infinity when information sharing with outsidersis legally barred), a client may prefer or need to con-duct that portion of the project internally but wouldbe willing to pay for the knowledge and decision-making tools offered through the self-service option.All of these phenomena support Proposition 8.

7. Conclusions and LimitationsIn this study we address service coproduction in con-sulting services with incomplete contracts. We focuson two key issues central to effective managementof knowledge-intensive service coproduction: the allo-cation of work between the service provider andthe coproducing client, and pricing. We draw onthree theoretical bases—information stickiness, ser-vice coproduction, and incomplete contract theory—to develop a model of the coproduction processin knowledge-intensive consulting environments. We

present three models under different assumptionsabout service need uncertainty, contract incomplete-ness, and contract formats, with the client alwaysmaximizing her utility and the consultant alwaysmaximizing his profit. Because this reduces to anexpense (i.e., process inefficiency) minimization inModel 1, we are able to explore how process efficiencyis lost with service need uncertainty and contractincompleteness in Models 2 and 3. In doing so,we have contributed to the development of infor-mation stickiness theory by disaggregating informa-tion transfer and processing costs and jointly con-sidering the performance implications of consultant-to-client and client-to-consultant information stick-iness. We show that information stickiness makesknowledge-intensive service operations such as con-sulting inherently coproduction processes that requireboth individual contributions and close collaborationbetween the client and the consultant (i.e., � �= 0 and� �= 1). We also show that contract incompleteness, acommon reality in consulting services, can be consid-ered a source of information stickiness, i.e., a barrierfor efficient information/knowledge transfer. Addi-tionally, we have applied incomplete contract theoryto service coproduction to help understand the impactof the residual right to determine how the workloadwill be allocated when it cannot be completely speci-fied at the time the contract is signed. In addition, themodels presented address a gap in the service copro-duction literature, which lacks models that explicitlyincorporate the coproducer role of clients. Overall,our findings indicate that, within an incomplete con-tract and Stackelberg game framework, the resultingprocess is less efficient than the socially optimal pro-cess. We also find that information stickiness, contractincompleteness, and the coproduction nature of con-sulting services all contribute to the loss of processefficiency.

With more specific assumptions, i.e., quadraticexpense functions for information transfer and pro-cessing, we find that, whether the client or the consul-tant has the residual right to specify the self-servicelevel when the service need emerges, the actual self-service level will be set lower than the efficient self-service level. Under the client choice model, whenthe consultant has a sufficiently high cost for infor-mation processing or information transfer is relativelyinexpensive for the consultant, the client gets a dis-count for doing more self-service. Under the consul-tant choice model, if the client-owned information istoo costly to transfer or her processing cost is rela-tively low, the client may be willing to pay for theself-service option, effectively enabling the consultantto charge a fee because of the client’s willingness topay. More importantly, to improve efficiency, we show


that the more productive party should have the resid-ual right to respecify the self-service level after thespecific service need actually emerges. As discussed,this result has significant managerial implications forconsulting practice.

We also performed sensitivity analyses to furtherexplore the role of information stickiness in settingthe self-service level and pricing within the incom-plete contract and coproduction framework. We findthat certain distortions occur because each party seeksto maximize only his or her own payoff rather thanoverall process efficiency. More specifically, the own-ership of the residual right to specify the self-servicelevel makes it possible for one party to set the self-service level that maximizes his or her own payoffbut not the overall process efficiency, despite the rene-gotiation mechanism that allows the other party to“influence” the pricing schedule.

Finally, field interviews offer support for the resultsfrom the model. Moreover, our results generate sig-nificant insights into the causes of the unproductiveor inefficient service delivery processes in consultingpractices and offer directions for achieving better effi-ciency from the design of contracts, operational pro-cesses, and pricing schedules.

Nevertheless, there are certain limitations of thecurrent study to be addressed in future research. First,the results in the illustrative model are made underspecific functional form assumptions, although suchassumptions are motivated by the theories that webuild on and empirical findings in the related lit-erature. This imposes limitations on the applicationof the results outside consulting services to otherknowledge-intensive services. Also, as previously dis-cussed, we implicitly incorporated the impact of theself-service level on quality in the model and focuson the impact of the self-service level on informa-tion transfer and processing expenses for the clientand consultant. In addition, we have been assum-ing symmetric information throughout. However, theconsultant may not know the client’s expenses forinformation transfer and processing (although he maybe able to infer it based on his experience with theclient), and the client may have incentives not to fullydisclose this information. Meanwhile, although theclient may be able to estimate the consultant’s costsfrom pricing policies or knowledge of the differenttypes of work and working hours of consultants withvarious seniority levels, it will still be only an esti-mate, because the consultant is unlikely to disclose hisreal costs. Models assuming such asymmetric infor-mation are left for future research. Moreover, becauseour model was developed to capture the coproduc-tion nature of knowledge-intensive consulting ser-vices with a focus on efficiency, information stick-iness, and contract forms, other factors that might

influence the outcome of the process may have beenexcluded. For example, in future research the cur-rent framework may be extended to incorporate pos-sible objectives for the consultant other than efficiencyand immediate financial gains, e.g., client satisfactionand loyalty or willingness to reengage the consultant.Third, it would also be interesting to extend the cur-rent framework beyond a single transaction betweenthe client and the consultant to take into account long-term relationships and the performance benefits offamiliarity (Huckman and Pisano 2006), which areoften the case for consultants and their clients. Finally,in the current base model we consider the expensesas additive. An extension in which the expenses aremodeled as multiplicative may further capture anyinteraction effects between the costs.

Although the research questions are exploredmainly in the consulting service context, given theknowledge-intensive nature of consulting servicesand the general framework used, the results may alsohelp to understand other knowledge-intensive opera-tions that face similar issues. In addition, we built ourmodels on three theoretical bases—with knowledgemanagement and transaction cost economics fromoutside operations management—and applied themin a novel manner in a service operations context,thereby contributing to enriching the theoretical foun-dation of service operations management in particularand operations management in general.

AcknowledgmentsThis research is supported by the National Science Foun-dation (Grant SES-0518931). The authors thank the senioreditor, three anonymous referees, Christian Terwiesch, JunZhang, and Mark Citsay for their valuable and construc-tive comments. The authors thank an anonymous refereefor suggesting the relevance of the Arrow core to this study.

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Service Coproduction with Information Stickiness and Incomplete Contracts: Implications for Consulting Services Design