OPENING THE BLACK BOX OF KNOWLEDGE TRANSFER: THE ROLE OF REPLICATION ACCURACY Gabriel Szulanski Wharton School, University of Pennsylvania Department of Management 2033 SH-DH Philadelphia, PA 19104-6370 Tel: (215) 573-9627 e-mail:
[email protected]
Sidney G. Winter Wharton School, University of Pennsylvania Department of Management 2018 SH-DH Philadelphia, PA 19104-6370 Tel: (215) 898-4140 e-mail:
[email protected]
Rossella Cappetta Bocconi University Department of Organization Viale Isonzo, 23 20135 Milan, Italy Tel: (+39) 02 5836-2633 e-mail:
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Christophe Van den Bulte Wharton School, University of Pennsylvania Department of Marketing 1472 SH-DH Philadelphia, PA 19104-6371 Tel: (215) 898-6532 e-mail:
[email protected]
January 10, 2002 Aknowledgement The authors acknowledge helpful conversations with and suggestions from Linda Argote, Russ Coff, Bill McEvilly, Dan Levinthal, John Paul MacDuffie, Joe Porac, Ray Reagans, Lori Rosenkopf, Nicolaj Siggelkow, Laurie Weingart, and participants of seminars at Carnegie Mellon GSIA and Emory School of Business. Financial support was graciously provided by the Reginald Jones Center and by the Hunstman Center at the Wharton School of the University of Pennsylvania. Errors and omissions are solely the authors’ responsibility.
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OPENING THE BLACK BOX OF KNOWLEDGE TRANSFER: THE ROLE OF REPLICATION ACCURACY
Abstract In this paper we present a model of the relationships among barriers to knowledge transfer, replication accuracy and transfer performance, and test that model with a sample of 109 transfers of best practice within eight firms. We posit, and find, that replication accuracy mediates the effect of knowledge transfer barriers on knowledge stickiness documented in previous research, and that the importance of replication accuracy varies across four phases of the transfer process. During the initiation phase we find no relationship between accuracy and stickiness. During the implementation phase, we find a strong relationship significant relationship between accuracy and stickiness, and strong evidence of mediation: full mediation for the perceived reliability of the source and the quality of the relationship, and partial mediation for the motivation of the source, causal ambiguity and absorptive capacity. During the ramp-up phase, we find a strong relationship between replication accuracy and stickiness, and strong evidence of mediation: full mediation for the source motivation, perceived reliability of the source, and causal ambiguity, and partial mediation for absorptive capacity and retentive capacity. During the integration phase, finally, we find a strong relationship between accuracy and stickiness but only moderate mediation: full mediation for causal ambiguity and partial mediation for absorptive capacity. Overall, these patterns provide strong support for a replication perspective on knowledge transfer and stickiness of organizational practices. . Keywords: Knowledge Transfer, Accuracy, Stickiness, Replication 2
1. Introduction In the past decade, organizational learning has become one of the foremost topics of interest to managers and management scholars interested in technology and organizations. An important focus of organizational learning concerns has been the transfer knowledge to close internal performance gaps, to realize synergies, and to shed avoidable deficits in performance (O'Dell, Grayson et al. 1998; Dixon 2000; Pfeffer and Sutton 2000). Such activities have proven to be surprisingly challenging and much less prevalent than expected. For example, in a survey of 431 U.S. and European organizations conducted in 1997 by Ernst & Young, only 14% of the respondents were satisfied with the performance of their organization in transferring existing knowledge internally. The remaining 86% found it lacking (Ruggles 1998). Internal knowledge remains stubbornly inert. The transfer of knowledge within the firm appears difficult. This problem of “internal stickiness” or difficulty to transfer knowledge is a central issue for the management of knowledge assets. Extant research on stickiness has identified a plethora of factors that can contribute to the difficulty of transferring knowledge and has offered empirical evidence in support of those claims (von Hippel 1994; Szulanski 1996; Ogawa 1998; 2000 [Gabriel: NOT IN REFS]). However, it has treated transfers as a ‘black box’ thus stopping short of inquiring into the generative mechanisms that underlie stickiness. This gap is understandable and perhaps inevitable because of the continuing reliance of knowledge transfer research on the signaling metaphor (Shannon and Weaver 1949). As several scholars have pointed out, reliance on the signaling metaphor is a limiting factor for efforts to understand the movement of knowledge in organizations (Attewell 1992; Putnam, Phillips et al. 1996). A paradigm for communication study (Rogers 1994: 438), the signaling metaphor has had a formative influence on the study of knowledge diffusion and transfer because it provides a
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single, easily understandable framework for the movement of knowledge. However, because this metaphor portrays the transfer as an instantaneous and costless act (Rogers 1994; Putnam, Phillips et al. 1996), it blurs the process of transfer and offers little incentive or opportunity to single out and understand generative mechanisms that underlie stickiness. This constrains the theoretical backdrop available to interpret findings and thus it limits the possibility to derive sound practical normative implications. Consequently, to advance research on stickiness one must begin to model explicitly the process of knowledge transfer, and this requires a new perspective. In this paper we draw on a replication perspective (Nelson and Winter 1982; Winter and Szulanski 2001) to propose and test the idea that accuracy mediates the relationship between barriers to knowledge transfer and stickiness. The key tenet in the replication perspective is that complex knowledge must be recreated by the recipient, rather than obtained through a single act of information transmission and absorption. We argue that barriers to knowledge transfer documented in previous investigations limit access to the working example that is being copied, and hence hinder efforts to accurately replicate organizational routines. The failure to achieve an accurate replica, in turn, is what makes knowledge hard to transfer or sticky. We test this claim empirically, distinguishing four stages of the transfer process: initiation, implementation, ramp-up and integration. We hypothesize that efforts to keep modifications in check are particularly important during the implementation and ramp-up phases which are the two phases where close scrutiny of the working example is most needed as a reference. We test these hypotheses with data on 109 transfers of organizational practices within eight firms.
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We find that good support for our hypotheses based on the replication perspective. During the initiation phase we find no relationship between accuracy and stickiness. During the implementation phase, we find a strong relationship significant relationship between accuracy and stickiness, and strong evidence of mediation: full mediation for the perceived reliability of the source and the quality of the relationship, and partial mediation for the motivation of the source, causal ambiguity and absorptive capacity. During the ramp-up phase, we find a strong relationship between replication accuracy and stickiness, and strong evidence of mediation: full mediation for the source motivation, perceived reliability of the source, and causal ambiguity, and partial mediation for absorptive capacity and retentive capacity. During the integration phase, finally, we find a strong relationship between accuracy and stickiness but only moderate mediation: full mediation for causal ambiguity and partial mediation for absorptive capacity. Overall, these patterns provide strong support for a replication perspective on knowledge transfer and stickiness of organizational practices.
2. Theory and Hypotheses 2.1 Stickiness and the Signaling Metaphor The signaling metaphor is a dominant influence on research on knowledge transfer. The mathematical theory of communication (Shannon and Weaver 1949)), the fundamental theoretical framework developed from the signaling metaphor, has been deemed the most important single stimulus for the development of other models and theories in communication (Serevin and Tankerd 1988). It served as the “paradigm for communication study, providing single, easily understandable specification of the main components of the communication act:
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source, message, channel, receiver” (Rogers 1994: 438), and has had a formative influence on the study of knowledge diffusion and transfer (Attewell 1992). As any other metaphor, the signaling perspective highlights some characteristics of the phenomenon being investigated, but does so at the detriment of other characteristics (cf. Morgan 1980 [Gabriel: ADD TO REFERENCES]). The signaling perspective frames knowledge transfer as an instantaneous and costless act and is hence not a very productive basis to understand stickiness. The frictionless view of knowledge transfer is especially evident in early studies of international transfer of technology (Teece 1977), technology diffusion (Nelson 1981) and diffusion of practices within organizations (Rogers 1983; Attewell 1992; Leonard-Barton 1995). From very early on, however, some scholars and practitioners were aware of this framing limitation and noted some discomfort with the signaling metaphor because the reality of knowledge transfer differed significantly from such costless and instantaneous portrayal. Deviations from this metaphorical view were attributed to the limited information processing capacity of ‘social channels’ (Arrow 1974), to the emotions and the experiences of sensemaking individuals (Rogers and Kincaid 1981), to the peculiarities of the relationship and of the social context in which the transfer is embedded (Szulanski 1996; Hansen 1999; Kostova 1999) and to inevitable distortions in the communication process (Stohl and Redding 1987; Putnam, Phillips et al. 1996). The nature of the transferred knowledge is another important area where the signaling metaphor has been found wanting (Nelson and Winter 1982; Winter 1987; Kogut and Zander 1992). As Winter and Szulanski (2001) pointed out, a causally ambiguous practice would normally have features that are irrelevant or even detrimental to the effectiveness of the transfer and ones that, though desirable, are impossible to transfer – such as unique human capital.
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Furthermore, some of its features may be tacit (Polanyi 1966; Kogut and Zander 1992; Nonaka 1994). Aware of these limitations, some scholars amended the signaling metaphor by acknowledging the existence of stickiness (Teece 1977; Nelson 1981) and of its consequences for innovation related problem solving (von Hippel 1994). In these efforts, stickiness was defined as the cost associated with technology transfer (Teece 1977), as the slowness of diffusion [Gabriel: do you have a reference for this?], as the incremental cost of consummating a transfer (von Hippel 1994), or as the eventfulness of a transfer (Szulanski 1996). Another response, mostly by communication scholars, has been to construct alternative metaphors altogether (Boland and Tenkasi 1995; Putnam, Phillips et al. 1996). All these different alternative metaphors share an implicit or explicit view of the transfer as an iterative process in which the participating parties converge on a shared meaning (Watzlawick, Beavin et al. 1967; Fisher 1978; Weick 1979). Also taking a process view, several students of technology transfer and innovation diffusion have contributed models of transfer that specify stages or states of the process; the most common distinction made is between a phase of initiation and one or several phases of implementation (Rogers 1983; Szulanski 2000).
2.2 Transfer of Knowledge as Replication The replication perspective (Nelson and Winter 1982; Winter and Szulanski 2001) on knowledge transfer helps further specify the process of transfer. Specifically, the source does not emit a message to be received, but offers or constitutes a template that must be replicated. In the transfer of complex organizational routines, replicating agents seek to create exact or partial
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replicas of a web of coordinating relationships connecting specific resources so that a different but similar set of resources is coordinated by a very similar web of relationships (Winter 1995). Rather than the instantaneous act depicted by the signaling metaphor, knowledge transfer is thus expected to be a protracted iterative process with the goal of replicating the source’s performance by recreating and adapting the source’s practices at the recipient’s end. Departing from the fundamental observation that complex knowledge must be recreated by the recipient, rather than obtained through a single act of information transmission and reception (Rosenberg 1982; Attewell 1992; Zander and Kogut 1995), the replication perspective acknowledges that several iterations are often necessary to produce acceptable results. Iterations are an inevitable consequence of the uncertainty and equivocality that stems from either the complexity of practices (Nelson and Winter 1982) or distortions and filtering in the communication process (Stohl and Redding 1987). The initial results may be unsatisfactory because crucial details of time and place have been left out from the initial replication attempt thus requiring further iterations to correct oversights (Jensen and Meckling 1992). The process of diagnosing and correcting performance deficits during the replication process typically benefits from close scrutiny of the original working example, when problems arise (Nelson and Winter 1982). Otherwise, when it is not possible or practical to consult the working example, performance improvement must rely on trial and error.
2.3 Barriers of Knowledge Transfer and Accuracy. An organizational unit attempting to reproduce superior results can draw in many different ways from the information available at the original site, i.e., the template site (Nelson and Winter 1982) where exemplar results are being obtained. At a minimum, knowledge of
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superior results can be used merely to set or adjust expectations for future performance, leaving every other aspect to trial and error. A unit attempting to replicate superior results however can study the actual practices that underlie those results. While less costly and less risky than trial and error, direct observation does not completely eliminate the possibility of oversight, and is likely to involve redundant and possibly costly rediscovery of information that already exists. For these reasons, when feasible, the recipient can plausibly be expected to seek the advice of the source.1 Such exchange may be less effective when the source and the recipient have a difficult relationship, the source lacks motivation to share, the source is not perceived as reliable, the recipient lacks motivation to share, lacks absorptive capacity, or lacks the ability to discard old practices and sustain new ones, and finally, when the organizational context does not provide incentive or support for the exchange (see Szulanski 1996). The net result of these factors limiting access to the template or to the advice of the source is that the replica is less likely to resemble the original template. Hypothesis 1: the higher the barriers to knowledge transfer the lower the accuracy of the transfer.
2.4 Accuracy and Stickiness. In the mathematical theory of communication, accuracy is seen as a central prerequisite for the effective transfer of information. In their analysis, Shannon and Weaver (1949) de-
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Heifetz (1994: 76) suggests an additional rationale for such “flight to the source”: economizing on learning. He distinguishes between technical work, where the problem and the solution are clear, and adaptive work where finding a solution and perhaps even understanding the problem may require an effort to learn. As he eloquently puts it: “By and large, we want answers, not questions. Even the toughest individual tends to avoid realities that require adaptive work, searching instead for an authority, [e.g.,] a physician, to provide the way out.”
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compose the problem of communication into three sub-problems or levels, technical accuracy – how accurately the symbols of the communication can be transmitted, semantic accuracy – how precisely the transmitted symbols convey the desired meaning, and effectiveness – how effectively the received meaning affects the conduct of the recipient in the desired way. Even though they focus primarily on the technical problem, on how accurately can the symbols be transmitted, Shannon and Weaver justify their focus by noting that it is hard to conceive that a communication episode would be effective, i.e., that the recipient’s behavior will be affected as intended by the source, when communication is technically inaccurate or semantically imprecise2. The replication perspective augments the “signaling metaphor” by taking note also of the difficulties at the “encoding” stage of the message transfer. When a production situation involves knowledge that is highly tacit and/or causally ambiguous, nobody knows what “message” – i.e., what symbolic representation of the knowledge – is needed to move that knowledge so that it yields satisfactory results in a new setting. There may be no such message. Consequently, regardless of the semantic and technical accuracy with which any actual message is conveyed, there is still a potential source of “stickiness” at the encoding stage. Thus, the replication perspective takes into consideration the part of the transfer process that is temporally and causally antecedent to any symbolic representation at the source. Replication is about “real to real” transfer of organizational practice, not “message to real” enactment of a symbolically
2
More recent contributions have placed less emphasis on the concept of accuracy because they conceive of communication less as a unidirectional act of signaling and more as a process of converging on a shared meaning which could differ from the original meaning imputed to the message by the source (e.g., Putnam et al. 1996; Rommetveit 1974; Weick 1979; Rogers and Kincaid 1981; Boland and Tenkasi 1995). Accuracy, however, has remained a relevant and rather central element for those branches of the communication field that focus primarily on directional exchanges from a source to a recipient, as is the case when technologies or best practices are transferred within organizations.
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rendered formula, or “mind to mind” transfer of individual understanding. When there is a stable template but there are significant contextual differences between the source site and the recipient site, exact replication of the template may produce unsatisfactory results. Indeed, in almost every practical situation, there are some contextual differences and what is at issue is their significance, how important they really are. There are two basic options to deal with the impact of contextual differences. One is to assume that most differences are significant, and thus the approach consists of understanding the differences and adjusting the practices for them. Such approach would favor the introduction of modifications, unless a specific rationale exists to oppose them. The other option, assumes that unsatisfactory performance reflects primarily a poor implementation of the template. The basic prescriptive thrust of this second option thus is to overcome those dispositional factors that make people choose adaptation too easily, or too hastily – such as the tendency to overlook the existence and importance of tacit knowledge, a bias towards optimism and overconfidence, the tendency towards cognitive simplification in perceiving complex systems and situations, leading in particular to a neglect of interaction effects. Therefore, it is wise to “lean against” those dispositional factors and be obsessive about accuracy. Adaptation will occur only after the original template is successfully reproduced in the host environment. A replication perspective favors delaying adaptation until after replication has been completed because modifications introduced a-priori may create new problems. Since adaptation makes the practices in the new site different from those in the template site, post-transfer problems at the new site will have to be solved in-situ through a costly process of trial and error, since they cannot be solved through reference to the established template. Accurate reproduction of the specific details of the original template shortens the time and effort required to pinpoint
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and correct differences that may exist between the replica and the original. When interactions among practice components are taken into consideration, it is easy to see that small and seemingly inconsequential departures from the specifications of the original template can rapidly disrupt a replication effort (Rivkin 2000). In this vein, Adler (1990) argues that it is desirable to stick to the original design of a technologically sophisticated practice as much as possible because such practices rely often on poorly mastered process techniques to such an extent that any substantial divergence from the existing, functioning design of the process risks multiplying operational problems beyond manageable levels. There are numerous examples of companies that zealously enforce accuracy. Intel’s “Copy Exactly” philosophy for building semiconductor plants provides a tangible example of this replication approach (McDonald 1998). Recognizing that semiconductor production processes have enormously complex and opaque causal structures, Intel requires that every change to the specifications of a semiconductor plant be approved by a central committee, and, when approved, the change must be implemented across all of the fabs built to that specification. Emphasis on precision is such that Intel personnel joke that “even the height of the process technicians must be identical at all fabs” (Iansiti 1998). Likewise, Rank Xerox, a benchmarking pioneer, allows a business unit to adapt a model process only after it has raised its performance to the same level achieved by the benchmark unit (Financial Times 1997). In the fast food industry, McDonalds quickly realized that it could only expand successfully abroad when it stuck to the very same menu and store design that worked in the US. For example, when McDonald's Australia finally restored the standard American menu, its operation moved into the black after eight consecutive losing years. Likewise, only when the McDonald's units in Germany began to look more like those in the United States did they begin to build volume (Love 1995). Similarly
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Bradach (1998: 23-24) found that franchise operators in his sample quickly learned to conform to franchise formats, despite idiosyncratic local pressures, because tinkering with isolated operating procedures of the complex interlocking franchise operating system brought numerous unproductive distractions. In a similar vein, Knott (2001) shows that franchisors add value by enforcing compliance with prescribed routines, which suggests that adaptations to idiosyncratic needs undertaken by individual franchisees are on average counter-productive. Likewise, the Great Harvest Bread Company explains to new franchisees that there are no acceptable reasons for diverging from the operating manual—and makes them agree to adhere to the “tiniest letter” of its instructions—in their first year of operation. Common to all these examples is the fact that the reference value of the original template is rapidly lost when even seemingly small modifications are introduced. Thus emphasis on accurate reproduction of the details of the original template mitigates stickiness because it maximizes the diagnostic value of causally ambiguous templates.
2.5 Accuracy, Stickiness and the Transfer Process One can analyze the relationship between accuracy and stickiness further by considering the different stages of the transfer process. Following Szulanski (2000), we consider four distinct stages of a transfer: initiation, initial implementation, ramp-up to satisfactory performance, and integration, i.e., subsequent follow-through and evaluation efforts to integrate the practice with other practices of the recipient. Initial implementation of a new practice and the subsequent ramp-up to satisfactory performance involve a two step sequence of first ‘learning before doing’(Pisano 1996)—achieved either by planning (Argote 1999) or by experimenting in a contrived setting before knowledge is actually put to use by the recipient—and then ‘learning by
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doing’ which entails the resolution of unexpected problems that arise when new knowledge is put to use by the recipient (von Hippel and Tyre 1995). Follow-through efforts typically aim at maintaining and improving the outcome of the transfer after satisfactory results are initially obtained. Thus, four different kinds of stickiness can be identified: initiation stickiness which is the difficulty to recognize opportunities to transfer and to act upon them to initiate the transfer; implementation stickiness which consists primarily of difficulties that arise during the exchange of information and resources between the source and the recipient; ramp-up stickiness which is created by unexpected problems that keep the recipient from matching or exceeding a-priori expectations of post transfer performance, and finally integration stickiness which emanates from efforts to remove obstacles and to deal with challenges to the routinization of the transferred practice. Obviously, there is little need for a referent for problem solving during the initiation phase, because the efforts to create a replica are yet to begin and in any case the replica is still not ready for comparison with the original. In the three subsequent phases, however, replication accuracy should help overcome stickiness, even though it may be less important in the penultimate integration phase than during implementation and ramp-up. The reason for the latter qualification is that the template is less likely to be needed as referent for problem solving once satisfactory results are initially obtained by the recipient. Moreover, the recipient is likely to gradually modify the replica during integration to fine tune its performance to the demands of the host environment, thus reducing the value of the template as referent. Even though the strength of the relationship between accuracy and integration stickiness thus will depend on the extent of those modifications and is likely to decline over time as both the replica and maybe even the
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original template are changed, we still expect accuracy to affect stickiness even at the integration stage—be it more weakly than in the implementation and ramp-up stages. . Thus, a replication perspective on knowledge transfer and stickiness suggests the following four hypotheses: Hypothesis 2: During initiation, accuracy in the replication is not related to stickiness. Hypothesis 3: During implementation, accuracy in the replication is negatively related to stickiness, and mediates the effect of barriers to transfer. Hypothesis 4: During ramp-up, accuracy in the replication is negatively related to stickiness, and mediates the effect of barriers to transfer. Hypothesis 5: During integration, accuracy in the replication is negatively related to stickiness, and mediates the effect of barriers to transfer.
3. Method 3.1 Sample and Research Process The transfer of best practices (O'Dell, Grayson et al. 1998) provides a propitious setting to observe transfers of complex knowledge within organizations, in which the main objective is to reproduce superior results already achieved somewhere within the organization. Data were collected through a two-step questionnaire survey. The first step of the survey asked companies to provide a list of transfers for study that included sufficient detail about the parties involved in those transfers [Gabriel: please add 1 or 2 sentences about how those companies were selected!]. More than 60 companies, with varying degrees of experience in the transfer of practices, expressed interest. Of that group, 12 were able to provide such a list. Of the 12, only eight provided entries of sufficient quality to warrant continuation of their involvement in the
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research. The eight companies were: AMP, AT&T Paradyne, British Petroleum, Burmah Castrol, Chevron Corporation, EDS, Kaiser Permanente, and Rank Xerox. The second step of the survey was devised to analyze accuracy at specific transfers. To provide practices for study, companies were directed to search for transfers of important activities or processes that showed evidence of difficulty during the transfer and in the adaptation of the practice by the recipient3. They were also instructed to rule out practices that could be performed by a single individual and to choose only practices that required the coordinated effort of many. The final sample consisted of 271 returned questionnaires, spanning 122 transfers of 38 practices4, for a response rate of 61%. To obtain a balanced perspective on each transfer, separate questionnaires were sent to the source, the recipient, and a third party to the transfer. The respondents were comprised 110 source units, 101 recipient units and 60 third parties. Average item non-response was lower than 5%. An average of 7.3 questionnaires were received for each practice studied. [Gabriel: please add 1 or 2 sentences about who the informants were, esp. function and level of involvement in the transfer!].
3.2 Construction of Measures Multiple-item scales were developed for all constructs to ensure the reliability and validity of the measurement system. Little empirical precedent was available to guide the development of the measures. A broad and thorough literature review informed the generation of the initial constructs and the a priori assignment of items to measure those constructs. In-depth
3
In an effort to increase the variance in the dependent variable, this directive was necessary to counter the inclination of firms to report only successful transfers. 4 The sample contained both technical and administrative practices. Examples of technical practices are software development procedures and drawing standards. Examples of administrative practices are upward appraisal and activity-based costing (ABC). Full disclosure of the practices studied is precluded by a guarantee of confidentiality.
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clinical work, consultation with subject experts and feedback obtained when piloting the questionnaire helped refine the choice of constructs, identify the most relevant items for those constructs and select their proper wording given the empirical context. Some items were discarded, but not re-assigned, after the full data set was obtained.5 [Gabriel: I suggest that we simply delete this last sentence and footnote] Having generated items to capture the relevant construct domains, we took several steps in the design and administration of the questionnaire to minimize measurement error (Nunnally 1978). Formulated only after extensive fieldwork, the questionnaire was pre-tested with all the participating companies, with experienced academics, and with respondents who volunteered to record their reactions while completing it. Finally, the cognitive effort of the respondents was reduced by minimizing the number of scales to be learned and by translating generic terms like “source” or “recipient” into the specifics of a particular transfer. Unless otherwise stated, a balanced five-point Likert-type agree-disagree scale was used to measure the items in the questionnaire. Following Nunnally’s (1978) recommendation, construct scores were computed by adding up the standardized item scores. Below we detail the operationalization of the central construct of this paper, accuracy of transfer. Remaining constructs are described in detail in Szulanski (1996). The accuracy of the transfer of a practice refers to the care invested in producing a close replica of the template. Thus a measure of accuracy must be sensitive to realized differences between the features of the replica and those of the original template (Shannon and Weaver 1949; Roberts and O'Reilly 1974; Muchinsky 1977) as well as to the intention to modify the original template (Stohl and Redding 1987; Eisenberg and Phillips 1991).
5
The a priori assignment of items was preserved for all constructs except accuracy. See description below.
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This scale has eight items that capture the realization of accuracy and the intent to be accurate. To assess intention, we asked whether the recipient performed unnecessary modifications to the practice, whether the recipient modified the practice in ways contrary to expert’s advice, and whether by altering the practice, the recipient created further problems which had to be solved. To assess both the intention and the need for a change we asked whether the recipient’s environment turned out to be different from that of the source forcing the recipient to make unforeseen changes to the practice. To assess realization, we asked whether the practice had to be adapted to make it workable at the recipient site; whether compared to the source’s practice, the recipient’s one is: 1 = “Exactly the same”; 2 = “Essentially the same”; 3 = “Slightly modified”, 4 = “Markedly modified”, 5 = “Completely different;” whether, by altering the practice, further problems were been created; whether original modules of the practice were replaced by existing ones at the recipient’s side; and we also measured the completeness of the replication by asking whether: 1 = “All modules have been transferred”; 2 = “Only selected, but all the essential modules have been transferred”; 3 = “Only the essential modules have been transferred”, 4 = “Only selected modules, some essential some not, have been transferred”, 5 = “None of the modules have been transferred,”. Unless otherwise specified, we used the 5 point default Likert agree-disagree scale mentioned above.
3.3 Performance of the measurement model Table 1 summarizes the performance of the measurement model. Convergent validity (reliability and unidimensionality) was evaluated separately for each construct (Gerbing and Anderson 1988). Cronbach’s alpha was used as a measure of reliability because it provides a lower bound to the reliability of a scale and is the most widely used measure (Nunnally 1978).
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All but two scales had alpha greater than .70, thus providing an adequate level of reliability for predictor tests and hypothesized measures of a construct (Nunnally 1978: 245-246). The two least reliable scales scored only marginally below that standard. Unidimensionality was assessed through factor analysis and computation of the theta coefficient (Armor 1974; Carmines and Zeller 1979; Zeller and Carmines 1980). The unidimensionality of all 11 scales was adequate. Finally, all variables meet reasonable assumptions of normality (see Table 1 for skewness and kurtosis values). Insert Table 1 about here Discriminant validity was evaluated for all construct pairs by examining the observed correlation matrix of the constructs. If the correlation between constructs i and j is 1, (i.e., if constructs i and j are perfectly correlated), the observed correlation should be (αi.5) * (αj.5) where αi and αj are the reliability coefficients for the constructs. In practical terms, testing for discriminant validity entails computing the upper limit for the confidence interval of the observed correlations and testing whether this limit is smaller than the maximum possible correlation between the scales as computed from their reliability coefficients. Table 2 reports the correlations for all the variables. All construct pairs met the discriminant validity test at p < .005. Insert Table 2 about here 3.4 Substantive Assumptions for the Statistical Analysis We make two substantive assumptions in our statistical analysis of the hypotheses. First, we assume that predictors remain invariant for the duration of the transfer. When such assumption holds, the timing of the measurement of the independent variables is not critical. We deem this assumption reasonable because most of our predictors typically change slowly. There may be exceptions, though. For instance, he motivation of the source, the motivation of the 19
recipient and the nature of the relationship between the units may be affected by the expected outcome of the transfer, a belief which transfer participants may update during the transfer process. Pre-existing relationships between source and recipient sub-units did exist for al least two years prior to the beginning of the transfer. [Gabriel: why is this sentence here? Why does it matter? Can’t we just delete it?] In addition, we assume that a comparison across transfers is warranted. Leonard-Barton (1990) argues that it is necessary to measure multi-item constructs at a “defined point” in time to make meaningful comparisons when the meaning of complex constructs depends on when during a transfer they are measured. As point of reference for her study she selected the “very first use of the technology in a routine production task” as the anchor point. She chose that point because it could be identified with a “satisfactory degree of accuracy”. [Gabriel: I suggest that we delete these two sentences—there is no need to compare this paper so closely to Leonard-Barton’s] In this study, all questionnaires were completed within a narrow window of 3.5 months, which started 5 months after the first day that knowledge was first put to use by the recipient. Thus, all transfers are measured at a defined and comparable point in time. Comparison across transfers is thus considered appropriate6.
3.5 Statistical Analysis We have complete data on 109 distinct transfers. For several of these transfers, we have data from multiple respondents—transfer source, recipient, or third party. Depending on which stage of the transfer we analyze, the number of available observations varies between 143 and
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Such a window of 3.5 months is narrow, because it means that all transfers were sampled early on in the integration stage which has been documented to last between 1.5 to 2 years. While 3.5 months is not literally a
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153. To adequately model the lack of independence between multiple observations of the same transfer, we use hierarchical linear modeling allowing for random effects in both the intercept and slopes. Using subscript i to denote a transfer, j to denote a respondent and k to denote a regressor, the model structure we use is:
Σk βki xkij + εij (k: 1, …, K), where
yij
=
β0i +
βki
=
βk + Uki (k: 0, …, K),
εij
i.i.d. N(0, σ2),
Uki
i.i.d. N(0, τk).
Since the average number of observations per transfer is quite small, we impose a variance components structure, i.e., we assume Cov (Uki, Uki’) = 0. As always in hierarchical linear models, the random effects Uki are assumed independent of the residuals εij. Our analysis proceeds in the standard fashion (Bryk and Raudenbusch 1992; Snijders and Bosker 1999). We estimate the model using residual maximum likelihood (Laird and Ware 1982). We originally allow for random effects in all regressors, and use likelihood ratio test to identify which random effects can be excluded from the model (i.e. random effects whose variance is not significantly from zero). Having found the model with the simplest yet statistically most defensible error structure, we use traditional Wald t-tests to assess whether (fixed) slope effects βk are significantly different from zero (one must not use likelihood ratio tests for testing the significance of fixed effects when using residual maximum likelihood).
"point in time", it is quite narrow a window considering the duration of the entire transfer processes being investigated, which warrants comparison across transfers.
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In addition to the regressors of substantive interest (i.e., barriers to transfer as antecedents and accuracy as mediator), we include two binary indicators in the models to indicate whether the respondent j participated in the transfer as source or third party (rather than as recipient, which is the baseline captured in the intercept). We do not expect the average level of stickiness reported to vary across types of respondents, and hence expect the coefficients of the dummy variables to be zero, but hold it more likely that third parties, being somewhat more remote from the transfer than sources and recipients, to exhibit more error variance. Allowing for random effects in the coefficients of the dummy control variables accounts for the possibility of such heteroscedasticity across types of respondents. To test the mediation effect of accuracy, we follow the procedure recommended by Baron and Kenny (1986). We run three regression models: a first model tests whether the antecedents (barriers to transfer) affect the mediator (accuracy); a second model tests whether the antecedents affect the dependent variable (stickiness); and a third model including both antecedents and mediator tests the relationship between the mediator and the dependent variable while controlling for the antecedents and allows one to assess the extent to which the antecedents operate on the dependent variable through the mediator. The second and the third models enable the comparison of the direct path (excluding the mediator) with the mediated path (including the mediator). Consequently, we first regress accuracy on a set of barriers. We then regress the different types of stickiness against the same barriers. Finally, we regress the different types of stickiness against the barriers, but this time including accuracy as an independent variable.
22
4. Results Table 3 displays the results. The results in the first column support H1. Most barriers to knowledge transfer are negatively related to accuracy. Specifically, source motivation, source perceived reliability, recipient absorptive capacity and the quality of the relationship are positively related to accuracy. Causal ambiguity and retentive capacity are negatively related to accuracy. Causal ambiguity makes it more difficult to identify and reproduce the essential features of the template. The negative effect of retentive capacity indicates that unlearning barriers impeding the shedding of legacies tend to decrease accuracy. Knowledge provenness, recipient motivation, and context are not significant. Insert Table 3 about here The other columns in Table 3 allow us to assess, for each of the four transfer phases, whether the barriers are related to stickiness when one does not control for accuracy and to assess whether this changes after including accuracy. In assessing the evidence of mediation, we focus only on the effect of those antecedents (barriers) that have a significant direct relationship to accuracy. H2 is supported. As predicted by the replication perspective, we find no mediation effect at the initiation stage. Controlling for accuracy has no effect on the effect of knowledge barriers. Furthermore, the coefficient of accuracy itself is not significant. H3 is supported. All five salient barriers have mediated effects, two fully and three partially. We observe full mediation of the effects of perceived source reliability and quality of the relationship, and partial mediation of the effects of source motivation, causal ambiguity, and absorptive capacity. The coefficient of accuracy itself is highly significant.
23
H4 is supported. All five salient barriers have mediated effects, three fully and two partially. We observe full mediation of the effects of the source motivation, source perceived reliability and causal ambiguity, and partial mediation of the effects of absorptive capacity and retentive capacity. The coefficient of accuracy itself is highly significant. H5 is supported, but slightly less strongly than H3 and H4 are. Three of the four salient barriers have mediated effects. We observe full mediation of the effects of causal ambiguity and partial mediation of the effects of absorptive capacity and quality of the relationship. The coefficient of accuracy itself is highly significant. In sum, the results are closely in line with the predictions of a replication perspective on stickiness. Accuracy is an important mediating mechanism during the implementation, but not during the initiation of the transfer.
5. Discussion and Conclusion While extant research on stickiness has identified, both theoretically and empirically, several factors that contribute to the difficulty to transfer knowledge, it did not specify—let alone investigate empirically—the generative mechanisms that underlie stickiness. The replication perspective on internal stickiness that we develop in this paper allows us to specify and test the workings of a generative mechanism that underlie stickiness. A replication perspective suggests that knowledge transfer occurs through replication, and that barriers cause stickiness by reducing replication accuracy. In this study, we advance the hypothesis that accuracy in the replication process mediates the relationship between barriers of transfer and stickiness. Using data on the transfer of best practices within firms, we find empirical support for this claim for the implementation, ramp-up and integration phases of knowledge transfer.
24
Our results document the role of accurate replication as a generative process of knowledge transfer. The empirical support for this replication perspective renders us optimistic about further exploring its potential for reframing the knowledge transfer process and for achieving a deeper understanding of how transfer barriers operate. For example, the replication perspective suggests possible hypotheses to guide an investigation of how the motivation of the source of knowledge could affect the eventfulness of a transfer. The source may impact accuracy by hindering access to the template or by withholding important details that preclude the possibility of creating an accurate enough replica at the first attempt. Similarly, causal ambiguity may impact accuracy by increasing the scope of template characteristics that must be considered for reproduction thus increasing the effort necessary for a successful transfer, because some of these characteristics will turn out to be immaterial to the functioning of the replica but that cannot be known in advance. The mediation effect of accuracy is important during the implementation phases of the transfer because it is during those moments that the replica is actually being produced. During the initiation of the transfer, other processes such as search and competition for attention may upstage accuracy seeking (Simon 1957; Cyert and March 1963; Hansen 1999). Likewise, during the integration phase, which includes institutionalization, accuracy requirements may be relaxed as adaptation efforts are made to fine tune the replica to its environment, thus reducing the value of the original template as a referent. A limitation of the evidence is that its cross-sectional nature precludes strong casual inferences. Data collected through a cross sectional survey could be valuable for a diachronic analysis because longitudinal archival data is virtually non-existent and most extant longitudinal examinations of the process of transfer span, at best, a handful of
25
transfers and, almost invariably, a single firm. Furthermore, observations taken through a fixed-interval periodic survey may not be comparable because the specific meaning of complex measures is sensitive to the stage of the transfer in which those measurements are taken. Thus such a survey may miss important dynamics when transfers are not synchronized, when the interval of sampling is long relative to the pace of events in the transfer and when respondent’s participation in the transfers is fluid. Analysis of a crosssectional survey is not subject to these other concerns. [Gabriel: I would delete all that stuff because it’s already taken care of in section 3.4!! Do you have OTHER limitations to mention?] Overall, our results allow us to sketch a contingency approach to the management of knowledge management transfer. Indeed, different managerial interventions may be more appropriate to different stages of the transfer. Our analysis suggests that interventions geared to increase accuracy could be particularly beneficial during the implementation phases of the transfer, especially during initial implementation and during ramp-up. Thus for example, stressing the use of training materials during the implementation could help improve accuracy thus reducing stickiness, while hiring new personnel during those phases of the transfer may increase the inflow of extraneous information from the outside thus reducing accuracy and increasing stickiness. The metaphor of replication provides a powerful yet simple framework from which to start specifying some of the mechanisms that underlie stickiness. It applies to all those instances of knowledge utilization where knowledge reuse is preferable to de novo learning. This metaphor has allowed us to augment the signaling metaphor in order to specify and test a specific mechanism, accuracy, underlying stickiness.
26
Progress in understanding the still substantial mysteries of knowledge transfer is being increasingly impaired by a treatment of the transfer as if it were a black box. We believe that opening that proverbial black box is a promising avenue to achieve further progress and we hope that we have shown one possible way to begin moving in that direction. Of course, not all transfers are equal and the importance of accuracy or other intervening mechanisms may vary from transfer to transfer. Future research in this direction may bring a more nuanced understanding of stickiness and increase our ability to leverage what we know by helping us unsticking sticky transfers.
27
Table 1: Measurement Model Cronbach α
Items
Valid N
Avg. Inter item Corr.
Skewness
Kurtosis
Motivation of the source unit to support the transfer
.93
13
271
.50
-.16
-1.34
2 Source credibility
Degree to which the source of the best practice is perceived as reliable
.64
8
210
.19
-.29
-.28
3 Context
Degree to which the organizational context supports the development of transfers
.77
14
247
.20
-.09
-.03
4 Causal ambiguity
Depth of knowledge
.86
8
250
.45
.19
-.74
5 Knowledge provenness
Degree of conjecture on the utility of the transferred knowledge
.67
3
251
.40
-.67
-.27
6 Recipient motivation
Motivation of the recipient unit to support the transfer
.93
14
271
.48
-.31
-1.27
7 Recipient absorptive capacity 8 Recipient retentive capacity 9 Relationship
Ability of the recipient unit to identify, value and apply new knowledge
.83
9
252
.36
-.22
-.65
Ability of the recipient unit to support the routinize the use of new knowledge
.81
6
249
.43
-.12
-.04
Ease of communication and intimacy of the relationship
.71
3
237
.46
-.30
-.61
10 Accuracy
Degree of similarity between the replica and the template.
.79
8
203
.32
-.08
-.30
11 Initiation stickiness
Difficulties experienced prior to the decision to transfer
.74
8
241
.27
.75
.26
12 Implementation stickiness
Difficulties experienced between the decision to transfer and start of actual use
.80
11
240
.27
.39
-.26
13 Ramp-up stickiness
Unexpected problems from the start of actual use until satisfactory perf obtains
.69
7
236
.25
-.00
-1.03
14 Integration stickiness
Difficulties experienced after satisfactory performance is achieved
.79
12
224
.25
.31
-.72
Construct
Description
1 Source motivation
28
Table 2: Correlation matrix (Casewise deletion of missing data, |r| values > .13 are significant at the .05 level) 1
2
3
4
5
6
7
8
9
10
11
12
13
1 Initiation stickiness 2 Implementation stickiness
.57
3 Ramp-up stickiness
.37
.56
4 Integration stickiness
.30
.47
.41
5 Source motivation
-.37
-.42
-.29
-.15
6 Source credibility
-.55
-.52
-.31
-.28
.46
7 Context
-.30
-.34
-.36
-.49
.25
.27
8 Causal ambiguity
.47
.43
.30
.31
-.32
-.48
-.36
9 Knowledge provenness
-.42
-.35
-.19
-.13
.27
.34
.26
-.43
10 Recipient motivation
-.32
-.36
-.25
-.39
.48
.35
.30
-.21
.17
11 Recipient absorptive capacity
-.28
-.48
-.34
-.60
-.07
.27
.44
-.23
.16
.39
12 Recipient retentive capacity
-.14
-.27
-.06
-.44
-.11
.09
.46
-.25
.08
.18
.62
13 Relationship
-.36
-.41
-.21
-.33
.21
.32
.35
-.29
.30
.30
.24
.15
14 Accuracy
-.36
-.62
-.62
-.52
.32
.47
.36
-.53
.32
.24
.34
.21
29
.29
14
Table 3: Regression Analysis Fixed Effects Stickiness Initiation
Stickiness Implementation
Stickiness Ramp-Up
Stickiness Integration
β coefficient (std error)
Accuracy
Intercept
-1.019
0.671
0.715
1.079
0.954
1.433**
0.705
3.419***
3.332***
(0.839)
(0.951)
(0.958)
(1.081)
(1.045)
(0.717)
(0.628)
(1.136)
(1.105)
0.145*
-0.066
-0.073
-0.218**
-0.176*
-0.156**
-0.069
-0.133
-0.088
(0.0856)
(0.087)
(0.089)
(0.101)
(0.099)
(0.069)
(0.060)
(0.109)
(0.107)
0.233**
-0.515***
-0.541***
-0.219*
-0.144
-0.194**
-0.081
0.109
0.242*
(0.105)
(0.102)
(0.106)
(0.118)
(0.118)
(0.080)
(0.072)
(0.125)
(0.127)
0.0411
0.042
0.036
0.003
0.019
-0.218***
-0.216***
-0.103
-0.106*
(0.050)
(0.051)
(0.051)
(0.059)
(0.058)
(0.044)
(0.037)
(0.061)
(0.059)
Src. Motivation Src. Credibility Context Causal Ambig. Provenness Rec Motivation Absorptive Cap. Retentive Cap. Relationship Source Third Party
-0.389***
0.140**
0.165**
0.297***
0.179**
0.132**
-0.078
0.240***
0.134
(0.062)
(0.065)
(0.070)
(0.071)
(0.077)
(0.053)
(0.054)
(0.079)
(0.082)
0.033
-0.498***
-0.494***
-0.099
-0.120
0.133
0.040
0.188
0.135
(0.175)
(0.173)
(0.173)
(0.166)
(0.162)
(0.148)
(0.127)
(0.186)
(0.182)
-0.023
-0.107
-0.100
0.102
0.0786
0.0452
0.029
-0.207*
-0.252**
(0.080)
(0.084)
(0.085)
(0.093)
(0.091)
(0.066)
(0.056)
(0.104)
(0.101)
0.210***
-0.0002
-0.017
-0.573***
-0.498***
-0.276***
-0.172***
-0.521***
-0.454***
(0.069)
(0.084)
(0.086)
(0.078)
(0.079)
(0.056)
(0.051)
(0.086)
(0.086)
-0.275***
-0.097
-0.072
0.084
-0.018
0.417***
0.302***
0.013
-0.028
(0.089)
(0.101)
(0.106)
(0.130)
(0.125)
(0.093)
(0.081)
(0.114)
(0.112)
0.251*
-0.058
-0.071
-0.302*
-0.233
0.094
0.269**
-0.627***
-0.530***
(0.142)
(0.152)
(0.153)
(0.164)
(0.162)
(0.119)
(0.105)
(0.189)
(0.185)
0.193
1.300*
1.193
-1.159
-1.168
-1.372**
-1.214**
-0.367
-0.165
(0.637)
(0.725)
(0.731)
(0.749)
(0.728)
(0.512)
(0.446)
(0.877)
(0.865)
0.267
2.235**
2.114**
0.974
1.111
-0.928
-0.417
-1.461
-1.488
(1.136)
(0.847)
(0.858)
(0.958)
(0.920)
(0.780)
(0.708)
(0.985)
(0.968)
Mediator Accuracy
0.0694
-0.275***
-0.415***
-0.288***
(0.080)
(0.086)
(0.059)
(0.091)
Random Effect Variance Intercept
2.875
Provenness
0.512
3.305 0.407
0.400
Absorptive Cap.
0.169
0.157
Retentive Cap.
0.138
0.160
4.011
2.056
1.057
0.182
0.142
0.464
0.350
0.269
0.187
5.875
4.231
11.828
12.113
Third Party
24.332
3.457
3.042
7.070
6.391
Residual
5.572
6.217
6.239
6.585
6.124
1.725
1.584
-2REML
852.0
844.2
846.7
833.9
827.4
723.3
685.9
822
815.3
N
154
153
153
148
148
143
143
143
143
* p
