Organizational Behavior and Human Decision Processes Vol. 82, No. 1, May, pp. 28–44, 2000 doi:10.1006/obhd.2000.2885, available online at http://www.idealibrary.com on
An Investigation of Partner Similarity Dimensions on Knowledge Transfer Eric D. Darr Anderson Graduate School of Management, University of California, Los Angeles
and Terri R. Kurtzberg Kellogg Graduate School of Management, Northwestern University
Learning from the experiences of others can provide significant benefits for an organization, but it can be difficult to know who has the most useful or applicable knowledge. Knowledge is acquired from many sources: from within the firm; from other firms; or from competitors, customers, suppliers, and channel partners. Managers must decide how to efficiently search through a universe of potential knowledge sources to select the knowledge that will be the most useful to them. This research examines the conditions under which partner similarity enhances knowledge transfer. Previous research has argued that partner similarity influences knowledge sharing through attraction. Building on past work, our research argues that attraction is only one mechanism by which partner similarity affects knowledge transfer and introduces the idea that partner similarity aids the search through a universe of potential knowledge sources. The dimensions of partner similarity that allow more efficient search will facilitate knowledge transfer, while those similarity dimensions that do not aid search will have a less important impact on transfer. Data from both quantitative and qualitative sources support these hypotheses. Quantitative analyses show that strategic similarity emerges as a more important dimension than customer or location similarities as a significant predictor of knowledge transfer. Qualitative interview data show that businesses are conscious of the strategic similarities within their industry and choose transfer partners accordingly. q 2000 Academic Press
Address correspondence and reprint requests to Eric Darr, 337 Futurity Drive, Camp Hill, PA 17011. E-mail:
[email protected]. 28 0749-5978/00 $35.00 Copyright q 2000 by Academic Press All rights of reproduction in any form reserved.
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Key Words: knowledge transfer; strategic similarity; partner selection; organizational learning.
The business world is changing at an increasingly faster rate, and it is impossible for any one business or manager to constantly monitor and understand all of the potentially relevant information in any domain. Businesses need to be able to share knowledge with each other and learn from the experiences of others in order to keep up with the changes that happen in every industry. To do this, businesses must first be able to efficiently search the overwhelming amount of information available and to select appropriate sources of knowledge. For the most part, managers are left on their own to sort out where the best lessons might be held. The more that one business has in common with another, particularly with respect to the problems that they face and the decisions that they make, the more likely it is that the lessons and examples of one will be of use to the other. The thesis of this research maintains that business similarity dimensions will attract the attention of managers and influence knowledge transfer among them. Knowledge transfer is conceived as an event through which one organization learns from the experience of another. However, successful knowledge transfer is not always easy to achieve. Research has shown that a firm may greatly improve its innovative capacity by leveraging the skills of others through the transfer of knowledge (Pennings & Harianto, 1992) both within and across firms (Garud & Nayyar, 1994; Gilbert & Cordey-Hayes, 1996; Szulanski, 1996). However, research also finds evidence of both incomplete transfer and no transfer at all. The conditions under which transfer occurs have yet to be clearly established. This research focuses specifically on the role of partner similarity as a condition that promotes knowledge transfer. The transfer of knowledge from one location to another can enhance organizational learning. New knowledge can promote innovations in new methods and practices, which can then be absorbed into the routines and culture of an organization. Although research shows that organizations can learn either from their own experiences or from the experiences of others (Huber, 1991; Levitt & March, 1988), much of the current theoretical and empirical work (e.g., Attewell, 1992; Brown & Duguid, 1991; Lant & Mezias, 1992; Starbuck, 1992), as well as prescriptive work (e.g., Nadler, Gerstein, & Shaw, 1992; Senge, 1990), focuses on learning from one’s own experiences. Thus, research focusing on learning from the experiences of others will complement these studies and enhance our understanding of knowledge transfer as one of the ways by which organizations learn. Our research argues that transfer has occurred when a contributor shares knowledge that is used by an adopter. Our definition differs from others that equate knowledge transfer simply with sharing (Huber, 1991) and that do not include the condition that knowledge transfer must involve use on the part of the adopter. It is difficult to identify and verify that knowledge has been transferred without direct evidence of sharing. Applying our definition makes
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it easier to verify that knowledge transfer has occurred by investigating those cases involving use which can be directly observed. SELECTION OF A TRANSFER PARTNER
Even after appropriate knowledge has been selected, transfer of that knowledge is an unsure process. To transfer knowledge successfully, a context of understanding needs to be created (Dove, 1996) between the transfer “partners.” This context of understanding should include mechanisms for both knowledge contribution and adoption. This research proposes that specific dimensions of similarity provide a heuristic for managers to choose both when to contribute knowledge and from whom to adopt knowledge. Podolny (1994) describes similarity as a selection heuristic for exchange partners in an uncertain market. This research shows specifically that firms facing an uncertain market tend to form exchange partnerships with past partners who share similar status. An effective heuristic reduces the cost of knowledge adoption by reducing the time and effort required to locate appropriate knowledge sources. Likewise, a heuristic can reduce the cost of knowledge contribution costs by decreasing the effort required to understand the needs of the other party and to frame contributions accordingly. Researchers have identified the positive relationship between similarity and attraction as one of the broadest and most reliable findings in all of social psychology (Sabini, 1992). Such research has shown that when others, even total strangers, agree with us, we will like them more (Newcomb, 1961; Singh & Tan, 1992), we will be more willing to share assets or information with them (Byrne, Clone, & Worchel, 1966; Tajfel, Billing, Bundy, & Flament, 1971), and we will experience more positive affect (Byrne & Nelson, 1964). This attraction is particularly salient when there is similarity on an issue that is important to daily interactions (Davis, 1981). Furthermore, information is more likely to be believed when it comes from similar others (O’Reilly, 1983). Finally, research has shown that knowledge transfer is more likely between individuals who display similar attitudes as well as between firms that have encountered similar problems in the past (Ounjian & Bryan, 1987) and that have had similar experiences (Burkhardt & Brass, 1990; Cohen & Levinthal, 1990). Knowledge transfer involves individuals within one organization communicating on specific problems and procedures with individuals from another organization. Therefore, the social psychological principles that influence and guide an individual’s choices and thought processes affect firm-level actions. Previous research has suggested that similarity dimensions guide attraction, and, thus, partner selection, among individuals. Partner similarity manifests itself in multiple forms. Our research explores three distinct dimensions of partner similarity: (a) strategic, (b) customer, and (c) geographic. Strategic and customer similarity most closely approximate the spirit of partner similarity that previous research has addressed in that business strategy and customers are directly chosen features and geography was most likely not selected by current managers. However, we have also analyzed
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geographic similarity in order to understand more fully the breadth of the similarity construct. It is possible that any form of similarity will impact the selection of a knowledge transfer partner. Geographic similarity may offer more opportunities for transfer because store managers may attend regional meetings or because managers in the same area may get better acquainted by virtue of their physical proximity. Partners may also be motivated to transfer knowledge because of the common marketing experience created by customer similarity. However, we believe there are four reasons why similarity in strategy should have more impact on knowledge transfer than other kinds of similarity. The similarity-attraction hypothesis (Byrne, 1971) suggests that managers making the same choices will likely be inherently attracted to each other. Unlike strategies driven by customer and location similarities, business strategy reflects a larger degree of choice on the part of the manager. Attributes that are consciously selected will be perceived as more important than those that happen by chance. Based on similarity theory (Byrne, Clone, & Worchel, 1966; Tajfel, Billing, Bundy, & Flament, 1971), there is reason to argue that strategic similarity has the biggest impact on transfer. Second, research on strategic alliances (Simonin, 1999) and strategic groups (Dranove, Peteraf, & Shanley, 1998) suggests that firms with common business strategies may be better aligned to transfer knowledge across organizational boundaries. Research on strategic groups demonstrates that these groups provide a reference point for member firms for purposes of identity and strategy formulation (Fiegenbaum & Thomas, 1995) and even for networks and intrafirm cooperation (Duysters & Hagedoorn, 1995). Strategic similarity has also been shown to promote better performance in horizontal mergers (Ramaswamy, 1997). The results suggest that strategic similarity may form a context in which firms can better understand each other and thus be more capable of effectively sharing knowledge. Third, organizations choosing the same strategy may assume a commonality of problems and experiences that geography or customer base would not imply. The definition of a common problem is particularly important for issues of knowledge transfer because the need for knowledge generally stems from a problem. It follows that a search for potential solutions would begin by seeking out those who have experienced the same problem and who have demonstrated a course of action consistent with one’s own strategy. The ability to transfer knowledge is improved between organizations or individuals sharing common problems. Finally, research demonstrates that information is more easily assimilated into long-term memory (Newell & Simon, 1972; Simon & Lea, 1974) and more easily implemented (Cohen & Levinthal, 1990) when it is similar to existing knowledge content and structure. Strategic similarity implies the closest fit of knowledge structures among organizations because the entire operation of an organization is likely to be guided by the overarching business strategy that has been chosen. This structural similarity among strategically similar organizations therefore increases the ability to use knowledge acquired from others.
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This research investigates the effects of three specific dimensions of partner similarity (business strategy, customer base, and proximity) on transfer of knowledge across organizations. It is expected that: 1: Greater business strategy similarity will lead to greater transfer of experience. 2: Greater customer similarity will lead to greater transfer of experience. 3: Greater partner proximity will lead to greater transfer of experience.
Although we predict that all three of the similarity dimensions will affect knowledge transfer, we also predict that business strategy similarity will have the greatest impact. Modeling techniques are used to test the hypotheses across a large number of organizations over time. Interview data are analyzed to provide potential explanations for the behaviors and actions of the organizations. METHOD: QUANTITATIVE AND QUALITATIVE DATA
Our research was conducted on pizza-delivery franchise organizations in England. All of the franchises in the study are affiliated with the same parent corporation, which has its headquarters in the United States. The sample consisted of 11 franchise organizations owning a total of 41 pizza stores. Twothirds of the United Kingdom pizza stores franchised by the American parent corporation are included in the sample. The largest franchise owned 11 stores, whereas 4 of the franchisees were single-store owners. Data for the modeling effort were collected from the English franchise organizations and from the corporation’s international headquarters. Corporate officials provided data concerning number of pizzas produced, production costs, and sales for each store by week. The production costs included food, labor, and coupon costs. These costs represent approximately 95% of the variable costs associated with pizza production. Structured interviews with the franchisees provided data on store location, customers, and strategy. These data were coded by three independent raters. The English franchise setting provides a useful set of data for isolating effects of partner similarity on knowledge transfer. The inputs, or raw materials, technology, and products are nearly homogeneous across all stores. Therefore, potentially important aspects of similarity associated with technology and product are controlled naturally in the sample. Additionally, there appears to be little or no competition between stores in the sample. In most English towns, the openness of the fast-food market allows pizza stores to be placed several miles from each other. Stores that are in the same town draw their customers from different districts. The lack of stack-rankings or direct store comparison by the corporation further reduces the possibility for competition between stores. Too much competition can reduce the likelihood of knowledge transfer. Finally, there appears to be minimal corporate control over store operations. Any transfer of knowledge occurring in this context is discretionary because the franchisees are not mandated to communicate with each other and only do so when they decide that it is in their own best interests.
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Darr, Argote, and Epple (1995) demonstrated on a different sample of stores that as knowledge and experience concerning pizza production accrues, the cost of production decreases at a decreasing rate. The result, that cost per unit changes as a function of cumulative experience, is referred to as a learning curve. Typically, research using learning curves analyzes the cost of producing units of output as a function of cumulative production experience at an organization (e.g., Argote, Beckman, & Epple, 1990; Zimmerman, 1982). However, knowledge transfer may also be investigated in the learning-curve framework by examining the effects of production experience generated in other organizations (Argote & Epple, 1990). For example, knowledge transfer may be inferred if cumulative production experience in store A influences the unit cost of production in store B. More generally, the approach to measuring knowledge gained from the experience of others involves (a) aggregating output through time and across stores and (b) estimating the effects of these aggregations on focal organizational outcomes. In this way, estimation of the learning curve allows us to relate knowledge to outcome and therefore to estimate the effects of knowledge transfer. In order to best explore knowledge-transfer practices, both qualitative and quantitative techniques were used. Quantitative modeling using a learningcurve framework allows for precise coefficient estimation and for differentiation between the effects of partner similarity and those of competing variables such as partner proximity. In addition, it allows for the demonstration of the specific, measurable effects of knowledge transfer and learning over a relatively long period of time. However, a quantitative analysis does not allow for a direct investigation of the relationship between partner similarity and the individual-level transfer activities of search, match, contribution, and adoption. Modeling efforts can provide results on the effect of various factors on knowledge transfer, but they cannot provide evidence for the actual behaviors and decisions undertaken by the organization. A qualitative field study builds on the strengths of the quantitative effort by allowing for an examination of the processes involved in transfer of knowledge. Quantitative Analysis The following model is used to test impacts of partner similarity on knowledge transfer: focal store 5 store-specific 1franchise 1 knowledge from stores 1 knowledge from stores unit cost knowledge knowledge with common business with different business (Q) (FQ) strategies strategies (MQ) (NQ) 1 knowledge from stores 1 knowledge from stores with 1 knowledge from proximate with similar customers different customers stores (PQ) (DQ) (ZQ) 1 knowledge from distant 1 store dummy 1 stores variables (VQ)
error term
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Several specific models are estimated in which pizza-production cost depends on store-specific experience, franchisee experience, aggregate similar-franchisee experience, and other control variables. These models, using nonlinear maximum-likelihood regression, allow us to test the hypotheses through a comparison of competing variables. In the analyses, the unit of time is a week. All variables (except for the store dummy variables and error term) are the cumulative pizza production through the end of the previous week. The lagged cumulative output appears on the right-hand side of the equations because cumulative output serves as a proxy for experience acquired as a result of past output. Partner similarity effects are investigated by estimating models (columns 1, 2, and 3 in Table 1) with aggregate similar-store experience versus aggregate different-store experience. The variable of business strategy similarity was determined through structured interviews with the 11 franchisees, which provided information on their business problems and strategies for business improvement. Research in strategic alignment has long recognized that organizational strategies can be categorized into distinct groupings (Miles & Snow, 1978; Porter, 1980). Three independent raters used respondents’ answers to categorize the franchisees as either “cost cutters” or “expansionists” (Kendall’s W 5 .706, c2 5 19.03, p 5 .024) according to the following definitions: Expansionist: A franchisee who behaves as if he/she believes that survival primarily depends
TABLE I Estimated Coefficients for Models Predicting Unit Cost Variable (b1): store-specific learning (b2): transfer between commonly owned stores (b3): transfer between stores with same strategies (b4): transfer between stores with different strategies (b5): transfer between stores with same customers (b6): transfer between stores with different customers (b7): transfer between near stores (b8): transfer between distant stores Autocorrelation coefficient (u) R2 N
Model 1
Model 2
Model 3
Model 4
2.033** (.011) 2.031** (.009) 2.026** (.013) .011 (.018)
2.034** (.010) 2.031** (.009)
2.033** (.010) 2.030** (.009)
2.029** (.009) 2.031** (.008) 2.026** (.014)
.018 (.012) .008 (.015)
.659** (.011) .553 4658
Note. Standard errors are shown in parentheses. ** p , .01.
.662** (.011) .412 4658
.019 (.012)
2.009 (.011) .006 (.014) .659** (.011) .409 4658
2.008 (.012)
.658** (.011) .592 4658
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upon product, service, and/or market expansion. Typical actions would include increasing hours of operation, improving training, and expanding customer contact. Cost cutter: A franchisee who behaves as if he/she believes that survival primarily depends upon improving operating efficiency. Typical actions would include decreasing staff, decreasing advertising, and eliminating operating hours that are not cost effective.
Indeed, during the interviews it became apparent that most cost cutters considered expansionists to have bad business sense and to be engaged in ruinous activities and vice versa. A nonparametric test—the Kendall test—was used to assess interrater reliability because of the inability to meet normal distribution assumptions and because of the dichotomous nature of the variables. Within the sample, six franchisees controlling 20 stores were identified as cost cutters. The remaining five franchisees owning 21 stores were labeled expansionists. Customer similarity was also determined to be a possible selection mechanism through structured interviews with the franchisees about their primary customers. Customer and market knowledge is essential for success, particularly in service organizations. Owners indicated in interviews that typical discussions with other owners generally begin with brief chats about current sales performance and customer responses to promotions. Responses were used to classify stores as serving (a) tourists and business people, (b) college employees or students, or (c) local residents. Within the sample, 46% of the stores cater primarily to tourists and business people, 12% serve college-affiliated customers, and the remaining 42% of the stores primarily serve residential customers. Geographic similarity was also discussed as a potential determinant of selection partners across the English franchise stores. Research suggests that partner proximity (Mahajan & Peterson, 1979) or the “neighborhood effect” (Brown, 1981) can increase contact and communication (Czepiel, 1974; Ghoshal & Bartlett, 1988), which can in turn facilitate innovation diffusion (Rothwell, 1978). Proximity may also influence motivation to engage in transfer of learning because of greater similarity of problems and experiences. The impact of partner proximity on transfer of learning is analyzed by comparing the effects of knowledge from near stores to the effects of knowledge from distant stores on focal organizational outcomes. Proximity is measured by categorizing four regions of store locations: London, central England, northern England, and Ireland. Stores located in the same region are treated as proximate. In order to be confident that the effects of strategy, customer, proximity, and franchise membership could be disentangled, the correlations among these categories were assessed. One of our concerns was that if all stores located in London were found to be cost cutters, for example, the effects of proximity and business strategy variables would be confounded. Results of a multivariate nonparametric test (Kendall correlation coefficients) of the relationship between store categorization on business strategy, customer, location, and franchise membership alleviated these concerns. As might be expected, the only significant correlation was between franchise membership and business strategy (T 5 .451, p 5 .003). The relationship between location and customers was
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marginally significant (T 5 .214, p 5 .093), indicating that stores in the same area may share similar types of customers. However, strategy remains statistically unrelated to the location or the customer base of the franchises. The three models estimated: focal store unit cost 5 b0 1 b1(store-specific knowledge) 1 b2(franchise knowledge) 1 b3(common strategy) 1 b4(different strategy) 1 store dummy variables 1 error term,
(1)
focal store unit cost 5 b0 1 b1(store-specific knowledge) 1 b2(franchise knowledge) 1 b5(similar customers) 1 b6(different customers) 1 store dummy variables 1 error term,
(2)
and focal store unit cost 5 b0 1 b1(store-specific knowledge) 1 b2(franchise knowledge) 1 b7(proximate stores) 1 b8(distant stores) 1 store dummy variables 1 error term.
(3)
In Eq. (1), if b3 (common strategy) is significant, transfer of knowledge between stores sharing similar business strategies has occurred. If b4 (different strategy) is significant, transfer of knowledge between stores with different strategies has occurred. Hypothesis 1 suggests that b3 will be significant. In Eq. (2), if b5 (similar customers) is significant, transfer of knowledge between stores sharing similar customers has occurred. If b6 (different customers) is significant, transfer of knowledge between stores serving different customers has occurred. Hypothesis 2 suggests that b5 will be significant. In Eq. (3), if b7 (proximate stores) is significant, transfer of knowledge between proximate stores has occurred. If b8 (distant stores) is significant, transfer of knowledge between distant stores has occurred. Hypothesis 3 suggests that b7 will be significant. The relative importance of the three partner similarity dimensions is investigated by estimating a model (column 4 in Table 1) with knowledge from stores with similar strategies, similar customer experience, and proximate locations. The last model estimated: focal store unit cost 5 b0 1 b1 (store-specific knowledge) 1 b2(franchise knowledge) 1 b3(common strategy) 1 b5(similar customers) 1 b7(proximate stores) 1 store dummy variables 1 error term.
(4)
Our discussion suggests that b3 (common strategy) will be more significant than b5 (similar customers) and b7 (close stores). Quantitative Results Store-specific learning results. As expected, store-specific learning is evident in the English pizza stores. The data demonstrate that the unit cost of producing pizza decreased at a decreasing rate as the cumulative number of pizzas produced increased. Franchise-specific learning results. Also as expected, transfer of knowledge across commonly owned stores is evident. The coefficient for franchise knowledge is significant in all models and is consistent with Darr, Argote, and Epple (1995). A franchisee is likely to implement a good process or procedure in all of his or her stores.
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Partner similarity results. Models 1, 2, and 3 investigate the effects of strategy, customer, and geographical similarity, respectively, in order to provide clear tests of the hypotheses. Hypothesis 1, which suggests that strategy similarity should facilitate transfer of knowledge, was supported by the data. Model 1 shows that the variable representing transfer between stores with the same strategy has a significant negative coefficient, whereas the variable representing transfer between stores with different strategies is not significant. This indicates that transfer between stores with the same strategy significantly decreases the unit cost of production, while transfer from stores with different strategies has no effect on unit cost. Results suggest that strategy similarity contributes to knowledge transfer, whereas customer similarity or proximity does not. Hypothesis 2, which suggests that customer similarity should facilitate transfer of knowledge (see Model 2 of Table 1), and Hypothesis 3, which suggests that proximity should facilitate transfer of knowledge (see Model 3 of Table 1), were not supported by the results. Additionally, the results from Model 4, which includes all three similarity dimensions, demonstrate that only knowledge from stores with similar strategies significantly decreases production costs. Extensions to the base analyses were conducted to examine alternative explanations for changes in unit cost. These analyses determined that the partner similarity results were not significantly affected by accounting for economies of scale, changes in the rate of learning, changes in the product mix, knowledge transfer along friendship networks, the age of these stores (Stinchcombe, 1965), or by their size in the models. Thus, the effect of partner similarity on knowledge transfer is robust. Furthermore, a logistic regression was conducted to assess whether there might be simultaneity in the determination of cost per pizza and strategy. Simultaneity or endogeneity would lead to biased coefficient estimates. The models shown in Table 1 allow business strategy to affect costs. Business strategy could potentially be a function of costs if a store with high cost per pizza, for example, charged high prices and performed poorly, thus requiring a cost-cutting strategy. An analysis using business strategy as the dependent variable (expansionist, yes or no) was conducted. The predictor variables (franchise membership, age, cost per unit, customer mix, location, rate of sales change over the sample time period, and rate of cost change over the sample time period) were entered in the model using a stepwise method. The only predictor to enter the equation was change in sales (B 5 24.942, p 5 .004). The negative coefficient suggests that a cost-cutting strategy is associated with a decrease in sales over the sample time period. The direction of causality cannot be determined from this analysis. The chi-square value for the model is 33.686, p 5 .7105, and the goodness-of-fit statistic is 35.303, p 5 .6392. The large significance levels indicate that the estimated model does not differ significantly from the “perfect” model. Results reveal that business strategy is not significantly related to cost per unit; thus, endogeneity does not appear to be a problem.
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Qualitative Work The qualitative field work focused on three of the franchise organizations included in the modeling analyses that volunteered to participate in the study. The first franchise (Franchise A), a multiple-store organization that follows an expansionistic business strategy, was visited for 8 days. The second franchise (Franchise B), a single-store organization which also follows an expansionist strategy, was visited for 2 days. The third franchise (Franchise C), a multiplestore organization that supports a cost-cutting business strategy, was visited for 7 days. Two broad questions guided the qualitative field study: (a) How does transfer of knowledge occur in English franchise organizations? and (b) What role does partner similarity play in the determination of knowledge transfer occurrence and success in franchise organizations? Three qualitative methods were employed. Structured interviews were used to collect information about such factors as culture, rewards, business strategy, problem-solving behavior, operations improvements, sharing behavior, and customer type. Respondents were asked to recall and reconstruct transfer events. Because respondents may have had incomplete or no knowledge about past transfer events, observations were also used to record detailed information about transfer events in real time. Observations focused on knowledge transfer events, problem-solving processes, store-level factors, knowledge characteristics (e.g., complexity), and pizza-production procedures. Finally, a record of daily communications was maintained by a few key informants. These diary data provide information on a larger number of transfer events than was possible to collect through observation alone. Interviews. In each franchise organization, structured interviews were conducted with the franchisee, any supervisors, all store managers, and some assistant managers. A total of 33 interviews were completed. Each interview lasted approximately an hour and a half. The interview questions focused on five conceptual domains. First, respondents were asked to describe in their own words their store’s business strategy and its primary goals and actions in their own words. They were asked to compare their strategy to other store strategies. Next, respondents were asked to describe whether they use their own or others’ experience for problem solving, in order to establish a frequency measure of problem solving via knowledge sharing. The respondents were also asked to describe the typical problems facing the store, in order to better understand the complexity of the problems that they face. Third, in order to assess methods for operations improvements, information was collected about sources for innovative knowledge. Respondents were asked to identify the best sources for new knowledge and to provide their opinions on the best methods for sharing knowledge. Fourth, in order to investigate sharing behavior, respondents were asked to provide information about the frequency with which they shared new ideas with other store managers, franchisees, and corporate officials. Motivation, method, and outcome of transfer events were investigated.
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Finally, respondents were asked about culture and rewards for sharing, innovativeness, cooperation, communication, and problem solving. Observations. Observations were conducted in 13 stores for approximately 4 h each. Factors affecting the motivation and ability to search, match, share, and adopt knowledge were observed. Efforts focused on a set of five priorities overlapping, but not duplicating, the domains covered by the interview questionnaire. Observations attempted to record detailed information about (a) all transfer events occurring during the period of observation, including antecedents, processes, and outcomes; (b) the problem-solving process and the propensity to share solutions; (c) culture, reward systems, and store-specific learning which facilitate and inhibit learning; (d) descriptions of knowledge characteristics in the store (e.g., complex or simple, ambiguous or clear); and (e) the pizzaproduction processes. Diaries. A simple diary format was used in this research to facilitate respondent motivation about daily recordkeeping. Diaries of daily communications were kept by a franchisee and his or her two area supervisors. Each record focused on five basic questions: (a) Who was the communication with? (b) How did you communicate? (c) Why did you engage in the communication? (d) What did you communicate about? and (e) What was the outcome of the communication? Over a period of 6 weeks, respondents were asked to complete this set of five questions for each interaction with someone outside of the focal franchise organization. Qualitative Results Qualitative results demonstrate that knowledge transfer occurred in these firms. Results indicate that this transfer happened most often between firms sharing similar strategies and problems. Results from the interviews provide evidence that two distinct strategies existed in the sample of pizza stores and that the franchisees were aware of these two strategies. In answering a freeresponse question, participants described one group of franchises as “concerned with running a tight ship,” “strict on food and labor goals,” “overly concerned with late pizzas,” and “interested in making a quality product with little waste.” These descriptors seem to fit well with a cost-conscious strategy. Respondents similarly described a second set of franchises as “concerned with customer response,” “driven to improve company awareness,” “concerned about training,” and “more concerned with image than with quality.” This second set of descriptors seems to fit well with an expansion-oriented strategy. Indeed, a franchisee who followed a cost-cutting strategy stated that he did not understand how other franchisees in England could run a profitable business while developing expensive training programs and using expensive marketing agencies. Alternatively, a multiple-store expansionist stated that some franchisees were inhibited by the recession in England. He did not understand how someone could be in business and not want to grow. Clearly, the two groups of franchisees did not agree on the most appropriate strategy.
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Results from the interviews demonstrate that respondents in cost-cutter stores focused on a different set of problems than respondents in expansionist stores. These results suggest that it is easier to share knowledge within, as opposed to across, strategy type. Respondents were asked to describe their most pressing concern during the past week. The top three problems faced by respondents in stores following a cost-cutting strategy were (a) meeting food or labor goals (44%), (b) crew member stealing (21%), and (c) competition (14%). Alternatively, the top three problems faced by respondents from stores following an expansionist strategy were (a) attracting new customers (38%), (b) crew training (22%), and (c) customer complaints (22%). These responses further support the existence of two distinct business strategies in the sample of pizza stores and demonstrate that store strategies are related to the types of problems most frequently encountered by managers. The pattern of responses suggests that business strategy focuses manager attention on specific sets of problems. Stores are less likely to share knowledge across different problem sets and will logically take advantage of synergistic associations with other stores that have solved similar problems. The results of cataloging transfer events through observation and interviews support the finding that transfer of knowledge is facilitated by strategic similarity. The vast majority of the 24 transfer events cataloged (79%) occurred between stores using the same strategy. An observed transfer event is defined as knowledge sharing that was confirmed by both the contributor and the adopter, while a reconstructed transfer event is defined as knowledge sharing described by either the adopter or contributor. Four events were observed in real time. The telephone was the transfer mechanism in two events, and a managers’ meeting was the mechanism in the other two. All four observed transfer events occurred between two store managers using the same strategy. The results of analyzing four observed transfer events and 20 reconstructed transfer events are consistent with the modeling results and show that transfer occurs among franchises displaying strategic similarity. For example, expansionistic stores transferred knowledge on such processes as new training programs, new recruiting procedures, and customer appreciation letters. Costcutting franchises, on the other hand, shared ideas for increasing efficiency such as redesigning inventory storage, using contract drivers rather than hourly ones, and using a food order sheet to forecast sales and orders. The same pattern of results is evident in the diary data. The multiple-store expansionist franchisee and his or her two supervisors each maintained a diary of communications with people outside of their franchise for a 6-week period. Ten of the 11 recorded communications occurred with other expansionist franchisees, showing overwhelmingly that franchisees choose to communicate with those who are strategically similar. Many different types of qualitative data have been presented: observations, interviews, and diaries. The pattern of results is the same for each type of data. Strategy similarity is positively associated with knowledge transfer.
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DISCUSSION
Our results indicate that business strategy similarity creates a context favorable to knowledge transfer. The pizza stores in the sample reduced their unit cost of production through store-specific learning, transfer of knowledge between stores owned by the same franchisee, and transfer of knowledge between stores following the same business strategy. These reductions in production cost occurred independently of calendar time, scale effects, or product mix. Both the modeling and the qualitative results highlight the value of strategic similarity as a potential facilitator of successful knowledge transfer. Our study shows that strategic similarity even facilitates knowledge transfer across stores not owned by the same franchisee. Store managers seem to use a selection heuristic based on strategic similarity for finding trusted sources of appropriate, useful knowledge. Managers must decide which cues to use in order to select knowledge transfer partners. These results, both quantitative and qualitative, suggest that, unlike strategic similarities, geographic and customer similarity dimensions are not sufficiently linked to the operations of a franchise in order to encourage knowledge sharing. A franchise member facing a production problem is motivated to acquire knowledge from someone who has already faced that same problem. Selecting a transfer partner based on business strategy similarity seems to be the most accurate way for store members to identify others with potentially useful knowledge. The modeling results are consistent with this perspective. Additionally, the interview, observation, and diary data demonstrate that the majority of knowledge transfers occurs between stores following the same business strategy, thereby reinforcing the quantitative results. The interview data provide insights into the perceptions of the franchisees about their own business and that of other franchisees and allow us to better understand the cognitive mechanisms driving the selection of a transfer partner. The interviews demonstrate that the franchisees in this sample are consciously aware of the differences between strategies, described here as cost cutting and expansionistic. Furthermore, the interview data highlight the mistrust and skepticism with which members of each category view the other. Although these results cannot directly demonstrate that managers consciously choose knowledge transfer partners based on strategic similarity, it does show that managers are cognizant of different strategies and, given a choice, tend to align themselves with potential knowledge transfer partners displaying a similar strategy to their own. Selecting potential transfer partners by customer similarity or store proximity does not seem as useful, perhaps because there is less problem convergence across customer type and store proximity than across strategy type. A costcutting franchisee serving residential customers may have very different problems from those of an expansionistic franchisee serving the same residential customers. A similar situation likely exists for the dimension of proximity. Just
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because a franchise is near another geographically does not mean that it has the same problem perceptions and assumptions. A primary limitation of the study may be its generalizability because the research was conducted in a single type of organization. For example, the results concerning transfer may be influenced by the simple technology used in pizza making. It is possible that in production settings with more advanced technology, a different set of factors determines transfer success. A second concern may involve the characteristics of the interview and diary respondents. It is possible that the respondents who agreed to participate in the study were generally more willing to share knowledge. However, we have no reason to believe that they would have been more likely to share with each other based on strategic dimensions than on any other dimension of similarity. This research furthers our understanding of the conditions and processes that promote knowledge transfer. Future research should also examine the effects of learning on alternative indicators of firm performance (e.g., service quality, store profitability, employee turnover). For example, does knowledge transfer affect service performance in these pizza stores in the same manner by which productivity is increased? Perhaps knowledge related to service performance is different from knowledge related to productivity. Our results demonstrate that organizations learn from their own experiences and from the experiences of other—but not all other—organizations. We observe that knowledge transfers occur across stores owned by the same franchisee and across stores following the same business strategy regardless of ownership. The evidence suggests that similarity of both problem framing and the proposed course of action can positively affect the motivation and the ability required for one organization to learn from another. REFERENCES Argote, L., & Epple, D. (1990). Learning curves in manufacturing. Science, 23, 920–924. Argote, L., Beckman, S. L., & Epple, D. (1990). The persistence and transfer of learning in industrial settings. Management Science, 36, 140–154. Attewell, P. (1992). Technology diffusion and organizational learning: The case of business computing. Organization Science, 3, 1–19. Brown, L. A. (1981). Innovation diffusion: A new perspective. New York: Methuen. Brown, S., & Duguid, P. (1991). Organizational learning and communities of practice: Toward a unified view of working, learning and innovation. Organization Science, 2, 40–57. Burkhardt, M. E., & Brass, D. J. (1990). Changing patterns or patterns of change: The effects of a change in technology on social network structure and power. Administrative Science Quarterly, 35, 105–127. Byrne, D. (1971). The attraction paradigm. New York: Academic Press. Byrne, D., Clore, G. L., & Worchel, P. (1966). The effect of economic similarity-dissimilarity on interpersonal attraction. Journal of Personality and Social Psychology, 4, 220–224. Byrne, D., & Nelson, D. (1964). Attraction as a function of attitude similarity–dissimilarity: The effect of topic importance. Psychonomic Science, 1, 93–94. Cohen, M. D., March, J. G., & Olsen, J. P. (1972). A garbage can model of organizational choice. Administrative Science Quarterly, 17, 1–25.
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