Knowledge structures and retail sales performance: an empirical examination

Knowledge Structures and Retail Sales Performance: An Empirical Examination ARUN SHARMA University of Miami

MICHAEL LEVY Babson College

AJITH KUMAR Arizona State University

Retailers are constantly trying to improve the performance of their salespeople. In this research, we suggest that elements of knowledge structures held by salespeople enhance their understanding of the antecedents to retail sales effectiveness. These structures also provide information that can help retail sales managers select, train and supervise salespeople more efficiently. For better salespeople, such knowledge structures contain useful insights about customers and how to respond to their needs effectively. We examine retail salespeople’s knowledge structures and find strong relationships between the complexity of knowledge structures of salespeople and their performance.

Due to the rising cost of personal selling, retail sales managers must continuously strive to increase the effectiveness of their salesforces. Because selling skills explain a high proportion of variance in sales performance, they are fundamental to the success of sales interactions. Traditionally, selling skills have been measured by concepts such as technical knowledge, sales comprehension, sales style, empathy, and ability to resolve conflicts (Churchill, Ford and Walker, 1993). Such structures also include an understanding of customer needs, signals that characterize these needs, and of the products that will serve these needs. A conceptual framework that reflects this linkage between selling skills and performance may be derived through the analysis of the knowledge structures of salespeople (Weitz, Sujan, and Sujan, 1986). Salespeople use knowledge structures to evaluate sales

Arun Sharma is associate professor at the University of Miami, Coral Gables, FL (e-mail: [email protected]). Michael Levy is professor of marketing at Babson College. Ajith Kumar is professor of marketing at Arizona State University. Journal of Retailing, Volume 76(1) pp. 53– 69, ISSN: 0022-4359 Copyright © 2000 by New York University. All rights of reproduction in any form reserved.

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situations and develop sales strategies for each customer. Weitz et al. (1986) pose several propositions with regard to salespeople’s knowledge structures. Some of these propositions have been examined by Sujan, Sujan and Bettman (1988, 1991), Leong, Busch, and John (1989), and Szymanski and Churchill (1990). However, these relationships have been examined in contexts of limited application to retailers. We build on this previous research by examining the relationship between various aspects of retail salespeople’s knowledge structures and their performance on the sales floor. The article is organized in three sections. First, based on theory from psychology and previous research in sales management, we present a conceptual framework for examining the knowledge structures of retail salespeople. The research results are presented in the second section. Finally, implications of the findings for future research and managerial practice are examined.

SALESPEOPLE’S KNOWLEDGE STRUCTURES OF CUSTOMERS People’s senses are inundated with stimuli and they need a method to determine which stimuli need attention (Rosch, 1975; Schneider, Hastorf, and Ellsworth, 1979; Shaw, 1990). As a result, they develop knowledge or category structures that allow them to determine the stimuli that need attention as well as allow them to process and organize information more efficiently. Categories allow people to structure, organize, and interpret new information. Retail salespeople’s knowledge structures about their customers are a critical issue in the personal selling process (Weitz, Sujan and Sujan, 1986). In a retail sales environment, salespeople proactively classify customers into groups, such as “sale” or “full price” shoppers, and use a unique strategy for each group. By utilizing such a categorization approach, rather than developing a unique strategy for each customer, salespeople decrease their cognitive effort and the uncertainty about which sales strategy to use. The domain of retail salespeople’s knowledge structures can be divided into declarative and procedural. Declarative knowledge is the content of categories that contains a set of prototypes representing the essential characteristics of category members (Crocker, Fiske, and Taylor, 1984; Weitz, Sujan, and Sujan, 1986; Szymanski, 1988). Procedural knowledge, also known as behavioral scripts, contains information about the sequences of behavior appropriate to particular situations (Gioia and Manz, 1985; Shaw, 1990). Procedural knowledge, in the present context, includes information about the sequences of events and actions commonly encountered in retail sales situations. Salespeople use this knowledge to guide their behavior when selling to specific customer categories (Leigh and Rethans, 1984; Leigh and McGraw, 1989; Weitz, Sujan, and Sujan, 1986). Our interest is in the structure of knowledge structures rather than the content. There has been no study of the relationship between retail salespeople’s categorization and their performance. Only two studies have examined salespeople’s categorical knowledge structures (Sujan, Sujan, and Bettman, 1988; Leong, Busch, and John, 1989)— hereafter referred to as SSB and LBJ, respectively. However, their findings may not be directly applicable to retail salespeople for several reasons. First, because the sample

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frames are different, the results may not be generalized to other settings. For example, the SSB sample consisted of student telemarketers with limited sales experience. Young salespeople with limited experience may not have sufficiently developed knowledge structures, compared to experienced retail salespeople who have been in the field for some period of time. The LBJ sample consisted of insurance salespeople who may have different knowledge structures for two different reasons. First, unlike the insurance industry, retail salespeople are not able to self-select their customers. Second, most retail salespeople see many more potential customers than the typical insurance salesperson and these are likely to be more heterogeneous. This intensity and greater variety may lead to a need for more complex knowledge structures for retail salespeople. Yet another reason for why further exploration into the knowledge structures of retail salespeople should be important is that both SSB and LBJ examined only a limited set of knowledge structure variables. Specifically, the LBJ study did not examine declarative knowledge and the SSB study examined a limited set of procedural knowledge variables. Finally, the methodology of LBJ and SSB are different and the findings often conflict. In the next two sections we broadly review the knowledge structure literature, and the SSB and LBJ studies in particular, to provide a link between declarative and procedural knowledge structures and sales performance.

DECLARATIVE KNOWLEDGE When one observes the behaviors and appearances of others, the perceptual experience is immediately ordered and classified (Schneider, Hastorf, and Ellsworth, 1979). In a selling context, salespeople listen to and observe customers, and then categorize them. The category descriptions, that is, declarative knowledge structures, are unique for each salesperson since he/she pays attention to different customer attributes. Four declarative knowledge variables have typically been examined in the literature—the number of categories used to classify customers, the richness of category description, category distinctiveness and descriptions based on needs versus physical characteristics.

Number of Categories Experts have been shown to use more categories to describe their domain (cf., Simon and Gilmartin, 1973; Brewer, Dull, and Lui, 1981). In a sales setting, more effective, that is, expert, salespeople are expected to also utilize more categories. A larger number of categories enables salespeople to categorize their customers more precisely and thus develop more appropriate sales strategies for a given sales situation (Weitz, Sujan, and Sujan, 1986). SSB, however, found no difference between the number of categories of customers provided by more and less effective salespeople. The lack of differences in the

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SSB study may be due to the sample of relatively inexperienced telemarketers. We expect a positive relationship between number of categories and sales performance.

Richness of Category Description SSB found that the internal category structure of experts is associated with both more concepts and a richer network of specific concepts. Richer descriptions allow experts to categorize customers more precisely, that is, a finer discrimination can be made with greater reliability (Murphy and Wright, 1984). We also expect better performing retail salespeople to have richer internal category structures.

Category Distinctiveness Categories are regarded as more distinctive when members are rated as more similar to each other and more different from members of other categories (Homa, Rhoads, and Chambliss, 1979). The evidence regarding the relationship between effectiveness and distinctiveness of categories is mixed. Some research suggests that more expert salespeople discern and utilize more distinctive customer categories. (Homa, Rhoads, and Chambliss, 1979). In contrast, other research suggests the opposite. Experts form theories about underlying causes and solutions that may contain similar characteristics across categories. As a result, experts are able to observe the similarities in a great number of categories, whereas those with less expertise look for dissimilarities when they construct categories (Chi, Feltovich, and Glaser, 1981). The SSB study provided mixed results. Our expectation is that the category descriptions of more effective salespeople are more distinctive than those of less effective salespeople.

Descriptions Based on Needs versus Physical Characteristics The internal category structure of experts contains more information regarding underlying characteristics than those of nonexperts. The latter reflect surface characteristics or perceptual inferences (Alba and Hutchinson, 1987; Chi, Fletovich, and Glaser, 1981). As a result, novice salespeople may be more prone to classify customers based on appearances and inferences that will lead to stereotyping behavior (cf., Alba and Hutchinson, 1987). Because stereotyping customers may lead to sales strategies that are not needbased, salespeople will be less effective (SSB; Weitz, Sujan, and Sujan, 1986). Our expectation is that the knowledge structures of more effective salespeople, when compared to less effective salespeople, will contain category descriptions based more on needs than on physical characteristics.

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PROCEDURAL KNOWLEDGE Ideally, salespeople should use different sales strategies for each of their customer categories. They might do so by drawing upon their procedural knowledge structures to emphasize different selling steps depending on the customer (Leigh and Rethans, 1984; Leigh and McGraw, 1989). Similar to declarative knowledge, we expect that more productive salespeople use richer sales strategy descriptions, more distinctive sales strategies, have an information acquisition focus, use a high level of abstraction, and have high commonality between procedural knowledge and prescriptive selling steps.

Richness of Procedural Knowledge The procedural knowledge structures of experts should contain more details or procedures for using their knowledge (Lurigio and Carroll, 1985). The procedural knowledge structures of experts are expected to be more elaborate, that is, are more articulate and contain more information units. SSB and LBJ found that better salespeople have richer procedural knowledge structures. Our expectation is that salespeople with rich procedural knowledge structures are better able to utilize this detailed information in sales interactions, thus leading to better performance.

Distinctiveness of Sales Strategies LBJ demonstrated that high performers exhibit a greater number of procedural knowledge tracks representing differences between stimuli because they have a greater ability to discriminate between stimuli. They suggest that a “canned” sales presentation for all customers is an example of a nondistinctive knowledge structure. A highly adaptive sales presentation (i.e., unique sales strategy for each customer category) is an example of a distinctive knowledge structure. SSB, however, predicted a negative relationship that their results support. They argue that more effective salespeople should have less distinctive sales strategies because they recognize that customers across categories have common needs. However, the preponderance of research suggests that the procedural knowledge structures of more effective retail salespeople are more distinctive than the knowledge structures of less effective retail salespeople.

Information Acquisition Focus The procedural knowledge structure of salespeople contains certain information acquisition steps that enable salespeople to acquire information about their customers. Information acquisition steps are those in which a salesperson probes for information, asks questions, and listens and observes customers (Weitz et al., 1986). LBJ demonstrated that

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a large number of information acquisition steps indicate a well-developed hierarchical knowledge structure, which was related to sales performance.

Level of Abstraction LBJ showed that the procedural knowledge of more effective salespeople contains a higher level of abstraction in that it is expressed in more general terms. LBJ have defined abstract or hypothetical knowledge structures as those that contain generalizations based on multiple experiences, and descriptions comprising more abstract terms. Considering earlier studies by John (1985) and John and Whitney (1986), LBJ found that more effective salespeople concentrate on the common elements of their experience (i.e., abstraction from experience). Less effective salespeople, on the other hand, concentrated on the specific elements. Ability to abstract from past experiences was also a key element of knowledge development (Abelson, 1981; Larkin, 1981). Thus, the procedural knowledge of more effective salespeople should contain a higher level of abstraction in that it is expressed in more general terms.

Commonality Between Procedural Knowledge and Prescriptive Selling Steps Sales training is used to develop procedural knowledge structures in salespeople. Specifically, most training programs utilize a prescribed set of selling steps (e.g., Weitz, Castleberry, and Tanner, 1998). Although the steps may be somewhat different across situations, it is widely accepted that successful sales interactions contain the following steps: preapproach, approach, presentation, objection handling, closing, and follow-up. The research in learning suggests that prior knowledge is used to develop new strategies (Heit, 1994). As a result, after training, salespeople are expected to adapt these steps to fit their own experiences. Salespeople learn as they compare new sales situations with what they were taught during training and notice features of the training program that do not cover the new sales situation (Spalding and Ross, 1994). Thus, more effective salespeople should utilize fewer prescribed selling steps than less effective salespeople.

METHOD

This section provides relevant details of our sample, data collection procedures, and a brief description of the measures used in the study and how they were developed.

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Sample Retail salespeople with at least one year’s experience were chosen from a division of a major department store chain with over 250 stores nationwide. They receive a formal two-day, sales training program plus ongoing in-store training from their sales manager that includes selling skills and product knowledge. Ten stores were randomly chosen and the salespeople with at least one year’s experience were sent a questionnaire with a cover letter from the Human Resources Vice President requesting cooperation. The questionnaires were returned to the researchers in sealed envelopes, thus assuring confidentiality. The respondents were from ten different locations and a variety of departments that required complex selling skills, such as electronics, furniture, and high fashion clothing. Two hundred fifteen out of four hundred questionnaires were returned providing a 53.7% response rate. The average age was 40 years; 75.8% were female; and average sales experience was ten years. The demographic profile of the sample was compared and found to be representative of salespeople for the entire chain. Data Collection Several methods have been used to obtain data on the knowledge structures of salespeople. For instance, LBJ presented salespeople with profiles of customers and asked them to describe orally the expected sales interaction. In contrast, SSB used a “paper and pencil” instrument. The salespeople were first asked to describe their categories, and the sales strategies they would use for each category. Thus, LBJ controlled their stimuli; whereas SSB used free elicitation. The major advantage of controlling the stimuli is the availability of comparable responses across subjects. However, there may be problems associated with providing standard stimuli. First, the stimuli may affect knowledge structure descriptions (Kaplan, 1975; Zanna and Hamilton, 1977). Second, not all traits may be important or used by all salespeople (Wiggins, Hoffman, and Taber, 1969). This study utilized a questionnaire that solicited open-ended information on salespeople’s knowledge structures. Similar processes have been successfully used by researchers to access knowledge structures (Ross and Sicoly, 1979; Scott, 1980). First, the respondents were asked to provide names for and descriptions of the types of customers that come into their department so that the categories could be defined by prototypical attributes (Rosch, 1975). Second, salespeople were asked to describe how they recognize the customers who most and least frequently visit their department and the selling steps they use for each. The respondents provided their employee number that was matched to corporate sales performance data. Measures Two graduate students were used as judges to code the measures. Each evaluative measure consisted of a two-item ten-point scale that was summed. Salespersons’ descrip-

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tions of their knowledge structures were coded using content analysis. Disagreements on objective information, for example, the number of selling steps, were resolved by discussion. The mean of the judges’ responses was used to resolve disagreements on evaluative information, for example, affect toward customers. Interjudge reliability ranged from .61 to 1.00, with a mean of .91 for both objective and evaluative measures.

Declarative Knowledge The number of types of customers the salespeople listed was used as the measure of the number of categories. Richness of category descriptions was measured in three ways. First, similar to SSB, richness was measured by the number of unique traits listed by the respondents. The second measure was the number of words used to describe the categories. Both measures capture completeness from slightly different perspectives. The number of words allows us to evaluate the quantity of information used to categorize customers. We also used the number of words to measure the descriptiveness or the richness of category descriptions. Finally, the judges evaluated the richness of category descriptions on a two-item scale (r ⫽ .62). The first item measured the depth of the descriptions, whereas the second item asked the judges to gauge how well the descriptions would help them classify customers. The distinctiveness of the category descriptions was measured by the level of overlap that salespeople provided in their customer descriptions. One measure of category overlap was the proportion of shared to total number of traits. Specifically, the overlap between any two categories was computed as the proportion of shared to shared plus distinctive features (SSB; Tversky 1977). Also, the judges evaluated distinctiveness on a two-item scale (r ⫽ .78). The first item measured the overlap between categories; whereas the second measured the likelihood that a customer belongs to two categories. To determine whether category descriptions were based more on needs than on physical characteristics, we classified the traits that describe categories with the following schema: needs are traits that describe what the customer wants such as, “customer wants designer clothes.” Physical characteristics are traits such as type of clothing, race, social class, and English language ability. Emphasis on needs was measured as the proportion of need-traits to total number of traits. The judges also evaluated salespeople’s emphasis on physical characteristics versus needs on a two-item scale (r ⫽ .88). The first item evaluated the category descriptions; whereas the second item evaluated the category titles.

Procedural Knowledge Richness of procedural knowledge was measured by the number of unique selling steps listed by the salespeople. This measure was similar to the measure suggested by SSB and the concept of “net events” suggested by LBJ. Additionally, the judges evaluated the richness of procedural knowledge on a two-item scale (r ⫽ .77). The first item measured the depth of the procedural knowledge. The second item asked the judges to determine how well they could predict the salesperson’s next action based on the selling steps listed.

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The distinctiveness of procedural knowledge was measured by the level of overlap that salespeople provided in their selling steps. The measure of overlap was the proportion of shared to total number of selling steps. This measure is similar to the “unique events” construct suggested by LBJ and also controls for number of selling steps mentioned by salespeople. Judges used a two-item scale to measure the degree to which salespeople sold to all of their customer categories the same way, and if the selling steps were common across all customers (r ⫽ .72). Information acquisition focus was measured as the proportion of information acquisition steps to the total number of selling steps. Examples of information acquisition steps are, “observe the racks the customer visits,” or “ask the customer what he/she wants.” These measures are similar to the “contingency” construct measured by the “if-then” statements used by LBJ. The judges also evaluated the degree to which each of the selling scripts had an information acquisition focus (r ⫽ .75). The three measures of abstraction were adapted from LBJ, and reflected the generalizations encompassed in salespeople’s procedural knowledge structures. The first measure was the number of roles mentioned. The second measure was the number of frequency indicators used. Finally, the judges evaluated the degree to which each of the sales scripts was specific or generalized. The prescribed selling steps—approach, collecting information, presenting the merchandise, handling reservations, making the sales and building future sales were adapted from Levy and Weitz (1998). The training provided by the participating retailer encompasses these six selling steps. The judges were asked to evaluate whether the selling steps listed by the salespeople coincided with the prescribed selling steps. Specifically, a salesperson’s score was based on the number of prescribed selling steps that matched the selling steps they listed. So, the highest possible score for each salesperson was six.

Sales Performance The measure of sales performance was the ratio of a salesperson’s average annual hourly sales volume divided by the average annual hourly sales of all salespeople working in that specific department. Thus, the sales volume data are adjusted for product and environment since the average performance of each department is one. This measure is more stringent than some other performance criteria used in previous research, for example, self-rated performance. The salespeople were split into 113 high (mean score of 1.29) and 112 low performers (mean score of .81) based on a median split. The sample retail chain has excellent revenues, suggesting that average sales per salesperson may be high. The average age of a salesperson was 39 years and the high performance group was older than the low performance group (43 versus 35 years). Similarly, the average sales experience of a salesperson was 9.25 years and the high performance group had more sales experience than the low performance group (twelve versus seven years). The correlation between age and performance was low (r ⫽ .243) but significant.

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TABLE 1 Means for Declarative Knowledge Means (Standard Deviation) Construct Number of categories Richness of categories Unique traits Number of words Evaluation of richness Category distinctiveness Shared to total number of traits Evaluation of distinctiveness Needs versus physical characteristics Proportion of underlying to total traits Evaluation of emphasis on underlying traits

Low Effectiveness

High Effectiveness

F-Ratio

P-Value

5.30 (1.87) MANOVA 3.64 (1.38) 23.73 (9.88) 3.07 (2.57) MANOVA 0.38 (0.75) 3.32 (0.93) MANOVA 0.14 (10.07) 4.32 (1.75)

6.08 (2.17)

8.30

0.004

19.17 33.53

0.001 0.001

49.19

0.001

14.85

0.001

11.51 16.25

0.001 0.001

8.62

0.01

41.29 22.78

0.001 0.001

82.45

0.001

4.85 (1.73) 34.55 (12.96) 4.49 (2.91) 0.07 (0.26) 3.65 (0.72) 0.18 (0.05) 6.31 (1.51)

RESULTS To test if there were overall differences in the individual elements of knowledge structures of more and less effective salespeople, we performed MANOVA and one way analysis of variance. We used sales performance as the between subject factor and the measures of category structures as the dependent variables. These results are discussed next and they’re summarized in Table 3 .

Declarative Knowledge The difference between more and less effective salespeople in their declarative knowledge was significantly different at p ⬍ .001. Please see Table 1 for Declarative Knowledge results. The difference in the number of customer categories of more and less effective salespeople was significant (p ⬍ .004). More effective salespeople had an average of 6.08 customer categories; whereas the less effective salespeople have an average of 5.30 categories. This finding supports studies in psychology, but is in contrast to the results found by SSB. The primary reason for the difference may be due to the different sample frames. SSB used a student sample. They worked part-time and had approximately fifteen weeks of experience. This compares to our sample of professional retail salespeople with

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TABLE 2 Means for Procedural Knowledge Means (Standard Deviation) Construct Richness of procedural knowledge Number of selling steps Evaluation of richness Distinctiveness of sales strategies Shared to total number of selling steps Evaluation of distinctiveness Information acquisition focus Proportion of information acquisition to total number of selling steps Evaluation of information acquisition focus Level of abstraction Roles mentioned Frequency indications Evaluation of level of abstraction Commonality between procedural knowledge and prescriptive selling steps

Low Effectiveness MANOVA 5.24 (2.33) 4.19 (1.72) MANOVA 0.20 (0.24) 3.29 (3.81) MANOVA

High Effectiveness 8.21 (3.43) 5.95 (1.59) 0.14 (0.13) 6.22 (3.92)

0.17 (0.16) 2.23 (1.39) MANOVA 0.40 (0.69) 0.05 (0.21) 5.20 (1.66)

0.30 (0.24) 4.85 (2.70)

2.53 (1.19)

3.20 (1.23)

1.46 (1.46) 0.48 (0.84) 6.31 (1.45)

F-Ratio

P-Value

33.01 57.02

0.001 0.001

62.64

0.001

16.00 4.16

0.001 0.042

32.25

0.001

40.43

0.001

18.27

0.001

74.99

0.001

19.15 48.16

0.001 0.001

26.75

0.001

27.93

0.001

16.90

0.001

significant training and experience. It is possible that all salespeople may start with a similar number of customer categories, but as they gain experience, more effective salespeople may tend to increase the number of customer categories. Therefore knowledge structures need time to develop, and salespeople may need to acquire a certain amount of sales experience before their knowledge structures stabilize. A MANOVA on the three measures was used to test if more effective salespeople have richer category descriptions indicated a significant relationship (p ⬍ .001). The average number of unique traits listed by more effective salespeople was 4.85; whereas the average for less effective salespeople was 3.64 (p ⬍ .001). The difference in the number of words used to describe categories was also significant (34.55 versus23.73; p ⬍ .001). Finally, the judges found more effective salespeople provided richer descriptions (4.49 versus 3.07 on a ten-point scale; p ⬍ .001). Using a MANOVA, there was support for the assumption that better salespeople have more distinctive category structures (p ⬍ .001). Generally, both more and less effective salespeople had a low proportion of overlapping to total number of traits (.07 versus .38). The difference, however, was statistically significant (p ⬍ .001). The judges also found a

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TABLE 3 Effect of Knowledge Structures on Performance Findings of Construct Declarative knowledge Number of categories Richness of description Category distinctiveness Needs vs. physical characteristics Procedural Knowledge Richness Distinctiveness Information Acquisition Focus Level of Abstraction Past Knowledge Structures

Note: SSB: LBJ: ⫹: ⫺: ne: mxd:

SSB Study

LBJ Study

Our Study ⫹ ⫹ ⫹ ⫹

ne ⫹ mxd ⫹ ⫹ ⫺

⫹ ⫹ ⫹ ⫹ ⫹

⫹ ⫹ ⫹ ⫹

Sujan, Sujan and Bettman (1988), and the Sujan, Sujan and Bettman (1991) study; Leong, Busch, and John (1989) study; Positive Relationship; Negative Relationship; No effect; Mixed Results.

difference in the distinctiveness of category descriptions of more and less effective salespeople (3.32 versus 3.65 on a ten-point scale; p ⬍ .01). This result is also in contrast to the SSB study, and as discussed earlier, possibly because of the way the customer traits were selected. These results highlight the need for further study of the process of category formation. The results of a MANOVA indicated that the category descriptions of more and less effective salespeople were different, based on their emphasis on needs versus physical characteristics (p ⬍ .001). The proportion of need-traits to total traits for more and less effective salespeople was 0.18 to 0.14 respectively (p ⬍ .001). The judges’ evaluation of salespeople’s emphasis on needs versus physical characteristics also indicated significant differences (6.31 versus 4.32; p ⬍ .001). Retail sales training programs stress the necessity to emphasize needs. It is therefore important to understand the process by which less effective salespeople move away from the prescribed approach of emphasizing needs and move toward emphasizing physical appearances.

Procedural Knowledge The difference between more and less effective salespeople in their completeness and complexity of procedural knowledge was significantly different at p ⬍ .001. Please see Table 2 for Procedural Knowledge results. More effective salespeople have an average of 8.21 selling steps; whereas the less effective salespeople have an average of 5.24 selling

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steps (p ⬍ .001). Also, the judges evaluated the procedural knowledge of more effective salespeople to be richer than less effective salespeople (5.95 versus 4.19; p ⬍ .001). More effective salespeople have more distinctive procedural knowledge structures when compared to less effective salespeople. The proportion of shared to total number of selling steps was .20 to .14 for less and more effective salespeople, respectively (p ⬍ .042). The judges also found the distinctiveness of procedural knowledge to be different for the two groups (6.22 versus 3.29; p ⬍ .001). In contrast to SSB, the results of both this and the LBJ study suggest that more effective salespeople do not have more overlapping structures than less effective salespeople. The notion that the procedural knowledge structures of more effective salespeople have more information acquisition steps than the knowledge structures of less effective salespeople was supported using MANOVA (p ⬍ .001). The proportion of information acquisition steps to the total number of selling steps for more and less effective salespeople was .30 to .17, respectively (p ⬍ .001). The judges evaluated more effective salespeople to have a higher degree of information acquisition focus in their selling scripts (4.85 versus 2.23; p ⬍ .001). Using MANOVA, we contended that the level of abstraction would be higher for good salespeople. This was supported (p ⬍ .001). More effective salespeople mentioned 1.46 versus .40 roles and had .48 versus .05 frequency statements. The judges evaluated the abstraction of procedural knowledge of more effective salespeople to be 6.31 versus 5.20 for less effective salespeople. All differences were significant at p ⬍ .001. Finally, selling steps of more effective salespeople matched 3.20 prescribed selling steps, compared to 2.53 for less effective salespeople.

DISCUSSION AND IMPLICATIONS The contributions of our study are in two major areas. First, we extend our understanding and measurement of knowledge structures in areas such as the impact of “past knowledge structures,” that salespeople acquired through training on sales performance. We determined that better performing salespeople stay with prescribed selling steps, whereas mediocre salespeople move away from prescribed selling steps. Also we developed evaluative measures for knowledge structure elements. We found the evaluative measures to be correlated with objective measures of knowledge structures. Second, we highlighted the effect of retail salespeople’s knowledge structures on sales performance. As discussed earlier, two previously published studies examined salespeople’s categorical knowledge structures. Our results were very similar to LBJ and distinct from SSB in key areas. These results lead to two interesting observations. First, the LBJ sample consisted of insurance salespeople whom we thought might be distinct from retail salespeople due to their ability to self-select their customers, and the lower number of customers that they meet. However, our results were similar to LBJ results and suggest that type of industry may not have a large impact on the relationship between knowledge structures and sales performance. Our results were distinct from SSB in four different areas—number of categories, richness of declarative knowledge, category distinctiveness

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and richness of procedural knowledge. These findings support studies in psychology, but are in contrast to the results found by SSB. The primary reason for the difference may be due to the different sample frames. SSB used a student sample who worked part-time with only fifteen weeks of experience. This compared to our sample of professional retail salespeople with significant training and experience. It is possible that all salespeople may start with a similar number of customer categories, but as they gain experience, more effective salespeople may tend to increase the number of customer categories. Therefore knowledge structures need time to develop, and salespeople may need to acquire a certain amount of sales experience before their knowledge structures stabilize. In support of this contention, we found a significant relationship between sales experience and sales performance (r ⫽ .27; p ⬍ .001). Similar to sales performance, positive relationships were found between knowledge structure variables and sales experience. This relationship exists despite the self-selection. Self-selection suggests that better salespeople (who have complex knowledge structures) are promoted leaving only a larger proportion of less successful, older salespeople. Although the skills needed for salespeople and sales managers are different, typically many of the best salespeople are promoted to managerial positions. Therefore, there seems a major effect of experience on knowledge structures.

Managerial Implications The results suggest that salespeople need to develop knowledge structures that include rich, distinctive categories based on customers’ needs; and develop rich, distinctive sales strategies for each type of customer. Because knowledge structures require development, retailers should be proactively involved in the training and supervision of salespeople. The first area of emphasis should be on selecting the right type of retail salespeople. Sales managers should attempt to evaluate the content and complexity of a recruit’s knowledge structures. In the case of inexperienced hires, the research of Levy and Sharma (1994), suggest that more educated recruits and women who enter the salesforce are more adaptive, therefore suggesting they have better developed knowledge structures. Salespeople need to be trained to develop better knowledge structures. Sujan, Weitz and Sujan (1988) suggested training programs for salespeople based on the knowledge structures of experienced and better performing salespeople. These steps include: teach salespeople to categorize information, develop skills that aid the development of knowledge structures, make salespeople work smartly, and provide continuous training. Most managers provide feedback to salespeople on their outcomes, for example, how close they are to meeting their sales goals. More importantly, however, sales managers have accumulated knowledge that can provide feedback on salespeople’s behaviors. This type of feedback is more valuable than simple outcome assessments since it can help salespeople develop more sophisticated knowledge structures. Finally, encouraging salespeople to manage themselves forces them to examine the causes of their successes and failures, which, in turn, strengthens their knowledge structures.

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LIMITATIONS AND DIRECTIONS FOR FUTURE RESEARCH

Our paper examined the effect of knowledge structures on sales performance. We also explicated the positive effect of sales experience on knowledge structures. In addition, we found that performance was based on the training programs that salespeople had undergone. These findings would suggest a need for programmatic research to determine how knowledge structures evolve generally and especially how the knowledge structure of better salespeople evolves. Additionally, the benefits of having “accurate” knowledge structures may be an area of research interest. Finally, it would be interesting to determine if well-developed knowledge structures allow salespeople to manage their time better. For example, do salespeople allocate their time to customers based on their knowledge structures? Acknowledgment: The authors gratefully acknowledge the helpful comments of A. Parasuraman, Barton A. Weitz, Howard Marmorstein, and Dhruv Grewal. The research was partly funded by School of Business and McLamore Summer Research Awards, University of Miami.

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