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Price Delegation in Sales Organizations: An Empirical Investigation

Price Delegation in Sales Organizations: An Empirical Investigation The allocation of decision rights is an integral component of designing organizational architecture. Econ- omists have long understood the importance of co-locating decision rights with the knowledge that is valu- able to those decisions. Following this prescription, marketing scholars have developed strong theoretical arguments in favor of delegating pricing authority to the sales force. Empirical work, however, reveals a significant number of sales organizations yielding only minimal authority to their salespeople. Given this divergence between theory and practice, we develop and empirically test two mitigating factors that could potentially explain why firms restrict pricing authority. We test our hypotheses on a sample of 222 Ger- man sales organizations and find that the data are generally consistent with our conceptualization. Keywords: pricing authority, delegation, determinants of price delegation, pricing, sales force manage- ment maximize simultaneously their own income and the 1. INTRODUCTION company’s profits. The allocation of decision rights is an integral com- Surprisingly, despite these powerful theoretical ponent of designing organizational architecture arguments in favor of delegating pricing authority to (Brickley, Smith, and Zimmerman 2001). Econo- the sales force, empirical work reveals a majority of mists have long understood the importance of co- sales organizations yielding little or no pricing au- locating decision authority with the knowledge that thority to their salespeople. In a study conducted by is valuable to those decisions. As early as 1945, Stephenson, Cron, and Frazier (1979) in the hospi- Hayek highlighted the inability of centralized deci- tal-supplies industry, for example, 29% of firms sion-makers to effectively solve organizational prob- yield no pricing authority and 48% yield only lim- lems lower down in the hierarchy. More recently, ited pricing authority. Only a minority of respond- Jensen and Meckling (1992) suggest that as long as ing firms, namely 23%, give full pricing authority to agency problems are minimal, assigning decision their salespeople. Moreover, firms that centralize rights to individuals, who have the decision-relevant pricing authority are actually found to be more prof- knowledge, increases efficiency. itable than firms that delegate pricing authority. Marketing scholars have presented similar argu- Broadly, our objective in this research is to shed ments in describing the allocation of pricing author- light on the observed divergence between theory ity within sales organizations. Lal (1986) makes the and practice. More specifically, our research goals case that delegating pricing authority to the sales are two-fold: (i) identify factors that could poten- force will be more profitable than centralization tially mitigate the optimality of delegating pricing because salespeople often possess superior informa- authority to the sales force, and (ii) investigate the tion about customer willingness-to-pay. Weinberg empirical validity of the proposed mitigating factors. (1975) shows that salespeople, who are paid a com- In our research, we describe two mitigating factors mission based on realized gross margin and who are that may cause centralization to be actually pre- given control over price, will set prices so as to ferred over delegation. First, following the work of 94 BuR - Business Research Official Open Access Journal of VHB Verband der Hochschullehrer für Betriebswirtschaft e.V. Volume 1 | Issue 1 | May 2008 | 94-104 Jensen and Meckling (1992), we posit that agency In our empirical research, we investigate the man- costs can potentially mitigate the benefits of delegat- ner in which pricing authority is delegated to the ing pricing authority to the sales force. Agency costs sales force in a sample of 222 German sales organi- refer to the inefficiencies arising from a lack of per- zations spanning multiple industries. Like Stephen- fect goal alignment between employees and owners, son, Cron, and Frazier (1979), we find considerable thereby lowering firm profit. The work by Joseph heterogeneity across firms with respect to this deci- (2001) is particularly germane in this connection. sion. Interestingly, we find that a significant propor- His model reveals that salespeople have a tendency tion of firms, namely 28%, choose to yield no pric- to make trade-offs between effort and price dis- ing authority to the sales force. In these cases, price counting that are inconsistent with the profit objec- is determined exclusively by management. Another tive of the firm. Interestingly, this type of agency 61% of the firms yield only limited pricing authority cost is also of great concern to practitioners. Specifi- to their salespeople. Here, salespeople are allowed cally, sales managers complain that price latitude to set prices within a pre-specified range. Finally, often causes salespeople to take the path of least only a relatively lower percentage of firms, namely resistance, i.e., use discounting rather than expend 11%, follow the theoretical prescription of providing effort on selling (Stephenson, Cron, and Frazier their salespeople with full pricing authority. In these 1979, p. 26). Given the possibility of such inefficient cases, salespeople are given the freedom to set any trade-offs, firms may withhold pricing authority price above marginal cost. even as they sacrifice the benefits of price customi- Our main empirical findings can be summarized as zation obtained via delegation. follows: The observed heterogeneity with respect to The second reason why centralization may be pre- price delegation can be explained by the aforemen- ferred to delegation pertains to the manner in which tioned mitigating factors. In particular, our proxies sales force control systems are designed. The design that identify conditions where firms are concerned of control systems includes such elements as the about inefficient trade-offs between price delegation choice of metrics utilized in the compensation plan, and effort are able to successfully predict the likeli- namely margins or sales, and the level of monitoring hood of price delegation. In addition, the nature of (Joseph and Thevaranjan 1998). Clearly, these con- the control system also predicts the likelihood of trol elements are designed not only to support the price delegation. price delegation decision but also to respond to Overall, these findings offer a more refined under- various other conditions facing the firm. For exam- standing of the price delegation decision. Early work ple, in some scenarios, the firm may wish to avoid in the marketing literature suggests that price dele- setting commissions based on margins because such gation will invariably improve firm profits (Lal an action could reveal the firm’s cost structure to the 1986; Weinberg 1975). The practitioner-oriented competition. This revelation could prove to be too literature, on the other hand, has generally been costly from a strategic point of view (Churchill, more circumspect about delegating pricing author- Ford, and Walker 1997, p. 226). In such situations, a ity to the sales force. For example, based on their profit-maximizing firm is pushed towards centrali- consulting experience, Dolan and Simon (1996) zation because incentives on sales provide no check comment that it seems to be better to err on the on indiscriminate price discounting. Similarly, a restrictive side, i.e., offer less pricing authority ra- firm faced with high monitoring costs may not be ther than too much pricing authority. They also able to install an adequate number of supervisory report the practitioner sentiment that “letting the personnel. This lack of supervision may prevent the sales force set prices is about the same as hiring a firm from verifying if the salesperson is making the fox to guard the hen house.” Clearly, the mitigating right trade-offs between effort and price; conse- factors proposed in this research have the potential quently, here also, centralization is the best strategy. to reconcile these divergent prescriptions. In short, our essential point here is that the decision The rest of the paper is organized in the following to delegate pricing authority will be influenced by manner: In the next section, we review the literature the extant control system. Consequently, any study and derive our hypotheses. We then explain our that examines the issue of delegating pricing author- empirical strategy and describe the data and meas- ity to the sales force must explicitly take into ac- ures utilized in our empirical research. Next, we count the nature of the overall control system. present our empirical findings and discuss the main 95 BuR - Business Research Official Open Access Journal of VHB Verband der Hochschullehrer für Betriebswirtschaft e.V. Volume 1 | Issue 1 | May 2008 | 94-104 implications. Finally, we summarize our contribu- Mishra and Prasad (2005) demonstrate similar tions and conclude by outlining directions for future implications in a competitive setting. Although this research. is in contrast to Lal’s (1986) result, it arises because Mishra and Prasad assume a different timing of information. In particular, in their work, the private 2. LITERATURE REVIEW AND information of the rep is garnered at the time of DEVELOPMENT OF HYPOTHESES contracting; consequently, an appropriate contract is able to elicit this information. In contrast to the 2.1 Literature Review efforts of Mishra and Prasad, we follow the work of Joseph (2001) considers the impact of two forces Lal (1986) and consider a context where the private that could potentially influence the optimality of information is obtained after the time of contracting price delegation. On the one hand, providing pricing when salespeople actually call on their clients. authority to individual salespeople empowers them to use their superior information about customer 2.2 Development of Hypotheses willingness-to-pay and thereby conclude a greater As suggested previously, there are two mitigating number of transactions. On the other hand, provid- factors which may negate the price-customization ing the salesperson with pricing authority could lead advantages of price delegation, namely agency costs to sub-optimal trade-offs between effort and price and the overall nature of the control system. We discounting. Given these opposing considerations, next develop hypotheses pertaining to these two Joseph's primary objective is to examine the net factors. effect of these two forces in determining the optimal level of pricing authority. His analysis reveals that Impact of Agency Costs on Price Delegation limiting pricing authority can, in some environ- Since our hypotheses here depend heavily on the ments, reduce the sub-optimal trade-off between work of Joseph (2001), it is instructive to review it price discounting and effort. In effect, limiting pric- in some detail. In his model, the market consists of ing authority forces the salesperson to expend two segments: A and B. Customers belonging to greater effort on prospecting because shirking on Segment A have reservation values that are inde- this task cannot be offset by price discounting. As pendently distributed and come from the uniform such, the benefit obtained from inducing greater distribution [1- , 2- ]. Customers belonging to effort on prospecting outweighs the loss arising Segment B have reservation values that are also from the inability to customize prices. independently distributed but come from the uni- Of course, other work in marketing has also exam- form distribution [0, 1]. Evidently, there is some ined the price delegation decision. Bhardwaj (2001) overlap between the two segments, A and B. In par- considers the strategic impact of the price delega- ticular, there are some customers in both segments tion decision. In particular, he examines how com- whose valuations lie in the interval [1- , 1]. The pa- petition impacts the price delegation decision. Our rameter  thus represents the overlap between the investigation differs from his analysis in that he two segments. Its expected values will be greater does not consider the price-customization advan- than 0 but substantially less than 1. Obviously, cus- tages of price delegation. That is, the issue of cus- tomers in Segment A comprise the firm’s target tomizing prices across customers is not considered segment because they tend to have higher reserva- in his model – when the rep has pricing authority, tion values in general. As such, the firm will encour- he (or she) sets a single price for the entire market. age the salesperson to identify and pursue custom- Mishra and Prasad (2004) also consider the issue of ers belonging to this segment (prospecting). Now, price delegation and conclude that centralized pric- high values of suggest that the two segments ing performs at least as well as price delegation. merge with respect to their reservation values which is likely to be the case in a highly competitive envi- From a technical point of view, Joseph (2001) obtains these ronment. In particular, due to the availability of effects because he allows the effort devoted to prospecting to several substitute goods, the reservation values of influence the type of customer (high valuation or low valua- tion) that the salesperson encounters. This is in contrast to both segments converge. In contrast, low values of Lal’s work wherein the price sensitivity of the sales response describe a distinct segment that is willing to pay function is better observed by the salesperson, but not influ- enced by the effort choices of the salesperson. 96 BuR - Business Research Official Open Access Journal of VHB Verband der Hochschullehrer für Betriebswirtschaft e.V. Volume 1 | Issue 1 | May 2008 | 94-104 higher prices for the firm’s offering which is more such, the firm yields pricing authority to the sales- characteristic for a low competitive landscape be- person in order to obtain the benefits of price cus- cause of the absence of alternative suppliers. Fol- tomization. Similarly, when is relatively high, both lowing these explanations,  is interpreted as a the firm and salesperson are in agreement that not measure of competitive intensity. much effort should be devoted to prospecting – this In this context, the effort  expended by the sales- activity is expensive in terms of effort costs. As such, here also, the firm yields pricing authority to the person is assumed to impact the quality of prospect- salesperson in order to obtain the benefits of price ing. Specifically, as the salesperson expends greater customization. However, when  takes on interme- effort on prospecting, a greater fraction of the cus- diate values, the salesperson’s preferred trade-off tomers encountered by the salesperson are drawn between effort and price is different from that of the from Segment A. This is because the more time the firm’s. In particular, the salesperson prefers to sub- salesperson devotes to market analysis in terms of stitute price discounting for effort whereas the firm identifying potential customers based on demo- prefers that this substitution not be done. Thus, in graphic or situational factors, the higher the likeli- this instance, the firm is better off limiting the ex- hood that potential customers are classified cor- tent of pricing authority. This limitation, in turn, rectly as Segment A customers. Clearly,  can take forces the salesperson to invest a sufficient amount on values between 0 (minimum effort) and 1 of effort on prospecting. (maximum effort). Hence, given effort level on prospecting and a cohort of N customers, N cus- Figure 1. Optimality of Price Delegation tomers are drawn from Segment A. Since [0,1], the remaining N (1- ) customers are drawn from Segment B. Within the model, the pa- rameter  scales the effort cost of prospecting, which is expressed as  . Clearly,  determines how ex- pensive prospecting effort is – a given level of pros- pecting effort incurs greater effort cost in those en- vironments where  is higher. Following this analysis, the main insight offered by Joseph (2001) is as follows: He finds that price del- egation is not optimal in all parts of the parameter space. Specifically, for a given value of the competi- tive intensity parameter, , the optimality of delegat- ing pricing authority varies nonmonotonically with the effort cost of following a high-quality prospect- ing strategy. In particular, when is relatively high or relatively low, delegating pricing authority to the sales force is the optimal strategy. However, when takes on intermediate values, limiting pricing au- For our purposes, the model analyzed by Joseph thority is the optimal strategy (please see basis for (2001) can be examined closely to obtain an H arrow in Figure 1, taken from Joseph (2001). 1a additional insight. Specifically, by looking at the The intuition behind this finding is as follows: When output of the model, it is also apparent that for a is relatively low, the salesperson is willing to invest given value of the parameter , the optimality of effort on prospecting because prospecting is not that delegating pricing authority varies nonmonotoni- expensive in terms of effort costs. In this situation, cally with the competitive intensity parameter, . there is no divergence in preferences between the Thus, when  is relatively high or relatively low, firm and the salesperson with respect to the amount delegating pricing authority to the sales force is the of effort that ought to be devoted to prospecting. As optimal strategy. However, when takes on inter- mediate values, limiting pricing authority is the Effort cost is the monetary equivalent of the disutility in- optimal strategy (please see basis for H arrow in 1b curred from effort (see also Basu, Lal, Srinivasan, and Stae- Figure 1). lin 1985). 97 BuR - Business Research Official Open Access Journal of VHB Verband der Hochschullehrer für Betriebswirtschaft e.V. Volume 1 | Issue 1 | May 2008 | 94-104 Although this is a new insight, the intuition behind mediate values, agency costs are salient and it is very similar to the previous case. When  is rela- the likelihood of delegating pricing author- tively small, the salesperson is willing to invest effort ity is expected to be low. on prospecting because the degree of competitive intensity is small and any effort expended on pros- Impact of Control System Elements on Price pecting yields Segment A customers who are, in this Delegation instance, distinct from Segment B customers. As As mentioned previously, the design of the control such, there is no divergence in preferences between system is likely to be influenced by several factors. the firm and the salesperson with respect to the Typically, the control system is designed not only to amount of effort that ought to be devoted to pros- support the price delegation decision but also to pecting. Consequently, the firm yields pricing au- accommodate various conditions facing the firm thority to the salesperson in order to obtain the such as task programmability, environmental un- benefits of price customization. Similarly, when  is certainty, risk preferences, etc (Basu, Lal, Sriniva- relatively large, competitive intensity is high and san, and Staelin 1985; Anderson and Oliver 1987). both the firm and salesperson are in agreement that Two important elements of the control system in- not much effort should be devoted to prospecting. clude the performance metrics employed by the firm Given the high overlap between the segments, pros- (sales or margin) and the extent of managerial mon- pecting does not yield customers who are willing to itoring. First, consider the impact of utilizing incen- pay much higher prices than Segment B customers. tives based on gross margins. We posit that the use As such, here also, the firm yields pricing authority of incentives based on gross margins in the control to the salesperson in order to obtain the benefits of system will increase the likelihood of delegating price customization. However, when takes on in- pricing authority. This is because offering incentives termediate values, the salesperson’s preferred trade- on gross margins (as opposed to sales revenue) off between effort and price is different from that of ensures that any reduction in price strongly affects the firm’s. Again, the salesperson prefers to substi- the compensation of the salesperson. To illustrate tute price discounting for effort whereas the firm this point, consider a product with a list price of € prefers that this substitution not be done. Thus, in 100 and marginal cost of € 90. If the salesperson is this instance, the firm is better off limiting the ex- compensated on sales with a commission rate of 1%, tent of pricing authority. This limitation, in turn, a sale at list price yields € 1 in income. Discounting ensures that the salesperson invests sufficient the product to € 95 leads to commission income of amounts of effort on prospecting. € 0.95 – a decrease of only 5 cents. On the other Overall, this discussion suggests that the price dele- hand, if the salesperson is compensated on realized gation decision will vary non-monotonically in the gross margins with commission rate of 10%, a sale parameters and . This leads to our first set of hy- at list price yields € 1 in income. Discounting the pothesis: product to € 95 leads to commission income of € 0.50, a decrease of 50%. Clearly, incentives based on H1a: When prospecting is relatively expensive or realized gross margins can substantially reduce the relatively inexpensive in terms of effort motivation to indiscriminately lower price. costs, agency costs are muted and the like- Next, consider the impact of the cost of monitoring lihood of delegating pricing authority is ex- (Joseph and Thevaranjan 1998). Monitoring can pected to be high. However, when prospect- significantly reduce the ability of the salesperson to ing is moderately expensive in terms of ef- engage in sub-optimal trade-offs between effort and fort costs, agency costs are salient and the price. In other words, managerial monitoring can likelihood of delegating pricing authority is ensure that the salesperson does not misuse pricing expected to be low. authority. This discussion involving the control system leads to our second set of hypotheses: H : When competitive intensity is relatively low 1b or relatively high, agency costs are muted H : The utilization of incentives based on gross 2a and the likelihood of delegating pricing au- margins in the control system will increase thority is expected to be high. However, the likelihood of delegating pricing author- when competitive intensity takes on inter- ity to the sales force. 98 BuR - Business Research Official Open Access Journal of VHB Verband der Hochschullehrer für Betriebswirtschaft e.V. Volume 1 | Issue 1 | May 2008 | 94-104 H : A high intensity of monitoring in the con- Dependent Variable 2b trol system will increase the likelihood of The survey measures the extent of pricing authority delegating pricing authority to the sales given to the sales force via the question, “The gen- force. eral pricing authority of your salespeople is” fol- lowed by the choices, “0: No pricing authority (prices are determined by the management,” “1: 3. EMPIRICAL STRATEGY, DATA, Restricted (salesperson determines prices within a MEASURES, AND ESTIMATION pre-specified range),” and “2: Unrestricted (sales- EQUATION person has full authority).” Empirical Strategy Proxies for and We now describe the essential features of our em- As discussed previously, we employ proxies to cap- pirical strategy. Guided by the trade-offs contained ture the hypothesized effects of the parameters within our first hypothesis, we first identify condi- and . With respect to the parameter  the survey tions wherein sub-optimal trade-offs between effort provides information about the fraction of time and price are likely to occur. We then expect these spent on: (i) sales calls, (ii) activities such as call conditions to predict instances wherein firms are preparation, merchandising, and service, (iii) travel likely to restrict pricing authority. Our second hy- / waiting. Recall that the parameter  reflects the pothesis is tested in straightforward fashion by ex- effort cost of perfectly targeting a customer in the amining the impact of control system elements on firm’s target segment. While items (ii) and (iii) do the likelihood of price delegation. not strictly measure prospecting, they are a gauge of We employ proxies to test the effects described the host of activities required to identify and pursue within our hypotheses. Broadly, in this approach, a target customers. Sales processes that require high- case is made for an observable variable to represent er levels of call preparation, merchandising, service, the conditions described within the hypotheses (see, and even travel/waiting all imply greater effective for example, the empirical work by Coughlan and cost to identify and pursue a prospect in the firm’s Narasimhan 1992 on sales-force compensation target segment. As such, we employ the sum of (ii) plans). Then, the data are analyzed to examine if the and (iii) to serve as a proxy for . proxies behave in a manner suggested by the pro- From a measurement point of view, however, the posed conceptualization. response pertaining to the proportion of time spent on sales calls (item (i)) is likely to be the most accu- Data rate. This is because it is often recorded in call logs. We utilize data collected by Krafft (1999) in his In addition, sales managers are likely to be very study pertaining to sales-force control systems. His conscious of this fraction since it is frequently used data were obtained via a mail survey of 1,099 chief for decisions regarding sales-force sizing and spe- sales executives of German sales forces (for details, cialization (see, for example, Moriarty and Swartz see Krafft 1999 or Krafft, Lal, and Albers 2004). A 1986). Thus, we measure the effort cost of prospect- second mailing followed the initial mailing four to ing-type activities by 100 less the fraction of time six weeks later. The survey was completed approxi- devoted to the actual sales call. In our sample, the mately twelve weeks after the first mailing and re- fraction of effort devoted to prospecting varies from sulted in a response rate of 24.6%. This sample is 30% to 100%. The two observations that take values characterized by large firms and comprises observa- greater than 95% are likely to be described by sup- tions from the financial-services sector, pharmaceu- port salespeople rather than field salespeople. Nev- tical-goods firms, industrial-goods companies, and ertheless, they also serve as useful observations to the consumer-goods industry. The average annual test our expectation here that the firm would like to sales volume in the data set is € 148.9 million. A offer pricing authority to the sales force. This is be- comparison of the sample with other German stud- cause the opportunity to make sub-optimal trade- ies shows that this data set corresponds well with offs between effort and price simply does not arise. typical levels of annual sales, sales-force size, age, With respect to the parameter , the survey tenure, and total pay. measures INTENSITY OF COMPETITION via the 99 BuR - Business Research Official Open Access Journal of VHB Verband der Hochschullehrer für Betriebswirtschaft e.V. Volume 1 | Issue 1 | May 2008 | 94-104 question: “How strong do you perceive the intensity Probability (Delegating Pricing Authority) = (1) of competition in your market segment?” The re- z i 1 e sponses to this question are coded via a 7-point semantic differential scale going from Low to High. with z + × PROSPECTING FRACTION + (2) = 0i 1 × (PROSPECTING FRACTION) + Utilization of Incentives Based on Gross × INTENSITY OF COMPETITION + Margins 3 The survey also reports the utilization of incentives × (INTENSITY OF COMPETITION) + based on gross margins. In straightforward fashion, × UTILIZATION OF GROSS MARGIN INCENTIVES + we employ a dummy variable which takes the value × INTENSITY OF MONITORING + 1 if such incentives are utilized, 0 otherwise. Overall, 7 CALLS TO CLOSE in our sample of firms, only about 18% of firms em- ploy incentives based on gross margins although and i = category of delegated pricing approximately 72% offer some amount of pricing authority (no vs. limited vs. full) authority to their salespeople. This empirical finding underscores the importance of including elements The powered terms in equation (2) allow us to test of the control system. the non-monotonic relationships developed in hy- potheses H and H . Following our hypotheses, we 1a 1b Intensity of Monitoring expect 1 < 0, 2 > 0, 3 < 0, 4 > 0, 5 > 0, and In straightforward fashion, we measure the intensity > 0. of monitoring via the number of salespeople super- vised by the sales manager. We assume that the 4. FINDINGS AND DISCUSSION greater the number of salespeople monitored by a Table 1 provides the overall characteristics of our sales manager, the lower is the intensity of monitor- sample with respect to the dependent and inde- ing. Since we further suppose that the impact of a pendent measures (by industry). Table 2 reports the unit increase in sales-force size is larger at relatively correlation matrix of our analysis variables. Table 2 small sales-force sizes – and hence an r-shaped suggests that multi-collinearity – with the exception relation is expected – we use a square-root trans- of the correlations between powered and dedicated formation for this variable. non-powered terms – is not an issue in our study. Despite the multi-collinearity between powered and Calls to Close non-powered terms we integrate the squared terms For proper specification, we also include the num- into our model because both terms add significantly ber of calls required to close a sale in our model. to the overall explanatory power of our model. As Firms characterized by long selling cycles are likely compared to a reduced model (with Log Likelihood to be promoting products that are intrinsically com- = -181.43) that does not include the variables plex. Such products may inherently require a good (PROSPECTING FRACTION) and (INTENSITY deal of negotiation. For this reason, we expect OF COMPETITION) , the model that includes all of length of the selling cycle to increase the likelihood our independent variables has significantly higher of delegating pricing authority to the sales force (see fit (the improvement = 8.615, p<.05). This signifi- Stephenson, Cron, and Frazier 1979, p. 21). CALLS cant improvement confirms our assumption that TO CLOSE is measured via the question, “How powered and non-powered terms of PROS- many sales calls are necessary in cases of first pur- PECTING FRACTION and INTENSITY OF chases to close a sale?” COMPETITION are necessary to estimate the de- pendent variable. We note that including powered Estimation Model terms does not change the remaining variables of Following our discussion, we specify the following model for estimation purposes: For i=0, equation (1) describes the probability that firms dele- gate full or limited (vs. no) pricing authority. For i=1, equation (1) describes the probability that firms delegate full vs. limited or no pricing authority. 100 BuR - Business Research Official Open Access Journal of VHB Verband der Hochschullehrer für Betriebswirtschaft e.V. Volume 1 | Issue 1 | May 2008 | 94-104 our model with regard to direction or significance assumed distribution of the residuals best. Further levels of the coefficients. reasons for preferring a logit model to probit are that both distributions tend to lead to similar re- sults, and probit has found rather limited applica- Table 1: Means Across Industries (standard deviations of independent tion in association with ordered models while logit variables by industry in parentheses) models are widely used (Greene 2003). The findings of our estimation are reported in Table 3, and we Financial Pharmaceutical Consumer Industrial Services Companies Goods Goods note that – with the exception of the variable UTILIZATION OF GROSS MARGIN INCENTIVES Percent of Firms .07 .10 .14 .13 with Full Price – all coefficients are significant at the 0.05 level or Delegation higher. Percent of Firms .36 .55 .6 .82 As hypothesized, we do find that the fraction of with Limited Price effort devoted to prospecting has a non-monotonic Delegation impact on the probability of delegating pricing au- Prospecting 62.03 71.56 66.82 67.66 thority to the sales force. Firms at which the sales Fraction (12.35) (13.38) (14.21) (13.89) process is characterized by relatively low or rela- Intensity of 5.70 5.91 5.76 5.94 Competition (.93) (1.18) (1.46) (1.19) tively high cost of prospecting tend to delegate pric- ing authority to the sales force. On the other hand, Utilization of .19 .15 .16 .21 Gross Margin (.39) (.36) (.37) (.41) firms at which the sales process is characterized by Incentives intermediate levels for the cost of prospecting tend Intensity of 10.70 8.82 9.07 8.44 to limit the extent of pricing authority given to their Monitoring (10.94) (3.14) (6.32) (5.19) salespeople. Similarly, we find that the intensity of Calls to Close 2.77 3.11 3.10 5.35 (1.54) (1.99) (2.29) (3.23) competition displays the hypothesized effects. Spe- cifically, we find that competitive intensity first de- Number of 47 33 75 67 Observations creases and then increases the extent of pricing authority given to the sales force. Taken together, these findings provide strong support for our pri- Table 2: Correlation matrix of independent mary hypothesis that agency costs can mitigate the variables (p-values in parentheses) Prospecting Prospecting Utilization of Gross Intensity Calls to Close Intensity of Intensity of 2 2 Fraction Fraction Margin Incentives of Competition Competition Monitoring Prospecting 1.00 Fraction (0.00) Prospecting 0.99** 1.00 Fraction (0.00) (0.00) Utilization of -0.01 -0.01 1.00 Gross Margin (0.46) (0.43) (0.00) Incentives Intensity of -0.16** -0.14* -0.19** 1.00 Monitoring (0.01) (0.02) (0.00) (0.00) Calls to Close 0.09 0.09 0.08 -0.11* 1.00 (0.11) (0.1) (0.12) (0.05) (0.00) Intensity of 0.08 0.09 0.08 -0.23** 0.13* 1.00 Competition (0.12) (0.09) (0.13) (0.00) (0.03) (0.00) Intensity of 0.07 0.08 0.09 0.24 0.13* 0.99** 1.00 Competition (0.17) (0.13) (0.09) (0.00) (0.03) (0.00) (0.00) **: Significant at the o.01 level *: Significant at the 0.05 level N=222 We next report findings from running our model as price-customization advantages of delegation. specified in (1) and (2). Since the dependent variable The hypothesized effect that the use of incentives is ordinal scaled and its potential categories can be based on gross margins increases the likelihood of ordered, we employ an ordered regression model for delegating pricing authority to the sales force is not our estimation (Long and Freese 2006). We use a supported by our model. logit model because the logistic distribution fits our 101 BuR - Business Research Official Open Access Journal of VHB Verband der Hochschullehrer für Betriebswirtschaft e.V. Volume 1 | Issue 1 | May 2008 | 94-104 One might speculate that this finding is affected by nature of the control system has a significant bear- the categorical nature of our dependent variable – ing on the price delegation decision. our largest sub-sample of sales forces with restrict- To account for the fact that the nature of the selling ted price delegation entails cases with low to high process may also influence the extent of pricing (but not full) degrees of pricing authority. The non- authority delegated to the sales force, we integrated significant effect could then be a consequence of a the variable CALLS TO CLOSE as a covariate. As leveling effect: firms with high degrees of delegation expected, we find that firms characterized by long that base their incentives on gross margins, are selling cycles are more likely to delegate pricing within the same category as firms with very re- authority to their salespeople. stricted pricing authority that do not apply incen- tives based on gross margins. This argument can be Table 4: Determinants of Delegating Pricing underlined by our results from a logistic regression Authority (two groups, logistic regression, where only cases with full vs. no delegation are con- p-values in parentheses) sidered. Though not significant due to low sample Variable Expected Sign Estimate size (n=87), firms using incentives based on gross Prospecting Fraction - - 0.269 margins are more likely to fully delegate pricing (0.103) authority (see Table 4). 2 (Prospecting Fraction) + + 0.002* (0.073) Intensity of Competi- - - 2.726*** Table 3: Determinants of Delegating Pricing tion (0.005) Authority (three groups, ordered (Intensity of + + 0.258*** regression, p-values in parentheses) Competition) (0.009) Variable Expected Sign Estimate Utilization of Gross + - 1.114 Prospecting Fraction - - 0.148** Margin Incentives (0.136) (0.037) Intensity of Monitoring - - 1.311*** (Prospecting Fraction) + + 0.0013** (R, Square root trans- (0.006) (0.023) formation) Intensity of Competition - - 1.594*** Calls to Close + + 0.467*** (0.009) (0.001) (Intensity of + + 0.147** Log likelihood -35.13 Competition) (0.013) χ (Likelihood-Ratio Test) 32.234*** Utilization of Gross + - 0.008 (degrees of freedom: 7) Margin Incentives (0.492) Nagelkerke 0.4 Intensity of Monitoring - - 0.569*** ***: Significant at the 0.01 level (one-tailed) (R, Square root trans- (0.001) **: Significant at the 0.05 level (one-tailed) *: Significant at the 0.1 level (one-tailed) formation) R: reversed measure Calls to Close + + 0.208*** N=87 (0.000) Finally, we also report findings from estimating our Log likelihood -177.123 χ (Likelihood-Ratio Test) 40.567*** model with logistic regression by excluding the (degrees of freedom: 7) middle group (delegating restricted price authority) Nagelkerke 0.2 and findings from ordered regression estimation for ***: Significant at the 0.01 level (one-tailed) equally sized groups. The logistic regression (results **: Significant at the 0.05 level (one-tailed) *: Significant at the 0.1 level (one-tailed) are reported in Table 4) is based on zero (n=63) vs. R: reversed measure N=222 full (n=24) delegation and confirms our findings. Due to limited sample size, some of the coefficients Next, we find that the intensity of monitoring also are not significant, but in the proposed direction. In has a significant impact on the probability of dele- Table 5, we report results from a balanced sample gating pricing authority to the sales force. Firms that (24 cases for no, restricted and full delegation – for monitor their salespeople intensely are relatively no and restricted delegation, we took a random more likely to delegate pricing authority to the sales sample of the 63 and 135 cases). Again, the results force. As postulated, these findings suggest that the are quite similar. We only find some non-significant 102 BuR - Business Research Official Open Access Journal of VHB Verband der Hochschullehrer für Betriebswirtschaft e.V. Volume 1 | Issue 1 | May 2008 | 94-104 relationships because of limited sample sizes. Over- to be weighed against the magnitude of the agency all, the results of the two additional estimations costs that are likely to emerge. In our empirical suggest that the findings of our model are robust work, we find that these agency costs can be sub- and thus confirm that our results as reported in stantial when the proportion of effort devoted to Table 3 are reliable. prospecting-type activities and the degree of com- petitive intensity take on intermediate levels. Clear- Table 5: Determinants of Delegating Pricing ly, these conditions are not transparent; conse- Authority (three groups of same size, quently, these implications have the potential to be ordered regression, p-values in fairly insightful. parentheses) A second substantive finding pertains to the impact Variable Expected Sign Estimate of the control system on the price delegation deci- Prospecting Fraction - - 0.073 sion. We find that firms that closely monitor their (0.342) sales personnel can minimize sub-optimal substitu- (Prospecting Fraction) + + 0.001 tion of selling effort by price discounting. Thus, (0.253) these firms can potentially benefit from delegating Intensity of Competition - - 4.607*** pricing authority to their sales personnel. Con- (0.004) versely, firms that employ low levels of supervision (Intensity of + + 0.454*** may restrict pricing authority because they suffer Competition) (0.003) from an inability to limit agency costs. These firms Utilization of Gross + - 0.305 thus cannot take advantage of the price-custo- Margin Incentives (0.380) mization advantages of delegation. Intensity of Monitoring - - 0.412* (R, Square root trans- (0.089) Based on the assumption that a firm’s decision formation) against price delegation is influenced by some miti- Calls to Close + + 0.486*** gating factors that make not delegating optimal for (0.001) them, our research suggests that the decision to Log likelihood -57.105 delegate pricing authority yields the advantages of χ (Likelihood-Ratio Test) 42.605*** price-customization benefits but is fraught with (degrees of freedom: 7) Nagelkerke 0.5 agency costs. However, to get a broader understand- ing of the optimality of the price delegation decision, ***: Significant at the 0.01 level (one-tailed) **: Significant at the 0.05 level (one-tailed) future research should more thoroughly investigate *: Significant at the 0.1 level (one-tailed) R: reversed measure the relationship between the degree of pricing au- N=72 thority and performance. Furthermore, additional research is required to verify the impact of the miti- 5. CONTRIBUTIONS AND gating factors with more precise dependent vari- IMPLICATIONS ables and direct measures rather than the proxies In very many markets, customers vary significantly that we employ in the current research. Additional in their valuation of the firm’s offerings. In these research is also required to examine how technology cases, managers need to decide whether and how (e.g., sales-force automation) can impact the delega- tion decision. Finally, future research could also much pricing authority should be delegated to the sales force. Our work illuminates the economic examine heterogeneity in the price delegation deci- trade-offs involved in this decision. sion within a salesforce (by levels of experience, Substantively, our empirical findings suggest that product lines, etc). We hope our research will stimu- late such efforts. although price delegation can yield tremendous advantages, this latitude gives rise to the possibility Acknowledgements of a specific type of agency cost, namely, the sub- The authors are grateful for several detailed and optimal substitution of selling effort by price dis- insightful comments by two anonymous BuR re- counting. A key finding in our empirical work is that viewers on previous versions of the paper. Kissan firms are less likely to delegate pricing authority Joseph also gratefully acknowledges financial sup- when these agency costs are likely to be fairly sub- port from the General Research Fund of the Univer- stantial. This finding demonstrates that the price- sity of Kansas. customization advantages of price delegation need 103 BuR - Business Research Official Open Access Journal of VHB Verband der Hochschullehrer für Betriebswirtschaft e.V. Volume 1 | Issue 1 | May 2008 | 94-104 Mishra, Birendra K. and Ashutosh Prasad (2004): Central- References ized Pricing versus Delegating Pricing to the Salesforce under Information Asymmetry, Marketing Science, 23 (1): Anderson, Erin and Richard L. Oliver (1987): Perspectives 21-28. on Behaviour-based Versus Outcome-Based Sales Force Control Systems, Journal of Marketing, 51 (4): 76-88. Mishra, Birendra K. and Ashutosh Prasad (2005): Delegat- ing Pricing Decisions in Competitive Markets with Sym- Akaike, Hirotugu (1974): New Look at the Statistical Model metric and Asymmetric Information, Marketing Science, Identification, IEEE Transactions on Automatic Control, 24 (3): 490-497. 19 (6): 716-723. Moriarty, Rowland T. and Gordon Swartz (1986): BOC Basu, Amiya K., Rajiv Lal, V. Srinivasan, and Richard Group: Ohmeda (A), Harvard Business School Case. Staelin (1985): Salesforce Compensation Plans: An Agency Theoretic Perspective, Marketing Science, 4 (4): 267-291. Stephenson, Ronald P., William L. Cron, and Gary L. Fra- zier (1979): Delegating Pricing Authority to the Sales Force: Bhardwaj, Pradeep (2001): Delegating Pricing Decisions, The Effects on Sales and Profit Performance, Journal of Marketing Science, 20 (2): 143-169. Marketing, 43 (2): 21-28. Brickley, James A., Clifford W. Smith, and Jerold L. Zim- Weinberg, Charles B. (1975): An Optimal Commission Plan merman (2001): Managerial Economics and Organiza- for Salesmen’s Control over Price, Management Science, 21 tional Architecture, Mc-Graw Hill Irwin: Boston, MA. (8): 937-943. Churchill, Gilbert A., Neil M. Ford, and Orville C. Walker (1997): Salesforce Management, 5th ed., Irwin: Chicago, IL. Biographies Coughlan, Anne T. and Chakravarthi Narasimhan (1992): Ann-Kristin Hansen is doctoral student at the Institute of An Empirical Analysis of Sales-Force Compensation Plans, Marketing at the Westphalian Wilhelms University of Muenster in Journal of Business, 65 (1): 93-121. Germany. After finishing her degree in business studies (majors: Dolan, Robert and Hermann Simon (1996): Power Pricing. marketing, sales and supply chain management) in 2006, she has The Free Press: New York. been employed as research assistant at the Institute of Marketing. Greene, William H. (2003): Econometric Analysis, 5th ed., Her research interests focus on delegation of pricing authority to Pearson Education: New Jersey. sales people and the Marketing-Sales interface. Hayek, Friedrich A. (1945): The Use of Knowledge in Soci- ety, American Economic Review, 35 (4): 519-530. Kissan Joseph is Associate Professor and Stockton Research Jensen, Michael and William Meckling (1992): Specific and Fellow at the University of Kansas. He obtained his Ph. D. in General Knowledge and Organizational Structure, Journal Marketing from Purdue University in 1992. His research interests of Applied Corporate Finance, 8 (2): 4-18. include pharmaceutical marketing, e-marketing, pricing, advertis- ing budgeting, and sales force compensation. His research has Joseph, Kissan (2001): On the Optimality of Delegating Pricing Authority to the Sales Force, Journal of Marketing, appeared in the Journal of Marketing, Marketing Science, Mar- 65 (1): 62-70. keting Letters, Industrial Marketing Management, Managerial and Decision Economics, and the Southern Economic Journal. Joseph, Kissan and Alex Thevaranjan (1998): Monitoring and Incentives In Sales Organizations: An Agency- Theoretic Perspective, Marketing Science, 17 (2): 107-123. Manfred Krafft is Professor and Director of the Institute of Marketing at the Westphalian Wilhelms University of Muenster in Krafft, Manfred (1999): An Empirical Investigation of the Germany since 2003. Postdoctoral, he was employed as Assistant Antecedents of Sales Force Control Systems, Journal of Marketing, 63 (3): 120-134. Professor of Marketing at the Institute of Marketing, University of Kiel. After finishing his Habilitation (2nd dissertation) with a Krafft, Manfred, Sönke Albers, and Rajiv Lal (2004): Rela- thesis on “Customer retention and customer value” in 1999, he tive Explanatory Power of Agency Theory and Transaction Cost Analysis in German Salesforces, International Jour- was endowed the Otto Beisheim Chair of Marketing at the nal of Research in Marketing, 21 (3): 265-283. Koblenz Graduate School Management in Germany. His main research interests focus on topics related to customer relationship Lal, Rajiv (1986): Delegating Pricing Responsibility to the management, direct marketing and sales management. His re- Salesforce, Marketing Science, 5 (2): 159-168. search has appeared in the Journal of Marketing, Marketing Long, J. Scott and Jeremy Freese (2006): Regression Mod- Science, Journal of Marketing Research, International Journal of els for Categorical Dependent Variables Using Stata, 2nd Research in Marketing and Interfaces. ed., Stata Press: Texas. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Business Research Springer Journals

Price Delegation in Sales Organizations: An Empirical Investigation

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Publisher
Springer Journals
Copyright
Copyright © 2008 by The Author(s)
Subject
Business and Management; Business and Management, general; Accounting/Auditing; Corporate Finance; Marketing; Business Strategy/Leadership
ISSN
2198-3402
eISSN
2198-2627
DOI
10.1007/BF03342704
Publisher site
See Article on Publisher Site

Abstract

The allocation of decision rights is an integral component of designing organizational architecture. Econ- omists have long understood the importance of co-locating decision rights with the knowledge that is valu- able to those decisions. Following this prescription, marketing scholars have developed strong theoretical arguments in favor of delegating pricing authority to the sales force. Empirical work, however, reveals a significant number of sales organizations yielding only minimal authority to their salespeople. Given this divergence between theory and practice, we develop and empirically test two mitigating factors that could potentially explain why firms restrict pricing authority. We test our hypotheses on a sample of 222 Ger- man sales organizations and find that the data are generally consistent with our conceptualization. Keywords: pricing authority, delegation, determinants of price delegation, pricing, sales force manage- ment maximize simultaneously their own income and the 1. INTRODUCTION company’s profits. The allocation of decision rights is an integral com- Surprisingly, despite these powerful theoretical ponent of designing organizational architecture arguments in favor of delegating pricing authority to (Brickley, Smith, and Zimmerman 2001). Econo- the sales force, empirical work reveals a majority of mists have long understood the importance of co- sales organizations yielding little or no pricing au- locating decision authority with the knowledge that thority to their salespeople. In a study conducted by is valuable to those decisions. As early as 1945, Stephenson, Cron, and Frazier (1979) in the hospi- Hayek highlighted the inability of centralized deci- tal-supplies industry, for example, 29% of firms sion-makers to effectively solve organizational prob- yield no pricing authority and 48% yield only lim- lems lower down in the hierarchy. More recently, ited pricing authority. Only a minority of respond- Jensen and Meckling (1992) suggest that as long as ing firms, namely 23%, give full pricing authority to agency problems are minimal, assigning decision their salespeople. Moreover, firms that centralize rights to individuals, who have the decision-relevant pricing authority are actually found to be more prof- knowledge, increases efficiency. itable than firms that delegate pricing authority. Marketing scholars have presented similar argu- Broadly, our objective in this research is to shed ments in describing the allocation of pricing author- light on the observed divergence between theory ity within sales organizations. Lal (1986) makes the and practice. More specifically, our research goals case that delegating pricing authority to the sales are two-fold: (i) identify factors that could poten- force will be more profitable than centralization tially mitigate the optimality of delegating pricing because salespeople often possess superior informa- authority to the sales force, and (ii) investigate the tion about customer willingness-to-pay. Weinberg empirical validity of the proposed mitigating factors. (1975) shows that salespeople, who are paid a com- In our research, we describe two mitigating factors mission based on realized gross margin and who are that may cause centralization to be actually pre- given control over price, will set prices so as to ferred over delegation. First, following the work of 94 BuR - Business Research Official Open Access Journal of VHB Verband der Hochschullehrer für Betriebswirtschaft e.V. Volume 1 | Issue 1 | May 2008 | 94-104 Jensen and Meckling (1992), we posit that agency In our empirical research, we investigate the man- costs can potentially mitigate the benefits of delegat- ner in which pricing authority is delegated to the ing pricing authority to the sales force. Agency costs sales force in a sample of 222 German sales organi- refer to the inefficiencies arising from a lack of per- zations spanning multiple industries. Like Stephen- fect goal alignment between employees and owners, son, Cron, and Frazier (1979), we find considerable thereby lowering firm profit. The work by Joseph heterogeneity across firms with respect to this deci- (2001) is particularly germane in this connection. sion. Interestingly, we find that a significant propor- His model reveals that salespeople have a tendency tion of firms, namely 28%, choose to yield no pric- to make trade-offs between effort and price dis- ing authority to the sales force. In these cases, price counting that are inconsistent with the profit objec- is determined exclusively by management. Another tive of the firm. Interestingly, this type of agency 61% of the firms yield only limited pricing authority cost is also of great concern to practitioners. Specifi- to their salespeople. Here, salespeople are allowed cally, sales managers complain that price latitude to set prices within a pre-specified range. Finally, often causes salespeople to take the path of least only a relatively lower percentage of firms, namely resistance, i.e., use discounting rather than expend 11%, follow the theoretical prescription of providing effort on selling (Stephenson, Cron, and Frazier their salespeople with full pricing authority. In these 1979, p. 26). Given the possibility of such inefficient cases, salespeople are given the freedom to set any trade-offs, firms may withhold pricing authority price above marginal cost. even as they sacrifice the benefits of price customi- Our main empirical findings can be summarized as zation obtained via delegation. follows: The observed heterogeneity with respect to The second reason why centralization may be pre- price delegation can be explained by the aforemen- ferred to delegation pertains to the manner in which tioned mitigating factors. In particular, our proxies sales force control systems are designed. The design that identify conditions where firms are concerned of control systems includes such elements as the about inefficient trade-offs between price delegation choice of metrics utilized in the compensation plan, and effort are able to successfully predict the likeli- namely margins or sales, and the level of monitoring hood of price delegation. In addition, the nature of (Joseph and Thevaranjan 1998). Clearly, these con- the control system also predicts the likelihood of trol elements are designed not only to support the price delegation. price delegation decision but also to respond to Overall, these findings offer a more refined under- various other conditions facing the firm. For exam- standing of the price delegation decision. Early work ple, in some scenarios, the firm may wish to avoid in the marketing literature suggests that price dele- setting commissions based on margins because such gation will invariably improve firm profits (Lal an action could reveal the firm’s cost structure to the 1986; Weinberg 1975). The practitioner-oriented competition. This revelation could prove to be too literature, on the other hand, has generally been costly from a strategic point of view (Churchill, more circumspect about delegating pricing author- Ford, and Walker 1997, p. 226). In such situations, a ity to the sales force. For example, based on their profit-maximizing firm is pushed towards centrali- consulting experience, Dolan and Simon (1996) zation because incentives on sales provide no check comment that it seems to be better to err on the on indiscriminate price discounting. Similarly, a restrictive side, i.e., offer less pricing authority ra- firm faced with high monitoring costs may not be ther than too much pricing authority. They also able to install an adequate number of supervisory report the practitioner sentiment that “letting the personnel. This lack of supervision may prevent the sales force set prices is about the same as hiring a firm from verifying if the salesperson is making the fox to guard the hen house.” Clearly, the mitigating right trade-offs between effort and price; conse- factors proposed in this research have the potential quently, here also, centralization is the best strategy. to reconcile these divergent prescriptions. In short, our essential point here is that the decision The rest of the paper is organized in the following to delegate pricing authority will be influenced by manner: In the next section, we review the literature the extant control system. Consequently, any study and derive our hypotheses. We then explain our that examines the issue of delegating pricing author- empirical strategy and describe the data and meas- ity to the sales force must explicitly take into ac- ures utilized in our empirical research. Next, we count the nature of the overall control system. present our empirical findings and discuss the main 95 BuR - Business Research Official Open Access Journal of VHB Verband der Hochschullehrer für Betriebswirtschaft e.V. Volume 1 | Issue 1 | May 2008 | 94-104 implications. Finally, we summarize our contribu- Mishra and Prasad (2005) demonstrate similar tions and conclude by outlining directions for future implications in a competitive setting. Although this research. is in contrast to Lal’s (1986) result, it arises because Mishra and Prasad assume a different timing of information. In particular, in their work, the private 2. LITERATURE REVIEW AND information of the rep is garnered at the time of DEVELOPMENT OF HYPOTHESES contracting; consequently, an appropriate contract is able to elicit this information. In contrast to the 2.1 Literature Review efforts of Mishra and Prasad, we follow the work of Joseph (2001) considers the impact of two forces Lal (1986) and consider a context where the private that could potentially influence the optimality of information is obtained after the time of contracting price delegation. On the one hand, providing pricing when salespeople actually call on their clients. authority to individual salespeople empowers them to use their superior information about customer 2.2 Development of Hypotheses willingness-to-pay and thereby conclude a greater As suggested previously, there are two mitigating number of transactions. On the other hand, provid- factors which may negate the price-customization ing the salesperson with pricing authority could lead advantages of price delegation, namely agency costs to sub-optimal trade-offs between effort and price and the overall nature of the control system. We discounting. Given these opposing considerations, next develop hypotheses pertaining to these two Joseph's primary objective is to examine the net factors. effect of these two forces in determining the optimal level of pricing authority. His analysis reveals that Impact of Agency Costs on Price Delegation limiting pricing authority can, in some environ- Since our hypotheses here depend heavily on the ments, reduce the sub-optimal trade-off between work of Joseph (2001), it is instructive to review it price discounting and effort. In effect, limiting pric- in some detail. In his model, the market consists of ing authority forces the salesperson to expend two segments: A and B. Customers belonging to greater effort on prospecting because shirking on Segment A have reservation values that are inde- this task cannot be offset by price discounting. As pendently distributed and come from the uniform such, the benefit obtained from inducing greater distribution [1- , 2- ]. Customers belonging to effort on prospecting outweighs the loss arising Segment B have reservation values that are also from the inability to customize prices. independently distributed but come from the uni- Of course, other work in marketing has also exam- form distribution [0, 1]. Evidently, there is some ined the price delegation decision. Bhardwaj (2001) overlap between the two segments, A and B. In par- considers the strategic impact of the price delega- ticular, there are some customers in both segments tion decision. In particular, he examines how com- whose valuations lie in the interval [1- , 1]. The pa- petition impacts the price delegation decision. Our rameter  thus represents the overlap between the investigation differs from his analysis in that he two segments. Its expected values will be greater does not consider the price-customization advan- than 0 but substantially less than 1. Obviously, cus- tages of price delegation. That is, the issue of cus- tomers in Segment A comprise the firm’s target tomizing prices across customers is not considered segment because they tend to have higher reserva- in his model – when the rep has pricing authority, tion values in general. As such, the firm will encour- he (or she) sets a single price for the entire market. age the salesperson to identify and pursue custom- Mishra and Prasad (2004) also consider the issue of ers belonging to this segment (prospecting). Now, price delegation and conclude that centralized pric- high values of suggest that the two segments ing performs at least as well as price delegation. merge with respect to their reservation values which is likely to be the case in a highly competitive envi- From a technical point of view, Joseph (2001) obtains these ronment. In particular, due to the availability of effects because he allows the effort devoted to prospecting to several substitute goods, the reservation values of influence the type of customer (high valuation or low valua- tion) that the salesperson encounters. This is in contrast to both segments converge. In contrast, low values of Lal’s work wherein the price sensitivity of the sales response describe a distinct segment that is willing to pay function is better observed by the salesperson, but not influ- enced by the effort choices of the salesperson. 96 BuR - Business Research Official Open Access Journal of VHB Verband der Hochschullehrer für Betriebswirtschaft e.V. Volume 1 | Issue 1 | May 2008 | 94-104 higher prices for the firm’s offering which is more such, the firm yields pricing authority to the sales- characteristic for a low competitive landscape be- person in order to obtain the benefits of price cus- cause of the absence of alternative suppliers. Fol- tomization. Similarly, when is relatively high, both lowing these explanations,  is interpreted as a the firm and salesperson are in agreement that not measure of competitive intensity. much effort should be devoted to prospecting – this In this context, the effort  expended by the sales- activity is expensive in terms of effort costs. As such, here also, the firm yields pricing authority to the person is assumed to impact the quality of prospect- salesperson in order to obtain the benefits of price ing. Specifically, as the salesperson expends greater customization. However, when  takes on interme- effort on prospecting, a greater fraction of the cus- diate values, the salesperson’s preferred trade-off tomers encountered by the salesperson are drawn between effort and price is different from that of the from Segment A. This is because the more time the firm’s. In particular, the salesperson prefers to sub- salesperson devotes to market analysis in terms of stitute price discounting for effort whereas the firm identifying potential customers based on demo- prefers that this substitution not be done. Thus, in graphic or situational factors, the higher the likeli- this instance, the firm is better off limiting the ex- hood that potential customers are classified cor- tent of pricing authority. This limitation, in turn, rectly as Segment A customers. Clearly,  can take forces the salesperson to invest a sufficient amount on values between 0 (minimum effort) and 1 of effort on prospecting. (maximum effort). Hence, given effort level on prospecting and a cohort of N customers, N cus- Figure 1. Optimality of Price Delegation tomers are drawn from Segment A. Since [0,1], the remaining N (1- ) customers are drawn from Segment B. Within the model, the pa- rameter  scales the effort cost of prospecting, which is expressed as  . Clearly,  determines how ex- pensive prospecting effort is – a given level of pros- pecting effort incurs greater effort cost in those en- vironments where  is higher. Following this analysis, the main insight offered by Joseph (2001) is as follows: He finds that price del- egation is not optimal in all parts of the parameter space. Specifically, for a given value of the competi- tive intensity parameter, , the optimality of delegat- ing pricing authority varies nonmonotonically with the effort cost of following a high-quality prospect- ing strategy. In particular, when is relatively high or relatively low, delegating pricing authority to the sales force is the optimal strategy. However, when takes on intermediate values, limiting pricing au- For our purposes, the model analyzed by Joseph thority is the optimal strategy (please see basis for (2001) can be examined closely to obtain an H arrow in Figure 1, taken from Joseph (2001). 1a additional insight. Specifically, by looking at the The intuition behind this finding is as follows: When output of the model, it is also apparent that for a is relatively low, the salesperson is willing to invest given value of the parameter , the optimality of effort on prospecting because prospecting is not that delegating pricing authority varies nonmonotoni- expensive in terms of effort costs. In this situation, cally with the competitive intensity parameter, . there is no divergence in preferences between the Thus, when  is relatively high or relatively low, firm and the salesperson with respect to the amount delegating pricing authority to the sales force is the of effort that ought to be devoted to prospecting. As optimal strategy. However, when takes on inter- mediate values, limiting pricing authority is the Effort cost is the monetary equivalent of the disutility in- optimal strategy (please see basis for H arrow in 1b curred from effort (see also Basu, Lal, Srinivasan, and Stae- Figure 1). lin 1985). 97 BuR - Business Research Official Open Access Journal of VHB Verband der Hochschullehrer für Betriebswirtschaft e.V. Volume 1 | Issue 1 | May 2008 | 94-104 Although this is a new insight, the intuition behind mediate values, agency costs are salient and it is very similar to the previous case. When  is rela- the likelihood of delegating pricing author- tively small, the salesperson is willing to invest effort ity is expected to be low. on prospecting because the degree of competitive intensity is small and any effort expended on pros- Impact of Control System Elements on Price pecting yields Segment A customers who are, in this Delegation instance, distinct from Segment B customers. As As mentioned previously, the design of the control such, there is no divergence in preferences between system is likely to be influenced by several factors. the firm and the salesperson with respect to the Typically, the control system is designed not only to amount of effort that ought to be devoted to pros- support the price delegation decision but also to pecting. Consequently, the firm yields pricing au- accommodate various conditions facing the firm thority to the salesperson in order to obtain the such as task programmability, environmental un- benefits of price customization. Similarly, when  is certainty, risk preferences, etc (Basu, Lal, Sriniva- relatively large, competitive intensity is high and san, and Staelin 1985; Anderson and Oliver 1987). both the firm and salesperson are in agreement that Two important elements of the control system in- not much effort should be devoted to prospecting. clude the performance metrics employed by the firm Given the high overlap between the segments, pros- (sales or margin) and the extent of managerial mon- pecting does not yield customers who are willing to itoring. First, consider the impact of utilizing incen- pay much higher prices than Segment B customers. tives based on gross margins. We posit that the use As such, here also, the firm yields pricing authority of incentives based on gross margins in the control to the salesperson in order to obtain the benefits of system will increase the likelihood of delegating price customization. However, when takes on in- pricing authority. This is because offering incentives termediate values, the salesperson’s preferred trade- on gross margins (as opposed to sales revenue) off between effort and price is different from that of ensures that any reduction in price strongly affects the firm’s. Again, the salesperson prefers to substi- the compensation of the salesperson. To illustrate tute price discounting for effort whereas the firm this point, consider a product with a list price of € prefers that this substitution not be done. Thus, in 100 and marginal cost of € 90. If the salesperson is this instance, the firm is better off limiting the ex- compensated on sales with a commission rate of 1%, tent of pricing authority. This limitation, in turn, a sale at list price yields € 1 in income. Discounting ensures that the salesperson invests sufficient the product to € 95 leads to commission income of amounts of effort on prospecting. € 0.95 – a decrease of only 5 cents. On the other Overall, this discussion suggests that the price dele- hand, if the salesperson is compensated on realized gation decision will vary non-monotonically in the gross margins with commission rate of 10%, a sale parameters and . This leads to our first set of hy- at list price yields € 1 in income. Discounting the pothesis: product to € 95 leads to commission income of € 0.50, a decrease of 50%. Clearly, incentives based on H1a: When prospecting is relatively expensive or realized gross margins can substantially reduce the relatively inexpensive in terms of effort motivation to indiscriminately lower price. costs, agency costs are muted and the like- Next, consider the impact of the cost of monitoring lihood of delegating pricing authority is ex- (Joseph and Thevaranjan 1998). Monitoring can pected to be high. However, when prospect- significantly reduce the ability of the salesperson to ing is moderately expensive in terms of ef- engage in sub-optimal trade-offs between effort and fort costs, agency costs are salient and the price. In other words, managerial monitoring can likelihood of delegating pricing authority is ensure that the salesperson does not misuse pricing expected to be low. authority. This discussion involving the control system leads to our second set of hypotheses: H : When competitive intensity is relatively low 1b or relatively high, agency costs are muted H : The utilization of incentives based on gross 2a and the likelihood of delegating pricing au- margins in the control system will increase thority is expected to be high. However, the likelihood of delegating pricing author- when competitive intensity takes on inter- ity to the sales force. 98 BuR - Business Research Official Open Access Journal of VHB Verband der Hochschullehrer für Betriebswirtschaft e.V. Volume 1 | Issue 1 | May 2008 | 94-104 H : A high intensity of monitoring in the con- Dependent Variable 2b trol system will increase the likelihood of The survey measures the extent of pricing authority delegating pricing authority to the sales given to the sales force via the question, “The gen- force. eral pricing authority of your salespeople is” fol- lowed by the choices, “0: No pricing authority (prices are determined by the management,” “1: 3. EMPIRICAL STRATEGY, DATA, Restricted (salesperson determines prices within a MEASURES, AND ESTIMATION pre-specified range),” and “2: Unrestricted (sales- EQUATION person has full authority).” Empirical Strategy Proxies for and We now describe the essential features of our em- As discussed previously, we employ proxies to cap- pirical strategy. Guided by the trade-offs contained ture the hypothesized effects of the parameters within our first hypothesis, we first identify condi- and . With respect to the parameter  the survey tions wherein sub-optimal trade-offs between effort provides information about the fraction of time and price are likely to occur. We then expect these spent on: (i) sales calls, (ii) activities such as call conditions to predict instances wherein firms are preparation, merchandising, and service, (iii) travel likely to restrict pricing authority. Our second hy- / waiting. Recall that the parameter  reflects the pothesis is tested in straightforward fashion by ex- effort cost of perfectly targeting a customer in the amining the impact of control system elements on firm’s target segment. While items (ii) and (iii) do the likelihood of price delegation. not strictly measure prospecting, they are a gauge of We employ proxies to test the effects described the host of activities required to identify and pursue within our hypotheses. Broadly, in this approach, a target customers. Sales processes that require high- case is made for an observable variable to represent er levels of call preparation, merchandising, service, the conditions described within the hypotheses (see, and even travel/waiting all imply greater effective for example, the empirical work by Coughlan and cost to identify and pursue a prospect in the firm’s Narasimhan 1992 on sales-force compensation target segment. As such, we employ the sum of (ii) plans). Then, the data are analyzed to examine if the and (iii) to serve as a proxy for . proxies behave in a manner suggested by the pro- From a measurement point of view, however, the posed conceptualization. response pertaining to the proportion of time spent on sales calls (item (i)) is likely to be the most accu- Data rate. This is because it is often recorded in call logs. We utilize data collected by Krafft (1999) in his In addition, sales managers are likely to be very study pertaining to sales-force control systems. His conscious of this fraction since it is frequently used data were obtained via a mail survey of 1,099 chief for decisions regarding sales-force sizing and spe- sales executives of German sales forces (for details, cialization (see, for example, Moriarty and Swartz see Krafft 1999 or Krafft, Lal, and Albers 2004). A 1986). Thus, we measure the effort cost of prospect- second mailing followed the initial mailing four to ing-type activities by 100 less the fraction of time six weeks later. The survey was completed approxi- devoted to the actual sales call. In our sample, the mately twelve weeks after the first mailing and re- fraction of effort devoted to prospecting varies from sulted in a response rate of 24.6%. This sample is 30% to 100%. The two observations that take values characterized by large firms and comprises observa- greater than 95% are likely to be described by sup- tions from the financial-services sector, pharmaceu- port salespeople rather than field salespeople. Nev- tical-goods firms, industrial-goods companies, and ertheless, they also serve as useful observations to the consumer-goods industry. The average annual test our expectation here that the firm would like to sales volume in the data set is € 148.9 million. A offer pricing authority to the sales force. This is be- comparison of the sample with other German stud- cause the opportunity to make sub-optimal trade- ies shows that this data set corresponds well with offs between effort and price simply does not arise. typical levels of annual sales, sales-force size, age, With respect to the parameter , the survey tenure, and total pay. measures INTENSITY OF COMPETITION via the 99 BuR - Business Research Official Open Access Journal of VHB Verband der Hochschullehrer für Betriebswirtschaft e.V. Volume 1 | Issue 1 | May 2008 | 94-104 question: “How strong do you perceive the intensity Probability (Delegating Pricing Authority) = (1) of competition in your market segment?” The re- z i 1 e sponses to this question are coded via a 7-point semantic differential scale going from Low to High. with z + × PROSPECTING FRACTION + (2) = 0i 1 × (PROSPECTING FRACTION) + Utilization of Incentives Based on Gross × INTENSITY OF COMPETITION + Margins 3 The survey also reports the utilization of incentives × (INTENSITY OF COMPETITION) + based on gross margins. In straightforward fashion, × UTILIZATION OF GROSS MARGIN INCENTIVES + we employ a dummy variable which takes the value × INTENSITY OF MONITORING + 1 if such incentives are utilized, 0 otherwise. Overall, 7 CALLS TO CLOSE in our sample of firms, only about 18% of firms em- ploy incentives based on gross margins although and i = category of delegated pricing approximately 72% offer some amount of pricing authority (no vs. limited vs. full) authority to their salespeople. This empirical finding underscores the importance of including elements The powered terms in equation (2) allow us to test of the control system. the non-monotonic relationships developed in hy- potheses H and H . Following our hypotheses, we 1a 1b Intensity of Monitoring expect 1 < 0, 2 > 0, 3 < 0, 4 > 0, 5 > 0, and In straightforward fashion, we measure the intensity > 0. of monitoring via the number of salespeople super- vised by the sales manager. We assume that the 4. FINDINGS AND DISCUSSION greater the number of salespeople monitored by a Table 1 provides the overall characteristics of our sales manager, the lower is the intensity of monitor- sample with respect to the dependent and inde- ing. Since we further suppose that the impact of a pendent measures (by industry). Table 2 reports the unit increase in sales-force size is larger at relatively correlation matrix of our analysis variables. Table 2 small sales-force sizes – and hence an r-shaped suggests that multi-collinearity – with the exception relation is expected – we use a square-root trans- of the correlations between powered and dedicated formation for this variable. non-powered terms – is not an issue in our study. Despite the multi-collinearity between powered and Calls to Close non-powered terms we integrate the squared terms For proper specification, we also include the num- into our model because both terms add significantly ber of calls required to close a sale in our model. to the overall explanatory power of our model. As Firms characterized by long selling cycles are likely compared to a reduced model (with Log Likelihood to be promoting products that are intrinsically com- = -181.43) that does not include the variables plex. Such products may inherently require a good (PROSPECTING FRACTION) and (INTENSITY deal of negotiation. For this reason, we expect OF COMPETITION) , the model that includes all of length of the selling cycle to increase the likelihood our independent variables has significantly higher of delegating pricing authority to the sales force (see fit (the improvement = 8.615, p<.05). This signifi- Stephenson, Cron, and Frazier 1979, p. 21). CALLS cant improvement confirms our assumption that TO CLOSE is measured via the question, “How powered and non-powered terms of PROS- many sales calls are necessary in cases of first pur- PECTING FRACTION and INTENSITY OF chases to close a sale?” COMPETITION are necessary to estimate the de- pendent variable. We note that including powered Estimation Model terms does not change the remaining variables of Following our discussion, we specify the following model for estimation purposes: For i=0, equation (1) describes the probability that firms dele- gate full or limited (vs. no) pricing authority. For i=1, equation (1) describes the probability that firms delegate full vs. limited or no pricing authority. 100 BuR - Business Research Official Open Access Journal of VHB Verband der Hochschullehrer für Betriebswirtschaft e.V. Volume 1 | Issue 1 | May 2008 | 94-104 our model with regard to direction or significance assumed distribution of the residuals best. Further levels of the coefficients. reasons for preferring a logit model to probit are that both distributions tend to lead to similar re- sults, and probit has found rather limited applica- Table 1: Means Across Industries (standard deviations of independent tion in association with ordered models while logit variables by industry in parentheses) models are widely used (Greene 2003). The findings of our estimation are reported in Table 3, and we Financial Pharmaceutical Consumer Industrial Services Companies Goods Goods note that – with the exception of the variable UTILIZATION OF GROSS MARGIN INCENTIVES Percent of Firms .07 .10 .14 .13 with Full Price – all coefficients are significant at the 0.05 level or Delegation higher. Percent of Firms .36 .55 .6 .82 As hypothesized, we do find that the fraction of with Limited Price effort devoted to prospecting has a non-monotonic Delegation impact on the probability of delegating pricing au- Prospecting 62.03 71.56 66.82 67.66 thority to the sales force. Firms at which the sales Fraction (12.35) (13.38) (14.21) (13.89) process is characterized by relatively low or rela- Intensity of 5.70 5.91 5.76 5.94 Competition (.93) (1.18) (1.46) (1.19) tively high cost of prospecting tend to delegate pric- ing authority to the sales force. On the other hand, Utilization of .19 .15 .16 .21 Gross Margin (.39) (.36) (.37) (.41) firms at which the sales process is characterized by Incentives intermediate levels for the cost of prospecting tend Intensity of 10.70 8.82 9.07 8.44 to limit the extent of pricing authority given to their Monitoring (10.94) (3.14) (6.32) (5.19) salespeople. Similarly, we find that the intensity of Calls to Close 2.77 3.11 3.10 5.35 (1.54) (1.99) (2.29) (3.23) competition displays the hypothesized effects. Spe- cifically, we find that competitive intensity first de- Number of 47 33 75 67 Observations creases and then increases the extent of pricing authority given to the sales force. Taken together, these findings provide strong support for our pri- Table 2: Correlation matrix of independent mary hypothesis that agency costs can mitigate the variables (p-values in parentheses) Prospecting Prospecting Utilization of Gross Intensity Calls to Close Intensity of Intensity of 2 2 Fraction Fraction Margin Incentives of Competition Competition Monitoring Prospecting 1.00 Fraction (0.00) Prospecting 0.99** 1.00 Fraction (0.00) (0.00) Utilization of -0.01 -0.01 1.00 Gross Margin (0.46) (0.43) (0.00) Incentives Intensity of -0.16** -0.14* -0.19** 1.00 Monitoring (0.01) (0.02) (0.00) (0.00) Calls to Close 0.09 0.09 0.08 -0.11* 1.00 (0.11) (0.1) (0.12) (0.05) (0.00) Intensity of 0.08 0.09 0.08 -0.23** 0.13* 1.00 Competition (0.12) (0.09) (0.13) (0.00) (0.03) (0.00) Intensity of 0.07 0.08 0.09 0.24 0.13* 0.99** 1.00 Competition (0.17) (0.13) (0.09) (0.00) (0.03) (0.00) (0.00) **: Significant at the o.01 level *: Significant at the 0.05 level N=222 We next report findings from running our model as price-customization advantages of delegation. specified in (1) and (2). Since the dependent variable The hypothesized effect that the use of incentives is ordinal scaled and its potential categories can be based on gross margins increases the likelihood of ordered, we employ an ordered regression model for delegating pricing authority to the sales force is not our estimation (Long and Freese 2006). We use a supported by our model. logit model because the logistic distribution fits our 101 BuR - Business Research Official Open Access Journal of VHB Verband der Hochschullehrer für Betriebswirtschaft e.V. Volume 1 | Issue 1 | May 2008 | 94-104 One might speculate that this finding is affected by nature of the control system has a significant bear- the categorical nature of our dependent variable – ing on the price delegation decision. our largest sub-sample of sales forces with restrict- To account for the fact that the nature of the selling ted price delegation entails cases with low to high process may also influence the extent of pricing (but not full) degrees of pricing authority. The non- authority delegated to the sales force, we integrated significant effect could then be a consequence of a the variable CALLS TO CLOSE as a covariate. As leveling effect: firms with high degrees of delegation expected, we find that firms characterized by long that base their incentives on gross margins, are selling cycles are more likely to delegate pricing within the same category as firms with very re- authority to their salespeople. stricted pricing authority that do not apply incen- tives based on gross margins. This argument can be Table 4: Determinants of Delegating Pricing underlined by our results from a logistic regression Authority (two groups, logistic regression, where only cases with full vs. no delegation are con- p-values in parentheses) sidered. Though not significant due to low sample Variable Expected Sign Estimate size (n=87), firms using incentives based on gross Prospecting Fraction - - 0.269 margins are more likely to fully delegate pricing (0.103) authority (see Table 4). 2 (Prospecting Fraction) + + 0.002* (0.073) Intensity of Competi- - - 2.726*** Table 3: Determinants of Delegating Pricing tion (0.005) Authority (three groups, ordered (Intensity of + + 0.258*** regression, p-values in parentheses) Competition) (0.009) Variable Expected Sign Estimate Utilization of Gross + - 1.114 Prospecting Fraction - - 0.148** Margin Incentives (0.136) (0.037) Intensity of Monitoring - - 1.311*** (Prospecting Fraction) + + 0.0013** (R, Square root trans- (0.006) (0.023) formation) Intensity of Competition - - 1.594*** Calls to Close + + 0.467*** (0.009) (0.001) (Intensity of + + 0.147** Log likelihood -35.13 Competition) (0.013) χ (Likelihood-Ratio Test) 32.234*** Utilization of Gross + - 0.008 (degrees of freedom: 7) Margin Incentives (0.492) Nagelkerke 0.4 Intensity of Monitoring - - 0.569*** ***: Significant at the 0.01 level (one-tailed) (R, Square root trans- (0.001) **: Significant at the 0.05 level (one-tailed) *: Significant at the 0.1 level (one-tailed) formation) R: reversed measure Calls to Close + + 0.208*** N=87 (0.000) Finally, we also report findings from estimating our Log likelihood -177.123 χ (Likelihood-Ratio Test) 40.567*** model with logistic regression by excluding the (degrees of freedom: 7) middle group (delegating restricted price authority) Nagelkerke 0.2 and findings from ordered regression estimation for ***: Significant at the 0.01 level (one-tailed) equally sized groups. The logistic regression (results **: Significant at the 0.05 level (one-tailed) *: Significant at the 0.1 level (one-tailed) are reported in Table 4) is based on zero (n=63) vs. R: reversed measure N=222 full (n=24) delegation and confirms our findings. Due to limited sample size, some of the coefficients Next, we find that the intensity of monitoring also are not significant, but in the proposed direction. In has a significant impact on the probability of dele- Table 5, we report results from a balanced sample gating pricing authority to the sales force. Firms that (24 cases for no, restricted and full delegation – for monitor their salespeople intensely are relatively no and restricted delegation, we took a random more likely to delegate pricing authority to the sales sample of the 63 and 135 cases). Again, the results force. As postulated, these findings suggest that the are quite similar. We only find some non-significant 102 BuR - Business Research Official Open Access Journal of VHB Verband der Hochschullehrer für Betriebswirtschaft e.V. Volume 1 | Issue 1 | May 2008 | 94-104 relationships because of limited sample sizes. Over- to be weighed against the magnitude of the agency all, the results of the two additional estimations costs that are likely to emerge. In our empirical suggest that the findings of our model are robust work, we find that these agency costs can be sub- and thus confirm that our results as reported in stantial when the proportion of effort devoted to Table 3 are reliable. prospecting-type activities and the degree of com- petitive intensity take on intermediate levels. Clear- Table 5: Determinants of Delegating Pricing ly, these conditions are not transparent; conse- Authority (three groups of same size, quently, these implications have the potential to be ordered regression, p-values in fairly insightful. parentheses) A second substantive finding pertains to the impact Variable Expected Sign Estimate of the control system on the price delegation deci- Prospecting Fraction - - 0.073 sion. We find that firms that closely monitor their (0.342) sales personnel can minimize sub-optimal substitu- (Prospecting Fraction) + + 0.001 tion of selling effort by price discounting. Thus, (0.253) these firms can potentially benefit from delegating Intensity of Competition - - 4.607*** pricing authority to their sales personnel. Con- (0.004) versely, firms that employ low levels of supervision (Intensity of + + 0.454*** may restrict pricing authority because they suffer Competition) (0.003) from an inability to limit agency costs. These firms Utilization of Gross + - 0.305 thus cannot take advantage of the price-custo- Margin Incentives (0.380) mization advantages of delegation. Intensity of Monitoring - - 0.412* (R, Square root trans- (0.089) Based on the assumption that a firm’s decision formation) against price delegation is influenced by some miti- Calls to Close + + 0.486*** gating factors that make not delegating optimal for (0.001) them, our research suggests that the decision to Log likelihood -57.105 delegate pricing authority yields the advantages of χ (Likelihood-Ratio Test) 42.605*** price-customization benefits but is fraught with (degrees of freedom: 7) Nagelkerke 0.5 agency costs. However, to get a broader understand- ing of the optimality of the price delegation decision, ***: Significant at the 0.01 level (one-tailed) **: Significant at the 0.05 level (one-tailed) future research should more thoroughly investigate *: Significant at the 0.1 level (one-tailed) R: reversed measure the relationship between the degree of pricing au- N=72 thority and performance. Furthermore, additional research is required to verify the impact of the miti- 5. CONTRIBUTIONS AND gating factors with more precise dependent vari- IMPLICATIONS ables and direct measures rather than the proxies In very many markets, customers vary significantly that we employ in the current research. Additional in their valuation of the firm’s offerings. In these research is also required to examine how technology cases, managers need to decide whether and how (e.g., sales-force automation) can impact the delega- tion decision. Finally, future research could also much pricing authority should be delegated to the sales force. Our work illuminates the economic examine heterogeneity in the price delegation deci- trade-offs involved in this decision. sion within a salesforce (by levels of experience, Substantively, our empirical findings suggest that product lines, etc). We hope our research will stimu- late such efforts. although price delegation can yield tremendous advantages, this latitude gives rise to the possibility Acknowledgements of a specific type of agency cost, namely, the sub- The authors are grateful for several detailed and optimal substitution of selling effort by price dis- insightful comments by two anonymous BuR re- counting. A key finding in our empirical work is that viewers on previous versions of the paper. Kissan firms are less likely to delegate pricing authority Joseph also gratefully acknowledges financial sup- when these agency costs are likely to be fairly sub- port from the General Research Fund of the Univer- stantial. This finding demonstrates that the price- sity of Kansas. customization advantages of price delegation need 103 BuR - Business Research Official Open Access Journal of VHB Verband der Hochschullehrer für Betriebswirtschaft e.V. Volume 1 | Issue 1 | May 2008 | 94-104 Mishra, Birendra K. and Ashutosh Prasad (2004): Central- References ized Pricing versus Delegating Pricing to the Salesforce under Information Asymmetry, Marketing Science, 23 (1): Anderson, Erin and Richard L. Oliver (1987): Perspectives 21-28. on Behaviour-based Versus Outcome-Based Sales Force Control Systems, Journal of Marketing, 51 (4): 76-88. 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(1975): An Optimal Commission Plan merman (2001): Managerial Economics and Organiza- for Salesmen’s Control over Price, Management Science, 21 tional Architecture, Mc-Graw Hill Irwin: Boston, MA. (8): 937-943. Churchill, Gilbert A., Neil M. Ford, and Orville C. Walker (1997): Salesforce Management, 5th ed., Irwin: Chicago, IL. Biographies Coughlan, Anne T. and Chakravarthi Narasimhan (1992): Ann-Kristin Hansen is doctoral student at the Institute of An Empirical Analysis of Sales-Force Compensation Plans, Marketing at the Westphalian Wilhelms University of Muenster in Journal of Business, 65 (1): 93-121. Germany. After finishing her degree in business studies (majors: Dolan, Robert and Hermann Simon (1996): Power Pricing. marketing, sales and supply chain management) in 2006, she has The Free Press: New York. been employed as research assistant at the Institute of Marketing. Greene, William H. (2003): Econometric Analysis, 5th ed., Her research interests focus on delegation of pricing authority to Pearson Education: New Jersey. sales people and the Marketing-Sales interface. Hayek, Friedrich A. (1945): The Use of Knowledge in Soci- ety, American Economic Review, 35 (4): 519-530. Kissan Joseph is Associate Professor and Stockton Research Jensen, Michael and William Meckling (1992): Specific and Fellow at the University of Kansas. He obtained his Ph. D. in General Knowledge and Organizational Structure, Journal Marketing from Purdue University in 1992. His research interests of Applied Corporate Finance, 8 (2): 4-18. include pharmaceutical marketing, e-marketing, pricing, advertis- ing budgeting, and sales force compensation. His research has Joseph, Kissan (2001): On the Optimality of Delegating Pricing Authority to the Sales Force, Journal of Marketing, appeared in the Journal of Marketing, Marketing Science, Mar- 65 (1): 62-70. keting Letters, Industrial Marketing Management, Managerial and Decision Economics, and the Southern Economic Journal. Joseph, Kissan and Alex Thevaranjan (1998): Monitoring and Incentives In Sales Organizations: An Agency- Theoretic Perspective, Marketing Science, 17 (2): 107-123. Manfred Krafft is Professor and Director of the Institute of Marketing at the Westphalian Wilhelms University of Muenster in Krafft, Manfred (1999): An Empirical Investigation of the Germany since 2003. Postdoctoral, he was employed as Assistant Antecedents of Sales Force Control Systems, Journal of Marketing, 63 (3): 120-134. Professor of Marketing at the Institute of Marketing, University of Kiel. After finishing his Habilitation (2nd dissertation) with a Krafft, Manfred, Sönke Albers, and Rajiv Lal (2004): Rela- thesis on “Customer retention and customer value” in 1999, he tive Explanatory Power of Agency Theory and Transaction Cost Analysis in German Salesforces, International Jour- was endowed the Otto Beisheim Chair of Marketing at the nal of Research in Marketing, 21 (3): 265-283. Koblenz Graduate School Management in Germany. His main research interests focus on topics related to customer relationship Lal, Rajiv (1986): Delegating Pricing Responsibility to the management, direct marketing and sales management. His re- Salesforce, Marketing Science, 5 (2): 159-168. search has appeared in the Journal of Marketing, Marketing Long, J. Scott and Jeremy Freese (2006): Regression Mod- Science, Journal of Marketing Research, International Journal of els for Categorical Dependent Variables Using Stata, 2nd Research in Marketing and Interfaces. ed., Stata Press: Texas.

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