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Journal of Business Logistics, 2011, 32(1): 99–114  Council of Supply Chain Management Professionals

Driving Trucks and Driving Sales? The Impact of Delivery Personnel on Customer Purchase Behavior Christoph Bode, Eckhard Lindemann, and Stephan M. Wagner Swiss Federal Institute of Technology Zurich

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his study investigates how and under what conditions delivery persons, specifically truck drivers of a supplier firm, affect customers’ purchase behavior in industrial customer–supplier relationships. Drawing on service quality and customer contact literature, we test a proposed theoretical model that suggests a positive direct effect of personal contact quality (provided by a delivery person) on sales as well as three situational constraints that determine the occurrence and strength of the direct effect. Keywords: customer contact employees; delivery personnel; logistics service quality; multilevel modeling; truck drivers

INTRODUCTION ‘‘People–Service–Profits’’ is a slogan of FedEx Corporation, one of the world’s largest logistics service providers (LSPs). The slogan implies that success in business logistics: (1) hinges first and foremost on people; and (2) is generated through the quality of services that are delivered to the customers. Few would argue against the slogan’s first assertion: logistics is a people business whose success depends critically on the human element (Ellinger et al. 2010). Accordingly, personnelfocused topics, such as professional skills (Gammelgaard and Larson 2001), recruitment (Richey et al. 2006), and customer orientation (Keller et al. 2006), have become a major area of attention for practitioners and researchers in business logistics. There is also little doubt about the validity of the slogan’s second assertion. Prior empirical studies suggested that firms that deliver excellent logistics services to their customers are in the best position to increase customer satisfaction, improve customer loyalty, and gain market share (Innis and La Londe 1994; Daugherty et al. 1998; Stank et al. 2003). Consequently, considerable efforts have been made to describe or predict how customer perceptions of service quality are formed by the performance dimensions of logistics services (Bienstock et al. 1997; Mentzer et al. 2001). However, when it comes to linking people with the quality of logistics services, the literature has focused predominantly on logistics managers, service staff of LSPs, dispatchers, or distribution center personnel (Voss et al. 2005; Keller and Ozment 2009). This stream of research has generated valuable insights, yet, despite the large body of literature, we know remarkably little about the role of delivery personnel, particularly company-employed truck drivers, in the service quality– performance sequence. This is astonishing given that delivery Corresponding author: Christoph Bode, Swiss Federal Institute of Technology Zurich, Department of Management, Technology, and Economics, Scheuchzerstrasse 7, 8092 Zurich, Switzerland; E-mail: cbode@ ethz.ch

personnel—the individuals who transport and deliver products from a supplier to the customers (outbound freight transport)— are situated at the customer–supplier interface and, thus, represent the supplier firm to customer firms. In some settings, delivery personnel may be the sole face-to-face interaction that customers have with a supplier firm. From a customer contact perspective, a delivery person may slip into a boundary-spanning role, thereby influencing the customers’ assessment of the customer–supplier relationship (Keller 2002; Bettencourt et al. 2005). Likewise, from a service quality perspective, the delivery person’s behavior during the faceto-face interactions (e.g., showing courtesy, friendliness, helpfulness) may influence the customers’ satisfaction with the supplier firm (Gro¨nroos 1984; Brady and Cronin 2001). Thus, the question arises as to how and under what conditions delivery personnel are able to affect the customers’ purchase behavior and thereby drive the supplier firm’s sales. We address this research question within an industrial business-to-business context and with a specific focus on private carriage performed by company-employed truck drivers. Drawing on theory from the service quality and customer contact literature, we hypothesize and test—based on a sample of 207 industrial customer–supplier relationships—a proposed theoretical model that links the sales of a supplier firm with the customers’ perceived quality of the personal contact provided by delivery personnel. Specifically, our model suggests a positive relationship between personal contact quality and sales, but also that this direct relationship may magnify under certain conditions and diminish under others. In contrast to the vast majority of research on logistics service quality, which relied either on suppliers’ or customers’ selfreported performance indicators, we use data from both sides of the relationship dyad to test the proposed hypotheses. To this end, we match perceptual data from customer firms with factual sales data from a specific supplier firm. The results provide considerable support for our model and yield important managerial implications, particularly for the training of drivers, marketing plans, and logistics outsourcing decisions.

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LITERATURE REVIEW To understand delivery personnel’s role in the service quality–performance sequence, we review the literature on service quality and customer contact employees. The literature review serves as a basis for the subsequent development of hypotheses. Service quality Service quality theory emerged from the product quality and customer satisfaction literature with two goals: (1) to conceptualize and measure the relationship between a provided service and the perceived service quality; and (2) to understand the behavioral consequences of perceived service quality (Mentzer et al. 1999). Concept and measurement During the last 30 years, numerous attempts have been made to conceptualize and measure service quality. The basic underlying theory is the confirmation ⁄ disconfirmation paradigm, which holds that service quality results from a comparison of perceived performance with (a priori) expected performance (Oliver 1980). Any discrepancy between the two leads to increased or decreased service quality perception. Early approaches drew on this theory and maintained that service quality can be decomposed into various subdimensions. The most prominent examples are: (1) the two-dimensional ‘‘Nordic’’ model (Gro¨nroos 1984), which distinguishes technical (or outcome) quality (i.e., what service is delivered) from functional (or process) quality (i.e., how a service is delivered), and (2) the five-dimensional ‘‘SERVQUAL’’ model (Parasuraman et al. 1988), which comprises tangibles, responsiveness, empathy, reliability, and assurance. Later works refined (Parasuraman et al. 1991) or expanded these general models (Cronin and Taylor 1992; Dabholkar et al. 1996), but the discussion of how best to conceptualize and measure service quality has remained controversial (Buttle 1996; Brady and Cronin 2001). Reeves and Bednar (1994) concluded that ‘‘no universal, parsimonious, or all-encompassing definition or model of quality exists’’ (p. 436) and suggested that different models are appropriate in different circumstances. Thus, to improve its reliability, the proposed general service quality models were adapted to industryspecific situations (Brown et al. 1993). In logistics, the first adaptations of the service quality concept focused solely on the technical ⁄ outcome aspects of logistics services; that is, delivering the right product, at the right time, to the right place (Perreault and Russ 1976; Mentzer et al. 1989). This approach is based on the notion that in industrial service contexts—where (1) the service provider and the service customer are physically separated and (2) the services are directed at things (e.g., the transportation of products) rather than at people—technical aspects dominate the service quality perception (Lovelock 1983). Accordingly, ‘‘physical distribution service quality’’ was initially conceptualized along three technical dimensions: timeliness (on-time delivery), availability of products, and condition (damage-free delivery) (Bienstock et al. 1997). In later

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studies, researchers realized that this approach might be too narrow. Even in industrial contexts, many logistics services do not only take place at ‘‘arm’s length,’’ but involve in-person interactions between employees of the customer firm and people facilitating in the logistics services (Mentzer et al. 1999; Stank et al. 1999). Hence, the attitudes and behaviors of logistics service personnel (i.e., functional ⁄ process aspects of the service) may also affect perceived service quality (Woo and Ennew 2005). Therefore, Mentzer et al. (2001) proposed the broader ‘‘logistics service quality’’ construct and accommodated functional ⁄ process aspects by adding the relational dimension of ‘‘personal contact quality.’’ which ‘‘refers to the customer orientation of the supplier’s logistics contact people’’ (p. 84). Behavioral consequences The significant interest in understanding service quality is based on the strong assumption that service quality is linked to firm performance (Buzzell and Gale 1987). However, empirical studies that investigated the relationship between service quality and customer behavior reported mixed results (Zeithaml et al. 1996). In particular, broadly defined multidimensional models of service quality such as SERVQUAL did not consistently perform well as predictors of behavioral outcomes (e.g., purchase behavior) in crossindustry studies (Cronin and Taylor 1992). In contrast, industry-adapted or channel-specific service quality models showed much better predictive validity. For example, the link between technical ⁄ outcome aspects of logistics service quality and customer satisfaction received considerable empirical support (Innis and La Londe 1994; Daugherty et al. 1998). Likewise, several studies that used logistics industry-adapted measures provided empirical evidence to suggest that functional ⁄ process aspects have a positive impact on customer satisfaction (Mentzer et al. 2001; Stank et al. 2003). Mentzer et al. (2001), for example, reported a positive relationship between personal contact quality (provided by logistics personnel who are in charge of placing orders and handling discrepancies) and customer satisfaction. These findings indicate that relational aspects are indeed an important complement to purely technical aspects in determining customer satisfaction. However, there is a research gap in that neither the direct link between personal contact quality and actual customer purchase behavior nor the role of delivery personnel in this linkage have been investigated so far. Customer contact employees In the business-to-business context, research on customer contact employees revolves around employees of supplier firms who interact regularly with employees of customer firms (Bitner et al. 1990; Czepiel 1990; Gwinner et al. 1998). Most of the extant literature focuses on sales representatives, but acknowledges that other employees may also assume some responsibility for customer contact (George 1990; Hartline and Ferrell 1996). Gummesson (1987), for example, used the term ‘‘part-time marketer’’ to emphasize that customer relations may be influenced by any employee.

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The major tenet of the customer contact employee literature is that social exchanges that take place at the interface between a supplier firm and a customer firm (e.g., service encounters) play an important role in the development of customer–supplier relationships (Bettencourt et al. 2005). Ulaga and Eggert (2006) suggested that regular personal interactions between employees of supplier firms and employees of customer firms are an important factor in the creation of value in the exchange relationship. Here, the terms ‘‘relationship manager’’ (Crosby et al. 1990) or ‘‘key contact employee’’ (Bendapudi and Leone 2001) were proposed to identify the person who is closest to the customer firm. In this regard, research has suggested that empathy and professionalism (Pilling and Eroglu 1994), likeability and attractiveness (Jones et al. 1998), as well as trust (Doney and Cannon 1997) contribute to personal relationships and may lead to positive emotional ties (Price and Arnould 1999). These personal bonds are important for the success of customer–supplier relationships (Seabright et al. 1992). In some instances, customer firms’ relationships with contact employees may even be more intense than relationships with the supplier firm itself (Czepiel 1990; Bendapudi and Leone 2002).

HYPOTHESES Drawing on the service quality and customer contact literature, this section develops a conceptual model, which explains how and under what conditions delivery personnel of a supplier firm, particularly company truck drivers, are able to affect the customers’ purchase behavior in an industrial business-to-business context. Figure 1 displays the relationships that are hypothesized in the following paragraphs. In essence, the proposed model posits a positive direct relationship between personal contact quality and the purchase behavior of a customer firm (or, conversely, the sales earned by the supplier firm), which, however, is contingent on several contextual variables.

Figure 1: Conceptual framework.

Contextual factors Relationship age

Customer firm size

H2 –

Personal contact quality

H1 +

H3 –

Frequency of contact

H4 –

Customer purchase behavior

Delivery personnel and personal contact quality Delivery personnel in physical distribution are frontline employees who transport and deliver products from a supplier firm (shipper) to the customer firms (usually door-todoor). These employees are involved in the broader logistics activity of physical distribution which is ‘‘the interrelated package of activities provided by a supplier which creates utility of time and place for a buyer, and insures form utility’’ (Perreault and Russ 1976, 3). In industrial business-tobusiness contexts, delivery personnel often consist of company-employed truck drivers (Nickerson and Silverman 2003). Not only do these drivers drive trucks, they usually also engage in nondriving service tasks such as handling the goods upon delivery (Baker and Hubbard 2003). In many industries, a typical delivery sequence would consist of arriving on time at the customer firm, assisting in unloading the freight, handling the receipts of goods, and departing for the next customer (Ouellet 1994). Obviously, this sequence of technical activities is paralleled by in-person interactions; that is, a period of time during which one or more employees from the customer firm interact directly with the delivery person (at least for the signing of receipts of goods) (Bitner 1990). In the following, we delineate how drivers are able to create personal contact quality during these service encounters and, thus, affect the purchase behavior of the customer firms (Mentzer et al. 1999, 2001). First, from a service quality perspective, in-person interactions (face-to-face contact and communication) within a service encounter are of key importance for the perception of service quality by the customer firm. Brady and Cronin (2001) noted that ‘‘the interpersonal interactions that take place during service delivery often have the greatest effect on service quality perceptions’’ (p. 38). Specifically, behaviors and attributes that go beyond the technical aspects of the service delivery such as courtesy (e.g., politeness, consideration, friendliness), credibility (e.g., honesty, ability to inspire trust, and confidence), or knowing the customer (e.g., learning the customer’s specific requirements) of the service employee are indispensable to the customer’s satisfaction with a service (Parasuraman et al. 1988; Bitner 1990; Stank et al. 1999). For example, friendliness and attentiveness of service employees have been reported to enhance customers’ perceptions of service quality (Bitner et al. 1990). Second, from a customer contact perspective, physical distribution allows for frequent service encounters between the same delivery personnel and employees of the customer firm, because it is a common industry practice to assign the same delivery person to a specific route (Keller 2002). These frequent in-person interactions may lead to relational bonds that affect the quality of the customer–supplier relationship (Crosby et al. 1990; Gwinner et al. 1998; Bendapudi and Leone 2002). Prior research described ‘‘rub-off’’ effects (Bendapudi and Leone 2002) in the sense that customers’ positive feelings toward the customer contact employee (e.g., trust) may ‘‘transfer’’ to customers’ perception of both the product and the supplier firm (Beatty et al. 1996) that, in turn, is positively associated with a reduced likelihood of customers switching to another supplier (loyalty), positive word

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of mouth, and increased purchase volume (Reynolds and Beatty 1999). Thus, customers’ satisfaction with attitudes and behaviors of customer contact employees may affect sales and performance of a customer–supplier relationship. Integrating both perspectives suggests that delivery personnel may create positive customer perceptions of themselves as well as their firm during the service encounter. The truck drivers’ friendliness, helpfulness, conscientiousness, and individualized attention to the customer is likely to increase customer satisfaction (Stank et al. 2003). This is consistent with prior research that proposed ‘‘personal contact quality’’ as a separate dimension of overall logistics service quality (Mentzer et al. 1999, 2001). Conversely, customer satisfaction is linked to an array of beneficial outcomes, which positively affect purchase behavior such as reducing price elasticity of demand, reducing customer defections, enhancing brand reputation, and increasing loyalty (Anderson et al. 1994). Thus, we expect to observe a positive relationship between personal contact quality created by a truck driver and the customer’s purchase behavior. Formally, H1: Personal contact quality has a positive effect on the customer firm’s purchase behavior. Contextual factors The hypothesized main effect is conditional on several contextual factors; that is, situational constraints that affect the occurrence and strength of the relationship between personal contact quality and customers’ purchase behaviors. Customer firm size In order for the hypothesized main effect to become operational, the driver and her service must be notified, directly or indirectly, by the employees who make the purchase decisions of the customer firms. If these decision makers do not receive information about functional ⁄ process aspects of the service, their perception of service quality will be formed by ‘‘tangible’’ technical ⁄ outcome aspects (timeliness, availability, and condition) (Lovelock 1983; Bienstock et al. 1997). Obviously, with increasing size of the customer firm, the hierarchical and physical distance between the service encounter and the purchase decision increases. Large firms have an incoming goods department with employees who are designated to interact with the truck drivers and take care of processing the products. The employees in the incoming goods department are usually not involved in making purchase decisions, so their perception of personal contact quality will not affect the customer–supplier relationship or the firm’s purchase behavior. Indeed, large firms are also likely to have formal supplier evaluation practices, but these evaluations are usually oriented toward the technical ⁄ output dimensions of physical distribution (Bienstock et al. 1997). In contrast, in small firms, where the decision maker is close to or even involved in the delivery service encounter, it is not only the outcome of the service that remains important, but also the process of the service delivery and, thus, the quality of the personal contact. Hence, we expect that a delivery person’s ability to positively affect the relevant decision-maker’s

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perception of service quality will shrink as the customer firm grows. Formally, H2: The larger the customer firm, the weaker the positive relationship between personal contact quality and the customer firm’s purchase behavior. Relationship age The life cycle theory of interfirm relationships holds that relationships between firms develop over time along distinct stages (or phases) that exhibit systematic differences in patterns of behavior, goal congruence, and processes of the two parties involved (Dwyer et al. 1987). Consequently, critical relationship properties (e.g., trust in the partner) and processes (e.g., performance assessments of the partner) are contingent on the age of the relationship (Rousseau et al. 1998). Although some inconsistencies exist across the proposed conceptualizations, particularly with regard to the question whether there is a stable series of stages occurring in a fixed order or whether it is a continuous cycle of stages (Dwyer et al. 1987; Ring and Van de Ven 1994), there is a consensus that relationships start with an exploration stage, evolve over time (e.g., build-up and maturity stages), and finally decline (Palmatier 2008b). In a recent study, Jap and Anderson (2007) provided considerable empirical support for this theory. Not surprisingly, they found that trust in the exchange partner is lower in the early stages of a relationship (i.e., exploration) than in its later stages. This finding is consistent with earlier studies that argued that ‘‘the exploration phase is a search and trial phase in which the potential obligations, benefits, and burdens of continued exchange are considered’’ (Jap and Ganesan 2000, 231). This dynamic view of customer–supplier relationships suggests that delivery personnel’s impact on purchase behavior is likely to be moderated by relationship age; that is, that the main effect changes over the course of an exchange relationship. Shorter relationships imply that the customer firm will evaluate the performance of the supplier firm along all relevant dimensions—the quality of personal contact with the delivery personnel being one of them. In later phases of the exchange relationship, when the relationship is mature or even in decline, the customer firm’s purchase behavior will be more habitual. Consequently, the driver and her interaction with the customer will have less of an effect on the customer’s purchase behavior (Jap 2001). Thus, H3: The older the relationship between the customer firm and the supplier firm, the weaker the positive relationship between personal contact quality and the customer firm’s purchase behavior. Frequency of contact According to the confirmation ⁄ disconfirmation paradigm, service quality is a function of the discrepancy between a consumer’s perception of the actual outcome and his or her a priori expectations (Oliver 1980; Parasuraman et al. 1985). If a service falls short of expectations (negative disconfirmation) the consumer is likely to be dissatisfied. In turn, if the service performance matches (confirmation) or

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exceeds prior expectations (positive disconfirmation), positive service quality perceptions are likely to be formed. The stronger the disconfirmation (either positive or negative), the more pronounced the perception of service quality and, thus, the subsequent behavioral consequences (e.g., repurchase or defection) (Parasuraman et al. 1985). Consequently, expectations play a pivotal role in service quality formation. However, present expectations are affected by past experiences (Oliver 1977). Laboratory experiments indicated that customers update their expectations after each consumption of a product (Boulding et al. 1993). Accordingly, if a customer firm is in frequent contact with the same delivery person, this firm may become accustomed to the level of personal contact quality and update its expectations about how delivery persons can and should behave when delivering products (Zeithaml et al. 1993; Parasuraman et al. 1994; Keller 2002). Thus, the delivery person may confound the customer firm’s prior expectation. If prior expectations accurately predict the actual performance (i.e., there is little discrepancy between the two), a weak positive or neutral perception of service quality will result. In contrast, if the frequency of contact is low, the customer firm’s expectations are unaffected by the delivery person’s past performance and disconfirmation (either positive or negative) is more likely to occur. Therefore, we expect the positive relationship between the personal contact quality and sales to be stronger when the frequency of contact is low and weaker when the frequency of contact is high. Formally, H4: The higher the frequency of contact between the driver and the customer firm, the weaker the positive relationship between personal contact quality and the customer firm’s purchase behavior.

METHOD Research setting The research setting chosen for this study was the outbound freight transport of a single supplier firm that owns and operates an internal truck fleet for the purpose of delivering its products to its customers. This private carrier was a medium-sized manufacturer (around 500 employees) of construction material located in Germany. In 2006, the supplier served approximately 2,500 customers in seven countries. About 99% of the customer base was located in Austria, Germany, and France. The remaining 1% was spread over the Netherlands, Switzerland, Belgium, and Luxembourg. Most of the customers were small and micro firms (with no more than 10 employees), particularly craft enterprises. The European economy is characterized by small- and mediumsized enterprises (SMEs). In 2005, SMEs in Europe (EU-27) provided two-thirds of total private-sector employment and generated more than half of the economy’s value added (Schmiemann 2008). Consequently, SMEs are important customers in many industries. In our setting, the largest customer accounted for about 3% of total sales.

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We opted for this specific context because it offers several characteristics that are conducive to testing the proposed conceptual framework. First, the problem in studying physical distribution services and delivery activities is that they may vary significantly in scope and complexity across industries and with the characteristics of the freight. In the chosen context, however, the activities that were performed by the drivers at the customer’s place of business were highly standardized and not extensive. All shipments were lessthan-truckload, packaged goods that did not require special treatment, equipment, or handling upon delivery. The fact that all customers received comparable physical distribution services in terms of timeliness, availability, and condition (Bienstock et al. 1997), reduces the risk that differences in these technical ⁄ outcome aspects would confound personal service quality perceptions. Second, the supplier employed 32 truck drivers (delivery personnel) who always covered the same districts and routes and who were fluent in the language of the customers. This setup allowed the drivers to have a solid knowledge of how to handle the products and the customers’ specific situation. Third, although the service encounter is straightforward and the extent of customer contact is low, there is necessarily a face-to-face interaction between the truck driver and an employee of the customer firm (e.g., unloading the product at the customers’ place of business, speaking with the customers to request a signature for goods). Fourth, as a private carrier, all transportation and distribution operations were managed and executed in-house with direct shipping to the customers (i.e., shipments were transported directly from the supplier’s production site to its destination without passing through any kind of hub). Specifically, a ‘‘one-to-many’’ shipment setup was used which means that customer destinations were clustered into regional districts and, in a single tour, a truck driver served several destinations within the same district (similar to a ‘‘milkrun’’ setup). Fifth, with more than eight direct competitors in the same market segment and because of low switching costs, the customer firms’ dependence on the supplier firm was low. Customers could easily switch to other suppliers or decide on a case-by-case basis where to buy. Finally, at the time of our investigation, the supplier did not attempt to actively manage or leverage the truck driver– customer interaction. Data and procedure The empirical basis of this study comprises primary and secondary data and perceptual and factual measures. We conducted a survey among the supplier’s customers in Austria, Germany, and France and subsequently matched this primary data with factual secondary data from the supplier’s databases (enterprise resource planning data, accounting records, customer-relationship management data). Each case in the resulting data set corresponds to a matched-pair customer–supplier dyad. Primary data were collected between June and August 2006 by means of a large-scale survey among the customers. As shown in Table 1, most of the customer firms were located in France and in Germany. Self-reports from single

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Table 1: Sample composition (customer firms) f Number of employees (in 2006) 1–4 106 5–9 54 10–14 24 15–19 7 20–29 6 30–49 5 50–99 5 Location Austria 7 France 122 Germany 78

% 51.2 26.1 11.6 3.4 2.9 2.4 2.4 3.4 58.9 37.7

key informants were used (Kumar et al. 1993). We addressed the survey directly to the person who is most knowledgeable about the interfirm material flows with the supplier. The questionnaire was framed as a customer satisfaction survey. Respondents were instructed to complete the entire questionnaire in reference to their firm’s relationship with the specific supplier and especially with the delivery truck driver. Therefore, our unit of analysis was the customer firm’s relationship with the supplier and the corresponding truck driver. We offered anonymity (on the level of the respondent) and confidentiality to reduce the chances of responses that were socially desirable or consistent with how respondents believe researchers want them to respond. The sampling frame was restricted to customers who ordered and received at least one item during the 18 months preceding data collection (N = 1,611). From this sampling frame, 708 customers with a valid e-mail address were stored in the supplier’s customer-relationship database. These firms were invited to participate in the study via an e-mail containing a hyperlink to the online questionnaire. The remaining 903 firms without valid e-mail address were contacted via fax and invited to complete the questionnaire on paper or to use the provided hyperlink to the online questionnaire. In exchange for participation in the survey, all respondents were offered entries in a drawing for two Apple iPod mp3-players. Three follow-ups via e-mail and telephone generated 207 usable questionnaires, yielding a response rate of 12.8%. The total annual revenues of the participating customer firms ranged from $US0.04 million to $US12.00 million (M = $US1.37 million, SD = 2.12 million).1 The number of employees (full-time equivalent) ranged from 1 to 90 (M = 7.74, SD = 10.94). Due to the small- and mediumsized nature of the firms in the sample, the majority of informants were owner managers. Other informants were managers from the fields of purchasing, logistics, or operations.

1

The informants provided €-values, which were converted into US$-values according to the official currency exchange rate of December 31, 2006 (€1 = $US1.32).

Two approaches were used to assess whether nonresponse bias was present in the sample (Wagner and Kemmerling 2010). First, we inspected the differences between early (initial invitation) and late respondents (second and third reminder) on all survey items in our model by means of a multivariate analysis of variance (MANOVA). No significant mean differences were found (p < .05), neither at the multivariate nor at the univariate level. Second, we compared the obtained sample with a sample of 100 nonresponding firms randomly drawn from the initial sampling frame (N = 1,611) on the basis of annual sales, annual number of delivered orders (sales volume), and relationship age (as per 2006). The performed t-tests for these three variables indicated no statistically significant differences between the two groups (p < .05). In sum, the performed analyses suggest that nonresponse bias does not pose a significant threat to the validity of the results. Measures Only the two variables personal contact quality and customer firm size were obtained from the primary data collection effort, whereas all other variables were taken from the supplier’s databases. Retrieving dependent and independent variables from separate sources and using both perceptual and factual data eliminates potential problems associated with common method variance (Podsakoff et al. 2003). Following standard techniques, the survey instrument and its measures were developed in several stages (Churchill 1979). First, a preliminary questionnaire was drafted on the basis of the literature from the logistics and marketing field. Second, five employees of the supplier firm (the CEO, the head of production, the head of physical distribution and logistics, and two employees from the sales department) commented on the measurement items and their feedback was incorporated into the questionnaire. Third, to refine the survey instrument further, it was pretested through interviews with employees from three customer firms. Their comments were incorporated into the final version of the questionnaire. To accommodate most of the respondents, the survey was administered in French and German. To this end, the original German questionnaire was translated first into French by a bilingual translator and then it was back translated into German by a second bilingual translator. Any differences between the two translations were reconciled (Brislin 1970). Summated five-point rating scales (Likert-type) were used and all items were formulated as indirect, reflective indicators. Translations of the measurement items appear in Table 2. Independent variables For personal contact quality, we used Keller’s (2002) measurement scale for truck drivers’ relationships with external customers as a template. To adapt the items to our context, we drew on the qualitative interviews and feedback from the pretests. The four-item scale captures the driver’s friendliness, conscientiousness, helpfulness, and knowledge of the customer. Customer firm size was measured as the number of employees in the customer firm. Relationship age was

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Table 2: Measurement scale

Construct name ⁄ item

Coefficient alpha

Composite reliability

Personal contact quality (PCQ) .92 .97 Please assess the driver(s) of supplier X that usually deliver(s) your orders along the following ers of other suppliers: (1: significantly worse; 5: significantly better) PCQ1 Friendliness of the driver(s) PCQ2 Conscientiousness of the driver(s) PCQ3 Helpfulness of the driver(s) PCQ4 Specific knowledge of the driver(s) about our company

k

t-Value*

SE

R2

criteria in comparison to the driv0.86 0.81 0.91 0.85

–a 14.33 17.36 15.67

–a 0.06 0.06 0.06

.74 .66 .83 .73

Notes: Items were measured on five-point rating scales (Likert-type) and scored such that higher scale points represented increases in the underlying construct. k, completely standardized factor loading; SE, standard error. *t-Values are from the unstandardized solution. All factor loadings are significant at the p < .001 level (two-tailed). a Factor loading was fixed at 1.0 for identification purposes.

measured as the period of time that the supplier had worked together with the specific customer. Frequency of contact was measured as the average number of deliveries per month during the 18 months preceding data collection. Dependent variable The dependent variable sales (i.e., customer firm’s purchase behavior) was measured as net operating sales earned by the supplier firm from a customer firm during the period between July 1, 2005 and December 31, 2006 (18 months). Control variables Perceptions of service quality may vary across cultural groups (Liu et al. 2000), which might affect the relationships under investigation. For this reason, we controlled for country effects by including two binary dummy variables: one for customers from France; and one for customers from Austria (Cohen et al. 2003). Germany was treated as the baseline category (coded ‘‘0’’).

significant (all k were above 0.80 and significant at p < .001) and all R2s exceeding .50 (Hair et al. 2006). Composite reliability (.97) and average variance extracted (.90) largely exceeded the common cut-off values of .70 (Nunnally and Bernstein 1994) and .50 (Fornell and Larcker 1981; Bagozzi and Yi 1988), respectively. Having established validity and reliability of the measurement model, we used the (unweighted) average of the measurement items as the latent variable score for the final estimation. For all observed variables, we examined the univariate distributions for outliers and for both skewness and kurtosis (i.e., absolute values of skewness below 2.0 and kurtosis below 7.0). No obvious outliers were detected by means of visual inspection. However, the distributions of the variables customer firm size, relationship age, and sales were relatively wide and highly positively skewed. Therefore, and as none of these variables were measured on interval scales, we employed a monotonic nonlinear transformation using the natural logarithm (Cohen et al. 2003). Interfactor correlations appear in Table 3.

Measure assessment The measurement model for personal contact quality was evaluated through confirmatory factor analysis (CFA) using maximum likelihood estimation and Mplus 6 (Muthe´n and Muthe´n 1998–2010). The data fit the CFA model very well (v2(df=2) = 0.42, p = .81; v2 ⁄ df = 0.21, CFI = 1.00, TLI = 1.00, SRMR = .00, RMSEA = .000 with 90% confidence interval = [.000; .085]).2 Details of the measurement model appear in Table 2. The results indicated that the reflective items that were used capture the underlying latent variable well and implied a satisfactory level of convergent validity, unidimensionality, and reliability. Convergent validity and unidimensionality were supported by all item loadings being large and 2

CFI refers to comparative fit index; TLI refers to Tucker– Lewis index (also nonnormed fit index, NNFI); SRMR refers to standardized root mean square residual; and RMSEA refers to root mean square error of approximation.

ANALYSES AND RESULTS A unique aspect of the collected data is its nested structure, such that multiple customers (level 1) are nested within a single delivery person (level 2). In particular, our main variable of interest, personal contact quality, is likely to be cluster dependent, because it can be assumed that there are individual differences among the 32 truck drivers. Ignoring such nonindependence due to groups can result in a substantial loss of power and increase in Type-I error (Bliese and Hanges 2004). As ordinary least square (OLS) regression may not take into account this nested nature of the data, we used a multilevel modeling approach to incorporate the two-level structure into the statistical analyses and to account for potential nonindependence of the observations. Specifically, we opted for an approach that takes the nesting effect into consideration by allowing a random slope (for personal contact quality) to vary across the truck drivers; that is, random

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Table 3: Correlation table and descriptive statistics Variable (1) (2) (3) (4) (5)

Personal contact quality Customer firm sizea Relationship agea Frequency of contact Salesa

M

SD

(1)

3.99 2.50 1.67 5.57 9.80

0.77 1.00 0.68 9.51 1.50

1 ).15* ).14* .09 .23**

(2)

(3)

(4)

(5)

.63**

1

1 .14* .37** .34**

1 .12 .19**

1

Notes: Pearson’s correlation coefficients are shown. *p < .05 (equals |r| > .14), **p < .01 (equals |r| > .18) (two-tailed). a Transformed using the natural logarithm.

variations from the slope of personal contact quality reflect unobserved heterogeneity in the driver groups (Raudenbush and Bryk 2002). Prior to testing the four developed hypotheses, all independent variables were mean-centered, and interaction terms were created by multiplying standardized variables scores (Cohen et al. 2003). Then, we analyzed the following regression equation in three hierarchical steps:3 Level 1 (Customer firms): SALES ¼ b0 þ b1 FRA þ b2 AUT þ b3 SZE þ b4 REL þ b5 FRQ þ b6;driver PCQ þ b7 ðPCQ  SZEÞ þ b8 ðPCQ  RELÞ þ b9 ðPCQ  FRQÞ þ e: Level 2 (Truck drivers):   b6;driver ¼ b6 þ u6;driver with u6;driver  Nð0; r2u6 Þ: All models were fitted by means of restricted maximum likelihood estimation using MLwiN 2.20 (West et al. 2007; Rasbash et al. 2009). Table 4 summarizes the results. For each model, variance inflation factors and the bivariate correlations between the independent variables were within acceptable ranges (i.e., bivariate correlations |r| < .70 and variance inflation factors < 3), thus indicating that multicollinearity did not pose a serious problem to the analyses (at the customer level). Diagnostic checks for multilevel models were conducted following the recommendations from Snijders and Berkhof (2008). In summary, these analyses did not give reason to assume that the chosen method was inappropriate. In addition, to check the robustness of the results, separate analyses were performed using full-information maximum likelihood estimation and, without consideration of the multilevel data structure, OLS regression. These analyses showed substantially similar results for all hypotheses. The primary method of comparing model fit of two nested multilevel models is through tests based on deviance ()2 · log(likelihood)). The deviance is a measure of the lack of fit between data and a model. In general, models with lower deviance fit better than models with higher deviance. 3

The variable identifiers are as follows: FRA, France (country dummy variable); AUT, Austria (country dummy variable); SZE, customer firm size; REL, relationship age; FRQ, frequency of contact; and PCQ, personal contact quality.

For nested models, the difference between the two deviances (DDeviance) is asymptotically v2-distributed with the degrees of freedom equal to the difference in the number of parameters between the two models (Raudenbush and Bryk 2002). In the present case, in every step of the estimation procedure, the decrease in deviance (i.e., improvement in model fit) was significant. The Bayesian information criterion also decreased in every step. We ask, first, whether the quality of the personal contact provided by the delivering truck driver has a positive effect on the purchase behavior of the customer and, thus, leads to more sales (H1). The results from the main-effects-only model (Model 2) support this hypothesis and suggest that personal contact quality does positively affect sales (standardized regression coefficient b6 = 0.20, p = .04). The inclusion of the main effect explained a small additional amount of variance in sales at the customer level (DR2 = .01), but significantly improved model fit (DDeviance(df=2) = )6.6, p = .04). Thus, the higher the perceived personal contact quality, the higher the customer’s contribution to the supplier firm’s sales. Beyond this main effect, the predictive power of the three hypothesized interaction terms was tested. Inclusion of the interaction terms (Model 3) resulted in a significant improvement of model fit (DDeviance(df=3) = )17.5, p < .001) and in an increase of variance explained at the customer level (DR2 = .04). To understand better the nature of the three interaction effects, we conducted a simple slope analysis following the approach outlined in Bauer and Curran (2005). Figure 2 depicts the results and the simple slopes for low (M ) 1SD), mean (M), and high (M + 1SD) levels of the moderators. H2 postulates that the positive relationship between personal contact quality and sales is weaker when the customer firm is large than when it is small. The associated regression coefficient was significant and in the expected direction (b7 = )0.17, p = .02). As shown in Figure 2a, the slope, which indicates the relationship between personal contact quality and sales is weaker for larger firms than for smaller firms. This provides support for H2. H3 posits that the positive relationship between personal contact quality and sales is weaker when the relationship between the two firms is long than when it is short. The corresponding regression coefficients was significant and in the

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Table 4: Results of regression analysis Model 1: Controls Variable Fixed part Intercept France (country dummy variable)a Austria (country dummy variable)a Customer firm size Relationship age Frequency of contact Personal contact quality Personal contact quality · Customer firm size Personal contact quality · Relationship age Personal contact quality · Frequency of contact Random part Between-customer variance (level 1) Between-driver variance (level 2) Model fit Deviance ()2 · log (likelihood)) DDeviance Bayesian information criterion R2 at customer level (level 1) R2 at driver level (level 2)

Model 2: Main effect

Model 3: Interaction effects

Estimate

t-Value

Estimate

t-Value

Estimate

t-Value

9.16*** 1.07*** )0.04 0.19** 0.24** 0.81***

74.58 6.83 )0.10 2.45 3.18 9.90

9.25*** 0.91*** )0.20 0.25** 0.24** 0.77*** 0.20*

71.06 5.47 )0.47 3.12 3.13 9.64 2.14

9.18*** 0.98*** )0.12 0.25** 0.21** 0.84*** 0.17* )0.17* )0.15* )0.24*

73.95 6.15 )0.28 3.20 2.84 10.39 2.08 )2.26 )2.04 )2.10

1.08*** –

10.02

1.02*** 0.08

9.49 0.90

0.97*** 0.01

9.40 0.17

598.2 – 618.8 0.54 –

591.6 )6.6* 616.9 0.55 0.69

574.1 )17.5*** 609.8 0.59 0.72

Notes: Dependent variable is Sales. Restricted maximum likelihood estimation was used. Except for the two dummy variables, reported estimates refer to standardized regression coefficients. Models 2 and 3 were treated as cluster-dependent random effects models (level 1 covariates with a random slope that varies over level 2 groups) (nlevel-1 = 207, nlevel-2 = 32). *p < .05, **p < .01, ***p < .001 (two-tailed). a Baseline category was Germany (coded ‘‘0’’).

expected direction (b8 = )0.15, p = .04), thus supporting H3. Consistent with our expectations, Figure 2b shows that the positive effect of personal contact quality on sales is high for ‘‘younger’’ customer–supplier relationships but vanished for ‘‘older’’ relationships. Finally, H4 was supported. It states that the positive relationship between personal contact quality and sales is weaker when the frequency of contact between the truck driver and the customer firm is high than when it is low. The associated regression coefficient was significant and in the expected direction (b9 = )0.24, p = .04). Figure 2c reveals that the positive effect of personal contact quality on sales becomes insignificant when there is greater frequency of contact.

DISCUSSION AND IMPLICATIONS Although prior studies stressed the importance of frontline employees in logistics and physical distribution (Bienstock et al. 1997; Mentzer et al. 1999; Voss et al. 2005; Ellinger et al. 2010), our knowledge of how and under what conditions delivery personnel are able to contribute to sales is very limited. The core of our contribution addresses this gap by providing insights into the relationship between delivery per-

sonnel in outbound freight transport and customer firms’ purchase behavior. Specifically, we have identified and tested the direct link between personal contact quality and purchase behavior (i.e., sales), in addition to three moderator effects that explain under what conditions the direct link is operational. Several important scholarly and managerial implications can be deduced from the results. Theoretical implications Drawing on the service quality and customer contact literature, we started from the premise that face-to-face interactions between delivery personnel and employees from customer firms constitute service encounters, which bear the potential to influence the purchase behavior of the customer firms (Brady and Cronin 2001). We focused on company truck drivers in a private carriage setup, although the proposed conceptual model might also be applicable to other types of delivery personnel in different settings (or even nonlogistics frontline employees). Our results suggest that truck drivers may, in fact, positively influence customer perceptions of service quality and the customers’ purchase behaviors. This finding provides support for recent research that stressed the importance of

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Figure 2: Interaction effects.

A

Personal Contact Quality × Customer Firm Size

C

B

Personal Contact Quality × Relationship Age

Personal Contact Quality × Frequency of Contact

Notes: Plots were calculated by following the approach outlined in Bauer and Curran (2005). Variables not taken into account in the plots were held constant at their mean values. Simple slopes termed ‘‘high’’ refer to M + 1SD and ‘‘low’’ refer to M ) 1SD. Personal contact quality-axis boundaries were set at M ) 1.5SD and M + 1.5SD. *p < .05, **p < .01, ***p < .001 (two-tailed); ‘‘n.s.’’ indicates that the simple slope is not significantly different from zero (p ‡ .05).

Journal of Business Logistics, Vol. 32, No. 1, 2011

frontline employees in logistics (Keller and Ozment 2009). We add to this stream of literature by highlighting the marketing potential residing in delivery personnel in general, and truck drivers in particular. Our result is also in line with the service quality literature and supports the call that a firm should coordinate its logistics function with its marketing function (Kahn and Mentzer 1996; Mentzer and Williams 2001). Our study also indicates that the direct relationship between personal contact quality and purchase behavior of the customer firms is not straightforward. We hypothesized three important contextual factors that determine under what conditions personal contact quality delivered by truck drivers affects the customers’ purchase behaviors. The three corresponding moderation hypotheses were supported. First, the effect of personal contact quality on sales becomes weaker with increasing size of the customer firm. Consistent with our expectations, Figure 2a shows that the effect of personal contact quality is operational for smaller firms, but vanishes for larger firms. In the investigated setting, the threshold at which personal contact quality no longer affects a customer firm’s purchase behavior was at around 20 employees. As firm size increases, so does the hierarchical and physical distance between the service encounter and the purchase decision. Larger firms emphasize technical ⁄ outcome aspects of the physical distribution service, as suggested by Bienstock et al. (1997). In smaller firms, however, the service encounter with the delivery person and hence the corresponding functional ⁄ process aspects are likely to be noticed by the decision makers and incorporated into the purchase decision. Second, as suggested by the life cycle theory of interfirm relationships (Dwyer et al. 1987; Jap and Anderson 2007), the results indicate that delivery personnel’s effect on sales is more pronounced when the relationship age is rather low. Palmatier (2008a), argued that ‘‘in the initial stages, the quality of the bonds may be most critical because these initial bonds form the seeds of interfirm norms’’ (p. 86). Indeed, the delivery person’s potential to influence the customer’s purchase behavior is greatest in the early stages of a customer–supplier relationship (i.e., exploration and build-up phase). Third and in a similar vein, our findings suggest that the frequency of contact between a specific delivery person and a customer firm influences the form of the investigated direct effect. With increasing frequency, the quality of personal contact becomes less relevant for the customer’s purchase behavior. At first sight, this finding seems to be counterintuitive as many relational constructs such as interpersonal trust and loyalty are formed through a repeated train of beneficial interactions (Bendapudi and Leone 2002). Studies in this context often carry an implicit assumption that more is better. However, in case of physical distribution service encounters, the delivery person also affects the expectation of the customer firm, similar to a habituation effect. If a customer firm has experience-based expectations that accurately predict the performance of the delivery person, the extent to which disconfirmation may occur is small. This leads to a perception of neutral or weak positive quality, and quality of personal contact might be considered a ‘‘hygiene factor.’’ In contrast, if there is little interaction, it is more likely that the

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delivery person ‘‘surprises’’ (either positively or negatively) the customer’s prior expectations. These results do certainly not imply that a good performance of the driver becomes irrelevant to the relationship when the frequency of contact is high. Customers might even be particularly vulnerable to defecting, because failed expectations have been reported to create discomfort and perplexity and to function as strong promoters for information-search processes and triggers for change (Weiner 1985; Ellis and Davidi 2005). If a customer firm is familiar with a delivery person and accustomed to a certain quality of service, a sudden change in this quality (e.g., after an outsourcing decision) may have unexpected repercussions on the customer–supplier relationship (Anderson and Jap 2005). Finally, from a methodological point of view, our study makes two noteworthy contributions. First, to understand customer–supplier relationships, it is crucial to investigate empirically the relationship from both sides of the dyad. Even though many researchers are aware of its importance, empirical dyadic research is still scarce and the absence of dyadic data is an often stated limitation of empirical research on interfirm relationships (Stanko et al. 2007). Second, previous studies have largely focused on the link between logistics service quality aspects and customer satisfaction (Daugherty et al. 1998; Stank et al. 2003) or on purchase intentions as the dependent variable (Bienstock et al. 2008). However, service quality and customer satisfaction are closely related (if not indistinguishable) concepts (Iacobucci et al. 1995) and purchase intentions may deviate significantly from actual purchase behavior (Newberry et al. 2003). Therefore, our findings constitute an important extension of the literature. Managerial implications Logistics managers and marketing managers know that service quality and customer satisfaction are indispensable to successful logistics service operations. However, it is not always clear how to achieve these goals and the desired effects (e.g., on sales). Therefore, it becomes important for managers to recognize that delivery persons are customer contact employees who, under certain conditions, are in a position to affect sales. First, to leverage the ‘‘sales force’’ potential of truck drivers, managers should promote customer-oriented values and beliefs in a way that encourages the drivers to be customer oriented (George 1990; Bell and Menguc 2002). While motivating drivers can be a complicated incentive problem (Baker and Hubbard 2003; Nickerson and Silverman 2003), instilling behavioral traits linked to personal contact quality (e.g., friendliness, courtesy) can be achieved through training and job enrichment. Prior research, for example, has suggested that high service quality results from high job satisfaction, because satisfaction leads to motivation and motivation encourages customer-oriented behaviors (Hartline and Ferrell 1996). Keller (2002) investigated the determinants of truck driver performance and showed that their compensation, amount of time at home, and dispatcher responsiveness all influence their job performance.

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Second, and even more important, the investigated moderator effects provide highly relevant insights into the circumstances in which personal contact quality effectively influences customers’ purchase behavior. Larger firms may not notice the functional ⁄ process dimension of delivery services. Thus, the desired ‘‘personal contact quality’’ effect might not become operational for larger customer firms. Smaller firms, however, notice and value experienced delivery persons who know the customer firm, are experienced in handling the products, and who listen to the customers’ requests and complaints. Third, the driver is particularly important during the early stages of a customer–supplier relationship. In the first transactions with a new customer, it is advisable to brief the delivering truck driver to provide excellent delivery service with the goal of generating personal contact quality. Furthermore, frequent interactions between delivery persons and customers may affect the customers’ expectations about how delivery persons can and should behave when delivering the products. Established personal contacts between drivers and customers may also imply a risk for supplier firms. If the driver suddenly fails to deliver as usual, the customer’s purchase behavior may be negatively affected. Finally, our results also yield implications for logistics outsourcing decisions, which are among the most frequent decisions facing logistics managers in industrial firms (Murphy and Poist 2000). Outsourcing decisions that concern physical distribution services are often driven by technical ⁄ outcome aspects (Hall and Racer 1995). This is confirmed by the fact that industrial firms rely more than ever on LSPs to reduce costs and to improve the service levels while focusing on their core competencies (Capgemini Consulting et al. 2009). Due to economies of scale (specialization, bundling opportunities, large networks), LSPs are often able to offer the same (or even better) technical ⁄ outcome service quality at lower costs than industrial firms could do on their own. However, physical distribution and associated outsourcing decisions should not be viewed solely through the lenses of technical aspects and logistics costs. This logic would neglect the central role of people in physical distribution services (Mentzer et al. 1999), which takes this discussion full circle and back to the FedEx slogan mentioned in the introduction: ‘‘People–Service–Profits.’’ The relationship marketing literature suggests that an important avenue for the creation of stable customer–supplier relationships, and thus robust sales, lies in the personal interaction of customer contact employees with employees of the customer firm (Reynolds and Beatty 1999; Ulaga and Eggert 2006). Certainly, LSPs can also provide excellent performance during the service encounter, but the link to customers’ purchase behaviors might be more complex, since customers might not directly relate the positive social interaction with an LSP employee to a specific goods supplier.

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pertain to our data. We investigated cross-sectional data, although the perception of personal contact quality might vary with time and among service encounters. Causal relationships can also only be established on the ground of longitudinal data. Therefore, the use of longitudinal data would significantly enhance our understanding of the dynamic nature of the variables investigated. Furthermore, like other studies that used dyadic data (Jap and Ganesan 2000), we surveyed the customer base of a single supplier firm and tested the proposed hypotheses within the boundaries of a particular, specified context. This approach has several advantages (e.g., manageable number of control variables, response rate), but also reduces the generalizability of the results. While we do not believe that the chosen context (private carriage in an industrial business-to-business context with small and medium-sized customer firms) is highly idiosyncratic, the setting may not be comparable to other industries and physical distribution service contexts. Therefore, replication across other industries would increase the generalizability of the results and make their interpretation and implications more robust. Likewise, further research ought to explore the effects of personal contact quality within an LSP ⁄ contract carriage setup. With regard to the managerial implications, it is important to note that the present study focused on purchasing behavior (sales) as the dependent variable, but did not consider costs. Hence, the results do not provide insights into the bottom-line impact of personal contact quality. It is obvious that there are diminishing returns to expenditures on service quality (Rust et al. 1995). Consequently, at the bottom-line, the costs of operating physical distribution with skilled delivery personnel might outweigh the additional sales induced by delivery persons’ service performance. Several additional directions for future research can be highlighted. Recent research has argued that it is important to distinguish level and strength of satisfaction with a service (Chandrashekara et al. 2007). Despite reporting a high level of personal contact quality, customers could still be vulnerable to defection if the strength, with which the contact quality judgment is held, is weak. An overtly satisfied customer could have a weakly held satisfaction if he is secretly concerned about losing the key contact employee (e.g., the driver) (Chandrashekara et al. 2007). Furthermore, Mittal et al. (1998) argued that perceptions of attributes of a contact employee might not be linked symmetrically with satisfaction. Thus, one unit of negative performance on an attribute (e.g., politeness) could have a stronger effect on satisfaction than a corresponding unit of positive performance. Further research should identify the key attributes of drivers that affect customers’ perception of personal contact quality. This would enable firms to allocate their resources in a way that most enhances customer satisfaction and purchase behavior.

CONCLUSION LIMITATIONS Several limitations of this study should be considered in the interpretation of its results. A few obvious limitations

This study began with the FedEx slogan ‘‘People–Service– Profits,’’ and by asserting that logistics is a service business whose key ingredient is the quality of the people involved.

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Company truck drivers, like any other customer contact employees, are the ‘‘calling card’’ of a firm and are able to affect customers’ purchase behavior. If their boundary-spanning role and behavior are managed appropriately, they may complement the relationship marketing efforts of a firm. Thus, firms should carefully consider how they can best leverage this potential.

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SHORT BIOGRAPHIES Christoph Bode (PhD WHU—Otto Beisheim School of Management) is an Assistant Professor of Supply Chain Management at the Swiss Federal Institute of Technology Zurich (ETH Zurich). His current research interests lie in the areas of supply chain and logistics management, with a special focus on supply chain strategies, disruptions and risk management, behavior within the supply chain, and interfirm relationships. His research has appeared or is forthcoming in such journals as Academy of Management Journal, European Journal of Operational Research, Journal of Business Logistics, International Journal of Production Economics, and Journal of Purchasing & Supply Management. Eckhard Lindemann (PhD WHU—Otto Beisheim School of Management) is a Research Fellow at the Swiss Federal Institute of Technology Zurich (ETH Zurich) and a manager at Bosch Rexroth AG. His research interests focus on the management of buyer–supplier relationships. He has

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published in Journal of Business Research, Journal of Business & Industrial Marketing, Production Planning & Control, Sociological Methods & Research, and in other journals. Stephan M. Wagner (PhD University of St. Gallen) holds the Kuehne Foundation-sponsored Chair of Logistics Management and is Director of the ‘‘Executive MBA in Supply Chain Management’’ at the Swiss Federal Institute of Technology Zurich (ETH Zurich). He has been Senior Manager for an international top-management consulting firm, Head of Supply Chain Management for a Swiss-based technology group, and taught at WHU—Otto Beisheim School of Management before joining the ETH Zurich. His research in logistics and supply chain management focuses particularly on strategy, networks, relationships, behavioral issues, risk, and innovation. Professor Wagner is an active researcher, presents regularly at international conferences, and has published 10 books and more than 100 academic and professional articles.