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The Impact of the Quality of Logistics Activities on Customer Commitment, Loyalty and Firm’s Performance

The Impact of the Quality of Logistics Activities on Customer Commitment, Loyalty and Firm’s... The aim of this study is to examine the role of logistics activities and on customer loyalty, commitment and performance. The main objective is to analyze the logistics service quality (LSQ), commitment, loyalty and performance in a supply chain context. Logistics services include activities that contribute to consistent quality, productivity and efficiency, as well as activities that help companies to better meet customer expectations and needs. Customer loyalty is increasingly recognized as a path to long-term success because finding new customers and doing business with them takes time, effort and money. Increasing the level of performance, while maintaining low costs create value for customers and for companies, too. A questionnaire based on personal survey was conducted among retail customers of a wholesale company in Albania. The study collected data from 204 companies. The hypotheses were tested using structural equation modeling (SEM) which is a comprehensive statistical approach for testing hypotheses about relations between observed and latent variables. The reliability and validity tests show satisfactory results. The conclusions confirm all the relationships hypothesized, and emphasize the crucial role of LSQ in the loyalty-commitment-performance chain of relations. Keywords: logistics service quality, commitment, loyalty, performance. JEL Classification: M31. 1. Introduction During the last two decades the idea of relationship marketing - establishing, developing, and maintaining successful relational exchanges - has gained significant importance (Morgan and Hunt, 1994). A vast body of literature supports the importance of developing and maintaining enduring relationships with customers (Ganesan, 1994), which leads to long-term customer retention (Mattila, 2001). In a supply chain context, relationships can be a sustainable source of competitive advantage due of their ability to create barriers to competition (Day, 2000). Since an important focus of supply chain research is the collaborative relationships with select trading partners (Bowersox and Daugherty, 1995) an important strategic result for companies is the building and growing of customer loyalty. Customer loyalty is increasingly recognized as a path to long-term success because finding new customers and doing business with them takes time, effort and money (Mittal and Lassar, 1998). It can be more expensive to obtain a new customer than retain one, and an organization's long-term success in a market is increasingly determined by its ability to expand and maintain a large and loyal customer base (Kandampully, 1998). Fay (1994) asserts that customer loyalty objectives have gained the same importance as other financial or strategic objectives. Reichheld et al. (2000) also contend that in the past, building loyalty with select customers was just one weapon to use against competition, but today it has become essential to survival. As products become more commoditized, companies can no longer maintain a loyal customer base or create sustainable advantages for themselves by only having a variety of tangible products. Rather, firms form complex relationships with customers and differentiate themselves by offering goods and service mixes in distinct ways to offer convenience, reliability, and support (Fuller et al., 1993). Improving logistic capabilities and the quality of logistic activities helps in building closer relationships with customers (Bowersox et al., 1995), gaining and maintaining customer loyalty (Bowersox et al., 1992). As the increasing competitive markets make hard for firms to find and retain important business customers (Flint and Mentzer, 2000) they can positively impact customer loyalty by providing high quality logistics service. A crucial goal of relationship marketing theory is to identify key drivers that influence important outcomes for the firm and to gain a better understanding of the links between drivers and outcomes (Hennig-Thurau et al., 2002). Therefore, understanding how logistics service can influence loyalty and commitment, and ultimately firm's performance, could be significant in examining and predicting supply chain relationship outcomes. However, logistics service is sometimes ignored as a competitive tool (Sharma, Grewal and Levy, 1995). The purpose of Journal of Advanced Research in Management this study is to examine the casual relationships between Logistics Service Quality (LSQ), loyalty, commitment and firm's performance. The main objective is to help companies assess the impact of logistics service in creating loyalty and commitment and their effect on market and financial indicators of performance. 2. Theory review and hypotheses Logistics Service Quality: Since the beginning of the 1980s, service quality has been a priority theme in for marketing researchers and practitioners, focusing mainly in quality, quality management and satisfaction (Fisk et al., 1993; Richey et al., 2007). Millen et al. (1999) contend that improved customer satisfaction is a key outcome of LSQ, while Mentzer et al. (2001) conclude that part of the value of a product is created by logistics service. In marketing, the focus of service performance has been on service quality, or the evaluation of service performance. The definition and measurement of service quality has occupied a prominent position in the services marketing literature. The service quality paradigm started with a qualitative study, where the differences in perceptions of service between managers and consumers were examined (Parasuraman et al., 1985). The authors developed a service quality model that showed several discrepancies ("gaps") between perceptions of customer service by the supplier and the customer. In an effort to empirically examine the gaps, Parasuraman, Zeithaml and Berry (1988) developed a service quality measurement instrument called SERVQUAL for assessing customer expectations and perceptions of service quality in service and retail organizations. This multi-item scale evaluates the five dimensions of quality: Tangibles: physical facilities, equipment and appearance of personnel; Reliability: ability to perform the promised service dependability and accurately; Responsiveness: willingness to help customers and provide prompt service; Assurance: knowledge and courtesy of employees and their ability to inspire trust and confidence; Empathy: caring, individualized attention that a firm provides to its customers. For almost three decades there has been a stream of research addressing the definition, conceptualization and measurement of service quality. Grönroos (1984) conceptualizes service quality to be formed by two components: Technical quality expressed as the service being technically acceptable and leading to a concrete result; and Functional quality which includes the way the customer is treated during the service provision process. The inclusion of customer perceptions in measuring service quality with SERVQUAL scale attracted logistics researchers to apply this scale in the logistics context. Beinstock, Mentzer and Bird (1997) argued that researchers should explore alternative dimensions of logistics service quality, since the service provider and the service customer are physically separated and the services are directed at "things" instead of people, thus technical or outcome dimensions are necessary for logistics service quality measurement instruments. Studies on service quality have proved that SERVQUAL items must be customized to the specific service environment (Carman, 1990; Finn and Lamb, 1991), in order to effectively address service quality in different industry contexts (Brown et al., 1993). Logistics researchers have utilized the studies on service quality and their measurement scales, adopting them to fit the specific study context. Mentzer, Flint and Hult (2001) developed a LSQ scale with specific logistics service dimensions. They conceptualized LSQ as a process and the scale was based on qualitative research from a large logistics service provider's customer base. According to Maltz and Maltz (1998), logistics service has two components. The first is basic logistics service, involving cycle time, on-time delivery, and inventory availability. The second component is responsiveness, representing the ability to adapt to market-driven change. Collier (1991) also suggests that service consists of two distinct dimensions, but he identifies them as the internal or operations-oriented dimension of service quality performance and the external or marketing-oriented dimension. Stank, et al. (1999) and Stank et al. (2003) also developed a scale to measure both the operational and relational elements of logistics service performance. Literature in supply chain management and logistics suggests that logistics service quality may contribute to a firm's market and financial performance (Bowersox et al., 2002). Based on previous research, the following hypotheses were developed: H1: LSQ has a positive influence on loyalty H2: LSQ has a positive influence on commitment Commitment: Commitment is recognized as an essential ingredient for successful long-term relationships (Dwyer et al., 1987, Gundlach and Mentzer, 1995). Dwyer et al. (1987) define commitment as an "implicit or explicit pledge of continuity between relational partners" (p. 19). Also defined by Gundlach and Mentzer (1995), "Commitment is thought to be closely related to mutuality, loyalty and forsaking of alternatives, variables which are the core of the meaning of relationalism" (p. 79). They propose three components of commitment: An input or instrumental component; this involves an affirmative action taken by one party that creates a self - interest stake in the relationship and demonstrates something, more than a mere promise. An attitudinal component signifying an enduring intention by the parties to develop and maintain a stable long-term relationship. This component can also be described in terms of affective commitment, with psychological attachment, identification, affiliation and value congruence. A temporal dimension indicating that commitment involves something long term. Studies of relationship marketing show that, if commitment is lacking, the relationship will come to an end (Wetzels et al., 1998). Meyer and Allen (1991) note that common to the various definitions of organizational commitment is "the view that commitment is a psychological state that (a) characterizes the employee's relationship with the organization, and (b) has implications for the decision to continue membership in the organization" (p. 67). Meyer and Allen (1991) described three components of commitment as (1) affective commitment, (2) continuance commitment, and (3) normative commitment. Dwyer, Schurr and Oh (1987) suggest that the key distinction of the commitment phase is that the parties purposefully engage resources to maintain a relationship fueled by the on-going benefit accrued by each partner. Morgan and Hunt (1994) theorize that trust and commitment lead directly to cooperative behavior which, in turn, contributes to the success of relationship marketing by promoting efficiency, productivity and effectiveness for both parties. Pritchard, Havitz and Howard (1999) found commitment to be strongly correlated with customer loyalty. Iwasaki and Havitz (1998) suggested that psychological commitment mediated the effects of enduring involvement on patrons' behavioral loyalty. Fullerton (2003) investigated the roles played by different forms of commitment in the relationship between customers and service providers. They found that, when customer commitment is based on shared values and identification, it has a uniformly positive impact on customer loyalty. Based on these previous findings, respective hypotheses are formed: H3: Commitment has a positive influence on performance H4: Commitment has a positive influence on loyalty Loyalty: Two approaches have been employed in the study of loyalty, including the stochastic approach and the deterministic approach. The deterministic approach holds that loyalty should be viewed as an attitude; therefore, the researcher can manipulate numerous factors that lead to loyalty (Jacoby, 1971; Jarvis and Wilcox, 1976). Deterministic research examines the psychological effect of loyalty, ignoring the outcomes of loyalty (i.e., purchase behavior). Dick and Basu (1994) define loyalty as the relationship between the relative attitude toward an entity (brand/product/service/store/vendor) and patronage behavior. In addition, researchers have noted the importance of distinguishing between true (intentional) loyalty and repeat purchase behavior (Jacoby and Kyner, 1973; Jarvis and Wilcox, 1976). The basis of this distinction is that true loyalty involves a psychological bond to the seller and requires a high degree of customer satisfaction and commitment, whereas repeat purchase behavior does not involve the psychological commitment. Repeat purchase behavior generally occurs because of time/energy costs, perceived risk, perceived absence of choice, probability or bias, temporary selling incentives, or legal and corporate policy constraints (Jarvis and Wilcox, 1976). Dick and Basu (1994) built upon their concept of the relationship of relative attitude with repeat patronage by cross-classifying four conditions of loyalty. A low relative attitude combined with low repeat patronage indicates an absence of loyalty. Spurious loyalty consists of a low relative attitude combined with high repeat patronage, indicating the possibility of non-attitudinal influences on the consumer's behavior. High relative attitude with low repeat patronage establishes latent loyalty, where it is assumed that situational effects and market conditions are equally as strong as attitudinal effects on the consumer's behavior. The final and most desirable of the four conditions is loyalty, where there is a positive relationship between relative attitude and patronage behavior. Journal of Advanced Research in Management Yim and Kannan (1999) developed a modeling framework of consumer behavioral loyalty that is useful for segmentation. The first segment of consumers identified by the model is the hard-core loyalty group. This group exclusively makes repeat purchases of one product alternative. The second consumer segment identified is the reinforcing loyalty group. In contrast to the hard-core loyalty group, this segment predominantly makes repeat purchases of one or more product alternatives. Dick and Basu (1994) also described several important cognitive, affective, and conative antecedents to consumer loyalty. Cognitive antecedents identified by the authors include the accessibility and ease with which an attitude can be retrieved from the consumer's memory, the attitudinal confidence (level of certainty) a consumer has about their attitude or evaluation, the centrality of the relationship between the consumer's attitude and the consumer's value system, and the clarity of the consumer's attitude or evaluation. The affective antecedents included emotions, moods, primary affect (independent of cognitions), and satisfaction. Finally, important conative antecedents to the development of consumer loyalty include switching costs, sunk costs, and expectations. From the above, the following hypothesis can be extracted: H5: Loyalty has a positive influence on performance 3. Research methodology and tests results To examine the proposed hypotheses, data were collected from the retail business customers of a powerful wholesaler in Albania. The person who managed each company contacted was also responsible for the purchase decisions, so the appropriate and necessary data could be obtained from the interview. From 251 businesses contacted, 224 agreed to cooperate, and finally only 204 questionnaires were considered valid for further examination. Thus the response rate was 81%. The managers were contacted directly, and 10 individuals were engaged in the field work of data gathering. These individuals had previous experience with interviewing; nevertheless, they were shortly trained on the specifics of this kind of study. The period of data collection was April ­ May 2012. Table 1 presents a summary of sample's characteristics. Table 1. Sample's characteristics 1 to 3 years 4 to 6 years 7 to 9 years 10 to 12 years More than 12 years Less than 15% 15% to 30% 31% to 45% 46% to 60% 61% to 75% More than 75% Under 15,000 15,001 to 30,000 30,001 to 45,000 45,001 to 60,000 60,001 to 75,000 More than 75,000 17 22 67 64 34 204 18 57 48 27 16 38 204 45 23 22 54 28 32 204 8.33% 10.78% 32.84% 31.37% 16.67% 100% 8.82% 27.94% 23.53% 13.24% 7.84% 18.63% 100% 22.06% 11.27% 10.78% 26.47% 13.73% 15.69% 100% Relationship length with the wholesaler Percentage of business conducted with the wholesaler Annual Revenue Development of the measurement scales for each construct in the model proceeded through a series of steps. A review of the relevant literature was first conducted to identify available measures. Since the sampling frame came from the retailing industry, it was critical to adapt the measures to fit the industry context. Based on the measures derived from the literature, preliminary interviews were conducted with the wholesale company managers, as well as with a dozen target retailing companies' managers. The interviews were particularly useful in adapting meaningful measures of operational and relational order fulfillment service to the retailing industry context. Then the complete customer survey instrument was developed. A Likert format where 1 reflected "strongly disagree" and 7 reflected "strongly agree" was used. Measures for Logistics Service Quality were constructed first in accordance with the existing scales from Stank et al. (2003), Stank et al., (1999), and Mentzer et al., (2001). Commitment was measured with items from both Hennig - Thurau et al. (2002) and Smith (1998). Loyalty was measured with items from Balogly (2002) and Plank and Newell (2007). The performance measures were adapted from Webster (1992), Cooper et al. (1997) and Carr and Pearson (1999). Before hypothesis testing, scale purification was performed. Following basic descriptive analyses, including examination for coding errors, normality, skewness, kurtosis, means, and standard deviations, the purification data set was subjected to confirmatory factor analyses (CFA) by means of SPSS AMOS 18.0. After these analyses, all the items were considered valid for the measurement of the respective hypothesized construct. The measurement scales are presented in Table 2. Table 2. Measure scales and respective loadings, average variance extracted and construct reliability Construct Measure 1. This supplier delivers its products/services on or before the requested delivery date 2. This supplier always provides us with the quantities we need. 3. The shipments from this supplier are accurate 4. This supplier handles discrepancies with orders very well 5. This supplier proactively communicate supply issues that may delay our order 6. This supplier cooperates with us to make order processing more efficient 7. This supplier makes recommendations for continuous improvement on an ongoing basis 8. This supplier knows our needs very well 9. This supplier is responsive to problems that may arise 1. Our relationship with this supplier is something that we are committed to 2. Our relationship with this supplier is very important to our company 3. Our relationship with this supplier is something we really care about 4. Our relationship with this supplier deserves our maximum efforts to maintain 5. We believe that this supplier and our firm are both committed to the relationship 6. This supplier is prepared to make short term sacrifices to maintain our relationship 7. I believe that this supplier and our firm view our relationship as a long-term partnership 1. We recommend this supplier to our partners 2. We encourage our partners to do business with this supplier 3. We consider this supplier our first choice when we purchase the goods/services they supply 4. We will continue to do business with this supplier for the next few years 5. We are willing to maintain our relationship with this supplier. 6. Our firm is loyal to this supplier 1. Our market share is much better than our competitors 2. Our rate of customer retention is much better than our competitors 3. Our sales growth is much better than our competitors Loading 0.812 0.798 0.871 0.778 0.801 0.846 0.764 0.783 0.791 0.931 0.894 0.847 0.741 0.852 0.794 0.913 0.869 0.847 0.914 0.746 0.797 0.793 0.967 0.961 0.849 0.817 0.931 0.784 0.847 0.689 0.899 AVE Construct Reliability Logistics Service Quality Commitment Loyalty Performance Journal of Advanced Research in Management Construct Reliability Construct Measure 4. Our current average profit per customer is much better than our competitors 5. Our current ROI is much better than our competitors Loading 0.837 0.834 AVE Psychometric properties of the four constructs were evaluated in order to confirm construct unidimensionality, validity, and reliability, using Confirmatory Factor Analysis with SPSS Amos 18 Software. Within this analysis, both theoretical and statistical considerations were incorporated in developing the scales (Anderson and Gerbing 1988). The model was evaluated using the DELTA 2 index, RMSEA, and the CFI, which have been shown to be the most stable fit indices (Gerbing and Anderson 1992). The ² statistics with corresponding degrees of freedom are included for comparison purposes (Jöreskog and Sorbom 1996). Using these criteria, the analysis resulted in acceptable fit of the data (Table 3). One criterion for establishing reliability is that the AVE should exceed 0.5 to ensure that, on average, the measures share at least half of their variation with the latent variable (Fornell and Larcker 1981). As demonstrated in Table 2, the AVE and the construct reliability criterion are met for each of the latent variables, which support the reliability of the measures. Table 3. Fit Statistics Measurement Model 0.918 0.918 0.54 828 331 Structural Model 0.911 0.911 0.056 872 335 CFI Delta 2 RMSEA ² d. f. To assess convergent validity, the overall fit of the measurement model, the magnitude, direction, and statistical significance of the estimated parameters between latent variables and their indicators were assessed (Anderson and Gerbing, 1988). As demonstrated in Table 2, the standardized factor loadings are all relatively large and positive. Then, discriminant validity was estimated in order to verify that items from one scale did not load or converge too closely with items from a different scale (Dabholkar et al., 1996). To ensure the discriminant validity of the constructs, Fornell and Larcker (1981) argue that the average variance extracted (AVE) of any two constructs should be greater than their squared correlation. Table 4 presents the square root of variance shared between the constructs and their measures, or average communalities (the values in diagonal) and correlations among constructs (the values off-diagonal). As it can be seen, the average communalities measures of each construct are greater than the variance shared with other constructs, demonstrating that the discriminant validity of all scales is adequate. The tested model approach was utilized in order to increase the confidence that items did discriminate. Using this approach, comparisons were made between the original measurement model and successive models with correlations (-s) among latent variables fixed to 1. As long as the alternate measurement models fail to demonstrate significantly better fit than the original model, discriminant validity exists (Bagozzi and Yi, 1998). The procedure included the evaluation of one pair of factors at a time, as suggested by Anderson and Gerbing (1988), and it was found that each alternate model did not demonstrate better fit. Table 4. Results of Discriminant Validity Analysis LSQ Commitment Loyalty Performance LSQ 0.678 0.357 0.167 0.211 Commitment 0.824 0.137 0.021 Loyalty 0.704 0.324 Performance The five hypotheses of the study were tested simultaneously in a structural equation model using SPSS AMOS 18.0. The fit statistics for the structural model presented in Table 3 are comparable to those of the measurement model, and demonstrate sound model fit (CFI=0.911, DELTA 2=0.911 and RMSEA=0.056). Given the overall sound assessment of the model fit, the subsequent step was the examination of the hypotheses. The results of the hypothesis tests are presented in Figure 1. The first hypothesis examines the direct influence that LSQ has on loyalty. The model results indicate a strong confirmation for Hypothesis 1, supporting the contention that as the wholesaler's customer personnel develop working relationships with company's customers, the wholesaler can learn more about the customers' operational needs. Therefore, the processes would be aligned processes to meet those needs, leading customers to a greater loyalty towards their supplier. Hypothesis 2 suggests that LSQ has a positive influence on commitment; this hypothesis was supported too. In the interviews with some of the customers and the wholesaler's representatives, respondents explained that logistics service quality is crucial to the retailers. In order to satisfy their customers, they rely on the performance of their suppliers' logistics activities. The better the quality of logistics services the more they will do business with their supplier, thus increasing their loyalty towards him. Logistics Service Quality H2 + 0.312** Commitment H1 + 0.424** H3 + 0.234** + 0.347** H5 H4 + 0.514** Loyalty Performance ** significant at 0.001 level Figure 1. Hypotheses test results Not surprisingly, commitment and customer loyalty were found to have a strong association between each other, positively confirming Hypothesis 4. This finding supports to studies conducted by Dwyer et al., 1987 and Hennig-Thurau et al., 2002. Because commitment has a crucial role in building and fostering long-term relationships, suppliers must exert everything possible to increase the levels of commitment. Bearing this as a "must-achieve" target, suppliers can draw and implement strategies to improve confidence and increase social and special benefits for their customers. Taking into the consideration the impact that communication has on commitment, companies must use it in a sincere and honest manner. Both commitment and loyalty were found to have positive influences on performance, thus confirming hypotheses 3 and 5. Having long term partnership with suppliers and being loyal to them is beneficial for companies, and one of these benefits is a better performance. These findings confirm previous research conducted by Uncles et al., 2003 and Reichheld, 2002. Both market indicators and financial indicators of performance are found to be influenced by loyalty and commitment. Discussion and implications In today's changing business environment, it is hard to compete on products alone as the global marketplace provides more and opportunities for customers to find similar products and product features. Therefore, counting on products alone to create loyalty among customers will be much harder to achieve. Sellers in all positions in the marketing channel have to differentiate their products by the quality of the service processes accompanying those products (Novack et al., 1994). Because of this, every industry is now potentially a "service" industry (Anderson et al., 1994). One goal of this study was to extend the understanding the potential of Logistics Service Quality in creating customer value outcomes, reflected in the bottom line of market and financial performance indicators. Thus, LSQ serves as a crucial stimulator of customer's loyalty, commitment, and his own Journal of Advanced Research in Management performance. The study has highlighted the significance of creating an order fulfillment capability in order to maintain a loyal customer base. Another purpose of this research was to extend the discipline's understanding of commitment and loyalty. A stream of marketing literature defines and measures loyalty as behavioral intentions. However, more than 30 years ago, Jacoby and Kyner (1973) argued that loyalty is more than just repeat purchase behavior and that it also engenders an emotional connection. Intentions or purchase behavior alone stem from many factors besides a genuine attachment, so referring to this as "loyalty" misrepresents the phenomenon. In order to make this distinction, loyalty should be defined and measured not simply as repurchase intentions or as a global construct with emotional and behavioral items, but as causal relationships steaming from service quality and commitment. Because of the increasing demands of customers to have exactly what they want when they want it, it is important to meet customer demands in a consistent manner. Understanding the impact of an improved Logistics Service Quality, i.e. firm's ability to execute order fulfillment service dependably and accurately, to provide customer personnel that are knowledgeable and empathetic to the needs of customers, can be very useful in differentiating a seemingly similar physical product. It is also important for firms to recognize that in order to meet the operational requirements; there must be processes in place to enable customer contact personnel to interface with those responsible for the operational elements. This allows for the operational processes to stay flexible and responsive to changing customer requirements. An important goal for firms is to grow a larger share of the profitable revenue available (Bowersox, Closs and Stank 1999), and maintaining the same level of commitment for firm's entire customer base may be ineffective from a profit perspective. Managers need to determine with which customers it makes the most sense to pursue stronger relationships and develop strategies for managing a portfolio of customer relationships. Finally, LSQ is one of many capabilities that integrate internal processes to become a strategic weapon in the dynamic and competitive environment that surrounds every company. Further research may focus on these other capabilities (such as manufacturing, new product development or human resource), and examine their potential in creating customer's commitment and loyalty. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Advanced Research in Management de Gruyter

The Impact of the Quality of Logistics Activities on Customer Commitment, Loyalty and Firm’s Performance

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de Gruyter
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10.2478/v10258-012-0007-5
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Abstract

The aim of this study is to examine the role of logistics activities and on customer loyalty, commitment and performance. The main objective is to analyze the logistics service quality (LSQ), commitment, loyalty and performance in a supply chain context. Logistics services include activities that contribute to consistent quality, productivity and efficiency, as well as activities that help companies to better meet customer expectations and needs. Customer loyalty is increasingly recognized as a path to long-term success because finding new customers and doing business with them takes time, effort and money. Increasing the level of performance, while maintaining low costs create value for customers and for companies, too. A questionnaire based on personal survey was conducted among retail customers of a wholesale company in Albania. The study collected data from 204 companies. The hypotheses were tested using structural equation modeling (SEM) which is a comprehensive statistical approach for testing hypotheses about relations between observed and latent variables. The reliability and validity tests show satisfactory results. The conclusions confirm all the relationships hypothesized, and emphasize the crucial role of LSQ in the loyalty-commitment-performance chain of relations. Keywords: logistics service quality, commitment, loyalty, performance. JEL Classification: M31. 1. Introduction During the last two decades the idea of relationship marketing - establishing, developing, and maintaining successful relational exchanges - has gained significant importance (Morgan and Hunt, 1994). A vast body of literature supports the importance of developing and maintaining enduring relationships with customers (Ganesan, 1994), which leads to long-term customer retention (Mattila, 2001). In a supply chain context, relationships can be a sustainable source of competitive advantage due of their ability to create barriers to competition (Day, 2000). Since an important focus of supply chain research is the collaborative relationships with select trading partners (Bowersox and Daugherty, 1995) an important strategic result for companies is the building and growing of customer loyalty. Customer loyalty is increasingly recognized as a path to long-term success because finding new customers and doing business with them takes time, effort and money (Mittal and Lassar, 1998). It can be more expensive to obtain a new customer than retain one, and an organization's long-term success in a market is increasingly determined by its ability to expand and maintain a large and loyal customer base (Kandampully, 1998). Fay (1994) asserts that customer loyalty objectives have gained the same importance as other financial or strategic objectives. Reichheld et al. (2000) also contend that in the past, building loyalty with select customers was just one weapon to use against competition, but today it has become essential to survival. As products become more commoditized, companies can no longer maintain a loyal customer base or create sustainable advantages for themselves by only having a variety of tangible products. Rather, firms form complex relationships with customers and differentiate themselves by offering goods and service mixes in distinct ways to offer convenience, reliability, and support (Fuller et al., 1993). Improving logistic capabilities and the quality of logistic activities helps in building closer relationships with customers (Bowersox et al., 1995), gaining and maintaining customer loyalty (Bowersox et al., 1992). As the increasing competitive markets make hard for firms to find and retain important business customers (Flint and Mentzer, 2000) they can positively impact customer loyalty by providing high quality logistics service. A crucial goal of relationship marketing theory is to identify key drivers that influence important outcomes for the firm and to gain a better understanding of the links between drivers and outcomes (Hennig-Thurau et al., 2002). Therefore, understanding how logistics service can influence loyalty and commitment, and ultimately firm's performance, could be significant in examining and predicting supply chain relationship outcomes. However, logistics service is sometimes ignored as a competitive tool (Sharma, Grewal and Levy, 1995). The purpose of Journal of Advanced Research in Management this study is to examine the casual relationships between Logistics Service Quality (LSQ), loyalty, commitment and firm's performance. The main objective is to help companies assess the impact of logistics service in creating loyalty and commitment and their effect on market and financial indicators of performance. 2. Theory review and hypotheses Logistics Service Quality: Since the beginning of the 1980s, service quality has been a priority theme in for marketing researchers and practitioners, focusing mainly in quality, quality management and satisfaction (Fisk et al., 1993; Richey et al., 2007). Millen et al. (1999) contend that improved customer satisfaction is a key outcome of LSQ, while Mentzer et al. (2001) conclude that part of the value of a product is created by logistics service. In marketing, the focus of service performance has been on service quality, or the evaluation of service performance. The definition and measurement of service quality has occupied a prominent position in the services marketing literature. The service quality paradigm started with a qualitative study, where the differences in perceptions of service between managers and consumers were examined (Parasuraman et al., 1985). The authors developed a service quality model that showed several discrepancies ("gaps") between perceptions of customer service by the supplier and the customer. In an effort to empirically examine the gaps, Parasuraman, Zeithaml and Berry (1988) developed a service quality measurement instrument called SERVQUAL for assessing customer expectations and perceptions of service quality in service and retail organizations. This multi-item scale evaluates the five dimensions of quality: Tangibles: physical facilities, equipment and appearance of personnel; Reliability: ability to perform the promised service dependability and accurately; Responsiveness: willingness to help customers and provide prompt service; Assurance: knowledge and courtesy of employees and their ability to inspire trust and confidence; Empathy: caring, individualized attention that a firm provides to its customers. For almost three decades there has been a stream of research addressing the definition, conceptualization and measurement of service quality. Grönroos (1984) conceptualizes service quality to be formed by two components: Technical quality expressed as the service being technically acceptable and leading to a concrete result; and Functional quality which includes the way the customer is treated during the service provision process. The inclusion of customer perceptions in measuring service quality with SERVQUAL scale attracted logistics researchers to apply this scale in the logistics context. Beinstock, Mentzer and Bird (1997) argued that researchers should explore alternative dimensions of logistics service quality, since the service provider and the service customer are physically separated and the services are directed at "things" instead of people, thus technical or outcome dimensions are necessary for logistics service quality measurement instruments. Studies on service quality have proved that SERVQUAL items must be customized to the specific service environment (Carman, 1990; Finn and Lamb, 1991), in order to effectively address service quality in different industry contexts (Brown et al., 1993). Logistics researchers have utilized the studies on service quality and their measurement scales, adopting them to fit the specific study context. Mentzer, Flint and Hult (2001) developed a LSQ scale with specific logistics service dimensions. They conceptualized LSQ as a process and the scale was based on qualitative research from a large logistics service provider's customer base. According to Maltz and Maltz (1998), logistics service has two components. The first is basic logistics service, involving cycle time, on-time delivery, and inventory availability. The second component is responsiveness, representing the ability to adapt to market-driven change. Collier (1991) also suggests that service consists of two distinct dimensions, but he identifies them as the internal or operations-oriented dimension of service quality performance and the external or marketing-oriented dimension. Stank, et al. (1999) and Stank et al. (2003) also developed a scale to measure both the operational and relational elements of logistics service performance. Literature in supply chain management and logistics suggests that logistics service quality may contribute to a firm's market and financial performance (Bowersox et al., 2002). Based on previous research, the following hypotheses were developed: H1: LSQ has a positive influence on loyalty H2: LSQ has a positive influence on commitment Commitment: Commitment is recognized as an essential ingredient for successful long-term relationships (Dwyer et al., 1987, Gundlach and Mentzer, 1995). Dwyer et al. (1987) define commitment as an "implicit or explicit pledge of continuity between relational partners" (p. 19). Also defined by Gundlach and Mentzer (1995), "Commitment is thought to be closely related to mutuality, loyalty and forsaking of alternatives, variables which are the core of the meaning of relationalism" (p. 79). They propose three components of commitment: An input or instrumental component; this involves an affirmative action taken by one party that creates a self - interest stake in the relationship and demonstrates something, more than a mere promise. An attitudinal component signifying an enduring intention by the parties to develop and maintain a stable long-term relationship. This component can also be described in terms of affective commitment, with psychological attachment, identification, affiliation and value congruence. A temporal dimension indicating that commitment involves something long term. Studies of relationship marketing show that, if commitment is lacking, the relationship will come to an end (Wetzels et al., 1998). Meyer and Allen (1991) note that common to the various definitions of organizational commitment is "the view that commitment is a psychological state that (a) characterizes the employee's relationship with the organization, and (b) has implications for the decision to continue membership in the organization" (p. 67). Meyer and Allen (1991) described three components of commitment as (1) affective commitment, (2) continuance commitment, and (3) normative commitment. Dwyer, Schurr and Oh (1987) suggest that the key distinction of the commitment phase is that the parties purposefully engage resources to maintain a relationship fueled by the on-going benefit accrued by each partner. Morgan and Hunt (1994) theorize that trust and commitment lead directly to cooperative behavior which, in turn, contributes to the success of relationship marketing by promoting efficiency, productivity and effectiveness for both parties. Pritchard, Havitz and Howard (1999) found commitment to be strongly correlated with customer loyalty. Iwasaki and Havitz (1998) suggested that psychological commitment mediated the effects of enduring involvement on patrons' behavioral loyalty. Fullerton (2003) investigated the roles played by different forms of commitment in the relationship between customers and service providers. They found that, when customer commitment is based on shared values and identification, it has a uniformly positive impact on customer loyalty. Based on these previous findings, respective hypotheses are formed: H3: Commitment has a positive influence on performance H4: Commitment has a positive influence on loyalty Loyalty: Two approaches have been employed in the study of loyalty, including the stochastic approach and the deterministic approach. The deterministic approach holds that loyalty should be viewed as an attitude; therefore, the researcher can manipulate numerous factors that lead to loyalty (Jacoby, 1971; Jarvis and Wilcox, 1976). Deterministic research examines the psychological effect of loyalty, ignoring the outcomes of loyalty (i.e., purchase behavior). Dick and Basu (1994) define loyalty as the relationship between the relative attitude toward an entity (brand/product/service/store/vendor) and patronage behavior. In addition, researchers have noted the importance of distinguishing between true (intentional) loyalty and repeat purchase behavior (Jacoby and Kyner, 1973; Jarvis and Wilcox, 1976). The basis of this distinction is that true loyalty involves a psychological bond to the seller and requires a high degree of customer satisfaction and commitment, whereas repeat purchase behavior does not involve the psychological commitment. Repeat purchase behavior generally occurs because of time/energy costs, perceived risk, perceived absence of choice, probability or bias, temporary selling incentives, or legal and corporate policy constraints (Jarvis and Wilcox, 1976). Dick and Basu (1994) built upon their concept of the relationship of relative attitude with repeat patronage by cross-classifying four conditions of loyalty. A low relative attitude combined with low repeat patronage indicates an absence of loyalty. Spurious loyalty consists of a low relative attitude combined with high repeat patronage, indicating the possibility of non-attitudinal influences on the consumer's behavior. High relative attitude with low repeat patronage establishes latent loyalty, where it is assumed that situational effects and market conditions are equally as strong as attitudinal effects on the consumer's behavior. The final and most desirable of the four conditions is loyalty, where there is a positive relationship between relative attitude and patronage behavior. Journal of Advanced Research in Management Yim and Kannan (1999) developed a modeling framework of consumer behavioral loyalty that is useful for segmentation. The first segment of consumers identified by the model is the hard-core loyalty group. This group exclusively makes repeat purchases of one product alternative. The second consumer segment identified is the reinforcing loyalty group. In contrast to the hard-core loyalty group, this segment predominantly makes repeat purchases of one or more product alternatives. Dick and Basu (1994) also described several important cognitive, affective, and conative antecedents to consumer loyalty. Cognitive antecedents identified by the authors include the accessibility and ease with which an attitude can be retrieved from the consumer's memory, the attitudinal confidence (level of certainty) a consumer has about their attitude or evaluation, the centrality of the relationship between the consumer's attitude and the consumer's value system, and the clarity of the consumer's attitude or evaluation. The affective antecedents included emotions, moods, primary affect (independent of cognitions), and satisfaction. Finally, important conative antecedents to the development of consumer loyalty include switching costs, sunk costs, and expectations. From the above, the following hypothesis can be extracted: H5: Loyalty has a positive influence on performance 3. Research methodology and tests results To examine the proposed hypotheses, data were collected from the retail business customers of a powerful wholesaler in Albania. The person who managed each company contacted was also responsible for the purchase decisions, so the appropriate and necessary data could be obtained from the interview. From 251 businesses contacted, 224 agreed to cooperate, and finally only 204 questionnaires were considered valid for further examination. Thus the response rate was 81%. The managers were contacted directly, and 10 individuals were engaged in the field work of data gathering. These individuals had previous experience with interviewing; nevertheless, they were shortly trained on the specifics of this kind of study. The period of data collection was April ­ May 2012. Table 1 presents a summary of sample's characteristics. Table 1. Sample's characteristics 1 to 3 years 4 to 6 years 7 to 9 years 10 to 12 years More than 12 years Less than 15% 15% to 30% 31% to 45% 46% to 60% 61% to 75% More than 75% Under 15,000 15,001 to 30,000 30,001 to 45,000 45,001 to 60,000 60,001 to 75,000 More than 75,000 17 22 67 64 34 204 18 57 48 27 16 38 204 45 23 22 54 28 32 204 8.33% 10.78% 32.84% 31.37% 16.67% 100% 8.82% 27.94% 23.53% 13.24% 7.84% 18.63% 100% 22.06% 11.27% 10.78% 26.47% 13.73% 15.69% 100% Relationship length with the wholesaler Percentage of business conducted with the wholesaler Annual Revenue Development of the measurement scales for each construct in the model proceeded through a series of steps. A review of the relevant literature was first conducted to identify available measures. Since the sampling frame came from the retailing industry, it was critical to adapt the measures to fit the industry context. Based on the measures derived from the literature, preliminary interviews were conducted with the wholesale company managers, as well as with a dozen target retailing companies' managers. The interviews were particularly useful in adapting meaningful measures of operational and relational order fulfillment service to the retailing industry context. Then the complete customer survey instrument was developed. A Likert format where 1 reflected "strongly disagree" and 7 reflected "strongly agree" was used. Measures for Logistics Service Quality were constructed first in accordance with the existing scales from Stank et al. (2003), Stank et al., (1999), and Mentzer et al., (2001). Commitment was measured with items from both Hennig - Thurau et al. (2002) and Smith (1998). Loyalty was measured with items from Balogly (2002) and Plank and Newell (2007). The performance measures were adapted from Webster (1992), Cooper et al. (1997) and Carr and Pearson (1999). Before hypothesis testing, scale purification was performed. Following basic descriptive analyses, including examination for coding errors, normality, skewness, kurtosis, means, and standard deviations, the purification data set was subjected to confirmatory factor analyses (CFA) by means of SPSS AMOS 18.0. After these analyses, all the items were considered valid for the measurement of the respective hypothesized construct. The measurement scales are presented in Table 2. Table 2. Measure scales and respective loadings, average variance extracted and construct reliability Construct Measure 1. This supplier delivers its products/services on or before the requested delivery date 2. This supplier always provides us with the quantities we need. 3. The shipments from this supplier are accurate 4. This supplier handles discrepancies with orders very well 5. This supplier proactively communicate supply issues that may delay our order 6. This supplier cooperates with us to make order processing more efficient 7. This supplier makes recommendations for continuous improvement on an ongoing basis 8. This supplier knows our needs very well 9. This supplier is responsive to problems that may arise 1. Our relationship with this supplier is something that we are committed to 2. Our relationship with this supplier is very important to our company 3. Our relationship with this supplier is something we really care about 4. Our relationship with this supplier deserves our maximum efforts to maintain 5. We believe that this supplier and our firm are both committed to the relationship 6. This supplier is prepared to make short term sacrifices to maintain our relationship 7. I believe that this supplier and our firm view our relationship as a long-term partnership 1. We recommend this supplier to our partners 2. We encourage our partners to do business with this supplier 3. We consider this supplier our first choice when we purchase the goods/services they supply 4. We will continue to do business with this supplier for the next few years 5. We are willing to maintain our relationship with this supplier. 6. Our firm is loyal to this supplier 1. Our market share is much better than our competitors 2. Our rate of customer retention is much better than our competitors 3. Our sales growth is much better than our competitors Loading 0.812 0.798 0.871 0.778 0.801 0.846 0.764 0.783 0.791 0.931 0.894 0.847 0.741 0.852 0.794 0.913 0.869 0.847 0.914 0.746 0.797 0.793 0.967 0.961 0.849 0.817 0.931 0.784 0.847 0.689 0.899 AVE Construct Reliability Logistics Service Quality Commitment Loyalty Performance Journal of Advanced Research in Management Construct Reliability Construct Measure 4. Our current average profit per customer is much better than our competitors 5. Our current ROI is much better than our competitors Loading 0.837 0.834 AVE Psychometric properties of the four constructs were evaluated in order to confirm construct unidimensionality, validity, and reliability, using Confirmatory Factor Analysis with SPSS Amos 18 Software. Within this analysis, both theoretical and statistical considerations were incorporated in developing the scales (Anderson and Gerbing 1988). The model was evaluated using the DELTA 2 index, RMSEA, and the CFI, which have been shown to be the most stable fit indices (Gerbing and Anderson 1992). The ² statistics with corresponding degrees of freedom are included for comparison purposes (Jöreskog and Sorbom 1996). Using these criteria, the analysis resulted in acceptable fit of the data (Table 3). One criterion for establishing reliability is that the AVE should exceed 0.5 to ensure that, on average, the measures share at least half of their variation with the latent variable (Fornell and Larcker 1981). As demonstrated in Table 2, the AVE and the construct reliability criterion are met for each of the latent variables, which support the reliability of the measures. Table 3. Fit Statistics Measurement Model 0.918 0.918 0.54 828 331 Structural Model 0.911 0.911 0.056 872 335 CFI Delta 2 RMSEA ² d. f. To assess convergent validity, the overall fit of the measurement model, the magnitude, direction, and statistical significance of the estimated parameters between latent variables and their indicators were assessed (Anderson and Gerbing, 1988). As demonstrated in Table 2, the standardized factor loadings are all relatively large and positive. Then, discriminant validity was estimated in order to verify that items from one scale did not load or converge too closely with items from a different scale (Dabholkar et al., 1996). To ensure the discriminant validity of the constructs, Fornell and Larcker (1981) argue that the average variance extracted (AVE) of any two constructs should be greater than their squared correlation. Table 4 presents the square root of variance shared between the constructs and their measures, or average communalities (the values in diagonal) and correlations among constructs (the values off-diagonal). As it can be seen, the average communalities measures of each construct are greater than the variance shared with other constructs, demonstrating that the discriminant validity of all scales is adequate. The tested model approach was utilized in order to increase the confidence that items did discriminate. Using this approach, comparisons were made between the original measurement model and successive models with correlations (-s) among latent variables fixed to 1. As long as the alternate measurement models fail to demonstrate significantly better fit than the original model, discriminant validity exists (Bagozzi and Yi, 1998). The procedure included the evaluation of one pair of factors at a time, as suggested by Anderson and Gerbing (1988), and it was found that each alternate model did not demonstrate better fit. Table 4. Results of Discriminant Validity Analysis LSQ Commitment Loyalty Performance LSQ 0.678 0.357 0.167 0.211 Commitment 0.824 0.137 0.021 Loyalty 0.704 0.324 Performance The five hypotheses of the study were tested simultaneously in a structural equation model using SPSS AMOS 18.0. The fit statistics for the structural model presented in Table 3 are comparable to those of the measurement model, and demonstrate sound model fit (CFI=0.911, DELTA 2=0.911 and RMSEA=0.056). Given the overall sound assessment of the model fit, the subsequent step was the examination of the hypotheses. The results of the hypothesis tests are presented in Figure 1. The first hypothesis examines the direct influence that LSQ has on loyalty. The model results indicate a strong confirmation for Hypothesis 1, supporting the contention that as the wholesaler's customer personnel develop working relationships with company's customers, the wholesaler can learn more about the customers' operational needs. Therefore, the processes would be aligned processes to meet those needs, leading customers to a greater loyalty towards their supplier. Hypothesis 2 suggests that LSQ has a positive influence on commitment; this hypothesis was supported too. In the interviews with some of the customers and the wholesaler's representatives, respondents explained that logistics service quality is crucial to the retailers. In order to satisfy their customers, they rely on the performance of their suppliers' logistics activities. The better the quality of logistics services the more they will do business with their supplier, thus increasing their loyalty towards him. Logistics Service Quality H2 + 0.312** Commitment H1 + 0.424** H3 + 0.234** + 0.347** H5 H4 + 0.514** Loyalty Performance ** significant at 0.001 level Figure 1. Hypotheses test results Not surprisingly, commitment and customer loyalty were found to have a strong association between each other, positively confirming Hypothesis 4. This finding supports to studies conducted by Dwyer et al., 1987 and Hennig-Thurau et al., 2002. Because commitment has a crucial role in building and fostering long-term relationships, suppliers must exert everything possible to increase the levels of commitment. Bearing this as a "must-achieve" target, suppliers can draw and implement strategies to improve confidence and increase social and special benefits for their customers. Taking into the consideration the impact that communication has on commitment, companies must use it in a sincere and honest manner. Both commitment and loyalty were found to have positive influences on performance, thus confirming hypotheses 3 and 5. Having long term partnership with suppliers and being loyal to them is beneficial for companies, and one of these benefits is a better performance. These findings confirm previous research conducted by Uncles et al., 2003 and Reichheld, 2002. Both market indicators and financial indicators of performance are found to be influenced by loyalty and commitment. Discussion and implications In today's changing business environment, it is hard to compete on products alone as the global marketplace provides more and opportunities for customers to find similar products and product features. Therefore, counting on products alone to create loyalty among customers will be much harder to achieve. Sellers in all positions in the marketing channel have to differentiate their products by the quality of the service processes accompanying those products (Novack et al., 1994). Because of this, every industry is now potentially a "service" industry (Anderson et al., 1994). One goal of this study was to extend the understanding the potential of Logistics Service Quality in creating customer value outcomes, reflected in the bottom line of market and financial performance indicators. Thus, LSQ serves as a crucial stimulator of customer's loyalty, commitment, and his own Journal of Advanced Research in Management performance. The study has highlighted the significance of creating an order fulfillment capability in order to maintain a loyal customer base. Another purpose of this research was to extend the discipline's understanding of commitment and loyalty. A stream of marketing literature defines and measures loyalty as behavioral intentions. However, more than 30 years ago, Jacoby and Kyner (1973) argued that loyalty is more than just repeat purchase behavior and that it also engenders an emotional connection. Intentions or purchase behavior alone stem from many factors besides a genuine attachment, so referring to this as "loyalty" misrepresents the phenomenon. In order to make this distinction, loyalty should be defined and measured not simply as repurchase intentions or as a global construct with emotional and behavioral items, but as causal relationships steaming from service quality and commitment. Because of the increasing demands of customers to have exactly what they want when they want it, it is important to meet customer demands in a consistent manner. Understanding the impact of an improved Logistics Service Quality, i.e. firm's ability to execute order fulfillment service dependably and accurately, to provide customer personnel that are knowledgeable and empathetic to the needs of customers, can be very useful in differentiating a seemingly similar physical product. It is also important for firms to recognize that in order to meet the operational requirements; there must be processes in place to enable customer contact personnel to interface with those responsible for the operational elements. This allows for the operational processes to stay flexible and responsive to changing customer requirements. An important goal for firms is to grow a larger share of the profitable revenue available (Bowersox, Closs and Stank 1999), and maintaining the same level of commitment for firm's entire customer base may be ineffective from a profit perspective. Managers need to determine with which customers it makes the most sense to pursue stronger relationships and develop strategies for managing a portfolio of customer relationships. Finally, LSQ is one of many capabilities that integrate internal processes to become a strategic weapon in the dynamic and competitive environment that surrounds every company. Further research may focus on these other capabilities (such as manufacturing, new product development or human resource), and examine their potential in creating customer's commitment and loyalty.

Journal

Journal of Advanced Research in Managementde Gruyter

Published: Dec 1, 2012

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