• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.67-78
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• 2021

The number of domestic e-commerce transactions has been breaking its own record by an annual average growth rate of over 20% based on volume for the past 5 years. Due to the rapid increase in e-commerce market, retail companies that have difficulty meeting consumers in person are in fierce competition to take the lead in the last mile service, which is the only point of contact with customers. Especially in the delivery area, where competition is most intense, the role of the fulfillment center is very important for service differentiation. It must be capable of fast product preparation ordered by consumers in accordance with the delivery service level. This study focuses on the order picking system for rapid order processing in the fulfillment center as an alternative for companies to gain competitive advantage in the e-commerce market. A mixed integer programming model was developed and implemented to optimize the stock replenishment in order picking facilities. The effectiveness was scientifically and objectively verified by simulation using the actual operation process and data.

• Journal of Global Scholars of Marketing Science
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• v.19 no.4
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• pp.57-63
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• 2009

With an exponential increase in electronic commerce (e-commerce), marketers are attempting to gain a competitive advantage by emphasizing service quality and post interaction service aspects, which leads to customer satisfaction or behavioral consequence. Particularly for apparel, service quality is one of the key determinants in encouraging customer e-loyalty, and hence the success of apparel retailing in the context of electronic commerce. Therefore, this study explores e-service quality (E-SERVQUAL) factors and their unique effects on e-loyalty for apparel online shopping based on Parasuraman et al' s (2005) framework. Specific objectives of this study are to identify underlying dimension of E-SERVQUAL, and analyze a structural model for examining the effect of E-SERVQUAL on e-loyalty for online apparel shopping. For the theoretical framework of service quality in the context of online shopping, literatures on traditional and electronic service quality factors were comparatively reviewed, and two aspects of core and recovery services were identified. This study hypothesized that E-SERVQUAL has an effect on e-loyalty; customer satisfaction has a positive effect on e-service loyalty for apparel online shopping; and customer satisfaction mediates in the effect of E-SERVQUAL on e-loyalty for apparel online shopping. A self-administered questionnaire was developed based on literatures. A total of 252 usable questionnaires were obtained from online consumers who had purchase experience with online shopping for apparel products and reside in standard metropolitan areas, in the United States. Factor analysis (e.g., exploratory, confirmatory) was conducted to assess the validity and reliability and the structural equation model including measurement and structural models was estimated via LISREL 8.8 program. Findings showed that the E-SERVQUAL of shopping websites for apparel consisted of five factors: Compensation, Fulfillment, Efficiency, System Availability, and Responsiveness. This supports Parasuraman (2005)'s E-S-QUAL encompassing two aspects of core service (e.g., fulfillment, efficiency, system availability) and recovery related service (e.g., compensation, responsiveness) in the context of apparel shopping online. In the structural equation model, there are five exogenous latent variables for e-SERVQUAL factors; and two endogenous latent variables (e.g., customer satisfaction, e-loyalty). For the measurement model, the factor loadings for each respective construct were statistically significant and were greater than .60 and internal consistency reliabilities ranged from .85 to .88. In the estimated structural model of the e-SERVEQUAL factors, the system availability was found to have direct and positive effect on e-loyalty, whereas efficiency had a negative effect on e-loyalty for apparel online shopping. However, fulfillment was not a significant predictor for explaining consequences of E-SERVQUAL for apparel online shopping. This finding implies that perceived service quality of system available was likely to increase customer satisfaction for apparel online shopping. However, it was not supported that e-loyalty was determined by service quality, because service quality has an indirect effect on e-loyalty (i.e., repurchase intention) by mediating effect of value or satisfaction in the context of online shopping for apparel. In addition, both compensation and responsiveness were found to have a significant impact on customer satisfaction, which influenced e-loyalty for apparel online shopping. Thus, there was significant indirect effect of compensation and responsiveness on e-loyalty. This suggests that the recovery-specific service factors play an important role in maximizing customer satisfaction levels and then maintaining customer loyalty to the online shopping site for apparel. The findings have both managerial and research implications. Fashion marketers can establish long-term relationship with their customers based on continuously measuring customer perceptions for recovery-related service quality, such as quick responses to problem and returns, and compensation for customers' problem after their purchases. In order to maintain e-loyalty, recovery services play an important role in the first choice websites for consumers to purchase clothing. Given that online consumers may shop anywhere, a marketing strategy for improving competitive advantages is to provide better service quality, maximize satisfaction, and turn to creating customers' e-loyalty for apparel online shopping. From a researcher's perspective, there are some limitations of this research that should be considered when interpreting its findings. For future research, findings provide a basis for the further study of this important topic along both theoretical and empirical dimensions. Based on the findings, more comprehensive models for predicting E-SERVQUAL's consequences can be developed and tested. For global fashion marketing, this study can expand to a cross-cultural approach into e-service quality for apparel by including multinational samples.

• The Journal of Bigdata
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• v.8 no.2
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• pp.149-163
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• 2023

Due to the continuous growth of the E-commerce market, the volume of orders that fulfillment centers have to process has increased, and various customer requirements have increased the complexity of order processing. Along with this trend, the operational efficiency of fulfillment centers due to increased labor costs is becoming more important from a corporate management perspective. Using historical performance data as training data, this study focused on real-time box recommendations applicable to packaging areas during fulfillment center shipping. Four types of data, such as product information, order information, packaging information, and delivery information, were applied to the machine learning model through pre-processing and feature-engineering processes. As an input vector, three characteristics were used as product specification information: width, length, and height, the characteristics of the input vector were extracted through a feature engineering process that converts product information from real numbers to an integer system for each section. As a result of comparing the performance of each model, it was confirmed that when the Gradient Boosting model was applied, the prediction was performed with the highest accuracy at 95.2% when the product specification information was converted into integers in 21 sections. This study proposes a machine learning model as a way to reduce the increase in costs and inefficiency of box packaging time caused by incorrect box selection in the fulfillment center, and also proposes a feature engineering method to effectively extract the characteristics of product specification information.