• International Journal of Advanced Culture Technology
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• v.8 no.1
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• pp.56-61
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• 2020

Customer satisfaction in retail stores are considerably affected by checkout services. Self-checkout counters have been installed in order to reduce waiting times at checkout in retail stores. However, it is uncertain whether the self-checkout lanes actually decrease the average waiting time of customers. Rather, there are some problems associated with self-checkout lanes such as theft or service failure due to technological problems. This study analyzes comparison between self-checkout and regular staffed-checkout lanes, based on the dataset collected from a retail store in Poland. As a result, we observe that the average transaction times were longer at the self-checkout lanes though fewer products were purchased than at the staffed-checkout lanes. In addition, the customers who buy more products tend to use self-checkouts less frequently. We also check that transaction times are proportional to the number of products customers purchase, and that both the time to scan one item and the fixed time related to checkout are significantly longer at the self-checkout counters. As there has been very few research on the effectiveness of self-checkouts, this study can be the first step to investigate managerial insights on checkout services in retail stores.

• Journal of Distribution Science
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• v.17 no.8
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• pp.67-76
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• 2019

Purpose - This research examined whether entrepreneurial proclivity of retail employees affects job engagement and satisfaction, which are job-related positive aspects; and whether job engagement affects job satisfaction. Research design, data, and methodology - To accomplish this purpose, data were collected for 224 retail employees working in the distribution industry in the Republic of Korea. Reliability, validity, and hypotheses were tested through structural equation modeling, and mediating effects of job engagement between entrepreneurial proclivity and job satisfaction were verified through the bootstrap method by using the process model. Results - The results show that innovativeness and progressiveness in entrepreneurial proclivity positively affected job engagement and job satisfaction, but risk-taking did not affect either job engagement or job satisfaction. Also, this research confirmed that job engagement positively affects job satisfaction. Conclusions - This study contributes to the retail literature by applying the concept of entrepreneurial proclivity in the retail employee context. This study puts forward empirical evidence that identifies the effect of entrepreneurial proclivity as a job resource that influences job engagement and job satisfaction in the JD-R model. Thus, this study surmounts the limitation of prior studies by examining entrepreneurial proclivity from the aspect of retail employees.

• Journal of Distribution Science
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• v.16 no.9
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• pp.53-58
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• 2018

Purpose - This study aims to examine how the time variations of customer satisfaction influence retail firms' performance. Research design, data, and methodology - The study employs yearly time series customer satisfaction data of Korean retail secured from the National Customer Satisfaction Index(NCSI) for the 2011~2016 period. Our data includes a total of 90 observations of 15 retail firms in 5 different sector(department store, filling station, large discount store, open market, TV home shopping). We obtained the firm performance data from the KIS Value database. The variables for financial performance include sales and net profit. Results - The results show that customer satisfaction has dynamic effects on retail firms' performance. More specifically, the time variation of customer satisfaction has the moderating effect on the linkage between customer satisfaction and financial performance as well as direct effects on the firms' financial performance. Conclusions - Customer satisfaction has the current effect lasting over time on firm performance and changes of customer satisfaction in positive direction also impact on firm performance. Retail firms need to not only focus on improving customer satisfaction in the current term, but make efforts to continuously enhance customer satisfaction in the long term.

• Journal of Distribution Science
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• v.19 no.10
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• pp.55-64
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• 2021

Purpose: This study empirically validates a research framework encompassing predictors hypothesized to affect the participation in sharing economy on O2O retail platforms. Research design, data, and methodology: The study examines the role of consumers' social capital and consumer citizenship as a net promoter of retail sales increase of sharing economy products. Using a convenience sampling method, this study used a questionnaire survey method to collect data from 400 adult consumers with previous experience of sharing economy who reside in the metropolitan areas of Seoul and Kyonggi Province, Korea. This study applied structural equation modeling to verify the structural relationships proposed as research hypotheses. Results: The study found a significant impact of social capital on sharing economy participation and the impact of consumer citizenship on sharing economy participation in retail settings. This study also confirmed that social identity and corporate image mediated the relationship between social capital (and citizenship) and sharing economy participation. Conclusions: The study results are expected to contribute to further understanding of the sharing economy's key success factors. The study results offer significant strategic implications for retail platform operators and individual retail operators of sharing economy.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.571-580
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• 2021

The study explores the factors affecting the brand selection behavior of retail chains in Vietnam and to what extent they have an impact on the customer's choice intention. This research employs a combination of both qualitative and quantitative mixed methods with the help of SPSS version 22.0 in data analysis. Expert interviews are used to design the questionnaire for the survey conducted on 700 consumers. Research results show that the eight factors of store image (1-to-3 split factor of store image including the display of goods and services), price perception, risk perception, brand attitude, brand awareness, and brand familiarity were determined. They all influence the intention to choose the retail chain brand. With a positive β coefficient, the more store image, price perception, brand attitude, and brand awareness are enhanced, the more likely the intention to choose the retail chain brand. The factor of risk perception has negative ��, resulting in an inverse impact on choosing a retail chain brand name. Price perception and risk perception have the strongest impact on retail chain decision behavior while commodity display factors the least. Based on these important results, the study proposes implications for retailers and manufacturers.

• Journal of Distribution Science
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• v.20 no.6
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• pp.109-123
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• 2022

Purpose: This research presents a practical tool aimed at increasing collaboration between designers and marketers for effective retail space branding. We present a design-related information processing model (DIP Model), which is a schematic map that includes cognitive theories which have design applications to retail space branding. Research design, data and methodology: Through literature review and practitioner opinion survey, 43 theories pertaining to the brand communication in retail spaces were selected, and design applications of the theories were analysed through field trips to stores of global brands. Results: The DIP Model consists of two axes: the information processing axis (i.e., encoding vsretrieval) and the regulatory focus axis(i.e., promotion vs prevention). Theories related to information processing axis are theories that facilitate the encoding and retrieval of information as intended by the company. Theories related to regulatory focus axis are theories that reinforce positive cognition and prevent negative cognition regarding the brand. Conclusions: The DIP Model is developed as a tool to categorise cognitive theories that are applicable to the design of brand communication in retail spaces. As such, the model can provide a better understanding of the role of behavioural design, with the aim of building stronger brands in retail spaces.

• Journal of Distribution Science
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• v.18 no.3
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• pp.87-97
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• 2020

Purpose: The aim of this study is to understand the internal and external responses that consumers experience when they are exposed to an innovative system in retail stores. This study considered the SST(Self-Service Technology) system in a retail setting as a type of functional environmental stimuli and selected a smart shopping cart as an example of SST system. The influences of functional environmental stimuli on consumers' emotional, cognitive, and behavioral responses were examined by applying S-O-R model. In addition, this study attempted to extend the traditional S-O-R model by (a) incorporating personal characteristics variables such as time pressure and perceived crowding and (b) considering not only emotional but also cognitive aspects of consumers' internal responses. Research Design, Data, and Methodology: This study used a video-scenario technique. Participants watched a video about grocery shopping situations using a smart shopping cart and responded to their emotional, cognitive, and behavioral responses. An online survey was conducted using Amazon's Mechanical Turk (N = 185). All participants were US consumers over 20 years old and had been shopping at the grocery store in the last month. Data were analyzed through structural equations modeling with AMOS 20. Results: Test results showed that consumers who perceived higher levels of time pressure and perceived crowding in usual shopping situations were more likely to evaluate the SST system favorably. The results showed that personal characteristics have a significant impact on consumers' evaluation of functional environmental stimuli in retail setting. As consumers evaluated the SST system favorably, they experienced more positive affect and less negative affect during their shopping behaviors. Positive affect led to good service quality inference, which further increased patronize intention. However, negative affect did not show a significant impact on service quality inference, but only on patronize intention. Conclusions: This study attempted to investigate the influence of SST system by extending the traditional S-O-R model. This study classified the SST system as functional environmental stimulus of retail stores and analyzed the effect of stimulus on consumers' internal and external responses. The results of this study showed that the introduction of innovative SST can serve as an effective store differentiation strategy in an increasingly competitive retail environment.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Journal of Intelligence and Information Systems
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• v.14 no.4
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• pp.117-134
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• 2008

The outpaced growth of online channel sales over the traditional retail sales is a result from superior shopping convenience that online stores offer to their customers. One major source of online shopping convenience is a personalized store that reduces customer's shopping time. Personalization of an online store is accomplished by using various in-store shopping behavior data that the Internet and Web Technology provides. Brick-and-mortar retailers have not been able to make this type of data available for their stores until now. However, RFID technology has now opened a new possibility to personalization of traditional retail stores. In this paper, we propose BRIMPS (BRIck-and-Mortar Personalization System) as a system that brick-and-mortar retailers may use to personalize their business and become more competitive against online retailers.