• Fourth Industrial Review
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• v.1 no.1
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• pp.23-29
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• 2021

Purpose - Faced with the great change of the 4th industrial revolution and the addition of the COVID-19 pandemic, great confusion and crises are occurring in the retail environment as well. The purpose of this study is to suggest the necessity of establishing a methodology for applying retail tech to offline distribution channels in crisis. Research design, data, and methodology - After examining the recent developments of representative fields to which retail technology is applied, it is rearranged through consideration through previous studies. Result - The retail industry must transform into digital commerce through digital transformation. According to the development of retail technology, the distribution industry is at a time of change from the stage of brokering product and service transactions to a structure that creates value based on information on production and consumption. The business model of the distribution industry must be converted to a platform business model in which both consumers and producers become users. Conclusion - In-depth analysis of the cases has not been conducted, and there are limitations in that the development is somewhat insufficient due to insufficient prior research data. However, it is meaningful to suggest the necessity of finding a methodology for applying retail technology to overcome the crisis of offline retailers through quantitative research on the retail technology area.

• The Research Journal of the Costume Culture
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• v.28 no.4
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• pp.409-419
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• 2020

Building on Technology Readiness and Acceptance Model(TRAM), the study aimed to examine how technology readiness affects consumers' perceptions of ease of use, usefulness, and risk, which in turn predict their intention to use retail service robots. Specifically, the study proposed that technology readiness motivators (optimism and innovativeness) would influence perceived ease of use and usefulness, while technology readiness inhibitors (discomfort and insecurity) would affect perceived risk. The study further examined if the perception factors (ease of use, usefulness, and risk) contribute to intention to use retail service robots. A survey method was used with data collected from Korean consumers. The final sample size was 418. The data was analyzed using structural equation modeling. Findings of the study revealed that technology readiness motivators positively affected perceived ease of use and usefulness while innovativeness had no impact on usefulness. All the inhibitors increased perceived risk. Lastly, as hypothesized, perceptions of ease of use, usefulness, and risk predicted intention to use retail service robots. This study extended the retail technology literature by applying and validating TRAM to the context of consumer acceptance of retail service robots. The study further helped marketers and retailers by highlighting the importance of technology readiness in improving consumer perceptions and responses towards retail service robots.

• Fashion & Textile Research Journal
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• v.21 no.6
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• pp.730-742
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• 2019

This study investigated the influence of the perceived characteristics of AR fashion retail technology on value co-creation and continued use intention. This study also examines the moderating role of technology readiness in the effects of the perceived characteristics of AR fashion retail technology on value co-creation. A total of 241 university students who had experience using AR fashion retail technology completed the questionnaire. The results were as follows. First, there were five factors in the perceived characteristics of AR fashion retail technology: presence, aesthetic attractiveness, ease of use, shopping usefulness, and perceived enjoyment. Second, aesthetic attractiveness, shopping usefulness, and perceived enjoyment had positive impacts on value co-creation. Third, value co-creation had a positive impact on continued use intention of AR retail technology. Fourth, there were significant differences in the effect of aesthetic attractiveness and shopping usefulness on value co-creation by the innovativeness dimension of technology readiness. Fifth, there was a significant difference in the effect of ease of use on value co-creation by the optimism dimension of technology readiness. The results of this study should provide guidance for marketers or retailers interested in the application of AR fashion retail technology in their stores.

• The Journal of Industrial Distribution & Business
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• v.14 no.2
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• pp.21-33
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• 2023

Purpose: After using the Internet, the world is changing through several paradigms, and the retail industry, which is essential to living in the world, is also changing rapidly. In this review paper, the requirements that the retail industry should consider and prepare in accordance with the rapidly changing paradigm were reviewed according to the current situation of the times. Research design, data, and methodology: It is a review of technological development using PRISMA flow diagram, retail change, and necessity in April 2022, and a review of the digital environment to be applied to the retail industry in the future. Results As the current situation and changes of retail, and the development of IT technology, reviews on the retail business applying the 4th Industrial Revolution, the Internet of Things and artificial intelligence were collected, and the direction of the retail industry was suggested. Conclusions: The direction for the retail industry in preparation for developing technologies was presented. In addition, this study is a review paper that suggests the need for research on active introduction of new technologies to the beauty market that is very close to human life and economically helpful as IT technology for the 4th industrial revolution develops rapidly.

• 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.19 no.3
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• pp.5-14
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• 2021

Purpose: This study aims to clarify the behavioral extraction and ability of venture merchants, who actively challenge commerce in the face of harsh living environments. Research design, data and methodology: Adopting the concept of retail functions and retail skills, this study examines how venture merchants perform retail functions, and identifies the required retail skills. This study analyzed primary data obtained through an interview with a bookstore called Lunuganga. Results: The venture merchant purchases products based on his self-assertion and creates an original "store identification." Moreover, he draws a changing "own-store customers image" and acquires "own-store customers," that is, customers acquired by him by building an original store identity. He sells products to "own-store customers" who identify with the store. The retail skills identified as required by venture merchants to carry out such retail functions were "skill to draw a store identification" and "skill to draw own-store customer image." Conclusions: Venture merchants' unique retail functions and retail skills suggest a new basis for the existence of small and medium-sized retailers. It is necessary to build a generalized theoretical hypothesis model by refining the concept presented in this paper by repeating research targeting venture merchants in the same industry and different industries.

• Journal of Distribution Science
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• v.19 no.3
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• pp.15-23
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• 2021

Purpose: This study aims to identify the role of technological progress in the distribution sector in Saudi Arabia. Research design, data, and methodology: The study applies the Autoregressive Distributed Lag (ARDL) approach to estimate the Cobb Douglas production function of the wholesale and retail trade sector in Saudi Arabia, relied on annual data from the General Authority for Statistics from 2005 to 2019. Results: The results show that there is a long run relationship between the production of the wholesale and retail trade sector in KSA and the factors of production labour, capital and technology progress. The elasticity of the wholesale and retail trade production with respect to capital and labour are 0.26 and 0.78 respectively; the coefficients are positive and statistically significant. The wholesale and retail trade sector is operating under increasing returns to scale. The main result indicates that the elasticity of the wholesale and retail production with respect to the technology progress is 4.62%, which is positive and statistically significant. Conclusions: The study concluded that technological progress has a positive contribution to the growth of the distribution sector in KSA. Therefore, the technological progress can improve the productivity and efficiency of the resources allocated to the dis.

• 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 Distribution Science
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• v.19 no.9
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• pp.79-90
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• 2021

Purpose: The covid-19 pandemic has led to the implementation of strict measure such as social distancing and lockdown around the globe and these measures has largely affected the retail industry. This study is to examine the negative impacts of the covid-19 pandemic on the acceleration of startups innovation in the retail industry. Research design, data and methodology: The current authors used the qualitative content approach and the data collection process in this procedure starts with a formulated and direct research question which means that rather than asking how a change in one variable leads to a change in the other, the research question seeks to understand the meanings and experiences derived from the piece of communication. Results: This section outlines how retail companies can overcome the adversely effect of the Covid-19 pandemic on the acceleration of startup innovation in the retail industry. The solutions are mostly from peer-reviewed articles. All retailers should respond to the negative impacts of the covid-19 pandemic to ensure their continuity while accelerating startups innovations in the sector. Conclusion: This study implies that the retailing industry, alongside other sectors, should respond to the negative effects of the covid-19 pandemic by encouraging innovations and adaptations. The study has shown that flexibility is very crucial to adapt during the crisis