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The paper attempts to discuss the optimal pricing decisions under the decentralized and centralized decision and analyze the influence of online reviews and in-sale service on dual-channel supply chain. Finally, the authors design a two-part tariff coordination mechanism.

To deal with this pricing conflict problems of dual-channel supply chain consisting of dominant manufacturer and a retailer, considering the fact that online reviews and in-sale service are important factors on consumers’ purchase decisions, the authors establish some basic models and exploit them to discuss the optimal pricing decisions under the decentralized and centralized decision and analyze the influence of online reviews and in-sale service on dual-channel supply chain. Finally, the authors design a profit-sharing coordination mechanism.

The results show that the optimal online direct selling price is positively correlated with product perceived quality obtained from online reviews and negatively correlated with the in-sale service. The traditional retail price is positively correlated with the in-sale service and weakly correlated with online reviews. For the manufacturer and retailer, whether decentralized decision or coordination contract, their profits increase with the increase of the in-sale service in a certain range and quality perceived from spontaneous online reviews. Online reviews and in-sale service are important factors on consumers’ purchase decisions. Positive in-sale services and online reviews can provide consumers with a better shopping experience, thereby promoting their enthusiasm for shopping and improving their quality of life. The two-part tariff coordination mechanism improves the profits of the manufacturer and the traditional retailer, respectively, through the transfer fee.

The proposed approach can well analyze the channel conflicts and pricing problems between retailers and manufacturers with respect to product offline price and online price. The analysis and results can inform decision-making for manufacturers and retailers.

We investigate the extended service strategy choices of competing manufacturers and examine their impact on the retail platform.

We construct a supply chain model with a retail platform as the leader and manufacturers as the followers. Manufacturers face differential consumer preferences on the same agency retail platform, and they can sell a bundled extended service product and sell a separate product without any extended service.

The sale of extended warranty services on the retail platform leads to lower pricing of the manufacturers' products and changes in the product market structure in response to differences in consumer preferences. The retailing platform tends to provide an extended warranty conditionally. The sale of extended warranty services on a retail platform would be detrimental to the interests of the manufacturer who sells products with extended warranty services and in favor of the manufacturer who sells products without them.

The equilibrium results of the retail platform’s non-sales and sales of extended warranty services for the no-extended warranty product under the same commission rate and differential commission rate models are discussed, and the product structure of the market is investigated, respectively.

COVID-19 accelerated change within the UK retail market. It encouraged the growth of online shopping, providing the necessary demand for grocers to invest in their operations, and transformed the economics of their businesses. As innovative new business models emerged, some existing retailers collapsed leading to significant changes on the high street. Landlords were also affected. As some retail tenants struggled to pay their rents, other parts of the sector prospered and sought additional warehouse capacity to cope with rising demand. Not only does this illustrate how different parts of the retail sector faired during COVID-19, but it also demonstrates how the move online has resulted in the emergence of new opportunities.

This paper aims to investigate the impact of acquisition and organic growth on the operating efficiency and total factor productivity change of retailing networks.

The assessment uses low-frequency data of newly opened stores and acquired stores of a large supermarket (S/M) network in Athens, for a period (financial year 2014) where the network began to refocus on its organic growth after a two-year period of deep recession (financial years 2012–2013). To evaluate the performance effects of both strategies, the authors employ the innovative benchmarking tool of bootstrap data envelopment analysis (DEA) for measuring operational efficiency and the Malmquist productivity index DEA approach for measuring productivity change over time.

The short-run evidence indicates that compared to organic growth, acquisitions lead to lower operating efficiency. However, this difference gradually converges over time as acquired stores show a higher rate of productivity compared to newly opened stores. The authors interpret this as a result of the smooth integration of the acquired chain store into the organizational structure of the existing store network given their significant similarities in terms of products and customers.

The authors inform managers of store chains that during the process of organic growth, a general improvement in efficiency takes place while in the case of acquisitions, the required post-acquisition streamlining actions cause a short delay on the realization of efficiency gains. Therefore, managers should not take it for granted that acquisitions cause a long-term decrease in efficiency.

The study contributes to the literature on growth strategies and retailing performance in general, by offering new evidence regarding the comparative effect of the horizontal growth modes on the efficiency of store chains.

Addressing stigmatized identity threat cues customers experience, this study aimed to uncover the effects of frontline employees' ethnic and body-size diversity which lead to customer self-objectification and negative store attitude.

The researchers conducted two studies: Study 1 utilized a one-way ANOVA and a PROCESS mediation model to test the effect of Western beauty-ideal stereotypes on stigmatized identity threat and self-objectification. Study 2 utilized a 2 × 2 experimental design to examine the effects of body-size diversity and ethnic diversity on perceived warmth of sales associates and store attitude.

Results demonstrate that retail store environments which present Western beauty-ideal stereotypes among sales associates cultivate higher levels of stigmatized identity threat cues with their customers, which leads to self-objectification. Also, our findings demonstrated that a lack of ethnic and body-size diversity (i.e. thin and White) among sales associates decreased the perceived sales associate warmth and in turn lowered store attitude.

Participants of the first study were limited by the White participants. The implications highlight the integral need for retailers to hire sales associates who fall outside the parameters of typical Western beauty standards because today's customers desire inclusive brands who do not discriminate based on ethnicity, body size and other characteristics.

This research utilized social identity theory to uncover the effect of retail sales associate stereotype on customer perception of employees, which has not to our knowledge been previously studied.

The best possible way for brick-and-mortar retailers to maximise engagement with personalised shoppers is capitalising on intelligent insights. The retailer operates differently with diversified items and services, but influencing retail atmospheric on personalised shoppers, the perception remains the same across industries. Retail atmospherics stimuli such as design, smell and others create behavioural modifications. The purpose of this study is to explore the atmospheric effects on brick-and-mortar store performance and personalised shopper's behaviour using cognitive computing based in-store analytics in the context of emerging market.

The data are collected from 35 shoppers of a brick-and-mortar retailer through questionnaire survey and analysed using quantitative method.

The result of the analysis reveals month-on-month growth in footfall count (46%), conversation rate (21%), units per transaction (27%), average order value (23%), dwell time (11%), purchase intention (29%), emotional experience (40%) and a month-on-month decline in remorse (20%). The retailers need to focus on three control gates of shopper behaviour: entry, browsing and exit. Attention should be paid to the cognitive computing solution to judge the influence of retail atmospherics on store performance and behaviour of personalised shoppers. Retail atmospherics create the right experience for individual shoppers and forceful use of it has an adverse impact.

The paper focuses on strategic decisions of retailers, the tactical value of personalised shoppers and empirically identifies the retail atmospherics effect on brick-and-mortar store performance and personalised shopper behaviour.

The aim of the study presented in this paper is to investigate the interrelationships among technologies in retail, webrooming and showrooming purchase intentions and customer experience.

The study was conducted in the fashion industry in three fashion groups: LPP, CCC and Inditex, which declare to use omnichannel solutions in their supply chains. The study focusses on 825 customers drawn from the emerging market in Poland. The research follows the partial least squares path model procedure.

Based on the study, it was concluded that out-store technologies are positively associated with omnichannel purchase intentions and also positively associated with cognitive and affective customer experience. This study proves that the boundary between traditional and online stores is beginning to blur, and thanks to new technologies, customers can experience traditional shopping resembling online shopping, and vice-versa.

Although the results provide several major contributions to theory and implications for practitioners, the study still demonstrates some methodological constraints. More specifically, although the study employs a relatively large research sample of 825 shoppers, it still focusses only on a selected group of customers in three fashion groups, LPP, CCC and Inditex, and is limited to investigating a particular type of customer experience solely in the fashion industry.

The results of this study not only verify the theoretical concepts and assumptions of technologies supporting omnichannel retail but also offer a practical roadmap for creating omnichannel solutions providing the best customer experience.

This study was carried out to analyze the importance of consumer preference data in forecasting demand in apparel retailing.

To collect preference data, 729 hypothetical stock keeping units (SKU) were derived using a full factorial design, from a combination of six attributes and three levels each. From the hypothetical SKU's, 63 practical SKU's were selected for further analysis. Two hundred two responses were collected from a store intercept survey. Respondents' utility scores for all 63 SKUs were calculated using conjoint analysis. In estimating aggregate demand, to allow for consumer substitution and to make the SKU available when a consumer wishes to buy more than one item in the same SKU, top three highly preferred SKU's utility scores of each individual were selected and classified using a decision tree and was aggregated. A choice rule was modeled to include substitution; by applying this choice rule, aggregate demand was estimated.

The respondents' utility scores were calculated. The value of Kendall's tau is 0.88, the value of Pearson's R is 0.98 and internal predictive validity using Kendall's tau is 1.00, and this shows the high quality of data obtained. The proposed model was used to estimate the demand for 63 SKUs. The demand was estimated at 6.04 per cent for the SKU cotton, regular style, half sleeve, medium priced, private label. The proposed model for estimating demand using consumer preference data gave better estimates close to actual sales than expert opinion data. The Spearman's rank correlation between actual sales and consumer preference data is 0.338 and is significant at 5 per cent level. The Spearman's rank correlation between actual sales and expert opinion is −0.059, and there is no significant relation between expert opinion data and actual sales. Thus, consumer preference model proves to be better in estimating demand than expert opinion data.

There has been a considerable amount of work done in choice-based models. There is a lot of scope in working in deterministic models.

The proposed consumer preference-based demand estimation model can be beneficial to the apparel retailers in increasing their profit by reducing stock-out and overstocking situations. Though conjoint analysis is used in demand estimation in other industries, it is not used in apparel for demand estimations and can be greater use in its simplest form.

This research is the first one to model consumer preferences-based data to estimate demand in apparel. This research was practically tested in an apparel retail store. It is original.

The case is written based on publicly available data from primary sources such as the company’s annual reports, company website and the company’s presentations, as well as from secondary sources comprising newspaper articles, research papers, research magazines, magazine articles, industry reports, research reports, etc. as indicated in the references. The company’s financials and peer data are sourced from the Thomson Reuters Eikon database.

The case examines the financial position of Macy’s, Inc., America’s largest and one of the oldest premier departmental stores, with a consolidated annual turnover of US$18,097m in the fiscal year 2020/2021 (FY, 2021). Over the previous few years, the company had been struggling with decreasing market share and profitability mainly due to increasing competition from online retailers and deep discounters, which was affecting the company’s share price. With the appointment of a new chief executive officer (CEO) in fiscal year (FY) 2017, Macy’s, Inc. undertook several changes to revive its financial health and improve its market share. However, it still registered heavy losses of US$3,944m in the FY 2020/2021, the company’s first time in the past decade. With many retailers filing for bankruptcy, was there more that Macy’s could do to improve the company’s position and regain lost investor confidence? Will its entry into emerging markets play a crucial role in its turnaround?

The case can be used in undergraduate and postgraduate courses such as accounting for managers, financial statement analysis, management accounting, introduction to accounting and advanced financial statement analysis. The case can also be effectively used to understand the primary fundamental analysis of the company that involves understanding the company’s positioning and strengths, weaknesses, opportunities and threats analysis. The case would also help business management and entrepreneurship students to get a preliminary idea about the change management process. Finally, the case can be used to familiarize students with using Microsoft Excel to build financial analysis worksheets.

Teaching notes are available for educators only.

The case is written based on publicly available data from primary sources like the company’s annual reports and presentations and from secondary sources, as indicated in the references.

Barnes & Noble Inc. (B&N), one of the oldest and largest American retail booksellers founded in 1917, was facing a grim business situation underpinned by a fall in demand, a change in consumer preference and stiff competition. After almost a century of being in the business, B&N was experiencing a fall in market share and weak stock market performance. In 2019, the company was sold to Elliot Advisors – a hedge fund – for US$638m. With the appointment of new chief executive officer (CEO) James Daunt in August 2019, a man known for the turnaround of similar businesses, B&N expected its business’s revival and reorganization strategy to turn profitable. Its long-term strategy of beating competitors with its offerings’ sheer volume and low prices was no longer viable. The turmoil was compounded by top management crises with the repeated changes and ousting of several CEOs in a short span, alongside the COVID-19 pandemic and subsequent lockdowns in 2020 and 2021. Daunt was considering how to overcome the crisis and act fast to reposition the company and regain the loyalty of its customers. Was there more that the company could do to improve the company’s position and restore profitability?

The case can be used in strategic management and entrepreneurship classes at undergraduate and postgraduate levels. The case can be used in an investment analysis and management course to teach students the industry analysis technique using Porter’s five forces model.