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This study examined the reciprocal influence of demand learning and preference matching in the context of store brand customization. The demand-learning effect refers to the collection of market demand information through production, based on pre-order demands, enabling retailers to accurately predict and allocate product quantities, thus improving inventory management. The preference-matching effect involves engaging consumers in the production and design processes of store brands to align fully with their preferences, thereby increasing the purchase impact of store brand products and promoting consumption.

We employ game-theoretic models to analyze a two-echelon supply chain consisting of a manufacturer and a retailer. The retailer offers both national brands, manufactured by the supplier and in-house store brands. To enhance their competitive edge, the retailer can adopt a customized strategy targeting the store brand to attract a wider consumer base.

The analysis reveals that, under low commission fees, the manufacturer consistently opts for high production quantities, irrespective of the level of demand uncertainty. However, when the perceived value of a store brand is low and demand uncertainty is either low or high, the retailer should choose a minimal or zero production quantity. The decision-making process is influenced by the customization process, wherein the effects of demand learning and preference matching occasionally mutually reinforce each other. Specifically, when the perceived value of a store brand is low, or the product cost is high, along with high customization costs, the interplay between demand learning and preference matching becomes mutually inhibiting. Consequently, the significance of store brand customization diminishes.

This study enhances the current body of knowledge by providing a deeper understanding of the theoretical value of store brand customization. In addition, it offers valuable decision-making support to enterprises by assisting them in selecting appropriate inventory and customization strategies.

Online fashion retailers offer body measurement guidance, alongside their target consumer body size charts and fit information, to help consumers select the right size garment as the garment cannot be tried on. Its use by retailers suggests it can act as a means of mitigating garment returns as there has been a noted increase in returns due to incorrect size selection even though body measurement instructions are provided online. The purpose of this research is to determine how consumers interact with body measurement guidance, how they interpret it and use it, thereby interrogating its efficacy.

An exploratory investigation was undertaken. Thirty participants were recruited and given a choice of fashion retailers' body measurement guidance and a tape measure. They took their body measurements over their clothes. A technician repeated the task, thereby providing two sets of body measurements. A paired t-test determined if there was a significant difference in both values in terms of their mean. Technicians also documented their observations of how the participants interacted with the task. The content of the fashion retailer measurement guidance was then compared to that of published anthropometric guidance for surveys.

Participants were familiar with the guidance and tape-measure; they were able to self-measure. The fashion retailer measurement guidance, however, lacked detail/clarity in visuals and written content when compared to anthropometric guidance. Interpretation of the guidance differed between participant and technician. This resulted in a significant difference in circumference measurements for the bust/chest and hips, yet no significant difference in waist and inside-leg measurements. For measurements that were difficult to take unaided, participants devised novel practices which resulted in little divergence from the technician taken body measurements. The results question the guidance efficacy in its current form.

There has been no study which addresses how consumers interpret and interact with fashion retailers' online measurement guidance. This is important as this can mitigate garment returns. This research provides insight to influence fashion retailers' measurement guidance policy. It also adds to the existing body of knowledge surrounding anthropometric practice for clothing.

The findings indicated that fashion retailers need to revise their body measurement guidance content. The content needs to be more comprehensive but still use accessible language and visuals. The broader implications of this study highlight that traditional anthropometrics for self-taken body measurement needs developing as an emerging concept through clothing-related academic study.

Fashion retailers' measurement guidance needs to be more comprehensive but still use accessible language and visuals. The broader implications of this study highlight that traditional anthropometrics for self-taken body measurement needs further investigation and documentation as an emerging concept through clothing-related academic study.

With the advancement of digital transformation, it is important for e-retailers to use artificial intelligence (AI) for customer engagement (CE), as CE enables e-retail brands to succeed. Essentially, AI e-marketing (AIeMktg) is the use of AI technological approaches in e-marketing by blending customer data, and Retail 4.0 is the digitisation of the physical shopping experience. Therefore, in the era of Retail 4.0, this study investigates the factors influencing the use of AIeMktg for transforming CE.

The primary data were collected from 305 e-retailer customers, and the analysis was performed using a quantitative methodology.

The results reveal that AIeMktg has tremendous applications in Retail 4.0 for CE. First, it enables marketers to swiftly and responsibly use data to anticipate and predict customer demands and to provide relevant personalised messages and offers with location-based e-marketing. Second, through a continuous feedback loop, AIeMktg improves offerings by analysing and incorporating insights from a 360-degree view of CE.

The main contribution of this study is to provide theoretical underpinnings of CE, AIeMktg, factors influencing the use of AIeMktg, and customer commitment in the era of Retail 4.0. Subsequently, it builds and validates structural relationships among such theoretical underpinning variables in transforming CE with AIeMktg, which is important for customers to expect a different type of shopping experience across digital channels.

The current research has developed a novel method to update the decisions regarding real-time data, named the dynamic adjusted real-time decision-making (DARDEM), for updating the decisions of a grocery supply chain that avoids both frequent modifications of decisions and apathy. The DARDEM method is an integration of unsupervised machine learning and mathematical modeling. This study aims to propose a dynamic proposed a dynamic distribution structure and developed a bi-objective mixed-integer linear program to make distribution decisions along with supplier selection in the supply chain.

The constantly changing environment of the grocery supply chains shows the necessity for dynamic distribution systems. In addition, new disruptive technologies of Industry 4.0, such as the Internet of Things, provide real-time data availability. Under such conditions, updating decisions has a crucial impact on the continued success of the supply chains. Optimization models have traditionally relied on estimated average input parameters, making it challenging to incorporate real-time data into their framework.

The proposed dynamic distribution and DARDEM method are studied in an e-grocery supply chain to minimize the total cost and complexity of the supply chain simultaneously. The proposed dynamic structure outperforms traditional distribution structures in a grocery supply chain, particularly when there is higher demand dispersion. The study showed that the DARDEM solution, the online solution, achieved an average difference of 1.54% compared to the offline solution, the optimal solution obtained in the presence of complete information. Moreover, the proposed method reduced the number of changes in downstream and upstream decisions by 30.32% and 40%, respectively, compared to the shortsighted approach.

Introducing a dynamic distribution structure in the supply chain that can effectively manage the challenges posed by real-time demand data, providing a balance between distribution stability and flexibility. The research develops a bi-objective mixed-integer linear program to make distribution decisions and supplier selections in the supply chain simultaneously. This model helps minimize the total cost and complexity of the e-grocery supply chain, providing valuable insights into decision-making processes. Developing a novel method to determine the status of the supply chain and online decision-making in the supply chain based on real-time data, enhancing the adaptability of the system to changing conditions. Implementing and analyzing the proposed MILP model and the developed real-time decision-making method in a case study in a grocery supply chain.

This study identifies benefits and challenges of PL partnerships, and recommendations to improve the PL partnership process.

Qualitative data was collected via semi-structured interviews (n = 8) with Irish PL retail buyers (n = 4) and producers (n = 4). Data was coded and thematically analysed.

Three key themes were identified. Theme 1 provides an overview of the benefits of PL partnerships for producers (e.g. volume driven orders, increased efficiencies) and for retailers (e.g. unique products, meeting consumer demand). Theme 2 presents challenges of PL partnerships specific to small and large producers (e.g. small producers may need significant investment to upgrade facilities, while larger producers may require significant volume to justify adaptation of production lines). Challenges common to both (e.g. risks related to short-term contracts, concerns about brand identity) are also discussed. Theme 3 summarised recommendations for successful PL partnerships generally (e.g. setting clear expectations and goals, building rapport and trust), and recommendations specific to producers and buyers specifically (e.g. producers should diversify customers to reduce risk, and retailers should communicate needs and direction).

There is currently limited research on PL partnerships between producers and retailers. This study addresses this gap by identifying key aspects for producers to consider when entering PL partnerships and key aspects for retailers to be aware of to help improve the attractiveness and success of these partnerships.

The advanced technology enables retailers to develop customer profile analysis (CPA) to implement personalized pricing. However, considering the efficiency of developing CPA, the benefit to different retailers of implementing more precise personalized pricing remains unclear. Thus, this essay aimed to investigate the impact of efficiency on participants’ strategies and profits in the supply chain.

A two-stage game model was introduced in the presence of a manufacturer who sets his wholesale price and a retailer that decides her CPA strategy. The equilibrium results were generated by backward induction.

Most retailers are willing to develop the highest CPA to implement perfect personalized pricing, but those inefficient retailers with high production costs would like to determine a middle CPA to implement bounded personalized pricing. The retailers’ profits may decrease with the efficiency of developing CPA when the efficiency is middle. In this case, as the efficiency improves, the manufacturer increases the wholesale price, resulting in lower demand and thus lower profits. Moreover, define a Pareto Improvement (PI) strategy as one that benefits both manufacturers and retailers. Therefore, uniform pricing is a PI when the unit cost is high and the efficiency is low; personalized pricing is a PI when the unit cost is low and the efficiency is low or high; otherwise, there is no PI.

This study is the first that investigates how the retailer develops CPA to implement personalized pricing on a comprehensive spectrum, which can provide practical insights for retailers with different efficiencies.

The present study considers how clothing and shoe retailers in New Zealand, Portugal and Hungary manage promotion campaigns and looks at the objectives that are most important to these retailers as well as the marketing activities that are undertaken to reach these objectives. Change‐of‐season sales are found to be the most frequently used sales type by the retailers studied and these sales are linked with objectives of moving a volume of stock and activities such as co‐ordination of media across all forms. Secondary sales types include Christmas and general sales, and these are linked with other promotional objectives and activities such as increasing profit and dollar sales, and stock management.

Explores the future development of the “big five” UK grocery retailers and their response to the challenges of the next decade. Presents the results of a survey of manufacturers, analysts and retailers, in which saturation and internationalization are the key themes. Recounts how UK grocery retailers have not been in the forefront of international retail activity – both non‐food UK retailers, and continental European food retailers, have established stronger international profiles. Suggests that the prospect of a saturated UK grocery market faces the “big five”, and addresses the question: when will opportunities in the UK become marginal, and how will retailers react to that prospect?

Suggests that gaps exist between the product ranges or lines offered by manufacturers and the assortments selected and stocked by retailers. Looks at the extent to which differing levels of “product volatility” affect retailers’ selectivity in stocking items from a manufacturer’s line. Provides a limited test of several hypotheses about how the degree of product volatility of the category within which a manufacturer’s line belongs might affect the number of items in the line that will be stocked by a retailer. Analysis of stock‐planning data for two retailers in each of two product categories offers some support for the hypotheses. Interprets these results in light of theories of distribution channel co‐ordination and retailer expertise. They may reflect an alternative explanation for widely observed increases in retailer power.

EFTPoS (electronic fund transfer at point of sale) seen as a potential development in the 1970s, increasingly became a realistic proposition during the 1980s. In 1985, Eftpos UK Ltd, a joint endeavour by banks and building societies, was established to develop a unified clearing system. In advance of national introduction, three cities, Edinburgh, Leeds and Southampton, were chosen to pilot the Eftpos UK Ltd system. These pilot schemes began in late 1989. This article presents survey findings from the Edinburgh pilot scheme. Retailer response was sought before and after the trial to ascertain changing perceptions. The results throw an interesting light on the evolving attitudes of retailers to the system and indicates areas where future marketing of EFTPoS systems should be modified.