Retail Chains Research Articles (Page 1)

With the rapid growth of the “Internet + Healthcare” model, pharmaceutical e-commerce has swiftly gained a substantial market share in China’s pharmaceutical retail sector, thanks to its competitive prices, convenient purchasing channels, and online consultation services. This has put considerable pressure on traditional retail chain pharmacies. This paper provides an overview of the current state of pharmaceutical e-commerce, and analyzes the main challenges faced by retail chain pharmacies, including intense price competition, changes in consumer habits, insufficient digitalization and severe homogenized competition. Drawing on industry practices and case studies, the paper proposes strategies to enhance competitiveness, such as promoting digital transformation and online-offline (O2O) integration, strengthening pharmacist services and health management capabilities, optimizing supply chain management, building differentiated brand advantages, and proactively responding to policy and regulatory requirements. The study argues that, while leveraging their advantages in specialization, community-based operations, service orientation and retail chain pharmacies, should actively embrace digitalization and industrial collaboration in order to achieve sustainable development in the context of e-commerce. This research provides theoretical references for the strategic transformation of retail chain pharmacies and offers practical significance for the high-quality development of the pharmaceutical retail industry.

This paper examines the role of advertising spending in shaping brand loyalty at FPT Shop, a leading retail chain in Vietnam. The study analyzes how advertising expenditure functions as an external stimulus that influences consumer perceptions using the Stimulus-Organism-Response model and signaling theory. Primary data were collected from 1,036 respondents across 12 FPT Shop outlets. Several data analysis techniques were executed to validate the measurement scales and test the proposed model. The results indicate that advertising spending has a significant direct positive effect on brand loyalty, while its impact is also mediated by improvements in store image and customer satisfaction. The mediating effect of perceived quality was observed to be relatively modest, its contribution to the overall influence of advertising on loyalty is acknowledged. These findings provide valuable insights for retail practitioners seeking to optimize advertising strategies, enhance customer perceptions, and strengthen brand loyalty in a competitive market environment.

Recent research in psychology, management, and more recently in economics, highlights the role of individual managers and their behavior in shaping employee performance. While emerging literature on harmful managerial behavior has focused primarily on severe forms of workplace mistreatment, especially various types of harassment, much less is known about its boundary conditions: How minor can a manager's bad behavior be and still negatively affect employee performance? We study what appears to be a very minor workplace mistreatment-failing to deliver an expected birthday gift and greeting card on time-and examine its effect on subsequent employee performance. Using a dynamic difference-in-differences approach with detailed data from a national retail chain, we find that this small slight leads to over a 50% increase in employee absenteeism and a reduction of more than two working hours per month. Our analysis suggests that emotional responses to perceived workplace mistreatment drive the results. These findings indicate that even modest slights can meaningfully harm employee performance.

The article examines the transformation of digital marketing communications in retail as an instrument for increasing business resilience to demand turbulence, supply disruptions, and rising costs. The purpose of the study is to provide a theoretical justification and operationalization of adaptive marketing strategies for retail chains through the integration of demand, availability, and price data into a single management loop, as well as to validate the resulting propositions using an applied case empirically. Relevance is framed by increasing frequency of market shocks and limitations; therefore, moving to real-time campaign management from traditional media plans. Methodology: Findings synthesis using a systematic literature review for 2022-2025, followed by validation on the City Capital Group case. Scientific novelty: Integrated holistic Sense–Decide–Act architecture for marketing communications. The contribution is in bringing out that signal-to-action time is central in measuring the effectiveness of any adaptive communication system. Key results: It is established here that a move to real-time campaigns eliminates between 10 and 20 percent of inefficient spend per week, with budgets reallocated to highly profitable microsegments, adding between 5 and 10 percent organic profit growth. Internal communications-enabled analytics integration reduces median response time below two-and-a-half hours and increases pipeline advance by 12 percent a quarter. The rising use of AI agents demands a product catalog and open APIs, which lowers both the cost of data exchange and the returns, too. Applied case shows that orchestrated Sense–Decide–Act increases tenant traffic and turnover due to budget flexibility and ability to scale successful initiatives fast; speed in closing the feedback loop forms a competitive advantage. The article will be helpful to researchers and practitioners of digital marketing, executives of retail chains, pricing and supply chain specialists, product and catalog managers, as well as data teams and ML engineers.

Variability of temperature exposure of dairy products during refrigerated e-commerce distribution and its impact on dairy quality.

Untapped Opportunities: The Unique Value Proposition of Women of Color Pharmacists.

The retail industry faces significant challenges in managing inventory across multiple nodes. Predictive modeling techniques have emerged as critical tools for optimizing inventory levels, reducing costs, and improving service levels. This study explores various predictive modeling techniques, including time series analysis, machine learning algorithms, and statistical forecasting methods. Through a comprehensive literature review and statistical analysis, this research identifies the most effective modeling techniques for multi-node inventory management in retail chains. The results indicate that advanced machine learning methods outperform traditional approaches, leading to enhanced inventory optimization and decision-making.

In recent times, the retail chain industry in Kenya has experienced challenges, leading to the closure of large retail outlets. After COVID-19, there was a re-emergence of retail supermarkets. This forms the motivation of the study. The study sought to investigate the relationship between supplier relationship management practice and the performance of large supermarkets in Nairobi Metropolitan. The study was grounded in Resource-Based Theory. The target population was 135 employees, consisting of managers, operations supervisors, stock coordinators, loss branch stock controllers, and transport and logistics supervisors of the selected large supermarkets. Primary data was obtained through a structured questionnaire. Descriptive and inferential statistical techniques were applied, with simple linear regression analysis used to predict the relationships between supplier relationship management practice and supermarket performance. The data was analyzed through SPSS and presented using tables. The study found that supplier relationship management (β =0.576, p value =0.00) has a significant and positive relationship with the performance of large supermarkets in Nairobi Metropolitan. The study recommends that the supermarkets review and strengthen their quality control mechanisms and regular performance reviews to ensure consistent delivery and product standards. Supermarkets should also standardize supplier evaluation procedures to improve reliability and accountability across all suppliers.

The retail sector is the most demand-sensitive echelon in the supply chain, where non-moving items accumulate and become dead stock. Existing inventory management studies focus on fast-moving products and income generation. This paper focuses on dead stock management and proposes a mixed strategy solution using a pure integer non-linear programming model that minimizes the dead stock management cost of a retail chain operator. The number of products and volume of product-related data in a retail chain system require big data analysis to ensure sustainable inventory practices that reduce waste generated from dead stock inventory. Through hypothetical data sets, the 3-store, 10-product run showed that discount percentage, expected sales success probability of a product in a store location, and disposition of unsold products were the main drivers of the decisions made by the model. The most significant cost contributors arising from these decisions were the unrecovered product cost (UPC), disposed product cost (PC), and salvage value from the successful sale of dead stock. Inventory managers must balance the effect on these cost components when they choose the strategies to use in managing dead stock.

This article examines the concept, functions, and principles of own labels, their advantages, the legal framework, and trends in their development and improvement. The main trends in improving own labels in retail chains are identified as follows: reducing the list of parent and family brands while simultaneously increasing the number of own label sub-brands and the number of products sold under own label, as well as increasing the share of store label products in the product range structure and turnover structure, growing demand for own label products in the premium price segment and goods produced in accordance with environmentally responsible principles.

A diet low in fermentable carbohydrates, oligosaccharides, disaccharides, monosaccharides and polyols (FODMAP) has been described as an effective nutritional approach in irritable bowel syndrome. There has been an increased demand for gluten- and lactose-free foodstuffs in the last few years, which are associated with gastrointestinal symptoms and are consumed by patients with gastrointestinal disease. This study aims to estimate the FODMAP content of industrial pre-packaged food products containing the ‘gluten-free’ and ‘lactose-free’ claims. The ingredient lists of the foodstuffs from a Portuguese food retail chain were verified and classified according to their estimated FODMAP content as ‘low’ and ‘high’, using as a reference the FODMAP cutoff values and the serving sizes established by the Monash University App. Descriptive statistics and the Pearson χ2 test were applied. From the 436 eligible products identified, most were classified as ‘low’ (53·0 %, n 231), 24·3 % (n 106) were classified as ‘high’ and 22·7 % (n 99) were classified as having ‘unknown’ FODMAP content. ‘High FODMAP’ products accounted for 12·2 % of those with ‘lactose-free’ claims and 31·6 % of those with ‘gluten-free’ claims. The ‘ready meals’ and ‘sauces, dressings, creams and soups’ were the food categories with the highest proportion of products with high FODMAP sources. This study showed that approximately a quarter of pre-packaged industrial foods targeted to gastrointestinal pathologies are high in FODMAP. The nutritional information on the label should be more specific, enabling more accurate dosing of FODMAP contents in foodstuffs and the establishment of the recommended serving sizes.

Introduction & BackgroundPain is a global threat to well-being and workplace productivity, yet current estimates of its prevalence vary greatly between studies. This is partly due to a lack of consistency in survey items and reliance on self-reported methods alone. Additionally, although pain can lead to absence from work, individuals are usually not excluded from workplace entirely, instead they might prefer to work despite a reduction in productivity, which further makes economic outcomes difficult to measure. In this paper, we propose an innovative approach to measure pain by harnessing large-scale shopping data and predict workplace attendance, providing evidence at two levels. Objectives & ApproachFirst and foremost, a key objective is to measure pain from self-medication behaviours. Self-medication is a common practice, with pain being a key motivator. While self-medication behaviours have been traditionally difficult to accurately examine, the emergence of digital trace data has opened new avenues. In Study 1 (regional-level evidence), via data partnership, we utilised shopping records obtained from a major retailer chain (20,500,952 customers, 2014-2015) and computed metrics to represent the prevalence of pain in each local authority district (LAD). Specifically, we calculated the proportion of customers who purchased painkiller products at least 6 times in each LAD. In the statistical models, we used labour statistics as outcomes for workplace attendance (e.g., average working hours and proportions of individuals working part-time in each LAD), controlling for median income and education levels. In Study 2 (individual-level evidence), with a data donation approach, we asked consenting 828 participants to donate their shopping history (2015-2024) with us via a survey and computed metrics to capture the presence of pain conditions at individual levels (e.g., the proportion of transactions with painkillers). Participants also reported their employment statuses, which we used as outcomes in statistical models, controlling for age, gender, and caring responsibilities. Relevance to Digital FootprintsWith two sets of novel digital footprints data, we inferred health conditions from shopping history and provided insights into the associations between pain and workplace productivity, a link that is traditionally difficult to accurately examine. ResultsIn Study 1, we found strong evidence that regions with more individuals suffering from pain were associated with shorter working hours and higher proportion of individuals working part-time. In Study 2, we found strong evidence that individuals who purchased proportionally more painkillers were less likely to work full-time, and also more likely to be restricted in their workplace attendance. Conclusions & ImplicationsThis paper investigates the impact of pain on workplace attendance and offers key insights into the future of health data collection and research, as well as providing the foundation for linking shopping patterns to pain conditions.

Abstract In several markets, retailers consider volume (size) as a crucial factor for competitiveness; this is driven, for example, by volume rebates from suppliers or technological economies of scale. This indicates that the retailers face decreasing marginal costs. We show that such economies of scale may imply that prices become strategic substitutes and that this has profound effects on firms’ behavior. A retail chain that controls several stores in the same market may prefer decentralized pricing rather than centralized pricing. This preference arises because a multi-store chain that employs centralized pricing essentially commits to setting higher prices than it otherwise would. If prices are strategic substitutes, the rivals of a multi-store chain would then, to the detriment of the chain stores, respond by reducing their prices. If this effect is sufficiently strong, centralized pricing will be unprofitable for a multi-store chain.

Purpose This study examines the role of brick-and-mortar retail store attributes (SA) on consumers' brand loyalty and perceived value (PV), which consequently influence repurchase and word-of-mouth (WOM) behavior. Design/methodology/approach Data were collected through a structured survey administered to 374 shoppers at leading physical retail chains. The relationships among key constructs were analyzed using partial least squares-structural equation modeling (PLS-SEM) in SmartPLS, enabling assessment of both direct and mediating effects in the proposed model. Findings Findings revealed that structural equation modeling indicated that store-related attributes had the strongest effect on brand loyalty and PV. Both mediators, in turn, enhanced repurchase and WOM intentions. Subgroup analysis highlighted age-based differences in these pathways. Practical implications Retail managers can boost customer loyalty and PV by enhancing retail SA. Investing in visual merchandising, staff training, competitive pricing and omnichannel integration leads to higher satisfaction, positive WOM and increased repurchase intentions (RI). Prioritizing these strategies helps retailers build loyal customers and drive business growth. Originality/value This study provides unique insights into shopper expectations of SA that may be used by global retailers to create consumer brand loyalty and PV and build thriving businesses.

Minimum unit pricing (MUP), which involves setting a floor price for alcoholic beverages based on their alcohol content, has been shown to reduce alcohol-related harms. This study identified alcoholic beverages in Western Australia that may be affected by three minimum unit price thresholds. In September 2024, we web-scraped product information for 16,645 alcoholic beverages listed on the online stores of two major alcohol retail chains in Perth, Australia. We identified the proportion of products that were priced below three floor price thresholds ($1.30, $1.50 and $1.75 per standard drink), both overall and across six alcohol categories. We further identified the proportion of all products with a promoted price (e.g., 'was $10, now $8') that fell below each threshold. A small proportion of alcohol products were priced below the $1.30, $1.50 and $1.75 floor price thresholds (4%, 5% and 7%, respectively). The majority of cask wine products were priced below each threshold (86%, 88% and 89%, respectively), whereas only a small fraction of cider (9%, 12% and 28%), bottled wine (6%, 8% and 12%), beer (1%, 1% and 4%), spirits (0%, 0% and 1%) and premix drinks (0% for each) fell below these levels. Few products were promoted at a price below the corresponding thresholds (3%, 4% and 5%). Most alcohol prices would be unaffected by MUP thresholds ranging from $1.30 to $1.75. Communicating this finding could increase the general acceptability of the policy.

Sustainability concerns and rising consumer environmental awareness (CEA) have fundamentally reshaped competitive dynamics in modern supply chains. This study examines the influence of CEA on pricing and environmental effort competition between store brand (SB) and national brand (NB) products in a two-stage supply chain with one manufacturer and one retailer. We develop a mathematical model to evaluate strategic interactions under three power structures: Manufacturer Stackelberg (MS), Retailer Stackelberg (RS), and Vertical Nash (VN), considering two environmental investment scenarios: NB-only investment and bilateral SB-NB investment. Our findings indicate that (i) when only NB products invest environmentally, CEA increases environmental effort levels, wholesale prices, and retail prices for both brands, expanding total channel value rather than merely redistributing profits; (ii) CEA and channel competition on jointly determine optimal channel power structure, with MS dominating in differentiated markets with low CEA while RS yields superior outcomes under high competition and high CEA; (iii) retailers consistently achieve maximum profits under VN structure through balanced negotiation positions; and (iv) bilateral environmental investment causes price convergence across structures, shifting competitive focus from governance to operational excellence. By integrating environmental investment, channel power structure, and channel competition into a unified framework, this study offers managers practical decision tools for selecting optimal channel structures based on observable market conditions. Furthermore, it demonstrates how grocery retail chains and consumer goods manufacturers can transform environmental initiatives from compliance costs into value creation mechanisms that enhance both profitability and sustainability.

Subject. The business process of retail chains (retailers) is studied based on the allocation of retail space in the form of a special complex resource considered as a unified store, which includes, along with the retail space, a standard set of retail equipment. A business process model of a chain retailer is formed, describing the function of trade revenue from such resources as the purchase price of goods sold, personnel employed in the retail chain and unified stores. Based on the business process model, methods have been developed for assessing the demand of chain retailers for commercial real estate in the form of the dependence of the number of unified stores on the planned values of revenue and business resources. Objectives. Development of an economic and mathematical model for determining the demand of retailers for commercial real estate provided by developers, and a mechanism for planning the development of a network of retail outlets. Methodology. Mathematical modeling of business processes using production functions and processing of statistical data using regression analysis. Results. A model of the business process of a network retailer has been developed in the form of the Solow production function. A methodology for assessing the technological efficiency of the business process of a network retailer has been proposed, which allows judging the degree of return on the resources used based on the coefficients of the retailer's business process model. A mechanism for calculating the target value of unified stores has been designed based on the planned values ??of revenue, cost of goods turnover and the number of employees. Numerical experiments based on data from the retail chains X5 Group, Magnit and Lenta have been conducted, demonstrating the adequacy of the proposed developments. Conclusions. The function of the retailer's trade revenue from key resources (purchase cost of goods, retail chain personnel and unified stores) has shown a power-law nature. The mechanism for planning the development of the retailer's network of outlets has demonstrated efficiency and sufficient accuracy.

The precise maturity quality classification of guava is crucial at farm level, retail, storage, and supply chain. The manual classification causes substantial postharvest losses, which increases the demand for material and resources. Therefore, the present study proposed an automated, precise, and accurate model for quality classification according to the maturity stages (Green, Mature Green, Ripe) of three varieties (Local Sindhi, Riyali, Thadhrami) of guava. The study aimed to develop a precise, accurate and automated model for the classification of guava according to their maturity stages. The guava images were used to extract color, shape, and texture features. Analysis of Variance (ANOVA) was used for the selection of important features. The six different machine learning (ML) classifiers; Artificial Neural Network (ANN), k-Nearest Neighbor (KNN), Support Vector Machine (SVM), Cubic SVM, Quadratic SVM, and Random Forest (RF) were used to find out the best classifier for maturity classification. Among the proposed classifiers, the RF classifier was found to be the best classifier for all three varieties of guava. The Quadratic SVM classifier showed the lowest classification accuracy. The study concluded that RF classifier was found to be a robust model for the maturity classification of guava.

Inuit have long utilized trail networks for subsistence harvest. Fueled by climate change, increasingly volatile environmental and weather conditions in the Canadian Arctic territory of Nunavut have made these routes less reliable and more dangerous-jeopardizing availability of traditional foods. Qualitative research indicates communities adapt by grocery shopping. We modeled consecutive days of trail inaccessibility on total grocery and meat product sales, respectively, of a market-dominant retailer in 13 Nunavut communities. Although we hypothesized positive associations between trail inaccessibility and store purchasing, we observed negligible negative associations; meanwhile, socioeconomic factors like pay dates yielded strong, positive associations. In light of the null findings with respect to trail inaccessibility, we discuss key limitations of our approach and potential alternative explanations that might account for these unexpected findings, including the ecological level of analysis potentially masking subgroup vulnerabilities relative to exposure or outcomes, hunters' possible risk tolerance elasticities, and food sourced beyond our partner retailer (such as from the other major retail chain or through food sharing networks). Given the local nutrition and economic transitions-away from traditional food and subsistence livelihoods-communities may face reduced day-to-day vulnerability to trail accessibility disruptions. As the wage-based economy expands and the contemporary diet includes more energy-dense, processed store-bought food, Inuit communities may become increasingly sensitive to macro-political and economic pressures on their food system and sovereignty.

Purpose: This study sought to establish the relationship between electronic communication and supply chain performance in Large Retail Chains in Kenya. The study was guided by disruptive innovation theory. Methodology: The study adopted descriptive research design and positivist research paradigm. The study targeted 12 Large Retail Chains in Kenya. The target population for this study was 240 respondents. Census method was used in this study. The study used research questionnaires to collect primary data. Qualitative data was analyzed using content analysis and presented in prose form. Qualitative data was analyzed using descriptive and inferential analysis. Descriptive statistics such as frequency, percentages, and means were used. Pearson correlation coefficient was used for testing strength and direction between the independent and the dependent variables. A multiple regression model was used to test the significance of the relationship between the independent variables and the dependent variable. Findings: The study findings revealed that electronic communication has a positive and significant effect on supply chain performance of Large Retail Chains in Kenya. The study concludes that electronic communication has a positive and significant effect on supply chain performance of Large Retail Chains in Kenya. Unique Contribution to Theory, Policy, and Practice: Based on the findings, this study recommends that the management of large retail chains in Kenya should implement integrated Electronic Data Interchange (EDI) systems to enhance communication across their supply chains. The study contributes to theory by showing electronic communication as a strategic resource for supply chain performance, informs policy by highlighting the need for supportive frameworks to enhance digital integration, and guides practice by urging retail managers to adopt EDI systems for greater efficiency and competitiveness.