Major Retail Research Articles

Artificial intelligence-driven music biometrics influencing customers’ retail buying behavior

This study examines the digital transformation effects of artificial intelligence (AI)-based facial and music biometrics on customers’ cognitive and emotional states, and how these effects influence their behavioral responses in terms of value creation. Using a real-life, major optical retail store in China, 386 customers participated in a five-day experiment with different types of music (enhanced by music-recognition biometrics). The findings show that for utilitarian-type customers in a high-involvement AI purchase condition, music-recognition biometric-induced emotion mediates cognition and behavioral intentions. Both likability and the tempo of the music affect the impact of music on cognition. This study contributes to a better understanding of the relationship between cognition and emotion induced by AI-based facial and music biometric systems in shaping customer behavior and it adds to the atmospheric literature. This is a significant contribution given the paucity of research in the context of the Chinese retail environment, which is now a significant retail market with global importance.

Analysis of sentiment expressions for user-centered design

Devising intelligent systems capable of identifying the idiosyncratic needs of users at scale and translating them into attribute-level design feedback and recommendations is a key prerequisite for successful user-centered design processes. Recent studies show that 49% of design firms lack systems and tools for monitoring external platforms, and only 8% have adopted digital, data-driven approaches for new product development despite acknowledging them as a high priority. The state-of-the-art attribute-level sentiment analysis approaches based on deep learning have achieved promising results; however, these methods pose strict preconditions, require manually labeled data for training and pre-defined attributes by experts, and only classify sentiments intro predefined categories which have limited implications for designers. This article develops a rule-based methodology for extracting and analyzing the sentiment expressions of users on a large scale, from myriad reviews available on social media and e-commerce platforms. The methodology further advances current unsupervised attribute-level sentiment analysis approaches by enabling efficient identification and mapping of sentiment expressions of individual users onto their respective attributes. Experiments on a large dataset scraped from a major e-commerce retail store for apparel and indicate 74.3%–93.8% precision in extracting attribute-level sentiment expressions of users and demonstrate the feasibility and potentials of the developed methodology for large-scale need finding from user reviews.

The effect of nWOM firestorms on South African retail banking

PurposeThis study analysed the effect of online negative word-of-mouth (nWOM) firestorms in the retail banking sector. By understanding negative sentiment and sentiment recovery across an entire retail banking sector, the research exposed a unique view of banking in South Africa.Design/methodology/approachThe study made use of both a sentiment and topic analysis of over 1.7 million social media posts in South Africa. The methodology made use of both NLP and human validation techniques to measure changes in social media sentiment during online firestorms. This measurement included each of South Africa's major retail banks over a twelve month period.FindingsFrom the analysis, key trigger characteristics for these firestorms (product failures, service failures, social failures and communication failures) were categorised. In addition, the average duration of a firestorm was calculated and factors that impact sentiment recovery were explored.Originality/valueThe study was located in South Africa and, unlike firm level studies, researched nWOM for the whole retail banking sector. A theoretical footprint depicting the typical anatomy of a firestorm was derived in order to aid stakeholders to be more vigilant and better equipped to provide correct intervention in such times of crisis.

The Future of Formal Retail in Sub-Saharan Africa: Brief Review and Opinion

As Sub-Saharan Africa economies boomed in the early 90s, formal retail trade grew as well. However, most of the Sub-Saharan economies’ wealth depends on commodity price performance which in turn has impeded the growth of retail sector. The focus on Africa from the major retailers’ perspective has always been short-term. As the challenges continue eroding opportunities, many are re-thinking whether there is hope in the long-term. While there is no heterogeneity, as each country is at a different stage in retail development, challenges of inflation, forex shortages, poor infrastructure development, policy shifts, and general government inefficiencies are not uncommon. Almost 90% of informal trade makes up the retail sector in Sub-Saharan Africa. The informal retail growth, riding on the political support of the governments, is crowding out the formalized retail growth. This work explores the body of existing knowledge on formalized retailing in Sub-Saharan Africa and focuses on how retailing has evolved in Sub-Saharan countries of Nigeria, Angola, South Africa, Zambia, and Malawi. The paper concludes by identifying the retail growth gaps from retail chains and the governments in the Sub-Saharan region. Recommendations for sustainability are highlighted. Future research gaps are equally identified to advance the understanding of formalized retail importance, challenges, and prospects for Sub-Saharan Africa.

Notes: Social media firestorms and the protection of smaller enterprises: Ubuntu Baba and Woolworths

In this note we explore the use of social media as a tool to help small enterprises exert pressure on large corporations. Specifically, we use the case study of a small South African business (Ubuntu Baba) that exerted a powerful non-legal sanction on major retailer Woolworths through social media. This entrepreneur-initiated social media firestorm led to victory in the court of public opinion and a quick settlement. This low-cost option was possibly chosen in the face of the costs and uncertainties of more conventional legal recourse. Small businesses are an important component of the South African government’s strategy for economic development and employment creation, yet the existing laws protecting weaker parties often leave small businesses exposed to corporate power-play. This form of corporate power imbalance is a core theme underlying our case study.

Reciprocity within major retail purchase channels and their effects on overall, offline and online loyalty

Although online stores drive offline stores and vice versa, knowledge of these reciprocal effects is still scarce. The authors analyze whether reciprocal effects between images of major purchase channels exist and how they affect consumers’ loyalty. Importantly, they differentiate cross-channel loyalty. Study 1 analyzes longitudinal data from 573 consumer evaluations of leading omni-channel fashion retailers using cross-lagged panel models. The results indicate that a positive offline channel image enhances the online channel image and vice versa. The reciprocal effects of offline (vs. online) images are stronger on overall retailer, offline channel and, surprisingly, online channel loyalty. Moreover, the effects change for retailers with which consumers have more or less favorable prior offline and online experiences. Study 2 tests identical relationships based on 380 consumer evaluations and reveals possible shortcomings of a typical cross-sectional design. The findings have direct implications for managers interested in understanding how reciprocity and prior experience affect consumer behavior.

The impact of the COVID-19 pandemic on retailer performance: empirical evidence from India

The study aims to synthesize the challenges that retailers are facing during the COVID-19 emergency. The research is definitive, informative, and based on a single design of cross-sectional research. Quantitative data based on the research instrument were produced (a questionnaire). Five hundred responses were collected from employees of major retail stores in India. Retailer performance is considered a dependent variable, whereas employee well-being, customer and brand protection, use of technology, government policies, and supply chain are used as independent variables. The current study results indicated that employee well-being and government policies have a significant positive impact on retailer performance, while customer and brand protection, use of technology, and supply chain have a significant positive impact on retailers’ performance. This study will help retailers develop strategies for their employees to protect them and understand that technology is needed in the new normal times. This study highlights the need to be flexible in executing strategic strategies, but retailers need to develop comprehensive action plans, including selecting managers of initiative and defining goals and deadlines. Provided that retailers’ current reality is different from the old normal, no time is lost in taking audacious action.

Analysis of Food Labels to Evaluate the Nutritional Quality of Bread Products and Substitutes Sold in Italy: Results from the Food Labelling of Italian Products (FLIP) Study.

Bread is one of the most common staple foods, despite the increasing consumption of the so-called “bread substitutes”. The aim of the present work is to survey the nutritional quality intended as a nutrition declaration of 339 pre-packed bread products and 1020 bread substitutes sold in the major retailers present on the Italian market. Comparisons of energy, macronutrient, and salt content within product types, and between regular and gluten-free (GF) products and products with or without nutrition claim (NC) and health claim (HC) declarations, were performed. A high inter-product variability was detected. The median energy contents were 274 (interquartile range 255–289) and 412 (380–437) kcal/100 for bread products and substitutes, respectively. Irrespective of the category, GF products had lower amounts of energy than their gluten-containing counterpart (p < 0.001), whereas products carrying NC had lower energy, sugar and salt amounts than the products without these declarations on the pack (p < 0.001 for all). A strong positive correlation was observed between energy and carbohydrate in bread (rho = 0.73, p < 0.001), but not in substitutes (rho = 0.033, p = 0.29). The present work highlighted a high variability in the apparent nutritional quality of bread products and substitutes sold on the Italian market, and suggested that bread alternatives should not be considered tout court as substitutes from a nutritional point of view.

Don't Waste that Free Lettuce! Impact of BOGOF Promotions on Waste

Buy-One-Get-One-Free (BOGOF), a ubiquitous sales promotion scheme by which one can get two items for the price of one, encourages consumers to buy and waste food. Motivated by the initiative of a major retailer, we consider an alternative promotional scheme where consumers receive a coupon for a second item available for free at a later time; a move which was promoted as an attempt to curb waste. We develop a two-period Newsvendor model for the sale of a perishable product under the two promotional schemes using an aggregate demand formulation. We obtain analytical comparisons of order quantities, profits and waste and use numerical experiments to generate further insights. We find that delaying when consumers receive the free item often leads to a win-win proposition: the retailer makes more profit and the fraction of product wasted by both the retailer and the consumers is reduced. Yet, interestingly, we find that for high gross margin products, the traditional implementation of BOGOF can be also be a win-win strategy compared to not running a promotion and compared to delaying the free item. We also find that the BOGOF promotion scheme can actually be an effective waste-reduction strategy for seasonal products. Overall our results paint a more favorable picture of this promotional scheme than commonly perceived and suggest that its environmental criticism emanates from a too narrow and too consumer-centric view of the waste problem.

Allergen Content of Best-Selling Ethnic Versus Nonethnic Shampoos, Conditioners, and Styling Products.

Hair products are a potential cause of allergic contact dermatitis. There are limited data on the allergen content of ethnic hair products. To identify allergens unique to ethnic hair products (shampoos, conditioners, styling products) and provide a resource for low allergen hair care products for patients with ethnic hair types. The top 100 best-selling shampoos, conditioners, and styling products for ethnic and nonethnic hair products were determined from 3 major online retailers (Walmart, Target, Walgreens). Allergen was defined as presence on the 2017 American Contact Dermatitis Society Core 80 allergen list. The 2017 American Contact Dermatitis Society Core 80 allergens were tabulated for ethnic and nonethnic shampoos, conditioners, and styling products. A list of low-allergen shampoos, conditioners, and styling products was identified. Fragrance was the most common allergen for ethnic shampoos, conditioners, and styling products. Other notable allergens included methylchloroisothiazolinone/methylisothiazolinone, formaldehyde releasers, cetyl steryl alcohol, tocopherol, decyl glucoside, sodium benzoate, and phenoxyethanol. This study identifies important differences in allergens found in products marketed for ethnic hair compared with those marketed for nonethnic hair.

Pathway Analysis to Determine Factors Contributing to Overall Quality Scores in Four Berry Crops

Abstract Fruit quality attributes are influenced by environmental, agronomic and genetic factors; both cultivars and growing conditions can vary substantially between UK production and imported fruit. This study aimed to record and dissect the most relevant fruit quality traits for berries imported into the UK in the winter months. Blackberry, blueberry, raspberry and strawberry fruit were imported from 11 countries into a Kent-based packhouse (UK) or purchased from major retailers between December 2018 and March 2019. Multiple fruit quality components were assessed for relative contribution towards a high “overall assessment” fruit quality score. It was found that strawberry and blackberry overall scores were affected by sweetness perception, whereas blueberry and raspberry organoleptics are more complex, with overall scores influenced by flavor perception. Multiple raspberry and strawberry fruit quality traits were found to be associated with genotypic differences, indicating a promising potential for genetic improvement through breeding. By contrast, the study findings suggest that there is less potential for genetic improvement in blueberry, and that the environment may have a large effect on blueberry fruit quality traits.

Financial Analysis of Major Retail Chains within a Turbulent Economic Environment

Abstract The Greek economic activity has been declined dramatically in recent years as many sectors of the economy that cannot cope with the new state brought about by the economic crisis. An economic crisis can be described as the phenomenon when an economy is characterized by a continuous and noticeable decline in its economic activity. As an economic activity, we are referring to all the macroeconomic dimensions of the economy, such as employment, the national product, prices, investments, etc. The downturn in the supermarket sector, which is attributed to the country’s financial situation, has led to a decline in the household disposable income. The present study was carried out at the time of the economic crisis, concerning a productive sector of the Greek economy, where millions of euros are exchanged hands. The objective of the study was to explore the impacts of the economic crisis on the sector by using a series of financial indicators. The results point to significant effects on the efficiency and earnings of the major actors of the sector.

Research and development in robotics with potential to automate handling of biological collections

This report investigates the current state of physical (mechanical) robotics, automated warehousing approaches and assistive technologies in relation to the storage, handling and processing (particularly digitisation) of natural history collections. Robotics can sound futuristic, however we provide case studies that show many and growing examples of physical automation in the natural history and cultural heritage sectors, including barcodes and conveyor belts for digitisation; robots that handle multiple vials for molecular and genetic work; robots for use in in display or exhibition contexts; and automated warehousing of library collections. We provide a non-exhaustive example of an end to end workflow of storage, retrieval and processing and discuss aspects of the tools and challenges relevant to these stages. The Distributed System of Scientific Collections (DiSSCo), a new Research Infrastructure for natural science collections, should build on this, leading a future programme of pilots that develop understanding of independent stages, and can be connected to make progress towards end-to-end solutions. Robots, or automated systems, excel at repetitive tasks, and are developing rapidly to be able to handle more complex object types, at lower cost. High volume, high variety of objects, and considerations such as fragility are not unique to the natural history sector - they apply for example to major retail operations - however natural history collections do offer some of the more extreme examples of these challenges, and in particular are not replaceable. Increased consistency of storage units is likely to be a critical factor in enabling automated handling in future, as well as looking at automation possibilities when new collections storage spaces are developed and built. Engagement with industry and subject matter experts has been patchy and again we recommend that DiSSCo help to ensure a joined up engagement with the right incentives in place, and with clear communication of requirements and challenges for shared R&amp;amp;D. When examining return on investment for particular automation, collections-holding institutions need to consider not only time and cost of automation compared to human labour, but wider factors including: health and safety such as physical environment and repetitve strain injury; security; quality and consistency of outputs; degree of criticality in response times (e.g. if digitising on demand); effective use of spaces; and freeing up staff to conduct other tasks. Purely software-based automation is outside the scope of this report, but is also in increasing use and has enormous potential, for example to transform the extraction of label and specimen data at scale from images. The challenges of managing and digitising collections at scale under DiSSCo are likely to require a combination of hardware and software automation approaches.

Eliciting Attribute-Level User Needs From Online Reviews With Deep Language Models and Information Extraction

Abstract Eliciting user needs for individual components and features of a product or a service on a large scale is a key requirement for innovative design. Synthesizing data as an initial discovery phase of a design process is usually accomplished with a small number of participants, employing qualitative research methods such as observations, focus groups, and interviews. This leaves an entire swath of pertinent user behavior, preferences, and opinions not captured. Sentiment analysis is a key enabler for large-scale need finding from online user reviews generated on a regular basis. A major limitation of current sentiment analysis approaches used in design sciences, however, is the need for laborious labeling and annotation of large review datasets for training, which in turn hinders their scalability and transferability across different domains. This article proposes an efficient and scalable methodology for automated and large-scale elicitation of attribute-level user needs. The methodology builds on the state-of-the-art pretrained deep language model, BERT (Bidirectional Encoder Representations from Transformers), with new convolutional net and named entity recognition (NER) layers for extracting attribute, description, and sentiment words from online user review corpora. The machine translation algorithm BLEU (BiLingual Evaluation Understudy) is utilized to extract need expressions in the form of predefined part-of-speech combinations (e.g., adjective–noun, verb–noun). Numerical experiments are conducted on a large dataset scraped from a major e-commerce retail store for apparel and footwear to demonstrate the performance, feasibility, and potentials of the developed methodology.

Optimal Inventory Level Control and Replenishment Plan for Retailers

In vendor managed inventory (VMI) operations, it is necessary for both the suppliers and the customers to reach an agreement on the maximal and minimal inventory level. In practice, in VMI, inventory levels are decided manually by planning personnel, based on their experience and past operating records. From a system optimization point of view, the determination of the upper and lower inventory levels in storage areas is complicated, necessitating analysis of many factors. Consequently, it may not be possible to obtain the best planning results, to determine and maintain the optimum levels of inventory. In this study, we utilize network flow techniques to build a replenishment model to deal with the upper-and-lower inventory level control problem, with the objective of minimizing the total cost in short-term operations, subject to inventory level control and other operating constraints. The model is formulated as an integer network flow problem with side constraints and is characterized as NP-hard in terms of optimization. To efficiently and effectively solve the large-scale problems that occur in the real world, a solution algorithm is also developed. Finally, numerical tests are conducted using real data from a major retail company in northern Taiwan. The results demonstrate the usefulness of the proposed model and solution algorithm for replenishment planning in actual practice.

Entry Timing in the Face of Switching Costs and its Welfare Effects: Evidence from Same-Day Grocery Delivery Platforms

The online grocery market has seen significant entry over the last five years by firms with different business models. Most firms offer subscriptions, and, in the data, consumers rarely switch between them. I find that switching costs significantly affect consumer platform choice, suggesting potential for future exercise of market power. I estimate the welfare impact of the acquisition of a national grocery chain by of a major online retailer and highlight the role played by this feature of demand and by delivery cost structures. I model competition between two large platforms. The first is the major online retailer engaging in the merger. The rival is an independent platform with low entry costs. The platforms compete in a dynamic entry game, and two opposing forces influence entry timing. I find that firms chase a first-mover advantage resulting from consumer lock-in at the expense of higher entry costs, leading to significant costs of early entry. I estimate that, before the acquisition, the major online retailer had very large entry costs. The acquisition reduced this cost, posing a competitive threat to the rival. Consumer lock-in then contributed to raising the stakes of early entry for both firms, accelerating entry across new markets and generating over \$ 800 M in welfare gains to consumers due to earlier entry. Further, I find that a merger between the two platforms, resulting in a monopoly, would delay entry significantly. These results show that strategic competition in entry timing plays an important role in mergers' welfare effect.

Navigating the Last Mile: The Demand Effects of Click-and-Collect Order Fulfillment

Many retailers are rushing into the click-and-collect (C&amp;C) format, where shoppers place orders online and pick up the goods themselves later. The authors study the demand implications of C&amp;C and postulate how different ways of organizing this format—each with its own convenience features—appeal to households with different shopper characteristics. Using two data sets, each covering the introduction of two C&amp;C fulfillment types by a major grocery retailer in a large number of local markets, the authors compare the impact of in-store fulfillment (pickup at existing stores), near-store fulfillment (pickup at outlets adjoining stores), and stand-alone fulfillment (pickup at free-standing locations). The authors find that the shift in online consumer spending significantly differs between the three order fulfillment types, as does the impact on total spending. No one order fulfillment type systematically dominates; the effects depend heavily on shopper characteristics. The study’s results provide guidance on which C&amp;C fulfillment type(s) to operate under what conditions and caution retailers not to take the easy in-store route routinely.

Integrating Anticipative Replenishment Allocation with Reactive Fulfillment for Online Retailing Using Robust Optimization

Problem definition: In each period of a planning horizon, an online retailer decides how much to replenish each product and how to allocate its inventory to fulfillment centers (FCs) before demand is known. After the demand in the period is realized, the retailer decides on which FCs to fulfill it. It is crucial to optimize the replenishment, allocation, and fulfillment decisions jointly such that the expected total operating cost is minimized. The problem is challenging because the replenishment allocation is done in an anticipative manner under a push strategy, but the fulfillment is executed in a reactive way under a pull strategy. We propose a multiperiod stochastic optimization model to delicately integrate the anticipative replenishment allocation decisions with the reactive fulfillment decisions such that they are determined seamlessly as the demands are realized over time. Academic/practical relevance: The aggressive expansion in e-commerce sales significantly escalates online retailers’ operating costs. Our methodology helps boost their competency in this cutthroat industry. Methodology: We develop a two-phase approach based on robust optimization to solve the problem. The first phase decides whether the products should be replenished in each period (binary decisions). We fix these binary decisions in the second phase, in which we determine the replenishment, allocation, and fulfillment quantities. Results: Numerical experiments suggest that our approach outperforms existing methods from the literature in solution quality and computational time and performs within 7% of a benchmark with perfect information. A study using real data from a major fashion online retailer in Asia suggests that the two-phase approach can potentially reduce the retailer’s cumulative cost significantly. Managerial implications: By decoupling the binary decisions from the continuous decisions, our methodology can solve large problem instances (up to 1,200 products). The integration, robustness, and adaptability of the decisions under our approach create significant value.

Leveraging Logistics Competence in New Product Sourcing: The Role of Strategic Intent and Impact on Performance

The purpose of the present research is to examine and compare product and logistics competencies in supplier selection decisions, which can serve as a crucial building block for competitive differentiation, in the context of the unique private label (PL) supply chain. This study also hypothesizes about the impact of product and logistics competence on the retailer’s financial performance. Lastly, the moderating role of the product type in the proposed research model is explored. Partial least squares path modelling is used to analyze the dataset drawn from major South Korean retailers, due to the exploratory nature of the research and the use of both reflective and formative construct measurement items. Overall, the results of this study demonstrate that relationships between the desire for a particular strategic intent and performance are more complex than previous studies have implied. The findings of this research offer possible explanations on an important but understudied aspect of PL success: why not all PLs (even of the same retailer) are thriving even in a rapidly growing PL industry. We further elicit strategic recommendations for retailers in selecting PL suppliers and for PL manufacturers to differentiate themselves and achieve a superior performance.

Market Integration and Price Transmission amongst the Major Wholesale and Retail Markets of Lentil (Lens culinaris / Lens esculenta) in India

Background: India has become self-sufficient for cereal production but is still dependent upon pulse imports for fulfilling its domestic demands. These imports are very much capable of influencing the domestic prices, so the present study was undertaken to study the integration and price transmission amongst the major lentil producing states of India i.e. Madhya Pradesh, Uttar Pradesh, West Bengal, Bihar, Rajasthan and Assam. Methods: Out of these six states, altogether ten markets and all India average values were undertaken for the present study based on the quantum of lentil marketed and availability of data. The secondary data was collected from the Agmarknet portal for the lentil crop while Minitab and R software were used for the analysis of the retrieved data. Various techniques like Seasonal Indices (SI), unit-root test, Johansen's cointegration test and Granger causality test were applied to find the results and conclusions of this study. Result: The results have stated that the prices of these ten wholesale and retail markets are highly integrated and most of the markets have a uni-directional relationship with each other. The wholesale and retail markets of Jaipur and Lucknow have little/no relationship with the other markets while they have a bidirectional relationship between their own wholesale and retail market prices indicating that they might be the markets where prices are discovered, as they are not found to be influenced by the other spatially separated markets.