Store-level Data Research Articles

The moderating role of servant leadership in the relationship between organizational structure and performance in fashion retailing

PurposeThis study aims to reveal how the organizational structure (vertical integration vs. franchising) of 308 stores in a Spanish fashion retail franchise chain affects their performance measured through two key performance indicators commonly used in this industry, namely, labor productivity and service quality ratings. We also appraise the moderating role played by the servant leadership of franchisees and managers of company-owned outlets to explore its influence on the relationship between organizational structure and store performance.Design/methodology/approachWe have used multivariate analyses to study the research questions, with a panel dataset of quarterly store-level data for the period January–December 2022.FindingsVertically-integrated stores record lower labor productivity than franchised ones. This impact is lower in stores run by individuals high in servant leadership than in those run by individuals low in it. Franchised outlets also record lower ratings in service quality than vertically-integrated stores, and this negative impact is weaker in stores run by individuals high in servant leadership.Originality/valueNothing has thus far been published on the moderating effect of servant leadership in the relationship between the organizational structure of different stores and their outcomes in franchise systems.

Relationship between the terminal built environment and airport retail revenue

Non-aeronautical business, including airport retail, is an essential source of airport revenue. The locations of retail stores in an airport terminal building significantly impact their revenue. Based on the retail store-level data from Dalian Zhoushuizi International Airport (DLC), the traditional panel data regression and machine learning methods were employed to explore the relationships between the terminal built environment and airport retail revenue. The results indicate the following: (1) non-linear relationships exist between airport retail store revenue and the terminal built environment. (2) Airport passenger traffic is still the principal factor in retail store revenue. (3) Variables including area, function halls, and per capita spending have different impacts on the revenue of food and beverage (F&B) and specialty retail (SR) stores. The optimal location sites for retail stores are as far away from the nearest washroom as possible while being as close to gates as possible.

The Impact of Store Brands on Overall Product Category Performance

A comprehensive store-level data set of 9 dairy product categories across 149 stores of a supermarket retailer chain over 32 weeks in 2021 was used to examine the impact of store brands on overall category performance as measured by net margin dollars. The study examines results on both the overall dairy department and category specific performance. The results for the overall dairy department indicate there is not a significant positive relationship between a change in overall store brand sales penetration and profitability. There is, however, a significant positive relationship between overall store brand net margin dollar penetration and category profitability.

Testing the Production Approach to Markup Estimation

Abstract Under the production approach to markup estimation, any flexible input should recover the markup. I test this implication using manufacturing datasets from Chile, Colombia, India, Indonesia, the U.S., and Southern Europe, as well as store-level data from a major U.S. retailer, and overwhelmingly reject that markups estimated using labour and materials have the same distribution. For every dataset, markups estimated using labour are negatively correlated with markups estimated using materials, exhibit greater dispersion, and have opposite time trends. I continue to find stark differences in markups estimated using energy and non-energy raw materials. Non-neutral productivity differences across firms can explain these findings.

CEO Political Leanings and Store-Level Economic Activity during the COVID-19 Crisis: Effects on Shareholder Value and Public Health.

ABSTRACTMaintaining economic output during the COVID‐19 pandemic results in benefits for firm shareholders but comes at a potential cost to public health. Using store‐level data, we examine how a CEO's political leaning impacts this trade‐off. We document that firms with a Republican‐leaning CEO experience a relative increase in store visits compared to firms with a Democratic‐leaning CEO. The increase in store visits is associated with higher sales and positive abnormal stock returns. However, we also document higher COVID‐19 transmission rates and more employee safety complaints in communities where establishments with higher store traffic are managed by a Republican‐leaning CEO.

Using Deep Learning to Improve Inventory Record Accuracy: Concept and Application

Maintaining accurate inventory records has been, and remains, a central problem for managing retail operations. Discrepancies between the physical and recorded stock lead to poor reordering decisions, the over- or understocking of products that increase waste or lost sales, respectively. In this study, we revisit this classic inventory management problem by investigating whether novel machine learning algorithms provide an improvement over established methods. Specifically, we explore the application of deep learning as a method to identify and correct impactful inventory record errors – those that may affect future reordering decisions – by leveraging product level, store level and inventory quality data. Our results show that a deep learning-based decision model outperforms traditional stock auditing strategies. We validate our model by running a field experiment on 450 large stores of a leading grocery retailer. The results demonstrate that a deep learning-based decision model can provide a significant improvement over traditional corrective strategies by refining the representation of the inventory record inaccuracy problem. More generally, our study highlights how generic algorithms, like deep learning, can be applied successfully to improve performance over existing specific methods, yet also, we identify the wider challenges that emerge when deploying those algorithms.

A semiparametric approach to estimating reference price effects in sales response models

It is well known that store-level brand sales may not only depend on contemporaneous influencing factors like current own and competitive prices or other marketing activities, but also on past prices representing customer response to price dynamics. On the other hand, non- or semiparametric regression models have been proposed in order to accommodate potential nonlinearities in price response, and related empirical findings for frequently purchased consumer goods indicate that price effects may show complex nonlinearities, which are difficult to capture with parametric models. In this contribution, we combine nonparametric price response modeling and behavioral pricing theory. In particular, we propose a semiparametric approach to flexibly estimating price-change or reference price effects based on store-level sales data. We compare different representations for capturing symmetric vs. asymmetric and proportional vs. disproportionate price-change effects following adaptation-level and prospect theory, and further compare our flexible autoregressive model specifications to parametric benchmark models. Functional flexibility is accommodated via P-splines, and all models are estimated within a fully Bayesian framework. In an empirical study, we demonstrate that our semiparametric dynamic models provide more accurate sales forecasts for most brands considered compared to competing benchmark models that either ignore price dynamics or just include them in a parametric way.

The Earliest Food Deserts: Availability of Infant and Follow-on Formula, and Lactation Support Products among Stores in Black and Non-Hispanic White Zip Codes in Los Angeles County.

This study investigates the availability, accessibility, and product depth of in-store infant feeding and galactagogues products in majority Black and majority white zip codes in Los Angeles County. A cross-sectional study was conducted to determine racial/ethnic neighborhood differences in the availability of infant and follow-on formula and galactagogues products in 47 retail stores in 21 zip codes. Store-level data were collected in June 2019 and an observational tool for galactagogues products and infant/follow-on formula (availability, accessibility, product depth) was employed at each store. Most of the stores were grocery stores (87.2%). Stores in majority Black zip codes had less availability of infant formula ready-to-use (p = 0.001), less accessibility of follow-on powder (p = 0.028), and availability of galactagogues beverages (p = 0.036) versus majority white zip codes. Product depth (number of brands sold) of stores with one or more brands of the aforementioned products was consistently higher in majority white zip codes compared to majority Black zip codes. Stores in majority Black zip codes were most likely to have lower availability of infant formula and galactagogues products, an important part of the food environment for infant feeding options, in particular, for lactation support. Most studies investigating the association of the food environment and health outcomes have focused only on solid foods. However, additional food products (e.g., liquids, powders) may be contributors to extensive disparities in infant mortality between Black and white infants and may lead to health disparities beyond infant stage (e.g., children, adolescents, and adults). Lastly, for breastfeeding inequities to decrease, pregnant and postpartum Black persons need equitable access and education onsafe and quality galactagogues products.

The effect of on-shelf sugar labeling on beverage sales in the supermarket: a comparative interrupted time series analysis of a natural experiment

BackgroundNutrition labels show potential in increasing healthy food and beverage purchases, but their effectiveness seems to depend on the type of label, the targeted food category and the setting, and evidence on their impact in real-world settings is limited. The aim of this study was to evaluate the effectiveness of an industry-designed on-shelf sugar label on the sales of beverages with no, low, medium and high sugar content implemented within a real-world supermarket.MethodsIn week 17 of 2019, on-shelf sugar labels were implemented by a Dutch supermarket chain. Non-alcoholic beverages were classified using a traffic-light labeling system and included the beverage categories “green” for sugar free (< 1.25 g/250 ml), “blue” for low sugar (1.25–6.24 g/250 ml), “yellow” for medium sugar (6.25–13.5 g/250 ml) and “amber” for high sugar (> 13.5 g/250 ml). Store-level data on beverage sales and revenue from 41 randomly selected supermarkets for 13 weeks pre-implementation and 21 weeks post-implementation were used for analysis. In total, 30 stores implemented the on-shelf sugar labels by week 17, and the 11 stores that had not were used as comparisons. Outcome measures were differences in the number of beverages sold in the four label categories and the total revenue from beverage sales in implementation stores relative to comparison stores. Analyses were conducted using a multiple-group Interrupted Time Series Approach. Results of individual store data were combined using random effect meta-analyses.ResultsAt the end of the intervention period, the changes in sales of beverages with green (B 3.4, 95%CI -0.3; 7.0), blue (B 0.0, 95%CI -0.6; 0.7), yellow (B 1.3, 95%CI -0.9; 3.5), and amber (B 0.9, 95%CI -5.5; 7.3) labels were not significantly different between intervention and comparison stores. The changes in total revenues for beverages at the end of the intervention period were also not significantly different between intervention and comparison stores.ConclusionThe implementation of an on-shelf sugar labeling system did not significantly decrease unhealthy beverage sales or significantly increase healthier beverage sales. Nutrition labeling initiatives combined with complementary strategies, such as pricing strategies or other healthy food nudging approaches, should be considered to promote healthier beverage purchases.

CEO Political Leanings and Store-Level Economic Activity during COVID-19 Crisis: Effects on Shareholder Value and Public Health

Maintaining economic output during COVID-19 pandemic results in benefits for a firm’s shareholders but comes at a potential cost to public health. Using store-level data, we examine how a CEO’s political leanings impact this tradeoff. We document that firms with Republican-leaning CEOs experience a relative increase in store visits compared to firms with Democratic-leaning CEOs. The increase in store visits is associated with higher sales and positive abnormal stock returns. However, we also document higher COVID-19 transmission rates and more employee safety complaints in communities where establishments with higher store traffic are managed by Republican-leaning CEOs.

Validation of a province-wide commercial food store dataset in a heterogeneous predominantly rural food environment.

Commercially available business (CAB) datasets for food environments have been investigated for error in large urban contexts and some rural areas, but there is a relative dearth of literature that reports error across regions of variable rurality. The objective of the current study was to assess the validity of a CAB dataset using a government dataset at the provincial scale. A ground-truthed dataset provided by the government of Newfoundland and Labrador (NL) was used to assess a popular commercial dataset. Concordance, sensitivity, positive-predictive value (PPV) and geocoding errors were calculated. Measures were stratified by store types and rurality to investigate any association between these variables and database accuracy. NL, Canada. The current analysis used store-level (ecological) data. Of 1125 stores, there were 380 stores that existed in both datasets and were considered true-positive stores. The mean positional error between a ground-truthed and test point was 17·72 km. When compared with the provincial dataset of businesses, grocery stores had the greatest agreement, sensitivity = 0·64, PPV = 0·60 and concordance = 0·45. Gas stations had the least agreement, sensitivity = 0·26, PPV = 0·32 and concordance = 0·17. Only 4 % of commercial data points in rural areas matched every criterion examined. The commercial dataset exhibits a low level of agreement with the ground-truthed provincial data. Particularly retailers in rural areas or belonging to the gas station category suffered from misclassification and/or geocoding errors. Taken together, the commercial dataset is differentially representative of the ground-truthed reality based on store-type and rurality/urbanity.

Point-of-Sale Marketing Mix and Brand Performance – The Moderating Role of Retail Format and Brand Type

We study differences in the effects of prices, non-price promotions, and brand line length on brand shares at different retail formats. Our conceptual framework rests on the presence of trip level fixed and category level variable utility components and shows how the trade-off between these components results in (i) different formats visited on different types of shopping trips; and (ii) differential marginal sensitivities of brand shares to changes in marketing mix variables across trip types. Together, these provide predictions on how marketing mix variables differentially impact brand shares at various retail formats. We use Nielsen Homescan and store level data from 2011-2014 and analyze the top ten spending product categories across four retail formats - convenience stores, drug stores, supermarkets and mass merchandisers, in over 200 Nielsen markets. We then describe how our findings linking the effects of marketing mix variables with different retail formats has implications for brand manufacturers.

Marketing-Mix Response Across Retail Formats: The Role of Shopping Trip Types

The authors study differences in the effects of prices, nonprice promotions, and brand line length on brand shares at different retail formats. Their conceptual framework rests on the presence of trip-level fixed and category-level variable utility components and shows how the trade-off between these components results in (1) different formats visited on different types of shopping trips and (2) differential marginal sensitivities of brand shares to changes in marketing-mix variables across trip types. Together, these provide predictions on how marketing-mix variables differentially affect brand shares at various retail formats. The authors use Nielsen Homescan and store-level data from 2011–2014 and analyze the top ten spending product categories across four retail formats—convenience stores, drugstores, supermarkets, and mass merchandisers—in over 200 Nielsen markets. Implications for brand manufacturers managing the marketing mix across different formats are offered. JEL codes: M310, L11, D4

Food Banks and Retail Markups

Food banks play a critical part in the food distribution system. In this paper, we examine the impact of food bank donations on retailer markups using a unique store-level data set on donations, labor employment, sales, and capital employment. We frame our empirical model of food bank donations and store-level markups in terms of a theoretical model of quality-based price discrimination and retail sales. We find that stores that donate more food to the local food bank are able to charge higher markups - 33% higher - after controlling for the well-known endogeneity problems that bedevil production analysis. Our findings suggest that donations are not necessarily only altruistic gestures on the part of retailers, but rather part of a symbiotic relationship between food banks and food retailers.

Media influences on consumption trends: Effects of the film Food, Inc. on organic food sales in the U.S.

In this paper, we empirically investigate the effect of Food, Inc. – an influential documentary film on the US food industry – on organic food sales. We use a novel dataset of Google search volume to measure the influence of the film. Organic food sales are measured using store-level data for three categories in 33 major US markets. For two out of the three studied categories – yogurt and peanut butter – we find that areas with more Google searches for the film experienced a greater increase in the share of organic sales subsequent to the film's release. The effect of the film is more pronounced for the yogurt category, which is closer to the farm and more relevant to animal welfare — one of the key issues featured in the film. The effects are economically significant: ceteris paribus, a one standard deviation increase in our Google search stock measure translates into a 0.66% increase in organic market share for the yogurt and a 0.15% increase for the peanut butter category. In addition, we provide a measure of the decay of the film's effect by deriving the 90% duration interval of the focal search stock variable. We find that, for both categories, the film's effect on organic shares faded within six months.

Supplemental Nutrition Assistance Program Data: Why Disclosure Is Needed.

The Supplemental Nutrition Assistance Program (SNAP) provides funding to low-income households to purchase food at participating stores. The goals of the program include reducing hunger, improving nutrition, and strengthening the US food system. These are interrelated, as food access and choice depend on availability.SNAP generates data that could be useful for program evaluation and evidence-based policymaking to reach public health goals. However, the US Department of Agriculture (USDA) does not collect or disclose all SNAP-related data. In particular, the USDA does not systematically collect food expenditure data, and although it does collect transaction (sales) and redemption data (the amount retailers are reimbursed through SNAP), it does not release these data at the store level.In 2018, Congress quietly changed the law to prohibit the USDA from disclosing store-level transaction and redemption data, and in 2019, the US Supreme Court blocked disclosure of these data. These federal proceedings can inform the outcome of additional efforts to disclose SNAP-related data, as well as future research and policy evaluation to support improved public health outcomes for SNAP beneficiaries.

Effects of multitier private labels on marketing national brands

Purpose This paper aims to understand empirically how shares of standard and premium private label (PL) products affect a retailer’s marketing mix decisions toward national brands (NBs). Design/methodology/approach Using a comprehensive store-level data set covering 52 categories and 130 stores of two retailer chains during 2003-2009, this paper examines how shares of standard and premium PLs affect retailer marketing strategies for NB retail prices, promotions and product assortments. The empirical analysis uses a simultaneous equations model estimated by the generalized method of moments approach and controls for endogeneity between PL shares and NB decisions and potential confounding variables including consumer, manufacturer and retailer factors. Findings Standard PL shares are associated positively with NB retail prices and negatively with NB promotions and assortments. In contrast, premium PL shares are associated positively with NB retail prices, promotions and assortments. Research limitations/implications The results indicate that retailers make strategic NB decisions through multitier PLs. Specifically, the evidence suggests that retailers use standard and premium PLs differently in promotion and assortment decisions toward NBs. NB manufacturers need to be cognizant of the increasing marketing power of retailers through their multitier PLs. Originality/value Prior research has mainly focused on the role of PLs as a strategic weapon to gain power in the channel and its impact on NB pricing decisions in a single PL context. After accounting for potential confounding factors (retailer, consumer and manufacturer) and endogeneity, the authors find empirical evidence that retailers appear to leverage standard and premium PLs differently in some marketing mix decisions toward NB. In particular, the results reveal PL performance to be a determinant of retailer NB assortment decisions.

Testing the Production Approach to Markup Estimation

Under the production approach to markup estimation, any flexible input should recover the markup. I test this implication using four manufacturing censuses and store-level data from a US retailer, and overwhelmingly reject that markups estimated using labor and materials have the same distribution. For every dataset, markups estimated using labor are negatively correlated with markups using materials, exhibit greater dispersion, and have opposite time trends. Non-neutral productivity differences can reconcile these findings. I develop a flexible cost share estimator to model such heterogeneity. Using this estimator, markups estimated with different inputs are positively correlated in the cross-section and time series.

P5 - Healthy Corners: What Variables Predict Sustainability?

Background (Background, Rationale, Prior Research, and/or Theory): Lack of access to healthful foods is a contributing factor to higher rates of chronic conditions. To address this determinant of health and increase access to fresh fruits and vegetables, corner stores have been targeted to transition from a traditional to a healthy corner store, particularly in food deserts. The question of whether these stores will sustain this initiative, after current subsidies end, remains unclear. Objective: The goal of the healthy corners' evaluation is to understand what variables predict continuity (sustainability) in providing healthy items should the financial and technical assistance supports for the program be reduced or eliminated. Study Design, Setting, Participants, Intervention: District of Columbia (DC) has been actively engaged in supporting 71 healthy corner stores to increase the sale of fresh fruits and vegetables. Using nine variables such as sales data, Nutrition Environment Measures Survey—Corner Store, and owner surveys, we created a Healthy Corner Store Scorecard to predict sustainability of the market. Outcome Measures and Analysis: Forty two stores had complete datasets that were used in the analysis. Using the Healthy Corner Scorecard, a higher score may predict long-term sustainability. Results: Years in the healthy corners program, store owner involvement, and variety and pounds of fruit and vegetables sold appear to predict the sustainability of a healthy corner store. Stores that have been in the healthy corner store program for an average of 58 months, earned the highest scores on the scorecard. Conclusions and Implications: Transitioning traditional corner stores to healthy corner stores takes long-term support, funding, and time. DC has made an investment to improve food access to its most vulnerable residents. It appears from the store-level data that DC is making great progress in having corner stores offer healthful food items. Additional research is needed to fully answer the sustainability question. Funding: Department of Health, Washington, DC.

The effect of payday lending restrictions on liquor sales

We exploit a change in lending laws to estimate the causal effect of restricting access to payday loans on liquor sales. Leveraging lender- and liquor store-level data, we find that the changes reduce sales, with the largest decreases at stores located nearest to lenders. By focusing on states with state-run liquor monopolies, we account for supply side variables that are typically unobserved. Our results are the first to quantify how credit constraints affect spending on liquor, and suggest mechanisms underlying some loan usage. These results illustrate that the benefits of lending restrictions extend beyond personal finance and may be large.