Assessing GIS for retail location planning

Proprietary GIS is rapidly becoming a popular tool in the retail industry for site location analysis. GIS vendors have been quick to exploit this market. At the same time, however, vendors have recognized the limitations concerning the level of spatial analysis available in many of these packages, and have invested in new techniques to add to the kitbag of available GIS solutions. The aim of this paper is to critically evaluate the use of proprietary GIS for retail location planning, using data built up for the city of Leeds in the UK. The paper addresses the appropriateness of simple functions such as mapping, overlay and ‘buffer and overlay’, which have been used in many examples of retail planning. Following this appraisal, we move on to look at more ‘sophisticated’ techniques, such as spatial interaction models, which have only recently begun to appear in proprietary GIS, but to date have received very little evaluation in the literature. In particular, we examine the ease of use of such methods and compare their results with much simpler forms of analysis. Finally, we compare the results from models run within GIS packages with those from more customized software.     

Incorporating big data within retail organizations: A case study approach

The rapid growth in consumer-generated Big Data that are mostly sourced from various types of mobile devices and sensor technologies has placed increasing competitive pressure on retailers to leverage such data within their location decision-making practices. This paper examines the incorporation of Big Data within retail organizations. Through the analysis of three in-depth case-studies of major retail-related organizations operating in Canada, this research addresses the following two inter-related objectives: (i) to identify the awareness, availability, and development of Big Data environments; and, (ii) to explore the opportunities and challenges associated with new Big Data-based decision support systems within retail organizations. The characteristics of an ideal data environment for Big Data adoption and development to take place are identified. The key findings reveal that while there was general awareness of the importance of Big Data, it was evident that the adoption and development of Big Data decision support was heavily reliant on a data environment that promotes transparency and a clear corporate data strategy.     

Using workplace population statistics to understand retail store performance

We explore the value of recently released workplace geographies and accompanying census-based workplace zone statistics (WZS) and an associated classification of workplace zones (COWZ). We consider how these data could support retailers in their operational and strategic decision-making, including the evaluation of retail demand and retail store performance in localities where trade is driven by non-residential demand. In collaboration with major UK grocery retailer The Co-operative Group we explore the relationship between workplace population composition and store trading characteristics using a series of case study stores within Inner London. We use empirical store trading data to identify store and product category level temporal sales fluctuations attributable to workplace populations. We also use census-derived flow data to identify the spatial origins of workplace population inflow. We identify that store performance exhibits characteristics attributable to demand driven by these populations. We conclude that workplace population geographies, WZS and the COWZ afford considerable potential for understanding drivers of store performance, observed store trading patterns and evaluation of retail store performance. We suggest that the next step is to build these populations and their micro geography spatial and temporal characteristics into predictive models and evaluate their potential for store performance evaluation and location-based store and network decision-making within this sector.     

农产品物流安全应急决策支持系统研究

针对农产品物流安全的特点，围绕运输车辆、农产品腐烂等突发事故，集成Internet GIS、无线数据传输、无线通讯等信息化技术，构建了分布式应急决策支持系统（DDSS）。最后基于GPS／GIS／GSM（3G）技术，以车辆调度为例阐述了典型功能区的远程数据采集与决策管理实现。     

A GIS-based spatial decision support system for tourists of Great Smoky Mountains National Park

Great Smoky Mountains National Park (GSMNP) is filled with an abundance of ecological diversity, historical significance, and recreational opportunities for visitors to explore and experience. The wide range of potential activities available in the park also present a major challenge for park visitors to plan activities that will better meet their preferences and constraints. With the large amounts of spatial and non-spatial data associated with the diverse resources and activities in the park, it is a logical choice to use geographic information systems (GIS) for storing, managing, analyzing, and visualizing the data. Nevertheless, GIS functions alone are insufficient to facilitate activity planning for park visitors. This paper presents a GIS-based spatial decision support system (SDSS) application that integrates GIS functions and SDSS designs with easy-to-use graphic user interfaces to help visitors of GSMNP choose and plan their activities more effectively to match their personal preferences and constraints.     

Spatial environmental risk factors for pedestrian injury collisions in Ciudad Juárez,Mexico (2008–2009): implications for urban planning

The aim of this study is to examine the spatial distribution of pedestrian injury collisions and analyse the environmental (social and physical) risk factors in Ciudad Juarez, Mexico. More specifically, this study investigates the influence of land use, density, traffic and socio-economic characteristics. This cross sectional study is based on pedestrian injury collision data that were collected by the Municipal Transit Police during 2008–2009. This research presents an analysis of vehicle–pedestrian collisions and their spatial risk determinants using mixed methods that included (1) spatial/geographical information systems (GIS) analysis of pedestrian collision data and (2) ordinary least squares (OLS) regression analysis to explain the density of pedestrian collisions data. In our model, we found a higher probability for pedestrian collisions in census tracts with population and employment density, large concentration of commercial/retail land uses and older people (65 and more). Interventions to alleviate this situation including transportation planning such as decentralisation of municipal transport system, investment in road infrastructure – density of traffic lights, pedestrian crossing, road design, improves lane demarcation. Besides, land use planning interventions should be implemented in commercial/retail areas, in particular separating pedestrian and vehicular spaces.     

零售业数字化转型机理研究

在数字经济时代,数字化转型已成为零售业高质量发展的必然趋势。近年来,我国零售企业纷纷进行数字化转型,呈现出从技术应用向数字赋能转变、从渠道线上化向线上线下一体化转变、从业务数据化向数据业务化转变、从营销数字化向全面数字化转变以及从大企业主导向大中小企业协同转变的特征,整体上处于探索阶段,存在着全面数字化战略规划缺失、数字化基础和能力较弱、需求驱动型供应链支撑不足和企业组织架构改革相对滞后等问题。零售业数字化转型的内在机理是数字化技术驱动的以消费需求为核心的生产供给体系和流通供给体系的变革,即以消费者需求为出发点,通过线上线下多维立体场景打造、供应链逆向整合、数据资源积累和数据分析能力构建、业务流程再造与组织架构变革构建数字化商业生态系统,打破商品生产与消费之间的时间与空间限制,重构人货场的关系,提升生产与流通体系供给质量和供给效率。     

Estimating small-area demand of urban tourist for groceries: The case of Greater London

Tourist retail demand within urban areas brings both opportunities and challenges to the local economy. Taking Greater London as the study area, this paper integrates conventional statistics and survey datasets with novel crowdsourcing big data sources to identify and estimate four types of tourist grocery demand at the small-area scale: travellers staying with Airbnb, tourists using traditional commercial accommodation, guests staying with relatives or friends and day trip visitors. Based on this combined tourist retail demand layer we show the spatial variations at the small-area level and as an illustration of the demand uplift, we estimate additional grocery expenditure that is associated with this tourist demand. Thus, the paper indicates the neighbourhoods with significant grocery demand uplift from tourist stays. We argue that the new retail demand layer has tremendous potential to be used as an additional input to retail location modelling tools to support new store revenue estimation and store performance evaluation within the grocery retail sector.     

Estimating attractiveness,hierarchy and catchment area extents for a national set of retail centre agglomerations

There is a legacy of research aiming to conceptualise and empirically estimate retail store catchment areas, however, a dearth that frames such considerations within the context of retail agglomerations and their position within regional or national networks. As a result, this paper provides an extension to single store or shopping centre retail catchment estimation techniques, and presents an empirically specified and tested production constrained model for a national network of retail centres in the UK. Our model takes into account the spatial interactions between potential customers and a hierarchical network of retail centres to estimate patronage probabilities and catchment extents. The model is tested for a large metropolitan area vis-à-vis real world shopping flows recorded through a survey of shoppers. Finally, we present an open source software tool for custom model fitting, and discuss a range of theoretical and empirical challenges that such a model presents.     

城市零售流通系统的空间竞争结构及其变化--以日本为例

本文以日本为例,对城市零售流通系统的空间竞争结构及其变化问题进行了较为深入的研究。研究的初步结论是:城市零售流通系统的空间结构会随着城市的发展而不断发生变化,城市发展阶段不同,城市零售流通系统的空间结构也不同;大城市首先发生零售流通系统规模结构的变化,进而波及到地方城市或中小城市;随着城市的发展,城市零售流通系统出现了许多相互区隔、相互竞争的零售市场空间;零售商业集聚的竞争优势会不断发生变化,一些新的零售商业集聚区也会不断出现。     

Sales employees’ polychronicity and sales-service ambidexterity: Mediation of work engagement and moderation of store manager support

Sales employees are encountered with increasing job demands and volatile changes in the retail environment. In particular, the adoption of smart technologies in the retail sector has pressurized sales employees to be versatile and agile in the new marketplace. In this sense, performing multiple tasks within a limited time frame has become an important quality required for sales employees. Grounded in the job demands-resources model, we proposed that sales employees’ individualized resource (i.e., polychronicity) would be positively related to their sales-service ambidexterity though work engagement. We further posited that an organizational resource (i.e., store manager support) synergistically interacted with polychronicity to predict work engagement and subsequent sales–service ambidexterity. To test propositions, we collected multilevel data from 292 sales employees in 43 home-furnishing stores in India. Results demonstrated that the positive association among work engagement, polychronicity, and sales–service ambidexterity was more pronounced when store manager support was high than when it was low. These findings offer implications to overcome challenges faced by retail stores by indicating factors predicting sales–service ambidexterity in retail.     

RFM-based repurchase behavior for customer classification and segmentation

Customer behavior modeling and classification are well-studied areas for applications in retail. Past studies implemented the purchase behavior modeling based on the physical behavior of a subject. In this research, we apply the recency, frequency, and monetary (RFM) model and data modeling techniques to detect behavior patterns for a customer. Each transaction attributed to a customer is part of one's behavior, and an instance of the feature vector, it is modeled on a set of transactions to constitute repurchase behavior. The proposed scheme is validated by simulating a publicly accessible real-world data set with a need-tailored multi-layer perceptron (MLP) and also support vector machine (SVM) and decision tree classification (DTC) methods. The experiments yield a high customer classification rate of more than 97% for the different numbers of the customers. Empirical analysis shows that eight transactions are sufficient to classify a customer with high accuracy.     

Technology revenue management system for customer groups in hotels

This paper discusses revenue management; a technique that focuses on decision making that will maximize profit from the sale of perishable inventory units. New technologies management plays an important role in the development of revenue management techniques. Each new advancement in technology management leads to more sophisticated revenue business capabilities. Today decision support revenue management systems and technologies management are crucial factors for the success of businesses in service industries. This paper addresses the specific case of customer groups in hotels. This paper introduces a new decision support system that sets the revenue maximization criteria for a hotel. The aforementioned system includes a set of demand forecasting methods for customers and addresses a general case considering individual guests and customer groups. The system also incorporates deterministic and stochastic mathematical programming models that help to make the best decisions. The actual revenue depends upon which reservation system the hotel uses. A simulation engine makes a comparison between different heuristics of room inventory control: the results include performance indexes such as occupancy rate, efficiency rate, and yield; it compares results and chooses one of them. The system proves its suitability for actual cases by testing against actual data and thus becoming an innovative and efficient tool in the management of hotels' reservation systems.     

Face and Emotion Recognition on Commercial Property under EU Data Protection Law

This paper integrates and cuts through domains of privacy law and biometrics. Specifically, this paper presents a legal analysis on the use of Automated Facial Recognition Systems (the AFRS) in commercial (retail store) settings within the European Union data protection framework. The AFRS is a typical instance of biometric technologies, where a distributed system of dozens of low‐cost cameras uses psychological states, sociodemographic characteristics, and identity recognition algorithms on thousands of passers‐by and customers. Current use cases and theoretical possibilities are discussed due to the technology's potential of becoming a substantial privacy issue. First, this paper introduces the AFRS and EU data protection law. This is followed by an analysis of European Data protection law and its application in relation to the use of the AFRS, including requirements concerning data quality and legitimate processing of personal data, which, finally, leads to an overview of measures that traders can take to comply with data protection law, including by means of information, consent, and anonymization.     

Now that's a Bright Idea: The Influence of Consumer Elaboration and Distance Perceptions on Sustainable Choices

Integrating theory related to psychological distance and elaboration on potential outcomes, hypotheses are offered that predict that consumers’ elaboration levels moderate the effect of temporal distance on choice of energy efficient products. In three experiments, the authors examine the influence of temporal and psychological distance and the moderating effect of a consumer's propensity to elaborate on potential outcomes (EPO) in retail choice-based situations. Results support these predictions with consumers lower in elaboration more likely to choose an energy efficient product when perceived distance is proximal versus distal, while the distance effect has less of an influence on consumers prone to higher elaboration. We test the effect of distance perceptions within a retail lab environment, as well as across ad and retail contexts. These results will help marketers better understand how to promote products in ad versus retail-based contexts and across different consumer groups. Findings offer implications for theory, retailers, product marketers, and NGO's interested in promoting energy efficient choices.     

The spatial knowledge of retail decision makers: capturing and interpreting group insight using a composite cognitive map

The paper argues that existing techniques for retail site evaluation ignore the useful "soft" insights of retail executives. It outlines a technique for constructing a composite map of retail decision makers' insights into the location influences affecting store performance. It has two objectives: to show how the method can be used to construct a basic knowledge base by helping to elicit decision makers' spatial knowledge; and to clarify the status of the maps in terms of the process of group decision making. Illustrations are given from current work drawn from interviews with executives of a major multiple superstore retailer in the UK. The paper shows how composite mapping provides a means to reduce the ideas of a group without any loss of detail, using key cognitive constructs as a "glue" to provide a template against which individual maps can be compared and better understood. The interpretative method provides a flexible and expedient means for constructing an organizational knowledge base for site location - the value of which can then be tested for modelling purposes - and as an effective counterweight to normative procedures.     

Toward a three-dimensional framework for omni-channel

The omni-channel, as an emerging trend in retail, aims to coordinate processes and technologies across supply and sales channels. The evolution of this concept is still nascent. This paper develops a conceptual framework for omni-channel systems, configured by three dimensions of channel stage, channel type and channel agent. Integration and visibility are also explored and discussed as the main enablers, which support the implementation of omni-channel framework.This research is built upon the empirical and secondary data. Multiple case studies and expert interview methods are employed for data collection to validate the recommended framework and to explore its applicability.The framework proposed, along with the key integration and visibility enablers identified for the omni-channel, can be applied to a wide range of retail supply chains. It helps managers to develop, run and monitor omni-channel systems; it may also serve as a stepping-stone for development of the literature on omni-channel systems.     

Black out of the blue light: An analysis of Kmart store closing decisions

We investigate the spatial and economic factors that influenced Kmart's decision to close about 600 under-performing stores as part of its Chapter 11 financial objectives review in 2002 and 2003. We develop a theoretical model of retail store location and estimate an empirical counterpart using a Logit model to investigate the economic and spatial factors that influenced this decision, including the degree and proximity of competition in the local market as well as local demographic characteristics. In general, our empirical results offer statistical support for the accepted paradigm, but our model offers modest predictive ability as to which stores were actually closed. One interesting extension is a discussion of the potential implications of store closing on the local population, especially with respect to low-income households’ access to discount stores.     

Exploiting consumer–employee–retailer interactions in technology-enriched retail environments through a relational lens

The aim of this paper is to explore the relationships between consumer, frontline employees and retailer (organization) subjected to the disruptive force prompted by the adoption of innovative technologies in retail settings. Starting from qualitative data involving 43 frontline employees with different experiences, our analysis revealed that the way for building and maintaining organizational relationships changes differently considering consumer–employee and employee–retailer perspective. In particular, the emergent computer-mediated relationships seem to produce beneficial effects, while combining employees׳ existing capabilities with new competencies, thus resulting in an improvement of the entire retail process. Similarly, they are more satisfied and appreciated organization effort in supporting their jobs through the innovative technologies. Hence, the study provides an empirical contribution to the emerging literature on consumer–employee–retailer (organization) relationships in the innovative scenario through the in-depth investigation of these relationships of four case firms.     

Change in technology-enabled omnichannel customer experiences in-store

This paper explores changes in technology-enabled omnichannel customer experiences in stores over a five-year period (2014–2019). It contributes to the omnichannel-experience-management literature through customer technology-enabled touchpoints within fashion retail. Adopting an exploratory qualitative approach, primary data were obtained using semi-structured interviews with millennial consumers. The findings demonstrate the growing importance of implementing and integrating in-store technologies to improve customer experience. From these, two models are developed: “technology-induced customer experience in-store”; and “technology-enabled customer shopping journey in-store”.