Multiple Stores Research Articles

Store profitability and public policies to improve food access in non-metro U.S. counties

Access to affordable and nutritious food is a widely-debated public policy issue in the U.S. In non-metro (including semi-urban and rural) U.S. areas, poor food access is mostly the result of lack of food stores, and in particular the absence of large ones (e.g., full-line groceries or superstores). Any policy designed to improve food access in non-metro U.S. areas should recognize that improving stores’ profitability is crucial to policy success in the long-run. We adapt an empirical entry model to assess the effectiveness of two types of policies to improve food access – demand-stimulating policies (DSP), such as increases in Supplemental Nutrition Assistance Program dollars, and supply-side policies (SSP), such as subsidies to reduce establishment costs – by estimating the minimum market size needed for one or more large food stores in non-metro U.S. counties to be profitable. We find that neither type of intervention is preferred a priori and that the cost-effectiveness of each policy type depends upon the presence of an adjacent metropolitan county, the number of pre-existing stores, and the duration of the demand-side stimuli. Additionally, our results suggest that the cost-effectiveness of broad-based policy solutions to improve physical access to large food stores or to stimulate demand may be limited when it comes to easing entry in areas with multiple stores.

The Lemonade Stand: An Elementary Case for Introducing Data Analytics

Accounting education has been encouraged to increase the business knowledge, analytical skills, and data analytic skills of accounting students. This case blends these areas in a single, multi-part project for Accounting Information Systems (AIS) courses. The case includes the technical function of extracting data from databases, integrating multiple data stores and using multiple software tools (MS Access and Tableau). Additionally, students learn to assess the business needs driving the use of integrated data stores to produce quality information for decision making. Using a basic business scenario (lemonade stand), this case provides a stand-alone project focusing on incorporating data analytics into an AIS course. Students assume the role of a professional consultant to a lemonade stand and will become familiar with the business processes and the data of the company, develop queries to answer various business questions, and integrate internal and external data to graphically analyze the combined data for a business analysis. The case allows integration of the course content of data extraction and reporting elements with data analytics. Students indicated that they perceived that they increased their knowledge about business analysis and data analytics tools. Student also indicated they enjoyed the case and had many positive comments about their experience. Results from a pre-/post-test quiz reflect that students did significantly increase their knowledge of business analysis and data analytics.

Magic components—why quantifying rain, snowmelt, and icemelt in river discharge is not easy

AbstractQuantifying the components of rain, snowmelt, and glacier ice melt in river discharge is an important but difficult task in hydrology. Although it forms the basis of many climate impact assessments, many published modelling results do not clearly describe how they derived the discharge components. Consequently, reported components such as absolute amounts or relative percentages of snow and ice melt from different studies are rarely comparable. This commentary revisits the differences in the terminology used, the modelling approaches, and the possible conclusions for effects at different time scales. We argue that for questions related to changes in discharge, not particle tracking, for which methodology is widely available, but instead, an “effect tracking” of the input contributions is important, that is, the representation of the signals of rainfall, snowmelt, and glacier ice melt in the discharge at the catchment outlet. We introduce and briefly describe a method for effect tracking and discuss the differences and advantages compared to other methods. This comparison supports our call to the modelling community for more precise descriptions of how the generated input contributions into a catchment from rainfall, snowmelt, and glacier ice melt are tracked through the catchments' multiple stores to finally compose the presented hydrographs.

Assortment planning for multiple chain stores

In retail, assortment planning refers to selecting a subset of products to offer that maximizes profit. Assortments can be planned for a single store or a retailer with multiple chain stores where demand varies between stores. In this paper, we assume that a retailer with a multitude of stores wants to specify her offered assortment. To suit all local preferences, regionalization and store-level assortment optimization are widely used in practice and lead to competitive advantages. When selecting regionalized assortments, a trade-off between expensive, customized assortments in every store and inexpensive, identical assortments in all stores that neglect demand variation is preferable. We formulate a stylized model for the regionalized assortment planning problem (APP) with capacity constraints and given demand. In our approach, a common assortment that is supplemented by regionalized products is selected. While products in the common assortment are offered in all stores, products in the local assortments are customized and vary from store to store. The model is both applicable for optimizing a total assortment as well as a particular category of an assortment. Concerning the computational complexity, we show that the APP is strongly NP-hard. We formulate the APP as an integer program and provide algorithms and methods for obtaining approximate solutions and solving large-scale instances. Moreover, we extend our model to include substitution effects. Lastly, we perform computational experiments to analyze the benefits of regionalized assortment planning depending on the variation in customer demands between stores.

The influence of consumer confidence on inter-format competition: An analysis based on the French Consumer Confidence Index

The multiple store formats (Drive, hypermarkets, hard discount stores, convenience stores) tend to strengthen inter-format competition in addition to the intra-format competition. Thus, when he or she has to shop, the consumer is now facing several solutions. Research in marketing was much interested in determining the choice of store formats using variables such as the composition of the assortment, price, and sales promotions. More recently, analysts have raised the issue of whether the economic context could affect households’ preferences for store formats without providing a convincing answer. To contribute to this debate, which is now important at a time when many economies are in crisis, this research examines the influence of consumer confidence on households’ spending per trip. An analysis of panel data during the 2004–2013 period and four store formats shows that the changes (increase, decrease) in consumer confidence have asymmetric and nonlinear effects on households’ spending per trip across the different store formats and this also depends on the economic cycle (expansion, contraction).

Assessing the contribution of nightly rechargeable grid-scale storage to generation capacity adequacy

Abstract This paper is concerned with assessing the contribution of grid-scale storage to generation capacity adequacy. Results are obtained for a utility-scale exemplar involving the Great Britain power system. All stores are assumed, for the purpose of capacity adequacy assessment, to be centrally controlled by the system operator, with the objective of minimising the Expected Energy Not Served over the peak demand season. The investigation is limited to stores that are sufficiently small such that discharge on one day does not restrict their ability to support adequacy on subsequent days. We argue that for such stores, the central control assumption does not imply loss of generality for the results. Since it may be the case that stores must take power export decisions without the benefit of complete information about the state of the system, a methodology is presented for calculating bounds on the value of such information for supporting generation adequacy. A greedy strategy is proven to be optimal for the case where decisions can be made immediately after a generation shortfall event has occurred, regardless of the decision maker’s risk aversion. The adequacy contribution of multiple stores is examined, and algorithms for coordinating their responses are presented.

A Multi-Objective Partition Method for Marine Sensor Networks Based on Degree of Event Correlation.

Existing marine sensor networks acquire data from sea areas that are geographically divided, and store the data independently in their affiliated sea area data centers. In the case of marine events across multiple sea areas, the current network structure needs to retrieve data from multiple data centers, and thus severely affects real-time decision making. In this study, in order to provide a fast data retrieval service for a marine sensor network, we use all the marine sensors as the vertices, establish the edge based on marine events, and abstract the marine sensor network as a graph. Then, we construct a multi-objective balanced partition method to partition the abstract graph into multiple regions and store them in the cloud computing platform. This method effectively increases the correlation of the sensors and decreases the retrieval cost. On this basis, an incremental optimization strategy is designed to dynamically optimize existing partitions when new sensors are added into the network. Experimental results show that the proposed method can achieve the optimal layout for distributed storage in the process of disaster data retrieval in the China Sea area, and effectively optimize the result of partitions when new buoys are deployed, which eventually will provide efficient data access service for marine events.

Data-driven assignment of delivery patterns with handling effort considerations in retail

We consider a supply chain with one warehouse and multiple stores. At the warehouse, the orders for the stores are picked and in the store, shelves are stacked from the backroom. We include handling costs at the warehouse and stores as these are main drivers for logistics costs. We find delivery patterns and order-up-to levels, both of which shall remain fixed for a certain time. As especially in retail stochastic non-stationary demand structures are prevalent, we extend the classic joint replenishment problem under dynamic demand by a stochastic yet distribution-free optimization approach based on historical data samples. We formulate a mixed integer linear program using the plant-location formulation and develop several hierarchical decomposition approaches and a genetic algorithm. We consider a cyclic approach for orders, which allows an order at the end of the time horizon to fulfill the demand at the beginning of the time horizon. Using this approach, there is no need for initial inventories to be set as an input; they are are optimized within the model. Furthermore, a metacalibration approach is introduced, which allows an automated setting of input parameters for the genetic algorithm. To derive insights into the performance of the models, random instances are solved and then the most promising models are used for a case study with a European retailer. The results for the controlled test instances are analyzed by a meta-modeling approach that provides insights into performance drivers for the investigated model variants. The average logistics cost savings of our model over a deterministic approach with safety stocks amount to 3.02 % for the controlled test instances. In a similar comparison for the case study, average results over different parameter combinations show a 20.60 % logistics costs saving potential.

DOES PRODUCT CONTEXT COMPLEXITY MATTER IN (IN)CONGRUENT SHOP ENVIRONMENTS? THE INFLUENCE OF PROCESSING FLUENCY ON PRODUCT EVALUATION

DOES PRODUCT CONTEXT COMPLEXITY MATTER IN (IN)CONGRUENT SHOP ENVIRONMENTS? THE INFLUENCE OF PROCESSING FLUENCY ON PRODUCT EVALUATION

Privacy preserving Internet of Things: From privacy techniques to a blueprint architecture and efficient implementation

The Internet of Things (IoT) is the latest web evolution that incorporates billions of devices that are owned by different organisations and people who are deploying and using them for their own purposes. IoT-enabled harnessing of the information that is provided by federations of such IoT devices (which are often referred to as IoT things) provides unprecedented opportunities to solve internet-scale problems that have been too big and too difficult to tackle before. Just like other web-based information systems, IoT must also deal with the plethora of Cyber Security and privacy threats that currently disrupt organisations and can potentially hold the data of entire industries and even countries for ransom. To realise its full potential, IoT must deal effectively with such threats and ensure the security and privacy of the information collected and distilled from IoT devices. However, IoT presents several unique challenges that make the application of existing security and privacy techniques difficult. This is because IoT solutions encompass a variety of security and privacy solutions for protecting such IoT data on the move and in store at the device layer, the IoT infrastructure/platform layer, and the IoT application layer. Therefore, ensuring end-to-end privacy across these three IoT layers is a grand challenge in IoT. In this paper, we tackle the IoT privacy preservation problem. In particular, we propose innovative techniques for privacy preservation of IoT data, introduce a privacy preserving IoT Architecture, and also describe the implementation of an efficient proof of concept system that utilises all these to ensure that IoT data remains private. The proposed privacy preservation techniques utilise multiple IoT cloud data stores to protect the privacy of data collected from IoT. The proposed privacy preserving IoT Architecture and proof of concept implementation are based on extensions of OpenIoT - a widely used open source platform for IoT application development. Experimental evaluations are also provided to validate the efficiency and performance outcomes of the proposed privacy preserving techniques and architecture.

Boxwood Borer Heterobostrychus brunneus (Coleoptera: Bostrichidae) Infesting Dried Cassava: A Current Record from Southern Ethiopia.

Insect specimens of adult beetles and larvae of 7–9 and 9–10 mm length, respectively were collected from infested dry cassava at two locations from multiple stores in southern Ethiopia. The specimens were identified as Heterobostrychus brunneus (Murray, 1867) commonly known as boxwood borer and auger beetle. The study presents a current record of H. brunneus in Ethiopia, particularly in the context of infesting food products. Additionally, a wide geographical distribution of the pest was reviewed and presented in this article. Current evidence suggests that H. brunneus is a serious pest of forest wood, structural timbers, and dried food products and that it carries a risk to be introduced into various other parts of the world via global trade.

Comparing Measures of Accessibility to Urban Supermarkets for Transit and Auto Users

Research on access to healthy foods often emphasizes the spatial proximity of residents to food stores like supermarkets as a way to gauge overall accessibility. Much of the literature has focused on locating the closest facility, assuming that access to one food store is sufficient. Given evidence that access to multiple healthy food stores can improve diets, however, this article examines how closest facility measures differ from cumulative opportunity accessibility measures across space in a medium-sized U.S. city. Differences in access between automobile and transit riders, using realistic travel time costs, are also considered. Results demonstrate that the two types of accessibility measures produce different accessibility geographies and that there are disparities in access for those dependent on transit. These findings indicate that researchers should carefully consider whether access to one supermarket opportunity is enough and pay special attention to the mode residents rely on to access food.

경기도 커피 전문점의 입점 전략에 대한 실증 연구

This article examines the location strategies of coffee franchises in Gyeonggi province. Due to its large population, broad area, and diverse industrial structure, Gyeonggi province is an ideal dataset for empirical testing of the location strategies. We collect the addresses of five major coffee franchises stores, convert them into geographic coordinates using Google Maps Geocoding API, and compute Haversine distances both between stores of the same franchise and between stores of different franchises. This novel approach leads to three discoveries. First, coffee-consuming age population is positively related to the number of stores and more strongly for commercial areas with a large floating population. Second, one third of Starbucks stores have another Starbucks store within a radius of 300m, which empirically confirms the 'Focused Destroy Strategy' of Starbucks that has multiple stores in central business districts. Third, for 80% of Starbucks stores, we can find Ediya stores within 500m, which supports Ediya's 'Next-to-Starbucks Strategy'. Our research methods can be efficiently applied to the analyses of other retail businesses such as convenience stores, fast food restaurants, and mobile phone shops.

Consumer Shopping Strategies and Prices Paid in Retail Food Markets

We examine price search in retail food markets. Previous studies have concentrated on one aspect of search, shopping at multiple stores. We expand this by examining several strategies used to find low prices, including buying on sale and concentrating purchases at a store with low average prices. Using scanner data from 38 markets, we find that the effect of factors motivating search vary by the type of search strategy considered. We also find evidence that financial savings is not the only motivation for search. In particular, transaction utility, in which the value of a purchase depends on price paid relative to a reference price, may also play a role.

Payoff interdependence and the multi-store paradox

We solve the multi-store paradox by introducing interdependent payoffs between the firms. We show that firms set up multiple stores unless the degree of payoff interdependence is low. We also show that multiple equilibria, namely intertwined and neighboring location equilibria, exist if the degree of payoff interdependence is intermediate.

The effects of online and offline information sources on multiple store patronage

The purpose of this study is to examine the relationship between multiple store patronage (MSP) and information source usage both online and offline. In particular, this study investigates the detailed effects of information sources on MSP rather than considering whether consumers choose online or offline sources. In prior studies, MSP has been conceptualized using the consumer cost–benefit framework and relates to consumer multiple store usage. However, even though prior studies have emphasized the importance of information sources in the cost–benefit framework, those that consider MSP have not tested the sources' effects. This current study conducts empirical count data analysis in the Japanese sports shoes retail market. The results reveal that consumers evaluate information sources using more detailed divisions than simply online and offline. This study contributes to studies on MSP because it is the first to identify the impact of information source usage on MSP.

Query processing in multistore systems: an overview

Building cloud data-intensive applications often requires using multiple data stores (NoSQL, HDFS, RDBMS, etc.), each optimized for one kind of data and tasks. However, the wide diversification of data store interfaces makes it difficult to access and integrate data from multiple data stores. This important problem has motivated the design of a new generation of systems, called multistore systems, which provide integrated or transparent access to a number of cloud data stores through one or more query languages. In this paper, we give an overview of query processing in multistore systems. We start by introducing the recent cloud data management solutions and query processing in multidatabase systems. Then, we describe and analyze some representative multistore systems, based on their architecture, data model, query languages and query processing techniques. To ease comparison, we divide multistore systems based on the level of coupling with the underlying data stores, i.e. loosely-coupled, tightly-coupled and hybrid. Our analysis reveals some important trends, which we discuss. We also identify some major research issues.

Challenges and Techniques for Testing of Big Data

Big Data, the new buzz word in the industry, is data that exceeds the processing and analytic capacity of conventional database systems within the time necessary to make them useful. With multiple data stores in abundant formats, billions of rows of data with hundreds of millions of data combinations and the urgent need of making best possible decisions, the challenge is big and the solution bigger, Big Data. Comes with it, new advances in computing technology together with its high performance analytics for simpler and faster processing of only relevant data to enable timely and accurate insights using data mining and predictive analytics, text mining, forecasting and optimization on complex data to continuously drive innovation and make the best possible decisions. While Big Data provides solutions to complex business problems like analyzing larger volumes of data than was previously possible to drive more precise answers, analyzing data in motion to capture opportunities that were previously lost, it poses bigger challenges in testing these scenarios. Testing such highly volatile data, which is more often than not unstructured generated from myriad sources such as web logs, radio frequency Id (RFID), sensors embedded in devices, GPS systems etc. and mostly clustered data for its accuracy, high availability, security requires specialization. One of the most challenging things for a tester is to keep pace with changing dynamics of the industry. While on most aspects of testing, the tester need not know the technical details behind the scene however this is where testing Big Data Technology is so different. A tester not only needs to be strong on testing fundamentals but also has to be equally aware of minute details in the architecture of the database designs to analyze several performance bottlenecks and other issues. Like in the example quoted above on In-Memory databases, a tester would need to know how the operating systems allocate and de-allocate memory and understand how much memory is being used at any given time. So, concluding, as the data- analytics Industry evolves further we would see the IT Testing Services getting closely aligned with the Database Engineering and the industry would need more skilled testing professional in this domain to grab the new opportunities.

Rapa Nui

Rapa Nui Forrest Wade Young (bio) Rapa Nui politics during the review period centered on the reclamation of ancestral territories that Chile had developed into a national park in 1935 without notifying or consulting Rapa Nui. A 2003 national truth commission on Chile’s indigenous peoples had recommended return of [End Page 237] the “park lands” to Rapa Nui along with all island territory (Gobierno de Chile 2008, 570), but this was never realized under the subsequent neoliberal presidency of Sebastián Piñera. The reclamation efforts reflect an intensification of Rapa Nui pursuit of their international indigenous human rights to self-determination in the face of statecraft that continues to foreground the development of economic growth threatening Rapa Nui culture, island ecology, and livelihood. Under the guidance of Ariki Valentino Riro Kainga (appointed king of Rapa Nui in 2011 and descendant of the last Rapa Nui king, Riro), the leadership of Parlamento Rapa Nui President Leviante Araki and Vice President Eriti Teave is distinguished in this review of the conflict and the broader context of the events of the year. As expansion of the tourism industry in Rapa Nui has intensified annually since the turn of the millennium, questions of sustainability have been constant. The late unesco assistant director general, Francesco di Castri, worried about the sustainability of tourism at the beginning of the new millennium, when he estimated 20,000 visitors as the maximum carrying capacity of the island (di Castri 2003, 45). He noted that this number had already been exceeded in 1990 when visitors climbed to 21,000 and by 2005 had reached 45,000 (Gonschor 2007, 244). Australian ecologist Petra Campbell echoed di Castri’s concerns with a 2004 study that projected current tourist development as likely to result in an “environmental catastrophe” that would involve future aquifer contamination, continued land degradation from long-term erosion and mismanagement, marine resource destruction, and a range of energy failures (Campbell 2006). While recent government statistics registered over 92,000 tourist visits to the island in 2013 (El Correo Del Moai, Oct 2014), during the review period the Chilean state continued to pursue development projects that will expand tourism even further beyond the 2003 unesco recommendations of di Castri. Three development proposals are most noteworthy. During October 2014, Chilean President Michelle Bachelet announced an approximately us$160 million plan for infrastructural changes to the island airport (including a second runway), docking, irrigation, power, and sewage systems to begin in 2015 and be completed by 2020 (gi, 20 Oct 2014). In strong support, island Mayor Petero Edmunds emphasized that the projects were necessary for maintaining the island like a “Rolls Royce”—a goal he conceived as having immeasurable value for Chile’s “image” (lt, 17 Oct 2014). In December, Edmunds announced an additional plan to build a shopping mall on the island. The project proposes to develop a four-hectare plot of land containing some of the world-famous moai statues within the municipality of Hanga Roa into a mall with multiple retail stores. The mayor called for a partnership with billionaire German-Chilean entrepreneur Horst Paulmann, chief executive officer and chairman of the Cencosud Corporation, reportedly “one of the largest and most prestigious retail conglomerates in Latin America” (Cencosud 2015). While the project would require the displacement of some of the moai for mall construction, [End Page 238] Edmunds promised coordination of moai movement by the Chilean National Monument Council, utilizing the most advanced technology in the world (ec, 28 Dec 2014). Rather than address projected aquifer contamination, in June 2015 the Chilean National Corporation of Forestry (conaf) announced a plan to build a desalinization plant for agricultural development. Carolina Cuevas (who is, ironically, the head of a Chilean sustainability foundation) emphasized that the new desalinization technology will provide more water to help local farmers realize new economic opportunities (Parque Nacional Rapa Nui, 25 June 2015). State plans to restructure the island ecology, infrastructure, and material culture to intensify economic growth are being coordinated with programs to develop Rapa Nui subjectivity in terms of the principles of “homo oeconomicus” that are increasingly dominant in the era of global neoliberal rationality (Brown 2015, 33). As part of Chile’s...

Supporting Multi Data Stores Applications in Cloud Environments

The production of huge amount of data and the emergence of cloud computing have introduced new requirements for data management. Many applications need to interact with several heterogeneous data stores depending on the type of data they have to manage: traditional data types, documents, graph data from social networks, simple key-value data, etc. Interacting with heterogeneous data models via different APIs, and multiple data store applications imposes challenging tasks to their developers. Indeed, programmers have to be familiar with different APIs. In addition, the execution of complex queries over heterogeneous data models cannot, currently, be achieved in a declarative way as it is used to be with mono-data store application, and therefore requires extra implementation efforts. Moreover, developers need to master and deal with the complex processes of cloud discovery, and application deployment and execution. In this paper we propose an integrated set of models, algorithms and tools aiming at alleviating developers task for developing, deploying and migrating multiple data stores applications in cloud environments. Our approach focuses mainly on three points. First, we provide a unifying data model used by applications developers to interact with heterogeneous relational and NoSQL data stores. Based on that, they express queries using OPEN-PaaS-DataBase API (ODBAPI), a unique REST API allowing programmers to write their applications code independently of the target data stores. Second, we propose virtual data stores, which act as a mediator and interact with integrated data stores wrapped by ODBAPI. This run-time component supports the execution of single and complex queries over heterogeneous data stores. Finally, we present a declarative approach that enables to lighten the burden of the tedious and non-standard tasks of (1) discovering relevant cloud environment and (2) deploying applications on them while letting developers to simply focus on specifying their storage and computing requirements. A prototype of the proposed solution has been developed and is currently used to implement use cases from the OpenPaaS project.