The problem of identifying the optimal location for a new retail store has been the focus of past research, especially in the field of land economy, due to its importance in the success of a business. Traditional approaches to the problem have factored in demographics, revenue and aggregated human flow statistics from nearby or remote areas. However, the acquisition of relevant data is usually expensive. With the growth of location-based social networks, fine grained data describing user mobility and popularity of places has recently become attainable. 
In this paper we study the predictive power of various machine learning features on the popularity of retail stores in the city through the use of a dataset collected from Foursquare in New York. The features we mine are based on two general signals: geographic, where features are formulated according to the types and density of nearby places, and user mobility, which includes transitions between venues or the incoming flow of mobile users from distant areas. Our evaluation suggests that the best performing features are common across the three different commercial chains considered in the analysis, although variations may exist too, as explained by heterogeneities in the way retail facilities attract users. We also show that performance improves significantly when combining multiple features in supervised learning algorithms, suggesting that the retail success of a business may depend on multiple factors.

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Geo-Spotting: Mining Online Location-based Services for Optimal Retail Store Placement

The problem of identifying the optimal location for a new retail store has been the focus of past research, especially in the field of land economy, due to its importance in the success of a business. Traditional approaches to the problem have factored in demographics, revenue and aggregated human flow statistics from nearby or remote areas. However, the acquisition of relevant data is usually expensive. With the growth of location-based social networks, fine grained data describing user mobility and popularity of places has recently become attainable. In this paper we study the predictive power of various machine learning features on the popularity of retail stores in the city through the use of a dataset collected from Foursquare in New York. The features we mine are based on two general signals: geographic, where features are formulated according to the types and density of nearby places, and user mobility, which includes transitions between venues or the incoming flow of mobile users from distant areas. Our evaluation suggests that the best performing features are common across the three different commercial chains considered in the analysis, although variations may exist too, as explained by heterogeneities in the way retail facilities attract users. We also show that performance improves significantly when combining multiple features in supervised learning algorithms, suggesting that the retail success of a business may depend on multiple factors.

/pdf/geo-spotting-mining-online-location-based-services-for-6wb1v53ody.pdf

Geo-spotting: mining online location-based services for optimal retail store placement

Purpose – The purpose of this paper is to investigate the relationships among destination image, perceived quality, perceived value, satisfaction and revisit intention of sport tourism in Iran. Design/methodology/approach – Statistical population of this research consisted of those tourists who traveled to Mazandaran province in Iran as a sport tourism destination in 2012. A random sample of 1,250 tourists was asked to respond to questionnaires from which 886 accurate questionnaires were used for analysing data. Structural equation modeling (SEM) was used to explore the relationships among the research variables. Findings – The results showed that sport tourists’ perception of destination image, perceived quality and perceived value influenced their satisfaction and revisit intention positively. Originality/value – The results of this research help to better understand the behavioral intentions, which can be an acceptable basis to increase sport tourists’ visit and revisit of a sport destination.

Factors influencing sport tourists’ revisit intentions: The role and effect of destination image, perceived quality, perceived value and satisfaction

This paper provides an integrated framework for assessing the relations between retailing attractiveness and both freight and shopping trips generated in urban areas. The proposed framework deploys a trip attraction (FTA) and a shopping trip attraction (STA) model then carries out a retailing attractiveness analysis. First, an overview on trip generation and attractiveness related to retailing is made to motivate and position the present work. The paper introduces then the methodological framework and describes the FTA and STA models as well as the attractiveness indicator proposed. After that, both FTA and STA flows are assessed on Lyon's conurbation. Then, the relations between retailing attractiveness and retailing-based trip attraction rates are analysed on the basis of linear regression analysis. The results show that both FTA and STA rates have a direct relation with retailing attractiveness but the category of urban space needs also to be included in those analyses. Finally, as a conclusion, main implications of the proposed framework are addressed for both researchers and practitioners.

Impacts of retailing attractiveness on freight and shopping trip attraction rates

The aim of this article is to review research in media innovation through a holistic, analytical, and concise approach. Although research in journalism innovation has experienced considerable growth in recent years, theoretical, methodological, and systematic contributions have received little and fragmented attention. Three hundred and two peer-reviewed publications, in both English and Spanish, were included in the sample. The most researched areas included diffusion theory, management, organizational culture, professional profiles, business models, genres and content, tools and technology, media labs and start-ups. Other less developed areas, such as policy, methodology, ethics, or journalism education, are also discussed. Finally, a number of proposals regarding further research on journalism innovation, considering the effect of Covid-19 on the media, are discussed.

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Review article: Journalism innovation research, a diverse and flourishing field (2000-2020)

Extant literature on site selection highlights the need for scientific research to aid location decisions. In spite of this call, most published research on site selection utilizes neither a well-developed theory of consumer behavior nor considers the influence of competitors' outlets on location decision. This research employs spatial interaction theory, customer density estimates and minimax decision criterion to address site selection issues for a new coffee shop at a university campus. The decision-support model specified in this paper should be of interest to practitioners. (ProQuest: ... denotes formulae omitted.) BACKGROUND Consider the following scenario about a meeting involving facilities planners in a university: "I think there is a need for coffee shop on campus", quipped a senior manager. "What if we built a state-of-the-art coffee shop on campus and nobody came", said another. "Location is the key", replied another. "Let us have it in the newly planned constructions within the Medical School precinct. That should attract the most customers" was the conclusion of the meeting. The above scenario appears to be a common tactic among managers for selecting locations or sites for businesses ranging from small coffee shops to multi-million dollar establishments (Quirk et al. 2002). Specifically, management intuition tends to replace market research in selecting new locations. More than four decades ago Applebaum (1965) highlighted the need for scientific research to replace intuition in site selection decisions. In spite of this call, most published research on site selection utilizes neither a well-developed theory of consumer behavior nor considers the influence of competitors' outlets on location decision (Lilien et al. 1992; Gonzalez-Benito et al. 2005). While Geographie Information Systems (GIS) do provide opportunities for spatial analysis for site selection purposes (Joerger et al. 1999; Lilien & Rangaswamy 2002), there is evidence in the literature that its usage is skewed towards large firms (Garry 1 996; Hernandez & Biasiotto 200 1 ; Rushton 2003). Furthermore, GIS driven geo-information processing functions do not provide the analyst with an opportunity to specify a theoretical model of consumer behavior to predict new business success at the marketplace. For instance, the GIS software "Enlighten for Retailers" employs demographics and psychographics to profile the population in trade areas (Garry 1996). However, as aptly observed by Rossiter (1 987), psychographics and demographics are far removed from actual customer behavior. To illustrate, consider two customers with similar activities, interests, and opinions (psychographics). While "imitation" or "aspiration" is said to cause or push people with similar psychographics towards a common behavior (Best 2005), it is unlikely that all individuals with similar psychographics will have the same reference group. Further, reference group theory, with "aspiration" as the energizer of behavior, should help better predict sales of socially conspicuous products such as mobile phones and cars; not all types of products. As regards demographics, the literature is replete with failures of demographic variables to differentiate preference for brands (see for example, Sheth & Mittal 2004). Finally, and more importantly, GIS is often unavailable for closed populations such as university campuses. This makes it difficult for managers in such settings to employ off-the-shelf spatial analyses software for site selection purposes. This research is an attempt to assist site selection decisions in such contexts. Specifically, it employs spatial interaction theory, customer density estimates and minimax decision criterion to address site selection issues for a new coffee shop at a university campus. In all, two research questions guided the construction of the paper: * Given full awareness of coffee shops, what fraction of a population would prefer each outlet, and * Which location will produce an optimum share of market potential, and minimum hazard for future sales erosion? …

Location Decision Making: The Case of Retail Service Development in a Closed Population

A common understanding among businesses is that a satisfied customer engages in positive word-of-mouth communication that affects the bottomline of the business It has been shown in various research that word-of-mouth recommendations from past students constitute a major source of influence on students' university choice In spite of its importance, little work has been done to address student satisfaction issues in the higher education sector This paper develops a model of student satisfaction with university services using three major theories of consumer satisfaction: expectancy-disconfirmation, equity, and attribution

Antecedents and Consequences of Student Satisfaction with University Services: A Longitudinal Analysis

Decision making could be described using 2 steps: (a) understanding the effects of a particular course of action and (b) selecting an action given this knowledge of effects. It is my contention that businesses wanting to advertise in radio have only partial information about the marketplace. There are approximately 500 commercial and community radio stations in Australia. However, only commercial radio stations, which make up 47% of the total radio stations in the country, systematically conduct radio surveys. In this article, I profile radio audiences in North Queensland, Australia using the list of values measure (LOV; Kahle & Kennedy, 1989). Also, I present the combinations of commercial and community radio stations that could deliver the maximum possible impact on the pertinent target market.

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Optimizing Radio Advertising in North Queensland, Australia: A Mathematical Programming Approach

INTRODUCTION Internet penetration among adult population in the US, people 18 years of age and above, is estimated at 73% or 147 million people (Madden, 2006). While rural adults lag behind urban and suburban adults in Internet usage as much as 8% (Horrigan and Murray, 2006), there is general consensus that Internet can improve perceptions about quality of life (Madden, 2006). Research also suggests that while rural users pursue many of the same online activities as their urban / suburban counterparts, they are less likely to engage in transactions (Bell, Reddy & Rainie, 2004). One reason could be that non-metro residents lack high-speed Internet facilities at home. In fact, Horrigan (2006) highlights a 15% gap in home broadband connection between rural and other residents. Another explanation for less use of the Internet by rural residents could be that they, being older, are more loyal to local land-based businesses with which they are more familiar and comfortable in transactions. In these cases, rural residents may buy essential, day-to-day products locally from land-based businesses and purchase hard to access products such as software, books, and plane tickets on-line. The literature generally lacks an analysis, rather than description, of Internet purchases by non-metro residents. This study contributes to the literature by addressing the following questions: i) Can the Internet purchasing behavior of non-metro residents be explained by type of good or service selected? ii) Is quality of life in non-metro communities influenced by access to high-speed Internet at home and, if so, how? THEORETICAL FOUNDATIONS AND RESEARCH HYPOTHESES Purchase Behavior It is a well-established fact that simple descriptive matrices, such as the Boston's Consulting Group's growth share matrix, are often used by business managers to analyze or predict business units' performance. Witness the popularity of "grid" analyses and prescriptions in publications such as the Harvard Business Review that are often read by business managers. One such grid analysis that summarizes four prominent theories of consumer behavior: utility maximizing economic theory, affective or emotions-laden behavioral theory, social theory, and stimulus-response psychology, is the Foote, Cone & Belding (FCB) matrix (Vaughn, 1986; Figure 1). The FCB matrix contends that purchase behavior varies for high versus low involvement products and those which require predominantly thinking or feeling judgment. Involvement is defined as a consumer's perceived riskiness of the purchase; a low risk purchase is categorized as low involvement and a "risky" purchase as high involvement (Rossiter, Percy & Donovan, 1991). The "think" and "feel" dimensions relate to motives for purchasing. Specifically, "thinking" products tend to solve or remove a problem; for example, Aspirin removes aches and pains. By contrast, "feeling goods" such as food and sweets stimulate and satisfy one's taste sensations and address sensory enjoyment needs. While space does not permit an explanation of all four theories included in FCB, the grid has been validated in 24 different nations, with general agreement among marketers that products such as shown in Figure 1, on average, represent each of the four cells. How can the FCB matrix explain and predict Internet purchase behavior? The Internet can provide both iconic (visual; relevant for "feeling" products; mainly used to elicit emotions) and echoic (oral; relevant for "thinking" products) learning in consumers. However personal income, or financial resources, strongly affects how much non-metro residents buy on the Internet. On this topic, it is well-documented that rural Americans, on average, have lower incomes than people living in metro areas (Horrigan & Murray 2006). Since financial resources often determine, or limit, consumption of "feeling" goods, we hypothesize that: H1: Non-metro residents will purchase more "thinking" goods than "feeling" goods on-line. …

E-Commerce in Non-Metro Regions: On-Line Purchases and Implications for Businesses

Consumerism is gaining momentum in the higher education sector (James, 2001; Lampley, 2001). Stakeholder satisfaction has become a principal objective of almos all universities in the industrialised nations. The ultimate stakeholders, it could be argued, are the employers of the graduates of the higher education institution, and interim stakeholders are the students themselves. However the students don't necessarily know what is important while they are still students, so that student satisfaction surveys taken in school may not be practically valid. This paper describes a measurement approach that would further institutional researchers' understanding of the specific skills required of graduates in the workplace and extent to which higher education has prepared them to meet these requirements. Specifically, using the techniques of multiattribute attitude models (Lilien, 1998), this study examines the beliefs of graduates of a medium-size university, after they had been employed from one to four years, about the importance of various skills and attributes in their career, and the extent to which these skills and attributes were developed during their study. The implications of the results of this study to practitioners in higher education institutions are discussed.

Adee Athiyaman

Papers

Location Decision Making: The Case of Retail Service Development in a Closed Population

Antecedents and Consequences of Student Satisfaction with University Services: A Longitudinal Analysis

Optimizing Radio Advertising in North Queensland, Australia: A Mathematical Programming Approach

E-Commerce in Non-Metro Regions: On-Line Purchases and Implications for Businesses

A measurement approach to assessing the outcomes of university education