Content uploaded by Yang Yang
Author content
All content in this area was uploaded by Yang Yang on Oct 03, 2017
Content may be subject to copyright.
A preview of the PDF is not available
Understanding and projecting the restaurantscape: Influence of neighborhood sociodemographic characteristics on restaurant location
Abstract and Figures
To better understand the location patterns of different types of restaurants across the United States, we investigate the relationship between neighborhood sociodemographic characteristics and restaurant location using a unique data set from 2013 covering 30,772 U.S. zip codes. The estimation results from negative binomial regression models confirm the significant impacts of various sociodemographic factors (e.g., population density, median age, median household income, average household size, educational attainment, gender distribution, housing tenure, neighborhood urbanization) on restaurant location. We also project future restaurant growth potential based on model estimates and projected changes in sociodemographic characteristics by 2020. The results are analyzed, and several metropolitan areas in Texas and Florida are identified as having high potential for growth. Lastly, implications are provided for restaurant real estate practitioners.
Figures - uploaded by Yang Yang
Author content
All figure content in this area was uploaded by Yang Yang
Content may be subject to copyright.
... Research has also found that the geographic locations of fast food outlets, which affect the spatial accessibility of customers to consume fast foods (Smith, 1983(Smith, , 1985, can be determined by several neighborhood characteristics, such as population flow, income level, traffic flow, and socioeconomic status (Block et al., 2004;Powell et al., 2007;Sharkey et al., 2011;Yang et al., 2017). These associations can be explained. ...
... These associations can be explained. First, neighborhood characteristics can be related to consumer habits and dining expenditures of residents who dine out: neighborhoods with more active local consumers generally have more restaurants from a supply-and-demand perspective (Binkley, 2006;Liu et al., 2013;Stewart and Yen, 2004;Yang et al., 2017). While restaurants are generally established given this logic, fast food outlets may have a narrower connection to the local neighborhood. ...
... The service area in our study refers to the neighborhood area in which the local residents are the main target consumers (i.e., the geographic area surrounding the fast food outlet accessible to local residents). Previous research has utilized administrative boundaries, such as census tracts (Morland et al., 2002), census block groups (Lamichhane et al., 2013), and zip codes (Yang et al., 2017), to examine the relationship between service areas and neighborhood characteristics in relation to fast food outlet locations. While the use of administrative units allows for ease of linking the census data to measure neighborhood characteristics, these units are arbitrary, vary in size, and do not consider spatial accessibility. ...
Fast food consumption is one of the major causes of rising obesity rates. Fast food consumers are mostly residents located in the service area—the fast food outlet's surrounding area. Conventional buffer approaches may exhibit bias in measuring service areas by ignoring the local community's detailed spatial configuration and transportation preferences.
This study uses fast food outlets and their visits provided by a mobile phone-based dataset named SafeGraph and applies a novel context-based crystal growth algorithm (CG) to delineate improved service areas of fast food outlets in Chicago. We also explore how socioeconomic variables in service areas by CG and buffer-based approaches are related to visits to fast food outlets.
Results show that compared to conventional buffers, CG produces improved service areas as it excludes inaccessible barriers and adjusts the accessible areas by transportation preferences. Further, the model using service areas of public transport users by CG yields the best performance. Additionally, the rate of single-mother households and the number of other fast food outlets nearby are positively related to fast food visits in all models. Findings acknowledge the advantages of CG and help make policy interventions to reduce fast food consumption.
... Selecting a right location that can generate profits is of strategic importance for the success of a restaurant (Chen & Tsai, 2016;Yang, Roehl, & Huang, 2017) or a hotel (Yang, Mao, & Tang, 2018), particularly in today's turbulent business environment (Godinho, Phillips, & Moutinho, 2018;Phillips & Moutinho, 2014). Many chain restaurants such as Chili's, BJ's and Cheesecake Factory are struggling to survive, despite that consumers love eating out regardless of the poor economic situations (Peltz, 2017). ...
... Previous studies have examined hospitality services' location selection patterns and how location determinants shape these patterns (Chen &Tsai, 2016;Pillsbury, 1987;Yang, Roehl, & Huang, 2017;Smith, 1983). Various location models have been proposed and widely adopted in the industry such as gravity model, Huff model, analog model (Yang Luo, & Law, 2014), and in recent years, the models have been more advanced and sophisticated, for example the use of multi-criteria decision-making (MCDM) methods in location selection (Chang and Hsieh, 2014;Temur, 2016;Velasquez & Hester, 2013). ...
... Aksoy and Ozbu (2017) identified three determinants for evaluating hotel location: accessibility, regional development, and tourist attractions. Yang, Roehl, and Huang (2017) found that the location determinants are traffic, population, and market geodemographic. Demographic factors were investigated on a family basis; for example, household income and composition etc., were considered (Yang, Roehl, & Huang, 2017). ...
This study proposes a machine learning-based model for location selection that focuses on the estimation of sales potential of a prospective site and helps to overcome the lack of historical data for the prospective site and the subjective criteria used in conventional models. The proposed model involves three major steps. First, we use an attribute selection algorithm to identify the key factors that contribute to the profitability of a specific location. Second, we evaluate the similarity between the candidate site and the existing stores by using an improved grey comprehensive evaluation method. Finally, we use a kernel regression model to predict the sales potential of the candidate site. A case study of a well-known international restaurant chain is used to illustrate the application of the proposed data-driven model. The results indicate that our proposed model helps to accurately select the most profitable locations.
... Puskesmas is an organization from the government that provides health services for the community in each sub-district. Each location attribute has a significant relationship with customer satisfaction (Yang et al., 2017;Han et al., 2020). ...
... The location has been considered strategic and easy to visit by the community as patients from the Puskesmas. These results are in agreement with previous studies (Yang et al., 2017;Han et al., 2020). ...
This paper aims to observe the causes of patient satisfaction based on service quality and location. The population in this study were patients at a health center in West Surabaya with a total sample of 96 respondents. The method of data analysis to determine the effect of service quality and location on patient satisfaction used an analytical tool, namely the multiple linear regression equation. This study found that service quality and location have a role in the formation of patient satisfaction at the puskesmas. This is an important determinant for achieving patient satisfaction. It is the obligation of the puskesmas to provide the best service for the community in the health sector. The results of this study are expected to be material for improving the quality of services in hospitals and become the basis for further research.
... Research results are consistent with other reports. This is concurrent with previous research, which places particular focus on factors driving demand for food services, including, specifically: the attractiveness of collective consumption (Namkung & Jang, 2007;Fatimah et al., 2011;Duarte Alonso et al., 2013;Yang et al., 2017), economic factors (Kwun & Oh, 2004;Nguyen et al., 2018), social and cultural factors (Kim et al., 2016). Undemanding customers make up a consumer segment which, as demonstrated by the research, had a low score for all variables. ...
The research aimed to explore preferences regarding the selection of collective consumption, with attention being given to the following three research constructs: the attractiveness of collective consumption, economic determinants for using the offer of food services, as well as social and cultural determinants for consumer behaviours on the food services market. The research was conducted through a direct channel, using a questionnaire prepared to this end. The research process, during which primary data was gathered, comprised the following three stages: qualitative research, quantitative pilot research and proper quantitative research. The proper quantitative research covered a representative random sample of 2,102 Poles aged above 18. The article presents the results of research carried out to examine preferences regarding the use of food services provided by restaurants and attempts to categorise consumers of food services into segments. Empirical material was gathered and analysed to identify five consumer segments. The data obtained not only supplements existing research by providing up-to-date pandemic-related information on changing preferences on the food services market and gives restaurant owners (restaurateurs) guidance on designing food product architecture for individual consumer segments, but also provides a starting point for research hypotheses that can be formulated in future scientific research.
... Locational choice has been listed as one of the most influential factors for a hotel's longterm business prosperity and success [1][2][3]. A superior location is significantly associated with higher demand [4], customer purchasing intention [5], and thus higher revenue, better performance and market profitability [6] and elevated customer satisfaction [7]. ...
This study aims to investigate the spatial associations of luxury hotels by using geographical information system (GIS) tools and the multiscale geographically weighted regression (MGWR) model to examine the relationships between the distribution of luxury hotels and exogenous (regional) determinants of urban subdistricts in which the luxury hotels are located. Shanghai City is used as an example. The study first introduces the spatial-temporal characteristics of luxury hotels in Shanghai City, and the key exogenous determinants that contribute to luxury hotel location choice are identified with the MGWR model. The nearest neighbor index decreased from 1.01 to 0.47 and Moran’s I statistics increased from 0.268 to 0.452, revealing that the spatial-temporal evolution pattern of luxury hotels presents a cluster trend from 1995 to 2015. The significance level of the standard regression coefficient shows that the institutional proximity, room rate, green space and the World Expo are the primary determining factors that influence the distribution of luxury hotels in Shanghai City. The analysis is important theoretically, as it presents new and novel methodologies for shedding light on the influencing factors of the locational dynamics of luxury hotels. Meanwhile, it enriches the methodologies for analyzing the relationships between luxury hotels and urban structures, and it is important for practitioners, as it provides strategic information that would enable them to globally select appropriate locations for luxury hotels.
... Based on the derived spatial patterns through exploratory analysis, explanatory methods further identify key factors that shape the spatial patterns. The widely used explanatory method is the regression analysis, which explains local influencing factors in areal data (Yang et al., 2017) or discrete destination choice in origin-destination or trajectory data (Nicolau, 2017). Besides, explanatory analysis can be used for both spatial pattern detection and explanatory analysis, including geodetector (Wang, 2016) and geographically weighted regression (Shabrina et al., 2021). ...
Community-based tourism is a sustainable form of tourism development where tourists visit residential communities to interact with local lives and cultures for an enhanced travel experience. Identifying and tracking tourist activities in community-based tourism is particularly challenging, as tourists have shared activity spaces with residents. The paper proposes a new method to study the space–time patterns of the tourist flow using Wi-Fi data. Specifically, we have tracked Wi-Fi probe requests over six months in the Shichahai scenic area, a famous community-based tourist attraction in Beijing, China. After deriving the tourist flow from the Wi-Fi data, we have applied the empirical orthogonal function (EOF) method to the identification of the spatial aggregation pattern and the temporality of the tourist flow. A follow-up explanatory analysis examines the environmental impacts, such as weather conditions, air quality, and travel days, on the space–time patterns. The study is among the first to employ Wi-Fi data to study travel behaviours in community-based tourism. The proposed method can shed insights into a better understanding of tourist behaviours in open-space, tourism-oriented urban communities.
This article introduces the special section on leveraging user behavior and data science technologies for management. It reviews 12 articles and discusses their contribution towards establishing a new dynamic paradigm of leveraging user behavior and data science technologies for management. User data has become a promising and relevant area to explore in order to improve decision-making. However, and despite increasing access to this kind of data, several challenges remain related to how to successfully collect, manage and incorporate user data to managerial decisions. In this special issue, we focus on exploring different facets related to impactful data practices in management as well as envisaging future developments related to new sources of user data and methods. Overall, the special issue contributes to deepening the understanding of data usage and management for business through a series of articles that highlight promising further developments in areas such as data collection, data disclosure and privacy, data usage and data analysis methods.
Issue addressed:
Socio-economic spatial patterning of fast-food outlets can result in disparities in the availability and access of food across geographic areas, contributing to health inequalities. This study investigated whether area-level socio-economic disparities exist in fast-food availability across the Perth metropolitan region of Western Australia.
Methods:
Fast-food outlet locations were sourced from Perth Local Governments in 2018/2019. All Perth suburbs (n=328) were allocated a decile ranking based on the Australian Bureau of Statistics Socio-Economic Index for Areas with decile 1 indicating relatively greater disadvantage and decile 10 indicating a relative lack of disadvantage. Zero-inflated negative binomial regression models, adjusted for suburb area and population density, were used to investigate the association between area-level disadvantage decile and availability of fast-food outlets.
Results:
A socio-economic gradient was identified; for every unit increase in disadvantage decile (i.e., a reduction in relative disadvantage), the count of fast-food outlets decreased by 6% (p<0.01), and the count of the "top ranking" fast-food chains (i.e., McDonalds, KFC, Hungry Jacks and Red Rooster) decreased by 10% (p < 0.001).
Conclusions:
Consistent with evidence internationally and from within Australia, socio-economic spatial patterning of fast-food outlet availability was shown to exist in Perth, with greater fast-food availability in areas with more relative socio-economic disadvantage. SO WHAT?: To address health inequities associated with fast-food consumption, policy and practice changes are needed that manage fast-food outlet proliferation in areas of greater socio-economic disadvantage.
The dynamics of a city's structures are determined by the coupling of functional components (such as restaurants) and human population. Yet, there lacks mechanism models to quantify the forces on the spatial distribution of the components. Here, a gradient model is explored to simulate the individual density curves of multiple types of city functional components based on the equilibria of gravitational and repulsive forces along with the urban–rural gradient. The model is concise by relying on four key variables, the attributes of components include net ecosystem service (m) and environmental index (γ); and the attributes of cities include land rent exponent (σ) and population attenuation coefficient (β). The model has been used to simulate the distribution curves of 22 types of components on the urban–rural gradients in 13 cities in two periods. The model reveals a bottom‐up mechanism that the patterns of the components in a city are determined by the economic, ecological, and social attributes of both cities and components. Strongly backed by empirical data, the model can predict the distribution curves of many types of components along with the development of cities. This model provides a general tool for analyzing the distribution of multiple objects on the gradients. A gradient model is explored to simulate the individual density curves of multiple types of city functional components based on the equilibrium of gravitational and repulsive forces along with the urban–rural gradient. The model can predict the distribution curves of many types of components along with the development of cities.
This paper examines purchase intentions and brand loyalty with five dimensions of Quick Service Restaurant. The objectives of the study are to investigate the associations between consumers' perceptions of Quick Service Restaurant and brand loyalty, and whether purchase intentions mediate these relationships. The measures used for the constructs were adapted from past studies and our hypotheses were tested using online survey data. This research was conducted from the perspective of Generation Y consumers in Singapore and we used McDonald's, the market leader in the Singapore fast food industry, as the context for this study. The findings provided support for all the hypotheses, namely, that consumers' perceptions of location, product menu, food quality, service crew and collectibles were positively related to purchase intentions. Moreover, the results indicate that the purchase intention variable played a mediating role in the relationship between location, product menu, food quality and brand loyalty. The findings have some useful managerial implications for the foodservice industry.
Although location is a significant factor in a restaurant’s survival chances, contrary to the commonly held belief, the presence of nearby homes did not help in lowering failure rates between 2000 and 2010 for restaurants in Boulder, Colorado. Instead, having a substantial population of apartment dwellers and transient residents (notably, university students) enhanced the restaurants’ success, as did the presence of people aged eighteen to twenty-four, those with higher educational levels, non-household families, and low- to middle-income families. Larger restaurants and those with chain affiliation had a greater probability of success than small, quick-service operations. Among the factors that had little effect on restaurant success or failure were unemployment rates, the nature of nearby residents’ profession, and the geographical presence of families with children under eighteen. The study’s results also support the long-held industry perception regarding avoiding locations where restaurants have already failed. When a location had experienced three ownership turnovers, the study found that the location generally ceased to host restaurants as tenants. As explained further in this article, this is the fourth in a series of examinations of the elements of restaurant success and failure.
The aim of the present research was to investigate the factors affecting customer loyalty in the restaurant industry. Data was collected using questionnaire distributed in 10 randomly selected branches of Boof Chain Restaurant in Tehran. In each branch, 40 customers were selected for the study. Ultimately, the research sample consisted of 268 customers. Structured equation modeling was used for data analysis and hypothesis testing. The obtained results show that food quality, service quality, restaurant environment, and perception of price fairness had a positive impact on customer satisfaction, but the impact of restaurant location on customer satisfaction was not confirmed. Also, food quality, service quality, and perception of price fairness had a positive effect on customer trust. The results show that food quality is the most important factor affecting customer satisfaction and trust in Boof Chain Restaurants. Customer satisfaction had a positive impact on customer loyalty, but the effect of customer trust on customer loyalty was not confirmed.
Mr. Chen has reported that he has no relationships relevant to the contents of this paper to disclose. Dr. Patel has received research grant support from AstraZeneca, Bayer, Janssen, Heartflow, and the National Heart, Lung, and Blood Institute; and has served on the advisory board for Janssen, Bayer, CSI, and AstraZeneca.
The combination of demographic and geographic information can be applied to identifying locations for restaurant units. By analyzing restaurant-density patterns, an operator can determine the number and types of restaurants in a market area. Next, the operator can acquire computerized street-level maps showing business generators and traffic patterns for existing restaurants. Finally, the operator can identify potential locations for new restaurants. As an example, the analysis of a neighborhood in Boston shows a location that would likely support a new casual-theme restaurant.
The undue importance that professors give to their choice of work location while searching for an academic position is discussed. It is suggested that a perfect job in an imperfect location is better than the reverse. The process of searching for the right job requires flexibility as things do not always go the expected way. The importance of taking advice and support from friends, family, and colleagues is highlighted.
Location selection plays a crucial role in the retail and service industries. A comprehensive location selection model and appropriate analytical technique can improve the quality of location decisions, attracting more customers and substantially impacting market share and profitability. This study developed a data mining framework based on rough set theory (RST) to support location selection decisions. The proposed framework consists of four stages: (1) problem definition and data collection; (2) RST analysis; (3) rule validation; and (4) knowledge extraction and usage. An empirical study focused on a restaurant chain to demonstrate the validity of the proposed approach. Twenty location variables relevant to five location aspects were examined, and the results indicated that latent knowledge can be identified to support location selection decisions.
This paper investigates household expenditures on food away from home by type of facility. A system of expenditures at full-service
restaurants, fast-food restaurants and other facilities are estimated with a multivariate sample selection procedure, for
three types of households by household composition. Statistical significance of error correlations suggests endogeneity of
sample selectivity and justifies estimation of the equations in a system to improve statistical efficiency. Differentiated
effects of economic and demographic factors are found on expenditures at different facilities and across household types.
Findings on the roles of economic and socio-demographic factors can inform marketing strategies and policy deliberations.
This paper explores the role of restaurants, as consumption spaces, in defining the consumptional identity of 243 American cities. Specifically, it is asked whether, and how, US cities can be classified on the basis of the local prevalence of specific types of restaurants—are some cities culinary deserts, while others are gastronomic oases? A two-stage cluster analysis reveals four distinct city types, which fall along two intersecting dimensions: a quantitative dimension of restaurant availability and a qualitative cultural dimension. These four city types are characterised and connected to the existing literature on consumption spaces, with particular attention to a strong parallel between these city types and the communities discussed by Richard Florida. Several directions for future research using the city classification as a conceptual framework are offered. Additionally, these analyses involve the development and application of a new method of measuring a city's number of consumption spaces, which is theoretically superior to traditional per capita measures and which is described in a methodological appendix.