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As ecommerce becomes more prevalent, traditional brick-and-mortar retailers such as warehouse clubs (WCs) face the challenging task of maintaining and growing their customer base. This study aims to unravel the combined impact of retail agglomeration and ecommerce activities on consumer foot traffic (also referred to as “footprint”) at WC stores, placing an emphasis on the locational strategies adopted by WCs in this evolving retail landscape.

Mobile-based customer foot traffic data for Costco, a major U.S. WC chain, is sourced for our analysis. We use Principal Component Analysis (PCA) to identify dimensions of general merchandise (GM) and narrow-range merchandise (NM) retail agglomeration. Two-stage least squares (2SLS) regressions are used to explore how the intensity of ecommerce activities and WC locational choices within retail agglomerations impact WC foot traffic.

Our analysis highlights a notable decline in WC store visits attributable to both GM and NM ecommerce activities, with GM ecommerce presenting a more significant competitive challenge to WCs. Regarding retail agglomerations, proximity to GM clusters that include a diverse range of supercenters, department stores, and club stores, is associated with an increase in WC customer visits within their vicinity. In contrast, the influence of NM agglomerations is mixed; clusters adjacent to grocery stores lead to higher WC customer traffic compared to those focused on other specialized stores. These findings underscore the strategic importance of location in mitigating the adverse effects of ecommerce competition. Additionally, our study uncovers intricate dynamics between GM and NM retail clusters and ecommerce activities, demonstrating varied impacts on WC customer footprint.

Access to customer footprint data illustrates the potential of this data source for retail decision making and researchers. Our analysis is limited to one chain, notably Costco.

Our findings underscore the need for retailers to adeptly navigate the evolving retail landscape, including the confluence between physical and digital retail environments, to secure future success. In particular, our results emphasize the benefits of locating stores within mixed retail agglomerations and underline the need to consider the broader retail landscape in location decisions.

The rise of ecommerce in the U.S. has reshaped consumer behavior and altered local shopping districts’ communal dynamics. This change may spur policy interventions to help physical stores compete with online retailers, emphasizing the importance of retail diversity and community-centric environments to sustain communal retail interactions amidst digital advancements.

The paper makes use of a unique dataset to provide a first assessment of the combined effects of retail agglomeration and ecommerce activities on consumer foot traffic for WC retailers. Thus, this paper provides insights into the impacts on consumer shopping behavior from the dynamic interactions between physical retail clusters and online shopping behaviors.

This paper aims to evaluate the early effects of the pandemic of coronavirus disease 2019 (COVID-19) and accompanying stay-at-home orders on restaurant demand in US counties.

The following two sets of daily restaurant demand data were collected for each US county: foot traffic data and card transaction data. A two-way fixed-effects panel data model was used to estimate daily restaurant demand from February 1 to April 30, 2020.

Results show that a 1% increase in daily new COVID-19 cases led to a 0.0556% decrease in daily restaurant demand, while stay-at-home orders were collectively associated with a 3.25% drop in demand. The extent of these declines varied across counties; ethnicity, political ideology, eat-in habits and restaurant diversity were found to moderate the effects of the COVID-19 pandemic and stay-at-home orders.

These results characterize the regional restaurant industry’s resilience to COVID-19 and identify particularly vulnerable areas that may require pubic policies and managerial strategies for intervention.

This study represents a pioneering attempt to investigate the economic impact of COVID-19 on restaurant businesses.

This study aims to analyze the impact of COVID-19 on housing price within four major metropolitan areas in Texas: Austin, Dallas, Houston and San Antonio. The analysis intends to understand economic and mobility drivers behind the housing market under the inclusion of fixed and random effects.

This study used a linear mixed effects model to assess the socioeconomic and housing and transport-related factors contributing to median home prices in four major cities in Texas and to capture unobserved factors operating at spatial and temporal level during the COVID-19 pandemic.

The regression results indicated that an increase in new COVID-19 cases resulted in an increase in housing price. Additionally, housing price had a significant and negative relationship with the following variables: business cycle index, mortgage rate, percent of single-family homes, population density and foot traffic. Interestingly, unemployment claims did not have a significant impact on housing price, contrary to previous COVID-19 housing market related literature.

Previous literature analyzed the housing market within the first phase of COVID-19, whereas this study analyzed the effects of the COVID-19 throughout the entirety of 2020. The mixed model includes spatial and temporal analyses as well as provides insight into how quantitative-based mobility behavior impacted housing price, rather than relying on qualitative indicators such as shutdown order implementation.

In this study, to investigate tourist mobility (i.e. hotel visits) during the COVID-19 pandemic, the authors developed three objectives with reference to protection motivation theory: (1) to examine changes in travel distances in the USA before and during the pandemic, (2) to identify distinct travel patterns across different regions during the pandemic; and (3) to explore threat- and coping-related factors influencing tourist mobility.

The authors used two primary sources of data. First, smartphone data from SafeGraph provided hotel-specific variables (e.g. location and visitor counts) and travel distances for 63,610 hotels in the USA. Second, state-level data representing various factors associated with travel distance were obtained from COVID-19 Data Hub and the US Census Bureau. The authors analyzed changes in travel distances over time at the state and regional levels and investigated clinical, policy and demographic factors associated with such changes.

The findings reveal actual travel movements and intraregional variances across different stages of the pandemic, as well as the roles of health-related policies and other externalities in shaping travel patterns amid public health risks.

To the best of the authors’ knowledge, this study is the first to empirically examine changes in travel distances to hotels as destinations using smartphone data along with state-level data on COVID-19 and demographics. The findings suggest that tourism enterprises and stakeholders can proactively adapt their strategies by considering threat appraisals and coping mechanisms, both of which are influenced by externalities such as health-related policies.

在我们的研究中, 为了调查COVID-19大流行期间的旅游出行（例如：酒店访问）, 我们根据保护动机理论制定了三个目标：（1）研究在COVID-19大流行前后美国的旅行距离的变化, （2）在大流行期间识别不同地区的不同旅行模式; 以及（3）探讨影响旅游出行的威胁和应对因素。

我们利用了两个主要数据源。首先, 来自SafeGraph的智能手机数据提供了63,610家美国酒店的酒店特定变量（例如位置和访客计数）以及旅行距离数据。其次, 代表与旅行距离相关的各种因素的州级数据来自COVID-19数据中心和美国人口普查局。我们分析了州级和地区级的旅行距离随时间的变化, 并调查了与这些变化相关的临床、政策和人口因素。

我们的研究结果揭示了不同阶段的实际旅行动态和地区内的差异, 以及在公共卫生风险中塑造旅行模式的健康相关政策和其他外部因素的作用。

我们的研究是第一个利用智能手机数据以及与COVID-19和人口统计数据相关的州级数据, 经验性地研究了旅行距离到酒店作为目的地的变化。我们的研究结果表明, 旅游企业和利益相关者可以通过考虑威胁评估和应对机制来主动调整他们的策略, 这两者都受到健康相关政策等外部因素的影响。

In the world of corporate real estate, there are as many intuitive methods for decision‐making as there are real estate executives. Some executives may use comparisons of new sites to existing sites. Others have a checklist of characteristics that sites must have in order to be a ‘go’. Still others examine neighbourhoods around sites to determine if they will be successful. The current paper takes a look at the intuitive ways that real estate executives make decisions and demonstrates how scientific modelling mimics these decision strategies.

The COVID-19 pandemic has significantly impacted global tourism, with international travel bearing the burden of restrictions. Domestic tourism has also faced substantial challenges. This paper aims to analyse the impact of the COVID-19 pandemic on domestic tourism in Spain, focusing on travel from Madrid (the country’s capital) to other tourist destinations.

Mobile phone data has been used to study the evolution of tourist trips over the summers of 2019, 2020 and 2021. Regression models are used to explain the number of visitors at destinations.

The pandemic not only caused a drastic drop in tourist flows but also disrupted the overall pattern of the domestic flow system. Winning destinations were typically areas in proximity to Madrid and less densely populated destinations, while urban destinations were major losers. The preferences of domestic tourists varied notably by income group, but the decrease in trip volumes showed only marginal differences.

The paper demonstrates the potential of mobile phone data analysis to study the uneven impact of external shocks, such as the COVID-19 pandemic, on tourist destinations. This approach considers spatial resilience heterogeneity within regions or provinces. By incorporating income information, the analysis introduces a social dimension to highly detailed spatial data, surpassing traditional studies conducted at the regional or national levels.

COVID-19大流行对全球旅游业产生了重大影响，国际旅行受到了限制的影响最为严重。国内旅游也面临着重大挑战。本文分析了COVID-19大流行对西班牙国内旅游的影响，重点关注从马德里（该国首都）到其他旅游目的地的旅行。

本研究使用移动电话数据研究了2019年、2020年和2021年夏季旅游出行的演变。采用回归模型解释了各目的地游客数量。

大流行不仅导致了旅游流量急剧下降，还扰乱了国内流动系统的总体模式。获胜的目的地通常是马德里附近的地区和人口较稀少的目的地，而城市目的地是主要的输家。国内游客的偏好在收入群体之间有明显差异，但旅行量的减少只显示出边际差异。

本文展示了使用移动电话数据分析研究外部冲击（如COVID-19大流行）对旅游目的地的不均匀影响的潜力。该方法考虑了区域或省份内的空间弹性异质性。通过整合收入信息，该分析为高度详细的空间数据引入了社会维度，超越了传统在区域或国家水平进行的研究。

This study aims to identify the antecedent factors influencing consumer attitudes and patronage intentions toward an intelligent unmanned convenience store (IUCVS) in Taiwan. The IUCVS is a new smart service that offers customers a novel shopping experience, given that it avoids queues and physical contacts with cashiers. However, studies discussing IUCVS remain scant owing to its brief history.

This research develops a synergistic model combining original unified theory of acceptance and use of technology (UTAUT) constructs with perceived risk and value to test differences between unexperienced and experienced customers’ attitudes and patronage intentions toward IUCVSs. Data collected from 268 experienced and 156 unexperienced consumers were tested against the proposed research model using partial least squares (PLS) structural equation modeling and multi-group analysis (PLS-MGA).

In line with expectations, three UTAUT variables (i.e. performance, effort expectancy and social influence) and perceived value significantly and positively influence consumer attitudes toward IUCVSs. This research confirms the significant and negative direct effect of perceived risk on consumers’ patronage intentions toward IUCVSs. Furthermore, the PLS-MGA results unveil that a significant difference exist in the effects of perceived convenience value on attitudes toward IUCVS between consumers who had experience of using self-service machines and those who have not.

This research successfully fills the research gap by offering a synergistic model for evaluating consumers’ attitudes and patronage intentions toward a new smart service. Several important theoretical and practical implications are provided to help retail managers develop service strategies.

The purpose of this paper is to report on the findings of completed case studies of two major multi‐channel grocery retailers in South Africa. The aim of the research was to establish the potential that online grocery retail has to undermine traditional retail by decreasing foot traffic and undermining rental income.

The growth of online shopping in the retail sector is a matter of concern for those involved in the development and management of shopping centres. Non‐probability convenience sampling was employed to interview shoppers in the five largest regional shopping centres in Cape Town tenanted by the two major grocery “e‐tailers” in South Africa.

The findings show that the online grocery market is an expanding market segment. Furthermore, diminished foot traffic is likely to affect the ability of smaller retailers to pay turnover rentals. Miller's revised rent model is adapted and used to illustrate the potential savings that may be generated by changing the rent models currently in use.

Future research into exactly what consumers buy online from food retailers needs to be undertaken in order to establish the maximum potential reduction in foot traffic attracted by food anchors.

It is concluded that the South African retail industry is heavily reliant on traditional retail centres and although the loss in rentals resulting from online grocery sales is not currently considerable, it does represent a potential future threat.

The paper speculates about the effects of growth in online buying on rental agreements in shopping centres. The paper would appeal to property investors, property developers and facilities managers.