Remedying food policy invisibility with spatial intersectionality: A case study in the Detroit Metropolitan Area

This study examines the intersectionality of race/ethnicity and poverty in terms of geographic access to 2,635 food stores of three types (supermarkets, grocery stores, and convenience stores) in the tricounty Detroit metropolitan area (DMA). Prior research not only lacks an intersectional view of sociodemographic categories in explicating food store access, but it also fails to provide place-based policies to remedy food policy invisibility. The authors explore whether spatial dependencies among food stores exist and whether these are linked to sociodemographic heterogeneity in the DMA. Food stores are clustered across suburban and rural areas surrounding urban boundaries but are less clustered in the inner city. Poor neighborhoods have varying access to different types of food stores depending on the predominant racial/ethnic composition of the neighborhood. This research can assist policy makers in implementing place-based food interventions and policies, especially attracting new supermarkets and grocery stores to the urban DMA

The importance of tourism clusters and community resilience for remedying Airbnb COVID-19 disruption

Peer-to-peer accommodation markets have been disrupted by the COVID-19 pandemic. However, little attention is paid to how to remedy the disruption in terms of Airbnb business performance. This study empirically investigates the spatially varying COVID-19 disruptions in the Airbnb business and offer place-based remedying strategies through local resources, including tourism clusters and community resilience. Using real data of Airbnb operating performance, COVID-19, and local resources in Florida, we apply geographically weighted regression to estimate the spatial effects in the Airbnb performance model. The results show that Airbnb listings in rural areas that specialized in leisure businesses were more disrupted by COVID-19 than those in urban areas that specialized in hospitality businesses. Furthermore, community resilience attenuated the negative impact of COVID-19 across locations, more in rural areas than in urban areas. These findings enable Airbnb hosts and policymakers to adopt localized resource-based remedying strategies to cope with the pandemic

Do price promotions drive consumer spending on luxury hotel services? The moderating roles of room price and user-generated content

Price promotion, as price information, and user-generated content (UGC), as non-price information, play an important role in generating luxury hotel revenue. This study empirically investigates how price promotion influences actual consumer spending on luxury hotel services except room price, by considering the contingency role of room price and volume and valence of UGC. Combined data of daily settlements and Tripadvisor customer reviews of a regional luxury hotel chain are used for the analyses. The results indicate that, overall, price promotion negatively influences consumer spending on luxury hotel services and its negative effect is strengthened when the room is higher priced or the valence of UGC is high. Furthermore, a larger volume of intrinsic attribute-related UGC–amenity and location–with price promotion leads to more consumer spending than a larger volume of extrinsic attribute-related UGC–food and staff. The findings provide hotel managers with important insights into pricing and UGC management

How Does Selling Capability Impact Firm Value? The Moderating Roles of Relative Strategic Emphasis, Market Volatility, and Technological Volatility

Firms develop and deploy selling capability to create and sustain a competitive advantage. Previous studies have focused predominantly on static, input-based selling capability, paying little attention to dynamic, efficiency-focused selling capability. This treatise reconceptualizes selling capability from a dynamic and efficiency (input–output) perspective and investigates the effect of selling capability on firm value with the contingent role of internal [i.e. relative strategic emphasis (SE)] and external (i.e. market volatility and technological volatility) factors. Using data from 341 US-based manufacturing and service firms over the period 2014–2020 and an endogeneity-robust dynamic estimation technique, the authors find that selling capability positively affects firm value, and firms with a relative SE on value appropriation (i.e. advertising) as opposed to value creation (i.e. R&D) reap more rewards from selling capability. That is, relative SE positively moderates the nexus between selling capability and firm value. Furthermore, the results demonstrate that the interactive effect of selling capability and relative SE is weaker when an industry experiences higher market volatility but stronger when technological volatility is higher. Overall, this study demonstrates that a firm's selling capability should be managed dynamically in light of its (internal) relative SE and (external) environmental conditions. The results are robust to several additional sensitivity analyses.© 2022 The Authors.British Journal of Management published by John Wiley & Sons Ltd on behalf of British Academy of Management. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioitu|en=peerReviewed

Seasonal spatial activity patterns of visitors with a mobile exercise application at Seoraksan National Park, South Korea

Visitors’ behavior in national parks can be influenced by seasonal variations in climate and preferred activities. Seasonality can produce different space consumption patterns, and impact visitor experience and natural resource use. The purpose of this study was to explore the seasonal spatial patterns of visitors’ activities using a mobile exercise application within the context of Seoraksan National Park in South Korea. A dataset composed of 5142 starting and ending points of 2639 activities (hiking and walking) created by 1206 mobile exercise application users (January–December 2015) were collected from a leading mobile exercise application operator. GIS-based spatial analytical techniques were used to analyze the spatial patterns of activity points across seasons and days (weekdays/weekends). Results indicated considerable seasonal and daily variations in activity distribution and hot spots (i.e., locations of potential congestion or crowding). The findings enable park managers to mitigate negative impacts to natural resources as well as enhance visitors’ experiences. Also, it allows potential visitors to decide when to visit certain sites via mobile application to ensure optimal conditions. Furthermore, the GPS-based exercise mobile application can be used as a new methodological approach to understand spatio-temporal patterns of visitors’ behavior within national parks and other natural protected areas

Enhancing exercise visitors' behavioral engagement through gamified experiences: a spatial approach

“Quantified” travelers often use mobile exercise apps and gamified features to manage their physical activities while visiting destinations. Accordingly, this study empirically explores the spatially varying relationships between visitors' gamified experiences—place curiosity and social recognition—and exercise behavioral engagement. Using novel data from exercise app users’ activity logs and spatial analytical methods, this study finds that gamified experiences have differential effects on the exercise times and distance of tourist and resident visitors. Furthermore, the visitor gamification-engagement relationship varies according to the type of gamified experience and individual and clustered locations. These findings offer important implications for integrating gamified exercise apps with destination management to improve visitor engagement, which enhances our knowledge of smart tourism design

An improved Bayesian inference model for auto healing of concrete specimen due to a cyclic freeze-thaw experiment

This paper presents an innovative solution for the auto healing porous structures damaged by cyclic freeze-thaw, followed by predicting the results of recovered damage due to freezing based on Bayesian inference. The additional hydration of high strength material, cured in high temperature, is applied as auto curing for the damaged micro-pore structures. Modeling of micro pore structure is prior to damage analysis. The amount of ice volume with temperature dependent surface tensions, freezing pressure and resulting deformations, and cycle and temperature dependent pore volume has been predicted and compared with available test results. By heating the selected area of specimen in frozen chamber, approximately 100 % of strength recovery has been observed after 10 days of freeze-thaw tests in the proposed nonlinear stochastic prediction models and the experimental results

Reliability assessment based on an adaptive response surface method considering correlation among random variables

Although the Monte-Carlo Simulation (MCS) technique can evaluate a reliability of most structural systems, its processing time equals, approximately, the reciprocal of the probability of failure. While the Stochastic Finite Element (SFE) method could help to solve such a drawback, it is limited to specific computer programs, in which the mean and the coefficient of random variables are estimated by a perturbation, or by a weighted integral method. Therefore, SFE may not be easily applicable when using commercial software or systems that are not prepared with the prerequisite programming. To overcome these limitations, the RSM can be applied, because its accuracy depends on both the distance of axial points, and the linearity of the Limit State Functions (LSFs). The correlation among random variables and the response of a system is evaluated by composing a Bayesian belief nets (BBN). Consequently, the proposed Linear Adaptive Weighted Response Surface Method (LAW-RSM) with BBN modeling produces improved converged reliability indices than conventional RSMs and detail observation for the uncertainties in structural components

Continuance use intention with mobile augmented reality games: Overall and multigroup analyses on Pokémon Go

As mobile augmented reality (AR) games enter the maturity stage, understanding how to improve players’ continuance use intention with mobile AR games is critical. Drawing upon the uses and gratifications (U&G) theory, we investigated the effects of four major gratifications – content, process, social, and technology – and other factors on continuance intention to play mobile AR games. Data collected from 280 Pokémon Go players were used to address research questions. Partial least squares method was employed to assess the relationships in the model and multigroup analysis was conducted based on survey participants’ demographics and their gaming experience. Content gratification (i.e., catching Pokémon), process gratification (i.e., entertainment), game knowledge, and achievement drive players’ continuance use intention. However, social and technology gratifications do not influence players’ continuance use intention. Multigroup analysis suggests that mobile AR game developers should capitalize on the fact that different types of gratifications prompt continuance use intention of different user segments in terms of demographics and experience in general mobile games and Pokémon Go. User behavior of mobile AR games has been studied at the early stage of the games, with less attention to variable continuance use intentions across different user segments. This study attempts to fill the gap by extending the U&G theory to continuance use intention with mobile AR games at the maturity stage and further investigating the importance of player heterogeneity in continuance use intention with mobile AR games. Findings of this study contribute to the literature on U&G, continuance use intention and mobile AR games