Distance and Local Competition in Mobile Geofencing

Ho, Yi‐Jen; Dewan, Sanjeev; Ho, Yi-Chun

doi:10.2139/ssrn.3154767

Cited by 4 publications

(4 citation statements)

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“…The PU of m-shopping can also pertain to good deals and savings, for example. These can be accessed on mobile, primarily via geofencing, which signals users discounts and promotions offered by merchants located nearby (i.e., locational targeting of mobile promotions and special events) (Fong et al, 2015;Ho et al, 2020). This would translate into productivity and value for users spurring m-shopping usage intentions.…”

Section: Hypothesis Developmentmentioning

confidence: 99%

Predicting m-shopping in the two largest m-commerce markets: The United States and China

Ertz

Kong³

et al. 2021

International Journal of Market Research

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This research examines the factors affecting consumers’ mobile shopping (m-shopping) intentions in China and the United States. Drawing on the hedonic-motivation system adoption model (HMSAM), it is proposed that perceived ease of use affects m-shopping intentions; furthermore, this relationship is mediated by perceived usefulness, perceived enjoyment, and control. A survey-based cross-sectional analysis involving a total of 720 respondents constitutes the methodology of this study. In the United States, 409 responses from American citizens or residents were obtained from surveys administered online by MTurk. In China, 311 responses from Chinese consumers were obtained from surveys administered online by Sojump. Perceived usefulness, an extrinsic motive, directly affects behavioral intentions, especially for Chinese consumers, and this effect is also much stronger and complemented by an indirect effect for the Chinese (relative to American) consumers. In contrast, intrinsic motives of joy and control, which are strongly affected by perceived ease of use, do not influence intentions in either market. However, joy exerts an indirect influence on m-shopping intentions, but only for Chinese consumers. These results pertain to the specific context of m-shopping and establish further the importance of distinguishing between utilitarian and hedonic factors, especially across different markets.

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Section: Hypothesis Developmentmentioning

confidence: 99%

Predicting m-shopping in the two largest m-commerce markets: The United States and China

Ertz

Kong³

et al. 2021

International Journal of Market Research

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“…Besides the tremendous popularity in the business world, LBS has also drawn attention from the academic world. Researchers have begun to explore this emerging field from multiple perspectives, such as the effectiveness of the mobile promotions delivered through LBS (Fang et al., 2015), the performance of geofencing (Ho et al., 2020), the relevant privacy concerns when using LBS (Xu, 2012), and the impact of social network structure on consumer decision‐making (Qiu et al., 2018). In addition, computer science researchers have examined LBS in different contexts by (1) exploring how to use the spatial and temporal information provided by LBS to make a better location prediction (Gao et al., 2012); (2) understanding users’ mobility pattern revealed by their activities on LBS to provide insights for recommender systems (Cho et al., 2011); (3) studying the venue popularities on LBS (Y. Li et al., 2013).…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting venue popularity on location‐based services using interpretable machine learning

Wang

Gopal

Shankar

et al. 2022

Production and Operations Management

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Customers are increasingly utilizing location-based services via mobile devices to engage with retail establishments. The focus of this paper is to identify factors that help to drive venue popularity revealed by location-based services, which then better facilitate companies' operational decisions, such as procurement and staff scheduling. Using data collected from Foursquare and Yelp, we build, evaluate, and compare a wide variety of machine learning methods including deep learning models with varying characteristics and degrees of sophistication. First, we find that support vector regression is the best performing model compared to other complex predictive algorithms. Second, we apply SHAP (Shapley Additive exPlanations) to quantify the contribution from each business feature at both the global and local levels. The global interpretability results show that customer loyalty, the agglomeration effect, and the word-of-mouth effect are the top three drivers of venue popularity. Furthermore, the local interpretability analysis reveals that the contributions of business features vary, both quantitatively and directionally. Our findings are robust with respect to different popularity measures, training and testing periods, and prediction horizons. These findings extend our knowledge of location-based services by demonstrating their potential to play a prominent role in attracting consumer engagement and boosting venue popularity. Managers can make better operational decisions such as procurement and staff scheduling based on these more accurate venue popularity prediction methods. Furthermore, this study also highlights the importance of model interpretability which enhances the ability of managers to more effectively utilize machine learning models for effective decision-making.

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“…Ho et al. (2020) show that geofencing ads can effectively entice consumers to visit local businesses and make subsequent purchases. Ghose et al.…”

Section: Literaturementioning

confidence: 99%

Is it all about you or your driving? Designing IoT‐enabled risk assessments

Liu

et al. 2022

Production and Operations Management

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Technological applications disrupt the way to assess risks in the auto‐insurance business. Contrasted with the common practice based on static demographics, usage‐based insurance predicts risks using driving data collected from Internet‐of‐things–enabled telematics. This study proposes a novel solution leveraging the synergy between big data and hierarchical modeling. We specifically consider two aspects of mobility, namely, trait and trajectory, monitored by global positioning system (GPS), on‐board diagnostics, and in‐vehicle cameras in real time. Traits here refer to drivers’ distinctive driving behaviors (styles), whereas trajectories consist of the vehicle motion sequences and the contextual factors on trips. We operationalize semantic features of the two to assess risks at both trip and driver levels. Using fine‐granular driving data and crash reports, we find that behavioral traits play a significant role in predicting crashes, given individual heterogeneity and temporal dynamics. In a series of empirical validations, the proposed solution outperforms the current practice and alternative predictive models considered by prior literature. We show that the mobility‐based models are superior to the demographic‐based ones. Moreover, our model achieves the comparable performance of neural networks, improving the recall of class‐weighted logistic regression, nested support vector machine, and cost‐sensitive random forests by 44.23%, 29.18%, and 24.59%, respectively. Last, our approach is robust, data independent, and computationally efficient for skewed and small samples. This study provides several managerial implications and a blueprint for the auto‐insurance industry to operationalize IoT‐enabled risk assessments in the era of 5G communication.

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Distance and Local Competition in Mobile Geofencing

Cited by 4 publications

References 0 publications

Predicting m-shopping in the two largest m-commerce markets: The United States and China

Predicting m-shopping in the two largest m-commerce markets: The United States and China

Forecasting venue popularity on location‐based services using interpretable machine learning

Is it all about you or your driving? Designing IoT‐enabled risk assessments

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