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Spatial Analytics with Hospitality Big Data: Examining the Impact of Locational Determinants on Customer Satisfaction in the U.S. Hotel Market

2023 | HICSS | Citations: 0
Authors: Lee, Minwoo; Kim, Jinwon; Shin, Hyejo

Abstract: Although hotel location has been recognized as one of the important factors affe ... Expand

Abstract: Although hotel location has been recognized as one of the important factors affecting hotel selection and guest satisfaction, relatively few studies have examined guest satisfaction with hotel location and its locational determinants at a macro level. This study aims to identify the locational determinants of hotel guest satisfaction through big data spatial analytics via a case study of 5,302 hotels in 151 cities in the U.S. Based on the framework of hotel location satisfaction, we classified all location-related factors into three categories: accessibility to points of interest, transport convenience, and surrounding environment. Our findings indicated that hotel property’s proximity to city area, landmark, park, shopping center, and highway as well as, attraction-driven tourism industry specialization, and hotel industry agglomeration were significant determinants. Furthermore, the impacts of these factors were spatially heterogeneous. These findings can provide geographical insights that are critical for developing a customer service experience and satisfaction model. Collapse

Semantic filters: corrected Akaike information criterion

Topics: big data geographic information system customer satisfaction online review criminality

Methods: regression analysis method spatial analysis spatial regression case study network analysis
Real-time temperature prediction in a cold supply chain based on Newton's law of cooling
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2021 | Decision Support Systems | Citations: 0
Authors: Konovalenko, Iurii; Ludwig, André; Leopold, Henrik

Abstract: Many goods, including pharmaceuticals, require close temperature monitoring. Thi ... Expand

Abstract: Many goods, including pharmaceuticals, require close temperature monitoring. This is important not only for complying with regulations but also for guaranteeing safety of use. A particular challenge in controlling a product’s temperature arises during transportation. In cold supply chains (SCs), temperature is maintained by refrigerated containers. However, many situations, e.g. cooling system failure, lead to ambient temperature changes, and this needs to be detected as early as possible to prevent product damage. Existing approaches to temperature prediction are confined to long-term forecasts with relatively stable ambient temperatures and/or rely on multiple sensors in the known fixed positions. Since interventions in a SC are required immediately, there is a need for methods that provide real-time predictions regarding regular ambient temperature instability, i.e. when the ambient temperature changes unexpectedly in the short term. We propose a novel method that extends the applicability of Newton’s law of cooling (NLC) to changeable ambient temperatures based on a set of tem perature stability conditions and a sensor measurement error. In the method, an optimal number of measure ments that characterize stable ambient temperatures and improve prediction reliability are selected. We compare the adapted NLC with artificial neural networks and autoregressive moving average models with respect to deviation prediction, prediction error, and execution time. Our evaluation based on real-world data shows that the adapted NLC outperforms existing baseline methods. In contrast to existing solutions, our method does not require any knowledge about the positioning of products within the container, further increasing its practical value. Collapse

Semantic filters: corrected Akaike information criterion

Topics: Python logistics management open source

Methods: autoregressive moving average model experiment longitudinal research machine learning artificial neural network
Longitudinal healthcare analytics for disease management: Empirical demonstration for low back pain
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2020 | Decision Support Systems | Citations: 0
Authors: Mueller-Peltzer, Michael; Feuerriegel, Stefan; Molgaard Nielsen, Anne; Kongsted, Alice; Vach, Werner; Neumann, Dirk

Abstract: Clinician guidelines recommend health management to tailor the form of care to t ... Expand

Abstract: Clinician guidelines recommend health management to tailor the form of care to the expected course of diseases. Hence, in order to decide upon a suitable treatment plan, health professionals benefit from decision support, i.e., predictions about how a disease is to evolve. In clinical practice, such a prediction model requires interpretability. Interpretability, however, is often precluded by complex dynamic models that would be capable of capturing the intrapersonal variability of disease trajectories. Therefore, we develop a cross-sectional ARMA model that allows for inference of the expected course of symptoms. Distinct from traditional time series models, it generalizes to cross-sectional settings and thus patient cohorts (i.e., it is estimated to multiple instead of single disease trajectories). Our model is evaluated according to a longitudinal 52-week study involving 928 patients with low back pain. It achieves a favorable prediction performance while maintaining interpretability. In sum, we provide decision support by informing health professionals about whether symptoms will have the tendency to stabilize or continue to be severe. Collapse

Semantic filters: corrected Akaike information criterion

Topics: decision support decision making healthcare data missing data personalization

Methods: cross sectional research longitudinal research time series analysis autoregressive moving average model generalized autoregressive conditional heteroskedasticity model
Quantifying the Dynamic Sales Impact of Location-Based Mobile Promotion Technologies
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2013 | International Conference on Information Systems | Citations: 0
Authors: Luo, Xueming; Gu, Bin; Fang, Zheng; Xu, Yunjie (Calvin)

Abstract: Location-based mobile promotion (LMP) is believed to trigger spontaneous and imp ... Expand

Abstract: Location-based mobile promotion (LMP) is believed to trigger spontaneous and impulsive purchases. This field study quantifies the dynamic impact of LMP, using aggregated daily mobile movie promotions and purchase data from over 3 million realworld users of a large mobile service provider. The developed multivariate time-series models confirm that LMP has a significant contemporaneous impact on movie ticket sales. Bayesian vector autoregressive results also confirm that LMP has significant immediate (next day) and cumulative impacts on sales over the next 9 days. The immediate and cumulative impacts account for 66% of the total effects. These findings suggest that without considering the dynamic effects, practitioners and researchers may substantially under-estimate the value of LMP. Our analysis also reveals the presence of cross-channel effects between LMP and other forms of mobile related promotions. Collapse

Semantic filters: corrected Akaike information criterion

Topics: mobile system accounting marketing management information storage and retrieval system information privacy concern

Methods: longitudinal research time series analysis Granger causality computational algorithm field study

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Found 4 articles

Spatial Analytics with Hospitality Big Data: Examining the Impact of Locational Determinants on Customer Satisfaction in the U.S. Hotel Market

Real-time temperature prediction in a cold supply chain based on Newton's law of cooling

Longitudinal healthcare analytics for disease management: Empirical demonstration for low back pain

Quantifying the Dynamic Sales Impact of Location-Based Mobile Promotion Technologies