Gratitude with Expectations: Exploring the Role of Restaurant Delivery and Food Availability on Online Customer Reviews During the COVID-19 Pandemic

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Avery, Atiya; Xu, Pei

Abstract: Online reviews have become a popular tool for understanding consumers' percepti ... Expand

Abstract: Online reviews have become a popular tool for understanding consumers' perceptions of service and product quality. The COVID-19 pandemic has significantly impacted the restaurant industry, including negative impacts on profitability. This study utilizes gratitude theory and expectation confirmation theory to investigate how food delivery and food desert status moderated the impact of the pandemic on consumers' perceptions of restaurant quality. We find that consumers still held expectations for restaurants despite the COVID-19 pandemic crisis and their lack of food availability. Our findings also highlight the importance of food accessibility and availability, particularly in underprivileged communities during a crisis. This study has broader implications for businesses operating during crisis events providing insights into how they can improve their service and product quality to meet the needs of their consumers. Collapse

Topics: pandemic service quality online review website geographic information system

Methods: product review robustness check

Theories: expectation confirmation theory

No Ground Truth at Sea – Developing High-Accuracy AI Decision-Support for Complex Environments

2023 | HICSS | Citations: 0

Authors: Bumann, Adrian

Abstract: As AI decision-support systems are increasingly developed for applications outsi ... Expand

Abstract: As AI decision-support systems are increasingly developed for applications outside of traditional organizational confinements, developers are confronted with new sources of complexity they need to address. However, we know little about how AI applications are developed for natural use domains with high environmental complexity, stemming from physical influences outside of the developers’ control. This study investigates what challenges emerge from such complexity and how developers mitigate them. Drawing upon a rich longitudinal single-case study on the development of AI decision-support for maritime navigation, findings show that achieving high output accuracy is complicated by the physical environment hindering training data creation. Further, developers chose to reduce the output accuracy and adapt the HMI design to successfully situate the AI application in an existing sociotechnical context. This study contributes to IS literature following recent calls for phenomenon-based examination of emerging challenges when extending the scope frontier of AI and provides practical recommendations for developing AI decision-support for complex environments. Collapse

Topics: artificial intelligence decision support decision support system geographic information system innovation management

Methods: qualitative interview single case study personal interview longitudinal research field study

Theories: socio technical theory

Business Inferences and Risk Modeling with Machine Learning; The case of Aviation Incidents

2023 | HICSS | Citations: 0

Authors: Cankaya, B.; Topuz, K.; Glassman, A.

Abstract: Machine learning becomes truly valuable only when decision-makers begin to depe ... Expand

Abstract: Machine learning becomes truly valuable only when decision-makers begin to depend on it to optimize decisions. Instilling trust in machine learning is critical for businesses in their efforts to interpret and get insights into data, and to make their analytical choices accessible and subject to accountability. In the field of aviation, the innovative application of machine learning and analytics can facilitate an understanding of the risk of accidents and other incidents. These occur infrequently, generally in an irregular, unpredictable manner, and cause significant disruptions, and hence, they are classified as "highimpact, low-probability" (HILP) events. Aviation incident reports are inspected by experts, but it is also important to have a comprehensive overview of incidents and their holistic effects. This study provides an interpretable machine-learning framework for predicting aircraft damage. In addition, it describes patterns of flight specifications detected through the use of a simulation tool and illuminates the underlying reasons for specific aviation accidents. As a result, we can predict the aircraft damage with 85% accuracy and 84% in-class accuracy. Most important, we simulate a combination of possible flight-type, aircraft-type, and pilot-expertise combinations to arrive at insights, and we recommend actions that can be taken by aviation stakeholders, such as airport managers, airlines, flight training companies, and aviation policy makers. In short, we combine predictive results with simulations to interpret findings and prescribe actions.A machine-learning (ML) algorithm was chosen for its fast performance, short run-time, and interpretability. The ML algorithms were compared and evaluated on a confusion matrix for their mean receiver operating characteristic curve (ROC), Collapse

Topics: decision making decision support system graphical user interface usability missing data

Methods: simulation machine learning multinomial logistic regression computational algorithm support vector machine

Outside the Bean Belt: A Study on the Suitability and Sustainability of Coffee Plants in Southern California due to Climate Change using GIS

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Casillas, Vanessa

Abstract: California has a unique climate that is suitable for growing a variety of agric ... Expand

Abstract: California has a unique climate that is suitable for growing a variety of agricultural crops. As stated by the California Department of Food and Agriculture, California's agricultural abundance includes more than 400 commodities, and of these, over a third of the vegetables and three-quarters of the fruits and nuts consumed in the United States, are grown in California. Mark Gaskell, Ph.D., a California Cooperative Extension Farm Advisor, recently began an experiment to grow coffee plants; he believed that due to changing climatic conditions, California farmers could begin to grow coffee plants in their fields. Several farmers agreed to experiment as well and found that the plants did indeed grow. The issue, however, is that farmers would like to know if they can reliably plant coffee on their farms and sustain coffee production for years to come. As a result, this study will utilize the maximum entropy approach, a wellestablished algorithm for modeling habitat suitability over geographical domains, to measure the impact of future climate change on the sustainability of growing coffee plants in California in the years ahead. Layers of worldwide climate data, that represent 19 bioclimatic variables, will be utilized in this study. WorldClim is an online repository that contains historic, monthly, meteorological data averaged across 30 years as well as climate data representing possible future conditions based on various climate prediction models. While this study is focusing on the impact of climate change on coffee plants, it is expected the methodological processes developed, and the resultant findings will be applicable to a wide variety of agricultural crops. Collapse

Topics: geographic information system climate change

Methods: design artifact literature study computational algorithm

Theories: biological theory

Trust in Online Ride-Sharing Transactions: Impacts of Heterogeneous Order Features

2023 | Journal of Management Information Systems | Citations: 0

Authors: Cheng, Xusen; Fu, Shixuan; Sun, Jianshan; Zuo, Meiyun; Meng, Xiangsong

Abstract: With the development of the sharing economy, online ride-sharing has become a pr ... Expand

Abstract: With the development of the sharing economy, online ride-sharing has become a primary form of commuting. Using secondary transaction data, this study investigates the associations between the heterogeneous features and mutual trust in sharing economy-driven online ride-sharing transactions. Based on an examination of 12,404 ride-sharing orders in Beijing, we propose a set of trust distribution maps using order location data to reveal heterogeneous spatial patterns of the relationship between online ride-sharing transactions and mutual trust. The results show that the historical order completion rate and order distance are positively associated with mutual trust in ride-sharing transactions, whereas order time and departure density negatively and significantly influence mutual trust. Furthermore, we use machine learning algorithms to predict trust. The implications for theory and practice and future research directions are discussed. Collapse

Topics: ride sharing platform sharing platform logistics management geographic information system online review

Methods: machine learning computational algorithm regression analysis method experiment exploratory data analysis

Theories: signaling theory

Does the new entrant eat my pie or enlarge my pie? Market entry investigation in the online-to-offline on-demand context

2023 | Decision Support Systems | Citations: 0

Authors: Dai, Hongyan; Ta, La; Xu, Xun

Abstract: The facilitation of information technology has led to the rapid development of o ... Expand

Abstract: The facilitation of information technology has led to the rapid development of online-to-offline commerce, and this mode is attracting increasing businesses to enter the market. Its unique on-demand features affect con­ sumers’ shopping behaviors. This study uses a difference-in-difference approach with propensity score matching and machine learning approaches to examine the impact of a new giant online-to-offline entrant on consumers’ ordering and spending behavior. We find that overall, the new entrant has a negative impact on consumers’ ordering and spending from the perspective of existing providers. This negative impact is amplified if the con­ sumers are highly engaged on the platform. However, the new entrant effect is reduced if the providers use inhouse rather than outsourced deliverymen or consumers have a relatively long distance from the new entrant. Furthermore, the new entrant effect is altered if the platform implements technological innovation. On a provider level, the new entrant has a direct negative impact on consumers’ ordering and spending from homogenous providers; however, a positive spillover effect is observed in terms of increasing consumers’ spending on het­ erogeneous providers. The direct effect occurs immediately, while the spillover effect appears in a time-lagged pattern. On a product level, we find that the new entrant negatively affects consumers’ ordering and spending on substitute products also sold by the new entrant, but that their spending on non-substitute products is boosted. Our findings provide decision support for the new entrant and existing providers to expand the market to stimulate consumers’ purchase from both, to enhance their financial performance. Collapse

Topics: decision support digital innovation innovation management electronic commerce financial performance

Methods: experimental group k-means clustering machine learning computational algorithm Student's t-test

The “holiday effect” in consumer satisfaction: Evidence from review ratings

2023 | Information & Management | Citations: 0

Authors: Deng, Lingfei; Ye, Qiang; Xu, DaPeng; Sun, Fangfang

Abstract: This study explores whether and how a holiday effect exists in consumers’ review ... Expand

Abstract: This study explores whether and how a holiday effect exists in consumers’ review rating behavior, which is well explained by expectancy-disconfirmation theory (EDT). After utilizing the secondary data of online restaurant reviews and conducting an online experiment, we find that consumers have the tendency to form high expec­ tations before holidays, thus leading to more negative disconfirmation and bad moods, which finally induce their negative review rating behavior on holidays. The consumer heterogeneity analyses provide evidence that con­ sumers who are tourists, tourists with less travel experience, and male natives are more likely to post lower review ratings on holidays. Collapse

Topics: online review service quality user-generated content consumer behavior website

Methods: experiment experimental group regression analysis method product review survey

Using Isolation Forest and Alternative Data Products to Overcome Ground Truth Data Scarcity for Improved Deep Learning-based Agricultural Land Use Classification Models

2023 | HICSS | Citations: 0

Authors: García Pereira, Agustín; Porwol, Lukasz; Ojo, Adegboyega

Abstract: High-quality labelled datasets represent a cornerstone in the development of dee ... Expand

Abstract: High-quality labelled datasets represent a cornerstone in the development of deep learning models for land use classification. The high cost of data collection, the inherent errors introduced during data mapping efforts, the lack of local knowledge, and the spatial variability of the data hinder the development of accurate and spatially-transferable deep learning models in the context of agriculture. In this paper, we investigate the use of Isolation Forest (IF), an anomaly detection algorithm, to reduce noise in a large-scale, low-resolution alternative ground truth dataset used to train land use deep learning models. We use a modest-size, high-resolution and high-fidelity manually collected ground-truth dataset to calibrate Isolation Forest parameters and evaluate our approach, highlighting the relatively low cost of the methodology. Our data-centric methodology demonstrates the efficacy of deep learning methods coupled with IF to create mid-resolution land-use models and map products for agriculture using an alternative ground-truth dataset. Moreover, we compare our deep learning approach with a traditional algorithm used in remote sensing and evaluate the spatial transferability of the created models. Finally, we reflect upon the lessons learnt and future work. Collapse

Topics: cloud computing Python programming language open source geographic information system

Methods: experiment deep learning computational algorithm isolation forest random forest classification

Estimation Method for Roof‐damaged Buildings from Aero-Photo Images During Earthquakes Using Deep Learning

2023 | Information Systems Frontiers | Citations: 0

Authors: Fujita, Shono; Hatayama, Michinori

Abstract: Issuing a disaster certificate, which is used to decide the contents of a victim ... Expand

Abstract: Issuing a disaster certificate, which is used to decide the contents of a victim’s support, requires accuracy and rapidity. However, in Japan at large, issuing of damage certificates has taken a long time in past earthquake disasters. Hence, the government needs a more efficient mechanism for issuing damage certificates. This study developed an estimation system of roof-damaged buildings to obtain an overview of earthquake damage based on aero-photo images using deep learning. To provide speedy estimation, this system utilized the trimming algorithm, which automatically generates roof image data using the location information of building polygons on GIS (Geographic Information System). Consequently, the proposed system can estimate, if a house is covered with a blue sheet with 97.57 % accuracy and also detect whether a house is damaged, with 93.51 % accuracy. It would therefore be worth considering the development of an image recognition model and a method of collecting aero-photo data to operate this system during a real earthquake. Collapse

Topics: earthquake database system geographic information system

Methods: computational algorithm deep learning machine learning experiment survey

Take It or Not? Impact of Taking Investments from Tech Giants on IT Startups’ Future Funding

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Huang, Linmei; Wang, Shuting (Ada); Lang, Karl

Abstract: While tech giants have become important external equity financing sources, it r ... Expand

Abstract: While tech giants have become important external equity financing sources, it remains unclear whether ITstartups should take investments from tech giants. In this study we empirically examine how taking investments from tech giants influences future funding of IT-startups. Our results indicate that taking investments from tech giants significantly reduces the amount of funding IT-startups could obtain in the following rounds. Additionally, we find that social ties between IT-startups' regions and locations with substantial capital will attenuate such negative impact of taking investments from tech giants. We further explore the mechanisms. Results suggest that when tech giants make investments in IT-startups, they predominate the new technologies by applying for more patents in the relevant fields and crowd out new investors. We contribute to the literature in IS by examining how investments from tech giants may influence the growth of IT-startups and how social ties may play a role in this interaction. Collapse

Topics: financial performance systems development artificial intelligence innovation management competitive advantage

Methods: propensity score matching experimental group theory development robustness check