Policy Change, Social and Cultural Capital and Medical Crowdfunding Use: A Quasi-Natural Experiment

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Liu, Yuanyuan; Fan, Rui; Ravichandran, T.

Abstract: This study investigates the impact of health policy, specifically the Individua ... Expand

Abstract: This study investigates the impact of health policy, specifically the Individual Mandate Repeal (IMR), on the use of medical crowdfunding. We employ a difference-in-differences research design and find that IMR has led to an increase in medical crowdfunding usage, with significant heterogeneity among users. Specifically, individuals with high cultural capital and bridging social capital, but low bonding social capital, are more likely to increase their use of medical crowdfunding. These results suggest that the sociotechnology for healthcare financing purpose (i.e., online medical crowdfunding) responds to the gap of the Affordable Care Act (i.e., IMR), and its usage is shaped by individual differences in cultural and social capital. The policy implication is that medical crowdfunding platform provides opportunities to take on the shortcomings of health policy, but the opportunity exploration or its usages are disparate across populations with varying levels of capital. Public health policymakers may develop policies fostering bridging social capital to expand the healthcare affordable opportunities for underserved populations without imposing taxes and mandate regulations. KeywordsMedical Crowdfunding Usage, individual mandate repeal, opportunity, forms of capital, cultural capital, bridging social capital, bonding social capital. Collapse

Topics: crowdsourcing crowdfunding social capital legal context social network

Methods: longitudinal research difference in differences quasi experiment experimental group

Fake review detection system for online E-commerce platforms: A supervised general mixed probability approach

2023 | Decision Support Systems | Citations: 0

Authors: Luo, Jiwei; Luo, Jian; Nan, Guofang; Li, Dahui

Abstract: Online consumer reviews play an important role in helping consumers judge the qu ... Expand

Abstract: Online consumer reviews play an important role in helping consumers judge the quality and authenticity of products on e-commerce platforms. However, the constant presence of fake reviews on these platforms has significantly impacted the operation and development of e-commerce platforms. In this study, we develop a novel supervised probabilistic method to detect fake reviews by utilizing the difference in the distribution of nonfraudulent reviews and that of fake reviews. Specifically, we first derive the univariate distributions of several unique features (linguistic, behavioral, and interrelationship features). We then integrate these distributions into two mixed distributions according to their labels to represent the overall difference between non-fraudulent reviews and fake reviews. Next, we randomly generate synthetic review data points with different labels from the above mixed distributions. Finally, we train a Multilayer Perceptron model by using these synthetic review data to obtain a classifier. We conducted several experiments to test the model using several original real-world review datasets. Numerical results indicated that the proposed supervised method outperformed some wellknown sampling models and fake review detection methods, in terms of classification accuracy. Moreover, we extend the proposed method to handle the scenarios with small samples of raw review data. This study con­ tributes to the literature by exploiting the difference in the distribution of non-fraudulent reviews and that of fraudulent reviews, which can improve the accuracy of fake review detection for online platforms. Collapse

Topics: fake review electronic commerce big data decision support system user behavior

Methods: computational algorithm experiment machine learning semi-supervised learning artificial neural network

Activist Protest Spillovers into the Regulatory Domain: Theory and Evidence from the U.S. Nuclear Power Generation Industry

2022 | Organization Science | Citations: 0

Authors: Fremeth, Adam R.; Holburn, Guy L. F.; Piazza, Alessandro

Abstract: We examine how social activism—in the form of public protests against contentiou ... Expand

Abstract: We examine how social activism—in the form of public protests against contentious business practices—can spill over into the regulatory domain, extending beyond activists’ articulated goals to affect firms’ regulatory outcomes in areas that are not directly targeted. We argue that firms are likely to experience broader regulatory repercussions after activist protests because public contention invites greater scrutiny of firm behavior by industry regulators, increasing the likelihood that instances of organizational noncompliance will be discovered. Protests can also cause regulators to evaluate targeted firms more negatively in regulatory assessments, especially firms with less favorable preexisting reputations or stakeholder relations, and to tighten regulations on nontargeted issues that signal their commitment to safeguarding the public interest. We further contend that the political context within which regulatory agencies operate shapes the extent of protest spillovers: When political institutions are aligned with activist goals, and when regulators are ideologically sympathetic too, protests have a more pronounced negative impact on firms’ regulatory outcomes in nontargeted domains. We find robust support for our predictions in a statistical analysis of the impact of antinuclear protests—which sought to block nuclear power plant development by electric utilities—on utilities’ subsequent regulated financial rates of return on their assets. Our analysis contributes new insights to research on the indirect consequences for targeted organizations of social activism. Collapse

Topics: database system organizational productivity website internet technology missing data

Methods: statistical hypothesis test regression analysis method longitudinal research descriptive statistic archival research

Theories: theory of economic growth

Fraudulent review detection model focusing on emotional expressions and explicit aspects: investigating the potential of feature engineering

2022 | Decision Support Systems | Citations: 5

Authors: Kumar, Ajay; Gopal, Ram D.; Shankar, Ravi; Tan, Kim Hua

Abstract: Reading customer reviews before purchasing items online has become a common prac ... Expand

Abstract: Reading customer reviews before purchasing items online has become a common practice; however, some companies use machine learning (ML) algorithms to generate false reviews in order to create positive brand images of their own products and negative images of competitors' offerings. Existing techniques use review content to identify fraudulent reviewers; however, spammers become more intelligent, started to learn from their mistakes, and changed their tactics in order to avoid detection techniques. Thus, investigating fraudulent ac­ counts' behaviour of generating fake negative or positive reviews for competitors or themselves and the necessity of ML classifiers to identify fraudulent reviews, is more important than ever. In this research, we present a novel feature engineering approach in which we (1) extract several “review-centric” and “reviewer-centric” features from a dataset; (2) combine the cumulative effects of features distributions into a unified model that represents overall behavior of the fraudulent reviewers; (3) investigate the role of effective data pre-processing to improve detection accuracy; and (4) develop a probabilistic approach to detect fraudulent reviewers by learning a novel M-SMOTE model over a derived balanced dataset and feature distributions, which outperforms other ML models. Our study contributes to the literature on digital platforms and fraudulent review detection with significant managerial and theoretical implications through these novel findings. Collapse

Topics: online review electronic commerce website marketing management logistics management

Methods: feature engineering machine learning computational algorithm XGBoost product review

Impact of Ride-Hailing Services on Transportation Mode Choices: Evidence from Traffic and Transit Ridership

2022 | Management Information Systems Quarterly | Citations: 0

Authors: Lee, Kyunghee; Jin, Qianran (Jenny); Animesh, Animesh; Ramaprasad, Jui

Abstract: The rise of technology-enabled ride-hailing services has affected individuals’ t ... Expand

Abstract: The rise of technology-enabled ride-hailing services has affected individuals’ transportation-related decisions. The impact of these ride-hailing services likely varies across traveler segments that differ in their usage of various modes of transportation. In this paper, we develop and leverage a framework that allows us to examine the impact of ride-hailing services on the transportation mode choice for three traveler segments: drivers (who primarily use a personal automobile to travel), riders (who primarily use public transit to travel), and walkers (who primarily use non-motorized modes of transport). We first develop a framework outlining how the behavior of different traveler segments would be impacted by the introduction of ride-hailing services and show how this affects traffic congestion and public transportation ridership. To test the framework, we compiled a rich dataset, combining data on public transportation ridership, traffic congestion, and individual transportation mode choice. Employing a difference-in-differences methodology, we show that the Uber entry in a market enabled those who were walkers and riders prior to the entry of Uber to travel more conveniently, leading to an increase in traffic congestion, and induced those who were drivers to substitute their use of private automobiles with a combination of Uber and public transit. We introduced urban compactness to assess the heterogeneous impact of ride-hailing services for cities that differ in their distribution of traveler segments. We found that Uber entry increases traffic congestion and reduces public transit demand more in cities with higher levels of urban compactness, i.e., where the proportion of riders and walkers is higher than that of drivers. This work provides a holistic framework to understand the mechanism underlying the impact of ride-hailing services on public transit and traffic congestion. Urban planners and policy makers can leverage our framework, methodology, and empirical results to guide city planning decisions that have implications for sustainability. Collapse

Topics: Uber ride sharing platform societal evaluation criteria Lyft mobile application

Methods: survey experimental group descriptive statistic difference in differences longitudinal research

Functional IT Complementarity and Hospital Performance in the United States: A Longitudinal Investigation

2022 | Information Systems Research | Citations: 9

Authors: Mishra, Abhay Nath; Tao, Youyou; Keil, Mark; Oh, Jeong-ha (Cath)

Abstract: This paper examines complementarity between clinical health information technolo ... Expand

Abstract: This paper examines complementarity between clinical health information technology (HIT) applications and their effects on three hospital-level performance measures: clinical quality, experiential quality, and healthcare cost. We emphasize two aspects of HIT use in hospitals. First, we focus on whether HIT applications are used to perform primary or support clinical functions. Contingent on whether the use of HIT applications is for performing only primary functions or both primary and support functions, we conceptualize symbiotic and pooled HIT complementarity, respectively. Second, we focus on whether HIT applications are implemented in the same time period or different time periods. Contingent on this temporal aspect, we conceptualize simultaneous and sequential HIT complementarity, respectively. We collected panel data on HIT implementation, clinical quality, experiential quality, and healthcare cost for 715 hospitals in the United States from four sources. Our results suggest that symbiotic, pooled, simultaneous, and sequential complementarity among HITs impact hospital quality and cost outcomes. Our results further indicate that these complementary effects differ across chronic and acute conditions. We also find that three-way complementarity has significant economic effects on quality and cost. In fact, post hoc analyses indicate that three-way sequential complementarity effects, which have not been previously examined, are particularly significant. This paper contributes to the literature by empirically examining different forms of HIT complementarity in hospitals. Our central message is that when assessing HIT value in hospitals, managers and researchers must pay attention to (1) the clinical functions to which these technologies are applied; (2) the sequence in which these HITs are implemented; and (3) the prevalence of chronic versus acute patients admitted in the hospital. Collapse

Topics: network effect health information system enterprise resource planning IS impact systems implementation

Methods: survey longitudinal research descriptive statistic theory development design artifact

Improving Imbalanced Machine Learning with Neighborhood-Informed Synthetic Sample Placement

2022 | Journal of Management Information Systems | Citations: 0

Authors: Nasir, Murtaza; Dag, Ali; Simsek, Serhat; Ivanov, Anton; Oztekin, Asil

Abstract: Machine learning is widely used in information systems design. Yet, training alg ... Expand

Abstract: Machine learning is widely used in information systems design. Yet, training algorithms on imbalanced datasets may severely affect performance on unseen data. For example, in some cases in healthcare, fintech, or cybersecurity contexts, certain subclasses are difficult to learn because they are underrepresented in training data. Our study offers a flexible and efficient solution based on a new synthetic average neighborhood sampling algorithm (SANSA), which, in contrast to other solutions, introduces a novel "placement" parameter that can be tuned to adapt to each dataset's unique manifestation of the imbalance. This package can be downloaded for R1. We tested SANSA against seven existing sampling methods used in conjunction with the four most frequently used machine learning models trained on 14 benchmark datasets. Our results provide suggestive evidence that SANSA offers a feasible solution to the imbalance problem for most datasets. Our findings provide practical recommendations for how SANSA can be effectively implemented while reducing the complexity level of an imbalanced learning pipeline. Collapse

Topics: marketing management customer service online review logistics management credit card fraud

Methods: computational algorithm machine learning support vector machine artificial neural network descriptive statistic

Utilizing Adaptive AI-based Information Systems to Analyze the Effectiveness of Policy Measures in the Fight of COVID-19

2020 | International Conference on Information Systems | Citations: 0

Authors: Baier, Lucas; Schoeffer, Jakob; Kã, Niklas

Abstract: A novel coronavirus has been spreading around the world since early 2020. Due to ... Expand

Abstract: A novel coronavirus has been spreading around the world since early 2020. Due to its high infectiousness and lethality, countries around the world have adopted drastic policy measures to contain the pandemic—with far-reaching implications for economies and societies. We analyze the effect of two nonpharmaceutical interventions (NPI), namely closure of schools as well as a lockdown, on the development of daily case numbers of COVID-19 patients in several countries and US states. By applying Adaptive AIbased Information Systems (AAIS) based on concept drift detection, we conclude that it takes more than two weeks on average until NPIs show a significant effect on the number of new cases. This information is crucial for policymakers as they are currently confronted with difficult decisions to trade off the reopening of the economy with a strict containment of the virus. Collapse

Topics: artificial intelligence pandemic systems development accounting digital dashboard

Methods: computational algorithm time series analysis longitudinal research conceptual modelling regression analysis method

Mitigating Traffic Congestion: The Role of Intelligent Transportation Systems

2020 | Information Systems Research | Citations: 16

Authors: Cheng, Zhi (Aaron); Pang, Min-Seok; Pavlou, Paul A.

Abstract: Despite massive investments in transportation infrastructure, traffic congestion ... Expand

Abstract: Despite massive investments in transportation infrastructure, traffic congestion remains a major societal and public policy problem. Intelligent transportation systems (ITS) have been proposed as a potential solution to this challenge, but their effectiveness has remained unclear in both research and practice. To understand whether and how ITS affect traffic congestion, we consolidate a unique longitudinal data set on road traffic and the deployment of a large federally supported ITS program in the United States—511 systems—in 99 urban areas between 1994 and 2014. The difference-in-differences estimates show that the adoption of 511 systems is associated with a significant decrease in traffic congestion, saving over $4.7 billion dollars and 175 million hours in travel time annually in U.S. cities. 511 systems also reduce about 53 million gallons of fossil fuel consumption and over 10 billion pounds of CO2 emissions. We offer two theoretical explanations for this effect: (i) ITS help individual commuters to make better travel decisions, and (ii) ITS help local governments to develop an urban traffic management capability. Empirical evidence supports the underlying theoretical mechanisms and shows that ITS help commuters to schedule travel more efficiently, choose better navigation routes, and optimize their worktrip transportation mode. Second, the effect of ITS is contingent on road supply and public transit services. We also find that the traffic-reducing effect of ITS is larger when commuters use more online services for traffic information and when state governments incorporate more functionalities into their 511 systems. This study contributes to the literature on IT capabilities, public-sector IT value, and the societal impact of IT, while also extending the transportation economics to IT-enabled traffic interventions. Finally, we inform policymakers of ITS as a cost-effective means to mitigating traffic congestion. Collapse

Topics: Google+ technology adoption website societal evaluation criteria information technology capability

Methods: longitudinal research instrumental variables estimation theory development difference in differences robustness check

Theories: dynamic capabilities theory

Discovering event episodes from sequences of online news articles: A time-adjoining frequent itemset-based clustering method

2020 | Information & Management | Citations: 0

Authors: Lee, Yen-Hsien; Hu, Paul Jen-Hwa; Zhu, Hongquan; Chen, Hsin-Wei

Abstract: Firms perform environmental surveillance to identify important events and their ... Expand

Abstract: Firms perform environmental surveillance to identify important events and their developments. To alleviate the stringent information processing and analysis requirements, automated methods are needed to discover from online news articles distinct episodes (stages) of an important event. We propose a time-adjoining frequent itemset-based method that incorporates essential temporal characteristics of news articles for event episode discovery. With a corpus of 1468 news articles that pertain to 248 episodes of 53 different events, we empirically evaluate the proposed method and include several prevalent techniques as benchmarks. The results show that our method outperforms the benchmark techniques consistently and significantly, attaining the cluster recall, cluster precision, and F-measure values at 0.706, 0.593, and 0.584, respectively. Collapse

Topics: digital news hurricane earthquake armed conflict tsunami

Methods: cluster analysis machine learning nonparametric test Wilcoxon signed-rank test parametric test