Managing an extreme event like a healthcare disaster requires accurate information about the event's circumstances to comprehend the full consequences of acting. However, information quality is rarely optimal since it takes time to determine the information of relevance. The COVID-19 pandemic showed that even official data sources are far from optimal since they suffer from reporting delays that slow decision-making. To support decision-makers with timely information, we utilize data from online social networks to propose an adaptable information extraction solution to create indices helping to forecast COVID-19 case numbers and hospitalization rates. We show that combining heterogeneous data sources like Twitter and Reddit can leverage these sources' inherent complementarity and yield better predictions than those using a single data source alone. We further show that the predictions run ahead of the official COVID-19 incidences by up to 14 days. Additionally, we highlight the importance of model adjustments whenever new information becomes available or the underlying data changes by observing distinct changes in the presence of specific symptoms on Reddit.

This paper investigates how information timeliness and richness affect public engagement using text data from China's largest social media platform during times of the COVID-19 pandemic. We utilize a similarity calculation method based on natural language processing (NLP) and text mining to evaluate three dimensions of information timeliness: retrospectiveness, immediateness, and prospectiveness. Public engagement is divided into breadth and depth. The empirical results show that information retrospectiveness is negatively associated with public engagement breadth but positively with depth. Both information immediateness and prospectiveness improved the breadth and depth of public engagement. Interestingly, information richness has a positive moderating effect on the relationships between information retrospectiveness, prospectiveness, and public engagement breadth but no significant effects on immediateness; meanwhile, it has a negative moderating effect on the relationship between retrospectiveness and depth but a positive effect on immediateness, prospectiveness. In the extension analysis, we constructed a supervised NLP model to identify and classify health emergency-related information (epidemic prevention and help-seeking) automatically. We find that public engagement differs in the two emergency-related information categories. The findings can promote a more responsive public health strategy that magnifies the transfer speed for critical information and mitigates the negative impacts of information uncertainty or false information.

The COVID-19 pandemic has had a severe impact on mankind, causing physical suffering and deaths across the globe. Even those who have not contracted the virus have experienced its far-reaching impacts, particularly on their mental health. The increased incidences of psychological problems, anxiety associated with the infection, social restrictions, economic downturn, etc., are likely to aggravate with the virus spread and leave a longer impact on humankind. These reasons in aggregation have raised concerns on mental health and created a need to identify novel precursors of depression and suicidal tendencies during COVID-19. Identifying factors affecting mental health and causing suicidal ideation is of paramount importance for timely intervention and suicide prevention. This study, thus, bridges this gap by utilizing computational intelligence and Natural Language Processing (NLP) to unveil the factors underlying mental health issues. We observed that the pandemic and subsequent lockdown anxiety emerged as significant factors leading to poor mental health outcomes after the onset of COVID-19. Consistent with previous works, we found that psychological disorders have remained pre-eminent. Interestingly, financial burden was found to cause suicidal ideation before the pandemic, while it led to higher odds of depressive (non-suicidal) thoughts for individuals who lost their jobs. This study offers significant implications for health policy makers, governments, psychiatric practitioners, and psychologists.

Travel restriction measures have been widely implemented to curb the continued spread of COVID-19 during the Chinese Lunar New Year celebrations. Many operation lines and train schedules of China's railway were either heavily adjusted or canceled. In this study, a mixed-integer linear programming model and a two-step solution algorithm were developed to handle such large-scale adjustments. The formulation considers a flexible time window for each operation line and locomotive traction operations, and minimizes the number of locomotives utilized with their total idle time for train rescheduling and locomotive assignment, respectively. The solution algorithm determines the minimum locomotive fleet size based on the optimal train rescheduling results; it then reduces the traction idle time of locomotives. In response to the uncertainty of COVID-19, two tailored approaches were also designed to recover and remove operation lines, which can insert and cut operation lines based on the results of locomotive assignment. Finally, we conducted a case study of the Beijing-Tianjin intercity railway from the start of the COVID-19 outbreak to the recovery of operations.

The COVID-19 pandemic has become a crucial public health problem in the world that disrupted the lives of millions in many countries including the United States. In this study, we present a decision analytic approach which is an efficient tool to assess the effectiveness of early social distancing measures in communities with different population characteristics. First, we empirically estimate the reproduction numbers for two different states. Then, we develop an age-structured compartmental simulation model for the disease spread to demonstrate the variation in the observed outbreak. Finally, we analyze the computational results and show that early trigger social distancing strategies result in smaller death tolls; however, there are relatively larger second waves. Conversely, late trigger social distancing strategies result in higher initial death tolls but relatively smaller second waves. This study shows that decision analytic tools can help policy makers simulate different social distancing scenarios at the early stages of a global outbreak. Policy makers should expect multiple waves of cases as a result of the social distancing policies implemented when there are no vaccines available for mass immunization and appropriate antiviral treatments.

One of the major challenges that confront medical experts during a pandemic is the time required to identify and validate the risk factors of the novel disease and to develop an effective treatment protocol. Traditionally, this process involves numerous clinical trials that may take up to several years, during which strict preventive measures must be in place to control the outbreak and reduce the deaths. Advanced data analytics techniques, however, can be leveraged to guide and speed up this process. In this study, we combine evolutionary search algorithms, deep learning, and advanced model interpretation methods to develop a holistic exploratory-predictive-explanatory machine learning framework that can assist clinical decision-makers in reacting to the challenges of a pandemic in a timely manner. The proposed framework is showcased in studying emergency department (ED) readmissions of COVID-19 patients using ED visits from a real-world electronic health records database. After an exploratory feature selection phase using genetic algorithm, we develop and train a deep artificial neural network to predict early (i.e., 7-day) readmissions (AUC = 0.883). Lastly, a SHAP model is formulated to estimate additive Shapley values (i.e., importance scores) of the features and to interpret the magnitude and direction of their effects. The findings are mostly in line with those reported by lengthy and expensive clinical trial studies.

Prior research has confirmed the importance of IT-business alignment (ITBA) and big data analytics capability (BDAC) in supporting firms' strategic decision-making under normal circumstances. However, the global outbreak of COVID-19 has significantly changed firms' strategic decision-making landscapes and raised questions regarding the effects of ITBA and BDAC on strategic decision-making as conditioned by COVID-19 characteristics. In this study, we contextualize two important event impact factors (i.e., event criticality and event disruption) in the context of COVID-19 and examine their contingent roles in the effects of ITBA and BDAC on strategic decision-making. Our analyses, based on two-round, multi-respondents matched survey data collected from 175 Chinese firms to elucidate the differential moderating roles of event criticality and disruption of COVID-19 in the impact of ITBA and BDAC on strategic decision speed and quality. The results indicate the event criticality of COVID-19 strengthens the effects of ITBA on decision speed and quality but weakens the influence of BDAC on decision quality. Meanwhile, the event disruption of COVID-19 weakens the influence of ITBA on decision speed and quality but strengthens the effect of BDAC on decision speed and quality. These findings have important theoretical and practical implications, which we discuss in the conclusion.

The COVID-19 pandemic and the lockdown bore a devastating impact on organizations across the globe. In this crisis, organizations belonged to the victim cluster, with a low crisis responsibility. Nevertheless, organizations needed to strategize their crisis responses and communicate with stakeholders to reduce the threat to reputational capital and manage stakeholder reactions in the pandemic. In this paper, we studied organizational Twitter communication during the COVID-19 crisis through the lens of the situational crisis communication theory (SCCT). We analyzed 325,627 tweets collected from the Twitter pages of 464 organizations belonging to the Fortune 500 list. The Twitter data reflected organizational COVID-19 crisis response strategies and demonstrated organizational use of Twitter for crisis communication. We applied lexicon-based emotion mining to identify and measure emotions, and topic mining to measure crisis response topic scores from this large multi-organization dataset. We performed path analysis to test our research model derived from the SCCT. The analysis showed that instructing and adjusting information can minimize threats to organizational reputation in a victim crisis and manage stakeholder reactions. Positive emotions showed a stronger association with behavioral outcomes. Emotion neutral tweets generated more favorable stakeholder reactions. The paper contributes to the literature on situational crisis communication for a victim crisis. The multi-organization data addresses the sensitive inter-organization dependencies and improves the understanding of crisis communication. It provides practitioners an insight into the effect of the COVID-19 crisis response strategies on stakeholder emotions and behavior.

The COVID-19 pandemic led to a great deal of financial uncertainty in the stock market. An initial drop in March 2020 was followed by unexpected rapid growth over 2021. Therefore, financial risk forecasting continues to be a central issue in financial planning, dealing with new types of uncertainty. This paper presents a stock market forecasting model combining a multi-layer perceptron artificial neural network (MLP-ANN) with the traditional Altman -Score model. The contribution of the paper is presentation of a new hybrid enterprise crisis warning model combining Z-score and MLP-ANN models. The new hybrid default prediction model is demonstrated using Chinese data. The results of empirical analysis show that the average correct classification rate of thew hybrid neural network model (99.40%) is higher than that of the Altman -score model (86.54%) and of the pure neural network method (98.26%). Our model can provide early warning signals of a company's deteriorating financial situation to managers and other related personnel, investors and creditors, government regulators, financial institutions and analysts and others so that they can take timely measures to avoid losses.

Crowdfunding was first used by individuals and entrepreneurs to collect small-sized investments from crowds to support for-profit ventures, but now it is being touted as a valuable alternative to raise money for non-profit causes. Similar to various online settings, a key challenge for online charitable crowdfunding platform is the problem of donor retention. In this research, we disentangle donor retention behavior and build up a structural model to jointly examine donors' donation and latent attrition. By incorporating donation relationship and action related covariates into the model, we illustrate the drivers of donor retention and quantitatively examine their influence on individual donor's contribution and attrition activity. After calibrating the model with longitudinal donation transaction data from a leading charitable crowdfunding platform which enables teachers to request materials and resources for their classrooms, we find that (1) Teacher-donors (people who can be both donation makers and fundraisers) usually exhibit higher donation rate and lower attrition rate than normal donors on the platform; (2) Compared with site-donors (donors directly acquired through website visit), donors acquired through teacher referral usually have lower contribution and attrition rates; (3) The provided "" and "" prosocial marketing programs on the platform seem to be a double-edged sword to donor retention. They have positive impact on donors' contribution rate, at the same time, they significantly increase donors' attrition rate; (4) Donors' initial contribution amount to the platform, successful donation result and "" feedback from fundraisers can significantly decrease their attrition rate. Our results provide insights on new donor acquisition and donor relationship management in online charitable crowdfunding market.

In recent years, new types of interactive analytical dashboard features have emerged for operational decision support systems (DSS). Analytical components of such features solve optimization problems hidden from the human eye, whereas interactive components involve the individual in the optimization process via graphical user interfaces (GUIs). Despite their expected value for organizations, little is known about the effectiveness of interactive analytical dashboards in operational DSS or their influences on human cognitive abilities. This paper contributes to the closing of this gap by exploring and empirically testing the effects of interactive analytical dashboard features on situation awareness (SA) and task performance in operational DSS. Using the theoretical lens of SA, we develop hypotheses about the effects of a what-if analysis as an interactive analytical dashboard feature on operational decision-makers' SA and task performance. The resulting research model is studied with a laboratory experiment, including eye-tracking data of 83 participants. Our findings show that although a what-if analysis leads to higher task performance, it may also reduce SA, nourishing a potential out-of-the-loop problem. Thus, designers and users of interactive analytical dashboards have to carefully mitigate these effects in the implementation and application of operational DSS. In this article, we translate our findings into implications for designing dashboards within operational DSS to help practitioners in their efforts to address the danger of the out-of-the-loop syndrome.

The recent COVID-19 pandemic has clearly shown how agricultural foods and e-commerce initiatives are critical for many organizations, regions, and countries worldwide. Despite this vital importance, prior IS research on the business value of IT has not paid enough attention to the potential specificities of the agribusinesses. This study examines the impact of e-commerce capability on business agility in agribusinesses. Using a sample of Chinese agriculture firms, we find that: 1) The e-commerce capability of agribusinesses enables two types of business agility: market capitalizing agility and operational adjustment agility, and 2) while environmental complexity positively moderates the effects of e-commerce capability on the market capitalizing agility and operational adjustment agility, environmental dynamism does not. This study contributes to the IS research on the business value of IT by providing an eloquent theoretical explanation and empirical evidence on how e-commerce capability help agricultural firms to thrive through complexity by enabling market capitalizing agility (strategic focus) and operational adjustment agility (operational focus).

In this paper, we propose a comprehensive analytics framework that can serve as a decision support tool for HR recruiters in real-world settings in order to improve hiring and placement decisions. The proposed framework follows two main phases: a local prediction scheme for recruitments' success at the level of a single job placement, and a mathematical model that provides a global recruitment optimization scheme for the organization, taking into account multilevel considerations. In the first phase, a key property of the proposed prediction approach is the interpretability of the machine learning (ML) model, which in this case is obtained by applying the Variable-Order Bayesian Network (VOBN) model to the recruitment data. Specifically, we used a uniquely large dataset that contains recruitment records of hundreds of thousands of employees over a decade and represents a wide range of heterogeneous populations. Our analysis shows that the VOBN model can provide both high accuracy and interpretability insights to HR professionals. Moreover, we show that using the interpretable VOBN can lead to unexpected and sometimes counter-intuitive insights that might otherwise be overlooked by recruiters who rely on conventional methods. We demonstrate that it is feasible to predict the successful placement of a candidate in a specific position at a pre-hire stage and utilize predictions to devise a global optimization model. Our results show that in comparison to actual recruitment decisions, the devised framework is capable of providing a balanced recruitment plan while improving both diversity and recruitment success rates, despite the inherent trade-off between the two.

Content sharing platforms such as product review websites largely depend on reviewers' voluntary contributions. In order to motivate reviewers to contribute more, many platforms established incentive mechanisms, either reputation-based or financial. Yet most of the existing research has focused on reputations that are everlasting, such as badges and virtual points, or financial rewards where no evaluation exists about the users' contributed content, such as rebates. There is still a significant gap in our understanding of how incentives with reevaluation mechanism actually influence reviewers' behaviors such as their contribution levels, the opinion they express, and how they express. In this paper, we fill this gap using data collected from Yelp Elite Squad where reviewers with good reviewing history are awarded into the elite group and most importantly reevaluated each year. We draw from the accountability theory and conduct a difference-in-differences analysis to empirically study the effect of incentives with reevaluation mechanism on reviewers' behaviors in both short term and long term. The results show that in short term, reviewers significantly increase their contribution levels, become more conservative with lower percentage of extreme ratings, and also increase the readability of their reviews. In long term, they continue improving the quality of reviews though their numerical rating behaviors stabilize. Our research has significant implications for business models that rely on user contributions.

Online smoking cessation communities help hundreds of thousands of smokers quit smoking and stay abstinent each year. Content shared by users of such communities may contain important information that could enable more effective and personally tailored cessation treatment recommendations. This study demonstrates a novel approach to determine individuals' smoking status by applying machine learning techniques to classify user-generated content in an online cessation community. Study data were from BecomeAnEX.org, a large, online smoking cessation community. We extracted three types of novel features from a post: domain-specific features, author-based features, and thread-based features. These features helped to improve the smoking status identification (quit vs. not) performance by 9.7% compared to using only text features of a post's content. In other words, knowledge from domain experts, data regarding the post author's patterns of online engagement, and other community member reactions to the post can help to determine the focal post author's smoking status, over and above the actual content of a focal post. We demonstrated that machine learning methods can be applied to user-generated data from online cessation communities to validly and reliably discern important user characteristics, which could aid decision support on intervention tailoring.

Toy-related injuries account for a significant number of childhood injuries and the prevention of these injuries remains a goal for regulatory agencies and manufacturers. Text-mining is an increasingly prevalent method for uncovering the significance of words using big data. This research sets out to determine the effectiveness of text-mining in uncovering potentially dangerous children's toys. We develop a danger word list, also known as a 'smoke word' list, from injury and recall text narratives. We then use the smoke word lists to score over one million Amazon reviews, with the top scores denoting potential safety concerns. We compare the smoke word list to conventional sentiment analysis techniques, in terms of both word overlap and effectiveness. We find that smoke word lists are highly distinct from conventional sentiment dictionaries and provide a statistically significant method for identifying safety concerns in children's toy reviews. Our findings indicate that text-mining is, in fact, an effective method for the surveillance of safety concerns in children's toys and could be a gateway to effective prevention of toy-product-related injuries.

The prevalence of social media has greatly catalyzed the dissemination and proliferation of online memes (e.g., ideas, topics, melodies, tags, etc.). However, this information abundance is exceeding the capability of online users to consume it. Ranking memes based on their popularities could promote online advertisement and content distribution. Despite such importance, few existing work can solve this problem well. They are either daunted by unpractical assumptions or incapability of characterizing dynamic information. As such, in this paper, we elaborate a model-free scheme to rank online memes in the context of social media. This scheme is capable to characterize the nonlinear interactions of online users, which mark the process of meme diffusion. Empirical studies on two large-scale, real-world datasets (one in English and one in Chinese) demonstrate the effectiveness and robustness of the proposed scheme. In addition, due to its fine-grained modeling of user dynamics, this ranking scheme can also be utilized to explain meme popularity through the lens of social influence.

Technologies for automated detection of neonatal seizures are gradually moving towards cot-side implementation. The aim of this paper is to present different ways to visualize the output of a neonatal seizure detection system and analyse their influence on performance in a clinical environment. Three different ways to visualize the detector output are considered: a binary output, a probabilistic trace, and a spatio-temporal colormap of seizure observability. As an alternative to visual aids, audified neonatal EEG is also considered. Additionally, a survey on the usefulness and accuracy of the presented methods has been performed among clinical personnel. The main advantages and disadvantages of the presented methods are discussed. The connection between information visualization and different methods to compute conventional metrics is established. The results of the visualization methods along with the system validation results indicate that the developed neonatal seizure detector with its current level of performance would unambiguously be of benefit to clinicians as a decision support system. The results of the survey suggest that a suitable way to visualize the output of neonatal seizure detection systems in a clinical environment is a combination of a binary output and a probabilistic trace. The main healthcare benefits of the tool are outlined. The decision support system with the chosen visualization interface is currently undergoing pre-market European multi-centre clinical investigation to support its regulatory approval and clinical adoption.

Organizations today regularly share their customer data with their partners to gain competitive advantages. They are also often requested or even required by a third party to provide customer data that are deemed sensitive. In these circumstances, organizations are obligated to protect the privacy of the individuals involved while still benefiting from sharing data or meeting the requirement for releasing data. In this study, we analyze the tradeoff between privacy and data utility from the perspective of the data owner. We develop an incentive-compatible mechanism for the data owner to price and disseminate private data. With this mechanism, a data user is motivated to reveal his true purpose of data usage and acquire the data that suits to that purpose. Existing economic studies of information privacy primarily consider the interplay between the data owner and the individuals, focusing on problems that occur in the of private data. This study, however, examines the privacy issue facing a data owner organization in the of private data to a third party data user when the real purpose of data usage is unclear and the released data could be misused.

Over time, data warehouse (DW) systems have become more difficult to develop because of the growing heterogeneity of data sources. Despite advances in research and technology, DW projects are still too slow for pragmatic results to be generated. Here, we address the following question: To answer this, we proposed methodological guidelines based on cycles of conceptual modeling and data analysis, to drive construction of a modular DW system. These guidelines were applied to the blood donation domain, successfully reducing the complexity of DW development.