Among modern information technologies, robots help reduce the effort employees expend on tasks that are repetitive and physically demanding. When helping employees, robots may be required to display enhanced efficiency, but such a design can also increase employees' effort required for operational troubleshooting. It is not yet known whether effort saving (i.e., increasing nurses' time and energy saved)

Financial markets, as complex adaptive systems, are characterized by historical data limitations, inherent evolution and non-stationarity, which challenge the effectiveness of deep learning models such as Long Short-Term Memory (LSTM). We address these challenges by generating synthetic data using Agent-Based Modeling (ABM) to simulate complex market conditions through “what-if” scenarios. Our method

This research employs interaction ritual model to explore the two-way interaction between streamers and viewers on a live streaming commerce platform. To be specific, the study investigates the impact of viewers' real-time comments and streamer's dialogue on product sales and follower growth, using minute-level data for detailed analysis. The results show that real-time comments exhibit a nonlinear

Facing the challenge of information overload, some travel websites have introduced systems for travelers to vote on helpful reviews, prompting researchers to focus on the determinants of review helpfulness. While evaluations from multiple reviews may provide travelers with more perspectives, inconsistent information within the reviews may cause confusion. Studies exploring the effects of multiple inconsistencies

This study explores the potential of the metaverse in enhancing job satisfaction for remote employees. With the increasing shift towards remote work, firms are investing more in the metaverse to create dynamic and immersive digital environments. Drawing upon media richness theory, we investigate the roles of gamification and online social connectedness within the metaverse, which are crucial factors

This study presents a significant advancement in Environmental, Social, Governance (ESG) evaluation by addressing critical gaps in transparency, consistency, and industry-specific relevance. The ESG-Keyword integrated bidirectional encoder representations from transformers (ESG-KIBERT) model, developed using advanced natural language processing (NLP) techniques, enhances ESG classification performance

Enterprise forecasting systems often involve modeling a large scale of heterogeneous time series using a pool of candidate algorithms, such as in the case of simultaneous sales forecasts of thousands of stock-keeping units. In such cases, it can be advantageous to automatically monitor and replace algorithms for each time series. We introduce TimeSpeaks, a framework that adapts sequence modeling in

Contemporary research has leveraged social network data as a predictive tool for decision-making process in the capital market. Yet, its effectiveness may be compromised by social contagion. This study addresses this problem by introducing conversation-level measures that capture how interactions among investors affect market predictions. Drawing on social contagion theory, we identified three conversation

While some donors and volunteers do not publicize their prosocial behaviors because of humility, many others fear that disclosing their prosocial behaviors may be perceived as bragging. With the rise of social networking sites (SNSs), this has become an essential issue with important business implications. As more companies encourage employees to volunteer a small portion of their work time and match

User engagement (e.g., likes, shares, and comments) is widely recognized as critical to business success. Although existing studies have explored the determinants of user engagement, relatively little attention has been paid to cognitive load. This study, based on cognitive load theory, expectation confirmation theory, and the stressor-strain-outcome framework, examines the heterogeneous effects of

Propensity Score Matching (PSM) is a widely used method for estimating causal treatment effects, but its performance can be limited in complex scenarios. This paper examines cases where a confounder also serves as an effect modifier and compares the bias-reduction performance of PSM with Inverse Probability Weighting (IPW). Using the University of California, Berkeley graduate admission data as an

By utilizing consumer behavioral data, targeted ads can enhance the click-through rates (CTRs) but, at the same time, cause consumer privacy concerns. In this paper, we investigate whether a streaming platform should purchase external consumer data to improve ad-targeting levels, whereby it gain revenue from cost-per-mille (CPM) and cost-per-click (CPC) advertising. We explore how advertising intensity

Most decision models of system resilience use static, deterministic optimization techniques while focusing on resilience assessment. At present, we lack appropriate decision support methodologies and computational tools that can offer dynamic control of resilience and balance the costs of resilience assurance. This paper presents a stochastic dynamic optimization model, based on an infinite horizon

When shopping online, customers may find user-generated images (UGIs) where existing buyers share their product experiences in an actual setting. Drawing on the constructivist theory of visual perception, we propose a cognitive inference process in which shoppers utilize the background objects in UGIs that contextualize a product (e.g., a snow-covered mountain implying cold weather) to infer its features

Wireless emergency alerts (WEAs) have become a crucial information system to notify residents of potential hazards in their vicinity. Using a large transaction dataset, we investigate (1) how WEAs influence offline and online transactions as a proxy to public mobility, and (2) how different types of information in WEAs affect transactions. Our results indicate that WEAs that only notify the occurrence

The metaverse is a transformative force in supply chain information systems, particularly in the context of decision-making processes focusing on sustainable development goals. Thus, this study examines: How does the metaverse among stakeholders contribute to the supply chain decision-making processes regarding sustainable development goals? This study is among the first to provide empirical data examining

In recent years, many online healthcare communities (OHCs) in China introduced the feature of free live-streamed medical consultations (FLSMC), which allows patients to communicate with physicians and have an interactive consultation for free through live streaming. Despite the rapid growth of FLSMC, little is known about whether FLSMC can bring benefits to patients when they have online consultation

Despite the pervasiveness of artificial intelligence (AI) technologies in the healthcare industry, physicians are reluctant to follow the recommendations suggested by AI-aided diagnostic systems. We conceptualize physicians' noncompliance use of AI-aided diagnostic systems and draw on the technology threat avoidance theory (TTAT) to investigate the phenomenon of interest. Specifically, we leverage

Depression is a serious and recurrent mental illness that significantly affects an individual's life and the society as a whole. Automatic detection of depression is crucial for early intervention and minimizing negative consequences. Existing studies on building deep learning models for automated depression detection have mainly used post-level emotion polarity (i.e., positive and negative emotions)

With the recent explosion of vicarious experiences in the metaverse (e.g. twitch, YouTube gaming, Facebook gaming, etc.), understanding the underlying mechanism of this phenomenon is key for researchers and practitioners. This research examines the rising phenomenon of vicarious experiences within the metaverse. Using a three-study experimental approach, results show that subjects attain equal levels

Accurate estimates of the length of stay (LOS) for patients who suffer traumatic fall injuries are crucial to inform physicians' clinical decisions and patient management. They also have important implications for resource utilization efficiency and cost containment efforts by healthcare organizations. Effective predictions should consider essential relationships across different variables pertaining

Health Information Technology (HIT) is revolutionizing healthcare by serving as the backbone for various decision support activities across the healthcare continuum, particularly within hospital settings. While existing literature highlights its positive impact on patient satisfaction, costs, and quality, its role in complementing other crucial hospital inputs to influence clinical healthcare outcomes

Manufacturers can effectively obtain precise demand information through the utilization of data analysis technologies. These better-informed manufacturers commonly distribute their products not only through traditional offline retailers but also via direct sales channels. In this context, distinct pricing strategies for online channels, namely holding pricing power and giving up pricing power, can

Existing Explainable AI (XAI) approaches, such as the widely used SHAP values or counterfactual (CF) explanations, are arguably often too technical for users to understand and act upon. To enhance comprehension of explanations of AI decisions and the overall user experience, we introduce XAIstories, which leverage Large Language Models (LLMs) to provide narratives about how AI predictions are made:

Customers' perception of review helpfulness entails a cognitive reasoning process influenced by the contextual information of reviews including product descriptions and review neighbors. Current studies on helpfulness prediction primarily focus on static features of individual reviews, neglecting the dynamic interaction among products, reviews and their contextual neighbors. To address this gap, we

The anonymity and convenience of social media platforms enable the public to express and even vent themselves, which drives a surge of cyberviolence behaviors (CVB). Recent advances in machine learning, especially in deep learning, have drastically benefited CVB detection. However, despite the wide use of state-of-the-art deep-learning models, previous studies only analyzed each post/comment for the

Online question and answer (Q&A) communities rely on the general audience or the question asker to determine the best answer. However, limited attention has been directed toward understanding the influence of the general audience-favored answer (i.e., most popular answer) on the question asker-selected best answer (i.e., best answer). This study examines whether and how the general audience-favored

This research investigates the disclosure of IT-related risk factors in U.S. public firms' periodic SEC filings. Drawing upon the Resource-Based View theory, we propose that a firm's IT capability determines the disclosure of its overall IT-related risk factors. We employ a machine learning-enhanced dictionary that captures emerging IT keywords from newly filed corporate reports to quantify the scope

As a nascent market in recent years, the NFT market has been widely scrutinized for its significant market bubble. To help investors make more informed trading decisions, several NFT marketplaces have introduced features that display the rarity information of NFTs directly on their interfaces. Existing literature on the rarity effect suggests that this feature generally increases trading activity.

In the domain of medical services, patients are frequently readmitted shortly after discharge due to inadequate discharge planning or relapses of their illnesses. Such occurrences not only deplete valuable medical resources but also compromise patient satisfaction with the medical care they receive. To address this issue, we propose an interpretable imbalance ensemble classification method incorporating

Flash sales are a widely adopted e-commerce marketing strategy that operate over a brief period, offering limited-time discounts, special promotions, or clearance items to create a sense of urgency and promote rapid sales. This study proposes panoramic sales insight (PSI), a multimodal revenue forecasting framework designed to improve the accuracy of revenue predictions for flash sales. Using historical

Given the “profit-seeking” behavior of task solvers and the “quality-seeking” focus of solution seekers in knowledge-intensive crowdsourcing contests, task recommender systems must manage the trade-off between their respective benefits. This study proposes a multitask deep learning model with a multigate hybrid expert structure to jointly model solver preference and ability, thereby balancing bilateral

Personal loan pricing requires accurate estimates of individual customer behavior, such as the willingness to take out a loan at a given price, the “bid response”. This is challenging due to the nonlinearity of responses hindering the discretionary definition of models, as well as the confoundedness of observational training data. This paper investigates the application of data-driven and machine learning

The metaverse allows users to interact with the real and virtual worlds naturally by stimulating multimodal sensations. Meanwhile, the attractive environments created by the metaverse may also bring challenges such as excessive use. There is a great deal of uncertainty about the undesirable risks of the metaverse. Therefore, this study makes efforts to introduce a theoretical framework and explain

Meal delivery is a fast-growing industry supported by couriers participating in the gig economy. This paper takes a single courier's perspective and provides decision support for an individual courier who works at will in repositioning between jobs and order-taking to optimize her profit during a work period. A hybrid discrete-time, discrete-event simulation environment was developed based on data

This study explores the impact of temporary account suspensions on users' engagement in online platforms. Using observational data obtained through a collaboration with a prominent online discussion forum in Asia, we conduct empirical analyses that are guided by regulatory focus theory and reactance theory, and we use both propensity score matching and a difference-in-differences regression analysis

In metaverse communities, users engage in various activities, such as socializing, gaming, and exploration. Presence in such communities refers to the feeling of being there and being fully immersed. This study examines the role of various dimensions of presence (e.g., social, spatial, and self) in driving user well-being in metaverse communities. Moreover, the study tests the mediating roles of social

The emerging technologies, immersive systems, and metaverse are evolving at a rapid pace. These advancements are creating and offering unique opportunities for individuals, platforms, and businesses through virtual interactions. This proliferation is also gathering attention of marketeers and advertising agencies as the metaverse can conduct campaigns with a recreational touch. However, little is known

The advent of the metaverse is fundamentally altering the relationship dynamics between brands and users. Brands that successfully navigate this new landscape create deeper engagement and foster lasting brand loyalty. Using a multi-method, multi-study approach, we examine the interplay between user characteristics and brand perception in a real metaverse environment called “Universe”, created by a

The metaverse has recently attracted significant attention from the business community as it offers various opportunities for enhanced business value creation. Nevertheless, there is a limited understanding as to why some firms are better able than others to realise the performance benefits afforded by the metaverses. To address this shortcoming, we draw on the ‘organisational excellence’ framework

The Metaverse is a virtual shared space that merges physical and digital realities, allowing users to have immersive experiences with avatars and interact in various ways such as socializing, gaming, and commerce. Despite ongoing research on rendering technology and its impact on user immersion, empathy, and virtual consumption from a perspective of social needs, the field is still in its infancy.

As the foundation of the metaverse, three-dimensional avatars are increasingly shaping our personal and professional lives. However, a nuanced reading of avatar literature proposes that avatars can exhibit both positive and negative dimensions, leading to a paradoxical phenomenon. This study aims to conceptualize a holistic framework elucidating the intricate interplay between perceptions, emotions

The growing popularity of online social networking has made it increasingly important to develop group recommender systems (RS) for delivering personalized services to the members of user groups. However, owing to the sparsity of data on group–item interactions (G–I interactions), existing group recommendation methods have concentrated on modeling user–item interactions (U–I interactions), which has

Financial statement frauds by listed firms pose significant challenges to public investors and jeopardize the stability of financial markets. Previous studies have identified deceptive verbal and vocal cues from earnings conference calls as indicators of financial statement fraud. However, these studies only extracted managers' verbal and vocal cues separately over the entire call, neglecting the utterance-level

Many organizations implement outsourcing solutions for information systems and/or information technology (IS/IT). Some of them face problems resulting from dissatisfaction with these outsourcing decisions. Reasons for dissatisfaction include an outsourcing agreement that did not meet expectations, organizational changes, and a loss of control over the business. As a result, companies are rethinking

Introducing the feature of “Buy Again” or “Order Again” is a common practice for online platforms to facilitate consumer repurchases. Although the adoption of these features can cultivate consumers' dependence on focal products and promote habitual purchases, it potentially hinders the promotion of new products and reduces consumer choice diversity. This raises a broader issue of how to inhibit habitual

To facilitate the promotion of sustainable consumption decision-making, organizations might employ blockchain technology to offer transparent information regarding the sustainability of their products. Nonetheless, consumers often seek supplementary information from diverse sources. The inconsistent or conflicting information across these sources can complicate the process of making sustainable consumption

In the natural sciences, many research activities now require the support of digital artifacts. This digitalization of science has led to the need to develop essential, specialized, devices and software. Computational science is a branch of science that especially requires such artifacts. This research examines computational science to identify its challenges and successes in developing and applying

Over a six-year period, employees and managers at Wells Fargo created 3.5 million false deposit and credit card accounts resulting in $4.8 billion in fines. Following this incident, there has been a newfound focus on effective internal controls. The purpose of the current study is to improve misstatement identification by formulating a novel hybrid decision support framework to a) accurately predict

In striking a balance between attracting more product reviews versus maintaining review quality, online platforms have started to label reviews with whether they are associated with verifiable purchases. This paper examines the impact of such disclosure policy on the strategic behavior of review writers and the helpfulness of verified reviews (VRs) and non-verified reviews (NVRs) for review users.

Extending the theory of perceived risk, this study examines how risk perception, a vital factor in determining investment decisions, comprising both initiative risk statement generated by fundraisers and passive risk disclosure published by backers, influences crowdfunding financing performance. Utilizing a corpus of 126,593 innovative projects from Kickstarter, text analytics is employed to classify

This paper presents the Heterogeneous Dynamic Seemingly Unrelated Regression with Dynamic Linear Models (HD-SURDLM), an innovative framework for stock return prediction that combines cutting-edge sentiment analysis with dynamic financial modeling. The model integrates sentiment data from 2.5 million Twitter posts and various news sources, utilizing state-of-the-art sentiment analysis tools such as

Businesses and industries are placing a greater emphasis on information systems for cybersecurity decision-making due to the rising cybersecurity threat landscape and the critical need to protect their digital assets. Threat hunting provides a data-driven and proactive approach to cybersecurity, enabling organizations to efficiently detect, analyze, and respond to cyber threats in real-time. Despite

Despite the potential of IoT-enriched digitized products, firms struggle to generate desired impact. We investigate the alignment of actualized digitized product potentials (i.e., affordances) with organizational goals, examining how the emergence of critical inhibiting factors affect the generation of desired effects. We conduct an embedded single case study of four actualized digitized product potentials

Enhancing a website's persuasiveness and improving users' satisfaction and intention are critical for companies and website designers. Based on the Fogg Behavior Model (FBM), this study explores the perspective of persuasive technology in the context of a website. We identify and design two types of persuasive features: a visual-preview feature and an information-sidedness feature. We propose that

Metaverse ecosystems are fast growing platforms which are witnessing wide adoption. Different digital platforms like social media are trying to evolve into metaverse ecosystems which are perceived to enhance the overall experiences of different users. However there is a lack of impactful empirical literature which have attempted to document diverse socio-technical perspectives surrounding these emerging

The advent of Large Language Models (LLMs) has shown the potential to improve relevance and provide direct answers in web searches. However, challenges arise in validating the reliability of generated results and the credibility of contributing sources due to the limitations of traditional information retrieval algorithms and the LLM hallucination problem. We aim to transform LLM into a relevant, responsible

This editorial essay argues the design science decision support literature has unduly focused on developing technical systems when organizational problem solving and decision making often require socio-technical ones. Decision making in uncertain environments requires other aspects the technical view actively suppresses, such as effectiveness and innovation. We explore this in a three-step argument

Using empirical data from a third-party platform and a comprehensive public hospital (equipped with an official online healthcare platform) in China, this study employs a two-stage Heckman selection model and find that third-party online healthcare platforms (OHPs) should not be considered an obstacle to promoting official OHPs. Instead, doctors' activities on third-party OHPs increase the demand for

We consider a content market with an ad-supported content platform and a representative producer in the presence of altruistic consumers. The platform may launch different subsidy policies (i.e., a monetary subsidy based on the content demand that directly improves marginal profit or a traffic subsidy that directly improves content quality), and the producer creates content under two pricing strategies