Decision Support Systems最新文献

This study investigates how organizations may increase innovation and productivity through the Metaverse environment efficacy (MVEE), Artificial intelligence usage (AIU), Internet of Things usage (IoTU), and Big Data Analytics usage (BDAU). The study gathers responses from the gaming, information technology, and entertainment industries, using a multi-method involving Partial Least Squares Structural Equation Modeling, Fuzzy-set Qualitative Comparative Analysis, and Artificial Neural Networks to investigate how these technologies might be used to improve the linking of disparate realities in a business context. The use of AI in personalized and decision-support applications, IoT for real-time data collecting, and BDAU for an insights-driven strategy all combine to create a dynamic MVEE ecosystem. The research also delves into theoretical implications concerning the viability of using the MVEE to boost innovation and productivity. This research identifies the applications of using AI, IoT, and BDA to drive organizational performance in terms of innovation and productivity. Also, the research lays out the role of AI, IoT, and BDA in creating a dynamic metaverse ecosystem.

The restaurant industry has been slow to adopt analytics for the supply chain, operations, and demand forecasting, with limited research on this sector. The COVID-19 pandemic's significant impact on the restaurant industry, one of the hardest-hit sectors, has underscored the need for digital technologies and advanced analytics for managing supply chains and making operational decisions. This paper presents a collaborative study with one of the largest restaurant chains in the United States, highlighting the value of advanced data analytics in forecasting restaurant demand. The study offers insights into the benefit of integrating external data, including macroeconomic and pandemic-related factors, into demand forecasting. It explores traditional machine learning algorithms and state-of-the-art deep learning architectures, evaluating their effectiveness in the context of the restaurant industry. The paper further discusses the implications of utilizing advanced forecasting models, providing valuable insights for the restaurant industry in the face of supply chain disruptions and pandemics.

In an era where user-generated content drives social media growth, effectively incentivizing contributions remains a challenge. This study explores the empirical impact of a crypto-integrated platform, Steemit, focusing on a system transition designed to enhance fairness in reward distribution. We assess how this shift affects user engagement, specifically through the volume of posts. Our findings indicate that a fairer crypto-reward distribution boosts user-generated posts, though the increase is less pronounced for users with higher capital or reputation. Further analysis reveals the complex dynamics of cryptocurrency rewards and their role in fostering individual contributions and platform growth, while offering financial incentives. The effects of fair distribution are consistent across diverse user groups, highlighting novel incentivization strategies in social media and the transformative potential of integrating cryptocurrencies into reward systems.

Are we more likely to believe a social media news story shared by someone with whom we have a strong or weak tie? We tend to trust close ties more than weak ties, but weak ties are sources of new information more often than strong ones. We conducted an online experiment to examine the effect of tie strength (strong ties vs. weak ties) on the decision to believe or not believe fake news stories. Participants perceived false stories from weak ties to be more believable than false stories from strong ties (after controlling for the trustworthiness of the sharer). We found that a sharer's perceived ability to share reliable information plays a significant role in individuals' decision to believe news stories on social media, regardless of whether the source is a strong or weak tie. Interestingly, a sharer's perceived integrity was found to be important only when the information came from weak ties, while a sharer's perceived benevolence was not important for either weak or strong ties. These findings show that the perceived integrity of the sharer is a key factor in the decision to believe stories from weak ties, more so than from strong ties. Furthermore, a sharer's perceived ability to share reliable information is less critical when weak ties share true stories. The impact of weak ties does not stem from the novelty of their information, as we used identical headlines across both study groups. Thus, while the strength of weak ties effect is present in this context, the underlying theoretical mechanism differs from the novelty of information traditionally observed in other settings.

The unprecedented COVID-19 has led to the collapse of numerous businesses, notably within the tourism and hospitality sectors. Despite the burgeoning research on resilience, few studies have embraced a theoretical lens, particularly from a social network perspective. In addition, most extant resilience studies have not explicitly considered the geographic accessibility prerequisite inherent to tourism and hospitality products. In this study, leveraging the social contagion theory, we present a holistic research framework to investigate the influence of geographic and social proximities, two pivotal social contagion mechanisms, on business resilience. We also delve into moderating factors to discern the conditions under which contagion effects are amplified or attenuated. To validate our theoretical model, we select the restaurant industry as our research context, given its severe impact from COVID-19. Utilizing an extensive dataset from Yelp, encompassing ten U.S. cities varying in sizes and geolocations, our findings indicate that both geographic and social influences exert significant direct effects on resilience. Additionally, these effects exhibit considerable variations contingent upon product attributes, customer characteristics, and geographic factors. Theoretically, we are the first to substantiate the role of social contagion theory in examining resilience, enriching our understanding of the social network mechanism of behavioral contagion among customers during the COVID-19 pandemic. We also offer valuable practical implications for various stakeholders in supporting their management strategies and decision-making in developing effective plans and preparations, minimizing adverse impacts, and ensuring sustainability in the face of future disruptions.

Comparative online reviews have evolved into a vital instrument for consumers in decision-making, offering valuable comparisons and available options. Drawing on the insights from the linguistic category model (LCM) and elaboration likelihood model (ELM), we propose that different types (attribute-based and experience-based) of comparative reviews can affect consumers' perceived credibility of online reviews, thus impacting product sales. We analyzed 136,260 reviews on e-commerce platforms to assess these effects and introduced review valence as a boundary condition. Utilizing a combination of pattern discovery, supervised learning techniques, and manual coding, we identified attribute-based and experience-based comparative reviews and subsequently classified them based on positive, neutral, and negative valence. Subsequently, we took the product sales as the dependent variable and applied a two-way fixed effects model. The results indicate that attribute-based comparative reviews exert a more favorable influence on product sales compared to experience-based ones. Additionally, positive comparative reviews, irrespective of their attribute-based or experience-based nature, demonstrate a greater impact than regular positive reviews. However, negative and neutral comparative reviews, only when associated with attribute-based information, exhibit a significant effect. The results highlight the value of different types of comparative reviews and illuminate the moderating role of review valence. Our findings offer new insights and practical guidance for marketers and e-commerce platforms in capitalizing on the important influence of comparative reviews and enhancing the presentation of online reviews.

Live streaming has become increasingly popular in recent years. Viewers of live streaming channels can interact with live streamers through various behaviors, such as sending virtual gifts and Danmaku. It is very critical to accurately model such viewers' behaviors, which reflect their interest, for recommending live streaming channels. However, existing studies on live streaming channel recommendation usually model viewers' interaction behaviors through traditional graphs where an edge only connects two nodes, which cannot capture interaction relationships between multi-viewers and multi-channels. In this study, we propose a novel approach to live streaming recommendation based on Viewers' Interaction Behavior Modeled by Hypergraphs (VIBM-Hyper). Specifically, VIBM-Hyper first constructs two hypergraphs to model viewers' interaction behaviors, including a channel-oriented behavior hypergraph and a viewer-oriented behavior hypergraph. Then, it employs a hypergraph convolution technique to learn the representations of viewers and live streaming channels, respectively, which are finally used to predict a viewer's preference for a certain live streaming channel. We analyzed viewers' multiple types of behaviors in live streaming channels and conducted empirical evaluation to investigate the effectiveness of VIBM-Hyper with two real-world datasets. The evaluation results demonstrate its superior performance in live streaming channel recommendation in comparison to the state-of-the-art methods.

Live streaming commerce thrives with a rich tapestry of multimodal information that intertwines with various entities, including the anchor, the commodities, and the live streaming environment. Despite the wealth of data at hand, the synthesis and analysis of this information to predict sales remains a significant challenge. This study introduces a framework for multi-entity multimodal fusion, which is characterized by the effective synthesis of multimodal data and its prioritization of entity-level fusion, thereby providing a comprehensive feature representation for improving predictive performance. In addressing the multimodal data associated with a diverse range of products, our framework improves the Transformer architecture to initially capture the intra-product modal features and subsequently integrate the inter-product features. Data experiments are conducted on a real-world dataset from Taobao Live. The framework outperforms both traditional machine learning methods and state-of-the-art multimodal fusion methods, which affirms its value as a robust decision-support tool for sales prediction, enabling more accurate pre-event predictions and strategic planning. We also examine the impact of different types of information in accurate sales prediction. It is found that harnessing a comprehensive suite of data leads to optimal performance across all evaluation metrics. Commodity-related data is primary factor in determining the prediction accuracy, followed by video data and streaming room-related data, providing insights regarding the resource allocation for collecting and analyzing multimodal data from live streaming platforms.

This paper contextualizes explainable artificial intelligence (AI) for enhanced decision-making and serves as an editorial for the corresponding special issue. AI is defined as the development of computer systems that are able to perform tasks that normally require human intelligence by understanding, processing, and analyzing large amounts of data. AI has been a dominant domain for several decades in the information systems (IS) literature. To this end, we define explainable AI (XAI) as the process that allows one to understand how an AI system decides, predicts, and performs its operations. First, we contextualize its current role for improved business decision-making. Second, we discuss three underlying dimensions of XAI that serve as broader innovation grounds to make better and more informed decisions, i.e., data, method, and application. For each of the contributing papers in this special issue, we describe their major contributions to the field of XAI for decision making. In conclusion, this paper further presents a future research agenda for IS researchers in the XAI field.

The emergence of online healthcare platforms has changed the competitive environment among physicians. However, little is known about how physicians can improve their performance in this new environment. Platforms also face challenges in comprehending the competitive mechanisms among physicians, which might hinder them from formulating strategic managerial decisions that foster sustained growth. In this light, we extract medical service-related information from physicians' response behavioral data on a prominent healthcare platform, and empirically investigate the factors affecting physicians' online performance from a competitive perspective as well as the gender differences in these effects. The results indicate that physicians' responses significantly impact their online performance, revealing a competitive relationship between physicians and their colleagues in the same department. Specifically, a fast response time and informative responses are positively correlated with the focal physician's performance, whereas colleagues' informative responses negatively impact the focal physician's performance, and this relationship is mediated by the focal physician's response informativeness. Nevertheless, there is no significant correlation between colleagues' response time and the focal physician's performance. The results also unveil that gender moderates the effect of response informativeness on the focal physician's performance. Specifically, colleagues' response informativeness has a more significant impact on male physicians' performance than on female physicians' performance, suggesting a greater propensity for competition among male physicians. Our findings could offer decision support for enhancing physician performance and healthcare platform management.