Decision Support Systems最新文献

Successful attended home delivery (AHD) is the most important aspect of e-commerce order fulfillment. Prior literature focuses on incentive scheme development for customers' choices of delivery windows and predictive analytics for delivery results, but it is not clear whether the effect of AHD on the appointment date set by customers increases the success rate of AHD. Therefore, we developed an uplift modeling method, PSM-NDML, as a relevant prescriptive analytic tool for AHD on an appointment date, which aims to estimate the causal effect of the by-appointment delivery on the delivery result. PSM-NDML integrates propensity score matching and double machine learning, effectively addressing sample selection bias, low predictive performance, and poor interpretability. Applied to a real-world product delivery dataset of a Chinese logistics company, PSM-NDML achieves superior performance relative to ten other state-of-the-art uplift models in terms of cumulative gain and the Qini coefficient. The predicted responses gained from PSM-NDML are also visually interpreted at the global and local levels, which reveals various managerial insights. In practice, the findings expand managers' understanding of the heterogeneous effects of AHD on appointment dates and provide decision support for logistics companies in the development of home delivery plans.

While many online platforms use incentive hierarchies to stimulate consumers to generate more online reviews, the extent to which these hierarchies influence reviewer behavior is not fully understood. This study, drawing on image motivation theory and consumer attention theory, takes a novel approach to investigate whether reviewers strategically adjust their review behavior after reaching higher ranks in a hierarchy. We use data from rank change timestamps on platforms to accurately identify reviewers' ranks when posting reviews and then employ a quasi-natural experimental design for causal inference. Additionally, we use Fisher's permutation test to explore the different effects at various ranks. The empirical results reveal that reviewers tend to increase their review length and insert more pictures into their reviews after they reach higher ranks. Reviewers at lower ranks tend to submit more extreme ratings upon rank advancement, whereas their higher-ranking counterparts do not demonstrate significant change. Unlike ratings, reviewers tend to consistently increase the sentiment intensity of their expressions in text after reaching higher ranks. Furthermore, our findings indicate that the magnitude of changes in reviewing behavior only shows an increasing trend in the early stages of rank progression. These insights contribute to a better understanding of the efficacy of incentive hierarchies and offer practical implications for decision-making by platform managers.

A critical part of flow-based conceptual modeling, such as process modeling, is visualizing the logical and temporal sequence in which activities in a process should be completed. While there are established standards and recommendations, there is limited empirical research examining the influence of process model layout on model comprehension. To address this research gap, we conducted a controlled eye-tracking experiment with 70 participants comparing different layouts. The experimental results confirm that the visibility of control flow patterns is critical for assisting users with visual processing, particularly attentional allocation, when comprehending process models for both local comprehension tasks and tasks requiring cognitive integration of model components. In models with more directional changes, users’ visual attention is more drawn to irrelevant regions, but comprehension is less affected as long as patterns remain visible. Our findings not only elucidate how cognitive fit between a visual representation and a task can manifest itself and the perceptual benefits it brings, but they can also guide the automated layout of models in tools and complement practical process modeling guidelines.

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.