Decision Support Systems最新文献

Blockchain technology has brought opportunities and challenges to many fields, including operations and supply chain management. Due to the innovative characteristics of decentralization, transparency, immutability, and anonymity, blockchain applications break new ground for users' decision-making in an operations environment. There is a lack of evidence regarding herding in blockchain technology in general and its potential effect on blockchain application platforms in particular. In response to this new technological phenomenon, we studied whether and how herding behaviors take place in the context of blockchain applications for operations in this research. We propose two new variables related to the operation of the blockchain application (i.e., technical assurance and decentralized management efforts) and a new variable related to user identity (i.e., identifiability). We found evidence of the existence of herding by utilizing the objective data of approximately 0.1 million transactions on a leading blockchain application platform providing self-service and collateral-based lending services. Our empirical findings also reveal that technical assurance and decentralized management efforts reduce users' herding tendencies. Moreover, we found that the higher the identifiability, the lower the users' herding tendency, and the less they are affected by technical assurance and decentralized management efforts.

In times of rapid and unpredictable developments, companies experience significant volatility in capacity utilization. Virtual capacity exchange platforms help to mitigate this challenge by exchanging capacities with anonymous participants in market-like peer-to-peer networks. However, its efficiency is hindered by behavioral uncertainties, including a lack of inter-organizational trust in other participants. To leverage the potential of such exchange platforms, this paper reports on a Design Science Research project aiming to derive and validate design principles for establishing trust in inter-organizational capacity exchange in two design iterations. Using blockchain technology, we instantiate six design principles into an artifact and perform experimental evaluations to investigate their effect on perceived trust. Our paper contributes to research and practice by identifying and applying prescriptive design knowledge and advancing our understanding of how to design trust-enabling inter-organizational systems. The originality lies within the empirical investigation of how and why different design principle combinations can be established through blockchain technology as one of the promising approaches for establishing trust. In doing this, we also disclose future pathways for IS and blockchain researchers and practitioners.

To respond to information systems (IS) researchers' on-going call for understanding what happens in new, social media-enabled processes in diverse contexts, this research investigated how Internet memes facilitate the emergence of new online travel activities and influence visit intentions in new forms of meme tourism. Meme tourism involves new forms of visit intention, where individuals are motivated to visit a destination by viral memes, and once on location, they recreate and re-enact elements of the meme before sharing the results via social media. Such use of memes in the tourism context – often considered as a casual reflection of individuality, pop culture, and a new way of communicating in social media – raises the question of how to influence and nurture travelers' behaviors and actions during an actual trip. To empirically test the ability of memes to induce specific actions during visits, this study developed an integrated model using affordance as an overarching theoretical framework to provide insights into the actualization of two types of meme affordances – Imitation and Self-presentation – during tourism visits. With survey data collected from individual tourists using a crowdsourcing platform, Amazon Mechanical Turk (MTurk), this research found that the relationship between both affordances (imitation and self-presentation) and travel intention was significant. The results of the study offer alternative explanations of the possible psychological influence of memes in inducing physical behaviors, which adds insight to behavioral research fields such as IS. Both the research and the practical implications of the results are discussed.

Effective financial distress prediction (FDP) can discover a company's potential financial risks and support relevant decisions in a timely manner. Previous studies on FDP have mostly focused on using financial indicators and periodic reports. Compared with periodic reports, current reports disclose major events in a timelier manner. But leveraging the information in current reports involves the critical challenges of capturing the complex semantics and measuring the importance of heterogeneous events. To this end, we propose a novel deep learning method, a user-response-guided deep attention network (URGDAN), to predict financial distress using current reports. In the proposed method, we construct a deep learning architecture to integrate financial indicators, current report texts, and user responses. URGDAN leverages the user responses to current reports to guide the semantic feature representation of the reports, it also identifies event information that has a significant correlation with company financial distress. Empirical evaluation shows that URGDAN significantly improves predictive performance and can accurately determine the importance of different current reports. Our work provides practical implications for creditors and investors.

The current dynamic market environment challenges successful incremental new product (INP) launches, compelling enterprise managers to promptly recognize and respond to competitive situations. Estimating INP competitiveness before sale helps enterprise managers adjust their strategies effectively and in a timely manner to ensure successful INP launches. However, a lack of historical evaluation information challenges the estimation. Given that INPs are updates of existing products, massive user-generated content (UGC) regarding existing products and the new product launch provides an appropriate data source for estimating INPs' competitiveness. Therefore, this study proposes a method for estimating the competitiveness of INP using UGC. First, the INP product attributes are classified as non-updated or updated. For the former, existing products containing the same attribute values are used as references, and their reference values are measured based on the time and price of the first sale. UGC regarding the launch of INPs serves as a data source for the latter. Sentiment analysis is performed on the UGC concerning product attributes to obtain the sentiment tendency and sentiment intensity for constructing an intuitive fuzzy number, which represents INP's attribute performance. INP's product performance is estimated by considering attribute weights determined by customer attention. INP's competitiveness in the market environment is then estimated by comparing its product performance with that of competing products. Finally, the proposed method is applied to a mobile phone, and its effectiveness and applicability are verified.

Focused online question and answer (Q&A) communities aid social coding. Despite the growing importance of social coding, knowledge transfer in this context remains under-researched. Our primary objective is to understand the knowledge transfer process in this context. We conceptualize knowledge transfer as a process that is impacted by the prior knowledge transfer interactions (network) among participants and is augmented by gamification. We argue that social capital resulting from prior knowledge transfer network interactions impact answer quality. Moreover, we also argue that the relationship between social capital and answer quality is moderated by the complexity of the knowledge transferred. Hence, our models draw from multiple related research streams: online communities, knowledge transfer, social capital, and gamification. These models are empirically tested using data from Stack Overflow (SO), a popular online Q&A community that aids social coding. Our results help to understand knowledge transfer in Q&A communities that aid social coding. Moreover, our results have implications for research on other types of Q&A communities and can inform development of platforms to support online communities.

Due to its vital role in financial risk management, credit scoring has been investigated extensively in extant information systems studies. However, most credit scoring studies rely on one-size-fits-all classifiers with logistic regression (LR) as a popular benchmark. Moreover, extant literature largely focuses on predictive performance as an evaluation criterion. To find a better balance between predictive performance and interpretability though, the current study investigates the beneficial impact of segmentation-based modelling by benchmarking the logit leaf model (LLM) which is based on LR and decision trees. By a large experimental setup using a real-life credit scoring data set containing 65,536 active customers, we find that LLM is a viable classifier over its constituent parts, i.e., LR and decision trees, and is very competitive to state-of-the-art credit decision making techniques (neural networks, support vector machines, bagging, boosting and random forests) on three evaluation metrics (AUC, top-decile lift and profit). Furthermore, we show its extraordinary interpretability capacities by proposing a new visualization based on the LLM output. In sum, the excellence of the LLM as a classifier for credit decision making problems stems from its ability to combine strong predictive performance with interpretable insights that in turn can inform managerial decisions.

The importance of voice has been well acknowledged in sensory decision-making. Yet, past literature on video creators' performance did not shed much light on the impact of video creators' acoustic characteristics. Building on signaling theory of portfolios, we examine how the acoustic characteristics of a video creator and the signals of video quantity affect the number of likes a video creator receives and the change in the number of the creator's followers. Using a longitudinal dataset obtained from Bilibili through automated speech recognition analytics and text analysis approach, econometrics models are employed to test the research model. Findings indicate that loudness variability of a video creator significantly increases the number of likes the creator receives, and exerts a positive effect on the change in the number of the creator's followers. In contrast, vocal pitch has a significant positive impact on the number of likes but a significant negative impact on the change in the number of followers. Results further suggest that a video creator's performance varies according to the signals of video quantity, i.e., the number of weekly published videos and the average length of videos. Our results could offer theoretical contributions and practical insights for video creators to enhance their performance by adjusting the acoustic characteristics and the signals of video quantity wisely.

Selling experience products online is usually more challenging than selling search products. Addressing the calls for future research in the literature, we study how the different combinations of different web-design features can explain people's intentions to purchase experience products online by mitigating three different product uncertainties associated with such products. The results of a detailed experiment using artworks showed that combining expert opinions with product descriptions or experience simulation was more effective than combining product description and experience simulation. Moreover, unlike the findings reported in the literature concerning search products, this study showed that trust explained people's intentions to purchase experience products online more than perceived usefulness and perceived ease of use. Theoretically, we refine the product uncertainty model by demonstrating the differential effects of different product uncertainties regarding experience products. Practically, our study informs firms about the roles of different combinations of web-design features in selling experience products online.

Sustainability has become a target in official policy rhetoric. However, the gap between scientific investigation and practical decision-making poses a significant challenge in achieving sustainability, particularly in endorheic regions. Addressing this challenge requires the translation of scientific outcomes into available decision-making information. In this study, we propose a sustainable decision-making methodology, namely, decision rehearsal, that has four stages: the scientification of decision-making problems, the politicization of scientific knowledge, the translation of scientific findings into decision-making information, and the translation of decision-making information into policy action. Based on this theoretical framework, an ensemble artifact, referred to as the River Basin Sustainable Development Decision Support System (RSDSS), was designed, built and evaluated from a design science research perspective to address a class of sustainability problems in endorheic basins. This system integrates a watershed-integrated model, disaggregated sustainable development goals (SDGs), localized shared socioeconomic pathways (SSPs), and a sustainability assessment model, which can assist decision- and policy-makers in more deeply understand the impacts of different policy assumptions on the interactions between human and natural systems and watershed sustainability. Some related RSDSS cases are discussed to demonstrate the ability of the RSDSS to resolve complex watershed problems, which implies that the RSDSS can promote sustainable river basin management.