Online hacker communities are meeting spots for aspiring and seasoned cybercriminals where they engage in technical discussions, share exploits and relevant hacking tools to be used in launching cyber-attacks on business organizations. Sometimes, the affected organizations can detect these attacks in advance, with the help of cyber-threat intelligence derived from the explicit and implicit features

This study investigates the differential roles of negative and positive reviews on the growth of branded and independent hotels and their underlying interplays. We construct a dataset by combining the revenue data with TripAdvisor review data of hotels in four Texas cities. Based on the resource-based view of firm growth and signaling theory, we obtain several novel findings. Positive reviews exert

Lending decisions are usually made with proprietary models that provide minimally acceptable explanations to users. In a future world without such secrecy, what decision support tools would one want to use for justified lending decisions? This question is timely, since the economy has dramatically shifted due to a pandemic, and a massive number of new loans will be necessary in the short term. We propose

Measuring the success of targeted marketing actions is challenging. Research on prescriptive analytics recommends uplift models to guide targeting decisions. Uplift models predict how much a marketing action will change customers' behavior, known as the individual treatment effect (ITE). Marketers can then solicit customers in decreasing order of their estimated ITE. We argue that the ITE-based targeting

Visual analytics (VA) is a visually assisted exploratory analysis approach in which knowledge discovery is executed interactively between the user and system in a human-centered manner. The purpose of this study is to develop a method for the VA of set data aimed at supporting knowledge discovery and member selection. A typical target application is a visual support system for team analysis and member

Detection and characterization of comorbidity, the presence of more than one distinct disorder or illness concurrently occurring among a specific cohort of patients, is an invaluable decision aid and a prominent challenge in healthcare research and practice. The aim of this paper is to design a novel visual analytics system that can support efficient pattern detection and intuitive visualization of

An important aspect of human resource management is understanding employees' values, motivations, and factors of satisfaction. Relationships between employee satisfaction, values, positivity and negativity, employee retention, and industry allow human resource managers to make decisions on employee recruitment techniques and budgets, employee retention efforts, and even company image initiatives. Traditional

Helpfulness prediction techniques have been widely incorporated into online decision support systems to identify high-quality reviews. Most current studies on helpfulness prediction assume that a review's helpfulness only relies on information from itself. In practice, however, consumers hardly process reviews independently because reviews are displayed in sequence; a review is more likely to be affected

Vulnerability disclosure has been a controversial topic among scholars and practitioners. Most scholars agree on adopting the responsible disclosure practices for vulnerability disclosures, which give firms a protected period to address the vulnerability before public disclosure is made. However, the firms may not fully utilize the protected period resulting in financial and reputational losses. The

Decision support systems often rely on time series forecasting, making the accuracy of such systems of paramount importance for their efficiency. Since most systems nowadays require the processing of massive amount of data, automatic identification of time series models has become inevitable. This automatism is inherent in artificial intelligence methods, but it often goes unnoticed that forecasting

Enterprise collaboration systems (ECS) have become the core of the digital workplace in many organizations. More and more companies have introduced this new business software for supporting computer-mediated collaboration among employees. The emergence of ECS in the technical and strategical landscape of companies leads to new challenges in business intelligence and reporting. In contrast to traditional

The digital service market is vast and constantly expanding. In the digital service market, platforms such as Netflix, Steam, etc. often seek to enter into exclusivity deals with service providers or developers in order to get exclusive access rights to their digital services in the hopes that offering exclusive access to a digital product will entice new consumers to use their platform and thus generate

A decision support system for cyber-security is here presented. The system aims to select an optimal portfolio of security controls to counteract multi-stage attacks. The system has several components: a preventive optimisation to select controls for an initial defensive portfolio, a learning mechanism to estimate possible ongoing attacks, and an online optimisation selecting an optimal portfolio to

Many open collaboration platforms (e.g., Wikipedia) have recently utilized automated algorithmic agents, called Bots, to solve the issue of stagnating user participation. However, understanding of how such a bot agent affects user activities in open collaboration is limited. In this study, we examine (i) whether bot intervention affects user participation and (ii) whether the impact of bot intervention

Project risks are mostly considered to be independent in risk management, ignoring interdependencies among them, which can lead to inappropriate risk assessment and reduced efficacy in risk treatment. This study introduces a new Monte Carlo simulation-based risk interdependency network model to support decision makers in assessing project risks and planning risk treatment actions more effectively.

With the widespread use of online social media, we have witnessed that fake news causes enormous distress and inconvenience to people's social life. Although previous studies have proposed rich machine learning methods for identifying fake news in social media, the task of detecting fake news in emerging news events/domains remains a challenging problem due to the wide range of news topics on social

This study examines the complexity of consumer experiences in the sharing economy (SE) from the perspective of the level of interaction between consumers and service providers. Consistent with service-dominant logic, the joint efforts of consumers and service providers co-create value. In the context of accommodation-sharing, this means not just the room that guests seek but, rather, the authentic

Decision-intensive business processes are performed by decision makers who gather different pieces of information to reach the process objective: a final decision of high quality, for instance, the final price of a quote or the diagnosis of a failure of a hightech machine, as a result of an information-gathering process with minimum costs and efforts. Gathering all possible pieces of information results

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

Blood supply chains (BSCs) play a strategic and crucial role in healthcare systems especially in unexpected situations such as earthquakes and pandemic outbreaks. Nevertheless, measuring the sustainability and resilience of BSCs is a major challenge for many decision-makers in healthcare systems. To this end, this paper presents an advanced network data envelopment analysis (NDEA) method to evaluate

Wells Fargo employees set up 3.5 million fraudulent accounts over several years. Wells Fargo ended up paying $4.5 billion in fines. The Wells Fargo scandal highlights the importance of management actions to prevent misstatements and potential frauds on a timely basis. This study utilized the design science research paradigm to develop a predictive framework (IT artifact) in order to stratify firms

We consider a newsvendor setting where the newsvendor elicits a demand forecast from an expert to determine the optimal inventory level for a product. Since the interests of both parties might not be aligned, we propose a proper scoring rule to elicit the expert's forecast that is tailored to the decision problem the newsvendor faces. Under the proposed scoring rule, both the newsvendor's expected

The cloud computing spot instance is one offering that vendors are leveraging to provide differentiated service to an expanding pay-per-use computing market. Spot instances have cost advantages, albeit at a trade-off of interruptions that can occur when the user's bid price falls below the spot price. The interruptions are often exacerbated since customers often require resources in bundles. For these

This study explores a platform's promotional strategies for its first-party products when the third-party seller carries identical products, taking into consideration consumers' redemption cost incurred in searching and using the coupons, gift cards, and so forth. We determine how the platform vendor's promotional pricing strategies vary with the market expansion effect, proportion of the price-sensitive

Why do organizations develop and execute social media initiatives to create business value? This study addresses this prominent and critical research question for IS research on the business value of social media. Drawing on the institutional theory, we argue that mimetic, coercive, and normative pressure persuade organizations to use social media to improve marketing performance. We test our core

Cross-domain collaborative filtering has attracted a great deal of attention for its capability of dealing with data sparsity by transferring valuable knowledge from auxiliary domains to assist in recommendation in a target domain. State-of-the-art research has mainly focused on the scenario in which auxiliary domains share users or items with a target domain. However, such auxiliary data is rare or

Missing input values weaken the ability of information systems (IS) researchers to make calculations, thereby reducing effective sample sizes and statistical power. Such technical problems with data cascade into scientific limitations resulting in the neglect of social and economic issues. Therefore, extensive missing values in data forces researchers to make crucial decisions, such as whether to impute

The creation of the AIS College of Senior Scholars' basket of journals (SSB) was an attempt to address the perception that, compared with other fields, only a few Information Systems (IS) journals were widely considered elite-level journals in tenure and promotion cases. We propose that the creation of the SSB provided a signal to the IS field that conveyed an additional stock of symbolic capital to

A thorough understanding of online customer's purchase behavior will directly boost e-commerce business performance. Existing studies have overtly focused on purchase intention and used sales rank as a natural proxy, which however has limited business application. Additionally, intention to purchase does not necessarily convert to actual retail purchases. We aim to further our understanding of online

Initial coin offering (ICO) is a new financing method that has been widely used in cryptocurrency projects. However, it has been reported that nearly 30% of cryptocurrency projects fail during ICO, indicating an important gap in research and an opportunity for more advanced research on ICO project assessment. This study reveals that previous studies primarily used project-related factors to predict

Despite the crucial role a crowdfunding campaign webpage can play in attracting potential backers, there has been a lack of theory-driven studies to investigate how to design effective campaign webpages for successful campaigns. Based on the Stimulus-Organism-Response (S-O-R) framework, HCI theories, and literature on information diagnosticity, this study investigates how the effects of progress presentation

Because most information systems in real-world domains are complex, practitioners often use different types of conceptual modeling scripts to understand them. Modeling methods such as UML, for example, provide more than a dozen scripts for practitioners to use. We study how script readers decide which of several different scripts to use during systems analysis and design tasks. We carried out a free-simulation

Most bankruptcy prediction models used by financial institutions rely on single-period data, that is to say data that characterize firms at a given moment of their life. However, the financial literature devoted to bankruptcy considers this phenomenon to be a protracted process and time to be a fundamental explanatory variable of firm failure. The few studies that attempted to incorporate a temporal

This research explores firms' digital presence in an online interactive network. Anchored in the literature on firm engagement and firm-generated content, this study discusses and empirically examines the impact of managerial response to online reviews on future ratings. A big data approach coupled with text analytics is employed on a large field dataset to analyse both structured review/response metrics

Frequent tweets of the former president of the United States, Donald Trump, provide a unique opportunity to study how financial markets respond to his statements. To do this, we utilize a precise timestamp of each tweet together with high-frequency financial data. We start by analyzing the impact of tweets in general, irrespective of their content. We find that tweets by President Trump are followed

The growing amount of cyberspace threats highlights the need to evaluate cybersecurity risks and to plan for effective investments. One internationally recognized document for cybersecurity risk management is the framework for Improving Critical Infrastructure Cybersecurity by the US National Institute of Standards and Technology (NIST). It provides guidelines, best practices and standards for cybersecurity

Achieving timely last-mile order delivery is often the most challenging part of an e-commerce order fulfillment. Effective management of last-mile operations can result in significant cost savings and lead to increased customer satisfaction. Currently, due to the lack of customer availability information, the schedules followed by delivery agents are optimized for the shortest tour distance. Therefore

Understanding customer experience is an essential part of service operations for sustaining business in the sharing economy. We investigate the relationship among customer experience, perceived value, and customer loyalty from a stimulus-organism-response (S-O-R) perspective. Using a dataset of 4166 listings in a leading Chinese accommodation-sharing platform and text mining and econometric methods

In recent years, online consumer credit services have emerged in e-commerce. Although such services boost sales, the best way to allocate credit to consumers is a critical issue to be explored. In this paper, a comprehensive scheme is proposed using data augmentation and model enhancement to infer online consumer credit risk. The proposed scheme augments consumer profiles by incorporating phone usage

Informative contributions are critical for the healthy development of online Q&A communities, which have gained increasing popularity in solving personalized open-ended problems. However, little is known about whether past contribution behaviors and corresponding community feedbacks received affect the characteristics of subsequent contributions. Drawing upon the social cognitive theory, we examine

The real world contains many complex networks, including research networks, social networks, biological networks, and transport networks. Real-world complex networks are unconstrained and can be characterized as undirected and unweighted. Understanding and controlling the process of information propagation in such networks is significant for decision-making activities and has many uses, such as disease

Asymmetric information could harm a stakeholder in a transaction when they are not aware of the disparity of the information among the various stakeholders. Understanding the relationship and interaction among stakeholders is imperative for making informed decisions. It becomes even more critical in the context of high-value low-frequency transaction (HVLFT). This paper delves into the different forms

This paper proposes a novel framework to support the generation of strategies for multi-criteria long-term improvement. It can be applied to general preference models but it is illustrated in this article on a Multi-Attribute Value Theory model. The novel contributions to the literature are twofold. Firstly, the framework addresses the issue of resistance to change that may arise during the implementation

Digital technologies and in particular the prevalence of mobile devices and high-speed mobile data networks enable new entertainment products such as location-based games (LBGs). Due to their requirement for constant user mobility, LBGs have the potential to affect individuals' consumption patterns of entertainment products and can thus change the nature of business in the context of this emerging

Many existing attention-based deep learning approaches to sentiment analysis have focused on words and represent an entire review text as a word sequence. However, these approaches overlook the differences in the importance of each sentence to the complete text. To solve this problem, some work has been performed to calculate sentence-level attention, but these studies use the same approach that is

Creating reliable and robust quality control systems that identify process errors while having a low number of false rejects is a considerable challenge in the automated manufacturing industry. Especially in the pharmaceutical industry, where a product's quality has to be ensured at all costs, a large amount of false rejects is acceptable to guarantee the integrity of all released products. As standard

The use of big data analytics (BDA) and related technologies by firms can raise concerns regarding data privacy and security (DPS). In this study, with stock return and risk as indicators of firm performance, we investigate how DPS investment affects the financial performance of BDA firms (firms with BDA investment) and non-BDA firms (firms without BDA investment). We use a difference-in-differences

In digital repositories, it is crucial to refine existing subject terms and exploit a taxonomy with subject terms, in order to promote information retrieval tasks such as indexing, cataloging and searching of digital documents. In this paper, we address how to refine an existing set of subject terms, often containing irrelevant ones or creating noise, that are used to index digital documents. Further

We use convolutional neural networks for model identification in ARMA time series models, where our networks are trained on synthetic data with known ground truths. Comparing the performance of these networks with traditional likelihood-based methods, in particular the Akaike and Bayesian Information Criteria, we are able to show that when it comes to statistical inference on ARMA orders, neural networks

Modern aircraft have thousands of parts, systems, and components that need to be recurrently inspected or replaced. To keep the fleet airworthy, maintenance planners have to schedule the maintenance checks for each aircraft and the associated tasks. In practice, these two complex problems are solved following the experience of planners, resulting in sub-efficient solutions. This paper presents the

Prior studies of paper recommendation methods that consider historical user preferences rarely adequately address the complexity of user preferences and interests. We propose a method to recommend personalized papers based on a heterogeneous network that includes papers, venues, authors, terms, and users as well as the relations among these entities. We investigate meta-paths in the network to capture

Platform-mediated network has received increasing attention from researchers and practitioners in recent years. The literature on this topic has been mainly derived from the model of two-sided network (2SN) where producers and consumers comprise the two sides and are considered the main user groups. Inadequacy, however, has been noticed where the two-sided network model cannot fully explicate the dynamics

Business Intelligence and Analytics (BI&A) systems have demonstrated their potential to enhance decision making; however, the linkage between BI&A and decision support systems (DSS) has been contested by some, if not completely denied by others. In this research, we investigate the foundations of BI&A by using foundational literature on DSS to open the ‘black box’ of BI&A systems. We argue that BI&A

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

The nature of people's web navigation has significantly changed in recent years. The advent of smartphones and other handheld devices has given rise to web users consulting websites with more than one device, or using a shared device. As a result, large volumes of seemingly disjoint data are available, which when analysed together can support decision-making. The task of identifying web sessions by

Can social media-driven customer engagement help movie producers to increase movie sales? Movie producers use social media to boost customer engagement; however, our understanding of whether there is a relationship between pre-consumption customer engagement and movie sales is limited. This research explores what drives the success of movies on the opening-weekend. We examine the relationship between

The rise of e-commerce has led to the increased reliance of users on electronic word-of-mouth (eWOM) to evaluate the products and services offered. Previous research has attempted to determine which reviews are more credible than others, as credible reviews tend to impact readers more than non-credible ones. However, one of the research questions in this domain that has not been addressed so far is

Channel integration services such as click-and-collect provide consumers with a seamless experience across channels. However, research on the effects of simultaneously offered but differently useful offline-to-online (OFF-ON) and online-to-offline (ON-OFF) integration services for consumers is lacking. Managers still find it challenging to decide for those services that increase channel quality and

Make or buy decisions are often decisions between investing in capital for production or building supply chain relationships upstream. These are strategic decisions about production interdependence: the degree to which materials and services are provided internally versus purchased externally. Information technology (IT) has increased productivity with hardware and software such as robotics and flexible

Digital Twin is one of the promising digital technologies being developed at present to support digital transformation and decision making in multiple industries. While the concept of a Digital Twin is nearly 20 years old, it continues to evolve as it expands to new industries and use cases. This has resulted in a continually increasing variety of definitions that threatens to dilute the concept and