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

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

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

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

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

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

The growing amount of cyberspace threats highlights the need to evaluate cybersecurity risks and to plan for effective investments. One document internationally recognized for cybersecurity risk management is the framework (Framework for Improving Critical Infrastructure Cybersecurity (Version 1.1), National Institute of Standards and Technology, 2014.) by the US National Institute of Standards and

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

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

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

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 scripts to use during systems analysis and design tasks. We carried out a free-simulation experiment

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

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

The online review systems of digital platforms feature a variety of designs. This study examines how closed-form evaluations and open-ended textual comment options affect customers' online review writing behaviors and reflect satisfaction with hotels. We find that direct and spillover effects exist. These refer to the effects of customers' closed-form evaluations of a product or service attribute on

Following the 2007–2008 global financial crisis, regulators introduced a series of supervisory tools for the closer monitoring of financial institutions. Among them, stress tests and capital exercises performed by the Federal Reserve System (Fed) and the European Banking Authority/European Central Bank (EBA/ECB) play a critical role in the surveillance of the U.S. and the European banking sector. However

In the music market, superstars significantly dominate the market share, while predicting the top hit songs is notoriously difficult. The music intelligence technology, retrieving and utilizing granular acoustic features of songs, provides opportunities to improve the prediction of top hit songs. Using data on 6209 unique songs that appeared in the weekly Billboard Hot 100 charts from 1998 to 2016

Setting the retail price as a part of marketing would affect customers' cognition regarding products and affect their post-purchase behavior of review writing. To deeply understand the relationships between retail prices and reviews, this paper designs an intelligent data-driven Generate/Test Cycle using a machine learning technique to automatically discover the relationship model from a huge amount

Database tuning is a task executed by Database Administrators (DBAs) based on their practical experience and on tuning systems, which support DBA actions towards improving the performance of a database system. It is notoriously a complex task that requires precise domain knowledge about possible database configurations. Ideally, a DBA should keep track of several Database Management Systems (DBMS)

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

Machine learning has proved to generate useful predictive models that can and should support decision makers in many areas. The availability of tools for AutoML makes it possible to quickly create an effective but complex predictive model. However, the complexity of such models is often a major obstacle in applications, especially in terms of high-stake decisions. We are experiencing a growing number

An understudied area in the field of social media research is the design of decision support systems that can aid the manager by way of automated message component generation. Recent advances in this form of artificial intelligence has been suggested to allow content creators and managers to transcend their tasks from creation towards editing, thus overcoming a common problem: the tyranny of the blank

The ever-increasing reliance on loot boxes by the video game industry has attracted scrutiny from consumer groups and regulators. For example, this practice of selling random assortments of virtual items for a price has been criticized for its lack of transparency since, before purchasing a loot box, players do not necessarily know the possible items they can win and the associated probabilities. Even

Process models generated through process mining depict the as-is state of a process. Through annotations with metrics such as the frequency or duration of activities, these models provide generic information to the process analyst. To improve business processes with respect to performance measures, process analysts require further guidance from the process model. In this study, we design Graph Relevance

Inconsistency in online consumer reviews (OCRs) may cause uncertainty and confusion to consumers when they make purchase decisions. However, there is a lack of a systematic and empirical investigation of review inconsistency in the literature. This research characterizes review inconsistency from multiple aspects, including rating-sentiment, content, and language, and proposes hypotheses about their

Online gaming has grown to be a very popular electronic entertainment for people throughout the world. However, the burgeoning popularity of online games in some cases leads to addiction, a phenomenon that has received considerable attention. The purpose of this study is to examine the effects of psychological ownership, gaming motivation, and primary–secondary control on online game addiction (OGA)

To comply with sustainability goals, many companies buy green energy to serve their energy demand. This is typically done by engaging in bilateral power purchase agreements (PPA) with renewable energy producers (REP). A PPA can be flexibly structured, but the core principle is that a buyer (company) agrees to buy future energy production of a seller (REP) at an agreed-upon fixed price. PPAs are financially

Artificial Intelligence is increasingly becoming integrated in many aspects of human life. One particular AI comes in the form of conversational agents (CAs) such as Siri, Alexa, and chatbots used for customer service on websites and other information systems. It is widely accepted that humans treat systems as social actors. Leveraging this bias, companies sometimes attempt to masquerade a CA as a

In this study, we investigate the potential for optimizing hot spot patrol routes through density ridge estimation. We explore the application of an extended version of the subspace-constrained mean shift algorithm by using 2018 and 2019 Part I crime data from Chicago. Ultimately, the goal of mapping hot spots is to show concentrations of crime, thus targeting the epicenters only focuses on one problem

As a newly-emerged financing method, crowdfunding has been gaining popularity among entrepreneurs in recent years. Two major functions are featured in crowdfunding campaigns: financing and marketing. Focusing on reward-based crowdfunding campaigns that adopt the “All-or-Nothing” mechanism, this paper mainly investigates how entrepreneurs set their funding targets and sales strategies with two cases

Traffic pumping is a type of fraud committed in several countries, in which small telephone operators inflate the number of incoming calls to their networks, profiting from a higher access charge in relation to the network operator associated with the origin of the call. The identification of traffic pumping is complex due to the lack of labels for performing supervised learning, and the scarce literature

Expert knowledge is the foundation of the interpretability of belief rule base (BRB) expert system. However, the rule explosion problem and weak extendability of BRB limit the utilization of expert knowledge. To solve this problem, a new approximate belief rule with single attributes is proposed, with which a new expert system named as ABRB is constructed. In the new rule, the correlation among attributes

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

Big data analytics examines millions, if not billions of records, to unmask hidden patterns, provide actionable insights and interpretable results for various domains. One area that has great potential to leverage the value of big data and analytics is the critical analysis of traffic accidents. Investigation results help in providing an in-depth understanding of the risks and provide measures to potentially

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

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

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

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

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

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

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

Due to low switching costs and stiff competition, customer relationship management has become a central component in the marketing strategy of telecommunication service providers. Since the costs of acquiring a new customer are five times higher than the costs of maintaining an existing customer, telecommunication service providers are eager to reduce the churn rate. A solid understanding of customer

This paper describes a decision support system designed for a Belgian Human Resource (HR) and Well-Being Service Provider. Their goal is to improve health and well-being in the workplace, and to this end, the task is to identify groups of employees at risk of sickness absence who can then be targeted with interventions aiming to reduce or prevent absences. To facilitate deployment, we apply a range

Timely identification of terrorist networks within civilian populations could assist security and intelligence personnel to disrupt and dismantle potential terrorist activities. Finding “small” and “good” communities in multi-layer terrorist networks, where each layer represents a particular type of relationship between network actors, is a vital step in such disruption efforts. We propose a community

Restaurant health inspections aim at identifying health violations and shall reduce the risk that restaurant visitors suffer from foodborne illness. Nevertheless, regulatory authorities' resources are limited, so an efficient mechanism that supports scheduling of health inspections is necessary. We build upon information efficiency theory and investigate whether information extracted from online review

Little is known about how organizations leverage business analytics (BA) to develop, process, and exploit analytical information in cybersecurity incident response (CSIR). Drawing on information processing theory (IPT), we conducted a field study using a multiple case study design to answer the following research question: How do organizations exploit analytical information in the process of cybersecurity

In sharp market competition, it is very important for enterprises to maintain high product competitiveness. The rich data on social network sites and e-commerce platforms provide a novel way to research product competitiveness. Some studies have mined product competitiveness from online reviews, which may be biased, since some fake information may be contained in online reviews, and the information

The electronic diagnostic records of patients, primarily collected by hospitals, comprise valuable data for the development of recommender systems to support physicians in predicting the risks associated with various diseases. For some diseases, the diagnostic record data are not sufficient to train a prediction model to generate recommendations; this is referred to as the data sparsity problem. Cross-domain

In paid search advertising firms need to make decisions on a matching strategy between broad match and exact match to target their current and prospective customers. Broad match broadens the scope of targeting while exact match provides more accurate targeting. This paper investigates the relative effectiveness of matching decisions using both secondary data and a field experiment data. Both studies

Student dropout is a major concern in higher education, as it leads to direct economic losses and substantial social costs. Public and private institutions spend considerable resources to prevent student dropout. The efficiency and effectiveness of these investments, however, may be improved by adopting a profit-driven perspective. In this paper, we propose a novel approach for implementing student

Predicting next events in predictive process monitoring enables companies to manage and control processes at an early stage and reduce their action distance. In recent years, approaches have steadily moved from classical statistical methods towards the application of deep neural network architectures, which outperform the former and enable analysis without explicit knowledge of the underlying process

In this paper, a Decision Support System (DSS) with digital twinning-based resilience analysis is proposed as a modern tool for port resilience computation and updating. The proposed DSS assesses the resilience of a port under possible disruptive events given its design, operations and potential pre-defined post-event recovery actions to mitigate the impact of the disruption. Digital twinning provides

The short-term room rental online platforms have boomed in the era of digital business acceleration, as they offer excellent opportunities for interactive behavior between host and guests. However, pursuing customer repeated bookings is a challenge on these platforms. How to increase customer repeated bookings in the short-term room rental market? We examine theoretically and empirically this crucial

Facilitating information search to support decision making is one of the core purposes of information technology. In both personal and workplace environments, advances in information technology and the availability of information have enabled people to perform much more search and access much more information for decision making than ever before. Because of this abundance of information, there is an

An important business domain that relies heavily on advanced statistical- and machine learning algorithms to support operational decision-making is customer retention management. Customer churn prediction is a crucial tool to support customer retention. It allows an early identification of customers who are at risk to abandon the company and provides the ability to gain insights into why customers