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

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

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

Financial institutions increasingly rely upon data-driven methods for developing fraud detection systems, which are able to automatically detect and block fraudulent transactions. From a machine learning perspective, the task of detecting suspicious transactions is a binary classification problem and therefore many techniques can be applied. Interpretability is however of utmost importance for the

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 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

Road signs, such as traffic signs, traffic lights or pavement markings, are essential elements for the regulation of driving. Sensors embedded in vehicles (e.g., cameras) are increasingly able to detect them to provide near real-time assistance to the driver, with features such as the current speed limitation at any moment. When sensors are not able to detect road signs (e.g., because of bad weather

The purpose of this study is to demonstrate the value of Facebook data in predicting first-time donation behavior. More specifically, we provide evidence that Facebook data can be used as a valuable data source for nonprofit organizations in acquiring new donors. To do so, we evaluate three different dimensionality reduction techniques (i.e., singular value decomposition, non-negative matrix factorization

The increased awareness of the importance of global healthcare which has formed an inseparable relationship with the quality of human life has led researchers to pay attention to trends in the development of the healthcare industry. This study identifies eight potential development trends designed to provide the healthcare industry with appropriate development strategy recommendations. The modified

Heterogeneous information networks (HINs) refer to logical networks involving entities of multiple types and their multiple relations, which are widely used for modeling real-world systems with rich features and intricate patterns. Link prediction in such networks is a consistent interesting research question due to its methodological and practical implications in the business field. This study develops

Crowdsourcing is a new trend that uses the wisdom of crowds on the Internet to solve certain problems that need vast amounts of human resources. There have been a number of crowdsourcing platforms developed for various domains. However, the landscape of crowdsourcing platforms is widely dispersed and most tasks remain hidden. Finding out the tasks closely matching contributors' personal preference

Many goods, including pharmaceuticals, require close temperature monitoring. This is important not only for complying with regulations but also for guaranteeing safety of use. A particular challenge in controlling a product's temperature arises during transportation. In cold supply chains (SCs), temperature is maintained by refrigerated containers. However, many situations, e.g. cooling system failure

Several recently published papers in Decision Support Systems discussed issues related to data quality in Information Systems research. In this short research note, I build on the work introduced in these papers and document two data quality issues discovered in a large open dataset commonly used in research. Inside Airbnb (IA) collects data from places and reviews as posted by users of Airbnb.com

Efficient detection of incorrectly filed tax returns is one of the main tasks of tax agencies. Value added tax (VAT) legislation requires buyers and sellers to communicate any exchanges that exceed a certain amount. Both statements should coincide, but sometimes the seller/buyer and its counterpart declare different amounts. This paper presents a method to detect those businesses that are more prone

Comparable entity identification plays an essential role in the decision making of both consumers and firms in competitive environment. In contrast to traditional cooccurrence approaches, this paper proposes a novel method, namely, ICE (identifying comparable entities) for effectively identifying comparable entities from web search logs, which are online user-generated contents that reflect users'

Advances in artificial intelligence create new opportunities for computers to support humans as peers in hybrid teams in several complex problem-solving situations. This paper proposes a decision-making architecture for adaptively informing decisions in human-computer collaboration for large-scale competitive problems under dynamic environments. The proposed architecture integrates methods from sequence

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

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

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

This paper examines the association between product networks generated by recommendation systems and the product ratings' convergence of products. It further investigates how different types of product networks are associated with a customers' perception of quality between product pairs in a product network. Additionally, this study examines whether the type of product networks are associated with

Crafting and executing the best cryptocurrency mining strategy is vital to succeeding in cryptocurrency market investments. This study aims to identify the best cryptocurrency mining strategy based on service providers' performance for cryptocurrency mining using a hybrid analytics approach, which integrates the Analytic Hierarchy Process (AHP) and Fuzzy-TOPSIS techniques, along with sensitivity analysis

Appointment no-shows are disruptive to healthcare clinics, and may increase patient waiting time and clinic overtime, resulting in increased clinic costs. Appointment scheduling models typically mitigate the negative effects of no-shows through appointment overbooking. Recent work has proposed a predictive overbooking framework, where a probabilisitic classifier predicts the no-show probability of

The study uses a novel psychological text-mining approach, language style matching (LSM), to examine the effect of the social interaction of top management team (TMT) members on conservative accounting reporting practices. We posit that similar language styles help to form consensus and social integration, leading to better cooperation in group decision-making. Using 10,531 earnings conference call

Automated identification (auto-ID) has been widely used in practice for more than five decades, beginning with the commercial use of barcodes in the early 1970s. More recently, since about 2003, RFID (Radio Frequency IDentification) use has seen widespread adoption. While these automated identification technologies help improve convenience, effectiveness, and efficiency, associated vulnerabilities

In this paper we present a multi-criteria classification of Vocational and Educational Programs in Extremadura (Spain) during the period 2009–2016. This ranking has been carried out through the integration into a complete database of the detailed information of individuals finishing such studies together with their labor data. The multicriteria method used is TOPSIS together with a new decision support

Online game research has explained the sources of gamer loyalty, but has not discussed the role of gaming team competition, i.e., who would engage in gaming team functions and what gaming team functions would foster members' loyalty. A lack of answers to these member-retaining questions keeps game providers in the dark about making game design decisions to retain gaming team members, threatening their

Unlike general e-business, e-commerce livestreaming innovatively enables anchors to use instant social functions to communicate with viewers and present products in more vivid ways. However, little research has been done to understand the effects of social cues in e-commerce livestreaming. Drawing on stimulus-organism-response (S-O-R) theory, we develop a two-phase research framework to examine how

Due to the increasing occurrence of disruptions across our global society, it has become critically important to understand the resilience of different socio-economic systems, i.e., to what extent those systems exhibit the ability both to resist a disruption and to recover from one once it occurs. In order to characterize this ability, however, one must be able to quantitatively measure the relative

Mobile health applications are considered to be powerful tools for activity-based wellness management. With the availability of multimodal sensors in smart devices used in our daily lives, it is possible to track human activity and deliver context-aware wellness services. The embedded sensors in naturally used devices such as smartphones, smartwatches, and wearables contain rich information that can

Crowdfunding was first used by individuals and entrepreneurs to collect small-sized investments from crowds to support for-profit ventures, but now it is being touted as a valuable alternative to raise money for non-profit causes. Similar to various online settings, a key challenge for online charitable crowdfunding platform is the problem of donor retention. In this research, we disentangle donor

In a competitive environment, determining when to enter the market and what homing policies should be adopted are crucial strategic decisions for platform-based businesses. In this study, we investigate the interactions between two platforms competing in two-sided markets with cross-sided network effects and determine their prices under different entry and homing strategies. We derive the equilibrium

Many bankruptcy prediction models for small and medium-sized enterprises (SMEs) are built using accounting-based financial ratios. This study proposes a bankruptcy prediction model for SMEs that uses transactional data and payment network–based variables under a scenario where no financial (accounting) data are required. Offline and online test results both confirmed the predictive capability and economic

Online home-sharing platforms, such as Airbnb, have recently become increasingly popular among travelers, including outbound travelers. This study investigates the impact of consumer animosity, a determinant of consumers' purchase decisions in international businesses, on outbound travelers' demand for sharing-based accommodations. In particular, we examine the differential effects of two types of

The new era of circular supply chain management (CSCM) produces a new complex decision area for process managers. Part of it can be attributed to green procurement, in which a large number of potential ideas need to be reviewed that can sustain business. Such a large amount of data can quickly lead to information overload, especially without the presence of appropriate decision support tools. While

Ideational impact refers to the uptake of a paper's ideas and concepts by subsequent research. It is defined in stark contrast to total citation impact, a measure predominantly used in research evaluation that assumes that all citations are equal. Understanding ideational impact is critical for evaluating research impact and understanding how scientific disciplines build a cumulative tradition. Research

For modern mortgage firms, the process of setting up and verifying a new loan, known as origination, is complex and multifaceted. The literature notes that this process is rife with delays that can stunt the firm's business opportunities, but no modern analytical techniques have been developed to address the problem. In this paper, we suggest the use of text analytic and machine learning techniques

Companies like Ritz Carlton, Disney and Verizon are among many who have invested in analytics to improve their customers' service experiences with the firms. Extensive data are collected on all aspects of how customers interact or experience the products or services. Research has shown the importance of the “peak-end” rule in service design; that is, providing a customer with good “peak” service levels

The on-demand economy has prospered with the rapid development of digital platforms. Many customers use on-demand service platforms to order services and then post online reviews. Using text-mining approaches, this study examines customers' online review-writing behavior and their overall satisfaction with restaurants in the context of on-demand food service. We use customers' overall ratings in their

Demand forecasting works as a basis for operating, business and production planning decisions in many supply chain contexts. Yet, how to accurately predict the manufacturer's demand for components in the presence of end-customer demand uncertainty remains poorly understood. Assigning the proper order quantities of components to suppliers thus becomes a nontrivial task, with a significant impact on

Nowadays, many companies face shorter product life cycles, increasing the need to properly forecast demand for newly introduced products. These forecasts allow them to support operational decisions, such as procurement and inventory control. However, forecasting the demand of new products is challenging compared to existing products, since historical sales data is not available as an indicator of future

Effective knowledge management and decision-making are essential for professional service firms. Consequences of poor decision-making are particularly significant in audit firms, where decisions affect the performance of financial markets. International corporate regulators and professional accounting bodies have raised serious concerns about the quality of audit decisions currently being made, suggesting

This study investigates the determinants of online review helpfulness, adopting various predictors from three dimensions of the online review management: source factors, review factors, and context factors. Based on a large, comprehensive dataset that includes 14,051,211 online reviews in 24 product categories from an ecommerce retailer, Amazon.com, this study provides empirical evidence on the effect

Microtasking is a type of crowdsourcing, denoting the act of breaking a job into several tasks and allocating them to multiple workers to complete. The assignment of tasks to workers is a complex decision-making process, particularly when considering budget and quality constraints. While there is a growing body of knowledge on the development of task assignment algorithms, the current algorithms suffer

One of the most common methods used in the social network analysis of criminal groups is node importance evaluation, which focuses on the links between network members to identify likely crime suspects. Because such traditional node evaluators do not take full advantage of group members' individual criminal propensities, a new evaluator called the social network criminal suspect evaluator (SNCSE) is

Online stores currently use recommendation systems (RSs) quasi-universally to provide their customers with added value and increase their profits, thus reshaping the world of e-commerce. RSs, however, depend on the availability of e-commerce user data to be effective. Nevertheless, data privacy regulations are increasingly becoming more restrictive and e-commerce users more aware of and concerned about

Financial statement fraud is an area of significant consternation for potential investors, auditing companies, and state regulators. The paper proposes an approach for detecting statement fraud through the combination of information from financial ratios and managerial comments within corporate annual reports. We employ a hierarchical attention network (HAN) to extract text features from the Management

In an online game, multiple players virtually play the game together and are ranked based on their in-game performance through competition. Online games update constantly and continuously (i.e., a modification in the game content after its release), changing the game every time. This research examines the effect that these game updates have on individual gameplay. We compared 9342 players' daily gameplay

Although the upper echelons theory posits that senior executives' personal characteristics influence firm performance, very few studies have examined the impact of the interplay between CEO and CFO characteristics on corporate activities. To fill this research gap, we propose an econometric analysis model to examine the interplay between the personality traits of CEOs and CFOs and corporate mergers

Expert finding in Community Question Answering (CQA) networks such as Stack Overflow is a practical issue facing a challenging problem called vocabulary gap. A widely used approach to overcome this problem is translation model. Different from prior works that only consider the relevancy of translations to a query, we intend to diversify query translations for better coverage of query topics. In this

In the majority of executive domains, a notion of normality is involved in most strategic decisions. However, few data-driven tools that support strategic decision-making are available. We introduce and extend the use of autoencoders to provide strategically relevant granular feedback. A first experiment indicates that experts are inconsistent in their decision making, highlighting the need for strategic

A large amount of research on recommender systems has focused on improving the accuracy of suggestions in offline settings. However, this focus and the commonly used techniques can lead to a “filter bubble”, severely limiting the diversity of content discovered by users. Several offline studies show that this can be mitigated by using experts for recommendation. In contrast to standard recommender

Big data analytics (BDA) are gaining importance in all aspects of business management. This is driven by both the presence of large-scale data and management's desire to root decisions in data. Extant research demonstrates that supply chain and operations management functions are among the biggest sources and users of data in the company. Therefore, their decision-making processes would benefit from

Social networking site (SNS) driven e-commerce, the latest social commerce (s-commerce) phenomenon, gains prominence with the introduction of the call-to-action feature. The call-to-action feature on any sponsored post or advertisement on SNS redirects the user to a linked e-commerce website that offers the product. Information cues available on the SNS are expected to influence user decision making

A rating system (RS) comprises a rating metric defined by a discrete set of integers contained in an interval (e.g., [0, N]), and an aggregation rule. RSs are widely used in various fields to capture and summarize individuals' opinions on alternatives. In this paper we argue that the multi-attribute additive value model (MAVM) should be used as a benchmark to analyze the reliability of RSs, and present

The use and popularity of online auctions is growing all over the world. Bidding strategies are important because they are related to an auction's final price and ultimately its revenue. This study investigates the bidding strategies adopted by online bidders and the factors of the bidders, including bidding motivations, time availability, bidding experience, and risk aversion. We use the data from

Low attendance levels in medical appointments have been associated with poor health outcomes and efficiency problems for service providers. To address this problem, healthcare managers could aim at improving attendance levels or minimizing the operational impact of no-shows by adapting resource allocation policies. However, given the uncertainty of patient behaviour, generating relevant information

We consider portfolio decision problems with positive interactions between projects. Exact solutions to this problem require that all interactions are assessed, requiring time, expertise and effort that may not always be available. We develop and test a number of fast and frugal heuristics – psychologically plausible models that limit the number of assessments to be made and combine these in computationally

Ransomware is a type of malware that affects the victim data by modifying, deleting, or blocking their access. In recent years, ransomware attacks have resulted in critical data and financial losses to individuals and industries. These disruptions force the need for developing effective anti-ransomware methods in the research community. However, most of the existing techniques are designed to detect