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

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

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

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

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

With the goal of devising algorithms for decision support in operational tasks, we introduce a new methodology for the automated configuration of algorithms for combinatorial online optimization problems. The procedure draws upon available instance data and is capable of recognizing data patterns which prove beneficial to the overall outcome. Since online optimization requires repetitive decision making

Due to its complex, time-demanding, and multifaceted structure, personnel selection is considered as a multicriteria decision-making problem, the framework of which includes both qualitative and quantitative criteria. Although various techniques have been proposed to address this problem in various industries, a robust methodology that is capable of explicitly considering the presence of uncertainty/vagueness

E-commerce consumers often face complex product choices and have to make decisions at various times of the day. Extant research has primarily focused on providing decision aids and eliminating “distracting” features to help consumers' conscious thought when making complex decisions. Our research adopts a novel perspective that consumers' unconscious thought may have some advantages and be exploited

The overarching goal of this paper is to develop a modeling framework that can be used to obtain personalized, data-driven and monotonically constrained probability curves. This research is motivated by the important problem of improving the predictions for organ transplantation outcomes, which can inform updates made to organ allocation protocols, post-transplantation care pathways, and clinical resource

There has been significant research into reject inference, with several statistical methods and machine learning techniques having been employed to infer the possible repayment behavior of rejected credit applicants. This study proposes a novel three-stage reject inference learning framework using unsupervised transfer learning and three-way decision theory that integrates: (1) the rejected credit

Digital services are a key driver of contemporary businesses. In order to scale the implementation of design-centric development processes, companies increasingly assign design work to design novices. As design novices have limited design knowledge and experience, they are challenged to select adequate design techniques throughout the entire lifecycle of digital services. Thus, providing decision aids

This study examines how the unique information environment of online forums affects consumers' information acquisitions, and further impacts their purchase behavior. Based on the information foraging theory, we hypothesize that the relevant information patches in online forums facilitate consumers' information acquisition processes and increase purchase intention. However, the mixed information scents

Product defects are a major concern for manufacturers and customers. Detecting product defects is vital for manufacturers to prevent enormous product failure costs. As the surge of social media is in vogue, social media data become an important information source for manufacturers to collect defect information. In this study, we propose a novel probabilistic graphic model to discover defects from social

In the framework of Industry 4.0, decision support systems (DSS) are an essential part in the process of converting information into managerial actions. This paper proposes a specific DSS to improve the daily operation of an industrial heat-recovery section (a network of heat exchangers) in a fiber-production factory. In this process, the aim is to optimize the resource utilization in real time while

The low quality of data in information systems poses enormous risks to business operations and decision making. In this paper, a single-period resource allocation problem for controlling the information system's data quality problem is considered. We develop a Data-Quality-Petri net to capture the process through which data quality problem generates, propagates, and accumulates in the information system

In recent decades, the implementation of electronic health record (EHR) systems has been evolving worldwide, leading to the creation of immense data volume in healthcare. Moreover, there has been a call for research studies to enhance personalized medicine and develop clinical decision support systems (CDSS) by analyzing the available EHR data. In EHR data, usually, there are millions of patients records

This paper considers a multi-item newsvendor problem with a capacity constraint (Z). The problem has already been addressed in the literature using the classical newsvendor problem. However, provided solutions made assumptions for demand distributions, which are often incorrect and led to errors in the inventory optimization. This research proposes a distribution-free and completely data-driven solution

The criteria for evaluating a crowdfunding project vary from person to person. Some investors are concerned with the entrepreneurs' profiles, while others are more interested in the creativity behind the project or the idea. Most investors evaluate a project by referring to founder-generated content. Entrepreneurs then face the choice of emphasizing their own profiles or the project creativity within

Online social networks convey rich information about geospatial facets of reality. However in most cases, geographic information is not explicit and structured, thus preventing its exploitation in real-time applications. We address this limitation by introducing a novel geoparsing and geotagging technique called Geo-Semantic-Parsing (GSP). GSP identifies location references in free text and extracts

While supporting the execution of business processes, information systems record event logs. Conformance checking relies on these logs to analyze whether the recorded behavior of a process conforms to the behavior of a normative specification. A key assumption of existing conformance checking techniques, however, is that all events are associated with timestamps that allow to infer a total order of

A complex decision-making challenge senior managers commonly face is the selection of the winning bid from multiple project proposals. Project selection decisions become more complex when providers deliberately choose to introduce ambiguity to their project proposals rather than address the client's predetermined set of desired specifications. In particular, providers suggest in their proposals a range

Loan application assessments of small and medium-sized enterprises (SMEs) are difficult because of information asymmetry. To mitigate the information asymmetry, this paper focuses on information found in legal judgments involving the company and its principles and combines this information with financial and firm-specific information to help evaluate the credit risk of SMEs. We propose a framework

Digitalization shifts human communication to online platforms, which has many benefits but also builds up a space for antisocial online behavior (AOB) such as harassment, insult and other forms of hateful textual content. Online platforms have good reasons to monitor and moderate such content. The paper examines the viability of automatic content monitoring using deep machine learning and natural language

Due to the ageing population and the prevalence of chronic diseases, Home Health Care (HHC) practices are significantly increasing in developed countries to provide coordinated health related services to patients at their homes. Accordingly, the scope of HHC services is also expanding from typical nursing and postoperative care at home to cover all types of needs of elderly patients (e.g., personal

The increased exposure of the average citizen and customer to polarised content from various sources has been of significant consequence for companies and governmental organisations. Such content has, for example, served as a catalyst for violent uprisings and shifts in stock market prices. The collection and study of opinion have therefore become a necessity in many industries. Due to the vast nature

Many real-world networks have signed relationships between the nodes. Identification of these relationships is an important aspect of decision making. The existing signed relationships in a network may impact the relationships between the other nodes, hence learning from the existing signed relationships in a network can be used for decision making in various mining tasks. These signed networks are

In recent years, new types of interactive analytical dashboard features have emerged for operational decision support systems (DSS). Analytical components of such features solve optimization problems hidden from the human eye, whereas interactive components involve the individual in the optimization process via graphical user interfaces (GUIs). Despite their expected value for organizations, little

Along with recent advancement of web technologies, social networking sites (SNSs) affect peoples lifestyles by enabling them to perform so many activities which were not easy to do before. However, protection of users' disclosed information becomes an important challenge not only for SNS users but also for governing organizations. This study focuses on SNS users' protection from privacy breaches that

Online learning has been adopted rapidly by educational institutions and organizations. Despite its many advantages, including 24/7 access, high flexibility, rich content, and low cost, online learning suffers from high dropout rates that hamper pedagogical and economic goal outcomes. Enhanced student dropout prediction tools would help providers proactively detect students at risk of leaving and identify

Reward-based crowdfunding provides a unique instrument to acquire capital from small investors for a specific project. To successfully campaign for the desired funds, however, entrepreneurs also need to understand the subconscious factors, which influence the funding behavior of potential investors. In pursuit of that goal, this study examines how facial emotional expressions, which are shown in the

Despite a consensus that sales of physical albums have declined with the increasing prevalence of digital music, this seemingly unequivocal observation may not be true. Physical formats still occupy a fraction of the market, with a noticeable increase in the sales of physical albums in a few countries where digital media is predominant. This study empirically examines the impact of two online music

When an aircraft experiences an unexpected issue during flight operations, a technician determines whether the aircraft can safely perform the next flight. This operational decision process - known as dispatch assessment - has to happen within limited available time between aircraft arrival and departure. Currently, technicians face two main problems during the assessment: lack of access to decision

The recent COVID-19 pandemic has clearly shown how agricultural foods and e-commerce initiatives are critical for many organizations, regions, and countries worldwide. Despite this vital importance, prior IS research on the business value of IT has not paid enough attention to the potential specificities of the agribusinesses. This study examines the impact of e-commerce capability on business agility

Hinterland intermodal transportation is the movement of containers between deep-sea ports and inland terminals by using trucks, trains, barges, or any combination of them. Synchromodal transportation, as an extension of intermodal transportation, refers to transport systems with dynamic updating of plans by incorporating real-time information. The trend towards spot markets and digitalization in hinterland

Uplift modeling is an approach for estimating the incremental effect of an action or treatment at the individual level. It has gained attention in the marketing and analytics communities due to its ability to adequately model the effect of direct marketing actions via predictive analytics. The main contribution of our study is the implementation of the uplift modeling framework to maximize the effectiveness

Deterioration rate is an important characteristic of perishable products. While deterioration in perishables is unavoidable, there are proven ways to lower their deterioration rates. Deterioration rate is commonly treated as an exogenous parameter in extant inventory management literature. We define deterioration rate as one of the controllable variables and propose a novel freshness-preservation effort

Shared parking firms offer a double-sided platform for parking space sharing. Many of these firms provide differentiated service levels to both suppliers and buyers. This new phenomenon in the parking industry materialized thanks to recent innovations in IoT-enabled automation and electric vehicle charging technologies. We study shared parking firms. Specifically, we formulate the firm's location and

In consideration of the general lack of trust in human professional financial advisors due to conflicts of interest, and given inadequacies in terms of the utility of FinTech alternatives for financial goal recommendations, this study establishes a framework for an ontology-based, multiagent recommender system designed to improve financial capability through the recommendation of financial goals, called

Imagine that you are a marketer with a good product but mediocre online reviews. When would be the best time to present the review score information to consumers: before the product description, with the product description, or after the product description? In order to answer this question, we carried out three online experiments in which we manipulated the order of information (reviews presented

In light of individuals' increasing concern regarding their physical health, mobile health applications (mHealth Apps) have gained popularity in recent years as important tools for addressing health problems. However, users find it challenging to choose appropriate mHealth Apps, as these Apps incorporate diverse behavior change techniques (BCTs), and their individual behavioral intervention effects

Process mining, as with any form of data analysis, relies heavily on the quality of input data to generate accurate and reliable results. A fit-for-purpose event log nearly always requires time-consuming, manual pre-processing to extract events from source data, with data quality dependent on the analyst's domain knowledge and skills. Despite much being written about data quality in general, a generalisable

Accurate prediction of no-show patients plays a crucial role as it enables researchers to increase the efficiency of their scheduling systems. The purpose of the current study is to formulate a novel hybrid data mining-based methodology to a) accurately predict the no-show patients, b) build a parsimonious model by employing a comprehensive variable selection procedure, c) build a model that does not

We develop a secure attribute-based search protocol for Radio Frequency Identification (RFID) systems. This protocol can be used to simultaneously identify groups of items that share a set of attribute values. To the best of our knowledge, this is the first such work with the potential to significantly enhance the security and intelligence of RFID-enabled applications in inventory control and supply

Clinician guidelines recommend health management to tailor the form of care to the expected course of diseases. Hence, in order to decide upon a suitable treatment plan, health professionals benefit from decision support, i.e., predictions about how a disease is to evolve. In clinical practice, such a prediction model requires interpretability. Interpretability, however, is often precluded by complex

Fake online reviews in e-commerce significantly affect online consumers, merchants, and, as a result, market efficiency. Despite scholarly efforts to examine fake reviews, there still lacks a survey that can systematically analyze and summarize its antecedents and consequences. This study proposes an antecedent–consequence–intervention conceptual framework to develop an initial research agenda for

The review systems of online platforms create a stream of online word-of-mouth that allows consumers to learn from others' purchasing experience. However, it is difficult for consumers to discern the authenticity of a review or the reviewer's level of experience with the product. Platforms can aid the authentication process by incorporating a verified purchase (VP) indication, or “badge” as is done

Humankind faces many “wicked” decision-making problems, which must be solved. One promising approach refers to crowdsourcing systems that hold the potential to solve any kind of problem – notably wicked ones. Crowdsourced solutions work well because crowds exchange knowledge from different domains – a concept known as “cross-fertilisation.” Thereby, the “facilitator” of a crowdsourcing system is the

A central concern for the long-term vibrancy of virtual communities is how to effectively motivate continuous contributions from community members. To address this challenge, a series of motivation factors that range from self-interest driven to prosocial-oriented motives have been identified by scholars. As members participate in continuous contributions and community activities alike, their status

Evidence suggests that firms which use customer data analytics perform better than those that do not. However, the current policy of voluntary profiling allows firms to collect and use customer information only if customers voluntarily disclose information with them. Further, surveys and literature show that many customers are not comfortable with firms collecting their information due to privacy concerns

This paper introduces the concept of IT wisdom from a perspective of meta-capability and establishes the relationship between IT wisdom and firm performance. We propose that IT wisdom as a holistic meta-IT capability of a firm can strategically enable and exercise suitable IT capabilities and organizational capabilities to adapt to changing environments for improving firm performance. We test our hypotheses