Simulation is a versatile technique for quantitative analysis of business processes. It allows analysts to estimate the performance of a process under multiple scenarios. However, the discovery, validation, and tuning of business process simulation models is cumbersome and error-prone. It requires manual iterative refinement of the process model and simulation parameters in order to match the observed

Trust plays an important role in sharing transactions on short-term rental platforms. However, the impact of host self-description on trust perception and whether trust perception can influence purchase behavior remain under-studied. Therefore, a text analytics framework was proposed to research the relationships among host self-description, trust perception and purchase behavior on Airbnb. Specifically

This paper studies a reward-based crowdfunding game wherein the creator first collects money through an E-crowdfunding platform and then, if successful, invests the raised money to fund promotional activities via online retailing. We derive the creator's equilibrium decisions and the corresponding payoff and show that they are significantly influenced by the potential demand and the gap between different

Effectively displaying goods in search results is valuable for B2C merchants to earn clicks from consumers and even increase sales. Taking down jackets and trousers sold on Tmall—China's largest B2C e-commerce platform—as the example, this paper collects data about three factors that influence consumers' first impression on their search results: the price of a product, the quantity of historical reviews

Safety, as the most important concern in civil aviation, needs to be maintained at an acceptable level at all times in the air transportation system. This paper aims to increase en-route flight safety through the development of deep learning models for trajectory prediction, where model prediction uncertainty is characterized following a Bayesian approach. The proposed methodology consists of four

Online auctions use a variety of unique tools to provide quality signals for products and sellers in an attempt to overcome the online marketplace's limitations. These tools include feedback ratings and symbols that indicate reputation. However, surprisingly few studies have investigated the effects of reputation indicators in the decision-making process in online marketplaces. No prior study in our

Digital Assistants (DA) such as Amazon Alexa, Siri, or Google Assistant are now gaining great diffusion, since they allow users to execute a wide range of actions through messages in natural language. Even though DAs are able to complete tasks such as sending texts, making phone calls, or playing songs, they do not yet implement recommendation facilities. In this paper, we investigate the combination

Companies are increasingly conscious of the importance of Enterprise Architecture (EA) to represent and manage IT and business in a holistic way. EA modelling has become decisive to achieve models that accurately represents behaviour and assets of companies and lead them to make appropriate business decisions. Although EA representations can be manually modelled by experts, automatic EA modelling methods

This paper proposes a decision support tool for maintenance capacity planning of complex product systems. The tool – ForeSim-BI – addresses the problem faced by maintenance organizations in forecasting the workload of future maintenance interventions and in planning an adequate capacity to face that expected workload. Developed and implemented from a predictive analytics perspective in the particular

We investigate the impacts of the decision support system's recommendations on decision makers' psychology and decision behaviors under uncertain contexts where optimal solutions exist. As a representative of such contexts, the newsvendor problem is studied by using the method of laboratory experiments. Through providing an elaborately designed decision support system in Experiment I, we validate that

Given the increasing competition to attract new customers, e-commerce providers (i.e., e-tailers) are being urged to optimize their website onboarding journeys. Design mechanisms employing social influence tactics seem to be promising avenues to effectively improve user onboarding experiences. Specifically, two social influence tactics—namely, reciprocity and social proof—are widely used in practice

The advent of new media, such as Google, Twitter, and Facebook, has offered the movie industry new opportunities to market movies and increase sales. In this study, we investigate the relationship between online media activities and movie sales. In particular, this paper examines how media coverage (online news and social media posts) and consumer engagement (information search and reading) affect

Today, customers can conveniently compare products and decide how to interact with companies. With customer centricity becoming an important success factor, companies must drive customer satisfaction not only through excellent products but also through customer-centric processes. As many companies face an abundance of action possibilities, fast-changing customer needs, and scarce resources, guidance

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 examines online users' perceived susceptibility to phishing attacks. We posit that an individual's phishing susceptibility may be shaped by recent phishing encounters and, more importantly, that the effect of new experience on susceptibility will be heterogeneous among users. To facilitate our investigation, we focus on both the process and outcome of phishing detection. Survey data from

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

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

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

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

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

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

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

This paper studies, designs and implements a new type of emergency decision support system that aims to improve the decision-making of emergency managers in crisis situations by connecting them to new, multiple data sources. The system combines event-driven and model-driven architectures and is dedicated to crisis cells. After its implementation, the system is evaluated using a realistic crisis scenario

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

Toy-related injuries account for a significant number of childhood injuries and the prevention of these injuries remains a goal for regulatory agencies and manufacturers. Text-mining is an increasingly prevalent method for uncovering the significance of words using big data. This research sets out to determine the effectiveness of text-mining in uncovering potentially dangerous children's toys. We

The prevalence of social media has greatly catalyzed the dissemination and proliferation of online memes (e.g., ideas, topics, melodies, tags, etc.). However, this information abundance is exceeding the capability of online users to consume it. Ranking memes based on their popularities could promote online advertisement and content distribution. Despite such importance, few existing work can solve

Technologies for automated detection of neonatal seizures are gradually moving towards cot-side implementation. The aim of this paper is to present different ways to visualize the output of a neonatal seizure detection system and analyse their influence on performance in a clinical environment. Three different ways to visualize the detector output are considered: a binary output, a probabilistic trace

Organizations today regularly share their customer data with their partners to gain competitive advantages. They are also often requested or even required by a third party to provide customer data that are deemed sensitive. In these circumstances, organizations are obligated to protect the privacy of the individuals involved while still benefiting from sharing data or meeting the requirement for releasing

Over time, data warehouse (DW) systems have become more difficult to develop because of the growing heterogeneity of data sources. Despite advances in research and technology, DW projects are still too slow for pragmatic results to be generated. Here, we address the following question: how can the complexity of DW development for integration of heterogeneous transactional information systems be reduced

The way information is presented influences human decision making and is consequently highly relevant to electronically supported negotiations. The present study analyzes in a controlled laboratory experiment how information presentation in three alternative formats (table, history graph and dance graph) influences the negotiators' behavior and negotiation outcomes. The results show that graphical

Discrete multi-criteria decision problems with numerous Pareto-efficient solution candidates place a significant cognitive burden on the decision maker. An interactive, aspiration-based search process that iteratively progresses toward the most preferred solution can alleviate this task. In this paper, we study three ways of representing such problems in a DSS, and compare them in a laboratory experiment

Methods for generating a random sample of networks with desired properties are important tools for the analysis of social, biological, and information networks. Algorithm-based approaches to sampling networks have received a great deal of attention in recent literature. Most of these algorithms are based on simple intuitions that associate the full features of connectivity patterns with specific values

We live in an increasingly mobile world, which leads to the duplication of information across domains. Though organizations attempt to obscure the identities of their constituents when sharing information for worthwhile purposes, such as basic research, the uncoordinated nature of such environment can lead to privacy vulnerabilities. For instance, disparate healthcare providers can collect information