-
Stopping information search: An fMRI investigation Decis. Support Syst. (IF 4.721) Pub Date : 2021-01-19 Glenn J. Browne; Eric A. Walden
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
-
How to increase customer repeated bookings in the short-term room rental market? A large-scale granular data investigation Decis. Support Syst. (IF 4.721) Pub Date : 2021-01-13 Jiang Wu; Jingxuan Cai; Xin Luo; Jose Benitez
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
-
Redefining profit metrics for boosting student retention in higher education Decis. Support Syst. (IF 4.721) Pub Date : 2021-01-12 Sebastián Maldonado; Jaime Miranda; Diego Olaya; Jonathan Vásquez; Wouter Verbeke
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
-
Data engineering for fraud detection Decis. Support Syst. (IF 4.721) Pub Date : 2021-01-12 Bart Baesens; Sebastiaan Höppner; Tim Verdonck
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
-
Process data properties matter: Introducing GCNN and KVP for next event prediction with deep learning Decis. Support Syst. (IF 4.721) Pub Date : 2021-01-11 Kai Heinrich; Patrick Zschech; Christian Janiesch; Markus Bonin
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
-
Broad or exact? Search Ad matching decisions with keyword specificity and position Decis. Support Syst. (IF 4.721) Pub Date : 2021-01-09 Shuai Yang; Joseph Pancras; Yiping Amy Song
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
-
Providing more regular road signs infrastructure updates for connected driving: A crowdsourced approach with clustering and confidence level Decis. Support Syst. (IF 4.721) Pub Date : 2020-11-04 Dieudonné Tchuente; Dominik Senninger; Holger Pietsch; Danilo Gasdzik
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
-
Predicting donation behavior: Acquisition modeling in the nonprofit sector using Facebook data Decis. Support Syst. (IF 4.721) Pub Date : 2020-11-05 Lisa Schetgen; Matthias Bogaert; Dirk Van den Poel
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
-
A group decision-making approach for exploring trends in the development of the healthcare industry in Taiwan Decis. Support Syst. (IF 4.721) Pub Date : 2020-11-10 Wan-Chi Jackie Hsu; James J.H. Liou; Huai-Wei Lo
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
-
Link prediction in heterogeneous information networks: An improved deep graph convolution approach Decis. Support Syst. (IF 4.721) Pub Date : 2020-11-19 Xi Wang; Yibo Chai; Hui Li; Danqin Wu
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
-
A social mechanism for task-oriented crowdsourcing recommendations Decis. Support Syst. (IF 4.721) Pub Date : 2020-11-17 Yung-Ming Li; Chin-Yu Hsieh; Lien-Fa Lin; Chi-Hsuan Wei
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
-
DMN4DQ: When data quality meets DMN Decis. Support Syst. (IF 4.721) Pub Date : 2020-11-18 Álvaro Valencia-Parra; Luisa Parody; Ángel Jesús Varela-Vaca; Ismael Caballero; María Teresa Gómez-López
-
Real-time temperature prediction in a cold supply chain based on Newton's law of cooling Decis. Support Syst. (IF 4.721) Pub Date : 2020-11-17 Iurii Konovalenko; André Ludwig; Henrik Leopold
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
-
Incorrect data in the widely used Inside Airbnb dataset Decis. Support Syst. (IF 4.721) Pub Date : 2020-11-28 Abdulkareem Alsudais
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
-
Identifying business misreporting in VAT using network analysis Decis. Support Syst. (IF 4.721) Pub Date : 2020-12-10 Christian González-Martel; Juan M. Hernández; Casiano Manrique-de-Lara-Peñate
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
-
Identifying comparable entities with indirectly associative relations and word embeddings from web search logs Decis. Support Syst. (IF 4.721) Pub Date : 2020-12-03 Liye Wang; Jin Zhang; Guoqing Chen; Dandan Qiao
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'
-
A strategic decision-making architecture toward hybrid teams for dynamic competitive problems Decis. Support Syst. (IF 4.721) Pub Date : 2021-01-04 Alparslan Emrah Bayrak; Christopher McComb; Jonathan Cagan; Kenneth Kotovsky
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
-
A cross-domain recommender system through information transfer for medical diagnosis Decis. Support Syst. (IF 4.721) Pub Date : 2020-12-31 Wenjun Chang; Qian Zhang; Chao Fu; Weiyong Liu; Guangquan Zhang; Jie Lu
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
-
Demystifying analytical information processing capability: The case of cybersecurity incident response Decis. Support Syst. (IF 4.721) Pub Date : 2020-12-29 Humza Naseer; Sean B. Maynard; Kevin C. Desouza
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
-
Mining product competitiveness by fusing multisource online information Decis. Support Syst. (IF 4.721) Pub Date : 2020-12-23 Zhao Liu; Chang-Xiong Qin; Yue-Jun Zhang
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
-
Leveraging online review platforms to support public policy: Predicting restaurant health violations based on online reviews Decis. Support Syst. (IF 4.721) Pub Date : 2020-12-17 Michael Siering
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
-
Recommendation systems and convergence of online reviews: The type of product network matters! Decis. Support Syst. (IF 4.721) Pub Date : 2020-12-15 David Robert Stöckli; Hamid Khobzi
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 performance-based cryptocurrency mining strategies using a hybrid analytics approach Decis. Support Syst. (IF 4.721) Pub Date : 2020-12-14 Umit Hacioglu; Dounia Chlyeh; Mustafa K. Yilmaz; Ekrem Tatoglu; Dursun Delen
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
-
On selecting a probabilistic classifier for appointment no-show prediction Decis. Support Syst. (IF 4.721) Pub Date : 2020-12-09 Shannon L. Harris; Michele Samorani
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
-
Top management team social interaction and conservative reporting decision: A language style matching approach Decis. Support Syst. (IF 4.721) Pub Date : 2020-12-08 Ting Zhang; Fang-Chun Liu; Baojun Gao; David Yen
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
-
Critical risk considerations in auto-ID security: Barcode vs. RFID Decis. Support Syst. (IF 4.721) Pub Date : 2020-12-08 Yu-Ju Tu; Wei Zhou; Selwyn Piramuthu
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
-
Applying data driven decision making to rank vocational and educational training programs with TOPSIS Decis. Support Syst. (IF 4.721) Pub Date : 2020-12-08 J.M. Conejero; J.C. Preciado; A.E. Prieto; M.C. Bas; V.J. Bolós
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
-
Impact of online gamers' conscientiousness on team function engagement and loyalty Decis. Support Syst. (IF 4.721) Pub Date : 2020-12-07 Gen-Yih Liao; T.C.E. Cheng; Wen-Lung Shiau; Ching-I Teng
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
-
Promoting or attenuating? An eye-tracking study on the role of social cues in e-commerce livestreaming Decis. Support Syst. (IF 4.721) Pub Date : 2020-12-01 Mengqi Fei; Huizhong Tan; Xixian Peng; Qiuzhen Wang; Lei Wang
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
-
Establishing a frame of reference for measuring disaster resilience Decis. Support Syst. (IF 4.721) Pub Date : 2020-09-30 Christopher W. Zobel; Cameron A. MacKenzie; Milad Baghersad; Yuhong Li
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
-
Recognition of human activities for wellness management using a smartphone and a smartwatch: A boosting approach Decis. Support Syst. (IF 4.721) Pub Date : 2020-10-28 Pratik Tarafdar; Indranil Bose
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
-
The role you play, the life you have: Donor retention in online charitable crowdfunding platform Decis. Support Syst. (IF 4.721) Pub Date : 2020-10-21 Shengsheng Xiao; Qing Yue
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
-
Platform entry and homing as competitive strategies under cross-sided network effects Decis. Support Syst. (IF 4.721) Pub Date : 2020-10-23 Cheng-Han Wu; Netnapha Chamnisampan
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
-
Bankruptcy prediction for SMEs using transactional data and two-stage multiobjective feature selection Decis. Support Syst. (IF 4.721) Pub Date : 2020-11-03 Gang Kou; Yong Xu; Yi Peng; Feng Shen; Yang Chen; Kun Chang; Shaomin Kou
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
-
The effects of consumer animosity on demand for sharing-based accommodations: Evidence from Airbnb Decis. Support Syst. (IF 4.721) Pub Date : 2020-10-25 Yating Li; Bin Li; Gang Wang; Shuai Yang
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
-
Exploring recommendations for circular supply chain management through interactive visualisation Decis. Support Syst. (IF 4.721) Pub Date : 2020-10-28 Guido van Capelleveen; Jesse van Wieren; Chintan Amrit; Devrim Murat Yazan; Henk Zijm
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
-
Classifying the ideational impact of Information Systems review articles: A content-enriched deep learning approach Decis. Support Syst. (IF 4.721) Pub Date : 2020-11-04 Julian Prester; Gerit Wagner; Guido Schryen; Nik Rushdi Hassan
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
-
Automated mortgage origination delay detection from textual conversations Decis. Support Syst. (IF 4.721) Pub Date : 2020-10-31 Arin Brahma; David M. Goldberg; Nohel Zaman; Mariano Aloiso
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
-
Peak cubes in service operations: Bringing multidimensionality into decision support systems Decis. Support Syst. (IF 4.721) Pub Date : 2020-11-05 Arash Barfar; Balaji Padmanabhan; Alan Hevner
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
-
What are customers commenting on, and how is their satisfaction affected? Examining online reviews in the on-demand food service context Decis. Support Syst. (IF 4.721) Pub Date : 2020-11-30 Xun Xu
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
-
A multivariate approach for multi-step demand forecasting in assembly industries: Empirical evidence from an automotive supply chain Decis. Support Syst. (IF 4.721) Pub Date : 2020-11-27 João N.C. Gonçalves; Paulo Cortez; M. Sameiro Carvalho; Nuno M. Frazão
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
-
Forecasting demand profiles of new products Decis. Support Syst. (IF 4.721) Pub Date : 2020-09-09 R.M. van Steenbergen; M.R.K. Mes
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
-
Drivers of and barriers to decision support technology use by financial report auditors Decis. Support Syst. (IF 4.721) Pub Date : 2020-09-16 Kirsty Meredith; Jacqueline Blake; Peter Baxter; Donald Kerr
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
-
An empirical investigation of online review helpfulness: A big data perspective Decis. Support Syst. (IF 4.721) Pub Date : 2020-09-20 Hoon S. Choi; Steven Leon
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
-
Optimizing microtask assignment on crowdsourcing platforms using Markov chain Monte Carlo Decis. Support Syst. (IF 4.721) Pub Date : 2020-09-18 Alireza Moayedikia; Hadi Ghaderi; William Yeoh
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
-
Integrating relations and criminal background to identifying key individuals in crime networks Decis. Support Syst. (IF 4.721) Pub Date : 2020-09-25 Fredy Troncoso; Richard Weber
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
-
Perceived usefulness: A silver bullet to assure user data availability for online recommendation systems Decis. Support Syst. (IF 4.721) Pub Date : 2020-10-02 Daniel Mican; Dan-Andrei Sitar-Tăut; Ovidiu-Ioan Moisescu
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
-
Deep learning for detecting financial statement fraud Decis. Support Syst. (IF 4.721) Pub Date : 2020-10-10 Patricia Craja; Alisa Kim; Stefan Lessmann
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
-
For whom does a game update? Players' status-contingent gameplay on online games before and after an update Decis. Support Syst. (IF 4.721) Pub Date : 2020-10-14 Ji Hyeon Hyeong; Kang Jun Choi; Jae Young Lee; Tae-Hyung Pyo
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
-
Does the interplay between the personality traits of CEOs and CFOs influence corporate mergers and acquisitions intensity? An econometric analysis with machine learning-based constructs Decis. Support Syst. (IF 4.721) Pub Date : 2020-10-17 Qiping Wang; Raymond Y.K. Lau; Kai Yang
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
-
An improvement in the quality of expert finding in community question answering networks Decis. Support Syst. (IF 4.721) Pub Date : 2020-10-22 Mahdi Dehghan; Ahmad Ali Abin; Mahmood Neshati
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
-
Autoencoders for strategic decision support Decis. Support Syst. (IF 4.721) Pub Date : 2020-10-14 Sam Verboven; Jeroen Berrevoets; Chris Wuytens; Bart Baesens; Wouter Verbeke
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
-
The crowd against the few: Measuring the impact of expert recommendations Decis. Support Syst. (IF 4.721) Pub Date : 2020-09-03 Nils Herm-Stapelberg; Franz Rothlauf
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
-
A note on big data analytics capability development in supply chain Decis. Support Syst. (IF 4.721) Pub Date : 2020-08-12 Ashish Kumar Jha; Maher A.N. Agi; Eric W.T. Ngai
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
-
S-commerce: Influence of Facebook likes on purchases and recommendations on a linked e-commerce site Decis. Support Syst. (IF 4.721) Pub Date : 2020-08-24 Samadrita Bhattacharyya; Indranil Bose
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
-
The reliability analysis of rating systems in decision making: When scale meets multi-attribute additive value model Decis. Support Syst. (IF 4.721) Pub Date : 2020-08-25 Sihai Zhao; Yucheng Dong; Ying He
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 effects of bidder factors on online bidding strategies: A motivation-opportunity-ability (MOA) model Decis. Support Syst. (IF 4.721) Pub Date : 2020-08-27 Xiling Cui; Vincent S. Lai; Paul Benjamin Lowry; Yang Lei
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
-
Improving healthcare access management by predicting patient no-show behaviour Decis. Support Syst. (IF 4.721) Pub Date : 2020-08-25 David Barrera Ferro; Sally Brailsford; Cristián Bravo; Honora Smith
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
-
Fast and frugal heuristics for portfolio decisions with positive project interactions Decis. Support Syst. (IF 4.721) Pub Date : 2020-09-06 Ian N. Durbach; Simón Algorta; Dieudonné Kabongo Kantu; Konstantinos V. Katsikopoulos; Özgür Şimşek
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
-
Automated dynamic approach for detecting ransomware using finite-state machine Decis. Support Syst. (IF 4.721) Pub Date : 2020-09-06 Gowtham Ramesh; Anjali Menen
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
Contents have been reproduced by permission of the publishers.