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  Subjects -> BUSINESS AND ECONOMICS (Total: 3104 journals)
    - ACCOUNTING (88 journals)
    - BANKING AND FINANCE (264 journals)
    - BUSINESS AND ECONOMICS (1148 journals)
    - CONSUMER EDUCATION AND PROTECTION (24 journals)
    - COOPERATIVES (4 journals)
    - ECONOMIC SCIENCES: GENERAL (166 journals)
    - ECONOMIC SYSTEMS, THEORIES AND HISTORY (177 journals)
    - FASHION AND CONSUMER TRENDS (13 journals)
    - HUMAN RESOURCES (93 journals)
    - INSURANCE (23 journals)
    - INTERNATIONAL COMMERCE (127 journals)
    - INTERNATIONAL DEVELOPMENT AND AID (82 journals)
    - INVESTMENTS (27 journals)
    - LABOR AND INDUSTRIAL RELATIONS (43 journals)
    - MACROECONOMICS (15 journals)
    - MANAGEMENT (524 journals)
    - MARKETING AND PURCHASING (88 journals)
    - MICROECONOMICS (24 journals)
    - PRODUCTION OF GOODS AND SERVICES (138 journals)
    - PUBLIC FINANCE, TAXATION (34 journals)
    - TRADE AND INDUSTRIAL DIRECTORIES (2 journals)

BUSINESS AND ECONOMICS (1148 journals)                  1 2 3 4 5 6 | Last

Showing 1 - 200 of 1566 Journals sorted alphabetically
4OR: A Quarterly Journal of Operations Research     Hybrid Journal   (Followers: 9)
Abacus     Hybrid Journal   (Followers: 12)
Accounting Forum     Hybrid Journal   (Followers: 23)
Acta Amazonica     Open Access   (Followers: 3)
Acta Commercii     Open Access   (Followers: 2)
Acta Oeconomica     Full-text available via subscription   (Followers: 2)
Acta Scientiarum. Human and Social Sciences     Open Access   (Followers: 4)
Acta Universitatis Danubius. Œconomica     Open Access  
Acta Universitatis Nicolai Copernici Zarządzanie     Open Access   (Followers: 3)
AD-minister     Open Access   (Followers: 2)
ADR Bulletin     Open Access   (Followers: 5)
Advances in Developing Human Resources     Hybrid Journal   (Followers: 21)
Advances in Economics and Business     Open Access   (Followers: 12)
AfricaGrowth Agenda     Full-text available via subscription   (Followers: 1)
African Affairs     Hybrid Journal   (Followers: 59)
African Development Review     Hybrid Journal   (Followers: 35)
African Journal of Business and Economic Research     Full-text available via subscription   (Followers: 1)
African Journal of Business Ethics     Open Access   (Followers: 7)
African Review of Economics and Finance     Open Access   (Followers: 3)
Afro-Asian Journal of Finance and Accounting     Hybrid Journal   (Followers: 7)
Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi     Open Access   (Followers: 3)
Agronomy     Open Access   (Followers: 11)
Akademika : Journal of Southeast Asia Social Sciences and Humanities     Open Access   (Followers: 4)
Alphanumeric Journal : The Journal of Operations Research, Statistics, Econometrics and Management Information Systems     Open Access   (Followers: 4)
American Economic Journal : Applied Economics     Full-text available via subscription   (Followers: 133)
American Journal of Business     Hybrid Journal   (Followers: 15)
American Journal of Business and Management     Open Access   (Followers: 51)
American Journal of Business Education     Open Access   (Followers: 10)
American Journal of Economics and Business Administration     Open Access   (Followers: 25)
American Journal of Economics and Sociology     Hybrid Journal   (Followers: 27)
American Journal of Evaluation     Hybrid Journal   (Followers: 13)
American Journal of Finance and Accounting     Hybrid Journal   (Followers: 19)
American Journal of Health Economics     Full-text available via subscription   (Followers: 13)
American Journal of Industrial and Business Management     Open Access   (Followers: 23)
American Journal of Medical Quality     Hybrid Journal   (Followers: 7)
American Law and Economics Review     Hybrid Journal   (Followers: 27)
ANALES de la Universidad Central del Ecuador     Open Access   (Followers: 1)
Annales de l'Institut Henri Poincare (C) Non Linear Analysis     Full-text available via subscription   (Followers: 1)
Annals in Social Responsibility     Full-text available via subscription  
Annals of Finance     Hybrid Journal   (Followers: 28)
Annals of Operations Research     Hybrid Journal   (Followers: 8)
Annual Review of Economics     Full-text available via subscription   (Followers: 30)
Applied Developmental Science     Hybrid Journal   (Followers: 3)
Applied Economics     Hybrid Journal   (Followers: 46)
Applied Economics Letters     Hybrid Journal   (Followers: 29)
Applied Economics Quarterly     Full-text available via subscription   (Followers: 10)
Applied Financial Economics     Hybrid Journal   (Followers: 24)
Applied Mathematical Finance     Hybrid Journal   (Followers: 7)
Applied Stochastic Models in Business and Industry     Hybrid Journal   (Followers: 5)
Arab Economic and Business Journal     Open Access   (Followers: 3)
Archives of Business Research     Open Access   (Followers: 5)
Arena Journal     Full-text available via subscription   (Followers: 1)
Argomenti. Rivista di economia, cultura e ricerca sociale     Open Access   (Followers: 2)
ASEAN Economic Bulletin     Full-text available via subscription   (Followers: 5)
Asia Pacific Business Review     Hybrid Journal   (Followers: 5)
Asia Pacific Journal of Human Resources     Hybrid Journal   (Followers: 316)
Asia Pacific Viewpoint     Hybrid Journal  
Asia-Pacific Journal of Business Administration     Hybrid Journal   (Followers: 3)
Asia-Pacific Journal of Operational Research     Hybrid Journal   (Followers: 3)
Asia-Pacific Management and Business Application     Open Access  
Asian Business Review     Open Access   (Followers: 2)
Asian Case Research Journal     Hybrid Journal   (Followers: 1)
Asian Development Review     Open Access   (Followers: 13)
Asian Economic Journal     Hybrid Journal   (Followers: 8)
Asian Economic Papers     Hybrid Journal   (Followers: 7)
Asian Economic Policy Review     Hybrid Journal   (Followers: 4)
Asian Journal of Accounting and Governance     Open Access   (Followers: 4)
Asian Journal of Business Ethics     Hybrid Journal   (Followers: 7)
Asian Journal of Social Sciences and Management Studies     Open Access   (Followers: 6)
Asian Journal of Sustainability and Social Responsibility     Open Access  
Asian Journal of Technology Innovation     Hybrid Journal   (Followers: 8)
Asian-pacific Economic Literature     Hybrid Journal   (Followers: 5)
AStA Wirtschafts- und Sozialstatistisches Archiv     Hybrid Journal   (Followers: 5)
Atlantic Economic Journal     Hybrid Journal   (Followers: 15)
Australasian Journal of Regional Studies, The     Full-text available via subscription   (Followers: 2)
Australian Cottongrower, The     Full-text available via subscription   (Followers: 1)
Australian Economic Papers     Hybrid Journal   (Followers: 23)
Australian Economic Review     Hybrid Journal   (Followers: 6)
Australian Journal of Maritime and Ocean Affairs     Hybrid Journal   (Followers: 10)
Balkan Region Conference on Engineering and Business Education     Open Access   (Followers: 1)
Baltic Journal of Real Estate Economics and Construction Management     Open Access   (Followers: 1)
Banks in Insurance Report     Hybrid Journal   (Followers: 1)
BBR - Brazilian Business Review     Open Access   (Followers: 4)
Benchmarking : An International Journal     Hybrid Journal   (Followers: 11)
BER : Consumer Confidence Survey     Full-text available via subscription   (Followers: 4)
BER : Economic Prospects : An Executive Summary     Full-text available via subscription  
BER : Economic Prospects : Full Survey     Full-text available via subscription   (Followers: 2)
BER : Intermediate Goods Industries Survey     Full-text available via subscription   (Followers: 1)
BER : Manufacturing Survey : Full Survey     Full-text available via subscription   (Followers: 2)
BER : Motor Trade Survey     Full-text available via subscription   (Followers: 1)
BER : Retail Sector Survey     Full-text available via subscription   (Followers: 2)
BER : Retail Survey : Full Survey     Full-text available via subscription   (Followers: 2)
BER : Survey of Business Conditions in Building and Construction : An Executive Summary     Full-text available via subscription   (Followers: 4)
BER : Survey of Business Conditions in Manufacturing : An Executive Summary     Full-text available via subscription   (Followers: 3)
BER : Survey of Business Conditions in Retail : An Executive Summary     Full-text available via subscription   (Followers: 3)
BER : Trends : Full Survey     Full-text available via subscription   (Followers: 2)
BER : Wholesale Sector Survey     Full-text available via subscription   (Followers: 1)
Berkeley Business Law Journal     Free   (Followers: 10)
Bio-based and Applied Economics     Open Access   (Followers: 1)
Biodegradation     Hybrid Journal   (Followers: 1)
Biology Direct     Open Access   (Followers: 7)
Black Enterprise     Full-text available via subscription  
Board & Administrator for Administrators only     Hybrid Journal  
Border Crossing : Transnational Working Papers     Open Access   (Followers: 2)
Briefings in Real Estate Finance     Hybrid Journal   (Followers: 5)
British Journal of Industrial Relations     Hybrid Journal   (Followers: 32)
Brookings Papers on Economic Activity     Open Access   (Followers: 48)
Brookings Trade Forum     Full-text available via subscription   (Followers: 3)
BRQ Business Research Quarterly     Open Access   (Followers: 2)
Building Sustainable Legacies : The New Frontier Of Societal Value Co-Creation     Full-text available via subscription   (Followers: 1)
Bulletin of Economic Research     Hybrid Journal   (Followers: 17)
Bulletin of Geography. Socio-economic Series     Open Access   (Followers: 7)
Bulletin of Indonesian Economic Studies     Hybrid Journal   (Followers: 3)
Bulletin of the Dnipropetrovsk University. Series : Management of Innovations     Open Access   (Followers: 1)
Business & Entrepreneurship Journal     Open Access   (Followers: 17)
Business & Information Systems Engineering     Hybrid Journal   (Followers: 5)
Business & Society     Hybrid Journal   (Followers: 9)
Business : Theory and Practice / Verslas : Teorija ir Praktika     Open Access   (Followers: 1)
Business and Economic Research     Open Access   (Followers: 6)
Business and Management Horizons     Open Access   (Followers: 12)
Business and Management Research     Open Access   (Followers: 17)
Business and Management Studies     Open Access   (Followers: 9)
Business and Politics     Hybrid Journal   (Followers: 6)
Business and Professional Communication Quarterly     Hybrid Journal   (Followers: 7)
Business and Society Review     Hybrid Journal   (Followers: 5)
Business Economics     Hybrid Journal   (Followers: 6)
Business Ethics: A European Review     Hybrid Journal   (Followers: 16)
Business Horizons     Hybrid Journal   (Followers: 8)
Business Information Review     Hybrid Journal   (Followers: 13)
Business Management and Strategy     Open Access   (Followers: 40)
Business Research     Hybrid Journal   (Followers: 2)
Business Strategy and the Environment     Hybrid Journal   (Followers: 12)
Business Strategy Review     Hybrid Journal   (Followers: 7)
Business Strategy Series     Hybrid Journal   (Followers: 6)
Business Systems & Economics     Open Access   (Followers: 2)
Business Systems Research Journal     Open Access   (Followers: 5)
Business, Management and Education     Open Access   (Followers: 17)
Business, Peace and Sustainable Development     Full-text available via subscription   (Followers: 3)
Bustan     Hybrid Journal   (Followers: 1)
Cadernos EBAPE.BR     Open Access   (Followers: 1)
Cambridge Journal of Economics     Hybrid Journal   (Followers: 56)
Cambridge Journal of Regions, Economy and Society     Hybrid Journal   (Followers: 11)
Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration     Hybrid Journal   (Followers: 1)
Canadian Journal of Economics/Revue Canadienne d`Economique     Hybrid Journal   (Followers: 27)
Canadian journal of nonprofit and social economy research     Open Access   (Followers: 2)
Capitalism and Society     Hybrid Journal   (Followers: 2)
Capitalism Nature Socialism     Hybrid Journal   (Followers: 11)
Case Studies in Business and Management     Open Access   (Followers: 8)
CBU International Conference Proceedings     Open Access   (Followers: 1)
Central European Business Review     Open Access   (Followers: 1)
Central European Journal of Operations Research     Hybrid Journal   (Followers: 5)
Central European Journal of Public Policy     Open Access   (Followers: 2)
CESifo Economic Studies     Hybrid Journal   (Followers: 17)
Chain Reaction     Full-text available via subscription  
Challenge     Full-text available via subscription   (Followers: 4)
China & World Economy     Hybrid Journal   (Followers: 15)
China : An International Journal     Full-text available via subscription   (Followers: 16)
China Economic Journal: The Official Journal of the China Center for Economic Research (CCER) at Peking University     Hybrid Journal   (Followers: 11)
China Economic Review     Hybrid Journal   (Followers: 10)
China Finance Review International     Hybrid Journal   (Followers: 5)
China Nonprofit Review     Hybrid Journal   (Followers: 3)
China perspectives     Open Access   (Followers: 11)
Chinese Economy     Full-text available via subscription  
Ciência & Saúde Coletiva     Open Access   (Followers: 2)
CLIO América     Open Access   (Followers: 1)
Cliometrica     Hybrid Journal   (Followers: 2)
COEPTUM     Open Access  
Community Development Journal     Hybrid Journal   (Followers: 24)
Compensation & Benefits Review     Hybrid Journal   (Followers: 6)
Competition & Change     Hybrid Journal   (Followers: 10)
Competitive Intelligence Review     Hybrid Journal   (Followers: 2)
Competitiveness Review : An International Business Journal incorporating Journal of Global Competitiveness     Hybrid Journal   (Followers: 5)
Computational Economics     Hybrid Journal   (Followers: 9)
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computer Law & Security Review     Hybrid Journal   (Followers: 15)
Computers & Operations Research     Hybrid Journal   (Followers: 10)
Construction Innovation: Information, Process, Management     Hybrid Journal   (Followers: 14)
Contemporary Wales     Full-text available via subscription   (Followers: 3)
Contextus - Revista Contemporânea de Economia e Gestão     Open Access   (Followers: 1)
Contributions to Political Economy     Hybrid Journal   (Followers: 6)
Corporate Communications An International Journal     Hybrid Journal   (Followers: 5)
Corporate Philanthropy Report     Hybrid Journal   (Followers: 2)
Corporate Reputation Review     Hybrid Journal   (Followers: 4)
Creative and Knowledge Society     Open Access   (Followers: 10)
Creative Industries Journal     Hybrid Journal   (Followers: 9)
CRIS - Bulletin of the Centre for Research and Interdisciplinary Study     Open Access   (Followers: 1)
Crossing the Border : International Journal of Interdisciplinary Studies     Open Access   (Followers: 4)
Cuadernos de Administración (Universidad del Valle)     Open Access   (Followers: 1)
Cuadernos de Economía     Open Access   (Followers: 1)
Cuadernos de Economia - Latin American Journal of Economics     Open Access   (Followers: 1)
Cuadernos de Estudios Empresariales     Open Access   (Followers: 1)
Current Opinion in Creativity, Innovation and Entrepreneurship     Open Access   (Followers: 8)
De Economist     Hybrid Journal   (Followers: 12)
Decision Analysis     Full-text available via subscription   (Followers: 8)
Decision Sciences     Hybrid Journal   (Followers: 15)
Decision Support Systems     Hybrid Journal   (Followers: 15)
Defence and Peace Economics     Hybrid Journal   (Followers: 16)
der markt     Hybrid Journal   (Followers: 1)
Desenvolvimento em Questão     Open Access  
Development     Full-text available via subscription   (Followers: 23)

        1 2 3 4 5 6 | Last

Journal Cover Decision Support Systems
  [SJR: 2.262]   [H-I: 95]   [15 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0167-9236
   Published by Elsevier Homepage  [3042 journals]
  • A systematic literature review and critical assessment of model-driven
           decision support for IT outsourcing
    • Abstract: Publication date: Available online 8 July 2017
      Source:Decision Support Systems
      Author(s): Mohammad Mehdi Rajaeian, Aileen Cater-Steel, Michael Lane
      Information technology outsourcing (ITO) is a widely-adopted strategy for IT governance. The decisions involved in IT outsourcing are complicated. Empirical research confirms that a rational and formalized decision-making process results in better decision outcomes. However, formal and systematic approaches for making ITO decisions appear to be scarce in practice. To support organizational decision-makers involved in IT outsourcing (including cloud sourcing), researchers have suggested several decision support methods. To date there is no comprehensive review and assessment of the research in this domain. In this study 133 model-driven decision support research articles for IT outsourcing and cloud sourcing were identified through a systematic literature review and assessed based on a highly-regarded research framework. An analysis of these 133 research articles suggested a range of Multiple Criteria Decision Making (MCDM), optimization and simulation methods to support different IT outsourcing decisions. Our findings raise concerns about the limited use of reference design theories, and the lack of validation and naturalistic evaluation of the decision support artifacts reported in ITO decision support literature. Based on the review, we provide future research directions, as well as a number of recommendations to enhance the rigor and relevance of ITO Decision Support Systems research.

      PubDate: 2017-07-09T07:29:48Z
       
  • Invention or incremental improvement' Simulation modeling and
           
    • Abstract: Publication date: Available online 6 July 2017
      Source:Decision Support Systems
      Author(s): Bin GUO, Peng DING
      This paper explores how firms determine their patenting strategy when faced with different performance situations. Patenting strategy in this study is defined in terms of either engaging more in inventions with more risk and higher profit or in more incremental improvements with less risk and lower profit. We develop two game-theoretical models to analyze how different kinds of performance discrepancies encountered by a firm influence the evolution of the firm’s propensity toward a patenting strategy. Then, an empirical analysis of 1,921 listed companies in China is conducted to test the propositions derived from the two game-theoretical models. The results reveal the decision-making pattern of a firm’s patenting strategy. Specifically, a firm with performance higher than its aspiration will prefer to engage more in invention-type patents, while a firm with lower performance than its aspiration will invest more in incremental improvement patents. Additionally, all else being equal, the patenting strategy more likely to succeed will be more appealing to firms, no matter what kinds of performance gaps they have.

      PubDate: 2017-07-09T07:29:48Z
       
  • Challenges of Smart Business Process Management: An Introduction to the
           Special Issue
    • Abstract: Publication date: Available online 5 July 2017
      Source:Decision Support Systems
      Author(s): Jan Mendling, Bart Baesens, Abraham Bernstein, Michael Fellmann
      This paper describes the foundations of smart business process management and serves as an editorial to the corresponding special issue. To this end, we introduce a framework that distinguishes three levels of business process management: multi process management, process model management, and process instance management. For each of these levels we identify major contributions of prior research and describe in how far papers assembled in this special issue extend our understanding of smart business process management.

      PubDate: 2017-07-09T07:29:48Z
       
  • Kernel-based features for predicting population health indices from
           geocoded social media data
    • Abstract: Publication date: Available online 4 July 2017
      Source:Decision Support Systems
      Author(s): Thin Nguyen, Mark E. Larsen, Bridianne O’Dea, Duc Thanh Nguyen, John Yearwood, Dinh Phung, Svetha Venkatesh, Helen Christensen
      When using tweets to predict population health index, due to the large scale of data, an aggregation of tweets by population has been a popular practice in learning features to characterize the population. This would alleviate the computational cost for extracting features on each individual tweet. On the other hand, much information on the population could be lost as the distribution of textual features of a population could be important for identifying the health index of that population. In addition, there could be relationships between features and those relationships could also convey predictive information of the health index. In this paper, we propose mid-level features namely kernel-based features for prediction of health indices of populations from social media data. The kernel-based features are extracted on the distributions of textual features over population tweets and encode the relationships between individual textual features in a kernel function. We implemented our features using three different kernel functions and applied them for two case studies of population health prediction: across-year prediction and across-county prediction. The kernel-based features were evaluated and compared with existing features on a dataset collected from the Behavioral Risk Factor Surveillance System dataset. Experimental results show that the kernel-based features gained significantly higher prediction performance than existing techniques, by up to 16.3%, suggesting the potential and applicability of the proposed features in a wide spectrum of applications on data analytics at population levels.

      PubDate: 2017-07-09T07:29:48Z
       
  • Lightweight Non-Distance-Bounding Means to Address RFID Relay Attacks
    • Abstract: Publication date: Available online 3 July 2017
      Source:Decision Support Systems
      Author(s): Yuju Tu, Selwyn Piramuthu
      A relay attack is accomplished by simply relaying messages between a prover (e.g., an RFID tag) and a verifier (e.g., an RFID reader) with the goal of convincing the verifier of its close physical proximity to the prover. In almost all relay attack scenarios, the verifier essentially communicates with a prover that is outside the verifier’s read-range. Relay attacks are notorious since they occur without the knowledge of the reader and/or tag, and has the potential to cause damage to honest parties (here, RFID reader and/or tag). Almost all means to address relay attacks in RFID systems to date are based on the proximity check idea that involves the measurement of message round trip times between tag and reader. With the speed of light at play, such measurements need not necessarily be accurate and could result in the false assumption of relay attack absence. Our review of published literature on approaches that use non-distance-based means to address relay attacks revealed ambient conditions’ potential. We critically evaluate ambient conditions and develop a lightweight mutual authentication protocol that is based on magnetometer readings to address relay attacks.

      PubDate: 2017-07-09T07:29:48Z
       
  • How e-WOM and local competition drive local retailers' decisions about
           daily deal offerings
    • Abstract: Publication date: Available online 1 July 2017
      Source:Decision Support Systems
      Author(s): Xue Bai, James R. Marsden, William T. Ross, Gang Wang
      Local retailers considering offering daily deals must take into account possible impacts of both electronic-word-of-mouth (e-WOM) and local competition. However, how e-WOM, local competition, and their interactions affect local retailers' decisions to offer daily deals remains unclear. Here we examine these effects utilizing a data set that contains details of daily deals, online reviews, and local competition measures for restaurants in the Chicago area. With a propensity score matching (PSA) method, we show: 1) local retailers with high ratings and high number of reviews were more likely to initiate daily deals; 2) local retailers in an area with a low level of local competition were more likely to initiate daily deals; and 3) the strength and direction of the impact of e-WOM depend on the level of local competition. Our results enhance understanding of local retailers' decisions to offer daily deals and yield important implications related to daily deal sites.

      PubDate: 2017-07-09T07:29:48Z
       
  • Analyzing Control Flow Information to Improve the Effectiveness of Process
           Model Matching Techniques
    • Abstract: Publication date: Available online 30 June 2017
      Source:Decision Support Systems
      Author(s): Christopher Klinkmüller, Ingo Weber
      Process model matchers automatically identify activities that represent similar functionality in different process models. As such, they support various tasks in business process management including model collection management and process design. Yet, comparative evaluations revealed that state-of-the-art matchers fall short of offering high performance across varied datasets. To facilitate the development of more effective matchers, we systematically study, if and how the analysis of control flow information in process models can contribute to the matching process. In particular, we empirically examine the validity of analysis options and use our findings to automate the adaptation of matcher configurations to model collections.

      PubDate: 2017-07-09T07:29:48Z
       
  • Understanding the determinants of online review helpfulness: A
           meta-analytic investigation
    • Abstract: Publication date: Available online 29 June 2017
      Source:Decision Support Systems
      Author(s): Hong Hong, Di Xu, G. Alan Wang, Weiguo Fan
      Online consumer reviews can help customers reduce uncertainty and risks faced in online shopping. However, the studies examining the determinants of perceived review helpfulness produce mixed findings. We review extant research about the determinant factors of perceived online review helpfulness. All review related determinants (i.e., review depth, review readability, linear review rating, quadratic review rating, review age) and two reviewer related determinants (i.e., reviewer information disclosure and reviewer expertise) are found to have inconsistent conclusions on how they affect perceived review helpfulness. We conduct a meta-analysis to examine those determinant factors in order to reconcile the contradictory findings about their influence on perceived review helpfulness. The meta-analysis results affirm that review depth, review age, reviewer information disclosure, and reviewer expertise have positive influences on review helpfulness. Review readability and review rating are found to have no significant influence on review helpfulness. Moreover, we find that helpfulness measurement, online review platform, and product type are the three factors that cause mixed findings in extant research.

      PubDate: 2017-07-09T07:29:48Z
       
  • Multi-objective optimization based ranking prediction for cloud service
           recommendation
    • Abstract: Publication date: Available online 28 June 2017
      Source:Decision Support Systems
      Author(s): Shuai Ding, Chengyi Xia, Chengjiang Wang, Desheng Wu, Youtao Zhang
      Performing effective ranking prediction for cloud services can help customers make prompt decisions when they are confronted by a large number of choices. This can also enhance web service user satisfaction levels. Improving ranking prediction of QoS-based services continues to be an active topic of research in cloud service recommendation. Most service recommendation algorithms focus on prediction accuracy, ignoring diversity, which also may be an important consideration. In this paper we view service recommendation as a multi-objective optimization problem, and give two modified ranking prediction and recommendation algorithms that simultaneously consider accuracy and diversity. Existing algorithm recommendations can be made much more diverse by adjusting weights on service origin and substantially reducing the risk of inappropriate recommendations. Our experiments show that the algorithms we propose can yield greater diversity without greatly sacrificing prediction accuracy.

      PubDate: 2017-07-09T07:29:48Z
       
  • Applying behavioral economics in predictive analytics for B2B churn:
           Findings from service quality data
    • Abstract: Publication date: Available online 28 June 2017
      Source:Decision Support Systems
      Author(s): Arash Barfar, Balaji Padmanabhan, Alan Hevner
      Motivated by the long-standing debate on rationality in behavioral economics and the potential of theory-driven predictive analytics, this paper examines the link between service quality and B2B churn. Using longitudinal B2B transactional data with service quality indicators provided by a large company, we present evidence that both rationality and bounded-rationality assumptions play significant roles in predicting organizational decisions on churn. Specifically, variables that relate to the assumed rationality of organizations appear to provide accurate predictions while, at the same time, variables that capture boundedly rational decision rules appear to play a role through “somatic states” that make organizations more sensitive to the rational variables. In addition to presenting a novel approach for predicting organizational decisions on churn, this paper offers theoretical and managerial insights as well as opportunities for future research at the intersection of behavioral economics and predictive analytics for decision-making.

      PubDate: 2017-07-09T07:29:48Z
       
  • Increasing firm agility through the use of data analytics: The role of fit
    • Abstract: Publication date: Available online 28 June 2017
      Source:Decision Support Systems
      Author(s): Maryam Ghasemaghaei, Khaled Hassanein, Ofir Turel
      Agility, which refers to a dynamic capability within firms to identify and effectively respond to threats and opportunities with speed, is considered as a main business imperative in modern business environments. While there is some evidence that information technology (IT) capabilities can help organizations to be more agile, studies have reported mixed findings regarding such effects. In this study, we identify the conditions under which IT capabilities translate into agility gains. We focus on a specific and critical IT capability, the use of data analytics, which is often leveraged by firms to improve decision making and achieve agility gains. We leverage dynamic capability theory to understand the influence of data analytics use as a lower-order dynamic capability on firm agility as a higher-order dynamic capability. We also draw on the fit perspective to suggest that this impact will only accrue if there is a high degree of fit between several elements that are closely related to the use of data analytics tools within firms including the tools themselves, the users, the firm tasks, and the data. The proposed research model is empirically validated using survey data from 215 senior IT professionals confirming the importance of high levels of fit between data analytics tools and key related elements. The findings provide the understanding of the impacts of data analytics use on firm agility, while also providing guidance to managers on how they could better leverage the use of such technologies. These findings could be more broadly used to inform the effective use of other forms of IT in organizations.

      PubDate: 2017-07-09T07:29:48Z
       
  • The time-varying nature of social media sentiments in modeling stock
           returns
    • Abstract: Publication date: Available online 22 June 2017
      Source:Decision Support Systems
      Author(s): Chi-San Ho, Paul Damien, Bin Gu, Prabhudev Konana
      The broad aim of this paper is to answer the following query: is the relationship between social media sentiments and stock returns time-varying' To provide a satisfactory response, a novel methodology—a symbiosis of Bayesian Dynamic Linear Models and Seemingly Unrelated Regressions —is introduced. Two sets of Dow Jones Industrial Average stock data and corresponding social media data from Yahoo! Finance stock message boards are used in a comprehensive empirical study. Some key findings are: (a) Affirmative response to the above question; (b) Models with only social media sentiments and market returns perform at least as well as models that include Fama-French and Momentum factors; (c) There are significant correlations between stocks, ranging from −0.8 to 0.6 in both data sets.

      PubDate: 2017-06-28T13:12:51Z
       
  • User Segmentation for Retention Management in Online Social Games
    • Abstract: Publication date: Available online 2 June 2017
      Source:Decision Support Systems
      Author(s): Xin Fu, Xi Chen, Yu-Tong Shi, Indranil Bose, Shun Cai
      This work proposes an innovative model for segmenting online players based on data related to their in-game behaviours to support player retention management. This kind of analysis is helpful to explore the potential reasons behind why players leave the game, analyse retention trends, design customised strategies for different player segments, and then boost the overall retention rate. In particular, a new similarity metric which is driven by players’ stickiness to the game is developed to cluster players. Three feature dimensions, namely engagement features (e.g., log-in frequency and length of log-in time), performance features (e.g., level, the number of completed tasks, coins and achievements), and social interactions features (e.g., the number of in-game friends, whether or not to join a guild, and the guild role), are employed and aggregated to derive the stickiness metric. The applicability and utility of this new segmentation model are illustrated through experiments that are conducted on a realistic MMORPG dataset. The derived results are also discussed and compared against two benchmark models. The results reveal that the new segmentation model not only achieves better clustering performance, but also improves player’s lifetime prediction by better distinguishing between loyal customers and churners. The empirical results confirm the effects of social interaction, which is usually underestimated in the current research, on player segmentation. From an operational perspective, the derived results would help game developers better understand the different retention-behaviour patterns of players, establish effective and customised tactics to retain more players, and boost product revenue.

      PubDate: 2017-06-06T07:25:17Z
       
  • Location analytics and decision support: Reflections on recent
           advancements, a research framework, and the path ahead
    • Abstract: Publication date: Available online 1 June 2017
      Source:Decision Support Systems
      Author(s): James B. Pick, Ozgur Turetken, Amit V. Deokar, Avijit Sarkar


      PubDate: 2017-06-06T07:25:17Z
       
  • Emotion classification of YouTube videos
    • Abstract: Publication date: Available online 26 May 2017
      Source:Decision Support Systems
      Author(s): Yen-Liang Chen, Chia-Ling Chang, Chin-Sheng Yeh
      Watching online videos is a major leisure activity among Internet users. The largest video website, YouTube, stores billions of videos on its servers. Thus, previous studies have applied automatic video categorization methods to enable users to find videos corresponding to their needs; however, emotion has not been a factor considered in these classification methods. Therefore, this study classified YouTube videos into six emotion categories (i.e., happiness, anger, disgust, fear, sadness, and surprise). Through unsupervised and supervised learning methods, this study first categorized videos according to emotion. An ensemble model was subsequently applied to integrate the classification results of both methods. The experimental results confirm that the proposed method effectively facilitates the classification of YouTube videos into suitable emotion categories.

      PubDate: 2017-05-27T07:22:22Z
       
  • Tour recommendations by mining photo sharing social media
    • Abstract: Publication date: Available online 19 May 2017
      Source:Decision Support Systems
      Author(s): Chih-Yuan Sun, Anthony J.T. Lee
      With the increasing popularity of photo and video sharing social networks, more and more people have shared their photos or videos with their family members and friends. Therefore, in this paper, we propose a framework for recommending top-k tours to meet user's interest and time frame by using user-generated contents in a photo sharing social network. The proposed framework contains four phases. First, we cluster geotagged locations into landmarks, and further cluster these landmarks into areas by the mean-shift clustering method. Second, we employ the Latent Dirichlet Allocation model to categorize the hashtags posted by users into landmark topics, and then use these topics to characterize landmarks and users. Third, to recommend tours for a user, we compute the tendency (or score) of the user visiting each landmark by the landmark popularity, the attraction of landmark to the user, and how many users similar to the user visit the landmark. Finally, based on the scores computed, we develop a method to recommend top-k tours with highest scores for the user. Unlike most previous methods recommending tours landmark by landmark, our framework recommends tours area by area so that users can avoid going back and forth from one area to another and save plenty of time on transportation, which in turn can visit more landmarks. The experiment results show that our proposed method outperforms the Markov-Topic method in terms of average score and precision. Our proposed framework may help users plan their trips and customize a trip for each user.

      PubDate: 2017-05-27T07:22:22Z
       
  • A bi-objective two-stage robust location model for waste-to-energy
           facilities under uncertainty
    • Abstract: Publication date: Available online 18 May 2017
      Source:Decision Support Systems
      Author(s): Chenlian Hu, Xiao Liu, Jie Lu
      Waste-to-energy (WTE) facilities have begun to play an increasingly important role in the management of municipal solid waste (MSW) worldwide. However, due to the environmental and economic impacts they impose on urban sustainability, the location of WTE facilities is always a sensitive issue. With the frequent involvement of private investors in WTE projects in recent years, the uncertainties associated with MSW generation often impose a huge financial risk on both the private investors involved and the government. Therefore, decision support for the location planning of WTE facilities is necessary and critical. A bi-objective two-stage robust model has been developed to help governments identify cost-effective and environmental-friendly WTE facility location strategies under uncertainty, in which one objective is to minimize worst-case annual government spending, while the other minimizes environmental disutility. To efficiently solve the model, a novel solution method has been developed based on a combination of the ϵ-constraint method and the column-and-constraint generation algorithm. The proposed model is demonstrated via a case study in the city of Shanghai where the government plans to locate incinerators to release pressure on sanitary landfills. The computational results show that the proposed model and solution method can effectively support decision-makers. A further sensitivity analysis reveals several useful MSW management insights.

      PubDate: 2017-05-27T07:22:22Z
       
  • A data analytics approach to building a clinical decision support system
           for diabetic retinopathy: Developing and deploying a model ensemble
    • Abstract: Publication date: Available online 15 May 2017
      Source:Decision Support Systems
      Author(s): Saeed Piri, Dursun Delen, Tieming Liu, Hamed M. Zolbanin
      Diabetes is a common chronic disease that may lead to several complications. Diabetic retinopathy (DR), one of the most serious of these complications, is the most common cause of vision loss among diabetic patients. In this paper, we analyzed data from more than 1.4 million diabetics and developed a clinical decision support system (CDSS) for predicting DR. While the existing diagnostic approach requires access to ophthalmologists and expensive equipment, our CDSS only uses demographic and lab data to detect patients' susceptibility to retinopathy with a high accuracy. We illustrate how a combination of multiple data preparation and modeling steps helped us improve the performance of our CDSS. From the data preprocessing aspect, we aggregated the data at the patient level and incorporated comorbidity information into our models. From the modeling perspective, we built several predictive models and developed a novel “confidence margin” ensemble technique that outperformed the existing ensemble models. Our results suggest that diabetic neuropathy, creatinine serum, blood urea nitrogen, glucose serum plasma, and hematocrit are the most important variables in detecting DR. Our CDSS provides several important practical implications, including identifying the DR risk factors, facilitating the early diagnosis of DR, and solving the problem of low compliance with annual retinopathy screenings.

      PubDate: 2017-05-27T07:22:22Z
       
  • Moving in time and space – Location intelligence for carsharing
           decision support
    • Abstract: Publication date: Available online 8 May 2017
      Source:Decision Support Systems
      Author(s): Christoph Willing, Konstantin Klemmer, Tobias Brandt, Dirk Neumann
      In this paper we develop a spatial decision support system that assists free-floating carsharing providers in countering imbalances between vehicle supply and customer demand in existing business areas and reduces the risk of imbalance when expanding the carsharing business to a new city. For this purpose, we analyze rental data of a major carsharing provider in the city of Amsterdam in combination with points of interest (POIs). The spatio-temporal demand variations are used to develop pricing zones for existing business areas. We then apply the influence of POIs derived from carsharing usage in Amsterdam in order to predict carsharing demand in the city of Berlin. The results indicate that predicted and actual usage patterns are very similar. Hence, our approach can be used to define new business areas when expanding to new cities to include high demand areas and exclude low demand areas, thereby reducing the risk of supply-demand imbalance.

      PubDate: 2017-05-12T05:54:15Z
       
  • Facility location using GIS enriched demographic and lifestyle data for a
           traveling entertainment troupe in Bavaria, Germany
    • Abstract: Publication date: Available online 8 May 2017
      Source:Decision Support Systems
      Author(s): Jeremy W. North, Fred L. Miller
      This paper presents the development and subsequent application of a facility location methodology for selecting good show locations for a traveling entertainment troupe in Bavaria, Germany. The troupe is headquartered at a theater in Munich and wishes to expand its audience by offering traveling shows to select sites across Bavaria. A spatial analysis of the region is completed via classic location theory modeling techniques, leading to the development of a multi-criteria facility location approach for application. Additionally, we use location analytics techniques on demographic and consumer spending data extracted from the Business Analyst Web App (BAWA) system for each of the 95 districts in Bavaria. This data is integrated into a decision support system to weight consumer demand values with district lifestyle population patterns aggregated at the postal code level. Lifestyle-weighted demand is then used to identify locations that maximize the amount of customers within a given travel distance to a show while maintaining dispersion of selected facilities.

      PubDate: 2017-05-12T05:54:15Z
       
  • Understanding the formation of reciprocal hyperlinks between e-marketplace
           sellers
    • Abstract: Publication date: Available online 8 May 2017
      Source:Decision Support Systems
      Author(s): Zhaoran Xu, Youwei Wang, Yulin Fang, Bernard Tan, Hai Sun
      Online sellers in the e-marketplace cooperate with each other to increase resources and reduce transaction costs, both of which are crucial to the success of small businesses. A commonly used IT-enabled strategy is to ally with other online sellers by exchanging hyperlinks. This paper provides theoretical guidance to sellers on how to choose partners to improve reciprocity rates in hyperlink formation. Using the resource-based view and transaction-cost rationale, we examine the effects of market conditions and seller reputation on reciprocity link formation, using real transaction data from the largest online marketplace in China. The findings indicate that partners are less likely to exchange hyperlinks if the two sellers sharing a link are in highly overlapping markets and are geographically distant from one another, but the two factors weaken each other's negative effects. The study also explores the moderating effect of seller reputation, and finds that the negative effect of market commonality is weakened by seller reputation. The results of this study can be extended to other types of small business cooperation and are also useful to platform operators for designing mechanisms to encourage cooperation among online sellers.

      PubDate: 2017-05-12T05:54:15Z
       
  • Early Detection of University Students with Potential Difficulties
    • Abstract: Publication date: Available online 7 May 2017
      Source:Decision Support Systems
      Author(s): Anne-Sophie Hoffait, Michaël Schyns
      Using data mining methods, this paper presents a new means of identifying freshmen’s profiles likely to face major difficulties to complete their first academic year. Academic failure is a relevant issue at a time when post-secondary education is ever more critical to economic success. We aim at early detection of potential failure using student data available at registration, i.e. school records and environmental factors, with a view to timely and efficient remediation and/or study reorientation. We adapt three data mining methods, namely random forest, logistic regression and artificial neural network algorithms. We design algorithms to increase the accuracy of the prediction when some classes are of major interest. These algorithms are context independent and can be used in different fields. Real data pertaining to undergraduates at the University of Liège (Belgium), illustrates our methodology.

      PubDate: 2017-05-12T05:54:15Z
       
  • An open-data approach for quantifying the potential of taxi ridesharing
    • Abstract: Publication date: Available online 7 May 2017
      Source:Decision Support Systems
      Author(s): Benjamin Barann, Daniel Beverungen, Oliver Müller
      Taxi ridesharing 1 1 Taxi ridesharing (TRS), also known as shared taxi or collective taxi, is an advanced form of public transportation with flexible routing and scheduling that matches at least two separate ride requests with similar spatio-temporal characteristics in real-time to a jointly used taxi, driven by an employed driver without own destination. TRS, therefore, differs from private ridesharing, which refers to sharing of rides among private people. TRS is a more restricted dynamic dial-a-ride problem, which considers the requirements of both multiple passengers and the service provider. Because of the pooled simultaneous utilization of a taxi, TRS is collaborative consumption. [This definition has been pasted from the paper, Section 2.2. References are provided there] (TRS) is an advanced form of urban transportation that matches separate ride requests with similar spatio-temporal characteristics to a jointly used taxi. As collaborative consumption, TRS saves customers money, enables taxi companies to economize use of their resources, and lowers greenhouse gas emissions. We develop a one-to-one TRS approach that matches rides with similar start and end points. We evaluate our approach by analyzing an open dataset of >5 million taxi trajectories in New York City. Our empirical analysis reveals that the proposed approach matches up to 48.34% of all taxi rides, saving 2,892,036km of travel distance, 231,362.89l of gas, and 532,134.64kg of CO2 emissions per week. Compared to many-to-many TRS approaches, our approach is competitive, simpler to implement and operate, and poses less rigid assumptions on data availability and customer acceptance.

      PubDate: 2017-05-12T05:54:15Z
       
  • Geography of online network ties: A predictive modelling approach
    • Abstract: Publication date: Available online 6 May 2017
      Source:Decision Support Systems
      Author(s): Swanand J. Deodhar, Mani Subramani, Akbar Zaheer
      Internet platforms are increasingly enabling individuals to access and interact with a wider, globally dispersed group of peers. The promise of these platforms is that the geographic distance is no longer a barrier to forming network ties. However, whether these platforms truly alleviate the influence of geographic distance remains unexplored. In this study, we examine the role of geographic distance with machine learning approach using a unique dataset of the network ties between traders in an online social trading platform. Specifically, we determine the extent to which, compared to other types of distances, geographic distance predicts the occurrences of the network ties in country dyads. Using cluster analysis and predictive modelling, we show that not only the geographic distance and network ties exhibit an inverse association but also that geographic distance is the strongest predictor of such ties.

      PubDate: 2017-05-07T05:38:09Z
       
  • Preventing Traffic Accidents with In-Vehicle Decision Support Systems -
           The Impact of Accident Hotspot Warnings on Driver Behaviour
    • Abstract: Publication date: Available online 6 May 2017
      Source:Decision Support Systems
      Author(s): Benjamin Ryder, Bernhard Gahr, Philipp Egolf, Andre Dahlinger, Felix Wortmann
      Despite continuous investment in road and vehicle safety, as well as improvements in technology standards, the total amount of road traffic accidents has been increasing over the last decades. Consequently, identifying ways of effectively reducing the frequency and severity of traffic accidents is of utmost importance. In light of the depicted challenge, latest studies provide promising evidence that in-vehicle decision support systems (DSSs) can have significant positive effects on driving behaviour and collision avoidance. Going beyond existing research, we developed a comprehensive in-vehicle DSS, which provides accident hotspot warnings to drivers based on location analytics applied to a national historical accident dataset, composed of over 266,000 accidents. As such, we depict the design and field evaluation of an in-vehicle DSS, bridging the gap between real world location analytics and in-vehicle warnings. The system was tested in a country-wide field test of 57 professional drivers, with over 170,000km driven during a four-week period, where vehicle data were gathered via a connected car prototype system. Ultimately, we demonstrate that in-vehicle warnings of accident hotspots have a significant improvement on driver behaviour over time. In addition, we provide first evidence that an individual’s personality plays a key role in the effectiveness of in-vehicle DSSs. However, in contrast to existing lab experiments with very promising results, we were unable to find an immediate effect on driver behaviour. Hence, we see a strong need for further field experiments with high resolution car data to confirm that in-vehicle DSSs can deliver in diverse field situations.

      PubDate: 2017-05-07T05:38:09Z
       
  • Designing utilization-based spatial healthcare accessibility decision
           support systems: A case of a regional health plan
    • Abstract: Publication date: Available online 6 May 2017
      Source:Decision Support Systems
      Author(s): Yan Li, Au Vo, Manjit Randhawa, Genia Fick
      In the U.S., myriad healthcare reforms have begun to show some positive effects on enabling “potential access”. One facet of healthcare access, “having access”, which is the availability and accessibility of health services for the surrounding populations, has not been adequately addressed. Research regarding “having access” is presently championed by a family of methods called Floating Catchment Area (FCA). However, existing scholarship is limited in integrating non-spatial factors within the FCA methods. In this research, we propose a novel utilization-based framework as the first attempt to adopt the Behavioral Model of Health Services Use as a theoretical lens to integrate non-spatial factors in spatial healthcare accessibility research. The framework employs a unique approach to derive categorical and factor weights for different population subgroup's healthcare needs using predictive analytics. The proposed framework is evaluated using a case study of a regional health plan. A Spatial Decision Support System (SDSS) instantiates the framework and enables decision makers to explore physician shortage areas. The SDSS validates the practicality of the proposed utilization-based framework and subsequently allows other FCA methods to be implemented in real-world applications.

      PubDate: 2017-05-07T05:38:09Z
       
  • RFID-Enabled Flexible Warehousing
    • Abstract: Publication date: Available online 4 May 2017
      Source:Decision Support Systems
      Author(s): Wei Zhou, Selwyn Piramuthu, Feng Chu, Chengbin Chu
      We propose a smart warehouse environment where not only inventory items but also the shelves are tracked by an RFID-based system. Both operational activities and warehouse configurations are continually monitored to facilitate real-time response. We study the dynamics of a flexible warehouse scenario where items of any type can be dropped off anywhere within the premises. Unlike existing models, we relax both the location constraint and local (e.g., item-type level) capacity constraints with a periodically renewable fixed global capacity. Dynamic decisions on location and local capacity are made based on the stochastic Markovian demand states. We optimize processing and routing constraints and compare the performance of this flexible storage setup with classical models through multiple levels of real-time decision support. Our results provide corroborating evidence to support the following observations: (1) “free pick-n-drop” combined with fluid warehousing mechanism greatly reduces trip costs and lead time for single trip demand, (2) there exists a lower bound on the performance in such a setup with fixed local capacities, and (3) the lower bound can be further improved when inventory capacity and location are dynamically adjusted according to actual demand patterns.

      PubDate: 2017-05-07T05:38:09Z
       
  • Process Querying: Enabling Business Intelligence through Query-Based
           Process Analytics
    • Abstract: Publication date: Available online 2 May 2017
      Source:Decision Support Systems
      Author(s): Artem Polyvyanyy, Chun Ouyang, Alistair Barros, Wil M.P. van der Aalst
      The volume of process-related data is growing rapidly: more and more business operations are being supported and monitored by information systems. Industry 4.0 and the corresponding industrial Internet of Things are about to generate new waves of process-related data, next to the abundance of event data already present in enterprise systems. However, organizations often fail to convert such data into strategic and tactical intelligence. This is due to the lack of dedicated technologies that are tailored to effectively manage the information on processes encoded in process models and process execution records. Process-related information is a core organizational asset which requires dedicated analytics to unlock its full potential. This paper proposes a framework for devising process querying methods, i.e., techniques for the (automated) management of repositories of designed and executed processes, as well as models that describe relationships between processes. The framework is composed of generic components that can be configured to create a range of process querying methods. The motivation for the framework stems from use cases in the field of Business Process Management. The design of the framework is informed by and validated via a systematic literature review. The framework structures the state of the art and points to gaps in existing research. Process querying methods need to address these gaps to better support strategic decision-making and provide the next generation of Business Intelligence platforms.

      PubDate: 2017-05-07T05:38:09Z
       
  • ProcessProfiler3D: A Visualisation Framework for Log-based Process
           Performance Comparison
    • Abstract: Publication date: Available online 2 May 2017
      Source:Decision Support Systems
      Author(s): M.T. Wynn, E. Poppe, J. Xu, A.H.M. ter Hofstede, R. Brown, A. Pini, W.M.P. van der Aalst
      An organisation can significantly improve its performance by observing how their business operations are currently being carried out. A great way to derive evidence-based process improvement insights is to compare the behaviour and performance of processes for different process cohorts by utilising the information recorded in event logs. A process cohort is a coherent group of process instances that has one or more shared characteristics. Such process performance comparisons can highlight positive or negative variations that can be evident in a particular cohort, thus enabling a tailored approach to process improvement. Although existing process mining techniques can be used to calculate various statistics from event logs for performance analysis, most techniques calculate and display the statistics for each cohort separately. Furthermore, the numerical statistics and simple visualisations may not be intuitive enough to allow users to compare the performance of various cohorts efficiently and effectively. We developed a novel visualisation framework for log-based process performance comparison to address these issues. It enables analysts to quickly identify the performance differences between cohorts. The framework supports the selection of cohorts and a three-dimensional visualisation to compare the cohorts using a variety of performance metrics. The approach has been implemented as a set of plug-ins within the open source process mining framework ProM and has been evaluated using two real-life data sets from the insurance domain to assess the usefulness of such a tool. This paper also derives a set of design principles from our approach which provide guidance for the development of new approaches to process cohort performance comparison.

      PubDate: 2017-05-07T05:38:09Z
       
  • Incorporating sequential information in bankruptcy prediction with
           predictors based on Markov for discrimination
    • Abstract: Publication date: Available online 29 April 2017
      Source:Decision Support Systems
      Author(s): Andrey Volkov, Dries F. Benoit, Dirk Van den Poel
      In this paper we make a contribution to the body literature that incorporates a dynamic view on bankruptcy into bankruptcy prediction modelling In addition to using financial ratios measured over multiple time periods, we introduce variables based on the Markov for discrimination (MFD) model. MFD variables are able to extract the sequential information from time-series of financial ratios and concentrate it in one score. Our results obtained from multiple samples of Belgian bankruptcy data show that using data collected from multiple time periods outperforms snap-shot data that contains financial ratios measured at one point in time. In addition, we demonstrate that inclusion of MFD variables in non-ensemble bankruptcy prediction models considered in the study can lead to better classification performance. The latter type of models, despite not achieving the top performance based on metric considered in our study, can still be used by practitioners who prefer simpler, more interpretable models.

      PubDate: 2017-05-01T11:25:25Z
       
  • The Value of Vehicle Telematics Data in Insurance Risk Selection Processes
    • Abstract: Publication date: Available online 28 April 2017
      Source:Decision Support Systems
      Author(s): Philippe Baecke, Lorenzo Bocca
      The advent of the Internet of Things enables companies to collect an increasing amount of sensor generated data which creates plenty of new business opportunities. This study investigates how this sensor data can improve the risk selection process in an insurance company. More specifically, several risk assessment models based on three different data mining techniques are augmented with driving behaviour data collected from In-Vehicle Data Recorders. This study proves that including standard telematics variables significantly improves the risk assessment of customers. As a result, insurers will be better able to tailor their products to the customers’ risk profile. Moreover, this research illustrates the importance of including industry knowledge, combined with data expertise, in the variable creation process. Especially when a regulator forces the use of easily interpretable data mining techniques, expert-based telematics variables are able to improve the risk assessment model in addition to the standard telematics variables. Further, the results suggest that if a manager wants to implement Usage-Based-Insurances, Pay-As-You-Drive related variables are most valuable to tailor the premium to the risk. Finally, the study illustrates that this new type of telematics-based insurance product can quickly be implemented since three months of data is already sufficient to obtain the best risk estimations.

      PubDate: 2017-05-01T11:25:25Z
       
  • Financial Concept Element Mapper (FinCEM) for XBRL interoperability:
           Utilizing the M3 Plus method
    • Abstract: Publication date: Available online 22 April 2017
      Source:Decision Support Systems
      Author(s): Ugochukwu Etudo, Victoria Yoon, Dapeng Liu
      The use of eXtensible Business Reporting Language (XBRL) to represent financial reports (particularly 10-K and 10-Q filings) is a requirement of all public companies in the United States. The intention of the XBRL mandate is to streamline the financial reporting pipeline by providing full automaticity with respect to the collection, collation and analysis of financial information on the Web. However, the current lack of acceptable XBRL interoperability prevents the realization of the mandate's potential. This paper reports on a comprehensive solution to this problematic situation. The proposed design artifact, called FinCEM, is undergirded by channel theory and seeks to capture and leverage the semantics of XBRL calculation linkbases towards improved XBRL interoperability. The design artifact is instantiated and evaluated against XBRL filings from companies included in the S&P 100. The artifact, which operates automatically and without human intervention, is shown to provide significant improvements over alternative approaches as it attains high accuracy with respect to its core information retrieval task.

      PubDate: 2017-05-01T11:25:25Z
       
  • Taxo-Semantics: Assessing similarity between multi-word expressions for
           extending e-catalogs
    • Abstract: Publication date: Available online 8 April 2017
      Source:Decision Support Systems
      Author(s): Heiko Angermann, Zeeshan Pervez, Naeem Ramzan
      Taxonomies, also named directories, are utilized in e-catalogs to classify goods in a hierarchical manner with the help of concepts. If there is a need to create new concepts when modifying the taxonomy, the semantic similarity between the provided concepts has to be assessed properly. Existing semantic similarity assessment techniques lack in a comprehensive support for e-commerce, as those are not supporting multi-word expressions, multilingualism, the import/export to relational databases, and supervised user-involvement. This paper proposes Taxo-Semantics, a decision support system that is based on the progress in taxonomy matching to match each expression against various sources of background knowledge. The similarity assessment is based on providing three different matching strategies: a lexical-based strategy named Taxo-Semantics-Label, the strategy Taxo-Semantics-Bk, which is using different sources of background knowledge, and the strategy Taxo-Semantics-User that is providing user-involvement. The proposed system includes a translating service to analyze non-English concepts with the help of the WordNet lexicon, can parse taxonomies of relational databases, supports user-involvement to match single sequences with WordNet, and is capable to analyze each sequence as (sub)-taxonomy. The three proposed matching strategies significantly outperformed existing techniques. Taxo-Semantics-Label could improve the accuracy result by more than 7 % as compared to state-of-the-art lexical techniques. Taxo-Semantics-Bk could improve the accuracy compared to structure-based techniques by more than 8 %. And, Taxo-Semantics-User could additionally increase the accuracy by on average 23 %.

      PubDate: 2017-04-09T23:08:28Z
       
  • Explicit versus Implicit information for job recommendation: A case study
           with the Flemish public employment services
    • Abstract: Publication date: Available online 7 April 2017
      Source:Decision Support Systems
      Author(s): Michael Reusens, Wilfried Lemahieu, Bart Baesens, Luc Sels
      Recommender systems have proven to be a valuable tool in many online applications. However, the multitude of user related data types and recommender system algorithms makes it difficult for decision makers to choose the best combination for their specific business goals. Through a case study on job recommender systems in collaboration with the Flemish public employment services (VDAB), we evaluate what data types are most indicative of job seekers’ vacancy interests, and how this impacts the appropriateness of the different types of recommender systems for job recommendation. We show that implicit feedback data covers a broader spectrum of job seekers’ job interests than explicitly stated interests. Based on this insight we present a user-user collaborative filtering system solely based on this implicit feedback data. Our experiments show that this system outperforms the extensive knowledge-based recommender system currently employed by VDAB in both offline and expert evaluation. Furthermore, this study contributes to the existing recommender system literature by showing that, even in high risk recommendation contexts such as job recommendation, organizations should not only hang on to explicit feedback recommender systems but should embrace the value and abundance of available implicit feedback data.

      PubDate: 2017-04-09T23:08:28Z
       
  • Do Customer Reviews Drive Purchase Decisions? The Moderating Roles of
           Review Exposure and Price
    • Abstract: Publication date: Available online 22 March 2017
      Source:Decision Support Systems
      Author(s): Ewa Maslowska, Edward C. Malthouse, Vijay Viswanathan
      Customers read reviews to reduce the risk associated with a purchase decision. While prior studies have focused on the valence and volume of reviews, this study provides a more comprehensive understanding of how reviews influence customers by considering two additional factors—exposure to reviews and price relative to other products in the category. Data provided by two online retailers are used for the analysis. The results reveal a four-way interaction with the effect of valence on purchase probability strongest when (1) there are many reviews, (2) the customer reads reviews, and (3) the product is higher priced. The effects of valence are smaller, but still positive, in the other conditions. We develop theoretical explanations for the effects based on dual processing models and prospect theory, and provide a sensitivity analysis. We discuss implications for academics, manufacturers and online retailers.

      PubDate: 2017-03-27T07:59:13Z
       
  • Utopia in the solution of the Bucket Order Problem
    • Abstract: Publication date: Available online 21 March 2017
      Source:Decision Support Systems
      Author(s): Juan A. Aledo, José A. Gámez, Alejandro Rosete
      This paper deals with group decision making and, in particular, with rank aggregation, which is the problem of aggregating individual preferences (rankings) in order to obtain a consensus ranking. Although this consensus ranking is usually a permutation of all the ranked items, in this paper we tackle the situation in which some items can be tied, that is, the consensus shows that there is no preference among them. This problem has arisen recently and is known as the optimal bucket order problem (OBOP). In this paper we propose two improvements to the standard greedy algorithm usually considered to approach the bucket order problem: the Bucket Pivot Algorithm (BPA). The first improvement is based on the introduction of the Utopian Matrix, a matrix associated to a pair order matrix that represents the precedences in a collection of rankings. This idealization constitutes a superoptimal solution to the OBOP, which can be used as an extreme (sometimes feasible) best value. The second improvement is based on the use of several items as pivots to generate the bucket order, in contrast to BPA that only uses a single pivot. The set of items playing the role of decision-maker is dynamically created. We analyze separately the contribution of each improvement and also their joint effect. The statistical analysis of the experiments carried out shows that the combined use of both techniques is the best choice, showing a significant improvement in accuracy (17%) with respect to the original BPA and providing an important reduction in the variance of the output. Moreover, we provide decision rules to help the decision maker to select the right algorithm according to the problem instance.

      PubDate: 2017-03-27T07:59:13Z
       
  • Patterns of business intelligence systems use in organizations
    • Abstract: Publication date: Available online 21 March 2017
      Source:Decision Support Systems
      Author(s): David Arnott, Felix Lizama, Yutong Song
      Business intelligence (BI) is often used as the umbrella term for large-scale decision support systems (DSS) in organizations. BI is currently the largest area of IT investment in organizations and has been rated as the top technology priority by CIOs worldwide for many years. The most important use patterns in decision support are concerned with the type of decision to be supported and the type of manager that makes the decision. The seminal Gorry and Scott Morton MIS/DSS framework remains the most popular framework to describe these use patterns. It is widely believed that DSS theory like this framework can be transferred to BI. This paper investigates BI systems use patterns using the Gorry and Scott Morton framework and contemporary decision-making theory from behavioral economics. The paper presents secondary case study research that analyzes eight BI systems and 86 decisions supported by these systems. Based on the results of the case studies a framework to describe BI use patterns is developed. The framework provides both a theoretical and empirically based foundation for the development of high quality BI theory. It also provides a guide for developing organizational strategy for BI provision. The framework shows that enterprise and smaller functional BI systems exist together in an organization to support different decisions and different decision makers. The framework shows that personal DSS theory cannot be applied to BI systems without specific empirical support.

      PubDate: 2017-03-27T07:59:13Z
       
  • A machine learning approach to product review disambiguation based on
           function, form and behavior classification
    • Abstract: Publication date: Available online 20 March 2017
      Source:Decision Support Systems
      Author(s): Abhinav Singh, Conrad S. Tucker
      Online product reviews have been shown to be a viable source of information for helping customers make informed purchasing decisions. In many cases, users of online shopping platforms have the ability to rate products on a numerical scale, and also provide textual feedback pertaining to a purchased product. Beyond using online product review platforms as customer decision support systems, this information rich data source could also aid designers seeking to increase the chances of their products being successful in the market through a deeper understanding of market needs. However, the increasing size and complexity of products on the market makes manual analysis of such data challenging. Information obtained from such sources, if not mined correctly, risks misrepresenting a product's true success/failure (e.g., a customer leaves a one star rating because of the slow shipping service of a product, not necessarily that he/she dislikes the product). The objective of this paper is three fold: i) to propose a machine learning approach that disambiguates online customer review feedback by classifying them into one of three direct product characteristics (i.e., form, function or behavior) and two indirect product characteristics (i.e., service and other), ii) to discover the machine learning algorithm that yields the highest and most generalizable results in achieving objective i) and iii) to quantify the correlation between product ratings and direct and indirect product characteristics. A case study involving review data for products mined from e-commerce websites is presented to demonstrate the validity of the proposed method. A multilayered (i.e., k-fold and leave one out) validation approach is presented to explore the generalizability of the proposed method. The resulting machine learning model achieved classification accuracies of 82.44% for within product classification, 80.84% for across product classification, 79.03% for across product type classification and 80.64% for across product domain classification. Furthermore, it was determined that the form of a product had the highest Pearson Correlation Coefficient relating to a product's star rating, with a value of 0.934. The scientific contributions of this work have the potential to transform the manner in which both product designers and customers incorporate product reviews into their decision making processes by quantifying the relationship between product reviews and product characteristics.

      PubDate: 2017-03-20T06:57:05Z
       
  • Review popularity and review helpfulness: A model for user review
           effectiveness
    • Abstract: Publication date: Available online 19 March 2017
      Source:Decision Support Systems
      Author(s): Jianan Wu
      The wide adoption and perceived helpfulness of online user reviews on consumers' decision making have energized academic research on the assessment of review effectiveness. Although the literature probed the impacts of user reviews on various elements of review effectiveness independently, little research has done to examine them jointly. Inspired by communication theories, we conceptualize a framework for user review effectiveness in which we focus on the joint assessment of its first two elements: Review Popularity and Review Helpfulness. We develop our hypotheses regarding the effects of the user review determinants on both Review Popularity and Review Helpfulness, and further develop an operational model to empirically test our hypotheses using data collected from Amazon. Our study suggests that disentangling Review Popularity and Review Helpfulness in assessing review effectiveness is not only conceptually sounding, but also managerially beneficial. We find that Review Popularity is as important as Review Helpfulness in review effectiveness evaluations. Review determinants may play opposite roles on Review Popularity and Review Helpfulness (e.g., valence), and can drive review effectiveness via Review Popularity or Review Helpfulness or both. These findings offer new insights for various decision makers to harvest user review effectiveness in online markets.

      PubDate: 2017-03-20T06:57:05Z
       
  • Effects of decision space information on MAUT-based systems that support
           purchase decision processes
    • Abstract: Publication date: Available online 14 March 2017
      Source:Decision Support Systems
      Author(s): Michael Scholz, Markus Franz, Oliver Hinz
      This paper shows that decision makers often have a misconception of the decision space. The decision space is constituted by the relations among the attributes describing the alternatives available in a decision situation. The paper demonstrates that these misconceptions negatively affect the usage and perceptions of MAUT-based decision support systems. To overcome these negative effects, this paper proposes to use a visualization method based on singular value decomposition to give decision makers insights into the attribute relations. In a laboratory experiment in cooperation with Germany's largest Internet real estate website, this paper moreover evaluates the proposed solution and shows that our solution improves decision makers' usage and perceptions of MAUT-based decision support systems. We further show that information about the decision space ultimately affects variables relevant for the economic success of decision support system providers such as reuse intention and the probability to act as a promoter for the systems.

      PubDate: 2017-03-20T06:57:05Z
       
  • Understanding and overcoming biases in online review systems
    • Abstract: Publication date: Available online 9 March 2017
      Source:Decision Support Systems
      Author(s): Georgios Askalidis, Su Jung Kim, Edward C. Malthouse
      This study addresses the issues of social influence and selection biases in the context of online review systems. We propose that one way to reduce these biases is to send email invitations to write a review to a random sample of buyers, and not exposing them to existing reviews while they write their reviews. We provide empirical evidence showing how such a simple intervention from the retailer mitigates the biases by analyzing data from four diverse online retailers over multiple years. The data include both self-motivated reviews, where the reviewer sees other reviews at the time of writing, and retailer-prompted reviews generated by an email invitation to verified buyers, where the reviewer does not see existing reviews. Consistent with previous research on the social influence bias, we find that the star ratings of self-motivated reviews decrease over time (i.e., downward trend), while the star ratings of retailer-prompted reviews remain constant. As predicted by theories on motivation, the self-motivated reviews are shown to be more negative (lower valence), longer, and more helpful, which suggests that the nature of self-motivated and retailer-prompted reviews is distinctively different and the influx of retailer-prompted reviews would enhance diversity in the overall review system. Regarding the selection bias, we found that email invitations can improve the representativeness of reviews by adding a new segment of verified buyers. In sum, implementing appropriate design and policy in online review systems will improve the quality and validity of online reviews and help practitioners provide more credible and representative ratings to their customers.

      PubDate: 2017-03-12T17:21:51Z
       
  • Bridging the gap between decision-making and emerging big data sources: an
           application of a model-based framework to disaster management in Brazil
    • Abstract: Publication date: Available online 9 March 2017
      Source:Decision Support Systems
      Author(s): Flávio E.A. Horita, João Porto de Albuquerque, Victor Marchezini, Eduardo M. Mendiondo
      With the emergence of big data and new data sources, a challenge posed to today’s organizations consists of identifying how to align their decision-making and organizational processes to data that could help them make better-informed decisions. This paper presents a study in the context of disaster management in Brazil that applies oDMN +, a framework that connects decision-making with data sources through an extended modeling notation and a modeling process. The study results revealed that the framework is an effective approach for improving the understanding of how to leverage big data in the organization’s decision-making.

      PubDate: 2017-03-12T17:21:51Z
       
  • A review of the nature and effects of guidance design features
    • Abstract: Publication date: Available online 8 March 2017
      Source:Decision Support Systems
      Author(s): Stefan Morana, Silvia Schacht, Ansgar Scherp, Alexander Maedche
      Guidance design features in information systems are used to help people in decision-making, problem solving, and task execution. Various information systems instantiate guidance design features, which have specifically been researched in the field of decision support systems for decades. However, due to the lack of a common conceptualization, it is difficult to compare the research findings on guidance design features from different literature streams. This article reviews and analyzes the work of the research streams of decisional guidance, explanations, and decision aids conducted in the last 25years. Building on and grounded by the analyzed literature, we theorize an integrated taxonomy on guidance design features. Applying the taxonomy, we discuss existing empirical results, identify effects of different guidance design features, and propose opportunities for future research. Overall, this article contributes to research and practice. The taxonomy allows researchers to describe their work by using a set of dimensions and characteristics and to systematically compare existing research on guidance design features. From a practice-oriented perspective, we provide an overview on design features to support implementing guidance in various types of information systems.

      PubDate: 2017-03-12T17:21:51Z
       
  • Information systems and task demand: An exploratory pupillometry study of
           computerized decision making
    • Abstract: Publication date: Available online 20 February 2017
      Source:Decision Support Systems
      Author(s): Dennis D. Fehrenbacher, Soussan Djamasbi
      Information systems (IS) play an important role in successful execution of organizational decisions, and the ensuing tasks that rely on those decisions. Because decision making models show that cognitive load has a significant impact on how people use information systems, objective measurement of cognitive load becomes both relevant and important in IS research. In this paper, we manipulate task demand during a decision making task in four different ways. We then investigate how increasing task demand affects a user's pupil data during interaction with a computerized decision aid. Our results suggest that pupillometry has the potential to serve as a reliable, objective, continuous and unobtrusive measure of task demand and that the adaptive decision making theory may serve as a suitable framework for studying user pupillary responses in the IS domain.

      PubDate: 2017-02-21T12:51:15Z
       
 
 
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