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  Subjects -> BUSINESS AND ECONOMICS (Total: 3075 journals)
    - ACCOUNTING (89 journals)
    - BANKING AND FINANCE (261 journals)
    - BUSINESS AND ECONOMICS (1154 journals)
    - CONSUMER EDUCATION AND PROTECTION (24 journals)
    - COOPERATIVES (4 journals)
    - ECONOMIC SCIENCES: GENERAL (158 journals)
    - ECONOMIC SYSTEMS, THEORIES AND HISTORY (167 journals)
    - FASHION AND CONSUMER TRENDS (13 journals)
    - HUMAN RESOURCES (94 journals)
    - INSURANCE (23 journals)
    - INTERNATIONAL COMMERCE (127 journals)
    - INTERNATIONAL DEVELOPMENT AND AID (81 journals)
    - INVESTMENTS (25 journals)
    - LABOR AND INDUSTRIAL RELATIONS (43 journals)
    - MACROECONOMICS (13 journals)
    - MANAGEMENT (517 journals)
    - MARKETING AND PURCHASING (86 journals)
    - MICROECONOMICS (24 journals)
    - PRODUCTION OF GOODS AND SERVICES (138 journals)
    - PUBLIC FINANCE, TAXATION (32 journals)
    - TRADE AND INDUSTRIAL DIRECTORIES (2 journals)

BUSINESS AND ECONOMICS (1154 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: 11)
Accounting Forum     Hybrid Journal   (Followers: 22)
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: 57)
African Development Review     Hybrid Journal   (Followers: 34)
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: 6)
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: 124)
American Economic Journal : Economic Policy     Full-text available via subscription   (Followers: 94)
American Journal of Business     Hybrid Journal   (Followers: 14)
American Journal of Business and Management     Open Access   (Followers: 50)
American Journal of Business Education     Open Access   (Followers: 10)
American Journal of Economics and Business Administration     Open Access   (Followers: 22)
American Journal of Economics and Sociology     Hybrid Journal   (Followers: 27)
American Journal of Evaluation     Hybrid Journal   (Followers: 12)
American Journal of Finance and Accounting     Hybrid Journal   (Followers: 16)
American Journal of Health Economics     Full-text available via subscription   (Followers: 12)
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: 26)
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: 26)
Annals of Operations Research     Hybrid Journal   (Followers: 8)
Annual Review of Economics     Full-text available via subscription   (Followers: 29)
Applied Developmental Science     Hybrid Journal   (Followers: 4)
Applied Economics     Hybrid Journal   (Followers: 44)
Applied Economics Letters     Hybrid Journal   (Followers: 28)
Applied Economics Quarterly     Full-text available via subscription   (Followers: 10)
Applied Financial Economics     Hybrid Journal   (Followers: 21)
Applied Mathematical Finance     Hybrid Journal   (Followers: 6)
Applied Stochastic Models in Business and Industry     Hybrid Journal   (Followers: 5)
Apuntes Universitarios     Open Access   (Followers: 1)
Arab Economic and Business Journal     Open Access   (Followers: 3)
Archives of Business Research     Open Access   (Followers: 4)
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: 309)
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)
Asian Business Review     Open Access   (Followers: 2)
Asian Case Research Journal     Hybrid Journal   (Followers: 1)
Asian Development Review     Open Access   (Followers: 14)
Asian Economic Journal     Hybrid Journal   (Followers: 6)
Asian Economic Papers     Hybrid Journal   (Followers: 7)
Asian Economic Policy Review     Hybrid Journal   (Followers: 3)
Asian Journal of Accounting and Governance     Open Access   (Followers: 3)
Asian Journal of Business Ethics     Hybrid Journal   (Followers: 7)
Asian Journal of Social Sciences and Management Studies     Open Access   (Followers: 6)
Asian Journal of Technology Innovation     Hybrid Journal   (Followers: 9)
Asian-pacific Economic Literature     Hybrid Journal   (Followers: 5)
AStA Wirtschafts- und Sozialstatistisches Archiv     Hybrid Journal   (Followers: 5)
Atlantic Economic Journal     Hybrid Journal   (Followers: 14)
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: 16)
Australian Economic Review     Hybrid Journal   (Followers: 7)
Australian Journal of Maritime and Ocean Affairs     Hybrid Journal   (Followers: 11)
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: 5)
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: 11)
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: 30)
Brookings Papers on Economic Activity     Open Access   (Followers: 46)
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: 16)
Bulletin of Geography. Socio-economic Series     Open Access   (Followers: 6)
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: 15)
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 Horizons     Open Access   (Followers: 2)
Business and Economic Research     Open Access   (Followers: 5)
Business and Management Horizons     Open Access   (Followers: 11)
Business and Management Research     Open Access   (Followers: 16)
Business and Management Studies     Open Access   (Followers: 7)
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: 9)
Business Information Review     Hybrid Journal   (Followers: 13)
Business Management and Strategy     Open Access   (Followers: 39)
Business Research     Hybrid Journal   (Followers: 1)
Business Strategy and the Environment     Hybrid Journal   (Followers: 11)
Business Strategy Review     Hybrid Journal   (Followers: 6)
Business Strategy Series     Hybrid Journal   (Followers: 5)
Business Systems & Economics     Open Access   (Followers: 1)
Business Systems Research Journal     Open Access   (Followers: 4)
Business, Management and Education     Open Access   (Followers: 16)
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: 55)
Cambridge Journal of Regions, Economy and Society     Hybrid Journal   (Followers: 9)
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: 25)
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: 1)
CESifo Economic Studies     Hybrid Journal   (Followers: 15)
Chain Reaction     Full-text available via subscription   (Followers: 1)
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: 17)
China Economic Journal: The Official Journal of the China Center for Economic Research (CCER) at Peking University     Hybrid Journal   (Followers: 10)
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: 1)
COEPTUM     Open Access  
Community Development Journal     Hybrid Journal   (Followers: 23)
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 Report     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: 4)
Corporate Philanthropy Report     Hybrid Journal   (Followers: 2)
Corporate Reputation Review     Hybrid Journal   (Followers: 5)
Creative and Knowledge Society     Open Access   (Followers: 10)
Creative Industries Journal     Hybrid Journal   (Followers: 8)
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  

        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  [3041 journals]
  • 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
       
  • Overcoming individual process model matcher weaknesses using
           ensemble matching
    • Abstract: Publication date: Available online 2 March 2017
      Source:Decision Support Systems
      Author(s): Christian Meilicke, Henrik Leopold, Elena Kuss, Heiner Stuckenschmidt, Hajo A. Reijers
      In recent years, a considerable number of process model matching techniques have been proposed. The goal of these techniques is to identify correspondences between the activities of two process models. However, the results from the Process Model Matching Contest 2015 reveal that there is still no universally applicable matching technique and that each technique has particular strengths and weaknesses. It is hard or even impossible to choose the best technique for a given matching problem. We propose to cope with this problem by running an ensemble of matching techniques and automatically selecting a subset of the generated correspondences. To this end, we propose a Markov Logic based optimization approach that automatically selects the best correspondences. The approach builds on an adaption of a voting technique from the domain of schema matching and combines it with process model specific constraints. Our experiments show that our approach is capable of generating results that are significantly better than alternative approaches.

      PubDate: 2017-03-07T13:15:30Z
       
  • Follow the herd or be myself? An analysis of consistency in behavior of
           reviewers and helpfulness of their reviews
    • Abstract: Publication date: March 2017
      Source:Decision Support Systems, Volume 95
      Author(s): Baojun Gao, Nan Hu, Indranil Bose
      This study investigates if reviewers' pattern of rating is consistent over time and predictable. Two interesting results emerge from the econometric analyses using publicly available data from TripAdvisor.com. First, reviewers' rating behavior is consistent over time and across products. Furthermore, most of the variation in their future rating behavior can be explained by their rating behavior in the past rather than by the observed average rating. Second, reviews by reviewers with higher absolute bias in rating in the past receive more helpful votes in future. We further divide the bias in rating into intrinsic bias (driven by intrinsic reviewer characteristics) and extrinsic bias (driven by influences beyond intrinsic bias) and document that intrinsic bias plays a more significant role in influencing helpful votes for reviews than extrinsic bias. Our results are robust to different product categories and different definition of bias. Overall our results indicate that in the online review context, the observed average rating or an attention grabbing strategy may not be as important as believed in the past. This study provides insights into reviewers' rating behavior and prescribes actionable items for online vendors so that they can proactively influence online opinion instead of passively responding to them.

      PubDate: 2017-03-07T13:15:30Z
       
  • A hybrid decision support system for managing humanitarian relief chains
    • Abstract: Publication date: March 2017
      Source:Decision Support Systems, Volume 95
      Author(s): Navid Sahebjamnia, S. Ali Torabi, S. Afshin Mansouri
      Decisions regarding location, allocation and distribution of relief items are among the main concerns of the humanitarian relief chain (HRC) managers in response to no-notice large-scale disasters such as earthquakes. In this paper, a hybrid decision support system (HDSS) consisting of a simulator, a rule-based inference engine, and a knowledge-based system (KBS) is developed to configure a three level HRC. Three main performance measures including the coverage, total cost, and response time are considered to make an explicit trade-off analysis between the cost efficiency and responsiveness of the designed HRC. In the first step, the simulator calculates the performance measures of the different configurations of the HRC under a number of generated disaster scenarios. Then, the rule-based inference engine attempts to build the best configuration of the HRC including facilities' locations, relief items' allocation and distribution plan of the scenario under investigation based on the calculated performance measures. Finally, the best configuration for each scenario is stored in the KBS as the extracted knowledge from the above analyses. In this way, the HRC managers can retrieve the most appropriate HRC configuration in accordance with the realized post-disaster scenario in an effective and timely manner. The results of a real case study in Tehran demonstrate that the developed HDSS is an effective tool for fast configuration of HRCs using stochastic data.

      PubDate: 2017-03-07T13:15:30Z
       
  • A comparative analysis of data preparation algorithms for customer churn
           prediction: A case study in the telecommunication industry
    • Abstract: Publication date: March 2017
      Source:Decision Support Systems, Volume 95
      Author(s): Kristof Coussement, Stefan Lessmann, Geert Verstraeten


      PubDate: 2017-03-07T13:15:30Z
       
  • The seaport service rate prediction system: Using drayage truck trajectory
           data to predict seaport service rates
    • Abstract: Publication date: March 2017
      Source:Decision Support Systems, Volume 95
      Author(s): Meditya Wasesa, Andries Stam, Eric van Heck
      For drayage operators the service rate of seaports is crucial for organizing their container pick-up/delivery operations. This study presents a seaport service rate prediction system that could help drayage operators to improve their predictions of the duration of the pick-up/delivery operations at a seaport by using the subordinate trucks' trajectory data. The system is constructed based on three components namely, trajectory reconstruction, geo-fencing analysis, and gradient boosting modelling. Using predictive analytic techniques, the prediction system is trained and validated using more than 15million data records from over 200 trucks over a period of 19months. The gradient boosting model-based solution provides better predictions compared with the linear model benchmark solution. Conclusions and implications are formulated.

      PubDate: 2017-03-07T13:15:30Z
       
  • Retraction notice to: Provider Feedback Information and Customer Choice
           Decisions on Crowdsourcing Marketplaces: Evidence from Two Discrete Choice
           Experiments [Decision Support Systems 82 (2016) 1-11]
    • Abstract: Publication date: March 2017
      Source:Decision Support Systems, Volume 95
      Author(s): Behrang Assemi, Daniel Schlagwein
      This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This article has been retracted at the request of the Editor-in-Chief following a dispute over authorship that the original listed authors were not able to resolve. The authorship of the paper was changed during the latter stages of review without notification to either the Editor-in-Chief or Elsevier.
      Authors hip should include and be limited to those who have made a significant contribution to the conception, design, execution, or interpretation of the reported study.

      PubDate: 2017-03-07T13:15:30Z
       
  • Retrieving batch organisation of work insights from event logs
    • Abstract: Publication date: Available online 1 March 2017
      Source:Decision Support Systems
      Author(s): Niels Martin, Marijke Swennen, Benoît Depaire, Mieke Jans, An Caris, Koen Vanhoof
      Resources can organise their work in batches, i.e. perform activities on multiple cases simultaneously, concurrently or intentionally defer activity execution to handle multiple cases (quasi-) sequentially. As batching behaviour influences process performance, efforts to gain insight on this matter are valuable. In this respect, this paper uses event logs, data files containing process execution information, as an information source. More specifically, this work (i) identifies and formalises three batch processing types, (ii) presents a resource-activity centered approach to identify batching behaviour in an event log and (iii) introduces batch processing metrics to acquire knowledge on batch characteristics and its influence on process execution. These contributions are integrated in the Batch Organisation of Work Identification algorithm (BOWI), which is evaluated on both artificial and real-life data.

      PubDate: 2017-03-01T12:58:44Z
       
  • Press accept to update now: Individual differences in susceptibility to
           malevolent interruptions
    • Abstract: Publication date: Available online 27 February 2017
      Source:Decision Support Systems
      Author(s): Emma J. Williams, Phillip L. Morgan, Adam N. Joinson
      Increasingly, connected communication technologies have resulted in people being exposed to fraudulent communications by scammers and hackers attempting to gain access to computer systems for malicious purposes. Common influence techniques, such as mimicking authority figures or instilling a sense of urgency, are used to persuade people to respond to malevolent messages by, for example, accepting urgent updates. An ‘accept’ response to a malevolent influence message can result in severe negative consequences for the user and for others, including the organisations they work for. This paper undertakes exploratory research to examine individual differences in susceptibility to fraudulent computer messages when they masquerade as interruptions during a demanding memory recall primary task compared to when they are presented in a post-task phase. A mixed-methods approach was adopted to examine when and why people choose to accept or decline three types of interrupting computer update message (genuine, mimicked, and low authority) and the relative impact of such interruptions on performance of a serial recall memory primary task. Results suggest that fraudulent communications are more likely to be accepted by users when they interrupt a demanding memory-based primary task, that this relationship is impacted by the content of the fraudulent message, and that influence techniques used in fraudulent communications can over-ride authenticity cues when individuals decide to accept an update message. Implications for theories, such as the recently proposed Suspicion, Cognition and Automaticity Model and the Integrated Information Processing Model of Phishing Susceptibility, are discussed.

      PubDate: 2017-03-01T12:58:44Z
       
  • An upper approximation based community detection algorithm for complex
           networks
    • Abstract: Publication date: Available online 24 February 2017
      Source:Decision Support Systems
      Author(s): Pradeep Kumar, Samrat Gupta, Bharat Bhasker
      The emergence of multifarious complex networks has attracted researchers and practitioners from various disciplines. Discovering cohesive subgroups or communities in complex networks is essential to understand the dynamics of real-world systems. Researchers have made persistent efforts to investigate and infer community patterns in complex networks. However, real-world networks exhibit various characteristics wherein existing communities are not only disjoint but are also overlapping and nested. The existing literature on community detection consists of limited methods to discover co-occurring disjoint, overlapping and nested communities. In this work, we propose a novel rough set based algorithm capable of uncovering true community structure in networks, be it disjoint, overlapping or nested. Initial sets of granules are constructed using neighborhood connectivity around the nodes and represented as rough sets. Subsequently, we iteratively obtain the constrained connectedness upper approximation of these sets. To constrain the sets and merge them during each iteration, we utilize the concept of relative connectedness among the nodes. We illustrate the proposed algorithm on a toy network and evaluate it on fourteen real-world benchmark networks. Experimental results show that the proposed algorithm reveals more accurate communities and significantly outperforms state-of-the-art techniques.

      PubDate: 2017-03-01T12:58:44Z
       
  • Data-driven Process Prioritization in process networks
    • Abstract: Publication date: Available online 24 February 2017
      Source:Decision Support Systems
      Author(s): Wolfgang Kratsch, Jonas Manderscheid, Daniel Reißner, Maximilian Röglinger
      Business process management (BPM) is an essential paradigm of organizational design and a source of corporate performance. The most value-creating activity of BPM is process improvement. With effective process prioritization being a critical success factor for process improvement, we propose the Data-Driven Process Prioritization (D2P2) approach. By addressing the weaknesses of extant process prioritization approaches, the D2P2 accounts for structural and stochastic process dependencies and leverages log data. The D2P2 returns a priority list that indicates in which future periods the processes from a process network should undergo the next in-depth analysis to check whether they actually require improvement. The D2P2 contributes to the prescriptive knowledge on process prioritization and process decision-making. As for evaluation, we discussed the D2P2's design specification against theory-backed design objectives and competing artefacts. We also instantiated the D2P2 as a software prototype and applied the prototype to a real-world scenario based on the 2012 BPI Challenge log.

      PubDate: 2017-03-01T12:58:44Z
       
  • 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
       
  • QPLAN: Decision support for evaluating planning quality in software
           development projects
    • Abstract: Publication date: Available online 20 February 2017
      Source:Decision Support Systems
      Author(s): Marco Antônio Amaral Féris, Ofer Zwikael, Shirley Gregor
      Decisions about whether or not to approve a project plan for execution are critical. A decision to continue with a bad plan may lead to a failed project, whereas requesting unnecessary additional planning for an already high-quality plan may be counterproductive. However, these decisions can be influenced by psychological biases, such as the endowment effect, optimism bias and ambiguity effect, which are enhanced when uncertainty is substantial and information incomplete. As a result, a non-biased model for evaluating the quality of project planning is important to improve planning approval decisions and resource allocation. This paper introduces a novel artifact (QPLAN) that evaluates and improves planning quality, and a case study to demonstrate its effectiveness within a business environment.

      PubDate: 2017-02-21T12:51:15Z
       
  • Sustainable production: Using simulation modeling to identify the benefits
           of green information systems
    • Abstract: Publication date: Available online 20 February 2017
      Source:Decision Support Systems
      Author(s): Lyubov A. Kurkalova, Lemuria Carter
      Researchers and practitioners highlight the potential for information systems to promote sustainability in agricultural production, but little is known about the private and social benefits of specific agricultural decision support tools. In this study, we utilize the resource-based view to assess a specific green technology using an agricultural-economics simulation to estimate the quantitative benefits of this technology expressed as dollars saved and reduced greenhouse gas emissions. In particular, we employ a five-step simulation modeling approach within a micro-economic model of crop production to assess the ability of yield monitors to promote liquefied petroleum (LP) gas savings and subsequently reduce production costs, reduce greenhouse gas (GHG) emissions associated with LP gas burning, and generate additional revenue at a market for GHG mitigation credits. We estimate that the total benefits of using the green IS to improve the harvesting decision would have been $82 million in post-harvest cost savings and a significant reduction in greenhouse gas emissions. We present this simulation modeling approach, a common methodology in environmental sciences and economics, as a viable methodology for IS researchers interested in modeling intricate decision-making processes that are impacted by technology.

      PubDate: 2017-02-21T12:51:15Z
       
  • Aircraft re-routing optimization and performance assessment
           under uncertainty
    • Abstract: Publication date: Available online 17 February 2017
      Source:Decision Support Systems
      Author(s): Xiaoge Zhang, Sankaran Mahadevan
      The need for aircraft re-routing arises when there is disruption in the system, such as when an airport is closed due to extreme weather. In this paper, we investigate a simulation-based approach to optimize the aircraft re-routing process, by considering multiple sources of uncertainty. The proposed approach has four main components: system simulation, uncertainty representation, aircraft re-routing algorithm, and system performance assessment. Several sources of uncertainty are accounted for in this approach, related to incoming aircraft, space availability in neighboring airports, radar performance, and communication delays. An aircraft re-routing optimization model is formulated to make periodic re-routing decisions with the objective of minimizing the overall distance travelled by all the aircraft, subject to the system resources. We analyze the performance of this aircraft re-routing system using system failure time as the metric. Since the simulation time is limited, right-censored data arises with respect to system failure time. A novel methodology is developed to compute the lower bound of system failure time in the presence of right-censored data, and to analyze the sensitivity of the system performance metric to the uncertain variables relating to the aircraft, radars, nearby airports, and communication system. Since the simulation is time-consuming, we build a Support Vector Regression (SVR) surrogate model to efficiently construct the system failure time distribution.

      PubDate: 2017-02-21T12:51:15Z
       
  • The Structured Process Modeling Method (SPMM) what is the best way for me
           to construct a process model?
    • Abstract: Publication date: Available online 13 February 2017
      Source:Decision Support Systems
      Author(s): Jan Claes, Irene Vanderfeesten, Frederik Gailly, Paul Grefen, Geert Poels
      More and more organizations turn to the construction of process models to support strategical and operational tasks. At the same time, reports indicate quality issues for a considerable part of these models, caused by modeling errors. Therefore, the research described in this paper investigates the development of a practical method to determine and train an optimal process modeling strategy that aims to decrease the number of cognitive errors made during modeling. Such cognitive errors originate in inadequate cognitive processing caused by the inherent complexity of constructing process models. The method helps modelers to derive their personal cognitive profile and the related optimal cognitive strategy that minimizes these cognitive failures. The contribution of the research consists of the conceptual method and an automated modeling strategy selection and training instrument. These two artefacts are positively evaluated by a laboratory experiment covering multiple modeling sessions and involving a total of 149 master students at Ghent University.

      PubDate: 2017-02-14T22:46:53Z
       
  • Designing an Intelligent Decision Support System for Effective Negotiation
           Pricing: A Systematic and Learning Approach
    • Abstract: Publication date: Available online 10 February 2017
      Source:Decision Support Systems
      Author(s): Xin Fu, Xiao-Jun Zeng, Xin(Robert) Luo, Di Wang, Di Xu, Qing-Liang Fan
      Automatic negotiation pricing and differential pricing aim to provide different customers with products/services that adequately meet their requirements at the “right” price. This often takes place with the purchase of expensive products/services and in the business-to-business context. Effective negotiation pricing can help enhance a company’s profitability, balance supply and demand, and improve the customer satisfaction. However, determining the “right” price is a rather complex decision-making problem that puzzles pricing managers, as it needs to consider information from many constituents of the purchase channel. To further advance this line of research, this study proposes a systematic and learning approach that consists of three different types of fuzzy systems (FSs) to provide intelligent decision support for negotiation pricing. More specifically, the three FSs include: 1) a standard FS, which is a typical multiple inputs and single output FS that forms a mathematical mapping from the input space to the output space; 2) an SFS-SISOM, which is a linear fuzzy inference model with a single input and a single output module; and 3) a hierarchical FS, which consists of several FSs in a hierarchical manner to perform fuzzy inference. To address the existing problem of a standard FS suffering from the high-dimensional problem with a large number of influential factors, a generalized type of FS (named hierarchical FS), including its mathematical models and suitability for tackling the negotiation pricing problem, is introduced. In particular, a proof-of-concept prototype system that integrates these three FSs is also developed and presented. From a system design perspective, this artifact provides immense potential and flexibility for end users to choose the most suitable model for the given problem. The utility and effectiveness of this proposed system is illustrated and examined by three experimental datasets that vary from dimensionality and data coverage. Moreover, the performances of three different approaches are compared and discussed with respect to some important properties of decision support systems (DSSs).

      PubDate: 2017-02-14T22:46:53Z
       
  • Discovering Work Prioritisation Patterns from Event Logs
    • Abstract: Publication date: Available online 8 February 2017
      Source:Decision Support Systems
      Author(s): Suriadi Suriadi, Moe T. Wynn, Jingxin Xu, Wil M.P. vander Aalst, Arthur H.M. ter Hofstede
      Business process improvement initiatives typically employ various process analysis techniques, including evidence-based analysis techniques such as process mining, to identify new ways to streamline current business processes. While plenty of process mining techniques have been proposed to extract insights about the way in which activities within processes are conducted, techniques to understand resource behaviour are limited. At the same time, an understanding of resources behaviour is critical to enable intelligent and effective resource management - an important factor which can significantly impact overall process performance. The presence of detailed records kept by today’s organisations, including data about who, how, what, and when various activities were carried out by resources, open up the possibility for real behaviours of resources to be studied. This paper proposes an approach to analyse one aspect of resource behaviour: the manner in which a resource prioritises his/her work. The proposed approach has been formalised, implemented, and evaluated using a number of synthetic and real datasets.

      PubDate: 2017-02-14T22:46:53Z
       
  • Online review helpfulness: Impact of reviewer profile image
    • Abstract: Publication date: Available online 5 February 2017
      Source:Decision Support Systems
      Author(s): Sahar Karimi, Fang Wang
      Despite the growing number of studies on online reviews, the impact of visual cues on consumer's evaluation of review helpfulness has remained underexplored. It is not yet known whether and how images influence the way online reviews are perceived. This paper introduces and empirically examines the potential effects of reviewer profile image, a photo/image displayed next to the reviewer name, on review helpfulness by drawing on the decorative and information functions of images. With a sample of 2178 reviews from mobile gaming applications, we report that reviewer profile image can significantly enhance consumer's evaluation of review helpfulness; whereas there is no differential effect among image types (i.e. self, family, or random images). Interestingly, the effect of reviewer profile image on review helpfulness is moderated by review length, but not review valence and equivocality. Results suggest that reviewer profile image enhances the perception of review helpfulness by serving mainly as a visual decoration that creates affective responses rather than identity information.

      PubDate: 2017-02-07T22:45:06Z
       
  • An empirical study of natural noise management in group recommendation
           systems
    • Abstract: Publication date: February 2017
      Source:Decision Support Systems, Volume 94
      Author(s): Jorge Castro, Raciel Yera, Luis Martínez
      Group recommender systems (GRSs) filter relevant items to groups of users in overloaded search spaces using information about their preferences. When the feedback is explicitly given by the users, inconsistencies may be introduced due to various factors, known as natural noise. Previous research on individual recommendation has demonstrated that natural noise negatively influences the recommendation accuracy, whilst it improves when noise is managed. GRSs also employ explicit ratings given by several users as ground truth, hence the recommendation process is also affected by natural noise. However, the natural noise problem has not been addressed on GRSs. The aim of this paper is to develop and test a model to diminish its negative effect in GRSs. A case study will evaluate the results of different approaches, showing that managing the natural noise at different rating levels reduces prediction error. Eventually, the deployment of a GRS with natural noise management is analysed.

      PubDate: 2017-02-07T22:45:06Z
       
  • The influence of influence: The effect of task repetition on persuaders
           and persuadees
    • Abstract: Publication date: February 2017
      Source:Decision Support Systems, Volume 94
      Author(s): Thomas Chesney, Swee-Hoon Chuah, Robert Hoffmann, Jeremy Larner
      We investigate how the experience of influencing and of being influenced impacts on a subsequent, immediate attempt to influence and be influenced. We conduct an experiment using participant dyads matched in a round-robin design which systematically measures the influence one individual has on another in a decision task using a short, anonymous, computer mediated, text based exchange. Findings show that being influenced in a round of the task tends to be positively related to being influenced in the subsequent two rounds with the effect weakening each time. We find no impact on the ability to influence.

      PubDate: 2017-02-07T22:45:06Z
       
  • Whose online reviews to trust? Understanding reviewer trustworthiness
           and its impact on business
    • Abstract: Publication date: Available online 27 January 2017
      Source:Decision Support Systems
      Author(s): Shankhadeep Banerjee, Samadrita Bhattacharyya, Indranil Bose
      Why do top movie reviewers receive invitations to exclusive screenings? Even popular technology bloggers get free new gadgets for reviewing. How much do these reviewers really matter for businesses? While the impact of online reviews on sales of products and services has been well established, not much literature is available on impact of reviewers for businesses. Source credibility theory expounds how a communication's persuasiveness is affected by the perceived credibility of its source. So, perceived trustworthiness of reviewers should influence acceptance of reviews, and consequently should have an indirect impact on sales. Using local business review data from Yelp.com, this paper successfully tests the premise that reviewer trustworthiness positively moderates the impact of review-based online reputation on business patronages. Given the importance of reviewer trustworthiness, the next logical question is – how to estimate and predict it, if no direct proxy is available? We propose a theoretical model with several reviewer characteristics (positivity, involvement, experience, reputation, competence, sociability) affecting reviewer trustworthiness, and find all factors to be significant using the robust regression method. Further, using these factors, a predictive classification of reviewers into high and low level of potential trustworthiness is done using logistic regression with nearly 83% accuracy. Our findings have several implications - firstly, businesses should focus on building a good review-based online reputation; secondly, they should encourage top trustworthy reviewers to review their products and services; and thirdly, trustworthy reviewers could be identified and ranked using reviewer characteristics.

      PubDate: 2017-02-07T22:45:06Z
       
  • Incorporating association rule networks in feature category-weighted naive
           Bayes model to support weaning decision making
    • Abstract: Publication date: Available online 26 January 2017
      Source:Decision Support Systems
      Author(s): Yuanyuan Gao, Anqi Xu, Paul Jen-Hwa Hu, Tsang-Hsiang Cheng
      Mechanical ventilation is an invasive intervention commonly used in the intensive care unit to assist patients' respirations. Physicians' decisions to wean patients from ventilation are critical: Effective weaning decisions improve patient care and well-being, but ineffective decisions can create serious severe consequences and complications. Data-driven approaches, enabled by appropriate data mining techniques, can support physicians' weaning decisions. A review of the existing techniques reveals several gaps. Specifically, most techniques assume that a feature can contribute equally to different outcome classes, overlook the “fuzzy region” issue, and assess the importance of individual features holistically rather than scrutinize the discriminant power of distinctive categories of a feature toward each decision outcome class. To address these backdrops, we propose an association rule network-based feature category-weighted naive Bayes method capable of dealing with the inherent challenges in weaning decision making. Our method analyzes feature category weights for each decision outcome by incorporating association rule learning with weighted network analysis, then applies a category-weighted naive Bayes model that can assign differential weights to various feature categories. The results of our empirical evaluation, including several prevalent techniques—artificial neural network (ANN), ANN with backward feature selection, support vector machine (SVM), and SVM with logistical regression based feature selection—indicate that the proposed method consistently outperforms all the benchmark techniques in terms of accuracy, precision, recall and F measure.

      PubDate: 2017-02-07T22:45:06Z
       
  • Agent Based Modelling as a Decision Support System for Shadow Accounting
    • Abstract: Publication date: Available online 21 January 2017
      Source:Decision Support Systems
      Author(s): Thomas Chesney, Stefan Gold, Alexander Trautrims
      We propose the use of agent based modelling to create a shadow account, that is, a secondary account of a business which is used to audit or verify the primary account. Such a model could be used to test the claims of industries and businesses. For example, the model could determine whether a business is generating enough funds to pay minimum wage. Parameters in the model can be set by observation or a range of values can be tested to determine points at which enough revenue could be generated. We illustrate the potential of agent based modelling as a tool for shadow accounting with a case study of a car wash business.

      PubDate: 2017-02-07T22:45:06Z
       
  • How visual cognition influences process model comprehension
    • Abstract: Publication date: Available online 18 January 2017
      Source:Decision Support Systems
      Author(s): Razvan Petrusel, Jan Mendling, Hajo A. Reijers
      Process analysts and other professionals extensively use process models to analyze business processes and identify performance improvement opportunities. Therefore, it is important that such models can be easily and properly understood. Previous research has mainly focused on two types of factors that are important in this context: (i) properties of the model itself, and (ii) properties of the model reader. The work in this paper aims at determining how the performance of subjects varies across different types of comprehension tasks, which is a new angle. To reason about the complexity of comprehension tasks we take a theoretical perspective that is grounded in visual cognition. We test our hypotheses using a free-simulation experiment that incorporates eye-tracking technology. We find that model-related and person-related factors are fully mediated by variables of visual cognition. Moreover, in comparison, visual cognition variables provide a significantly higher explanatory power for the duration and efficiency of comprehension tasks. These insights shed a new perspective on what influences sense-making of process models, shifting the attention from model and reader characteristics to the complexity of the problem-solving task at hand. Our work opens the way to investigate and develop effective strategies to support readers of process models, for example through the context-sensitive use of visual cues.

      PubDate: 2017-02-07T22:45:06Z
       
  • On Sensor-Based Solutions for Simultaneous Presence of Multiple RFID Tags
    • Abstract: Publication date: Available online 14 January 2017
      Source:Decision Support Systems
      Author(s): Selwyn Piramuthu, Robin Doss
      A majority of RFID authentication scenarios involve a single tag that is identified independent of other tags in the field of the reader. However, there are situations that necessitate simultaneous authentication of multiple tags as well as the verification of their simultaneous physical proximity to the reader. Juels (2004) introduced yoking proof for simultaneous authentication of multiple RFID tags. Several researchers have since then developed variants of yoking proof using both symmetric and asymmetric cryptography. Given that the ambient conditions are bound to be very similar for all objects that are in close physical proximity to one another, we critically evaluate the use of various relevant ambient conditions for this purpose. Based on our evaluation, we choose to use tag temperature and develop a variant of yoking proof protocol for simultaneous authentication of multiple tags.

      PubDate: 2017-01-17T19:27:00Z
       
  • A data mining based system for credit-card fraud detection in e-tail
    • Abstract: Publication date: Available online 7 January 2017
      Source:Decision Support Systems
      Author(s): Nuno Carneiro, Gonçalo Figueira, Miguel Costa
      Credit-card fraud leads to billions of dollars in losses for online merchants. With the development of machine learning algorithms, researchers have been finding increasingly sophisticated ways to detect fraud, but practical implementations are rarely reported. We describe the development and deployment of a fraud detection system in a large e-tail merchant. The paper explores the combination of manual and automatic classification, gives insights into the complete development process and compares different machine learning methods. The paper can thus help researchers and practitioners to design and implement data mining based systems for fraud detection or similar problems. This project has contributed not only with an automatic system, but also with insights to the fraud analysts for improving their manual revision process, which resulted in an overall superior performance.

      PubDate: 2017-01-10T19:14:09Z
       
  • Expediting analytical databases with columnar approach
    • Abstract: Publication date: Available online 6 January 2017
      Source:Decision Support Systems
      Author(s): Nenad Jukic, Boris Jukic, Abhishek Sharma, Svetlozar Nestorov, Benjamin Korallus
      The approaches and discussions given in this paper offer applicable solutions for a number of scenarios taking place in the contemporary world that are dealing with performance issues in development and use analytical databases for the support of both tactical and strategic decision making. The paper introduces a novel method for expediting the development and use of analytical databases that combines columnar database technology with an approach based on denormalizing data tables for analysis and decision support. This method improves the feasibility and quality of tactical decision making by making critical information more readily available. It also improves the quality of longer term strategic decision making by widening the range of feasible queries against the vast amounts of available information. The advantages include the improvements in the performance of the ETL process (the most common time-consuming bottleneck in most implementations of data warehousing for quality decision support) and in the performance of the individual analytical queries. These improvements in the critical decision support infrastructure are achieved without resulting in insurmountable storage-size increase requirements. The efficiencies and advantages of the introduced approach are illustrated by showing the application in two relevant real-world cases.

      PubDate: 2017-01-10T19:14:09Z
       
  • The interplay between free sampling and word of mouth in the online
           software market
    • Abstract: Publication date: Available online 5 January 2017
      Source:Decision Support Systems
      Author(s): Hong Chen, Wenjing Duan, Wenqi Zhou
      Free sampling in digital format has become a common business practice in the online market offering consumers first-hand experience with products, due to its low marginal cost and extensive online distribution. At the same time, online word of mouth (WOM) has also been a prevalent strategy on the Internet for increasing product visibility and providing trustworthy product information. Those two online marketing strategies are generally considered to stand alone by marketers and prior research. Nevertheless, by drawing on integrated information response theory as well as theories for explaining online consumers' review sharing, we argue that free sampling complements WOM in the online market by amplifying its sales effect and facilitating its implementation. We provide supportive empirical evidence through a Bayesian analysis of software free sampling on CNET Download.com (CNETD) and sales and WOM from Amazon.com over a 25-week data set. Our results show that adoptions of CNETD free sampling positively interact with Amazon WOM in influencing Amazon software sales. In addition, more adoptions of CNETD free sampling lead to a larger volume of Amazon WOM, and this impact is more significant for less popular products. These findings contribute to our understanding of free sampling in the online market such that, in addition to its direct sales effect, free sampling can also potentially affect sales through influencing online WOM. Therefore, we suggest that marketers evaluate the free sampling strategy by including its interplay with online WOM and apply low-cost free sampling to facilitate the relatively more expensive online WOM marketing strategy, especially for unpopular products.

      PubDate: 2017-01-10T19:14:09Z
       
  • Enabling effective workflow model reuse: A data-centric approach
    • Abstract: Publication date: January 2017
      Source:Decision Support Systems, Volume 93
      Author(s): Zhiyong Liu, Shaokun Fan, Harry Jiannan Wang, J. Leon Zhao
      With increasingly widespread adoption of workflow technology as a standard solution to business process management, a large number of workflow models have been put in use in companies in the era of electronic commerce. These workflow models form a valuable resource for workflow domain knowledge, which should be reused to support workflow model design. However, current workflow modeling approaches do not facilitate workflow model reuse as a fundamental requirement, leading to a research gap in effective workflow model reuse. In this paper, we propose a novel approach called Data-centric Workflow Model Reuse framework (DWMR) to provide a solution to workflow model reuse. DWMR compliments existing control-flow-focused workflow modeling approaches by explicitly storing workflow data information, such as data dependency, data task relationships, and data similarity scores. DWMR also provides data-driven workflow model search and composition algorithms to satisfy user query requirements by automatically combining multiple workflow models. We demonstrate the feasibility of the DWMR approach by applying it to data from a well-known industry workflow model repository.

      PubDate: 2017-01-10T19:14:09Z
       
  • An empirical investigation on the impact of XBRL adoption on information
           asymmetry: Evidence from Europe
    • Abstract: Publication date: January 2017
      Source:Decision Support Systems, Volume 93
      Author(s): Chunhui Liu, Xin (Robert) Luo, Fu Lee Wang
      Given the high cost of developing and implementing data standards such as eXtensible Business Reporting Language (XBRL), it is critical to assess their influences before they are adopted on a large scale. The European Parliament has voted for the new Transparency Directive that calls for the mandatory preparation of annual business performance reports in a single electronic reporting from January 1, 2020 based on a cost–benefit analysis by European Securities and Markets Authority (ESMA), with due reference to current and future technological options such as XBRL. Regulators in many other jurisdictions such as Canadian Securities Administrators are also assessing the costs and benefits from XBRL adoption. This paper informs such analysis by examining whether the expected benefit of information asymmetry reduction is realized through XBRL adoption in a European context. XBRL adoption among European non-financial firms is found to significantly increase market liquidity and thus reduce information asymmetry. The association is stronger for larger firms that have sufficient resources and expertise to properly implement the technology. The empirical findings also suggest that the association is stronger for non-high-technology firms whose financial statements affected by XBRL are more reliant upon by investors. Based on these findings, XBRL evidences a viable option as an electronic reporting format with effective implementation for businesses.

      PubDate: 2017-01-10T19:14:09Z
       
  • The technology and economic determinants of cryptocurrency exchange rates:
           The case of Bitcoin
    • Abstract: Publication date: Available online 26 December 2016
      Source:Decision Support Systems
      Author(s): Xin Li, Chong Alex Wang
      Cryptocurrencies, such as Bitcoin, have ignited intense discussions. Despite receiving extensive public attention, theoretical understanding is limited regarding the value of blockchain-based cryptocurrencies, as expressed in their exchange rates against traditional currencies. In this paper, we conduct a theory-driven empirical study of the Bitcoin exchange rate (against USD) determination, taking into consideration both technology and economic factors. To address co-integration in a mix of stationary and non-stationary time series, we use the autoregressive distributed lag (ARDL) model with a bounds test approach in the estimation. Meanwhile, to detect potential structural changes, we estimate our empirical model on two periods separated by the closure of Mt. Gox (one of the largest Bitcoin exchange markets). According to our analysis, in the short term, the Bitcoin exchange rate adjusts to changes in economic fundamentals and market conditions. The long-term Bitcoin exchange rate is more sensitive to economic fundamentals and less sensitive to technological factors after Mt. Gox closed. We also identify a significant impact of mining technology and a decreasing significance of mining difficulty in the Bitcoin exchange price determination.

      PubDate: 2017-01-10T19:14:09Z
       
  • A new web personalization decision-support artifact for utility-sensitive
           customer review analysis
    • Abstract: Publication date: Available online 23 November 2016
      Source:Decision Support Systems
      Author(s): Long Flory, Kweku-Muata Osei-Bryson, Manoj Thomas
      In recent years there has been increased consumer use of the vast array of online reviews. Given the increasingly high volume of such reviews, automatic analyses of their quality have become imperative. Not surprisingly, this situation has attracted the interest of researchers. However, prior approaches are insufficient to address the consumers' need for non-burdensome sense making of online reviews. This research attempts to close this gap by proposing novel design science artifacts (i.e. construct, architecture, algorithms and prototype) to address the consumers' need. We evaluate these artifacts using a set of experiments and hypothesis tests. The results validate the effectiveness and efficiency of the proposed artifacts. We demonstrate their practical utility and relevance using real world pilot experiments. This paper contributes theoretical knowledge to the review quality literature and, what we believe is the first exemplifier for adequately validating the solutions of review quality research.

      PubDate: 2016-12-01T03:42:21Z
       
  • A social route recommender mechanism for store shopping support
    • Abstract: Publication date: Available online 22 November 2016
      Source:Decision Support Systems
      Author(s): Yung-Ming Li, Lien-Fa Lin, Chun-Chih Ho
      To survive in a fiercely competitive business environment, it has become increasingly important for physical retailers to provide customers with services offering a better shopping experience. Many renovate and enlarge their shopping spaces to make their stores more enjoyable places to visit. The growth in social media and the use of mobile devices provide retailers with an opportunity to offer a context-aware guidance service to enhance customers' in-store shopping experience. In this research, by extracting and analysing shopping information (shopping context, visiting trajectory) and social information (user's interest, friends' influence), a contextual store shopping recommendation system is proposed to provide an appropriate route for first-time customers or those who are unfamiliar with a retailer's shopping space. Our experimental results show that the proposed model is effective in providing an appropriate shopping route and enhancing users' shopping experience, which could significantly improve the profitability and competitive advantage of the retailers.

      PubDate: 2016-12-01T03:42:21Z
       
  • The power of the “like” button: The impact of social media on
           box office
    • Abstract: Publication date: Available online 12 November 2016
      Source:Decision Support Systems
      Author(s): Chao Ding, Hsing Kenneth Cheng, Yang Duan, Yong Jin
      The mainstream research of social factors and box office performance has concentrated on post-consumption opinion mining and sentiment analysis, which are difficult to operationalize to the benefits of the industry practitioners whose objective is to maximize box office sales. In this study, we propose the Facebook “like” as an effective social marketing tool before the release of movies for several reasons. Firstly, people's prerelease “liking” of movies can be influenced by marketing campaigns. Secondly, the clicks of “likes” create social impact, as suggested by the Social Impact Theory, on moviegoers' consumption behaviors. And thirdly, Facebook “like” provides practitioners with real-time visible updates. By studying the impact of prerelease “likes” on box office sales, we not only contribute to the literature by offering a new social metric to evaluate the box office performance, but also provide the industry practitioners with quantitative support for the effectiveness of their social marketing activities. Our empirical results indicate that the prerelease “likes” exert a significantly positive impact on box office performance. More specifically, 1% increase in the number of “likes” in the one week prior to release is associated with an increase of the opening week box office by about 0.2%. As it approaches the release date, the prerelease “like” impact becomes stronger, suggesting that the latest prerelease “likes” are more effective in driving box office performance.

      PubDate: 2016-12-01T03:42:21Z
       
  • Sentiment analysis in financial texts
    • Abstract: Publication date: Available online 5 November 2016
      Source:Decision Support Systems
      Author(s): Samuel W.K. Chan, Mickey W.C. Chong
      The growth of financial texts in the wake of big data has challenged most organizations and brought escalating demands for analysis tools. In general, text streams are more challenging to handle than numeric data streams. Text streams are unstructured by nature, but they represent collective expressions that are of value in any financial decision. It can be both daunting and necessary to make sense of unstructured textual data. In this study, we address key questions related to the explosion of interest in how to extract insight from unstructured data and how to determine if such insight provides any hints concerning the trends of financial markets. A sentiment analysis engine (SAE) is proposed which takes advantage of linguistic analyses based on grammars. This engine extends sentiment analysis not only at the word token level, but also at the phrase level within each sentence. An assessment heuristic is applied to extract the collective expressions shown in the texts. Also, three evaluations are presented to assess the performance of the engine. First, several standard parsing evaluation metrics are applied on two treebanks. Second, a benchmark evaluation using a dataset of English movie review is conducted. Results show our SAE outperforms the traditional bag of words approach. Third, a financial text stream with twelve million words that aligns with a stock market index is examined. The evaluation results and their statistical significance provide strong evidence of a long persistence in the mood time series generated by the engine. In addition, our approach establishes grounds for belief that the sentiments expressed through text streams are helpful for analyzing the trends in a stock market index, although such sentiments and market indices are normally considered to be completely uncorrelated.

      PubDate: 2016-12-01T03:42:21Z
       
  • Predicting heart transplantation outcomes through data analytics
    • Abstract: Publication date: Available online 3 November 2016
      Source:Decision Support Systems
      Author(s): Ali Dag, Asil Oztekin, Ahmet Yucel, Serkan Bulur, Fadel M. Megahed
      Predicting the survival of heart transplant patients is an important, yet challenging problem since it plays a crucial role in understanding the matching procedure between a donor and a recipient. Data mining models can be used to effectively analyze and extract novel information from large/complex transplantation datasets. The objective of this study is to predict the 1-, 5-, and 9-year patient's graft survival following a heart transplant surgery via the deployment of analytical models that are based on four powerful classification algorithms (i.e. decision trees, artificial neural networks, support vector machines, and logistic regression). Since the datasets used in this study has a much larger number of survival cases than deaths for 1- and 5-year survival analysis and vice versa for 9-year survival analysis, random under sampling (RUS) and synthetic minority over-sampling (SMOTE) are employed to overcome the data-imbalance problems. The results indicate that logistic regression combined with SMOTE achieves the best classification for the 1-, 5-, and 9-year outcome prediction, with area-under-the-curve (AUC) values of 0.624, 0.676, and 0.838, respectively. By applying sensitivity analysis to the data analytical models, the most important predictors and their associated contribution for the 1-, 5-, and 9-year graft survival of heart transplant patients are identified. By doing so, variables, whose importance changes over time, are differentiated. Not only this proposed hybrid approach gives superior results over the literature but also the models and identification of the variables present important retrospective findings, which can be the basis for a prospective medical study.

      PubDate: 2016-12-01T03:42:21Z
       
  • Adapting sentiment lexicons to domain-specific social media texts
    • Abstract: Publication date: Available online 3 November 2016
      Source:Decision Support Systems
      Author(s): Shuyuan Deng, Atish P. Sinha, Huimin Zhao
      Social media has become the largest data source of public opinion. The application of sentiment analysis to social media texts has great potential, but faces great challenges because of domain heterogeneity. Sentiment orientation of words varies by content domain, but learning context-specific sentiment in social media domains continues to be a major challenge. The language domain poses another challenge since the language used in social media today differs significantly from that used in traditional media. To address these challenges, we propose a method to adapt existing sentiment lexicons for domain-specific sentiment classification using an unannotated corpus and a dictionary. We evaluate our method using two large developing corpora, containing 743,069 tweets related to the stock market and one million tweets related to political topics, respectively, and five existing sentiment lexicons as seeds and baselines. The results demonstrate the usefulness of our method, showing significant improvement in sentiment classification performance.

      PubDate: 2016-12-01T03:42:21Z
       
  • Privacy concerns for mobile app download: An elaboration likelihood model
           perspective
    • Abstract: Publication date: Available online 1 November 2016
      Source:Decision Support Systems
      Author(s): Jie Gu, Yunjie (Calvin) Xu, Heng Xu, Cheng Zhang, Hong Ling
      In the mobile age, protecting users' information from privacy-invasive apps becomes increasingly critical. To precaution users against possible privacy risks, a few Android app stores prominently disclose app permission requests on app download pages. Focusing on this emerging practice, this study investigates the effects of contextual cues (perceived permission sensitivity, permission justification and perceived app popularity) on Android users' privacy concerns, download intention, and their contingent effects dependent on users' mobile privacy victim experience. Drawing on Elaboration Likelihood Model, our empirical results suggest that perceived permission sensitivity makes users more concerned about privacy, while permission justification and perceived app popularity make them less concerned. Interestingly, users' mobile privacy victim experience negatively moderates the effect of permission justification. In particular, the provision of permission justification makes users less concerned about their privacy only for those with less mobile privacy victim experience. Results also reveal a positive effect of perceived app popularity and a negative effect of privacy concerns on download intention. This study provides a better understanding of Android users' information processing and the formation of their privacy concerns in the app download stage, and proposes and tests emerging privacy protection mechanisms including the prominent disclosure of app permission requests and the provision of permission justifications.

      PubDate: 2016-12-01T03:42:21Z
       
  • On computing probabilities of dismissal of 10b-5 securities class-action
           cases
    • Abstract: Publication date: Available online 29 October 2016
      Source:Decision Support Systems
      Author(s): Sumanta Singha, Steve Hillmer, Prakash P. Shenoy
      The main goal of this paper is to propose a probability model for computing probabilities of dismissal of 10b-5 securities class-action cases filed in United States Federal district courts. By dismissal, we mean dismissal with prejudice in response to the motion to dismiss filed by the defendants, and not eventual dismissal after the discovery process. The proposed probability model is a hybrid of two widely-used methods: logistic regression, and naïve Bayes. Using a dataset of 925 10b-5 securities class-action cases filed between 2002 and 2010, we show that the proposed hybrid model has the potential of computing better probabilities than either LR or NB models. By better, we mean lower root mean square errors of probabilities of dismissal. The proposed hybrid model uses the following features: allegations of generally accepted accounting principles violations, allegations of lack of internal control, bankruptcy filing during the class period, allegations of Section 11 violations of Securities Act of 1933, and short-term drop in stock price. Our model is useful for those insurance companies which underwrite Directors and Officers liability policy.

      PubDate: 2016-12-01T03:42:21Z
       
  • Computational intelligent hybrid model for detecting disruptive trading
           activity
    • Abstract: Publication date: Available online 23 September 2016
      Source:Decision Support Systems
      Author(s): Jia Zhai, Yi Cao, Yuan Yao, Xuemei Ding, Yuhua Li
      The term “disruptive trading behaviour” was first proposed by the U.S. Commodity Futures Trading Commission and is now widely used by US and EU regulation (MiFID II) to describe activities that create a misleading appearance of market liquidity or depth or an artificial price movement upward or downward according to their own purposes. Such activities, identified as a new form of financial fraud in EU regulations, damage the proper functioning and integrity of capital markets and are hence extremely harmful. While existing studies have explored this issue, they have, in most cases, either focused on empirical analysis of such cases or proposed detection models based on certain assumptions of the market. Effective methods that can analyse and detect such disruptive activities based on direct studies of trading behaviours have not been studied to date. There exists, accordingly, a knowledge gap in the literature. This paper seeks to address that gap and provides a hybrid model composed of two data-mining-based detection modules that effectively identify disruptive trading behaviours. The hybrid model is designed to work in an on-line scheme. The limit order stream is transformed, calculated and extracted as a feature stream. One detection module, “Single Order Detection,” detects disruptive behaviours by identifying abnormal patterns of every single trading order. Another module, “Order Sequence Detection,” approaches the problem by examining the contextual relationships of a sequence of trading orders using an extended hidden Markov model, which identifies whether sequential changes from the extracted features are manipulative activities (or not). Both models were evaluated using huge volumes of real tick data from the NASDAQ, which demonstrated that both are able to identify a range of disruptive trading behaviours and, furthermore, that they outperform the selected traditional benchmark models. Thus, this hybrid model is shown to make a substantial contribution to the literature on financial market surveillance and to offer a practical and effective approach for the identification of disruptive trading behaviour.

      PubDate: 2016-12-01T03:42:21Z
       
  • Utilizing customer satisfaction in ranking prediction for personalized
           cloud service selection
    • Abstract: Publication date: Available online 21 September 2016
      Source:Decision Support Systems
      Author(s): Shuai Ding, Zeyuan Wang, Desheng Wu, David L. Olson
      With the rapid development of cloud computing, cloud service has become an indispensable component of modern information systems where quality of service (QoS) has a direct impact on the system's performance and stability. While scholars have concentrated their efforts on the monitoring and evaluation of QoS in cloud computing, other service selection characteristics have been neglected, such as the scarcity of evaluation data and various customer needs. In this paper, we present a ranking-oriented prediction method that will assist in the process of discovering the cloud service candidates that have the highest customer satisfaction. This approach encompasses two basic functions: ranking similarity estimation and cloud service ranking prediction that takes into account customer's preference and expectation. The comparative experimental results show that the proposed method outperforms other competing methods.

      PubDate: 2016-12-01T03:42:21Z
       
 
 
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