for Journals by Title or ISSN
for Articles by Keywords
help
  Subjects -> BUSINESS AND ECONOMICS (Total: 3080 journals)
    - ACCOUNTING (90 journals)
    - BANKING AND FINANCE (261 journals)
    - BUSINESS AND ECONOMICS (1139 journals)
    - CONSUMER EDUCATION AND PROTECTION (24 journals)
    - COOPERATIVES (4 journals)
    - ECONOMIC SCIENCES: GENERAL (158 journals)
    - ECONOMIC SYSTEMS, THEORIES AND HISTORY (176 journals)
    - FASHION AND CONSUMER TRENDS (13 journals)
    - HUMAN RESOURCES (93 journals)
    - INSURANCE (23 journals)
    - INTERNATIONAL COMMERCE (126 journals)
    - INTERNATIONAL DEVELOPMENT AND AID (82 journals)
    - INVESTMENTS (27 journals)
    - LABOR AND INDUSTRIAL RELATIONS (43 journals)
    - MACROECONOMICS (15 journals)
    - MANAGEMENT (522 journals)
    - MARKETING AND PURCHASING (86 journals)
    - MICROECONOMICS (25 journals)
    - PRODUCTION OF GOODS AND SERVICES (137 journals)
    - PUBLIC FINANCE, TAXATION (34 journals)
    - TRADE AND INDUSTRIAL DIRECTORIES (2 journals)

BUSINESS AND ECONOMICS (1139 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: 33)
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: 126)
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: 24)
American Journal of Economics and Sociology     Hybrid Journal   (Followers: 28)
American Journal of Evaluation     Hybrid Journal   (Followers: 12)
American Journal of Finance and Accounting     Hybrid Journal   (Followers: 18)
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: 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: 28)
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: 3)
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: 23)
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: 318)
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: 8)
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 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: 22)
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: 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: 47)
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: 16)
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: 54)
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: 26)
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 Journal of Operations Research     Hybrid Journal   (Followers: 5)
Central European Journal of Public Policy     Open Access   (Followers: 1)
CESifo Economic Studies     Hybrid Journal   (Followers: 16)
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: 9)
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: 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  
Development     Full-text available via subscription   (Followers: 23)
Development and Change     Hybrid Journal   (Followers: 46)
Development and Learning in Organizations     Hybrid Journal   (Followers: 7)

        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  [3031 journals]
  • 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
       
  • Prediction from regional angst – A study of NFL sentiment in Twitter
           using technical stock market charting
    • Abstract: Publication date: Available online 29 April 2017
      Source:Decision Support Systems
      Author(s): Robert P. Schumaker, Chester S. Labedz, A. Tomasz Jarmoszko, Leonard L. Brown
      To predict NFL game outcomes, we examine the application of technical stock market techniques to sentiment gathered from social media. From our analysis we found a $14.84 average return per sentiment-based wager compared to a $12.21 average return loss on the entire 256 games of the 2015–2016 regular season if using an odds-only approach. We further noted that wagers on underdogs (i.e., the less favored teams) that exhibit a “golden cross” pattern in sentiment (e.g., the most recent sentiment signal crosses the longer baseline sentiment), netted a $48.18 return per wager on 41 wagers. These results show promise of cross-domain research and we believe that applying stock market techniques to sports wagering may open an entire new research area.

      PubDate: 2017-05-01T11:25:25Z
       
  • 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
       
  • The BIG CHASE: A decision support system for client acquisition applied to
           financial networks
    • Abstract: Publication date: Available online 22 April 2017
      Source:Decision Support Systems
      Author(s): Lara Quijano-Sanchez, Federico Liberatore
      Bank agencies daily store a huge volume of data regarding clients and their operations. This information, in turn, can be used for marketing purposes to acquire new clients or sell products to existing clients. A Decision Support System (DSS) can help a manager to decide the sequence of clients to contact to reach a designed target. In this paper we present the BIG CHASE, a DSS that translates bank data into a reliability graph. This graph models relationships based on a probability of traversal function that includes social measures. The proposed DSS, developed in close collaboration with Banco Santander, S.A., fits the parameters of the probability function to explicit solution evaluations given by experts by means of a specifically designed Projected Gradient Descent algorithm. The fitted probability function determines the reliabilities associated to the edges of the graph. An optimization procedure tailored to be efficient on very large sparse graphs with millions of nodes and edges identifies the most reliable sequence of clients that a manager should contact to reach a specific target. The BIG CHASE has been tested with a case study on real data that includes Banco Santander, S.A. 2015 Spain bank records. Experimental results show that the proposed DSS is capable of modeling the experts' evaluations into probability function with a small error.

      PubDate: 2017-05-01T11:25:25Z
       
  • Fodina: A robust and flexible heuristic process discovery technique
    • Abstract: Publication date: Available online 19 April 2017
      Source:Decision Support Systems
      Author(s): Seppe K.L.M. vanden Broucke, Jochen De Weerdt
      In this paper, we present Fodina, a process discovery technique with a strong focus on robustness and flexibility. To do so, we improve upon and extend an existing process discovery algorithm, namely Heuristics Miner. We have identified several drawbacks which impact the reliability of existing heuristic-based process discovery techniques and therefore propose a new algorithm which is shown to be better performing in terms of process model quality, adds the ability to mine duplicate tasks, and allows for flexible configuration options.
      Graphical abstract image

      PubDate: 2017-04-22T07:52:29Z
       
  • Predicting process behaviour using deep learning
    • Abstract: Publication date: Available online 17 April 2017
      Source:Decision Support Systems
      Author(s): Joerg Evermann, Jana-Rebecca Rehse, Peter Fettke
      Predicting business process behaviour is an important aspect of business process management. Motivated by research in natural language processing, this paper describes an application of deep learning with recurrent neural networks to the problem of predicting the next event in a business process. This is both a novel method in process prediction, which has largely relied on explicit process models, and also a novel application of deep learning methods. The approach is evaluated on two real datasets and our results surpass the state-of-the-art in prediction precision.

      PubDate: 2017-04-22T07:52:29Z
       
  • 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
       
  • Harnessing the frontline employee sensing of capabilities for decision
           support
    • Abstract: Publication date: Available online 21 March 2017
      Source:Decision Support Systems
      Author(s): Carina Antonia Hallin, Torben Juul Andersen, Sigbjørn Tveterås
      The ability to sense developments in operational (steady-state) and dynamic (growth) capabilities provides early signals about how the firm adapts its operations to ongoing changes in the environment. Frontline employees engage in the daily transactions and sense the firm's operating conditions and ability to deal with the environment that eventually will affect performance and strategic outcomes. The environmental sensing is a central cognitive feature and constitutes an information source for operations strategy decisions. Drawing on aggregated judgmental time-series forecasting techniques, this article develops a sensing instrument an employee-sensed operational conduct (ESOC) index for updated information as an essential decision support mechanism. This sensing capacity is firm-specific and difficult to replicate once in place and thus can provide a basis for sustainable competitive advantage.

      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
       
  • 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
       
  • 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
       
  • 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
       
  • 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
       
  • 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
       
  • 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
       
  • 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
       
  • 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
       
 
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
 
Home (Search)
Subjects A-Z
Publishers A-Z
Customise
APIs
Your IP address: 54.162.44.105
 
About JournalTOCs
API
Help
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-2016