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  Subjects -> BUSINESS AND ECONOMICS (Total: 3120 journals)
    - ACCOUNTING (92 journals)
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    - BUSINESS AND ECONOMICS (1155 journals)
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    - MANAGEMENT (524 journals)
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BUSINESS AND ECONOMICS (1155 journals)                  1 2 3 4 5 6 | Last

Showing 1 - 200 of 1566 Journals sorted alphabetically
4OR: A Quarterly Journal of Operations Research     Hybrid Journal   (Followers: 9)
Abacus     Hybrid Journal   (Followers: 12)
Accounting Forum     Hybrid Journal   (Followers: 24)
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   (Followers: 1)
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: 11)
AfricaGrowth Agenda     Full-text available via subscription   (Followers: 1)
African Affairs     Hybrid Journal   (Followers: 60)
African Development Review     Hybrid Journal   (Followers: 35)
African Journal of Business and Economic Research     Full-text available via subscription   (Followers: 1)
African Journal of Business Ethics     Open Access   (Followers: 6)
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: 144)
American Journal of Business     Hybrid Journal   (Followers: 15)
American Journal of Business and Management     Open Access   (Followers: 53)
American Journal of Business Education     Open Access   (Followers: 10)
American Journal of Economics and Business Administration     Open Access   (Followers: 26)
American Journal of Economics and Sociology     Hybrid Journal   (Followers: 27)
American Journal of Evaluation     Hybrid Journal   (Followers: 13)
American Journal of Finance and Accounting     Hybrid Journal   (Followers: 19)
American Journal of Health Economics     Full-text available via subscription   (Followers: 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: 25)
ANALES de la Universidad Central del Ecuador     Open Access   (Followers: 1)
Annales de l'Institut Henri Poincare (C) Non Linear Analysis     Full-text available via subscription   (Followers: 1)
Annals in Social Responsibility     Full-text available via subscription  
Annals of Finance     Hybrid Journal   (Followers: 28)
Annals of Operations Research     Hybrid Journal   (Followers: 8)
Annual Review of Economics     Full-text available via subscription   (Followers: 30)
Applied Developmental Science     Hybrid Journal   (Followers: 3)
Applied Economics     Hybrid Journal   (Followers: 48)
Applied Economics Letters     Hybrid Journal   (Followers: 29)
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: 312)
Asia Pacific Viewpoint     Hybrid Journal   (Followers: 1)
Asia-Pacific Journal of Business Administration     Hybrid Journal   (Followers: 3)
Asia-Pacific Journal of Operational Research     Hybrid Journal   (Followers: 3)
Asia-Pacific Management and Business Application     Open Access  
Asian Business Review     Open Access   (Followers: 2)
Asian Case Research Journal     Hybrid Journal   (Followers: 1)
Asian Development Review     Open Access   (Followers: 14)
Asian Economic Journal     Hybrid Journal   (Followers: 8)
Asian Economic Papers     Hybrid Journal   (Followers: 7)
Asian Economic Policy Review     Hybrid Journal   (Followers: 4)
Asian Journal of Accounting and Governance     Open Access   (Followers: 4)
Asian Journal of Business Ethics     Hybrid Journal   (Followers: 7)
Asian Journal of Social Sciences and Management Studies     Open Access   (Followers: 7)
Asian Journal of Sustainability and Social Responsibility     Open Access   (Followers: 1)
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: 27)
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)
Benefit : Jurnal Manajemen dan Bisnis     Open Access  
BER : Consumer Confidence Survey     Full-text available via subscription   (Followers: 4)
BER : Economic Prospects : An Executive Summary     Full-text available via subscription  
BER : Economic Prospects : Full Survey     Full-text available via subscription   (Followers: 2)
BER : Intermediate Goods Industries Survey     Full-text available via subscription   (Followers: 1)
BER : Manufacturing Survey : Full Survey     Full-text available via subscription   (Followers: 2)
BER : Motor Trade Survey     Full-text available via subscription   (Followers: 1)
BER : Retail Sector Survey     Full-text available via subscription   (Followers: 2)
BER : Retail Survey : Full Survey     Full-text available via subscription   (Followers: 2)
BER : Survey of Business Conditions in Building and Construction : An Executive Summary     Full-text available via subscription   (Followers: 4)
BER : Survey of Business Conditions in Manufacturing : An Executive Summary     Full-text available via subscription   (Followers: 3)
BER : Survey of Business Conditions in Retail : An Executive Summary     Full-text available via subscription   (Followers: 3)
BER : Trends : Full Survey     Full-text available via subscription   (Followers: 2)
BER : Wholesale Sector Survey     Full-text available via subscription   (Followers: 1)
Berkeley Business Law Journal     Free   (Followers: 10)
Bio-based and Applied Economics     Open Access   (Followers: 1)
Biodegradation     Hybrid Journal   (Followers: 1)
Biology Direct     Open Access   (Followers: 7)
Black Enterprise     Full-text available via subscription  
Board & Administrator for Administrators only     Hybrid Journal  
Border Crossing : Transnational Working Papers     Open Access   (Followers: 2)
Briefings in Real Estate Finance     Hybrid Journal   (Followers: 5)
British Journal of Industrial Relations     Hybrid Journal   (Followers: 34)
Brookings Papers on Economic Activity     Open Access   (Followers: 48)
Brookings Trade Forum     Full-text available via subscription   (Followers: 3)
BRQ Business Research Quarterly     Open Access   (Followers: 2)
Building Sustainable Legacies : The New Frontier Of Societal Value Co-Creation     Full-text available via subscription   (Followers: 1)
Bulletin of Economic Research     Hybrid Journal   (Followers: 17)
Bulletin of Geography. Socio-economic Series     Open Access   (Followers: 7)
Bulletin of Indonesian Economic Studies     Hybrid Journal   (Followers: 3)
Bulletin of the Dnipropetrovsk University. Series : Management of Innovations     Open Access   (Followers: 1)
Business & Entrepreneurship Journal     Open Access   (Followers: 18)
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: 9)
Business Information Review     Hybrid Journal   (Followers: 14)
Business Management and Strategy     Open Access   (Followers: 42)
Business Research     Hybrid Journal   (Followers: 2)
Business Strategy and the Environment     Hybrid Journal   (Followers: 13)
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: 18)
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: 58)
Cambridge Journal of Regions, Economy and Society     Hybrid Journal   (Followers: 11)
Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration     Hybrid Journal   (Followers: 1)
Canadian Journal of Economics/Revue Canadienne d`Economique     Hybrid Journal   (Followers: 28)
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: 12)
Case Studies in Business and Management     Open Access   (Followers: 8)
CBU International Conference Proceedings     Open Access   (Followers: 1)
Central European Business Review     Open Access   (Followers: 1)
Central European Journal of Operations Research     Hybrid Journal   (Followers: 5)
Central European Journal of Public Policy     Open Access   (Followers: 2)
CESifo Economic Studies     Hybrid Journal   (Followers: 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: 11)
China Economic Review     Hybrid Journal   (Followers: 9)
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: 7)
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: 6)
Computational Economics     Hybrid Journal   (Followers: 9)
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computer Law & Security Review     Hybrid Journal   (Followers: 16)
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: 6)
Corporate Philanthropy Report     Hybrid Journal   (Followers: 2)
Corporate Reputation Review     Hybrid Journal   (Followers: 4)
Creative and Knowledge Society     Open Access   (Followers: 10)
Creative Industries Journal     Hybrid Journal   (Followers: 9)
CRIS - Bulletin of the Centre for Research and Interdisciplinary Study     Open Access   (Followers: 1)
Crossing the Border : International Journal of Interdisciplinary Studies     Open Access   (Followers: 4)
Cuadernos de Administración (Universidad del Valle)     Open Access   (Followers: 1)
Cuadernos de Economía     Open Access   (Followers: 1)
Cuadernos de Economia - Latin American Journal of Economics     Open Access   (Followers: 1)
Cuadernos de Estudios Empresariales     Open Access   (Followers: 1)
Current Opinion in Creativity, Innovation and Entrepreneurship     Open Access   (Followers: 8)
De Economist     Hybrid Journal   (Followers: 12)
Decision Analysis     Full-text available via subscription   (Followers: 8)
Decision Sciences     Hybrid Journal   (Followers: 16)
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  [3044 journals]
  • Comparing alternatives to account for unobserved heterogeneity in direct
           marketing models
    • Abstract: Publication date: Available online 8 September 2017
      Source:Decision Support Systems
      Author(s): Nadine Schröder, Harald Hruschka
      We are dealing with mailing decisions of a direct marketing company and focus on assessing three alternative approaches to model unobserved heterogeneity, which are based on finite mixtures, continuous mixtures, and a mixture of Dirichlet processes (MDP), respectively. Models are estimated by Markov Chain Monte Carlo (MCMC) simulation. Based on Pseudo Bayes factors (PsBF), we find that a finite mixture model turns out to be superior both to models based on either a MDP or a continuous mixture. Whereas the MDP finds similar estimates compared to the finite mixture approach, estimates of the continuous mixture differ for some variables. According to the finite mixture, type of mailing has an effect on purchase behavior. In addition, some customers show supersaturation effects of mailings. Due to different coefficient estimates, managerial implications differ depending on which model they relate. In particular, a continuous mixture model would recommend more mailings than a finite mixture approach.

      PubDate: 2017-09-13T12:48:09Z
  • A game theoretic analysis of multichannel retail in the context of
    • Abstract: Publication date: Available online 6 September 2017
      Source:Decision Support Systems
      Author(s): Preetam Basu, Shounak Basak, Balram Avittathur, Soumyen Sikdar
      “Showrooming” as a market phenomenon in multichannel retailing has grown in importance over the last few years. Consumers nowadays use the brick-and-mortar store to research about a product before purchasing it online. This leads to the offline stores being converted into showrooms for the online retailers. Therefore, popular notion suggests that showrooming should benefit the online retailer. In this paper, our objective is to analyze multichannel retailing under showrooming and determine the veracity of the popularly held belief. We develop a series of game theoretic models that involve a traditional retailer and an online retailer under showrooming. We determine optimal pricing strategies for each player and also the sales effort expended by the traditional retailer based on the interplay of “power” dynamics, market potential and the impact of showrooming. Our results indicate that profit for the traditional as well as the online retailer decreases with rising levels of showrooming. Hence, high levels of showrooming are not beneficial from the perspective of the online retailer. Thus, contrary to popular intuition, lessening of showrooming benefits not only the traditional retailer but also the online retailer. Nevertheless, from the consumer's point of view showrooming is beneficial as it leads to overall reduction in retail prices. We also analyze the viability of a click-and-mortar model as a strategy of the traditional retailer to counter the threat of showrooming.

      PubDate: 2017-09-07T13:30:44Z
  • A context-aware researcher recommendation system for university-industry
           collaboration on R&D projects
    • Abstract: Publication date: Available online 5 September 2017
      Source:Decision Support Systems
      Author(s): Qi Wang, Jian Ma, Xiuwu Liao, Wei Du
      University-industry collaboration plays an important role in the success of R&D projects. One of the main challenges of university-industry collaboration is the identification of suitable partners. Due to the information asymmetry problem, it is difficult for companies to identify researchers from universities for collaboration on their R&D projects. Various expert recommendation systems (e.g., question responder recommenders and co-author recommenders) have been proposed, but they fail to characterize companies' needs in identifying suitable researchers. This paper proposes a context-aware researcher recommendation system to encourage university-industry collaboration on industrial R&D projects. The system has two modules: an offline preparation module and an online recommendation module. In the offline preparation module, candidate researchers are identified in advance to improve the efficiency of the context-aware recommendation. In the online recommendation module, contextual information (i.e., R&D projects) is captured from a social network platform, and then, candidate researchers are recommended based on a contextual trust analysis model, which combines the expertise relevance, quality, and trust relations of researchers to profile and evaluate candidate researchers for the R&D project collaboration. An offline experiment and a user study are conducted to evaluate the effectiveness of the proposed recommendation system. The results show that the proposed method achieves better performance than the baseline methods.

      PubDate: 2017-09-07T13:30:44Z
  • Early detection of university students with potential difficulties
    • Abstract: Publication date: September 2017
      Source:Decision Support Systems, Volume 101
      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: 2017-09-02T02:41:25Z
  • An analytic approach to assessing organizational citizenship behavior
    • Abstract: Publication date: Available online 1 September 2017
      Source:Decision Support Systems
      Author(s): Ozlem Ayaz Arda, Dursun Delen, Ekrem Tatoglu, Selim Zaim
      This study examines the organizational citizenship behavior (OCB) of employees by designing and developing an analytic network process (ANP) methodology. The viability of the proposed methodology is demonstrated via the sales representatives of Beko, a brand name of domestic appliance and consumer electronics giant of Arçelik Inc., controlled by Koç Group. We first develop a conceptual framework based on qualitative research methods – in-depth interviews and focus group sessions. We employ the principles of ANP methodology to examine and discover the inter-relationships among the OCBs. This process results in a descriptive model that encapsulates the findings from both qualitative and analytics methods. Necessity, altruism, departmental, compliance, and independence are the underlying dimensions of OCBs found to be the most influential/important. The key novelty of this study resides in designing and developing a prescriptive analytics (i.e. ANP) methodology to objectively evaluate the OCBs, which is rare in the area of organizational behavior (a managerial field of study that have been dominated by traditional statistical methods), and thus serves as a useful contribution/augmentation to the business/managerial research methods, and also extends the reach/coverage of analytics-based decision support systems research and practice into a new direction.

      PubDate: 2017-09-02T02:41:25Z
  • Solvency prediction for small and medium enterprises in banking
    • Abstract: Publication date: Available online 14 August 2017
      Source:Decision Support Systems
      Author(s): Silvia Figini, Federico Bonelli, Emanuele Giovannini
      This paper describes novel approaches to predict default for SMEs. Multivariate outlier detection techniques based on Local Outlier Factor are proposed to improve the out of sample performance of parametric and non-parametric models for credit risk estimation. The models are tested on a real data set provided by UniCredit Bank. The results at hand confirm that our proposal improves the results in terms of predictive capability and support financial institutions to make decision. Single and ensemble models are compared and in particular, inside parametric models, the generalized extreme value regression model is proposed as a suitable competitor of the logistic regression.

      PubDate: 2017-09-02T02:41:25Z
  • Online to offline (O2O) service recommendation method based on
           multi-dimensional similarity measurement
    • Abstract: Publication date: Available online 10 August 2017
      Source:Decision Support Systems
      Author(s): Yuchen Pan, Desheng Wu, David L. Olson
      With the rapid development of information technology, consumers are able to search for and buy services or products online, and then consume them in an offline store. This emerging ecommerce model is called online to offline (O2O) service, which has attracted business and academic attention. The large number of O2O services on the Internet creates a scalability problem, creating massive but highly sparse matrices relating customers to items purchased. In this paper, we proposed a novel O2O service recommendation method based on multi-dimensional similarity measurements. This approach encompasses three similarity measures: collaborative similarity, preference similarity and trajectory similarity. Experimental results show that a combination of multiple similarity measures performs better than any one single similarity measure. We also find that trajectory similarity performs better than the rating-based similarity metrics (collaborative similarity and preference similarity) in sparse matrices.

      PubDate: 2017-09-02T02:41:25Z
  • Movie aspects, tweet metrics, and movie revenues: The influence of iOS vs.
    • Abstract: Publication date: Available online 8 August 2017
      Source:Decision Support Systems
      Author(s): David Zimbra, Kumar R. Sarangee, Rupinder P. Jindal
      Microblogging word of mouth (MWOM) using Twitter has been found to impact the success of experiential products such as movies. However, the influence of the type of device or platform used for tweeting (iOS or Android) on the relationship between well-established tweet metrics - valence, volume, and time period of tweeting - and movie performance is not yet known. Furthermore, it is not known if users of these platforms differ in the aspects of movies they discuss and how that may influence tweet metrics. In this study, we investigated these gaps by analyzing more than four million tweets for 29 movies from both iOS and Android users and conducted a robustness check on another 8 movies. Results from mixed model estimations show that valence of tweets on Android before a movie's release and volume of tweets on iOS after the release significantly influence the revenues of a movie. Results also show that mentions of director and script are more important in the case of Android users whereas mentions of production and music are more important in the case of iOS users. Finally, results show that it may be more productive for movie studios and advertisers to reach the more prolific Twitter users on Android but relatively newer Twitter users on iOS. These findings have significant implications for movie studios as well as mobile advertisers to target their promotions to these platform users accordingly.

      PubDate: 2017-09-02T02:41:25Z
  • Decision support to customer decrement detection at the early stage for
           theme parks
    • Abstract: Publication date: Available online 2 August 2017
      Source:Decision Support Systems
      Author(s): Yen Chung-en, Chun-Che Huang, Marian (Dan-Wei) Wen, Wang You-Ping
      In recent years, a theme park drives significant attention in tourism industry due to the provision of quality and integrated service, and issuing annual pass cards help the theme park to differentiate long-term customers from short-term ones. Customer Value Analysis is demanded for theme parks to identify potential customers as well as to appraise customer value through the setting of the annual pass. Moreover, customer value often alters from time to time since theme park industry is relevantly competitive and innovation demanded than other industries, and customer preferences are frequently changed. This study provides an early warning system to support the theme park to detect, monitor and analyze the changes of customer value. By applying the aggregated approach based on Rough Set Theory and Recency, Frequency and Monetary architecture, the tourist satisfaction levels can be captured after the aforementioned approach is executed. In addition, the rule comparison approach is contributed to predicting customer behavior from technical viewpoint. This study aims at providing an early correction strategy for the theme park to avoid losing VIP customers and identify latent customers.

      PubDate: 2017-08-03T04:39:01Z
  • Data-driven Process Prioritization in Process Networks
    • Abstract: Publication date: August 2017
      Source:Decision Support Systems, Volume 100
      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: 2017-08-03T04:39:01Z
  • The Structured Process Modeling Method (SPMM) what is the best way for me
           to construct a process model'
    • Abstract: Publication date: August 2017
      Source:Decision Support Systems, Volume 100
      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: 2017-08-03T04:39:01Z
  • Discovering work prioritisation patterns from event logs
    • Abstract: Publication date: August 2017
      Source:Decision Support Systems, Volume 100
      Author(s): Suriadi Suriadi, Moe T. Wynn, Jingxin Xu, Wil M.P. van der 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: 2017-08-03T04:39:01Z
  • Fodina: A robust and flexible heuristic process discovery technique
    • Abstract: Publication date: August 2017
      Source:Decision Support Systems, Volume 100
      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: 2017-08-03T04:39:01Z
  • Retrieving batch organisation of work insights from event logs
    • Abstract: Publication date: August 2017
      Source:Decision Support Systems, Volume 100
      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: 2017-08-03T04:39:01Z
  • Predicting process behaviour using deep learning
    • Abstract: Publication date: August 2017
      Source:Decision Support Systems, Volume 100
      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: 2017-08-03T04:39:01Z
  • Bankruptcy prediction for SMEs using relational data
    • Abstract: Publication date: Available online 18 July 2017
      Source:Decision Support Systems
      Author(s): Ellen Tobback, Tony Bellotti, Julie Moeyersoms, Marija Stankova, David Martens
      Bankruptcy prediction has been a popular and challenging research area for decades. Most prediction models are built using financial figures, stock market data and firm specific variables. We complement such traditional low-dimensional data with high-dimensional data on the company’s directors and managers in the prediction models. This information is used to build a network between small and medium-sized enterprises (SMEs), where two companies are related if they share a director or high-level manager. A smoothed version of the weighted-vote relational neighbour classifier is applied on the network and transforms the relationships between companies into bankruptcy prediction scores, thereby assuming that a company is more likely to file for bankruptcy if one of the related companies in its network has already failed. An ensemble model is built that combines the relational model’s output scores with structured data and is applied on two data sets of Belgian and UK SMEs. We find that the relational model gives improved predictions over a simple financial model when detecting the riskiest firms. The largest performance increase is found when the relational and financial data are combined, confirming the complementary nature of both data types.

      PubDate: 2017-07-22T21:40:49Z
  • Emergency Response Community Effectiveness: A simulation modeler for
           comparing Emergency Medical Services with smartphone-based Samaritan
    • Abstract: Publication date: Available online 14 July 2017
      Source:Decision Support Systems
      Author(s): Michael Khalemsky, David G. Schwartz
      Mobile emergency response applications involving location-based alerts and physical response of networked members increasingly appear on smartphones to address a variety of emergencies. EMS (Emergency Medical Services) administrators, policy makers, and other decision makers need to determine when such systems present an effective addition to traditional Emergency Medical Services. We developed a software tool, the Emergency Response Community Effectiveness Modeler (ERCEM) that accepts parameters and compares the potential smartphone-initiated Samaritan/member response to traditional EMS response for a specific medical condition in a given geographic area. This study uses EMS data from the National EMS Information System (NEMSIS) and analyses geographies based on Rural-Urban Commuting Area (RUCA) and Economic Research Service (ERS) urbanicity codes. To demonstrate ERCEM's capabilities, we input a full year of NEMSIS data documenting EMS response incidents across the USA. We conducted three experiments to explore anaphylaxis, hypoglycemia and opioid overdose events across different population density characteristics, with further permutations to consider a series of potential app adoption levels, Samaritan response behaviors, notification radii, etc. Our model emphasizes how medical condition, prescription adherence levels, community network membership, and population density are key factors in determining the effectiveness of Samaritan-based Emergency Response Communities (ERC). We show how the efficacy of deploying mHealth apps for emergency response by volunteers can be modelled and studied in comparison to EMS. A decision maker can utilize ERCEM to generate a detailed simulation of different emergency response scenarios to assess the efficacy of smartphone-based Samaritan response applications in varying geographic regions for a series of different conditions and treatments.

      PubDate: 2017-07-22T21:40:49Z
  • A systematic literature review and critical assessment of model-driven
           decision support for IT outsourcing
    • Abstract: Publication date: Available online 8 July 2017
      Source:Decision Support Systems
      Author(s): Mohammad Mehdi Rajaeian, Aileen Cater-Steel, Michael Lane
      Information technology outsourcing (ITO) is a widely-adopted strategy for IT governance. The decisions involved in IT outsourcing are complicated. Empirical research confirms that a rational and formalized decision-making process results in better decision outcomes. However, formal and systematic approaches for making ITO decisions appear to be scarce in practice. To support organizational decision-makers involved in IT outsourcing (including cloud sourcing), researchers have suggested several decision support methods. To date there is no comprehensive review and assessment of the research in this domain. In this study 133 model-driven decision support research articles for IT outsourcing and cloud sourcing were identified through a systematic literature review and assessed based on a highly-regarded research framework. An analysis of these 133 research articles suggested a range of Multiple Criteria Decision Making (MCDM), optimization and simulation methods to support different IT outsourcing decisions. Our findings raise concerns about the limited use of reference design theories, and the lack of validation and naturalistic evaluation of the decision support artifacts reported in ITO decision support literature. Based on the review, we provide future research directions, as well as a number of recommendations to enhance the rigor and relevance of ITO Decision Support Systems research.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

      PubDate: 2017-05-12T05:54:15Z
  • 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
  • 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
  • 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
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