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  Subjects -> BUSINESS AND ECONOMICS (Total: 3182 journals)
    - ACCOUNTING (97 journals)
    - BANKING AND FINANCE (270 journals)
    - BUSINESS AND ECONOMICS (1165 journals)
    - COOPERATIVES (4 journals)
    - ECONOMIC SCIENCES: GENERAL (175 journals)
    - HUMAN RESOURCES (95 journals)
    - INSURANCE (24 journals)
    - INTERNATIONAL COMMERCE (128 journals)
    - INVESTMENTS (27 journals)
    - MACROECONOMICS (15 journals)
    - MANAGEMENT (534 journals)
    - MARKETING AND PURCHASING (92 journals)
    - MICROECONOMICS (24 journals)
    - PUBLIC FINANCE, TAXATION (35 journals)

BUSINESS AND ECONOMICS (1165 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: 10)
Abacus     Hybrid Journal   (Followers: 13)
Accounting Forum     Hybrid Journal   (Followers: 25)
Acta Amazonica     Open Access   (Followers: 5)
Acta Commercii     Open Access   (Followers: 4)
Acta Oeconomica     Full-text available via subscription   (Followers: 2)
Acta Scientiarum. Human and Social Sciences     Open Access   (Followers: 7)
Acta Universitatis Danubius. Œconomica     Open Access   (Followers: 3)
Acta Universitatis Nicolai Copernici Zarządzanie     Open Access   (Followers: 4)
AD-minister     Open Access   (Followers: 3)
ADR Bulletin     Open Access   (Followers: 6)
Advances in Developing Human Resources     Hybrid Journal   (Followers: 23)
Advances in Economics and Business     Open Access   (Followers: 11)
AfricaGrowth Agenda     Full-text available via subscription   (Followers: 1)
African Affairs     Hybrid Journal   (Followers: 61)
African Development Review     Hybrid Journal   (Followers: 33)
African Journal of Business and Economic Research     Full-text available via subscription   (Followers: 3)
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: 6)
Alphanumeric Journal : The Journal of Operations Research, Statistics, Econometrics and Management Information Systems     Open Access   (Followers: 5)
American Economic Journal : Applied Economics     Full-text available via subscription   (Followers: 179)
American Journal of Business     Hybrid Journal   (Followers: 16)
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: 29)
American Journal of Evaluation     Hybrid Journal   (Followers: 14)
American Journal of Finance and Accounting     Hybrid Journal   (Followers: 21)
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: 28)
ANALES de la Universidad Central del Ecuador     Open Access   (Followers: 2)
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: 10)
Annual Review of Economics     Full-text available via subscription   (Followers: 32)
Applied Developmental Science     Hybrid Journal   (Followers: 3)
Applied Economics     Hybrid Journal   (Followers: 46)
Applied Economics Letters     Hybrid Journal   (Followers: 29)
Applied Economics Quarterly     Full-text available via subscription   (Followers: 10)
Applied Financial Economics     Hybrid Journal   (Followers: 24)
Applied Mathematical Finance     Hybrid Journal   (Followers: 7)
Applied Stochastic Models in Business and Industry     Hybrid Journal   (Followers: 5)
Arab Economic and Business Journal     Open Access   (Followers: 3)
Archives of Business Research     Open Access   (Followers: 6)
Arena Journal     Full-text available via subscription   (Followers: 1)
Argomenti. Rivista di economia, cultura e ricerca sociale     Open Access   (Followers: 3)
ASEAN Economic Bulletin     Full-text available via subscription   (Followers: 5)
Asia Pacific Business Review     Hybrid Journal   (Followers: 6)
Asia Pacific Journal of Human Resources     Hybrid Journal   (Followers: 327)
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: 3)
Asian Journal of Business Ethics     Hybrid Journal   (Followers: 8)
Asian Journal of Social Sciences and Management Studies     Open Access   (Followers: 6)
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: 10)
Australasian Journal of Regional Studies, The     Full-text available via subscription   (Followers: 1)
Australian Cottongrower, The     Full-text available via subscription   (Followers: 1)
Australian Economic Papers     Hybrid Journal   (Followers: 31)
Australian Economic Review     Hybrid Journal   (Followers: 6)
Australian Journal of Maritime and Ocean Affairs     Hybrid Journal   (Followers: 9)
Balkan Region Conference on Engineering and Business Education     Open Access   (Followers: 1)
Baltic Journal of Real Estate Economics and Construction Management     Open Access   (Followers: 2)
Banks in Insurance Report     Hybrid Journal   (Followers: 1)
BBR - Brazilian Business Review     Open Access   (Followers: 4)
Benchmarking : An International Journal     Hybrid Journal   (Followers: 10)
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: 4)
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: 49)
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: 19)
Business and Management Studies     Open Access   (Followers: 10)
Business and Politics     Hybrid Journal   (Followers: 8)
Business and Professional Communication Quarterly     Hybrid Journal   (Followers: 7)
Business and Society Review     Hybrid Journal   (Followers: 5)
Business Economics     Hybrid Journal   (Followers: 8)
Business Ethics: A European Review     Hybrid Journal   (Followers: 17)
Business Horizons     Hybrid Journal   (Followers: 7)
Business Information Review     Hybrid Journal   (Followers: 14)
Business Management and Strategy     Open Access   (Followers: 43)
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: 59)
Cambridge Journal of Regions, Economy and Society     Hybrid Journal   (Followers: 10)
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: 29)
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: 16)
Case Studies in Business and Management     Open Access   (Followers: 10)
CBU International Conference Proceedings     Open Access   (Followers: 1)
Central European Business Review     Open Access   (Followers: 1)
Central European Journal of Operations Research     Hybrid Journal   (Followers: 5)
Central European Journal of Public Policy     Open Access   (Followers: 2)
CESifo Economic Studies     Hybrid Journal   (Followers: 17)
Chain Reaction     Full-text available via subscription  
Challenge     Full-text available via subscription   (Followers: 4)
China & World Economy     Hybrid Journal   (Followers: 15)
China : An International Journal     Full-text available via subscription   (Followers: 19)
China Economic Journal: The Official Journal of the China Center for Economic Research (CCER) at Peking University     Hybrid Journal   (Followers: 12)
China Economic Review     Hybrid Journal   (Followers: 8)
China Finance Review International     Hybrid Journal   (Followers: 5)
China Nonprofit Review     Hybrid Journal   (Followers: 3)
China perspectives     Open Access   (Followers: 12)
Chinese Economy     Full-text available via subscription  
Ciência & Saúde Coletiva     Open Access   (Followers: 2)
CLIO América     Open Access   (Followers: 1)
Cliometrica     Hybrid Journal   (Followers: 4)
COEPTUM     Open Access  
Community Development Journal     Hybrid Journal   (Followers: 25)
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: 12)
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: 5)
Corporate Communications An International Journal     Hybrid Journal   (Followers: 7)
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: 2)
Cuadernos de Economía     Open Access   (Followers: 2)
Cuadernos de Economia - Latin American Journal of Economics     Open Access   (Followers: 2)
Cuadernos de Estudios Empresariales     Open Access   (Followers: 2)
Current Opinion in Creativity, Innovation and Entrepreneurship     Open Access   (Followers: 10)
De Economist     Hybrid Journal   (Followers: 12)
Decision Analysis     Full-text available via subscription   (Followers: 10)
Decision Sciences     Hybrid Journal   (Followers: 18)
Decision Support Systems     Hybrid Journal   (Followers: 16)
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]   [16 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0167-9236
   Published by Elsevier Homepage  [3120 journals]
  • Cannibalization and competition effects on a manufacturer's retail channel
           strategies: Implications on an omni-channel business model
    • Abstract: Publication date: Available online 31 January 2018
      Source:Decision Support Systems
      Author(s): Jae-Cheol Kim, Se-Hak Chun
      This paper analyzes two effects caused by “channel conflict”, which occurs when firms newly add a direct online channel via the Internet or a mobile device. The first is an “intra-cannibalization effect” between the firms' existing retail channel and the new online channel, and the second is the “inter-competition effect” between manufacturers and retailers in the supply chain. In particular, this paper investigates a manufacturer's retailing channel strategy considering the relative market power between a manufacturer and a retailer in the supply chain, which has been rarely considered in previous studies. This paper shows the manufacturer's channel strategies: (i) if customers are very heterogeneous with regard to their receptiveness to online shopping, the manufacturer may use a multi-channel strategy. (ii) if the customer sector becomes homogeneous, the manufacturer will become more willing to adopt an omni-channel strategy. (iii) if customers are neither similar nor very different, the manufacturer uses a brick-and-mortar strategy. This paper also shows results on the issue of channel conflict in terms of market power: (i) the retailer may voluntarily limit its market power and thus, self-created competition in the retail market alleviates the problem of double-markup to some extent. (ii) the manufacturer can use an online channel when inter-competition effect becomes severe.

      PubDate: 2018-02-05T13:41:08Z
  • Channel integration quality, perceived fluency and omnichannel service
           usage: The moderating roles of internal and external usage experience
    • Abstract: Publication date: Available online 31 January 2018
      Source:Decision Support Systems
      Author(s): Xiao-Liang Shen, Yang-Jun Li, Yongqiang Sun, Nan Wang
      Along with the rapid development of in-store technology, multichannel service is being shifted to omnichannel. By integrating different parallel channels, omnichannel service delivers customers an integrated, seamless and consistent cross-channel shopping experience. To better understand this emerging phenomenon, this study intends to explore the potential drivers of omnichannel service usage. Drawing upon Wixom & Todd framework, this study develops a research model by including object-based beliefs (i.e., channel integration quality) and behavioral beliefs (i.e., perceived fluency). In addition, behavior-based traits (i.e., internal and external usage experience) are hypothesized as moderating the effects of behavioral beliefs on usage behavior. Using an online survey of 401 omnichannel users, the findings demonstrate that channel integration quality significantly affects perceived fluency across different channels, which in turn explains 55% of the variance in omnichannel service usage. The results also show that internal usage experience weakens, whereas external usage experience enhances the effect of perceived fluency on omnichannel service usage. Limitations and implications of this study are further discussed.

      PubDate: 2018-02-05T13:41:08Z
  • Mindfully going omni-channel: An economic decision model for evaluating
           omni-channel strategies
    • Abstract: Publication date: Available online 31 January 2018
      Source:Decision Support Systems
      Author(s): Sabiölla Hosseini, Marieluise Merz, Maximilian Röglinger, Annette Wenninger
      In the digital age, customers want to define on their own how to interact with organizations during their customer journeys. Thus, many organizations struggle to implement an omni-channel strategy (OCS) that meets their customers' channel preferences and can be operated efficiently. Despite this high practical need, research on omni-channel management predominantly takes a descriptive perspective. What is missing is prescriptive knowledge that guides organizations in the valuation and selection of an appropriate OCS. Most existing studies investigate single facets of omni-channel management in detail while neglecting the big picture. They also require customer journeys to follow sequential and organization-defined purchase decision processes. To address this research gap, we propose an economic decision model that considers online and offline channels, the opening and closing of channels, non-sequential customer journeys, and customers' channel preferences. Drawing from the principles of value-based management, the decision model recommends choosing the OCS with the highest contribution to an organization's long-term firm value. We applied and validated the decision model based on real-world data from a German bank.

      PubDate: 2018-02-05T13:41:08Z
  • Omnichannel businesses in the publishing and retailing industries:
           Synergies and tensions between coexisting online and offline business
    • Abstract: Publication date: Available online 31 January 2018
      Source:Decision Support Systems
      Author(s): Martin Wiener, Nadja Hoßbach, Carol Saunders
      Since the emergence of the Internet, many brick-and-mortar companies from various industries have established an online business model (BM) alongside their traditional offline BM. Despite the increasing coexistence of online and offline BMs within a single company, however, most prior research has focused on studying online and offline BMs in isolation. Consequently, still little is known about the interplay of dual BMs in omnichannel businesses. We address this research gap through an empirical investigation of the synergies and tensions that arise from coexisting online and offline BMs as well as the factors that influence the emergence of such synergies and tensions. Drawing on a series of six case studies with three publishers and three retailers, we identify an extended set of BM synergies and tensions, which concern all major BM dimensions. In addition, our case analysis reveals that companies are able to exploit different synergies, but also face different tensions between their online and offline BMs. The observed differences can be traced back to the level of online-offline BM integration, online-offline product distinctions (e.g., in terms of product content and publication cycles), and general organization context factors (e.g., offline brand strength, organization structure). By uncovering both the benefits and the complexity of running online and offline BMs in parallel, our study contributes to the theoretical understanding of omnichannel businesses, and provides managers with practical guidance on how to design, integrate, and manage their dual BMs successfully.

      PubDate: 2018-02-05T13:41:08Z
  • Automatic feature weighting for improving financial Decision Support
    • Abstract: Publication date: Available online 31 January 2018
      Source:Decision Support Systems
      Author(s): Yosimar Oswaldo Serrano-Silva, Yenny Villuendas-Rey, Cornelio Yáñez-Márquez
      We propose a novel methodology for improving financial Decision Support Systems (DSS) through automatic feature weighting. Using this methodology, we show that automatic feature weighting leads to a significant improvement in the performance of decision-making algorithms over financial data, which are the key of financial DSS. The statistical analysis carried out shows that metaheuristic algorithms are good for automatic feature weighting, and that Differential Evolution (DE) offers a good trade-off between decision-making performance and computational cost. We believe these results contribute to the development of novel financial DSS.
      Graphical abstract image

      PubDate: 2018-02-05T13:41:08Z
  • Detection of Online Phishing Email using Dynamic Evolving Neural Network
           Based on Reinforcement Learning
    • Abstract: Publication date: Available online 17 January 2018
      Source:Decision Support Systems
      Author(s): Sami Smadi, Nauman Aslam, Li Zhang
      Despite state-of-the-art solutions to detect phishing attacks, there is still a lack of accuracy for the detection systems in the online mode which leading to loopholes in web-based transactions. In this research, a novel framework is proposed which combines a neural network with reinforcement learning to detect phishing attacks in the online mode for the first time. The proposed model has the ability to adapt itself to produce a new phishing email detection system that reflects changes in newly explored behaviours, which is accomplished by adopting the idea of reinforcement learning to enhance the system dynamically over time. The proposed model solve the problem of limited dataset by automatically add more emails to the offline dataset in the online mode. A novel algorithm is proposed to explore any new phishing behaviours in the new dataset. Through rigorous testing using the well-known data sets, we demonstrate that the proposed technique can handle zero-day phishing attacks with high performance levels achieving high accuracy, TPR, and TNR at 98.63%, 99.07%, and 98.19% respectively. In addition, it shows low FPR and FNR, at 1.81% and 0.93% respectively. Comparison with other similar techniques on the same dataset shows that the proposed model outperforms the existing methods.
      Graphical abstract image

      PubDate: 2018-02-05T13:41:08Z
  • Disentangling consumer recommendations: Explaining and predicting airline
           recommendations based on online reviews
    • Abstract: Publication date: Available online 11 January 2018
      Source:Decision Support Systems
      Author(s): Michael Siering, Amit V. Deokar, Christian Janze
      Consumer recommendations of products and services are important performance indicators for organizations to gain feedback on their offerings. Furthermore, they are important for prospective customers to learn from prior consumer experiences. In this study, we focus on user-generated content, in particular online reviews, to investigate which service aspects are evaluated by consumers and how these factors explain a consumer's recommendation. Further, we investigate how recommendations can be predicted automatically based on such user-driven responses. We disentangle the recommendation decision by performing explanatory and predictive analyses focusing on a sample of airline reviews. We identify core and augmented service aspects expressed in the online review. We then show that service aspect-specific sentiment indicators drive the decision to recommend an airline and that these factors can be incorporated in a predictive model using data mining techniques. We also find that the business model of an airline being reviewed, whether low cost or full service, is also an applicable consideration. Our results are highly relevant for practitioners to analyze and act on consumer feedback in a prompt manner, along with the ability of gaining a deeper understanding of the service from multiple aspects. Also, potential travelers can benefit from this approach by getting an aggregated view on service quality.

      PubDate: 2018-02-05T13:41:08Z
  • Time-aware cloud service recommendation using similarity-enhanced
           collaborative filtering and ARIMA model
    • Abstract: Publication date: Available online 8 January 2018
      Source:Decision Support Systems
      Author(s): Shuai Ding, Yeqing Li, Desheng Wu, Youtao Zhang, Shanlin Yang
      The quality of service (QoS) of cloud services change frequently over time. Existing service recommendation approaches either ignore this property or address it inadequately, leading to ineffective service recommendation. In this paper, we propose a time-aware service recommendation (taSR) approach to address this issue. We first develop a novel similarity-enhanced collaborative filtering (CF) approach to capture the time feature of user similarity and address the data sparsity in the existing PITs (point in time). We then apply autoregressive integrated moving average model (ARIMA) to predict the QoS values in the future PIT under QoS instantaneity. We evaluate the proposed approach and compare it to the state-of-the-art. Our experimental results show that taSR achieves significant performance improvements over existing approaches.

      PubDate: 2018-02-05T13:41:08Z
  • Platform adoption by mobile application developers: A multimethodological
    • Abstract: Publication date: Available online 5 January 2018
      Source:Decision Support Systems
      Author(s): Jaeki Song, Jeff Baker, Ying Wang, Hyoung Yong Choi, Anol Bhattacherjee
      This paper investigates the factors that influence the adoption of IT platforms by software developers and how those factors differ from those that influence IT adoption by end-users. We take a multi-methodological approach, beginning with an interpretive field study where we interview mobile application developers. In the initial interpretive phase, we identify a comprehensive set of influences on developers' platform adoption, comparing them with the factors that have been identified in previous studies of end-user adoption, noting key differences. In the second phase, we empirically test the factors identified in our interviews. We find several key differences between end-user adoption of IT and developer adoption of IT platforms. Most notably, we observe the importance of network externality considerations when developers make an adoption decision, a consideration that is largely absent for end-users. Our study is among the first to comment on B2B and B2C issues in the adoption phenomenon where developers adopt a platform as technology producers (a B2B consideration) in order to ultimately provide mobile applications to end-users who are technology consumers (a B2C consideration).

      PubDate: 2018-02-05T13:41:08Z
  • Can irrelevant benchmark information help when making business decisions
           under uncertainty' An empirical investigation of the newsvendor game
    • Abstract: Publication date: Available online 2 January 2018
      Source:Decision Support Systems
      Author(s): Tong Wu, Abraham Seidmann
      Firms often compensate employees based on their relative performance in the most recent business period. These firms need to consider what type of performance information to share with their employees in order to obtain better outcomes in the long run, without diminishing staff motivation. In this paper, we empirically investigate the impact of sharing irrelevant benchmark “information” (e.g., information about the interim winner's performance) when individuals are making repeated decisions under uncertainty. The decision-making context used is the newsvendor problem, which is a canonical framework for operations management decision making. The newsvendor problem occurs in many business contexts, such as buying fashion goods for retail, setting safety stock levels, setting target inventory levels for perishable goods, selecting the right capacity for a service facility, and overbooking customers. Theoretically, information about the interim winner's performance has no value for making improved decisions in future rounds, and it might even be misleading. Surprisingly, we find that displaying such irrelevant benchmark information results in significantly improved decisions overall, as compared to a control group; this additional display may motivate participants to perform better. We also identify two personality traits related to impulsivity which moderate this positive information display effect.

      PubDate: 2018-02-05T13:41:08Z
  • Augmenting processes with decision intelligence: Principles for integrated
    • Abstract: Publication date: Available online 28 December 2017
      Source:Decision Support Systems
      Author(s): Faruk Hasić, Johannes De Smedt, Jan Vanthienen
      Until recently decisions were mostly modelled within the process. Such an approach was shown to impair the maintainability, scalability, and flexibility of both processes and decisions. Lately, literature is moving towards a separation of concerns between the process and decision model. Most notably, the introduction of the Decision Model and Notation (DMN) standard provides a suitable solution for filling the void of decision representation. This raises the question whether decisions and processes can easily be separated and consistently integrated. We introduce an integrated way of modelling the process, while providing a decision model which encompasses the process in its entirety, rather than focusing on local decision points only. Specifically, this paper contributes formal definitions for decision models and for the integration of processes and decisions. Additionally, inconsistencies between process and decision models are identified and we remedy those inconsistencies by establishing Five Principles for integrated Process and Decision Modelling (5PDM). The principles are subsequently illustrated and validated on a case of a Belgian accounting company.

      PubDate: 2018-02-05T13:41:08Z
  • Customer's reaction to cross-channel integration in omnichannel retailing:
           The mediating roles of retailer uncertainty, identity attractiveness, and
           switching costs
    • Abstract: Publication date: Available online 27 December 2017
      Source:Decision Support Systems
      Author(s): Yang Li, Hefu Liu, Eric T.K. Lim, Jie Mein Goh, Feng Yang, Matthew K.O. Lee
      Although omnichannel retailing has gained significant interest among academics and practitioners, the mechanisms through which customers react to Cross-Channel Integration (CCI) in omnichannel retailing remain unclear. To this end, this study builds on the Push-Pull-Mooring (PPM) framework to unpack the processes through which uncertainty, identity attractiveness, and switching costs of omnichannel retailers play pushing, pulling, and mooring roles in shaping customers' reaction to CCI. We further explore the moderating influence of showrooming in these relationships. Survey findings reveal that uncertainty, identity attractiveness, and switching costs of omnichannel retailers partially mediate the effect of CCI on customer retention while fully mediating the relationship between CCI and interest in alternatives. We also uncovered that customer showrooming strengthens the negative relationship between CCI and retailer uncertainty. We conclude this paper with theoretical and practical implications of our findings.

      PubDate: 2018-02-05T13:41:08Z
  • Will firm's marketing efforts on owned social media payoff' A
           quasi-experimental analysis of tourism products
    • Abstract: Publication date: Available online 24 December 2017
      Source:Decision Support Systems
      Author(s): Hsin-Lu Chang, Yen-Chun Chou, Dai-Yu Wu, Sou-Chein Wu
      A growing number of travel agencies in the tourism industry use social media to promote their services and reach target customers despite some doubt regarding the effectiveness of these tools. Nevertheless, most prior studies adopt a customer-centric perspective to explore the usefulness of earned social media (e.g., eWOM) and its influences on customer behavior. Few have examined a firm's owned social media strategy (e.g., a Facebook brand page) in online social interactions. This paper distinguishes owned media from earned media by site ownerships and communication paths. We study a firm's marketing efforts on its owned media (Facebook brand page) and evaluate the resulting effect on sales. Based on the cognitive fit theory, we further explore whether a firm can moderate such effects by promoting different types of products. Working with a leading travel agency in Taiwan, we collected a matched sample of products with Facebook marketing (treatment group) and those without Facebook marketing (control group). Using a quasi-experimental design and difference-in-difference (DID) estimation, we evaluate the effect of a firm's efforts on Facebook marketing campaigns after controlling time-fixed selection bias and common time-series heterogeneity. While the method is powerful and intuitive, its validity largely relies on the common trend assumption. A concise discussion on caveats of DID estimation is provided to carefully examine our findings, as well as serve as a simple guidance for IS research. The results show that Facebook campaign activities have a positive impact on purchases of tourism products. Furthermore, sales are more likely to increase when a travel agency promotes tourism products that are highly structured, medium-priced, or medium-length, or that require more tourist involvement. Such effects are further examined across different quantiles of sales and in different time spans to see when product moderations are more prominent. The empirical findings facilitate decision-making of e-commerce managers in the tourism industry not only by justifying the effectiveness as well as budget allocation of owned social media marketing, but also by providing a rudimentary guidance on the product selection in Facebook marketing campaigns.

      PubDate: 2017-12-26T17:01:30Z
  • Integrating KPSO and C5.0 to analyze the omnichannel solutions for
           optimizing telecommunication retail
    • Abstract: Publication date: Available online 22 December 2017
      Source:Decision Support Systems
      Author(s): Shen-Tsu Wang
      Telecommunication system providers offer many special number commodities, and all marketing staff sell the commodities and special number combinations according to their marketing experience; consequently, telecommunication retailers find it difficult to consider both user demands and profits in their marketing strategies. This study proposes a classification model integrating K-means Particle Swarm Optimization (KPSO) and C5.0. The particles' PSO only followed pbest and gbest when moving, resulting in the disadvantage that PSO may easily fall into a local optimal solution. First, clustering analysis is carried out using the KPSO clustering method. Second, classification rules for clustering results are formulated by the C5.0 classification method, and a classification model is established in order to achieve effective descriptions of the clustering rules. The methods proposed herein can help retailers find and utilize complementary tariff products for mobile numbers as the basis for future sales and procurement. This study also analyzes the best media for customers' mobile phone purchase methods and utilizes different groups of buyers of the omnichannel. The proposed model is also able to categorize new future tariffs and can further conceptualize the clustering results in the analysis of telecom tariffs and product mix. Finally, the results effectively assist the telecommunications retail industry when considering procurement, product projects, sales, and marketing solutions.

      PubDate: 2017-12-26T17:01:30Z
  • Improving prognosis and reducing decision regret for pancreatic cancer
           treatment using artificial neural networks
    • Abstract: Publication date: Available online 16 December 2017
      Source:Decision Support Systems
      Author(s): Steven Walczak, Vic Velanovich
      Cancer is a worldwide health problem with extremely high morbidity and mortality. Pancreatic cancer specifically is the fourth leading cause of death by cancer in the United States and is a leading cause of cancer deaths worldwide. The optimal treatment for pancreatic cancer is resection surgery, but even with surgery many patients suffer high morbidity and mortality, leading to regret in physicians over whether or not the optimal course of treatment with regard to the patient's quality of life was made. Patients also suffer regret concerning the morbidity associated with treatment. An artificial neural network is developed to predict 7-month survival of pancreatic cancer patients that achieves over a 91% sensitivity and an overall accuracy above 70%. The artificial neural network outcome predictions may be used as an additional source of information to assist physicians and patients in selecting the treatment that provides the best quality of life for the patient and reduces treatment decision regret.

      PubDate: 2017-12-26T17:01:30Z
  • Optimal Pricing in E-Commerce Based on Sparse and Noisy Data
    • Abstract: Publication date: Available online 14 December 2017
      Source:Decision Support Systems
      Author(s): Josef Bauer, Dietmar Jannach
      In today’s transparent markets, e-commerce providers often have to adjust their prices within short time intervals, e.g., to take frequently changing prices of competitors into account. Automating this task of determining an “optimal” price (e.g., in terms of profit or revenue) with a learning-based approach can however be challenging. Often, only few data points are available, making it difficult to reliably detect the relationships between a given price and the resulting revenue or profit. In this paper, we propose a novel machine-learning based framework for estimating optimal prices under such constraints. The framework is generic in terms of the optimality criterion and can be customized in different ways. At its core, it implements a novel algorithm based on Bayesian inference combined with bootstrap-based confidence estimation and kernel regression. Simulation experiments show that our method is favorable over existing dynamic pricing strategies. Furthermore, the method led to a significant increase in profit and revenue in a real-world evaluation.

      PubDate: 2017-12-26T17:01:30Z
  • Visibility of corporate websites: The role of information prosociality
    • Abstract: Publication date: Available online 14 December 2017
      Source:Decision Support Systems
      Author(s): Gautam Pant, Shagun Pant
      With an ever expanding content and user base, the Web presents information discovery and consumption challenges for both consumers and producers of information. Producers of information strive for visibility among consumers who have limited attention. Corporate websites are a primary digital marketing channel for firms through which they seek to gain a bigger share of their stakeholders' (i.e., customers, investors, communities) attention. Using observations spanning several years we study the website visibility, as measured by user traffic, of more than 2500 public firms and its association with properties of corporate websites and the corresponding firms. One property that is of particular interest to us is the availability of “community-engaging” pages, i.e., pages that support blogs or forums on the website or provide links to external social media platforms such as Facebook. These community-engaging pages signify online prosocial services provided by firms. We find that websites with larger number of community-engaging pages are associated with higher visibility. This provides a novel empirical support for the promotion and use of social media content and tools on websites of firms. We also find that websites with more specific content are associated with lower visibility while providing more out-links is associated with higher visibility. We observe these results consistently over time. These associations are observed while controlling for the size of the firms, types of their industries, the magnitude of media attention and other firm-level heterogeneity. Finally, machine learning models derived from our empirical analysis provide strong predictive utility for out-of-sample data.

      PubDate: 2017-12-26T17:01:30Z
  • Know who to give: Enhancing the effectiveness of online product sampling
    • Abstract: Publication date: January 2018
      Source:Decision Support Systems, Volume 105
      Author(s): Xianghua Lu, Chee Wei Phang, Sulin Ba, Xinlin Yao
      Product sampling is an established marketing strategy to increase product exposure and sales, and its use has recently been extended online. Online product sampling affords the advantages of reaching mass audiences, as well as opportunities for firms to select promising sample recipients. In this study, we leverage on the data from an online platform's early effort in administering online product sampling campaigns, whereby sample recipients were randomly selected from among the consumers who indicated interest in the product sample. This affords a relatively “clean” environment for us to investigate the behaviors of consumers in terms of their subsequent purchase-making after being given a product sample, with minimal biases arising from purposive selection issues. We find that, overall, receiving a product sample could increase the consumers' purchase probability by around 300%. Furthermore, the effects vary among different type of consumers. Specifically, average consumers who have few or moderate experience on the platform demonstrated highest purchase probability compared to mature shoppers and also “opportunists”, after they received a product sample. This study contributes to the literature by unveiling consumer heterogeneity in response to online product sampling when receiving a sample, and provides guidance to firms' decision-making in targeting potential consumers so as to better economize on these campaigns.

      PubDate: 2017-12-13T01:14:36Z
  • Fighting money laundering with technology: A case study of Bank X in the
    • Abstract: Publication date: January 2018
      Source:Decision Support Systems, Volume 105
      Author(s): Dionysios S. Demetis
      This paper presents a longitudinal interpretive case study of a UK bank's efforts to combat Money Laundering (ML) by expanding the scope of its profiling of ML behaviour. The concept of structural coupling, taken from systems theory, is used to reflect on the bank's approach to theorize about the nature of ML-profiling. The paper offers a practical contribution by laying a path towards the improvement of money laundering detection in an organizational context while a set of evaluation measures is extracted from the case study. Generalizing from the case of the bank, the paper presents a systems-oriented conceptual framework for ML monitoring.

      PubDate: 2017-12-13T01:14:36Z
  • Decision support to product configuration considering component
           replenishment uncertainty: A stochastic programming approach
    • Abstract: Publication date: January 2018
      Source:Decision Support Systems, Volume 105
      Author(s): Dong Yang, Xiaohong Li, Roger J. Jiao, Bill Wang
      Product configuration is to make decisions on component selections and combination to constitute a customized product under mass customization production. However, the uncertainties (such as component supplies) in product configuration setting are not considered in the existing product configurators. To handle the uncertainty in component replenishment lead-time, a new stochastic decision model is proposed in this paper using two-stage stochastic programming approach. Further, a pre-procuring strategy for component supply is employed to reduce total configuration costs and shorten the delivery date of customized products. The stochastic decision model for product configuration is solved by using Lagrangian relaxation algorithm. The effectiveness of the stochastic decision model is demonstrated through case studies from both computer configuration and ranger drilling machine configuration. Computational comparisons with a commercial solver (CPLEX) indicate that the proposed stochastic decision model provides competitive solution results.

      PubDate: 2017-12-13T01:14:36Z
  • Predicting graft survival among kidney transplant recipients: A Bayesian
           decision support model decision support systems
    • Abstract: Publication date: Available online 9 December 2017
      Source:Decision Support Systems
      Author(s): Kazim Topuz, Ferhat D. Zengul, Ali Dag, Ammar Almehmi, Mehmet Bayram Yildirim
      Predicting the graft survival for kidney transplantation is a high stakes undertaking considering the shortage of available organs and the utilization of healthcare resources. The strength of any predictive model depends on the selection of proper predictors. However, despite improvements in acute rejection management and short-term graft survival, the accurate prediction of kidney transplant outcomes remains suboptimal. Among other approaches, machine-learning techniques have the potential to offer solutions to this prediction problem in kidney transplantation. This study offers a novel methodological solution to this prediction problem by: (a) analyzing the retrospective database including >31,000 U.S. patients; (b) introducing a comprehensive feature selection framework that accounts for medical literature, data analytics methods and elastic net (EN) regression (c) using sensitivity analyses and information fusion to evaluate and combine features from several machine learning approaches (i.e., support vector machines (SVM), artificial neural networks (ANN), and Bootstrap Forest (BF)); (d) constructing several different scenarios by merging different sets of features that are optioned through these fused data mining models and statistical models in addition to expert knowledge; and (e) using best performing sets in Bayesian belief network (BBN) algorithm to identify non-linear relationships and the interactions between explanatory factors and risk levels for kidney graft survival. The results showed that the predictor set obtained through fused data mining model and literature review outperformed the all other alternative predictors sets with the scores of 0.602, 0.684, 0.495 for F-Measure, Average Accuracy, and G-Mean, respectively. Overall, our findings provide novel insights about risk prediction that could potentially help in improving the outcome of kidney transplants. This methodology can also be applied to other similar transplant data sets.

      PubDate: 2017-12-13T01:14:36Z
  • The effect of intrinsic and extrinsic quality cues of digital video games
           on sales: An empirical investigation
    • Abstract: Publication date: Available online 8 December 2017
      Source:Decision Support Systems
      Author(s): Hoon S. Choi, Myung S. Ko, Dawn Medlin, Charlie Chen
      This study examines the effect of product quality cues on sales of digital video games, using signaling theory as a theoretical model. The quality cues are examined from two angles: intrinsic and extrinsic. The intrinsic cues, in this study, include company reputation, newness, and retro features and extrinsic cues include review valence, product popularity, price, and user engagement. Based on a publicly available panel data of 142,590 observations for 5415 digital video games, our empirical results suggest that both intrinsic and extrinsic quality cues affect sales of digital video games. Company reputation of a digital video game, however, does not have a significant effect on sales. Although an overall relationship between price and sales is positive, this is not the case for less popular digital video games. This study provides the implications for IS research and practice.

      PubDate: 2017-12-13T01:14:36Z
  • Feature selection using firefly optimization for classification and
           regression models
    • Abstract: Publication date: Available online 7 December 2017
      Source:Decision Support Systems
      Author(s): Li Zhang, Kamlesh Mistry, Chee Peng Lim, Siew Chin Neoh
      In this research, we propose a variant of the Firefly Algorithm (FA) for discriminative feature selection in classification and regression models for supporting decision making processes using data-based learning methods. The FA variant employs Simulated Annealing (SA)-enhanced local and global promising solutions, chaotic-accelerated attractiveness parameters and diversion mechanisms of weak solutions to escape from the local optimum trap and mitigate the premature convergence problem in the original FA algorithm. A total of 29 classification and 11 regression benchmark data sets have been used to evaluate the efficiency of the proposed FA model. It shows statistically significant improvements over other state-of-the-art FA variants and classical search methods for diverse feature selection problems. In short, the proposed FA variant offers an effective method to identify optimal feature subsets in classification and regression models for supporting data-based decision making processes.

      PubDate: 2017-12-13T01:14:36Z
  • The privacy trade-off for mobile app downloads: The roles of app value,
           intrusiveness, and privacy concerns
    • Abstract: Publication date: Available online 6 December 2017
      Source:Decision Support Systems
      Author(s): Verena M. Wottrich, Eva A. van Reijmersdal, Edith G. Smit
      Today, mobile app users regularly “pay” for various mobile services, such as social networking or entertainment apps, by accepting app permission requests, thereby sharing personal data with apps. Privacy calculus theory has established that individuals disclose personal information based on a cost-benefit trade-off. In the mobile app context, however, this notion needs more support, because existing studies have only measured costs and benefits or forced a trade-off. Conducting two online experiments among Western European app users (N 1 =183; N 2 =687), this study replicates earlier findings and provides more-profound insights into the boundary conditions of the privacy calculus by showing that app value (i.e., benefits) trumps the costs (i.e., intrusiveness, privacy concerns) in the privacy trade-off.

      PubDate: 2017-12-13T01:14:36Z
  • Change detection model for sequential cause-and-effect relationships
    • Abstract: Publication date: Available online 5 December 2017
      Source:Decision Support Systems
      Author(s): Tony Cheng-Kui Huang, Pu-Tai Yang, Jen-Hung Teng
      Detecting changes of behaviors or events is crucial when updating existing knowledge in a dynamic business environment. Currently, data analysts can immediately collect data and easily access existing knowledge. However, that knowledge can also rapidly become outdated. This study discusses a form of knowledge, classifiable sequential patterns (CSPs), defined as s → c, where s is a temporal sequence; c is a class label; and “→” is a sign which implies the sequential relationships between s (cause) and c (effect). If the CSP evolves into another, and the new knowledge is not updated, decision-makers would continue to work with the obsolete CSP. To the authors' knowledge, no study has addressed the topic of change mining in CSPs. To address this research gap, this study proposes a novel change-mining model, SeqClassChange, to identify changes in CSPs. Experiments were conducted with a real-world dataset to evaluate the proposed model.

      PubDate: 2017-12-13T01:14:36Z
  • A synthetic informative minority over-sampling (SIMO) algorithm leveraging
           support vector machine to enhance learning from imbalanced datasets
    • Abstract: Publication date: Available online 29 November 2017
      Source:Decision Support Systems
      Author(s): Saeed Piri, Dursun Delen, Tieming Liu
      Developing decision support systems (DSS) based on imbalanced datasets is one the critical challenges in data mining and decision-analytics. A dataset is called imbalanced when the number of examples from one class outnumbers the number of the instances from another class. Learning from imbalanced datasets is one of the major challenges in machine learning. While a standard classifier could have a very good performance on a balanced dataset, when applied to an imbalanced dataset, its performance deteriorates dramatically. This poor performance is rather troublesome, especially in detecting the minority class, which usually is the class of interest. Therefore, the poor performance of machine learning techniques, which are used to develop DSS, negatively affect the practicality of DSS in real word problems. Over-sampling the minority class is one of the most promising remedies for imbalanced data learning. In this study, we propose a new synthetic informative minority over-sampling (SIMO) algorithm leveraging support vector machine (SVM). In this algorithm, first SVM is applied to the original imbalanced dataset, then, minority examples close to the SVM decision boundary, as the informative minority examples are over-sampled. We also developed another version of SIMO and call it weighted SIMO (W-SIMO). W-SIMO is different from SIMO in the degree of over-sampling the informative minority examples. In W-SIMO, incorrectly classified informative minority examples are over-sampled with a higher degree compared to the correctly classified informative minority examples. In this way, there is more focus on incorrectly classified minority examples. The over-sampled dataset can be used to train any classifier. We applied these algorithms to the 15 publicly available benchmark imbalanced datasets and assessed their performance in comparison with existing approaches in the area of imbalanced data learning. The results showed that our algorithms had the best performance in all datasets compared to other approaches.

      PubDate: 2017-12-13T01:14:36Z
  • Comparing fingerprint-based biometrics authentication versus traditional
           authentication methods for e-payment
    • Abstract: Publication date: Available online 21 November 2017
      Source:Decision Support Systems
      Author(s): Obi Ogbanufe, Dan J. Kim
      Biometrics authentication for electronic payment is generally viewed as a quicker, convenient and a more secure means to identify and authenticate users for online payment. This view is mostly anecdotal and conceptual is nature. The aim of the paper is to shed light on the comparison of perceptions and beliefs of different authentication methods for electronic payment (i.e., credit card, credit card with PIN, and fingerprint biometrics authentication) in an e-commerce context. As theoretical foundation, the valence framework is used in understanding and explaining the individual's evaluation of benefit and risk concerning the payment methods. We propose a research model with hypotheses that evaluate and compare the individual's perceptions of the payment authentication methods, trust of the online store, and the willingness to continue using the website account associated with the payment authentication method. An experiment is used to test the hypotheses. The results show that biometrics authentication significantly influences the individual's security concern, perceived usefulness, and trust of online store. Theoretically, through the study's context – biometrics versus credit card authentication – evidence is provided for the importance of the individual's perceptions, concerns, and beliefs in the use of biometrics for electronic payments. Managerial implications include shedding light on the perceptions and concerns of secure authentication and the need for implementing biometrics authentication for electronic payments.

      PubDate: 2017-12-13T01:14:36Z
  • Leveraging deep learning with LDA-based text analytics to detect
           automobile insurance fraud
    • Abstract: Publication date: Available online 13 November 2017
      Source:Decision Support Systems
      Author(s): Yibo Wang, Wei Xu
      Automobile insurance fraud represents a pivotal percentage of property insurance companies' costs and affects the companies' pricing strategies and social economic benefits in the long term. Automobile insurance fraud detection has become critically important for reducing the costs of insurance companies. Previous studies on automobile insurance fraud detection examined various numeric factors, such as the time of the claim and the brand of the insured car. However, the textual information in the claims has rarely been studied to analyze insurance fraud. This paper proposes a novel deep learning model for automobile insurance fraud detection that uses Latent Dirichlet Allocation (LDA)-based text analytics. In our proposed method, LDA is first used to extract the text features hiding in the text descriptions of the accidents appearing in the claims, and deep neural networks then are trained on the data, which include the text features and traditional numeric features for detecting fraudulent claims. Based on the real-world insurance fraud dataset, our experimental results reveal that the proposed text analytics-based framework outperforms a traditional one. Furthermore, the experimental results show that the deep neural networks outperform widely used machine learning models, such as random forests and support vector machine. Therefore, our proposed framework that combines deep neural networks and LDA is a suitable potential tool for automobile insurance fraud detection.

      PubDate: 2017-11-15T16:49:14Z
  • Rebuilding sample distributions for small dataset learning
    • Abstract: Publication date: Available online 3 November 2017
      Source:Decision Support Systems
      Author(s): Der-Chiang Li, Wu-Kuo Lin, Chien-Chih Chen, Hung-Yu Chen, Liang-Sian Lin
      Over the past few decades, a few learning algorithms have been proposed to extract knowledge from data. The majority of these algorithms have been developed with the assumption that training sets can denote populations. When the training sets contain only a few properties of their populations, the algorithms may extract minimal and/or biased knowledge for decision makers. This study develops a systematic procedure based on fuzzy theories to create new training sets by rebuilding the possible sample distributions, where the procedure contains new functions that estimate domains and a sample generating method. In this study, two real cases of a leading company in the thin film transistor liquid crystal display (TFT-LCD) industry are examined. Two learning algorithms—a back-propagation neural network and support vector regression—are employed for modeling, and two sample generation approaches—bootstrap aggregating (bagging) and the synthetic minority over-sampling technique (SMOTE)—are employed to compare the accuracy of the models. The results indicate that the proposed method outperforms bagging and the SMOTE with the greatest amount of statistical support.

      PubDate: 2017-11-09T06:31:21Z
  • A Tabu search heuristic for smoke term curation in safety defect discovery
    • Abstract: Publication date: Available online 24 October 2017
      Source:Decision Support Systems
      Author(s): David M. Goldberg, Alan S. Abrahams
      The ability to detect and rapidly respond to the presence of safety defects is vital to firms and to regulatory agencies. In this paper, we employ a text mining methodology to generate industry-specific “smoke terms” for identifying these defects in the countertop appliances and over-the-counter medicine industries. Building upon prior work, we propose several methodological improvements to enhance the precision of our industry-specific terms. First, we replace the subjective manual curation of these terms with an automated Tabu search algorithm, which provides a statistically significant improvement over a sample of human-curated lists. Contrary to the assumptions of prior work, we find that shorter, targeted smoke term lists produce superior precision. Second, we incorporate non-textual review features to enhance the performance of these smoke term lists. In total, we find greater than a twofold improvement over typical human-curated lists. As safety surveillance is vital across industries, our method has great potential to assist firms and regulatory agencies in identifying and responding quickly to safety defects.

      PubDate: 2017-10-26T06:09:41Z
  • Predictive analytics and disused railways requalification: insights from a
           Post Factum Analysis perspective
    • Abstract: Publication date: Available online 24 October 2017
      Source:Decision Support Systems
      Author(s): Krzysztof Ciomek, Valentina Ferretti, Miłosz Kadziński
      Strategic decision making problems in the public policy domain typically involve the comparison of competing options by different stakeholders. This paper considers a real case study oriented toward ranking potential actions for the regeneration of disused railways in Italy. The study involves multiple conflicting criteria such as an~expected duration of construction work, costs, a number of potential users, and new green areas. Within this context, we demonstrate that Post Factum Analysis (PFA) coupled with Decision Aiding supports the development of robust recommendations. The role of PFA is to highlight how the actions’ performances need to be modified so that the recommendation is changed in a desired way. In particular, it highlights the minimal improvements that would warrant the feasibility of a~currently impossible outcome (e.g., achieving a better position in the ranking) or the maximal deteriorations that alternatives can afford to maintain a target result (e.g., not losing their advantage over some other options). The use of a focus group with both experts and participants in the decision making process provided insights on how PFA can support: (i) the creation of arguments in favour or against the respective options under analysis, (ii) understanding of the results’ sensitivity with respect to possible changes in the performances assigned to action on different criteria, (iii) a better informed discussion about the results among the participants in the process, and (iv) the development of new/better alternatives.

      PubDate: 2017-10-26T06:09:41Z
  • Academic paper recommender system using multilevel simultaneous citation
    • Abstract: Publication date: Available online 21 October 2017
      Source:Decision Support Systems
      Author(s): Jieun Son, Seoung Bum Kim
      Researchers typically need to filter several academic papers to find those relevant to their research. This filtering is cumbersome and time-consuming because the number of published academic papers is growing exponentially. Some researchers have focused on developing better recommender systems for academic papers by using citation analysis and content analysis. Most traditional content analysis is implemented using a keyword matching process, and thus it cannot consider the semantic contexts of items. Further, citation analysis-based techniques rely on the number of links directly citing or being cited in a single-level network. Consequently, it may be difficult to recommend the appropriate papers when the paper of interest does not have enough citation information. To address these problems, we propose a recommendation system for academic papers that combines citation analysis and network analysis. The proposed method is based on multilevel citation networks that compare all the indirectly linked papers to the paper of interest to inspect the structural and semantic relationships among them. Thus, the proposed method tends to recommend informative and useful papers related to both the research topic and the academic theory. The comparison results based on real data showed that the proposed method outperformed the Google Scholar and SCOPUS algorithms.

      PubDate: 2017-10-26T06:09:41Z
  • Using contextual features and multi-view ensemble learning in product
           defect identification from online discussion forums
    • Abstract: Publication date: Available online 20 October 2017
      Source:Decision Support Systems
      Author(s): Yao Liu, Cuiqing Jiang, Huimin Zhao
      As social media are continually gaining more popularity, they have become an important source for manufacturers to collect information related to defects on their products from consumers. Researchers have started to develop automated models to identify mentions of product defects from social media, such as online discussion forums. In this paper, we propose a novel method for product defect identification from online forums, addressing two inadequacies in previous studies, namely, the inadequate use of information contained in replies and the straightforward use of standard single classifier methods. Our method incorporates contextual features derived from replies and uses a multi-view ensemble learning method specifically tailored to the problem on hand. A case study in the automotive industry demonstrates the utilities of both novelties in our method.

      PubDate: 2017-10-26T06:09:41Z
  • Workforce management in omnichannel service centers with heterogeneous
           channel response urgencies
    • Abstract: Publication date: Available online 19 October 2017
      Source:Decision Support Systems
      Author(s): Noyan Ilk, Michael Brusco, Paulo Goes
      Workforce staffing and assignment decisions are of critical importance for meeting the challenge of minimizing operational costs while providing satisfactory customer service. These decisions are particularly challenging for omnichannel service centers, where customers can request services via different communication channels (e.g., phone, e-mail, live-chat, social media) that have different service quality and response requirements. We present a formulation of the omnichannel workforce management problem that accounts for variations in response urgencies of different channels as well as diminishing agent performances due to channel switching. We develop an algorithm that efficiently provides solutions for this problem and determines the number and channel allocation of service agents within the service center. Through numerical experiments, we study the performance of the algorithm among various service center configurations with equal cost characteristics. The results indicate that the proposed algorithm can identify service center structures that outperform many alternative structures, including those commonly-adopted in the real-world.

      PubDate: 2017-10-26T06:09:41Z
  • Evaluating the effect of best practices for business process redesign: An
           evidence-based approach based on process mining techniques
    • Abstract: Publication date: Available online 18 October 2017
      Source:Decision Support Systems
      Author(s): Minsu Cho, Minseok Song, Marco Comuzzi, Sooyoung Yoo
      The management of business processes in modern times is rapidly shifting towards being evidence-based. Business process evaluation indicators tend to focus on process performance only, neglecting the definition of indicators to evaluate other concerns of interest in different phases of the business process lifecycle. Moreover, they usually do not discuss specifically which data must be collected to calculate indicators and whether collecting these data is feasible or not. This paper proposes a business process assessment framework focused on the process redesign lifecycle phase and tightly coupled with process mining as an operational framework to calculate indicators. The framework includes process performance indicators and indicators to assess whether process redesign best practices have been applied and to what extent. Both sets of indicators can be calculated using standard process mining functionality. This, implicitly, also defines what data must be collected during process execution to enable their calculation. The framework is evaluated through case studies and a thorough comparison against other approaches in the literature.

      PubDate: 2017-10-26T06:09:41Z
  • Integrated framework for profit-based feature selection and SVM
           classification in credit scoring
    • Abstract: Publication date: Available online 18 October 2017
      Source:Decision Support Systems
      Author(s): Sebastián Maldonado, Cristián Bravo, Julio López, Juan Pérez
      In this paper, we propose a profit-driven approach for classifier construction and simultaneous variable selection based on linear Support Vector Machines. The main goal is to incorporate business-related information such as the variable acquisition costs, the Type I and II error costs, and the profit generated by correctly classified instances, into the modeling process. Our proposal incorporates a group penalty function in the SVM formulation in order to penalize the variables simultaneously that belong to the same group, assuming that companies often acquire groups of related variables for a given cost rather than acquiring them individually. The proposed framework was studied in a credit scoring problem for a Chilean bank, and led to superior performance with respect to business-related goals.

      PubDate: 2017-10-18T17:04:09Z
  • Automatic trading method based on piecewise aggregate approximation and
           multi-swarm of improved self-adaptive particle swarm optimization with
    • Abstract: Publication date: Available online 17 October 2017
      Source:Decision Support Systems
      Author(s): Rodrigo C. Brasileiro, Victor L.F. Souza, Adriano L.I. Oliveira
      Financial time series represent the stock prices over time and exhibit behavior similar to a data stream. Many works report on the use of data mining techniques to predict the future direction of stock prices and to discover patterns in the time series data to provide decision support for trading operations. Traditional optimization methods do not take into account the possibility that the function to be optimized, namely, the final financial balance for operations considering some stock, may have multiple peaks, i.e., be represented by multimodal functions. However, multimodality is a known feature of real-world financial time series optimization problems. To deal with this issue, this article proposes the PAA-MS-IDPSO-V approach (Piecewise Aggregate Approximation - Multi-Swarm of Improved Self-adaptive Particle Swarm Optimization with Validation). The proposed method aims to find patterns in financial time series to support investment decisions. The approach uses multi-swarms to obtain a better particle initialization for the final optimization phase since it aims to tackle multimodal problems. Furthermore, it uses a validation set with early stopping to avoid overfitting. The patterns discovered by the method are used together with investment rules to support decisions and thus help investors to maximize the profit in their operations in the stock market. The experiments reported in this paper compare the results obtained by the proposed model with the Buy-and-Hold, PAA-IDPSO approaches and another approach found in the literature. We report on experiments conducted with S&P100 index stocks and using the Friedman Non-Parametric Test with the Nemenyi post-hoc Test both with 95% confidence level. The results show that the proposed model outperformed the competing methods and was able to considerably reduce the variance for all stocks.

      PubDate: 2017-10-18T17:04:09Z
  • The platform shapes the message: How website design affects abstraction
           and valence of online consumer reviews
    • Abstract: Publication date: Available online 16 October 2017
      Source:Decision Support Systems
      Author(s): Goele Aerts, Tim Smits, Peeter Verlegh
      Online consumer reviews provide relevant information about products and services for consumers. In today's networked age, the online consumer review platform market is hyper-competitive. These platforms can easily change different design characteristics to get more reviewers and to nudge reviewers to deliver higher quality reviews. This study explored the relation between online consumer review platforms' design characteristics and the reviewers' construal level. A psycholinguistic coding scheme was used to assess which social and physical design characteristics impact the language abstraction in accompanying online consumer reviews. To this end, we content analyzed reviews of services and products posted on eight different online consumer review platforms (N =400). This resulted in a number of key design characteristics (e.g., reviewer identification, reviewer status, order of instructions and length instructions) that led to a decrease in language abstraction used in online consumer reviews. Moreover, results showed that language abstraction mediated the relationship between the four design characteristics and valence. The findings and their broader theoretical, methodological and practical implications are discussed. Online consumer review platforms could capitalize on our findings in adaptive design choices.

      PubDate: 2017-10-18T17:04:09Z
  • Decision support from financial disclosures with deep neural networks and
           transfer learning
    • Abstract: Publication date: Available online 9 October 2017
      Source:Decision Support Systems
      Author(s): Mathias Kraus, Stefan Feuerriegel
      Company disclosures greatly aid in the process of financial decision-making; therefore, they are consulted by financial investors and automated traders before exercising ownership in stocks. While humans are usually able to correctly interpret the content, the same is rarely true of computerized decision support systems, which struggle with the complexity and ambiguity of natural language. A possible remedy is represented by deep learning, which overcomes several shortcomings of traditional methods of text mining. For instance, recurrent neural networks, such as long short-term memories, employ hierarchical structures, together with a large number of hidden layers, to automatically extract features from ordered sequences of words and capture highly non-linear relationships such as context-dependent meanings. However, deep learning has only recently started to receive traction, possibly because its performance is largely untested. Hence, this paper studies the use of deep neural networks for financial decision support. We additionally experiment with transfer learning, in which we pre-train the network on a different corpus with a length of 139.1 million words. Our results reveal a higher directional accuracy as compared to traditional machine learning when predicting stock price movements in response to financial disclosures. Our work thereby helps to highlight the business value of deep learning and provides recommendations to practitioners and executives.

      PubDate: 2017-10-11T16:43:19Z
  • How Can Online Marketplaces Reduce Rating Manipulation' A New Approach
           on Dynamic Aggregation of Online Ratings
    • Abstract: Publication date: Available online 7 October 2017
      Source:Decision Support Systems
      Author(s): Olga Ivanova, Michael Scholz
      Retailers’ incentives to manipulate online ratings can undermine consumers’ trust in online marketplaces. Finding ways to avoid fake ratings has become a fundamental problem. Most marketplaces update product ratings immediately, i.e., display new ratings as soon as they are submitted. Some platforms have proposed to reduce the frequency of rating updates, as hiding ratings for a certain amount of time allows identifying and eliminating bursts of suspicious ratings. Reducing the update frequency also allows aggregating ratings and displaying only a summary statistic (e.g., mean of ratings). Although such aggregation helps to reduce the amount of fake ratings, as multiple fake ratings get represented by only one value, it might also distort legitimate ratings from real customers and hence have negative impact on honest retailers. In the present study, we propose and evaluate a novel method that instead of displaying every new rating immediately, aggregates a sequence of most recent ratings to k-values, with k determined dynamically based on the distribution of the recent ratings. In a simulation, we demonstrate that our proposed method outperforms state-of-the-art aggregation methods – it effectively reduces the impact of fake ratings on sales, and at the same time only marginally affects sales of honest retailers. Our proposed method can be easily integrated in online rating systems and can be especially used for designing fraud-resistant ranking algorithms.

      PubDate: 2017-10-11T16:43:19Z
  • A cross-domain recommender system with consistent information transfer
    • Abstract: Publication date: Available online 6 October 2017
      Source:Decision Support Systems
      Author(s): Qian Zhang, Dianshuang Wu, Jie Lu, Feng Liu, Guangquan Zhang
      Recommender systems provide users with personalized online product and service recommendations and are a ubiquitous part of today's online entertainment smorgasbord. However, many suffer from cold-start problems due to a lack of sufficient preference data, and this is hindering their development. Cross-domain recommender systems have been proposed as one possible solution. These systems transfer knowledge from one domain that has adequate preference information to another domain that does not. The outlook for cross-domain recommendation is promising, but existing methods cannot ensure the knowledge extracted from the source domain is consistent with the target domain, which may impact the accuracy of the recommendations. To address this challenging issue, we propose a cross-domain recommender system with consistent information transfer (CIT). Knowledge consistency is based on user and item latent groups, and domain adaptation techniques are used to map and adjust these groups in both domains to maintain consistency during the transfer learning process. Experiments were conducted on five real-world datasets in three categories: movies, books, and music. The results for nine cross-domain recommendation tasks show that CIT outperforms five benchmarks and increases the accuracy of recommendations in the target domain, especially with sparse data. Practically, our proposed method is applied into a telecom product recommender system and a business partner recommender system (Smart BizSeeker) to enhance personalized decision making for both businesses and individual customers.

      PubDate: 2017-10-11T16:43:19Z
  • The added value of social media data in B2B customer acquisition systems:
           A real-life experiment
    • Abstract: Publication date: Available online 1 October 2017
      Source:Decision Support Systems
      Author(s): Matthijs Meire, Michel Ballings, Dirk Van den Poel
      Business-to-business organizations and scholars are becoming increasingly aware of the possibilities social media and predictive analytics offer. Despite the interest in social media, only few have analyzed the impact of social media on the sales process. This paper takes a quantitative view to examine the added value of Facebook data in the customer acquisition process. In order to do so, we devise a customer acquisition decision support system to qualify prospects as potential customers, and incorporate commercially purchased prospecting data, website data and Facebook data. Our system is subsequently used by Coca Cola Refreshments Inc. (CCR) to generate calling lists of beverage serving outlets, ranked by their likelihood of becoming a customer. In this paper we report the results, in terms of prospect-to-customer conversion, of a real-life experiment encompassing nearly 9000 prospects. The results show that Facebook is the most informative data source to qualify prospects, and is complementary with the other data sources in that it further improves predictive performance. We contribute to literature in that we are the first to investigate the effectiveness of social media information in acquiring B2B-customers. Our results imply that Facebook data challenge current best practices in customer acquisition.

      PubDate: 2017-10-04T00:57:49Z
  • On a participation structure that ensures representative prices in
           prediction markets
    • Abstract: Publication date: Available online 1 October 2017
      Source:Decision Support Systems
      Author(s): Arthur Carvalho
      The logarithmic market scoring rule (LMSR) is now the de facto market-maker mechanism for prediction markets. We show how LMSR can have more representative final prices by simply imposing a participation structure where the market proceeds in rounds and, in each round, traders can only trade up to a fixed number of contracts. Focusing on markets over binary outcomes, we prove that under such a participation structure, the market price converges after a finite number of rounds to the median of traders' private information for an odd number of traders, and to a point in the median interval for an even number of traders. Thus, the final market price effectively represents all agents' private information since those equilibria are measures of central tendency. We also show that when traders use market price data to revise their private information, the aforementioned equilibrium prices do not change for a broad class of learning methods.

      PubDate: 2017-10-04T00:57:49Z
  • Counterfeit product detection: Bridging the gap between design science and
           behavioral science in information systems research
    • Abstract: Publication date: Available online 21 September 2017
      Source:Decision Support Systems
      Author(s): Hayden Wimmer, Victoria Y. Yoon
      In IS research, there is a dichotomy where design science and behavioral science are distinct research paradigms. IS researchers should view these paradigms as complementary with research drawing upon the strengths of both, yet few have done so. This work demonstrates how design science and behavioral science can be united in IS research via counterfeit product detection based on product reviews in an online marketplace. Product authenticity in the online marketplace is a common issue plaguing consumers. The decision process involved in determining product authenticity is lengthy and complex. Despite the pressing need for an automatic authenticity rating system for online shopping, little research has been done to develop such a system and assess its effects on consumer purchase behavior. To respond to this need, our study develops a design artifact, called OnCDS, to automatically calculate the likelihood that a product is counterfeit based on online customer reviews. Drawing upon lexicon-based sentiment analysis approaches and TF-IDF as kernel theories for our design, we employ web scraping, natural language processing, and topic analysis methods to process customer reviews and calculate the counterfeit score of a product. In assessing the effects of OnCDS on consumer behavior, we develop a research model that encompasses trust and perceived risk based on the valence framework. Results show that our design artifact's efficacy is validated and that the counterfeit score affects perceived risk and trust, which in turn influences attitude toward purchase.

      PubDate: 2017-09-25T20:30:56Z
  • 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
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