Decision Support System Research Papers
The common thread of articles published in Decision Support Systems is their relevance to theoretical and technical issues in the support of enhanced decision making. The areas addressed may include foundations, functionality, interfaces, implementation, impacts, and evaluation of decision support systems (DSSs). Manuscripts may draw from diverse methods and methodologies, including those from decision theory, economics, econometrics, statistics, computer supported cooperative work, data base management, linguistics, management science, mathematical modeling, operations management, cognitive science, psychology, user interface management, and others. However, a manuscript focused on direct contributions to any of these related areas should be submitted to an outlet appropriate to the specific area.
Examples of research topics that would be appropriate for Decision Support Systems include the following:
1. DSS Foundations e.g. principles, concepts, and theories of enhanced decision making; formal languages and research methods enabling improvements in decision making. It is important that theory validation be carefully addressed.
2. DSS Functionality e.g. methods, tools, and techniques for developing thefunctional aspects of enhanced decision making; solver, model, and/or data management in DSSs; rule formulation and management in DSSs; DSS development and use in computer supported cooperative work, negotiation, research and product.
3. DSS Interfaces e.g. methods, tools, and techniques for designing and developing DSS interfaces; development, management, and presentation of knowledge in a DSS; coordination of a DSS's interface with its functionality.
4. DSS Implementation - experiences in DSS development and utilization; DSS management and updating; DSS instruction/training. A critical consideration must be how specific experiences provide more general implications.
5. DSS Evaluation and Impact e.g. evaluation metrics and processes; DSS impact on decision makers, organizational processes and performance.Hide full Aims & Scope
More than 300 papers have been published in the last 15 years on the topic of green or sustainable (forward) supply chains. Looking at the research methodologies employed, only 36 papers apply quantitative models. This is in contrast to, for example, the neighboring field of reverse or closed-loop supply chains where several reviews on respective quantitative models have already been provided. The paper summarizes research on quantitative models for forward supply chains and thereby contributes to the further substantiation of the field. While different kinds of models are applied, it is evident that the social side of sustainability is not taken into account. On the environmental side, life-cycle assessment based approaches and impact criteria clearly dominate. On the modeling side there are three dominant approaches: equilibrium models, multi-criteria decision making and analytical hierarchy process. There has been only limited empirical research so far. The paper ends with suggestions for future research. © 2012 Elsevier B.V.
Haluk Demirkan | Dursun Delen
Using service-oriented decision support systems (DSS in cloud) is one of the major trends for many organizations in hopes of becoming more agile. In this paper, after defining a list of requirements for service-oriented DSS, we propose a conceptual framework for DSS in cloud, and discus about research directions. A unique contribution of this paper is its perspective on how to servitize the product oriented DSS environment, and demonstrate the opportunities and challenges of engineering service oriented DSS in cloud. When we define data, information and analytics as services, we see that traditional measurement mechanisms, which are mainly time and cost driven, do not work well. Organizations need to consider value of service level and quality in addition to the cost and duration of delivered services. DSS in CLOUD enables scale, scope and speed economies. This article contributes new knowledge in service science by tying the information technology strategy perspectives to the database and design science perspectives for a broader audience.
Jie Lu | Dianshuang Wu | Mingsong Mao | Wei Wang | Guangquan Zhang
© 2015 Elsevier B.V. A recommender system aims to provide users with personalized online product or service recommendations to handle the increasing online information overload problem and improve customer relationship management. Various recommender system techniques have been proposed since the mid-1990s, and many sorts of recommender system software have been developed recently for a variety of applications. Researchers and managers recognize that recommender systems offer great opportunities and challenges for business, government, education, and other domains, with more recent successful developments of recommender systems for real-world applications becoming apparent. It is thus vital that a high quality, instructive review of current trends should be conducted, not only of the theoretical research results but more importantly of the practical developments in recommender systems. This paper therefore reviews up-to-date application developments of recommender systems, clusters their applications into eight main categories: e-government, e-business, e-commerce/e-shopping, e-library, e-learning, e-tourism, e-resource services and e-group activities, and summarizes the related recommendation techniques used in each category. It systematically examines the reported recommender systems through four dimensions: recommendation methods (such as CF), recommender systems software (such as BizSeeker), real-world application domains (such as e-business) and application platforms (such as mobile-based platforms). Some significant new topics are identified and listed as new directions. By providing a state-of-the-art knowledge, this survey will directly support researchers and practical professionals in their understanding of developments in recommender system applications.
Retaining users and facilitating their continuance usage are crucial for mobile payment service providers. Drawing on the information systems success model and flow theory, this research identified the factors affecting continuance intention of mobile payment. We conducted data analysis with structural equation modeling. The results indicated that service quality is the main factor affecting trust, whereas system quality is the main factor affecting satisfaction. Information quality and service quality affect flow. Trust, flow and satisfaction determine continuance intention of mobile payment. The results imply that service providers need to offer quality system, information and services in order to facilitate users' continuance usage of mobile payment. © 2012 Elsevier B.V.
Hefu Liu | Weiling Ke | Kwok Kee Wei | Zhongsheng Hua
Researchers and practitioners regard information technology (IT) as a competitive tool. However, current knowledge on IT capability mechanisms that affect firm performance remains unclear. Based on the dynamic capabilities perspective and the view of a hierarchy of capabilities, this article proposes a model to examine how IT capabilities (i.e., flexible IT infrastructure and IT assimilation) affect firm performance through absorptive capacity and supply chain agility in the supply chain context. Survey data show that absorptive capacity and supply chain agility fully mediate the influences of IT capabilities on firm performance. In addition to the direct effects, absorptive capacity also has indirect effects on firm performance by shaping supply chain agility. We conclude with implications and suggestions for future research. © 2012 Elsevier B.V.
Yang Yu | Wenjing Duan | Qing Cao
This study aims to investigate the effect of social media and conventional media, their relative importance, and their interrelatedness on short term firm stock market performances. We use a novel and large-scale dataset that features daily media content across various conventional media and social media outlets for 824 public traded firms across 6 industries. Social media outlets include blogs, forums, and Twitter. Conventional media includes major newspapers, television broadcasting companies, and business magazines. We apply the advanced sentiment analysis technique that goes beyond the number of mentions (counts) to analyze the overall sentiment of each media resource toward a specific company on the daily basis. We use stock return and risk as the indicators of companies' short-term performances. Our findings suggest that overall social media has a stronger relationship with firm stock performance than conventional media while social and conventional media have a strong interaction effect on stock performance. More interestingly, we find that the impact of different types of social media varies significantly. Different types of social media also interrelate with conventional media to influence stock movement in various directions and degrees. Our study is among the first to examine the effect of multiple sources of social media along with the effect of conventional media and to investigate their relative importance and their interrelatedness. Our findings suggest the importance for firms to differentiate and leverage the unique impact of various sources of media outlets in implementing their social media marketing strategies. © 2012 Elsevier B.V.
Huaxia Rui | Yizao Liu | Andrew Whinston
Social broadcasting networks such as Twitter in the U.S. and "Weibo" in China are transforming the way online word of mouth (WOM) is disseminated and consumed in the digital age. In the present study, we investigated whether and how Twitter WOM affects movie sales by estimating a dynamic panel data model using publicly available data and well-known machine learning algorithms. We found that chatter on Twitter does matter; however, the magnitude and direction of the effect depend on whom the WOM is from and what the WOM is about. Incorporating the number of followers the author of each WOM message had into our study, we found that the effect of WOM from users followed by more Twitter users is significantly larger than those followed by less Twitter users. In support of some recent findings about the importance of WOM valence on product sales, we also found that positive Twitter WOM is associated with higher movie sales, whereas negative WOM is associated with lower movie sales. Interestingly, we found that the strongest effect on movie sales comes from those tweets in which the authors expressed their intention to watch a certain movie. We attribute this finding to the dual effects of such intention tweets on movie sales: the direct effect through the WOM author's own purchase behavior, and the indirect effect through either the awareness effect or the persuasive effect of the WOM on its recipients. Our findings provide new perspectives to understand the effect of WOM on product sales and have important managerial implications. For example, our study reveals the potential values of monitoring people's intentions and sentiments on Twitter and identifying influential users for companies wishing to harness the power of social broadcasting networks. © 2012 Elsevier B.V.
Matthew S. Gerber
Twitter is used extensively in the United States as well as globally, creating many opportunities to augment decision support systems with Twitter-driven predictive analytics. Twitter is an ideal data source for decision support: its users, who number in the millions, publicly discuss events, emotions, and innumerable other topics; its content is authored and distributed in real time at no charge; and individual messages (also known as tweets) are often tagged with precise spatial and temporal coordinates. This article presents research investigating the use of spatiotemporally tagged tweets for crime prediction. We use Twitter-specific linguistic analysis and statistical topic modeling to automatically identify discussion topics across a major city in the United States. We then incorporate these topics into a crime prediction model and show that, for 19 of the 25 crime types we studied, the addition of Twitter data improves crime prediction performance versus a standard approach based on kernel density estimation. We identify a number of performance bottlenecks that could impact the use of Twitter in an actual decision support system. We also point out important areas of future work for this research, including deeper semantic analysis of message content, temporal modeling, and incorporation of auxiliary data sources. This research has implications specifically for criminal justice decision makers in charge of resource allocation for crime prevention. More generally, this research has implications for decision makers concerned with geographic spaces occupied by Twitter-using individuals. ©2014 Elsevier B.V. All rights reserved.
Young Hoon Kim | Dan J. Kim | Kathy Wachter
The growth of mobile technology mediated environments is accelerated by its accessibility and easy use tools, such as smartphones and tablets. User friendly and intuitive features drive user value and satisfaction. These features motivate and drive further mobile user engagement. Smartphones, for example, allow users to control when, where, and how they engage in chosen activities that serve their needs, saving time, completing a task (utilitarian), entertain them (hedonic), or connect with others (social). Few studies have examined why and how mobile users are continually engaging mobile activities. Focusing on mobile engagement which has not previously been explored, this study investigates, proposes, and tests a mobile user engagement (MoEN) model to explain mobile user engagement intention through user's motivations, perceived value and satisfaction. Findings indicate that mobile users' engagement motivations do influence perceived value, satisfaction and mobile engagement intention. © 2013 Elsevier B.V.
Mutaz M. Al-Debei | Enas Al-Lozi | Anastasia Papazafeiropoulou
This study examines the continuance participation intentions and behaviour on Facebook, as a representative of Social Networking Sites (SNSs), from a social and behavioural perspective. The study extends the Theory of Planned Behaviour (TPB) through the inclusion of perceived value construct and utilizes the extended theory to explain users' continuance participation intentions and behaviour on Facebook. Despite the recent massive uptake of Facebook, our review of the related-literature revealed that very few studies tackled such technologies from the context of post- adoption as in this research. Using data from surveys of undergraduate and postgraduate students in Jordan (n = 403), the extended theory was tested using statistical analysis methods. The results show that attitude, subjective norm, perceived behavioural control, and perceived value have significant effect on the continuance participation intention of post-adopters. Further, the results show that continuance participation intention and perceived value have significant effect on continuance participation behaviour. However, the results show that perceived behavioural control has no significant effect on continuance participation behaviour of post-adopters. When comparing the extended theory developed in this study with the standard TPB, it was found that the inclusion of the perceived value construct in the extended theory is fruitful; as such an extension explained an additional 11.6% of the variance in continuance participation intention and 4.5% of the variance in continuance participation behaviour over the standard TPB constructs. Consistent with the research on value-driven post-adoption behaviour, these findings suggest that continuance intentions and behaviour of users of Facebook are likely to be greater when they perceive the behaviour to be associated with significant added-value (i.e. benefits outperform sacrifices). © 2013 Elsevier B.V. All rights reserved.
Farhan Hassan Khan | Saba Bashir | Usman Qamar
Twitter has become one of the most popular micro-blogging platform recently. Millions of users can share their thoughts and opinions about different aspects and events on the micro-blogging platform. Therefore, Twitter is considered as a rich source of information for decision making and sentiment analysis. Sentiment analysis refers to a classification problem where the main focus is to predict the polarity of words and then classify them into positive and negative feelings with the aim of identifying attitude and opinions that are expressed in any form or language. Sentiment analysis over Twitter offers organisations a fast and effective way to monitor the publics' feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. The primary issues in previous techniques are classification accuracy, data sparsity and sarcasm, as they incorrectly classify most of the tweets with a very high percentage of tweets incorrectly classified as neutral. This research paper focuses on these problems and presents an algorithm for twitter feeds classification based on a hybrid approach. The proposed method includes various pre-processing steps before feeding the text to the classifier. Experimental results show that the proposed technique overcomes the previous limitations and achieves higher accuracy when compared to similar techniques. © 2013 Elsevier B.V.
Ray M. Chang | Robert J. Kauffman | Youngok Kwon
The era of big data has created new opportunities for researchers to achieve high relevance and impact amid changes and transformations in how we study social science phenomena. With the emergence of new data collection technologies, advanced data mining and analytics support, there seems to be fundamental changes that are occurring with the research questions we can ask, and the research methods we can apply. The contexts include social networks and blogs, political discourse, corporate announcements, digital journalism, mobile telephony, home entertainment, online gaming, financial services, online shopping, social advertising, and social commerce. The changing costs of data collection and the new capabilities that researchers have to conduct research that leverages micro-level, meso-level and macro-level data suggest the possibility of a scientific paradigm shift toward computational social science. The new thinking related to empirical regularities analysis, experimental design, and longitudinal empirical research further suggests that these approaches can be tailored for rapid acquisition of big data sets. This will allow business analysts and researchers to achieve frequent, controlled and meaningful observations of real-world phenomena. We discuss how our philosophy of science should be changing in step with the times, and illustrate our perspective with comparisons between earlier and current research inquiry. We argue against the assertion that theory no longer matters and offer some new research directions. © 2013 Elsevier B.V. All rights reserved.
Gang Wang | Jianshan Sun | Jian Ma | Kaiquan Xu | Jibao Gu
With the rapid development of information technologies, user-generated contents can be conveniently posted online. While individuals, businesses, and governments are interested in evaluating the sentiments behind this content, there are no consistent conclusions on which sentiment classification technologies are best. Recent studies suggest that ensemble learning methods may have potential applicability in sentiment classification. In this study, we conduct a comparative assessment of the performance of three popular ensemble methods (Bagging, Boosting, and Random Subspace) based on five base learners (Naive Bayes, Maximum Entropy, Decision Tree, K Nearest Neighbor, and Support Vector Machine) for sentiment classification. Moreover, ten public sentiment analysis datasets were investigated to verify the effectiveness of ensemble learning for sentiment analysis. Based on a total of 1200 comparative group experiments, empirical results reveal that ensemble methods substantially improve the performance of individual base learners for sentiment classification. Among the three ensemble methods, Random Subspace has the better comparative results, although it was seldom discussed in the literature. These results illustrate that ensemble learning methods can be used as a viable method for sentiment classification. © 2013 Elsevier B.V.
Nádia F.F. Da Silva | Eduardo R. Hruschka | Estevam R. Hruschka
© 2014 Elsevier B.V. All rights reserved. Twitter is a microblogging site inwhich users can post updates (tweets) to friends (followers). It has become an immense dataset of the so-called sentiments. In this paper,we introduce an approach that automatically classifies the sentiment of tweets by using classifier ensembles and lexicons. Tweets are classified as either positive or negative concerning a query term. This approach is useful for consumers who can use sentiment analysis to search for products, for companies that aim at monitoring the public sentiment of their brands, and for many other applications. Indeed, sentiment classification in microblogging services (e.g., Twitter) through classifier ensembles and lexicons has not been well explored in the literature. Our experiments on a variety of public tweet sentiment datasets show that classifier ensembles formed by Multinomial Naive Bayes, SVM, Random Forest, and Logistic Regression can improve classification accuracy.
Yiming Zheng | Kexin Zhao | Antonis Stylianou
An information-exchange virtual community (VC) is an IT-supported virtual space that is composed of a group of people for accessing, sharing and disseminating topic-related experiences and knowledge through communication and social interaction [36,43]. With the increasing number of VCs and low switching cost, it is challenging to retain existing users and encourage their continued participation. By integrating the IS post-adoption research and IS Success model, we propose a research framework to investigate VC users' continuance intention from a quality perspective. Based on a field survey, we find that information and system quality directly affect perceived individual benefits and user satisfaction, which ultimately determine user continuance intention to consume and to provide information. Furthermore, by modeling information quality and system quality as multifaceted constructs, our results reveal key quality concerns in information-exchange VCs. Implications for VC design and management are also discussed. © 2012 Elsevier B.V.
Nan Xiao | Raj Sharman | H. R. Rao | Shambhu Upadhyaya
People are increasingly using the Internet to access health information and the information obtained has an impact on their healthcare outcomes. This paper examines the impacts of IT enablers and health motivators on peoples' online health information search behavior. We characterize users' online health information search behavior along three dimensions: the frequency of online health information search, the diversity of online health information usage, and the preference of the Internet for initial search. Using the 2003 Health Information National Trends Survey (HINTS) data on cancer, we find that ease of access to Internet services and trust in online health information could affect the three dimensional search behavior listed above. While perceived quality of communication with doctors has an impact on diversity of use and preference of use, we surprisingly do not find an impact on the frequency of search for online health information. In addition, our results find that perceived health status could affect both frequency and diversity of search for online health information. But we do not find evidence that perceived health status could lead to a preference for using the Internet as a source for health information. © 2012 Elsevier B.V.
Guiwu Wei | Jiamin Wang | Jian Chen
The traditional approach for multiple attribute decision analysis with incomplete information on alternative values and attribute weights is to identify alternatives that are potentially optimal. However, the results of potential optimality analysis may be misleading as an alternative is evaluated under the best-case scenario of attribute weights only. Robust optimality analysis is a conservative approach that is concerned with an assured level of payoff for an alternative across all possible scenarios of weights. In this study, we introduce two measures of robust optimality that extend the robust optimality analysis approach and classify alternatives in consideration into three groups: strong robust optimal, weak robust optimal and robust non-optimal. Mathematical models are developed to compute these measures. It is claimed that robust optimality analysis and potential optimality analysis together provide a comprehensive picture of an alternative's variable payoff. © 2013 Elsevier B.V.
G. Alan Wang | Jian Jiao | Alan S. Abrahams | Weiguo Fan | Zhongju Zhang
With increasing knowledge demands and limited availability of expertise and resources within organizations, professionals often rely on external sources when seeking knowledge. Online knowledge communities are Internet based virtual communities that specialize in knowledge seeking and sharing. They provide a virtual media environment where individuals with common interests seek and share knowledge across time and space. A large online community may have millions of participants who have accrued a large knowledge repository with millions of text documents. However, due to the low information quality of user-generated content, it is very challenging to develop an effective knowledge management system for facilitating knowledge seeking and sharing in online communities. Knowledge management literature suggests that effective knowledge management should make accessible not only written knowledge but also experts who are a source of information and can perform a given organizational or social function. Existing expert finding systems evaluate one's expertise based on either the contents of authored documents or one's social status within his or her knowledge community. However, very few studies consider both indicators collectively. In addition, very few studies focus on virtual communities where information quality is often poorer than that in organizational knowledge repositories. In this study we propose a novel expert finding algorithm, ExpertRank, that evaluates expertise based on both document-based relevance and one's authority in his or her knowledge community. We modify the PageRank algorithm to evaluate one's authority so that it reduces the effect of certain biasing communication behavior in online communities. We explore three different expert ranking strategies that combine document-based relevance and authority: linear combination, cascade ranking, and multiplication scaling. We evaluate ExpertRank using a popular online knowledge community. Experiments show that the proposed algorithm achieves the best performance when both document-based relevance and authority are considered. © 2012 Elsevier B.V.
Dursun Delen | Haluk Demirkan
While organizations are trying to become more agile to better respond to market changes in the midst of rapidly globalizing competition by adopting service orientation - commoditization of business processes, architectures, software, infrastructures and platforms - they are also facing new challenges. In this article, we provide a conceptual framework for service oriented managerial decision making process, and briefly explain the potential impact of service oriented architecture (SOA) and cloud computing on data, information and analytics. Today, SOA, cloud computing, Web 2.0 and Web 3.0 are converging, and transforming the information technology ecosystem for the better while imposing new complexities. With this convergence, a large amount of structured and unstructured data is being created and shared over disparate networks and virtual communities. To cope and/or to take advantage of these changes, we are in need of finding new and more efficient ways to collect, store, transform, share, utilize and dispose data, information and analytics. © 2012 Elsevier B.V. All rights reserved.