bluuebunny/crossref_metadata_2025 huggingface dataset:
Visualizing web search results using glyphs
Authors: Chau Michael
Published: April 2014

While the Web provides a lot of useful information to managers and decision makers in organizations for decision support, it requires a lot of time and cognitive effort for users to sift through a search result list returned by search engines to find useful information. Previous research in information visualization has shown that visualization techniques can help users comprehend information and accomplish information tasks more efficiently and effectively. However, only a limited number of such techniques have been applied to Web search result visualization with mixed evaluation results. Using a design science approach, this research designed and implemented a glyph (a graphical object that represents the values of multiple dimensions using multiple visual parameters) and a system for visualizing Web search results. A flower metaphor was adopted in the glyph design to represent the characteristics and metadata of Web documents. Following the cognitive fit theory, an experimental study was conducted to evaluate three displays: a numeric display, a glyph display, and a combined display which showed numbers only, glyphs only, and both, respectively. Experimental results showed that the glyph display and the combined display performed better when task complexity was high, and the numeric display and the combined display performed better when task complexity was low. The combined display also received the best perceived usability from the subjects. Based on the findings, the implications of the study to research and practice are discussed and some future research directions are suggested.
A multimethod study of information quality in wiki collaboration
Authors: Kane Gerald C.
Published: April 2014

In this article, the author presents the results of a two-phase, multimethod study of wiki-based collaboration in an attempt to better understand how peer-produced collaboration is done well in wiki environments. Phase 1 involves an in-depth case study of the collaborative processes surrounding the development of the Wikipedia article on the 2007 Virginia Tech massacre. The rich data collected are used to develop an initial set of testable hypotheses of factors that enhance the quality of peer-produced information in wiki environments. Phase 2 tests these theories through a quantitative analysis of the collaborative features associated with 188 similar articles that Wikipedia considered for recognition as their best (i.e., the top 0.1%). Four collaborative features are examined for their effects on quality: volume of contributor activity, type of contributor activity, number of anonymous contributors, and top contributor experience. Volume of contributor activity is the only feature that is unsupported, a particularly interesting result because previous literature connects that factor most clearly to success in wiki-based collaboration. Implications are discussed.
Computer use and wage returns
Authors: Dey Debabrata , Fan Ming , Peng Gang
Published: April 2014

The effect of computer use on individual workers is relatively underresearched in the IS literature. Prior studies on computer use usually treat technologies as a “black box” and rarely look into how computers are used in workplaces and why IT-related skills are important there. In this study, we use the data from the Current Population Survey (CPS) and the data on job requirements for over 12,000 occupations from the Dictionary of Occupational Titles (DOT), to examine the complementary roles of skill sets and nonroutine tasks in providing computer-use wage returns for individual workers. We find that computer use is associated with increased levels of interactive and numerical skills required for the general workforce. In addition, workers who use computers at work and possess higher levels of interactive skills receive higher wages. We also find that computer use complements performing nonroutine tasks, particularly nonroutine abstract tasks in contributing to the wage premium. As the tasks become increasingly routine, the impact of computer use on wage returns diminishes.
Digital goods and markets
Authors: Bhattacharjee Sudip , Gopal Ram D. , Marsden James R. , Sankaranarayanan Ramesh
Published: April 2014

This research commentary examines the changing landscape of digital goods, and discusses important emerging issues for IS researchers to explore. We begin with a discussion of the major technological milestones that have shaped digital goods industries such as music, movies, software, books, video games, and recently emerging digital goods. Our emphasis is on economic and legal issues, rather than on design science or sociological issues. We explore how research has been influenced by the major technological milestones and discuss the major findings of prior research. Based on this, we offer a roadmap for future researchers to explore the emergent changes in the digital goods arena, covering different aspects of digital goods industries such as risk management, value chain, legal aspects, transnational and cross-cultural issues.
Who does what
Authors: Liu Jun , Ram Sudha
Published: April 2014

The quality of Wikipedia articles is debatable. On the one hand, existing research indicates that not only are people willing to contribute articles but the quality of these articles is close to that found in conventional encyclopedias. On the other hand, the public has never stopped criticizing the quality of Wikipedia articles, and critics never have trouble finding low-quality Wikipedia articles. Why do Wikipedia articles vary widely in quality? We investigate the relationship between collaboration and Wikipedia article quality. We show that the quality of Wikipedia articles is not only dependent on the different types of contributors but also on how they collaborate. Based on an empirical study, we classify contributors based on their roles in editing individual Wikipedia articles. We identify various patterns of collaboration based on the provenance or, more specifically, who does what to Wikipedia articles. Our research helps identify collaboration patterns that are preferable or detrimental for article quality, thus providing insights for designing tools and mechanisms to improve the quality of Wikipedia articles.
Eliciting a sense of virtual community among knowledge contributors
Authors: Sutanto Juliana , Kankanhalli Atreyi , Tan Bernard Cheng Yian
Published: April 2014

Member-initiated virtual communities for product knowledge sharing and commerce purposes are proliferating as useful alternatives to company information and commerce Web sites. Although such communities are easy to create with the availability of numerous tools, the challenge lies in keeping the community alive and thriving. Key to sustainability is members' Sense Of Virtual Community (SOVC) so that they feel responsible for contributing their knowledge and creating value for others. However, it is unclear what leads to the SOVC among knowledge contributors. Building on appraisal theory, we hypothesize that the fulfillment of contributors' informational, instrumental, entertainment, self-discovery, and social enhancement needs will increase their SOVC. To test the hypotheses, we surveyed knowledge contributors in a beauty-product-related community to examine the relationship between their needs' fulfillment and SOVC levels. Other than the social enhancement need, all other needs' fulfillment were found to be positively related to SOVC levels. To further understand how the SOVC of knowledge contributors changes over time, we conducted a longitudinal analysis of a panel of these members. We discovered that over time, changes in the perceived fulfillment of their instrumental, entertainment, and self-discovery needs determined the change of their SOVC. The results have implications for future research as well as for the sustainability and value generation from such virtual communities.
Latent subject-centered modeling of collaborative tagging
Authors: Peng Jing , Zeng Daniel D. , Huang Zan
Published: April 2014

Collaborative tagging or social bookmarking is a main component of Web 2.0 systems and has been widely recognized as one of the key technologies underpinning next-generation knowledge management platforms. In this article, we propose a subject-centered model of collaborative tagging to account for the ternary cooccurrences involving users, items, and tags in such systems. Extending the well-established probabilistic latent semantic analysis theory for knowledge representation, our model maps the user, item, and tag entities into a common latent subject space that captures the “wisdom of the crowd” resulted from the collaborative tagging process. To put this model into action, we have developed a novel way to estimate the probabilistic subject-centered model approximately in a highly efficient manner taking advantage of a matrix factorization method. Our empirical evaluation shows that our proposed approach delivers substantial performance improvement on the knowledge resource recommendation task over the state-of-the-art standard and tag-aware resource recommendation algorithms.
A multilabel text classification algorithm for labeling risk factors in SEC form 10-K
Authors: Huang Ke-Wei , Li Zhuolun
Published: April 2014

This study develops, implements, and evaluates a multilabel text classification algorithm called the multilabel categorical K-nearest neighbor (ML-CKNN). The proposed algorithm is designed to automatically identify 25 types of risk factors with specific meanings reported in Section 1A of SEC form 10-K. The idea of ML-CKNN is to compute a categorical similarity score for each label by the K-nearest neighbors in that category. ML-CKNN is tailored to achieve the goal of extracting risk factors from 10Ks. The proposed algorithm can perfectly classify 74.94% of risk factors and 98.75% of labels. Moreover, ML-CKNN is empirically shown to outperform ML-KNN and other multilabel algorithms. The extracted risk factors could be valuable to empirical studies in accounting or finance.
Stock price movement prediction using representative prototypes of financial reports
Authors: Lin Ming-Chih , Lee Anthony J. T. , Kao Rung-Tai , Chen Kuo-Tay
Published: April 2014

Stock price movement prediction is an appealing topic not only for research but also for commercial applications. Most of prior research separately analyzes the meanings of the qualitative or quantitative features, and does not consider the categorical information when clustering financial reports. Since quantitative or qualitative features contain only partial information, there may be no synergy by considering them individually. It is more appropriate to predict stock price movements by simultaneously taking both quantitative and qualitative features into account. Therefore, in this study, we utilize a weighting scheme to combine both qualitative and quantitative features of financial reports together, and propose a method to predict short-term stock price movements. The proposed method employs the categorical information to localize the clusters and improve the purity of each resultant cluster. We gathered 26,255 reports of companies listed in the S&P 500 index from the EDGAR database and conducted the GICS (Global Industrial Classification System) experiments based on the industry sectors. The empirical evaluation results show that the proposed method outperforms the SVM, naïve Bayes, and PFHC methods in terms of accuracy and average profit.
Six principles for redesigning executive information systems—findings of a survey and evaluation of a prototype
Authors: Marx Frederik , Mayer Jörg H. , Winter Robert
Published: April 2014

Information Systems (IS) meant to help senior managers are known as Executive Information Systems (EIS). Despite a five-decade tradition of such IS, many executives still complain that they bear little relevance to managing a company and, even more, fail to accommodate their working style. The increasing acceptance of IS among today's executives and technological advances of the Internet era make the present moment favorable for redesigning EIS. Following the design science paradigm in IS research, this article provides six principles for such a redesign. To do so, we survey executives regarding their requirements and the IS they currently use. We then derive principles for a redesign to fill the gaps. They address diverse areas: a comprehensive information model, functions to better analyze and process information, easy-to-use IS handling, a more flexible IS architecture and data model, a proper information management, and fast prototype implementation. Finally a field test demonstrates and evaluates the utility of our proposal by means of a prototype.
Design science and the accumulation of knowledge in the information systems discipline
Authors: Niederman Fred , March Salvatore T.
Published: April 2014

Design science has emerged as an important research paradigm in the information systems (IS) discipline, and much has been written on how it should be conducted and evaluated (e.g., Hevner et al. [2004]; Walls et al. [1992]; Vaishnavi and Kuechler [2007]; Kuechler and Vaishnavi [2008]; Peffers et al. [2007]; Iivari [2010]; Pigneur [2011]). We contend that, as a socio-technical discipline, IS research must address the interaction between design and behavior. We begin with a background discussion of what we mean by IS research and the nature of the relationship between design and behavioral approaches to IS research. We discuss the nature of design, design science, and IT artifacts within information systems research and describe the importance of linking design and behavioral perspectives. We illustrate several key points using selected articles recently published in
ACM Transactions on Management Information Systems
[Schmidt-Rauch and Schwabe 2011; Lau et al. 2011]. We conclude with a vision of IS research in which the capabilities and affordances of IT artifacts are incorporated into behavioral studies; the results of behavioral studies are utilized in the development and evaluation of IT artifacts; and both behavioral and design perspectives are used to address the important problems of our constituent community.
Enhancement of recall within technology-mediated teams through the use of online visual artifacts
Authors: Basoglu K. Asli , Fuller Mark A. , Valacich Joseph S.
Published: April 2014

Given the distributed nature of modern organizations, the use of technology-mediated teams is a critical aspect of their success. These teams use various media that are arguably less personal than face-to-face communication. One factor influencing the success of these teams is their ability to develop an understanding of who knows what during the initial team development stage. However, this development of understanding within dispersed teams may be impeded because of the limitations of technology-enabled communication environments. Past research has found that a limited understanding of team member capabilities hinders team performance. As such, this article investigates mechanisms for improving the recall of individuals within dispersed teams. Utilizing the input-process-output model to conceptualize the group interaction process, three input factors—visual artifacts (i.e., a computer-generated image of each team member), team size, and work interruptions—are manipulated to assess their influence on a person's ability to recall important characteristics of their virtual team members. Results show that visual artifacts significantly increase the recall of individuals' information. However, high-urgency interruptions significantly deteriorate the recall of individuals, regardless of the visual artifact or team size. These findings provide theoretical and practical implications on knowledge acquisition and project success within technology-mediated teams.
Impact of data characteristics on recommender systems performance
Authors: Adomavicius Gediminas , Zhang Jingjing
Published: April 2014

This article investigates the impact of rating data characteristics on the performance of several popular recommendation algorithms, including user-based and item-based collaborative filtering, as well as matrix factorization. We focus on three groups of data characteristics: rating space, rating frequency distribution, and rating value distribution. A sampling procedure was employed to obtain different rating data subsamples with varying characteristics; recommendation algorithms were used to estimate the predictive accuracy for each sample; and linear regression-based models were used to uncover the relationships between data characteristics and recommendation accuracy. Experimental results on multiple rating datasets show the consistent and significant effects of several data characteristics on recommendation accuracy.
Deciphering word-of-mouth in social media
Authors: Zhang Zhu , Li Xin , Chen Yubo
Published: April 2014

Enabled by Web 2.0 technologies, social media provide an unparalleled platform for consumers to share their product experiences and opinions through word-of-mouth (WOM) or consumer reviews. It has become increasingly important to understand how WOM content and metrics influence consumer purchases and product sales. By integrating marketing theories with text mining techniques, we propose a set of novel measures that focus on sentiment divergence in consumer product reviews. To test the validity of these metrics, we conduct an empirical study based on data from Amazon.com and BN.com (Barnes & Noble). The results demonstrate significant effects of our proposed measures on product sales. This effect is not fully captured by nontextual review measures such as numerical ratings. Furthermore, in capturing the sales effect of review content, our divergence metrics are shown to be superior to and more appropriate than some commonly used textual measures the literature. The findings provide important insights into the business impact of social media and user-generated content, an emerging problem in business intelligence research. From a managerial perspective, our results suggest that firms should pay special attention to textual content information when managing social media and, more importantly, focus on the right measures.
How virtual teams use their virtual workspace to coordinate knowledge
Authors: Malhotra Arvind , Majchrzak Ann
Published: April 2014

Virtual team members increasingly rely on virtual workspace tools to coordinate knowledge that each individual brings to the team. How the use of these tools affects knowledge coordination within virtual teams is not well understood. We distinguish between tools as features and the use of the virtual workspace as providing affordances for behaviors. Using situational awareness theory, we hypothesized two affordances of virtual workspaces that facilitate knowledge coordination. Using trading zone theory, we hypothesized two forms of trading zones created by features of virtual workspaces and the impact of these trading zones on the creation of affordances for team members. Members of 54 teams were asked about the affordances of the virtual workspace, and team leaders were asked about specific tools provided to the team. Our hypothesized model was supported: the different forms of trading zones were differentially related to the different affordances and on affordances were related to knowledge coordination satisfaction. Theoretical implications focus on the distinction between features and affordances and on the identification of specific features that affect specific affordances. Practical implications for managers and engineers supporting virtual teams include the utility of becoming knowledgeable about different forms of trading zones that virtual workspaces can provide and understanding the relationship between trading zones and different affordances.
Two New Prediction-Driven Approaches to Discrete Choice Prediction
Authors: Huang Zan , Zhao Huimin , Zhu Dan
Published: April 2014

The ability to predict consumer choices is essential in understanding the demand structure of products and services. Typical discrete choice models that are targeted at providing an understanding of the behavioral process leading to choice outcomes are developed around two main assumptions: the existence of a utility function that represents the preferences over a choice set and the relatively simple and interpretable functional form for the utility function with respect to attributes of alternatives and decision makers. These assumptions lead to models that can be easily interpreted to provide insights into the effects of individual variables, such as price and promotion, on consumer choices. However, these restrictive assumptions might impede the ability of such theory-driven models to deliver accurate predictions and forecasts. In this article, we develop novel approaches targeted at providing more accurate choice predictions. Specifically, we propose two prediction-driven approaches: pairwise preference learning using classification techniques and ranking function learning using evolutionary computation. We compare our proposed approaches with a multiclass classification approach, as well as a standard discrete choice model. Our empirical results show that the proposed approaches achieved significantly higher choice prediction accuracy.
“Enforced” vs. “Casual” Transparency -- Findings from IT-Supported Financial Advisory Encounters
Authors: Nussbaumer Philipp , Matter Inu , Schwabe Gerhard
Published: April 2014

In sales-oriented service encounters like financial advice, the client may perceive information and interest asymmetries as a lack of
transparency
regarding the advisor’s activities. In this article, we will discuss two design iterations of a supportive tabletop application that we built to increase process and information transparency as compared to the traditional pen and paper encounters. While the first iteration’s design was “enforcing” transparency and therefore proved to be a failure [Nussbaumer et al. 2011], we built the second iteration on design rationales enabling more “casual” transparency. Experimental evaluations show that the redesigned system significantly increases the client’s perceived transparency, her perceived control of the encounter and improves her perceived trustworthiness of and satisfaction with the encounter. With these findings, we contribute to (1) insight into the role of transparency advisory encounter design; (2) design solutions for establishing particular facets of transparency and their potential instantiations in tabletop systems; and (3) insight into the process of designing for transparency with socio-technical artifacts that are emergent as a result of design activities.
Business Intelligence and Analytics Education, and Program Development
Authors: Chiang Roger H. L. , Goes Paulo , Stohr Edward A.
Published: April 2014

“Big Data,” huge volumes of data in both structured and unstructured forms generated by the Internet, social media, and computerized transactions, is straining our technical capacity to manage it. More importantly, the new challenge is to develop the capability to understand and interpret the burgeoning volume of data to take advantage of the opportunities it provides in many human endeavors, ranging from science to business. Data Science, and in business schools, Business Intelligence and Analytics (BI&A) are emerging disciplines that seek to address the demands of this new era. Big Data and BI&A present unique challenges and opportunities not only for the research community, but also for Information Systems (IS) programs at business schools. In this essay, we provide a brief overview of BI&A, speculate on the role of BI&A education in business schools, present the challenges facing IS departments, and discuss the role of IS curricula and program development, in delivering BI&A education. We contend that a new vision for the IS discipline should address these challenges.
Do Vendors’ Pricing Decisions Fully Reflect Information in Online Reviews?
Authors: Hu Nan , Cavusoglu Hasan , Liu Ling , Ni Chenkai
Published: April 2014

By using online retail data collected from Amazon, Barnes & Nobel, and Pricegrabber, this paper investigates whether online vendors’ pricing decisions fully reflect the information contained in various components of customers’ online reviews. The findings suggest that there is inefficiency in vendors’ pricing decisions. Specifically, vendors do not appear to fully understand the incremental predictive power of online reviews in forecasting future sales when they adjust their prices. However, they do understand demand persistence. Interestingly, vendors reduce price if the actual demand is higher than the expected demand (positive demand shock). This phenomenon is attributed to the advertising effect suggested in previous literature and the intense competitiveness of e-Commerce. Finally, we document that vendors do not change their prices directly in response to online reviews; their response to online reviews is through forecasting consumer’s future demand.
Contrasting Multiple Social Network Autocorrelations for Binary Outcomes, With Applications To Technology Adoption
Authors: Zhang Bin , Thomas Andrew C. , Doreian Patrick , Krackhardt David , Krishnan Ramayya
Published: April 2014

The rise of socially targeted marketing suggests that decisions made by consumers can be predicted not only from their personal tastes and characteristics, but also from the decisions of people who are close to them in their networks. One obstacle to consider is that there may be several different measures for closeness that are appropriate, either through different types of friendships, or different functions of distance on one kind of friendship, where only a subset of these networks may actually be relevant. Another is that these decisions are often binary and more difficult to model with conventional approaches, both conceptually and computationally. To address these issues, we present a hierarchical auto-probit model for individual binary outcomes that uses and extends the machinery of the auto-probit method for binary data. We demonstrate the behavior of the parameters estimated by the multiple network-regime auto-probit model (m-NAP) under various sensitivity conditions, such as the impact of the prior distribution and the nature of the structure of the network. We also demonstrate several examples of correlated binary data outcomes in networks of interest to information systems, including the adoption of caller ring-back tones, whose use is governed by direct connection but explained by additional network topologies.
Fast, Scalable, and Context-Sensitive Detection of Trending Topics in Microblog Post Streams
Authors: Pervin Nargis , Fang Fang , Datta Anindya , Dutta Kaushik , Vandermeer Debra
Published: April 2014

Social networks, such as Twitter, can quickly and broadly disseminate news and memes across both real-world events and cultural trends. Such networks are often the best sources of up-to-the-minute information, and are therefore of considerable commercial and consumer interest. The trending topics that appear first on these networks represent an answer to the age-old query “what are people talking about?” Given the incredible volume of posts (on the order of 45,000 or more per minute), and the vast number of stories about which users are posting at any given time, it is a formidable problem to extract trending stories in real time. In this article, we describe a method and implementation for extracting trending topics from a high-velocity real-time stream of microblog posts. We describe our approach and implementation, and a set of experimental results that show that our system can accurately find “hot” stories from high-rate Twitter-scale text streams.
A Dispatch-Mediated Communication Model for Emergency Response Systems
Authors: Valecha Rohit , Sharman Raj , Rao H. Raghav , Upadhyaya Shambhu
Published: April 2014

The current state of emergency communication is dispatch-mediated (the messages from the scene are directed towards the responders and agencies through the dispatch agency). These messages are logged in electronic documents called incident reports, which are useful in monitoring the incident, off-site supervision, resource allocation, and post-incident analysis. However, these messages do not adhere to any particular structure, and there is no set format. The lack of standards creates a problem for sharing information among systems and responders and has a detrimental impact on systems interoperability. In this article, we develop a National Information Exchange Model (NIEM) and Universal Core (UCORE) compliant messaging model, considering message structures and formats, to foster message standardization.
A Fitness-Utility Model for Design Science Research
Authors: Gill T. Grandon , Hevner Alan R.
Published: April 2014

Current thinking in design science research (DSR) defines the usefulness of the design artifact in a relevant problem environment as the primary research goal. Here we propose a complementary evaluation model for DSR. Drawing from evolutionary economics, we define a fitness-utility model that better captures the evolutionary nature of design improvements and the essential DSR nature of searching for a satisfactory design across a fitness landscape. Our goal is to move DSR to more meaningful evaluations of design artifacts for sustainable impacts. A key premise of this new thinking is that the evolutionary fitness of a design artifact is more valuable than its immediate usefulness. We conclude with a discussion of the strengths and challenges of the fitness-utility model for the performance of rigorous and relevant DSR.
Distributed Privacy-Preserving Decision Support System for Highly Imbalanced Clinical Data
Authors: Mathew George , Obradovic Zoran
Published: April 2014

When a medical practitioner encounters a patient with rare symptoms that translates to rare occurrences in the local database, it is quite valuable to draw conclusions collectively from such occurrences in other hospitals. However, for such rare conditions, there will be a huge imbalance in classes among the relevant base population. Due to regulations and privacy concerns, collecting data from other hospitals will be problematic. Consequently, distributed decision support systems that can use just the statistics of data from multiple hospitals are valuable. We present a system that can collectively build a distributed classification model dynamically without the need of patient data from each site in the case of imbalanced data. The system uses a voting ensemble of experts for the decision model. The imbalance condition and number of experts can be determined by the system. Since only statistics of the data and no raw data are required by the system, patient privacy issues are addressed. We demonstrate the outlined principles using the Nationwide Inpatient Sample (NIS) database. Results of experiments conducted on 7,810,762 patients from 1050 hospitals show improvement of 13.68% to 24.46% in balanced prediction accuracy using our model over the baseline model, illustrating the effectiveness of the proposed methodology.
Accurate and Efficient Algorithms that Adapt to Privacy-Enhanced Video for Improved Assistive Monitoring
Authors: Edgcomb Alex , Vahid Frank
Published: April 2014

Automated monitoring algorithms operating on live video streamed from a home can effectively aid in several assistive monitoring goals, such as detecting falls or estimating daily energy expenditure. Use of video raises obvious privacy concerns. Several privacy enhancements have been proposed such as modifying a person in video by introducing blur, silhouette, or bounding-box. Person extraction is fundamental in video-based assistive monitoring and degraded in the presence of privacy enhancements; however, privacy enhancements have characteristics that can opportunistically be adapted to. We propose two adaptive algorithms for improving assistive monitoring goal performance with privacy-enhanced video: specific-color hunter and edge-void filler. A nonadaptive algorithm, foregrounding, is used as the default algorithm for the adaptive algorithms. We compare nonadaptive and adaptive algorithms with 5 common privacy enhancements on the effectiveness of 8 automated monitoring goals. The nonadaptive algorithm performance on privacy-enhanced video is degraded from raw video. However, adaptive algorithms can compensate for the degradation. Energy estimation accuracy in our tests degraded from 90.9% to 83.9%, but the adaptive algorithms significantly compensated by bringing the accuracy up to 87.1%. Similarly, fall detection accuracy degraded from 1.0 sensitivity to 0.86 and from 1.0 specificity to 0.79, but the adaptive algorithms compensated accuracy back to 0.92 sensitivity and 0.90 specificity. Additionally, the adaptive algorithms were computationally more efficient than the nonadaptive algorithm, averaging 1.7% more frames processed per second.
Effects of Long-Term Exposure on Sensitivity and Comfort with Stereoscopic Displays
Authors: Stransky Debi , Wilcox Laurie M. , Allison Robert S.
Published: April 2014

Stereoscopic 3D media has recently increased in appreciation and availability. This popularity has led to concerns over the health effects of habitual viewing of stereoscopic 3D content; concerns that are largely hypothetical. Here we examine the effects of repeated, long-term exposure to stereoscopic 3D in the workplace on several measures of stereoscopic sensitivity (discrimination, depth matching, and fusion limits) along with reported negative symptoms associated with viewing stereoscopic 3D. We recruited a group of adult stereoscopic 3D industry experts and compared their performance with observers who were (i) inexperienced with stereoscopic 3D, (ii) researchers who study stereopsis, and (iii) vision researchers with little or no experimental stereoscopic experience. Unexpectedly, we found very little difference between the four groups on all but the depth discrimination task, and the differences that did occur appear to reflect task-specific training or experience. Thus, we found no positive or negative consequences of repeated and extended exposure to stereoscopic 3D in these populations.
Business Benefits or Incentive Maximization? Impacts of the Medicare EHR Incentive Program at Acute Care Hospitals
Authors: Mirani Rajesh , Harpalani Anju
Published: April 2014

This study investigates the influence of the Medicare EHR Incentive Program on EHR adoption at acute care hospitals and the impact of EHR adoption on operational and financial efficiency/effectiveness. It finds that even before joining the incentive program, adopter hospitals had more efficient and effective Medicare operations than those of non-adopters. Adopters were also financially more efficient. After joining the program, adopter hospitals treated significantly more Medicare patients by shortening their stay durations, relative to their own non-Medicare patients and also to patients at non-adopter hospitals, even as their overall capacity utilization remained relatively unchanged. The study concludes that many of these hospitals had implemented EHR even before the initiation of the incentive program. It further infers that they joined this program with opportunistic intentions of tapping into incentive payouts which they maximized by taking on more Medicare patients. These findings give credence to critics of the program who have questioned its utility and alleged that it serves only to reward existing users of EHR technologies.
Mining Deviations from Patient Care Pathways via Electronic Medical Record System Audits
Authors: Zhang He , Mehotra Sanjay , Liebovitz David , Gunter Carl A. , Malin Bradley
Published: April 2014

In electronic medical record (EMR) systems, administrators often provide EMR users with broad access privileges, which may leave the system vulnerable to misuse and abuse. Given that patient care is based on a coordinated workflow, we hypothesize that care pathways can be represented as the progression of a patient through a system and introduce a strategy to model the patient’s flow as a sequence of accesses defined over a graph. Elements in the sequence correspond to features associated with the access transaction (e.g., reason for access). Based on this motivation, we model patterns of patient record usage, which may indicate deviations from care workflows. We evaluate our approach using several months of data from a large academic medical center. Empirical results show that this framework finds a small portion of accesses constitute outliers from such flows. We also observe that the violation patterns deviate for different types of medical services. Analysis of our results suggests greater deviation from normal access patterns by nonclinical users. We simulate anomalies in the context of real accesses to illustrate the efficiency of the proposed method for different medical services. As an illustration of the capabilities of our method, it was observed that the area under the receiver operating characteristic (ROC) curve for the Pediatrics service was found to be 0.9166. The results suggest that our approach is competitive with, and often better than, the existing state-of-the-art in its outlier detection performance. At the same time, our method is more efficient, by orders of magnitude, than previous approaches, allowing for detection of thousands of accesses in seconds.
I Can Help You Change! An Empathic Virtual Agent Delivers Behavior Change Health Interventions
Authors: Lisetti Christine , Amini Reza , Yasavur Ugan , Rishe Naphtali
Published: April 2014

We discuss our approach to developing a novel modality for the computer-delivery of Brief Motivational Interventions (BMIs) for behavior change in the form of a personalized On-Demand VIrtual Counselor (ODVIC), accessed over the internet. ODVIC is a multimodal Embodied Conversational Agent (ECA) that empathically delivers an evidence-based behavior change intervention by adapting, in real-time, its verbal and nonverbal communication messages to those of the user’s during their interaction. We currently focus our work on excessive alcohol consumption as a target behavior, and our approach is adaptable to other target behaviors (e.g., overeating, lack of exercise, narcotic drug use, non-adherence to treatment). We based our current approach on a successful existing patient-centered brief motivational intervention for behavior change---the Drinker’s Check-Up (DCU)---whose computer-delivery with a text-only interface has been found effective in reducing alcohol consumption in problem drinkers. We discuss the results of users’ evaluation of the computer-based DCU intervention delivered with a text-only interface compared to the same intervention delivered with two different ECAs (a neutral one and one with some empathic abilities). Users rate the three systems in terms of acceptance, perceived enjoyment, and intention to use the system, among other dimensions. We conclude with a discussion of how our positive results encourage our long-term goals of
on-demand conversations
, anytime, anywhere, with virtual agents as personal health and well-being helpers.
Building Online Trust in a Culture of Confucianism
Authors: Huang Lihua , Ba Sulin , Lu Xianghua
Published: April 2014

The success of e-commerce companies in a Confucian cultural context takes more than advanced IT and process design that have proven successful in Western countries. The example of eBay’s failure in China indicates that earning the trust of Chinese consumers is essential to success, yet the process of building that trust requires something different from that in the Western culture. This article attempts to build a theoretical model to explore the relationship between the Confucian culture and online trust. We introduce two new constructs, namely process flexibility and perceived control, as particularly important factors in online trust formation in the Chinese cultural context. A survey was conducted to test the proposed theoretical model. This study offers a new explanation for online trust formation in the Confucian context. The findings of this article can provide guidance for companies hoping to successfully navigate the Chinese online market in the future.
Risk Mitigation Decisions for IT Security
Authors: Yeo M. Lisa , Rolland Erik , Ulmer Jackie Rees , Patterson Raymond A.
Published: April 2014

Enterprises must manage their information risk as part of their larger operational risk management program. Managers must choose how to control for such information risk. This article defines the flow risk reduction problem and presents a formal model using a workflow framework. Three different control placement methods are introduced to solve the problem, and a comparative analysis is presented using a robust test set of 162 simulations. One year of simulated attacks is used to validate the quality of the solutions. We find that the math programming control placement method yields substantial improvements in terms of risk reduction and risk reduction on investment when compared to heuristics that would typically be used by managers to solve the problem. The contribution of this research is to provide managers with methods to substantially reduce information and security risks, while obtaining significantly better returns on their security investments. By using a workflow approach to control placement, which guides the manager to examine the entire infrastructure in a holistic manner, this research is unique in that it enables information risk to be examined strategically.
Social-Sensed Image Search
Authors: Cui Peng , Liu Shao-Wei , Zhu Wen-Wu , Luan Huan-Bo , Chua Tat-Seng , Yang Shi-Qiang
Published: April 2014

Although Web search techniques have greatly facilitate users’ information seeking, there are still quite a lot of search sessions that cannot provide satisfactory results, which are more serious in Web image search scenarios. How to understand user intent from observed data is a fundamental issue and of paramount significance in improving image search performance. Previous research efforts mostly focus on discovering user intent either from clickthrough behavior in user search logs (e.g., Google), or from social data to facilitate vertical image search in a few limited social media platforms (e.g., Flickr). This article aims to combine the virtues of these two information sources to complement each other, that is, sensing and understanding users’ interests from social media platforms and transferring this knowledge to rerank the image search results in general image search engines. Toward this goal, we first propose a novel social-sensed image search framework, where both social media and search engine are jointly considered. To effectively and efficiently leverage these two kinds of platforms, we propose an example-based user interest representation and modeling method, where we construct a hybrid graph from social media and propose a hybrid random-walk algorithm to derive the user-image interest graph. Moreover, we propose a social-sensed image reranking method to integrate the user-image interest graph from social media and search results from general image search engines to rerank the images by fusing their social relevance and visual relevance. We conducted extensive experiments on real-world data from Flickr and Google image search, and the results demonstrated that the proposed methods can significantly improve the social relevance of image search results while maintaining visual relevance well.
Theoretical, Qualitative, and Quantitative Analyses of Small-Document Approaches to Resource Selection
Authors: Markov Ilya , Crestani Fabio
Published: April 2014

In a distributed retrieval setup, resource selection is the problem of identifying and ranking relevant sources of information for a given user’s query. For better usage of existing resource-selection techniques, it is desirable to know what the fundamental differences between them are and in what settings one is superior to others. However, little is understood still about the actual behavior of resource-selection methods. In this work, we focus on small-document approaches to resource selection that rank and select sources based on the ranking of their documents. We pose a number of research questions and approach them by three types of analyses. First, we present existing small-document techniques in a unified framework and analyze them theoretically. Second, we propose using a qualitative analysis to study the behavior of different small-document approaches. Third, we present a novel experimental methodology to evaluate small-document techniques and to validate the results of the qualitative analysis. This way, we answer the posed research questions and provide insights about small-document methods in general and about each technique in particular.
Conservation Laws and Traveling Wave Solutions of a Generalized Nonlinear ZK-BBM Equation
Authors: Adem Khadijo Rashid , Khalique Chaudry Masood
Published: April 2014

We study a generalized two-dimensional nonlinear Zakharov-Kuznetsov-Benjamin-Bona-Mahony (ZK-BBM) equation, which is in fact Benjamin-Bona-Mahony equation formulated in the ZK sense. Conservation laws for this equation are constructed by using the new conservation theorem due to Ibragimov and the multiplier method. Furthermore, traveling wave solutions are obtained by employing the(G'/G)-expansion method.
Holistic Management: Misinformation on the Science of Grazed Ecosystems
Authors: Carter John , Jones Allison , O’Brien Mary , Ratner Jonathan , Wuerthner George
Published: April 2014

Over 3 billion hectares of lands worldwide are grazed by livestock, with a majority suffering degradation in ecological condition. Losses in plant productivity, biodiversity of plant and animal communities, and carbon storage are occurring as a result of livestock grazing. Holistic management (HM) has been proposed as a means of restoring degraded deserts and grasslands and reversing climate change. The fundamental approach of this system is based on frequently rotating livestock herds to mimic native ungulates reacting to predators in order to break up biological soil crusts and trample plants and soils to promote restoration. This review could find no peer-reviewed studies that show that this management approach is superior to conventional grazing systems in outcomes. Any claims of success due to HM are likely due to the management aspects of goal setting, monitoring, and adapting to meet goals, not the ecological principles embodied in HM. Ecologically, the application of HM principles of trampling and intensive foraging are as detrimental to plants, soils, water storage, and plant productivity as are conventional grazing systems. Contrary to claims made that HM will reverse climate change, the scientific evidence is that global greenhouse gas emissions are vastly larger than the capacity of worldwide grasslands and deserts to store the carbon emitted each year.
Quantum Tunnelling for Hawking Radiation from Both Static and Dynamic Black Holes
Authors: Chakraborty Subenoy , Saha Subhajit
Published: April 2014

The paper deals with Hawking radiation from both a general static black hole and a nonstatic spherically symmetric black hole. In case of static black hole, tunnelling of nonzero mass particles is considered and due to complicated calculations, quantum corrections are calculated only up to the first order. The results are compared with those for massless particles near the horizon. On the other hand, for dynamical black hole, quantum corrections are incorporated using the Hamilton-Jacobi method beyond semiclassical approximation. It is found that different order correction terms satisfy identical differential equation and are solved by a typical technique. Finally, using the law of black hole mechanics, a general modified form of the black hole entropy is obtained considering modified Hawking temperature.