Conan C. Albrecht

Motivating Content Contributions to Online Communities: Toward a More Comprehensive Theory

This paper extends previous research by proposing a model that can help explain ways to motivate member contributions to online communities (OCs). New features in the model will allow researchers to test the relative effects of extrinsic and intrinsic rewards as motivators in OCs. Some OCs have introduced extrinsic re-enforcements like gifts, social recognition, and feedback to entice their community members to contribute. However, some research in non-OC settings has suggested that extrinsic rewards can be detrimental to intrinsic motivation. The new model presents findings from organizational behavior and psychology literature that suggest extrinsic rewards can increase a persons intrinsic motivation under some conditions.

Marketplace and technology standards for B2B e-commerce: progress, challenges, and the state of the art

We have examined standards required for successful e-commerce (EC) architectures and evaluated the strengths and limitations of current systems that have been developed to support EC. We find that there is an unfilled need for systems that can reliably locate buyers and sellers in electronic marketplaces and also facilitate automated transactions. The notion of a ubiquitous network where loosely coupled buyers and sellers can reliably find each other in real time, evaluate products, negotiate prices, and conduct transactions is not adequately supported by current systems. These findings were based on an analysis of mainline EC architectures: EDI, company Websites, B2B hubs, e-Procurement systems, and Web Services. Limitations of each architecture were identified. Particular attention was given to the strengths and weaknesses of the Web Services architecture, since it may overcome some limitations of the other approaches.

Metafraud: a meta-learning framework for detecting financial fraud

Financial fraud can have serious ramifications for the long-term sustainability of an organization, as well as adverse effects on its employees and investors, and on the economy as a whole. Several of the largest bankruptcies in U.S. history involved firms that engaged in major fraud. Accordingly, there has been considerable emphasis on the development of automated approaches for detecting financial fraud. However, most methods have yielded performance results that are less than ideal. In consequence, financial fraud detection continues as an important challenge for business intelligence technologies. In light of the need for more robust identification methods, we use a design science approach to develop MetaFraud, a novel meta-learning framework for enhanced financial fraud detection. To evaluate the proposed framework, a series of experiments are conducted on a test bed encompassing thousands of legitimate and fraudulent firms. The results reveal that each component of the framework significantly contributes to its overall effectiveness. Additional experiments demonstrate the effectiveness of the meta-learning framework over state-of-the-art financial fraud detection methods. Moreover, the MetaFraud framework generates confidence scores associated with each prediction that can facilitate unprecedented financial fraud detection performance and serve as a useful decision-making aid. The results have important implications for several stakeholder groups, including compliance officers, investors, audit firms, and regulators.

Current Trends in Fraud and its Detection

ABSTRACT This article discusses the basic nature of fraud, including the major accounting scandals of the last decade. The article also discusses the role of auditors and if auditors should be held liable for not detecting financial statement fraud. The article examines recent standards, rules, and acts put in place after the major frauds of the 1990s and early 2000s, including Sarbanes-Oxley, new rules by the NYSE and NASDAQ, and SAS 92. Finally, the article discusses whether these new standards, rules, and acts will have an impact to deter financial statement frauds from occurring in the future.

Evolutionary development and research on Internet-based collaborative writing tools and processes to enhance eWriting in an eGovernment setting

The Center for the Management of Information (CMI) at the University of Arizona has been actively involved in research with various U.S. government organizations for nearly twenty years. This article details the years of evolutionary development and research conducted by CMI in an eGovernment setting that resulted in the creation of an Internet-based collaborative writing (eWriting) tool, called Collaboratus. By embracing persistence, serendipity, and years of multi-methodological research in the field and in the lab, CMI has built on the foundation of eWriting research that was largely abandoned at the beginning of the eBusiness revolution. This research shows the promising potential for Collaboratus and eWriting tools to help improve digital government through improved document production and collaboration, and highlights many future research opportunities.

Facilitator-in-a-Box: Process Support Applications to Help Practitioners Realize the Potential of Collaboration Technology

The potential benefits of collaboration technologies are typically realized only in groups led by collaboration experts. This raises the facilitator-in-the-box challenge: Can collaboration expertise be packaged with collaboration technology in a form that nonexperts can reuse with no training on either tools or techniques? We address that challenge with process support applications (PSAs). We describe a collaboration support system (CSS) that combines a computer-assisted collaboration engineering platform for creating PSAs with a process support system runtime platform for executing PSAs. We show that the CSS meets its design goals: (1) to reduce development cycles for collaboration systems, (2) to allow nonprogrammers to design and develop PSAs, and (3) to package enough expertise in the tools that nonexperts could execute a well-designed collaborative work process without training.

How clean is the future of SOAP

If developers are not wise with its application, SOAP may lose the ability to tunnel through firewalls---an ability that represents one of its primary advantages.

Facilitator in a Box: Computer Assisted Collaboration Engineering and Process Support Systems for Rapid Development of Collaborative Applications for High-Value Tasks

This paper proposes Computer Assisted Collaboration Engineering (CACE) and Process Support Systems (PSS) as a new approach to move beyond limitations of the current generation of group support systems (GSS) and other collaboration technologies in supporting recurring collaborative work practices. It argues the need for certain capabilities in CACE and PSS systems, and illustrates key concepts with examples from proof of concept systems. It summarizes early results with these systems in the field.

Users' experiences in collaborative writing using Collaboratus, an Internet-based collaborative work

This paper reviews several user experiences that researchers at the University of Arizona have had in building Internet-based tools to support collaborative writing. First, the technological framework for an advanced collaborative writing tool called Collaboratus is presented. Next, we review the tools that make up Collaboratus, and then provide an overview of the various user experiences we have had. Finally, many suggestions are provided for developing the next generation of collaborative writing tools.

Machine Learning Methods For Detecting Patterns Of Management Fraud

Discovery of financial fraud has profound social consequences. Loss of stockholder value, bankruptcy, and loss of confidence in the professional audit firms have resulted from failure to detect financial fraud. Previous studies that have attempted to discover fraud patterns from publicly available information have achieved only moderate levels of success. This study explores the capabilities of recently developed statistical learning and data mining methods in an attempt to advance fraud discovery performance to levels that have potential for proactive discovery or mitigation of financial fraud. The partially adaptive methods we test have achieved success in a number of complex problem domains and are easily interpretable. Ensemble methods, which combine predictions from multiple models via boosting, bagging, or related approaches, have emerged as among the most powerful data mining and machine learning methods. Our study includes random forests, stochastic gradient boosting, and rule ensembles. The results for ensemble models show marked improvement over past efforts, with accuracy approaching levels of practical potential. In particular, rule ensembles do so while maintaining a degree of interpretability absent in the other ensemble methods.