Amit V. Deokar

Detecting Concealment of Intent in Transportation Screening: A Proof of Concept

Transportation and border security systems have a common goal: to allow law-abiding people to pass through security and detain those people who intend to harm. Understanding how intention is concealed and how it might be detected should help in attaining this goal. In this paper, we introduce a multidisciplinary theoretical model of intent concealment along with three verbal and nonverbal automated methods for detecting intent: message feature mining, speech act profiling, and kinesic analysis. This paper also reviews a program of empirical research supporting this model, including several previously published studies and the results of a proof-of-concept study. These studies support the model by showing that aspects of intent can be detected at a rate that is higher than chance. Finally, this paper discusses the implications of these findings in an airport-screening scenario.

Knowledge Management Systems for Knowledge-Intensive Processes: Design Approach and an Illustrative Example

In order to gain sustainable competitive advantage in todays knowledge economy, organizations are looking beyond routine transactional workflow processes to support knowledge-intensive processes. Traditional business process management systems are effective in providing coordination support, but are not geared towards providing relevant knowledge support as well. Also, knowledge management systems are used in an ad hoc manner without explicitly linking them to the underlying organizational processes. Process-based knowledge management (PKM) systems have emerged as a potential solution to support knowledge-intensive processes. However, design guidelines for developing PKM systems are minimal. This paper highlights this research problem, identifies kernel theories governing the design and development of PKM systems, and synthesizes various kernel theories to propose a comprehensive design process for PKM systems. Feasibility and a comparative evaluation of the proposed design process is also discussed.

Detecting Fraudulent Behavior on Crowdfunding Platforms: The Role of Linguistic and Content-Based Cues in Static and Dynamic Contexts

Abstract Crowdfunding platforms offer founders the possibility to collect funding for project realization. With the advent of these platforms, the risk of fraud has risen. Fraudulent founders provide inaccurate information or pretend interest toward a project. Within this study, we propose deception detection support mechanisms to address this novel type of Internet fraud. We analyze a sample of fraudulent and nonfraudulent projects published at a leading crowdfunding platform. We examine whether the analysis of dynamic communication during the funding period is valuable for identifying fraudulent behavior—apart from analyzing only the static information related to the project. We investigate whether content-based cues and linguistic cues are valuable for fraud detection. The selection of cues and the subsequent feature engineering is based on theories in areas of communication, psychology, and computational linguistics. Our results should be helpful to the stakeholders of crowdfunding platforms and researchers of fraud detection.

Prescriptive Workflow Design for Collaboration-intensive Processes using the Collaboration Engineering Approach

Modern organizations are investing in organizational process coordination tools such as collaboration support systems and workflow management systems for better decision-making and business process execution. However, these systems are deployed in a standalone manner with the result that business processes that span these systems are highly fragmented. Work processes are designed and managed independently for each system, resulting in large administration overheads to coordinate activities in business processes that use these systems. Lack of a structured approach to modeling and designing collaborative work processes has been attributed as a primary reason for this disconnect. In this regard, this paper reports on the advances in the area of Collaboration Engineering and its applicability in designing collaborative workflows that can lead to a unified approach for designing organizational processes consisting of interleaved individual and collaborative tasks. The paper also motivates the need for an interface between the workflow activities and the more detailed collaboration support prescriptions.

Dynamic collaboration: participant-driven agile processes for complex tasks

Groups and decision makers are increasingly running headlong into complexity as interconnections and interdependencies between individuals and organizations continue to grow, decision time horizons shrink and more work is being performed by distributed teams. These factors are driving up overall problem space complexity and limiting the effectiveness of time-tested decision and collaboration processes. Increases in problem space complexity lead to higher equivocality in collaborative processes and associated products. Electronic collaboration support tools and associated process management schemes have proven successful in many challenging contexts. However, current collaboration process management schemes and tools may not be able to effectively handle more complex tasks. We posit that heightened problem space complexity must be addressed with commensurate process and technological support for collaborative efforts. To achieve truly agile collaborative solutions, we propose Dynamic Collaboration -- a process management scheme that utilizes group consensus, and process evolution via iterative process alignment and product refinement phases to meet the challenges posed by complexity and equivocality.

Evaluating task-technology fit and user performance for an electronic health record system

Assessing user satisfaction, acceptance and performance impacts of information systems have long traditions in information systems research. With an increasing focus on broader international adoption and implementation of electronic health records, research examining performance impact resulting from system use will play an essential role in the successful design, implementation, and efficient use of these systems. In this study, we analyse user evaluations of an electronic health record system and assess the impact on self-reported, perceived individual performance using the task-technology fit theory. Overall, user evaluations for the eight dimensions of task-technology fit considered in this study are positive.

GDSS Parameters and Benefits

Group decision support system (GDSS) technology is designed to directly impact and change the behavior of groups to improve group effectiveness, efficiency, and satisfaction, and has been studied for more than two decades by researchers. However, as a tool, it is appropriate for use in situations with certain characteristics and is not useful in others. This chapter delineates and discusses some of the key parameters of this approach based on a research model. Parameters are classified based on whether they are related to the technology, group, task, or context. Their benefits for GDSS outcomes are discussed to provide insight into the group decision-making process.

Decision-enabled dynamic process management for networked enterprises

Enterprises in today’s networked economy face numerous information management challenges, both from a process management perspective as well as a decision support perspective. While there have been significant relevant advances in the areas of business process management as well as decision sciences, several open research issues exist. In this paper, we highlight the following key challenges. First, current process modeling and management techniques lack in providing a seamless integration of decision models and tools in existing business processes, which is critical to achieve organizational objectives. Second, given the dynamic nature of business processes in networked enterprises, process management approaches that enable organizations to react to business process changes in an agile manner are required. Third, current state-of-the-art decision model management techniques are not particularly amenable to distributed settings in networked enterprises, which limits the sharing and reuse of models in different contexts, including their utility within managing business processes. In this paper, we present a framework for decision-enabled dynamic process management that addresses these challenges. The framework builds on computational formalisms, including the structured modeling paradigm for representing decision models, and hierarchical task networks from the artificial intelligence (AI) planning area for process modeling. Within the framework, interleaved process planning (modeling), execution and monitoring for dynamic process management throughout the process lifecycle is proposed. A service-oriented architecture combined with advances from the semantic Web field for model management support within business processes is proposed.

An Ontology-Based Information Extraction (OBIE) Framework for Analyzing Initial Public Offering (IPO) Prospectus

With the large amounts of information associated with the Initial Public Offering (IPO) process, an intelligent tool is needed for assisting the decision-making activities for both the investors and the underwriters. Even though a large body of related studies exists in extant literature, minimum attention has been devoted to the aspect of understanding hidden semantics within the informative contents of IPO prospectus. In this paper, we present a framework for processing the textual content of IPO prospectus based on an emerging technique named Ontology Based Information Extraction (OBIE). Preliminary results indicates that the framework is capable of meeting the design requirements identified. Moreover, lessons learned during the design and implementation span technical and organizational considerations and can serve as guidance for future research and development in related areas.

Information Security Policy Compliance: An Empirical Study of Ethical Ideology

Information security policy compliance (ISP) is one of the key concerns that face organizations today. Although technical and procedural measures help improve information security, there is an increased need to accommodate human, social and organizational factors. Despite the plethora of studies that attempt to identify the factors that motivate compliance behavior or discourage abuse and misuse behaviors, there is a lack of studies that investigate the role of ethical ideology per se in explaining compliance behavior. The purpose of this research is to investigate the role of ethics in explaining Information Security Policy (ISP) compliance. In that regard, a model that integrates behavioral and ethical theoretical perspectives is developed and tested. Overall, analyses indicate strong support for the validation of the proposed theoretical model.