TAXONOMY DEVELOPMENT IN INFORMATION SYSTEMS: DEVELOPING A TAXONOMY OF MOBILE APPLICATIONS

The complexity of the information systems field often lends itself to classification schemes, or taxonomies, which provide ways to understand the similarities and differences among objects under study. Developing a taxonomy, however, is a complex process that is often done in an ad hoc way. This research-in-progress paper uses the design science paradigm to develop a systematic method for taxonomy development in information systems. The method we propose uses an indicator or operational level model that combines both empirical to deductive and deductive to empirical approaches. We evaluate this method by using it to develop a taxonomy of mobile applications, which we have chosen because of their ever-increasing number and variety. The resulting taxonomy contains seven dimensions with fifteen characteristics. We demonstrate the usefulness of this taxonomy by analyzing a range of current and proposed mobile applications. From the results of this analysis we identify combinations of characteristics where applications are missing and thus are candidates for new and potentially useful applications.taxonomy, design science, mobile application

Correspondence Truth and Quantum Mechanics

The logic of a physical theory reflects the structure of the propositions referring to the behaviour of a physical system in the domain of the relevant theory. It is argued in relation to classical mechanics that the propositional structure of the theory allows truth-value assignment in conformity with the traditional conception of a correspondence theory of truth. Every proposition in classical mechanics is assigned a definite truth value, either 'true' or 'false', describing what is actually the case at a certain moment of time. Truth-value assignment in quantum mechanics, however, differs; it is known, by means of a variety of 'no go' theorems, that it is not possible to assign definite truth values to all propositions pertaining to a quantum system without generating a Kochen-Specker contradiction. In this respect, the Bub-Clifton 'uniqueness theorem' is utilized for arguing that truth-value definiteness is consistently restored with respect to a determinate sublattice of propositions defined by the state of the quantum system concerned and a particular observable to be measured. An account of truth of contextual correspondence is thereby provided that is appropriate to the quantum domain of discourse. The conceptual implications of the resulting account are traced down and analyzed at length. In this light, the traditional conception of correspondence truth may be viewed as a species or as a limit case of the more generic proposed scheme of contextual correspondence when the non-explicit specification of a context of discourse poses no further consequences.Comment: 19 page

Solving multiple-criteria R&D project selection problems with a data-driven evidential reasoning rule

In this paper, a likelihood based evidence acquisition approach is proposed to acquire evidence from experts'assessments as recorded in historical datasets. Then a data-driven evidential reasoning rule based model is introduced to R&D project selection process by combining multiple pieces of evidence with different weights and reliabilities. As a result, the total belief degrees and the overall performance can be generated for ranking and selecting projects. Finally, a case study on the R&D project selection for the National Science Foundation of China is conducted to show the effectiveness of the proposed model. The data-driven evidential reasoning rule based model for project evaluation and selection (1) utilizes experimental data to represent experts' assessments by using belief distributions over the set of final funding outcomes, and through this historic statistics it helps experts and applicants to understand the funding probability to a given assessment grade, (2) implies the mapping relationships between the evaluation grades and the final funding outcomes by using historical data, and (3) provides a way to make fair decisions by taking experts' reliabilities into account. In the data-driven evidential reasoning rule based model, experts play different roles in accordance with their reliabilities which are determined by their previous review track records, and the selection process is made interpretable and fairer. The newly proposed model reduces the time-consuming panel review work for both managers and experts, and significantly improves the efficiency and quality of project selection process. Although the model is demonstrated for project selection in the NSFC, it can be generalized to other funding agencies or industries.Comment: 20 pages, forthcoming in International Journal of Project Management (2019

The emergence of a new form of IS offshore enterprise - The modern heterarchy

The complexity of the information systems field often lends itself to classification schemes, or taxonomies, which provide ways to understand the similarities and differences among objects under study. Developing a taxonomy, however, is a complex process that is often done in an ad hoc way. This research-in-progress paper uses the design science paradigm to develop a systematic method for taxonomy development in information systems. The method we propose uses an indicator or operational level model that combines both empirical to deductive and deductive to empirical approaches. We evaluate this method by using it to develop a taxonomy of mobile applications, which we have chosen because of their ever-increasing number and variety. The resulting taxonomy contains seven dimensions with fifteen characteristics. We demonstrate the usefulness of this taxonomy by analyzing a range of current and proposed mobile applications. From the results of this analysis we identify combinations of characteristics where applications are missing and thus are candidates for new and potentially useful applications

A TAXONOMY OF MACHINE LEARNING-BASED FRAUD DETECTION SYSTEMS

As fundamental changes in information systems drive digitalization, the heavy reliance on computers today significantly increases the risk of fraud. Existing literature promotes machine learning as a potential solution approach for the problem of fraud detection as it is able able to detect patterns in large datasets efficiently. However, there is a lack of clarity and awareness on which components and functionalities of machine learning-based fraud detection systems exist and how these systems can be classified consistently. We draw on 54 identified relevant machine learning-based fraud detection systems to address this research gap and develop a taxonomic scheme. By deriving three archetypes of machine learning-based fraud detection systems, the taxonomy paves the way for research and practice to understand and advance fraud detection knowledge to combat fraud and abuse

Enabling Factors in Successful Product Development

The research literature and industry best-practices report a vast number of enabling factors that contribute to successful product development (PD). Collectively this body of work also establishes the causal linkages between these enabling factors and overall success in PD. But what specific factors will produce what specific outcomes are vague and ambiguous. To address this apparent void, we find distinct sets of PD enabling factors that are statistically accurate predictors of the specific project outcomes of profit, market share, customer satisfaction, organizational effectiveness, and product quality. We are also motivated to help organizations improve their PD. To that end, we develop a diagnostic tool using the factors that predict our five PD outcomes. The tool is used to pinpoint weaknesses and focus improvements to achieve specific desired outcomes. Results of in situ testing of the tool are reported in this article. The guiding principles of this work are specificity and actionability: specific enabling factors that can produce specific results, and an actionable diagnostic-tool that practitioners can use to improve the practice and results of their PD projects