Bidding for brains: Intellectual property rights and the international migration of knowledge workers

We introduce international mobility of knowledge workers into a model of Nash equilibrium IPR policy choice among countries. We show that governments have incentives to use IPRs in a bidding war for global talent, resulting in Nash equilibrium IPRs that can be too high, rather than too low, from a global welfare perspective. These incentives become stronger as developing countries grow in size and wealth, thus allowing them to prevent the ‘poaching’ of their ‘brains’ by larger, wealthier markets.     

CHILD MENTAL HEALTH AND EDUCATIONAL ATTAINMENT: MULTIPLE OBSERVERS AND THE MEASUREMENT ERROR PROBLEM

We examine the effect of survey measurement error on the empirical relationship between child mental health and personal and family characteristics, and between child mental health and educational progress. Our contribution is to use unique UK survey data that contain (potentially biased) assessments of each child's mental state from three observers (parent, teacher and child), together with expert (quasi‐)diagnoses, using an assumption of optimal diagnostic behaviour to adjust for reporting bias. We use three alternative restrictions to identify the effect of mental disorders on educational progress. Maternal education and mental health, family income and major adverse life events are all significant in explaining child mental health, and child mental health is found to have a large influence on educational progress. Our preferred estimate is that a one‐standard‐deviation reduction in ‘true’ latent child mental health leads to a 2‐ to 5‐month loss in educational progress. We also find a strong tendency for observers to understate the problems of older children and adolescents compared to expert diagnosis. © 2015 The Authors. Journal of Applied Econometrics published by John Wiley & Sons, Ltd.     

Opportunities for artificial intelligence development in the accounting domain: the case for auditing

This paper reviews the nature of accounting and auditing problems and the need for application of artificial intelligence (AI) technologies to the discipline. The discussion includes current accounting issues for which new AI development should be fruitful, particularly auditing and assurance. Copyright © 2007 John Wiley & Sons, Ltd.     

HRD involvement in merger and acquisition decisions and strategy development: four organizational portraits

Although the financial and strategic aspects of mergers and acquisitions are well researched, little work has been done on the human resource development implications of this popular topic. The purpose of this case study research project was to explore the timing and scope of HRD involvement in four organizations recently involved in a merger or acquisition, by using three theoretical models as lenses. The results demonstrated that HRD normally was not involved in the initial decision‐making to merge or acquire, even though post‐deal HRD initiatives, particularly related to change management, were perceived as critical indicators of success.     

Artificial neural networks in accounting and finance: modeling issues

This article reviews the literature on artificial neural networks (ANNs) applied to accounting and finance problems and summarizes the ‘suggestions’ from this literature. The first section reviews the basic foundation of ANNs to provide a common basis for further elaboration and suggests criteria that should be used to determine whether the use of an ANN is appropriate. The second section of the paper discusses development of ANN models including: selection of the learning algorithm, choice of the error and transfer functions, specification of the architecture, preparation of the data to match the architecture, and training of the network The final section presents some general guidelines and a brief summary of research progress and open research questions. Copyright © 2000 John Wiley & Sons, Ltd.     

Artificial Neural Networks Applied to Ratio Analysis in the Analytical Review Process

Experts claim that artificial neural network (ANN) technology can outperform standard statistical methods when applied to examine actual financial data. Researchers have used ANNs to analyze bankruptcy prediction, bond rating and the going-concern problem. Financial firms have employed ANNs commercially to predict commercial bank failures, detect credit card fraud and verify signatures. For accounting and auditing problems, however, application of ANN technology has been limited. Preliminary experiments tested whether an ANN offered improved performance in recognizing material misstatements during the analytical review process of auditing. Four years of audited financial data from a medium-sized distributor were input as data streams to calibrate the ANN across fifteen financial accounts. Researchers compared a presumed lack of actual errors and certain seeded material errors with signals from the ANN analytical review process to evaluate performance. Results were compared to analyses where financial ratios and regression methods were employed as analytical review techniques. Results tentatively suggest that the ANN method recognized patterns within financial accounts more effectively than did financial ratio and regression methods. ANNs applied as a forecasting tool seem useful for identifying patterns that can indicate potential investigations of a firm's unaudited financial data in the current year.     

Advancing consumer behaviour theory in tourism via visual narrative art

This paper advances (i) propositions of consumer behaviour theory in tourism via storytelling theory and (ii) skills in decoding elements and subtle details that appear in ‘good stories’ versus not‐so‐good stories within travel contexts. The present paper presents a set of iconic story symbols for use in decoding stories and shows how to use these tools for decoding tourism‐related narratives. Practice using these visual iconic symbols in decoding stories achieves effective storytelling learning via cognitive sculpting; that is, learning improves from moving and arranging (sculpting) potentially three‐dimensional story or electronically movable icons while talking or writing narratives. Copyright © 2009 John Wiley & Sons, Ltd.     

Evaluating Expert Systems in a Financial Domain: A Study Demonstrating Issues in Case Selection,Validation Standards and Evaluator Bias

This article describes and generalizes a validation study of four commercially available personal financial planning expert systems and the rationale for the research methodology used. Our evaluation of these systems adds to the understanding of verification and validation issues related to case selection, validation standards and evaluator bias. The article describes the systems, their domain and the empirical method—field tests using hypothetical cases—and relates that method to the literature. Comparing same-task systems combines multiple system perspectives and multiple models. Our methodology did efficiently and effectively identify conflicting terminology, omissions and system weaknesses but was inadequate for comparing the complex plan recommendations. The results re-emphasize the importance of continual knowledge base updating, formal system testing and the need for external evaluation. The results also show the value of comparing multiple, same-task systems.