Flow Signals: Evidence from Patent and Alliance Portfolios in the US Biopharmaceutical Industry

Integrating signalling theory and the portfolio diversity literature, we theorize that diversity in a firm's patent and alliance portfolios sends contrasting flow signals impacting its market value in a nuanced way. Diversity in an alliance portfolio mediates the patent portfolio diversity – market value relationship by suppressing the negative effect of patent portfolio diversity creating an overall positive effect. We test our mediation model on a longitudinal set of 225 US biopharmaceutical firms that were awarded 17,078 patents and participated in 37,744 alliances between 1990 and 2006. Our theory and findings contribute three novel insights. First, we demonstrate the value of a temporal lens in explaining why diversity in a firm's patent and alliance portfolios send flow signals that establish expectations among market observers and have performance implications. Second, establishing that patent and alliance portfolio diversity are temporally sequenced provides compelling evidence for the value of studying multiple types of portfolios, their temporal relationships and effects on firm outcomes. Third, since diversity in a firm's portfolios can send contrasting flow signals conditioned on the cognitive demands and proximity involved in interpreting the signals, firms that do not maintain a ‘signalling fit’ with market observers increase the probability of unintentional negative signalling effects.     

Regulating,joint bargaining,and the demise of precedent

Recent decades have seen a fundamental shift in the nature of economic regulation in the United States. Unauthorized by congress, and largely unnoted in legal and academic circles, regulatory agencies such as the Federal Communications Commission have changed the regulatory process by linking otherwise unrelated regulatory issues. Examples include tying merger approval to firm commitments to engage in conceptually unrelated build‐outs and other projects of political importance. This linking of issues has several effects, the most prominent being (a) tying regulatory issues changes the outcomes obtained, plausibly in predictable ways; (b) tying in some circumstances allows regulators to extend their authority to issues for which they have little or no legal authority; and (c) tied regulatory bargaining fails to produce valid legal precedent for firm decision making. We provide an analysis of these conclusions by examining the increasing use of consent decrees, voluntary merger commitments, and merger conditions by the Federal Communications Commission, referencing our discussion with a simple model of joint bargaining applicable to regulatory practice.     

The role of accounting and the accountancy profession in economic development: A research agenda

A widely held view in the accountancy profession and the donor community is that accounting and the accountancy profession play an essential role in economic development. However, our review of the academic literature finds limited empirical research evidence on the relation between accounting and the accountancy profession and economic development, including specifically whether any such relation is causal, and if so, the direction of the causality. Entities, including those comprising the donor community, policymakers, and professional accountancy organizations (PAOs), need evidence on the question of whether and how the accountancy profession contributes to economic development. Such research could assist donors in evaluating the outcomes of interventions aimed at building the capacity of PAOs in emerging and developing economies and inform future interventions. We summarize the limited academic research addressing the relation between PAOs and economic development and present insights from two roundtables facilitated by the International Association for Accounting Education and Research. We identify research opportunities and research design considerations. We hope our paper will stimulate accounting researchers to advance this literature.     

Determinants of Fair Trade Product Purchase Intention of Dutch Consumers According to the Extended Theory of Planned Behaviour

The study reported in this paper investigated the determinants of fair trade (FT) product purchase intention among Dutch consumers according to the extended Theory of Planned Behaviour and determined whether the effects of those determinants differ between male and female consumers. To test the various research hypotheses, an online survey with 499 respondents from a Dutch research panel was employed. Results of the multi-group analysis using a structural equation modelling approach reveal that FT product purchase intention of both male and female consumers are predicated on moral obligation and self-identity. The impact of subjective norm on purchase intention is statistically significant for male consumers only. Analyses reveal that, indeed, the impact of subjective norm on FT product purchase intention is moderated by consumers’ gender.     

The U-shaped relationship between happiness and age: evidence using world values survey data

The paper retests the U-shaped relationship between happiness and age using the cross-classified multilevel regression procedure and the World Values Survey data. The analysis accounts for period and cohort effects. The results reconfirm the pattern that happiness is U-shaped in the life course. That is, happiness decreases from a high-point in young adulthood, reaches a low-point in midlife, and thereafter increases to arrive at another high-point in old age. The results show that the high-point of happiness in old age is lower than the high-point of happiness in young adulthood. That happiness does not return to its initial high-point after it drops to a low-point in midlife is perhaps another stylized fact in the relationship between happiness and age.     

Russian Nanotechnology Market: High-Technology Industry or Statistical Phenomenon

This article presents a medium-term forecast of the development of the Russian nanoindustry, taking into account the assumed transition of the industry from the formation stage to the investment growth stage. Problems associated with the estimated output volumes of products in the Russian nanoindustry are considered, since the trends recorded in the primary statistical data are very far from reflecting the real situation due to the imperfection of the system of statistics of nano-containing products, as well as a reduction of state funding and a decline in the intensity of research in the industry. It is concluded that a full-fledged industry has not been yet completely formed in Russia and it actually exists only as a statistical phenomenon; in addition, the project really set different objectives from the declared creation of a breakthrough high-technology branch.     

Do retail mergers affect competition? Evidence from grocery retailing

This study estimates the price effects of horizontal mergers in the U.S. grocery retailing industry. We examine fourteen regions affected by mergers, including mergers in highly concentrated and relatively unconcentrated markets. We identify price effects by comparing markets affected by mergers to unaffected markets using difference‐in‐difference estimation with three different comparison groups, propensity score weights, and by using the synthetic control method. Our results are robust to the choice of control group and estimation technique. We find that mergers in highly concentrated markets are most frequently associated with price increases, and mergers in less concentrated markets are most often associated with price decreases.     

Predicting credit card delinquencies: An application of deep neural networks

The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.