Street-level new public governances in integrated services-as-a-system

The paper explores the emergence of street-level public service integration in eight cities, in five countries using a new framework: services-as-a-system to explore new public governances in health and social care integration. Data is analysed from eight cities in six countries (Australia, Canada, The Netherlands, Spain, UK, and USA) gathered in over 100 semi-structured interviews with key agents. We show that whilst culture and context shape the form and processes of governances there are underlying processual drivers of new public governances, in particular where users are involved in the co-design and co-production of integrated services.     

Unemployment and econometric learning

We apply well-known results of the econometric learning literature to the Mortensen and Pissarides real business cycle model. Agents can always learn the unique rational expectations equilibrium (REE), for all possible well-defined sets of parameter values, by using the minimum-state-variable solution to the model and decreasing gain learning. From this perspective the assumption of rational expectations in the model could be seen as reasonable. But using a parametrisation with UK data, simulations show that the speed of convergence to the REE is slow. This type of learning dampens the cyclical response of unemployment to small structural shocks.     

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.     

Organizational learning in exporting: A bibliometric analysis and critical review of the empirical research

In the latest decades, empirical research on organizational learning in the export context has recorded an increasing trend. On the other hand, in spite of the wealth of research on the subject, no effort has yet been made to offer an integrative review to investigate the improvement of this body of knowledge. To fill this gap, this study endeavors to synthesize the stream of empirical research on organizational learning in the field of exporting published between 1997 and 2017. Initially, the relevant studies were bibliometrically analyzed on the basis of the data gathered from the Web of Science database. The bibliometric data analysis indicates the most influential publication outlets, most influential institutions, most influential scholars, and most influential articles in relation to organizational learning in the export literature. Additionally, each article was content-analyzed in terms of scope of research, research methodology, and empirical issues. The content analysis reveals that albeit significant progress, organizational learning research within the context of exporting is still at the introduction stage and certain weaknesses in scope, research designs, and conceptual understanding attract criticism.     

Firm-specific assets and the internationalization–performance relationship in the U.S. movie studio industry

We study the role that firm-specific assets (FSAs) play in the processes underlying the internationalization–performance relationship. International business scholars have begun studying the interrelationship between FSAs, internationalization, and performance; however, this research is still emergent, and has produced inconclusive results. We believe that this may be due, in part, to research designs involving the same FSAs across many industries, even though individual industries may rely on different FSAs in their internationalization strategies. We address this issue in a single industry study of U.S. movie studios, which typically rely on blockbuster production capabilities as FSAs in their internationalization efforts. We show how these FSAs co-evolve with firms’ degree of internationalization, and how each factor mediates the positive effect of the other on performance. Our results highlight the importance of studying specific industries and their salient FSAs when assessing their role of such resources in the internationalization–performance relationship.     

The behaviour of betting and currency markets on the night of the EU referendum

We study the behaviours of the Betfair betting market and the sterling/dollar exchange rate (futures price) during 24 June 2016, the night of the EU referendum. We investigate how the two markets responded to the announcement of the voting results by employing a Bayesian updating methodology to update prior opinion about the likelihood of the final outcome of the vote. We then relate the voting model to the real-time evolution of the market-determined prices as the results were announced. We find that, although both markets appear to be inefficient in absorbing the new information contained in the vote outcomes, the betting market seems less inefficient than the FX market. The different rates of convergence to the fundamental value between the two markets lead to highly profitable arbitrage opportunities.     

Mobile learning adoption for tourism education in a developing country

Since developing countries are gradually introducing mobile-based tourism education, it is a growing demand to understand the students’ intention to adopt mobile learning. The study used partial least squares-based structural equation modelling to analyse survey data from 176 questionnaires at three tourism education institutes in Bangladesh. The study contributes to the theory of planned behaviour by examining the antecedent impact of innovativeness and moderating effect of self-efficacy. Results confirmed innovativeness as a significant antecedent on the attitude–intention relationship; however, the moderating effect of self-efficacy has not been supported. The study has marketing implications for tourism education institutes and government bodies.     

Organizational attention and learning under regulatory intervention: Governmental investigation into auto engine quality

We examine the efficacy of government regulation on a firm's product. We draw on the behavioural approach of organization research in order to understand the micromechanisms whereby the regulatory intervention process affects corporate operation. We suggest that while government investigations may limit the improvements in product quality by distracting a firm's attention, this unintended outcome depends on the extent to which the firm engages in a substantive problem‐solving process with the regulator during an investigation process. A longitudinal analysis of the US government's investigation into motor vehicle engine production offers overall support for our argument. The paper concludes with a discussion of the implications that our findings present to learning theory and institutional literature. Copyright © 2017 ASAC. Published by John Wiley & Sons, Ltd.     

One size doesn’t fit all: A project designing small-scale economic development projects

The authors describe an innovative active learning strategy for a course on the economics of developing countries—a project designing a small-scale economic development project. Student teams research issues faced by developing countries and identify a specific problem in a specific locale. Students then create a detailed, feasible plan to alleviate the problem. Student plans include five key components: justification, implementation, budget, funding and evaluation. After having implemented this project for six years, the authors believe it has the potential to enhance learning and improve analytical, creative problem-solving and research skills. Results from a spring 2017 survey, where students respond favorably to the project, are discussed.