The Economics and Psychology of Personality Traits

This paper explores the interface between personality psychology andeconomics. We examine the predictive power of personality and the stability ofpersonality traits over the life cycle. We develop simple analytical frameworksfor interpreting the evidence in personality psychology and suggest promisingavenues for future research.education, training and the labour market;

The Economics and Psychology of Personality Traits

This paper explores the interface between personality psychology and economics. We examine the predictive power of personality and the stability of personality traits over the life cycle. We develop simple analytical frameworks for interpreting the evidence in personality psychology and suggest promising avenues for future research.personality traits, lifecycle effects, psychology, economics

The Economics and Psychology of Personality Traits

This paper explores the interface between personality psychology and economics. We examine the predictive power of personality and the stability of personality traits over the life cycle. We develop simple analytical frameworks for interpreting the evidence in personality psychology and suggest promising avenues for future research.lifecycle effects, personality traits

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

Investigating the 'latent' deficit hypothesis : age at time of head injury, executive and implicit functions and behavioral insight

This study investigated the 'latent deficit' hypothesis in two groups of frontotemporal headinjured patients, those injured prior to steep morphological and corresponding functional maturational periods for frontotemporal networks (≤ age 25), and those injured >28 years. The latent deficit hypothesis proposes that early injuries produce enduring cognitive deficits manifest later in the lifespan with graver consequences for behavior than adult injuries, particularly after frontal pathology (Eslinger, Grattan, Damasio & Damasio, 1992). Implicit and executive deficits both contribute to behavioral insight after frontotemporal head injury (Barker, Andrade, Romanowski, Morton & Wasti, 2006). On the basis of morphological and behavioral data, we hypothesised that early injury would confer greater vulnerability to impairment on tasks associated with frontotemporal regions than later injury. Patients completed experimental tasks of implicit cognition, executive function measures and the DEX measure of behavioural insight (Behavioral Assessment of the Dysexecutive Syndrome: Wilson, Alderman, Burgess, Emslie, & Evans, 1996). The Early Injury group were more impaired on implicit cognition tasks compared to controls that Late Injury patients. There were no marked group differences on most executive function measures. Executive ability only contributed to behavioral awareness in the Early Injury Group. Findings showed that age at injury moderates the relationship between executive and implicit cognition and behavioral insight and that early injuries result in longstanding deficits to functions associated with frontotemporal regions partially supporting the latent deficit hypothesis

The estimation of Human Capital in structural models with flexible specification

The present paper focuses on statistical models for estimating Human Capital (HC) at disaggregated level (worker, household, graduates). The more recent literature on HC as a latent variable states that HC can be reasonably considered a broader multi-dimensional non-observable construct, depending on several and interrelate causes, and indirectly measured by many observed indicators. In this perspective, latent variable models have been assuming a prominent role in the social science literature for the study of the interrelationships among phenomena. However, traditional estimation methods are prone to different limitations, as stringent distributional assumptions, improper solutions, and factor score indeterminacy for Covariance Structure Analysis and the lack of a global optimization procedure for the Partial Least Squares approach. To avoid these limitations, new approaches to structural equation modelling, based on Component Analysis, which estimates latent variables as exact linear combinations of observed variables minimizing a single criterion, were proposed in literature. However, these methods are limited to model particular types of relationship among sets of variables. In this paper, we propose a class of models in such a way that it enables to specify and fit a variety of relationships among latent variables and endogenous indicators. Specifically, we extend this new class of models to allow for covariate effects on the endogenous indicators. Finally, an application aimed to measure, in a realistic structural model, the causal impact of formal Human capital (HC), accumulated during Higher education, on the initial earnings for University of Milan (Italy) graduates is illustrated.

Lowering the pirate flag: a TPB study of the factors influencing the intention to pay for movie streaming services

The launch of several movie streaming services has raised new questions about how online consumers deal with both legal and illegal options to obtain their desired products. This paper investigates the factors influencing consumers’ intentions to subscribe to online movie streaming services. These services have challenged the dramatic growth in their illegal counterpart in recent years. Taking the theory of planned behavior as a starting point, we extended existing models in the literature by incorporating factors that are specific to consumer behavior in this particular field. A quantitative survey was conducted for the Italian market, and structural equation modeling was used for data analysis. Attitudes, involvement with products, moral judgement and frequency of past behavior were found to be the most important factors in explaining the intention to pay for movie streaming services. The paper provides insights for policy makers and industry managers on the marketing communication strategies needed to minimize the risk of digital piracy