El Diario de Murcia : Periódico para todos: Año VII Número 1812 - 1885 Marzo 18

Jorge Ruiz-Menjivar, Wookjae Heo, and John Grable are contributing co-authors, The Effects of Situational and Dispositional Factors on the Change in Financial Risk Tolerance. pp. 201-220. DOI: 10.4018/978-1-4666-7484-4.ch012 Utilizing the lens of Heider\u27s (1958) attribution theory and Grable and Joo\u27s (2004) conceptual framework, this chapter studies the effect of situational and dispositional attributions on changes in financial risk tolerance. Situational factors are assessed through changes in household situation and changes in macroeconomic factors. For dispositional factors, changes upon sensation seeking attitudes are explored. The data employed in this research come from the 1993, 1994, and 2006 National Longitudinal Survey of Youth (N = 5,449). Results from structural equation modeling indicate that changes in internal attributions have a significant and positive effect (coefficient = 0.12, p \u3c0.01) on the change in risk tolerance, as is true for changes in external attributions where a significant effect is seen (coefficient = 0.30, p \u3c0.01). Thus, the findings from this study support the conceptual framework premised on Heider\u27s attribution theory and Grable and Joo\u27s (2004) conceptual model.https://openprairie.sdstate.edu/consumer-sci_book/1001/thumbnail.jp

Understanding the Investment Behavior of Individual Investors:An Empirical Study on FOREX Markets

The FOREX market has become a popular ground amongst all kinds of market players. The leverage transactions of the market that may generate higher profit levels with low capital/investments make it very attractive for the individual risk takers. The research investigates the trading behavior of FOREX investors relying on the survey data collected from 167 Turkish investors in 2019. Within the scope of the research, the authors evaluate whether and to what extent behavioral factors, namely demographic characteristics; personal characteristics such as personality traits, love of money, and biases like disposition effect influence investment performance. The results reveal that among the personality traits, openness to experience and conscientiousness have a positive impact while disposition effect and love of money have a negative impact on the performance of investors. Additional analysis suggests that the effects of personality traits and biases on trading performance remarkably change among subgroups of investors regarding their income level. © 2020, IGI Global

Affective tutoring systems: Enhancing e-learning with the emotional awareness of a human tutor

This paper introduces the field of affective computing, and the benefits that can be realized by enhancing e-learning applications with the ability to detect and respond to emotions experienced by the learner. Affective computing has potential benefits for all areas of computing where the computer replaces or mediates face to face communication. The particular relevance of affective computing to e-learning, due to the complex interplay between emotions and the learning process, is considered along with the need for new theories of learning that incorporate affect. Some of the potential means for inferring users’ affective state are also reviewed. These can be broadly categorized into methods that involve the user’s input, and methods that acquire the information independent of any user input. This latter category is of particular interest as these approaches have the potential for more natural and unobtrusive implementation, and it includes techniques such as analysis of vocal patterns, facial expressions or physiological state. The paper concludes with a review of prominent affective tutoring systems and promotes future directions for e-learning that capitalize on the strengths of affective computing

The application of affective computing technology to e-learning

This chapter discusses the domain of affective computing and reviews the area of affective tutoring systems: e-learning applications that possess the ability to detect and appropriately respond to the affective state of the learner. A significant proportion of human communication is non-verbal or implicit, and the communication of affective state provides valuable context and insights. Computers are for all intents and purposes blind to this form of communication, creating what has been described as an “affective gap.” Affective computing aims to eliminate this gap and to foster the development of a new generation of computer interfaces that emulate a more natural human-human interaction paradigm. The domain of learning is considered to be of particular note due to the complex interplay between emotions and learning. This is discussed in this chapter along with the need for new theories of learning that incorporate affect. Next, the more commonly applicable means for inferring affective state are identified and discussed. These can be broadly categorized into methods that involve the user’s input and methods that acquire the information independent of any user input. This latter category is of interest as these approaches have the potential for more natural and unobtrusive implementation, and it includes techniques such as analysis of vocal patterns, facial expressions, and physiological state. The chapter concludes with a review of prominent affective tutoring systems in current research and promotes future directions for e-learning that capitalize on the strengths of affective computing