In sports education, psychological strength is considered as an integral component in the performance of any athlete. One of the effective ways to improve many psychological attributes involves training. The sample consisted of 450 athletes divided into an experimental group (EG) (n = 225) that received eight-week imagination training and a control group (CG) (n = 225) that continued with conventional training. Data was examined using the latest version of SPSS 17.0, using descriptive statistics, paired t-tests, and Analysis of covariance (...) (ANCOVA) to control for pre-existing differences. Psychological strength, mental resilience, focus and concentration, confidence, and stress management are important in athletic performance. It is proposed as a method of enhancing these psychological attributes in sports education. There were differences in significance in the EG in psychological strength, focus and concentration, confidence, and stress management compared with the CG. Paired t-tests in these variables reflect p< 0.05. ANCOVA supported the efficiency of imagination training. Imagination training significantly develops psychological strength and other related attributes in athletes. In all the variables studied, the EG performed better compared to the CG, which provided evidence that imagination training is indeed a helpful tool in enhancing psychological strength and performance in sports education. (shrink)

In business ethics, there is a large body of literature focusing on the conditions, factors, and influences in the ethical decision-making processes. This work builds upon the past critical reviews by updating and extending the literature review found in Craft’s :221–259, 2013) study, extending her literature review to include a total of 141 articles. Since past reviews have focused on categorizing results based upon various independent variables, we instead synthesize and look at the trends of these based upon the four (...) ethical decision making categories: Awareness, Behavior, Judgment, and Intention. We focus on the moderation and mediation effects found within these studies and provide an in-depth analysis of future trends. Furthermore, we also highlight key statistical and methodological concerns, outline overarching trends, and directions of future research in empirical ethical decision making. (shrink)

Deep learning models are powerful tools for representing the complex learning processes and decision-making strategies used by humans. Such neural network models make fewer assumptions about the underlying mechanisms thus providing experimental flexibility in terms of applicability. However, this comes at the cost of involving a larger number of parameters requiring significantly more data for effective learning. This presents practical challenges given that most cognitive experiments involve relatively small numbers of subjects. Laboratory collaborations are a natural way to increase overall (...) dataset size. However, data sharing barriers between laboratories as necessitated by data protection regulations encourage the search for alternative methods to enable collaborative data science. Distributed learning, especially federated learning, which supports the preservation of data privacy, is a promising method for addressing this issue. To verify the reliability and feasibility of applying FL to train neural networks models used in the characterization of decision making, we conducted experiments on a real-world, many-labs data pool including experiment data-sets from ten independent studies. The performance of single models trained on single laboratory data-sets was poor. This unsurprising finding supports the need for laboratory collaboration to train more reliable models. To that end we evaluated four collaborative approaches. The first approach represents conventional centralized learning and is the optimal approach but requires complete sharing of data which we wish to avoid. The results however establish a benchmark for the other three approaches, federated learning, incremental learning, and cyclic incremental learning. We evaluate these approaches in terms of prediction accuracy and capacity to characterize human decision-making strategies. The FL-based model achieves performance most comparable to that of the CL-based model. This indicates that FL has value in scaling data science methods to data collected in computational modeling contexts when data sharing is not convenient, practical or permissible. (shrink)

Qualitative studies are an important component of business ethics research. This large amount of research covers a wide array of factors and influences on ethical decision making published between 2004 and 2014. Following the methodology of past critical reviews, this work provides a synopsis of the diverse array of qualitative studies in ethical decision making within the business ethics literature. We highlight the distinct and investigative nature of qualitative research, synthesize and summarize findings, and suggest opportunities for future research. We (...) conclude with a recommendation for developing qualitative studies in business ethics and a call for an increased openness when considering this valuable and underrepresented strategy of inquiry. (shrink)