The aim of the present study was to investigate the relation of technology, organizational culture and emotional intelligence with knowledge management using the mediators of organizational structure and empowerment. The methodology of the research was descriptive-correlational and the population of the study consisted of all the physical education instructors of Zanjan universities with three-year teaching record (61 people). The population size using the census sample criterion. Research tool included Stankosky and Baldanza’s technology, organizational culture and structure, Bar-On’s emotional intelligence inventory, Spreitzer and Mishra’s empowerment, Kordnaij et al. and Newman and Conrad’s knowledge management framework questionnaires. The structural equation modelling was used via Smart PLS 2 software for analyzing the data. The results showed that there is a negative and significant relation between technology and knowledge management. Also, there is significant relation between organizational culture and knowledge management, emotional intelligence and knowledge management, technology and organizational structure, organizational culture and organizational structure, technology and empowerment, organizational culture and empowerment, organizational structure and empowerment and empowerment and knowledge management; while the significance of relations between organizational structure and knowledge management and emotional intelligence and empowerment were not confirmed. The results of the present study can help the people in charge of education and research in the universities in order to produce, keep and use the needed knowledge related to proper time and place by making decisions and educating people.
Physical activity can contribute to societal health and prevent antisocial behaviors. This study explored the driving forces facilitating these goals in Iran’s socio-cultural context. Through a literature review, investigation of available political documents, interviews with experts and consensus of the research team, seventy-three driving forces were explored from different domains and then categorised via the STEEPV framework. This framework considers drivers from Social, Technological, Environmental, Economic, Political, and Value/Cultural dimensions. The “sport/sport sciences” domain was also considered as an additional domain. In the next step, a questionnaire with an answer scale of 1 to 7 was distributed among experts. The fuzzy Delphi method was used to analyse the collected data. Results showed eighteen drivers from five domains (social, environmental, economic, technological and sport/sports sciences) dramatically influenced leisure time physical activity (LTPA) in Iran. “Physical activity opportunities for vulnerable groups” was identified as the most important driver for participation in LTPA. Results suggest the need for a multidimensional and thorough consideration by organisations, leisure managers and policymakers to discover methods to promote health-related physical activities in the future.
The aims of this study were to adapt the Hungarian version of the Sport Commitment Questionnaire-2 and test an expanded Sport Commitment Model (SCM) with psychological variables.
Participants were 526 adolescent athletes (aged 14-18 years, 52.3% males). Applied scales were the following: Hungarian version of the Sport Commitment Questionnaire-2, Consideration of the Future Consequences Scale and Health Attitudes Scale. Exploratory, confirmatory, and path analysis were used for statistical analysis.
Our result showed adequate construct validity of the Hungarian version of Sport Commitment Questionnaire-2. We found several positive predictors of Enthusiastic Commitment and three positive predictors of Constrained Commitment. We found that Health Attitudes had positive relationship with Constrained Commitment and it was associated with future goals and plans; whereas Enthusiastic Commitment had a positive relationship, and Constrained Commitment had a negative relationship with Future Orientation.
Information about sport commitment provided by Sport Commitment Questionnaire may be useful as a tool to prevent dropout among young athletes.
European Research Council Executive Agency, (ERCEA), has the mission to encourage the highest quality research in Europe through competitive funding and to support investigator-driven frontier research across all field, on the basis of scientific excellence. In 2019, European Research Council (ERC) updates the Panel Structure in 3 areas: Social Sciences and Humanities SH, Physical Sciences and Engineering PE, Life Sciences LS, 25 panels and 333 sub-panels. Every UE countries are updating own academic body system to align to the ERC. In Italy, this alignment is not possible because Movement and sport science has been together place SH and LS as academic disciplines of Physical training and Sport sciences. This is the vexata quaestio that makes the Italian academic system different from the other EU countries with consequences on the development of Italian research in Europa. Historical review explains why this division exists and why it begun after the second great war and developed to nowadays, determining an atypical model than others European countries. Movement and sport science should to be reasonably placed in an unique scientific area or alignments coherently at the related subpanels according to the scientific evidences, even if they are placed in more ERC areas. Both options can be applied according to ERC thought to resolve the actual problem.
The aim of the study was to evaluate the correlation between temperament and stress, to assess the stress level and perform comparative analysis of feeling of stress before and after the race. The test group consisted of 30 competitors from Mazovian cycling clubs between the ages of 15 and 16 (M = 15.5, SD = 0.50). Standard psychological questionnaires were used for the study. The level of stress was tested using the PSS 10 questionnaire by S. Cohen, T. Kamarck and R. Mermelstein. In addition, temperament was studied with Formal Characteristics of Behaviour – Temperament Inventory by Zawadzki and Strelau (1997). Measures were used to determine the constant predisposition of cyclists to feel the level of stress, as well as to show the intensity of stress during sports competitions (before and after the start). Statistical analyses carried out with the Wilcoxon test showed a significant difference between the initial and final value of the stress level as a condition in the subjects. It was found that in the same people, stress reached a higher average level after the race (M = 17.8, SD = 6) than before the performance (M = 11.83, SD = 5.9). The results show that the state of stress does not decrease after the start, as occurs with other variables (including emotional arousal). The results showed that stress measured before and after the start of a competition positively correlates with perseverance and emotional reactivity, while stress before the start negatively correlates with briskness. Observations from the analyses carried out may broaden the understanding of the phenomenon of stress, especially in aspects of sport competition and track cyclists.
While discussion and media coverage of esports (i.e., organized competitive video gaming) has dramatically increased since 2016, the use of esports by established consumer brands has not been emphasized in the sport marketing and sponsorship literature. Though appearing in limited sport management research, esports is a non-traditional sport form that generated just under $1.2 billion in revenue as an industry in 2019. However, many non-endemic traditional consumer brands have resisted capitalizing on esports brand-building opportunities. This paper provides a literature review of the past and current esports and sport marketing literature, resulting in the creation of a figure depicting the esports endemic and non-endemic company evolution of esports brand utilization. The evolution of the competitive video game market details how endemic companies are more apt to establish themselves in the esports space before non-endemic companies because of the way that the industry moves and has acceptance by gamers and non-gamers. Marketers and brand managers that have historically employed traditional sports may glean ideas on how to best enhance and extend their brand through the burgeoning esports industry. Moreover, ideas regarding when companies should enter the esports ecosystem is provided.
Most historical National Football League (NFL) analysis, both mainstream and academic, has relied on public, play-level data to generate team and player comparisons. Given the number of oft omitted variables that impact on-field results, such as play call, game situation, and opponent strength, findings tend to be more anecdotal than actionable. With the release of player tracking data, however, analysts can better ask and answer questions to isolate skill and strategy. In this article, we highlight the limitations of traditional analyses, and use a decades-old punching bag for analysts, fourth-down strategy, as a microcosm for why tracking data is needed. Specifically, we assert that, in absence of using the precise yardage needed for a first down, past findings supporting an aggressive fourth down strategy may have been overstated. Next, we synthesize recent work that comprises this special Journal of Quantitative Analysis in Sports issue into player tracking data in football. Finally, we conclude with some best practices and limitations regarding usage of this data. The release of player tracking data marks a transition for the league and its’ analysts, and we hope this issue helps guide innovation in football analytics for years to come.
Statistical analysis of defensive players in football has lagged behind that of offensive players, special teams, and coaching decisions, largely because data on individual defensive players has historically been lacking. With the introduction of player tracking data from the NFL, researchers can now tackle these problems. However, event and strategy annotations in the NFL’s tracking data are limited, especially when it comes to describing what defensive players do on each play. Moreover, methods for creating these annotations typically require extensive human labeling, which is difficult and expensive. Because of the importance of the passing game and the limited prior research on the defensive side of passing, we provide annotations for the pass coverage types of cornerbacks using unsupervised learning techniques, which require no training data. We define a set of features from the tracking data that distinguish between “man” and “zone” coverage. We use mixture models to create clusters corresponding to each group, allowing us to provide probabilistic assignments to each coverage type (or cluster). Additionally, we quantify each feature’s influence in distinguishing defensive pass coverage types. Our work makes possible several potential avenues of future NFL research into defensive backs and pass coverage strategies.