درخت حوزه‌های تخصصی

This study aimed to investigate the role of gender stereotypes in women's participation in sports. The present study was conducted with a qualitative, constructivist approach, serving its purpose. The participants in this study were 15 Iranian women and girls aged 15 to 55 who participated in sports recreationally or professionally and were selected as the final sample using a theoretical and purposive sampling method. Based on the results of the interviews, the focused codes extracted from the initial codes, which are presented in the form of theoretical codes of gender stereotypes in this study, include negative views of families towards girls' sports, social and cultural norms governing in sports, male dominance in some sports, breaking taboos in sports, dominant forces in sports, distrust of female athletes, and common stereotypes in sports that were identified. Therefore, changing social and cultural norms, educating and informing families through increased media coverage of women's sports, and role modeling will reduce gender stereotypes in sports and create conditions where women and girls can engage in sports without cultural and social restrictions.

The research utilizes bibliometric analysis and negative binomial regression to conduct a retrospective evaluation of the Journal of Sport & Tourism (JS&T), widely recognized as the oldest and most prominent publication in the industry. The objective is to gain a comprehensive understanding of the development and patterns seen in sports tourism area. We analyzed 243 articles published in Journal of Sport & Tourism from 2006 to 2022 (Volume 26, Issue 2). The first bibliometric analysis of Journal of Sport & Tourism reveals that the journal has significantly contributed to the sport tourism area for academics and professionals. For example, the percentage of female co-authors is, on average, 35.8%. However, a significant portion (42.38%) of all publications have been authored solely by men, with no female co-authors. These findings suggest an opportunity to enhance gender diversity in the journal's publications and research. In addition, the negative binomial regression results show that the article age variable positively correlates with citation, while the demeaned age squared variable displays a negative association. Also, we found a significant association between gender and citations per year but no significant relationship between gender and total citations. The detailed interpretation of these findings contributes to a deeper understanding of the intricate dynamics surrounding gender-related factors in scholarly impact, emphasising the importance of considering annualised and cumulative citation metrics for a comprehensive evaluation. As a results, the study provides a lens for academics and practitioners to follow to understand the evolution of sport tourism.

The purpose of this paper was to analyze the performance of elite taekwondo athletes using machine learning approaches to achieve three objectives: categorizing performance into four clusters ranging from excellent to poor, identifying key physical characteristics influencing performance, and predicting medal-winning potential in world competitions. This study employs the National Olympic Academy dataset of Iranian taekwondo athletes’ physical fitness and anthropometric records from 1996 to 2019 to develop descriptive and predictive models. In datasets comprising 999 female and 1560 male records, SOM-means and SOM-spectral clustering algorithms achieved average test efficiencies of 80%, for Silhouette, and 20% for Davies-Bouldin in the female dataset, and 79% and 34% respectively, in the male dataset, identifying four performance clusters based on physical attributes and medal distribution. A semi-supervised learning model with the CPLE-Learning algorithm demonstrated medal prediction capabilities, with accuracy rates of 68%, 59%, and 73% for predicting gold, silver, and bronze medals in the female dataset, and 58%, 61%, and 54% in the male dataset. These findings highlight the effectiveness of machine learning in sports performance analysis, offering valuable insights for managing taekwondo athletes and enhancing physical preparation strategies.

The aim of this study was to develop a comprehensive model for strategic thinking in sports federations in Iran. This research is a qualitative exploratory study and a practical one that has been done using thematic analysis. The research population consisted of experts and elites in the field of strategic sport management, with having knowledge of the sport federations. The research participants based on the triangulation technique included: faculty members of sports management of universities, senior managers with the experience in the field of sports, as well as athletes and referees with experience of professional sports. 16 participants were interviewed through purposive and snowball sampling until reaching theoretical saturation. The tools used to collect data included a semi-structured interview based on the main questions and objectives of the research. To increase the accuracy and validity of the analysis at different stages, the codes, sub-themes and main themes were provided to research team as well as two interviewed experts and the necessary modifications were made in accordance with their opinions. The retest reliability method was used to ensure the reliability of the findings. Credibility, dependability, transformability, and confirmability were also used to confirm the validity. Finally, based on the six-stage thematic analysis model of Braun and Clarke (2006), the final research model was developed which included: “effective factors”, “strategic thinking elements” in sport federations, and “Strategic thinking consequences” in sports federations. Considering the findings of this study can pave the way for developing strategic thinking in sport federations.

This study aimed to identify the challenges and drivers of AI-based electronic marketing in the sports industry of Iran. The research employed a qualitative approach with thematic analysis. The research population consisted of academic and organizational experts in the sports industry. Sampling was conducted using purposive (judgmental) and snowball methods, and interviews continued until theoretical saturation reached at the twelfth interview. To validate the research, the triangulation of data sources, peer review, and participant review method was utilized. Additionally, to check reliability, inter-coder reliability and test-retest reliability were employed. Data analysis was performed using MAXQDA2020 software in three stages of coding (instance, main theme, sub-theme). The results indicated that the challenges of AI-based electronic marketing in the Iranian sports industry include "service quality assurance, customer rights enforcement, security and privacy protection, inadequate employee knowledge and skills, lack of appropriate statutes and regulations, incorrect audience recognition and needs assessment, lack of focus on disruptive technologies, improper use of AI, increased internet consumption for unsolicited advertisements." The drivers encompass "providing low-interest loans, training and skill development, utilizing digital technologies, applying marketing mix, knowledgeable and informed management, holding briefing sessions, customer communication channels, research development, information dissemination and advertising, standard formulation, governmental support, smart technology development and application, investment, motivation and hope for progress, tax exemptions, culture building, business coaching, academic education, infrastructure and equipment, and branding.