• 한국벤처창업학회:학술대회논문집
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• 2022.11a
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• pp.227-236
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• 2022

벤처기업 CEO의 본질적 역량과 역할은 조직의 자원을 얼마나 효율적으로 운영하는가에 따라 관리자로서 조직성과에 큰 영향을 미친다. 이러한 중요성에도 불구하고 CEO 역량 수준이 조직이 내재하는 자원에 미치는 영향에 대한 이론적 고찰과 CEO의 역량 수준과 조직성과 간의 관계가 조직 구성원의 역량에 따라서 어떻게 달라지는가에 관한 실증연구는 매우 부족한 실정이다. 기존 선행연구의 한계점을 보완하기 위해 본 연구에서는 자원기반이론(resource-based view of the firm)을 바탕으로 프로스포츠 산업에서의 조직 구성원의 역량에 따른 관리자의 감독역할에 대해 실증적으로 분석하였다. 구체적으로, 본 연구에서는 기존의 스포츠 기업가정신(sport entrepreneurship) 연구 분야의 이론을 경영전략의 자원기반관점과 융합하여 벤처기업 CEO와 프로스포츠 감독의 역할이 조직구조와 성과 메커니즘의 측면에서 매우 흡사하며 조직의 자원을 효율적으로 운영하고 성과를 도출하는 측면에서 모두 기업가(entrepreneur)적 특성을 반드시 내재해야 한다고 본다. 이러한 맥락에서 프로스포츠팀에서의 관리자로서의 감독 역량과 조직성과 간의 관계에서 조직의 자원 효율성의 매개효과와 조직 구성원 역량에 대한 조절효과를 설명하고자 한다. 미국프로농구(NBA) 30개 구단과 한국프로농구(KBL) 10개 구단의 9개 시즌(2013~2014시즌 - 2021~2022시즌)의 감독과 팀 데이터를 실증분석에 있어 프로세스 매크로 58 모형을 적용하여 본 연구의 가설을 검증하였다. 실증분석 결과, 미국프로농구과 한국프로농구 데이터 모두에서 (1) 프로농구팀의 자원 효율성은 감독의 역량과 승률 간의 정(+)의 관계를 매개하고, (2) 조직 구성원의 역량은 농구팀의 자원 효율성을 통한 감독 역량이 승률에 미치는 간접효과를 조절(p<.05) 하는 것으로 나타났다. 본 연구는 비교적 객관적으로 조직의 성과측정이 가능한 프로스포츠 데이터를 활용하여 프로스포츠 산업에서 벤처기업의 CEO와 유사한 기업가적 역할을 수반해야 하는 감독 및 조직 구성원의 역량이 조직의 성과에 미치는 영향을 실증분석하는 한편 스포츠 애널리틱스(sport analytics) 분야와 경영학 연구를 융합하였다는 의의가 있다.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.6
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• pp.195-208
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• 2022

The role of venture CEO and their intrinsic capabilities on organizational performance can be determined by the level of resource synchronization initiated by the focal managers. Despite the important role of venture CEOs, a systematic lack of in-depth theoretical and empirical studies on ruminating the relationship between the effects of a CEO's capabilities and organizational performance depending on the level of resource synchronization exist for the rationale of investigation. To supplement the limitations of previous studies, this research empirically analyzes the role of managers specifically synchronizing organizational resources that affect organizational performance in the professional sports industry. Based on the entrepreneurship theory and resource-based view (RBV), this research conceptualizes the roles of venture CEO and basketball head coach in the professional sports industry as very similar in terms of organizational structure and performance mechanism embedding entrepreneurial characteristics necessary for managing organizational resources. In this research, we hypothesized (1) organizational resource synchronization will mediate the positive relationship between the ability of professional basketball head coach and organizational performance and (2) the indirect effect of the professional basketball head coach's capabilities on organizational performance mediated by resource synchronization will be moderated by the capabilities of players. To test these hypotheses, we utilized the PROCESS macro model 58 with the empirical data of 9 seasons (2013~2014-2021~2022) of 30 National Basketball Association (NBA) and 10 Korean Basketball League (KBL) teams. The statistical results showed that (1) resource synchronization mediates the positive relationship between professional basketball head coach capabilities and organizational performance and (2) the capabilities of players moderated the indirect effects of the abilities of head coaches on team performance via resource synchronization. This paper contributes to both academic and practical domains of entrepreneurship by empirically testing the research model through objective professional sports data.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.117-129
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• 2023

Organizations must seek change in order to adapt to environmental changes and achieve better performance. However, despite this obvious statement, empirical analysis has been almost non-existent due to the difficulty of manipulating organizational performance or change. In this study, we overcame these limitations and analyzed the causes and effects of organizational change by assuming a professional sports team as a venture company, which is relatively easy to objectively measure and evaluate organizational change or performance. We systematically collected and preprocessed traditional and advanced metrics of National Basketball Association (NBA) statistics along with preprocessed trade data from eight years of regular seasons (2014~2015-2021~2022) to analyze our research model. Assessment of process macro model 7 derives the following empirical result. The results of the empirical analysis depict that NBA teams with low organizational performance in the previous season are more likely to make organizational changes through player trades to improve performance. Into the bargain player trades mediate the static relationship between the winning percentage in the previous season and the winning percentage in the current season. However, the indirect effect of a team's previous season's performance on player trades appears to vary depending on the current situations and context of each NBA team. Teams that made the playoffs in the previous season tend to make fewer trades than teams that did not and the previous season's performance is highly correlated with the current season's performance. On the other hand, teams that did not make the playoffs in the previous season tend to make a relatively larger amount of player trades in total, and the mediating effect of trades vanishes in this case. In other words, teams that did not make the playoffs in the previous season experience a larger change in performance due to trades than teams that made the playoffs, even if they achieved the same winning percentage. This empirical analysis of the inverse relationship between organizational change and the performance of professional sports teams has both theoretical and practical implications in the field of sports industry and management by analyzing the fundamentals of organizational change and the performance of professional sports teams.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.263-286
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• 2022

This study acquires NBA statistical information for a total of 32 years from 1990 to 2022 using web crawling, observes variables of interest through exploratory data analysis, and generates related derived variables. Unused variables were removed through a purification process on the input data, and correlation analysis, t-test, and ANOVA were performed on the remaining variables. For the variable of interest, the difference in the mean between the groups that advanced to the playoffs and did not advance to the playoffs was tested, and then to compensate for this, the average difference between the three groups (higher/middle/lower) based on ranking was reconfirmed. Of the input data, only this year's season data was used as a test set, and 5-fold cross-validation was performed by dividing the training set and the validation set for model training. The overfitting problem was solved by comparing the cross-validation result and the final analysis result using the test set to confirm that there was no difference in the performance matrix. Because the quality level of the raw data is high and the statistical assumptions are satisfied, most of the models showed good results despite the small data set. This study not only predicts NBA game results or classifies whether or not to advance to the playoffs using machine learning, but also examines whether the variables of interest are included in the major variables with high importance by understanding the importance of input attribute. Through the visualization of SHAP value, it was possible to overcome the limitation that could not be interpreted only with the result of feature importance, and to compensate for the lack of consistency in the importance calculation in the process of entering/removing variables. It was found that a number of variables related to three points and errors classified as subjects of interest in this study were included in the major variables affecting advancing to the playoffs in the NBA. Although this study is similar in that it includes topics such as match results, playoffs, and championship predictions, which have been dealt with in the existing sports data analysis field, and comparatively analyzed several machine learning models for analysis, there is a difference in that the interest features are set in advance and statistically verified, so that it is compared with the machine learning analysis result. Also, it was differentiated from existing studies by presenting explanatory visualization results using SHAP, one of the XAI models.

• The Journal of the Convergence on Culture Technology
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• v.6 no.1
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• pp.179-183
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• 2020

Seligman argues that an optimistic athlete wins based on a study of US professional baseball and professional basketball players.(trans. Choi, 2008). So, the purpose of this study was to investigate whether optimism can predict the performance of badminton youth representatives. 60 high school students were divided into two groups according to their performance. We used the revised Life Orientation Test (LOT-R) developed by Scheier, Carver and Bridges(1994) and translated into Korean version by Shin Hyun-sook(2005). For this study, the data were analyzed by frequency analysis, reliability analysis, and independent t - test. As a result, there was a statistically significant higher level of optimism in the excellent group than in the non-excellent group. So, in order to improve sports performance, we need psychological skills training that can improve optimism as well as physical training and skills training.