臺灣運動產業生產總額預測與趨勢分析之研究型

Gross domestic product forecasting and trend analysis in the Taiwan sport industry

緒論：本文運用AI人工智慧與策略管理預測國內運動產業發展，透過政府公開資訊，掌握運動產業脈動、分析國內運動產業與運動行業現況，探討國家體育運動政策與發展議題，建構符合政府單位與業界的「臺灣運動產業生產總額預測模型」。方法：探討2009年至2018年國內12個運動產業與40個運動行業時間序列資料，運用類神經網路預測未來生產總額，運用移動平均法求算趨勢走向，運用策略管理與品牌策略定位分析產業定位與趨勢發展。結果：僅1個「運動場館或設施營建業」呈現正向預測成長，其餘11個運動產業之生產總額皆為下降預測；2017、2018年運動產業生產總額預估準確率平均值分別為94%、97.2%。共計8個運動行業呈現成長預測，其餘32個運動行業之生產總額皆為下降預測；2017、2018年運動行業生產總額預估準確率平均值分別為99.5%、99.4%。12項運動產業經由策略矩陣分析全數呈現成長趨勢，輔以運動產業生產總額時間序列，得知全數運動產業趨勢線同樣成長趨勢，意即整體運動產業發展持續上升趨勢。40項運動行業經由策略矩陣定位結合運動行業生產總額時間序列，得知明星行業與金牛行業趨勢與趨勢線為成長趨勢，問題行業與瘦狗行業趨勢與趨勢線則為下降趨勢。結論：本文提出六項具體建議：一、「預測」與「推估」尋求運動產業解方；二、「策略管理開創運動產業發展方向」；三、「運動產業未來趨勢探索與建議」；四、「運動行業未來趨勢探索與建議」；五、「運動產業範疇定義，大數據管理與因應」；六、「科技管理研究發展與學術應用」。

Introduction: This study used artificial intelligence and strategic management to predict sports industry development in Taiwan. Through public government information, we aimed to determine the dynamics of the sports industry and analyze the current situation of domestic sports industries and sports industry sectors. We also explored national sports policies and development issues to construct a Taiwan Sports Industry Gross Domestic Product Forecasting Model that aligns with the requirements of government agencies and the sports industry. Methods: This study explored time series data for 12 sports industries and 40 sports industry sectors from 2009 to 2018. We used artificial neural networks to predict future gross domestic product (GDP). We also used the moving average method to calculate the trend direction and used strategic management and brand positioning analysis to analyze industry positioning and trend development. Results: Only one sports industry, Sports Venue or Facility Construction, had an optimistic forecast for growth, while the GDP of the remaining 11 sports industries was predicted to decline. The average GDP forecasting accuracy for the sports industry was 94% in 2017 and 97.2% in 2018. Among the 40 sports industry sectors, eight had growth forecasts, while the GDP of the other 32 sectors was predicted to decline. The average GDP forecasting accuracy for sports industry sectors was 99.5% in 2017 and 99.4% in 2018. In the strategic matrix analysis, all 12 sports industries showed a growth trend. All sports industries showed a consistent upward trend in the GDP time series, indicating overall continuous growth in the sports industry. Similarly, among the 40 sports industry sectors, the Star and Cash Cow sectors showed growth trends and trend lines in the strategic positioning matrix and GDP time series. In contrast, the Question Marks and Dogs sectors showed declining trends and trend lines. Conclusion: This study makes six specific recommendations: (1) Seek solutions for the sports industry through prediction and estimation, (2) use strategic management to chart the course for developing the sports industry, (3) explore and propose future trends in the sports industry, (4) explore and propose future trends in the various sports industry sectors, (5) define the scope of the sports industry and address significant data management and its implications, and (6) foster research and development in technology management and its academic applications. These recommendations aim to provide valuable insights and guidance for the advancement and strategic planning of the sports industry, considering future trends and technological advances.