研究生: |
蔡雅欣 Tsai, Ya-Hsin |
---|---|
論文名稱: |
臺灣大數據論文研究之現況分析 The Study on Taiwan’s Theses and Dissertations of Big Data |
指導教授: |
程紹同
Cheng, Shao-Tung |
學位類別: |
碩士 Master |
系所名稱: |
體育學系 Department of Physical Education |
論文出版年: | 2017 |
畢業學年度: | 105 |
語文別: | 中文 |
論文頁數: | 119 |
中文關鍵詞: | 運動 、大數據 、內容分析法 |
英文關鍵詞: | Sport, Big Data, Content analysis |
DOI URL: | https://doi.org/10.6345/NTNU202202181 |
論文種類: | 學術論文 |
相關次數: | 點閱:271 下載:19 |
分享至: |
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
在資訊經濟的時代裡,數據已經成為各領域間競爭之利器,如何分析、運用資料以解決各項議題,進而獲得價值,將成為各領域的首要課題。然而,在國內運動領域中,是否也能善用大數據的效益尚不得而知。鑑此,本研究蒐集並分析臺灣大數據碩博士論文內容數量及相關聯情形,進而推估運動大數據未來研究方向。研究對象以2011年-2016年間所發表過的大數據論文為主,採用內容分析法,並以自編「臺灣大數據論文內容分析登錄表」做為研究工具,運用次數分配表及百分比分析進行資料處理。透過臺灣大數據論文全面分析與探討,本研究結論如下:
一、臺灣大數據論文研究現況
自2013年始有論文的產出,2015年論文篇幅數量最多,期刊以社會科學領域最多,學位論文以國立臺灣大學等七所學校佔近五成最多。
二、臺灣大數據論文研究內容
(一)研究主題:在各研究主題統計上,以「其他」為最多,「運動」則相對較少。
(二)研究目的:研究目的以「分析性研究」佔近五成為最多,「敘述性研究」與「理論性研究」兩者並重。
(三)研究方法:研究方法以「次級分析法」佔近五成為最多,其次為「系統建構法」,第三多為「調查研究法」及「實驗研究法」,四者所佔比例超過八成。
(四)統計方法:統計方法以「未使用統計方法」超過五成為最多,其次為「其他」,兩者所佔比例超過八成。
三、臺灣運動大數據未來研究趨勢
目前臺灣運動大數據研究議題較為單一,然而,在穿戴式科技的普及下,個人體適能等相關議題將會成為臺灣運動大數據未來研究趨勢。
基於上述結果,建議大數據研究者及相關單位:(一)增加實證性研究。(二)結合專業(運動領域)與實務課程規劃。(三)強化各種研究方法之應用。對後續研究建議:(一)增加關鍵字數量。(二)研究方法加入德爾菲法 (Delphi method)。(三)增加次要類目,進一步探討各主題之趨勢。(四)研究對象加入其他論文形式。
In the era of information economy, data has become a global tool for international competition in the world. The information analysis and applications have been critical for decision makers of governments, industries, (non) profit organizations, to solve various issues and create values. However, there is few Big Data academic studies have been done including in the field of sport in Taiwan. Thus, the purpose of this study was to analyze Taiwan’s these and dissertations of Big Data during 2011-2016. Content analysis was applied for discussion. Through the comprehensive analysis and discussion of Taiwan’s these and dissertations of Big Data, the conclusions of this study are as follows:
A. The status of Big data papers in Taiwan
Since 2013 the first paper came out, the quantity of the paper kept growing. The highest production of publication is in 2015 recently. The highest production of journal publication is from social science. For thesis and dissertation, the highest production (50%) is from National Taiwan University among seven universities.
B. The research content of Big data papers in Taiwan
(a)Theme of the study: The subject of statistics in the “other” have the largest number, “sport” is relatively few.
(b)Purpose of the study: “Analytical research” have the largest number, “Narrative research” and “Theoretical research” are as much as two.
(c)Research methods: “Secondary analysis”accounted for almost fifty percent, the second one is “System construction”, the third one is “Survey Research” and the forth one is “Experimental Research”. The proportion of the two are more than eighty percent.
(d)Statistical methods: The large number of statistical method is “No statistical method” more than fifty percent, the second is “other”. The proportion of the two are more than eighty percent.
C. The trend of Sport Big data future research in Taiwan
The research topic of Taiwan's Sports Big Data is relatively simple. However, under the popularization of wearable device, personal fitness and other related issues will become the future research trend of Taiwan's Sport Big data.
Based on the results, the recommendation is as follow: (a) increase the empirical study. (b) combined with professional (sports field) and practical curriculum. (c) strengthen the various methods. For the future researcher, the recommendation is (a) increase the number of keywords. (b) use Delphi method in method. (c) increase secondary categories. (d) increase other forms of paper into research object.
一、中文文獻
Insider(2015,9月17日)。看亞馬遜、eBay、Bikeberry,如何妙用大數據優化產品組合和服務。取自:https://www.inside.com.tw/2015/09/17/bigdata-e-commerce
TechNews(2016,8月5日)。引進高科技,外國運動協會靠大數據幫助奧運國手。取自http://technews.tw/2016/08/05/rio-olympics-athletes-are-using-tech/
王旭正(2015)。巨量資料安全技術與應用。臺北市:博碩。
王文科、王智弘(2014)。教育研究法。臺北市:五南。
王豐勝、黃彥文(2013)。台灣雲端巨量資料的策略與啟動-巨量資料分析工具與平台。經濟前瞻,(148),116-120。
王振宇(2011)。臺灣運動贊助碩士論文研究之現況分析(未出版碩士論文)。國立臺灣師範大學,臺北市。
王石番(1991)。傳播內容分析法-理論與實證。臺北市:幼獅文化。
何亦婕(2015)。日本推動智慧醫療照護與巨量資料應用之趨勢觀察。科技法律透析,27(12),51-69。
余孝先、趙祖佑(2015)。巨量資料應用,打造資料驅動決策的智慧政府。國土及公共治理季刊,3(4),27-37。
余承叡(2015,5月14日)。大數據時代的資料分析與應用案例。取自:http://www.math.scu.edu.tw/under/course/speech20150514.pdf
李雅竺(2016,8月10日)。倫敦奧運會是“大數據-SNS奧運會”。取自http://chinese.donga.com/BIG/List/3/all/29/445975/1
沈長振(2003)。我國運動休閒管理碩士論文內容分析之研究(未出版碩士論文)。國立體育大學,桃園市。
車品覺(2014)。大數據的關鍵思考。臺北市:天下雜誌。
林文政(2004)。台灣運動管理學碩士論文研究之現況分析(未出版碩士論文)。國立臺灣師範大學,臺北市。
邱皓政(2015)。量化研究法與統計分析:SPSS (PASW) 資料分析範例解析。臺北市:五南。
施致平、張琪、倪瑛蓮(2012)。運動管理學:臺灣之研究現況與趨勢分析。體育學報,45(3),167 -178。
胡世忠(2015)。雲端時代的殺手級應用:海量資料分析。臺北市:天下雜誌。
飛玉萍(2013)。臺灣運動賽會碩士論文現況分析研究(未出版碩士論文)。國立臺灣體育運動大學,臺中市。
紐文英(2015)。研究方法與論文寫作二版(修訂版)。臺北市:雙葉書廊。
紐文英(2012)。質性研究方法與論文寫作。臺北市:雙葉書廊。
國際貿易局(2016,8月3日)。打造亞太地區貿易新動能 我在APEC提出大數據 (Big Data) 在貿易領域應用倡議獲採認。取自:https://www.moea.gov.tw/MNS/populace/news/News.aspx?kind=1&menu_id=40&news_id=55447
張維君、陳玉倫、蔡國手(2016)。 ITRI IoT PaaS 大數據服務平台介紹。電腦與通訊,52-61。
張書瑋(2015)。物聯網來了!會計大數據的挑戰。會計研究月刊, (357),76-81。
梁定澎(2012)。資訊管理理論。新北市:前程文化。
許禎元(2003)。內容分析法的研究步驟與在政治學領域的應用。師大政治論叢。
郭嘉欣(2001)。我國電子商務研究現況與趨勢 - 碩博士論文之分析(未出版碩士論文)。銘傳大學,臺北市。
陳子軒(2015,3月26日)。魔球咒語:「大數據」真能人定勝天?取自http://opinion.udn.com/opinion/story/5769/790308
陳傑豪(2015)。大數據玩行銷。臺北市:30雜誌。
陳國光(2002)。我國體育運動管理學碩士論文及其引用文獻之分析研究(未出版碩士論文)。國立體育大學,桃園市。
曾龍(2016)。大數據與巨量資料分析。科學發展,(524),66-71。
程紹同(2016)。運動產業4.0時代之大數據新思維。運動管理,(33),19-44。
黃瑞祥(2015,5月28日)。一個分析師的閱讀時間:《魔球》──「大數據」是萬靈丹還是難打的變化球?取自:https://www.smartlinkin.com.tw/article/1123
黃金柱(2009)。體育研究法。臺北市:師大書苑。
楊修(2016,3月10日)。挑戰NIKE龍頭寶座!Under Armour怎麼做到的?取自https://www.managertoday.com.tw/articles/view/52199
楊晨欣(2015,11月9日)。你以為亞馬遜實體店是為了賣書?不,它是為了大數據。取自:http://www.bnext.com.tw/article/37907/BN-2015-11-09-063630-81
楊世文(2006)。臺灣地區運動與休閒管理碩士及期刊論文趨勢研究:2001至2005年(未出版碩士論文)。國立臺灣師範大學,臺北市。
葉志良(2016)。大數據應用下個人資料定義的檢討:以我國法院判決為例。資訊社會研究。(31),1-33。
資策會(2014,9月16日)。行動雲端、巨量資料的資服市場將呈現快速成長態勢。取自:https://mic.iii.org.tw/IndustryObservations_PressRelease02.aspx?sqno=368
遠見(2015,9月17日)。麥爾荀伯格:不用大數據換腦袋,就無法擺脫微利代工。取自:https://www.gvm.com.tw/webonly_content_6326.html
數位天下(2016,7月15日)。大數據會消失,資料科學不會!你該知道的資料科學第一堂課。取自http://www.bnext.com.tw/article/view/id/40220
蔡明學、黃建翔(2015)。大數據分析在我國教育發展應用上之探討。教育脈動。(4),154-164。
蔡清田(2013)。社會科學研究方法新論。臺北市:五南。
謝邦昌、鄭宇庭(2016)。大數據概論。臺北市:新陸。
謝邦昌、陳文慧(2014)。海量資料時代發展與未來應用趨勢之研究。數據分析,9(6),133-143。
闕月清、陳詠儒(2015)。臺灣運動教育學博碩士學位論文研究趨勢:1994-2013。中華體育季刊,29(3),181-187。
顏理謙(2017,4月28日)。New Balance百年品牌的科學創新。取自:https://www.bnext.com.tw/article/44217/new-balance-innovation。
鐘嘉德、柴惠珍、高崎鈞、曹元良(2015)。我國大數據政策推動現況。國土及公共治理季刊,3(4),77-84。
二、英文文獻
ACSM (2016). Annual survey reveals new #1 fitness trend in 2016. Retrieved July 15, 2017, from https://www.acsm.org/about-acsm/media-room/news-releases/2015/10/26/annual-survey-reveals-new-1-fitness-trend-in-2016
Baig, A. R., & Jabeen, H. (2016). Big data analytics for behavior monitoring of students. Procedia Computer Science, 82, 43-48.
Berg, N. (2014, June 25). Predicting crime, LAPD-style. The Guardian, 25. Retrieved November 23, 2016, from https://www.theguardian.com/cities/2014/jun/25/predicting-crime-lapd-los-angeles-police-data-analysis-algorithm-minority-report
Bojanova, I. (2014). IT enhances football at world cup 2014. IT Professional, 16(4), 12-17.
Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662-679.
Brown, B., Chui, M., & Manyika, J. (2011). Are you ready for the era of ‘big data’ . McKinsey Quarterly, 4(1), 24-35.
Business Dictionary (2016). Data definitions. Retrieved October 13, 2016 from http://www.businessdictionary.com/definition/data.html
Church, A. H., & Dutta, S. (2013). The promise of big data for OD: Old wine in new bottles or the next generation of data-driven methods for change. OD Practitioner, 45(4), 23-31.
Cohen, R. A., Gindi, R. M., & Kirzinger, W. K. (2012, March). Financial burden of medical care: early release of estimates from the National Health Interview Survey, January-June 2011. National Center for Health Statistics.
Desouza, K. C., & Jacob, B. (2014). Big data in the publicsector lessons for practitioners and scholars. Administration & Society.
Fanning, K., & Grant, R. (2013). Big data: Implications for financial managers. Journal of Corporate Accounting & Finance, 24(5), 23-30.
Ferreira, J. (2013, July 18). Big data in education: The 5 types that matter. Retrieved October 18, 2016 from http://www.knewton.com/blog/ceo-jose-ferreira/big-data-in-education/
Franzosi, R. (2008). Content analysis: Objective, systematic, and quantitative description of content. Content analysis, 1, XXI-XLX.
Frizzo-Barker, J., Chow-White, P. A., Mozafari, M., & Ha, D. (2016). An empirical study of the rise of big data in business scholarship. International Journal of Information Management, 36(3), 403-413.
Gantz, J., & Reinsel, D. (2011, June). Extracting value from chaos. Retrieved October 18, 2016 from https://www.emcgrandprix.com/collateral/analyst-reports/idc-extracting-value-from-chaos-ar.pdf
Gartner (2016, July 7). Gartner says worldwide IT spending is forecast to be flat in 2016. Retrieved October 10, 2016 from http://www.gartner.com/newsroom/id/3368517
Grove, P., Kayyali, B., Knott, D., & Van Kuiken, S. (2013). The “big data” revolution in healthcare. McKinsey & Company, Center for US Health System Reform Business Technology Office.
Hashem, I. A. T., Chang, V., Anuar, N. B., Adewole, K., Yaqoob, I., Gani, A., ... & Chiroma, H. (2016). The role of big data in smart city. International Journal of Information Management, 36(5), 748-758.
IBM (2016, February 26). See your fans from every angle with behavior based fan insight from IBM analytics. Retrieved December 6, 2016 from http://www.ibmbigdatahub.com/video/see-your-fans-every-angle-behavior-based-fan-insight-ibm-analytics
IDC (2011). Big data: What it is and why you should care. White Paper, IDC.
International Data Corporation (2016, May 23). Worldwide big data and business analytics revenues forecast to reach $187 billion in 2019, according to IDC. Retrieved Septemper 25, 2016 from https://www.idc.com/getdoc.jsp?containerId=prUS41306516
Jee, K., & Kim, G. H. (2013). Potentiality of big data in the medical sector: Focus on how to reshape the healthcare system. Healthcare Informatics Research, 19(2), 79-85.
Jin, X., Wah, B. W., Cheng, X., & Wang, Y. (2015). Significance and challenges of big data research. Big Data Research, 2(2), 59-64.
Kim, G. H., Trimi, S., & Chung, J. H. (2014). Big-data applications in the government sector. Communications of the ACM, 57(3), 78-85.
Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META Group Research Note, 6, 70.
Laney, D. (2012, January 14). Deja VVVu: Others claiming Gartner’s construct for big data. Retrieved Septemper 26, 2016 from http://blogs.gartner.com/doug-laney/deja-vvvue-others-claiming-gartners-volume-velocity-variety-construct-for-big-data/
Logica, B., & Magdalena, R. (2015). Using big data in the academic environment. Procedia Economics and Finance, 33, 277-286.
LTA (2014, June 2). LTA, SMRT, starhub and IBM collaborate to improve transport with data for Singapore commuters. Retrieved November 15, 2016 from https://www.lta.gov.sg/apps/news/page.aspx?c=2&id=407a5053-0345-40f5-8d64-51fb31bfb2a0
Magpi (2016). The impact of wearable devices and big data on health. Retrieved July 20, 2017, from http://home.magpi.com/impact-wearable-devices-big-data-health/
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011, May). Big data: The next frontier for innovation, competition, and productivity. Retrieved Septemper 25, 2016 from http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/big-data-the-next-frontier-for-innovation
Miller, K. (2012, January 2). Big data analytics in biomedical research, Biomedical Co-mputation Review. Retrieved October 15, 2016 from http://biomedicalcomputationreview.org/content/big-data-analytics-biomedical-research
Picciano, A. G. (2012). The evolution of big data and learning analytics in american higher education. Journal of Asynchronous Learning Networks, 16(3), 9-20.
Press, G. (2014, September 3). Big data definitions: What’s yours. Forbes Tech News. Retrieved October 13, 2016 from http://www.forbes.com/sites/gilpress/2014/09/03/12-big-data-definitions-whats-yours/#452d873c21a9
Roberts, B. (2013, October 1). Cover story: The benefits of big data. Retrieved October 15, 2016 from https://www.shrm.org/hr-today/news/hr-magazine/pages/1013-big-data.aspx
SAS (2013, March 19). Five big data challenges and how to overcome them with visual analytics, according to SAS. Retrieved October 14, 2016 from https://www.sas.com/content/dam/SAS/en_us/doc/other1/five-big-data-challenges-106263.pdf
SAS (2016). What is big data? Retrieved October 13, 2016 from https://www.sas.com/en_us/insights/big-data/what-is-big-data.html
Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30.
STATS (2016). Engage your basketball audience with content and statistics driven by our revolutionary tracking data – The same data used by every NBA team. STATS. Retrieved December 7, 2016 from http://www.stats.com/sportvu-basketball-media/
Olenski, S. (2015, March 19). Big data solving big problems. Retrieved October 15, 2016 from http://www.forbes.com/sites/steveolenski/2015/03/19/big-data-solving-big-problems/#62b92b16a2c9
Triconinfotech (2014, August 11). Big data for big sports. Retrieved December 12, 2016 from http://www.triconinfotech.com/blog/2014/08/11/big-data-for-big-sports/
Zaslavsky, A., Perera, C., & Georgakopoulos, D. (2013). Sensing as a service and big data. Proceedings of the international conference on advances in cloud computing. Bangalore, India.