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題名 大數據資料收集品質要素之研究
A study of the quality factors of big data collection on decision making作者 楊茜宜
Yang, Chien-I貢獻者 尚孝純
Shang, Xiao-Chun
楊茜宜
Yang, Chien-I關鍵詞 大數據
大數據分析
大數據收集
資料收集品質
決策制定
Big data
Big data analysis
Big data collection
Quality of data collection
Decision-making日期 2021 上傳時間 4-Aug-2021 14:48:19 (UTC+8) 摘要 近年來，大數據分析（BDA）在商業決策中的應用引起人們的極大關注。然而，幾乎沒有研究討論最基本的大數據問題，即數據收集的適當性，本研究探討如何正確收集數據以提高決策的準確性。首先，本研究透過文獻回顧找出會影響決策制定的數據收集的品質因素（the quality factors of data collection），其中數據收集品質因素為領域、來源、頻率、長度、量、再生性和折舊度。其次，本研究探索更有層次的問題，即是，在什麼情況下，收集越全面數據收集品質因素，對決策的有用性、有效性有影響；以及，身為調節變數的再生性、貶值度，如何影響資料收集品質因素和決策。為了解決這些問題，本研究分析五個不尋常的啟示個案，並考慮實務上數據分析和收集在不同部門的差異。最後研究發現數據收集品質因素在製造業和服務業表現截然不同，並且本研究也提出在哪些情境需要收集、分析全面的數據收集品質因素。本研究期望發展成為企業在數據收集和分析方面的衡量標準和指南。
The use of big data analysis (BDA) in business decision-making has attracted significant attention in recent years. However, hardly any research discussing the most basic big data issues which is the appropriateness of the data collection, this study investigate how data can be properly collected to improve the accuracy of decision-making.First, this study shows that quality factors in data collection affect decision-making, where quality factors are domain, source, frequency, length, quantity, regeneration, and depreciation. Second, this study explores hierarchical questions, indicating the conditions under which the comprehensiveness of the quality factors of data collected impact the effectiveness and efficiency of decision-making, and the contexts under which the data characteristics of the collected data can moderate the relationship between data collection quality and decision-making quality.To address these questions, this study analyzes five cases of successful companies and considers the gaps between the collection and analysis departments in practice. Finally, it concludes that the quality factors in the data collection show different performance in the manufacturing and service industries and then presents a proposal for appropriate data collection. This study may develop into a measurement standard and guideline for enterprises in data collection and analysis.參考文獻 Acharya, A., Singh, S. K., Pereira, V., &Singh, P. (2018). Big data, knowledge co-creation and decision making in fashion industry. International Journal of Information Management, 42(May), 90–101. https://doi.org/10.1016/j.ijinfomgt.2018.06.008Akter, S., &Wamba, S. F. (2016). Big data analytics in E-commerce: a systematic review and agenda for future research. Electronic Markets, 26(2), 173–194. https://doi.org/10.1007/s12525-016-0219-0Amie Tsang. (2018). Passengers Are Stranded as Another European Airline Collapses. The New York Times.Baxter, P., &Jack, S. (2008). Qualitative Case Study Methodology: Study Design and Implementation for Novice Researchers. The Qualitative Report, 13(4), 544–559. https://doi.org/10.1039/c6dt02264bBelhadi, A., Zkik, K., Cherrafi, A., Yusof, S. M., &Elfezazi, S. (2019). Understanding Big Data Analytics for Manufacturing Processes: Insights from Literature Review and Multiple Case Studies. Computers and Industrial Engineering, 137(September), 106099. https://doi.org/10.1016/j.cie.2019.106099Bizer, C., Boncz, P., Brodie, M. L., &Erling, O. (2012). 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國立政治大學
資訊管理學系
108356032資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108356032 資料類型 thesis dc.contributor.advisor 尚孝純 zh_TW dc.contributor.advisor Shang, Xiao-Chun en_US dc.contributor.author (Authors) 楊茜宜 zh_TW dc.contributor.author (Authors) Yang, Chien-I en_US dc.creator (作者) 楊茜宜 zh_TW dc.creator (作者) Yang, Chien-I en_US dc.date (日期) 2021 en_US dc.date.accessioned 4-Aug-2021 14:48:19 (UTC+8) - dc.date.available 4-Aug-2021 14:48:19 (UTC+8) - dc.date.issued (上傳時間) 4-Aug-2021 14:48:19 (UTC+8) - dc.identifier (Other Identifiers) G0108356032 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/136347 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理學系 zh_TW dc.description (描述) 108356032 zh_TW dc.description.abstract (摘要) 近年來，大數據分析（BDA）在商業決策中的應用引起人們的極大關注。然而，幾乎沒有研究討論最基本的大數據問題，即數據收集的適當性，本研究探討如何正確收集數據以提高決策的準確性。首先，本研究透過文獻回顧找出會影響決策制定的數據收集的品質因素（the quality factors of data collection），其中數據收集品質因素為領域、來源、頻率、長度、量、再生性和折舊度。其次，本研究探索更有層次的問題，即是，在什麼情況下，收集越全面數據收集品質因素，對決策的有用性、有效性有影響；以及，身為調節變數的再生性、貶值度，如何影響資料收集品質因素和決策。為了解決這些問題，本研究分析五個不尋常的啟示個案，並考慮實務上數據分析和收集在不同部門的差異。最後研究發現數據收集品質因素在製造業和服務業表現截然不同，並且本研究也提出在哪些情境需要收集、分析全面的數據收集品質因素。本研究期望發展成為企業在數據收集和分析方面的衡量標準和指南。 zh_TW dc.description.abstract (摘要) The use of big data analysis (BDA) in business decision-making has attracted significant attention in recent years. However, hardly any research discussing the most basic big data issues which is the appropriateness of the data collection, this study investigate how data can be properly collected to improve the accuracy of decision-making.First, this study shows that quality factors in data collection affect decision-making, where quality factors are domain, source, frequency, length, quantity, regeneration, and depreciation. Second, this study explores hierarchical questions, indicating the conditions under which the comprehensiveness of the quality factors of data collected impact the effectiveness and efficiency of decision-making, and the contexts under which the data characteristics of the collected data can moderate the relationship between data collection quality and decision-making quality.To address these questions, this study analyzes five cases of successful companies and considers the gaps between the collection and analysis departments in practice. Finally, it concludes that the quality factors in the data collection show different performance in the manufacturing and service industries and then presents a proposal for appropriate data collection. This study may develop into a measurement standard and guideline for enterprises in data collection and analysis. en_US dc.description.tableofcontents Abstract 2Chapter 1: Introduction 81.1 Industry background 81.2 Motivation 81.3 Research objectives 91.4 Structure 10Chapter 2: Literature review 112.1 Definition of big data 112.2 Big data process 142.2.1 Data collection 152.2.2 Data transformation 162.2.3 Data analysis stage 162.2.4 Data visualization/interpretation 172.2.5 Decision making 172.3 Quality of data collection 182.3.1 Domain of data collection 182.3.2 Source of data collection 192.3.3 Frequency of data collection 192.3.4 Length of data collection 202.3.5 Quantity of data collection 212.4 Typical data characteristics 222.4.1 Regeneration 222.4.2 Depreciation 23Chapter 3: Research design 243.1 Research framework 243.2 Research approach 253.3 Data collection 263.4 Data analysis 26Chapter 4: Research results 284.1 Manufacturing industry 284.1.1 Case M1 284.1.2 Case M2 364.2 Service industry 434.2.1 Case S1 434.2.2 Case S2 524.2.3 Case S3 584.3 Cross-case analysis 654.3.1 Decision type 654.3.2 Domain of data collection 664.3.3 Source of data collection 694.3.4 Frequency of data collection 714.3.5 Length of data collection 734.3.6 Quantity of data collection 754.3.7 Regeneration of data characteristics 774.3.8 Depreciation of data characteristics 794.3.9 Summary 81Chapter 5: Conclusion 835.1 Research summary 835.2 Managerial implication 845.3 Theoretical implication 855.4 Research contribution 855.5 Limitation and future research 85References 87Appendix 1: Questionnaire 93 zh_TW dc.format.extent 1286967 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108356032 en_US dc.subject (關鍵詞) 大數據 zh_TW dc.subject (關鍵詞) 大數據分析 zh_TW dc.subject (關鍵詞) 大數據收集 zh_TW dc.subject (關鍵詞) 資料收集品質 zh_TW dc.subject (關鍵詞) 決策制定 zh_TW dc.subject (關鍵詞) Big data en_US dc.subject (關鍵詞) Big data analysis en_US dc.subject (關鍵詞) Big data collection en_US dc.subject (關鍵詞) Quality of data collection en_US dc.subject (關鍵詞) Decision-making en_US dc.title (題名) 大數據資料收集品質要素之研究 zh_TW dc.title (題名) A study of the quality factors of big data collection on decision making en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Acharya, A., Singh, S. K., Pereira, V., &Singh, P. (2018). Big data, knowledge co-creation and decision making in fashion industry. International Journal of Information Management, 42(May), 90–101. https://doi.org/10.1016/j.ijinfomgt.2018.06.008Akter, S., &Wamba, S. F. (2016). Big data analytics in E-commerce: a systematic review and agenda for future research. Electronic Markets, 26(2), 173–194. https://doi.org/10.1007/s12525-016-0219-0Amie Tsang. (2018). Passengers Are Stranded as Another European Airline Collapses. The New York Times.Baxter, P., &Jack, S. (2008). Qualitative Case Study Methodology: Study Design and Implementation for Novice Researchers. The Qualitative Report, 13(4), 544–559. https://doi.org/10.1039/c6dt02264bBelhadi, A., Zkik, K., Cherrafi, A., Yusof, S. M., &Elfezazi, S. (2019). Understanding Big Data Analytics for Manufacturing Processes: Insights from Literature Review and Multiple Case Studies. Computers and Industrial Engineering, 137(September), 106099. https://doi.org/10.1016/j.cie.2019.106099Bizer, C., Boncz, P., Brodie, M. L., &Erling, O. (2012). The Meaningful Use of Big Data: Four Perspectives – Four Challenges. ACM SIGMOD Record, 40, 56–60.Chen, M., Mao, S., &Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171–209. https://doi.org/10.1007/s11036-013-0489-0Clark, T. D., Jones, M. C., &Armstrong, C. P. (2007). The dynamic structure of management support systems: Theory development, research focus, and direction. MIS Quarterly, 31(3), 579–615. https://doi.org/10.2307/25148808Constantiou, I. D., &Kallinikos, J. (2015). New games, new rules: Big data and the changing context of strategy. Journal of Information Technology, 30(1), 44–57. https://doi.org/10.1057/jit.2014.17Davenport, T. H., Barth, P., &Bean, R. (2012). How “big data” is different. MIT Sloan Management Review, 54(1).Dean, J., &Ghemawat, S. (2008). 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