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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/136347


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/136347


    Title: 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
    Authors: 楊茜宜
    Yang, Chien-I
    Contributors: 尚孝純
    Shang, Xiao-Chun
    楊茜宜
    Yang, Chien-I
    Keywords: 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
    Date: 2021
    Issue Date: 2021-08-04 14:48:19 (UTC+8)
    Abstract: 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.
    Reference: 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.2018.06.008
    Akter, 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/s12525-016-0219-0
    Amie 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/c6dt02264b
    Belhadi, 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.2019.106099
    Bizer, 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/s11036-013-0489-0
    Clark, 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/10.2307/25148808
    Constantiou, 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.2014.17
    Davenport, T. H., Barth, P., &Bean, R. (2012). How “big data” is different. MIT Sloan Management Review, 54(1).
    Dean, J., &Ghemawat, S. (2008). MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM, 51(1), 107–113. http://www.usenix.org/events/osdi04/tech/full_papers/dean/dean_html/
    Eisenhardt, K. M. (1989). Building Theories from Case Study Research. Academy of Management Review, 14(4), 532–550. https://doi.org/10.1016/s0140-6736(16)30010-1
    Eisenhardt, K. M., &Graebner, M. E. (2007). Theory building from cases: Opportunities and challenges. Academy of Management Journal, 50(1), 25–32. https://doi.org/10.5465/AMJ.2007.24160888
    Erevelles, S., Fukawa, N., &Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897–904. https://doi.org/10.1016/j.jbusres.2015.07.001
    Fan, J., Han, F., &Liu, H. (2014). Challenges of Big Data analysis. National Science Review, 1(2), 293–314. https://doi.org/10.1093/nsr/nwt032
    Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G., &Gnanzou, D. (2015). How “big data” can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246. https://doi.org/10.1016/j.ijpe.2014.12.031
    Gartner. (2015). Gartner Says Business Intelligence and Analytics Leaders Must Focus on Mindsets and Culture to Kick Start Advanced Analytics. Library Catalog: Www.Gartner.Com.
    Geng, B., Li, Y., Tao, D., Wang, M., Zha, Z. J., &Xu, C. (2012). Parallel lasso for large-scale video concept detection. IEEE Transactions on Multimedia, 14(1), 55–65. https://doi.org/10.1109/TMM.2011.2174781
    George, G., Lavie, D., Osinga, E. C., &Scott, B. A. (2016). Big data and data science methods for management research. Academy of Management Journal, 59(5), 1493–1507. http://www.scopus.com/inward/record.url?eid=2-s2.0-84900399014&partnerID=40&md5=1226b227def2d1b2fd0a11ef65f0180b
    Goedeking, P. (2018). Collapse of Primera shows the risks of low-cost long haul | Financial Times. Financial Times, 15. https://www.ft.com/content/22d28864-c62c-11e8-86b4-bfd556565bb2
    Grover, V., Chiang, R. H. L., Liang, T. P., &Zhang, D. (2018). Creating Strategic Business Value from Big Data Analytics: A Research Framework. Journal of Management Information Systems, 35(2), 388–423. https://doi.org/10.1080/07421222.2018.1451951
    Gudivada, V. N., Baeza-Yates, R., Labs, Y., &Raghavan, V.V. (2015). GUEST EDITORS’ INTRODUCTION Big Data: Promises and Problems. Computer, 21. http://www.cs.rug.nl/~roe/courses/isc/BigDataPromises.pdf
    Hagiu, A., &Julian, W. (2020). When Data Creates Competitive Advantage. Harvard Business Review. https://hbr.org/2020/01/when-data-creates-competitive-advantage
    Hand, D. J., &Adams, N. M. (2015). Data Mining. Wiley StatsRef: Statistics Reference Online, 1–7. https://doi.org/10.1002/9781118445112.stat06466.pub2
    Heer, J., Mackinlay, J. D., Stolte, C., &Agrawala, M. (2008). Graphical histories for visualization: Supporting analysis, communication, and evaluation. IEEE Transactions on Visualization and Computer Graphics, 14(6), 1189–1196. https://doi.org/10.1109/TVCG.2008.137
    Hulland, J., &Wade, M. (2004). The resource-based view and information systems research: review, extension, and suggestions for future research. MIS Quarterly, 28(1), 107–142.
    Janssen, M., van derVoort, H., &Wahyudi, A. (2017). Factors influencing big data decision-making quality. Journal of Business Research, 70, 338–345. https://doi.org/10.1016/j.jbusres.2016.08.007
    LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., &Kruschwitz, N. (2011). Big Data, Analytics and the Path from Insights to Value. MIT Sloan Management Review, Vol. 52, Iss. 2, 21–32. https://sloanreview.mit.edu/article/big-data-analytics-and-the-path-from-insights-to-value/
    Lu, J. (2020). Data Analytics Research-Informed Teaching in a Digital Technologies Curriculum. INFORMS Transactions on Education, May.
    Ma, K.-L., &S. Parker. (2001). Massively parallel software rendering for visualizing large-scale data sets. IEEE Computer Graphics and Applications, 21(4), 72–83.
    Maroufkhani, P., Tseng, M. L., Iranmanesh, M., Ismail, W. K. W., &Khalid, H. (2020). Big data analytics adoption: Determinants and performances among small to medium-sized enterprises. International Journal of Information Management, 54(February), 102190. https://doi.org/10.1016/j.ijinfomgt.2020.102190
    Masi, I., Trân, A. T., Hassner, T., Leksut, J. T., &Medioni, G. (2016). Do we really need to collect millions of faces for effective face recognition? Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9909 LNCS, 579–596. https://doi.org/10.1007/978-3-319-46454-1_35
    Mcafee, A., &Brynjolfsson, E. (2012). Big Data: The Management Revolution. Harvard Business Review, October, 1–9. http://tarjomefa.com/wp-content/uploads/2017/04/6539-English-TarjomeFa-1.pdf
    Mikalef, P., Krogstie, J., Pappas, I. O., &Pavlou, P. (2020). Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Information and Management, 57(2), 103–169. https://doi.org/10.1016/j.im.2019.05.004
    Müller, O., Fay, M., &vomBrocke, J. (2018). The Effect of Big Data and Analytics on Firm Performance: An Econometric Analysis Considering Industry Characteristics. Journal of Management Information Systems, 35(2), 488–509. https://doi.org/10.1080/07421222.2018.1451955
    Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., &Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data, 2(1), 1–21. https://doi.org/10.1186/s40537-014-0007-7
    Opresnik, D., &Taisch, M. (2015). The value of big data in servitization. International Journal of Production Economics, 165, 174–184. https://doi.org/10.1016/j.ijpe.2014.12.036
    Philip Chen, C. L., &Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314–347. https://doi.org/10.1016/j.ins.2014.01.015
    Provost, F., &Fawcett, T. (2013). Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data, 1(1), 51–59. https://doi.org/10.1089/big.2013.1508
    Ransbotham, S., &Kiron, D. (2017). Analytics as a Source of Business Innovation. MITSM Report, 58380, Foundation of Marketing 5th editionFoundation of M.
    Roh, Y., Heo, G., &Whang, S. E. (2019). A survey on data collection for machine learning: A big data - AI integration perspective. IEEE Transactions on Knowledge and Data Engineering, 33(4), 1328–1347. https://doi.org/10.1109/tkde.2019.2946162
    Russom, P. (2011). Big data analytics. TDWI Best Practices Report, 1–34. https://doi.org/10.1017/9781108566506.005
    Ryan, C., &Riggs, W. E. (1996). Redefining the product life cycle: the five-element product wave. Business Horizons, 39(5), 33+.
    Seddon, J. J. J. M., &Currie, W. L. (2017). A model for unpacking big data analytics in high-frequency trading. Journal of Business Research, 70, 300–307. https://doi.org/10.1016/j.jbusres.2016.08.003
    Shamim, S., Zeng, J., Shariq, S. M., &Khan, Z. (2019). Role of big data management in enhancing big data decision-making capability and quality among Chinese firms: A dynamic capabilities view. Information and Management, 56(6), 103–135. https://doi.org/10.1016/j.im.2018.12.003
    Sivarajah, U., Kamal, M. M., Irani, Z., &Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263–286. https://doi.org/10.1016/j.jbusres.2016.08.001
    Sun, E. W., Chen, Y. T., &Yu, M. T. (2015). Generalized optimal wavelet decomposing algorithm for big financial data. International Journal of Production Economics, 165, 194–214. https://doi.org/10.1016/j.ijpe.2014.12.033
    Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. fan, Dubey, R., &Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009
    White, T. (2009). Hadoop: The Definitive Guide. O’Reilly Media.
    Wu, X., Zhu, X., Wu, G.-Q., &Ding, W. (2014). Data Mining with Big Data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97–107. https://doi.org/10.1109/ISCO.2017.7855990
    Yaqoob, I., Hashem, I. A. T., Gani, A., Mokhtar, S., Ahmed, E., Anuar, N. B., &Vasilakos, A.V. (2016). Big data: From beginning to future. International Journal of Information Management, 36(6), 1231–1247. https://doi.org/10.1016/j.ijinfomgt.2016.07.009
    Yin, R. K. (2009). Case study research: Design and methods. Sage.
    Zaharia, M., Chowdhury, M., Franklin, M. J., Shenker, S., &Stoica, I. (2010). Spark: Cluster computing with working sets. 2nd USENIX Workshop on Hot Topics in Cloud Computing, HotCloud 2010.
    Zhou, K., Fu, C., &Yang, S. (2016). Big data driven smart energy management: From big data to big insights. Renewable and Sustainable Energy Reviews, 56(2016), 215–225. https://doi.org/10.1016/j.rser.2015.11.050
    Zhou, Z. H., Chawla, N.V., Jin, Y., &Williams, G. J. (2014). Big data opportunities and challenges: Discussions from data analytics perspectives [Discussion Forum]. IEEE Computational Intelligence Magazine, 9(4), 62–74. https://doi.org/10.1109/MCI.2014.2350953
    車品覺. (2020). 大數據的關鍵思考（增訂版）：行動╳多螢╳碎片化時代的商業智慧. 天下雜誌.
    Description: 碩士
    國立政治大學
    資訊管理學系
    108356032
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0108356032
    Data Type: thesis
    DOI: 10.6814/NCCU202100884
    Appears in Collections:[資訊管理學系] 學位論文

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