系統識別號 | U0002-0307201714123000 |
---|---|
DOI | 10.6846/TKU.2017.00057 |
論文名稱(中文) | 大數據應用對新產品開發績效之影響-以產品特性為干擾變數 |
論文名稱(英文) | The Effects of Big Data on New Product Development:Product Characteristics as Moderating Variables |
第三語言論文名稱 | |
校院名稱 | 淡江大學 |
系所名稱(中文) | 企業管理學系碩士班 |
系所名稱(英文) | Department of Business Administration |
外國學位學校名稱 | |
外國學位學院名稱 | |
外國學位研究所名稱 | |
學年度 | 105 |
學期 | 2 |
出版年 | 106 |
研究生(中文) | 陳思瑾 |
研究生(英文) | Szu-Chin Chen |
學號 | 604610153 |
學位類別 | 碩士 |
語言別 | 繁體中文 |
第二語言別 | |
口試日期 | 2017-05-17 |
論文頁數 | 75頁 |
口試委員 |
指導教授
-
楊立人
指導教授 - 張敬珣 委員 - 顏敏仁 委員 - 方郁惠 |
關鍵字(中) |
大數據應用 需求品質 新產品開發績效 產品特性 |
關鍵字(英) |
Big Data Requirement quality New product development performance Product characteristic |
第三語言關鍵字 | |
學科別分類 | |
中文摘要 |
世界正用超乎想像的速度隨著科技與技術的創新發展持續不斷的進步。在這數據化的時代,人們默默的被記錄與分析著,這些都顯示著「數據」已無所不在的圍繞在我們的身邊,來分析消費者的需求動向,因而顯示出大數據的重要性。在過去新產品開發績效文獻中發現鮮少與大數據應用做連結。因此,本研究欲探討大數據應用、需求品質及新產品開發績效彼此之間的關係,並從大數據探討需求品質對新產品開發績效之影響。 本研究採用SPSS統計軟體作為分析工具,並以便利抽樣之方式收集製造業及科技業為樣本,問卷發放時間為2017年3月至2017年4月,以線上問卷方式共回收有效問卷142份,進行分析後,研究的結果如下: 1. 大數據應用對需求品質有顯著正向之影響。 2. 需求品質對新產品開發績效有顯著正向之影響。 3. 大數據應用對新產品開發績效有顯著正向之影響。 4. 需求品質在大數據應用與新產品開發績效之間具有中介效果。 5. 產品特性對大數據應用與需求品質間具有干擾效果。 |
英文摘要 |
The world is constantly growing along with the development of technology and innovation beyond imagination. In the digitization age, we are recorded and analyzed secretly. It indicates that we are surrounded by these data invisibly. As the result, Big Data takes an important role of realizing the tendency of customer demand. Therefore, the primary purpose of this study was to explore the application of Big Data, requirement quality and their relationship with new product development performance. This study used SPSS statistical software to analyze data. The sample were collected from Taiwan manufacturing and high-tech industry. The data were collected from March 2017 to April 2017. A total of 142 valid questionnaires were analyzed. The results of this study are as follows: 1.Big Data has a significant positive influence on requirement quality. 2.Requirement quality has a significant positive impact on new product development performance. 3.Big Data has a significant positive influence on new product development performance. 4.Requirement quality has mediating effect on the relationship between Big Data and new product development performance. Product characteristics moderate the relationship between Big Data and requirement quality. |
第三語言摘要 | |
論文目次 |
目錄 目錄 I 表目錄 III 圖目錄 V 第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的 3 第三節 研究範圍 3 第三節 研究流程 3 第二章 文獻探討 5 第一節 大數據應用 5 第二節 需求品質 11 第三節 新產品開發績效 14 第四節 產品特性 16 第五節 研究假設發展 18 第三章 研究方法 21 第一節 研究架構 21 第二節 研究假設 22 第三節 問卷設計 22 第四節 研究樣本來源 26 第五節 資料分析方法 26 第四章 資料分析與研究結果 30 第一節 敘述性統計 30 第二節 效度與信度分析 38 第三節 信度分析 42 第四節 相關性分析 42 第五節 迴歸分析 44 第六節 層級迴歸分析 49 第七節 產品特性之干擾效果分析 51 第八節 研究假設驗證之結果 57 第五章 結論與建議 59 第一節 研究結論 59 第二節 管理意涵 60 第三節 研究限制與建議 61 參考文獻 63 中文文獻: 63 英文文獻: 64 附錄:研究問卷 71 表目錄 表2-1大數據決策與傳統商業智慧之比較 7 表2-2需求品質之屬性 12 表2-2需求品質之屬性 (續) 13 表3-1大數據衡量問項 23 表3-2需求品質問項 24 表3-3新產品開發績效問項 25 表3-4產品特性問項 26 表3-5專家前測之建議 27 表4-1公司所從事產業類型次數分配表 31 表4-2公司產品種類次數分配表 32 表4-3產品創新程度次數分配表 32 表4-4產品複雜度次數分配表 33 表4-5產品開發之環境不確定性程度次數分配表 33 表4-6產品總預算次數分配表 34 表4-7產品開發專案團隊人數次數分配表 34 表4-8產品開發的完成時間次數分配表 35 表4-9產品開發的持續時間次數分配表 35 表4-10工作內容次數分配表 36 表4-11職位次數分配表 36 表4-12年齡次數分配表 37 表4-13受測者教育程度次數分配表 37 表4-14各變數之Bartlett球形檢定及KMO檢定 38 表4-15大數據應用構面之因素分析表 39 表4-15大數據應用構面之因素分析表(續) 40 表4-16新產品開發績效構面之因素分析表 41 表4-17各構面之信度分析表 42 表4-18各構面之相關分析表 43 表4-19大數據應用子構面對需求品質構面之迴歸分析表 45 表4-20需求品質構面對新產品開發績效子構面之迴歸分析表 46 表4-21需求品質構面對新產品開發績效子構面之迴歸分析表 47 表4-22大數據應用子構面對新產品開發績效子構面之迴歸分析表 48 表4-23大數據應用子構面對新產品開發績效子構面之迴歸分析表 49 表4-24需求品質對大數據應用與新產品開發績效層級迴歸分析表 50 表4-25大數據應用之兩構面集群分析表 51 表4-26大數據應用與產品種類對需求品質之二因子變異數分析表 52 表4-27大數據應用與產品創新程度對需求品質之二因子變異數分析表 53 表4-28大數據應用與產品複雜度對需求品質之二因子變異數分析表 54 表4-29大數據應用與產品開發環境不確定性對需求品質二因子變異數分析表 55 表4-30大數據應用與產品總預算對需求品質之二因子變異數分析表 56 表4-31本研究假設驗證之結果 58 圖目錄 圖1-1研究流程圖 4 圖3-1研究架構 21 圖4-1產品種類對大數據應用與需求品質交互作用圖 52 圖4-2產品創新程度對大數據應用與需求品質交互作用圖 53 圖4-3產品複雜度對大數據應用與需求品質交互作用圖 54 圖4-4產品開發環境不確定性對大數據應用與需求品質交互作用圖 55 圖4-5產品總預算對大數據應用與需求品質交互作用圖 56 |
參考文獻 |
參考文獻 中文文獻: 1. 麥爾荀伯格(2014)。大數據帶來決策挑戰。哈佛商業評論全球繁體中文版,2014年7月號。 2. 王興良(2011)。需求管理實務作法對於新產品開發績效之影響。淡江大學企業管理學研究所碩士論文。 3. 車品覺(2014)。大數據的關鍵思考:行動×多螢×碎片化時代的商業智慧。台北市:天下雜誌。 4. 何瑋莉(2016)。工業4.0概念應用對新產品開發績效之影響-以產品特性為干擾變數。淡江大學企業管理學研究所碩士論文。 5. 吳珮雯、楊維漢(2005)。流行創新接受程度及流行涉入程度和衝動購買行為關係之研究,華岡紡織期刊,12(1), 16-22. 6. 司徒達賢(2005)。策略管理新論。台北:智勝文化。 7. 國家發展委員會(2014)。巨量資料的發展將改變世界,2014年7月11日,取自:http://www.ndc.gov.tw/News_Content.aspx?n=C90548F2DB23E8B9&sms=AB593F5AE64A02BE&s=72B0ABCF9B11B8B5#。 8. 國際商業機器股份有限公司(2015)。製造業大數據分析,打造新一代智慧工廠,天下雜誌,2015年6月25日,取自:http://www.cw.com.tw/article/article.action?id=5068696。 9. 劉欣(譯)(2015)。工業4.0:結合物聯網與大數據的第四次工業革命(原作者:AIfons Botthof, Ernst Andreas Hartmann)。台北市:四塊玉文創出版社。 10. 劉慧真、梁世英(譯)(2013)。Big Data大數據的獲利模式:圖解.案例.策略.實戰(原作者:城田真琴)。台北市:經濟新潮社出版:家庭傳媒城邦分公司發行。 11. 羅耀宗(譯)(2015)。簡單用數據,做出好決策:降低成本、提升績效,商業分析一次達成(原作者:Piyanka Jain, Puneet Sharma)。台北市:城邦商業周刊。 英文文獻: 1. Abdullah, N., Ismail, S. A., Sophiayati, S., & Sam, S. M. (2015). Data Quality in Big Data: A Review. International Journal of Advances in Soft Computing & Its Applications, 7(3). 2. Adner, R., & Levinthal, D. (2001). Demand heterogeneity and technology evolution: implications for product and process innovation. Management science, 47(5), 611-628. 3. Anderson, S. W. (2001). Direct and indirect effects of product mix characteristics on capacity management decisions and operating performance. International Journal of Flexible Manufacturing Systems, 13(3), 241-265. 4. Bailetti, A. J., & Litva, P. F. (1995). Integrating customer requirements into product designs. Journal of Product Innovation Management, 12(1), 3-15. 5. Barczak, G. (1995). New product strategy, structure, process, and performance in the telecommunications industry. Journal of Product Innovation Management, 12(3), 224-234. 6. Barki, H., & Hartwick, J. (2001). Interpersonal conflict and its management in information system development. Mis Quarterly, 195-228. 7. Brown, B., Chui, M., & Manyika, J. (2011). Are you ready for the era of ‘big data’. McKinsey Quarterly, 4(1), 24-35. 8. Brown, S. L., & Eisenhardt, K. M. (1995). Product development: Past research, present findings, and future directions. Academy of Management Review, 20(2), 343-378. 9. BSC’04 (2004). The challenges of complex IT projects. The report of a working group from the Royal academy of engineering and the British computer society. ISBN 1-903496-15-2. Access on 20th October 2004. http://www.bcs.org/BCS/News/ Positions And Responses/ Positions/ complexity.htm. 10. Calantone, R. J., Vickery, S. K., & Droge, C. (1995). Business performance and strategic new product development activities: An empirical investigation. Journal of Product Innovation Management, 12(3), 214-223. 11. Chen, M., Mao, S., & Liu, Y. (2014). Big data: a survey. Mobile Networks and Applications, 19(2), 171-209. 12. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, 36(4), 1165-1188. 13. Chihoub, H. E. (2013). Managing consistency for big data applications: tradeoffs and self-adaptiveness (Doctoral dissertation, Ecole normale superieure de Cachan-ENS Cachan). 14. Cooper, R. G., & De Brentani, U. (1984). Criteria for screening new industrial products. Industrial Marketing Management, 13(3), 149-156. 15. Copeland, M. T. (1923). Relation of consumers' buying habits to marketing methods. Harvard business review, 1(3), 282-289. 16. Cox, M., & Ellsworth, D. (1997). Managing big data for scientific visualization. In ACM Siggraph, Vol. 97, pp. 146-162. 17. Craig, A., & Hart, S. (1992). Where to now in new product development research?. European Journal of Marketing, 26(11), 2-49. 18. Dahlberg, T., & Nokkala, T. (2015). A Framework For The Corporate Governance of Data-Theoretical Background and Empirical Evidence. Business, Management and Education, 13(1), 25. 19. Davenport, T. (2014). Big data at work: dispelling the myths, uncovering the opportunities. Harvard Business Review Press. 20. Dawar. N.(2016). Use Big Data to create value for customers, not just target them. Harvard Business Review. 21. Deuse, J., Weisner, K., Hengstebeck, A., & Busch, F. (2015). Gestaltung von Produktionssystemen im Kontext von Industrie 4.0. In Zukunft der Arbeit in Industrie 4.0 (pp. 99-109). Springer Berlin Heidelberg. 22. Dougherty, D. (1992). Interpretive barriers to successful product innovation in large firms. Organization science, 3(2), 179-202. 23. Filippini, R., Salmaso, L., & Tessarolo, P. (2004). Product development time performance: Investigating the effect of interactions between drivers. Journal of Product Innovation Management, 21(3), 199-214. 24. Fisher, D., DeLine, R., Czerwinski, M., & Drucker, S. (2012). Interactions with big data analytics. interactions, 19(3), 50-59. 25. Frankova, P., Drahošova, M., & Balco, P. (2016). Agile Project Management Approach and its Use in Big Data Management. Procedia Computer Science, 83, 576-583. 26. Garcia, N., Sanzo, M. J., & Trespalacios, J. A. (2008). New product internal performance and market performance: Evidence from Spanish firms regarding the role of trust, interfunctional integration, and innovation type. Technovation,28(11), 713-725. 27. George, G., Haas, M. R., & Pentland, A. (2014). Big data and management. Academy of Management Journal, 57(2), 321-326. 28. Gobble, M. M. (2013). Outsourcing Innovation. Research-Technology Management, 56(4), 64-67. 29. Gorschek, T., Svahnberg, M., Borg, A., Loconsole, A., Borstler, J., Sandahl, K., & Eriksson, M. (2007). A controlled empirical evaluation of a requirements abstraction model. Information and Software Technology, 49(7), 790-805. 30. Griffin, A. (1997). Modeling and measuring product development cycle time across industries. Journal of engineering and technology management, 14(1), 1-24. 31. Griffin, A., & Hauser, J. R. (1992). Patterns of communication among marketing, engineering and manufacturing—A comparison between two new product teams. Management science, 38(3), 360-373. 32. Griffin, A., & Page, A. L. (1993). An interim report on measuring product development success and failure. Journal of product innovation management,10(4), 291-308. 33. Hart, S. (1993). Dimensions of success in new product development: an exploratory investigation. Journal of marketing management, 9(1), 23-41. 34. Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98-115. 35. Hauser, J. R., & Clausing, D. (1988). The house of quality. Harvard business review, 66(3). 36. Henard, D. H., & Szymanski, D. M. (2001). Why some new products are more successful than others. Journal of marketing Research, 38(3), 362-375. 37. Hill, T. J. (1983). Manufacturing's strategic role. Journal of the Operational research Society, 34(9), 853-860. 38. Holbrook, M. B., & Howard, J. A. (1977). Frequently purchased nondurable goods and services. Selected Aspects of Consumer Behavior, 1, 189-222. 39. Homburg, C., & Bucerius, M. (2006). Kundenzufriedenheit als Managementherausforderung. In Kundenzufriedenheit: Konzepte – Methoden – Erfahrungen (S. 64–65). Wiesbaden: Gabler. 40. The Standish Report (2004), http://www.standishgroup.com/chronicles/index.php. 41. Ioannidis, C., & Silver, M. (1997). Estimating the worth of product characteristics. Journal of the Market Research Society, 39(4), 559-570. 42. Jain, P., & Sharma, P. (2014). Behind Every Good Decision: How Anyone Can Use Business Analytics to Turn Data Into Profitable Insight. AMACOM Div American Mgmt Assn. 43. Kauppinen, M., & Kujala, S. (2001). Starting improvement of requirements engineering processes: An experience report. In International Conference on Product Focused Software Process Improvement (pp. 196-209). Springer Berlin Heidelberg. 44. Kotler, P., & Scheff, J. (1997). Standing room only: Strategies for marketing the performing arts. Harvard business press. 45. Kotler, P. & Modahl, K. (2000). Marketing management. New Jersey: Prentice-Hall l, Inc.. 46. Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META Group Research Note, 6, 70. 47. Li, J., Hou, L., Qin, Z., Wang, Q., & Chen, G. (2008). An Empirically–Based Process to Improve the Practice of Requirement Review. In International Conference on Software Process (pp. 135-146). Springer Berlin Heidelberg. 48. Li, J., Tao, F., Cheng, Y., & Zhao, L. (2015). Big Data in product lifecycle management. The International Journal of Advanced Manufacturing Technology, 81(1-4), 667-684. 49. Lilien, G. L., & Yoon, E. (1989). Determinants of new industrial product performance: A strategic re-examination of the empirical literature. IEEE Transactions on Engineering Management, 36(1), 3-10. 50. Mayer-Schonberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt. 51. McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data. The management revolution. Harvard Bus Rev, 90(10), 61-67. 52. McGrath, M. E., & Romeri, M. N. (1994). The R&D effectiveness index: a metric for product development performance. Journal of product innovation Management, 11(3), 213-220. 53. Mckinsey, B. D. (2011). The Next Frontier for Innovation. Competition, and Productivity. 54. Millson, M. R., & Wilemon, D. (2002). The impact of organizational integration and product development proficiency on market success. Industrial Marketing Management, 31(1), 1-23. 55. Ming, Z., Luo, C., Gao, W., Han, R., Yang, Q., Wang, L., & Zhan, J. (2013). Bdgs: A scalable big data generator suite in big data benchmarking. In Workshop on Big Data Benchmarks (pp. 138-154). Springer International Publishing. 56. Mohamed, N., & Al-Jaroodi, J. (2014). Real-time big data analytics: Applications and challenges. In HPCS (pp. 305-310). 57. Munns, A. K., & Bjeirmi, B. F. (1996). The role of project management in achieving project success. International journal of project management, 14(2), 81-87. 58. Nystrom, S., & Lonnegren, J. (2016). Processing data sources with big data frameworks. 59. O'Leary, D. E. (2013). Big Data’, the ‘Internet of Things’ and the ‘internet of signs. Intelligent Systems in Accounting, Finance and Management, 20(1), 53-65. 60. Olson, E. M., Walker Jr, O. C., & Ruekert, R. W. (1995). Organizing for effective new product development: The moderating role of product innovativeness. The Journal of Marketing, 48-62. 61. Ovtcharova, J., Hafner, P., Hafner, V., Katicic, J., & Vinke, C. (2015). Innovation braucht Resourceful Humans Aufbruch in eine neue Arbeitskultur durch Virtual Engineering. In Zukunft der Arbeit in Industrie 4.0 (pp. 111-124). Springer Berlin Heidelberg. 62. Pheng, L. S., & Chuan, Q. T. (2006). Environmental factors and work performance of project managers in the construction industry. International Journal of Project Management, 24(1), 24-37. 63. Rein, G. L. (2004). From experience: creating synergy between marketing and research and development. Journal of Product Innovation Management, 21(1), 33-43. 64. Rogerson, W. P. (1983). Reputation and product quality. The Bell Journal of Economics, 508-516. 65. Salleh, K. A., & Janczewski, L. (2016). Technological, Organizational and Environmental Security and Privacy Issues of Big Data: A Literature Review. Procedia Computer Science, 100, 19-28. 66. Shukla, S. (2016). Study of big data analytics landscape: considerations for market entry of an E-commerce analytics vendor (Doctoral dissertation, Massachusetts Institute of Technology). 67. Smolan, R. (2013). The human face of big data. 68. Song, X. M., & Parry, M. E. (1997). The determinants of Japanese new product successes. Journal of Marketing Research, 64-76. 69. Storey, V. C., & Wang, Y. Y. R. (1995). Modeling quality requirements in conceptual database design. International Financial Services Research Center, Sloan School of Management, Massachusetts Institute of Technology. 70. Strahilevitz, M. A. (1999). The effects of product type and donation magnitude on willingness to pay more for a charity-linked brand. Journal of Consumer Psychology, 8(3), 215-241. 71. Takeuchi, H., & Nonaka, I. (1986). The new new product development game. Harvard business review, 64(1), 137-146. 72. Teory, T.K.(1990). Database Modeling and Design: The Entity. Relationship Approach∥, San Mateo, CA: Morgan Kaufman Publisher. 73. Tzokas, N., Hultink, E. J., & Hart, S. (2004). Navigating the new product development process. Industrial Marketing Management, 33(7), 619-626. 74. Wilson, W. M., Rosenberg, L. H., & Hyatt, L. E. (1997). Automated analysis of requirement specifications. In Proceedings of the 19th international conference on Software engineering (pp. 161-171). ACM. 75. Yang, L. R., Chen, J. H., & Wang, X. L. (2015). Assessing the effect of requirement definition and management on performance outcomes: Role of interpersonal conflict, product advantage and project type. International Journal of Project Management, 33(1), 67-80. 76. Zarate Santovena, A. (2013). Big data: evolution, components, challenges and opportunities (Doctoral dissertation, Massachusetts Institute of Technology). 77. Zhang, Y., Ren, S., Liu, Y., & Si, S. (2017). A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products. Journal of Cleaner Production, 142, 626-641. 78. Zikopoulos, P., & Eaton, C. (2011). Understanding big data: Analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media. 79.Zikopoulos, P., Parasuraman, K., Deutsch, T., Giles, J., & Corrigan, D. (2012). Harness the power of big data The IBM big data platform. McGraw Hill Professional. |
論文全文使用權限 |
如有問題,歡迎洽詢!
圖書館數位資訊組 (02)2621-5656 轉 2487 或 來信