跳到主要內容

臺灣博碩士論文加值系統

(44.200.86.95) 您好!臺灣時間:2024/05/20 08:54
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:胡德燿
研究生(外文):OH TECK YAU
論文名稱:數據分析對國際市場知識:國際行銷策略之調節效果
論文名稱(外文):Data Analyitcs and International Market Knowledge: International Marketing Strategy as a Moderator
指導教授:譚大純譚大純引用關係
指導教授(外文):TARN, DA-CHUN
口試委員:李俊賢田祖武譚大純
口試委員(外文):LEE, CHUN HSIENTIEN, TSU-WUTARN, DA-CHUN
口試日期:2019-07-10
學位類別:碩士
校院名稱:國立高雄師範大學
系所名稱:事業經營學系
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:中文
論文頁數:81
中文關鍵詞:國際行銷策略數據分析取向國際市場知識
外文關鍵詞:international marketing strategy,data analysis orientationinternational market knowledge
相關次數:
  • 被引用被引用:0
  • 點閱點閱:203
  • 評分評分:
  • 下載下載:8
  • 收藏至我的研究室書目清單書目收藏:0
摘要
數據分析學(坊稱「巨量分析」、「數據分析」,就近幾年看來,越來越有一種像通用技能發展的趨勢,從生產、研發、市場、銷售到運營,多少會存在數據分析的需求。諸多領域已經由數據分析後獲得顯著成果。然迄今而文獻尚未見以較完整性且系統性之研究架構探討組織執行數據分析之內涵與行為,因此尚未能準確檢驗其所產生之效益。數據分析目的之一在於使組織能更快速瞭解國際市場知識。
本研究有下列目的:
一、探討國際企業進行數據分析之內容,並套以合適之架構將數據分析取向模式話。
二、數據分析取向與國際行銷策略是否對國際市場知識有影響。
三、探討數據分析取向在影響行銷機敏度之過程中,是否需先產生市場知識。亦即市場知識在國際行銷策略與分析取向之關中所扮演之中介角色。
基於前上述之目的,研究可分為三個構念組成:(1)數據分析取向:根據 Parise et al. (2012)與Zack (1999),建構為數據探索、績效管考、決策評估與社會分析等四面向;(2)市場知識則:依據 De Luca and Atuahene-Gima (2007) ,將市場知識劃分為四個主要子構念,且由內隱性、知識寬廣度、專屬性與深入度組成;(3)行銷機敏度:根據Sambamurthy et al.(2003),由作業機敏度、協同與顧客三構念組成。
首先,績效管考取向正向影響其國際市場知識之間的關係,數據分析取向的四個面向中,績效管考,決策評估及數據探索對國際市場知識皆產生正向影響,而社會分析對國際市場知識無顯著影響。
其次,數據分析取向對國際市場知識之間的關係,數據分析取向的四個面向中,績效管考、數據探索、與決策評估對國際市場知識皆產生正向影響。
第三,社會分析取向對國際市場知識之間的關係,績效管考及社會分析對國際市場知識度皆無顯著影響。
第四,決策評估取向對國際市場知識之間的關係,績效管考對深入度、專屬性皆產生顯著影響,僅對內隱性無顯著影響;數據探索對寬廣度及專屬性皆產生顯著影響,僅對內隱性無顯著影響;社會分析對深入度無顯著影響;決策評估對寬廣度正向影響。

Abstract
Data analytics (called "mass analysis" and "data analysis", in recent years, there is a growing trend of general skills development, from production, research and development, marketing, sales to operations, how much data will exist. Analytical requirements. Many fields have been significantly improved by data analysis. However, the literature has not yet seen the connotation and behavior of organizational execution data analysis with a more complete and systematic research structure, so it has not been able to accurately test its Benefits. One of the purposes of data analysis is to enable organizations to more quickly understand international market knowledge.
This study has the following purposes:
First, explore the content of data analysis by international companies, and apply the appropriate structure to analyze the data orientation pattern.
Second, whether the data analysis orientation and international marketing strategy have an impact on international market knowledge.
Third, to explore the direction of data analysis in the process of affecting marketing intelligence, whether it is necessary to first generate market knowledge. That is, the intermediary role of market knowledge in the relationship between international marketing strategies and analytical orientation.
Based on the above objectives, the research can be divided into three constructs: (1) Data analysis orientation: According to Parise et al. (2012) and Zack (1999), constructing data exploration, performance management, decision evaluation and society Analysis and other aspects; (2) Market knowledge: According to De Luca and Atuahene-Gima (2007), market knowledge is divided into four main sub-constructions, and implicit, knowledge broadness, specificity and depth Composition; (3) Marketing sensibility: According to Sambamurthy et al. (2003), it consists of work machine sensitivity, synergy and customer structure.
First, the orientation of performance management is positively affecting the relationship between knowledge in the international market. The four aspects of data analysis orientation, performance management, decision evaluation and data exploration have a positive impact on international market knowledge, while social analysis International market knowledge has no significant impact.
Secondly, the relationship between data analysis orientation and international market knowledge, and the four orientations of data analysis orientation, performance management, data exploration, and decision evaluation have positive impact on international market knowledge.
Third, the relationship between social analysis orientation and knowledge in the international market, performance management and social analysis have no significant impact on the international market knowledge.
Fourth, the relationship between decision-making assessment and international market knowledge, performance management has a significant impact on depth and specificity, and has no significant impact on implicitness; data exploration has a significant impact on breadth and specificity. There is no significant impact on implicitness; social analysis has no significant impact on depth; decision-making assessment has a positive impact on broadness.

目錄
摘要 II
Abstract IV
表目錄 VIII
圖目錄 IX
第一章 緒論 1
第一節 研究背景與研究問題 1
第二節 研究目的 3
第二章 文獻探討與假說推論 5
第一節 國際行銷策略 5
第二節 國際市場知識 7
第三節 數據分析 9
第四節 績效管考 12
第五節 數據探索 13
第六節 社會分析 13
第七節 決策評估 14
第八節 績效管考取向、國際行銷策略與國際市場知識 15
第九節 數據探索取向、國際行銷策略與國際市場知識 16
第十節 社會分析取向、國際行銷策略與國際市場知識 21
第十一節 決策評估取向、國際行銷策略與國際市場知識 22
第三章研究方法 24
第一節 量表衡鑑 24
第二節 調查方法 24
第三節研究樣本與抽樣 29
第四節 資料分析方法 29
第四章 研究結果 30
第一節 基本資料敘述統計分析 30
第二節 各構念內部一致性檢定 31
第三節 各構念相關分析 38
第四節數據分析、國際行銷策略對國際市場知識影響之分析 41
第五章 研究結論與建議 71
第一節 研究結論 71
第二節 研究討論 73
第三節 實務建議 74
參考之文獻 76
表目錄
表 1 數據分析取向與內涵 ………………………………………………………………17
表 2 量表定義及初步題項 ………………………………………………………………25
表 4-1 有效樣本之基本資料統計分析表…………………………………………………30
表 4-2 廠商數據分析取向量表之一致性檢定……………………………………………31
表 4-3 廠商國際市場知識取向量表之一致性檢定………………………………………34
表 4-4 廠商國際行銷策略取向量表之一致性檢定………………………………………36
表 4-5 績效管考與國際市場之深入度、專屬性與內隱性相關分析……………………38
表 4-6 數據探索與國際市場知識之寬廣度、專屬性與內隱性相關分析………………39
表 4-7 社會分析與國際市場知識之深入度相關分析……………………………………40
表 4-8 決策評估與國際市場知識之寬廣度相關分析……………………………………40
表 4-9 國際行銷策略之調節效果-績效管考對國際市場知識(深入度)之關係 ………44
表 4-10 國際行銷策略之調節效果-績效管考對國際市場知識(專屬性)之關係 ………47
表 4-11 國際行銷策略之調節效果-績效管考對國際市場知識(內隱性)之關係 ………51
表 4-12 國際行銷策略之調節效果-數據探索對國際市場知識(寬廣度)之關係 ………55
表 4-13 國際行銷策略之調節效果-數據探索對國際市場知識(專屬性)之關係 ………58
表 4-14 國際行銷策略之調節效果-數據探索對國際市場知識(內隱性)之關係 ………62
表 4-15 國際行銷策略之調節效果-社會分析對國際市場知識(深入度)之關係 ………66
表 4-16 國際行銷策略之調節效果-決策評估對國際市場知識(寬廣度)之關係 ………70
表 5-1 研究假說與實證結果………………………………………………………………72
圖目錄
圖 1-1 研究流程圖…………………………………………………………………4
圖1-2 數據分析模式…………………………………………………………………12
圖2-1 研究架構圖……………………………………………………………………23



譚大純、汪昭芬、楊淑雲(2016)。組織數據分析行為取向之初探:模式建構與個案研究。第十屆海峽兩岸企業管理學術研討會論文集,台北市:國立台灣大學。
譚大純(2017),數據分析取向、市場知識與行銷機敏度。行政院科技部專題研究計畫(MOST-105-2410-H-017-006-)。
Akter, S., Wamba, S. F., Gunasekaran, A., Ren, S. J., Dubey, R., & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment? International Journal of Production Economics, 182, 113-131.
Barney, J. B. (2001). Is the resource-based "view" a useful perspective for strategic management research? Yes. The Academy of Management Review, 26(1), 41-56.
Bartlett, C. A., & Ghoshal, S. (1989). Managing Across Borders: The Transnational Solution, Harvard Business School Press.
Bharadwaj, A. S. (2000). A resource-based perspective on information technology capability and firm performance: An empirical investigation. MIS Quarterly, 169-196.
Blau, J., & Gobble, M. M. (2015). Big demand for big data scientists in Europe. Research Technology Management, 58(3), 3-6.
Byrd, T. A., & Turner, D. E. (2000). Measuring the flexibility of information technology infrastructure: Exploratory analysis of a construct. Journal of Management Information Systems, 17(1), 167-208.
Cachon, G. P., & Fisher, M. (2000). Supply chain inventory management and the value of shared information. Management Science, 46(8), 1032-1048.
Chen, C.L.P. & Zhang, C.Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on big data, Information Sciences, 275, 314–347.
Chen, H., Chiang, R. H. L., & Stirey, V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS Quarterly, 36(4), 1165-1188.
Cieślik, A. (2008). Multinational firms, international knowledge flows, and dual labor markets in developing economies. Review of Development Economics, 12(1), 160-179.
Clark, C. E., Cavanaugh, N. C., Brown, C. V., & Sambamurthy, V. 1997. Building change-readiness capabilities in the IS Organization: Insights from the Bell Atlantic experience. MIS Quarterly, 21(4), 425-455.
Cohen, D. (1998). Toward a knowledge context: report on the First Annual U.C. Berkeley Forum on Knowledge and the Firm. California Management Review, 40(3), 23-39.
Collis, D. J. (1994). How valuable are organizational capabilities? Strategic Management Journal, 15(Winter), 143-152.
Côrte-Real, N., Oliveira, T., & Ruivo, P. (2017). Assessing business value of big data analytics in European firms. Journal of Business Research, 70, 379-390.
Cummings, J. N. (2004). Work groups, structural diversity, and knowledge sharing in a global organization. Management Science, 50, 352–364.
Day, G. S. (1994). The capabilities of market-driven organizations. Journal of Marketing, 58(4), 37-52.
DeLuca, L. M., & Atuahene-Gima, K. (2007). Market knowledge dimensions and cross-functional collaboration: examining the different routes to product innovation performance. Journal of Marketing, 71(1), 95–112.
Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897-904.
Fahey, L. (1999). Competitors: Outwitting, Out Maneuvering, and Out Performing. John Wiley & Sons.
Fawcett, S. E. & Scully, J. (1995). A contingency perspective of just-in-time purchasing: globalization, implementation, and performance. International Journal of Production Research, 33(4), 915-931.
Fiol, C. M., & Lyles, M. A. (1985). Organizational learning. Academy of Management Review, 10, 803–813.
Fulgoni, G. (2013). Big data: friend or foe of digital advertising? Five ways marketers should use digital big data to their advantage. Journal of Advertising Research, 53(4), 372-376.
Gammeltoft, P., & Hobdari, B. (2017). Emerging market multinationals, international knowledge flows and innovation. International Journal of Technology Management, 74(1-4), 1-22.
George, G., Haas, M. R., & Pentland, A. (2014). Big data and management. Academy of Management Journal, 57(3), 321-326.
Gibson, C. B., & Zellmer-Bruhn, M. E. (2002). Applying the concept of teamwork metaphors to management of teams in multicultural contexts. Organizational Dynamics, 31(2), 101–116.
Goes, P. B. (2014). Big data & IS Research. MIS Quarterly, 38(3), iii-viii.
Goldman, S. L., Nagel, R. N., & Preiss, K. (1995). Agile competitors and virtual organizations: strategies for enriching the customer, New York: Van Nostrand Reinhold.
Gosain, S., Malhotra, A., & El Sawy, O. A. (2005). Coordinating for flexibility in e-business supply chains. Journal of Management Information Systems, 21(3), 7-45.
Grant, R. M. (1996). Toward a knowledge-based theory of the firm. Strategic Management Journal, 17, 109 –122.
Gupta, A. K., & Govindarajan, V. (2000). Knowledge flows within multinational corporations. Strategic Management Journal, 21(4): 473–496.
Gutmann, J. (2015). Humanizing big data: marketing at the meeting of social science and consumer insight. International Journal of Market Research, 57(3), 503-505.
Hakansson, H., & Johanson, J. (1988). Formal and informal cooperation strategies in international industrial networks. In Cooperative Strategies in International Business, Contractor, F. J, & Lorange, P. (eds). Lexington Books: Lexington, MA; 369–379.
Hart, S. (1995). A natural-resource-based view of the firm. Academy of Management Review, 20(4), 986 –1014.
He, W., Wang, F., & Akula, V. (2017). Managing extracted knowledge from big social media data for business decision making. Journal of Knowledge Management, 21(2), 275-294.
Hewett, K. & Krasnikov, A. V. (2016). Investing in buyer-seller relationships in transitional markets: a market-based assets perspective. Journal of International Marketing, 24(1), 57-81.
Hsinchun, C., Chiang, R. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188.
Hult, G. T. M., Ferrell, O. C., & Hurley, R.F. (2002). Global organizational learning effects on cycle time performance. Journal of Business Research, 55(5), 377–387.
Hult, G. T. M., Ketchen, D. J. Jr., & Slater, S.F. (2004). Information processing, knowledge development, and strategic supply chain performance. Academy of Management Journal, 47(2), 241–253.
Hunt, S. D. (2012). The evolution of resource-advantage theory: six events, six realizations, six contributions. Journal of Historical Research in Marketing, 4(1), 7-29.
Jobs, C. G., Aukers, S. M., & Gilfoil, D. M. (2015). The impact of big data on your firms marketing communications: a framework for understanding the emerging marketing analytics industry. Academy of Marketing Studies Journal, 19(2), 81-92.
Jobs, C. G., Gilfoil, D. M., & Aukers, S. M. (2016). How marketing organizations can benefit from big data advertising analytics. Academy of Marketing Studies Journal, 20(1), 18-35.
Khan, Z., & Vorley, T. (2017). Big data text analytics: an enabler of knowledge management. Journal of Knowledge Management, 21(1), 18-34.
Kim, G.H., Trimi, S., & Chung, J.H. (2014). Big-data applications in the government sector. Communications of The ACM, 57(3), 78-85.
Larson, D., & Chang, V. (2016). A review and future direction of agile, business intelligence, analytics and data science. International Journal of Information Management, 36(5), 700-710.
Lawrence, P. R., & Lorsch, J. W. (1967). Managing Differentiation and Integration. Harvard Business School Press: Boston, MA.
Lee, O.K. D., Xu, P., Kuilboer, J.-P., & Ashrafi, N. (2012). IT impacts on performance of service firms through operation-level dynamic capability. Journal of Applied Business Research, 28(6), 1283-1294
Levin, D., & Cross, R. (2004). The strength of weak ties you can trust: the mediating role of trust in effective knowledge transfer. Management Science, 50, 1477–1490.
Lim, L. K. S., Acito, F., & Rusetski, A. (2006). Development of archetypes of international marketing strategy. Journal of International Business Studies, Vol. 37(4), pp. 499-524.
Lin, C.H., Yen, D., & Tarn, D.D.C. (2007). An industry-level knowledge management model-a study of information-related industry in Taiwan. Information & Management, 44(1), 22-39.
Liu, C., Wang, J. S., & Lin, C. (2017). The concepts of big data applied in personal knowledge management. Journal of Knowledge Management, 21(1), 213-230.
MacDuffie, J. P., & Helper, S. (1997). Creating lean suppliers: diffusing lean production through the supply chain. California Management Review, 39(4): 118–151.
Parise, S., Iyer, B. & Vesset, D. (2012). Four strategies to capture and create value from big data. Ivey Business Journal, 4, (on-line jounral).
Pauleen, D. J. (2017). Davenport and Prusak on KM and big data/analytics: interview with David J. Pauleen. Journal of Knowledge Management, 21(1), 7-11.
Pauleen, D. J., & Wang, W. C. (2017). Does big data mean big knowledge? KM perspectives on big data and analytics. Journal of Knowledge Management, 21(1), 1-6.
Pavlou, P. A., & El Sawy, O. A. (2006). From IT leveraging competence to competitive advantage in turbulent environments: The case of new product development. Information Systems Research, 17(3), 198-227.
Priem, R. L., & Butler, J. E. (2001). Is the resource-based "view" a useful perspective for strategic management research? The Academy of Management Review, 26(1), 22-40.
Rai, A., Patnayakuni, R., & Seth, N. (2006). Firm performance impacts of digitally enabled supply chain integration capabilities. MIS Quarterly, 30(2), 225-246.
Rashi, G. (1991). Marketing in an information-intensive environment: strategic implications of knowledge as an asset. Journal of Marketing, 55(4), 1-19.
Ravichandran, T., Lertwongsatien, C., & Lertwongsatien, C. (2005). Effect of information systems resources and capabilities on firm performance: A resource-based perspective. Journal of Management Information Systems, 21(4), 237-276.
Reed, R. & Defillippi, R. J. (1990). Causal ambiguity, barriers to imitation, and sustainable competitive advantage. The Academy of Management Review, Vol. 15(1), pp. 88-102.
Rothberg, H. N., & Erickson, G. S. (2017). Big data systems: knowledge transfer or intelligence insights?. Journal of Knowledge Management, 21(1), 92-112.
Salvatore P., Bala I., & Dan V. (2012). Four strategies to capture and create value from big data. Ivey Business Journal, 76(4), 1-9.
Seddon, J., & Currie, W. L. (2017). A model for unpacking big data analytics in high-frequency trading. Journal of Business Research, 70, 300-307.
Varian, H. R. (2014), Big data: new tricks for econometrics. Journal of Economic Perspectives, 28(2), 3-28.
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365.
Wang, Y., & Hajli, N. (2017). Exploring the path to big data analytics success in healthcare. Journal of Business Research, 70, 287-299.
Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97-121.
Xu, Z., Frankwick, G. L., & Ramirez, E. (2016). Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective. Journal of Business Research, 69(5), 1562–1566.
Yang, C. C., Yang, K. J., & Peng, S. Y. (2011). Exploration strategies and key activities for the system of environmental management. Total Quality Management & Business Excellence, 22(11), 1179 –1194.
Yang, D., Zhao, P., Lou, R., & Wei, H. (2013). Environmental marketing strategy effects on market-based assets. Total Quality Management & Business Excellence, 24(5/6), 707-718.
Zack, M. H. (1999). Developing a knowledge strategy. California Management Review, 41(3), 125-145
Zaheer, A., & Zaheer, S. (1997). Catching the wave: alertness, responsiveness, and market influence in global electronic networks. Management Science 43(11), 1493-1509
Zahra, S. A., Ireland R. D., & Hitt, M. A. (2000). International expansion by new venture firms: international diversity, mode of market entry, technological learning, and performance. The Academy of Management Journal, Vol. 43(5), pp. 925-950.
Zellmer-Bruhn, M., & Gibson, C. (2006). Multinational organization context: implications for team learning and performance. Academy of Management Journal, 49(3): 501–518.
Zou, A. & Cavusgil, S. T. (2002). The GMS: A broad conceptualization of global marketing strategy and its effect on firm performance. Journal of Marketing, Vol. 66(4), pp. 40-56.

QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top