隨著資訊科技的發展，大數據已經越來越受到各產業以及政府的重視。這些巨量的資料將會對企業與供應鏈帶來巨大的影響。透過大數據分析可以改善企業的分析結果，能夠更貼近真實情況。企業可以利用這些數據創造競爭對手企業難以匹配的能力。透過大數據分析的能力，能讓供應鏈更加透明，獲取更多不同於企業的新知識，來對快速變化的環境做出回應，進而能提升供應鏈敏捷性的績效。
本研究之研究對象為天下雜誌 2016 年所評選出之台灣四百五十大製造業、四百五十大服務業與一百大金融業的資訊部門主管，以郵寄問卷方式進行調查。研究結果顯示:(1) 大數據分析與基礎建設的能力與知識重組模型無顯著之影響;大數據分析管理的能力知識重組模型有顯著且正向的影響;大數據分析人員能力對知識重組模型中知識轉移之構面有顯著且正向的影響;在大數據分析人員能力對知識重組模型中知識互補性未有顯著的影響。(2)供應鏈知識重組之構面對供應鏈敏捷性顯著且正向的影響。

In today’s competitive business environment, firms face tremendous pressure to continually innovate and improve their processes, products, and services. Big data analytics has become an important area of study for both government and practitioners.These enormous data will have a huge impact on the business and supply chain. Big data analytics can improve operational and strategic capabilities, and ultimately, to positively impact business performance. Big data analytics has also become a competitive necessity for the management of supply chains, with practitioners and scholars focused almost entirely on how it capability is used to increase supply chain performance. Big data analytics capability can make the supply chain more transparent and acquire new knowledge that is different from the enterprise. Through these new knowledge to respond to the rapidly changing environment and can improve the performance of supply chain agility.
This study is investigates the top450 Taiwanese manufacturers, top450 service industries and top100 financial industry issued by CommonWealth Magazine of Taiwan in 2016. The research results reveal that: (1) BDA infrastructure capabilities have a nonsignificant impact on knowledge recombination. BDA management capability has a significant and positive impact on knowledge recombination. BDA personnel capability has a significant and positive impact on knowledge transfer, but a nonsignificant impact on knowledge complementarily (2) Knowledge recombination has a significant positive impact on supply chain agility.

中文部分
方亮淵. (2006). 資源基礎, 跨組織間知識共同演化行為與創新績效之研究.
金瑞. (2015). 大數據的無限商機(一版)
陳雲黃. (2000). 知識管理程序運用與工作角色行為認知之研究─ 以趨勢科技公司為例
英文部分
A. Bharadwaj, O.A. El Sawy, P.A. Pavlou, N. Venkatraman, Digital business
strategy: toward a next generation of insights, Mis Q. 37 (2013) 471–482.
Abbasi, A., Sarker, S., & Chiang, R. H. (2016). Big data research in information systems: Toward an inclusive research agenda. Journal of the Association for Information Systems, 17(2), 3.
Agarwal, A., Shankar, R., & Tiwari, M. (2007). Modeling agility of supply chain. Industrial marketing management, 36(4), 443-457.
Agarwal, R., & Dhar, V. (2014). Editorial—Big data, data science, and analytics: The opportunity and challenge for IS research: INFORMS.
Akter, S., Wamba, S. F., Gunasekaran, A., 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.
Alavi, M., & Leidner, D. E. (2001). Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS quarterly, 107-136.
Almeida, P., Song, J., & Grant, R. M. (2002). Are firms superior to alliances and markets? An empirical test of cross-border knowledge building. Organization science, 13(2), 147-161.
Ambrosini, V., & Bowman, C. (2009). What are dynamic capabilities and are they a useful construct in strategic management? International Journal of Management Reviews, 11(1), 29-49.
Anderson, C. (2008). The end of theory: The data deluge makes the scientific method obsolete. Wired magazine, 16(7), 16-07.
Anh, P. T. T., Baughn, C. C., Hang, N. T. M., & Neupert, K. E. (2006). Knowledge acquisition from foreign parents in international joint ventures: An empirical study in Vietnam. International Business Review, 15(5), 463-487.
Argote, L., & Ingram, P. (2000). Knowledge transfer: A basis for competitive advantage in firms. Organizational behavior and human decision processes, 82(1), 150-169.
Barratt, M., & Oke, A. (2007). Antecedents of supply chain visibility in retail supply chains: a resource-based theory perspective. Journal of operations management, 25(6), 1217-1233.
Barton, D., & Court, D. (2012). Making advanced analytics work for you. Harvard business review, 90(10), 78-83.
Bean, R., & Kiron, D. (2013). Organizational alignment is key to big data success. MIT Sloan Management Review, 54(3).
Bedi, P., Jindal, V., & Gautam, A. (2014). Beginning with big data simplified. Paper presented at the Data Mining and Intelligent Computing (ICDMIC), 2014 International Conference on.
Bonavia, M., Digiaro, M., Boscia, D., Boari, A., Bottalico, G., Savino, V., & Martelli, G. (2015). Studies on''corky rugose wood''of grapevine and on the diagnosis of grapevine virus B. VITIS-Journal of Grapevine Research, 35(1), 53.
Brandon‐Jones, E., Squire, B., Autry, C. W., & Petersen, K. J. (2014). A contingent resource‐based perspective of supply chain resilience and robustness. Journal of Supply Chain Management, 50(3), 55-73.
Braunscheidel, M. J., & Suresh, N. C. (2009). The organizational antecedents of a firm’s supply chain agility for risk mitigation and response. Journal of operations management, 27(2), 119-140.
Brown, B., Chui, M., & Manyika, J. (2011). Are you ready for the era of ‘big data’. McKinsey Quarterly, 4(1), 24-35.
Buhl, H., & Heidemann, J. (2013). Big Data: A Fashionable Topic with (out) Sustainable Relevance for Research and Practice?'Editorial, Business & Information Systems Engineering 2 (2013) 65-69.
Cai, Z., Huang, Q., Liu, H., Davison, R. M., & Liang, L. (2013). Developing Organizational Agility through IT Capability and KM Capability: The Moderating Effects of Organizational Climate. Paper presented at the PACIS.
Cao, M., & Zhang, Q. (2011). Supply chain collaboration: Impact on collaborative advantage and firm performance. Journal of operations management, 29(3), 163-180.
Chandler, N., Hostmann, B., Rayner, N., & Herschel, G. (2011). Gartner’s business analytics framework. Processes and platforms that need to be integrated and aligned to take a more strategic approach to business intelligence (BI).
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.
Christopher, M. (2000). The agile supply chain: competing in volatile markets. Industrial marketing management, 29(1), 37-44.
Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 128-152.
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.
Corte Real, N., Oliveira, T., & Ruivo, P. (2014). Understanding the hidden value of business intelligence and analytics (BI&A).
Cui, T., Ye, H. J., Teo, H. H., & Li, J. (2015). Information technology and open innovation: A strategic alignment perspective. Information & Management, 52(3), 348-358.
Cummings, J. L., & Teng, B.-S. (2003). Transferring R&D knowledge: the key factors affecting knowledge transfer success. Journal of Engineering and Technology Management, 20(1–2), 39-68. doi:https://doi.org/10.1016/S0923-4748(03)00004-3
Davenport, T. H. (2006). Competing on analytics. harvard business review, 84(1), 98.
Davenport, T. H., Barth, P., & Bean, R. (2012). How big data is different. MIT sloan management review, 54(1), 43.
Davis, J. P., & Eisenhardt, K. M. (2011). Rotating leadership and collaborative innovation: Recombination processes in symbiotic relationships. Administrative Science Quarterly, 56(2), 159-201.
de Freitas, P. A., dos Reis, E. A., Michel, W. S., Gronovicz, M. E., & de Macedo Rodrigues, M. r. A. (2013). Information Governance, Big Data and Data Quality. Paper presented at the Computational Science and Engineering (CSE), 2013 IEEE 16th International Conference on.
DeGroote, S. E., & Marx, T. G. (2013). The impact of IT on supply chain agility and firm performance: an empirical investigation. International Journal of Information Management, 33(6), 909-916.
Dibiaggio, L., Nasiriyar, M., & Nesta, L. (2014). Substitutability and complementarity of technological knowledge and the inventive performance of semiconductor companies. Research policy, 43(9), 1582-1593.
Dosi, G., & Nelson, R. R. (2010). Technical change and industrial dynamics as evolutionary processes. Handbook of the Economics of Innovation, 1, 51-127.
Dosi, G., & Nelson, R. R. (2013). The evolution of technologies: An assessment of the state-of-the-art. Eurasian Business Review, 3(1), 3-46.
Duan, L., & Xiong, Y. (2015). Big data analytics and business analytics. Journal of Management Analytics, 2(1), 1-21.
Dumbill, E. (2012). Planning for big data: " O'Reilly Media, Inc.".
E.Y. (2014). Big data Changing the way businessescompete and operate. Insights ongovernance, risk and compliance.
Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic capabilities: what are they? Strategic management journal, 1105-1121.
Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897-904.
Findlay, R. (1978). Relative backwardness, direct foreign investment, and the transfer of technology: a simple dynamic model. The Quarterly Journal of Economics, 92(1), 1-16.
Finkelstein, S. (2009). The effects of strategic and market complementarity on acquisition performance: Evidence from the US commercial banking industry, 1989–2001. Strategic management journal, 30(6), 617-646.
Fleming, L. (2001). Recombinant uncertainty in technological search. Management science, 47(1), 117-132.
Fliedner, G., & Vokurka, R. J. (1997). Agility: competitive weapon of the 1990s and beyond? Production and Inventory Management Journal, 38(3), 19.
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.
Frohlich, M. T., & Westbrook, R. (2002). Demand chain management in manufacturing and services: web-based integration, drivers and performance. Journal of operations management, 20(6), 729-745.
Galunic, D. C., & Rodan, S. (1998). Resource recombinations in the firm: Knowledge structures and the potential for Schumpeterian innovation. Strategic management journal, 1193-1201.
Gardner, D. (2013). Ford scours formore big data to bolster quality, improvemanufacturing,
streamline processes.
Germann, F., Lilien, G. L., Fiedler, L., & Kraus, M. (2014). Do retailers benefit from deploying customer analytics? Journal of Retailing, 90(4), 587-593.
Gibson, C. B., & Birkinshaw, J. (2004). The antecedents, consequences, and mediating role of organizational ambidexterity. Academy of management Journal, 47(2), 209-226.
Gold, A. H., & Arvind Malhotra, A. H. S. (2001). Knowledge management: An organizational capabilities perspective. Journal of management information systems, 18(1), 185-214.
Goldman, S. L. (1995). Agile competitors and virtual organizations: strategies for enriching the customer: Van Nostrand Reinhold Company.
Grant, R. M. (1996). Toward a knowledge‐based theory of the firm. Strategic management journal, 17(S2), 109-122.
Griffin, J. (2010). Implementing a data governance initiative. Information Management Journal, 27-28.
Gunasekaran, A., Lai, K.-h., & Cheng, T. E. (2008). Responsive supply chain: a competitive strategy in a networked economy. Omega, 36(4), 549-564.
Guo, Z., Fang, F., & Whinston, A. B. (2006). Supply chain information sharing in a macro prediction market. Decision Support Systems, 42(3), 1944-1958.
Hagel, J. (2015). Bringing analytics to life. Journal of Accountancy, 219(2), 24.
Hansen, M. T. (1999). The search-transfer problem: The role of weak ties in sharing knowledge across organization subunits. Administrative Science Quarterly, 44(1), 82-111.
Hansmann, T., & Niemeyer, P. (2014). Big data-characterizing an emerging research field using topic models. Paper presented at the Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2014 IEEE/WIC/ACM International Joint Conferences on.
Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72-80. doi:https://doi.org/10.1016/j.ijpe.2014.04.018
Helfat, C. E. (1994). Evolutionary trajectories in petroleum firm R&D. Management science, 40(12), 1720-1747.
Henderson, R., & Cockburn, I. (1994). Measuring competence? Exploring firm effects in pharmaceutical research. Strategic management journal, 15(S1), 63-84.
I. van Hoek, R., Harrison, A., & Christopher, M. (2001). Measuring agile capabilities in the supply chain. International Journal of Operations & Production Management, 21(1/2), 126-148.
Inkpen, A. C., & Dinur, A. (1998). Knowledge management processes and international joint ventures. Organization science, 9(4), 454-468.
Jiang, C. (2005). The impact of entrepreneur's social capital on knowledge transfer in Chinese high-tech firms: the mediating effects of absorptive capacity and guanxi development. International Journal of Entrepreneurship and Innovation Management, 5(3-4), 269-283.
Jones, B. F. (2009). The burden of knowledge and the “death of the renaissance man”: Is innovation getting harder? The Review of Economic Studies, 76(1), 283-317.
Kim, G., Shin, B., & Kwon, O. (2012). Investigating the value of sociomaterialism in conceptualizing IT capability of a firm. Journal of management information systems, 29(3), 327-362.
Kim, K. K., Umanath, N. S., Kim, J. Y., Ahrens, F., & Kim, B. (2012). Knowledge complementarity and knowledge exchange in supply channel relationships. International Journal of Information Management, 32(1), 35-49.
Kiron, D., Prentice, P. K., & Ferguson, R. B. (2014). The analytics mandate. MIT sloan management review, 55(4), 1.
Kogut, B., & Zander, U. (1992). Knowledge of the firm, combinative capabilities, and the replication of technology. Organization science, 3(3), 383-397.
Lambrechts, F., Taillieu, T., Grieten, S., & Poisquet, J. (2012). In-depth joint supply chain learning: towards a framework. Supply Chain Management: An International Journal, 17(6), 627-637.
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, 52(2), 21.
Lee, H. L., & Whang, S. (2004). E-business and supply chain integration The practice of supply chain management: Where theory and application converge (pp. 123-138): Springer.
Li, J. J., Poppo, L., & Zhou, K. Z. (2010). Relational mechanisms, formal contracts, and local knowledge acquisition by international subsidiaries. Strategic management journal, 31(4), 349-370.
Liu, H., Wei, S., Ke, W., Wei, K. K., & Hua, Z. (2016). The configuration between supply chain integration and information technology competency: A resource orchestration perspective. Journal of operations management, 44, 13-29.
Liu, Y. (2014). Big data and predictive business analytics. The Journal of Business Forecasting, 33(4), 40.
Liyanage, S., & Barnard, R. (2003). Valuing of firms' prior knowledge: a measure of knowledge distance. Knowledge and Process Management, 10(2), 85-98.
Lorenzoni, G., & Lipparini, A. (1999). The leveraging of interfirm relationships as a distinctive organizational capability: a longitudinal study. Strategic management journal, 317-338.
Lu, R. (2011). Ying Lu, K. Ramamurthy: Understanding the link between information technology capability and organizational agility: an empirical examination. MIS Q(4), 931-954.
Lycett, M. (2013). 'Datafication': Making sense of (big) data in a complex world. European Journal of Information Systems, 22(4), 381.
Makri, M., Hitt, M. A., & Lane, P. J. (2010). Complementary technologies, knowledge relatedness, and invention outcomes in high technology mergers and acquisitions. Strategic management journal, 31(6), 602-628.
Malhotra, A., Gosain, S., & Sawy, O. A. E. (2005). Absorptive capacity configurations in supply chains: gearing for partner-enabled market knowledge creation. MIS quarterly, 145-187.
Mariotti, F. (2007). Learning to share knowledge in the Italian motorsport industry. Knowledge and Process Management, 14(2), 81-94.
Mason-Jones, R., & Towill, D. R. (1999). Total cycle time compression and the agile supply chain. International Journal of Production Economics, 62(1), 61-73.
Maurer, I., & Ebers, M. (2006). Dynamics of social capital and their performance implications: Lessons from biotechnology start-ups. Administrative Science Quarterly, 51(2), 262-292.
McAfee, A., & Brynjolfsson, E. (2012). Big data: the management revolution. Harvard business review, 90(10), 60-68.
Meredith, S., & Francis, D. (2000). Journey towards agility: the agile wheel explored. The TQM Magazine, 12(2), 137-143.
Mohanty, S., Jagadeesh, M., & Srivatsa, H. (2013). Big data imperatives: Enterprise ‘Big Data’warehouse,‘BI’implementations and analytics: Apress.
Morabito, V. (2015). Big data and analytics: strategic and organizational impacts: Springer.
Mortenson, M. J., Doherty, N. F., & Robinson, S. (2015). Operational research from Taylorism to Terabytes: A research agenda for the analytics age. European journal of operational research, 241(3), 583-595. doi:http://dx.doi.org/10.1016/j.ejor.2014.08.029
Nelson, R. R., & Winter, S. G. (1982). The Schumpeterian tradeoff revisited. The American Economic Review, 72(1), 114-132.
Nerkar, A. (2003). Old is gold? The value of temporal exploration in the creation of new knowledge. Management science, 49(2), 211-229.
Ngai, E. W., Chau, D. C., & Chan, T. (2011). Information technology, operational, and management competencies for supply chain agility: Findings from case studies. The Journal of Strategic Information Systems, 20(3), 232-249.
Powell, W. W., Koput, K. W., & Smith-Doerr, L. (1996). Interorganizational collaboration and the locus of innovation: Networks of learning in biotechnology. Administrative Science Quarterly, 116-145.
Ransbotham, S., Kiron, D., & Prentice, P. K. (2015). Minding the analytics gap. MIT sloan management review, 56(3), 63.
Redman, T. C. (2008). Data driven: profiting from your most important business asset: Harvard Business Press.
Rhodes, J., Hung, R., Lok, P., Ya-Hui Lien, B., & Wu, C.-M. (2008). Factors influencing organizational knowledge transfer: implication for corporate performance. Journal of knowledge management, 12(3), 84-100.
Richardson, G. B. (1972). The organisation of industry. The economic journal, 82(327), 883-896.
Ringle, C. M., Wende, S., & Will, A. (2005). SmartPLS 2.0 (beta): Hamburg.
Rodan, S., & Galunic, C. (2004). More than network structure: How knowledge heterogeneity influences managerial performance and innovativeness. Strategic management journal, 25(6), 541-562.
Rothberg, H. N., Rothberg, H. N., Erickson, G. S., & Erickson, G. S. (2017). Big data systems: knowledge transfer or intelligence insights? Journal of knowledge management, 21(1), 92-112.
Sahin, F. (2000). Manufacturing competitiveness: Different systems to achieve the same results. Production and Inventory Management Journal, 41(1), 56.
Sambamurthy, V., Bharadwaj, A., & Grover, V. (2003). Shaping agility through digital options: Reconceptualizing the role of information technology in contemporary firms. MIS quarterly, 237-263.
Saraf, N., Langdon, C. S., & Gosain, S. (2007). IS application capabilities and relational value in interfirm partnerships. Information systems research, 18(3), 320-339.
Savino, T., Messeni Petruzzelli, A., & Albino, V. (2015). Search and recombination process to innovate: A review of the empirical evidence and a research agenda. International Journal of Management Reviews.
Schoenherr, T., & Speier‐Pero, C. (2015). Data science, predictive analytics, and big data in supply chain management: Current state and future potential. Journal of Business Logistics, 36(1), 120-132.
Schoenherr, T., & Swink, M. (2012). Revisiting the arcs of integration: Cross-validations and extensions. Journal of operations management, 30(1), 99-115.
Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D., & Tufano, P. (2012). Analytics: The Real-World Use of Big Data (IBM Institute for Business Value-Executive Report). IBM Institute for Business Value.
Sher, P. J., & Lee, V. C. (2004). Information technology as a facilitator for enhancing dynamic capabilities through knowledge management. Information & Management, 41(8), 933-945.
Swafford, P. M., Ghosh, S., & Murthy, N. (2006). The antecedents of supply chain agility of a firm: scale development and model testing. Journal of operations management, 24(2), 170-188.
Swafford, P. M., Ghosh, S., & Murthy, N. (2008). Achieving supply chain agility through IT integration and flexibility. International Journal of Production Economics, 116(2), 288-297.
Szulanski, G. (1996). Exploring internal stickiness: Impediments to the transfer of best practice within the firm. Strategic management journal, 17(S2), 27-43.
Szulanski, G. (2000). The process of knowledge transfer: A diachronic analysis of stickiness. Organizational behavior and human decision processes, 82(1), 9-27.
Tallon, P. P. (2013). Corporate governance of big data: Perspectives on value, risk, and cost. Computer, 46(6), 32-38.
Tamer Cavusgil, S., Calantone, R. J., & Zhao, Y. (2003). Tacit knowledge transfer and firm innovation capability. Journal of business & industrial marketing, 18(1), 6-21.
Teece, D., Peteraf, M., & Leih, S. (2016). Dynamic Capabilities and Organizational Agility. California Management Review, 58(4), 13-35.
Thomas, D. J., & Griffin, P. M. (1996). Coordinated supply chain management. European journal of operational research, 94(1), 1-15.
Tweney, D. (2013). Walmart scoops up Inkiru to bolster its ‘big data’capabilities online: Retrieved.
Urban, G. L., & Von Hippel, E. (1988). Lead user analyses for the development of new industrial products. Management science, 34(5), 569-582.
Vance, A. (2011). The data knows. Bloomberg Businessweek, September 12, 70-74.
Waller, M. A., & Fawcett, S. E. (2013). Click here for a data scientist: Big data, predictive analytics, and theory development in the era of a maker movement supply chain. Journal of Business Logistics, 34(4), 249-252.
Wamba, S. F., 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.
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J.-f., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365.
Wielki, J. (2013). Implementation of the Big Data concept in organizations-possibilities, impediments and challenges. Paper presented at the Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on.
Wilkins, J. (2013). Big data and its impact on manufacturing.
Wixom, B. H., Yen, B., & Relich, M. (2013). Maximizing Value from Business Analytics. MIS Quarterly Executive, 12(2).
Wu, F., Yeniyurt, S., Kim, D., & Cavusgil, S. T. (2006). The impact of information technology on supply chain capabilities and firm performance: A resource-based view. Industrial marketing management, 35(4), 493-504.
Yao, Z., Yang, Z., Fisher, G. J., Ma, C., & Fang, E. E. (2013). Knowledge complementarity, knowledge absorption effectiveness, and new product performance: The exploration of international joint ventures in China. International Business Review, 22(1), 216-227.
Yiu, C. (2012). The big data opportunity. Policy Exchange, 1-36.
Zahra, S. A., & George, G. (2002). Absorptive capacity: A review, reconceptualization, and extension. Academy of management review, 27(2), 185-203.
Zheng, S., Zhang, W., & Du, J. (2011). Knowledge-based dynamic capabilities and innovation in networked environments. Journal of knowledge management, 15(6), 1035-1051.