面對VUCA這種難以預測的多變環境，生成式AI(Generative Artificial Intelligence) 能夠幫助不同規模的企業發展、維持競爭優勢的策略。生成式 AI 具備自動生成多種 形式內容的能力，包括文字、圖像、影音以及程式語言，並且應用層面依然持續在擴 張。由於這項技術有助於提升員工效率，使其更能迅速應對外部環境變化，許多企業 紛紛開始將生成式 AI 導入日常工作流程中。而過去的學術研究多集中在企業的訊息 科技敏捷性，尚未有研究深入探討生成式 AI 敏捷性。因此，本研究參考動態能力理 論觀點與目前台灣企業應用生成式 AI 分級，設置了吸收能力、識別能力、關係能力 以及大數據分析能力四個前因變數來探討企業生成式 AI 敏捷性，並進一步研究對企 業績效表現的影響。
本研究以擁有生成式 AI 相關科技使用經驗的業界人士為樣本對象，透過網路發 放結構式問卷進行調查。在研究期間，成功回收了 202 份有效樣本。為確保研究資料 無共同方法變異偏誤以及無反應偏差，我們運用 Harman’s 單因素檢驗法與卡方檢定 進行檢驗。進一步，透過因素分析、信效度分析、Pearson 相關分析以及迴歸分析等 統計方法，我們探究了吸收能力、識別能力、關係能力、大數據分析能力、生成式 AI 敏捷性以及企業績效表現之間的相互關係。研究發現顯示:(一)吸收能力對生成式 AI 敏捷性呈現正相關;(二)識別能力對生成式 AI 敏捷性存在正相關;(三)關係 能力對生成式 AI 敏捷性呈現正相關;(四)大數據分析能力對生成式 AI 敏捷性存在 正相關;(五)生成式 AI 敏捷性對企業績效表現存在正相關。這些研究結果有助於 深化我們對企業在應用生成式 AI 時能力和績效相互作用的理解。

By the end of 2022, generative AI (artificial intelligence), a technology that helps firms deal with VUCA environment changes, has rapidly proliferated globally, gaining widespread recognition from scholars for its transformative potential. A growing number of firms were incorporating this technology into their daily operations. Despite this, limited research had explored how firms enhance their agility through generative AI. To address this gap, our study identifies four antecedents of generative AI agility: absorptive capacity, recognition capability, relational capability, and big data analytics. Furthermore, we investigate the relationship between generative AI agility and firm performance to assess the investment worthiness of this technology.
Our study involved collecting data from 202 employees with experience in using generative AI technology via the Internet. The results indicate positive correlations between the four antecedents and generative AI agility, with absorptive capacity showing particularly strong relevance. Additionally, generative AI agility is positively associated with firm performance. Through a comprehensive examination of generative AI and its applications, our study offers insights into its role in fostering firm agility.

林震岩, 再版, 學歷, 經歷, 著作, & 算過程. (2007). 多變量分析—SPSS 的操作與應用. 台北: 智勝.
商業週刊. (2023). 擁抱生成式AI 聯發科技採用微軟Azure OpenAI企業級資安規格取得競爭優勢. Retrieved from https://www.businessweekly.com.tw/business/indep/1003492
許靜之. (2023). 生成式AI沒退燒！企業如何五層次深度應用ChatGPT？. 未來城市Podcast精華. Retrieved from https://futurecity.cw.com.tw/article/3167
陳仲興. (2023). 【AI浪潮進台積】劉德音下令打造AI祕密武器 台積電tGenie智能機器人揭密. Retrieved from https://www.mirrormedia.mg/story/20230627fin001
陳寬裕. (2017). 應用統計分析: SPSS 的運用: 五南圖書出版股份有限公司.
彭台光, 高月慈, & 林鉦棽. (2006). 管理研究中的共同方法變異: 問題本質, 影響, 測試和補救. 管理學報, 23(1), 77-98.
Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley interdisciplinary reviews: computational statistics, 2(4), 433-459.
Alghamdi, O. A., & Agag, G. (2023). Boosting Innovation Performance through Big Data Analytics Powered by Artificial Intelligence Use: An Empirical Exploration of the Role of Strategic Agility and Market Turbulence. Sustainability, 15(19), 14296.
Arcidiacono, F., Ancarani, A., Di Mauro, C., & Schupp, F. (2022). The role of absorptive capacity in the adoption of Smart Manufacturing. International Journal of Operations & Production Management, 42(6), 773-796.
Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of marketing research, 14(3), 396-402.
Ashok, M., Madan, R., Joha, A., & Sivarajah, U. (2022). Ethical framework for Artificial Intelligence and Digital technologies. International Journal of Information Management, 62, 102433.
Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2021). Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. Technological Forecasting and Social Change, 163, 120420.
Baldwin, J., & Lin, Z. (2002). Impediments to advanced technology adoption for Canadian manufacturers. Research policy, 31(1), 1-18.
Barrar, P., & Gervais, R. (2006). Global outsourcing strategies: An international reference on effective outsourcing relationships: Gower Publishing, Ltd.
Bennett, N., & Lemoine, G. J. (2014). What a difference a word makes: Understanding threats to performance in a VUCA world. Business horizons, 57(3), 311-317.
Boxall, P., & Purcell, J. (2022). Strategy and human resource management: Bloomsbury Publishing.
Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, communication & society, 15(5), 662-679.
Broady, T., Chan, A., & Caputi, P. (2010). Comparison of older and younger adults' attitudes towards and abilities with computers: Implications for training and learning. British Journal of Educational Technology, 41(3), 473-485.
Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at work. Retrieved from
Cameron, A. C., & Windmeijer, F. A. (1997). An R-squared measure of goodness of fit for some common nonlinear regression models. Journal of econometrics, 77(2), 329-342.
Catsaros, O. (2023). Generative AI to Become a $1.3 Trillion Market by 2032, Research Finds. Bloomberg Intelligence: New Report Finds That the Emerging Industry Could Grow at a CAGR of 42% Over the Next 10 Years. Rising demand for generative AI products could add about $280 billion of new software revenue. Retrieved from https://www.bloomberg.com/company/press/generative-ai-to-become-a-1-3-trillion-market-by-2032-research-finds/
Cegarra-Navarro, J.-G., Soto-Acosta, P., & Wensley, A. K. (2016). Structured knowledge processes and firm performance: The role of organizational agility. Journal of Business Research, 69(5), 1544-1549.
Chakravarty, A., Grewal, R., & Sambamurthy, V. (2013). Information technology competencies, organizational agility, and firm performance: Enabling and facilitating roles. Information systems research, 24(4), 976-997.
Chan, H. K., Griffin, J., Lim, J. J., Zeng, F., & Chiu, A. S. (2018). The impact of 3D Printing Technology on the supply chain: Manufacturing and legal perspectives. International Journal of Production Economics, 205, 156-162.
Chang, Y., Wong, S. F., Eze, U., & Lee, H. (2019). The effect of IT ambidexterity and cloud computing absorptive capacity on competitive advantage. Industrial Management & Data Systems, 119(3), 613-638.
Chen, L., Jiang, M., Jia, F., & Liu, G. (2022). Artificial intelligence adoption in business-to-business marketing: toward a conceptual framework. Journal of Business & Industrial Marketing, 37(5), 1025-1044.
Chen, X., Guo, M., & Shangguan, W. (2022). Estimating the impact of cloud computing on firm performance: An empirical investigation of listed firms. Information & management, 59(3), 103603.
Chen, Y., Wang, Y., Nevo, S., Jin, J., Wang, L., & Chow, W. S. (2014). IT capability and organizational performance: the roles of business process agility and environmental factors. European Journal of Information Systems, 23, 326-342.
Cho, H. E., Jeong, I., Kim, E., & Cho, J. (2023). Achieving superior performance in international markets: the roles of organizational agility and absorptive capacity. Journal of Business & Industrial Marketing, 38(4), 736-750.
Choi, J., & Wang, H. (2009). Stakeholder relations and the persistence of corporate financial performance. Strategic management journal, 30(8), 895-907.
Coch, L., & French Jr, J. R. (1948). Overcoming resistance to change. Human relations, 1(4), 512-532.
Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative science quarterly, 128-152.
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.
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. psychometrika, 16(3), 297-334.
Cubric, M. (2020). Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study. Technology in Society, 62, 101257.
D’Este, P., Iammarino, S., Savona, M., & Von Tunzelmann, N. (2012). What hampers innovation? Revealed barriers versus deterring barriers. Research policy, 41(2), 482-488.
Demchenko, Y., Grosso, P., De Laat, C., & Membrey, P. (2013). Addressing big data issues in scientific data infrastructure. Paper presented at the 2013 International conference on collaboration technologies and systems (CTS).
Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., Giannakis, M., Foropon, C., . . . Hazen, B. T. (2020). Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations. International Journal of Production Economics, 226, 107599.
Durbin, J., & Watson, G. S. (1971). Testing for serial correlation in least squares regression. III. Biometrika, 58(1), 1-19.
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., . . . Ahuja, M. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642.
Dyer, J. H., & Singh, H. (1998). The relational view: Cooperative strategy and sources of interorganizational competitive advantage. Academy of management review, 23(4), 660-679.
Eisfeldt, A. L., Schubert, G., & Zhang, M. B. (2023). Generative ai and firm values. Retrieved from
Flatten, T. C., Engelen, A., Zahra, S. A., & Brettel, M. (2011). A measure of absorptive capacity: Scale development and validation. European Management Journal, 29(2), 98-116.
Fuller, C. M., Simmering, M. J., Atinc, G., Atinc, Y., & Babin, B. J. (2016). Common methods variance detection in business research. Journal of Business Research, 69(8), 3192-3198.
García-Morales, V. J., Ruiz-Moreno, A., & Llorens-Montes, F. J. (2007). Effects of technology absorptive capacity and technology proactivity on organizational learning, innovation and performance: An empirical examination. Technology Analysis & Strategic Management, 19(4), 527-558.
Ghasemaghaei, M., & Calic, G. (2020). Assessing the impact of big data on firm innovation performance: Big data is not always better data. Journal of Business Research, 108, 147-162.
Grover, V. (2022). Digital agility: responding to digital opportunities. European Journal of Information Systems, 31(6), 709-715.
Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California management review, 61(4), 5-14.
Harrigan, K. R. (1988). Joint ventures and competitive strategy. Strategic management journal, 9(2), 141-158.
iKala. (2023). iKala KOL Radar. Retrieved from https://www.kolradar.com/solution?gclid=CjwKCAiA9dGqBhAqEiwAmRpTC7zXresRcQyvbq8-0oGe_xRfJ8njDYRS70V2N6qMOs-4Ze37XqgRpRoCYPoQAvD_BwE
Jo, A. (2023). The promise and peril of generative AI. nature, 614(1), 214-216.
Kaiser, H. F. (1974). An index of factorial simplicity. psychometrika, 39(1), 31-36.
Kale, P., & Singh, H. (2007). Building firm capabilities through learning: the role of the alliance learning process in alliance capability and firm‐level alliance success. Strategic management journal, 28(10), 981-1000.
Kang, M., & Sung, M. (2017). How symmetrical employee communication leads to employee engagement and positive employee communication behaviors: The mediation of employee-organization relationships. Journal of Communication Management, 21(1), 82-102.
Kibria, M. G., Nguyen, K., Villardi, G. P., Zhao, O., Ishizu, K., & Kojima, F. (2018). Big data analytics, machine learning, and artificial intelligence in next-generation wireless networks. IEEE access, 6, 32328-32338.
Kleinbaum, D. G., Kupper, L. L., Nizam, A., & Rosenberg, E. S. (2013). Applied regression analysis and other multivariable methods: Cengage Learning.
Kovacevic-Opacic, L., & Marjanovic, O. (2021). Digital Agility: Balancing Situational Turbulence with Digital Platforms. Paper presented at the AMCIS.
Lacity, M. C., Khan, S. A., & Willcocks, L. P. (2009). A review of the IT outsourcing literature: Insights for practice. The journal of strategic information systems, 18(3), 130-146.
Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META group research note, 6(70), 1.
Lee, Y., & Kim, J. (2021). Cultivating employee creativity through strategic internal communication: The role of leadership, symmetry, and feedback seeking behaviors. Public Relations Review, 47(1), 101998.
Li, J. J., Bonn, M. A., & Ye, B. H. (2019). Hotel employee's artificial intelligence and robotics awareness and its impact on turnover intention: The moderating roles of perceived organizational support and competitive psychological climate. Tourism Management, 73, 172-181.
Li, L., Lin, J., Turel, O., Liu, P., & Luo, X. (2020). The impact of e-commerce capabilities on agricultural firms’ performance gains: the mediating role of organizational agility. Industrial Management & Data Systems, 120(7), 1265-1286.
Lorenz, R., Benninghaus, C., Friedli, T., & Netland, T. H. (2020). Digitization of manufacturing: the role of external search. International Journal of Operations & Production Management, 40(7/8), 1129-1152.
Lorenzoni, G., & Lipparini, A. (1999). The leveraging of interfirm relationships as a distinctive organizational capability: a longitudinal study. Strategic management journal, 20(4), 317-338.
Lu, Y., & Ramamurthy, K. (2011). Understanding the link between information technology capability and organizational agility: An empirical examination. MIS quarterly, 931-954.
Mahmood, T., & Mubarik, M. S. (2020). Balancing innovation and exploitation in the fourth industrial revolution: Role of intellectual capital and technology absorptive capacity. Technological Forecasting and Social Change, 160, 120248.
Malik, A., Sharma, P., Kingshott, R., & Laker, B. (2022). Leveraging cultural and relational capabilities for business model innovation: The case of a digital media EMMNE. Journal of Business Research, 149, 270-282.
Mao, H., Liu, S., Zhang, J., Zhang, Y., & Gong, Y. (2021). Information technology competency and organizational agility: roles of absorptive capacity and information intensity. Information Technology & People, 34(1), 421-451.
Mariani, M. M., Perez‐Vega, R., & Wirtz, J. (2022). AI in marketing, consumer research and psychology: A systematic literature review and research agenda. Psychology & Marketing, 39(4), 755-776.
Martinez-Caro, E., Cepeda-Carrión, G., Cegarra-Navarro, J. G., & Garcia-Perez, A. (2020). The effect of information technology assimilation on firm performance in B2B scenarios. Industrial Management & Data Systems, 120(12), 2269-2296.
Michie, S., & Marteau, T. (1999). Non-response bias in prospective studies of patients and health care professionals. International Journal of Social Research Methodology, 2(3), 203-212.
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics capabilities and innovation: the mediating role of dynamic capabilities and moderating effect of the environment. British Journal of Management, 30(2), 272-298.
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 & management, 57(2), 103169.
Millar, C. C., Groth, O., & Mahon, J. F. (2018). Management innovation in a VUCA world: Challenges and recommendations. California management review, 61(1), 5-14.
Möller, K. K., & Törrönen, P. (2003). Business suppliers' value creation potential: A capability-based analysis. Industrial Marketing Management, 32(2), 109-118.
Mondal, S., Das, S., & Vrana, V. G. (2023). How to bell the cat? A theoretical review of generative artificial intelligence towards digital disruption in all walks of life. Technologies, 11(2), 44.
Müller, J. M., Buliga, O., & Voigt, K.-I. (2021). The role of absorptive capacity and innovation strategy in the design of industry 4.0 business Models-A comparison between SMEs and large enterprises. European Management Journal, 39(3), 333-343.
Neves, P., & Eisenberger, R. (2012). Management communication and employee performance: The contribution of perceived organizational support. Human performance, 25(5), 452-464.
Nugroho, A., Prijadi, R., & Kusumastuti, R. D. (2022). Strategic orientations and firm performance: the role of information technology adoption capability. Journal of Strategy and Management, 15(4), 691-717.
ÓhÉigeartaigh, S. S., Whittlestone, J., Liu, Y., Zeng, Y., & Liu, Z. (2020). Overcoming barriers to cross-cultural cooperation in AI ethics and governance. Philosophy & technology, 33, 571-593.
Overby, E., Bharadwaj, A., & Sambamurthy, V. (2006). Enterprise agility and the enabling role of information technology. European Journal of Information Systems, 15, 120-131.
Pagano, A. (2009). The role of relational capabilities in the organization of international sourcing activities: A literature review. Industrial Marketing Management, 38(8), 903-913.
Pellegrino, G. (2018). Barriers to innovation in young and mature firms. Journal of Evolutionary Economics, 28, 181-206.
Pham, T. S. H., Monkhouse, L. L., & Barnes, B. R. (2017). The influence of relational capability and marketing capabilities on the export performance of emerging market firms. International Marketing Review, 34(5), 606-628.
Podolny, J. M. (1994). Market uncertainty and the social character of economic exchange. Administrative science quarterly, 458-483.
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of applied psychology, 88(5), 879.
Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of management, 12(4), 531-544.
Pospelov, G. (1983). Artificial Intelligence as a Basis for a New Information Technology. IFAC Proceedings Volumes, 16(20), 1-14.
Richard, P. J., Devinney, T. M., Yip, G. S., & Johnson, G. (2009). Measuring organizational performance: Towards methodological best practice. Journal of management, 35(3), 718-804.
Roberts, N., Galluch, P. S., Dinger, M., & Grover, V. (2012). Absorptive capacity and information systems research: Review, synthesis, and directions for future research. MIS quarterly, 625-648.
Ruck, K., & Welch, M. (2012). Valuing internal communication; management and employee perspectives. Public Relations Review, 38(2), 294-302.
Rungsithong, R., Meyer, K. E., & Roath, A. S. (2017). Relational capabilities in Thai buyer-supplier relationships. Journal of Business & Industrial Marketing.
Sætra, H. S. (2023). Generative AI: Here to stay, but for good? Technology in Society, 102372.
Salmela, H., Baiyere, A., Tapanainen, T., & Galliers, R. D. (2022). Digital Agility: Conceptualizing Agility for the Digital Era. Journal of the Association for Information Systems, 23(5), 1080-1101.
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.
Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: appropriate use and interpretation. Anesthesia & analgesia, 126(5), 1763-1768.
Shen, Y., Heacock, L., Elias, J., Hentel, K. D., Reig, B., Shih, G., & Moy, L. (2023). ChatGPT and other large language models are double-edged swords. In (Vol. 307, pp. e230163): Radiological Society of North America.
Smirnova, M., Naudé, P., Henneberg, S. C., Mouzas, S., & Kouchtch, S. P. (2011). The impact of market orientation on the development of relational capabilities and performance outcomes: The case of Russian industrial firms. Industrial Marketing Management, 40(1), 44-53.
Sun, G. H., & Hoelscher, S. H. (2023). The ChatGPT storm and what faculty can do. Nurse Educator, 48(3), 119-124.
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach's alpha. International journal of medical education, 2, 53.
Teece, D. J. (2007). Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strategic management journal, 28(13), 1319-1350.
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic management journal, 18(7), 509-533.
Terwee, C. B., Bot, S. D., de Boer, M. R., van der Windt, D. A., Knol, D. L., Dekker, J., . . . de Vet, H. C. (2007). Quality criteria were proposed for measurement properties of health status questionnaires. Journal of clinical epidemiology, 60(1), 34-42.
Todorova, G., & Durisin, B. (2007). Absorptive capacity: Valuing a reconceptualization. Academy of management review, 32(3), 774-786.
Troise, C., Corvello, V., Ghobadian, A., & O'Regan, N. (2022). How can SMEs successfully navigate VUCA environment: The role of agility in the digital transformation era. Technological Forecasting and Social Change, 174, 121227.
Venkatraman, N., & Ramanujam, V. (1987). Measurement of business economic performance: An examination of method convergence. Journal of management, 13(1), 109-122.
Wamba, S. F. (2022). Impact of artificial intelligence assimilation on firm performance: The mediating effects of organizational agility and customer agility. International Journal of Information Management, 67, 102544.
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.
Warner, K. S., & Wäger, M. (2019). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long range planning, 52(3), 326-349.
Wong, I. A., Lian, Q. L., & Sun, D. (2023). Autonomous travel decision-making: An early glimpse into ChatGPT and generative AI. Journal of Hospitality and Tourism Management, 56, 253-263.
Yadegaridehkordi, E., Nilashi, M., Shuib, L., Nasir, M. H. N. B. M., Asadi, S., Samad, S., & Awang, N. F. (2020). The impact of big data on firm performance in hotel industry. Electronic Commerce Research and Applications, 40, 100921.
Young, M., & Post, J. E. (1993). Managing to communicate, communicating to manage: How leading companies communicate with employees. Organizational Dynamics, 22(1), 31-43.
Yue, C. A., Men, L. R., & Ferguson, M. A. (2021). Examining the effects of internal communication and emotional culture on employees’ organizational identification. International Journal of Business Communication, 58(2), 169-195.
Zahra, S. A., & George, G. (2002). Absorptive capacity: A review, reconceptualization, and extension. Academy of management review, 27(2), 185-203.
Zhang, C., & Lu, Y. (2021). Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration, 23, 100224.