SÜREKSİZ TEKNOLOJİLERE VE TAMAMLAYICI YENİLİKLERE ÇALIŞANLAR NASIL UYUM SAĞLAR?

Yıl 2024, Cilt: 22 Sayı: Özel Sayı: Endüstri 4.0 ve Dijitalleşmenin Sosyal Bilimlerde Yansımaları, 1925 - 1945, 30.09.2024

Mehmet Fatih Vural

https://doi.org/10.35408/comuybd.1516756

Öz

Bu çalışma, Endüstri 4.0 bağlamında, tamamlayıcı yeniliklere ve süreksiz teknolojilere çalışanların daha iyi nasıl uyum sağlayabileceklerini keşfetmeyi amaçlamaktadır. Siber-fiziksel sistemler, yapay zeka ve büyük veri analitiği gibi teknolojik gelişmeler geleneksel iş modellerini bozarken, örgütlerin bu yenilikleri yapılarına etkili bir şekilde nasıl entegre edebileceklerini anlamak, rekabet gücünü korumak ve sürdürülebilir büyüme sağlamak adına önem taşımaktadır. Çalışmada kapsamlı bir literatür incelemesini içeren nitel bir araştırma metodolojisi kullanılmıştır. Çalışma, tamamlayıcı yeniliklerin, yıkıcı teknolojiler karşısında çalışanların adaptasyonunu ve örgütsel dönüşümü nasıl kolaylaştırdığına odaklanmaktadır. Alanyazın incelendiğinde, tamamlayıcı yeniliklerin, süreksiz teknolojilerin yıkıcı etkisini azaltmada kritik bir rol oynadığını göstermektedir. Bu yenilikleri başarıyla uygulayan kuruluşlar daha fazla çeviklik, dayanıklılık ve inovasyon kapasitesi göstermektedir. Etkili adaptasyon için temel faktörler arasında güçlü liderlik, sürekli öğrenme kültürü ve yeni teknolojilerin kurumsal hedeflerle stratejik olarak uyumlu hale getirilmesi yer almaktadır. Çalışma, ikincil verilere olan bağımlılığı nedeniyle çeşitli örgütsel bağlamlarda uyum süreçlerinin tüm karmaşıklığını yakalama konusunda sınırlılıkları söz konusudur. Bu araştırma, mevcut literatürde yeterince araştırılmamış olan süreksiz teknolojilere yönelik örgütsel uyum çerçevesine tamamlayıcı yenilikler kavramını entegre ederek özgün bir bakış açısı sunmaktadır. Süreksiz teknolojilerin oluşturduğu zorlukları azaltmak için tamamlayıcı yeniliklere yatırım yapmanın ve destekleyici bir örgütsel kültür geliştirmenin önemi vurgulanmaktadır. Gelecekteki araştırmalar, tamamlayıcı yeniliklerin belirli örgütsel ihtiyaçlara ve teknolojik bağlamlara nasıl uyarlanabileceğine dair daha ayrıntılı bir anlayış geliştirmek için farklı endüstrilerde ampirik araştırmalara odaklanmalıdır.

Anahtar Kelimeler

Süreksiz Teknolojiler, Tamamlayıcı Yenilikler, Endüstri 4.0, Teknolojiye Uyum, Çalışan Davranışları

HOW DO EMPLOYEES ADAPT TO DISCONTINUOUS TECHNOLOGIES AND COMPLEMENTARY INNOVATIONS?

Öz

This study aims to explore how complementary innovations can better adapt employees to discontinuous technologies in the context of Industry 4.0. While technological developments such as cyber-physical systems, artificial intelligence, and big data analytics disrupt traditional business models, understanding how organizations can effectively integrate these innovations into their structures is important in maintaining competitiveness and achieving sustainable growth. A qualitative research methodology including a comprehensive literature review was used in the study. The study focuses on how complementary innovations facilitate employee adaptation and organizational transformation in the face of disruptive technologies. When the literature is reviewed, it is shown that complementary innovations play a critical role in reducing the disruptive impact of discontinuous technologies. Organizations that successfully implement these innovations demonstrate greater agility, resilience, and innovation capacity. Key factors for effective adaptation include strong leadership, a culture of continuous learning, and strategic alignment of new technologies with organizational goals. The study demonstrates weakness in capturing the full complexity of adaptation processes in various organizational contexts due to its dependence on secondary data. Further empirical research is needed to validate the findings and explore industry-specific adaptation strategies. This research offers a unique perspective by integrating the concept of complementary innovations into the organizational adaptation framework for discontinuous technologies, which has been under-researched in the existing literature. The importance of investing in complementary innovations and developing a supportive organizational culture to mitigate the challenges posed by discontinuous technologies is emphasized. Future research should focus on empirical studies in different industries to develop a more nuanced understanding of how complementary innovations can be adapted to specific organizational needs and technological contexts.

Anahtar Kelimeler

Discontinuous Technologies, Complementary Innovations, Industry 4.0, Adaptation to Technology, Employee Behaviors

Kaynakça

• Al-alak, B. A., & Tarabieh, S. A. (2011). Gaining competitive advantage and organizational performance through customer orientation, innovation differentiation and market differentiation. International journal of economics and management sciences, 1(5), 80-91.
• Al Hadwer, A., Tavana, M., Gillis, D., & Rezania, D. (2021). A systematic review of organizational factors impacting cloud-based technology adoption using technology-organization-environment framework. Internet of Things, 15, 100407.
• Anderson, P., & Tushman, M. L. (2018). Technological discontinuities and dominant designs: A cyclical model of technological change. In Organizational innovation (pp. 373-402). Routledge.
• Bower, J. L., & Christensen, C. M. (1995). Disruptive technologies: Catching the wave. Harvard Business Review. 73(1), 43- 53.
• Coccia, M. (2023). Endüstriyel ve kurumsal değişim için yıkıcı teknolojiler. Küresel kamu yönetimi, kamu politikası ve yönetişim ansiklopedisinde (s. 3121-3127). Cham: Springer Uluslararası Yayıncılık.
• Brown, J. S., & Hagel, J. (2005). The only sustainable edge: Why business strategy depends on productive friction and dynamic specialization. Harvard Business School Press.
• Burns, T. & Stalker, G.M. (1971). The Management of Innovation. London: Tavistok publications. Cinar, B., & Bharadiya, J. P. (2023). Cloud computing forensics; challenges and future perspectives: A review. Asian Journal of Research in Computer Science, 16(1), 1-14.
• Christensen, C. M. (1997). The innovator's dilemma: When new technologies cause great firms to fail. Harvard Business School Press.
• Christensen, C., Raynor, M. E., & McDonald, R. (2013). Disruptive innovation (pp. 20151-20111). Brighton, MA, USA: Harvard Business Review.
• Christensen, C. M., & Raynor, M. E. (2003). The innovator's solution: Creating and sustaining successful growth. Harvard Business School Press.
• Dai, H. N., Wong, R. C. W., Wang, H., Zheng, Z., & Vasilakos, A. V. (2019). Big data analytics for large-scale wireless networks: Challenges and opportunities. ACM Computing Surveys (CSUR), 52(5), 1-36.
• Eisenreich, A., Füller, J., Stuchtey, M., & Gimenez-Jimenez, D. (2022). Toward a circular value chain: Impact of the circular economy on a company's value chain processes. Journal of Cleaner Production, 378, 134375.
• Esmer, Y., & Alan, M. A. (2019). Endüstri 4.0 perspektifinde inovasyon. Avrasya Uluslararası Araştırmalar Dergisi, 7(18), 465-478.
• Ghobakhloo, M. (2018). The future of manufacturing industry: A strategic roadmap toward Industry 4.0. Journal of Manufacturing Technology Management, 29(6), 910-936.
• Govindarajan, V., & Trimble, C. (2010). The other side of innovation: Solving the execution challenge. Harvard Business School Press.
• Grover, V., Chiang, R. H., 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.
• Gupta, S., Chen, H., Hazen, B. T., Kaur, S., & Gonzalez, E. D. S. (2019). Circular economy and big data analytics: A stakeholder perspective. Technological Forecasting and Social Change, 144, 466-474.
• Hamilton, R. H., & Sodeman, W. A. (2020). The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources. Business Horizons, 63(1), 85-95.
• Hassan, O. F., Aderibigbe, O. O., Efijemue, O. P., & Onasanya, T. D. (2024). The Impact of Cloud Computing in Promoting Economic Growth through SMEs in the United States. International Journal of Computer Science and Information Technology, 16, 11-23.
• Helm, J. M., Swiergosz, A. M., Haeberle, H. S., Karnuta, J. M., Schaffer, J. L., Krebs, V. E., ... & Ramkumar, P. N. (2020). Machine learning and artificial intelligence: definitions, applications, and future directions. Current reviews in musculoskeletal medicine, 13, 69-76.
• Hilbolling, S., Berends, H., Deken, F., & Tuertscher, P. (2020). Complementors as connectors: managing open innovation around digital product platforms. R&d Management, 50(1), 18-30.
• Hoffman, D. L., Moreau, C. P., Stremersch, S., & Wedel, M. (2022). The rise of new technologies in marketing: A framework and outlook. Journal of Marketing, 86(1), 1-6.
• Kandarkar, P. C., & Ravi, V. (2024). Investigating the impact of smart manufacturing and interconnected emerging technologies in building smarter supply chains. Journal of Manufacturing Technology Management.
• Kahraman, E. D., & Erdirençelebi, M. (2024). Çalışanların dijital dönüşüme bakış açısının motivasyon ve performansa etkisi. Fivezero, 4(1), 25-50. https://doi.org/10.54486/fivezero.2024.34
• Kaplan, R. S., & Norton, D. P. (2006). Alignment: Using the balanced scorecard to create corporate synergies. Harvard Business School Press.
• Kitsios, F., & Kamariotou, M. (2021). Artificial intelligence and business strategy towards digital transformation: A research agenda. Sustainability, 13(4), 2025.
• Kotter, J. P. (1996). Leading change. Harvard Business School Press.
• Laskurain-Iturbe, I., Arana-Landin, G., Landeta-Manzano, B., & Jimenez-Redal, R. (2023). Assessing the uptake of Industry 4.0 technologies: barriers to their adoption and impact on quality management aspects. International Journal of Quality & Reliability Management, 40(10), 2420-2442.
• Laudon, K. C., & Traver, C. G. (2018). E-commerce: Business, technology, society. Pearson Education.
• Laghari, A. A., Wu, K., Laghari, R. A., Ali, M., & Khan, A. A. (2021). A review and state of art of Internet of Things (IoT). Archives of Computational Methods in Engineering, 1-19.
• Laghari, A. A., Zhang, X., Shaikh, Z. A., Khan, A., Estrela, V. V., & Izadi, S. (2024). A review on quality of experience (QoE) in cloud computing. Journal of Reliable Intelligent Environments, 10(2), 107-121.
• Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3(5), 18-23.
• Li, Z., Li, W., Carusone, R., & Profita, S. (2024). Adapting to discontinuous technological change from the perspective of knowledge management: a case study from the lighting industry in Lin’an, China. Journal of Knowledge Management.
• Macedo, M. I., Ferreira, F. A., Dabić, M., & Ferreira, N. C. (2024). Structuring and analyzing initiatives that facilitate organizational transformation processes: A sociotechnical approach. Technological Forecasting and Social Change, 209, 123739.
• Manyika, J., Chui, M., Bughin, J., Dobbs, R., Bisson, P., & Marrs, A. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.
• McAfee, A., & Brynjolfsson, E. (2008). Investing in the IT that makes a competitive difference. Harvard Business Review, 86(7-8), 98-107.
• Miles, R.E & Snow, C.C. (1978). Organization Strategy, Structure and Process. New York: McGraw Hill. Mintzberg, H. (1979). The Structuring of Organizations. Englewood Cliffs: Prentice Hall.
• Naghavi, A., & Ottaviano, G. I. (2010). Outsourcing, complementary innovations, and growth. Industrial and Corporate Change, 19(4), 1009-1035.
• Mouha, R. A. R. A. (2021). Internet of things (IoT). Journal of Data Analysis and Information Processing, 9(02), 77.
• Oks, S. J., Jalowski, M., Lechner, M., Mirschberger, S., Merklein, M., Vogel-Heuser, B., & Möslein, K. M. (2022). Cyber-physical systems in the context of industry 4.0: A review, categorization and outlook. Information Systems Frontiers, 1-42.
• O'Reilly, C. A., & Tushman, M. L. (2016). Lead and disrupt: How to solve the innovator's dilemma. Stanford University Press.
• Palos-Sánchez, P. R., Baena-Luna, P., Badicu, A., & Infante-Moro, J. C. (2022). Artificial intelligence and human resources management: A bibliometric analysis. Applied Artificial Intelligence, 36(1), 2145631. Parsons, T. (1960). Structures and Process in Modern Society. New York: Free press.
• Peláez-Sánchez, J., & Velarde-Camaqui, A. (2024). Complementary innovations and their role in enhancing primary innovations. Journal of Innovation Management, 11(1), 22-35.
• Perifanis, N. A., & Kitsios, F. (2023). Investigating the influence of artificial intelligence on business value in the digital era of strategy: A literature review. Information, 14(2), 85.
• Porter, M. E. (1985). Competitive advantage: Creating and sustaining superior performance. Free Press. Putnik, G. D., Ferreira, L., Lopes, N., & Putnik, Z. (2019). What is a Cyber-Physical System: Definitions and Models Spectrum. Fme Transactions, 47(4).
• Robla-Gómez, S., Becerra, V. M., Llata, J. R., Gonzalez-Sarabia, E., Torre-Ferrero, C., & Perez-Oria, J. (2017). Working together: A review on safe human-robot collaboration in industrial environments. Ieee Access, 5, 26754-26773.
• Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
• Rothaermel, F. T. (2000). Technological discontinuities and the nature of competition. Technology Analysis & Strategic Management, 12(2), 149-160.
• Sağsan, M., & Medeni, T. D. (2009). Geleneksel olmayan örgüt yapılarında bilgi haritalaması için alternatif yol arayışı. Akademik Bilişim’09 Konferansı, 561-567.
• Schalock, R. L., Verdugo, M. A., & van Loon, J. (2018). Understanding organization transformation in evaluation and program planning. Evaluation and program planning, 67, 53-60.
• Sabatier, V., Craig-Kennard, A., & Mangematin, V. (2012). When technological discontinuities and disruptive business models challenge dominant industry logics: Insights from the drugs industry. Technological Forecasting and Social Change, 79(5), 949-962.
• Schäper, T., Bendig, D., Foege, J. N., & Wagner, R. (2024). Shaping innovation outcomes: The role of CIOs for firms’ digital exploration. Journal of Information Technology, 02683962241258213.
• Senge, P. M. (2006). The fifth discipline: The art and practice of the learning organization. Doubleday. Yin, Y., Stecke, K. E., & Li, D. (2018). The evolution of production systems from Industry 2.0 through Industry 4.0. International Journal of Production Research, 56(1-2), 848-861.
• Sun, Y., & Zhou, Y. (2024). Specialized complementary assets and disruptive innovation: digital capability and ecosystem embeddedness. Management Decision.
• Teece, D. J. (1986). Profiting from technological innovation: Implications for integration, collaboration, licensing and public policy. Research Policy, 15(6), 285-305.
• Teece, D. J., Peteraf, M. A., & Leih, S. (2016). Dynamic capabilities and organizational agility: Risk, uncertainty, and strategy in the innovation economy. California Management Review, 58(4), 13-35.
• Thibaud, M., Chi, H., Zhou, W., & Piramuthu, S. (2018). Internet of Things (IoT) in high-risk Environment, Health and Safety (EHS) industries: A comprehensive review. Decision Support Systems, 108, 79-95.
• Tushman, M. L., & O'Reilly, C. A. (1997). Winning through innovation: A practical guide to leading organizational change and renewal. Harvard Business School Press.
• Votto, A. M., Valecha, R., Najafirad, P., & Rao, H. R. (2021). Artificial intelligence in tactical human resource management: A systematic literature review. International Journal of Information Management Data Insights, 1(2), 100047.
• Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: State of the art and future trends. International Journal of Production Research, 56(8), 2941-2962.
• Yu, B., Zhou, J., & Hu, S. (2020). Cyber-physical systems: An overview. Big data analytics for cyber-physical systems, 1-11.
• Zakir, J., Seymour, T., & Berg, K. (2015). Big data analytics. Issues in Information Systems, 16(2).
• Zhang, Y., Xu, S., Zhang, L., & Yang, M. (2021). Big data and human resource management research: An integrative review and new directions for future research. Journal of Business Research, 133, 34-50.
• Zhang, C., Wang, X., Cui, A. P., & Han, S. (2020). Linking big data analytical intelligence to customer relationship management performance. Industrial Marketing Management, 91, 483-494.
• Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of Industry 4.0: A review. Engineering, 3(5), 616-630.