İletişimISSN:e-ISSN: 2578-8965
Yazar GirişiHakem GirişiYeni Üyelik
TürkçeEnglish
AnasayfaAmaç ve KapsamDergi KuruluYazarlar için RehberMakale GönderSon SayıArşivİletişim

Firma Düzeyinde Teknoloji Adaptasyonunun Değerlendirilmesi: TOE Çerçevesine Dayalı Entegre Bir Geometrik Ortalama Modeli (M-BOOST)

Turan Paksoy,BÜŞRA YİĞİTOL,MUSTAFA ATİLLA ARICIOĞLU,DEMİR SERCAN

  • BÜŞRA YİĞİTOL: NECMETTİN ERBAKAN ÜNİVERSİTESİ
  • MUSTAFA ATİLLA ARICIOĞLU: NECMETTİN ERBAKAN ÜNİVERSİTESİ
  • DEMİR SERCAN: HARRAN ÜNİVERSİTESİ
  •  Yıl : 2024
  •  Cilt : 4
  •  Sayı : 2
  •  Sayfa : 153-187
Teknolojinin getirdiği hız ve kolaylıklar, işletmeler için kritik öneme sahiptir ve göz ardı edilemez. Rekabet gücünü korumaya çalışan şirketler, verimliliği artırmak, ürün kalitesini geliştirmek ve operasyonel maliyetleri azaltmak için giderek daha fazla teknolojiye entegre olmaktadır. Teknolojinin sağladığı avantajlardan yararlanma yeteneği, doğrudan teknoloji benimsenmesi ile orantılıdır. Teknolojik gelişmeleri yakından takip etmek, sektör fark etmeksizin tüm işletmeler için küresel pazarda rekabetçi kalmanın anahtarı haline gelmiştir. Yeni teknolojileri hızla benimseyen şirketler, rakiplerine göre daha avantajlı bir konum elde etmekte ve piyasa pozisyonlarını sağlamlaştırmaktadır. Ancak, teknolojideki hızlı değişim, adaptasyon sürecinin de daha hızlı yürütülmesini gerektirmektedir. Bu çalışma, firma düzeyinde teknoloji adaptasyon performansını nicel olarak ölçmek ve uyum çerçevesini şirket bazında sayısal bir çıktı olarak sunmak üzere revize edilmiş bir Teknoloji-Organizasyon-Çevre (TOE) çerçevesine dayalı yeni bir model önermektedir. M-BOOST modeli, Alt Üst Etme, Organizasyon, Paydaş, Davranış, Teknoloji ve Yönetim olmak üzere altı boyuttan oluşmaktadır ve her boyut kendi parametrelerine sahiptir. Her bir boyutun skoru, bu parametrelerin aritmetik ortalaması olarak hesaplanırken, genel M-BOOST Teknoloji Adaptasyon Skoru, altı boyut skorunun geometrik ortalaması ile belirlenir. Model, tüm boyutların ayrı ayrı değerlendirilmesine olanak tanımaktadır. Ayrı değerlendirmeler yapılmasının yanı sıra, önerilen formülasyona göre genel bir uyum skoru belirlemek için bir hesaplama yapılmış ve örnek olay çalışması için bir değer ortaya konmuştur. M-BOOST skoru 1'in altında olan şirketler, teknoloji uyumunda sorun yaşayan şirketleri işaret ederken, 1-3 arasında bir değere sahip olan şirketlerin teknoloji uyumunda gelişime açık olduğu, 3'ün üzerinde bir değere sahip olan şirketlerin ise başarılı olduğu söylenebilir. Örnek olay uygulamasından elde edilen sonuçlar çalışmanın sonucunda yer verilmiş ve modelin kullanışlılığı ve gelecek çalışmalar için aktarımlar yapılmıştır.
Atıf yapmak için : Paksoy, T., Yiğitol, B., Arıcıoğlu, M. A., & Demir, S. (2024). Evaluating firm-level technology Adaptation: An ıntegrated geometric mean model based on the TOE framework (M-BOOST). Fivezero, 4(2), 153-187. https://doi.org/10.54486/fivezero.2024.41

Çıkar İlişkisi : Yazarlar çıkar çatışması olmadığını beyan etmiştir.

Bu makale CC BY-NC 4.0 lisansı ile yayımlanmıştır.
Fivezero Dergisi
2024, Cilt4, Sayı2
E-ISSN: 2578-8965
Geliş Tarihi : , Kabul Tarihi : , Yayın Tarihi :

Kaynakça

  1. Abed, S. S. (2020). Social commerce adoption using TOE framework: An empirical investigation of Saudi Arabian SMEs. International Journal of Information Management, 53, 102118.
  2. Ajzen, I. (1980). Understanding attitudes and predictiing social behavior. Englewood cliffs.
  3. Aksak, O. (2022 November 16 ). İşletmelerde Teknoloji. https://www.fhomebilisim.com/blog/isletmelerde-teknoloji.htm
  4. Ale Ebrahim, N., & Bong, Y. (2017). Open innovation: A bibliometric study. International Journal of Innovation (IJI), 5(3).
  5. Alqahtani, M. S. A., & Erfani, E. (2021). Exploring the relationship between technology adoption and cyber security compliance: A quantitative study of UTAUT2 model. International Journal of Electronic Government Research (IJEGR), 17(4), 40-62.
  6. Alqahtani, M., & Braun, R. (2021). Reviewing influence of UTAUT2 factors on cyber security compliance: a literature review. Journal of Information Assurance & Cyber Security.
  7. Asplund, F., Björk, J., Magnusson, M., & Patrick, A. J. (2021). The genesis of public-private innovation ecosystems: Bias and challenges✰. Technological Forecasting and Social Change, 162, 120378.
  8. Bailey, J. L. (2014). Non-technical skills for success in a technical world. International Journal of Business and Social Science, 5(4).
  9. Baker, J. (2012). The technology–organization–environment framework. Information Systems Theory: Explaining and Predicting Our Digital Society, Vol. 1, 231-245.
  10. Brown, S. A., Massey, A. P., Montoya-Weiss, M. M., & Burkman, J. R. (2002). Do I really have to? User acceptance of mandated technology. European journal of information systems, 11(4), 283-295.
  11. Chatterjee, S., Nguyen, B., Ghosh, S. K., Bhattacharjee, K. K., & Chaudhuri, S. (2020). Adoption of artificial intelligence integrated CRM system: an empirical study of Indian organizations. The Bottom Line, 33(4), 359-375.
  12. Chatzoglou, P., & Chatzoudes, D. (2016). Factors affecting e-business adoption in SMEs: an empirical research. Journal of Enterprise Information Management, 29(3), 327-358.
  13. Collins, P. D., Hage, J., & Hull, F. M. (1988). Organizational and technological predictors of change in automaticity. Academy of Management Journal, 31(3), 512-543.
  14. Cruz-Jesus, F., Pinheiro, A., & Oliveira, T. (2019). Understanding CRM adoption stages: empirical analysis building on the TOE framework. Computers in Industry, 109, 1-13.
  15. da Silva, S. B. (2021). Improving the firm innovation capacity through the adoption of standardized innovation management systems: a comparative analysis of the ISO 56002: 2019 with the literature on firm innovation capacity. International Journal of Innovation, 9(2), 389-413.
  16. Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results [Thesis, Massachusetts Institute of Technology]. https://dspace.mit.edu/handle/1721.1/15192
  17. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
  18. Dearing, J. W., & Cox, J. G. (2018). Diffusion of innovations theory, principles, and practice. Health affairs, 37(2), 183-190.
  19. Fonseka, K., Jaharadak, A. A., & Raman, M. (2022). Impact of E-commerce adoption on business performance of SMEs in Sri Lanka; moderating role of artificial intelligence. International Journal of Social Economics, 49(10), 1518-1531.
  20. Gafni, R., & Geri, N. (2013, February). Generation Y versus generation X: Differences in smartphone adaptation. In Learning in the technological era: Proceedings of the Chais conference on instructional technologies research (pp. 18-23).
  21. Ganguly, K. K. (2024). Understanding the challenges of the adoption of blockchain technology in the logistics sector: the TOE framework. Technology Analysis & Strategic Management, 36(3), 457-471.
  22. Goulart, V. G., Liboni, L. B., & Cezarino, L. O. (2022). Balancing skills in the digital transformation era: The future of jobs and the role of higher education. Industry and Higher Education, 36(2), 118-127.
  23. Güngör, T. (2018). Endüstri 4.0 çerçevesinde inovasyon ve teknoloji temelli büyüme (Master's thesis, Sosyal Bilimler Enstitüsü).
  24. Hameed, W. U., Basheer, M. F., Iqbal, J., Anwar, A., & Ahmad, H. K. (2018). Determinants of Firm’s open innovation performance and the role of R & D department: an empirical evidence from Malaysian SME’s. Journal of Global Entrepreneurship Research, 8(1), 1-20.
  25. Hashimy, L., Jain, G., & Grifell-Tatjé, E. (2023). Determinants of blockchain adoption as decentralized business model by Spanish firms–an innovation theory perspective. Industrial Management & Data Systems, 123(1), 204-228.
  26. Hashmi, M., Governatori, G., Lam, H. P., & Wynn, M. T. (2018). Are we done with business process compliance: state of the art and challenges ahead. Knowledge and Information Systems, 57(1), 79-133.
  27. Hausman, A., & Stock, J. R. (2003). Adoption and implementation of technological innovations within long-term relationships. Journal of Business Research, 56(8), 681-686.
  28. Huang, K. F. (2011). Technology competencies in competitive environment. Journal of Business Research, 64(2), 172-179.
  29. Jensen, P. H., & Webster, E. (2004). Examining biases in measures of firm innovation. Melbourne, Australia: Melbourne Institute of Applied Economic and Social Research, University of Melbourne.
  30. Kaminski, J. (2011). Diffusion of innovation theory. Canadian Journal of Nursing Informatics, 6(2), 1-6.
  31. Kemmerer, R. A. (2003, May). Cybersecurity. In 25th International Conference on Software Engineering, 2003. Proceedings. (pp. 705-715). IEEE.
  32. Khan, N. A., Khan, A. N., Bahadur, W., & Ali, M. (2021). Mobile payment adoption: a multi-theory model, multi-method approach and multi-country study. International Journal of Mobile Communications, 19(4), 467-491.
  33. Kim, D. Y., Kumar, V., & Kumar, U. (2012). Relationship between quality management practices and innovation. Journal of operations management, 30(4), 295-315.
  34. Kulviwat, S., Bruner II, G. C., & Al-Shuridah, O. (2009). The role of social influence on adoption of high tech innovations: The moderating effect of public/private consumption. Journal of Business research, 62(7), 706-712.
  35. LaMorte, W.W. (2022) Behavioral change models, Diffusion of Innovation Theory. Available at: https://sphweb.bumc.bu.edu/otlt/mph-modules/sb/behavioralchangetheories/behavioralchangetheories4.html (Accessed: 16 July 2024).
  36. Lawrence, P. R. (1968). How to deal with resistance to change. Harvard Business Review.
  37. Malik, S., Chadhar, M., Vatanasakdakul, S., & Chetty, M. (2021). Factors affecting the organizational adoption of blockchain technology: Extending the technology–organization–environment (TOE) framework in the Australian context. Sustainability, 13(16), 9404.
  38. Marangunić, N., & Granić, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal access in the information society, 14, 81-95.
  39. Marikyan, D., & Papagiannidis, S. (2023). Technology acceptance model—TheoryHub—Academic theories reviews for research and T&L. Technology Acceptance Model: A review. https://open. ncl. ac. uk/theories/1/technology-acceptance-model.
  40. Marquardt, K., Just, V., Geldmacher, W., Sommer, M., & Pamfilie, R. (2017). Disruptive innovations, their characteristics and implications on economy and people. New Trends in Sustainable Business and Consumption-2017, 410-418.
  41. Millar, C., Lockett, M., & Ladd, T. (2018). Disruption: Technology, innovation and society. Technological Forecasting and Social Change, 129(4), 254-260.
  42. Minishi-Majanja, M. K., & Kiplang'at, J. (2005). The diffusion of innovations theory as a theoretical framework in library and information science research. South African journal of libraries and information science, 71(3), 211-224.
  43. Morris, M. G., & Venkatesh, V. (2000). Age differences in technology adoption decisions: Implications for a changing work force. Personnel psychology, 53(2), 375-403.
  44. Mueller, J. S., Melwani, S., & Goncalo, J. A. (2012). The bias against creativity: Why people desire but reject creative ideas. Psychological science, 23(1), 13-17.
  45. Nadeau, M. C., Kar, A., Roth, R., & Kirchain, R. (2010). A dynamic process-based cost modeling approach to understand learning effects in manufacturing. International Journal of Production Economics, 128(1), 223-234.
  46. Ngah, A. H., Zainuddin, Y., & Thurasamy, R. (2017). Applying the TOE framework in the Halal warehouse adoption study. Journal of Islamic Accounting and Business Research, 8(2), 161-181.
  47. Nguyen, T. H., Le, X. C., & Vu, T. H. L. (2022). An extended technology-organization-environment (TOE) framework for online retailing utilization in digital transformation: Empirical evidence from Vietnam. Journal of Open Innovation: Technology, Market, and Complexity, 8(4), 200.
  48. Oliveira, T., & Martins, M. F. (2010, September). Information technology adoption models at firm level: review of literature. In The European conference on information systems management (p. 312). Academic Conferences International Limited.
  49. Olivieri F (2014) Compliance by design. Synthesis of business processes by declarative specifications. [Doctoral dissertation, Griffith University]. https://iris.univr.it/handle/11562/772161.
  50. Pietrewicz, L. (2019). Technology, business models and competitive advantage in the age of industry 4.0. Problemy Zarządzania, 17(2 (82)), 32-52.
  51. Quazi, A., & Talukder, M. (2011). Demographic determinants of adoption of technological innovation. Journal of Computer Information Systems, 52(1), 34-42.
  52. Reinders, M. J., Banovic, M., & Guerrero, L. (2019). Introduction. In Innovations in Traditional Foods (pp. 1-26). Woodhead Publishing.
  53. Riddell, W. C., & Song, X. (2017). The role of education in technology use and adoption: Evidence from the Canadian workplace and employee survey. ILR Review, 70(5), 1219-1253.
  54. Rogers, E. (2003). Diffusion of innovations. Revised. New York: Simon & Schuster.
  55. Salgues, B. (2016). Acceptability and Diffusion. In Health Industrialization (ss. 53-69). Elsevier.
  56. Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & education, 128, 13-35.
  57. Schumm, D., Leymann, F., Ma, Z., Scheibler, T., & Strauch, S. (2010). Integrating compliance into business processes. In Multikonferenz Wirtschaftsinformatik (Vol. 2010, p. 421).
  58. Schumpeter, J. A. (1942). Capitalism, socialism and democracy. Routledge.
  59. Seong, H. E., & Kim, B. Y. (2021). Critical factors affecting venture capital investment decision on innovative startups: a case of south korea. International Journal of Management (IJM), 12(3), 768-781.
  60. Sterns, H. L., & Doverspike, D. (1989). Aging and the training and learning process. In I. L. Goldstein, Training and development in organizations (pp. 299–332). Jossey-Bass.
  61. Stjepić, A. M., Pejić Bach, M., & Bosilj Vukšić, V. (2021). Exploring risks in the adoption of business intelligence in SMEs using the TOE framework. Journal of Risk and Financial Management, 14(2), 58.
  62. Sun, W., & Huo, J. (2005). Essential factor analysis on the cluster-based technological innovation. In Fourth Wuhan International Conference on E-Business: The Internet Era & The Global Enterprise (Vols. 1 and 2, pp. 1469-1474). Alfred Univ.
  63. Şimşek, O. (2023 November 10). Teknolojik Yetkinlik: Çalışanların Sürekli Öğrenme Yolculuğu. https://www.iienstitu.com/blog/teknolojik-yetkinlik-calisanlarin-surekli-ogrenme-yolculugu.
  64. Tajudeen, F. P., Jaafar, N. I., & Ainin, S. (2018). Understanding the impact of social media usage among organizations. Information & management, 55(3), 308-321.
  65. Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic management journal, 18(7), 509-533.
  66. Tornatzky, L., & Fleischer, M. (1990). The process of technology innovation, Lexington, MA.
  67. Ullah, F., Qayyum, S., Thaheem, M. J., Al-Turjman, F., & Sepasgozar, S. M. (2021). Risk management in sustainable smart cities governance: A TOE framework. Technological Forecasting and Social Change, 167, 120743.
  68. Uyumaz, M. (2002). Organizasyonların Teknolojik Gelişmelere Uyum Sağlama Yolları (Master's thesis, Anadolu University (Turkey)).
  69. Van Raaij, E. M., & Schepers, J. J. (2008). The acceptance and use of a virtual learning environment in China. Computers & education, 50(3), 838-852.
  70. Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information systems research, 11(4), 342-365.
  71. Volkom, M. V., Stapley, J. C., & Amaturo, V. (2014). Revisiting the digital divide: Generational differences in technology use in everyday life. North American Journal of Psychology, 16(3).
  72. Wallace, S., Green, K., Johnson, C., Cooper, J., & Gilstrap, C. (2021). An extended TOE framework for cybersecurity adoption decisions. Communications of the Association for Information Systems, 47(2020), 51.
  73. Watson, G. (1971). Resistance to change. American behavioral scientist, 14(5), 745-766.
  74. Wright, T. P. (1936). Factors affecting the cost of airplanes. Journal of the aeronautical sciences, 3(4), 122-128.
  75. Yasuda, R., & Batres, R. (2012). An Agent-based Model for Analyzing Diffusion of Biodiesel Production Schemes. In Computer Aided Chemical Engineering (Vol. 30, pp. 192-196). Elsevier.