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2020 | nr 2 | 32--55
Tytuł artykułu

Defining Stages of the Industry 4.0 Adoption via Indicator Sets

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
As Industry 4.0 offers significant productivity improvements, its relevance has grown across various organisations. While it captures the attention of both the industry and the academia, very few efforts have been made to streamline useful indicators across stages of its implementation. Such work facilitates the development of strategies that are appropriate for a specific stage of implementation; therefore, it would be significant to a variety of stakeholders. As a result, this paper aims to establish an indicator system for adopting Industry 4.0 within the context of the three stages of the innovation adoption: (i) pre-adoption, (ii) adoption, and (iii) post-adoption. First, a comprehensive review was performed with a search expanding into the literature on innovation and technology adoption. Second, the resulting indicators were filtered for relevance, redundancy, description, and thorough focus discussions. Finally, they were categorised by their stage of adoption. From 469 innovation adoption indicators found in the literature, this work identified a total of 62 indicators relevant for the Industry 4.0 adoption, in which 11, 14, and 37 of them comprised the three stages, respectively. Case studies from two manufacturing firms in the Philippines were reported to demonstrate the applicability of the proposed indicator system. This work pioneers the establishment of an indicator system for the Industry 4.0 adoption and the classification of such indicators into three stages - pre-adoption, adoption, and post-adoption - which would serve as a framework for decision-makers, practitioners, and stakeholders in planning, strategy development, resource allocation, and performance evaluation of the Industry 4.0 adoption. (original abstract)
Rocznik
Numer
Strony
32--55
Opis fizyczny
Twórcy
  • Cebu Technological University, Philippines
  • Cebu Technological University, Philippines
  • Cebu Technological University, Philippines
autor
  • Cebu Technological University, Philippines
  • Cebu Technological University, Philippines
  • Cebu Technological University, Philippines
  • Commission on Higher Education, Philippines
  • Cebu Technological University, Philippines
Bibliografia
  • Adegbola, P., & Gardebroek, C. (2007). The effect of information sources on technology adoption and modification decisions. Agricultural Economics, 37(1), 55-65. doi: 0.1111/j.1574-0862.2007.00222.x
  • Aduda, K., Thomassen, T., Zeiler, W., Labeodan, T., Boxem, G., van der Velden, J., & Dubbeldam, J.W. (2014). The human in the loop: An approach to individualize smart process control. Procedia Environmental Sciences, 22, 302-312. doi: 10.1016/j.pro-env.2014.11.029
  • Agarwal, R., & Prasad, J. (1998). The antecedents and consequents of user perceptions in information technology adoption. Decision Support Systems, 22(1), 15-29. doi: 10.1016/S0167-9236(97)00006-7
  • Alekseev, A.N., Buraeva, E.V., Kletskova, E.V., & Rykhtikova, N.A. (2018). Stages of Formation of Industry 4.0 and the Key Indicators of Its Development. Industry 4.0: Industrial Revolution of the 21st Century, 169, 93-100. doi: 10.1007/978-3-319-94310-7_9
  • Alguliyev, R., Imamverdiyev, Y., & Sukhostat, L. (2018). Cyber-physical systems and their security issues. Computers in Industry, 100, 212-223. doi: doi.org/10.1016/j.compind.2018.04.017
  • Almada-Lobo, F. (2016). The Industry 4.0 revolution and the future of manufacturing execution systems (MES). Journal of Innovation Management, 3(4), 16-21. doi: 10.24840/2183-0606_003.004_0003
  • Arnold, C., Veile, J., & Voigt, K.I. (2018). What drives industry 4.0 adoption? An examination of technological, organizational, and environmental determinants. 27th International Conference on Management of Technology (IAMOT), Birmingham, United Kingdom.
  • Attaran, M. (2017). The rise of 3-D printing: The advantages of additive manufacturing over traditional manufacturing. Business Horizons, 60(5), 677-688. doi: 10.1016/j.bushor.2017.05.011
  • Birchall, D., Chanaron, J. J., Tovstiga, G., & Hillenbrand, C. (2011). Innovation performance measurement: Current practices, issues and management challenges. International Journal of Technology Management, 56(1), 1-20. doi: 10.1504/ijtm.2011.042492
  • Blanchard, B.S., Verma, D., & Peterson, E.L. (1995). Maintainability: A key to effective serviceability and maintenance management. New York, United States: Wiley.
  • Bleicher, J., & Stanley, H. (2016). Digitization as a catalyst for business model innovation a three-step approach to facilitating economic success. Journal of Business Management, 4(2), 62-71.
  • Boh, W.F., Evaristo, R., & Ouderkirk, A. (2014). Balancing breadth and depth of expertise for innovation: A 3M story. Research Policy, 43(2), 349-366. doi: 10.1016/j.respol.2013.10.009
  • Bohnsack, R., & Pinkse, J. (2017). Value propositions for disruptive technologies: Reconfiguration tactics in the case of electric vehicles. California Management Review, 59(4), 79-96. doi: 10.1177/0008125617717711
  • Borrás, S., & Edquist, C. (2013). The choice of innovation policy instruments. Technological Forecasting and Social Change, 80(8), 1513-1522. doi: 10.1016/j.tech-fore.2013.03.002
  • Brettel, M., Friederichsen, N., Keller, M., & Rosenberg, M. (2014). How virtualization, decentralization and network building change the manufacturing landscape: An Industry 4.0 Perspective. International Journal of Information and Communication Engineering, 8(1), 37-44.
  • Browne, J., Dubois, D., Rathmill, K., Sethi, S.P., & Stecke, K.E. (1984). Classification of flexible manufacturing systems. The FMS Magazine, 2(2), 114-117.
  • Caiazza, R., & Volpe, T. (2017). Innovation and its diffusion: Process, actors and actions. Technology Analysis & Strategic Management, 29(2), 181-189. doi: 10.1080/09537325.2016.1211262
  • Castelo-Branco, I., Cruz-Jesus, F., & Oliveira, T. (2019). Assessing Industry 4.0 readiness in manufacturing: Evidence for the European Union. Computers in Industry, 107, 22-32. doi: 10.1016/j.compind.2019.01.007
  • Cavdar, S.C., & Aydin, A.D. (2015). An empirical analysis about technological development and innovation indicators. Procedia-Social and Behavioral Sciences, 195, 1486-1495. doi: 10.1016/j.sbspro.2015.06.449
  • Chang, V., Ramachandran, M., Yao, Y., Kuo, Y.H., & Li, C.S. (2016). A resiliency framework for an enterprise cloud. International Journal of Information Management: The Journal for Information Professionals, 36(1), 155-166. doi: 10.1016/j.ijinfomgt.2015.09.008
  • Chor, K.H.B., Wisdom, J.P., Olin, S.C.S., Hoagwood, K.E., & Horwitz, S.M. (2014). Measures for predictors of innovation adoption. Administration and Policy in Mental Health and Mental Health, 42(5), 545-573. doi: 10.1007/s10488-014-0551-7
  • Christensen, C.M., Bartman, T., & van Bever, D. (2016). The hard truth about business model innovation. Retrieved from http://sloanreview.mit.edu/article/the-hardtruth-about-business-model-innovation/
  • Crossan, M. M., & Apaydin, M. (2010). A multi-dimensional framework of organizational innovation: A systematic review of the literature. Journal of Management Studies, 47(6), 1154-1191. doi: 10.1111/j.1467-6486.2009.00880.x
  • Damanpour, F., & Schneider, M. (2006). Phases of the adoption of innovation in organizations: Effects of environment, organization and top managers 1. British Journal of Management, 17(3), 215-236. doi: 10.1111/j.1467-8551.2006.00498.x
  • Danquah, M. (2018). Technology transfer, adoption of technology and the efficiency of nations: Empirical evidence from sub Saharan Africa. Technological Forecasting and Social Change, 131, 175-182. doi: 10.1016/j.techfore.2017.12.007
  • Datta, A., Mukherjee, D., & Jessup, L. (2015). Understanding commercialization of technological innovation: Taking stock and moving forward. R&D Management, 45(3), 215-249. doi: 10.1111/radm.12068
  • Datta, A., Reed, R., & Jessup, L. (2013). Commercialization of innovations: An overarching framework and research agenda. American Journal of Business, 28(2), 147-191. doi: 10.1108/AJB-08-2012-0048
  • Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and acceptance of information technology. MIS Quarterly, 13(3), 319-340. doi: 10.2307/249008
  • De Sousa Jabbour, A.B., Jabbour, C.J., Foropon, C., & Godinho Filho, M. (2018). When titans meet - Can industry 4.0 revolutionise the environmentally-sustainable manufacturing wave? The role of critical success factors. Technological Forecasting and Social Change, 132, 18-25. doi: 10.1016/j.tech-fore.2018.01.017
  • Dewangan, V., & Godse, M. (2014). Towards a holistic enterprise innovation performance measurement system. Technovation, 34(9), 536-545. doi: 10.1016/ j.technovation.2014.04.002
  • Dimara, E., & Skuras, D. (2003). Adoption of agricultural innovations as a two-stage partial observability process. Agricultural Economics, 28(3), 187-196. doi: 10.1111/j.1574-0862.2003.tb00137.x
  • Dodgson, M., & Hinze, S. (2000). Indicators used to measure the innovation process: Defects and possible remedies. Research Evaluation, 9(2), 101-114. doi: 10.3152/147154400781777368
  • Dziallas, M., & Blind, K. (2018). Innovation indicators throughout the innovation process: An extensive literature analysis. Technovation, 80-81, 3-29. doi: 10.1016/j.technovation.2018.05.005
  • Edison, H., Bin Ali, N., & Torkar, R. (2013). Towards innovation measurement in the software industry. Journal of Systems and Software, 86(5), 1390-1407. doi: 10.1016/j.jss.2013.01.013
  • Egorova, I.E., Gagarin, A.G., Kuznetsov, S.Y., Simonov, A.B., & Velikanov, V.V. (2017). Successful Commercialization of Innovations as a Basis of Development of Modern Human Society. In Perspectives on the use of New Information and Communication Technology (ICT) in the Modern Economy (pp. 1156-1162). Cham, United Kingdom: Springer. doi: 10.1007/978-3-319-90835-9_130
  • Espitia-Escuer, M., García-Cebrián, L.I., & Muñoz-Porcar, A. (2014). Location as a competitive advantage for entrepreneurship an empirical application in the Region of Aragon (Spain). International Entrepreneurship and Management Journal, 11(1), 133-148. doi: 10.1007/s11365-014-0312-9
  • Evanschitzky, H., Eisend, M., Calantone, R.J., & Jiang, Y. (2012). Success factors of product innovation: An updated meta-analysis. Journal of Product Innovation Management, 29, 21-37. doi: 10.1111/j.1540-5885.2012.00964.x
  • Ezzi, F., & Jarboui, A. (2016). Does innovation strategy affect financial, social and environmental performance? Journal of Economics, Finance and Administrative Science, 21(40), 14-24. doi: 10.1016/j.je-fas.2016.03.001
  • Frijns, B., Gilbert, A., Lehnert, T., & Tourani-Rad, A. (2013). Uncertainty avoidance, risk tolerance and corporate takeover decisions. Journal of Banking & Finance, 37(7), 2457-2471. doi: 10.1016/j.jbank-fin.2013.02.010
  • Gallaud, D., & Torre, A. (2005). Geographical proximity and the diffusion of knowledge. In Rethinking Regional Innovation and Change (pp. 127-146). New York, United States: Springer. doi: 10.1007/0-387-23002-5_7
  • Garwood, S.G., Cox, L., Kaplan, V., Wasserman, N., & Sulzer, J.L. (1980). Beauty is only "name" deep: the effect of first-name on ratings of physical attraction. Journal of Applied Social Psychology, 10(5), 431-435. doi:10.1111/j.1559-1816.1980.tb00721.x
  • Gault, F. (2018). Defining and measuring innovation in all sectors of the economy. Research Policy, 47(3), 617-622. doi: 10.1016/j.respol.2018.01.007
  • Glass, R., Meissner, A., Gebauer, C., Stürmer, S., & Metternich, J. (2018). Identifying the barriers to Industrie 4.0. Procedia CIRP, 72, 985-988. doi: 10.1016/j.procir.2018.03.187
  • Gopalakrishnan, S., & Damanpour, F. (1997). A review of innovation research in economics, sociology and technology management. Omega, International Journal of Management Science, 25(1), 15-28. doi: 10.1016/S0305-0483(96)00043-6
  • Gorecky, D., Schmitt, M., Loskyll, M., & Zuhlke, D. (2014). Human-machine-interaction in the industry 4.0 era. 2014 12th IEEE International Conference on Industrial Informatics (INDIN). doi: 10.1109/indin.2014.6945523
  • Greenhalgh, T., Robert, G., Macfarlane, F., Bate, P., & Kyriakidou, O. (2004). Diffusion of innovations in service organizations: systematic review and recommendations. The Milbank Quarterly, 82(4), 581-629. doi: 10.1111/j.0887-378X.2004.00325.x
  • Gülbahar, Y. (2007). Technology planning: A roadmap to successful technology integration in schools. Computers & Education, 49, 943-956. doi: 10.1016/j.compedu.2005.12.002
  • Habicht, H., Möslein, K.M., & Reichwald, R. (2012). Open innovation maturity. International Journal of Knowledge-Based Organizations, 2(1), 92-111. doi: 0.1142/S1363919611003696
  • Ham, J., Lee, J.N., Kim, D.J., & Choi, B. (2015). Open innovation maturity model for the government: An open system perspective. Proceedings of the 36th International Conference on Information Systems, Fort Worth, Texas, United States.
  • Hameed, M.A., Counsell, S., & Swift, S. (2012). A conceptual model for the process of IT innovation adoption in organizations. Journal of Engineering and Technology Management, 29(3), 358-390. doi: 10.1016/j.jen-gtecman.2012.03.007
  • Hart, S., Jan Hultink, E., Tzokas, N., & Commandeur, H.R. (2003). Industrial companies' evaluation criteria in new product development gates. Journal of Product Innovation Management, 20(1), 22-36. doi: 10.1111/1540-5885.201003
  • Hassan, H. (2017). Organizational factors affecting cloud computing adoption in small and medium enterprises (SMEs) in service sector. International Conference on Enterprise Information Systems, Barcelona, Spain, 976-981. doi: 10.1016/j.procs.2017.11.126
  • Hermann, M., Pentek, T., & Otto, B. (2016). Design principles for Industrie 4.0 scenarios. 2016 49th Hawaii International Conference on System Sciences (HICSS). doi: 10.1109/hicss.2016.488
  • Hinnant, C.C., & O'Looney, J.A. (2003). Examining preadoption interest in online innovations: An exploratory study of e-service personalization in the public sector. IEEE Transactions on Engineering Management, 50(4), 436-447. doi: 0.1109/TEM.2003. 820133
  • Hoffman, D.G. (2002). Managing operational risk: 20 organization-wide best practice strategies. New York, United States: John Wiley & Sons.
  • Hsu, C.L., & Lin, J.C.C. (2016). Exploring factors affecting the adoption of internet of things services. Journal of Computer Information Systems, 58(1), 49-57. doi: 10.1080/08874417.2016.1186524
  • Issa, A., Hatiboglu, B., Bildstein, A., & Bauernhansl, T. (2018). Industrie 4.0 roadmap: Framework for digital transformation based on the concepts of capability maturity and alignment. Procedia CIRP, 72, 973-978.
  • Issar, G., & Navon, L.R. (2016). Operational Excellence. In G. Issar, & L.R. Navon (Eds.), Manufacturing Overhead (MOH) and Departmental Expense Control (pp. 91-93). Springer International Publishing.
  • Jazdi, N. (2014). Cyber-physical systems in the context of Industry 4.0. 2014 IEEE International Conference on Automation, Quality and Testing, Robotics. doi: 10.1109/aqtr.2014.6857843
  • Jeyaraj, A., Rottman, J.W., & Lacity, M.C. (2006). A review of the predictors, linkages, and biases in IT innovation adoption research. Journal of Information Technology, 21, 1-23. doi: 10.1057/palgrave.jit.2000056
  • Joachim, V., Spieth, P., & Heidenreich, S. (2018). Active innovation resistance: An empirical study on functional and psychological barriers to innovation adoption in different contexts. Industrial Marketing Management, 71, 95-107. doi: 10.1016/j.indmarman.2017.12.011
  • Joia, L.A., Gutman, L.F., & Moreno, V. (2016). Intention of use of home broker systems from the stock market investors' perspective. The Journal of High Technology Management Research, 27(2), 184-195. doi: 10.1016/j.hitech.2016.10.008
  • Kagermann, H., Helbig, J., Hellinger, A., & Wahlster, W. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the future of German manufacturing industry, final report of the Industrie 4.0 Working Group. Forschungs Union.
  • Kamble, S.S., Gunasekaran, A., & Gawankar, S.A. (2018). Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives. Process Safety and Environmental Protection, 117, 408-425. doi: 10.1016/j.psep.2018.05.009
  • Kang, H.S., Lee, J.Y., Choi, S.S., Kim, H., Park, J.H., Son, J.Y., Kim, B.H., & Noh, S.D. (2016). Smart manufacturing: Past research, present findings, and future directions. International Journal of Precision Engineering and Manufacturing-Green Technology, 3(1), 111-128. doi: 10.1007/s40684-016-0015-5
  • Kerschner, C., & Ehlers, M. (2016). A framework of attitudes towards technology in theory and practice. Ecological Economics, 126, 139-151. doi: 10.1016/j.ecolecon.2016.02.010
  • Kolberg, D., & Zühlke, D. (2015). Lean automation enabled by industry 4.0 technologies. IFAC-PapersOnLine, 48(3), 1870-1875. doi: 10.1016/j.ifa-col.2015.06.359
  • Lee, J., Bagheri, B., & Kao, H.A. (2015). A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18-23. doi: 10.1016/j.mfglet.2014.12.001
  • Lee, J.H., Phaal, R., & Lee, S.-H. (2013). An integrated service-device-technology roadmap for smart city development. Technological Forecasting and Social Change, 80(2), 286-306. doi: 10.1016/j.tech-fore.2012.09.020
  • Letia, T., & Kilyen, A. (2018). Using unified enhanced time Petri net models for cyber-physical system development. International Federation of Automatic Control PapersOnLine, 51(2), 248-253. doi: 10.1016/j.ifa-col.2018.03.043
  • Li, X., Ishii, H., & Masuda, T. (2012). Single machine batch scheduling problem with fuzzy batch size. Computers & Industrial Engineering, 62(3), 688-692. doi: 10.1016/j.cie.2011.12.021
  • Liao, Y., Deschamps, F., Loures, E.D.F.R., & Ramos, L.F.P. (2017). Past, present and future of Industry 4.0-a systematic literature review and research agenda proposal. International Journal of Production Research, 55(12), 3609-3629. doi: 10.1080/00207543.2017.1308576
  • Lira, V., Tavares, E., & Maciel, P. (2015). An automated approach to dependability evaluation of virtual networks. Computer Networks, 88(9), 89-102. doi: 10.1016/j.comnet.2015.05.016
  • Lombardi, P., Giordano, S., Farouh, H., & Yousef, W. (2012). Modelling the smart city performance. Innovation: The European Journal of Social Science Research, 25(2), 137-149. doi: 10.1080/13511610.2012.660325
  • Lopez, J., & Rubio, J.E. (2018). Access control for cyber-physical systems interconnected to the cloud. Computer Networks, 134, 46-54. doi: 10.1016/j.com-net.2018.01.037
  • Lu, Y. (2017). Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 6, 1-10. doi: 10.1016/j.jii.2017.04.005
  • Manral, L. (2010). Demand competition and investment heterogeneity in industries based on systemic technologies: Evidence from the US long-distance telecommunications services industry, 1984-1996. Journal of Evolutionary Economics, 20(5), 765-802. doi: 10.1007/s00191-010-0175-3
  • Martínez-Noya, A., & García-Canal, E. (2017). Location, shared suppliers and the innovation performance of R&D outsourcing agreements. Industry and Innovation, 25(3), 308-332. doi: 10.1080/13662716.2017.1329085
  • Mathiassen, L., & Munk-Madsen, A. (2007). Formalizations in systems development. Behaviour and Information Technology, 5(2), 145-155. doi: 10.1080/01449298608914507
  • Mehrad, D., & Mohammadi, S. (2017). Word of Mouth impact on the adoption of mobile banking in Iran. Telematics and Informatics, 34(7), 1351-1363. doi: https://doi.org/10.1016/j.tele.2016.08.009
  • Meyer, A.D., & Goes, J.B. (1988). Organizational assimilation of innovations: A multilevel contextual analysis. Academy of Management Journal, 31(4), 897-923. doi: 10.5465/256344
  • Miranda, M.Q., Farias, J.S., De Araújo Schwartz, C., & De Almeida, J.P. (2016). Technology adoption in diffusion of innovations perspective: Introduction of an ERP system in a non-profit organization. RAI Revista de Administração e Inovação, 13(1), 48-57. doi: 10.1016/j.rai.2016.02.002
  • Miremadi, I., Saboohi, Y., & Jacobsson, S. (2018). Assessing the performance of energy innovation systems: Towards an established set of indicators. Energy Research & Social Science, 40, 159-176. doi: 10.1016/j.erss.2018.01.002
  • Molina, E., & Jacob, E. (2018). Software-defined networking in cyber-physical systems: A survey. Computers & Electrical Engineering, 66, 407-419. doi: 10.1016/j.compeleceng.2017.05.013
  • Monostori, L., Kádár, B., Bauernhansl, T., Kondoh, S., Kumara, S., Reinhart, G., Sauer, O., Schuh, G., Sihn, W., & Ueda, K. (2016). Cyber-physical systems in manufacturing. CIRP Annals - Manufacturing Technology, 65(2), 621-641. doi: 10.1016/j.cirp.2016.06.005
  • Morrar, R., Arman, H., & Mousa, S. (2017). The fourth industrial revolution (Industry 4.0): A social innovation perspective. Technology Innovation Management Review, 7(11), 12-20. doi: 10.22215/timreview/1117
  • Müller, J.M. (2019). Antecedents to digital platform usage in Industry 4.0 by established manufacturers. Sustainability, 11(4), 1121. doi: 10.3390/su11041121
  • Müller, J.M., Kiel, D., & Voigt, K. (2018). What drives the implementation of Industry 4.0? The role of opportunities and challenges in the context of sustainability. Sustainability, 10(1), 247. doi:10.3390/su10010247
  • O'Hern, M.S., & Rindfleisch, A. (2017). Customer co-creation: A typology and research agenda. In Review of marketing research (pp. 108-130). Routledge.
  • Oesterreich, T.D., & Teuteberg, F. (2016). Understanding the implications of digitisation and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry. Computers in Industry, 83, 121-139. doi: 10.1016/j.compind.2016.09.006
  • Organization for Economic Cooperation and Development (OECD). (2005). Oslo Manual: The measurement of scientific and technological activities. Proposed Guidelines for Collecting an Interpreting Technological Innovation Data.
  • Oyemomi, O., Liu, S., Neaga, I., Chen, H., & Nakpodia, F. (2019). How cultural impact on knowledge sharing contributes to organizational performance: Using the fsQCA approach. Journal of Business Research, 94, 313-319. doi: 10.1016/j.jbusres.2018.02.027
  • Pilke, E. (2004). Flow experiences in information technology use. International Journal of Human-Computer Studies, 61(3), 347-357. doi: 10.1016/j.ijhcs.2004.01.004
  • Plsek, P. (2003). Complexity and the adoption of innovation in health care. Accelerating quality improvement in health care: Strategies to accelerate the diffusion of evidence-based innovations. Washington, United States: National Institute for Healthcare Management Foundation and National Committee for Quality in Health Care.
  • Prest A.R., & Turvey R. (1966) Cost-Benefit Analysis: A Survey. In Surveys of Economic Theory. London, United Kingdom: Palgrave Macmillan. doi: 10.1007/978-1-349-00210-8_5
  • Priyadarshinee, P., Raut, R.D., Jha, M.K., & Kamble, S.S. (2017). A cloud computing adoption in Indian SMEs: Scale development and validation approach. Journal of High Technology Management Research, 28(2), 221-245. doi: 10.1016/j.hitech.2017.10.010
  • Rajnai, Z., & Kocsis, I. (2018). Assessing industry 4.0 readiness of enterprises. 2018 IEEE 16th World Symposium on Applied Machine Intelligence and Informatics(SAMI), IEEE.
  • Rogers, E.M. (1995). Diffusion of Innovations. Fourth Ed. New York, United States: Free Press.
  • Rojko, A. (2017). Industry 4.0 concept: Background and overview. International Journal of Interactive Mobile Technologies, 11(5), 77-90. doi: 10.3991/ijim.v11i5.7072
  • Sabherwal, R., & King, W. (1991). Towards a theory of strategic use of information resources: An inductive approach. Information and Management, 20, 191-212. doi: 10.1016/0378-7206(91)90055-7
  • Salleh, M., Bahari, M., & Zakaria, N.H. (2017). An overview of software functionality service: A systematic literature review. Procedia Computer Science, 124, 337-344. doi: 10.1016/j.procs.2017.12.163
  • Schumpeter, J.A. (1934). Change and the Entrepreneur. Essays of JA Schumpeter, 4(23), 45-91.
  • Shamim, S., Cang, S., Yu, H., & Li, Y. (2016). Management approaches for Industry 4.0: A human resource management perspective. 2016 IEEE Congress on Evolutionary Computation (CEC), IEEE.
  • Sharma, S.K., Al-Badi, A.H., Govindaluri, S.M., & Al-Kharusi, M.H. (2016). Predicting motivators of cloud computing adoption: A developing country perspective. Computers in Human Behavior, 62, 61-69. doi: 10.1016/j.chb.2016.03.073
  • Siderska, J., & Mubarok, K. (2018). Cloud Manufacturing Platform and Architecture Design. Multidisciplinary Aspects of Production Engineering, 1(1), 673-680. doi: 10.2478/mape-2018-0085
  • Slater, S.F., & Mohr, J.J. (2006). Successful development and commercialization of technological innovation: Insights based on strategy type. Journal of Product Innovation Management, 23(1), 26-33. doi: 10.1111/j.1540-5885.2005.00178.x
  • Solis, B. (2016). The six stages of digital transformation maturity. Retrieved from https://www.prophet.com/2016/04/the--six--stages--of--digital--transformation.
  • Song, J. (2014). Understanding the adoption of mobile innovation in China. Computers in Human Behavior, 38, 339-348. doi: 10.1016/j.chb.2014.06.016
  • Sosna, M., Trevinyo-Rodriguez, R.N., & Velamuri, S.R. (2010). Business model innovation through trial-and-error learning: The Naturhouse case. Long Range Planning, 43(2), 383-407. doi: 10.1016/ j.lrp.2010.02.003
  • Stock, T., & Seliger, G. (2016). Opportunities of sustainable manufacturing in industry 4.0. Procedia CIRP, 40, 536-541. doi: 10.1016/j.procir.2016.01.129
  • Straub, E.T. (2009). Understanding technology adoption: Theory and future directions for informal learning. Review of Educational Research, 79(2), 625-649. doi: 10.3102/0034654308325896
  • Sung, T.K. (2018). Industry 4.0: A Korea perspective. Technological Forecasting & Social Change, 132, 40-45. doi: 10.1016/j.techfore.2017.11.005
  • Suomala, P. (2004). The life cycle dimension of new product development performance measurement. International Journal of Innovation Management, 8(02), 193-221. doi: 10.1142/S1363919604001039
  • Szczerbicki, E. (2008). Smart Systems Integration: Toward overcoming the problem of complexity. Cybernetics and Systems, 39(2), 190-198. doi: 10.1080/01969720701853475
  • Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, 157-169. doi: 10.1016/j.jmsy.2018.01.006
  • Terziyan, V., Gryshko, S., & Golovianko, M. (2018). Patented intelligence: Cloning human decision models for Industry 4.0. Journal of Manufacturing Systems, 48, 204-217. doi: 10.1016/j.jmsy.2018.04.019
  • Tweedale J.W. (2015). Enhancing the degree of autonomy by creating automated components within a multi-agent system framework. In J. Tweedale, L. Jain, J. Watada, & R. Howlett (Eds.), Knowledge-Based Information Systems in Practice. Smart Innovation, Systems and Technologies. Cham, United Kingdom: Springer. doi: 10.1007/978-3-319-13545-8_15
  • van Oorschot, J.A., Hofman, E., & Halman, J.I. (2018). A bibliometric review of the innovation adoption literature. Technological Forecasting and Social Change, 134, 1-21. doi: 10.1016/j.techfore.2018.04.032
  • Vogel-Heuser, B., & Hess, D. (2016). Guest editorial Industry 4.0-prerequisites and visions. IEEE Transactions on Automation Science and Engineering, 13(2), 411-413. doi: 10.1109/TASE.2016.2523639
  • Wang, B., Zhao, J., Wan, Z., Ma, J., Li, H., & Ma, J. (2016). Lean intelligent production system and value stream practice. 3rd International Conference on Economics and Management (ICEM 2016). doi:10.12783/dtem/icem2016/4106
  • Wegner, A., Graham, J., & Ribble, E. (2017). A new approach to cyberphysical security in industry 4.0. In Cybersecurity for Industry 4.0 (pp. 59-72). Cham, United Kingdom: Springer. doi: 10.1007/978-3-319-50660-9_3
  • Xu, Z. (2006). A note on linguistic hybrid arithmetic averaging operator in multiple attribute group decision making with linguistic information. Group Decision and Negotiation, 15(6), 593-604. doi: 10.1007/s10726-005-9008-4
  • Yigitcanlar, T., Sabatini-Marques, J., da-Costa, E. M., Kamruzzaman, M., & Ioppolo, G. (2019). Stimulating technological innovation through incentives: Perceptions of Australian and Brazilian firms. Technological Forecasting and Social Change, 146, 403-412. doi: 10.1016/j.techfore.2017.05.039
  • Zhang, M., & Hartley, J.L. (2018). Guanxi, IT systems, and innovation capability: the moderating role of proactiveness. Journal of Business Research, 90, 75-86. doi: 10.1016/j.jbusres.2018.04.036
  • Zhu, K., Dong, S., Xu, S.X., & Kraemer, K.L. (2006). Innovation diffusion in global contexts: Determinants of post-adoption digital transformation of European companies. European Journal of Information Systems, 15(6), 601-616. doi: 10.1057/palgrave.ejis.3000650
Typ dokumentu
Bibliografia
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