نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی دکتری مدیریت فناوری اطلاعات، دانشگاه فردوسی مشهد، مشهد، ایران
2 استاد گروه مدیریت، دانشگاه فردوسی مشهد، مشهد، ایران
چکیده
فناوری های جدید در زمیته صنعت 4.0 به شرکت ها اجازه میدهد تا فرآیندهای تجاری خود را بهبود بخشند و محصولات و خدمات را از طریق دانش جدیدی تولید شده سفارشی کنند. ایجاد و اشتراک دانش جدید هم به استفاده بهینه از فناوری های جدید صنعت 4.0 و هم به تعاملات در طول زنجیره ارزش بستگی دارد. با این حال، دستیابی به مزایای کسب و کار به شدت به منابع انسانی و به مهارتها و شایستگی های دیجیتالی آنها بستگی دارد. از این منظر، شرکتهایی که به پارادایم صنعت 4.0 نزدیک میشوند، باید چنین فناوریهای جدیدی را به عنوان ابزار جدیدی در نظر بگیرند که ایجاد و اشتراک دانش جدید را امکانپذیر میسازد. بنابراین، آنها باید به مهارتها و شایستگیهای دیجیتالی مورد نیاز برای مدیریت این دگرگونی فنآوری توجه کنند و ارتقای شایستگیهای داخلی را تقویت کنند. هدف تحقیق حاضر ترکیب نتایج و یافته های بدست آمده از مطالعات کیفی است. بنابراین بینش جدیدی از یافتههای مطالعات قبلی ارائه میگردد. در این پژوهش از یک رویکرد فراترکیبی برای بررسی مطالعات موردی کیفی استفاده شد که در آن رابطه بین مدیریت دانش و صنعت 4.0 و قابلیت های آنها در سازمان بررسی میشود. نتایج نشان میدهد که قابلیت های مدیریت دانش در حوزه صنعت 4.0 در دو بعد بررسی می شود: مدل های کسب و کار و نوآوری سازمانی. این تحقیق همچنین بیانگر آن است که جهت رفع چالش های سازمانی می بایست استراتژی های مدیریت دانش و سطح بلوغ فناوری های صنعت 4.0 در سازمانها درک شود.
کلیدواژهها
موضوعات
عنوان مقاله [English]
The impact of knowledge management and Industry 4.0 technologies in organizations: a meta-synthesis approach
نویسندگان [English]
- Nahid Entezarian 1
- Mohammad Mehraeen 2
1 Ph.D. Student in Information Technology Management, Ferdowsi University of Mashhad, Mashhad, Iran
2 Professor of Department of Management, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran Corresponding Author: mehraeen@um.ac.ir
چکیده [English]
New technologies in the field of Industry 4.0 enable companies to enhance their business processes and customize products and services through the generation of new knowledge. The creation and sharing of this new knowledge depends on both the optimal use of Industry 4.0 technologies and interactions along the value chain. However, achieving business benefits is highly dependent on human resources and their digital skills and competencies. Therefore, companies approaching the Industry 4.0 paradigm should consider these new technologies as tools that facilitate the creation and sharing of new knowledge. They should pay attention to the digital skills and competencies required to manage this technological transformation and enhance internal competencies. The purpose of this research is to combine the results and findings obtained from qualitative studies, providing new insights from previous research. In this study, a meta-composite approach was used to investigate qualitative case studies, examining the relationship between knowledge management and Industry 4.0 capabilities in organizations. The results show that knowledge management capabilities in the field of Industry 4.0 are examined in two dimensions: business models and organizational innovation. This research also emphasizes that in order to address organizational challenges, knowledge management strategies and the maturity level of Industry 4.0 technologies within organizations must be understood.
Introduction
Industry 4.0, driven by digital technologies such as smart sensors, IoT, cloud computing, big data, and AI, holds significant importance in the realm of organizational knowledge management. It enables convenient access to vast repositories of data that can be meticulously scrutinized to drive improvements in processes. Moreover, Industry 4.0 seamlessly merges the physical and virtual domains, thereby enhancing both production processes and resulting products (Wilkesmann, 2018). This study endeavors to propose a model that seamlessly integrates knowledge management and Industry 4.0 to gain a competitive advantage. The researchers will utilize the Meta-synthesis method to identify capabilities and develop a new framework, thus contributing to a deeper understanding in this field.
Literature Review
The theoretical foundations are categorized into two components: Industry 4.0 and knowledge management.
2.1. Industry 4.0
Industry 4.0 emerged in 2011 as the fourth industrial revolution, focusing on fully automated and intelligent production systems. It involves the integration of production systems through real-time information exchange and flexible production. The internet and related technologies play a crucial role in connecting physical objects, machines, and processes across organizations (Ghobakhloo, 2018). Industry 4.0 relies on data-driven decision-making and recognizes the value of real-time data utilization. It disrupts traditional competition and impacts various aspects of organizational strategy, business models, innovation, supply chains, production processes, and stakeholder relationships (Pozzi et al., 2023).
2.2. Knowledge management strategies and approaches in Industry4.0
Knowledge is essential for decision-making in implementing Industry 4.0 technologies. Industry 4.0 significantly influences knowledge management within organizations. These technologies facilitate knowledge management by enhancing existing knowledge and generating new knowledge. Knowledge sharing and storage are key components of knowledge management in the context of Industry 4.0 (Salvadorinho & Teixeira, 2021). The cost-effective and high-performance nature of Industry 4.0 technologies makes them suitable for storing and sharing knowledge. Industry 4.0 technologies enhance value creation through knowledge sharing within organizations and enable organizational innovation and competitive advantage maximization through knowledge management (Gupta et al., 2022).
Methodology
This research proposes Meta-synthesis as a suitable method for effectively combining the various factors involved in knowledge management capabilities and Industry 4.0 technologies within organizations. Meta-synthesis serves as a valuable instrument in formulating a comprehensive theory by systematically amalgamating these elements. The selection of the Hoon model (Hoon, 2013) for this research is based on its comprehensive and innovative nature in comparison to other Meta-synthesis models. It is characterized as an exploratory and inductive research design that integrates qualitative case studies to extend the findings of the original studies. Hoon's proposed Metasynthesis entails eight specific steps, which are briefly outlined below:
Step 1 involves designing and framing the research question related to knowledge management capabilities in Industry 4.0. Step 2 includes searching for articles using specific keywords and selecting relevant research. Step 3 involves screening and selecting suitable texts based on inclusion criteria. Step 4 entails extracting and coding evidence from selected studies. Step 5 analyzes individual studies using a causal network technique. Step 6 synthesizes findings on an across-study level. Step 7 involves building theory from meta-synthesis.
Results and Discussion
The convergence of Industry 4.0 and knowledge management within organizational frameworks serves to amplify the influence of knowledge management on the performance of organizational innovation (Tortorella et al., 2022). This study furnishes valuable perspectives for formulating an adoption strategy and prioritizing tasks in the integration of Industry 4.0. It underscores the significance of knowledge dissemination in expediting the assimilation of Industry 4.0 and recommends a focus on cultivating affiliations with strategic counterparts. The development of internal capabilities and competencies stands as pivotal for meaningful engagement in knowledge dissemination for Industry 4.0. Effective knowledge exchange among organizations can offset the dearth of internal resources and knowledge during the adoption process. This study accentuates the cost-effectiveness of knowledge sharing as an alternative to external consultants. In sum, it furnishes invaluable insights for managers seeking to augment organizational innovation, fortify stakeholder associations, and attain a competitive edge in the landscape of Industry 4.0.
Conclusion
The Meta-synthesis approach used in this study has limitations, including a smaller sample size of only 8 studies, which raises concerns about the generalizability of the findings. The reliance on a limited number of keywords for searching and identifying studies is another limitation. However, the study's analysis revealed similarities among the chosen articles, and the selection process followed the criteria set by Hoon (2013). The Meta-synthesis protocol allows for the development of causal networks, meta-causal network, and case comparison table, showing a wider context of knowledge management and Industry 4.0 capabilities in organizations. Future studies should encompass a wider scope, as organizations in the Industry 4.0 environment need to share and manage knowledge both internally and externally. The Meta causal network developed in this study can be used as a foundation for developing strategies that generate value and foster a competitive advantage in the realm of Industry 4.0.
Keywords: Knowledge Management, Industry 4.0, Meta-Synthesis, Case Study.
Figure 1. Meta-causal network of selected analyzed studies (research findings)
کلیدواژهها [English]
- Keywords: Knowledge management
- industry 4.0
- meta-synthesis
- case study
- Abubakar, A. M., Elrehail, H., Alatailat, M. A., & Elçi, A. (2019). Knowledge management, decision-making style and organizational performance. Journal of Innovation & Knowledge, 4(2), 104-114. https://doi.org/10.1016/j.jik.2017.07.003
- Ansari, F. (2019). Knowledge management 4.0: theoretical and practical considerations in cyber physical production systems. IFAC-PapersOnLine, 52(13), 1597-1602. https://doi.org/10.1016/j.ifacol.2019.11.428
- Bibby, L., & Dehe, B. (2018). Defining and assessing industry 4.0 maturity levels–case of the defence sector. Production Planning & Control, 29(12), 1030-1043. https://doi.org/10.1080/09537287.2018.1503355
- Cárcel-Carrasco, J., & Gómez-Gómez, C. (2021). Qualitative analysis of the perception of company managers in knowledge management in the maintenance activity in the era of industry 4.0. Processes, 9(1), 121. https://doi.org/10.3390/pr9010121
5. Cruzara, G., Takahashi, A. R. W., Sandri, E. C., & Cherobim, A. P. M. (2020). The impact of digital transformation and industry 4.0 on the aspects of value: Evidence from a meta-synthesis. Contextus: Revista Contemporânea de economia e gestão, 18(1), 92-106. https://doi.org/10.19094/contextus.2020.43717
- Eslami, M. H., Achtenhagen, L., Bertsch, C. T., & Lehmann, A. (2023). Knowledge-sharing across supply chain actors in adopting Industry 4.0 technologies: An exploratory case study within the automotive industry. Technological Forecasting and Social Change, 186, 122118. https://doi.org/10.1016/j.techfore.2022.122118
- Ferraris, A., Mazzoleni, A., Devalle, A., & Couturier, J. (2019). Big data analytics capabilities and knowledge management: impact on firm performance. Management Decision, 57(8), 1923-1936.https://doi.org/10.1108/MD-07-2018-0825
- García-Holgado, A., García-Peñalvo, F. J., Hernández-García, Á, & Llorens-Largo, F. (2015). Analysis and improvement of knowledge management processes in organizations using the business process model notation. In New Information and Communication Technologies for Knowledge Management in Organizations: 5th Global Innovation and Knowledge Academy Conference, GIKA 2015, Valencia, Spain, July 14-16, 2015, Proceedings 5 (pp. 93-101). Springer International Publishing. https://doi.org/10.1007/978-3-319-22204-2_9
- Ghobakhloo, M. (2018). The future of manufacturing industry: a strategic roadmap toward Industry 4.0. Journal of manufacturing technology management, 29(6), 910-936. https://doi.org/10.1108/JMTM-02-2018-0057
- Gupta, A., Kr Singh, R., Kamble, S., & Mishra, R. (2022). Knowledge management in industry 4.0 environment for sustainable competitive advantage: a strategic framework. Knowledge Management Research & Practice, 20(6), 878-892. https://doi.org/10.1080/14778238.2022.2144512
- He, W., Wang, F. K., & Akula, V. (2017). Managing extracted knowledge from big social media data for business decision making. Journal of Knowledge Management, 21(2), 275–294. https://doi.org/10.1108/JKM-07-2015-0296
- Hoon, C. (2013). Meta-synthesis of qualitative case studies: an approach to the building. Organizational Research Methods, 16(4), 522-556. https://doi.org/10.1177/1094428113484969
- Lee, J., Davari, H., Singh, J., & Pandhare, V. (2018). Industrial artificial intelligence for Industry 4.0-based manufacturing systems. Manufacturing Letters, 18, 20–23. https://doi.org/10.1016/j.mfglet.2018.09.002
- Librita Arifiani, S. K., Dyah Budiastuti, M. M., & Wibowo Kosasih, E. (2019). The effect of disruption technology, and the future knowledge management toward service innovation for telecommunication industry 4.0 in Indonesia. Int. J. Eng. Adv. Technol, 8, 247-257. https://doi.org/10.35940/ijeat.F1040.0986S319
- Ludvigsen, M. S., Hall, E. O., Meyer, G., Fegran, L., Aagaard, H., & Uhrenfeldt, Lu, Y. (2016). Using Sandelowski and Barroso’s meta-synthesis method in advancing qualitative evidence. Qualitative health research, 26(3), 320-329. https://doi.org/10.1177/1049732315576493
- Klingenberg, C. O., Viana Borges, M. A., & Valle Antunes Jr., J. A. (2019). Industry 4.0 as a data-driven paradigm: a systematic literature review on technologies. Journal of ManufacturingTechnology Management. https://doi.org/10.1108/JMTM-09-2018-0325
- Kolyasnikov, M. S., & Kelchevskaya, N. R. (2020). Knowledge management strategies in companies: Trends and the impact of Industry 4.0. Upravlenec, 11(4). https://doi.org/10.29141/2218-5003-2020-11-4-7
- Malik, N., Tripathi, S. N., Kar, A. K., & Gupta, S. (2021). Impact of artificial intelligence on employees working in industry 4.0 led organizations. International Journal of Manpower, 43(2), 334-354. https://doi.org/10.1108/IJM-03-2021-0173
- Miao, M., Zaman, S. I., Zafar, A., Rodriguez, C. G., & Ali Zaman, S. A. (2022). The augmentation of Knowledge Management through Industry 4.0: case of Aviation sector of emerging economy. Knowledge Management Research & Practice, 20(6), 893-912. https://doi.org/10.1080/14778238.2022.2113345
- Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. sage.
- Morais-Da-Silva, R. L., Takahashi, A. R. W., & Segatto, A. P. (2016). Scaling up social innovation: a meta-synthesis. RAM. Revista de Administração Mackenzie, 17, 134-163. https://doi.org/10.1590/1678-69712016/administracao.v17n6p134-163
- Müller, J. M., Buliga, O., & Voigt, K. I. (2018). Fortune favors the prepared: How SMEs approach business model innovations in Industry 4.0. Technological Forecasting and Social Change, 132, 2-17. https://doi.org/10.1016/j.techfore.2017.12.019
- North, K., & Maier, R. (2018). Wissen 4.0–Wissens management in digital Wandel. HMD Praxis der Wirtschaftsinformatik: Vol. 55, No. 4. DOI: https://doi.org/10.1365/s40702-018-0426-6
- Nunez-Merino, M., Maqueira-Marín, J. M., Moyano-Fuentes, J., & Martínez-Jurado, P. J. (2020). Information and digital technologies of Industry 4.0 and Lean supply chain management: a systematic literature review. International Journal of Production Research, 58(16), 5034-5061. https://doi.org/10.1080/00207543.2020.1743896
- Obermayer, N., & Toth, V. E. (2020). Organizational dynamics: exploring the factors affecting knowledge sharing behavior. Kybernetes, 49(1), 165-181. https://doi.org/10.1108/K-04-2019-0300
- Piccarozzi, M., Aquilani, B., & Gatti, C. (2018). Industry 4.0 in management studies: A systematic literature review. Sustainability, 10(10), 3821. https://doi.org/10.3390/su10103821
- Pozzi, R., Rossi, T., & Secchi, R. (2023). Industry 4.0 technologies: critical success factors for implementation and improvements in manufacturing companies. Production Planning & Control, 34(2), 139-158. https://doi.org/10.1080/09537287.2021.1891481
- Prim, M. F., de Oliveira Gomes, J., Kohl, H., Orth, R., Will, M., & Vargas, G. B. (2022). Identifying the Dynamics of Intangible Resources for Industry 4.0 Adoption Process. IEEE Access, 10, 101029-101041. https://doi.org/10.1109/ACCESS.2022.3208250
- Ringen, G., Paalsrud, F., & Lodgaard, E. (2020). Interorganizational learning in manufacturing networks. In Advances in Production Management Systems. The Path to Digital Transformation and Innovation of Production Management Systems: IFIP WG 5.7 International Conference, APMS 2020, Novi Sad, Serbia, August 30–September 3, 2020, Proceedings, Part I (pp. 680-686). Springer International Publishing. https://doi.org/10.1007/978-3-030-57993-7_77
- Roblek, V., Meško, M., & Krapež, A. (2016). A complex view of industry 4.0. Sage open, 6(2), 2158244016653987. https://doi.org/10.1177/2158244016653987
- Salvadorinho, J., & Teixeira, L. (2021). Organizational knowledge in the I4. 0 using BPMN: a case study. Procedia Computer Science, 181, 981-988. https://doi.org/10.1016/j.procs.2021.01.266
- Sandelowski, M., Barroso, J., & Voils, C. I. (2007). Using qualitative metasummary to synthesize qualitative and quantitative descriptive findings. Research in nursing & health, 30(1), 99-111. https://doi.org/10.1002/nur.20176
- Sartori, J. T. D., Frederico, G. F., & de Fátima Nunes Silva, H. (2022). Organizational knowledge management in the context of supply chain 4.0: A systematic literature review and conceptual model proposal. Knowledge and Process Management, 29(2), 147-161. https://doi.org/10.1002/kpm.1682
- Saucedo-Martínez, J. A., Pérez-Lara, M., Marmolejo-Saucedo, J. A., Salais-Fierro, T. E., & Vasant, P. (2018). Industry 4.0 framework for management and operations: a review. Journal of ambient intelligence and humanized computing, 9, 789-801. https://doi.org/10.1007/s12652-017-0533-1
- Sony, M., & Naik, S. (2020). Industry 4.0 integration with socio-technical systems theory: A systematic review and proposed theoretical model. Technology in society, 61, 101248. https://doi.org/10.1016/j.techsoc.2020.101248
- Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, 157–169. https://doi.org/10.1016/j.jmsy.2018.01.006
- Thoben, K. D., Wiesner, S., & Wuest, T. (2017). “Industrie 4.0” and smart manufacturing-a review of research issues and application examples. International journal of automation technology, 11(1), 4-16. https://doi.org/10.20965/ijat.2017.p0004
- Tortorella, G., Prashar, A., Vassolo, R., Cawley Vergara, A. M., Godinho Filho, M., & Samson, D. (2022). Boosting the impact of knowledge management on innovation performance through industry 4.0 adoption. Knowledge Management Research & Practice, 1-17. https://doi.org/10.1080/14778238.2022.2108737
- Wilkesmann, M., & Wilkesmann, U. (2018). Industry 4.0 – organizing routines or innovations? VINE Journal of Information and Knowledge Management Systems, 48(2), 238–254. https://doi.org/10.1108/VJIKMS-04-2017-0019
- Xu, Z., Frankwick, G. L., & Ramirez, E. (2016). Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective. Journal of Business Research, 69(5), 1562–1566. https://doi.org/10.1016/j.jbusres.2015.10.017