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واکاوی عوامل موثر بر رصد فناوری مبتنی بر هوش مصنوعی در شرکت های فناوری محور

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه مدیریت تکنولوژی، دانشکده مدیریت و اقتصاد، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران

2 دانشیار گروه مدیریت صنعتی، واحد کرج، دانشگاه آزاد اسلامی، کرج، ایران

3 استادیار گروه مدیریت، واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران

چکیده

در چشم‌انداز معاصر صنایع فناوری محور، ادغام هوش مصنوعی در رصد فناوری برای تقویت نوآوری و حفظ رقابت ضروری است. هدف این پژوهش، توسعه چارچوبی برای رصد فناوری مبتنی بر هوش مصنوعی با تمرکز بر شرکت‌های فناوری محور است. این پژوهش با رویکرد کیفی به گردآوری داده‌ها از طریق روش فراترکیب سندلوسکی و بارسو پرداخت. این روش با بررسی نظام‌مند 28 مقاله مرتبط با هدف پژوهش از بین 253 مقاله اولیه انجام شد. مقاله‌های نهایی براساس معیارهای ورود به مطالعه انتخاب شدند. روایی پژوهش برطبق معیارها، برگزاری جلسات با اعضای تیم پژوهش، استفاده از کارشناس و ممیزی کل فرایند برای اجماع نظری تأیید شد و پایایی نیز از طریق برنامه مهارت‌های ارزیابی انتقادی مشخص شد. چارچوب رصد فناوری مبتنی بر هوش مصنوعی در شرکت‌های فناوری محوردر پنج بعد ترسیم شده است: ابزارهای رصد فناوری، چرخه عمر فناوری، محیط شرکت، رویکرد شرکت در مواجهه با محیط، و ظرفیت جذب شرکت. یافته‌ها بر نقش محوری ابزارهای رصد فناوری مبتنی بر هوش مصنوعی تأکید می‌کنند، پویایی‌های ظریف چرخه عمر فناوری را روشن می‌کنند، جنبه‌های چندوجهی محیط شرکت را آشکار می‌سازند، رویکردهای استراتژیک در مواجهه با چشم‌انداز فناوری درحال تحول را ترسیم می‌کنند، و بر ضرورت ظرفیت جذب برای استفاده موثر از فناوری‌های هوش مصنوعی تأکید می‌کنند. این پژوهش با ارائه بینش‌های عملی و راهنمایی استراتژیک، شرکت‌های فناوری محور را با پایه‌ای قوی برای پیمایش در تقاطع پیچیده هوش مصنوعی و رصد فناوری مجهز می‌کند، درنتیجه رشد پایدار، افزایش مزیت رقابتی و نوآوری پایدار را تسریع می‌کند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Analyzing the Factors Affecting the Technology Scouting Based on Artificial Intelligence in technology-Oriented Companies

نویسندگان [English]

  • Shiva sadat Ghasemi 1
  • Abbas khamseh 2
  • Seyed Javad Iranban 3

1 Ph.D. Student, Department of Technology Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran.

2 Associate Professor, Department of Industrial Management, Karaj Branch, Islamic Azad University, Karaj, Iran. Corresponding Author: abbas.khamseh@kiau.ac.ir

3 Assistant Professor, Department of Management, Shiraz Branch, Islamic Azad University, Shiraz

چکیده [English]

In the contemporary landscape of technology-driven industries, the integration of artificial intelligence into technology scouting is imperative for enhancing innovation and sustaining competitiveness. This research aims to forge a framework for technology scouting based on artificial intelligence, with a specific focus on technology-based companies. Employing a qualitative approach, data collection utilized the meta-synthesis method devised by Sandelowski and Barroso. This involved a systematic review of 28 articles relevant to the research goal out of a pool of 253 primary articles. The final selection of articles was based on predefined inclusion criteria. The research's validity was confirmed through adherence to criteria, team meetings, expert consultations, and an exhaustive audit for theoretical consensus, while reliability was ascertained through the Critical Evaluation Skills Programme. The framework spans five dimensions: technology scouting tools, technology life cycle, firm environment, firm's approach to the environment, and firm's absorptive capacity. The findings underscore the pivotal role of AI-based technology scouting tools, elucidate the nuanced dynamics of the technology life cycle, and reveal the multifaceted aspects of the enterprise environment. The research outlines strategic approaches for navigating the evolving technology landscape, underscoring the imperative of absorptive capacity for the effective utilization of artificial intelligence technologies. By delivering actionable insights and strategic counsel, this research serves to furnish technology-based companies with a robust underpinning for negotiating the intricate intersection of AI and technology surveillance. In doing so, it propels sustainable growth, fortifies competitive advantage, and fosters enduring innovation.

Introduction

In the dynamic world of technology-driven industry, the role of strategic technology management, particularly in the technology selection and acquisition phases, cannot be overemphasized if success is sought in innovation-driven companies. Focusing on technology-oriented companies that currently face a rapid industrial evolution, the present study highlights the indispensable role of technology scouting, equipped specifically with artificial intelligence (AI), in grappling with the imminent competitive environment. The study proposes a framework that anticipates a future where AI plays a central role in technology acquisition and that strives to enhance absorptive capacities by bridging the adaptation gap. Drawing upon AI, the propsoed framework not only ensures proper technology selection by firms but also drives them toward cutting-edge technological innovations. Serving as a guide for decision-makers, technology strategists, and specialists, the study is expected to contribute, both theoretically and practically, to the understanding and advancement of technology scouting in tech-driven companies. Moreover, it explores and identifies the needs of organizations navigating the intricate technology landscape to derive actionable insights that ensure sustainable innovation leadership.
What is the framework for technology scouting based on artificial intelligence in technology-oriented companies?

Literature Review

In today's rapidly evolving tech landscape, it is essential to cope with the changing business environment (Kujawa and Paetzold, 2019). Ahammad et al. (2021) linked strategic agility to search strategies. Wang and Quan (2021) studied the impact of technology selection uncertainty on firms’ absorptive capacity. Vuorio et al. (2018) explored the significance of competitive edge in tech-driven enterprises. Kerr and Phall (2018) developed a scouting process model. Nasullaev et al. (2020) reiterated the alignment of strategy and tech scouting. Xu et al. (2021) advocated patent analysis in scouting. Sikandar et al. (2021) reiterated patents' innovation measure. Tabrizi et al. (2019) observed a shift to tech-centric business models. Stute et al. (2021) noted the importance of AI in supply chain enhancement. Mariani et al. (2023) classified the motivations underlying AI adoption. Stahl et al. (2023) addressed AI ethics while D'Almeida et al. (2022) categorized AI applications. Wang et al. (2020) identified AI algorithms. Despite these efforts, scant research has been reported on tech transformation, especially AI. This study adopts the meta-synthesis method to explore the digital transformation complexities, focusing on AI's transformative potential and bridging the gaps to derive a roadmap for navigating tech-driven industries.

Methodology

Employing a qualitative approach and the meta-synthesis method, a seven-step process (including goal setting, review, selection, extraction, analysis, quality control, and model development) was meticulously followed to develop an AI-based technological scouting model for advanced tech firms. A systematic search yielded 253 articles, 28 of which met the inclusion criteria and were validated through team meetings, software analysis, and expert consultation. Reliability was ensured since 89% of the articles received excellent scores via the Critical Evaluation Skills Program, indicating high quality.

Results

The research adopted a classified analysis perspective, utilizing inductive analysis based on Sandelowski and Barroso (2007). This involves extracting primary codes related to AI-based technology observation in high-tech companies, identifying patterns through open coding, and classifying concepts into sub-categories and main categories via axial coding.
Table 1. Factors Affecting AI-Based Technology Scouting




Category


Subcategory


Concepts






Technology Scouting Tool


Open Source Intelligence (OSINT) Tools


Web scraping tools, social media monitoring, online forums, patent databases, news aggregators, competitive intelligence tools, and data analytics platforms.




Machine Learning and AI Tools


Natural Language Processing (NLP), predictive analytics, pattern recognition, chatbots, sentiment analysis, machine learning, and cognitive computing tools.




Collaboration and Communication Platforms


Online collaboration tools, project management platforms, virtual team collaboration, idea management, crowdsourcing, communication apps, and workflow automation.




Technology Life Cycle


Innovation and Invention


Idea generation, R&D, concept testing, prototyping, patenting, technology transfer, proof of concept, funding, collaborative research, and feasibility studies.




Technology Adoption and Diffusion


Technology readiness, market analysis, adoption theories, market penetration, standardization, compliance, user testing, and overcoming adoption barriers.




Technology Evolution and Obsolescence


Continuous improvement, iterative development, versioning, obsolescence management, legacy systems, discontinuation planning, sustainability, disruptive tech, and sunset planning.




Company Environment


Competitive Landscape Analysis


Competitor mapping, SWOT analysis, industry benchmarking, market share analysis, competitive intelligence, PESTLE analysis, collaboration strategies, positioning, and sustainable advantage.




Regulatory and Legal Environment


Intellectual property management, standards compliance, regulatory impact, patent landscape analysis, legal risk, data protection, ethics, antitrust, government policies, and international regulations.




Internal Organizational Environment


Culture, cross-functional collaboration, governance, change management, talent, agile structures, infrastructure, decision-making, metrics, and employee engagement.




The Company's Approach in Facing the Environment


Innovation Strategy Formulation


Roadmapping, open innovation, blue ocean strategy, core competency analysis, innovation ecosystems, portfolio management, ambidextrous approach, horizon scanning, lean methodologies, and design thinking.




Adaptive and Resilient Practices


Crisis management, scenario planning, risk management, agile project management, supply chain resilience, continuous learning, adaptive capabilities, technology portfolio flexibility, and fostering innovation culture.




Strategic Alliances and Partnerships


Collaborative innovation, joint ventures, technology ecosystems, university-industry collaborations, innovation networks, open source, licensing, technology transfer, competition, and strategic partnerships.




Absorption Capacity of the Company


Learning and Knowledge Management


Organizational learning, knowledge creation, sharing platforms, communities of practice, intellectual capital, training programs, technology scouting, learning culture, and tacit knowledge transfer.




Resource Allocation and Utilization


Technology budgeting, allocation models, ROI analysis, portfolio management, cross-functional sharing, resource efficiency, project prioritization, dynamic reallocation, innovation finance, and risk management.




Adoption of Emerging Technologies


Scanning trends, piloting new tech, foresight methodologies, early adoption, readiness assessments, and collaborative ecosystems for adoption, mitigating risks, cross-functional teams, integration, and continuous monitoring.




 

Discussion

To address the crucial gap in technology scouting in technology-oriented companies involved in the joint AI and technology scouting, the study develops a framework of five dimensions. Open-source smart tools and machine learning are explored as essential components of the "Technology Scouting Tool"dimension to contribute to the development of a cohesive strategy. The "Technology Life Cycle" dimension guides the firm through the innovation, adoption, and evolution stages. The "Company Environment" dimension adopts a multifaceted approach, considering competitive analysis, regulatory factors, and internal dynamics. The strategic components of the "Firm's Approach to the Environment" underline the contributions of innovation strategy, adaptability, and alliances while "Firm's Absorptive Capacity" offers practical insights by underscoring learning, resource allocation, and technology adoption.

Conclusion

The proposed framework provides a strategy tailored for tech-oriented firms incorporating AI into scouting and offers strategic insights across the five dimensions to tackle nuanced challenges in the technology landscape. Advocating advanced open-source tools and strategic approaches, it explores the technology life cycle, considers diverse aspects of firm environment, and launches an AI-driven future. Acknowledging limitations and emphasizing proper deployment of AI, the study lays the foundations for future studies to validate and expand the framework while ensuring responsive and sustainable application of AI-based surveillance technologies in corporate contexts.
Keywords: Artificial Intelligence, Technology Scouting, Technology-Oriented Companies, Digital Transformation.
 
 
 

کلیدواژه‌ها [English]

  • Artificial intelligence
  • technology scouting
  • technology-based companies
  • digital transformation
مراجع
  1. حاجی غلام سریزدی، علی. (1399). پویایی تغییرات سطح فناوری شرکت‌های فناور در پارک علم و فناوری یزد. مدیریت نوآوری، 9(2)، 63 – https://dorl.net/dor/‌20.1001.1.23225386.1399.9.2.3.3
  2. خمسه، عباس و بهروزی، علیرضا. (1396). شناسایی و بررسی عوامل کلیدی مؤثر بر رصد تکنولوژی‌های پیشرفته آینده در مراکز طراحی هوافضا. آینده‌پژوهی دفاعی، 2(7)، 129 –http://www.dfsr.ir/article_33784.html
  3. میرشاه ولایتی، فرزانه و نظری زاده، فرهاد. (1398). الگوی دیدبانی فناوری: فرایند و ساختاری برای رصد تحول‌های فناورانه. آینده‌پژوهی دفاعی، 4(13)، 41– doi:10.22034/dfsr.2019.36542
  4. نظامی‌پور، قدیر، طبائیان، سیدکمال، الهی، سیدمجید، ناظمی، امیر و میرشاه ولایتی، فرزانه. (1395). معرفی معیارهای دیدبانی فناوری به‌عنوان ابزار آینده‌پژوهی.راهبرد دفاعی، 14(1)، 137 – https://ds.sndu.ac.ir/article_231.html?lang

References

  1. Abramov, O., Fimin, P., & Smirnova, E. (2019). Technology scouting in chemistry: roadmap and case studies. Proceedings of MATRIZ TRIZfest-2019 International Conference. September 11-14, 2019. Heilbronn, Germany. 319-326.
  2. Ahammad, M.F., Basu, S., Munjal, S., Clegg, J., & Shoham, O.B. (2021). Strategic agility, environmental uncertainties and international performance: The perspective of Indian firms. Journal of World Business, 56(4), 101218. https://doi.org/10.1016/j.jwb.2021.101218
  3. Armenia, S., Franco, E., Iandolo, F., Maielli, G., & Vito, P. (2024). Zooming in and out the landscape: Artificial intelligence and system dynamics in business and management. Technological Forecasting and Social Change, 200, 123131. https://doi.org/10.1016/j.techfore.‌2023.123131
  4. Benkhider, N., & Meziani, M. (2021). Digital transformation process based-technology infrastructure and employee training evidence from World Bank. Recherchers Economiques Manageriales, 15(1): 537-552. https://www.asjp.cerist.dz/en/article/153140
  5. Brügmann, S., Bouayad-Agha, N., Burga, A., Carrascosa, S., Ciaramella, A., Ciaramella, M., Codina-Filba, J., Escorsa, E., Judea, A., Mille, S., Müller, A., Saggion, H., Ziering, P., Schütze, H., & Leo, W. (2015). Towards content-oriented patent document processing: Intelligent patent analysis and summarization. World Patent Information, 40: 30-42. https://doi.org/10.1016/j.wpi.2014.10.003
  6. Cascio, W.F., & Montealegre, R. (2016). How technology is changing work and organizations. Annual Review of Organizational Psychology and Organizational Behavior, 3: 349-375. https://doi.org/‌10.1146/annurev-orgpsych-041015-062352
  7. Cho, Y., & Daim, T. (2013). Technology Forecasting Methods. Research and Technology Management in the Electricity Industry: Methods, Tools and Case Studies (pp.67-112), Edition: 2013, Publisher: Springer.
  8. D’Almeida, L., Bergiante, N.C.R., Ferreira, G.S., Leta, F.R., Campos Lima, C.B., Alves Lima, G.B. (2022). Digital transformation: a review on artificial intelligence techniques in drilling and production applications. The International Journal of Advanced Manufacturing Technology, 119: 5553–5582. https://doi.org/10.1007/s00170-021-08631-w
  9. de Weck, O.L. (2022). Technology Scouting. In: Technology Roadmapping and Development. Springer, Cham. https://doi.org/10.1007/978-3-030-88346-1_14
  10. Dzhengiz, T., & Niesten, E.(2020). Competences for environmental sustainability: A systematic review on the impact of absorptive capacity and capabilities. Journal of Business Ethics, 162: 881–906. DOI: 10.1007/s10551-019-04360-z
  11. Evangelista, , Ardito, L., Boccaccio, A., Fiorentino, M., Petruzzelli, A.M., Uva, A.E. (2020). Unveiling the technological trends of augmented reality: A patent analysis. Computers in Industry, 118, 103221. http://dx.doi.org/10.1016/j.compind.2020.103221
  12. Gonçalves, L.R., & Almeida, F.C. (2018). How technology intelligence is applied in different contexts? International Journal of Innovation, 7(1): 104-118. http://dx.doi.org/10.5585/iji.v7i1.393
  13. Guida, M., Caniato, F., Moretto, A., & Ronchi, S.(2023). Artificial intelligence for supplier scouting: an information processing theory approach. International Journal of Physical Distribution & Logistics Management, 53(4): 387-423. https://doi.org/10.1108/IJPDLM-12-2021-0536
  14. Halicka, K. (2017). Main concepts of technology analysis in the light of the literature on the subject. Procedia Engineering, 182: 291–298. https://doi.org/10.1016/j.proeng.2017.03.196
  15. Jaehyeong, A., Kim, K., Mortara, L., & Lee, (2018). Deriving technology intelligence from patents: Preposition-based semantic analysis. Journal of Informetrics, 12: 217-236. DOI: 10.1016/j.joi.2018.01.001
  16. Kerr, C., & Phaal, R. (2018). Directing the technology intelligence activity: An ‘information needs’ template for initiating the search. Technological Forecasting & Social Change, 134: 265-276. DOI: 10.1016/j.techfore.2018.06.033
  17. Kim, M., Park, Y., & Yoon, J. (2016). Generating patent development maps for technology monitoring using semantic patent-topic analysis. Computers & Industrial Engineering, 98: 289–299. https://doi.cir-mcs.e.corpintra.net/10.1016/j.cie.2016.06.006
  18. Kujawa, K.A., Paetzold, K. (2019). External technology searching methods: A literature review. International Conference on Engineering Design, ICED19, 5-8 AUGUST 2019, Delft, Netherlands. https://doi.org/10.1017/dsi.2019.232
  19. Leachu, S., Clemens, F., Stich, V. (2022). Development of a pre-competitive business model for AI-Based autonomous technology scouting. In: Herberger, D.; Hübner, M. (Eds.): Proceedings of the Conference on Production Systems and Logistics: CPSL 2022. Hannover: publish-Ing., 2022, S. 612-621. DOI: https://doi.org/‌10.15488/12163
  20. Loseto, G., Scioscia, F., Ruta, M., Gramegna, F. et al. (2023). A Cloud-Edge Artificial Intelligence Framework for Sensor Networks. 2023 9th International Workshop on Advances in Sensors and Interfaces (IWASI), Monopoli (Bari), Italy, 149-154, doi: 10.1109/IWASI58316.2023.10164335.
  21. Madani, F., & Khormaei, R. (2013). Overview of evolution in study of external technology search. In 2013 Proceedings of PICMET'13: Technology Management in the IT-Driven Services (PICMET) (pp. 2058-2060). IEEE.
  22. Manzini, R., & Nasullaev, A. (2017). Technology intelligence in practice: A systematic literature review of empirical studies and agenda for further research. http://dx.doi.org/10.25428/2532-554X/2
  23. Mariani, M.M., Machado, I., Magrelli, V., Dwivedi, Y.K. (2023). Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions. Technovation, 122: 102623. https://doi.org/10.1016/j.technovation.2022.102623
  24. Mühlroth, C., & Grottke, M. (2022). Artificial intelligence in innovation: How to spot emerging trends and technologies, in IEEE Transactions on Engineering Management, 69(2), 493-510, doi: 10.1109/TEM.2020.2989214.
  25. Nasullaev, A., Manzini R., & Kalvet, B. (2020). Technology intelligence practices in SMEs: Evidence from Estonia. Journal of Intelligence Studies in Business, 10(1): 6-22. https://doi.org/10.37380‌/jisib.‌v1i1.560
  26. Pantano, E., & Pizzi, G. (2020). Forecasting artificial intelligence on online customer assistance: Evidence from chatbot patents analysis. Journal of Retailing and Consumer Services, 55, 102096. https://doi.org/10.1016/j.jretconser.2020.102096
  27. Peter, M., Grivas, S., Horn, D., Rüegg, F., Barba, R., & Graf, M. (2019). The technology matrix – A tool-based method for technology scouting. 131-138. http://dx.doi.org/10.33965/is2019_201905L017
  28. Porter, A.L., & Cunningham, S.W. (2005), Tech Mining: Exploiting New Technologies for Competitive Advantage. John Wiley & Sons, Inc, Hoboken, NJ, USA. https://doi.org/10.1002/0471698466.ch10
  29. Reez, N. (2021). Foresight-based leadership. Decision-making in a growing AI environment. In: Jacobs, G., Suojanen, I., Horton, K., Bayerl, P. (eds) International Security Management. Advanced Sciences and Technologies for Security Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-42523-4_22
  30. Rodriguez-Salvador, M., & Castillo-Valdez, P.F. (2021). Integrating science and technology metrics into a competitive technology intelligence methodology. Journal of Intelligence Studies in Business, 11(1): 69-77. https://doi.org/10.37380/jisib.v1i1.696
  31. Sahoo, S., Kumar, S., Donthu, N., Kumar Singh, A. (2024). Artificial intelligence capabilities, open innovation, and business performance – Empirical insights from multinational B2B companies. Industrial Marketing Management, 117: 28-41. https://doi.org/10.1016/‌j.indmarman.2023.12.008
  32. Sandelowski, M., & Barroso, J. (2007). Handbook for Synthesizing Qualitative Research. New York: Springer Publishing Company. https://doi.org/10.4236/crcm.2022.118046
  33. Schilling, M.A., & Esmundo, M. (2009). Technology S-curves in renewable energy alternatives: Analysis and implications for industry and government. Energy Policy, 37(5): 1767–1781. https://doi.org/10.1016/j.enpol.2009.01.004
  34. Schuh, G., Hicking, J., Stroh, M.F., & Benning J (2020). Using AI to Facilitate Technology Management – Designing an Automated Technology Radar. In 53rd CIRP Conference on Manufacturing Systems, Procedia CIRP 93 (2020): 419–424. Science Direct. http://dx.doi.org/10.1016/j.procir.2020.04.089
  35. Schuh, G., Boßmeyer, H.J., & Bräkling, A. (2021). Data-driven technology management supported by artificial intelligence solutions. Journal of Production Systems and Logistics, 1(4). DOI: https://doi.org/10.15488/10528
  36. Sikandar, H., Vaicondam, Y., Khan, N., Qureshi, M.I., & Ullah A (2021). Scientific mapping of industry 4.0 research: A bibliometric analysis. International Journal of Interactive Mobile Technologies (iJIM), 15(18): 129-147. https://doi.org/10.3991/ijim.v15i18.25535
  37. Stahl, B. C., Brooks, L., Hatzakis, T., Santiago, N., Wright, D. (2023). Exploring ethics and human rights in artificial intelligence – A Delphi study. Technological Forecasting & Social Change, 191, 122502. https://doi.org/10.1016/j.techfore.2023.122502
  38. Stock, A., Stein, F., & Brecht, L. (2020). Usage of bibliometric tools in foresight and technology scouting. Paper presented at the ISPIM Connects Global 2020, Virtual.
  39. Stute, M., Sardesai, S., Parlings, M., Senna, P.P., Fornasiero, R., & Balech, (2021). Technology scouting to accelerate innovation in supply chain. Lecture Notes in Management and Industrial Engineering (Springer), 129-145. http://dx.doi.org/10.1007/978-3-030-63505-3_6
  40. Sundhararajan, , Gao, X.Z., & Vahdatnejad, H. (2018). Artificial intelligent techniques and its applications. Journal of Intelligent & Fuzzy Systems, 34: 755-760. DOI: 10.3233/JIFS-169369.
  41. Tabrizi, B., Lam, E., Girard, K., & Irvin, V. (2019). Digital transformation is not about technology. Harvard Business Review, 13, 1-6.
  42. Vuorio, A.M., Puumalainen, K., & Fellnhofer, K. (2018). Drivers of entrepreneurial intentions in sustainable entrepreneurship. International Journal of Entrepreneurial Behavior & Research, 24(2): 359-381. http://dx.doi.org/10.1108/IJEBR-03-2016-0097
  43. Wang, C.H. & Quan, X.I. (2021). The role of external technology scouting in inbound open innovation generation: evidence from high-technology industries. In IEEE transactions on Engineering Management, 68(6): 1558-1569. http://dx.doi.org/10.1109/TEM.2019.2956069
  44. Wang, Y., Yu, Y., Cao, S., Zhang, X., & Gao, S. (2020). A review of applications of artificial intelligent algorithms in wind farms. Artificial Intelligence Review, 53: 3447–3500. https://doi.org/10.1007/s10462-019-09768-7
  45. Wang, X., Daim, T., Huang, L., Li, Z., Shaikh, R., & Francois Kassi, D. (2022). Monitoring the development trend and competition status of high technologies using patent analysis and bibliographic coupling: The case of electronic design automation technology. Technology in Society, 71, 102076. DOI: 10.1016/j.techsoc.2022.102076
  46. Wustmans, M., Haubold, T., & Bruens, B. (2022). Bridging trends and patents: Combining different data sources for the evaluation of innovation fields in blockchain technology, in IEEE Transactions on Engineering Management, 69(3), 825-837, doi: 10.1109/TEM.2020.3043478.
  47. Xu, Z., Ge Z., Wang W., Skare M. (2021). Bibliometric analysis of technology adoption literature published from 1997 to 2020. Technological Forecasting and Social Change, 170. 120896. http://dx.doi.org/10.1016/j.techfore.2021.120896

 

References [In Persian]

  1. Hajigholamsaryazdi, A. (2020). Dynamics of technology changes in technology-based firms in Yazd Science and Technology Park. Innovation Management Journal, 9(2), 63-94. https://dorl.net/dor/20.1001.1.23225386.1399.9.2.3.3
  2. Khamseh, A., & Behroozi, A. (2018). Identifying and studying the key factors affecting scouting the future high technologies in aerospace design centers. Defensive Future Studies, 2(7), 129-152. http://www.dfsr.ir/article_33784.html
  3. Mir Shahvelayati, F., & Nazarizadeh, F. (2019). Technology scouting model: A process & structure for monitoring technological changes. Defensive Future Studies, 4(13), 41-68. doi:10.22034/dfsr.2019.36542
  4. Nezamipour, G., Tabaeian, S.K., Elahi, S.M., Nazemi, A., & Mirshahvelayati, F. (2016). Introduce of surveillance measures technology as a tool for future study. Defence Studies, 14(1), 137-171. https://ds.sndu.ac.ir/article_231.html?lang

 

 

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مطالعات مدیریت کسب و کار هوشمند
دوره 13، شماره 49
مهر 1403
صفحه 37-76
فایل ها
سابقه مقاله
  • تاریخ دریافت: 19 آذر 1402
  • تاریخ بازنگری: 08 بهمن 1402
  • تاریخ پذیرش: 14 بهمن 1402
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