دانلود مقاله چگونگی تحول مدیریت نوآوری توسط هوش مصنوعی
نکات برجسته
چکیده
کلید واژه ها
1. مقدمه
2. پیشینه نظری
3. مطالعه تجربی
4. یافته ها
5. بحث و مفاهیم
6. نتیجه گیری و دیدگاه تحقیق
پیوست A
پیوست B
منابع
Highlights
Abstract
Keywords
1. Introduction
2. Theoretical background
3. Empirical study
4. Findings
5. Discussion and implications
6. Conclusion and research outlook
CRediT authorship contribution statement
Appendix A
Appendix B
References
چکیده
انتظار میرود که کاربرد هوش مصنوعی فرصت های جدیدی را برای مدیریت نوآوری و تغییر شکل عملکرد نوآوری در سازمان ها ایجاد کند. مطالعه اکتشافی ما در میان 150 مدیر نوآوری با هوش مصنوعی، چهار خوشه مختلف را از نظر نحوه استفاده و پیاده‌سازی هوش مصنوعی در مدیریت نوآوری سازمان‌ها نشان می‌دهد که شامل (1) هوش مصنوعی پیشگام، (2) هوش مصنوعی متخصص و (3) هوش مصنوعی نوآوران گاه به گاه تا (4) مبتکران غیر هوش مصنوعی است. گروه‌های مختلف نه تنها از نظر استراتژی، ساختار سازمانی و مهارت‌سازی، بلکه در پتانسیل درک شده، درک تغییرات مورد نیاز، چالش‌های مواجه شده و زمینه‌های سازمانی متفاوت هستند. مطالعه ما به درک بهتر وضعیت فعلی مدیریت نوآوری مبتنی بر هوش مصنوعی، تأثیر آن بر رویه نوآوری آینده، و تفاوت در جاه‌طلبی‌های هوش مصنوعی سازمان‌ها و رویکردهای پیاده‌سازی انتخاب شده کمک می‌کند.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
The application of AI is expected to enable new opportunities for innovation management and reshape innovation practice in organizations. Our exploratory study among 150 AI-savvy innovation managers reveals four different clusters in terms of how organizations may use and implement AI in their innovation management ranging from (1) AI-Frontrunners, (2) AI-Practitioners, and (3) AI-Occasional innovators to (4) Non-AI innovators. The different groups vary not only in their strategy, organizational structure, and skill-building but also in their perceived potential, understanding of the required changes, encountered challenges, and organizational contexts. Our study contributes to a better understanding of the current state of AI-based innovation management, its impact on future innovation practice, and differences in organizations’ AI ambitions and chosen implementation approaches.
Introduction
The prospects for (AI) in business and the global economy are thrilling. The idea that AI – and machine learning in particular – will increasingly match or exceed human performance, take on work roles, fundamentally transform the operational foundation of business, and disrupt management practices holds considerable potential (Agrawal et al., 2017; Lakhani and Iansiti, 2020; von Krogh, 2018). Generally, the premise is that AI will enhance human capacities, perform tasks or solve problems faster, deliver better outcomes, and deliver higher efficiencies (Agrawal et al., 2019; Wilson and Daugherty, 2018). AI is not only a new technology leading to game-changing products and services and transforming existing processes to be done faster, cheaper, and with higher quality; it is considered the most important general-purpose technology of our times (Brynjolfsson and McAfee, 2017). AI is expected to transform every industry, just as the Internet did 30 years ago or electricity 100 years ago, creating an estimated GDP growth of $13 trillion between now and 2030 (Bughin et al., 2018).
Conclusion and research outlook
While our study provides a first glimpse of the application of AIbased management, its potential, affordances, challenges and potential implementation approaches, it also evokes additional research questions and asks for further studies on AI-based innovation management. We are aware of potential biases and limitations due to our study’s given scope and operationalization. Managers with limited or no interest in the topic might not even have started our online survey, leading to potential biases towards the potential of AI-based innovation. Though we included organizations not planning to apply AI-based methods in our comparative analysis, future research could focus more on companies’ reasons for not considering the technology. Studies may also explore ways to lower the barriers in case AI-based innovation management makes sense in the specific organizational context. The study has also shown that there is a lack of understanding about the necessary changes and impact of AI-based innovation management, as well as a surplus of perception-based rather than fact-based interpretations of the potential of AI in the innovation context.