Open Innovations

2026/2027

Учебный год

ENG

Обучение ведется на английском языке

5

Кредиты

Кто читает:

Департамент бизнес-информатики

Статус:

Курс обязательный

Когда читается:

4-й курс, 1, 2 модуль

Преподаватель

Текич Желько

Course Syllabus

Abstract

Open Innovation (OI) is defined as a distributed innovation process based on purposively managed knowledge flows across organizational boundaries. In essence, it encompasses a wide range of practices related to external knowledge acquisition and commercialization—from simple crowd engagement (such as choosing a new ice cream flavor) to the involvement of lead users in developing medical devices, R&D purchases, venturing, licensing agreements, and free revealing of inventions. In the OI approach, firms look beyond their boundaries to exploit the creativity and expertise of users, customers, experts, and online communities to co-create new products and services. By expanding firm boundaries, open innovation reshapes how companies—whether large corporations, SMEs, or startups—strategize, compete, create, deliver, and capture value. The additional layer of complexity brought by artificial intelligence (AI) further increases the importance of collaboration. In the era of AI, open innovation becomes a vital driver of competitiveness: it enables companies to gather and access data, acquire cutting-edge technologies, attract top talent, and leverage diverse expertise. To thrive in this landscape, organizations must master the art of complex, open collaboration. To prepare students for this context, the Open Innovation course covers the fundamentals of open innovation ideas, tools, practices, and strategies, aiming to equip students with the understanding, knowledge, and skills required to manage open and user innovation projects in their future workplaces. A distinctive feature of the course is an innovation sprint / hackathon, where students participate in an intensive, practice-based co-creation process. The innovation sprint allows them to experience open innovation both as active participants—collaborating, prototyping, and pitching solutions—and as innovation-aware observers, who develop insight into what motivates companies to engage in such collaborations and how these processes are managed.

Learning Objectives

• The course is designed to help students understand, experience, and manage open innovation (OI), reflecting the fact that collaboration has become a central feature of contemporary innovation. Few firms today rely solely on closed, internal R&D; instead, most combine in-house efforts with partnerships, user involvement, and ecosystem collaboration. As a result, open innovation has become a mainstream approach in many industries, and future managers need to be fluent in its logic and practice. While grounded in research and theory, the course is practice-oriented and built around an innovation sprint, which gives students a concentrated co-creation experience. They learn from both perspectives: as active participants, developing and pitching solutions to authentic company problems, and as innovation-aware observers, analyzing the motives, practices, and challenges companies face when engaging in open innovation. The course highlights the role of the innovation manager, who must be able to design, coordinate, and extract value from collaborative innovation processes. By integrating case studies, analytical frameworks, and the innovation sprint experience, students gain a holistic understanding of how OI works across large corporations, SMEs, startups, and non-commercial organizations. In doing so, the course prepares students to contribute as innovation managers, product and project managers, ecosystem collaborators, and co-creators who can thrive in a world where innovation is increasingly open and collaborative, particularly in the development of new products and services.

Expected Learning Outcomes

• Understand, explain and critically discuss the differences between open and closed innovation. Understand, explain and apply fundamental open innovation concepts and practices. Understand and explain the main motivation for organizations to use OI.
• Understand, explain and critically discuss the role of open innovation in business model change, and how that impacts management of Digital transformation and AI projects.
• Analyze and synthesize companies’ open innovation strategies. Shape technology-based ideas into workable business concepts and learn how to test them in the marketplace. Differentiate and distinguish the different process activities associated with new product/process/service development, inside or outside an established firm.
• Effectively communicate innovative initiatives in oral and written form. Productively work in groups. Critically reflect on its own learning.
• Differentiate between the different types of OI tools (co-creation with users, crowdsourcing, Lead Users, Innovation Intermediaries, in-licensing, open source…) and partners, and choose the right OI tool for different problem sets.
• Understand the basics of intellectual property rights and their role in OI. Understand, explain and critically discuss the role of the business model change. Understand how value appropriation works in the OI context.

Course Contents

• Innovation - what is it and why does it matter (intro to the topic and the course)
• From closed to open innovation
• Where does innovation come from? (Sources of innovation)
• Value appropriation mechanisms, Intellectual Property (IP) rights, and Open innovation
• Business models and open innovation
• OI in Practice
• Open innovation and AI

Assessment Elements

• Innovation Sprint
  This is a team activity. The Innovation Sprint is an intensive, week-long challenge in which students work in teams to identify and address a meaningful real-world problem within a context announced at the start of the Sprint. Unlike traditional project work, the Sprint begins with problem discovery rather than with a predefined solution task. Students are expected to investigate the context, gather and assess relevant evidence, formulate a clear problem statement, and only then proceed to solution development. The Sprint combines human inquiry with the purposeful use of large language models (LLMs) for search, ideation, analysis, recombination, and critical evaluation. Students are expected to use AI as a tool that supports, rather than replaces, their own judgment and to validate important assumptions using appropriate evidence. By working under time constraints and with an initially ambiguous challenge, students develop a range of transferable skills, including: Problem identification and framing – recognizing meaningful problems, distinguishing symptoms from underlying causes, and formulating clear and actionable problem statements. Evidence-based innovation – gathering and interpreting information from users, stakeholders, existing solutions, and other relevant sources. Creativity and solution development – generating, comparing, refining, and testing alternative solutions. AI-enabled problem solving – using LLMs critically for exploration, synthesis, ideation, and evaluation. Collaboration and teamwork – coordinating work, allocating responsibilities, integrating different perspectives, and making collective decisions under time pressure. Communication and pitching – presenting the problem, supporting evidence, proposed solution, and key learning clearly and persuasively. Adaptability and learning – responding to evidence and feedback, revising assumptions, and changing direction when necessary. The Innovation Sprint serves as an applied learning environment in which students move from problem discovery to solution development within a compressed timeframe. It enables them to apply course concepts in practice while developing entrepreneurial, innovation, analytical, collaborative, and AI-enabled capabilities relevant to contemporary organizations. team members anonymously assess each other contribution to the project result (peer evaluation)
• In-Class Discussion & Engagement
  In-Class Discussion & Engagement This component assesses active engagement and participation in class, rather than attendance alone. Simply being present does not earn engagement points. Engagement is demonstrated through timely, relevant, and substantive contributions to class discussions and activities. Contributions may draw on the current or previous sessions, assigned readings and videos, personal or professional experience, other courses, or relevant external examples. Students are expected to respond thoughtfully to questions, engage with the ideas of others, and support their views with reasoning and evidence. Engagement is assessed by the course instructor during and immediately after each class session. The quality and relevance of contributions are more important than the number or length of interventions. To earn engagement points, students are expected to: Attend the class and actively and voluntarily participate in discussions and in-class activities. Demonstrate familiarity with assigned readings and other preparation materials and engage with them critically. Make relevant connections to previous course material, other knowledge, experience, or evidence. Provide clear, analytical, and concise contributions, supporting arguments with facts, examples, concepts, or references rather than unsupported opinions. Respond constructively to questions and to contributions made by the instructor and other students. Contribute in ways that advance the discussion rather than merely increase speaking time. Length or frequency of speaking is not in itself rewarded. Monopolising discussion through repetitive, unstructured, off-topic, or unsupported contributions will not be considered positive engagement.
• Attendance
  Attendance at scheduled teaching sessions. Attendance is tracked per 80-minute session, not per day. Students register attendance using the method and timing specified by the instructor. Attendance cannot be replaced by extra tasks or later participation.
• Case-Based Test
  An individual assessment based on the mandatory readings and cases discussed in class. The test uses multiple-choice questions to assess students’ understanding of the key arguments, findings, mechanisms, and lessons from the assigned materials, as well as their ability to distinguish between alternative interpretations and applications. preliminary reading list: Antorini, Y. M., & Muñiz, A. M., Jr. (2013). The benefits and challenges of collaborating with user communities. Research-Technology Management, 56(3), 21–28. Lüthje, C., Herstatt, C., & von Hippel, E. (2005). User-innovators and “local” information: The case of mountain biking. Research Policy, 34(6), 951–965. doi:10.1016/j.respol.2005.05.005. Bilgram, V., Bartl, M., & Biel, S. (2011). Getting closer to the consumer—How Nivea co-creates new products. Marketing Review St. Gallen, 28(1), 34–40. doi:10.1007/s11621-011-0005-5. Boudreau, K. J., & Lakhani, K. R. (2013). Using the crowd as an innovation partner. Harvard Business Review, 91(4), 61–69. Dąbrowska, J., Lopez-Vega, H., & Ritala, P. (2019). Waking the sleeping beauty: Swarovski’s open innovation journey. R&D Management, 49(5), 775–788. doi:10.1111/radm.12374. Dąbrowska, J., Keränen, J., & Mention, A.-L. (2024). Beyond the buzz: Unpacking the forms and practices of dedicated open innovation functions. California Management Review, 67(1), 114–137. doi:10.1177/00081256241276566. Ho, W. R., Kazantsev, N., & Netland, T. (2024). The critical catalyst: Getting open innovation projects right in times of disruption. California Management Review, 67(1), 96–113. doi:10.1177/00081256241277238. Tekic, Z., Svirskaya, M. D., Tekic, A., & Titov, S. A. (2025). The shift to localized open innovation: The impact of sanctions on co-creation in Russian companies. Russian Management Journal, 23(1), 52–75. doi:10.21638/spbu18.2025.103. Boussioux, L., Lane, J. N., Zhang, M., Jacimovic, V., & Lakhani, K. R. (2024). The crowdless future? Generative AI and creative problem solving. Organization Science, 35(5), 1589–1607. doi:10.1287/orsc.2023.18430. Tekic, Z., & Füller, J. (2023). Managing innovation in the era of AI. Technology in Society, 73, 102254. Holgersson, M., Dahlander, L., Chesbrough, H. W., & Bogers, M. L. A. M. (2024). Open innovation in the age of AI. California Management Review, 67(1), 5–20. doi:10.1177/00081256241279326.
• Exam
  Exam – An individual assessment combining multiple-choice questions, which test conceptual understanding, and long-answer questions, which assess application, analysis, and critical thinking. The exam covers key theories and frameworks in open innovation and their application to case-based problems and managerial decisions.

Interim Assessment

• 2026/2027 2nd module
  0.1 * Attendance + 0.25 * Innovation Sprint + 0.15 * Case-Based Test + 0.4 * Exam + 0.1 * In-Class Discussion & Engagement

Bibliography

Recommended Core Bibliography

• Holgersson, M., Dahlander, L., Chesbrough, H., & Bogers, M. L. A. M. (2024). Open Innovation in the Age of AI. California Management Review, 67(1), 5–20.
• Strategic management of technological innovation, Schilling, M. A., 2023
• Z. Tekic , A. Tekic , S.A. Titov , & M.D. Svirskaya (2025). The shift to localized open innovation: the impact of sanctions on co-creation in Russian companies. Российский журнал менеджмента, (1), 52-75

Recommended Additional Bibliography

• Ritala, P., & Stefan, I. (2021). A paradox within the paradox of openness: The knowledge leveraging conundrum in open innovation. Industrial Marketing Management ; Volume 93, Page 281-292 ; ISSN 0019-8501. https://doi.org/10.1016/j.indmarman.2021.01.011
• Открытые инновации в России : тенденции и лучшие практики, Текич, Ж., 2025
• Сотворчество в России : как российские компании создают инновации вместе с внешними партнерами, Текич, Ж., 2025

Authors

• Tekich Zhelko