Series of AI-INNOM Project Seminars. Day #1
After two years of working on a project titled Adding Data and Artificial Intelligence into Innovation Equation: Conceptualizing and Exploring AI-Based Innovation Management (AI-INNOM), we are approaching its closure. We are inviting you to discuss our results and outlook for the future research through the series of seminars organized on Saturday, May 27 and Monday, May 29. Detailed schedule is below.
DAY #1 – SATURDAY, May 27
Seminar: Local knowledge base and entrepreneurial opportunities in regions: a configurational perspective
Saturday, May 27, starts at 10:45
Online, in English
According to the central hypothesis of the knowledge spillover theory of entrepreneurship – the more knowledge is produced in a region, the more entrepreneurial opportunities are available in it. Recent research extended the theory further, showing that the level of entrepreneurial activities, depends on the configuration of the local knowledge base not only on its size. This seminar aims at discussing the recent results which reveal which of knowledge profiles are more (and which less) supportive of creating entrepreneurial opportunities. The research present here is motivated by disagreements and conflicting evidence on fundamental matters across all three frequently used dimensions - the type of knowledge source that contributes to new knowledge within a region (i.e., universities or private incumbent firms); the presence/absence of a dominant source of new knowledge within a region (i.e., an anchor tenant); and the specialization/diversity of technological knowledge that is created within a region.
About the speaker: Zeljko Tekic is an Associate Professor at the Graduate School of Business, HSE University, Moscow. Zeljko earned his PhD in Engineering Management at the University of Novi Sad (Serbia). He was a postdoctoral scholar at the Fraunhofer Institute for Industrial Engineering in Stuttgart and at Freie Universität Berlin. More recently, he was a visiting researcher and a visiting professor at the Massachusetts Institute of Technology (MIT). Zeljko’s research interest evolves around topics at the interplay of digital transformation, open innovation, and startups formation. His latest research focuses on understanding 1) how startups grow using big data instruments; and 2) how artificial intelligence is revolutionizing innovation management. In these two directions, as a part of international research teams, he is investigating how Google Trends data can be used to analyze entrepreneurial phenomena, and how far AI could improve innovation management and what kind of innovation tasks could be assisted by machines.
Seminar: Organizing the implementation of AI: Case of Payment Service Provider
Saturday, May 27, starts at 11:30
Online, in English
Fintech companies are investing in technologies such as cloud technology and artificial intelligence to speed up transaction processing, improve accuracy and reduce expenses. Overall, AI presents the potential for payment service providers, however there is still a gap in applied research when it comes to organising the process of its implementation. The goal of the seminar is to present the recent findings related to the critical organisational elements that need to be considered in the process of implementing and adapting AI in the payment service provider industry.
About the speaker: Maxim Goryunov is a 2nd year Master's student of the Strategic Management and Consulting educational program at HSE, specialization - Digital Transformation of Business. His research interests relate to exploring the possibilities of new disruptive technologies, as well as their impact on manufacturing, team management, and socioeconomics. Moreover, having experience in managing the implementation of a cloud platform in IT consulting, as well as creating and delivering a digital product in a bank team, I am attracted by the opportunity to optimize internal products in practice.
Seminar: How AI revolutionizes innovation management – Perceptions and implementation preferences of AI-based innovators
Saturday, May 27, starts at 12:15
Online, in English
AI is fundamentally changing the way companies work – how they operate and how they compete. AI will also challenge the core axioms and assumptions underlying the innovation process and its management. The central proposition is that AI has the potential to transform the innovation management practice by enabling a much more effective and efficient innovation process and so herald a new innovation era. However, our knowledge of how to apply AI for innovation management is still sparse, and managers are struggling to find the most appropriate approach for applying AI in their innovation efforts. In this seminar we are exploring how managers perceive the potential of AI for innovation management and what kind of impact they expect on the setup of their innovation processes. More precisely, we explore how innovation managers differ in their views on how AI may impact their innovation management practices as well as how they plan to implement and use AI for certain innovation tasks within their organizations.
About the speaker: Zeljko Tekic is an Associate Professor at the Graduate School of Business, HSE University, Moscow. Zeljko earned his PhD in Engineering Management at the University of Novi Sad (Serbia). He was a postdoctoral scholar at the Fraunhofer Institute for Industrial Engineering in Stuttgart and at Freie Universität Berlin. More recently, he was a visiting researcher and a visiting professor at the Massachusetts Institute of Technology (MIT). Zeljko’s research interest evolves around topics at the interplay of digital transformation, open innovation, and startups formation. His latest research focuses on understanding 1) how startups grow using big data instruments; and 2) how artificial intelligence is revolutionizing innovation management. In these two directions, as a part of international research teams, he is investigating how Google Trends data can be used to analyze entrepreneurial phenomena, and how far AI could improve innovation management and what kind of innovation tasks could be assisted by machines.