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Regular version of the site
Language Proficiency
English
Serbian
Contacts
Phone:
00000
E-mail:
Address: 26 Shabolovka Ulitsa, Building 1, room 1219
Timetable
ORCID: 0000-0001-6101-4447
ResearcherID: AAV-7286-2021
Scopus AuthorID: 55338332400
Google Scholar
Supervisor
E. Zaramenskikh
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Zeljko Tekic

  • Zeljko Tekic has been at HSE University since 2021.

Education and Degrees

  • 2013

    PhD
    University of Novi Sad

  • 2008

    Master's in Industrial Engineering and Engineering Management
    University of Novi Sad

  • 2007

    Master's in Electrical and Electronic Engineering and Entrepreneurship
    University of Nottingham

  • 2004

    Degree in Computer Science
    University of Novi Sad

Awards and Accomplishments

Courses (2021/2022)

Courses (2020/2021)

Publications12

Grants

The Skoltech1Million Entrepreneurial Challenge, (2020-2022) MIT - Skoltech Next Generation Program, budget: ~$500.000; Skoltech PL Zeljko Tekic, MIT PL Douglas Hart

Technological Domain Formation, MIT Skoltech Seed Fund; one-year project; MIT PI: Christopher L. Magee, Institute for Data, Systems and Society; Skoltech co-PI: Zeljko Tekic.

Extracting and Employing IP Constraints in the Space of Design Alternatives, MIT Skoltech Seed Fund; one-year project; MIT PI: Warren Seering, Department of Mechanical Engineering; Skoltech co-PI: Zeljko Tekic

Mastering innovation in Serbia through development and implementation of interdisciplinary post-graduate curricula in innovation management; Project No 544278-TEMPUS-1-2013-1-RS-TEMPUS-JPCR; European Commission; 2013-2016; TEMPUS; budget: 950.000 Euro; the author of the project and its coordinator (while at University of Novi Sad);

Fostering Students’ Entrepreneurship and Open Innovation in University-Industry Collaboration – Idea Lab; Project No 544373-TEMPUS-1-2013-1-RS-TEMPUS-JPHES; European Commission; 2013-2016; TEMPUS; budget: 900.000 Euro; the author of the project and project coordinator assistant (while at University of Novi Sad)

Friend to Understand, European Researchers' Night; Project No: 633396; European Commission; 2014-2015; Horizon 2020; budget: 90.000 Euro; the author of the project and its coordinator (while at University of Novi Sad).

Employment history

Current: Associate Professor, Graduate School of Business, HSE University, Moscow, Russia

2014 – 2021: Assistant Professor, Skolkovo Institute of Science and Technology, Moscow, Russia

2017 (Mar – June): Visiting Researcher, Massachusetts Institute of Technology, Cambridge, US; Department of Mechanical Engineering (host: Professor Warren Seering)

2015 (Sep – Oct): Visiting Assistant Professor Massachusetts Institute of Technology, Cambridge, US; Skoltech-MIT Initiative (host: Professor Douglas Hart); 

2013 – 2014: Assistant Professor, University of Novi Sad, Faculty of Technical Sciences, Department of Industrial Engineering and Engineering Management; Novi Sad, Serbia

2008 – 2010: Junior Engineer, RT-RK Institute for Computer Based Systems, Novi Sad, Serbia

2008 – 2013: Teaching Assistant, University of Novi Sad, Faculty of Technical Sciences, Department of Industrial Engineering and Engineering Management; Novi Sad, Serbia


Topics for potential PhD students

Priority topics (please, read details below):

  • Topic 1: Managing innovation in the era of Artificial Intelligence (AI)
  • Topic 2: Data driven innovation and ecosystems

  • Topic 3: Google Trends data for analyzing startups

 

Topic 1: Managing innovation in the era of Artificial Intelligence (AI)

AI is fundamentally changing the way companies work – how they operate and how they compete (Lakhani and Iansiti, 2020). At the same time, AI is challenging and transforming the core axioms and assumptions underlying the innovation process and its management (Benner and Tushman, 2015; Cockburn et al., 2018; Haefner et al., 2021; Nambisan et al., 2017, Verganti et al., 2020). 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 on how companies should staff, organize, and strategize their innovation in the era of AI is still sparse, and managers are still struggling to find the most appropriate approach for applying AI in their innovation efforts. In parallel, a growing number of scholars sees existing theories of innovation uncoupled from the phenomena and the complex context within which it emerges (Benner & Tushman, 2015; Greenstein et al., 2013; Nambisan et al., 2017; Nylén & Holmström, 2015; Yoo et al., 2012), calling for new and alternative conceptualizations of innovation management (Nambisan et al., 2017).

Starting from overall research question How is AI changing the way companies manage their innovation process? we are interested in understanding how innovation management changes and how companies should staff, organize, and strategize to profit from that change. Many research perspectives and questions are open. For example:

  • How AI may be seen and considered in the innovation context? What generic new technology affordances are enabled by AI and how do they influence innovation trajectories and outcomes?
  • How innovation managers evaluate the future importance of AI applications at various innovation tasks along the idea-to-launch process?
  • What are preferred implementation patterns of AI-based innovation management relative to organizational context such as company size, degree of maturity, skill sets, and AI affinity?
  • What are different “recipes” for successful AI innovation management, assuming that there is more than one pathway to it? What are best and worst practices? What are critical success factors?
  • What is impact of AI-based innovation management on innovation output?
  • What are the changes in strategy, structure, functions, workforce, alignment, processes, and control that flow from management of AI?
  • What are AI-enabled changes to business strategy, business models, and value creation processes?

For full overview of relevant questions, please see the above cited papers and recent special issues of, for example: MISQ (Sep 2021), Technological Forecasting and Social Change (work in progress), Journal of Business Research (Jan 2021), California Management Review (Dec 2019) and Business Horizons (Mar 2020).

Supervisor: Associate Professor Zeljko Tekic, in collaboration with Prof. Dr. Johann Füller (University of Innsbruck and HYVE AG). Opportunities for research visits to Munich / Innsbruck (Professor Johann Füller).

Candidates are expected to have strong interest in innovation management and basic understanding how AI and machine learning “work”. Mathematical / quantitative background and programming skills (e.g., Python and/or R) are an advantage. This research may be ideal for candidates with strong (business) informatics / information systems backgrounds.

 

Topic 2: Data driven innovation and ecosystems

As AI-based innovations depend on having access not just to the underlying invention (e.g., algorithm), but also to large and granular datasets from activities and behaviors of interest, AI puts data into innovation equation, next to invention and exploitation. Thus, instead of Innovation = Invention + Exploitation (Roberts, 1988), in data-driven context innovation should be defined as: Innovation = Invention + Data + Exploitation.

Starting from this change, we are interested in understanding how innovation and technology management, are changing with the increasing implementation of data-driven practices, and how companies should staff, organize, and strategize to profit from that change. Many research perspectives and questions are open. For example:

  • What is the role of data in AI-based innovation? And how does it change innovation management and understanding of innovation?
  • Who owns the data and how the need for data access / ownership influences patterns of collaboration? What is the role of open innovation and ecosystems in that change? What are the relevant ecosystems for designing data-driven innovation?
  • Which organizational forms do support value creation from data in a better way? How do organizations transform with data-informed decision-making?
  • What are the different strategies to create value from data? And how do different organizations have to approach them to leverage data efficiently?

For full overview of relevant questions please see, for example, the latest call for papers in Technovation: www.journals.elsevier.com/technovation/call-for-papers/beyond-the-data-fads-consequences-of-big-data-to-contemporar

Supervisor: Associate Professor Zeljko Tekic, in collaboration with Prof. Dr. Johann Füller (University of Innsbruck and HYVE AG). Opportunities for research visits to Munich / Innsbruck (Professor Johann Füller).

Candidates are expected to have interest in innovation management and strong background in business and management. Ideal candidate should have strong background in quantitative or qualitative research methods. Knowledge in programming, ideally, in Python and/or R is an advantage.   

 

Topic 3: Google Trends data for analyzing startups

Startups and high growth technology-based new ventures (TBNVs) do not have time, interest, or obligation to share much data about what they achieved, when, and how. Thus, for outside observers, startups are “black boxes” for which it is almost impossible to get enough objective information to assess their progress. In the recently published work, a new metric: Google Trends (GT) search-query big data is proposed and explored as a powerful source of high-quality data for analyzing growth trajectories of high potential technology-based new ventures emerged from startups. The results suggest that proposed approach may become what X-ray chamber is for studying the human body – cheap, easy, and non-invasive way to understand what is going on inside a technology-based new venture.

These findings open many research questions and opportunities.  For instance, TBNVs’ GT data may be valuable for a better understanding of marketing strategies, business models, and intellectual property management practices used in technology-based new ventures and their results. That especially may be the case in understanding, in practice frequently used term, like product-market fit or business model validation, which still lacks appropriate tools for a fuller explanation. In another example, the methodology of using GT data for analyzing the growth dynamics of a particular venture can be slightly modified and applied for growth prediction purposes. Since GT data is very comprehensive (time series can be presented even in the minutes scale) and since we demonstrated its correlation with companies’ valuation dynamics, we expect that it can serve as a basis for building company-related mathematical models of evolution and future growth.

For more info and the full overview of the topic, please see:

  • Malyy, M., Tekic, Z., & Podladchikova, T. (2021). The value of big data for analyzing growth dynamics of technology-based new ventures. Technological Forecasting and Social Change, 169, 120794.
  • Jun, S. P., Yoo, H. S., & Choi, S. (2018). Ten years of research change using Google Trends: From the perspective of big data utilizations and applications. Technological forecasting and social change, 130, 69-87.

Supervisor: Associate Professor Zeljko Tekic

Candidates are expected to have interest in innovation management and strong mathematical and analytical backgrounds. Ideally, candidates are fluent in Python and/or R. Besides the theoretical contribution, commercially attractive solutions may be developed that lay the basis for successful business.

 

Organization and Set-Up for all topics:

Expected duration: 3 years including coursework.

Expected outcome: at least 3 publications, of which at least 2 in Q1/Q2 journals, and at least one in the leading role, all covering topic of the PhD research

Place of work is Graduate School of Business, HSE University, Moscow.

In all cases students will work as a part of a team, within specific project. 

Those accepted into HSE’s PhD program will conduct research in challenging environment, under the supervision of international faculty in brand new facility at Shabolovka.

Next to available state and competitive stipends (from the HSE Graduate School of Business), students are eligible for (additional) project stipend up to 30k RUB per month, subject to performance and results.

Working language is English.

To apply, visit: https://aspirantura.hse.ru/management/about

For all questions mail to Associate Professor Zeljko Tekic, ztekic@hse.ru (it is highly recommended to contact the supervisor BEFORE applying for the position).

Timetable for today

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