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Текич Желько

Высшая школа бизнеса

Профиль на hse.ru ↗ тел.: +7 (495) 772-95-90 | 26111
Публикаций
29
Языков
2
Наград
5
Конференций
0
Профиль Публикации (29) Курсы (12)

Должности

  • Профессор — Высшая школа бизнеса, Департамент бизнес-информатики

Био

  • · Начал работать в НИУ ВШЭ в 2021 году.
  • · Научно-педагогический стаж: 5 лет.

Образование

  • 2013 · PhD: Нови-Садский университет
  • 2008 · Магистратура: Нови-Садский университет, специальность «Промышленный инжиниринг и инженерный менеджмент», квалификация «Магистр»
  • 2007 · Магистратура: Ноттингемский университет, специальность «Электрическая и электронная техника и предпринимательство», квалификация «Магистр»
  • 2004 · Специалитет: Нови-Садский университет, специальность «Компьютерные науки», квалификация «Инженер-электрофизик»

Опыт работы

  • · В настоящее время: Доцент департамента бизнес-информатики Высшей школы бизнеса НИУ ВШЭ
  • · 2014 – 2021: Доцент, Сколковский институт науки и технологий
  • · 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

Награды и поощрения

  • · Благодарственное письмо ректора НИУ ВШЭ (июнь 2025)
  • · Благодарность Высшей школы бизнеса НИУ ВШЭ (август 2024)
  • · Надбавка за публикацию в журнале из Списка А (и приравненном к нему научном издании) (2025–2026, 2024–2025, 2023–2024)
  • · Надбавка за публикацию в международном рецензируемом научном издании (2022–2023)
  • · Лучший преподаватель — 2025, 2021

Гранты и проекты

  • — · на соискание учёной степени кандидата наук

Идентификаторы исследователя

Публикации (29)

Complex patterns of ICTs' effect on sustainable development at the national level: The triple bottom line perspective

2024 · ARTICLE · en

Building upon the complexity theory, we explore the interaction effects of the development of key digital information and communication technologies (ICTs) at the national level on a country's sustainability performance. By the means of the fuzzy-set Qualitative Comparative Analysis on a sample of 77 countries, we show that 1) a country's ICT profile matters for national sustainability performance, not a single ICT element; 2) national sustainability performance is not (only) about the level of ICT development of a country, but about composition and a quality of its ICT profile; 3) there are multiple distinctive ICT profiles that may lead to high or low sustainability performance (social, economic or environmental) at the national level; and 4) environmental sustainability may have conflicting ICT-related antecedents in comparison to social and economic sustainability. These results offer two primary contributions to research focused on the role of ICTs in supporting (or deterring) sustainable development. First, moving away from simplified perspective of a country's ICT development by focusing on a country's ICT profile allows us to distinguish effects (both positive and negative) of different ICT elements, as well as to take synergies brought by technological convergences into account. This allows us to capture effects of ICT elements in their totality, avoiding to lose richness and comprehensiveness. Second, by focusing on the triple-bottom line sustainability, we add to the growing evidence that environmental-focused goals often conflict with those related to social and economic aspects of sustainable development.

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Market-first or technology-first? Exploring unicorns’ pathways to extreme valuations

2024 · ARTICLE · en

In this study, we explore: 1) whether unicorns predominantly emerge from a focus on developing novel technologies, understanding market needs, or a balanced integration of both; 2) how these three strategic orientations correlate with unicorns’ growth rates; and 3) how various unicorn features, including early access to funding, moderate answers to the first two questions. To address these questions, we hand-collect data on 137 unicorns and examine the timing of their first patent and trademark filings, initial institutional funding, and company characteristics. Our findings indicate that all three strategic orientations – technology-first, market-first, and balanced orientation – can lead to extreme valuations. Specifically, the market-first approach emerges as the most common, with the balanced orientation most frequently associated with high growth, and the technology-first approach linked with the highest growth. Additionally, unicorns receiving seed funding before filing their first patents or trademarks tend to exhibit significantly higher median growth rates compared to those filing for intellectual property rights before securing seed funding. This study contributes to the nascent literature on unicorns, and established literature streams on strategic orientation and the value of patents, trademarks, and initial funding as signals of unobserved quality for startups.

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Rethinking Innovation Management—How AI Is Changing the Way We Innovate

2024 · ARTICLE · en

This paper examines the transformative impact of Artificial Intelligence (AI) on innovation management, highlighting the profound organizational shifts necessary to fully leverage its potential. As AI adoption surges, organizations face unprecedented opportunities to accelerate their innovation processes, from early trend identification to product diffusion. However, realizing these benefits requires a comprehensive strategic alignment. This includes rethinking innovation strategy, restructuring organizational set-up, redefining roles, and collaborative practices. By examining these pivotal aspects, this paper identifies critical knowledge gaps and further poses thought-provoking questions designed to stimulate both academic inquiry and managerial innovation. Our goal is to inspire researchers and managers to rethink their approach to innovation management in an AI-driven landscape.

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Managing innovation in the era of AI

2023 · ARTICLE · en

This paper conceptualizes how artificial intelligence (AI) may impact the way companies innovate and manage their innovation process. A research framework we use in investigation builds upon three pillars – data, new tech, and talent. Based on it, we map and discuss changes for organizations applying AI in innovation management. We conceptualize innovation management in the era of AI as a data-driven process in which AI significantly affects all dimensions of the innovation process and its management. Further, our framework suggests that the need for data, technology, and talents will lead to more open and collaborative innovation approaches, novel strategies for innovation protection, and the emergence of new roles in innovation teams. Using AI for innovation management also creates challenges like ethical data usage, navigation through diversity emerging from humans collaborating with artificial intelligence, and escaping from the incremental innovation trap. We summarize our main conclusions as research propositions and outline their practical implications.

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How AI revolutionizes innovation management–Perceptions and implementation preferences of AI-based innovators

2022 · ARTICLE · en

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Evolution of project studies through the lens of engaged scholarship: A longitudinal bibliometric analysis

2022 · ARTICLE · en

Building on the debate on engaged scholarship in project studies, this article aims to explore the extent and potential of practitioner involvement in research on projects and thereby characterise the evolution of the field through the lens of engaged scholarship. We conduct a longitudinal bibliometric analysis of 6584 articles published on projects between 1964 and 2017 to capture the volume and citation impact of publications featuring practitioner involvement in comparison to purely academic publications. The analysis identifies distinct research production patterns, allowing us to delineate and characterise three evolutionary periods in project studies: projects as an execution methodology (1964-1989), projects as an organisational concept (1990-2001), and projects as a theoretical framework (from 2002). In this way, the article enriches the ongoing debate about engaged scholarship in project studies, and discusses the endemic challenges, as well as unused potential, of actively involving practitioners in the production of research on projects.

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Culture as antecedent of national innovation performance: Evidence from neo-configurational perspective

2021 · ARTICLE · en

As an exogenous antecedent of national innovation performance, culture has been receiving significant attention in cross-cultural research. However, relying primarily on Hofstede’s framework of national culture, this research has so far been predominantly inclined to treating culture as a collection of independent dimensions, thereby ignoring the complex notion of culture profiles that refer to distinctive patterns of interrelated dimensions, which cannot be considered in isolation, but only in combination. Employing the lens of neo-configurational theory and with the support of the fuzzy-set Qualitative Comparative Analysis (fsQCA), the present study aims to fill this gap by exploring how multiple Hofstede’s dimensions interact and combine to influence national innovation performance. In this way, this study goes beyond the existing theory and empirical evidence about the relationship between distinctive culture profiles and innovation performance at national level, while broadening our understanding more generally about how to conceptualize and operationalize culture in business research.

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The value of big data for analyzing growth dynamics of technology-based new ventures

2021 · ARTICLE · en

This study demonstrates that web-search traffic information, in particular, Google Trends data, is a credible novel source of high-quality and easy-to-access data for analyzing technology-based new ventures (TBNVs) growth trajectories. Utilizing the diverse sample of 241 US-based TBNVs, we comparatively analyze the relationship between companies’ evolution curves represented by search activity on the one hand and by valuations achieved through rounds of venture investments on another. The results suggest that TBNV's growth dynamics are positively and strongly correlated with its web search traffic across the sample. This correlation is more robust when a company is a) more successful (in terms of valuation achieved) – especially if it is a “unicorn”; b) consumer-oriented (i.e., b2c); and 3) develops products in the form of a digital platform. Further analysis based on fuzzy-set Qualitative Comparative Analysis (fsQCA) shows that for the most successful companies (“unicorns”) and consumer-oriented digital platforms (i.e., b2c digital platform companies) proposed approach may be extremely reliable, while for other high-growth TBNVs it is useful for analyzing their growth dynamics, albeit to a more limited degree. The proposed methodological approach opens a wide range of possibilities for analyzing, researching and predicting the growth of recently formed growth-oriented companies, in practice and academia.

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Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future

2021 · ARTICLE · en

We analyze how artificial intelligence changes a significant part of the energy sector, the oil and gas industry. We focus on the upstream segment as the most capital-intensive part of oil and gas and the segment of enormous uncertainties to tackle. Basing on the analysis of AI application possibilities and the review of existing applications, we outline the most recent trends in developing AI-based tools and identify their effects on accelerating and de-risking processes in the industry. We investigate AI approaches and algorithms, as well as the role and availability of data in the segment. Further, we discuss the main non-technical challenges that prevent the intensive application of artificial intelligence in the oil and gas industry, related to data, people, and new forms of collaboration. We also outline three possible scenarios of how artificial intelligence will develop in the oil and gas industry and how it may change it in the future (in 5, 10, and 20 years).

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Review of technology trends in new space missions using a patent analytics approach

2021 · ARTICLE · en

This review paper analyzes technology trends observed in New Space missions, using a patent analytics approach. The analysis is complemented with a literature review of the subjects identified. The main objective of this review is to draw a comprehensive picture of technology trends in New Space and to discuss potential scenarios for further development of this novel type of missions. Building on the existing scientific literature, we survey alternative definitions and then propose our own definition of New Space missions as synthesis of the ongoing debate in the field. We identified more than two hundred organizations active in the development of both upstream and downstream products and services for New Space missions. Using a commercially available patent analytics tool, we collected 933 patents protecting technologies disclosed by those organizations. We used the Latent Dirichlet Allocation (LDA), a topic modeling algorithm, to analyze patents' data and to identify the underlying structures in New Space's technology trends. Ten major topics have been identified respectively named “Remote sensing & image acquisition”, “Flying/launch systems”, “Telecommunication systems”, “Constellation management”, “Digital Processing Architectures”, “Image analysis”, “Manufacturing process & materials”, “Feature recognition and extraction”, “Antenna systems”, and “Space platforms”. A deeper analysis of patents' claims revealed a core cluster defined as “data cluster” including data analysis topics (Remote sensing & image acquisition, Image analysis, and Feature recognition and extraction), data transmission topics (Telecommunication systems and Antenna systems from both hardware and software perspective) and related enabling technologies such as constellation management. Based on this analysis, the evolution of data cluster related technologies is surveyed in order to review the current technology state of the art as well as identifying potential areas that will benefit the most from further technology developments to improve New Space missions’ performance. Lastly, we identify technology trends for future New Space development based on the insights obtained by the synthesis of our literature and patent survey. We discuss the key directions for future development emerging from our review, such as the use of commercial off the shelf components, the increasing use of miniaturized technology, and software defined technology.

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Курсы (12)