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Attitudes and digital choices of society towards the green AI trend

Jelonek Dorota, Pawełoszek Ilona

Economics and Environment

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2026

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No. 1

art. no. 1160

EN

Purpose: This study investigates whether AI users are aware of its environmental impact and whether they are willing to change their behaviour or pay for more sustainable solutions. Methodology/Approach: A mixed-methods survey (N = 224) was conducted using a non-probability, snowball sampling technique. A composite Environmental Awareness Index (EAI) was constructed, combining cognitive, evaluative, and behavioural items. Findings: Over two-thirds of respondents expressed willingness to support greener AI financially. Higher awareness was significantly associated with willingness to pay. Open-ended responses revealed strategies for reducing AI’s footprint, including technical, behavioural, and regulatory solutions. Research limitations/implications: The study relied on self-reported data and non-random sampling, limiting generalizability. Practical implications: The findings support the need for transparency in AI's environmental cost and promotion of sustainable design. Social implications: Educating users about AI’s ecological impact can foster more responsible digital behaviours. Originality/value: This is one of the first studies to explore public attitudes toward “green AI” and link awareness to behavioural intent.

PL

Cel: Celem badania było określenie, czy użytkownicy AI są świadomi jej wpływu na środowisko oraz czy są skłonni ograniczyć jej użycie lub zapłacić za bardziej ekologiczną wersję. Metodyka: Przeprowadzono badanie ankietowe (N = 224) z wykorzystaniem próby śnieżnej. Na podstawie odpowiedzi skonstruowano złożony wskaźnik świadomości ekologicznej (EAI), który łączył komponenty poznawcze, oceniające i behawioralne. Wyniki: Ponad dwie trzecie respondentów zadeklarowało gotowość do wspierania „zielonej AI” finansowo. Wyższa świadomość była istotnie związana z gotowością do zapłaty. Odpowiedzi otwarte ujawniły różnorodne propozycje ograniczenia wpływu AI na środowisko. Ograniczenia: Zastosowano niereprezentatywną próbę oraz dane samoopisowe. Implikacje praktyczne i społeczne: Wyniki podkreślają potrzebę transparentności kosztów środowiskowych AI oraz edukacji użytkowników. Oryginalność: Jest to jedno z pierwszych badań łączących świadomość ekologiczną z intencją prośrodowiskowego działania w kontekście technologii AI.

2

Artificial intelligence applications in brand management

Jelonek Dorota, Kumar Narendra, Pawełoszek Ilona

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2024

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z. 202

153--170

EN

Purpose: This research explores the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in brand management. It aims to understand how AI technologies can optimize brand awareness, enhance personalized communication, and improve brand equity measurement, thereby redefining brand management strategies in the digital era. Design/methodology/approach: A systematic literature review was conducted, analyzing research studies published between 2014 and 2024. The methodology focused on articles that delve into the integration of AI and ML within brand management contexts. This approach was chosen to consolidate and synthesize a broad spectrum of findings on AI’s impact on brand management practices. Findings: The review identified significant enhancements in brand management facilitated by AI, particularly in consumer engagement and customization of consumer interactions through data-driven insights. AI's capability to analyze big data has enabled more precise consumer segmentation and targeting, thereby influencing brand loyalty and overall brand equity positively. Research limitations/implications: The primary limitation of this study is its reliance on data from articles indexed only in Scopus, potentially omitting relevant studies published in other languages or databases. Future research should expand the scope to include these sources and explore empirical validations of the proposed theoretical impacts of AI on brand management. Practical implications: This research highlights the practical applications of AI in improving brand management strategies, offering insights into effective AI integration into marketing practices. Businesses are encouraged to adopt AI-driven tools for better market segmentation, consumer behavior predictions, and enhanced customer relationship management. Social implications: The findings could influence public attitudes towards brand interaction by fostering greater acceptance of AI in consumer relations. The research supports enhanced corporate social responsibility through AI's ability to provide more targeted and meaningful consumer interactions. Originality/value: This paper contributes to the academic and practical understanding of AI’s role in brand management by synthesizing existing research and identifying future research directions. It is valuable to academicians, marketing professionals, and policymakers interested in the implications of AI technology in brand strategies.

3

Trip planning mobile application: a perspective case study of user experience

Pawełoszek Ilona, Wieczorkowski Jędrzej

Engineering Management in Production and Services

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2023

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Vol. 15, no. 2

55--71

EN

Mobile trip planning applications may contribute to popularising public transport, provided they work efficiently and gain high user acceptance. This article aims to take a closer look at the functioning of the JakDojade application, which has been the most popular platform in Poland for several years, supporting travel planning by public transport. In the presented case study, the authors tried to diagnose problems and indicate the directions of application development. At the same time, through this analysis, the authors aimed to demonstrate the usefulness of researching user comments from the viewpoint of managing the development of mobile applications and related services. A case study methodology was used to perform a descriptive study. Data on user feedback on JakDojade mobile application in Poland comes from Google Play Store. Semantic categorisation of user comments and sentiment analysis allowed for identifying user problems and diagnosing emotions related to its use. The presented methodology allowed for diagnosing typical user problems for the JakDojade application, which may help indicate further development directions. The authors attempted to demonstrate the usefulness of researching user comments from the point of view of managing the development of mobile applications and related services. The semi-automatic approach to text analysis presented in the article highlights the problems related to the study of user reviews. The limitations of the proposed methodology and the possibilities for its improvement were indicated.