Vol. 9, No. 4, December 2025
Editor-in-chief:
Prof. Johannes (Joost) Platje
Deputy Editor-in-chief
Prof. Kazim Baris Atici, Hacettepe University, Ankara, Turkey
Co-Editor:
Prof. Ali Emrouznejad, University of Surrey, United Kingdom
Dr. Wim Westerman, University of Groningen, The Netherlands
Vol. 9, No.4, December 2025, 7-28
Received: 18.06.2025, Revised: 21.07.2025, Revised: 14.08.2025, Revised: 01.10.2025, Accepted: 03.11.2025
Bulls or Bears: how does investor sentiment shape energy profit spreads?
Author: Naima BENTOUIR
University of Ain Temouchent, LMELSPM, AlgeriaE-mail: naima.bentouir@univ-temouchent.edu.dz
ORCID: http://orcid.org/0000-0002-7091-3839
Aim: The purpose of this study is to look into how investor sentiment affected the profit spreads of three significant oil and gas companies from 2014 to 2024: PB PLC, Exxon Mobil, and Chevron. The study investigates the relationship between investor sentiment and market performance, especially during times of global upheaval like the COVID-19 pandemic and the crisis in Russia and Ukraine.
Design / Research methods: The study evaluates the correlation between daily profit spreads and weekly investor sentiment indices using a Mixed Data Sampling (MIDAS) regression framework. Analysis is done on data from January 2014 to May 2024. To assess the MIDAS model’s performance and explanatory power, the results are contrasted with those of conventional linear regressions.
Conclusions / findings: The findings show a complicated and firm-specific relationship between profit spreads and investor sentiment. However, there are mixed positive and negative MIDAS regression coefficients for lagged sentiment. Because of its strong mean reversion and emphasis on long-term projects, Chevron exhibits little sensitivity. Although the direction and interpretation are still unclear, Exxon Mobil exhibits some notable sentiment effects. The oil and gas industry’s long-term orientation is reflected in PB PLC’s inconsistent sentiment effects. In general, it seems that investor sentiment has little effect on daily stock prices, but it might have a more subtle effect on profit spreads.
Originality / value of the article: By integrating high-frequency sentiment data into asset pricing models, this paper adds to the expanding body of research on behavioural finance. In a turbulent decade characterised by pandemics and war, it employs the MIDAS approach in a novel way to evaluate the sentiment-profitability relationships among three multinational oil companies
Keywords: Bulls, Bears, investors’ sentiment, profit spread, Impact, MIDAS.
JEL: C32, C58, G11, G12, G14, Q40.
Vol. 9, No.4, December 2025, 29-48
Received: 16.07.2025, Revised: 16.12.2025, Accepted: 28.12.2025
Paradigm of Artificial Intelligence Based on Conversion of Tacit to Explicit Knowledge
Author: Tadeusz GOSPODAREK
WSB Merito University in Wrocław, PolandE-mail: tadgospo@gmail.com
Aim: This paper proposes a novel paradigm of Artificial Intelligence (AI) grounded in the epistemological process of converting tacit knowledge into explicit knowledge. Drawing on the foundational philosophies of science, particularly the works of Popper, Kuhn, Lakatos, and Gospodarek, the study conceptualizes AI not merely as a computational tool but as a systemic method for epistemic transformation. The paradigm is structured as a Lakatosian Research Programme, with a clearly defined hard core asserting that AI enables the symbolic representation of internalized, experiential knowledge. Surrounding this core is a protective belt of auxiliary hypotheses derived from general systems theory, cybernetics, machine learning, and symbolic processing. The programme’s heuristics guide theoretical and technological advancements while preserving its epistemological foundation. By formalizing the tacit-to-explicit knowledge conversion, this paradigm repositions AI as a critical instrument for knowledge creation, management, and application in digital and socio-technical systems. This allows one to build measures and values of generative and language models, which is important from an economic point of view.
This research tries to clarify the framework of use AI models for converting tacit knowledge inside a learning data of neural network systems to explicit information requested by the asking. It is important for economic evaluation of AI systems where accuracy considered utility as a criterion.
Design / Research methods: Research programme in Lakatos’ sense and multidisciplinary heuristic related to the theory of systems.
Conclusions / findings: Artificial Intelligence should be understood not only as a technological artefact but as a systemic method for transforming tacit knowledge into explicit knowledge. The proposed AI paradigm adheres to the structure of a Kuhnian paradigm and a Lakatosian research programme. Its hard core is defined by the thesis that AI operationalizes the conversion of experiential, intuitive, or unconscious knowledge into symbolic, formalized, and actionable representations. Lakatosian protective belt as a dynamic epistemic layer. This AI paradigm offers a progressive problem-shift capacity by enabling novel ways of organizing, analyzing, and applying knowledge in digital and socio-technical environments. It also provides a coherent framework for developing AI systems that are more aligned with human cognitive and organizational processes.
Originality / value of the article: This paper introduces: new concepts of usefulness of AI systems, new definition of AI systems based on conversion of the knowledge, original conversion paradigm and research program in Lakatos sense. It is original conceptional heuristic based on philosophy of science in relation to economic usefulness of view AI systems.
Keywords: Conversion paradigm, Artificial Intelligence, Tacit knowledge, Model LLM, Definition of AI, Lakatos’ Programme, Accuracy Estimation of AI, Usefulness of AI Model, Epistemology of AI
JEL: C67, C18.
Vol. 9, No.4, December 2025, 49-68
Received: 06.07.2025, Revised: 18.09.2025, Accepted: 28.11.2025
A Privacy-Preserving Method for Longitudinal Participant Linkage in Web Surveys
Author: Rafał PALAK
Wrocław University of Science and Technology, PolandE-mail: rafal.palak@pwr.edu.pl
Aim: To enable longitudinal linkage in online panel surveys without collecting direct identifiers and while aligning with modern data-protection requirements
Design / Research methods: The article proposes a client-side protocol where participants create a reproducible secret from a self-chosen pseudonym and an ordered image sequence. The browser normalizes and cryptographically hashes these inputs to derive a short alphanumeric core code, adds a modulus-97 checksum for strict local validation, and the backend stores only a salted hash scoped to a specific study (form-family) context.
Conclusions / findings: This paper introduces a client-side protocol for generating anonymous yet linkable participant identifiers in web-based surveys by deriving a reproducible code from a user-chosen pseudonym and image sequence entirely in the browser, and by storing only a form-family–salted hash on the server for longitudinal linkage within a study. The design incorporates a checksum for strict client-side validation and is intended to reduce spurious identifiers caused by typographical errors; empirical validation of matching performance, usability, and security properties is left for future work.
Originality / value of the article: The work refines SGIC-style respondent-generated linkage by combining graphical secrets with browser-based cryptographic processing, checksum-based client-side validation, and form-family salting-yielding a concrete, implementable algorithm that improves privacy-respecting longitudinal linkage.
Keywords: longitudinal survey methodology, anonymous respondent linkage, self-generated identification codes (SGIC), data privacy in empirical research
JEL: C81, C83.
