Leaders and managers frequently face the need to make highly complex decisions with incomplete or fragmented information. Traditional decision support systems largely emphasize the visualization of data but often fall short in producing context-sensitive insights that can directly inform decision-making. This paper examines how narrative techniques combined with machine learning can strengthen communication across organizational hierarchies, particularly by improving the transfer of tacit expertise and contextual knowledge. To explore this, a transdisciplinary literature review was conducted using articles published within the last five years from databases such as Scopus, Web of Science, and ScienceDirect. The review highlights that narrative-driven reporting has been most commonly applied in fields such as accounting and sustainability, where expert interpretation replaces purely numerical summaries with more meaningful analytical explanations. Such approaches can also embed sentiment and personalization, commonly referred to as Narrative Disclosure Tone. Building on this foundation, the study investigates how Artificial Intelligence-driven decision support can formally integrate narrative elements to enhance report clarity, usability, and strategic relevance. Findings suggest that combining machine learning with expert-driven narrative reporting supports more innovative decision support systems and facilitates the alignment of tacit knowledge with data-driven insights.

Algorithmic management (AM) is spreading rapidly in platform work and traditional employment, yet empirical research remains platform-centric and skewed towards Western Europe, North America and China, leaving Central and Eastern Europe, especially Hungary, largely absent. This paper reports qualitative fieldwork on AM in two contrasting Hungarian firms, Data Analytics, a knowledge-intensive business services company, and ConLog, a contract logistics provider. Fieldwork in the EU-funded INCODING project (January 2022–July 2023) combined 15 semi-structured interviews, twelve internal and three external, with participant observation and documentary analysis. A sample limitation applies to one of the two firms: the Data Analytics evidence rests on only four internal interviews (two employees and two managers), so the findings for that case are indicative and exploratory rather than firm-level conclusions. A deliberately asymmetric comparison sets these findings against Hungarian platform labor, using secondary evidence on Uber, Wolt and Bolt from the CrowdWork21 project (2019–2021). Results show heterogeneity. In traditional firms, algorithmic systems sit within managerial hierarchies and produce hybrid human-algorithmic control. The older platform evidence documents real-time monitoring, opaque decision rules and market-mediated feedback that tighten control and erode autonomy. The contrast is a proposition for testing, not a measured difference. Workers in both settings adapt opportunistically, accommodating or partially resisting control. Hungary’s weak industrial relations institutions remain theoretically instructive despite limited generalizability. The contribution lies in the platform versus non-platform comparison, the institutional-bypassing argument and the design authority continuum.

Algorithmic management (AM) has become a key lens for analyzing the digital transformation of work, yet empirical research remains centered on platform labor in North America, Western Europe, and China. This paper applies the five-principles framework covering organizational form, object of management, ideology, modality, and accountability in an exploratory comparison of contrasting organizational settings within one country, Hungary. We define AM as the exercise of managerial functions such as direction, allocation, evaluation, discipline, and remuneration through software systems that continuously capture worker data, process them automatically, and feed the resulting decisions back into the labor process. The analysis draws on a base of 43 semi-structured interviews conducted between 2019 and 2024, all of which, drawn from the five interview-based cases (Wolt, Bolt, Upwork, Data Analytics and ConLog), constitute the coded analytical corpus, together with 21 h of participant observation and documentary evidence, contrasting four platform cases (Wolt, Bolt, Uber, and the freelance marketplace Upwork) with two traditional organizations, a Data Analytics company and a multinational logistics subsidiary. Evidence on Uber is confined to documentary sources (regulatory, parliamentary, legal and media records); the case is therefore used as an institutional and regulatory comparator only, and no worker-level generalizations are drawn from it. Findings suggest that AM is neither homogeneous nor confined to platforms. Full co-optation, including twisted accountability and dissolved organizational boundaries, appears mainly in platform work, whereas AM in traditional firms operates within hierarchies and formal employment, producing constrained co-optation, bounded ideology, and partially re-anchored accountability. We propose the partial Möbius effect as a testable hypothesis rather than as an established theoretical result: it was derived inductively from two traditional cases only (Data Analytics and ConLog) and has not yet been tested against a third, independent case. Its scope, boundary conditions, and durability are left as questions for future comparative and longitudinal research.