The present study focused on the development, optimization, and performance evaluation of a harvesting robot for heavyweight agricultural products. The main objective of developing this system is to improve the harvesting process of the mentioned crops. The pumpkin was selected as a heavyweight target crop for this study. The main components of the robot consist of mobile platforms (the main robot tractor and a parallel robot tractor), a manipulation system and its end-effector, and an integrated control unit. The development procedure was divided into four stages: stage I (designed system using Solidworks), stage II (installation of the developed system on a temporary platform), stage III (developed system on an RT-1 (Yanmar EG453)), and stage IV (developed system on an RT-2 (Yanmar YT5113)). Various indicators related to the performance of the robot were evaluated. The accuracy of 5.8 and 4.78 mm in x and y directions and repeatability of 5.11 mm were observed. The harvesting success rate of 87~92%, and damage rate of 5% resulted in the evaluation of the final version. The average cycle time was 35.1 s, 42.6 s, and 43.2 s for stages II, III, and IV, respectively. The performance evaluations showed that the system’s indicators are good enough to harvest big-sized and heavy-weighted crops. Development of the unique and unified system, including a mobile platform, a manipulation system, an end-effector, and an integrated algorithm, completed the targeted harvesting process appropriately. The system can increase the speed and improve the harvesting process because it can work all day long, has a precise robotic manipulation and end-effector, and a programmable controlling system that can work autonomously.

In our study, we analyse data from the Hungarian Microcensus (2016) in order to map the proportion of Hungarian jobs threatened by the spread of automation. In doing so, we use the internationally well-known methodology of Carl Benedict Frey and Michael A. Osborne who estimated the probability of computerization for 702 occupations. The analysis was then repeated by Panarinen and Rouvinen for the Finnish labour market by converting the probabilities defined for the US occupational statistics to the European International Standard Classification of Occupations. Similar calculations were conducted for the Swedish and Norwegian labour markets. According to our results, almost every second Hungarian employee (44%) works in a job that is threatened by the development of digital technologies. The same ratio is 47% in the US and 53% in Sweden, while it is much lower in Finland (35%) and Norway (33%). It is especially alarming that 13% of the Hungarian workforce (i.e., almost 600,000 employees) works in an occupation where the probability of computerization is above 95%, while the number of those working in occupations where the same ratio is above 90% exceeds one million (i.e., 25% of the total Hungarian labour force). Diving deeper into the analysis, we can state that those with higher educational qualifications are more likely to work in an occupation that is more protected against computerization. Overall, there are no significant differences in the probability of computerization by gender; however, women are over-represented in the most endangered occupations.

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.