A strategic contingencies' theory of intraorganizational power is presented in which it is hypothesized that organizations, being systems of interdependent subunits, have a power distribution with its sources in the division of labor. The focus is shifted from the vertical-personalized concept of power in the literature to subunits as the units of analysis. The theory relates the power of a subunit to its coping with uncertainty, substitutability, and centrality, through the control of strategic contingencies for other dependent activities, the control resulting from a combination of these variables. Possible measures for these variables are suggested.
In light of the environmental consequences of linear production and consumption processes, the circular economy (CE) is gaining momentum as a concept and practice, promoting closed material cycles by focusing on multiple strategies from material recycling to product reuse, as well as rethinking production and consumption chains toward increased resource efficiency. Yet, by considering mainly cost‐effective opportunities within the realm of economic competitiveness, it stops short of grappling with the institutional and social predispositions necessary for societal transitions to a CE. The distinction of noncompetitive and not‐for‐profit activities remains to be addressed, along with other societal questions relating to labor conditions, wealth distribution, and governance systems. In this article, we recall some underlying biophysical aspects to explain the limits to current CE approaches. We examine the CE from a biophysical and social perspective to show that the concept lacks the social and institutional dimensions to address the current material and energy throughput in the economy. We show that reconsidering labor is essential to tackling the large share of dissipated material and energy flows that cannot be recovered economically. Institutional conditions have an essential role to play in setting the rules that differentiate profitable from nonprofitable activities. In this context, the social and solidarity economy, with its focus on equity with respect to labor and governance, provides an instructive and practical example that defies the constraints related to current institutional conditions and economic efficiency. We show how insights from the principles of the social and solidarity economy can contribute to the development of a CE by further defining who bears the costs of economic activities.
This study examines the determinants of technological innovation and its impact on firm labor productivity across six Latin American countries (Argentina, Chile, Colombia, Costa Rica, Panama, and Uruguay) using micro data from innovation surveys. In line with the literature, in all countries firms that invest in knowledge are more able to introduce new technological advances, and those that innovate have greater labor productivity than those that do not. Yet firm-level determinants of innovation investment are much more heterogeneous than in OECD countries. Cooperation, foreign ownership, and exporting increase the propensity to invest in innovation activities and encourage innovation investment in only half of the countries studied. Scientific and market sources of information have little or no impact on firm innovation efforts, which illustrates the weak linkages that characterize national innovation systems in those countries. The results in terms of productivity, however, highlight the importance of innovation in enabling firms to improve economic performance and catch up.
Erik Arvidsson, Anders Rowell, Linnea Hansson
et al.
In Finland and Scandinavia, even-aged forest management predominates, often including mechanical site preparation and manual planting. Growing labor shortages and increased demand for sustainability have driven interest in mechanized and autonomous planting systems. This study evaluates two automated Coverage Path Planners (CPP), Pathfinder and TerraTrail, developed to optimize planting routes for mechanized forest regeneration. Their performance is compared to the routes of the manually operated mechanized planting machine, PlantMax. Three operational sites in Sweden, representing varied terrain and hydrological conditions are evaluated. The evaluation focuses on coverage, Euclidean and Dubins path lengths. Both CPPs incorporate Digital Elevation Models (DEM), Depth-to-Water (DTW) maps and vehicle-specific kinematics to generate planting routes. Two scenarios are evaluated: one where the CPPs neglect the DTW map, and another where the CPPs are constrained to avoid DTW values below 0.3 m. Results show that automated CPPs achieve 15–19% higher coverage than manual planning on average. Pathfinder showed similar normalized path lengths in an unconstrained scenario as the manual operator, but 14% shorter in the constrained environment. TerraTrail shows 7% longer normalized path lengths in an unconstrained scenario, while the constrained scenario shows similar path lengths as the manual operator. These findings emphasize the potential of deploying automated CPP systems to enhance precision, sustainability, and labor efficiency of silvicultural operations. The CPPs support both autonomous deployment and decision support tool for operators. Further refinement, including combining both CPPs to leverage the best functions of each, along with reversible path planning, could enhance their value in forestry practices.
Abstract Predicting workplace conflicts before they escalate into formal disputes or collective action represents a persistent challenge in organizational management, one that traditional statistical methods and expert judgment systems address inadequately due to labor relations’ complex, multi-factorial nature. We present a deep learning-based early warning system that integrates heterogeneous organizational data—HR records, communication logs, performance trajectories, satisfaction surveys, and external economic indicators—through attention-based multi-modal fusion, explicitly designed to capture both structural vulnerabilities and evolving behavioral dynamics that precede conflict emergence. The architecture combines MLP processing of cross-sectional organizational characteristics with LSTM modeling of temporal patterns, learning to weight modality contributions adaptively rather than applying fixed fusion schemes. Rigorous evaluation across twelve enterprise deployments spanning four industries demonstrates 89.2% prediction accuracy, substantially exceeding modern gradient boosting baselines (XGBoost: 83.4%, LightGBM: 84.1%) and recent tabular deep learning architectures (FT-Transformer: 86.1%), with ablation studies confirming that multi-modal integration contributes 4.5–12.8% performance gains beyond any single data source. Real-world deployment achieved 87.3% early warning success rates with 5–21 day lead times enabling proactive intervention, though 12.7% false positive rates and systematic errors during high-stress operational periods reveal important limitations. We provide detailed deployment statistics, case studies illustrating practical value and failure modes, validation on public benchmarks for reproducibility, and extensive discussion of ethical considerations surrounding workplace prediction algorithms, contributing methodological foundations for organizational risk assessment through multi-source data fusion while identifying clear directions for future enhancement.
Brain oscillations are one of the core mechanisms underlying episodic memory. However, while some studies highlight the role of synchronized oscillatory activity, others highlight the role of desynchronized activity. We here describe a framework to resolve this conundrum and integrate these two opposing oscillatory behaviors. Specifically, we argue that the synchronization and desynchronization reflect a division of labor between a hippocampal and a neocortical system, respectively. We describe a novel oscillatory framework that integrates synchronization and desynchronization mechanisms to explain how the two systems interact in the service of episodic memory.
This study presents a knowledge graph construction scheme leveraging large language models (LLMs) for task-oriented semantic communication systems. The proposed methodology systematically addresses four critical stages: corpus collection, entity extraction and relationship analysis, knowledge base generation, and dynamic updating mechanisms. It is worth noting that prompt engineering is combined with few-shot learning to enhance reliability and accuracy in this methodology. Experimental demonstration showed that this methodology had superior entity extraction performance, achieving 89.7% precision and 92.3% recall rate. This scheme overcomes the demand for domain knowledge and the labor cost of traditional knowledge base construction schemes. It greatly improves the construction efficiency of knowledge graphs. This paper provides an efficient and reliable task knowledge base construction scheme for task-oriented semantic communication, which is expected to promote its wider application.
Risk management and resilience of agriculture are among the most important issues in the ongoing discussion on the shape of the Common Agricultural Policy (CAP). Farming systems face various risks that increase their vulnerability, which necessitates the strengthening of their resilience. This raises critical questions whether CAP policies adequately support the resilience of farming systems in addressing these challenges. The study investigates the resilience of the Polish fruit and vegetable farming system within the context of the CAP. Employing a mixed-methods approach that includes interviews and stakeholder workshops, the research identifies critical risks such as market volatility, climate change, labor shortages, or international competition. The study reveals that while farmers adopt various coping strategies, existing CAP measures predominantly support robustness, often neglecting adaptability and transformability, which are essential for addressing long-term risks. Stakeholder feedback highlights bureaucratic inefficiencies, limited access to resources for innovation, and an overemphasis on short-term interventions. Recommendations emphasize the need for policy adjustments to foster long-term adaptability through enhanced vertical and horizontal integration, support for innovation, and knowledge transfer. Under future scenarios, policy priorities vary but consistently call for resilience-focused reforms. These findings underscore the benefits of integrating resilience-thinking frameworks into agricultural policy to enable sustainable development and competitiveness of farming systems.
This paper addresses the issue of how intermittent work performances - now increasingly enabled by new technologies - affect remuneration, and how such work contributes to in-work poverty. The analysis takes platform-based work as a model, considering it a prototype of a productive organisation relying on intermittent performances. It begins by examining whether there are legal mechanisms that allow for periods of inactivity between tasks to be taken into account when calculating remuneration. This part of the analysis first focuses on hetero-organised work, with reference to both the rules established by law under Article 2, paragraph 1, of Legislative Decree 81/2015, and the provisions of national collective labour agreements under Article 2, paragraph 2, of the same Decree. It then turns to the concept of intermittent subordinate employment as regulated by Articles 13 et seq. of Legislative Decree 81/2015. The paper concludes that the current legal framework is inadequate to ensure that workers performing intermittent tasks receive remuneration that is nonetheless adequate under Article 36 of the Constitution, and puts forward several corrective proposals.
S. Taugourdeau, S. Taugourdeau, M. Machdoud
et al.
This position article advocates for the creation of a participatory pastoral observatory, leveraging accessible technologies, including smartphones, to monitor rangeland ecosystems. Rather than reiterating the already accepted need for monitoring, we focus on how technological progress, ranging from ground-based field plots to satellite imagery, UAVs, and smartphones using Structure from Motion (SfM) methods, has transformed rangeland observation. We argue that an imagery-based community monitoring system can provide accurate, relevant, and timely data while empowering local stakeholders and informing policy decisions. We detail the operational steps of smartphone-based observatory, highlight its capacity to reduce labor-intensive biomass sampling, and discuss its feasibility when applied by pastoralists. We also draw lessons from related participatory approaches in Mongolia, Ireland, and East Africa. By integrating traditional ecological knowledge with scientific approaches, this initiative can strengthen the resilience of pastoral systems, support sustainable management practices, and contribute to evidence-based policymaking. The proposed observation framework builds on existing research and technological innovations to promote a decentralized and inclusive monitoring system. We imagine such a type of observatory could be useful in Sahel Region or in Northern Africa, could describe practical challenges (smartphone penetration, network coverage, training for low-literacy users), and outline next steps for implementation.
Rusman Rusni Fitri Y., Salman Darmawan, Munir Abdul Razak
et al.
The poultry industry has faced two significant challenges in the last 4 years: the coronavirus disease (COVID-19) pandemic and El Niño drought, which pose significant business risk. This study aims to fill this knowledge gap by conducting a comparative analysis of the vulnerability of poultry farms to COVID-19 and El Niño events and identifying potential mitigation strategies to reduce their impacts. This study was conducted using a qualitative approach and case study methodology on two different types of farms: broiler and layer. Data were collected through semi-structured and in-depth interviews, observations, and document analyses of 36 farmers and agri-food companies. The results showed that both types of farms were more vulnerable during the COVID-19 pandemic than during the El Niño drought period. However, based on farm characteristics, layer farms using independent systems were more vulnerable to both events than broiler farms using contract systems. The COVID-19 pandemic has resulted in several economic vulnerabilities, including fluctuations in the prices of feed and poultry products, reduction in labor, and the reallocation of investment funds. In contrast, El Niño droughts pose different challenges, including feed scarcity and the need to develop poultry breeds that can withstand extreme weather conditions. Different mitigation strategies are recommended for these two types of farms facing different disasters: the new disaster due to the current pandemic and decades-old climate change disasters, such as drought. Developing specific mitigation strategies based on disaster types and farm characteristics, such as improving reserve funds and market strategies, government-provided financial assistance, biosecurity measures, strengthening relationships with farmers and companies, using family labor, diversifying feed sources, and adopting climate-resilient housing, can provide practical solutions to reduce vulnerability and enhance the economic resilience of both broiler and layer farms in future crises.