Lappeenranta-Lahti University of Technology

Purpose This study examines how digital platforms (DPs) can drive social sustainability (SS) by influencing user behaviour in the context of sustainable mobility. While DPs have been widely discussed for economic and environmental outcomes, their role in fostering SS through user engagement remains underexplored. Design/methodology/approach A behavioural model was developed by integrating the Theory of Planned Behaviour (TPB), Technology Acceptance Model (TAM) and Equity Theory. This study used a time-lagged survey of 406 users of a DP for SS based in a major city in central in India, which incentivises responsible traffic behaviour. Confirmatory factor analysis was employed to validate the measurement model, and ridge regression was used to test the proposed hypotheses. Findings Attitude, captured through belief- and affect-based evaluations of social responsibility, along with perceived ease of use and effort fairness, significantly predicted intention to use the platform. Social norms were non-significant, suggesting that normative pressure plays a limited role in this mobility setting. Intention, in turn, predicted socially sustainable mobility behaviour. Overall, the results show that users respond more strongly to their own perceptions, usability and fairness than to social expectations, emphasising the personal agentic role of DPs in supporting socially sustainable practices. Research limitations/implications This study opens new pathways for platforms and sustainability research by demonstrating how user engagement can facilitate socially sustainable behaviours through incentivisation, It encourages scholars to expand behavioural models beyond traditional constructs by incorporating perceived fairness, emotional alignment and usability in the digital context. Although limited to one platform and cultural setting, the findings provide a replicable model for exploring user behaviour in digital ecosystems. Future research should adopt longitudinal and cross-cultural designs to assess habit formation, test the framework in varied policy environments and examine how DPs can be co-designed with communities to embed SS into everyday mobility behaviours. Practical implications DPs aiming to promote socially sustainable mobility must go beyond superficial incentives. Designing user engagement strategies that align with individuals’ beliefs and emotions around social responsibility can strengthen behavioural intention. Platforms should ensure that rewards are perceived as fair and proportional to effort, and streamline ease of use to reduce participation barriers. Public–private collaborations can integrate such platforms into mobility governance, using data-driven, user-centred design to complement weak institutional enforcement. By embedding sustainability into everyday interactions, DPs can become powerful tools for shaping long-term, pro-social behaviours in transport and beyond. Social implications This study emphasises the role of DPs as enablers of socially sustainable behaviour. By linking individual actions to collective outcomes through incentives, platforms can foster safer, more equitable mobility systems. The findings highlight the importance of fairness, accessibility and emotional engagement in driving participation, suggesting that digital interventions can build civic responsibility from the ground up. As platforms increasingly mediate everyday life, their design choices hold significant social power, shaping norms, enabling inclusive participation and addressing systemic gaps in public governance and infrastructure through bottom-up engagement. Originality/value The study develops and tests a behavioural framework based on the TPB-TAM-Equity theory in the often-overlooked dimension of SS. The study incorporates the personal agentic role of DPs in influencing SS behaviour in the specific context of sustainable mobility. It goes on to identify drivers of SS behaviour and how designing DPs for SS can use these to address systemic social issues like road safety. The findings enrich platform, behavioural and sustainability literature, and point to practical strategies for DPs.

ABSTRACT Robot swarms can be deployed as moving surveillance systems, for instance, as mobile anti‐poaching systems for monitoring wildlife and detecting poaching activities. Since poachers have an interest in evading detection, robots are at risk of being hijacked and manipulated to behave antagonistically, for example, to prevent the correct surveillance of a group of animals in a certain area. While the detection of cyber‐attacks on robots is commonly considered in the context of network activity and communication, this work focuses on the detection of anomalies that manifest in the robots' physical motion behavior. It builds upon our previous work, where a contextual data‐driven anomaly detection method was proposed and applied to motions of a robot swarm monitoring a static area. Such data‐driven, unsupervised methods for detecting anomalies in the physical behavior of robots are a suitable way of evading the need to define behavioral signatures or rules but only use information on the behavior of “normal,” cooperating robots, and the method proposed earlier demonstrated a strong performance both for recognizing normal robots and for the detection of a single antagonistic robot within a swarm monitoring a static area. Motivated by anti‐poaching scenarios, this article goes beyond the earlier work by considering multiple antagonists and a dynamic scenario, where the robots monitor a continually changing area around a herd of animals and therefore do not converge to fixed, optimal positions as in a static scenario. The statistical evaluation shows that the anomaly detection method consistently yields a correct categorization of normal and antagonistic behavior, and that the method's performance is unaffected by an increased number of antagonistic robots in a swarm. Furthermore, the results indicate that, given the availability of a limited amount of new data, the method can be fine‐tuned to efficiently adapt to new settings.

Overfishing is a key barrier to sustainable fisheries management in South Asia. Focusing on Sri Lanka’s tuna sector, this article examines how global value chain dynamics interact with local socio-economic realities, national fisheries policy and institutional constraints, and regional governance mechanisms—and the gaps within them—to perpetuate unsustainable fishing practices. Our analysis provides four actionable insights for lead buyers, fishing communities, and policymakers, including aligning market incentives with sustainable fishing practices, empowering fishers as ocean stewards, integrating science-based management into national policy, and fostering regional cooperation. These insights offer pathways to promote a resilient blue economy and advance progress toward UN Sustainable Development Goal 14.

Abstract This paper presents the validation of a novel code for the dynamic analysis of flexible multibody systems, which leverages projective geometric algebra for the kinematic description of the system. The theoretical foundations of geometric algebra are introduced briefly. A closed-form solution for the interpolation of motion is derived within the proposed framework and a finite element formulation of geometrically exact beams is developed. The accuracy of this code is assessed by comparing its prediction with those of eight multibody dynamics codes developed by independent researchers. Results are presented for three benchmark problems: the flexible four-bar mechanism, the lateral buckling of a thin beam, and the stability of a rotating shaft. Excellent agreement is found between the predictions of the proposed code and the previously published results, confirming the accuracy of the PGA-based approach. These findings demonstrate the potential of geometric algebra as a robust and elegant basis for the analysis of complex multibody systems.

Abstract BACKGROUND Alkaline digestion offers a promising route for converting low‐grade cellulosic wastes into valuable hydroxy carboxylic acids (HAs). However, the feasibility of producing HAs at process scale, particularly the influence of reactor design on product yields, energy demand, and production costs, remains less explored. This study integrates a detailed kinetic model with process simulation to evaluate techno‐economic performance of three process options that differ in reactor configuration and operating temperature. RESULTS The batch reactor with a temperature profile (BR‐TP) showed the highest selectivity toward glucoisosaccharinic acid (GISA), reaching approximately 65% of total HAs at target temperature of 200 °C, whereas the isothermal batch (BR‐IsoT) and isothermal CSTR (CSTR‐IsoT) favored smaller hydroxy acids (SHA). The CSTR‐IsoT offered shorter residence times at moderate conversions but required dramatically longer times at high conversions; at 200 °C, achieving the target conversion required 38‐fold longer time compared with BR‐TP. Techno‐economic analysis identified BR‐TP‐200 with the lowest total production cost (TPC) at 0.532 €/kg of product. In contrast, CSTR‐IsoT‐200 exhibited the highest TPC (0.928 €/kg). Energy consumption varied from 3.93 kW/kg of product in the BR‐TP (lowest) to 5.1 kW/kg of product in the CSTR‐IsoT. Sensitivity analysis confirmed raw materials as the dominant contributor to cost variability, while utility price fluctuations had minimal effects. CONCLUSION Batch operation, particularly BR‐TP, delivers the most favorable trade‐off between selectivity, energy use, and production cost, confirming the feasibility of alkaline digestion for HAs production. Future studies should validate the model with real wastes and develop improved reactor and alkali‐recovery strategies for scale‐up. © 2026 The Author(s). Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).