23 March 2026

Boosting self-efficacy to cope with workplace social undermining

A study of more than 500 employees in the fast-moving consumer goods sector has demonstrated how employers might mitigate social undermining in the workplace. Social undermining is a pattern of behaviour in which colleagues or supervisors hinder an individual’s performance or professional relationships. This might include withholding critical information, spreading rumours, or criticising colleagues in a public setting. Unlike overt harassment, such actions are often subtle and cumulative, gradually weakening an employee’s capacity to function effectively within a team.

Social undermining leads to stress, anxiety, and burnout. Such problems are not only detrimental to the employee being targeted but are also linked to reduced productivity and higher staff turnover within an organisation.

The research looks at self-efficacy, an individual’s belief in their own abilities. The team found that self-efficacy acts as a psychological buffer so that those who have greater self-efficacy are less likely to succumb to the effects of social undermining. The work also found that hostility from supervisors had a more pronounced emotional impact than similar actions by peers, but strong self-efficacy could buffer targeted individuals even more effectively in such situations.

Fundamentally, employees with greater confidence in their abilities were more likely to interpret negativity from supervisors as a challenge to be managed rather than as evidence of personal failure. This personal reframing of issues reduces the psychological toll of that kind of interaction for those individuals.

In contrast, negativity from peers affects social standing and workplace relationships, making it more difficult for even those with the greatest level of self-efficacy to cope with such issues. In these cases, the harm is less about task performance and more about belonging and reputation within a group.

The findings suggest that employers might address toxic behaviour in the workplace by strengthening how well their employees can cope given that some degree of interpersonal conflict is inevitable in any organisation and might not always be something that can be stopped directly. By promoting the development of personal resources and self-efficacy, they may have a more practical way to intervene without recourse to disciplinary approaches.

Tosun, B., Güner Kibaroğlu, G. and Basim, H.N. (2026) ‘Self-efficacy as the saviour: defending psychological well-being against the destructive power of social undermining’, Middle East J. Management, Vol. 13, No. 2, pp.137–159.

Free Open Access issue published by International Journal of Economics and Business Research

The International Journal of Economics and Business Research has published an Open Access issue. All of the issue’s papers can be downloaded via the full-text links available here.
  • Credit control and firm performance: evidence from Vietnam's listed real estate sector with firm size and corporate governance quality as moderators
  • Modelling entrepreneurial passion, self-leadership and resilience as drivers of innovative behaviour among SMEs: a social cognitive theory perspective from Indonesia
  • Expert-elicited weights in multi-criteria decision analysis: theory and practice

Free sample articles newly available from International Journal of Information Technology and Management

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  • Blockchain governance: reducing trusted third parties with Decred project
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  • AdaptTrans: a complexity-aware iterative optimisation framework for efficient code translation
  • Intelligent physical health testing method integrating Fast-OpenPose and BlazePose
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20 March 2026

Research pick: Dodging the distro inferno - "Fire warning of lithium battery energy storage power stations for environmental sustainable development"

A new fire detection system designed for lithium battery energy storage facilities described in the International Journal of Environmental Technology and Management could improve safety in the renewable energy sector.

Electricity generation that uses intermittent energy sources, such as wind and solar, relies on large-scale rechargeable batteries for storage. Unfortunately, a phenomenon known as thermal runaway is a well-known issue with lithium batteries. It refers to the feedback that occurs when battery temperature rises, triggering chemical reactions that generate further heat and so on. Thermal runaway can lead to catastrophic fire or explosion, causing damage to infrastructure and releasing hazardous substances, including toxic gases and heavy metals, into the surrounding environment.

The new approach discussed in IJETM addresses the risk through a more responsive and reliable method of fire detection. It uses a combination of sensors to monitor key indicators of potential failure, including temperature changes, smoke levels and the presence of carbon monoxide.

The system integrates these multiple data streams using a mathematical approach known as Dempster–Shafer evidence theory instead of using a single measurement. The framework works with uncertain or incomplete information from different sources and so can make reliable judgements on whether the system is stable or on the verge of catastrophic failure. In so doing, it reduces the number of false alarms and improves detection of genuine fire risk. The processing unit analyses the data in real time and can trigger an alarm and response within two seconds with over 95 per cent accuracy. Both response time and accuracy improve on earlier systems.

The same multi-factorial approach might be used in other sectors that rely on interconnected, sensor-driven technologies, including industrial safety monitoring, transportation networks, and urban infrastructure, where early detection of anomalies can prevent accidents and improve efficiency.

Deng, D.L. and Du, X.C. (2025) ‘Fire warning of lithium battery energy storage power stations for environmental sustainable development’, Int. J. Environmental Technology and Management, Vol. 28, Nos. 4/5/6, pp.355–366.

New Open Access article available: "E-government implementation and internal user satisfaction in a Peruvian naval tertiary medical centre: a cross-sectional study"

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It can be downloaded via the full-text link available here.

19 March 2026

Research pick: Balancing ecology and industry in China - "Marine ecological governance and green development in Beibu Gulf of Guangxi under the digital context"

A new study of the vast Guangxi Beibu Gulf Marine Region (GBGMR) in southern China takes a close look at how environmental limits are being stretched by economic growth. It highlights the disparities between provinces and asks how more effective environmental policies might be put in place across different parts of the region.

The GBGMR is an important coastal zone spanning several provinces. It lies along southern China’s coast on the Beibu Gulf near the border with Vietnam. It acts as an ecological barrier stabilising environmental conditions as well as supporting fisheries, water supply, and industry. The GBGMR encompasses an incredibly varied geography but represents an uneven distribution of natural resources. Both these factors make it especially vulnerable to all kinds of pressures from human activity.

Research in the International Journal of Global Energy Issues has shown that while the region currently operates within what we might call environmental limits, the buffer zone is steadily shrinking based on an assessment of its Ecological Carrying Capacity (ECC). ECC is a measure of an ecosystem’s ability to support human activity without causing long-term damage to the natural environment. In their study, the team combined two indicators of impact: carbon footprint and water footprint.

Their analysis shows clear variation across regions in the GBGMR and over time. Provinces that depend on energy-intensive industries, such as coal and chemicals, face much higher ecological stress whereas areas that have diversified are more resilient and can maintain a better balance between growth and environmental limits.

The findings could help guide policymakers so that locally pertinent regulations are put in place instead of blanket measures. The team suggests that regions with high emissions should accelerate the move to sustainable energy, while water-scarce areas should prioritise conservation and move away from water-intensive industries.

Song, H., Wang, X., Zhao, J., Yuan, S. and Yu, J. (2026) ‘Marine ecological governance and green development in Beibu Gulf of Guangxi under the digital context’, Int. J. Global Energy Issues, Vol. 48, No. 7, pp.1–20.

Free Open Access issue published by International Journal of Information and Communication Technology

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  • An intelligent decision support framework for financial market risk using big data and optimised XGBoost
  • Optimisation of vocal music teaching strategy in colleges and universities based on neural network
  • Cross-prompt English composition automatic scoring method integrating CNN, LSTM, and attention mechanism
  • The solidification effect of airport road bases using an improved BP neural network and visualisation evaluation model
  • Semantic analysis and translation optimisation of English sentences using long short-term memory network

New Open Access article available: "Aspect-level sentiment classification with emotional keywords attention network"

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Free Open Access issue published by International Journal of Information and Communication Technology

The International Journal of Information and Communication Technology has published an Open Access issue. All of the issue’s papers can be downloaded via the full-text links available here.
  • Construction and empirical study of a multi-source data fusion model for adolescent health literacy assessment
  • Hierarchical fusion of multi-scale features with transformer for crime scene trace identification
  • Visual depth models coupled with 3D pose estimation for sports body training evaluation
  • Antennas in the oral English test scenario: meta-learning assisted fast reconstruction
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Free Open Access special issue on "Nano-Evolution: From Science To Technologies" published by International Journal of Nanotechnology

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  • Synthesis of nanopowders Nd2Fe14B by chemical method
  • The second quantum revolution: the development of quantum subatomic nanotechnologies
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  • Synthesis and perspectives of Ag/In2O3 inverse opal
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18 March 2026

Free Open Access issue published by International Journal of Information and Communication Technology

The International Journal of Information and Communication Technology has published an Open Access issue. All of the issue’s papers can be downloaded via the full-text links available here.
  • Analysis of common error patterns in German software localisation and automated detection tools
  • Cross-modal retrieval of Korean intangible cultural heritage multimedia content using deep hashing networks
  • Cross-platform adult learning behaviour profiling based on multimodal data fusion
  • Harnessing multimodal graph neural networks to predict graduate employment anxiety
  • An edge computing system for live-line detection based on multi-sensor data fusion

Free Open Access issue published by International Journal of Information and Communication Technology

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  • Neural differential equations based diffusion models for high-jump posture prediction
  • Convergence criteria for iterative formats in high-dimensional optimisation problems discretised from mathematical partial differential equations
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New Open Access article available: "Competitive advantage versus cooperation in strategic management: a framework for success"

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Free Open Access issue published by International Journal of Information and Communication Technology

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  • Intelligent education recommendation under dual constraints: collaborative mechanism of federated architecture and genetic optimisation
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Research pick: The online protection racket - "Personal data protection in the age of digital financial systems"

Research in the journal Electronic Government discusses the growing need for protecting one’s personal financial data as the online world faces increasingly sophisticated cyber threats. The researchers argue that no single measure is sufficient to secure the modern financial ecosystem. As such, they set out a framework that combines technological tools, regulatory oversight, and individual responsibility to combat the problem.

There are three foundational principles in online financial security: confidentiality, integrity, and availability. Confidentiality is about making sure that sensitive information, such as account details and biometrics, is accessible only to authorised users. Integrity involves maintaining the accuracy and reliability of data and blocking unauthorised changes. Availability ensures that only legitimate users can access their financial information and no third party.

The researchers explain that a breakdown in any one of these areas can lead to personal financial loss, reputational harm for institutions, and more broadly, an erosion of trust in digital services.

Phishing, in which attackers pose as legitimate entities to extract sensitive information via a rogue email or website, is the most common digital fraud. Malware, software designed to infiltrate or damage systems, is a close second and continues to evolve to evade antivirus systems and get around firewalls. Insider threats, involving individuals within organisations misusing access, add another layer of risk. Then there are institutional, industrial-scale breaches where data is sold to malicious third parties on the dark web.

Financial institutions operate within stringent regulatory systems to reduce the risks but even with protections in place such as data regulation laws, encryption, multi-factor authentication, and routine security audits, vulnerabilities still exist.

All the protection in the world cannot save users from themselves, though. Even the least naïve digital native can succumb to social engineering or the sleekest of phishing attacks. The researchers suggest that user education is key. Users need to learn about avoiding weak passwords, about not repeating passwords, about how to recognise phishing attempts, and about how to be consistent in their practices online to avoid being caught out.

Kumari, A. (2026) ‘Personal data protection in the age of digital financial systems’, Electronic Government, Vol. 22, No. 2, pp.220–240.

17 March 2026

Research pick: AI, who drives the cars? - "Transformer-GNN hybrid architecture for optimising real-time traffic forecasting on highways"

Urban congestion is a big problem in our cities. It leads to commuter delays  and economic inefficiency. More tragically, though, it leads to a million deaths annually worldwide. Research in the International Journal of Reasoning-based Intelligent Systems shows how artificial intelligence (AI) might be able to carry out real-time traffic forecasting and so provide a way for the authorities to manage our road networks better.

Road vehicles do not behave as individual entities, traffic flow is a dynamic system in which there are no truly isolated events at individual locations, but conditions that ebb and flow over time. The researchers describe this phenomenon as spatiotemporal dependency. Events at one point at a given time influence conditions elsewhere on the roads. For example, a slowdown on a motorway might trigger congestion further down the route or in areas fed by the motorway some time later.

The researchers explain that capturing these delayed and distributed effects has long proved difficult for conventional forecasting models. Existing systems rely on simplified assumptions or short-term data patterns. The new approach using a hybrid deep learning system known as STG-Former. This brings together two computational approaches: graph neural networks and transformer models. A graph neural network represents the road system as a network of connections. The model can thus learn about traffic conditions over an area. The transformer component uses an attention mechanism to identify the most relevant information at any given time. It can thus detect patterns as they change through time.

Tests with this new system on standard traffic datasets show the model is much more accurate in its predictions than even the leading rivals and works well during periods of peak congestion when those other models often fail. The improvement is significant in the context of urban congestion, where even a small improvement in predictions can help traffic management improve its operational decisions and so avoid gridlock or major stalls in the flow of traffic.

Cheng, H., Cao, Y. and Li, W. (2026) ‘Transformer-GNN hybrid architecture for optimising real-time traffic forecasting on highways’, Int. J. Reasoning-based Intelligent Systems, Vol. 18, No. 9, pp.38–50.

New Open Access article available: "An edge computing-based fast restoration for urban medium- and low-voltage distribution networks"

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New Open Access article available: "Fuzzy best-worst method for analysing the threats of AI in education"

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Free Open Access issue published by International Journal of Information and Communication Technology

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  • Low-power mesh networks for off-grid communication systems: a 5G-fibre hybrid integration solution
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16 March 2026

Research pick: Grid vibrations – AI detects power supply cyberattacks in less than two seconds - "Network security threat identification based on GNN-transformer fusion model in energy cyber systems"

Modern energy infrastructure is increasingly defined as cyber-physical systems where physical power distribution and digital communication are closely tied together. While this digitalisation boosts efficiency, it exposes electricity grids to sophisticated cybersecurity risks. To combat such threats, researchers have developed an artificial intelligence (AI) method that integrates network structure analysis with data tracking to identify complex attacks that conventional security systems might miss. Details are reported in the International Journal of Global Energy Issues.

Energy infrastructure is vulnerable to Advanced Persistent Threats (APTs). Unlike localised glitches, APTs involve long-term infiltration where attackers quietly gather data or manipulate operational signals. A major problem is the False Data Injection (FDI) attack, where sensor measurements are altered to feed operators misleading information. Such changes can cause catastrophic errors in energy flow and paralyse physical fuel supplies across entire regions. Such vulnerabilities are manifest as ransomware attacks, but increasingly, there are the risks associated with international conflict.

Detecting these incursions is difficult because malicious commands often mimic routine operational activity. Legacy detection systems use “signatures”, predefined rules based on known past threats. Such an approach is generally ineffectual in the face of new, “zero-day” exploits or attacks that otherwise do not match existing patterns.

The new AI approach uses two distinct types of information: structural information (the physical and digital layout of devices and control centres) and temporal information (the chronological sequence of commands and signals) to identify an ongoing attack. The dual-layered deep learning architecture is based on a Graph Neural Network (GNN) that maps the system’s spatial layout, and a Transformer model analyses data sequences over time. The former gives the AI a picture of the physical aspects of the infrastructure, and the latter understands how it changes over time. Such a spatiotemporal AI detection system can identify coordinated, multi-stage attacks that appear harmless when viewed as isolated events.

Testing with standard cybersecurity datasets proved the new AI model to have an accuracy of more than 93 per cent. Critically, it identifies suspicious activity in less than two seconds of it starting. This offers a viable way to near-real-time protection of power infrastructure, the research suggests.

Dai, Y., Lu, J., Li, Z., Li, J. and Rafieipour, M. (2026) ‘Network security threat identification based on GNN-transformer fusion model in energy cyber systems’, Int. J. Global Energy Issues, Vol. 48, No. 7, pp.64–84.

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Free Open Access issue published by International Journal of Information and Communication Technology

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  • Intelligent scoring method for English articles leveraging large language models
  • An English vocabulary pronunciation evaluation model based on multidimensional audio features and machine learning
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Free Open Access special issue on "Interdisciplinary Research of Energy Application, Governance, and Policy for Sustainability – Part 1" published by International Journal of Innovation and Sustainable Development

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  • Stability of school-enterprise cooperation in energy education: interaction mechanisms and policy implications
  • Modelling and diagnosis method of power transformer sound characteristics by integrating sparse representation and deep autoencoder network
  • Spatial distribution of geographical indication agricultural product clusters and their impact on the sustainable development of the agricultural energy-economy system
  • The high-quality development of China's green energy economy for promotion of digital finance under deep learning technology
  • Graph neural network model for cable tunnel cost prediction under high-dimensional construction data

Associate Prof. Fa Zhu appointed as new Editor in Chief of International Journal of Granular Computing, Rough Sets and Intelligent Systems

Associate Prof. Fa Zhu from Nanjing Forestry University in China has been appointed to take over editorship of the International Journal of Granular Computing, Rough Sets and Intelligent Systems.

14 March 2026

Free sample articles newly available from International Journal of Markets and Business Systems

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  • The electrification of passenger transport for sustainable mobility: challenges and opportunities from the perspective of key stakeholders
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  • Low-altitude UAVs and IoT-empowered fresh cold chain logistics system for mountainous areas
  • Artistic image restoration and semantic reconstruction driven by multimodal AIGC
  • Deep learning-based power transformer condition monitoring and fault diagnosis algorithm
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