Adapting to AI adoption

Research in the International Journal of Business Information Systems suggests that the adoption of artificial intelligence (AI) is remarkably uneven across Italian firms. While some may have made a deliberate choice not to use AI, of the many that are planning to use it, some still lack the organisational structures needed to deploy the technology effectively.

This is one of the first systematic studies of AI adoption in Italy. It found that there are lots of early innovators eagerly integrating AI into their operations, but others are moving more cautiously and remain in the preliminary stages of exploration. This uneven uptake is seen elsewhere and reflects a broader international pattern, as businesses look for AI opportunities but struggle with the complexities of this rapidly evolving area of computing.

Despite the growing interest and investment in, specifically, generative AI, this research shows that many firms do not have a structured approach to the technology. The researchers propose an “AI Readiness Level” (AIRL) framework that could help organisations develop their AI strategy.

This notion of readiness is not just about technical capability, it takes into account the quality of a company’s data infrastructure, the availability of skilled personnel, leadership support, and external factors such as regulatory pressures or market competition. AIRL provides a model of the progressive stages of development, from initial awareness to full operational integration.

The team points out that firms that have adopted AI have reported improvements in operational efficiency, enhanced customer engagement, and more informed decision-making through predictive analytics. The research suggests that adopting AI is less a matter of installing new software than carrying out organisational transformation. Companies need to align their technological capabilities with workforce skills, management strategies, and governance structures, the authors explain. Those that fail to do so risk falling behind competitors that are already using this technology to their advantage.

Garlatti Costa, G., Pugliese, R. and Venier, F. (2026) ‘Exploring artificial intelligence adoption among Italian firms: the AI readiness level’, Int. J. Business Information Systems, Vol. 51, No. 7, pp.1–22.

Greening the supply chain

Research in the International Journal of Environment and Pollution has looked at carbon-reduction strategies across supply chains. The findings suggest that uncertainty in consumer demand need not preclude environmental gains.

The team looked at a four-stage supply chain, encompassing suppliers, producers, retailers, and consumers. They used a structured economic model, the Stackelberg game, to examine the dominant “actor”, in this case the manufacturer. The dominant actor makes the initial decisions, and the other players adjust their behaviour accordingly. Such a sequential decision-making framework models the way many industries function, where firms exert influence over pricing and production conditions downstream.

In contrast to other studies that have isolated individual parts of the supply chain, this latest study adopts a system-wide perspective. In it, retailers are not merely intermediaries but are active participants shaping demand. As such, retailers then influence consumer behaviour through pricing strategies and promotional efforts, such as emphasising low-carbon products or highlighting environmental credentials. This affects consumer decisions about the price of “greener” goods, and this then feeds back into the incentives at the manufacturer level for reducing emissions and pollution earlier in production.

The challenge in green manufacturing is demand uncertainty. Firms somehow need to be able to predict how positively consumers would respond to those greener, low-carbon products. This uncertainty complicates investment decisions. The research indicates that supply chain participants can still achieve what economists term Pareto improvements, where at least one party benefits without leaving others worse off, through coordinated adjustments in pricing, subsidies and emission reduction efforts.

The results reveal a set of trade-offs. Subsidies aimed at boosting retail promotion tend to increase marketing efforts and allow retailers to charge higher prices, reflecting stronger consumer demand for environmentally friendly products. However, these same measures weaken the producers’ incentives to invest in their own emission reductions and may lead to higher wholesale prices. The overall effect, however, is emission reduction across the supply chain, suggesting that policies or strategies that appear inefficient at the manufacturer level may still deliver environmental benefits.

Shen, Q. and Hou, X. (2026) ‘Carbon reduction coordination and pricing strategy of a four-level supply chain under demand uncertainty’, Int. J. Environment and Pollution, Vol. 76, No. 5, pp.36–57.

The Internet of Things can only get better

The rapid expansion of the Internet of Things (IoT) has changed how digital systems interact with the physical world. Millions, if not billions, of connected devices, from household appliances to industrial machinery, environmental sensors, medical diagnostic tools, and more, collect and exchange data with minimal human intervention.

This growing “network” has led to the automation of many mundane tasks as well as enormous improvements in efficiency across all these areas and beyond. However, researchers writing in the International Journal of Critical Infrastructures warn that the increasing complexity of the digital world brings with it vulnerabilities. This is perhaps of growing interest and concern as artificial intelligence is incorporated into the way in which IoT devices work.

The team explains that many IoT devices have limited computing resources, and so they are constrained in terms of how well they can address security issues. As a result, many devices are security targets and can, for instance, be added to so-called botnets, networks of affected machines used to carry out bigger attacks on networks and infrastructure using Distributed Denial of Service (DDoS) attacks and other methods.

Addressing these problems is vital if critical IoT systems are to be protected in energy grids, medical environments, factories, and across so-called smart cities. The research focuses on anomaly detection as a powerful strategy for identifying potential threats and system failures. Unlike standard rule-based security systems that use predefined patterns of known threats, anomaly detection can use machine learning to identify patterns based on training data and algorithmic analysis rather than explicit programming.

As IoT technology spreads, anomaly detection in real time is an essential part of implementation and a requirement for maintaining system integrity. Failures or breaches in interconnected systems could have cascading effects, disrupting essential services and undermining public trust.

Ultimately, securing IoT networks through this kind of proactive monitoring is not just a technical necessity but a safeguard for infrastructure that depends on all those millions of devices.

Xu, J. (2026) ‘Integrating IoT and machine learning for scalable anomaly detection in smart city infrastructure’, Int. J. Critical Infrastructures, Vol. 22, No. 10, pp.1–16.

Location, emplacement, posizione

A new way for computers to recognise and translate complex place names is reported in the International Journal of Information and Communication Technology. The approach offers a roadmap to address a long-standing weakness in digital language systems used for mapping, navigation, and international communication.

Place names often carry historical, geographical, and cultural significance, and errors in translation can lead to confusion or loss of context. More accurate handling of such names could improve digital maps, navigation systems, logistics platforms, and multilingual communication tools.

The research focuses on English-derived place names, those created by adding prefixes, suffixes, or descriptive elements to existing names. While common in geographic data, these constructions are hard for automated systems to work with because they combine meaning and pronunciation in ways that do not transfer neatly across languages.

To address this, the researchers developed a computational model that integrates two complementary approaches: a knowledge graph and a phonetic generation algorithm. A knowledge graph is a structured representation of information that maps relationships between concepts, allowing the system to understand how place names are formed and how their components relate to one another. This captures the semantic dimension of language, its meaning and contextual associations.

The phonetic generation algorithm focuses on the sound of the spoken names. It converts written words into standardised representations of pronunciation, enabling the system to align how a place name is written with how it is spoken. This is particularly important in translation, where names often need to preserve recognisable sounds alongside meaning.

These two elements interact using what the team refers to as a bidirectional dynamic interaction fusion mechanism. In this system, the semantic and phonetic information feed each other to improve recognition and translation. The system also uses a Long Short-Term Memory (LSTM) network, a type of neural network commonly used for language processing.

The model demonstrated an error rate of just 1.3 per cent in recognising place names and 0.8 per cent in translating them. Its outputs are more than 95 per cent fluent and consistent.

Ma, D. (2026) ‘English-derived place name recognition and translation based on knowledge graph and phonetic generation algorithm’, Int. J. Information and Communication Technology, Vol. 27, No. 27, pp.109–132.

When we’re old and wise

China is facing a rapidly ageing population, with almost a quarter of its population over the standard retirement age in many regions of 60 years. This coincides with a declining birth rate and given more flexible retirement policies, the workforce itself is getting older. Research in the International Journal of Economics and Business Research recognises that within this workforce, older, experienced knowledge workers are a growing human resource asset. Understanding their needs and ensuring they are not so disenfranchesed that they take retirement as early as possible is now high on the organisational agenda and a critical part of modern management.

The research emphasises career capital, a concept that brings together human capital, social capital, and decision-making capital. Human capital refers to an individual’s skills, knowledge, and experience. Social capital encompasses professional networks and relationships. Decision-making capital involves accumulated judgement and problem-solving abilities. The research found that these all contribute to ongoing professional effectiveness in the later stages of employment.

Two psychological factors specifically were identified as important in mediating the relationship between career capital and workplace success: self-efficacy and job crafting. Self-efficacy is an individual’s belief in their abilities, while job crafting refers to the adjustment they make to tasks and work relationships to align with personal strengths and interests. The accumulation of skills, networks, and decision-making abilities are all fully realised when older employees feel capable and empowered to shape their roles.

In an effort to ensure older employees are not disenfranchised and continue to play an important role, the researchers suggest that the various dynamics at play need to be integrated into a new model of human resource management. This model should pay attention to different forms of career capital, activation of self-efficacy and adaptability, and flexible organisational support strategies tailored to age-specific needs. If such an approach is implemented, organisations will be able to sustain productivity, encourage innovation, and preserve the professional value of older knowledge workers.

Wei, J-l. and Chen, C-s. (2026) ‘Exploring the impact of older knowledge workers’ career capital on career success: with self-efficacy and job crafting as mediators and perceived organisational support as a moderator’, Int. J. Economics and Business Research, Vol. 30, No. 1, pp.1–28.

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.

• An AI-driven dual neural network framework for enhancing and evaluating university teachers' informatisation teaching capacity under TPACK
• Gradient optimisation and cross-language transfer mechanism of English translation model based on LSTM-transformer
• Emotions and dissemination trends of Sichuan handicraft intangible cultural heritage inheritance groups based on ALBERT and TCN
• Deep learning-based automatic labelling of English syntactic variation and cross-dialect comparison
• Real-time health-aware emergency optimisation scheduling under fault scenarios in PV-fuel cell microgrids

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New Open Access article available: "Exploring artificial intelligence adoption among Italian firms: the AI readiness level"

The following International Journal of Business Information Systems article, "Exploring artificial intelligence adoption among Italian firms: the AI readiness level", is freely available for download as an open access article.

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

Research pick: AI second guess that emotion - "Aspect-level sentiment classification with emotional keywords attention network"

Research in the International Journal of Computational Intelligence Studies has looked at how we might improve artificial intelligence (AI) systems for interpreting human emotion in written communication. The new system is capable of identifying sentiment not only in broad terms, positive, negative, and neutral, but also at a more detailed, aspect-specific level.

Sentiment analysis usually evaluates entire sentences or documents as a single unit. This can hide the subtleties of human expression. For instance, a restaurant review may praise the food while criticising the service. Previous AI models could struggle to separate these differing opinions, often assigning a generalised sentiment score. The new model overcomes this limitation by emphasising emotionally charged keywords, the words that carry the most significant emotional weight in a sentence. It does this using an attention network, a computational mechanism that allows AI to prioritise certain inputs over others.

This focus on the most emotional terms in a piece of text allows the AI to classify sentiment directed at specific aspects of a text. In the restaurant example, the model can distinguish the positive sentiment aimed at the food from the negative sentiment about the service, producing a more nuanced interpretation. Moreover, the system’s ability to pay attention to the most emotionally charged words is a useful advance in natural language processing.

Such a tool could help businesses that rely on customer feedback, social media analysis, and online reviews. With it a company could spot concerns being discussed online as they arise and so make a timely response to help manage their image and refine their marketing. They might even be able to offer targeted responses to individuals or groups to improve customer satisfaction and perception.

This research is part of a growing trend in AI research towards improving the way in which computers interpret language and emotion. By enabling machines to analyse sentiment at the level of individual aspects rather than entire texts, this approach contributes to the development of more perceptive, context-aware AI.

Yuan, Z. and Yuan, J. (2026) ‘Aspect-level sentiment classification with emotional keywords attention network’, Int. J. Computational Intelligence Studies, Vol. 13, No. 5, pp.1–13.

New Open Access article available: "Integrating IoT and machine learning for scalable anomaly detection in smart city infrastructure"

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Free Open Access special issue on "Achieving Carbon Neutrality from Environmental Impact Monitoring and Assessment Technologies – Part II" published by International Journal of Environment and Pollution

The International Journal of Environment and Pollution has published an Open Access issue. All of the issue’s papers can be downloaded via the full-text links available here.

• Green logistics distribution route algorithm based on carbon emissions optimisation
• Using soil water content and energy balance model to promote sustainable development of green and low-carbon economy
• Carbon reduction coordination and pricing strategy of a four-level supply chain under demand uncertainty
• Evaluation of petroleum safety management system based on embedded intelligent image sensor
• State diagnosis technology of metal enclosed gas insulation equipment based on Apriori algorithm in cloud computing environment
• Investigation sustainable development of ecological environment and economic technology in the context of supply chain management
• Innovation-driven development path of the new energy industry based on immune optimisation and simulated annealing algorithm
• Environmental risk assessment and early warning system construction for forest tourism sites under the background of climate change

New Open Access article available: "Business perspectives on value co-creation as a mediator for entrepreneurial performance in educational contexts"

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New Open Access article available: "Sectoral total factor productivity and its determinants: firm-level evidence from Kazakhstan"

The following International Journal of Services, Economics and Management article, "Sectoral total factor productivity and its determinants: firm-level evidence from Kazakhstan", is freely available for download as an open access article.

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Research pick: Is the AI black box right on time? - "Artificial intelligence’s effect and influence on multiple disciplines and sectors"

Irrespective of the ethics and the apocalyptic predictions, artificial intelligence (AI) has already become a central component of economic and institutional decision-making. Research in the International Journal of Intelligent Systems Design and Computing has gone beyond an industry-specific analysis of the state-of-the-AI-art and offers a detailed framework of how the many different AI tools are being adopted.

The main point that arises from the analysis is that while AI technologies are being used widely across sectors, organizations do not yet have a strategy that allows AI to be integrated in a way that balances innovation with accountability.

AI encompasses so-called machine learning for recognising patterns in data, natural language processing that can interpret and human language, and generative tools that produce text, images, video, computer code, and other output. All these tools are changing many sectors from healthcare diagnostics to processing industrial and financial data, to produce hit pop songs and accompanying videos.

Education and business operations are undergoing similar shifts. Adaptive learning platforms in education adjust course material to suit the way individual students learn. In retail and logistics, AI is being used to refine supply chains, manage inventory, and personalize the customer “experience”. Even in the world of law, law enforcement is using AI to assess crime scenes and weigh evidence, while judges are using these tools to summarise their concluding remarks from massive briefs.

One of the most pressing issues highlighted by the research is data privacy, as AI systems depend on large volumes of often sensitive and personal information. In addition, there is the notion of algorithmic transparency, wherein we are are losing the ability to understand how a given AI system is arriving at a specific decision. Indeed, many of the most advanced AI models now work essentially as black boxes, meaning their internal processes simply cannot be interpreted…perhaps without resorting to another AI to do the interpretation! Such a lack of transparency might undermine trust in high-stakes contexts such as medical diagnoses or judicial decisions.

To address the issues, the researchers propose a framework based on stakeholder theory, which maintains an emphasis on the importance of all parties affected by the decisions AI might make. In the business context, they stress that organisations should bot focus solely on efficiency or profit, they must have perspective that them to weigh the interests of employees, customers, regulators, and society at large when adopting AI. This might only come about, of course, with governance, regulations, and ethical obligations.

Idemudia, E.C. (2025) ‘Artificial intelligence’s effect and influence on multiple disciplines and sectors’, Int. J. Intelligent Systems Design and Computing, Vol. 3, Nos. 3/4, pp.254–274.

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