Planning for planned obsolescence

A study in the International Journal of Intellectual Property Management suggests that planned obsolescence drives software innovation but also leads to customer lethargy or worse piracy.

The research has looked at the software upgrade cycle and highlights the complex role of planned obsolescence in shaping user behaviour across both legitimate and pirate markets. Planned obsolescence, in the context of software, involves discontinuing updates and technical support for older versions to encourage users to adopt newer releases. While often criticised as a tactic to extract additional revenue, the study notes that this strategy reflects practical considerations in software development. Companies continually invest in new features, security improvements, and interface enhancements, and revenue from upgrades sustains ongoing innovation.

However, the research, which focuses primarily on personal computer operating systems (OS), suggests that when companies end support for older versions of their software, this influences not only consumer choice but also broader patterns of technology adoption.

The team has analysed how users respond to these transitions using a push-pull-mooring (PPM) model. This framework was originally used to study geographic relocation but couches OS updates in terms of push and pull factors. Push factors are the drawbacks to remaining with outdated software, such as vulnerability to security breaches or incompatibility with modern applications and hardware. Pull factors represent advantages of upgrading, including enhanced functionality and a better user experience. The third type of factor, mooring factors, by contrast, are the costs or attachments that inhibit switching, such as financial expense, learning curves, or habit.

The team surveyed almost 300 users of perhaps the most common operating system on personal computers the world over. They found that the recognition of planned obsolescence increased a person’s intention to upgrade but that there is a split between users following the official channel to upgrade or turning to a pirate source. They also found that social influences and the appeal of improved features were particularly strong motivators for legitimate upgrades, whereas high switching costs, including technical challenges and monetary considerations, drove some users almost inevitably towards pirated software.

There exists a dynamic tension that the software companies face. If they discontinue older products, this eventually forces users to upgrade and so leads to new revenues. But that constant cycle of upgrade and obsolescence pushes people towards software piracy, especially in regions where higher cost sensitivity is a major decisive factor, such as in the developing world.

The work suggests that planned obsolescence is more than a marketing tactic. This hints that software companies could increase legitimate adoption and reduce piracy by designing upgrade processes that lower learning costs, clearly communicate benefits, and carefully manage the phasing-out of older products.

Thi, T.D.P. and Duong, N.T. (2026) ‘Intentions to upgrade software: evidence from Microsoft Windows users’, Int. J. Intellectual Property Management, Vol. 16, No. 1, pp.45–69.

New Open Access article available: "Automated simulation testing for complex software environments using multi-agent reinforcement learning"

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• Carbon emission prediction and management under the coordinated operation of renewable energy and smart grid
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• The role and importance of human capital in exports and growth: the case of Türkiye
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Research pick: Ask not what AI can do for your business… - "Business transformation in the age of generative AI: from strategy to societal impact"

The advent of generative artificial intelligence, GenAI, has changed how businesses use digital technologies. Where for many years AI was used as a predictive, analytical, and diagnostic tool, now it can produce ideas, articles, computer code, images, video, and music.

The turning point perhaps came in late 2022 with the public release of systems such as ChatGPT. These new tools allowed users to interact with complex AI models through conversational prompts. They could give the GenAI written, and more recently, spoken instructions, and the system would respond. These tools have since then become increasingly sophisticated and are now used across the corporate world and beyond.

The change happened partly because there were major developments in machine learning, a branch of computer science in which algorithms learn patterns from large datasets and can produce an output to a given prompt based on what they have learned. Central to this process is the so-called transformer model. This is a type of neural network architecture that can analyse relationships between different entries in a large volume of data. Neural networks are computational systems loosely inspired by the structure of the human brain. Transformer-based systems, including the GPT family of models, are particularly effective at generating coherent language from their training data given an appropriate prompt.

There are other approaches to GenAI. Generative adversarial networks (GANs), for instance, use two neural networks that play off each other. One creates synthetic data based on its training, and the second evaluates how real that data is based on its own training. The process goes back and forth until the output is deemed optimal and the system can no longer improve the synthetic output or make it any more real than it is.

There are various other approaches, such as variational autoencoders, which compress and simplify data and then generate variations on the themes. Diffusion models, widely used for image generation, begin with random noise and gradually transform it into structured images. More often than not, a GenAI might be using at least two of these approaches in a multimodal system that can produce text, images, and audio together.

Writing in the International Journal of Generative Artificial Intelligence in Business, researchers discuss how well all of these systems work, the value they create, and the ethics associated with GenAI. Where GenAI is augmenting one-on-one human interaction or helping make business decisions, there are issues of bias inherent in training data as well as labour disruption to consider.

As AI systems assist increasingly in analytic, writing, and creative work, knowledge workers and many other people will collaborate more and more with machines. The change is disruptive, it is likely that many jobs will become redundant. However, with automation there will be a greater need for critical thinking and ethical judgement.

Zouaghi, I. and Fosso Wamba, S. (2026) ‘Business transformation in the age of generative AI: from strategy to societal impact’, Int. J. Generative Artificial Intelligence in Business, Vol. 1, Nos. 1/2, pp.238–262.

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

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• Integrated knowledge graph and reinforcement learning: a graph modelling approach for international trade behaviour analysis and economic trend prediction
• Knowledge tracing in mathematical problem-solving processes: a spatio-temporal graph neural network approach
• EC-RLPA: a dynamic pricing law framework for smart connected vehicles integrating edge computing and reinforcement learning
• Collaborative multi-agent Q-learning-empowered vocational education resource matching system
• A dynamic evaluation model for sports sponsorship value based on neural-symbolic fusion learning

New Open Access article available: "Visualisation of Chinese phonemes based on three-dimensional tongue model and ultrasound images"

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Research pick: Coming to your educational rescue - "Emotional support and satisfaction with university campus life: mediation of self-efficacy and problem-solving"

Emotional support from parents and teachers can play an important role in how satisfied students feel with university life in Pakistan, according to research in the International Journal of Services, Economics and Management based on a survey of almost 600 undergraduates. The study suggests that encouragement and understanding from family and faculty do more than provide comfort: they appear to strengthen students’ psychological resources in ways that make campus life more manageable and rewarding for them.

The researchers turned to social support theory, a framework for understanding how caring relationships enhance psychological well-being and resilience, to help them investigate campus life.

Their analysis of the survey data did not just ask whether support improves satisfaction but explored how it does so. In particular, they assessed whether two psychological characteristics, self-efficacy and problem-solving ability, act as mediators of support. Self-efficacy describes a person’s belief in their own ability to succeed. Problem-solving capacity refers to one’s skills and confidence in resolving difficulties.

The team found that parental support is linked to stronger self-efficacy and improved problem-solving skills, which in turn contribute to greater satisfaction. Encouragement from home seems to foster confidence and a sense of competence. Emotional support from teachers follows a different pattern. Students who see their instructors as respectful, attentive, and supportive also report higher satisfaction with campus life. This relationship, the researchers suggest, is partly explained by enhanced problem-solving ability. Supportive teachers appear to help students think through challenges and develop strategies to address them. Teacher support did not significantly influence self-efficacy in this study. In others, words, teachers might help students tackle specific problems without fundamentally shaping the student’s self-belief.

The team adds that the cultural setting is important. In a society where family bonds and collective aspirations remain central even into early adulthood, parental influence may continue to outweigh that of teachers in shaping self-belief. This contrasts with studies in the West, where support from teachers in higher education, are more strongly associated with a student’s sense of competence.

Ahmad, M.S., Ahmad, M.A. and Elgammal, I. (2026) ‘Emotional support and satisfaction with university campus life: mediation of self-efficacy and problem-solving’, Int. J. Services, Economics and Management, Vol. 17, No. 1, pp.81–101.

New Open Access article available: "Sustainable fashion in the digital age: investigating consumer responsiveness to value-driven in-app pre- and post-purchase marketing strategies"

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New Open Access article available: "The mediating role of psychological capital in linking environmental dynamism to entrepreneurial persistence among nascent entrepreneurs in China"

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New Open Access article available: "Environmental, social and governance reporting quality and firm lag vs. lead performance: evidence from Sri Lankan listed companies"

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Research pick: Throwing shade on the dark side of AI - "The dark side of artificial intelligence"

Artificial intelligence, AI, has become one of the defining technologies of what economists and policymakers describe as the Fourth Industrial Revolution. This is an era in which digital, physical, and biological systems are increasingly intertwined. In practical terms, AI refers to computer systems capable of performing tasks that typically require human intelligence, such as recognising patterns, learning from data, making predictions, and assisting in complex decisions.

Aside from the generative AI and search tools that are at the forefront of the media and economic hyperbole, analytical and related AI systems already underpin smart manufacturing platforms, digital twins for testing and optimising equipment performance, adaptive cybersecurity tools, medical diagnostics, and much more. It is unlikely that within a decade or so many occupations will not have been augmented or displaced by AI tools. The potential for productivity, innovation, and economic growth is great.

As with any new technology, however, there are good reasons to look closely at the social and economic impact AI might have. It would be prudent to put safeguards in place urgently given the way in which technologies have often amplified inequality, weakened democratic norms, and introduced new systemic risks in the past.

Research in the International Journal of Generative Artificial Intelligence has looked closely at many of the issues that are coming to the fore, such as labour disruption, deepfakes, the opacity of advanced AI models, bias, copyright, privacy, and security issues. Then, there is the issue of whether a superintelligent AI might surpass human abilities and redefine our very existence, perhaps even determining, algorithmically or some kind of awareness, that we as a species are redundant, or worse, a problem that needs to be removed.

The researchers suggest that at the geopolitical level, international coordination is a major challenge, not least given the rogue behaviour of some so-called state actors. The trajectory that AI takes in this Fourth Industrial Revolution is not fixed, nor is it predictable. We need to work together to ensure that it works for the benefit of humanity and the planet.

Min, H. (2026) ‘The dark side of artificial intelligence’, Int. J. Generative Artificial Intelligence in Business, Vol. 1, Nos. 1/2, pp.199–209.

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New Open Access article available: "AI in sustainable higher education: an interpretive structural model and MICMAC approach"

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• Entrepreneurship at the base of the pyramid: effectual logics and sustainable development
• Potential of digital marketing and determinants of the 5A marketing strategy: a case in Thailand

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Research pick: Sites of the underground - "Application of deep learning algorithms in the design of urban subway public art space"

Underground metro (subway) stations are no longer merely points of departure and arrival. As cities grow denser and transit networks expand, these spaces have the potential to function as some of the most widely shared public interiors in urban life. They are places where millions pass daily, cutting across age, income, and neighbourhood. They offer a rare platform for collective cultural experience. Stations can, suggests research in the International Journal of Environment and Sustainable Development, anchor local identity, narrate a city’s history, and shape how residents and visitors alike perceive the character of the urban environment.

The research addresses a practical question confronting transport authorities and urban designers: how can large-scale public art projects fit into this infrastructure as it changes? Traditional artist-led design processes, though highly creative, can be time-intensive. By contrast, deep learning has allowed computers to generate high-quality images at speed. The missing link is that the computer-generated images may not understand the cultural meaning that the images need to convey. There is also a need to take into account how well a design might be installed in a real site.

The researchers hope to bridge this gap and have developed a multi-stage framework that integrates cultural analysis, visual cognition modelling, and spatial feasibility testing into a single pipeline.

Their approach is based on a semantic labelling system. The system can organise cultural concepts, such as local history, regional traditions, and environmental identity, into a knowledge graph. This graph can map relationships between ideas, enabling the computer to understand individual symbols and how they fit with broader narratives.

The framework then uses Contrastive Language-Image Pretraining, CLIP, is a deep neural network trained on vast datasets of containing pairings of images and text. An additional layer simulates human perception through a visual attention prediction network, considering composition, spatial layout, and pedestrian flow. By predicting where passengers are likely to focus while moving through a station, the system can position key symbolic elements in high-attention zones. The researchers suggest this could improve not only the aesthetic impact of the art installation but also the way in which pedestrians navigate the subway stations.

Wang. Q. (2026) ‘Application of deep learning algorithms in the design of urban subway public art space’, Int. J. Environment and Sustainable Development, Vol. 25, No. 5, pp.44–72.

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• Cross-modal sentiment analysis of new media content based on an enhanced question-answering framework
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Research pick: Rebuilding Syria ethically - "Mediating role of ethical intention between social norms, code of ethics and ethical decision-making"

In a country where the physical scars of war remain visible in shattered buildings and disrupted markets, research in the International Journal of Diplomacy and Economy suggests that the moral architecture of business may be just as important to recovery in Syria as capital investment and bricks & mortar.

A study of 200 business leaders working in international companies in Aleppo and Damascus finds that ethical decision-making in Syria can be explained, to a significant degree, by a well-established psychological framework known as the Theory of Planned Behaviour. This theory suggests that human behaviour is primarily shaped by intention, a person’s conscious plan or readiness to act. Those intentions, in turn, are influenced by three factors: personal attitudes, perceived social expectations, and perceived control over whether the behaviour is realistically achievable.

In practical terms, individuals are more likely to act ethically if they believe ethical conduct is right, think that others expect it of them, and feel capable of acting accordingly.

The researchers applied this theory in the context of Syria as part of an effort to understand how business leaders make ethical choices amid conflict, economic disruption, and institutional fragility. Their focus was Syria’s post-war reconstruction drive, a national strategy aimed at restoring infrastructure, reviving markets, and rebuilding social trust after years of violence.

Trust, the study notes, is not an abstract virtue in such an environment. It is a prerequisite for attracting investment, stabilising supply chains, and enabling cooperation between domestic firms and international partners. Ethical business conduct is thus a functional prerequisite of economic recovery.

For practitioners, the implications are concrete. The findings indicate that organisations seeking to strengthen ethical leadership cannot rely solely on written rules. Codes of ethics must be actively communicated and embedded within organisational culture, the shared values and practices that shape everyday work. When ethical expectations become part of that culture, they function as powerful social norms, guiding behaviour even in the absence of direct oversight.

Amoozegar, A., Lata, A., Falahat, M., Shakib, S., Kumar, M., Ramzani, S.R. and Yadav, M. (2026) ‘Mediating role of ethical intention between social norms, code of ethics and ethical decision-making’, Int. J. Diplomacy and Economy, Vol. 12, No. 5, pp.1–20.

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• Generational technological change, organisational search, and firm product innovation performance in new generational markets
• Artificial intelligence and business applications, an introduction

Research pick: The drugs do work - "Deep learning-based virtual screening system for drug molecules"

The earliest stage of drug discovery is governed by a simple constraint: there are far more possible drug-like molecules than any pharmaceutical laboratory could ever test. A new deep learning system, reported in the International Journal of Reasoning-based Intelligent Systems, offers a way to speed up research and could unblock industry bottlenecks.

Bringing a new pharmaceutical to market can take more than a decade and will inevitably cost billions of dollars in research and development, testing, regulatory compliance, and marketing. A large share of that investment is spent on identifying compounds that bind to biological targets, these are commonly proteins involved in disease, whether a protein found in a pathogen or a protein in our bodies involved in the disease. Virtual screening, so-called in silico studies, has for decades used computer models to predict which molecules from a library of candidates might be suitable for testing in vitro (in the laboratory) and ultimately in vivo (in animals, then humans).

That said, established methods fall into two categories. The first are receptor-based approaches, such as molecular docking, that simulate how a molecule fits into a protein’s three-dimensional binding site and estimate the strength of the bond that forms between. The accuracy of this approach depends on high-quality protein structures and simplified scoring formulae. A second approach is the ligand-based approach and this instead looks for compounds resembling known active molecules, using predefined chemical features, or descriptors.

These techniques can be computationally efficient and heavily successfully led to many pharmaceuticals on the market today. However, they rely heavily on prior knowledge and expert assumptions. In both cases, human-designed rules limit how much chemical complexity can be captured. The advent of deep learning systems is opening up a new approach.

Instead of manual feature selection, deep learning, a form of machine learning that uses multi-layered neural networks to detect patterns directly from raw data, can treat drug candidate molecules as graphs, with atoms as nodes and chemical bonds as edges. A graph neural network updates each atom’s representation based on its neighbours, allowing the model to learn subtle structural relationships.

Crucially, this new approach uses another information channel in addition to the graph. It handles the drug candidate’s SMILES string. A SMILES string is a unique text-based representation of the chemical structure of a molecule. By using structural and sequential representations together, the researchers could improve performance significantly. In tests on standard public benchmarks, the model achieved a score of 0.889; where 1.000 would be a perfect score. This score is a measure of how well the system distinguishes between active and inactive drug candidates. A score of 1 is ideal prediction whereas 0.5 reflects a 50:50 chance, a guess. Incredibly, the system could screen one million molecules in a quarter of an hour, which is 80 per cent faster than conventional approaches.

Zhang, C. (2026) ‘Deep learning-based virtual screening system for drug molecules’, Int. J. Reasoning-based Intelligent Systems, Vol. 18, No. 8, pp.44–55.