As social media becomes increasingly a part of marketing strategies, businesses are investing heavily in the platforms to reflect the ability to reach and engage with target audiences. However, according to work in the International Journal of Internet Marketing and Advertising one aspect of social media marketing remains relatively underexplored, the impact on building long-term brand loyalty.

Sohail Ahmad and Li Liang of Southwest Jiaotong University in Chengdu, China, Ahmad Iqbal of The Islamia University of Bahawalpur, and Irshad Hussain Sarki of the National College of Business Administration and Economics both in Pakistan, have considered this gap in our knowledge and focused on the role of community engagement as a mediating factor in the development of brand loyalty through social media. By examining how consumer participation in online communities influences loyalty, the team shows how companies might better improve engagement and increase loyalty through judicious choices surrounding digital channels.

The concept of brand loyalty can be central to the success of any business. Loyal customers are more likely to make repeat purchases as well as advocating for the brand. Marketers have long recognized the importance of loyalty, but fostering such loyalty has become more complex in the age of social media. The study shows that while social media marketing activities can directly influence brand loyalty, this influence is most effective when mediated by active engagement within online communities. The findings were built on three key theoretical frameworks: Stimulus-Organism-Response theory, Service-Dominant logic, and the concept of privacy calculus. S-D logic has perhaps the greatest relevance in showing how the collaborative nature of value creation, where brands and consumers co-create value through interactions, affects loyalty.

Fundamentally, consumers who engage more with brands and other community members on social media are more likely to feel a stronger connection to the brand, which leads to greater loyalty over time.

Ahmad, S., Liang, L., Iqbal, A. and Sarki, I.H. (2025) 'Beyond likes and shares: the secret to building stronger brands on social media from a privacy calculus perspective', Int. J. Internet Marketing and Advertising, Vol. 22, No. 1, pp.72–97.
DOI: 10.1504/IJIMA.2025.144205

As the number of digital resources expands and expands it becomes increasingly difficult to recommend reading matter. Research in the International Journal of Information and Communication Technology has led to a new artificial intelligence (AI) system that improve on precision and variety of book recommendations for online library goers. The new approach blends two established techniques, content-based filtering (CBF) and collaborative filtering (CF), and then has its roots in an advanced machine learning algorithm – Extreme Learning Machine (ELM) – which allows it to come up with the perfect personalized recommendation for the reader.

In traditional recommendation systems, Tianhao Wu of Changchun University of Technology, China explains, content-based filtering suggests books based on a book's attributes, such as its title, author, and genre. Collaborative filtering by contrast makes recommendations based on user behaviour, what books they have read previously and how they rated them. By combining both systems with ELM, the new hybrid model aims to improve the accuracy of suggestions while also increasing their diversity, better reflecting a user's unique preferences and opening them up to new books they may not have encountered otherwise but will hopefully enjoy.

ELM can process large datasets quickly and efficiently, which is particularly useful in the context of online libraries, where both the number of books and user interactions can be immense. Unlike traditional neural networks, ELM reduces the complexity of training by randomly generating weights for each entry. This allows it to adapt to new data much more quickly than other approaches and with greater accuracy.

As digital libraries continue to grow, this new hybrid system holds the potential to transform how books are recommended to users, making their library experiences more personalized and efficient. The team will attempt to address remaining challenges such as the cold-start problem facing new users about which there is initially no reading experience data and similarly with new books from new authors that equally lack a data history.

Wu, T. (2025) 'An ELM-based approach to promoting reading of library books', Int. J. Information and Communication Technology, Vol. 26, No. 2, pp.82–95.
DOI: 10.1504/IJICT.2025.144057

Small and medium-sized enterprises (SMEs) play an important role in the economy of the United Arab Emirates. Indeed, SMEs represent 94% of businesses and employ 86% of the workforce. When the COVID-19 pandemic struck, these businesses faced numerous challenges, including closures, financial instability, and disruptions to supply chains. A study in the International Journal of Entrepreneurship and Small Business has looked at how those SMEs addressed the issues and the role new media technologies played in how they were able to adapt and recover from the pandemic.

Bharti Pandya, Shreesha Mairaru, Asma Buhannad, and Leena Daroo of the Higher Colleges of Technology, Abu Dhabi, United Arab Emirates, highlight the immediate impact the pandemic had and how widespread this was as well as looking at the significant longer-term consequences for many SMEs. The team explains that these businesses were forced to adjust their operations quickly in order to survive. The team showed that social media, e-commerce platforms, and other digital tools were critical in this, helping businesses shift their strategies during the crisis. Once lockdowns and social distancing measures had begun to limit traditional business practices, those technologies allowed businesses to continue reaching customers, marketing their products, and managing operations remotely.

However, although new media technologies were important in the short term, the study shows that they led to longer-term changes in SMEs. Once these digital tools were integrated into the core business functions of those companies, they became critical to sustaining competitiveness as the economy changed after the pandemic. The researchers suggest that digital adoption was not simply a sticking plaster for the pandemic times, but a necessary treatment for the ongoing health of SMEs. Indeed, they explain that the integration of digital tools into communication and customer outreach have helped sustain growth beyond the initial crisis.

Of course, some SMEs needed different digital strategies and not all were able to adapt and survive. Response depended on sector and company size, with some SMEs needing more time and resources to adopt the digital tools and others finding that there were no platforms to meet their specific needs. The team concludes that any business needs to consider which digital tools are best suited to its objectives and resources carefully, and particularly in responding to a global crisis.

Pandya, B., Mairaru, S., Buhannad, A. and Daroo, L. (2025) 'New media technologies and small and medium enterprises: evidence from the COVID-19 period', Int. J. Entrepreneurship and Small Business, Vol. 54, No. 3, pp.403–422.
DOI: 10.1504/IJESB.2025.144230

Research in the International Journal of Computational Systems Engineering has looked at the challenges facing online education systems in terms of improving efficiency and avoiding redundancy in cloud computing platforms. Muchao Zhang of Nanjing Xiaozhuang University, China, offers a new approach to integrating educational data from various sources, models, and formats, all with the aim of improving cloud the efficiency.

Zhang shows how cloud platforms, known for their scalability, flexibility, and security, have already become an essential component of online education. However, the diverse nature of educational data, video lectures and digital textbooks, for instance, creates problems. Different data formats and structures can lead to redundancy, confusion, and inefficient allocation of computing resources. This then reduces the potential for the educational content to be as streamlined as possible.

To address this, Zhang has developed an approach that combines algorithms to help integrate the disparate data types. The various algorithms can each resolve a different issue associated with data integration. For example, the PMI-Simhash algorithm helps identify similarities between data sets, the BSM model aids in classifying the information more accurately, and the US-EM algorithm improves the matching of entities across different systems without needing manual intervention. The result is an integrated approach that minimizes redundancy and ensures that educational resources are much better organized than they otherwise would be.

Zhang has how these algorithms can work together using an online painting course for art students. The approach merges multimodal data, text, images, and video, and proved highly effective in terms of accuracy, speed, and resource usage. By improving the accuracy of data matching, Zhang's approach could ensure that students access the right resources at the right time, improving both the learning experience and resource management.

Zhang, M. (2025) 'Online education resource integration method for painting teaching of art majors based on cloud platform', Int. J. Computational Systems Engineering, Vol. 9, No. 5, pp.1–10.
DOI: 10.1504/IJCSYSE.2025.144358

A team from India, Netherlands, Poland, and Switzerland has looked at how to improve data analysis and to reduce the inherent bias in social network analysis. Writing in the International Journal of Applied Management Science, the researchers recognise that in quantitative surveys and social network analysis, the accuracy of data can often be skewed by biases in how respondents answer the questions. One particular form of bias, known as declarative bias, poses a significant threat to the reliability of survey results, particularly when addressing complex social issues.

Declarative bias occurs when survey participants, consciously or unconsciously, provide answers influenced by social expectations, fatigue, or external pressures rather than reflecting their true attitudes or beliefs. This type of bias is particularly problematic when the research seeks to inform public policy, as it can lead to misleading conclusions about society's attitudes and behaviour and thus inappropriate policies.

Response time testing could offer an answer. The assumption is that a more immediate response tends to reflect a stronger, more internalized opinion, while a slower response may reflect uncertainty or a response swayed by external factors, such as social desirability or reading into the questions themselves to work out what the right answer might be. By distinguishing between these types of responses, the researchers suggest that it might be possible to segregate strong answers from the flimsy.

They tested their approach on an international survey conducted in Spain and Sweden to explore attitudes toward the COVID-19 pandemic. Their results were striking. By homing in on high-confidence, fast responses, the team could see a much greater diversity of opinion. By contrast, a conventional analysis, where declarative bias was present, showed much more homogeneous opinions.

The findings have implications for public policy and health interventions based on surveys of the public or stakeholders on a given topic. For instance, public health policies based on the assumption of uniform public opinion on issues such as the pandemic might fail to address the subtleties of diverse opinions from different groups. By reducing declarative bias in the analysis of surveys, it should be possible to form policy that takes into account diverse opinions and needs.

Fernandez, G.P., Norré, B.F., Reykowska, D., Dutta, K., Nguyen-Phuong-Mai, M., Fernandez, J. and Ohme, R. (2024) 'Social network of confident attitudes with response time testing', Int. J. Applied Management Science, Vol. 16, No. 5, pp.1–31.
DOI: 10.1504/IJAMS.2024.144419

Research in the International Journal of Shipping and Transport Logistics has looked at the environmental sustainability of the Yangtze River Economic Belt (YREB) and raises important points about the region's ability to balance rapid economic growth with ecological preservation. Zhimei Lei, Shanshan Cai and Shaoxin Zhuo of Kunming University of Science and Technology, Yui-yip Lau of The Hong Kong Polytechnic University, and Ming Kim Lim of the University of Glasgow, UK, explain that the YREB encompasses eleven provinces and cities. The region thus plays a pivotal role in the national economy of China. However, its development has often been marred by significant environmental challenges, such as pollution, resource depletion, and ecological degradation.

The team examined almost two decades of data on sustainability levels across the YREB, using an innovative evaluation framework and a "pressure-state-response" (PSR) model. This latter tool allowed the team to link environmental pressures to the condition of the environment and the responses to the problems set in motion by policymakers. As such, the work integrates both qualitative indicators, such as government policies and key speeches, and quantitative data, making it particularly well-suited for the complex realities of the YREB.

Improving environmental sustainability over the study period could be seen in the data with the middle and upper regions of the YREB showing the most progress. However, the research also showed that there are persistent regional disparities. The lower regions of the YREB, in particular, lag behind in terms of environmental sustainability, which could have long-term implications for the overall ecological health of the area. Moreover, despite some obvious progress, there is no clear improvement in sustainability levels even between neighbouring provinces.

This, the researchers suggest, implies that effective collaboration across the YREB is not occurring. The team explains the disparities as perhaps being due to a combination of intra-regional and inter-regional factors: levels of industrialization, policy implementation approaches, and investment in green technologies. The implication is that there is a pressing need for more coordinated action between the YREB's provinces and cities. The team adds that the creation of a platform for sharing environmental data and research could be used to improve governance and decision-making across the whole region.

Lei, Z., Cai, S., Zhuo, S., Lau, Y-y. and Lim, M.K. (2024) 'Analysis of the differences and spatial-temporal dynamic evolution of the environmental sustainability of the Yangtze River Economic Belt in China', Int. J. Shipping and Transport Logistics, Vol. 19, No. 5, pp.1–41.
DOI: 10.1504/IJSTL.2024.144404

Research in the International Journal of Economics and Business Research has looked at the relationship between employee empowerment and job satisfaction, with a particular focus on the banking sector in Greece. As digital technologies reshape the modern workplace, are traditional concepts of empowerment being put to the test, the study asks. George Papageorgiou, Kyriakos Christofi, Aikaterini Gelinou, Andreas Efstathiades, and Elena Tsappi of the European University Cyprus in Nicosia, Cyprus, found which strategies can boost job satisfaction in an increasingly digitalized environment and offer managers insights for navigating this transformation.

The team identified four important empowerment practices that apparently contribute positively to an employee's level of job satisfaction. First, a well-defined organizational mission, combined with performance-based rewards, strengthens how much the employee aligns themselves in a positive way with company goals, thus giving them more of a sense of purpose. Secondly, organisations that allow employees a degree of autonomy in decision-making gives them a sense of so-called ownership over their role. This too increases engagement and involvement in the organisation's success. Thirdly, by delegating certain managerial responsibilities to lower-level employees, an organisation can promote a sense of trust and accountability even in more junior employees. Finally, effective communication between departments ensures that employees feel informed and supported by the organisation and their colleagues above and below them in the hierarchy.

However, the team also found that problems can arise when there is excessive standardization. While consistency and efficiency are important to success within an organisation, overly rigid structures can stymie initiative and limit career growth opportunities. The team suggests that as workplaces become more digitalized, organisations must find the right balance between structured processes and allow sufficient flexibility to encourage innovation and employee development.

The team adds that job-enrichment strategies, such as decentralization, team-based collaboration, and the use of digital tools, can boost engagement and job satisfaction. Specifically, with regard to the latter, technologies that allow flexible work arrangements and facilitate communication across different locations can improve engagement and satisfaction.

Papageorgiou, G., Christofi, K., Gelinou, A., Efstathiades, A. and Tsappi, E. (2025) 'Employee empowerment and job satisfaction in the evolving digital banking workplace', Int. J. Economics and Business Research, Vol. 29, No. 8, pp.41-60.
DOI: 10.1504/IJEBR.2025.144288

Electronic waste, including PCBs, is a rapidly growing problem as consumers endlessly replace their electronic gadgets. Regulations can go so far to nudge this waste into a recycling stream, but there is still the pressing need for the technology to process the waste.

The retrieval and extraction of useful metals from electronic waste will be a critical part of creating a sustainable future if that is to be technology led. Many metals are relatively rare or found only in geopolitically sensitive regions of the world. More to the point, we have tonnes of discarded devices, circuit boards, and wiring sitting in recycling dumps and landfills. If there were a simple way to extract metals, such as copper, from these resources, that use less energy and fewer resources than mining the ores, then that would offer us a more environmentally friendly option to sourcing copper.

Jayashree Mohanty, Puspita Biswal, Subhashree Subhasmita Mishra, and Tamasa Rani Das Mohapatra of the C.V. Raman Global University in Bhubaneswar, Odisha, India, have now demonstrated an approach to extracting copper from printed circuit boards that does not require the PCBs to be dismantled. Their approach, reported in the International Journal of Environmental Engineering, uses pieces of chopped up PCBs as one electrode in an acidic solution, the electrolyte, with the other electrode is a stainless steel plate. By passing an electric current through the electrodes and the solution it is possible to dissolve the copper as positive ions into the solution. The current then drives these ions towards the negative electrode, the steel plate, where they are deposited as metallic copper. This copper plating can be readily removed from the steel electrode.

This simplified electrochemical copper extraction process avoids the usually energy-intensive mechanical shredding or chemical leaching process used in recycling and so uses less energy overall as well as minimizing processing waste and chemical pollutants. It thus has the potential to extract copper from the electrical waste stream much more effectively than was previously possible.

The team add a not-so-secret sauce to their copper extraction recipe, a salt called sodium sulfate. This substance, added to the electrolyte, buffers the solution and at a certain concentration improves the current density and efficiency increasing the amount of copper dissolved from the PCBs and deposited on to the steel cathode. The researchers found that a concentration of 0.03 molar sodium sulfate gave them the highest current efficiency, at 77%, However, the highest copper purity (99%) was obtained at 0.02 molar. There will thus be a compromise in process efficiency and retrieval rates using this additive.

Mohanty, J., Biswal, P., Mishra, S.S. and Mohapatra, T.R.D. (2025) 'Electrochemical recovery of copper from the waste computer printed circuit board', Int. J. Environmental Engineering, Vol. 13, No. 1, pp.1–11.
DOI: 10.1504/IJEE.2025.143562

Research in the International Journal of Information and Communication Technology has examined the relationship between local government debt and economic growth. Lian Pan of Hunan International Economics University in Hunan, China, used the Panel Smooth Transition Regression (PSTR) model to analyse data in combination with a federated learning data enhancement algorithm. Pan could thus explore how different economic structures influence the effects of borrowing. The findings suggest that while local government debt can support growth, its impact depends on the structure of the local economy. This raises important questions for policymakers.

One of the findings from the research is that industrial composition can shape the outcomes of government borrowing. In areas with well-established industries, debt-financed investment can contribute to economic expansion. However, in less diversified economies, the benefits are less obvious. Indeed, debt may place additional strain on financial resources. The research indicates that simply managing the level of debt is not enough, it is equally as important to define clearly the allocation of borrowed funds.

The findings come at a time when many local governments are facing increasing financial pressures. Economic shifts, rising borrowing costs, and "changing revenue structures" have made fiscal planning even more complex than it was ever before. Some authorities, facing shortfalls, turn to less sustainable sources of revenue, such as land sales or off-budget financing. The study highlights the risks associated with such approaches and stresses the need for greater transparency and more structured debt management practices.

It is worth noting, that the use of federated learning, a machine learning method, has allowed for more precise analysis while maintaining data privacy. By integrating this approach with the PSTR model, Pan's work has enhanced our ability to assess financial relationships without exposing sensitive information. The method could be further refined through vertical federated learning. This would account for variations in the data distribution across different regions. Addressing these differences could improve the accuracy of economic models and their application to policymaking.

Pan, L. (2024) 'Correlation analysis between local government debt and economic growth combined with PSTR model', Int. J. Information and Communication Technology, Vol. 25, No. 9, pp.22–42.
DOI: 10.1504/IJICT.2024.143319

Facial emotion recognition could have broad applications across healthcare, education, marketing, transportation, and entertainment. It might be used to help monitor patients remotely or in over-stretched hospitals or emergency response settings, or patients unable to communicate well for any number of reasons. It could be used to personalize learning, allowing a computerised training system to respond more appropriately to the user. Similarly, such a system could improve customer service and might even be used to create immersive entertainment experiences.

Computer systems that can identify emotions from our facial expressions are in development, but still face man challenges. The earliest systems relied on a single method, such as mapping a person's face and matching it to a database of annotated expressions. Some approaches based on this simplified method are more accurate than others, but none yet captures all the nuance of human emotion as it is expressed in our faces.

Research in the International Journal of Biometrics introduces a new approach based on machine learning that could address this problem and make an emotion detector viable for a wide range of applications. The biggest issue that is addressed by the new work is that it can extract a complex emotion from real-world situations where environmental factors, incomplete data, or complex emotions might affect the accuracy of the results. However, the new approach brings together facial expression recognition and uses the person's speech and tone of voice or even what they might be writing to give a more accurate result.

In their experiments, researchers Jian Xie and Dan Chu of Fuyang Normal University in Anhui, China, achieved a recognition accuracy of 98.6% with their approach. The system was particularly adept at identifying happiness or a neutral emotional state when compared with earlier systems. The system could not cope quite as well with the identification of disgust and surprise, however.

Xie, J. and Chu, D. (2025) 'Character emotion recognition algorithm in small sample video based on multimodal feature fusion', Int. J. Biometrics, Vol. 17, Nos. 1/2, pp.1–14.
DOI: 10.1504/IJBM.2025.143720