An area of increasing importance in digital marketing is the role of the influencer. Influencers are individuals with some degree of fame online, a large and loyal following, and great reach, usually across a number of social media platforms, such as Instagram, TikTok, and YouTube in the International Journal of Information and Communication Technology has looked at how personality traits shape an individual's attitudes towards influencers.

Influencers have gained a lot of prominence in industries such as fashion, beauty, technology, and food and the biggest can affect public attitudes to brands quite significantly. Indeed, many people are reliant on these modern-day celebrities to guide their purchasing decisions and follow closely their favourite influencer's advice on brands. Brands know this and invest vast sums in influencer marketing to encourage the influencers to help them sell their products and services.

In the current research, a survey of almost 400 people from Colombia and Spain was conducted in order to fill the knowledge gap with regards to what leads to someone being "influenced". The team used the statistical approach partial least squares analysis, to help them identify cause and effect relationships in the data. They found that people with extrovert and disorganized personalities were more likely to have favourable attitudes toward influencers. That said, there was a gender gap: calm men and sympathetic women were particularly drawn to influencers.

The results suggest that the success of an influencer markting campaign may depend not only on the influencer's content but also on the psychological makeup of their audience. Armed this knowledge marketers might craft more personalized and targeted campaigns. Such an approach could be particularly beneficial in highly competitive sectors where influencer marketing has become a near-essential part of brand promotion.

Future research in this area might look at the specifics of whether various personality traits and being influenced are associated with specific influence types, such as beauty influencer as opposed to tech influencer.

Sánchez-Torres, J.A., Roldan-Gallego, J.S., Arroyo-Cañada, F-J. and Argila-Irurita, A.M. (2024) 'Which people are loyal followers of influencers? An exploratory study', Int. J. Information and Communication Technology, Vol. 25, No. 1, pp.25–34.
DOI: 10.1504/IJICT.2024.139828

Various recent technological advances allowed people to reshape their physical exercise during the COVID-19 pandemic. Those technologies are still in place and continue to allow people to engage in physical activity and sports in a virtual training setting. While many people have gone back to their traditional exercise venues, the outdoors, sports fields, and the gym, the paradigm shift wrought by the pandemic pressed alternatives on us with regard to our fitness routines that might continue to be a natural part of future public health.

Research in the International Journal of Healthcare Technology and Management has looked at how the integration of technology into everyday exercise routines affected people in Colombia, Pakistan, and Spain. It offers insight into how the pandemic affected those people, how virtual training continues to be a part of people's lives, and how we might keep fit during the next pandemic or another global crisis.

The researchers used the Theory of Planned Behaviour, a psychological model often used to explain and predict individual actions based on attitudes, social influences (subjective norms), and perceived control over actions. This approach allowed them to understand the human response to abrupt closure of gyms and restrictions on outdoor movement during the pandemic lockdowns. They added structural equation modelling, a statistical technique, to analyse data from surveys to reveal the relationships between psychological factors and the adoption of virtual sports activities.

Earlier work has shown that psychological factors influence conventional sports participation, but the focus on virtual training during a global crisis, shows just how useful technology, such as fitness-monitoring watches, smartphones, and other devices, was during the lockdowns. In addition, people with access to fitness tutorials and online classes commonly used those in parallel with their devices to help them follow a structured routine and monitor their progress.

From the opposite perspective, the virtual world allowed many trainers and instructors to continue teaching but remotely from their students. Indeed, the notion of virtual training, which had been around for a while, but necessarily widely adopted, allowed trainers to teach students around the world and many did so during and after the height of the pandemic.

The pandemic emphasised once again the need to stay physically active even in times of crisis. Future public health initiatives might now prioritize accessible home-based sports and exercise options. This could happen with more investment in virtual training platforms, the promotion of digital fitness tools, and efforts to ensure that such resources are widely available to all before and after a period of crisis.

Sánchez-Torres, J.A., Arroyo-Cañada, F-J., Argila-Irurita, A., Montoya-Restrepo, A. and Saleem-ahmed, M. (2024) 'At-home virtual workouts: embracing exercise during the COVID-19 pandemic', Int. J. Healthcare Technology and Management, Vol. 21, No. 2, pp.129–142.
DOI: 10.1504/IJHTM.2024.140383

The invasive North American plant species, Parthenium hysterophorus, commonly known as Santa Maria Feverfew and Famine Weed. The species is now present in Africa, Australia, and India, where it is locally known in English as Congress Grass. According to researchers writing in the International Journal of Environment and Waste Management, Congress Grass, has become a serious concern for food security, biodiversity, and public health in India and beyond. The species is highly resilient and can quickly displace native plants and crops, threatening agricultural systems. It grows well even under poor climate and soil conditions in which crops usually struggle, and it is one of the most destructive weeds agriculture sees.

Not only is P. hysterophorus very resilient, it has allelopathic properties, which means it releases chemicals that suppress the growth of nearby plants. This gives it even more of an advantage over native plants and crop plants, allowing it to soak up water and nutrients and block sunlight from reaching seedlings. This results in even worse impact on biodiversity and ecosystems and on agricultural productivity where it is rife. In addition to ecological and agricultural problems, the plant is very allergenic and toxic to livestock.

The current research proposes a new approach to dealing with this weed. Instead of focusing on attempting to eradicate it, the team suggests that it might be harvested and composted so that any nutrient loss can be reincorporated into the farm. Moreover, proper composting will destroy the plant's seeds and so reduce the risk of it spreading. The approach benefits from all the economic and ecological advantages of avoiding herbicide use.

Satish Kumar Ameta of Mewar University in Rajasthan, India, and colleagues suggest that governmental and non-governmental organizations have an important role to play in educating farmers about the potential of exploiting a weed in this way so that it might be adopted as a sustainable practice.

Ameta, S.K., Bhatt, J., Joshi, M., Ameta, R. and Ameta, S.C. (2024) 'Management of an obnoxious weed Parthenium hysterophorus through composting: a contrivance for recycling the nutrients', Int. J. Environment and Waste Management, Vol. 34, No. 2, pp.187–208.
DOI: 10.1504/IJEWM.2024.139280

Thermal, infrared (IR), facial recognition technology has advanced apace recently. Research in the International Journal of Information and Communication Technology, moves us another step towards a tenable system that overcomes some of the limitations of traditional visible-light systems.

Naser Zaeri of the Faculty of Computer Studies at the Arab Open University in Ardiya and Rusul R. Qasim of Kuwait Technical College in Abu-Halifa, Kuwait, explain how IR imaging sidesteps the problem of ambient lighting conditions and variations in skin tone seen with visible-light facial recognition. The use of thermal imaging relies on capturing the unique heat patterns emitted by the face rather than reflected light. The heat pattern observed is determined almost wholly by a person's facial vasculature and tissue structures beneath the skin. These are consistent, broadly speaking, regardless of environmental lighting and skin tone. This could make thermal IR a much more reliable alternative to visible-light imaging for biometric identification.

However, thermal recognition has faced challenges. The technology often has to cope with degraded image quality due to factors such as noise, blurring, reduced spatial resolution, and temperature drift. Additionally, variations in facial expression and pose can complicate the recognition process. Overcoming these issues requires advanced methods capable of accurately processing and recognizing faces even in less-than-ideal conditions.

Zaeri and colleagues have demonstrated the potential of Convolutional Neural Networks (CNNs) in enhancing the recognition of degraded thermal face images. CNNs are a class of deep learning models that have made a significant impact on the field of computer vision, thanks to their ability to automatically extract and learn complex features from raw images without requiring extensive pre-processing. This capability makes CNNs particularly well-suited to face the biometric challenge.

The team has worked with the well-known ResNet-50 CNN architecture. They applied it to a database of 7500 thermal images in order to evaluate performance with images of different quality and where facial expression and pose are different. The promising results show that this CNN-based system can achieve better recognition accuracy even with degraded thermal images and works across a range of scenarios. The work will have applications in security and the military world.

Zaeri, N. and Qasim, R.R. (2024) 'Resilient recognition system for degraded thermal images using convolutional neural networks', Int. J. Information and Communication Technology, Vol. 25, No. 5, pp.50–71.
DOI: 10.1504/IJICT.2024.140327

Research published in the European Journal of International Management has looked at how positive attitudes towards cultural diversity can significantly enhance team performance. The study, conducted with over 1000 leaders from highly globalized academic research teams in the Nordic region, shows that teams open to diverse cultural values, especially when combined with openness to language diversity, perform better and are more creative than others.

The findings from Jakob Lauring of Aarhus University, Denmark, Christina L. Butler of Kingston Business School, London, UK, Minna Paunova of Copenhagen Business School in Copenhagen, Denmark, Timur Uman of Jönköping University, Sweden, and Lena Zander of Uppsala University, Sweden, have some implications for better management of multicultural teams across various sectors, particularly in an increasingly globalized work environment.

It is important from the management perspective to understand how cultural values and language interact and how they affect workplace behaviour and success. "Cultural values" usually refers to the underlying principles that guide decisions in different societies, such as beliefs about hierarchy, individualism, and communication styles. Language diversity, by contrast, refers to the inclusion of multiple languages within a team, reflecting the varied backgrounds of its members. Both factors are critical to success in increasingly international teams.

Openness to cultural values it appears influences team performance positively. Moreover, teams that are receptive to both cultural and language diversity are commonly better positioned to capitalize on the benefits of each. Essentially, a willingness to embrace different languages within a team enhances the positive effects of being open to various cultural values, leading to improved collaboration, creativity, and overall team outcomes. The impact is synergistic, whereas earlier studies had not necessarily demonstrated that these two factors work together and were perhaps considered as operating independently. It is therefore time to refine research models concerning the way teams work Different types of diversity attitude and how they interact and influence each other must be embedded in such models.

Lauring, J., Butler, C.L., Paunova, M., Uman, T. and Zander, L. (2024) 'Openness towards language differences and cultural differences in multicultural teams: how do they interact?', European J. International Management, Vol. 24, No. 1, pp.1–24.
DOI: 10.1504/EJIM.2024.140297

Research published in the International Journal of Healthcare Technology and Management has looked at how additive manufacturing, colloquially referred to as "3D printing", is changing healthcare. This transformative technology, long associated with advances in traditional manufacturing, is increasingly being recognized for its capacity to produce highly customized, patient-specific medical models. Such models can be used in surgical planning, training, and the production of custom prosthetics and other medical devices.

However, despite its promise, Ethan Sanekane, Jill Speece, Mohamed Awwad, and Xuan Wang of California Polytechnic State University in Obispo and Sara Moghtadernejad California State University Long Beach, California, USA, suggest that access to this technology in healthcare is rather limited. There is an information gap that the current research seeks to fill.

Additive manufacturing, as the name suggests, involves the creation of objects by adding material layer by layer. This approach, pioneered in the 1980s, but having come to the fore in many areas in the last couple of decades, can be used to produce highly complex and detailed structures that would be beyond economic viability in conventional manufacturing. In healthcare, this technology enables the creation of models that might be an exact replica of a patient's anatomy, for instance. Surgeons could, for example, then use such a model of a disease site to plan a sophisticated procedure with unprecedented precision. Such a model might be even more useful with robotic surgery, where the model could be used to train the robot with no risk to the patient.

The same technology could be used to craft bespoke orthotics and prosthetics that are precisely tailored to the patient's unique needs, rather than being off-the shelf components that might be cut to fit, as it were.

The research has taken an important step forward in identifying the full potential of additive manufacturing in healthcare. By addressing the barriers to access and strategically locating additive manufacturing hubs, the researchers have perhaps paved the way for greater adoption of this transformative technology.

Sanekane, E., Speece, J., Awwad, M., Wang, X. and Moghtadernejad, S. (2024) 'Healthcare industry input parameters for a deterministic model that optimally locates additive manufacturing hubs', Int. J. Healthcare Technology and Management, Vol. 21, No. 2, pp.111–128.
DOI: 10.1504/IJHTM.2024.140392

Research in the International Journal of Computational Systems Engineering has demonstrated a new image compression tool that combines recursive algorithms with convolutional neural networks (CNNs) to out-perform other approaches to the compression of images from computer art and interaction design. Digital art and design increasingly rely on large volumes of visual data, so effective image compression is important for reducing the computer storage requirements without compromising quality. Duan Song of the Department of Fine Arts at Hebei Vocational Art College in Shijiazhuang, China, has proposed an algorithm that works to address the issues by integrating traditional and modern techniques.

Recursive algorithms, which simplify complex problems through repeated application of rules. Song explains that the approach works by breaking an image down into simpler components. By applying the process iteratively, quality can be maintained. The integration of CNNs into the compression approach builds on the way in which such systems were initially inspired by the way the human brain processes visual information. They are widely used in deep learning for image recognition and processing. Song's innovative merging of the recursive methods with CNNs allows him to overcome some of the limitations of earlier image compression techniques, which commonly struggle to achieve useful compression ratios because of the increasing complexity and scale of modern image data.

Song has tested the algorithm on two well-known image datasets, Kodak1 and Kodak2, to evaluate its performance. The results indicate that the algorithm consistently reduced the mean square error (MSE) between the original and compressed images. A lower MSE means better conservation of image quality. After 800 iterations, the algorithm achieved the lowest MSE compared to other methods and also performed well in terms of peak signal-to-noise ratio and multi-scale structural similarity. These results suggest that the proposed method can compress images effectively with no significant loss of quality.

The approach will be useful in the field of computer art but might also be useful in animation modelling, art interface design, and medical imaging.

Song, D. (2024) 'Recursive quantitative analysis modelling of computer art design interaction', Int. J. Computational Systems Engineering, Vol. 8, No. 5, pp.1–11.
DOI: 10.1504/IJCSYSE.2024.139715

A paper in the International Journal of Healthcare Technology and Management, has highlighted the potential benefits of adopting a new approach to collaboration in eHealth initiatives. The approach suggested by Maria Qvarfordt, Stefan Lagrosen, and Lina Nilsson of Linnaeus University in Kalmar, Sweden, braids together the four strands of stakeholder relationships – academia, business, the public sector, and citizens – into what the team calls a quadruple helix (QH).

Digitalisation in healthcare encompasses the adoption of digital technologies across various sectors and is crucial for global healthcare advancements. eHealth specifically refers to the use of electronic tools and methods to improve healthcare delivery and outcomes. For eHealth to be effective, collaboration among various stakeholders is critical. Previous studies have shown that the involvement of different stakeholders can be understood and developed with a traditional triple helix model (academia, government, and industry).

By incorporating a fourth strand – the public – which we might more formally refer to as civil society, an emphasis on the importance of the end-user perspective can be incorporated into eHealth solutions. Each stakeholder group brings unique knowledge, resources, and perspectives and so can benefit the outcomes as a whole, with that whole being more than the sum of its parts in some instances.

To develop the QH approach, the researchers used a grounded theory methodology and collected and analysed stakeholder perspectives on eHealth collaboration. They then aligned their findings from the study with an actor-resource-activity (ARA) model – a framework designed to understand business relationships. The team emphasise the importance of promoting value and quality in eHealth development collaborations. They highlight the role of knowledge and competence.

Overall, the QH approach is more inclusive and participatory and will hopefully lead to more effective and more widely accepted eHealth implementations. The paper thus offers an invaluable framework for understanding and improving stakeholder collaboration in the digitalisation of healthcare.

Qvarfordt, M., Lagrosen, S. and Nilsson, L. (2024) 'Quadruple helix collaboration for eHealth: a business relationship approach', Int. J. Healthcare Technology and Management, Vol. 21, No. 2, pp.89–110.
DOI: 10.1504/IJHTM.2024.140387

We are living in an era of astonishing data proliferation and the sharing of user-created content across all kinds of media, from social networks to news sites, e-commerce reviews to endless forums for every kind of interest and niche. Being able to accurately interpret emotions conveyed through such messages is increasingly important for social science and politics, in marketing, business, and economics, and elsewhere.

Recent advancements in the field of so-called "sentiment analysis" have led to the development of more sophisticated models capable of extracting and interpreting emotional subtleties in textual data. One such model is the BERT-ABiLSTM – Bidirectional Encoder Representations from Transformers, Attention Bidirectional Long Short-Term Memory. Research in the International Journal of Information and Communication Technology reports on how this large-scale pre-trained algorithmic can be used for sentiment analysis. However, as author Zhubin Luo, of the Hunan University of Humanities, Science and Technology in China, points out the system's use of ABiLSTM, means there are some limitations as it focuses on global features and can overlook nuance.

BERT, Luo explains, can learn language representations from extensive bodies of text. The ABiLSTM, a recurrent neural network, processes text sequences. Luo has now added TextCNN (Text Convolutional Neural Network) to the system to make BERT-CNN-ABiLSTM, a more sophisticated version of the model.

Overall, the underlying bidirectional approach allows the model to understand context from both past-to-future and future-to-past segments of text. This is important for capturing long-term dependencies in text. The attention mechanism within ABiLSTM further refines this by enabling the model to focus on the most pertinent parts of the text when making predictions, thus improving the accuracy of sentiment analysis.

The TextCNN component then uses convolutional kernels of various sizes to detect different granularities of features within the text. This allows the model to capture much more subtle local patterns within the text that would have been missed by simpler models, thus providing a yet more detailed analysis of textual content.

The improvements reported by Luo are particularly relevant for scenarios that require detailed text classification and recognition. This might include sentiment analysis on social media, evaluating customer feedback in e-commerce platforms, or empowering "intelligent" online question-and-answer systems.

Luo, Z. (2024) 'A study into text sentiment analysis model based on deep learning', Int. J. Information and Communication Technology, Vol. 24, No. 8, pp.64–75.
DOI: 10.1504/IJICT.2024.139869

In 2015, the United Nations initiated the Sustainable Development Goals (SDGs) an ambitious plan to end poverty, safeguard the environment, and promote prosperity for all by 2030. These 17 interconnected goals recognise that progress in one area can affect another area in positive ways. For instance, improving water quality (SDG 6) can have a ripple effect, enhancing health (SDG 3) and education (SDG 4). It is important to understand the connections so that effective policies can be put in place to help us achieve the goals in what might be referred to as a holistic manner.

A review in the International Journal of Sustainable Development has looked at the state of research in this area and provide a systematic summary, as well as incorporating insights from a workshop with SDG experts. The findings show that work is still in its infancy and focuses largely on statistical and conceptual associations rather than causal relationships. The reviewers suggest that this emphasis on correlation rather than causation means it is difficult to reproduce findings and apply them to policymaking. This, they add, is a particular issue at the local level.

The SDGs are the successor to the Millennium Development Goals and emerged from a participatory process that required consensus among UN member states. Unlike those earlier projects, the SDGs are broader in their scope and more integrated. They emphasize the need to understand how progress in one goal might affect other goals. This, the team suggests, means that policymakers and other stakeholders must recognise the interconnectedness of the goals and dismantle the old policy silos to help promote integrated approaches to sustainable development.

A new framework to address the gaps in research and understanding, emerges from this current review. The framework stresses the importance of the local context, as well as the interconnections that might vary critically because of regional socio-economic and environmental conditions. As the 2030 deadline approaches, there is an increasing urgency in attempting to achieve the SDGs. The consolidation of research findings and the standardization of data collection could help us implement real-world action that works to achieve those goals.

Chaniotakis, E., Siragusa, A., Tzanis, D. and Stamos, I. (2024) 'Scoping SDG interlinkages and methods to infer them', Int. J. Sustainable Development, Vol. 27, No. 5, pp.1–54.
DOI: 10.1504/IJSD.2024.140326