User Behaviour Research Articles

Knowledge, perceptions, and consumption behaviour of cosmetics among undergraduates of Sri Lanka: a descriptive cross-sectional study.

Despite the increasing usage of personal care products among young adolescents in Sri Lanka, limited studies have been conducted to understand the behaviour of cosmetic users and associated health complications. Therefore, the current study was conducted to evaluate the cosmetic consumption related behaviour of the undergraduate community in Sri Lanka and to identify the driving factors behind the incidences of cosmetic related adverse health effects. An analytical cross-sectional study was conducted by recruiting 421 undergraduates from five state universities in Sri Lanka through stratified random sampling, as the study population. Information on socio-demographic factors and cosmetic consumption behaviour of the participants were acquired through a self-administrated structured questionnaire, along with Knowledge, Attitudes and Practices (KAP) relevant for cosmetic usage. The Binary Logistic Regression model was used to determine the significant socio-demographic driving factors on cosmetic usage among the undergraduate community in Sri Lanka at a confidence level of 95%. Around 96.4% of the study population used one or more cosmetic products (77%) out of which, 75.3% experienced cosmetic related adverse health effects. Skin dryness (24%), acne (21%), allergies (20.5%) and rashes (19.8%), were identified as the most dominant adverse health effects, related to cosmetic usage. Perfumes (65.6%), face cream (63.2%) and body lotion/hand cream (60.6%), were the mostly used cosmetic products. Even though half of the study population exhibited higher levels of good practices during purchase (54.9%) and application (52%) of cosmetics, around 47.5% were characterized with a low level of knowledge on cosmetics. Gender, academic year, knowledge on cosmetics, monthly expenditure on cosmetics, source of recommendation for cosmetics, practices related to purchase and consumption of cosmetics and preference to receive medical care in case of cosmetic related emergency were recognized as significant risk factors (p < 0.05) associated with the incidence of cosmetic related adverse health effects among undergraduate students. Given the high prevalence of cosmetic related adverse health effects, the health authorities of Sri Lanka should pay more attention towards the wellbeing and responsible cosmetic usage among undergraduates. Designing of effective tools and regulations to monitor the cosmetic market and improving the knowledge on cosmetics are recommended to ensure safe cosmetic usage within the country in general and of adolescent users in particular.

Detecting Positive and Negative Deviations in Cross-Domain Product Reviews using Adaptive Stochastic Deep Networks

The analysis of sentiment in product reviews across diverse platforms such as e-commerce website and social media presents a challenging task due to the inherent differences in user behaviour and review formats. This research introduces an innovative methodology for detecting positive and negative deviations in cross-domain product reviews using Adaptive Stochastic Deep Networks (ASDN) tailored for multi-platform sentiment analysis. ASDNs possess mechanisms that enable dynamic adaptation to changes in data distributions, domain shifts, or varying complexities within the input data. The proposed framework aims to capture refined variations in sentiment expression across disparate platforms by incorporating adaptive stochasticity within deep neural networks. By adapting dynamically to changes in review styles, language use, and sentiment patterns unique to each platform, the ASDN architecture facilitates the identification of nuanced sentiment shifts. Through extensive experimentation on comprehensive datasets spanning Amazon, Facebook, and Instagram, the efficacy of the ASDN model in detecting positive and negative sentiment deviations across diverse platforms is demonstrated. This research contributes to advancing the understanding of sentiment dynamics across distinct social platforms and e-commerce sites, paving the way for more robust and adaptable models in cross-domain sentiment analysis.

IoT in Home Automation: A Data-Driven User Behaviour Analysis and User Adoption Test

This research carried out a thorough data-driven examination of user behaviour, adoption rates, satisfaction, and energy efficiency in the context of IoT in home automation, within the quickly changing environment of smart homes and Internet of Things (IoT) technologies. The study found that users interacted with various kinds of IoT devices in diverse ways. Smart security systems and thermostats, for example, were quickly adopted and received high levels of satisfaction. The potential for significant energy savings demonstrated the contribution of IoT devices to sustainability. These results highlight the significance of making well-informed decisions when using IoT technology to create smarter, more efficient, and greener living environments. They also provide useful insights for manufacturers, legislators, and homeowners.

Tensile Membranes and Public Spaces Thermal Comfort: An Analytical Review

Urbanization altered urban microclimates intensifying the Urban Heat Island (UHI) effect and thereby increasing outdoor thermal discomfort. Tensile membrane structures (TMS), due to their shading and cooling properties, present a viable solution for enhancing thermal comfort in urban public spaces. This paper synthesizes numerical outcomes from several studies integrating empirical data, computational models, and subjective assessments to evaluate the impact of TMS on thermal comfort. Results indicate that TMS can reduce ambient temperatures by 2-3°C and Physiological Equivalent Temperature (PET) by approximately 2.3-2.4°C or up to 5.4ºC on Mean Radiant Temperature (MRT) values, therefore enhancing user thermal comfort. The study underscores the critical role of design factors such as form, aperture, location, and strategic placement in optimizing TMS effectiveness. It also highlights the importance of considering local climatic conditions and user behaviours in the design and implementation of shading solutions. Subjective assessments reveal higher comfort levels in shaded areas, emphasizing the psychological benefits of TMS. Overall, TMS not only mitigate heat stress but also enhances the aesthetic and functional quality of urban environments, contributing to sustainable and liveable urban development. This comprehensive review provides valuable insights for urban planners, designers, and policymakers in creating more comfortable and resilient public spaces.

Securing the Future of Digital Marketing through Advanced Cybersecurity Approaches and Consumer Data Protection Privacy and Regulatory Compliance

The SecureDigitalGuard framework gets recognition for its all-encompassing strategy, which combines strict consumer data protection laws with state-of-the-art security safeguards with ease. This all-encompassing approach is designed to guarantee the longevity of digital marketing in the face of constantly changing cyberthreats. This cutting-edge system is based on three key strategies: the Behavioural Threat Detection (BTD) algorithm, the Adaptive Access Control (AAC) algorithm, and the Homomorphic Privacy Guard (HPG) programme. The vital task of dynamically controlling user access levels in response to continuing risk evaluations is taken on by the AAC algorithm. This dynamic control technique improves the framework's capacity to adjust to constantly shifting security circumstances. However, the BTD algorithm is proactive in spotting abnormalities in user behaviour, allowing for quick reactions to any dangers. The SecureDigitalGuard architecture gains an additional degree of protection from this preventive method. In addition, the HPG programme is responsible for doing analytics while maintaining user privacy. This careful approach shows a dedication to finding a fine balance between user protection and data analysis, making sure the framework complies with the strictest privacy regulations. Test results provide empirical evidence that SecureDigitalGuard is effective and that it can keep up with the dynamic and often changing nature of cyber threats. As a result, the architecture makes traditional cybersecurity techniques outdated. In an increasingly complex and dynamic cybersecurity world, SecureDigitalGuard provides a strong solution for protecting digital marketing through the seamless integration of state-of-the-art technology and strict adherence to privacy regulations.

Advances in Wearable Sensors for Real-Time Internet of things based Biomechanical Analysis in High-Performance Sports

Interest in wearable technology and the need for eco-friendly solutions have spurred new methodologies. This research examines how sophisticated deep learning and biomimetic designs benefit each other. The results may change smart technology forever. The introduction highlights the global appeal of wearable technology and the importance of environmental considerations in design. Deep learning and biomimicry are a fresh and exciting combination that might increase smart device accuracy, energy efficiency, and biomimicry. This project seamlessly integrates biomimetic design elements with deep learning methods. Biomimicry affects wearable technology design and functioning. However, deep learning techniques based on artificial neural networks boost user flexibility and predictive analytics. The controlled experiment allows a thorough examination of a number of datasets designed to cover a wide range of biomimetic settings and user behaviours. The data prove that the proposed technique beats alternatives across several performance parameters. Integrating biomimetic principles with deep learning systems boosts accuracy. This proves the system's reliability. The biomimetic method is eco-friendly since energy efficiency grows dramatically. Biological mimicry indications show that the suggested strategy resembles natural systems. A new exploratory method enhances sustainable technologies. Integrating biomimicry and deep learning efficiently enhances gadget performance and meets environmental standards. This research emphasizes the transformational power of nature-friendly technology, changing our worldview. Our study helps ensure that upcoming wearable technologies are cutting-edge and ecologically beneficial. Deep learning and biomimetic designs are converging, marking a tipping point in sustainable technology. This helps move toward an eco-friendly future.

Organizational complexity and road safety: dialectical contradiction and its behavioural interpretation

The main organizational and technical idea implemented in recent years in Russia in the field of road safety is to reduce the conflict of traffic and pedestrian flows. This is realised through the use of multiphase traffic light cycle modes. The consequence of this is a sharp increase in the duration of the traffic light cycle (up to 180-200 sec) and a decrease in the capacity of the street and road network. However, more surprisingly, it does not lead to an increase in road safety. The article presents the author’s view on the non-obvious effectiveness of such attempts to reduce the conflict of traffic and pedestrian flows and the author’s reflections on the dialectical contradiction between the organizational complexity of traffic light cycles and the behaviour of road users.

A data mining method based on label mapping for long-term and short-term browsing behaviour of network users

A data mining method based on label mapping for long-term and short-term browsing behaviour of network users

Service Quality and Brand Switching Behaviour of Flour Users in Federal Capital Territory, Nigeria.

This study examined the association between service quality and brand switching behaviour of flour users in the Federal Capital Territory (FCT), Nigeria. In line with the objectives of the study, two hypotheses were formulated. The survey research design was adopted for the study. The study population comprised all the bakers in FCT, Nigeria with a sample size of sixty (60) which was conveniently drawn from the population. The questionnaire was the instrument for data collection. Kendall tau_b was employed in testing the hypotheses formulated. The findings revealed a significant positive relationship between value-added service; promotion activities and brand switching behaviour. The study concludes that excellent service quality attracts customers to the company’s brand. The study recommends, amongst others, that Firms should prioritize the integration of special pricing, free trials, and gifts strategies to stimulate large-volume sales. Flour milling firms should consider employees’ training to enhance their capacities in effectively managing customer interactions and mitigating unnecessary customer turnover.

Understanding the actual buying behaviour of organic food users in India: a PLS-SEM approach

Understanding the actual buying behaviour of organic food users in India: a PLS-SEM approach

Unlocking Learning: Investigating the Replayability of Educational Games

Recently, gamification has emerged as an effective approach to captivate users, particularly in the context of online education. Although there has been a rise in the recent surge of articles on gamification, there is still a lack of research on the impact of replayability. We conducted a systematic literature review to address this gap by elucidating the impact of replayability in educational games on learners’ behaviour in e‐learning. The analysis of the investigations reveals the game elements that are predominantly utilised in the literature, and their corresponding effects on learners are identified and mapped. Moreover, we categorise these observed impacts of replayability on learners into six components: variety and performance, experience and nostalgia, learning, goals and completion, social aspect, and difficulty. Additionally, this study also examined the cognitive and affective behaviour of users in relation to replayability. The results of our comprehensive analysis of existing literature indicate that the study of replayability in educational games and its effects on learning is still at the outset. There is a scarcity of experimental tests and evidence in this area, with a prevailing tendency to primarily utilise educational games as external rewards. The study’s findings indicate that educational games that can be played multiple times have a substantial positive impact on learning outcomes and engagement. The study suggests that educators and game developers should prioritise the creation of games that include features supporting repeated play and diverse learning experiences.

Clustering related behaviour of users by the use of partitioning and parallel transaction reduction algorithm

Clustering related behaviour of users by the use of partitioning and parallel transaction reduction algorithm

Clustering related behaviour of users by the use of partitioning and parallel transaction reduction algorithm

Clustering related behaviour of users by the use of partitioning and parallel transaction reduction algorithm

Predicting the usability of mobile applications using AI tools: the rise of large user interface models, opportunities, and challenges

This article proposes the so-called large user interface models (LUIMs) to enable the generation of user interfaces and prediction of usability using artificial intelligence in the context of mobile applications. To this end, we synergized an integrated framework for the effective testing of the usability of mobile applications following a selective review of the most influential models of mobile usability testing. Next, we identified and analysed 13 recent AI tools that generate user interfaces for mobile apps, and systematically tested these tools to identify their AI capabilities. Our striking findings demonstrate that current generative UI tools fail to address mobile usability attributes, such as efficiency, learnability, effectiveness, satisfaction, and memorability. Our large UI models’ architecture proposes to leverage the capabilities of large language models, large vision models, and large code models to overcome the challenges of AI-driven UI/UX design and front-end implementations. This fascinating UI eco-system must be augmented with sufficient UI data and multi-sensory input regarding user behaviour to train the models. We anticipate LUIMs to create ample opportunities, like expedited frontend software development, enhanced personalised user experience, and wider accessibility of smart technologies. However, the research challenges hindering the UI generation and usability prediction of mobile apps include the seamless integration of complex generative AI models, semantic understanding of non-uniform visual designs, scarcity of UX datasets, and modelling of realistic user interactions.

A social network user behaviour data recommendation system based on fuzzy partition clustering

A social network user behaviour data recommendation system based on fuzzy partition clustering

Movie recommendation app with cosine similarity and flask

People are constantly busy with their professions, businesses, and other endeavours in the world in which we currently live. The majority of them find that watching movies is the greatest way to unwind during the limited free time they have between jobs. However, with so many movies available in different languages, choosing which one to watch may be a time-consuming task. The recommendation may be collaborative filtering or content-based. As the name implies, collaborative filtering bases its filtering method on the interactions between relevant user behaviour and that of other users. In order to correlate movies with recommendations, this study describes cosine similarity. To receive the top 5 recommendations, the user must enter the name of a movie they have previously enjoyed. We've also introduced a feature that allows the user to optionally provide the year data that they want the engine to use to propose movies that were published after that specific year. For example, if the user wants to filter recommendations for new movies, they can provide a recent year.

A social network user behaviour data recommendation system based on fuzzy partition clustering

A social network user behaviour data recommendation system based on fuzzy partition clustering

Digital transformation challenges in higher education institutions post COVID-19

Higher education institutions (HEIs) encountered an unanticipated and unprepared digital transformation because of COVID-19, which obliged most services, including teaching, learning, and working, to shift from physical to digital and virtual platforms. Digital transformation among employees provided an opportunity for technological upgrades and challenged technological competence and adaptability among employees to enable transitions between physical and virtual platforms. As a result, this paper through the technology acceptance model (TAM) reflects on factors that influence successful transformation of technological adoption among support staff members in a university of technology (UOT). This paper argues that the use of TAM to predict staff members’ attitudes toward adopting new technology can assist HEIs in decision-making regarding technology selection. Using descriptive research design and non-probability sampling method, data was collected from 177 support staff members from different departments. The findings of the study, which emanated from surveys, established that employees’ digital skills need improvement; lack of digital infrastructure funding; poor planning and unclear forms of communication proved to be a recipe for failure to implement a successful digital shift and in creating acceptance digital user behaviour among the employees.

Factors Affecting Consumer’s Intention to Use Electric Vehicles: Mediating Role of Awareness and Knowledge

This paper explores the role of environmental consequences, perceived barriers, policy interventions, public opinions, and knowledge and awareness in using electric vehicles (EVs). We collected 506 responses about their intention to use the EVs to develop our hypothesis. This study uses knowledge and awareness of the EVs as mediating variables towards adopting the EVs and consumers’ residences as moderating variables. It also introduces several control variables in this proposed research model to measure the effect on the intention to use the EVs. In this research study, we test the importance‐performance map analysis and check Cohen’s f2 to identify a better output. The measurement and structural equation modelling results show that the environmental consequences are a stronger predictor of intention and policy interventions. The findings suggest that government policies can also have an attractive position in the EV segment. In addition, knowledge and awareness mediate the adoption of the EVs. Perceived barriers do not influence consumers to use an EV in India. We test the moderating role of residence with our construct and find a partial moderation role with policy intervention and public opinion. We introduce three self‐created constructs, i.e., intention to use, knowledge, and public opinion. Public opinion for the EVs supports consumers’ intention to use, and knowledge also plays a significant role in using EVs. These newly added constructs will be essential to manufacturers and policymakers while promoting EVs. Also, environmental consequences and policy interventions emerge as significant factors of user behaviour. The paper highlights the critical predictors of consumers’ intention to use the EVs. Thus, it helps society, policymakers, and managers formulate and implement schemes to boost EV purchasing.

Wearable Clothing Drop Box Design Using Persuasive Design Approach

The phenomenon of fast fashion (the trend of dressing in a short period of just six months to two years) is a trend that occurs today. This trend raises the problem of stacking clothes. Clothes that are not reused should be used and distributed to others in need or known as the recycling concept. This study aims to design a drop box that can collect and distribute suitable clothes by using Persuasive Design approach. Dropbox is used to improve the concept of sustainability of clothing products. The initial stage of the study was an interview to explore the target of user behaviour in using clothes and explore the inhibiting factors experienced by users due to confusion in distributing their used clothes. Affective design approach is chosen by applying tunnelling, reduction and conditioning technology methods. Dropbox design built in a physical and digital display of the product. The product evaluation process using Usability Testing with the results obtained an efficiency rate of 100%, an effectiveness of 90.625%, and a System Usability Score (SUS) of 83.44, showing that the prototype has good usability. In the persuasive aspect, namely effectiveness, quality, and capability, average values of 5.75, 6.125, and 5.958 were obtained. Improvement recommendations are made based on the results of evaluation by respondents, namely make a clear information at drop box product and display improvement.