User Behaviour Research Articles

Mixed Poisson Processes with Dropout for Consumer Studies

We adapt the classical mixed Poisson process models for investigation of consumer behaviour in a situation where after a random time we can no longer identify a customer despite the customer remaining in the panel and continuing to perform buying actions. We derive explicit expressions for the distribution of the number of purchases by a random customer observed at a random subinterval for a given interval. For the estimation of parameters in the gamma–Poisson scheme, we use the estimator minimizing the Hellinger distance between the sampling and model distributions, and demonstrate that this method is almost as efficient as the maximum likelihood being much simpler. The results can be used for modelling internet user behaviour where cookies and other user identifiers naturally expire after a random time.

Digital Revolution: How AI is Transforming Content Marketing

Artificial Intelligence (AI) is revolutionising content marketing by automating key processes and enabling personalisation on a massive scale. Using technologies such as Machine Learning and Natural Language Processing, AI can generate content quickly, identify SEO-relevant keywords and improve campaign performance. It analyses user behaviour, anticipates their needs and optimises marketing strategies in real time. However, this automation poses ethical challenges, such as algorithmic bias and over-reliance on technology. To exploit these tools responsibly, it is crucial that companies adopt ethical and transparent practices, respecting data confidentiality rules. In the future, AI will continue to transform marketing, with innovations such as virtual agents and even more personalised strategies, while requiring a balance between technological efficiency and human authenticity.

Exploring the negative perceptions of tree-based urban green space. People's behaviour and management are crucial

Studies dealing with services and benefits of urban green space are overrepresented compared to those addressing disservices, perceived constraints and other negative perceptions. Even when focusing on disservices and perceived constraints, the studies often take into account only a limited number of reasons for the negative perception. In this study we conducted a series of focus group interviews with the members of the public in the city of Zagreb (Croatia) with the goal of grasping the variety of reasons for negative perception, providing classification of those perceptions and exploring whether and how the perception is related to sociodemographic characteristics. Participants mostly discussed negative perceptions due to the behaviour of other users and perceived inadequate management of urban green space, while the least about the perception of safety. Tree disservices accounted only for a small portion of overall reasons for the negative perception. Age, gender, household income, employment status, number of preschool children and the length of living in Zagreb proved statistically significant for the perception, while education was not. Focus groups as a method enabled cataloguing the variety of reasons for negative perception. Understanding the range of reasons leading to the negative public perception of urban green space contributes to better planning, management and meeting public needs and expectations.

A Weibo local network growth model constructed from the perspective of following-followed

Abstract In order to explore the evolution process of the Weibo local network, this study first defines four factors influencing the evolution of the Weibo network. On this basis, the BA scale-free network model was enhanced by incorporating these four factors and accounting for directionality, resulting in a Weibo local network evolution model based on user attributes and behavioral similarity. The model's validity was validated by comparing simulation results with real data. The findings indicate that the Weibo local network exhibits both small-world characteristics and distinctive features. The results show that the Weibo local network exhibits both small-world characteristics and distinctive properties. The in-degree distribution follows a mixed pattern of exponential and power-law distributions, the degree-degree shows isomatching, and both the in-degree centrality and eigenvector centrality values are relatively low. This research contributes to our understanding of user behaviour in the Weibo network, and provides a structural basis for exploring the impact of Weibo network structure on information dissemination.

Digital Advocacy Strategies with Data Analytics Framework: A Case Study for Effective Campaigns

This paper presents a case study of a digital campaign on social media to formulate an extensive novel framework using data analytics method to measure the effectiveness of a social media campaign. The Social Media Impact Discovery (SMID) framework is built from the concept of digital advocacy strategies and data analytics techniques, which will provide a systematic approach in both domains to carry-out an effective advocacy campaign. Since the advent of social media such as Twitter in providing platforms for freedom of speech, digital advocacy has become the new approach for delivering campaigns to a wider range of targeted audiences. The use of digital advocacy has generated interest among politicians and advocators to spread their messages across. Despite the numerous and widespread use of digital advocacy, there is still no formal framework that study the success of the technique as end outcomes in terms of the technological aspect of knowledge and the data analytic. The impact of the campaign is evaluated in terms of its effectiveness using deep machine learning methods on the acquired data on social media of a digital campaign. The results have shown that continuous loops using Support Vector Machine (SVM) and the Naïve Bayes classifier support the dynamic approach in the SMID framework to increase campaign effect and is in line with the iterative structure. The loop's cyclical structure is in tune with the shifting user trends and social media user behaviours. The framework's ability to include fresh data and modify models as advocacy campaigns change over time provides a tactical advantage responsive to shifting conditions. Thus, the overall processes of the campaign should be supported with accurate and reliable data to build trust among its audiences. A case study of sexual harassment on Twitter is used to assess the SMID framework’s practicality. It centred on a young Malaysian advocate named Ain Husniza, whose notable catchphrase is #MakeSchoolASaferPlace. For the first time, digital advocacy for sexual harassment campaigns has been used and analysed in this study. It has demonstrated its contribution by amplifying survivors’ voices and fostering online solidarity, with the aim of raining public awareness of the issue and promoting informed policy.

Applications of Google Trends as a Data Source for Statistical Models

As technology advances, there is a growing number of potential data sources that can provide an alternative to traditional surveys. An example of this is the real time search popularity data made available through Google Trends. This type of data makes it possible to study public opinion, behaviour and attitudes in society or forecast economic phenomena. A definite advantage of using search popularity data is the immediate availability and low cost of obtaining such data. Also of significance is the fact that the Google Trends tool allows for direct research into the behaviour of Internet users, and not just their declarations as in the case of a survey. This can make a difference if respondents consider one of the answers to be more morally correct. Nevertheless, the use of Google Trends requires selecting correct search topics and terms to be included in the study and an awareness of the fact that the research sample is limited to Google search engine users. The paper will present the advantages and disadvantages of Google Trends and review its usefulness as a data source especially in times of higher market volatility.

MIC: Memory analysis of IndexedDB data on Chromium-based applications

As Chromium-based applications continue to gain popularity, it is necessary for forensic investigators to obtain a comprehensive understanding of how they store and manage browsing artifacts from both filesystem and memory perspectives. In particular, the incognito mode developed in the current version of Chromium uses only physical memory to manage data related to active sessions. Therefore, handling physical memory is essential for tracking a user's browsing behaviour in incognito mode. This paper provides an in-depth examination of LevelDB, a lightweight key-value database utilized as Chromium's implementation for IndexedDB. In particular, we delve into the details of how IndexedDB data is managed through LevelDB, taking into account its low-level database file format. Furthermore, we thoroughly explore the possibility of residual data, both complete and incomplete, being retained as applications create and initialize IndexedDB-related data. Based on our research findings, we propose a systematic methodology for inspecting the internal structures of LevelDB-related C++ classes, carving these structures from binary streams, and interpreting the data for forensic analysis. In addition, we develop a proof-of-concept tool based on our approach and demonstrate its performance and effectiveness through case studies.

Editorial: Perspectives on Machine Learning

Progress in machine learning technology has truly impacted our lives by tailoring many of our daily experiences to be seamless and intuitive. This innovation has brought about changes in day-to-day routines; from suggesting music based on our emotions to offering recommendations for places to visit or meals to try out. This special issue explores various Machine Learning technologies. Among some are Machine Learning advances that improve human interaction, predict user behaviours, analyse user reviews, and optimize high-risk investments like Bitcoin trading. These technologies enhance user experiences, help businesses refine marketing strategies, and provide quick insights from vast amounts of information, elevating AI to new heights. With the rise of transformation into advanced technologies taking prominence in our lives, we expect to see these machine learning innovations being integrated across many sectors and uses.

Authorship Dynamics in Multidisciplinary Research: A Focus on Library and Information Science Professionals in RRIJM

This study looks into bibliometric studies in library and information science (LIS) to learn more about the influence of professional collaborations and multidisciplinary publication. It centers on user behaviour, digital libraries, and information management. The study uses bibliometric methodologies to examine authorship trends, geographic distribution, research paper length, and references. The results enhanced comprehension of the importance of library science in academic communities by emphasizing its function in handling intricate problems and promoting information sharing. Data from the Research Review International Journal of Multidisciplinary has been used in the study.

Exploring the effects of location information on perceptions of news credibility and sharing intention

In recent years, the integration of location-based services into social media platforms has seen a significant surge, coinciding with the growing challenges posed by the proliferation of fake news online. However, the influence of location data on readers’ perceptions of online news credibility, particularly in relation to the reporters’ whereabouts, remains unclear. To investigate this relationship, we conducted a 3 (Topics: crime, science, health) × 2 (Location anchor: event-anchored or participant-anchored) × 4 (Proximity to location anchor - no, same, close-by or faraway location) mixed-method online study (N = 288) on Prolific. Our data collection involved presenting participants with news articles and assessing their credibility assessments and sharing intentions based on the proximity of those disseminating the news to both the subject matter of the news and the audience consuming it. Our findings reveal that the proximity of the reporter’s location to the readers’ location had a noticeable adverse impact on perceptions of news credibility and the likelihood of sharing it. Furthermore, we also identified a weak positive correlation between sharing intentions and trust in social media platforms. In addition, we observed that crime news were generally perceived as less credible compared to health and science news. Our research contributes significantly to a nuanced understanding of how location-based cues impact user behaviour when interacting with online news articles. Furthermore, it provides design insights for social media platforms aiming to enhance user trust and promote pro-social behaviours.

Evolution of user behaviour on social media during 2018 road safety movement in Bangladesh

Evolution of user behaviour on social media during 2018 road safety movement in Bangladesh

Enhancing Building Sustainability: Integrating User Behaviour and Solar Orientation in the Thermal Performance of Houses

Enhancing Building Sustainability: Integrating User Behaviour and Solar Orientation in the Thermal Performance of Houses

Supporting Indonesian Online Waqf as Islamic Economics Source: Waqif Behaviour Approach in Java Island

Indonesia has great potential for asset waqf and cash waqf. Technological advancements show significant growth through fintech platforms for donations, zakat, and waqf. However, the magnitude of the potential of waqf and the advancement of financial technology is not in line with its realisation; this is a fundamental problem in Indonesia. The study aims to analyse the support of online waqf, an approach to waqf behaviour in Indonesia, by examining the influence of waqf literacy, trust, and attitude on behavioural intentions and behaviour of users who pay online cash waqf with age as a moderating variable. The study used a quantitative method with a survey approach to waqif in Indonesia, totalling 242 respondents through purposive sampling domiciled on the island of Java. Data analysis uses a structural equations model with the partial least squares (SEM-PLS). The study found that waqf literacy, beliefs, and attitudes have a significant relationship and a positive influence on behavioural intentions and user behaviour. Age has a significant impact on moderating the relationship between literacy and behavioural intentions. Behavioural intentions are significantly related to online waqf user behaviour. The implications of the research are increasing the support of literacy, trust and attitude to increase the potential for the realisation of online waqf in Indonesia, especially on the island of Java. The waqif behaviour approach strengthens the literature on Muslim social contributions. Further research examines the behavioural aspects of online waqf recipients (nazir) with qualitative and quantitative method approaches.

Can solar water kiosks generate sustainable revenue streams for rural water services?

Providing a sustainable supply of safe drinking water in rural Africa depends on sufficient revenue from user payments to maintain services. While handpumps have been the primary source of drinking water for rural Africans for decades, local revenue generation has been unstable, contributing to service disruptions and welfare losses. We examine the effect of upgrading manual handpumps to solar kiosks in rural Mali from 2019 to 2023. We model 452 monthly records of observed payments and metered water usage to estimate changes in volumetric use and revenue generation. Average revenues increase four-fold indicating stronger financial performance with solar kiosks. In contrast, we find no significant increase in the volume of water people use when a handpump is upgraded to a solar kiosk. We estimate that a 1 °C temperature increase is associated with a $9 increase in average monthly revenue and 366 more litres of water used every day per waterpoint. Our study suggests that rural Malians are more inclined to pay for water from professionally managed solar kiosks. However, seasonal volatility in water demand and uncertainty in the long-term revenue effect suggests caution in assuming solar kiosks are a definitive solution to the nuanced and dynamic nature of water user behaviours in rural Africa.

Investigating the Behavior of Consumers Using Digital Payment: Comparative Study between Rural and Urban Areas

The primary aim of the present research is to investigate the factors that precede digital payments and their effect on the behavioural intentions of consumers residing in both rural and urban areas. In this research, different factors, such as perceived usefulness (PU), compatibility (CO), performance expectancy (PE), transaction speed (TS), trust (TR), unavailability of facilitating conditions (UFC), and operational constraints (OC) have been taken from various theories. To collect data, an online survey was administered, and a total of 557 participants provided their responses. The model’s validity and reliability are established using confirmatory factor analysis (CFA), while the hypothetical relationships were analysed using structural equation modelling (SEM) in AMOS. It is concluded that PU, CO, PE, TS, TR, UFC, and OC have impacted the behavioural intentions of both urban and rural users. Moreover, the multi-group analysis is also applied in this research, to know the difference in behaviour of rural and urban users. It is concluded that PU, CO, PE, TS, TR, UFC, and OC have impacted the behavioural intentions of the users. Through the multi-group AMOS analysis, it was discovered that the path from drivers such as TS, CO, TR, and UFC to intent to use showed a difference between the two groups, whereas OC, PE, and PU showed the same path coefficient. Finally, at the end, some managerial implications are made for further study.

A Mathematical Modelling for Three-Factor Quantum Biometric Authentication Using Machine Learning, Double-Layer Encryption, Cryptographic Algorithms

Traditional authentication mechanisms are vulnerable to sophisticated attacks in the ever-changing cybersecurity paradigms. In this paper, a new three-factor quantum biometric system is proposed that were analysed to increase security level and reduce threats. This model employs multiple strategies, like quantum key distribution, a biometric identification system and machine learning which encrypts data based on their unique properties in an innovative way to provide not only one but two layers of encryption from unauthorized access. The system in its essence combines three critical elements: quantum generated cryptographic keys, biometric data (fingerprint or retinal scans) and dynamic behaviour-based identification using machine learning algorithms. By adding QKD to the hardware, an encryption key that should be practically impossible for anyone else (except sender and receiver) to intercept is securely generated and distributed through quantum mechanics principles of detection preventing possible eavesdropper. Secondly, the biometric data means that an imposter would have to not only steal your smartphone but also be able to authenticate themselves using unique physiology which takes even more of the likelihood out. Machine learning models study user behaviour patterns and work to adapt against evolving threats, gradually improving the accuracy of authentication with time. For added security, the system uses a double-layer encryption routine. The newly proposed system has a two-level encryption, and in the first layer, quantum keys encrypted bio-metric data while classical algorithms are applied to encrypt overall communication as well storage process. Besides ensuring the safety of private biometric information through dual encryption, this added redundancy also allows them to function as extra fail safe so that if one layer is breached, the entire system will still stand uncompromised. We evaluate the implementation of this new authentication scheme to a series-simulations and real-world test, showing the performance in different scenarios including high threat level environments. The results show major security enhancement compared to the traditional implementation of authentication methods with an observed amount decrease in data breaches and unsanctioned access attempts.

Multimodal presentation of E-commerce product reviews and ratings: empirical investigation using multimodality

PurposeThe aim of the research is to evaluate different modality for product reviews presentation and its impact on users’ performance, purchase intention and enjoyment.Design/methodology/approachThe study utilized an experimental approach with 48 opportunistic participants in three groups (16 users per group). Participants were randomly assigned to experimental conditions to ensure unbiased treatment. Data were collected through controlled interventions or manipulations, with pre-defined measures to assess specific outcomes. Statistical techniques such as ANOVA were employed to analyse the data, allowing for comparisons between experimental variables.FindingsThe findings revealed that integrating facial expression avatars and emojis into an e-commerce platform effectively communicates product reviews and ratings. Moreover, the use of animation significantly enhanced user enjoyment. This suggests that visual representations not only convey information effectively but also contribute to a more engaging and enjoyable user experience.Research limitations/implicationsWhile this experiment offers valuable insights into the impact of different e-commerce presentation layouts on user behaviour, further research could delve deeper into specific aspects such as the influence of individual user characteristics and the long-term effects of layout preferences.Originality/valueThis study contributes original insights by demonstrating the efficacy of facial expressive avatars and emojis in conveying product reviews and ratings within e-commerce platforms. Moreover, it adds value by highlighting the positive impact of animation on user enjoyment. By combining these elements, the research offers a novel approach to enhancing user engagement and understanding of customer feedback in online shopping environments. The findings provide valuable guidance for e-commerce platforms seeking innovative ways to communicate product information effectively and enhance the overall user experience, ultimately benefiting both businesses and consumers.

Driving engagement in metaverse-mediated tourism environments: exploring the role of perceived realism

PurposeDespite the promising development and marketing potential of the metaverse, our understanding of how realistic metaverse environments impact user engagement and behaviours remains limited. This study investigates the role of perceived realism in influencing user engagement, thereby affecting external search behaviour and visit intentions.Design/methodology/approachWe surveyed 270 active metaverse users to test the research model. The data were analysed using partial least squares structural equation modelling (PLS-SEM).FindingsThe results of our study show that three dimensions of realism – avatar involvement, perceptual pervasiveness and social realism – significantly enhance user engagement, which in turn influences external search behaviour and visit intention. In contrast, simulation realism and freedom of choice have minimal effects on absorption and dedication.Research limitations/implicationsThis study highlights the role of perceived realism in enhancing user engagement with the metaverse and its impact on physical world behaviours. It contributes to metaverse literature by demonstrating that engagement within the metaverse significantly influences physical world behaviours, including visit intentions and external search behaviours.Practical implicationsThis study offers practical guidance for developers to enhance user engagement in metaverse environments. Specifically, our findings advocate for visual and audio quality enhancements, greater persuasiveness of virtual spaces, improved avatar representativeness and a closer alignment of metaverse activities with real-life events.Originality/valueThis study advances the theoretical understanding of perceived realism by examining how its dimensions – such as visual and audio quality, avatar representativeness and alignment with real-life events – impact user engagement in the metaverse. It also explores how this engagement influences offline behaviours, thus bridging the gap between virtual and real-world interactions.

Enhancing 5G energy efficiency through digital twin networks: A comprehensive review

AbstractIn the era of 5G, seamless mobility handovers are critical in densely populated regions like Malaysia to mitigate disruptions and inefficiencies. 5G networks offer unprecedented data speeds and reliability, essential for advancing mobile communication and Internet of Things applications. However, ensuring continuous connectivity and service during mobility remains challenging, especially in urban settings. Digital twin technology presents a promising solution to enhance 5G handover mechanisms. A digital twin network (DTN) mirroring Malaysia's 5G infrastructure is proposed, utilising real‐time data and user behaviour insights to optimise energy consumption during handovers. The focus is on energy‐efficient protocols and algorithms, reviewed through a systematic literature review. The DTN aims to enhance mobility handover efficiency through predictive handovers and adaptive resource allocation, bolstered by sustainable practices such as edge computing. The potential of DTNs to optimise 5G handover processes is explored, starting with the foundational concepts of 5G mobility and digital twins, highlighting the need for improved strategies in high‐mobility scenarios. Methodologies leveraging digital twins to predict network conditions, simulate handover scenarios, and proactively manage decisions are examined, reducing latency and packet loss. Case studies demonstrate how digital twins adapt dynamically to network changes and user mobility, thereby improving quality of service and user experience. Malaysia's specific 5G mobility challenges are addressed with a tailored DTN emphasising energy efficiency, evaluated through practical applications. Evaluation criteria assess effectiveness with in‐depth analysis of methods, performance metrics, limitations, and recommendations for future research. Challenges and future directions including scalability, security, and real‐time data processing, are discussed, aiming to integrate digital twin technology with 5G networks for enhanced connectivity. This abstract provides a roadmap for leveraging digital twins to optimise 5G network performance sustainably, guiding future research and implementation strategies.

Temporal Matrix Factorization: A polynomial approach to latent factor estimation

Matrix Factorization (MF) is a simple and efficient Machine Learning (ML) technique to discover latent factors that help in explaining the underlying behaviour of actors (for instance, in the domain of recommender systems the actors could be users/items). The technique uses observed data, generated by the actors, to derive the latent factors. With time, more and more observed data that spans across a wide range of time period gets accumulated. The abundance of this data, allows us to discover new behavioural patterns. One such pattern is that, the latent factor values change with time. Most of the existing matrix factorization techniques are agnostic to time of occurrence of the event and fail to foresee the temporal changes in the actor behaviour. Deep neural networks are effective in predicting the recommendations with time, however, the ability to explain the underlying pattern and the associated rationale is poor. In our work, we propose a novel matrix factorization technique to estimate the temporal changes in the user behaviour and demonstrate its effectiveness in achieving time dependent recommendations.