International Journal of Mobile Communications (15 papers in press)

Regular Issues

• Switch to a new version or keep status quo? The explanation for users to update software from push-pull-mooring perspective  
  by Xue Sun, Yuhao Li 
  Abstract: Companies invest high-volume costs in software maintenance but often failed to receive equivalent feedback from users (e.g., resistance to update). This research aims to shed light on the update motivation of users. In the context of online meetings (OMs), this study is guided by the status quo bias and the application technology innovation theory to establish an updating motivation model framed by push-pull-mooring (PPM). The model was verified using the PLS method based on an online survey with 380 samples. The results indicated that push factors, pull factors, and mooring factors have significant impacts on the OM users’ update intention. A multi-group comparison further shows that the cognitive resource moderates the effects of pull and mooring factors on the OM update.
  Keywords: status quo bias; SQB; cognition resource; push-pull-mooring; PPM; software update; migration in IS; online meeting.
  DOI: 10.1504/IJMC.2025.10063219
   
  
• Elucidating mobile AR continuance intention: a dual fitting mechanism  
  by Hao Chen, Shuiqing Yang, Yuangao Chen, Shuang Zheng, Miao Zhang, June Wei 
  Abstract: Mobile Augmented Reality (AR) technology has revolutionized conventional human-computer interfaces and dramatically reshaped consumer behaviours. Based on task-technology fit (TTF) model and emotional fit theory, a research model reflected the dual fitting mechanism underlying mobile AR continuance intention was developed and analyzed by using data collected from 408 users of mobile AR services. Results from the structural equation modelling reveal that task mobility and spatial presence affect mobile AR continuance intention mainly via emotional fit, while confidence in AR influences mobile AR continuance intention predominantly through task-technology fit. In addition, technology fluency affects mobile AR continuance intention via both task-technology fit and emotional fit. Compared with mobile AR utilitarian applications, technology fluency has a greater impact on task-technology fit when using mobile AR hedonic applications. However, in terms of mobile AR hedonic applications, spatial presence exerts a greater impact on task-technology fit when compared with mobile AR utilitarian applications.
  Keywords: mobile augmented reality; continuance intention; task-technology fit; emotional fit; mobile AR application.
  DOI: 10.1504/IJMC.2026.10069686
   
  
• Multi-objective resource allocation for satellite-based cognitive IoT communications using power-domain NOMA  
  by Jizheng Yang, Amin Mohajer 
  Abstract: The rapid growth of IoT devices and the demand for efficient data transmission have made advancements in satellite communication essential. This paper introduces a novel framework for multi-objective resource allocation in satellite-based cognitive IoT communications using power-domain NOMA. Our method integrates serverless computing and multi-objective optimisation to enhance satellite network performance. The framework dynamically manages resources like bandwidth and power, ensuring low latency and optimal performance using backhaul traffic optimisation. Key steps include analysing network data to predict future needs, employing real-time functions for adaptive resource allocation, and using multi-objective optimisation to configure satellite constellations for minimal latency, reduced power consumption, and maximised throughput. Extensive simulations validate the framework’s effectiveness, demonstrating significant improvements in data rate, energy efficiency, and overall performance. Specifically, our proposed NOMA.QAM framework achieves a 25% reduction in bit error rate (BER) and a 30% increase in spectral efficiency compared to traditional NOMA with SIC. Additionally, it demonstrates a 20% improvement in energy efficiency, highlighting its potential for energy-constrained applications. These results indicate that the integration of power-domain NOMA with serverless computing and multi-objective optimisation provides a promising solution for advancing satellite-based cognitive IoT communication systems, ensuring efficient, reliable, and scalable network management.
  Keywords: cognitive IoT; multi-objective optimisation; satellite communications; dynamic resource management; power-domain NOMA.
  DOI: 10.1504/IJMC.2026.10069895
   
  
• Enhancing customer satisfaction and customer loyalty by means of mobile service: the case of fitness club  
  by Chien-Chung Teng, Cheng-Chung Cheng, Di-Qun Xu, Chang-Dian Huan 
  Abstract: This study analysed the impact of mobile service quality in fitness clubs on customer satisfaction and loyalty, identifying key service quality items that require improvement. This study adopts a questionnaire survey method, collecting data on mobile service quality, customer satisfaction, and loyalty from members of fitness clubs in Fujian, China. A total of 381 valid questionnaires were collected. Data analysis was conducted using Statistical Package for the Social Sciences (SPSS), linear structural relations (LISREL) statistical software, and importance-performance and gap analysis (IPGA). Statistical analysis indicates that mobile service quality positively impacts customer satisfaction and loyalty. Customer satisfaction acts as a mediator between mobile service quality and customer loyalty, facilitating the indirect effect of service quality on loyalty. Using IPGA, we identified five key quality items of fitness clubs mobile service that require improvement.
  Keywords: service management; mobile service quality; quality improvement; fitness club management; IPGA.
  DOI: 10.1504/IJMC.2026.10070818
   
  
• Deep learning-driven mobile crowd sensing for secure and efficient Hajj pilgrimage management  
  by Mohammed Naif Alatawi  
  Abstract: The annual Hajj pilgrimage attracts millions of people, necessitating effective crowd management strategies to ensure safety and enhance the experience for all participants. With the advent of digital technologies, machine learning techniques have become pivotal in analysing crowd behaviour and managing resources in real-time. This study explores the integration of deep learning methods in mobile crowd sensing frameworks, specifically within an IOTA-based decentralised environment, to address challenges such as data authenticity, privacy protection, and real-time anomaly detection. Using a logit-boosted convolutional neural network model, this research achieved an accuracy of 99.5% in identifying anomalous events, outperforming traditional machine learning models. The results demonstrate the models capacity to provide a robust framework for detecting potential threats and ensuring secure, efficient information exchange during large-scale events. These findings underscore the potential of deep learning-enhanced crowd sensing in transforming the management of high-density gatherings. This approach not only enhances safety but also optimises resource allocation based on crowd density predictions. Future applications of this framework could extend to other large-scale gatherings, offering scalable solutions for various crowd management scenarios.
  Keywords: mobile crowd sensing; MCS; deep learning; IOTA framework; convolutional neural network; CNN; real-time anomaly detection; crowd behaviour analysis; privacy protection; large-scale event management.
  DOI: 10.1504/IJMC.2026.10070819
   
  
• Exploring how system, information, and service quality shape goal-congruent usage and outcome in intelligent voice assistants  
  by Hyeon Jo 
  Abstract: In an increasingly digital age, intelligent voice assistants (IVAs) are becoming an integral part of daily life, providing both utility and entertainment value to their users. The global pandemic has accelerated this trend, amplifying the importance of these digital companions. In light of this shift, our study aims to elucidate the factors leading to goal-congruent usage and outcomes of IVAs. Data was collected from 248 IVA users to validate the proposed research model. We employed the partial least squares method to test our hypotheses. Our findings revealed that system quality significantly influences both the perceived utilitarian and hedonic value of IVAs. Similarly, information quality was found to significantly impact both utilitarian and hedonic value. Interestingly, service quality was predominantly linked to utilitarian value. Furthermore, the study indicates that the utilitarian value is a key enabler of goal-congruent outcome and usage. Similarly, hedonic value plays a substantial role in driving both the goal-congruent outcome and usage.
  Keywords: intelligent voice assistant; IVA; goal-congruent outcome; GCO; information system success factors.
  DOI: 10.1504/IJMC.2026.10071057
   
  
• Determinants of people’s online participation in public affairs: the role of locus of control and big-five personality traits  
  by Guan-Yu Lin, Ching-Yun Chen, Yi-Shun Wang 
  Abstract: This study was designed to explore the determinants of e-participation in public affairs by developing and validating an e-participation model that elucidates the relationships among personality traits (i.e., big-five personality traits and locus of control), motivations (i.e., intrinsic motivation and extrinsic motivation), e-participation intention and actual e-participation. The developed model was tested on data from 244 participants using the partial least squares (PLS) approach. The results indicate: e-participation intention positively influences actual e-participation; intrinsic motivation mediates the relationships between agreeableness and e-participation intention and between extraversion and e-participation intention; extrinsic motivation mediates the relationship between agreeableness and e-participation intention; openness to experience positively and directly impacts e-participation intention; and internal locus of control negatively and directly impacts e-participation intention. The findings of this study provide several important theoretical and practical implications for promoting greater e-participation by citizens in public affairs.
  Keywords: online participation; intrinsic motivation; extrinsic motivation; locus of control; big-five personality traits; public affairs.
  DOI: 10.1504/IJMC.2026.10071297
   
  
• Examining generative AI user payment intention: a perceived value perspective  
  by Tao Zhou, Yan Liu 
  Abstract: As a knowledge payment behavior, user payment is crucial to the profitability and continuous development of generative AI platforms. However, users often lack the motivation and intention to make payment. The purpose of this study is to investigate the payment intention of generative AI users based on the perceived value theory. We used both methods of SEM and fsQCA to conduct data analysis. The results found that perceived benefits (functional benefits and social benefits) positively affect perceived value, whereas perceived costs (privacy risk, misinformation, and low transparency) negatively affect perceived value, which further affects payment intention. The results suggest that generative AI platforms should increase perceived benefits and diminish perceived costs in order to improve the perceived value and facilitate user payment intention.
  Keywords: generative AI; perceived value; payment intention; privacy risk; misinformation.
  DOI: 10.1504/IJMC.2026.10072180
   
  
• Optimised deep convolutional spiking neural network for accurate long-term and short-term rainfall forecasting in climate prediction systems  
  by M. Amanullah, K. Ananthajothi, Moorthy Agoramoorthy 
  Abstract: The rainfall forecast is essential to the fields of hydrology and meteorology. However, the prediction accuracy of existing methods for both shorter and longer-term rainfall forecasting is consistently low. The decreased performance of atmospheric forecasting models under various circumstances causes fluctuations in predicting accuracy. This paper proposes a novel method called Deep Convolutional Spiking Neural Network optimised with Sandpiper optimisation algorithm fostered Long-term and Short-term Rainfall forecasting (RP-DCSNN-SPOA). The primary source of the long and short-term rainfall (LSTR) data is the Sudan IRA rainfall forecast dataset. Then, the gathered data is pre-processed using anisotropic diffusion Kuwahara filtering to recover the missing values. The DCSNN is used to predict the rainfall forecast. Then, the Sandpiper Optimisation Algorithm (SPOA) is used to enhance the DCSNN classifier that accurately forecasts the rainfall. The proposed method achieves 28%, 22.64% and 28.35%, greater accuracy, 20%, 26.64% and 23.35% greater precision when compared with existing models.
  Keywords: sandpiper optimisation technique; Kuwahara filtering; rainfall forecasting; deep convolutional spiking neural network.
  DOI: 10.1504/IJMC.2026.10072144
   
  
• Beneath the prosperity of social commerce: a perspective from a trust-centred online review ecology  
  by Yan Li, Hongjie He, Bojiao Mu, Yaru Jin, Kun Shi 
  Abstract: The fast development of social commerce (s-commerce) has matured the online review ecology. However, it lacks an in-depth theoretical analysis of this ecology and its functions in consumers’ purchases in s-commerce. By dissecting this ecology into its three pillars: the review community, content, and system, this study proposes the relationships among those pillars by extending the TAM with trust and identifies four information cues that are helpful for building this trust. The structural equation modeling was employed to test the proposed research model based on 391 valid samples. The results verify the distinctiveness of trust in the review community in s-commerce and show that this trust can facilitate consumers’ use of the s-commerce platform for shopping via increasing the perceived usefulness of the review system and reducing the perceived risk of online shopping. Moreover, four information cues contribute to the development of trust in the review community.
  Keywords: online review ecology; trust in the review community; social commerce; technology acceptance model; TAM; trust development.
  DOI: 10.1504/IJMC.2026.10072447
   
  
• Be congruent vs. be diverse? The effect of basic app types on the intent to upgrade incremental functions  
  by Youjia Zhang, Xiaoqin Wang 
  Abstract: This study examines how basic app types (utilitarian vs. hedonic) influence users intent to upgrade when incremental functions with congruent or incongruent goals are introduced. We employ a multi-method approach, including laboratory experiments, simulated real-world experiments, and online surveys (via Credamo), to ensure robust and reliable findings. Across four studies, we find that users exhibit a higher intent to upgrade when utilitarian incremental functions are added to a utilitarian app. However, for hedonic apps, users upgrade intent does not significantly differ based on whether the added functions are utilitarian or hedonic (studies 1 and 2). Further, cognitive flexibility mediates these effects (study 3), and the frequency of basic app usage moderates the preference for congruent vs. incongruent incremental functions (study 4). These findings contribute to the literature on usage frequency by examining its role in upgrade decisions and provide practical insights into enhancing user satisfaction post-download.
  Keywords: app type; upgrade intent; cognitive flexibility; usage frequency; incremental features.
  DOI: 10.1504/IJMC.2026.10073210
   
  
• A study of the blended teaching model from the perspective of ecological education  
  by Lan Lan  
  Abstract: Blended online instruction is a rapidly growing approach widely adopted by educational institutions. Combining virtual learning networks with face-to-face education, it allows students to experience integrated learning. Schools have implemented blended teaching to adapt to the demands of the internet era. However, this shift brings challenges due to the impact of digital technologies on education. Stakeholders, including teachers, school leaders, and students, face significant changes as blended teaching evolves. Many educators enhance their teaching by incorporating online content into traditional methods. Recognising the implications of blended models for teachers, students, investors, and leaders is vital. A thorough investigation of how schools execute blended teaching is essential, using appropriate data collection methods and case studies. Finally, understanding the internet’s role in transforming teaching practices is crucial for successfully implementing blended instruction in the modern educational landscape.
  Keywords: blended teaching; ecological education; information and communication technology; ICT; knowledge; mixed learning; e-learning; ecological perspective.
  DOI: 10.1504/IJMC.2026.10074124
   
  
• Evaluating the impact of colour congruence on marketing effectiveness within Instagram campaigns  
  by Chiao-Chieh Chen, Yu-Ping Chiu 
  Abstract: Social media platforms are pivotal arenas for brands to engage with audiences and disseminate brand-centric content. The impact of visual elements, particularly colour composition in brand posts, on the effectiveness of Instagram marketing campaigns remains underexplored. This research examined the influence of colour congruence between the background and thematic elements of brand posts on Instagram. Utilising a 2x2 between-subjects experimental design, where colour congruence and post type (brand layout vs. brand post) were the variables, this study investigated how these factors affect processing fluency and, consequently, marketing effectiveness. The findings reveal that consistent colour schemes enhance processing fluency, which in turn mediates the relationship between colour congruence and marketing outcomes. This research not only advances our understanding of congruence theory within the context of social media marketing but also offers practical insights for brands on optimising visual content strategy on their Instagram accounts.
  Keywords: Instagram campaigns; photo; color congruence; processing fluency; marketing effectiveness.
  DOI: 10.1504/IJMC.2026.10074425
   
  
• The Shaping of Public Opinion and Healthcare Influence through Mobile-Mediated Social Media: A Case Study of Azad Kashmir during the COVID-19 Pandemic  
  by Raja Gulfraz Ali, Zakir Shah, Jie Li 
  Abstract: Rumours, infodemics, and vaccine uncertainty related to COVID-19 pose a major global health risk, impacting individuals psychological state and fear of vaccination. This study, conducted in Azad Kashmir with 663 respondents, examines that mobile-mediated social media, particularly with visual content (e.g., vaccine selfies), can help combat rumours and shape public opinion towards COVID-19 vaccines. The study uses a conceptual model grounded in the extended parallel process model (EPPM) and Appraisal Theory, which together explain the cognitive and emotional pathways of persuasion and offer a nuanced understanding of digital health behaviour in the post-pandemic era. The study finds that mobile-mediated social media visuals (MSMV) are significantly associated with risk perception (RP), infodemic rejection (IR), and vaccine hesitancy (VH) during the pandemic, which in turn shape vaccine behaviour (VB). The findings highlight the role of platform-specific dynamics, such as trust and misinformation exposure, on Facebook and WhatsApp. These findings underscore the urgent need for public health authorities and communication strategists to engage public figures and celebrities to promote the sharing of positive content on social media during health crises, thereby encouraging vaccination uptake.
  Keywords: Mobile-mediated Social Media Visuals; Infodemic Rejection; Risk Perception; Vaccine Hesitancy; Vaccine Behavior.
  DOI: 10.1504/IJMC.2026.10074702
   
  
• Class E power amplifier design and optimisation for internet of things application using IAO strategy  
  by Rajukkannu Shankar, Ramasamy Gandhi, Kabilan Mohanraj, Androse Joseph Sheela 
  Abstract: This manuscript proposes a class-E power amplifier (CEPA), which drives an inductive-link for IoT applications. The proposed technique is a combination of Aquila Optimiser (AO), Pelican Optimisation Algorithm (POA). The catching-behaviour of AO is improvised with help of POA technique. Hence, it is named as Improved AO strategy. The proposed configuration resolves the trade-off between switch gate capacitance and ON resistance. A differential class-E PA with the inductor of split-slab is intended to satisfy the operational requirements set by the power amplifier. The performance of the proposed technique is validated in MATLAB site and it is compared with existing techniques.
  Keywords: internet of things; class-E power amplifier; CEPA; inductive link; switch-on resistance WPT.
  DOI: 10.1504/IJMC.2026.10075062