International Journal of Mobile Communications (14 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
   
  
• 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
   
  
• 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
   
  
• E-WOM and outcomes on perceived values and consumer purchase intention  
  by Yuan-shuh Lii, May-Ching Ding, Erin Sy 
  Abstract: This research examines the utilitarian, hedonic, social, and epistemic values consumers derive from e-WOM exchanges (both quality and volume) in online communities and the subsequent impact of these values on consumers' purchase intentions. A survey of 201 members of the online community from community forums in Southeast Asia was collected and structural equation modeling was applied to test the hypothesized relationship. The results show that only the quality of the e-WOM exchange affects four different types of value; moreover, only the perceived hedonic and epistemic values significantly affect the intention of consumers to buy. This paper contributes to the literature by studying e-WOM from a framework of consumption value theory. From a management point of view, practical applications of this research include obtaining real-time information from consumers, which can be applied in different stages of product launch, development, and finally sales and marketing stages.
  Keywords: E-WOM; consumption values; perceived utilitarian value; perceived hedonic value; perceived social value; perceived epistemic value; consumer purchase intention.
  DOI: 10.1504/IJMC.2026.10075549
   
  
• Classification of malicious android applications by machine learning methods using permission properties  
  by Abdullah Batuhan Yilmaz, Yavuz Selim Taspinar, Murat Koklu 
  Abstract: This study applies machine learning methods to classify Android applications as malicious or benign using permission features. A dataset consisting of 2854 malware and 2870 non-malware apps with 117 features was used. Classification was performed with Adaboost (AB), Random Forest (RF), and Artificial Neural Networks (ANN), while the Information Gain (IG) algorithm was used to select relevant features. The classification process was carried out in three steps: first using all 117 features, second with 60 selected features, and third with 20 selected features. The highest accuracy, 98.4%, was achieved using 117 features and ANN. The models were evaluated using precision, recall, F1 score, ROC curve, and AUC metrics. Additionally, the training and testing times of all models were analysed. The study also employed correlation and weighted correlation analysis to assess the importance of permission features.
  Keywords: malware detection; feature selection; efficient features. android malware dataset; machine learning.
  DOI: 10.1504/IJMC.2026.10075550
   
  
• Why Chinese listeners stay engaged with podcasts: insights from a modified ECM-UTAUT2 framework  
  by Yanyan Li, Xinru Sun 
  Abstract: Podcasts have emerged as a popular mobile media in China, yet factors driving sustained user engagement remain underexplored. This study extends the ECM-UTAUT2 model by introducing a novel internal factor, perceived concomitance, which captures users' ability to integrate podcast listening into daily routines and psychological accompaniment. In addition, two external factors, content gratification and social gratification, derived from Uses and Gratifications theory, are incorporated. Analysis of survey data from Chinese podcast users highlights the critical roles of perceived concomitance, content gratification, user satisfaction, performance expectancy, and confirmation in sustaining engagement. However, social gratification and social influence do not exhibit significant impacts, reflecting cultural and contextual differences in Chinese podcast consumption compared to Western contexts. Our research offers practical insights for fostering sustained engagement in the Chinese podcast market.
  Keywords: ECM-UTAUT2; podcast engagement; continuance intention; perceived concomitance; content gratification; Chinese market.
  DOI: 10.1504/IJMC.2026.10076182
   
  
• Combining DEMATEL and ANP to establish a decision-making evaluation model for farmers’ association to introduce mobile technology  
  by Chien-Heng Chou, Kuang-Husn Shih, Chien-Ta Hsieh 
  Abstract: In the face of increasing environmental threats, the farmers’ association, which has the closest relationship with Taiwan’s agricultural development, has also been forced to transform and change its previous business model. The farmers’ associations serve as the bridge between the government and the people. In order to understand how the farmers’ association can effectively introduce mobile technology, this study combined the Decision Making Trial and Evaluation Laboratory (DEMATEL) and the Analytic Network Process (ANP) to establish a decision evaluation framework for the digital transformation of farmers’ associations, which can be used as an important reference for managers to plan business strategies and allocate resources. The results show that, the management of the farmers’ associations is deeply influenced by the government's laws, regulations, and policies, but the resources and professional capabilities possessed by the farmers’ associations are the most important factors for introducing mobile technology. Managers can also establish the priority order of decision-making according to the correlation between criteria and elements, in order to enhance the success rate of introducing innovative science and technology into the farmers’ associations.
  Keywords: mobile technology; MT; farmers’ association; decision making trial and evaluation laboratory; DEMATEL; analytic network process; ANP; digital transformation.
  DOI: 10.1504/IJMC.2026.10076425
   
  
• Enhancing customer satisfaction and customer loyalty by means of mobile service: the case of the 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
   
  
• 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
   
  
• 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 the help of the POA technique. Hence, it is named the 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
   
  
• 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. To address these, 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
   
  
• Examining generative AI user payment intention: a perceived value perspective  
  by Tao Zhou, Yan Liu 
  Abstract: As a knowledge payment behaviour, 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