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Due to the continuous and rapid evolution of telecommunication equipment, the demand for more efficient and noise-robust detection of dual-tone multi-frequency (DTMF) signals is most significant.

A novel machine learning-based approach to detect DTMF tones affected by noise, frequency and time variations by employing the k-nearest neighbour (KNN) algorithm is proposed. The features required for training the proposed KNN classifier are extracted using Goertzel's algorithm that estimates the absolute discrete Fourier transform (DFT) coefficient values for the fundamental DTMF frequencies with or without considering their second harmonic frequencies. The proposed KNN classifier model is configured in four different manners which differ in being trained with or without augmented data, as well as, with or without the inclusion of second harmonic frequency DFT coefficient values as features.

It is found that the model which is trained using the augmented data set and additionally includes the absolute DFT values of the second harmonic frequency values for the eight fundamental DTMF frequencies as the features, achieved the best performance with a macro classification F1 score of 0.980835, a five-fold stratified cross-validation accuracy of 98.47% and test data set detection accuracy of 98.1053%.

The generated DTMF signal has been classified and detected using the proposed KNN classifier which utilizes the DFT coefficient along with second harmonic frequencies for better classification. Additionally, the proposed KNN classifier has been compared with existing models to ascertain its superiority and proclaim its state-of-the-art performance.

Employing the extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework, this study examines the potential differences between two groups of hotel guests – business and leisure travelers – in terms of factors influencing their intention to purchase hotel products on Douyin (TikTok) in China.

Data gathered from 700 Chinese hotel guests was analyzed using partial least squares-structural equation modeling (PLS-SEM) and multigroup analysis (MGA).

The MGA results reveal that three newly added variables – personalization, perceived interactivity and perceived creativity – significantly influence the purchase intention of leisure travelers but not business travelers. Regarding the conventional UTAUT2 variables, leisure travelers are more influenced by hedonic motivation and price value in their purchasing decisions. In contrast, performance expectancy and effort expectancy have a greater impact on the decision-making process of business travelers than their leisure counterparts.

Theoretically, this paper is among the first to explore traveler types as moderators in the purchase of hotel products on Douyin. Practically, the findings offer valuable guidance for hotel marketers aiming to leverage Douyin to promote hotel products to these two different traveler segments.

Instead of using “one-size-fits-all” strategies, hotel managers should design marketing strategies that address the diverse needs of business and leisure travelers on Douyin. By implementing this strategy, they can effectively attract target customers and, in turn, increase hotel revenue.

This study expands the UTAUT2 framework and contributes to the scarce knowledge about the differences between business and leisure travelers regarding the relative importance of factors that influence their purchase intention for hotel products on Douyin among business and leisure travelers.

This study aims to explore the factors affecting investment behaviour in cryptocurrencies among Jordanian investors. Specifically, it aims to assess how various motivational and behavioural drivers impact the intention to use cryptocurrencies, grounded in the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. The choice of Jordan as the research context is particularly relevant due to the lack of adequate regulations on cryptocurrency investment.

This study uses a quantitative research approach, using an online survey as the primary method for data collection. The final data set consists of 285 responses collected through a self-administered questionnaire to cryptocurrency users in Jordan. Next, structural equation modelling (SEM) was used to test the developed theoretical framework based on the UTAUT2 model.

The findings reveal that performance expectancy, trust, hedonic motivation and price value significantly enhance the intention to invest in cryptocurrencies, with performance expectancy acting as a mediator. Effort expectancy is not directly related to behavioural intention; however, it positively impacts performance expectancy, validating the mediation hypothesis. Trust affects both the intention to use and the performance expectancy, reinforcing its role as a mediator in cryptocurrency adoption. Hedonic motivation and price value also positively affect the intention to use cryptocurrency. In contrast, social influence and facilitating conditions do not significantly impact behavioural intention, suggesting that cryptocurrency adoption decisions are less influenced by external opinions or the availability of necessary conditions. The findings also show that the demographic profiles of the cryptocurrency users were young, educated males, which suggests a demographic skew in cryptocurrency usage in Jordan.

This study innovatively adapts the UTAUT2 model, focusing on the mediating role of performance expectancy between effort expectancy, trust, and behavioural intention. This study pioneers by examining the mediation effect of performance expectancy, showing how users' ease in using cryptocurrencies positively affects their belief in positive outcomes, subsequently influencing their behavioural intention to use cryptocurrencies. Moreover, this study sheds light on the factors driving cryptocurrency adoption in developing countries like Jordan. It also underscores the demographic trends in cryptocurrency use and proposes targeted recommendations for policymakers and cryptocurrency platforms to foster more inclusive and informed investment environments.

Three-dimensional (3D) food printers are revolutionizing food production with personalized, sustainable and efficient meal creation. This study aims to explore the factors driving consumer intentions to purchase three-dimensional (3D) food printers. These innovative devices are gaining popularity for their ability to produce intricate, customizable food designs with remarkable precision and convenience. By leveraging the Unified Theory of Acceptance and Use of Technology (UTAUT) framework, the research examines key variables such as performance expectancy, effort expectancy, social influence, facilitating conditions (FCD), hedonic motivation and perceived product value. The aim is to understand how these factors shape consumer behavior and decision-making, providing insights into the adoption dynamics of 3D food printers for professional and domestic use.

This study collected 973 valid responses through an online survey. The data were analyzed using partial least squares structural equation modeling.

Performance expectancy, social influence and perceived product value significantly enhance consumers’ intention to purchase 3D food printers. In contrast, effort expectancy, FCD and hedonic motivation show no statistically significant impact on their usage intention.

Companies in the 3D food printing industry should prioritize improving product performance and leveraging social influencers to spark consumer interest. Educating the public about the benefits of 3D food printing is essential for building market acceptance and demand. Governments should contemplate implementing policies and regulations encouraging companies to invest in research and development in this field. This study acknowledges its limitations and recommends directions for future research.

This study establishes its originality by integrating hedonic motivation and perceived product value with the original UTAUT framework to investigate Chinese households’ intentions to use a 3D food printer.

This study aims to identify the significant factors that influence the continuation intention (CI) to use a digital wallet in the regions with low technology infrastructure and among the consumers possessing low levels of digital and financial literacy.

Data for the study were collected from the rural parts of South India with 295 digital wallet users. Co-variance-based structural equation modelling (SEM) (CB-SEM) using maximum likelihood estimation method and Bayesian SEM (BSEM) approaches were executed to test the influence of independent variables on the dependent variable and to ensure the validation of the proposed hypothetical model.

The results showed that trust, incentives, technology satisfaction (TS), facilitating condition, performance expectancy, effort expectancy, habit and hedonic motivation significantly influenced the CI to use a digital wallet. In addition, incentives positively impact habit for the CI to use a digital wallet.

The present study is based on the samples from the regions with low technology infrastructure and among the consumers possessing low levels of digital and financial literacy in the rural parts of South India, which limits the generalisation of results.

The results provide impetus to the government, digital wallet marketers and users regarding how the CI to use a digital wallet can be encouraged among the low-adoption regions.

This study remains unique as the assessment of CI to use a digital wallet was conducted in low-adoption regions (rural parts of India) in extending Unified theory of acceptance and use of technology 2 with TS. A comparison of results arrived from CB-SEM with those of the BSEM ensures that the validation of the hypothetical model is found to be another major methodological contribution towards the consumer behaviour literature.

Social networks can not only mobilize individuals for collective action but also pose risks, potentially leading to political challenges and societal unrest. Information consumption varies across platforms, with platform characteristics influencing user interactions and information sharing; yet this has received limited attention in scholarly literature. Acknowledging platform-specific differences, this paper seeks to enhance our understanding of the mechanisms driving information diffusion on social networks in the context of geopolitical tensions.

The structural communication features on Twitter and Reddit are explored using schema theory and the concept of social media platform schema. Comparisons are drawn with social network analysis and content analysis of communication dynamics surrounding geopolitical tensions in India–Qatar relations, followed by the context of geopolitical tensions between India and Pakistan.

The results illustrate how content-based connections on Reddit foster closer ties within subreddits but less connectivity between them, contrasting with Twitter’s profile-based connections. These distinct characteristics lead to varied information diffusion patterns and shape the diversity of opinions, influencing community structures and affecting the emotional tenor of discourse.

Social networks can potentially influence geopolitical events, but focusing on one platform overlooks differences in how information spreads and the influence each platform holds. Recognizing this, our comparative analysis of social networks’ structural attributes highlights their crucial roles in shaping user engagement and information diffusion. It lends theoretical support to the notion of social media platform schema with empirical insights into how users’ perceptions of these schemas impact thematic and emotional differences in platform discourse related to geopolitical tensions.