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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.

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.

The purpose of this study is to investigate important factors that help explain customer willingness to adopt mobile banking (M-banking). To this end, the unified theory of acceptance and use of technology 2 (UTAUT2) was applied and to more accurately predict customer behavioral intentions, it was attempted to extend it.

The research data were collected from 396 customers of Iranian private banks who had the experience of using M-banking. The structural equation modeling technique was used to test the research hypotheses.

Findings suggest that performance expectancy, effort expectancy, social influence, facilitating conditions, habit, hedonic motivation, perceived value and trialability are endorsed as proponents of M-banking adoption intention. On the other hand, M-banking adoption intention has also had a significant positive effect on actual use behavior and word-of-mouth (WOM). WOM has also influenced actual use behavior and mediated the relationship between M-banking adoption intention and actual use behavior.

The present study focuses on private banks, therefore, although it is sufficient, it is limited to private cases. This study contributes to the literature on M-banking services and actual use behavior. By appropriately focusing on M-banking adoption intention and the service quality provided, banks can strengthen their relationships with customers, thereby stimulating actual customer behavior such as actual use behavior and WOM.

From theoretical and managerial aspects, this study has particular value for the literature on M-services’ intention in general and banking in particular. The present study provides a conceptual framework for M-banking adoption intention, which could be used in M-banking services. In addition, this study sought to extend UTAUT2 and to examine the mediating role of WOM in actual use behavior motivation as well.

The authors aim to study a conceptual model based on behavioural theories (UTAUT-3 model) to evaluate the adoption, usage and recommendation for neobanking services in India.

The authors propose this model based on the UTAUT-3 integrated with perceived risk constructs. Hypotheses were developed to determine the relationships and empirically validated using the PLSs-SEM method. Using the survey method, 680 Delhi NCR respondents participated in the survey.

Empirical results suggested that behavioural intention (BI) to usage, adoption and recommendation affects neobanking adoption positively. The research observed that performance expectancy (PE), effort expectancy (EE), perceived privacy risk (PYR) and perceived performance risk (PPR) are the essential constructs influencing the adoption of neobanking services.

Limited by geographic and Covid-19 constraints, a cross-sectional study was conducted. It highlights the BI of neobanking users tested using the UTAUT-3 model during the Covid-19 period.

The study's outcome offers valuable insights into Indian Neobanking services that researchers have not studied earlier. These insights will help bank managers, risk professionals, IT Developers, regulators, financial intermediaries and Fintech companies planning to invest or develop similar neobanking services. Additionally, this research provides significant insight into how perceived risk determinants may impact adoption independently for the neobanking service.

In a context where retail stores are closing down and high streets are declining, the purpose of this paper is to analyse on-site shopping by tourists. This work identifies the drivers that lead tourists to use digital information sources at the beginning of the shopping process. Understanding these drivers can help destination managers and retailers encourage tourists to shop.

A personal survey was conducted in a Spanish city noted for its shopping facilities (Logroño), using a sample of 430 tourists with purchase intention. The survey was designed based on the extended unified theory of acceptance and use of technology (UTAUT2) model. A multivariate analysis, based on structural equation modelling, was carried out using partial least squares (PLS), based on variance.

The study’s finding is that performance expectancy, effort expectancy, social influence, facilitating conditions and habit influence intention to use digital sources of information to make purchases in a destination. Tourists prioritise utilitarian over hedonic motivations in the intention to use digital sources of information in tourist shopping.

It has been recognised that tourists are the perfect target to revitalise on-site shopping and, therefore, destinations must provide attractive shopping experiences from the outset. Prior to purchase, the search for available information is the first stage of the tourist shopping journey. Although many studies have analysed tourist shopping behaviour, none have focused, using the UTAUT2, on the digital information sources tourists consult pre-purchase. This research develops understanding of tourist shopping behaviour in this new technological context. This can help retailers/destinations provide better services and optimise the shopper's experience from the first stage of the process.

零售商店陸續倒閉，商業街的經營業務逐漸式微；本文旨在分析遊客在這背景下的現場購物活動。本文擬確定遊客在購物過程的初期驅使他們使用數位資訊來源的誘因；了解這些誘因，將有助目的地管理經理和零售商推動遊客購物活動。

研究人員在一個以購物設施馳名的西班牙城市(洛格羅尼奧) 進行個人調查，樣本為430名有意購物的遊客。調查是以整合性科技接受使用理論的延伸模型(UTAUT2)為基礎而設計的。研究人員使用以方差為基礎的偏最小平方，來進行以結構方程模型為基礎的多變數分析。

績效期望、付出期望、社群影響、促成條件和習慣均影響遊客為目的地購物而使用數位資訊來源的意慾。而就這意慾而言，功利動機在優先次序上比享樂動機佔更高的位置。

我們承認，要使現場購物得以復甦，遊客是最適當的目標。因此，旅遊目的地必須從一開始就要給遊客提供愉快的購物體驗。遊客購物前、尋找有關的購物資訊便是這個旅遊購物旅程的第一個階段。分析遊客購物行為的研究為數不少，唯使用第2代整合型科技接受理論(UTAUT2) 、重點探討遊客購物前使用數位資訊來源來尋找資訊的研究則從未見過。本研究讓我們更深入了解遊客在這個新技術背景下的購物行為，這有助零售商/目的地經營者為遊客提供更佳的服務、及優化遊客從購物過程首階段開始的購物體驗。

Enrolling in an academic discipline or selecting the college major choice is a dynamic process. Very few studies examine this aspect in India. This paper makes a humble attempt to fill this gap using NSSO 71st round data on social consumption on education. The purpose of this paper is to use multinomial regression model to study the different factors that influence course choice in higher education. The different factors (given the availability of information) considered relate to ability, gender, cost of higher education, socio-economic and geographical location. The results indicate that gender polarization is apparent between humanities and engineering. The predicated probabilities bring out the dichotomy between the choice of courses and levels of living expressed through consumption expenditures in terms of professional and non-professional courses. Predicted probabilities of course choices bring in a clear distinction between south and west regions preferring engineering and other professional courses, whereas north, east and NES prefer humanities.

The present paper follows the same approach as that of Turner and Bowen (1999). The Multinomial regression is specified as P( M i = j ) = ( exp ( β j × X i ) / ∑ j − 15 exp ( β j × X i ) ), where P (M_i=j) denotes the probability of choosing outcome j, the particular course/major choice that categorizes different disciplines. This response variable is specified with five categories: such as medicine, engineering, other professional courses, science and humanities. The authors’ primary interest is to determine the factors governing an individual’s decision to choose a particular subject field as compared to humanities. In other words, to make the system identifiable in the MLR, humanities is treated as a reference category. The vector X_i includes the set of explanatory variables and β_j refers to the corresponding coefficients for each of the outcome j. From an aggregate perspective, the distribution of course choices is an important input to the skill (technical skills) composition of future workforce. In that sense, except humanities, the rest of the courses are technical-intensive courses; hence, humanities is treated as a reference category.

The results indicate that gender polarization is apparent between humanities and engineering. The predicated probabilities bring out the dichotomy between the choice of courses and levels of living expressed through consumption expenditures in terms of professional and non-professional courses. Predicted probabilities of course choices bring in a clear distinction between south and west regions preferring engineering and other professional courses, whereas north, east and NES prefer humanities.

Predicted probabilities of course choices bring in a clear distinction between south and west regions preferring engineering and other professional courses, whereas north, east and NES prefer humanities. This course and regional imbalance need to be worked with multi-pronged strategies of providing both access to education and employment opportunities in other states. But the predicted probabilities of medicine and science remain similar across the board. Very few research studies on the determinants of field choice in higher education prevail in India. Research studies on returns to education by field or course choices hardly exist in India. These evidences are particularly important to know which course choices can support student loans, which can be the future area of work.

The research evidence is particularly important to know which course choices can support student loans, which can be the future area of work, as well as how to address the gender bias in the course choices.

The paper has social implications in terms of giving insights into the course choices of students. These findings bring in implications for practice in their ability to predict the demand for course choices and their share of demand, not only in the labor market but also across regions. India has 36 states/UTs and each state/UT has a huge population size and large geographical areas. The choice of course has state-specific influence because of nature of state economy, society, culture and inherent education systems. Further, within the states, rural and urban variation has also a serious influence on the choice of courses.

The present study is a value addition on three counts. First, the choice of courses includes the recent trends in the preference over market-oriented/technical courses such as medicine, engineering and other professional courses (chartered accountancy and similar courses, courses from Industrial Training Institute, recognized vocational training institute, etc.). The choice of market-oriented courses has been examined in relation to the choice of conventional subjects. Second, the socio-economic background of students plays a significant role in the choice of courses. Third, the present paper uses the latest data on Social Consumption on Education.