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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.

In recent years, the proliferation of social commerce (s-commerce) has attracted many researchers to investigate the drivers of individuals' intentions. However, the empirical results reported in these studies were fragmented and inconsistent. This has led various meta-analyses to synthesize these findings, but without including a large number of s-commerce studies. In addition, investigating meta-analytically the effects of moderators such as the six dimensions of Hofstede's national culture is still lacking.

Drawing on nine theories and models, this meta-analysis aims to summarize the findings reported in 109 s-commerce studies published between 2011 and 2021 and to examine the moderating role of national culture. The correlation coefficient (r) has been used as the main effect size for this study. Based on the random-effects method, the CMA V3 software has been employed to calculate the weighted mean effect sizes.

The meta-analysis results showed that all the 11 hypothesized direct relationships are positive and significant. The moderator results also revealed that five out of six cultural dimensions significantly moderate the examined associations.

This research serves to enrich the existing s-commerce literature by addressing contradictory and mixed results reported in the empirical studies. This study is one of the first of its kind to investigate the role of Hofstede's six cultural dimensions as moderators in the field of s-commerce using the meta-analytic techniques.

Service providers have a vested interest in enhancing adoption of technologies that improve the customer service experience. Buoyed by this idea, this paper aims to explore Latin American consumers’ mobile payment (MP) adoption, conceptualized as bank-sponsored mobile wallets that facilitate payment at the point-of-purchase. This paper applies a revised unified theory of acceptance and use of technology 2 (UTAUT2) model as theoretical framework for this exploration.

To test the conceptual model of MP adoption in Latin America put forward in this paper, the authors used Colombia as a sample site and conducted two studies among a sample of consumers in this country. Completed questionnaires from 186 participants (Study 1) and 398 participants (Study 2) were used in data analyses, which were conducted using Mplus 8.4 and PROCESS.

In Study 1, performance expectancy, social influence, bank trust, confidence in MP system and consumer innovativeness all impact consumers’ MP use intention; and use intention impacts MP behavior. In Study 2, involving a wider sample, performance expectancy, effort expectancy, facilitating conditions, perceived quality of the MP system, bank trust, consumer innovativeness, consumer optimism and consumer insecurity all affect MP use intention; and use intention significantly impacts MP behavior. Across both studies, follow-up analysis showed that effort expectancy influences performance expectancy for MP and indirectly influences MP use intention through its impact on performance expectancy. Bank trust also indirectly affects MP use intention through its effects on system confidence. In Study 2, age did not affect MP use intention or MP use; however, education affected MP use.

The theoretical underpinning for the conceptual model was the UTAUT2, and the results across the two studies support previous research in which this revised model has been useful in explaining technology adoption. Core elements of the UTAUT2 such as performance expectancy, effort expectancy, facilitating conditions and social influence had different impact on MP adoption in Latin America, depending on the sample. Technology readiness index motivators and inhibitors also aid understanding of MP adoption.

The research provides insights on the variables that members of the MP ecosystem in Latin America (e.g. banks and other service providers, card issuers) need to address in getting Latin American consumers to use MP.

This research extends the exploration of MP to a region of the world that has not been the focus of prior studies on the adoption of this technology and responds to calls by some researchers to increase research in this region. The conceptual models in the two studies also incorporate trust in the banks that are part of the MP ecosystem in Latin America and consumer overall confidence in this MP ecosystem. The results show that both these factors are influential in Latin American consumers’ adoption of MP. System confidence also mediates the relationship between bank trust and MP use intention.

Traditional case-adaptation methods have poor accuracy, low efficiency and limited applicability, which cannot meet the needs of knowledge users. To address the shortcomings of the existing research in the industry, this paper proposes a case-adaptation optimization algorithm to support the effective application of tacit knowledge resources.

The attribute simplification algorithm based on the forward search strategy in the neighborhood decision information system is implemented to realize the vertical dimensionality reduction of the case base, and the fuzzy C-mean (FCM) clustering algorithm based on the simulated annealing genetic algorithm (SAGA) is implemented to compress the case base horizontally with multiple decision classes. Then, the subspace K-nearest neighbors (KNN) algorithm is used to induce the decision rules for the set of adapted cases to complete the optimization of the adaptation model.

The findings suggest the rapid enrichment of data, information and tacit knowledge in the field of practice has led to low efficiency and low utilization of knowledge dissemination, and this algorithm can effectively alleviate the problems of users falling into “knowledge disorientation” in the era of the knowledge economy.

This study provides a model with case knowledge that meets users’ needs, thereby effectively improving the application of the tacit knowledge in the explicit case base and the problem-solving efficiency of knowledge users.

The adaptation model can serve as a stable and efficient prediction model to make predictions for the effects of the many logistics and e-commerce enterprises' plans.

This study designs a multi-decision class case-adaptation optimization study based on forward attribute selection strategy-neighborhood rough sets (FASS-NRS) and simulated annealing genetic algorithm-fuzzy C-means (SAGA-FCM) for tacit knowledgeable exogenous cases. By effectively organizing and adjusting tacit knowledge resources, knowledge service organizations can maintain their competitive advantages. The algorithm models established in this study develop theoretical directions for a multi-decision class case-adaptation optimization study of tacit knowledge.

Consumers worldwide are increasingly ordering groceries from grocery delivery platforms (GDPs). This study aimed to explore the role of brick-and-mortar (B&M) retailers and GDPs in online grocery shopping (OGS) experience, attitude and continuous purchase intention under the platform model of online grocery retailing.

This study used a mixed method approach. A qualitative analysis was conducted based on 30 in-depth interviews and relevant literature to identify key attributes of the OGS experience. Then, data from 352 online grocery shoppers was used to examine the associations between service attributes, attitude and continuous purchase intention using a structural equation model.

The authors identified six key attributes of the OGS experience related to B&M retailers and GDPs. The quantitative study results showed that customer service, price value and instant delivery significantly impact attitude towards GDPs, while product quality, product assortment, customer service, price value and attitude toward GDPs positively impact online attitude toward B&M retailers. Online attitude toward B&M retailers significantly influences continuous purchase intention.

B&M retailers and GDPs should strengthen cooperation and joint oversight.

This study identified key attributes of the OGS experience associated with B&M retailers and GDPs under the platform model, giving a comprehensive understanding of the relationship between the OGS experience and behavioural intention when B&M retailers collaborate with GDPs.

This study aims to focus on delineating the drivers of intention to adopt mobile banking (m-banking) and its actual use among Islamic banking customers by extending the UTAUT2 model with the trust factor. The study also examined the moderating roles of age, gender and experience in the model.

An explanatory research design was used, and an online survey was conducted to collect responses from Islamic banking customers. A total of 329 completed responses were used to analyze the data. The partial least squares method was used for data analysis, and a multi-group analysis was applied for moderation-related analysis.

Trust positively and significantly influences the behavioral intention to adopt m-banking among Islamic banking customers. In addition, social influence, effort expectancy, hedonic motivation and habits significantly influence behavioral intentions among Islamic banking customers.

This study provides an extended UTAUT2 model that has never been tested in the context of Islamic m-banking. In addition, this study is expected to be the first scholarly research on Islamic banking in the Maldives.

This study identifies the facilitators and inhibitors for the adoption of e-learning for the undergraduate students of architecture. Nine constructs are identified as facilitators and five constructs are identified as inhibitors to the adoption of online learning systems in the context of the study. These constructs were used to propose a research model.

596 architecture undergraduates responded to a structured questionnaire. The questionnaire was finalized after a pilot study and included standard scale items drawn from previous studies. An exploratory factor analysis was followed by structural equation modeling (SEM) to test the proposed model.

All the identified facilitators emerged significant except social influence and price value. Furthermore, technology risk emerged insignificant while all other inhibitors had significant impact on Behavioral Intention to adopt e-learning.

The study has strong implications in academia as HEIs in developing countries need to make their students computer proficient, boost the implications of e-learning services by mitigating risks and motivating students to acquire knowledge through flexible e-learning modules.

The COVID-19 pandemic forced educational institutions to switch to online modes of learning. For students of architectural programs in a developing country like India, this has been unprecedented and has brought in a new set of challenges and opportunities. With the extension of the pandemic induced lockdown in educational institutions, students – and other stakeholders – have no choice but to adapt to this new normal of dependence on remote learning.

The purpose of this study is to investigate the impact of audit committee characteristics on firm performance. In particular, the authors employ the random-effects variant of the Hunter–Schmidt meta-analyze procedure to analyze the effects of key audit committee attributes, namely audit committee independence, audit committee expertise, audit committee size, audit committee meeting along with big four impact on firm performance. The authors hope to gain a better understanding of the function of audit committees in enhancing firm performance and to uncover potential discrepancies in prior findings due to varying economic levels or performance metrics.

This study uses the Hunter–Schmidt method to conduct a meta-analysis of 39 previous studies published between 2012 and 2022 to investigate the relationship between audit committee characteristics and firm performance.

The results indicate that audit committee independence, expertise, size and affiliation with the big four have a significant and positive effect on firm performance, while audit committee meetings have a non-significant effect. Furthermore, findings suggest that companies should carefully consider the contextual factors that may impact the effectiveness of their corporate governance structures, such as economic level, when designing and implementing governance mechanisms.

This study is significant as it is the first to combine and analyze previous research on this topic and highlights the importance of certain audit committee characteristics in enhancing financial reporting quality and corporate governance.

Drawing on the adapted unified theory of acceptance and use of technology (UTAUT2) framework in the bottom of pyramid (BoP) context, this paper examines the number of causal recipes that foster m-health adoption in a developing country (Bangladesh). This paper aims to propose an extended UTAUT2 model along with identifying the necessary and sufficient factors affecting the m-health adoption intention in the BoP market.

The research model was empirically tested, combining two approaches: structural equation modelling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). Data were collected from 221 housemaids and female security guards who earn around US$6 per day.

The SEM results revealed that while performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions, perceived cost (PC) and technology anxiety (TA) significantly influence the behavioural intention of BoP markets, hedonic motivation is the non-significant predictor. The fsQCA revealed that the two necessary conditions, PC and SI, can be combined with TA to increase the possibility of the success of m-health adoption in the BoP market.

For practitioners concerned with fostering the m-health adoption intention in BoP markets, the present study, which points out equifinality, recommends integrating the PC and SI in several combinations with PE, EE and TA.

To the best of the authors’ knowledge, no previous studies using the UTAUT2 theory examined the m-health services in the BoP market. This study contributes empirical data to the predominantly theoretical literature by offering a deeper understanding of the inclusion of TA and PC in several combinations with other UTUAT2 factors as predictors for explaining the m-health adoption intention of BoP markets.

The purpose of this study is to examine the factors that influence Malaysian Small and Medium Enterprises (SMEs) to adopt mobile commerce (m-commerce) by integrating the constructs of Technology Acceptance Model 3 (TAM 3), Universal Theory of Acceptance and Use of Technology 2 and Technology-Organization-Environment model. Though numerous m-commerce adoption (MCA) studies have been conducted, lesser attention is paid to how hedonic motivation (HM) can influence organizational users such as SMEs. This study bridges the gap by integrating the three models to provide a new lens to guide SMEs.

To examine the factors that influence the adoption of m-commerce, the researchers collected data from SMEs in Malaysia using an online survey. The sample size of the participants was determined through the available list provided by SME Corp Malaysia. The researchers also used Krejcie and Morgan’s sample size and G * Power techniques to determine that the sample size was appropriate. The data collected were analyzed using partial least square-structural equation modeling.

The findings of this study reveal that technological factors (computer self-efficacy [CSE], result demonstrability [RD] and computer anxiety [CA]) positively and significantly influence MCA. Likewise, the organizational/environmental factors (m-commerce knowledge, pressure from trading partners and pressure from competitors) positively and significantly influence MCA. The moderating influence of HM was also achieved on the relationship between CSE and RD. However, the proposed hedonic moderating relationship between CA and the adoption of m-commerce is not significant.

This study integrates three models to explain the adoption of m-commerce among SMEs in Malaysia and tested the moderating influence of HM. The results obtained better explain the decision by the SMEs to use m-commerce.

The study critically considered how m-commerce can be adopted by SMEs in Malaysia, which previous studies have largely ignored. Considering this, the study, therefore, advances a new relationship by integrating Technology-organization-environment model with TAM 3 and the moderating influence of HM to explain MCA among SMEs. This paper is one of the few research studies to test the moderating influence of HM in this regard.