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The purpose of this paper is to investigate the organizational readiness perspective of adopting artificial intelligence and big data analytics in the textile and garment industry in Bangladesh along with identifying the associated factors.

The research uses a qualitative method using semi-structured interviews with representatives of business organizations and stakeholders of Bangladesh’s textile and garment industry.

The research reveals that the textile and garment industry in Bangladesh currently has low organizational readiness to adopt artificial intelligence and big data analytics. This is due to moderate knowledge- and leadership-readiness along with low human-, finance- and engagement-readiness of most of the business organizations. The readiness aspects interplay with each other and need to be improved holistically.

Considering the significant global and national importance of Bangladesh’s textile and garment industry, gaining insights into the industry’s current state of readiness for adopting artificial intelligence and big data analytics would offer valuable assistance to both national and global economies and may enhance economic outcomes.

Since no exploratory study was conducted to understand the organizational readiness aspects of adopting artificial intelligence and big data analytics of the globally significant textile and garment industry in Bangladesh, the paper analyzes five key aspects of such readiness and offers a basis for conducting similar studies in other emerging economies.

Despite grave public concerns over information privacy and ongoing academic explorations of privacy policy, there is a general lack of understanding toward this issue in the legal context in China, the largest e-commerce market in the world. Departing from the extant literature of general discussion in nature, the authors undertook an exploratory study on the efficacy of e-commerce websites' privacy policies in China from the legal perspective.

The authors drew on a qualitative grounded theory approach to identify selective codes relating to the focal issue and established a theoretical framework therefrom. The authors then conducted theoretical integration by linking them to the Theory of Development Blocks and the System Justification Theory.

The research identifies a general distrust of Chinese consumers toward privacy policies and highlights that despite their growing concerns about privacy, the privacy policies are largely ineffective in reflecting legal enforcement, changing their perceptions or influencing purchase behaviors. It also reveals that the current Chinese legislation is unable to fully render consumers' confidence in e-commerce websites' privacy policies effectiveness and privacy protection due to its limited recognition and influences among them.

The research has multiple ramifications. The authors empirically confirmed a mismatch between customers' perception of privacy policies and their actual behaviors and then theoretically explained the seemingly conflicting scenario in the context of development block of legal enforcement and system justification. The authors theorized the absence of the legal enforcement in privacy policies to supplement the legal perspective to the literature. The research further leads us to suggest that the time has come to update and strongly enforce privacy regulation in China to fuel the further development of e-commerce sector in practice.

This research article aims to understand the role of brand image, service quality and price (charge) in revitalising functional mass brands into prestigious mass brands.

The empirical research framework was developed by synthesising the past literature on masstige marketing and brand extension. Data was collected using a survey questionnaire from 396 respondents availing M-Wallet. Structural equation modelling was used to validate the brand revitalization attributes; further, the binary logistic regression model examined the effect of revitalization attributes on the chance of increasing customer's perception of masstige.

The exploratory study suggested brand image, service quality and value for money pricing as essential attributes to revitalize mass brands into masstige brands; furthermore, path analysis validated the positive effects of these attributes on the perception of masstige. The proposed binary logistic regression model suggested brand image as sensitive attributes, increasing the odds ratio by 9.39 times in favour of perceiving brand as masstige followed by the perceived service quality that is 5.87 times. The prediction capability of the proposed binary logistic regression model is found to be 96%.

The methodology of this study provides the basis for future researchers to advance research on masstige. This study will assist the marketers of mass brands to make better marketing decisions related to how masstige image can be sustained or a new or less known brand can be revitalized into a prestigious brand.

This study is the first to provide empirical evidence of how the mass brand can be revitalised as masstige brands by considering image, quality and price attributes.

Because the success of m-payment services depends on the enablers and barriers that affect user satisfaction, the present research explores the effects of perceived value and sacrifices on users' satisfaction with m-payment services. The predicted relationships among perceived value, perceived sacrifices, users' satisfaction, continuance intention, word-of-mouth (WOM), shopping effectiveness, quality of life (QOL) and stickiness were established based on the mobile technology acceptance model (MTAM) and the value-based adoption model (VAM).

A representative data sample of 430 Egyptian banking clients was analyzed to test the hypotheses using partial least squares-structural equation modeling (PLS-SEM).

The findings revealed that all perceived value constructs significantly positively affect users' satisfaction. Moreover, all perceived sacrifice constructs significantly negatively affect users' satisfaction. Users' satisfaction, in turn, has a significant positive effect on continuance intention, WOM, shopping effectiveness, QOL and stickiness with m-payment services.

This is the first study to examine several levels of m-payment outcomes, including m-payment, consumer and bank outcomes, based on the integration of MTAM and VAM models.

The main purpose of this study is to identify the determinants that inhibit the adoption or usage of digital payment systems (DPSs) in India.

This study used a qualitative technique, including in-depth semi-structured interviews. Data analysis was conducted using thematic analysis, incorporating both deductive categorisation and inductive coding to identify factors responsible for the non-adoption or discontinuation of DPS use.

The findings are in the form of themes and sub-themes that were generated from the data analysis: digital divide (DD), which includes the digital access divide, digital capability divide and digital innovativeness divide; socio-demographic divide (SD), which includes education, geographical location, gender, age and income; psychological barriers, which include a lack of perceived ease of use, vulnerability to risks, technophobia and a lack of trust; and other barriers, which include a lack of awareness, a cash-dominated society and a lack of interoperability.

The factors identified in this research can be further validated and tested in future studies using quantitative data. This will enable stakeholders to better comprehend the impacts of these factors on DPS adoption or usage.

The study’s practical implications are specifically relevant to the Union Territory (UT) administration of Ladakh, as there is a DD and an SD among different sections of the population of the UT of Ladakh. UT administrations must prioritise efforts to eliminate these divides. The implications for banks and DPS providers are that they should conduct financial literacy training about DPSs in remote rural areas and invest in developing user-friendly and simplified DPS user interfaces to improve relationships with DPS users and their long-term retention.

The findings of this study reveal the three levels of the DD that determine DPS adoption or usage, which have not been discussed together in the literature in the DPS context and that must be addressed to expand DPS adoption, thus providing a more holistic view of the DD in the context of DPS.

Consumers in India are increasingly using mobile payment systems (MPSs) to make online and offline payments. Digital payment applications are gradually being used as surrogates for cash, checks and plastic money. The motive behind this research is to analyze the different antecedents that impact the users' willingness to continue using the MPS in India.

An extensive study of the literature review supports the creation of a framework that describes the continuance intention of using MPS. Data from a survey of 612 respondents from India were collected to assess the research model. The study used partial least squares (PLS)–structural equation modeling (SEM) technique to empirically validate the framework developed.

The outcomes of the research suggest that service quality, attitude, effort expectancy and perceived risk act as influencing antecedents of continuance intention to use MPS. Determinants like perceived trust, convenience and social value have no influence on users' continuance intention. SEM analysis has verified the proposed model, which explains 50.7% of the variance of the users' continuance intention of using MPSs.

The research is built upon cross-sectional data carried out in India. Hence, the outcomes of the study are limited to this region only.

Engaging with the consumers for a long time and enabling their continuance usage are extremely important for firms offering mobile payment services. The managerial implications provide insights into the different ways to capture new business opportunities to the firms rendering mobile payment services in the wake of changing consumer behavior.

This research tries to analyze users' continuance intention to use MPS in India. Although many research studies have investigated the willingness of the individuals to adopt novel technology in different frameworks, there are hardly any empirical studies carried out to analyze the antecedents of users' continuance intention to use MPSs.

Several industries including banking are booming because of COVID-19. However, it is still unknown whether this growth is momentary or permanent in nature. Hence, this study aims to identify the role of health-related concerns and trust as stimuli on M-payment loyalty.

Data were collected through Google Forms from 431 participants. Subjects were M-payment users. The hypothesized model was tested using structural equational modeling.

Results of the study indicate that perceived severity and trust act as stimuli for M-payment loyalty. Further, trust not only influences loyalty directly but also through intimacy. Additionally, no linear relationship was found between perceived usefulness and M-payment loyalty.

This work is an early attempt to consider health-related concerns and trust as stimuli to predict M-payment loyalty. Further, this study focused on three new constructs, namely perceived severity, perceived susceptibility and intimacy, that are underexplored in digital banking literature.

The study intends to evaluate students’ intention to shift from cash payment to mobile payment system for academic fee payments through push, pull and mooring framework. Push factors comprise risk and service-related factors, pull factors consist of subjective and aspect-based factors and mooring factors include cost and cognitive factors.

Sample of the study consists of around 296 undergraduate and postgraduate students from different higher educational institutions located in India. The questionnaire for data collection comprises 21 Likert scale-based items distributed among seven constructs. Partial least square structural equation modeling is used to identify the significant factors influencing students’ intentions.

Five of the factors, namely, risk, service, subjective, aspect and cognitive significantly influence student’s intention to switch to mobile payment system for academic fee payments. Moderation analysis indicates that the impact of the push and pull factors on switching intention towards mobile payments has a more positive influence among male students.

This study is probably the only study that tested the specific push, pull and mooring factors influencing intention to switch to mobile payment from cash payment in the Indian education system based on the incentive, Fogg behavior and status quo bias theory for academic fee payment.

The purpose of this study is to develop a conceptual framework which takes into account the perceived risk (PR) and the perceived benefits (PB) of using mobile payment systems (MPS) in the context of COVID-19 pandemic.

The study proposes a conceptual framework incorporating the uncertainties/risks associated with MPS like perceived technology uncertainty (PTU), perceived regulatory uncertainty (PRU), perceived service intangibility (PSI) and perceived information asymmetry (PIA), along with the benefits of using MPS such as trust, mobility, health consciousness (HC) and fear of Coronavirus (FOC). A survey comprising 1,253 participants was conducted in India. The proposed model was empirically examined through partial least square structural equation modelling.

The outcomes of the study revealed a significant positive influence of PTU, PRU, PIA and PSI on PR. On the other hand, HC and FOC were identified as the major antecedents having a significant positive influence on PB. Both PR and PB had a significant influence on the intention to adopt MPS, but the influence of PB was greater than the influence of PR.

The enablers and inhibitors play a crucial role in understanding the intention to adopt MPS. HC and fear of acquiring Coronavirus can be aggressively marketed by the government and service providers as a strategy to maintain social distancing. Government should address the regulatory concerns associated with the usage of MPS so as to alleviate any negative perception among the general public.

The current study is a novel attempt to understand the intention to adopt MPS in India as precautionary health behaviour to curb the transmission of Coronavirus pandemic. The study uses two constructs, HC and FOC, to better understand the behaviour of the people and explain the intention to adopt MPS during the COVID-19 pandemic.

The purpose of this paper is to study the combined effect of human error, common‐cause failure, redundancy, and maintenance policies on the performance of a system composed of three‐state devices.

Generalized expressions for time‐dependent and steady state availability of a generalized maintainable three‐state device parallel system subjected to human errors and common‐cause failures are developed in the paper under two maintenance policies: Type I repair policy (i.e. only the completely failed system is repaired); and Type II repair policy (i.e. both partially and completely failed system is repaired). The Markov method is used to develop general and special case expressions for state probabilities, and system time‐dependent and steady state availabilities.

In the case of three‐state devices, it is demonstrated that by increasing the number of redundant devices in parallel do not necessarily lead to the improvement in the system availability. In fact, the availability of the system depends significantly on the dominant failure mode of the devices (i.e. short‐mode or open‐mode). When comparing the effect of maintenance policies on the system availability, it is observed that the Type II repair policy does not lead to an improvement in the system availability. Furthermore, it is observed that both human error and common‐cause failure independently lead to lower system availability.

This study will help maintenance engineers and reliability practitioners to become aware of the combined impact of redundancy, human error, common‐cause failure, and maintenance policies on the performance of the three‐state device systems. Consequently, they will make better maintenance related decisions in organizations such as oil refineries and power stations that use three state devices quite extensively.

Most of the past models have independently studied the effects of redundancy, human error, and common‐cause failure on maintainable system made up of three‐state devices. This effort is one of the first attempts to study the combined effects of all these factors in a parallel system composed of three state devices.