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WeChat official account (WCOA) is an emerging and important platform for academic library services, which greatly accelerates the development of this field. This article aims to identify key evaluation indicators for users' satisfaction of the Academic Library WeChat Official Account (ALWCOA) as a reference for future improvements.

Based on the updated DeLone and McLean (D&M)’s model and Delphi method, an evaluation system of ALWCOA satisfaction was constructed. Then 212 university students were recruited to fill out a questionnaire on evaluation indicators. The grey relational analysis (GRA) and Pareto's principle were employed to analyze the questionnaire and select key evaluation indicators.

An ALWCOA service satisfaction evaluation system with three evaluation dimensions and 15 evaluation indicators was constructed, and three key evaluation indicators were identified, including service responsiveness, information timeliness and system security.

This article provides a strategy for assessing ALWCOA service satisfaction, as well as insights for improving of ALWCOA service. Specifically, academic libraries should pay more attention to improving service responsiveness, information timeliness and system security.

This article innovatively applied the updated D&M model in academic library service. Additionally, it facilitates the development of research fields, such as academic library services, microservices and user service evaluation, and provides a case study to better understand the WCOA.

Bicycle sharing offers a novel way to create smart and sustainable mobility solutions for the future. The purpose of this study is to draw on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) framework for identifying the factors necessary to predict bike-sharing intention among users in India.

Data were collected through a questionnaire distributed across four major cities in India, and 515 responses were analyzed. A sequential approach was employed to analyze the data using Partial Least Square–Structural Equation Modeling (PLS-SEM) and Fuzzy-set Qualitative Comparative Analysis (fsQCA).

The findings from PLS analysis revealed that performance expectancy, effort expectancy, facilitating conditions, hedonic motivation and price value are the salient variables that affect users' intentions to participate in bike sharing. In addition, based on fsQCA, six configurations of causal conditions are presented as intermediate solutions that produce the same results. Although antecedent conditions, such as habit and social influence, had an insignificant effect on individuals' BSI, they create conditions sufficient to encourage users' participation in bike sharing in combination with other variables.

A few limitations of this research and the implications of the findings in terms of theory and policy implications are also discussed.

The reported study is one of the earliest to explain bike-sharing adoption in India using the UTAUT 2 model.

Sustainability has become a global need for survival in every field since the side effects of financial development have resulted in environmental devastation. Green banking (GB) has been proposed as a way to reduce the carbon footprint caused by banking operations by promoting paperless financial services through the intensive use of technology. However, the adoption level of GB technology remains unsatisfactory among customers in the United Arab Emirates (UAE). Therefore, using the unified theory of acceptance and use of technology (UTAUT), this study aims to comprehensively investigate the challenges affecting the intention of bank consumers in the UAE to adopt GB technology.

This study used exploratory sequential mixed-methods research. Preliminary semi-structured interviews were conducted with ten banking professionals using a purposive sampling technique to explore the challenges affecting consumers’ intention to adopt GB technology. Sequentially, the study tested various factors through a quantitative cross-sectional online survey of a sample of 332 bank customers and used the convenience sampling technique to obtain further empirical support for the research framework. Thematic content analysis using NVivo 11 was used for the qualitative data analysis. Meanwhile, partial least square structural equation modelling in Smart PLS 3.3 was used for the quantitative data analysis.

The qualitative analysis identified six new challenges affecting customers’ intention to adopt GB technology, including customer awareness, personal innovativeness, bank reputation, security and privacy, system quality and government support. The preliminary qualitative findings were confirmed mainly through quantitative data analysis, whereby customer awareness, personal innovativeness, system quality and bank reputation were found to significantly impact customers’ intention to adopt GB technology. However, the effects of security and privacy and government support were insignificant.

To the best of the authors’ knowledge, this study is the first to propose a comprehensive model that considers individual, technological, organisational and environmental factors to address the issue of customers’ low GB technology adoption rates in the UAE. Meanwhile, this study extends the UTAUT by integrating new factors. This paper is also among the first to investigate customers’ GB technology adoption intention using a mixed-methods approach, which combines the strengths of quantitative and qualitative methods within the same study to offer better insights than a single-method approach.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

This study aims to improve the Unified Theory of Acceptance and Use of Technology (UTAUT) model by examining technological anxiety and other influential factors on international students' adoption of mobile learning (m-learning) during COVID-19 emergency remote teaching (ERT).

This study utilized the modified UTAUT framework to test hypotheses through a cross-sectional survey method. Participants were university students studying Chinese as a foreign language who were selected using a convenience sampling approach. An online questionnaire was then administered. The data collected from the surveys were analyzed using the partial least squares method with SmartPLS 4 software.

The study examined 16 hypotheses and found support for six of them. The results confirmed that performance expectancy (PE) is a significant predictor of behavioral intention (BI), and anxiety influences both PE and effort expectancy. The negative effect of social influence on anxiety was found to be significant, while facilitating conditions had a negative impact on learners' self-efficacy. The model fit indices indicated a good overall fit for the model.

This study presents a valuable contribution to the literature on m-learning in emergency education by incorporating technological anxiety into the enhanced UTAUT model. Examining the relationships between the key factors of the model provides a better understanding of learners' intentions and can inspire researchers to establish new theoretical foundations to evaluate the roles of these factors in diverse educational settings.

The study found that performance expectations are linked to learners' intentions, and anxiety indirectly affects BIs to use mobile learning platforms. Thus, these platforms should be designed to meet learners' expectations with minimum effort and eliminate anxiety triggers to facilitate ease of use. Language curriculum developers and policymakers should incorporate mobile learning applications to support diverse language skills, address students' needs and encourage their use through professional development opportunities for instructors.

Social factors have been found to significantly influence anxiety levels among learners. Therefore, it is crucial for teachers and family members to play an essential role in mitigating anxiety's adverse effects. Discussing related issues can enhance the quality of mobile learning and stimulate social initiative by providers, ultimately improving the learning experience for all learners, regardless of their location or circumstances. This can also contribute to the growth and development of society.

This study contributes to the originality of m-learning development by proposing an enhanced UTAUT model that considers anxiety and emphasizes the critical role of foreign learners' BIs. It provides fundamental guidelines for designing and evaluating m-learning in ERT contexts.

This study aims to verify the antecedents influencing mobile gamers’ intentions to purchase mobile phones. To this end, the constructs price–quality relationship, price sensitivity, perceived quality, identification with the group of gamers and referrals from other gamers were identified in the literature.

Data were collected through an online questionnaire with 335 consumers. The model was tested using partial least squares.

It was verified that the price–quality relationship directly influences mobile gamers' intentions to purchase mobile phones. Also, it was observed that price sensitivity of mobile gamers directly influences the price–quality relationship and indirectly influences the intention to purchase mobile phones to play games. It was further verified that this price sensitivity is directly influenced by the perceived quality of mobile gaming devices. Finally, it was observed that referrals from other gamers directly influences the perceived quality and onès identification with the group of gamers.

This study concluded that developing strategies focused on prices of mobile phones gamers use to play games tends to influence mobile gamers' purchase intentions. This paper extends the study of mobile device purchase behavior, uniting constructs studied separately and proposing connections not yet tested, assisting in the theoretical understanding of the factors that contribute to the intention to purchase mobile devices.

This study is theoretically justified for four reasons: it focused on a specific group of consumers, mobile gamers, which is a constantly growing audience; it brought an innovative model, testing the influence of the perceived quality of mobile gaming devices on the price sensitivity of mobile gamers; there are spaces for new research on mobile device purchase intention; and it may assist in the theoretical understanding of the factors contributing to mobile device purchase intention.

Meal ordering apps (MOAs) have transformed the customers' dining habits, particularly during mobility restrictions of the COVID-19 pandemic. Under the theoretical cover of the extended stimulus–organism–response (SOR) model, this paper attempts to explore the critical antecedents and outcomes of customer MOA engagement which predict the continuous purchase intentions using these apps. A multigroup analysis is conducted to investigate the difference between the hypothesized relationships between the Chinese and Indonesian consumers.

A mixed-method approach, including a systematic literature review, an open-ended essay (qualitative) with 139 MOA users and an online survey (quantitative) with 1,207 MOA users in total, was used for hypotheses testing.

The structural equation model results revealed that customer MOA experience factors such as mobile online reviews (MR), food quality (FQ), restaurant reputation (RR), service quality and system quality (SyQ) are the absolute positive factors that influence customer MOA cognitive, affective and behavioral engagement, which in turn affect continuous purchase intentions. The multigroup analysis results reveal that Chinese customers prioritized MR and FQ for customer MOA engagement (cognitive, affective and behavioral). Comparatively, Indonesian customers placed most importance on RR and SyQ.

Considering a market-specific setting and based on the extended SOR framework, this study is one of the first to take a comprehensive look at the critical antecedents and outcome of multidimensional customer MOA engagement in the developing countries’ (China and Indonesia) online to offline meal delivery context. Further, this study investigates the customer continuous purchase intentions as an outcome of MOA engagement during the COVID-19 pandemic. The findings also reveal the differences in consumer behavior across the two developing but culturally diverse countries samples during the pandemic.

The use of mobile technologies and learning devices has been increasing in every field of life, and library and information sciences are no exception. This study aims to explore the perceptions of library and information science (LIS) students regarding mobile learning (m-learning) along with their primary purposes, social media applications, advantages, disadvantages, barriers, impact and overall satisfaction with using these devices.

A survey method was used, and data was collected from the students of 12 library schools in Pakistan for a total sample of 250 students.

Their main purposes of usage, their needs, advantages, disadvantages, barriers, impacts and the level of overall satisfaction were also identified. The inferential statistics (t-test and ANOVA) also identified the difference of opinion on the basis of gender, programs, types and number of m-learning devices. Overall, the findings highlight the need for academic libraries to give mobile access (launching mobile applications) to better use library services. To ensure this, libraries must keep an eye on new advances in mobile technology, researchers’ needs and related electronic library services and observe how these services are being used.

Theoretical and practical implications have been highlighted to understand the perceptions of LIS students about m-learning devices.

The mobile library service providers and management need to improve their services by offering services that fit the diverse needs of their users and should know how to attract modern library users.

With the increasing use of Internet and mobile handsets, mobile-based electronic payment apps (MEPAs) are becoming very popular around the world due to having various advantages. The intention of this exploratory study is to investigate the role/impact of selected factors in adopting three MEPAs of China: Wechat, Alipay and UnionPay based on the extended technology acceptance model (ETAM). After conducting a thorough and careful literature review, this study identified and divided seven such factors into three broad categories: (1) technological factors: perceived ease of use (PEU) and perceived usefulness (PU); (2) personal factors: perceived trust (PT), perceived privacy (PP) and perceived risk (PR); and (3) social factors: social influence (SI) and peer influence (PI) that were assumed to affect the intention to adopt MEPAs (IAMEPAs).

1,597 Chinese individuals were selected through purposive sampling technique who regularly used MEPAs at the time of collecting data. For collecting primary data from the selected respondents, a cross-sectional survey instrument was used. The study utilized IBM SPSS 25 for descriptive statistics and a second generation covariance-based structural equation modeling (CB-SEM) technique through AMOS 25 for testing the hypothesized relationships.

From statistical analysis, it was identified that five factors: PEU, PU, PT, SI and PI have significant positive impact on the dependent variable, IAMEPAs while PR and PP have insignificant influence on IAMEPAs.

This is one of the studies ever conducted to discover the factors that can have impact on the adoption of MEPAs using ETAM. It is strongly expected that this exploratory study can motivate the scholars to commence additional investigations regarding this increasingly popular financial technology (Fin-Tech). In addition, it can be anticipated that the MEPA service providers can widen their service effectiveness according to the users’ opinion reflected in this study. Furthermore, policymakers involved with Fin-Tech can also formulate and implement effective policies and guidelines based on the empirical outcomes.

Consumers have pervasively relied on mobile reviews in digital economy. However, little knowledge exists regarding how customers adopt several mobile reviews to make purchasing decisions. With the assistance of reader-response theory, this study investigates how the consistency of product reviews, in terms of their adherence to both other reviews and the prior experience of the customer, affect perceived quality, confirmation of the customer's expectations, the customer's level of trust in the seller and the consequent purchase intention.

Based on a scenario simulation and an online experiment to collect data, the authors employed AMOS to test the proposed hypotheses using survey data collected from 314 customers in Study 1 and 420 consumers in Study 2.

The results indicate that global consistency positively and significantly contributes to confirmation, perceived quality and trust in sellers while sequential inconsistency positively and significantly influences perceived quality. Meanwhile, purchase intention is positively and significantly promoted by confirmation, perceived quality and trust in sellers, and initial valence has some moderating effects on these relationships.

This study contributes to the understanding of how customers apply product reviews to make purchasing decisions from a new angle. It also elucidates the way in which the perceived consistency of product reviews affects how reviewers are perceived and the consequent effect of these perceptions on a customer's purchase intentions.