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This study aims to explore the determinants influencing consumer purchase intentions towards electric vehicles (EVs) within the Asian market. It specifically examines how perceived value and perceived risk interact with the moderating effects of environmental, policy, and social factors to shape consumer behaviors towards EV adoption. The purpose is to delineate the intricate mechanisms driving consumer intentions in the context of sustainable mobility solutions.

Adopting a quantitative research design, this investigation collected data via a survey targeting consumers across various Asian countries. The study utilized Structural Equation Modeling (SEM) to analyze the responses, enabling a robust examination of the relationships between perceived value, perceived risk, and the potential moderating roles of external factors on consumer purchase intentions towards EVs.

The analysis revealed that both perceived value and perceived risk significantly influence consumers' intentions to purchase EVs. It was also found that environmental factors effectively moderate the relationship between perceived risk and purchase intentions, while social factors moderate the relationship between perceived risk and purchase intentions, highlighting the complex influence of external elements on consumer decisions. Contrarily, policy factors did not exhibit a significant moderating impact on the relationships examined.

This research enriches the domain of sustainable technology adoption by providing nuanced insights into the factors driving consumer intentions towards EVs in the Asian context. It underscores the critical roles of perceived value and risk, along with the distinct moderating effects of environmental and social factors, offering strategic implications for stakeholders within the EV ecosystem. The study's findings contribute to the broader discourse on consumer behavior in green technology adoption, laying groundwork for future investigations across diverse settings.

This study aims to clarify influencing factors of overcapacity of new energy enterprises in China and accurately predict whether these enterprises have overcapacity.

Based on relevant data including the experience and evidence from the capital market in China, the research establishes a generic univariate selection-comparative machine learning model to study relevant factors that affect overcapacity of new energy enterprises from five dimensions. These include the governmental intervention, market demand, corporate finance, corporate governance and corporate decision. Moreover, the bridging approach is used to strengthen findings from quantitative studies via the results from qualitative studies.

The authors' results show that the overcapacity of new energy enterprises in China is brought out by the combined effect of governmental intervention corporate governance and corporate decision. Governmental interventions increase the overcapacity risk of new energy enterprises mainly by distorting investment behaviors of enterprises. Corporate decision and corporate governance factors affect the overcapacity mainly by regulating the degree of overconfidence of the management team and the agency cost. Among the eight comparable integrated models, generic univariate selection-bagging exhibits the optimal comprehensive generalization performance and its area under the receiver operating characteristic curve Area under curve (AUC) accuracy precision and recall are 0.719, 0.960, 0.975 and 0.983, respectively.

The proposed integrated model analyzes causes and predicts presence of overcapacity of new energy enterprises to help governments to formulate appropriate strategies to deal with overcapacity and new energy enterprises to optimize resource allocation. Ten main features which affect the overcapacity of new energy enterprises in China are identified through generic univariate selection model. Through the bridging approach, the impact of the main features on the overcapacity of new energy enterprises and the mechanism of the influence are analyzed.

Rhetorical figures are frequently used in English-Chinese advertisement translation, but their impact on consumers is an under-studied issue. This research aims to explore Chinese consumers’ perception of two categories of rhetorical figures in bilingual advertising, namely schemes and tropes, via an eye-tracking experiment.

It takes rhyme and antithesis as representative examples of schemes and takes metaphor as an example of tropes. Eighty-six native Chinese speakers with satisfactory English proficiency participated in the experiment. Thirty English slogans without rhetorical figures were selected for the experiment. Two parallel Chinese versions were prepared, one with rhetorical figures and the other without. Participants were randomly assigned to read the English version and one Chinese version.

Data analyses show that the translations with rhetorical figures generally involve higher fixation duration or count, smaller saccade amplitude and higher subjective ratings. In particular, the impact of metaphor is more prominent than that of rhymes and antitheses. Still, English texts receive higher ratings than Chinese translations except for cases where metaphors are used in translations.

The results suggest that incorporating rhetorical figures particularly tropes into Chinese translations is effective in increasing Chinese consumers’ attention and processing depth and to present English advertisements together with Chinese translations can be helpful for affecting their attitude.

It applies the eye-tracking approach to explore Chinese consumers’ perception of rhetorical figures. It provides empirical evidence for the effectiveness of tropes.

In social media context, service failures become public domain, making them visible to vast audiences of customers who are virtually present. Thus, this study aims to discuss the roles of service failure type and management response on observers’ perceived helpfulness.

The authors conducted econometric analyses on a dataset incorporating 107,984 reviews and 34,641 management responses to negative reviews.

The results reveal that, for process failures, specifying a form of action (initiatives for solving the problem) is associated with more perceived helpfulness of reviews than accounts (explanation) or acknowledgments (recognition and acceptance), while responding to an outcome failure by providing an account is associated with increased perceived helpfulness of reviews.

For process failures, managers should make every effort to convince observers through specific actions that similar failures are less likely to occur in the future. For outcome failures, managers should strive to provide clear and fast explanations of the failure causes to restore observers’ evaluation of the firm’s capabilities.

The authors’ work extends sparse insights on observers and sheds new light on the effect of service failure type and response strategy on observers’ actual behaviors. The interplay between service failure type and response strategy provides guidance on how to use management responses to influence potential customers.

This study explores the impact of difficult coworkers on employees' turnover intention. Additionally, this study investigates the roles of employees' attitude toward difficult coworkers, perceived organizational support and affective commitment in the relationship between difficult coworkers and turnover intention.

Based on the stimulus-organism-response theory, a theoretical model was established that linked difficult coworkers to employees' attitude toward the, then to turnover intention directly and indirectly through perceived organizational support and affective commitment. The model was validated using responses from 343 Chinese employees in Macao's banking industry.

Results of the partial least squares-structural equation modeling (PLS-SEM) showed that difficult coworkers significantly influenced employees' attitude toward them. Employees' attitude toward difficult coworkers had a small and significant effect on turnover intention while perceived organizational support and affective commitment mediated the relationship between attitude toward difficult coworkers and turnover intention.

The study is the first empirical study to employ the stimulus-organism-response theory to characterize the impact of difficult coworkers on turnover intention. Fortunately, perceived organizational support and affective commitment were able to lessen the impact of difficult coworkers on turnover intention.

We seek to contribute to the literature by comparing and analyzing the relationship between Australian and Taiwanese students regarding environmental value attitude, action intention and green marketing intention. Specifically, by comparing the green marketing intention of hospitality and tourism (H&T) students in the East with that in the West.

A well-designed curriculum examines student thinking and behavior (learn). This study compares and analyzes the value and attitude and the application of big data to green marketing among Taiwanese and Australian university students using the stimulus-organism-response (S-O-R) model. Structural equation modeling (SEM) was used to test the hypotheses in a sample of 633 H&T students in Taiwan (389) and Australia (244).

This study also shows how the national differences between Australia and Taiwan have interference effects on the relationship between value attitudes and action intentions and between action intentions and the green marketing intention. We also combined the application of big data and related variables and estimated the mediating effect of related variables to evaluate the impact on action intentions and green marketing of big data applications.

There are significant differences in the sustainable behavior and intentions of H&T higher education students that reflect the educational differences between the East and the West. These different results may be due to a lack of natural resources and the relatively smaller size of Taiwan. With the strengthening of environmental action intention (AI) and green marketing intention (GMI), Taiwanese hospitality and management (H&M) students' sense of crisis increases, and their performance in GMI is slightly higher than that in Australia.

These findings indicate that it is necessary to consider students' ecological concepts, environmental knowledge, environmental value attitude and environmental action intention to improve their intentions to engage in green marketing under the stimulus-organism-response (S-O-R) framework. We also found that environmental knowledge has a mediating effect on the relationship between ecological concepts and environmental value attitude.

With the advent of AI-federated technologies, it is feasible to perform complex tasks in industrial Internet of Things (IIoT) environment by enhancing throughput of the network and by reducing the latency of transmitted data. The communications in IIoT and Industry 4.0 requires handshaking of multiple technologies for supporting heterogeneous networks and diverse protocols. IIoT applications may gather and analyse sensor data, allowing operators to monitor and manage production systems, resulting in considerable performance gains in automated processes. All IIoT applications are responsible for generating a vast set of data based on diverse characteristics. To obtain an optimum throughput in an IIoT environment requires efficiently processing of IIoT applications over communication channels. Because computing resources in the IIoT are limited, equitable resource allocation with the least amount of delay is the need of the IIoT applications. Although some existing scheduling strategies address delay concerns, faster transmission of data and optimal throughput should also be addressed along with the handling of transmission delay. Hence, this study aims to focus on a fair mechanism to handle throughput, transmission delay and faster transmission of data. The proposed work provides a link-scheduling algorithm termed as delay-aware resource allocation that allocates computing resources to computational-sensitive tasks by reducing overall latency and by increasing the overall throughput of the network. First of all, a multi-hop delay model is developed with multistep delay prediction using AI-federated neural network long–short-term memory (LSTM), which serves as a foundation for future design. Then, link-scheduling algorithm is designed for data routing in an efficient manner. The extensive experimental results reveal that the average end-to-end delay by considering processing, propagation, queueing and transmission delays is minimized with the proposed strategy. Experiments show that advances in machine learning have led to developing a smart, collaborative link scheduling algorithm for fairness-driven resource allocation with minimal delay and optimal throughput. The prediction performance of AI-federated LSTM is compared with the existing approaches and it outperforms over other techniques by achieving 98.2% accuracy.

With an increase of IoT devices, the demand for more IoT gateways has increased, which increases the cost of network infrastructure. As a result, the proposed system uses low-cost intermediate gateways in this study. Each gateway may use a different communication technology for data transmission within an IoT network. As a result, gateways are heterogeneous, with hardware support limited to the technologies associated with the wireless sensor networks. Data communication fairness at each gateway is achieved in an IoT network by considering dynamic IoT traffic and link-scheduling problems to achieve effective resource allocation in an IoT network. The two-phased solution is provided to solve these problems for improved data communication in heterogeneous networks achieving fairness. In the first phase, traffic is predicted using the LSTM network model to predict the dynamic traffic. In the second phase, efficient link selection per technology and link scheduling are achieved based on predicted load, the distance between gateways, link capacity and time required as per different technologies supported such as Bluetooth, Wi-Fi and Zigbee. It enhances data transmission fairness for all gateways, resulting in more data transmission achieving maximum throughput. Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation.

Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation. It also shows that AI- and IoT-federated devices can communicate seamlessly over IoT networks in Industry 4.0.

The concept is a part of the original research work and can be adopted by Industry 4.0 for easy and seamless connectivity of AI and IoT-federated devices.

There has been a lot of research on how to set marketing budgets, but the overlooked aspect was how allocating funds influences business performance in a multi-goal context. This study aims to examine the relationship between business performance, the process of allocating funds to multiple goals and the interaction among the goals.

Ratio data were generated through “a constant sum scale” from a sample of 362 managers from the B2C sector, besides data on after-tax revenue for two years. The data file was created. Then, a factor analysis was performed on the data. Furthermore, an econometric model with interaction terms was fitted to the data.

The results show that allocating funds to multiple marketing goals – demand generation, customer experience, brand image, marketing competency and purchase intention – influences business performance. Furthermore, a goal’s impact on business performance is higher when coupled with other goals than in isolation.

The findings of the study should assist managers in increasing revenue while spending less on marketing and shifting funds from less efficient goals and pairs of goals to highly efficient ones.

By extending the relevant theory on the relationship between the process of marketing fund allocation, multiple goals and business performance, this study contributes to the literature on marketing.

Since the COVID-19 epidemic, the number of restaurant service quality studies has continued to increase. However, until now, there has not been an overall perspective or accurate instructions for research on restaurant service quality and experiential value enhancement. This study conducts multiple comparison studies to discover differences between consumer-perceived service quality and satisfaction perspectives on hotel fine dining and chain restaurants.

This study integrates a hotel’s fine dining and chain restaurant to obtain 636 participants (e.g. Study 1 has 318 hotel fine dining customers; Study 2 has 318 chain restaurant customers), mainly expanding the SERVQUAL model and stimulus–organism–response (S–O–R) theory.

The results of Study 1 show that value co-creation has a mediating effect on the relationship between service quality and satisfaction. In addition, customer experiences have a significant moderating effect on customer satisfaction. The outcomes of Study 2 showed that experiential value has a significant mediating effect on the relationship between service quality and satisfaction. In addition, customer relationship quality is a critical criterion in regulating the process of experience value delivery.

Hotels’ fine dining should pay attention to the item risk in the value co-creation factor, while chain restaurants should enhance the item service excellence in the experiential value factor to satisfy the changing customer requirements.

This study provides several alternative models to verify the robustness of the empirical results.

1. This research has brought clarity to the diverse mediation-moderation models that compare of hotel fine dining and chain restaurant consumer perceived service quality and satisfaction predictions.

2. These models delve into how different service quality requirements after the epidemic that affect customer satisfaction, as perceived by customers consumed in hotel fine dining and chain restaurant.

3. Value cocreation and experiential value emerge as pivotal factors, they act as mediators between service quality and satisfaction.

4. They demonstrate a moderation effect of customer experiences between value cocreation and satisfaction, as well as customer relationship quality between experiential value and satisfaction.

This research has brought clarity to the diverse mediation-moderation models that compare of hotel fine dining and chain restaurant consumer perceived service quality and satisfaction predictions.

These models delve into how different service quality requirements after the epidemic that affect customer satisfaction, as perceived by customers consumed in hotel fine dining and chain restaurant.

Value cocreation and experiential value emerge as pivotal factors, they act as mediators between service quality and satisfaction.

They demonstrate a moderation effect of customer experiences between value cocreation and satisfaction, as well as customer relationship quality between experiential value and satisfaction.

The purpose of this study is to examine the applicability of the technology acceptance model (TAM) in libraries considering the advanced technologies and users’ behaviour.

The research uses a critical reflective approach to review and synthesize a body of recent academic literature on the use of TAM in libraries. The review included assessing TAM’s historical evolution, its limitations and how it could be improved.

The findings indicated that, although TAM can be viewed as an appropriate theoretical model to explain the users’ intention towards technology acceptance, it is limited in explaining both the users’ attitude towards advanced technology and their behaviour in advanced library settings.

To enhance the practicality of TAM in libraries, several recommendations for strategic advancements have been proposed such as contextualizing TAM to libraries, exploring AI-driven adoption, integrating library-specific constructs, understanding cultural differences and using holistic research approaches.

The findings contribute to a deeper understanding of technology adoption in libraries and to the future possibilities of TAM.