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An energy-absorbing device is an important tool that is capable of increasing the survivability of passengers in vehicles. Generally, empty metallic tubes are used, and it is found that the energy absorption capability is lower than the energy obtained using composite materials. Therefore, this paper numerically presents the crushing performances of hybrid tubes under axial and oblique compressions.

Three important parameters are used such as fiber thicknesses, fiber orientations and oblique compression angles. Epoxy-reinforced fibers are wrapped around the steel tubes and it is then modeled numerically using the ANSYS finite element program. Belytscho – Tsay shell element is used to model the composites, while a bilinear kinematic hardening model is used to model the steel tube. A proper contact algorithm is implemented to prevent interpenetration among elements and surfaces.

A proper contact algorithm is implemented to prevent interpenetration among elements and surfaces. Hybrid tubes are quasi-statically crushed and force–displacement curves are extracted and analyzed.

It is found that the introduction of oblique compressions has induced bending moments and therefore decreases the energy absorption capability. Varying fiber orientations also insignificantly changed the crushing performances. However, wrapping carbon/epoxy composite that is capable of strengthening the tubes, is also subjected to oblique compression compared with the glass/epoxy composites.

This study aims to investigate Sri Lankan Government university students’ acceptance of Chat Generative Pretrained Transformer (ChatGPT) for educational purposes. Using the unified theory of acceptance and use of technology 2 (UTAUT2) model as the primary theoretical lens, this study incorporated personal innovativeness as both a dependent and moderating variable to understand students’ ChatGPT use behaviour.

This quantitative study used a questionnaire survey to collect data. A total of 500 legitimate undergraduates from 17 government universities in Sri Lanka were selected for this study. Items for the variables were adopted from previously validated instruments. Partial least squares structural equation modelling (PLS-SEM) using SmartPLS 4 was used to investigate latent constructs’ relationships. Furthermore, the variables’ relative relevance was ranked using a two-stage artificial neural network analysis with the SPSS 27 application.

The results of the analysis revealed that eight of the nine proposed hypotheses were confirmed. The most significant determinants of behavioural intention were habit and performance expectancy, closely followed by hedonic motivation and perceived ease of use. Use behaviour was highly influenced by both behavioural intention and personal inventiveness. Though personal innovativeness (PI) was suggested as a moderator, the relationship was not significant.

The research highlights the impact of habit, performance expectancy and perceived ease of use on students’ acceptance of AI applications such as ChatGPT, emphasising the need for efficient implementation techniques, individual variations in technology adoption and continuous support and training to improve students’ proficiency.

This study enhances the comprehension of how undergraduate students adopt ChatGPT in an educational setting. The study emphasises the significance of certain variables in the UTAUT2 model and the importance of PI in influencing the adoption of ChatGPT in educational environments.

This research sought to examine the differential effect of chatbot banking artificial intelligence (AI) on consumer experience in the banking industry. A positivist paradigm was adopted to sample 389 consumers who were previously exposed to chatbot banking in Zimbabwe. A causal research design was employed whilst a quantitative approach was followed. In analysing data, the research study applied the structural equation modelling (SEM) technique. The authors found that chatbot banking significantly improves customer experience (CX) in the banking industry. Reliability and responsiveness of the chatbot need to be enhanced for effective improvements in CX. A need was also identified to enhance CX through the development of an ease-to-use chatbot which is embedded in everyday messaging applications of consumers. A significant association was also found between perceived benefits of chatbot banking and CX. This study informs the development of competitive advantage by banks and other related companies through AI-based CX management strategies. In times of pandemics and beyond, chatbot banking can be very instrumental in improving CX.

The purpose of this paper is to design and implement a fully integrated low‐phase noise and large tuning range dual‐band LC voltage‐controlled oscillator (VCO) in 0.13 μm complementary metal oxide semiconductor (CMOS) technology.

Two parallel‐connected single‐band VCOs are designed to implement the proposed VCO. Adopting a simple and straight‐forward architecture, the dual‐band VCO is configured to operate at two frequency bands, which are from 1.48 GHz to 1.78 GHz and from 2.08 GHz to 2.45 GHz. A band selection circuit is designed to perform band selection process based on the controlling input signal.

The proposed VCO features phase noise of −104.7 dBc/Hz and −108.8 dBc/Hz at 1 MHz offset frequency for both low corner and high corner end of the low‐band operation. For high‐band operation, phase‐noise performance of −101.1 dBc/Hz and −110.4 dBc/Hz at 1 MHz offset frequency are achieved. The measured output power of the dual‐band VCO ranges from −8.4 dBm to −5.8 dBm and from −9.6 dBm to −8.0 dBm for low‐band and high‐band operation, respectively. It was also observed that the power differences between the fundamental spectrum and the nearby spurious tone range from −67.5 dBc to −47.7 dBc.

The paper is useful to both the academic and industrial fields since it promotes the concept of multi‐band or multi‐standard system which is currently in demand in the telecommunication industry.

This research aimed to analyze and prioritize the factors affecting sustainable marketing (SM) and sustainable operation (SO) of manufacturing small and medium-sized enterprises SMEs through big data adoption (BDA).

The technology-organization-environment (TOE) framework was used as a theoretical base and data were gathered from manufacturing SMEs in Malaysia. The 159 questionnaire replies of chief executive officer (CEO)/managers were analyzed using a hybrid approach of structural equation modeling-artificial neural network (SEM-ANN).

The findings of this study showed that perceived benefits (PB), technological complexity (TC), organization's resources (OR), organization's management support (OMS) and government legislation (GL) are the factors that influence BDA and promote SM and SO. The findings of ANN showed that a perceived benefit is the most important factor, followed by OMS.

The findings of this study can assist SMEs managers in making strategic decisions and improving sustainable performance and thus contribute to overall economic development.

The manufacturing industry is under immense pressure to integrate sustainable practices for long-term success. BDA can assist industries in aligning industries' operational capabilities. The majority of the current research have mainly emphasized on BDA in corporations. However, the associations between BDA and sustainable performance of manufacturing SMEs have been less explored. To address this issue, this study developed a theoretical model and examined the influence of BDA on SM and SO of manufacturing SMEs. Meanwhile, the hybrid methodological approach can help to uncover both linear and non-linear relationships better.

The purpose of this study is twofold. The first objective is to identify the factors that affect Gen Z tourists' M-payment behaviour. Next, this study investigates the inter-relationship between Gen Z tourist’s perception of M-payment benefits, adoption behaviour, usage risk and future usage intention.

The sample comprised Malaysian Gen Z individuals (n = 326) who had experience using M-payment methods while travelling outbound. Given the complex nature of the model and the goal to predict and explain relationships within Gen Z's M-payment usage, partial-least square-structural equation modelling was used to assess the study framework and test the proposed relationships.

This study reveals significant influences on Gen Z tourists' behavioural intentions towards M-payment usage. Perceived benefits, performance expectancy, social influence and perceived trust positively impact behavioural intentions, while effort expectancy exhibits no significant effect. Furthermore, perceived trust is strongly influenced by perceived security, which also positively influences behavioural intentions. A mediated relationship is evident as trust mediating the effect of perceived security on behavioural intentions.

This study’s findings contribute to understanding the intricate relationships influencing Gen Z's M-payment behaviour and underscore trust's pivotal role in mediating the security–behavioural intention relationship.

This study is among the first to consider Mental Accounting Theory and the Unified Theory of Acceptance and Use of Technology as crucial underpinning theories in comprehending the intricate relationships that influence Gen Z travellers' perceptions and behaviours concerning M-payment systems.

This study aims to explore the consumers’ choices of mobile payments (m-payments) using a comprehensive unified model. The financial technology for digital m-payment has been increasingly introduced in the market, yet their acceptance has remained low.

This study uses the unified theory of acceptance and use of technology (UTAUT) with additional constructs of social influence, trust, anxiety, personal innovativeness and grievance redressal (GR). Structural equation modeling is used to evaluate the predictive model of attitudes toward m-payment. Individuals’ responses to questions regarding their attitude and intention to accept m-payment were gathered and examined through the lens of extended UTAUT model.

While the model supports TAM classical role, empirical examination of the model revealed that users’ attitudes and intentions are influenced by trust, personal innovativeness and social influence. Moreover, intention to use and GR are significant positive predictors of m-payment usage behavior.

M-payment provides customers with new digital payment platforms while providing businesses and marketing agents with more alternatives for online marketing. However, there is not much reported about m-payment adoption in Pakistan. This research introduces and evaluates new constructs that were not included in the original model. In Pakistan, to the best of the authors’ knowledge, this is a first of its kind of research which is purely based on the customers’ perspective of m-payment adoption.

The purpose of this study is to gain empirical insights into whether accounting information systems (AIS) usage matters among Jordanian small and medium-sized enterprises (SMEs) during the period of COVID-19 pandemic.

The suggested research model in the current study is based on the extending technology acceptance model (TAM) to test the antecedents’ factors that impact on AIS usage among SMEs. To test the proposed research model, partial least squares structural equation modeling (PLS-SEM) was used.

The empirical findings revealed all postulated hypotheses were accepted except H3. Contrary to what is expected, the empirical outcomes confirmed that perceived compatibility does not affect the perceived usefulness of AIS, and hence, the related hypothesis was rejected.

The results of the current research could be beneficial to a number of managers (owners) to obtain a better understanding of the benefits of AIS success usage among Jordanian SMEs performance during crises time as the COVID-19 pandemic crisis.

This study aims to explore the interplay of cognitive, affective, and normative constituents for their potential acceptance or rejection of artificial intelligence (AI) and ChatGPTs in the hospitality and tourism context.

Using an advanced analytical approach (i.e. a fuzzy-set qualitative comparative analysis), the study tested hypotheses based on 474 responses from individuals who have used ChatGPT for hospitality and tourism information.

The study found multiple solutions, including cognitive, affective and normative drivers for strong and weak continuance intentions toward AI-based ChatGPT. Informativeness, one of the cognitive drivers, was found to be a necessary condition for achieving the desired outcome.

This research provides novel insights into the functionality of developing multiple configurations to predict complex travelers behaviors in the context of hospitality and tourism technology consumption.

本研究旨在探讨认知、情感和规范成分之间的相互作用, 以及它们在接受或拒绝在酒店和旅游环境中使用人工智能（AI）和 ChatGPT 的潜力。

使用先进的分析方法（即模糊集定性比较分析）, 本研究基于 474 份使用 ChatGPT 获取酒店和旅游信息的个人的回答, 测试了假设。

本研究发现了多个解决方案, 包括认知、情感和规范驱动, 对基于 AI 的 ChatGPT 的强弱持续意图。信息性, 作为认知驱动之一, 被发现是实现期望结果的必要条件。

本研究为在酒店和旅游技术消费环境中预测复杂旅行者行为的功能性开发多个配置提供了新颖见解。