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The purpose of this paper is to develop a customer online behaviour analysis tool, segment high-value customers, analyse their online purchasing behaviour and predict their next purchases from an online air travel corporation.

An operations review of the customer online shopping process of an online travel agency (OTA) is conducted. A customer online shopping behaviour analysis tool is developed. The tool integrates competitors’ pricing data mining, customer segmentation and predictive analysis. The impacts of competitors’ price changes on customer purchasing decisions regarding the OTA’s products are evaluated. The integrated model for mining pricing data, identifying potential customers and predicting their next purchases helps the OTA recommend tailored product packages to its individual customers with reference to their travel patterns.

In the customer segmentation analysis, 110,840 customers are identified and segmented based on their purchasing behaviour. The relationship between the purchasing behaviour in an OTA and the price changes of different OTAs are analysed. There is a significant relationship between the flight duration time and the purchase lead time. The next travel destinations of segmented high-value customers are predicted with reference to their travel patterns and the significance of the relationships between destination pairs.

The developed model contributes to pricing evaluation, customer segmentation and package customization for online customers.

This study provides novel method and insights into customer behaviour towards OTAs through an integrated model of customer segmentation, customer behaviour and prediction analysis.

The effectiveness of product replenishment and responsiveness of customer service delivery impact largely on satisfaction and retention of customers in retail chain logistics distribution. The fast moving goods in the complex delivery network and limited vehicle resource often lead to long customer waiting time in stock replenishment. With lack of literature systematically reviewing factors affecting retail distribution in inter-store stock transfer services and improving the operations, the purpose of this paper is to analyse and enhance this service for the retail to reduce customer dissatisfaction by developing an integrated quality service improvement methodology and an optimisation tool to improve the product delivery services.

This paper reviews inter-store stock transfer operations and the process capability of an international retail chain, and proposes improvements by integrating Six Sigma, factor analysis, and optimisation modelling. User experience and expectations are evaluated through an empirical survey. A novel principle component factored inter-store stock transfer model is developed to improve replenishment operations. A total of 11 factors affecting inter-store stock transfer delivery time are analysed. An extended model with principal component factors incorporated is developed for the simulation.

The C_pk value of 0.51 shows significant difference between the experienced and expected waiting time. With the inter-store stock transfer optimisation model developed, the model assists traffic personnel on the vehicle route planning with multiple pick-up and drop-off locations. The system also ensures the best routing with a minimal travelling time planned, facilitating a reduction of the inter-store stock transfer time, thus improving the customer waiting time. Four significant factors affecting the delivery time are also identified from exploratory and confirmatory factor analysis. The results are analysed with an extended principal component factored inter-store stock transfer model.

The developed inter-store stock transfer models minimise stock transfer time, increase customer satisfaction, and reduce loss of sales. An integrated service quality improvement methodology has been developed and applied in reviewing significant factors affecting inter-store stock transfer operations.

This paper presents an analysis on inter-store stock transfer operations of an international retail and proposes enhancements on the operations by integrating Six Sigma, factor analysis, and optimisation modelling. A novel principal component factored inter-store stock transfer model is developed to improve the stock replenishment operations.

The purpose of this paper is to explore user engagement (UE) within the Metaverse (MV) environment, emphasising the crucial role of immersive experiences (IEs). This study aims to understand how IEs influence UE and the mediating effects of hedonic value (HV) and utilitarian value (UV) on this relationship. Additionally, the authors examine the moderating impacts of user perceptions (UPs) such as headset comfort, simulation sickness, prior knowledge and ease of use on the utilisation of the MV. This study seeks to elucidate the dynamics of virtual travel at a pre-experience stage, enhancing the comprehension of how digital platforms can revolutionise UE in travel and tourism.

This study used a triangulation methodology to provide a thorough investigation into the factors influencing UE in the MV. A systematic literature review (SLR) was conducted to frame the research context and identify relevant variables. To gather empirical data, 25 interviews were performed with active MV users, supplemented by a survey distributed to 118 participants. The data collected was analysed using structural equation modelling (SEM) to test the hypothesised relationships between IEs, UPs, HV and UV and their combined effect on UE within the MV.

The findings from the SEM indicate that engaging in the MV leads to a positive IE, which significantly enhances UE. Additionally, it was discovered that HV and UV play a mediating role in strengthening the link between IEs and UE. Furthermore, UPs, including headset comfort, simulation sickness, prior knowledge and ease of use, are significant moderators in the relationship between IEs and MV usage. These insights provide a nuanced understanding of the variables that contribute to and enhance UE in virtual environments.

This research contributes original insights into the burgeoning field of digital tourism by focusing on the MV, a rapidly evolving platform. It addresses the gap in the existing literature by delineating the complex interplay between IEs, UPs and value constructs within the MV. By using a mixed-method approach and advanced statistical analysis, this study provides a comprehensive model of UE specific to virtual travel platforms. The findings are particularly valuable for developers and marketers in the hospitality and tourism sectors seeking to capitalise on digital transformation and enhance UE through immersive technologies.

Through the theoretical lenses of media richness, perceived realism and customer engagement, this study aims to investigate the mechanisms that promote customer engagement in metaverse-mediated environments in the meetings, incentives, conferences and exhibitions (MICE) context, as well as the impact of customer engagement on customers’ metaverse usage intensity and future visit intention.

A survey of customers who have experience with metaverse-mediated MICE activities was conducted. Data from 267 respondents were analysed using partial least squares-structural equation modelling and fuzzy-set qualitative comparative analysis (fsQCA) to test our research framework.

Media richness dimensions, including multiple cues, immediate feedback and personal focus, were found to enhance perceived metaverse realism, which in turn affects the dimensions of customer engagement, leading to customers’ metaverse usage intensity and future visit intention. The fsQCA analysis identifies three configurations that lead to high event visit intention.

This research helps developers and marketers better understand how rich media contents create realistic experiences in the metaverse, aiding them to devise strategies for customer engagement and improve resource allocation.

Despite its potentially revolutionary impacts, empirical studies on the mechanisms driving customer engagement in the metaverse and its effects are scarce. This study contributes by revealing the multiple-phase mechanism of the customer engagement journey in the metaverse-mediated MICE context. By expanding the media richness theory into this area, our study provides new insights by illustrating how media richness dimensions create multisensory experiences and real-time interactions, enhancing perceived metaverse realism and customer engagement. It also addresses the debate on whether metaverse-mediated events substitute or complement real-life events.

This study aims to investigate the corrosion behavior of stir-cast hybrid aluminum composite reinforced with CeO₂ and graphene nanoplatelets (GNPs) nanoparticulates used as cylinder liner material in the engines (automotive, aerospace and aircraft industries).

The composites were prepared using the stir-casting technique, and their microstructure and corrosion behavior was evaluated using scanning electron microscopy (SEM) and potentiodynamic polarization test, respectively.

The results showed that the addition of CeO₂ and GNPs improved the corrosion resistance of the composites, and the optimal combination of these two nanoparticles was found to be 3 wt.% CeO₂ and 3 wt.% GNPs. The enhanced corrosion resistance was attributed to the formation of a protective layer on the surface of the composite, as well as the effective dispersion and uniform distribution of nanoparticles in the matrix. The 0.031362 was noted as the lowest corrosion rate (mmpy) and was noticed in 94% Al-6061 alloy + (3 Wt.% CeO₂ + 3 Wt.% GNPs) sample at room temperature and at elevated temperatures; the corrosion rate (mmpy) was observed as 0.0601 and 0.0636 at 45 °C and 75 °C, respectively.

In the vast majority of the published research publications, either cerium oxide or graphene nanoplatelets were utilized as a single reinforcement or in conjunction with other types of reinforcement such as alumina, silicon carbide, carbon nano-tubes, tungsten carbide, etc., but on the combination of the CeO₂ and GNPs as reinforcements have very less literatures with 2 wt.% each only. The prepared hybrid aluminum composite (reinforcing 1 wt.% to 3 wt.% in Al-6061 alloy) was considered for replacing the cylinder liner material in the piston-cylinder arrangement of engines.

The launch of ChatGPT has brought the large language model (LLM)-based generative artificial intelligence (GAI) into the spotlight, triggering the interests of various stakeholders to seize the possible opportunities implicated by it. Nevertheless, there are also challenges that the stakeholders should observe when they are considering the potential of GAI. Given this backdrop, this study presents the viewpoints gathered from various subject experts on six identified areas.

Through an expert-based approach, this paper gathers the viewpoints of various subject experts on the identified areas of tourism and hospitality, marketing, retailing, service operations, manufacturing and healthcare.

The subject experts first share an overview of the use of GAI, followed by the relevant opportunities and challenges in implementing GAI in each identified area. Afterwards, based on the opportunities and challenges, the subject experts propose several research agendas for the stakeholders to consider.

This paper serves as a frontier in exploring the opportunities and challenges implicated by the GAI in six identified areas that this emerging technology would considerably influence. It is believed that the viewpoints offered by the subject experts would enlighten the stakeholders in the identified areas.

Digitalisation is reshaping the learning process, shifting it towards personalisation and online learning, and fuelling the growth of massive open online courses (MOOCs) and micro-credentials. Despite being a popular global trend, limited studies have looked at micro-credentials and their impact on learners' behavioural outcomes. The purpose of this study is to investigate the impact of programme design factors on learning experience, as well as the inter-relationships between programme design, learning experience and behavioural responses (e.g. engagement and willingness to pay more (WTPM)) towards micro-credentials. In addition, the study aims to investigate learning enjoyment as a moderator.

A survey questionnaire was used to collect data from 354 respondents who are working adults living in the major economic states in Malaysia. Data analysis was performed using the analysis of a moment structures (AMOS) statistical software and SPSS (Statistical Package for the Social Sciences) PROCESS macro.

The results show the significance of programme design factors (i.e., flexibility, system quality and content quality) in determining the learning experience. The learning experience is found as a mediator in the relationship between programme design factors and learner engagement and WTPM. In addition, the moderation assessment confirms that enjoyment during learning strengthens the relationship between experience and behavioural responses.

This study is amongst a few selected studies that focus on engagement in and WTPM for micro-credentials. In addition, it emphasises the mediating role of the learning experience and the moderating role of enjoyment in understanding the impact of programme design on learners' experiences and behavioural responses.

The surging entrance of new mobile payment merchants into the growing market has prompted the need for an in-depth understanding of loyalty formation to retain customers. This study examines customers' loyalty generation process in mobile payment services by exploring the serial effect of cognitive drivers (i.e. brand awareness, perceived quality, brand image, perceived value and layout) on affective response, satisfaction and loyalty.

A survey using self-administered questionnaires was conducted. The data was collected from 370 consumers who have experience using mobile payment services in Vietnam. The data were submitted to partial least square structural equation modeling (PLS-SEM) and artificial neural networks (ANN) analysis.

The results indicated that all the proposed cognitive drivers show significant impacts on affective response, which, in turn, translates into satisfaction and loyalty. The post-hoc analysis revealed enjoyment as the vital affective response in determining satisfaction. Moreover, the multigroup analysis indicated that the relationship between affective response and satisfaction is stronger for the female group. In addition, the ANN's nonlinear result revealed complementary insight into the importance of cognitive drivers.

The current study revealed both linear and nonlinear mechanisms that explicate the roles of cognitive drivers and affective responses in fostering loyalty toward mobile payment merchants. The findings add to the existing literature that emphasizes consumers' initial mobile payment adoption.

The purpose of this paper is to investigate political influential spreaders in Twitter at the juncture before and after the Malaysian General Election in 2013 (MGE2013) for the purpose of understanding if the political sphere within Twitter reflects the intentions, popularity and influence of political figures in the year in which Malaysia has its first “social media election.”

A Big Data approach was used for acquiring a series of longitudinal data sets during the election period. The work differs from existing methods focusing on the general statistics of the number of followers, supporters, sentiment analysis, etc. A retweeting network has been extracted from tweets and retweets and has been mapped to a novel information flow and propagation network we developed. The authors conducted quantitative studies using k-shell decomposition, which enables the construction of a quantitative Twitter political propagation sphere where members posited at the core areas are more influential than those in the outer circles and periphery.

The authors conducted a comparative study of the influential members of Twitter political propagation sphere on the election day and the day after. The authors found that representatives of political parties which are located at the center of the propagation network are winners of the presidential election. This may indicate that influential power within Twitter is positively related to the final election results, at least in MGE2013. Furthermore, a number of non-politicians located at the center of the propagation network also significantly influenced the election.

This research is based on a large electoral campaign in a specific election period, and within a predefined nation. While the result is significant and meaningful, more case studies are needed for generalized application for identifying potential winning candidates in future social-media fueled political elections.

The authors presented a simple yet effective model for identifying influential spreaders in the Twitter political sphere. The application of the authors’ approach yielded the conclusion that online “coreness” score has significant influence to the final offline electoral results. This presents great opportunities for applying the novel methodology in the upcoming Malaysian General Election in 2018. The discovery presented here can be used for understanding how different players of political parties engage themselves in the election game in Twitter. The approach can also be adopted as a factor of influence for offline electoral activities. The conception of a quantitative approach in electoral results greatly influenced by social media means that comparative studies could be made in future elections.

Existing works related to general elections of various nations have either bypassed or ignored the subtle links between online and offline influential propagations. The modeling of influence from social media using a longitudinal and multilayered approach is also rarely studied. This simple yet effective method provides a new perspective of practice for understanding how different players behave and mutually shape each other over time in the election game.