Helping a company organize and capture the knowledge used by its employees and business processes is a daunting task. In this work we examine several proposed methodologies and synthesize them into a new methodology that we demonstrate through a case study of an electric power distribution company. This is a practical research study. First, the research approach for creating the knowledge map is process-oriented and the processes are considered as the main elements of the model. This research was done in four stages: literature review, model editing, model validation and case study. The Delphi method was used for the research model validation. Some of the important outputs of this research were mapping knowledge flows, determining the level of knowledge assets, expert-area knowledge map, preparing knowledge meta-model, and updating the knowledge map according to the company’s processes. Besides identifying, auditing and visualizing tacit and explicit knowledge, this knowledge mapping enables us to analyze the knowledge areas’ situation and subsequently help us to improve the processes and overall performance. So, a process map does knowledge mapping in a clear and accurate frame. Once the knowledge is used in processes, it creates value.

Aim/Purpose: Facebook has made it possible for organisation to embrace social and network centric knowledge processes by creating opportunities to connect, interact, and collaborate with stakeholders. We have witnessed a significant increase in the popularity and use of this tool in many organisations, especially in the private sector. But the utilisation of Facebook in public organisations is at its infancy, with many also believing that the use of Facebook is not a common practice in many public organisations in Nigeria. In spite of this fact, our discernment on the implications of Facebook usage in public organisations in Nigeria, especially organisations at the local level, seem to be remarkably limited. This paper specifically sought to ascertain if Facebook usage influenced inward and outward knowledge sharing in the selected local government councils in Delta State, Nigeria Methodology: The qualitative method was adopted. The study used interview as the primary means of data gathering. The study purposively sampled thirty-six employees as interviewees, twenty from Oshimili South and sixteen from Oshimili North local government councils respectively. The thematic content analysis method was used to analyse interview transcripts. Contribution: This research made distinct contributions to the available literature in social knowledge management, specifically bringing to the fore the intricacies surrounding the use of Facebook for knowledge sharing purposes in the public sector. Findings: The local government councils were yet to appreciate and utilise the interactive and collaborative nature of Facebook in improving stakeholders’ engagement, feedback, and cooperation. Facebook was used for outward knowledge sharing but not for inward knowledge sharing. Recommendations for Practitioners: Local government councils should encourage interaction via Facebook, show willingness to capture knowledge from identifiable sources, and effectively manage critical knowledge assets in order to build trust, cooperation, and confidence in the system. To gain strategic benefits from the use of Facebook for synchronous communication of knowledge, local government councils should ensure that the use of such technology is aligned with strategic plans and that directional change is in line with the new knowledge economy, where interaction and collaboration through technology are seen as strategic imperatives for continued success and sustainability. In addition, local government councils need to train stakeholders on effective use of Facebook for knowledge sharing, with special emphasis on how, why, who, when, and where to use such tool for knowledge sharing activities.

Aim/Purpose: The objective of this study was to assess the interrelationships among knowledge management infrastructure, knowledge management process, intellectual capital, and organization performance. Background: Although knowledge management capability is extensively used by organizations, reaching their maximum financial and non-financial performances has not been fully researched. Therefore, organizations need to optimize their performance by exploiting knowledge management capability through the accumulation of intellectual capital, where the new competitiveness is shifting from tangible to intangible resources. Methodology: This study adopted a positivist philosophy and deductive approach to accomplish the main goal of this research. Moreover, this research employed a quantitative approach since this study is concerned with causal relationship between variables. A questionnaire-based survey was designed to evaluate the research model using a convenience sample of 134 employees from the food industry sector in Jordan. Surveyed data was examined following the structural equation modeling procedures. Contribution: This study highlighted the potential benefits of applying the knowledge management capabilities, intellectual capital, and organizational performance to the food industrial sector in Jordan. Future research suggestions are also provided. Findings: Results indicated that knowledge management infrastructure had a positive effect on knowledge management process. In addition, knowledge management process impacted positively intellectual capital and organization performance and mediated the relationship between knowledge management infrastructure and intellectual capital. However, knowledge management infrastructure did not positively associate to organization performance. Recommendations for Practitioners: The current model is designed to help managers and decision makers to improve their management capabilities as well as their organization financial and non-financial performance through exploiting the organizational knowledge management infrastructure and intellectual capital approaches. Recommendation for Researchers: Our findings can be used as a base of knowledge to conduct further studies about knowledge management capabilities, intellectual capital, and organization performance following different criteria and research procedures. Impact on Society: The designed model highlights a significant organizational performance approach that can influence Jordanian food industrial sector positively. Future Research: The current designed research model can be applied and assessed further in other sectors including banking and industrial sectors across developed and developing countries. Also, we suggest that in addition to focusing on knowledge management process and intellectual capital as mediating variables, future research could test our findings in a longitudinal study and examine how to affect financial and non-financial performance.

Aim/Purpose: This paper aims to discuss the activities involved in facilitating self-selecting teams for Agile software development projects. This paper also discussed how these activities can influence the successful expertise coordination in Agile teams. Background: Self-selecting teams enable Agile team members to choose teams based on whom they prefer to work with. Good team bonding allows Agile team members to rely on each other in coordinating their expertise resources effectively. This is the focal point where expertise coordination is needed in Agile teams. Methodology: This study employed Grounded Theory by interviewing 48 Agile practitioners from different software organizations mainly based in New Zealand. This study also carried out several sessions of observations and document analysis in conjunction with interviews. Contribution: This study contributes to the body of knowledge by identifying the way self-selecting teams support expertise coordination. Findings: Our findings indicated that the activities involved tend to influence the successful expertise coordination in Agile teams. Self-selecting teams are essential to supporting expertise coordination by increasing inter-dependencies between Agile team members, ensuring a diverse range of knowledge and skills in teams. Recommendations for Practitioners: The self-selecting team activities can be used as a guideline for Agile software organizations in forming self-selecting teams in the fastest and most efficient way. It is vital for management to facilitate the process of self-selecting teams in order to optimize successful expertise coordination. Recommendation for Researchers: There is potential for further Grounded Theory research to explore more activities and strategies involved in self-selecting teams. Impact on Society: Self-selecting teams in Agile software developments projects tend to boost the productivity of software development. Future Research: Several hypotheses can be tested through a deductive approach in future studies.

Aim/Purpose: This article proposes a methodology for selecting the initial sets for clustering categorical data. The main idea is to combine all the different values of every single criterion or attribute, to form the first proposal of the so-called multiclusters, obtaining in this way the maximum number of clusters for the whole dataset. The multiclusters thus obtained, are themselves clustered in a second step, according to the desired final number of clusters. Background: Popular cluster methods for categorical data, such as the well-known K-Modes, usually select the initial sets by means of some random process. This fact introduces some randomness in the final results of the algorithms. We explore a different application of the clustering methodology for categorical data that overcomes the instability problems and ultimately provides a greater clustering efficiency. Methodology: For assessing the performance of the proposed algorithm and its comparison with K-Modes, we apply both of them to categorical databases where the response variable is known but not used in the analysis. In our examples, that response variable can be identified to the real clusters or classes to which the observations belong. With every data set, we perform a two-step analysis. In the first step we perform the clustering analysis on data where the response variable (the real clusters) has been omitted, and in the second step we use that omitted information to check the efficiency of the clustering algorithm (by comparing the real clusters to those given by the algorithm). Contribution: Simplicity, efficiency and stability are the main advantages of the multicluster method. Findings: The experimental results attained with real databases show that the multicluster algorithm has greater precision and a better grouping effect than the classical K-modes algorithm. Recommendations for Practitioners: The method can be useful for those researchers working with small and medium size datasets, allowing them to detect the underlying structure of the data in an intuitive and reasonable way. Recommendation for Researchers: The proposed algorithm is slower than K-Modes, since it devotes a lot of time to the calculation of the initial combinations of attributes. The reduction of the computing time is therefore an important research topic. Future Research: We are concerned with the scalability of the algorithm to large and complex data sets, as well as the application to mixed data sets with both quantitative and qualitative attributes.

Aim/Purpose: This paper reports a case study of organizational transition from a non-competitive selection method to a novel bidding method for the selection of consultants in the Architectural and Engineering (A/E) industry. Background: Public procurement agencies are increasingly relying on external consultants for the design of construction projects. Consultant selection can be based on either competitive bidding, or quality-based criteria, or some combination between these two approaches. Methodology: Different sources of information were reviewed: internal documents, and quantitative data from the enterprise software platform (ERP). In addition, informal and unstructured interviews were conducted with relevant officials. Contribution: As there are mixed opinions in the scientific literature regarding the use of competitive bidding for the selection of consultants in the A/E industry, this paper contributes a detailed review of a transition to a competitive selection method and provides a financial and qualitative comparison between the two methods. In addition, the method implemented is novel, as it delegates most of the responsibility of hiring and managing consultants to one main contractor. Findings: While the new selection method was intended to reduce bureaucratic overload, it has unexpectedly also succeeded to reduce costs as well. Recommendations for Practitioners: It may be more efficient and profitable to adopt the selection method described in this study. Recommendation for Researchers: Similar methods can be applied to other industries successfully. Impact on Society: Our method was applied in a public organization and resulted in a better outcome, both financial and managerial. Adopting this approach can benefit public budgets. Future Research: The selection, data storage, and analysis methods are interrelated components. Future analysis of these components can help better shape the consultant selection process.

Aim/Purpose: Currently, the world faces unprecedented challenges due to COVID-19, particularly concerning individuals’ health and livelihood and organizations and industrial performance. Indeed, the pandemic has caused rapid intensifying socio-economic effects. For instance, organizations are shifting from traditional working patterns toward telecommuting. By adopting remote working, organizations might mitigate the impact of COVID-19 on their workforce, explicitly concerning their safety, wellbeing, mobility, work-life balance, and self-efficiency. From this perceptive, this study examines the factors that influence employees’ behavioral intention to adopt telecommuting in the banking industry. Background: The study’s relevance stems from the fact that telecommuting and its benefits have been assumed rather than demonstrated in the banking sector. However, the pandemic has driven the implementation of remote working, thereby revealing possible advantages of working from home in the banking industry. The study investigated the effect of COVID-19 in driving organizations to shift from traditional working patterns toward telecommuting. Thereby, the study investigates the banking sector employees’ behavioral intention to adopt telecommuting. Methodology: The study employed a survey-based questionnaire, which entails gathering data from employees of twelve banks in Jordan, as the banking sector in Jordan was the first to transform from traditional working to telecommuting. The sample for this research was 675 respondents; convenience sampling was employed as a sampling technique. Subsequently, the data were analyzed with the partial least square structural equation modeling (PLS-SEM) to statistically test the research model. Contribution: Firstly, this study provides a deep examination and understanding of facilitators of telecommuting in a single comprehensive model. Secondly, the study pro-vides a deeper insight into the factors affecting behavioral intention towards telecommuting from the employees’ perspective in the banking sector. Finally, this study is the first to examine telecommuting in the emerging market of Jordan. Thereby, this study provides critical recommendations for managers to facilitate the implementation of telecommuting. Findings: Using the Technology Acceptance Model (TAM), this study highlights significant relationships between telecommuting systems, quality, organizational support, and the perceived usefulness and ease of use in telecommuting. Employees who perceive telecommuting systems to be easy and receive supervision and training for using these systems are likely to adopt this work scheme. The results present critical theoretical and managerial implications regarding employees’ behavioral intentions toward telecommuting. Recommendations for Practitioners: This study suggests the importance of work-life balance for employees when telecommuting. Working from home while managing household duties can create complications for employees, particularly parents. Therefore, flexibility in terms of working hours is needed to increase employees’ acceptance of telecommuting as they will have more control over their life. These increase employees’ perceived self-efficacy with telecommuting, which smooths the transition toward remote working in the future. In addition, training will allow employees to solve technical issues that can arise from using online systems. Recommendation for Researchers: This study focused on the context of the banking sector. The sensitivity of data and transactions in this sector may influence employers’ and employees’ willingness to work remotely. In addition, the job descriptions of employees in banks moderate specific factors outlined in this model, including work-life balance. For instance, executive managers may have a higher overload in banks in contrast to front-line employees. Thus, future studies should explore different contexts, including manufacturing and consultation, to understand the industry’s effect on remote working. Similarly, future research should concentrate on the influence of job descriptions on employees’ intentions toward telecommuting. Impact on Society: The COVID-19 pandemic created a sudden shift towards telecommuting, which made employees struggle to adopt new work schemes. Therefore, managers had to provide training for their employees to be well prepared and increase their acceptance of telecommuting. Furthermore, telecommuting has a positive effect on work-life balance, it provides employees with the flexibility to organize their daily schedule into more activities. Along the same line, the study highlighted the correlation between work-life balance and telecommuting. Such a relationship provides further evidence for the need to understand employees’ lifestyles in facilitating the adoption of telecommuting. Moreover, the study extends the stream of literature by outlining critical factors affecting employees’ acceptance of telecommuting. Future Research: Future studies should explore different contexts, including manufacturing and consultation, to understand the industry’s effect on remote working. Similarly, future research should concentrate on the influence of job descriptions on employees’ intentions toward telecommuting. Furthermore, the research team conducted the study by surveying 12 banks. Future research recommends surveying the whole banking industry to add more validation to the model.

Aim/Purpose: In this paper, we present an RFM model-based telecom customer churn system for better predicting and analyzing customer churn. Background: In the highly competitive telecom industry, customer churn is an important research topic in customer relationship management (CRM) for telecom companies that want to improve customer retention. Many researchers focus on a telecom customer churn analysis system to find out the customer churn factors for improving prediction accuracy. Methodology: The telecom customer churn analysis system consists of three main parts: customer segmentation, churn prediction, and churn factor identification. To segment the original dataset, we use the RFM model and K-means algorithm with an elbow method. We then use RFM-based feature construction for customer churn prediction, and the XGBoost algorithm with SHAP method to obtain a feature importance ranking. We chose an open-source customer churn dataset that contains 7,043 instances and 21 features. Contribution: We present a novel system for churn analysis in telecom companies, which encompasses customer churn prediction, customer segmentation, and churn factor analysis to enhance business strategies and services. In this system, we leverage customer segmentation techniques for feature construction, which enables the new features to improve the model performance significantly. Our experiments demonstrate that the proposed system outperforms current advanced customer churn prediction methods in the same dataset, with a higher prediction accuracy. The results further demonstrate that this churn analysis system can help telecom companies mine customer value from the features in a dataset, identify the primary factors contributing to customer churn, and propose suitable solution strategies. Findings: Simulation results show that the K-means algorithm gets better results when the original dataset is divided into four groups, so the K value is selected as 4. The XGBoost algorithm achieves 79.3% and 81.05% accuracy on the original dataset and new data with RFM, respectively. Additionally, each cluster has a unique feature importance ranking, allowing for specialized strategies to be provided to each cluster. Overall, our system can help telecom companies implement effective CRM and marketing strategies to reduce customer churn. Recommendations for Practitioners: More accurate churn prediction reduces misjudgment of customer churn. The acquisition of customer churn factors makes the company more convenient to analyze the reasons for churn and formulate relevant conservation strategies. Recommendation for Researchers: The research achieves 81.05% accuracy for customer churn prediction with the Xgboost and RFM algorithms. We believe that more enhancements algorithms can be attempted for data preprocessing for better prediction. Impact on Society: This study proposes a more accurate and competitive customer churn system to help telecom companies conserve the local markets and reduce capital outflows. Future Research: The research is also applicable to other fields, such as education, banking, and so forth. We will make more new attempts based on this system.

Aim/Purpose: In this study, the research proposes and experiments with a new model of collecting, storing, and analyzing big data on customer feedback in the tourism industry. The research focused on the Vietnam market. Background: Big Data describes large databases that have been “silently” built by businesses, which include product information, customer information, customer feedback, etc. This information is valuable, and the volume increases rapidly over time, but businesses often pay little attention or store it discretely, not centrally, thereby wasting an extremely large resource and partly causing limitations for business analysis as well as data. Methodology: The study conducted an experiment by collecting customer feedback data in the field of tourism, especially tourism in Vietnam, from 2007 to 2022. After that, the research proceeded to store and mine latent topics based on the data collected using the Topic Model. The study applied cloud computing technology to build a collection and storage model to solve difficulties, including scalability, system stability, and system cost optimization, as well as ease of access to technology. Contribution: The research has four main contributions: (1) Building a model for Big Data collection, storage, and analysis; (2) Experimenting with the solution by collecting customer feedback data from huge platforms such as Booking.com, Agoda.com, and Phuot.vn based on cloud computing, focusing mainly on tourism Vietnam; (3) A Data Lake that stores customer feedback and discussion in the field of tourism was built, supporting researchers in the field of natural language processing; (4) Experimental research on the latent topic mining model from the collected Big Data based on the topic model. Findings: Experimental results show that the Data Lake has helped users easily extract information, thereby supporting administrators in making quick and timely decisions. Next, PySpark big data processing technology and cloud computing help speed up processing, save costs, and make model building easier when moving to SaaS. Finally, the topic model helps identify customer discussion trends and identify latent topics that customers are interested in so business owners have a better picture of their potential customers and business. Recommendations for Practitioners: Empirical results show that facilities are the factor that customers in the Vietnamese market complain about the most in the tourism/hospitality sector. This information also recommends that practitioners reduce their expectations about facilities because the overall level of physical facilities in the Vietnamese market is still weak and cannot be compared with other countries in the world. However, this is also information to support administrators in planning to upgrade facilities in the long term. Recommendation for Researchers: The value of Data Lake has been proven by research. The study also formed a model for big data collection, storage, and analysis. Researchers can use the same model for other fields or use the model and algorithm proposed by this study to collect and store big data in other platforms and areas. Impact on Society: Collecting, storing, and analyzing big data in the tourism sector helps government strategists to identify tourism trends and communication crises. Based on that information, government managers will be able to make decisions and strategies to develop regional tourism, propose price levels, and support innovative programs. That is the great social value that this research brings. Future Research: With each different platform or website, the study had to build a query scenario and choose a different technology approach, which limits the ability of the solution’s scalability to multiple platforms. Research will continue to build and standardize query scenarios and processing technologies to make scalability to other platforms easier.

Aim/Purpose: The use of digital technology, such as an electronic records management system (ERMS), has prompted widespread changes across organizations. The organization needs to support its operations with an automation system to improve production performance. This study investigates ERMS’s potentiality to enhance organizational performance in the oil and gas industry. Background: Oil and gas organizations generate enormous electronic records that lead to difficulties in managing them without any system or digitalization procedure. The need to use a system to manage big data and records affects information security and creates several problems. This study supports decision-makers in oil and gas organizations to use ERMS to enhance organizational performance. Methodology: We used a quantitative method by integrating the typical partial least squares (SEM-PLS) approach, including measurement items, respondents’ demographics, sampling and collection of data, and data analysis. The SEM-PLS approach uses a measurement and structural model assessment to analyze data. Contribution: This study contributes significantly to theory and practice by providing advancements in identity theory in the context of big data management and electronic records management. This study is a foundation for further research on the role of ERMS in operations performance and Big Data Management (BDM). This research makes a theoretical contribution by studying a theory-driven framework that may serve as an essential lens to evaluate the role of ERMS in performance and increase its potentiality in the future. This research also evaluated the combined impacts of general technology acceptance theory elements and identity theory in the context of ERMS to support data management. Findings: This study provides an empirically tested model that helps organizations to adopt ERMS based on the influence of big data management. The current study’s findings looked at the concerns of oil and gas organizations about integrating new technologies to support organizational performance. The results demonstrated that individual characteristics of users in oil and gas organizations, in conjunction with administrative features, are robust predictors of ERMS. The results show that ERMS potentiality significantly influences the organizational performance of oil and gas organizations. The research results fit the big ideas about how big data management and ERMS affect respondents to adopt new technologies. Recommendations for Practitioners: This study contributes significantly to the theory and practice of ERMS potentiality and BDM by developing and validating a new framework for adopting ERMS to support the performance and production of oil and gas organizations. The current study adds a new framework to identity theory in the context of ERMS and BDM. It increases the perceived benefits of using ERMS in protecting the credibility and authenticity of electronic records in oil and gas organizations. Recommendation for Researchers: This study serves as a foundation for future research into the function and influence of big data management on ERMS that support the organizational performance. Researchers can examine the framework of this study in other nations in the future, and they will be able to analyze this research framework to compare various results in other countries and expand ERMS generalizability and efficacy. Impact on Society: ERMS and its impact on BDM is still a developing field, and readers of this article can assist in gaining a better understanding of the literature’s dissemination of ERMS adoption in the oil and gas industry. This study presents an experimentally validated model of ERMS adoption with the effect of BDM in the oil and gas industry. Future Research: In the future, researchers may be able to examine the impact of BDM and user technology fit as critical factors in adopting ERMS by using different theories or locations. Furthermore, researchers may include the moderating impact of demographical parameters such as age, gender, wealth, and experience into this study model to make it even more robust and comprehensive. In addition, future research may examine the significant direct correlations between human traits, organizational features, and individual perceptions of BDM that are directly related to ERMS potentiality and operational performance in the future.

Aim/Purpose: This research utilized a learn-to-rank algorithm to provide medical recommendations to prescribers. The algorithm has been utilized in other domains, such as information retrieval and recommender systems. Background: Ranking the possible medical treatments according to diagnoses of the medical cases is very beneficial for doctors, especially during the coding process. Methodology: We developed two deep learning pointwise learn-to-rank models within one prediction pipeline: one for predicting the top possible active ingredients from disease features, the other for ranking actual medicines codes from diseases and the ingredients features. Contribution: A new learn-to-rank deep learning model has been developed to rank medical procedures based on datasets collected from insurance companies. Findings: We ran 18 cross-validation trials on a confidential dataset from an insurance company. We obtained an average normalized discounted cumulative gain (NDCG@8) of 74% with a 5% standard deviation as a result of all 18 experiments. Our approach outperformed a known approach used in the information retrieval domain in which data is represented in LibSVM format. Then, we ran the same trials using three learn-to-rank models – pointwise, pairwise, and listwise – which yielded average NDCG@8 of 71%, 72%, and 72%, respectively. Recommendations for Practitioners: The proposed model provides an insightful approach to helping to manage the patient’s treatment process. Recommendation for Researchers: This research lays the groundwork for exploring various applications of data science techniques and machine learning algorithms in the medical field. Future studies should focus on the significant potential of learn-to-rank algorithms across different medical domains, including their use in cost-effectiveness models. Emphasizing these algorithms could enhance decision-making processes and optimize resource allocation in healthcare settings. Impact on Society: This will help insurance companies and end users reduce the cost associated with patient treatment. It also helps doctors to choose the best procedure and medicines for their patients. Future Research: Future research is required to investigate the impact of medicine data at a granular level.

Aim/Purpose: This study investigates the relationship between Electronic Word-of-Mouth (EWOM), Information Adoption, and the stock investment of Vietnamese investors. Background: Misinformation spreads online, and a lack of strong information analysis skills can lead Vietnamese investors to make poor stock choices. By understanding how online conversations and information processing influence investment decisions, this research can help investors avoid these pitfalls. Methodology: This study applies Structural Equation Modelling (SEM) to investigate how non-professional investors react to online information and which information factors influence their investment decisions. The final sample includes 512 investors from 18 to 65 years old from various professional backgrounds (including finance, technology, education, etc.). We conducted a combined online and offline survey using a convenience sampling method from August to November 2023. Contribution: This study contributes to the growing literature on Electronic Word-of-Mouth (EWOM) and its impact on investment decisions. While prior research has explored EWOM in various contexts, we focus on Vietnamese investors, which can offer valuable insights into its role within a developing nation’s stock market. Investors, particularly those who are new or less experienced, are often susceptible to the influence of EWOM. By examining EWOM’s influence in Vietnam, this study sheds light on a crucial factor impacting investment behavior in this emerging market. Findings: The results show that EWOM has a moderate impact on the Information Adoption and investment decisions of Vietnamese stock investors. Information Quality (QL) is the factor that has the strongest impact on Information Adoption (IA), followed by Information Credibility (IC) and Attitude Towards Information (AT). Needs for Information (NI) only have a small impact on Information Adoption (IA). Finally, Information Adoption (IA) has a limited influence on investor decisions in stock investment. We also find that investors need to verify information through official sites before making investment decisions based on posts in social media groups. Recommendations for Practitioners: The findings suggest that state management and media agencies need to coordinate to improve the quality of EWOM information to protect investors and promote the healthy development of the stock market. Social media platform managers need to moderate content, remove false information, prioritize displaying authentic information, cooperate with experts, provide complete information, and personalize the experience to enhance investor trust and positive attitude. Securities companies need to provide complete, accurate, and updated information about the market and investment products. They can enhance investor trust and positive attitude by developing news channels, interacting with investors, and providing auxiliary services. Listed companies need to take the initiative to improve the quality of information disclosure and ensure clarity, comprehensibility, and regular updates. Use diverse communication channels and improve corporate governance capacity to increase investor trust and positive attitude. Investors need to seek information from reliable sources, compare information from multiple sources, and carefully check the source and author of the information. They should improve their investment knowledge and skills, consult experts, define investment goals, and build a suitable investment portfolio. Recommendation for Researchers: This study synthesized previous research on EWOM, but there is still a gap in the field of securities because each nation has its laws, regulations, and policies. The relationships between the factors in the model are not yet clear, and there is a need to develop a model with more interactive factors. The research results need to be further verified, and more research can be conducted on the influence of investor psychology, investment experience, etc. Impact on Society: This study finds that online word-of-mouth (EWOM) can influence Vietnamese investors’ stock decisions, but information quality is more important. Policymakers should regulate EWOM accuracy, fund managers should use social media to reach investors, and investors should diversify their information sources. Future Research: This study focuses solely on the stock market, while individual investors in Vietnam may engage in various other investment forms such as gold, real estate, or cryptocurrencies. Therefore, future research could expand the scope to include other investment types to gain a more comprehensive understanding of how individual investors in Vietnam utilize electronic word-of-mouth (EWOM) and adopt information in their investment decision-making process. Furthermore, while these findings may apply to other emerging markets with similar levels of financial literacy as Vietnam, they may not fully extend to countries with higher financial literacy rates. Hence, further studies could be conducted in developed countries to examine the generalizability of these findings. Finally, future research could see how EWOM’s impact changes over a longer period. Additionally, a more nuanced understanding of the information adoption process could be achieved by developing a research model with additional factors.

This paper aims to share the author's vision on possible research directions for big knowledge-based AI. A renewed definition of big knowledge (BK) and big knowledge systems (BKS) is first introduced. Then the first BKS model, called cloud knowledge social intelligence (CKEI) is provided with a hierarchy of knowledge as a service (KAAS). At last, a new semantics, the big-and-broad step axiomatic structural operational semantics (BBASOS) for applications on BKS is introduced and discussed with a practical distributed BKS model knowledge graph network KGN and a mini example.

Self-efficacy is essential to learning but what happens when learning is done as a result of a collective process? What is the role of individual self-efficacy in collective problem solving? This research examines the manifestation of self-efficacy in prediction markets that are configured as collective problem-solving platforms and whether self-efficacy of traders affects the collective outcome. Prediction markets are collective-intelligence platforms that use a financial markets mechanism to combine knowledge and opinions of a group of people. Traders express their opinions or knowledge by buying and selling “stocks” related to questions or events. The collective outcome is derived from the final price of the stocks. Self-efficacy, one’s belief in his or her ability to act in a manner that leads to success, is known to affect personal performance in many domains. To date, its manifestation in computer-mediated collaborative environments and its effect on the collective outcome has not been studied. In a controlled experiment, 632 participants in 47 markets traded a solution to a complex problem, a naïve framing of the knapsack problem. Contrary to earlier research, we find that technical and functional self-efficacy perceptions are indistinguishable, probably due to a focus on outcomes rather than on resources. Further, results demonstrate that prediction markets are an effective collective problem-solving platform that correctly aggregates individual knowledge and is resilient to traders’ self-efficacy.

Aim/Purpose: As online video lectures rapidly gain popularity in formal and informal learning environments, one of their main challenges is student retention. This study investigates the influence of adding interactivity to online video lectures on students’ attention span. Background: Interactivity is perceived as increasing the attention span of learners and improving the quality of learning. However, interactivity may be regarded as an interruption, which distracts students. Furthermore, adding interactive elements to online video lectures requires additional investment of various resources. Therefore, it is important to investigate the impact of adding interactivity to online video lectures on the attention span of learners. Methodology: This study employed a learning analytics approach, obtained data from Google Analytics, and analyzed data of two Massive Open Online Courses (MOOCs) that were developed by the Open University of Israel in order to make English for academic purposes (EAP) courses freely accessible. Contribution: The paper provides important insights, based on quantitative empirical research, on: integrating interactive elements in online videos; the impact of video length; and differences between two groups of advanced and basic learners. Furthermore, it demonstrates how learning analytics may be used for improving instructional design. Findings: The findings suggest that interactivity may increase the attention span of learners, as measured by the average online video lecture viewing completion percentage, before and after the addition of interactivity. However, when the lecture is longer than about 15 minutes, the completion percentages decrease, even after adding interactive elements. Recommendations for Practitioners: Adding interactivity to online video lectures and controlling their length is expected to increase the attention span of learners. Recommendation for Researchers: Learning analytics is a powerful quantitative methodology for identifying ways to improve learning processes. Impact on Society: Providing practical insights on mechanisms for increasing the attention span of learners is expected to improve social inclusion. Future Research: Discovering further best practices to improve the effectiveness of online video lectures for diverse learners.

Aim/Purpose: Although a literature review is the fundamental base for any research, it is often considered tedious and conducted with a lack of methodology and rigor. The paper presents a method for systematically searching and screening literature using modern search technologies. The method focuses on minimizing the amount of manual screening by employing the references among papers. Background: A method to select quality literature effectively using modern search technologies is presented and evaluated. Methodology: The method starts with a keywords search in which the most suitable keywords are identified. In the backward search, promising resources are collected based on the keywords and their reference sections are searched for duplicates to find often cited basic literature. Then, the forward search identifies current literature that cites the basic sources. Contribution: Modern search technologies have the potential to improve the effectiveness of the use of information channels significantly and thus of traditional literature searches. Findings: The selection method was applied to the field of literature review itself and to the field of functional modelling. In both cases, relevant literature was identified within a surprisingly short time. Recommendation for Researchers: Literature reviews should be done systematically by using modern search technologies. Future Research: The presented method may be adapted according to the evolution of search technologies. The tool support for the automated extraction of references should be improved and a quantitative evaluation of the method in comparison to traditional reviews may foster the findings.

Labor pain and its relieving methods is one of the anxieties of mothers having a great impact on the quality of care during delivery as well as the patients' satisfaction. The propensity of using non-medicinal pain relief methods is increasing. The present study aimed to compare the effect of Acupressure at two GB-21 and SP06 points on the severity of labor pain. In this quasi-experimental single blind study started on December 2010 and ended on June 2011 in which 150 primiparous women were divided into three groups of Acupressure at GB-21 point, Acupressure at SP-6 point and control group. The intervention was carried out for 20 min at 3-4 and 20 min at 7-8 cm dilatation of Cervix. The pain severity was measured by Visual Analog Scale before and immediately, 30 and 60 min after the intervention. Then, the data were statistically analyzed. No significant difference was found among the 3 groups regarding the pain severity before the intervention. However, the pain severity it was reduced at 3-4 and 7-8 cm dilatation immediately, 30 and 60 min after the intervention in the two intervention groups compared to the control group (p<0.001). Nonetheless, no statistically significant difference was observed between the two intervention groups (p = 0.93). The results of the study showed that application of Acupressure at two GB-21 and SP-6 points was effective in the reduction of the severity of labor pain. Therefore, further studies are recommended to be performed on the application of Acupressure together with non-medicinal methods.

Lihan TONG,Weijia LI,Qingxia YANG,Liyuan CHEN,Peng CHEN, Vol.E107-D, No.11, pp.1472-1475
We present Ksformer, utilizing Multi-scale Key-select Routing Attention (MKRA) for intelligent selection of key areas through multi-channel, multi-scale windows with a top-k operator, and Lightweight Frequency Processing Module (LFPM) to enhance high-frequency features, outperforming other dehazing methods in tests.
Publication Date: 2024/11/01

Using data from 607 subjects organized in 161 teams (84 laboratory teams and 77 organizational field teams), we examined how leader humility influences team interaction patterns, emergent states, and team performance. We developed and tested a theoretical model arguing that when leaders behave humbly, followers emulate their humble behaviors, creating a shared interpersonal team process (collective humility). This collective humility in turn creates a team emergent state focused on progressively striving toward achieving the team's highest potential (collective promotion focus), which ultimately enhances team performance. We tested our model across three studies wherein we manipulated leader humility to test the social contagion hypothesis (Study 1), examined the impact of humility on team processes and performance in a longitudinal team simulation (Study 2), and tested the full model in a multistage field study in a health services context (Study 3). The findings from these lab and field studies collectively supported our theoretical model, demonstrating that leader behavior can spread via social contagion to followers, producing an emergent state that ultimately affects team performance. Our findings contribute to the leadership literature by suggesting the need for leaders to lead by example, and showing precisely how a specific set of leader behaviors influence team performance, which may provide a useful template for future leadership research on a wide variety of leader behaviors.

We draw on the historiographical concepts of "generational units" and "collective memories" as a framework for understanding the emergence of entrepreneurially oriented cohesive groups within regions. Generational units are localized subgroups within generations that have a self-referential, reflexive quality, by virtue of the members' sense of their own connections to each other and the events that define them. Collective memories are shared accounts of the past shaped by historical events that mold individuals' perceptions. The two concepts provide a valuable point of departure for incorporating historical concepts into the study of entrepreneurial dynamics and offer a framework for understanding how entrepreneurs' historically situated experiences affect them. Our framework breaks new theoretical ground in several ways. First, we synthesize disparate literatures on generational units, collective memory, and organizational imprinting. Second, we specify mechanisms through which imprinting occurs and persists over time. We develop analytical arguments framed by sociological and historiographical theories, focusing on the conditions under which meaningful generational units of entrepreneurs may emerge and benefit from leadership and legacy building, technologies of memory, and institutional support that increases the likelihood of their persistence.

Contemporary business schools are expected to educate their students to embrace ethical and pro-social values. But can business schools rise to this challenge? Comparing a business school to another professional school that encourages pro-social values, social work, we investigated value profiles as reflected in school websites and among their students. The findings show that the business school expresses self-enhancement values (power and achievement) more, and pro-social values (benevolence and universalism) less than the social work school. We further investigated self-selection and socialization as complementary organizational processes that may lead to, and sustain, the value profile of each school. Our findings show that as early as the first week of studies, freshmen's values are congruent with the value profile of their department, indicating a value-based self-selection process. To investigate socialization, we compared freshmen and seniors and conducted a yearlong study among freshmen. The findings revealed a small change in students' values throughout their training, providing only some support for value socialization. Altogether, our findings suggest that business schools that are interested in pro-social students should attract and select students that emphasize these values, rather than rely on socialization attempts.

President-Elect Donald Trump reaffirmed his commitment to protecting the Second Amendment by announcing his push for national concealed carry reciprocity.

STARBUCKS is once again collaborating with Stacey Bendet, CEO and Creative Director of top fashion house alice + olivia to offer a stylish designer merchandise collection, available for a limited time at select Starbucks stores across Malaysia.

With Stacey’s fun and sophisticated eye for design, the highly anticipated Starbucks X alice + olivia collection showcases two whimsical designs, including the iconic Stace Face, and a modern interpretation of the Stace Face with a colourful twist.

“Starbucks and Stacey Bendet are united by their aspiration to create unique and delightful experiences,” said Erin Silvoy, vice president, Product and Marketing, Starbucks Asia Pacific.

“Since our very first collaboration with alice + olivia, our customers have kept asking for more. Now, we’re excited to launch a new Starbucks X alice + olivia collection with bold, yet chic designs fit for everyday occasions, to encourage our customers to embrace self-expression and give them the confidence to live a life in style.”

Bendet herself added: “Both Starbucks and alice + olivia love creating unique and empowering experiences.

“With our rainbow Stace Face designs we hope to bring some colourful fun to the world!”

The exclusive collection will bring fashion and style to life once more, with a unique lineup that includes mugs and waterbottles, such as:

► Small Tote – The timeless look of the humble tote bag is reimagined with a modern interpretation of the ‘Stace Face’ with a colorful and stunning twist. Featuring an interior pocket that is lightweight, this bag is great for on the go.

► Bearista Bear – A soft and fluffy reinterpretation of the Bearista Bear wearing a custom sweater designed in the renowned alice + olivia style, which is matched only by the embroidered alice + olivia shoes.

► 12oz Ceramic Mug – The glossy clear-glazed stoneware gives this mug its special character. The handle is painted by hand with the mug body available in two different designs, one with the colorful spectrum of the Rainbow Stace Squad, and one of the Iconic Stace Face.

► 16oz Stainless Steel Tumbler – This tumbler is sure to keep your beverage at a perfect temperature whether it’s hot or cold with the innovative thermo 3D Double Wall vacuum insulation technology, as well as the medical-grade stainless steel so that there is not transfer of flavours or metal after taste. Available in two designs, the Rainbow Stace Squad and Iconic Stace Face.

► 16oz Ceramic Double Wall Traveler – Insulated with a double-wall construction with flat-white paint and an opaque black lid, which uses a slide open/close function for convenient use. Available in two designs, the Rainbow Stace Squad and Iconic Stace Face.

The limited-edition designer collaboration will be available beginning Sept 28, and priced from RM98 onwards, at select Starbucks stores across Malaysia, while supplies last.

ALLIES and supporters of the United States who praise it as the champion of democracy, freedom and human rights will now be rushing to join the media queue to congratulate the incoming president.

In their public messaging, they are likely to extol the outcome as yet another example of American exceptionalism and a role model for the countries of the world they regard as autocratic and necessary to bring down to uphold their definition of democracy and the Western rule of law.

Privately though, they will be feeling and reacting differently. They are also likely to be afraid of what will now follow.

The explanation is not far to find. Though portrayed in Western media as offering vastly different visions of the US for the next four years as well as being diametrically opposed in their foreign policy objectives, Kamala Harris and Donald Trump concur in adherence to the slogan made famous by Trump: that is to “Make America Great Again” (Maga).

It is a slogan that Democrat party leaders embrace just as strongly but would rather not
let the rest of the world be aware of or knowledgeable about.

How will Maga impact US foreign policy?

Post-election, the Maga agenda will be pushed hard and at the expense of the interests and concerns of the rest of the world. Maga foreign policy impact will be felt not only by countries that the US sees as rivals and enemies – China, Russia, North Korea, Iran, Cuba, Afghanistan, Pakistan, Serbia, Venezuela, Belarus and others.

It will also inflict costs on allies including Canada, European Union nations, Ukraine, United Kingdom, Australia, New Zealand, Japan, South Korea, and a few others such as the Philippines, previously provided with generous financial and military support by a moneyed and powerful benefactor, which is now relatively impoverished and less influential.

Countries not hitched to the American ideological bandwagon that see themselves as independent such as Mexico, India and Vietnam will find that sitting on the fence in the next four years will be much less comfortable as the new US president will not shield or spare them from the looming policy changes in trade, immigration, security, climate change and wherever else he or she sees as important and necessary to uplift the US and stem its decline.

Earlier in July, The Economist drew up a table ranking the vulnerability of various countries likely to be impacted by a new Trump presidency’s core policies. The table, The Trump Risk Index, assessed the exposure and vulnerability of America’s 70 largest trading partners to potential policy changes.

Although no similar table was drawn up for a Harris presidency, if one were to be drawn up, it is likely that there will be little or no difference in the index finding and ranking.

Increasingly, we find that liberal and conservative American analysts – both now recognising that the US is in an existential crisis – are converging in support of Maga to be the focus of US foreign policy.

The crisis, a long-developing one, exposes not only the deep divisions within American society with equal numbers on Republican and Democrat sides of the political fence in disagreement on the domestic policy reforms that the country badly needs.

It also brings to attention the current status of the US described by Trump as “a failing country”. It is a description that some Americans have taken umbrage with but which many Democrat supporters agree on while denouncing the Republican and Trumpian rhetoric and record on failing to improve the state of the nation.

What is perhaps most unsettling is that the disorder and instability in the US may see the new president become more reliant on US military superiority to ensure American dominance in global geopolitics.

The US military may again be called upon to underpin the foreign policy actions needed to make America great again.

Is a last hurrah coming to ensure that the US continues its defence of the unipolar world that it has shaped and is fixated on preserving?

Lim Teck Ghee’s Another Take is aimed at demystifying social orthodoxy.
Comments: letters@thesundaily.com

NEW YORK: The judge overseeing Donald Trump’s criminal hush money case has put off ruling on whether the president-elect’s conviction should be thrown out on immunity grounds, enabling prosecutors to weigh next steps following his Nov. 5 election victory.

Justice Juan Merchan had been due to rule on Tuesday on Trump’s argument that the U.S. Supreme Court’s decision in July that presidents are immune from prosecution involving their official acts meant the New York state case should be dismissed.

Instead, Merchan granted a request by Manhattan District Attorney Alvin Bragg’s office to have until Nov. 19 to consider how to approach the case in light of Trump’s looming inauguration in January 2025, email correspondence made public on Tuesday showed.

Trump’s scheduled Nov. 26 sentencing is now widely expected to be postponed.

Trump in May became the first U.S. president - former or sitting - convicted of a crime when a jury in Manhattan found him guilty on 34 felony counts of falsifying business records to cover up a potential sex scandal shortly before his first election win in 2016. Trump, who pleaded not guilty, has vowed to appeal the verdict after sentencing.

Prosecutor Matthew Colangelo wrote there were “competing interests” between ensuring a criminal case proceeds as usual and protecting the office of the president.

“The People agree that these are unprecedented circumstances,“ Colangelo wrote.

Trump is set to be the first felon inaugurated as president after his victory over Vice President Kamala Harris.

At issue in the six-week Manhattan trial was a $130,000 payment made by Trump’s then-lawyer Michael Cohen to adult film actress Stormy Daniels to keep quiet about a sexual encounter she said she had with him in 2006 but which he has denied.

Trump’s defense lawyer Emil Bove wrote that the case ultimately needed to be dismissed to avoid interfering with Trump’s presidential duties.

“The stay, and dismissal, are necessary to avoid unconstitutional impediments to President Trump’s ability to govern,“ Bove wrote.

TRUMP FACED FOUR CRIMINAL CASES

Trump, 78, is hoping to enter office unencumbered by any of four criminal cases he has faced and which once were thought to have threatened to derail his 2024 candidacy to return to the White House after having served from 2017-2021.

The Republican Trump has portrayed the hush money case brought by Bragg, a Democrat, and the three other state and federal criminal indictments brought in 2023 as politically motivated attempts to harm his presidential campaign. He pleaded not guilty in all four cases.

“It is now abundantly clear that Americans want an immediate end to the weaponization of our justice system,“ Trump campaign spokesperson Steven Cheung said in a statement on Tuesday.

Special Counsel Jack Smith brought two of the cases against Trump, one involving classified documents he kept after leaving office and the other involving his efforts to overturn his 2020 election loss. A Florida-based federal judge in July dismissed the documents case. The Justice Department is now evaluating how to wind down Smith’s election-related case.

Trump also faces state criminal charges in Georgia over his bid to reverse his 2020 loss in that state, but the case remains in limbo.

The Supreme Court, in a decision arising from one of Smith’s two cases against Trump, decided that presidents are immune from prosecution involving their official acts and that juries cannot be presented evidence of official acts in trials over personal conduct. It marked the first time that the court recognized any degree of presidential immunity from prosecution.

In making the case for immunity, Trump’s lawyers said the jury that convicted Trump in the hush money case was shown evidence by prosecutors of his social media posts as president and heard testimony from his former aides about conversations that occurred in the White House during his 2017-2021 term.

Bragg’s office countered that the Supreme Court’s ruling has no bearing on the case, which they said concerned “wholly unofficial conduct.” The Supreme Court in its ruling found no immunity for a president’s unofficial acts.

KUALA LUMPUR: Bursa Malaysia Bhd is experiencing a resurgence in IPOs as 2024 draws to a close, reflecting renewed investor confidence in the local bourse.

With 44 initial public offerings to date, Bursa Malaysia has outpaced other markets in Southeast Asia, emerging as an attractive IPO destination amid a stable economic and political landscape.

According to Mohd Sedek Jantan, UOB Kay Hian Wealth Advisors’ head of investment research, several factors have contributed to this surge. “The risk of doing business in the fourth quarter has subsided as major economic and political uncertainties have passed, such as the US presidential election while Malaysia’s active role in international forums has bolstered the country’s global standing,” he told Bernama.

He reckons that Malaysia’s stable economic indicators, including positive trade figures, healthy employment rates and steady industrial production have fostered a predictable business environment that encourages IPO activity. “Political stability and a clear government policy framework further enhance investor confidence,” he said.

The surge in IPOs on Bursa Malaysia underscores the local bourse’s resilience compared to other regional markets.

Mohd Sedek noted that Malaysia has recorded 36 IPOs so far this year, raising about US$450 million in the first half alone, which accounts for 33% of Southeast Asia’s total IPO proceeds.

“This stands in contrast to a subdued IPO market across the Asia-Pacific, where proceeds have dropped by 63%, largely due to challenges in China and Hong Kong.

“Malaysia has outperformed both Indonesia and Singapore in IPO activity this year,” he pointed out, highlighting that Indonesia faces political uncertainty following its recent presidential election, while Singapore has seen a slowdown in activity due to high regulatory costs and weak investor demand.

In contrast, he said Malaysia’s IPO market benefits from a stable macroeconomic backdrop, business-friendly regulations, and the supportive Madani Economy Framework.

Mohd Sedek said the growth in IPOs reflects optimism in key Malaysian sectors, with recent listings from the construction, manufacturing, and healthcare industries.

He said in the construction sector, which expanded by 22.9% in the third quarter, private and public investments in residential, non-residential, and large-scale infrastructure projects are expected to drive further growth. “Key government initiatives, such as RM9 billion for private finance initiatives and RM25.5 billion from government-linked investment companies are expected to sustain this momentum,” he added.

In the manufacturing sector, Malaysia’s transformation under the New Industrial Master Plan 2030 aims to drive growth in high-value, technology-driven industries. “The government’s focus on digitalisation, green technology, and advanced manufacturing techniques is expected to attract further investments, solidifying Malaysia’s position as a competitive manufacturing hub in Asean,” he said.

Malaysia’s healthcare sector is also expanding due to demographic shifts and rising health awareness. The integration of technology, such as telemedicine and digital health solutions, is anticipated to boost the sector’s growth by improving care accessibility and efficiency. “This trend, coupled with government support for medical tourism, positions Malaysia as a key player in the healthcare industry in the region,” Mohd Sedek said.

Bursa Malaysia CEO Datuk Muhamad Umar Swift expressed satisfaction with the IPO momentum, noting that three Main Market IPOs were listed this week alone.

“This surge reflects a thriving capital market with strong regulatory support and a diverse investor pool. Malaysia has experienced a bull run, making us the Asean exchange with the highest number of IPOs to date this year,” he said.

Echoing this sentiment, the exchange regulator’s chairman Tan Sri Abdul Wahid Omar highlighted the significance of Monday’s listings, which took place on the auspicious date of 11.11. (Nov 11)

“Both companies chose that date for its auspicious nature, marking a rare occasion of two listings on the same day. The last time Bursa hosted two listings on a single day was in November 2017, following the demerger of Sime Darby Group, which saw both Sime Darby Plantation Bhd and Sime Darby Property Bhd debut together,” he said.

Bank Muamalat Malaysia Bhd chief economist Dr Mohd Afzanizam Abdul Rashid noted that the strong IPO pipeline signals positive prospects for the Malaysian economy, as stable policies and a clear path towards becoming a high-income nation attract investor interest.

“Malaysia’s equities are undervalued, offering upside potential. The economic and policy stability enhances investor confidence, while companies’ growth trajectories inspire optimism for the market’s future,” he said.

Mohd Afzanizam said that as Bursa Malaysia continues to attract IPOs, he expects the exchange’s momentum to inspire small and medium enterprises to pursue similar growth opportunities. “The record-setting IPO activity underscores Malaysia’s resilience and strong capital market position in Asean, providing a positive outlook for 2025,” he added. – Bernama

KUALA LUMPUR: Malaysia’s goal of reaching 50% electric vehicle (EV) adoption by 2040 and 80% by 2050 in terms of total industry volume (TIV) is within reach, according to Deputy Investment, Trade, and Industry Minister Liew Chin Tong.

Liew said that the target – in accordance with the National Energy Transition Roadmap – aligns with the global shift towards sustainable transport.

“According to the International Energy Agency in the Global EV Outlook, globally in 2018, only 2% of total global sales was from EV, but by 2022, it was 14%, and by 2023, 18% of total global sales of cars comes from electric vehicles. In China this year, there were several months that EV overtook internal combustion cars, ICE cars. So these are all possible,” he told reporters at E-Mobility Asia 2024 (EMA 2024) today.

To achieve the target, Liew said that Malaysia needs to work together to develop a national effort to electrify its vehicles as much as possible.

He added that this is necessary to reduce national oil consumption and create more opportunities for various forms of manufacturing, including crossings of semiconductor and automotive industries.

Additionally, he said that the government is hoping that Malaysia will not just manufacture parts of the cars, but it is hoping that there will be horizontal crossing between the automotive industry and the semiconductor industry.

“So that one day, we are also known for designing chips for the automotive industry. That is one of our aspirations,” he remarked.

Liew said that another aspiration is to take advantage of the electrification of mobility, so that through this transition, Malaysia can reduce its overall national petroleum consumption.

“In most of our discussions, we are talking about shifting the burden of who pays for the petroleum consumption in this country. To address the question of the RON95 subsidy, I think E-Mobility has a big role to play. Electrification has a big role to play,” he added.

The event, EMA 2024 unveils electromobility and sustainable solutions as the way forward to reduce global emissions and tackle climate change.

China’s electric car manufacturers BYD, Chery and GWM are showcasing their latest models at the event, while Malaysia’s Eclimo is unveiling its new bikes.

EMA 2024 comes as EV demand surges in Southeast Asia and amid the global outlook that more than one in four vehicles on the road will be electric by 2035 according to the International Energy Agency.

Liew officiated the opening of the event that has drawn stakeholder and industry support including the state-owned Malaysia Automotive, Robotics & IoT Institute, and Electric Vehicle Association of Malaysia as strategic partners.

JAGUAR LAND ROVER’S (JLR) ambitious “Reimagine” strategy, announced nearly four years ago, is fast approaching a major milestone: transforming Jaguar into an all-electric luxury brand by 2025. While the company has not yet unveiled any new electric models, the transition away from combustion engines is in full swing. As of this November, Jaguar has officially stopped selling new cars with conventional powertrains in the UK.

In a recent statement, JLR confirmed the halt: “From November 2024, new Jaguar sales will come to an end. We have now ceased allocation of our current generation of Jaguar vehicles.” This decision means that models like the E-Pace, XE, XF, and F-Type—already phased out—are now joined by the F-Pace SUV, the final model of Jaguar’s internal combustion era in the UK.

While the F-Pace and other models are still available in some markets abroad, their production days are numbered. British customers, however, can still acquire certified pre-owned Jaguars. Notably, the F-Pace was Jaguar’s best-selling model in 2023, with 21,943 units sold globally—though this figure underscores the brand’s recent struggles in today’s competitive SUV market.

Looking ahead, Jaguar’s transformation will see it target an entirely new echelon of luxury. Instead of competing with BMW, Mercedes-Benz, and Audi, the brand is positioning itself against ultra-luxury names like Bentley and Aston Martin. The first model of Jaguar’s electric lineup will be a high-performance saloon, aimed at rivaling the Porsche Taycan, followed by an SUV set to compete with the Bentley Bentayga in 2026. Both models will be built on the Jaguar Electrified Architecture, with a flagship sedan expected later in the decade.

Meanwhile, Jaguar plans to debut a concept vehicle in the United States by year-end. This ultra-luxurious four-door grand tourer will lay the groundwork for a production model starting at over £100,000 (RM565,858). According to Jaguar’s Managing Director, Rawdon Glover, the transition to an electric-only brand has been “hugely frustrating,” yet the focus remains on moving into the ultra-luxury market with fewer, more profitable sales.

With the first new electric Jaguar not set to launch until 2026, the UK will see an unusual absence of new Jaguar vehicles over the coming year.