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 study presents a novel deep learning-based framework designed to enhance spontaneous micro-expression recognition by effectively increasing the amount and variety of data and balancing the class distribution to improve recognition accuracy. Background: Micro-expression recognition using deep learning requires large amounts of data. Micro-expression datasets are relatively small, and their class distribution is not balanced. Methodology: This study developed a framework using a deep learning-based model to recognize spontaneous micro-expressions on a person’s face. The framework also includes several technical stages, including image and data preprocessing. In data preprocessing, data augmentation is carried out to increase the amount and variety of data and class balancing to balance the distribution of sample classes in the dataset. Contribution: This study’s essential contribution lies in enhancing the accuracy of micro-expression recognition and overcoming the limited amount of data and imbalanced class distribution that typically leads to overfitting. Findings: The results indicate that the proposed framework, with its data preprocessing stages and deep learning model, significantly increases the accuracy of micro-expression recognition by overcoming dataset limitations and producing a balanced class distribution. This leads to improved micro-expression recognition accuracy using deep learning techniques. Recommendations for Practitioners: Practitioners can utilize the model produced by the proposed framework, which was developed to recognize spontaneous micro-expressions on a person’s face, by implementing it as an emotional analysis application based on facial micro-expressions. Recommendation for Researchers: Researchers involved in the development of a spontaneous micro-expression recognition framework for analyzing hidden emotions from a person’s face are playing an essential role in advancing this field and continue to search for more innovative deep learning-based solutions that continue to explore techniques to increase the amount and variety of data and find solutions to balancing the number of sample classes in various micro-expression datasets. They can further improvise to develop deep learning model architectures that are more suitable and relevant according to the needs of recognition tasks and the various characteristics of different datasets. Impact on Society: The proposed framework could significantly impact society by providing a reliable model for recognizing spontaneous micro-expressions in real-world applications, ranging from security systems and criminal investigations to healthcare and emotional analysis. Future Research: Developing a spontaneous micro-expression recognition framework based on spatial and temporal flow requires the learning model to classify optimal features. Our future work will focus more on exploring micro-expression features by developing various alternative learning models and increasing the weights of spatial and temporal features.

Aim/Purpose: In this study, we developed a recommendation system model designed to support decision-makers in identifying consumers eligible for pay-later options via consensus-based decision-making. This approach was chosen due to the high and complex risks involved, such as delayed payments, challenges in reaching consumers, and issues of bad credit. Background: The “pay-later” option, which allows consumers to postpone payment for e-commerce purchases, offers convenience and flexibility but also introduces several challenges: (i) by enabling payment deferral, merchants face financial risks, including potential delays or defaults in payment, adversely affecting their cash flow and profitability; and (ii) this payment delay can also heighten the risk of fraud, including identity theft and unauthorized transactions. Methodology: This study initiated a risk analysis utilizing the ROAD process. Considering contemporary economic developments and advancements in neural networks, integrating these networks into risk assessment has become crucial. Consequently, model development involved the amalgamation of three deep learning methods – CNN (Convolutional Neural Networks), RNN (Recurrent Neural Networks), and LSTM (Long Short-Term Memory) – to address various risk alternatives and facilitate multi-stage decision-making recommendations. Contribution: Our primary contribution is threefold. First, our study identified potential consumers by prioritizing those with the smallest associated problem consequence values. Second, we achieved an optimal recall value using a candidate generator. Last, we categorized consumers to assess their eligibility for pay-later rights. Findings: The findings from this study indicate that our multi-stage recommendation model is effective in minimizing the risk associated with consumer debt repayment. This method of consumer selection empowers policymakers to make informed decisions regarding which consumers should be granted pay-later privileges. Recommendations for Practitioners: This recommendation system is proposed to several key parties involved in the development, implementation, and use of pay-later systems. These parties include E-commerce Executive Management for financial analysis and risk evaluation, the Risk Management Team to assess and manage risks related to users utilizing Pay-Later services, and Sales Managers to integrate Pay-Later services into sales strategies. Recommendation for Researchers: Advanced fraud detection mechanisms were implemented to prevent unauthorized transactions effectively. The goal was to cultivate user confidence in the safety of their financial data by ensuring secure payment processing. Impact on Society: Ensuring consumers understand the terms and conditions of pay-later arrangements, including interest rates, repayment schedules, and potential fees, is crucial. Providing clear and transparent information, along with educating consumers about their financial responsibilities, helps prevent misunderstandings and disputes. Future Research: Our future development plans involve the ongoing assessment of the system’s performance to enhance prediction accuracy. This includes updating models and criteria based on feedback and changes in economic or market conditions. Upholding compliance with security and data privacy regulations necessitates the implementation of protective measures to safeguard consumer information. The implementation of such a system requires careful consideration to ensure fairness and adherence to legal standards. Additionally, it is important to acknowledge that algorithms and models may evolve over time through the incorporation of additional data and continuous evaluations.

Aim/Purpose: This study aims to develop a solution for personalized tourism recommendations that addresses information overload, data sparsity, and the cold-start problem. It focuses on enabling tourists to choose the most suitable tourism-related facilities, such as restaurants and hotels, that match their individual needs and preferences. Background: The tourism industry is experiencing a significant shift towards digitalization due to the increasing use of online platforms and the abundance of user data. Travelers now heavily rely on online resources to explore destinations and associated options like hotels, restaurants, attractions, transportation, and events. In this dynamic landscape, personalized recommendation systems play a crucial role in enhancing user experience and ensuring customer satisfaction. However, existing recommendation systems encounter major challenges in precisely understanding the complexities of user preferences within the tourism domain. Traditional approaches often rely solely on user ratings, neglecting the complex nature of travel choices. Data sparsity further complicates the issue, as users might have limited interactions with the system or incomplete preference profiles. This sparsity can hinder the effectiveness of these systems, leading to inaccurate or irrelevant recommendations. The cold-start problem presents another challenge, particularly with new users who lack a substantial interaction history within the system, thereby complicating the task of recommending relevant options. These limitations can greatly hinder the performance of recommendation systems and ultimately reduce user satisfaction with the overall experience. Methodology: The proposed User-based Multi-Criteria Trust-aware Collaborative Filtering (UMCTCF) approach exploits two key aspects to enhance both the accuracy and coverage of recommendations within tourism recommender systems: multi-criteria user preferences and implicit trust networks. Multi-criteria ratings capture the various factors that influence user preferences for specific tourism items, such as restaurants or hotels. These factors surpass a simple one-star rating and take into account the complex nature of travel choices. Implicit trust relationships refer to connections between users that are established through shared interests and past interactions without the need for explicit trust declarations. By integrating these elements, UMCTCF aims to provide more accurate and reliable recommendations, especially when data sparsity limits the ability to accurately predict user preferences, particularly for new users. Furthermore, the approach employs a switch hybridization scheme, which combines predictions from different components within UMCTCF. This scheme leads to a more robust recommendation strategy by leveraging diverse sources of information. Extensive experiments were conducted using real-world tourism datasets encompassing restaurants and hotels to evaluate the effectiveness of UMCTCF. The performance of UMCTCF was then compared against baseline methods to assess its prediction accuracy and coverage. Contribution: This study introduces a novel and effective recommendation approach, UMCTCF, which addresses the limitations of existing methods in personalized tourism recommendations by offering several key contributions. First, it transcends simple item preferences by incorporating multi-criteria user preferences. This allows UMCTCF to consider the various factors that users prioritize when making tourism decisions, leading to a more comprehensive understanding of user choices and, ultimately, more accurate recommendations. Second, UMCTCF leverages the collective wisdom of users by incorporating an implicit trust network into the recommendation process. By incorporating these trust relationships into the recommendation process, UMCTCF enhances its effectiveness, particularly in scenarios with data sparsity or new users with limited interaction history. Finally, UMCTCF demonstrates robustness towards data sparsity and the cold-start problem. This resilience in situations with limited data or incomplete user profiles makes UMCTCF particularly suitable for real-world applications in the tourism domain. Findings: The results consistently demonstrated UMCTCF’s superiority in key metrics, effectively addressing the challenges of data sparsity and new users while enhancing both prediction accuracy and coverage. In terms of prediction accuracy, UMCTCF yielded significantly more accurate predictions of user preferences for tourism items compared to baseline methods. Furthermore, UMCTCF achieved superior coverage compared to baseline methods, signifying its ability to recommend a wider range of tourism items, particularly for new users who might have limited interaction history within the system. This increased coverage has the potential to enhance user satisfaction by offering a more diverse and enriching set of recommendations. These findings collectively highlight the effectiveness of UMCTCF in addressing the challenges of personalized tourism recommendations, paving the way for improved user satisfaction and decision-making within the tourism domain. Recommendations for Practitioners: The proposed UMCTCF approach offers a potential opportunity for tourism recommendation systems, enabling practitioners to create solutions that prioritize the needs and preferences of users. By incorporating UMCTCF into online tourism platforms, tourists can utilize its capabilities to make well-informed decisions when selecting tourism-related facilities. Furthermore, UMCTCF’s robust design allows it to function effectively even in scenarios with data sparsity or new users with limited interaction history. This characteristic makes UMCTCF particularly valuable for real-world applications, especially in scenarios where these limitations are common obstacles. Recommendation for Researchers: The success of UMCTCF can open up new avenues in personalized recommendation research. One promising direction lies in exploring the integration of additional contextual information, such as temporal (time-based) or location-based information. By incorporating these elements, the model could be further improved, allowing for even more personalized recommendations. Furthermore, exploring the potential of UMCTCF in domains other than tourism has considerable significance. By exploring its effectiveness in other e-commerce domains, researchers can broaden the impact of UMCTCF and contribute to the advancement of personalized recommendation systems across various industries. Impact on Society: UMCTCF has the potential to make a positive impact on society in various ways. By delivering accurate and diverse recommendations that are tailored to individual user preferences, UMCTCF fosters a more positive and rewarding user experience with tourism recommendation systems. This can lead to increased user engagement with tourism platforms, ultimately enhancing overall satisfaction with travel planning. Furthermore, UMCTCF enables users to make more informed decisions through broader and more accurate recommendations, potentially reducing planning stress and leading to more fulfilling travel experiences. Future Research: Expanding upon the success of UMCTCF, future research activities can explore several promising paths. Enriching UMCTCF with various contextual data, such as spatial or location-based data, to enhance recommendation accuracy and relevance. Leveraging user-generated content, like reviews and social media posts, could provide deeper insights into user preferences and sentiments, improving personalization. Additionally, applying UMCTCF in various e-commerce domains beyond tourism, such as online shopping, entertainment, and healthcare, could yield valuable insights and enhance recommendation systems. Finally, exploring the integration of optimization algorithms could improve both recommendation accuracy and efficiency.

Aim/Purpose: This study aims to identify the characteristics of YouTube video reviews that consumers utilize to evaluate review helpfulness and explores how they process such information. This study aims to investigate the effect of argument quality, review popularity, number of likes, and source credibility on consumers’ perception of YouTube’s video review helpfulness. Background: Video reviews posted on YouTube are an emerging form of online reviews, which have the potential to be more helpful than textual reviews due to their visual and audible cues that deliver more vivid information about product features and specifications. With the availability of an enormous number of video reviews with unpredictable quality, it becomes challenging for consumers to find helpful reviews without consuming significant time and effort. In addition, YouTube does not provide a specific feature that indicates a review helpfulness similar to the one found on e-commerce websites. Consequently, consumers have to examine the characteristics of video reviews that are readily available on YouTube, evaluate them, and form a perception of whether a review is helpful or not. Despite the increasing popularity of YouTube’s video reviews, video reviews’ helpfulness received inadequate attention in the literature. The antecedents of the helpfulness of online video reviews are still underinvestigated, and more research is needed to identify the characteristics that consumers depend upon to assess video review helpfulness. Furthermore, it is important to understand how consumers process the information they gain from these characteristics to form a perception of their helpfulness. Methodology: Following an extended investigation of the relevant literature, we identified four key video characteristics that consumers presumably utilize to evaluate review helpfulness on YouTube (i.e., review popularity, number of likes, source credibility, and argument quality). By employing the Elaboration Likelihood Model (ELM), we classified these characteristics along the central and peripheral routes. The central route characteristics require a high cognitive effort by consumers to process the review’s message and reach a logical decision. In contrast, the peripheral route assumes that consumers judge the review’s message based on superficial qualities without substantial cognitive effort. A research model is introduced to investigate the effect of central and peripheral cues and their corresponding video review characteristics on review helpfulness. Accordingly, argument quality is proposed in the central route of the model, while review popularity, number of likes, and source credibility are proposed in the peripheral route. Furthermore, the study investigates how consumers process the information they obtain from these routes jointly or independently. To empirically test the proposed model, a convenient sample of 361 YouTube users was obtained through an online survey. The partial least squares method was used to investigate the effect of the proposed characteristics on video review helpfulness. Contribution: This study contributes to the literature in several ways. First, it is one of the few studies that investigate online video reviews’ helpfulness. Second, this study identifies several unique characteristics of YouTube’s video reviews that span peripheral and central routes, which potentially contribute to review helpfulness. Third, this study proposes a conceptual model based on the ELM to explore the effect of central and peripheral cues and their corresponding review characteristics on review helpfulness. Fourth, the research findings provide implications for research and practice that advance the theoretical understanding of video reviews’ helpfulness and serve as guidelines to create more helpful video reviews by better understanding the consumer’s cognitive processes. Findings: The results show that among the four characteristics proposed in the research model, argument quality in the central route is the strongest determinant factor affecting video review helpfulness. Results also show that review popularity, source credibility, and the number of likes in the peripheral route have significant effects on video review helpfulness. Altogether, our results show that the effect of the peripheral route adds up to 0.463 compared to 0.430, which is the impact magnitude of the argument quality construct in the central route. Based on the comparable effect magnitude of the central and peripheral routes of the model on video review helpfulness, our results indicate that both peripheral and central cues significantly affect consumers’ perception of video review helpfulness. The two routes are not mutually exclusive, and their cues can be processed in parallel or consecutive ways. Recommendations for Practitioners: The study recommends creating a dedicated category for reviews on YouTube with a specific feature for consumers to indicate the helpfulness of a video review, similar to the helpful vote button in textual reviews. The study also recommends that reviewers deliver more appealing and convincing argument quality, work toward improving their credibility, and understand the factors that contribute to video popularity. Impact on Society: Identifying the characteristics that affect video review helpfulness on YouTube helps consumers access helpful reviews more efficiently and improves their purchase decisions. Future Research: Future research could look into different types of data that could be extracted from YouTube to investigate the helpfulness of online video reviews. Future studies could employ machine learning and sentiment analysis techniques to reach more insights. Future research could also investigate the effect of product types in the context of online video reviews.

DSSP stands as a foundational tool in the domain of protein secondary structure prediction, yet it encounters notable challenges in accurately annotating irregular structures, such as β-turns and γ-turns, which constitute approximately 25%-30% and 10%-15% of protein turns, respectively. This limitation arises from DSSP's reliance on hydrogen-bond analysis, resulting in annotation gaps and reduced consensus on irregular structures. Alternatively, PROMOTIF excels at identifying these irregular structure annotations using phi-psi information. Despite their complementary strengths, previous methodologies utilised DSSP and PROMOTIF separately, leading to disparate prediction methods for protein secondary structures, hampering comprehensive structure analysis crucial for drug development. In this work, we bridge this gap using an annotation fusion approach, combining DSSP structures with beta, and gamma turns. We introduce IRNN-SS, a model employing deep inception and bidirectional gated recurrent neural networks, achieving 77.4% prediction accuracy on benchmark datasets, outpacing current models.

The world's economies are attempting to transform themselves to have a greater focus on developing knowledge as a commodity through innovation. Innovation starts with a creative activity that yields an invention but is augmented through a systematic value driven knowledge management system to yield new knowledge that can create a competitive advantage. To succeed in such an economy, organisations must have or develop the talent that can produce and use information effectively, they must have an ambidextrous organisational structure that allows them to innovate and produce simultaneously, and they must have an innovation management system to sustain effective innovation. In this paper we show how to augment existing university courses to simultaneously develop subject matter and innovation skills in students. We also suggest the incorporation of the new Innovations Management System Standard Series ISO 56000 into business curricula to better prepare students to function in the knowledge economy.

The emergence of service ecosystems can accelerate the industrial renewal required because of urgent global challenges. However, existing research has not sufficiently grasped the social dynamics of coevolution in ecosystems that enhance industrial renewal. This study aimed to advance ecosystem research through a practice lens and to present the key characteristics of industrial service ecosystem practice involved in industrial renewal. Consequently, its three characteristics - <i>accomplishment</i>, <i>attractiveness</i> and <i>actionability</i> - were configured based on an abductive study derived from the ecosystem literature, three practice-oriented approaches to learning, and two case ecosystem examinations. These features created the logic for resource integration and enhanced ecosystems to evolve as units, thus exceeding the actors' independent avenues of renewal. The findings of this study provided a deeper understanding of the coevolution in ecosystems needed to accelerate industrial renewal as well as a novel conceptualisation of an <i>ecosystem-as-practice</i> for further studies.

Campus discovery is an important feature of a university student induction process. Approaches towards campus discovery differ from course to course and can comprise guided tours that are often lengthy and uninspiring, or self-guided tours that run the risk of students failing to complete them. This paper describes a campus self-discovery induction game (Geospatial Crypto Reconnaissance) which aims to make students aware of campus resources and facilities, whilst at the same time allowing students to make friends and complete the game in an enthusing and exciting way. In this paper we describe the game construct, which comprises of a location, message, and artefact, and also the gameplay. Geospatial Crypto Reconnaissance requires students to identify a series of photographs from around the campus, to capture the GPS coordinates of the location of the photograph, to decipher a ciphered message and then to return both the GPS coordinates and the message for each photograph, proving that the student has attended the location. The game had a very high satisfaction score and we present an analysis of student feedback on the game and also provide guidance on how the game can be adopted for less technical cohorts of students.

Personalization is one of the most expected features in the current educational systems. User modeling is supposed to be the first stage of this process, which may incorporate learning style as an important part of the model. Learning style, which is a non-stable characteristic in the case of children, differentiates students in learning preferences. This paper identifies a new hybrid method to initiate and update the information of children’s learning style in an educational system. At the start-up phase, children’s learning style information is gathered through the modified Murphy-Meisgeier Type Indicator for Children (MMTIC) questionnaire, which is based on the well-known Myers-Briggs Type Indicator (MBTI). This primary information will be tuned by tracking children’s behaviors during the learning process. Analytical data mining helped us to cluster these behaviors and find their patterns. The proposed method was applied on 81 fourth grade children in elementary school. Delivering results suggest that this method provides a good precision in recognizing children learning style and may be an appropriate solution for non-stability problems in their preferences.

Aim/Purpose: Students face many challenges improving their soft skills such as critical thinking. This paper offers one possible solution to this problem. Background: This paper considers one method of enhancing critical thinking through a problem-solving game called the Coffee Shop. Problem-solving is a key component to critical thinking, and game-playing is one method of enhancing this through an interactive teaching method. Methodology: Three classes of Vietnamese high school students engaged in the Coffee Shop game. The method seeks outcome measurements through the use of analysis of multiple surveys to assess and interpret if critical thinking may have been improved. Contribution: The study may help to understand the importance of problem-solving in the context of an entrepreneurial setting and add to the variation of methods used to deliver the lesson to students in the classroom. Findings: The findings show that practicing problem-solving scenarios with a focus on critical thinking in a time limited setting results in a measured improvement of this skill. Recommendations for Practitioners: The findings suggest that educators could use games more as tools for problem-solving to contribute to their students’ learning outcomes around developing critical thinking. Recommendation for Researchers: More research could be devoted to developing problem-solving and critical thinking skills through game-play models. Impact on Society: Improved critical thinking skills in individuals could make a greater contribution to society. Future Research A comparative study between different high school grades and genders as well as between different countries or cultures.

Aim/Purpose: In this paper we propose an ecosystem for translational learning that combines core learning principles with a multilevel construct that embraces the tenets of translational research, namely, teaming, translating, and implementing. The goal of the paper is to argue that knowledge of learning sciences is essential at the individual, team, and organizational levels in the translational science enterprise. Background: The two decades that we can now call the translational era of health and medicine have not been without challenges. Many inroads have been made in navigating how scientific teaming, translating knowledge across the health spectrum, and implementing change to our health systems, policies, and interventions can serve our changing global environment. These changes to the traditional health science enterprise require new ways of understanding knowledge, forging relationships, and managing this new tradition of science. Competency requirements that have become important to the enterprise are dependent on a deep understanding about how people learn as individuals, in teams, and within organizations and systems. Methodology: An individual, team, and organizational conceptual framework for learning in translational ecosystems is developed drawing on the learning science literature, a synthesis of 9 key learning principles and integrated with core competencies for translational science. Contribution: The translational learning ecosystem is a means by which to understand how translational science competencies can be reinforced by core learning principles as teaming, translating, and implementation intersect as part of the translational science enterprise. Findings: This paper connects learning science to tailored principles in a simplified way so that those working translational science with less knowledge of theories of learning and pedagogy may be able to access it in a clear and concise way. Recommendation for Researchers: This paper provides a framework for researchers who engage in the education of translational scientists as well as those who are charged with training new scientists in an emerging field critical to health and medicine. Future Research: The translational ecosystem described can serve to expand how teaching and learning impact scientific advances. In addition, it serves as a means in which to understand the impact of learning on micro, meso, and macro levels.

Aim/Purpose: Developing Digital Platform Ecosystems (DPE) to transform conventional Knowledge Management Systems (KM/KMS) scenarios promises significant benefits for individuals, institutions, as well as emerging knowledge economies. Background: The academic entrepreneurship project presented is aiming for such a KMS-DPE configuration. Having consolidated this author’s own and external re-search findings, realization is currently commencing with a start-up in a business incubator. Methodology: Design science research applying mixed one-sample case study and illustrative scenario approach focusing on conceptual analysis and entrepreneurship. Contribution: Although (academic) entrepreneurship is a young research area with recently growing interest, publications focusing on this transitional stage between maturing research and projected commercial viability of digital technologies are rare. Findings: A roadmap looking beyond the immediate early-start-up perspective is out-lined by integrating recent development-stage-related DPE-research and by addressing stakeholders diverse informing needs essential for system realization. Recommendations for Practitioners and Researchers: As this transdisciplinary perspective combines KM, informing, design science, and entrepreneurial research spaces, it may assist other researchers and practitioners facing similar circumstances and/or start-up opportunities. Impact on Society: The article advances the understanding of how DPE communities may serve members with highly diverse skills and ambitions better to gainfully utilize the platform’s resources and generative potential in their personal and local settings. Future Research: As the entrepreneurial agenda will complement (not substitute) the academic research, research priorities have been highlighted aligned to three future stages.

Aim/Purpose: The amount of information published has increased dramatically due to the information explosion. The issue of managing information as it expands at this rate lies in the development of information extraction technology that can turn unstructured data into organized data that is understandable and controllable by computers Background: The primary goal of named entity recognition (NER) is to extract named entities from amorphous materials and place them in pre-defined semantic classes. Methodology: In our work, we analyze various machine learning algorithms and implement K-NN which has been widely used in machine learning and remains one of the most popular methods to classify data. Contribution: To the researchers’ best knowledge, no published study has presented Named entity recognition for the Kikuyu language using a machine learning algorithm. This research will fill this gap by recognizing entities in the Kikuyu language. Findings: An evaluation was done by testing precision, recall, and F-measure. The experiment results demonstrate that using K-NN is effective in classification performance. Recommendation for Researchers: With enough training data, researchers could perform an experiment and check the learning curve with accuracy that compares to state of art NER. Future Research: Future studies may be done using unsupervised and semi-supervised learning algorithms for other resource-scarce languages.

This paper proposes an efficient solution for personalised web directories recommendation using fast FCM+DQN. At first, web directory usage file obtained from given dataset is fed into the accretion matrix computation module, where visitor chain matrix, visitor chain binary matrix, directory chain matrix and directory chain binary matrix are formulated. In this, directory grouping is accomplished based on fast FCM and matching among query and group is conducted based on Kumar Hassebrook and Kulczynski similarity. The user preferred directory is restored at this stage and at last, personalised web directories are recommended to the visitors by means of DQN. The proposed approach has received superior results with respect to maximum accuracy of 0.910, minimum mean squared error (MSE) of 0.0206 and root mean squared error (RMSE) of 0.144. Although the system offered magnificent outcomes, it failed to order web directories in the form of highly, medium and low interested directories.

Congcong FANG,Yun JIN,Guanlin CHEN,Yunfan ZHANG,Shidang LI,Yong MA,Yue XIE, Vol.E107-D, No.11, pp.1463-1467
Currently, an increasing number of tasks in speech emotion recognition rely on the analysis of both speech and text features. However, there remains a paucity of research exploring the potential of leveraging large language models like GPT-3 to enhance emotion recognition. In this investigation, we harness the power of the GPT-3 model to extract semantic information from transcribed texts, generating text modal features with a dimensionality of 1536. Subsequently, we perform feature fusion, combining the 1536-dimensional text features with 1188-dimensional acoustic features to yield comprehensive multi-modal recognition outcomes. Our findings reveal that the proposed method achieves a weighted accuracy of 79.62% across the four emotion categories in IEMOCAP, underscoring the considerable enhancement in emotion recognition accuracy facilitated by integrating large language models.
Publication Date: 2024/11/01

Basics of the Adtech Ecosystem This guide delves into the intricacies of the adtech ecosystem, an elaborate mesh of platforms and technologies designed to facilitate and enhance the purchase and sale of digital advertising. Within this system, crucial elements such as ad servers, DSPs (Demand Side Platforms), SSPs (Supply Side Platforms), and ad [...]

How does the presence of domain experts on a corporate board—directors whose primary professional experience is within the focal firm's industry—affect organizational outcomes? We argue that under conditions of significant decision uncertainty, a higher proportion of domain experts on a board may detract from effective decision making and thus increase the probability of organizational failure. Building on exploratory interviews with board members and CEOs, we derive hypotheses from this argument in the context of local banks in the United States. We predict that the greater the level of decision uncertainty—due to rapid asset growth or operation in less predictable markets—the stronger the relationship between the proportion of banking expert directors and the probability of bank failure. Longitudinal analyses of 1,307 banks between 1996 and 2012 support this prediction, even after accounting for both the overall level of professional diversity among directors and banks' different propensities to have an expert-heavy board. We discuss implications for the key dimensions of board composition, the conditions under which the professional background of directors is more or less consequential, and the mechanisms whereby board composition affects organizational outcomes.

This essay presents a comprehensive briefing on the past and present of a business educational culture that is significantly different in ethos and structure to the widely known systems in the US and UK. That is the history and culture of the French Grandes Ecoles de Commerce. A brief reminder of extant literature on the utility of business education and its seeming misalignment with the competencies and skills as specified by practitioners is then given. Key pressures and trends on and within this system - such as internationalisation, accreditation and a greater emphasis on publications are identified and discussed. These threads are then combined in a partial replication of the work of Dierdorff and Rubin (2006; 2009). Specifically, information on 1582 classes from 542 programmes at the top Grandes Ecoles de Commerce is presented alongside further secondary data and then analysed in respect of alignment with Rubin and Dierdorff's identified behavioural competencies. We argue that whilst well intentioned, the outcome of these pressures may well be that inherent and historical strengths of great value are being discarded, and that the degree of irrelevance and misalignment between educational provision and required managerial competence will stay the same or even get worse.

This paper examines when firms pursue structural realignment through the recombination of business units. Our results refine and extend contingency theory and studies of organization design by drawing on theories of decision avoidance and delay to describe conditions when firms pursue or postpone structural realignment. Our empirical analysis of 46 firms from 1978 to 1997 operating within the U.S. medical device and pharmaceutical sectors demonstrates that while decision makers initiate structural recombination during periods of industry growth (i.e., munificence), they reduce their recombination efforts during periods of industry turbulence (i.e., dynamism) and managerial turbulence (i.e., growth in top management team size). We also find evidence that firms delay realignment and bide their time for better environmental conditions of declining turbulence and industry growth before pursuing more structural realignment. Together, these findings suggest that decision makers often delay initiating structural recombination until they can effectively process information and assess how structural changes will help them realign the organization to the environment.

Labor codes have been voluntarily adopted and used by manufacturers in emerging economies for the past two decades, as a means of ensuring minimally acceptable or core labor standards for workers. However, far too little is known of the potential benefits from the voluntary adoption of labor codes to the manufacturer, and prior human resource management research has been virtually silent on the business implications of their use for emerging economy manufacturers participating in global supply chains. Drawing on previous work across multiple disciplines and proposing a framework that extends human resource management theory more explicitly and rigorously to the context of emerging economy manufacturing, I theorize and demonstrate that the voluntary adoption of a labor code may constitute an effective human resource investment in emerging economies in improving establishment-level employee outcomes and operational and financial performance. The hypotheses are tested using longitudinal data on a sample of apparel manufacturing plants in Sri Lanka. Implications of this study include providing insight into how to expand the scope and relevance of human resource management theory to better understand research and practice in emerging economies.

The burgeoning economic inequality between the richest and the poorest is a cause of concern for social, political, and ethical reasons. While businesses are both implicated and affected by growing inequality, business schools have largely neglected to subject the phenomenon to sufficient critique. This is, in part, because far too many management educators rely on orthodox economic perspectives—often represented by neoliberal capitalism—which have dominated the curricula and the teaching philosophy of business schools. To address this issue, this article underscores the need for business schools to critically examine the relationship between neoliberal capitalism and economic inequalities, and to overtly engage with this nexus in pedagogical practice. The article concludes by revisiting the concepts of relationality and answerability as paths by which to address the current predicament. Relationality and answerability collectively offer: i) conceptual and reflexive tools by which to re-imagine business school education, and, ii) space for business schools to debate important questions about the taken-for-granted, but problematic, assumptions underlying the ideology of neoliberal capitalism

The tech billionaire joins the incoming administration to "dismantle government bureaucracy" - but what's in it for both of them?

For advocates of universal gun rights, this debate represents a fundamental question about the nature of the Second Amendment: is it an American right or a human right?

DONALD Trump’s victory in the 2024 US presidential election has raised global concerns about how his economic policies may impact countries like Malaysia.

With an “America First” approach focused on protecting domestic interests, the Trump administration is expected to reshape international trade, shift investment flows and influence geopolitical relationships.

For Malaysia, this outcome presents not only challenges but also opportunities in key economic sectors, including trade, foreign investment and commodities.

Trump is anticipated to continue protectionist policies that prioritise US jobs and domestic production. His proposal to impose a 10% import tariff on all goods entering the US aims to reduce reliance on foreign products and bolster domestic manufacturing.

Additionally, Trump’s plan to impose tariffs as high as 60% on Chinese products could have significant implications for Malaysia, one of the major exporters of electronic products and components to the US. If high tariffs are applied to Chinese goods, Malaysian products incorporating Chinese components could also be impacted, potentially diminishing US demand for Malaysian exports.

While this situation presents risks, it also provides opportunities as companies diversify supply chains away from China. Malaysia benefitted from the “China+1” strategy during Trump’s first term, as exports to the US increased amid US-China trade tensions.

Malaysia’s semiconductor industry, a focus of large investments from multinational companies such as Intel and Infineon, may continue to attract interest as a stable manufacturing base.

Currently, Malaysia holds around 13% of the global market in chip packaging and testing, making it a favourable location for companies seeking to expand operations outside of China. These conditions indicate Malaysia’s potential to further establish itself as a manufacturing hub if it can maintain political stability and investor-friendly economic policies.

The energy sector is also likely to be affected. Trump’s pro-oil stance could lead to increased US production and exports of fossil fuels. Should global oil prices rise, Malaysia, as an oil exporter, stands to benefit from higher national revenue.

However, rising oil prices also carry inflationary risks, as increased energy costs could drive up production costs and consumer prices domestically. While the energy sector may gain, higher energy costs could pressure consumer purchasing power and escalate operational costs for local industries.

To maximise these potential gains, Malaysia will need to balance these impacts on the consumer sector and ensure monetary policies support price stability.

The Malaysian commodity sector, particularly palm oil, faces potential challenges as well. During Trump’s first term, the US imposed import restrictions on Malaysian palm oil companies such as FGV Holdings and Sime Darby Plantation over allegations of forced labour. These restrictions affected Malaysian palm oil exports to the US, reducing revenue and harming the country’s image as a responsible producer.

Should similar policies persist, Malaysia will need to strengthen sustainable labour practices and meet international standards to retain access to global markets and protect its reputation as an ethical producer.

Trump’s policies could bring added uncertainty to Malaysia’s capital markets and the ringgit’s value. With US interest rates currently at 4.75%-5.00%, any influence Trump may exert on the Federal Reserve to raise rates could lead global investors to favour US assets, potentially causing capital outflows from Malaysia.

In 2023, Malaysia saw a 6.8% decline in foreign equity inflows, and the ringgit depreciated by around 8% against the US dollar. This shift reduces liquidity in local capital markets, and foreign investors may approach Malaysian equities with greater caution, especially if Trump’s policies introduce additional tariffs or trade restrictions.

As demand for the US dollar rises, the ringgit may face continued downward pressure. A weaker ringgit could increase import costs, particularly in vital sectors like food and technology, compounding domestic inflationary pressures, which currently stand at 2.8%.

To address these challenges, Malaysia needs a strong risk management strategy to maintain market stability and support the ringgit amid growing uncertainties.

In addition, Trump’s protectionist stance may directly impact Foreign Direct Investment (FDI) into Malaysia. As a manufacturing hub in Southeast Asia, Malaysia could see reduced FDI if the US pursues an aggressive stance on countries with significant trade surpluses.

Trump’s emphasis on protecting US jobs and domestic economic interests may lead to decreased investment from US companies in Malaysia.

Concurrently, prolonged US-China trade tensions could make investors more cautious about Malaysia, which may be perceived as politically and economically vulnerable. Any decline in FDI could affect job creation, technology growth and Malaysia’s long-term economic stability.

Furthermore, Trump’s victory raises concerns about the future of the US-led Indo-Pacific Economic Framework (Ipef). Trump has previously expressed a desire to withdraw from trade agreements like Ipef, which he sees as “another TPP”. If this happens, Malaysia may face challenges in maintaining market access and regional economic integration.

To prepare, Malaysia must diversify its trade partnerships, strengthen local industries and foster growth in resilient sectors. Malaysia’s involvement in Ipef reflects its commitment to regional economic integration, which could help mitigate the negative effects of US protectionist policies.

In summary, Trump’s victory could have significant implications for Malaysia’s economy. Protectionist policies and prolonged trade tensions could disrupt global supply chains, increase market uncertainty and challenge Malaysia’s economic growth.

Malaysia must be prepared with sustainable and adaptable strategies to tackle these challenges while capitalising on emerging opportunities to maintain economic resilience amid an increasingly complex global landscape.

The writer is a researcher and Islamic Finance consultant.
Comments: letters@thesundaily.com

SHAH ALAM: A total of 387 cases of child abuse were recorded by the Selangor police from January to October, said state police chief Datuk Hussein Omar Khan.

He said that of the total, 139 victims were aged between 0 and 1 year, 96 were between two and five years old, while the and remaining victims were aged up to 18 years.

“Childcare providers were the main perpetrators of these crimes, followed by biological parents, teachers and stepparents,“ he said.

He made these comments to the press after officiating the second Child Interview Centre (CIC) under the Sexual, Abuse and Child Investigation Division (D11) of the Criminal Investigation Department (CID) at the Selangor police headquarters in Seksyen 11 police station today.

Hussein said police investigations found that most child abuse cases were caused by negligence, such as leaving babies or young children alone, which posed risks to the victims and led to neglect.

He also noted that there had been a trend of increasing reports of child abuse cases, partly due to growing awareness of violence against children among the public and various organisations.

“Some people are now coming forward and bravely making reports, thanks to numerous awareness programmes and initiatives by the Royal Malaysia Police (PDRM) in the community to provide information,“ he said.

Regarding the second CIC, Hussein said that RM180,000 had been allocated to refurbish an existing premises at the Seksyen 11 Police Station for this purpose.

He said the establishment of the second CIC, which has been operational since March 5, was in response to the increasing number of child-related cases that require interviews each year, with an average of 400 to 500 cases annually.

“The establishment of this CIC takes into account the rising number of cases, with 875 children already interviewed this year alone, involving various cases such as abuse, neglect and sexual offences.

“Given current needs, we are also planning to expand this service. Both CIC facilities are currently located in Shah Alam, so there is a need to extend them to Kuala Selangor, Sabak Bernam, Hulu Selangor or the southern part of the state,“ he said.

Hussein also said that the first CIC, established in 2014 and located in Seksyen 7, serves the police districts (IPD) of South Klang, North Klang, Gombak, Shah Alam, Hulu Selangor, Kuala Selangor, Kajang and KLIA.

“The second CIC caters to the IPDs of Petaling Jaya, Subang Jaya, Sabak Bernam, Kuala Langat, Sungai Buloh, Sepang, Serdang and Ampang Jaya,“ he added, noting that the centre conducts interviews with children under the age of 16, as referred by investigating officers from the 16 IPDs.

KUALA LUMPUR: Integrated palm oil milling services provider Ecoscience International Bhd (EIB), via its wholly-owned subsidiary Ecoscience Manufacturing & Engineering Sdn Bhd, has accepted a letter of award (LoA) for a RM200 million engineering, procurement, and construction (EPC) contract from renewable energy company, Wilhelmina Energy Malaysia Sdn Bhd (WEMSB).

Under the LOA, EIB will provide comprehensive EPC services for the TG2 black pellet plant in Kuantan, Pahang.

These services will include design and engineering, sourcing and quality assurance of equipment and materials, plant infrastructure construction, and testing and start-up activities to support commissioning and ensure operational standards are met.

The specific terms and conditions of the EPC works will be outlined in a binding EPC agreement, which is expected by November 30, 2024.

The LoA was built upon the collaboration agreement (CA) with WEMSB in March 2024, aimed at transforming agricultural waste into sustainable energy, thereby reducing coal consumption and carbon emissions.

EIB managing director Wong Choi Ong expressed confidence in delivering a robust waste-to-energy solution that aligns with WEMSB’s vision for sustainable energy transformation.

“This project is a strategic fit for our expansion into environmental and energy efficiency sectors, building on our core strengths in constructing palm oil mills, supporting facilities, and equipment fabrication.

“As the largest project to be undertaken in our corporate history, we see this as a valuable opportunity to broaden our customer base, enhance our project portfolio, and strengthen our market position.

“The LoA will significantly boost our order book, providing our group with healthy earnings visibility over the next two years,“ he said.

The TG2 black pellet plant will convert oil palm empty fruit bunch (EFB) waste into TG2 black pellets – a drop-in coal replacement fuel.

TG2 black pellets are an advanced type of biofuel pellet, providing benefits over traditional biomass pellets, including enhanced grindability, water resistance, and higher energy density.

As a drop-in fuel, it is renewable and can be used in existing pulverised coal power plants without requiring significant infrastructure modifications.

EIB will continue supporting WEMSB as it expands TG2 black pellet plants across the region.

“Beyond the EPC scope for the TG2 black pellet plant, the CA signed in March 2024 also outlined the possibility of WEMSB outsourcing the plant’s operation and maintenance (O&M) to EIB.

“We are currently exploring this opportunity, and both parties will decide in due course.

“This potential arrangement, if materialise, would create a new, recurring revenue stream for us, complementing our current project-based work,“ Wong added.

WEMSB is a subsidiary of the Netherlands-based renewable energy company Maatschappij Wilhelmina NV, specialising in converting agricultural waste streams into sustainable energy using TG2 black pellets.

The EPC works are expected to commence by December 2024, with an expected project completion timeline of 24 months from the commencement date.

GOODYEAR is proud to be the official tyre sponsor of the Tokyo Auto Salon Kuala Lumpur 2024, happening from 8 – 10 November 2024 at MITEC, Kuala Lumpur. Known as the world’s premier customised car show, this event promises to showcase the latest in automotive technology, design, and more, drawing car enthusiasts from across the region.

Event Details

Date: 8 – 10 November 2024

Time: 10:00 am – 10:00 pm

Venue: MITEC, Kuala Lumpur

At the Goodyear booth, attendees can explore the latest in high-performance tyre technology and see how Goodyear is driving innovation in tyre performance and quality. This event offers automotive fans the perfect chance to engage with Goodyear and witness the exceptional standards that Goodyear tyres bring to every journey.

Don’t miss this exciting opportunity to connect with industry leaders and fellow car enthusiasts!

Earnings of Cosco Capital Inc., the listed retail holding firm of tycoon Lucio Co, increased by 10 percent in the nine months ending September on the back of strong operating performance across all its business segments.

Dell OS Recovery Tool is a freeware app that can be used to create a USB recovery drive to reinstall the Windows or Linux that came with your PC. Dell OS Recovery Tool is easy enough for anyone to use following the simple four steps in order. [License: Freeware | Requires: 11|10|8 | Size: 25 MB ]

Money expert Clark Howard extolls the virtues of 529 plans every chance he gets. You put money into those plans and invest it, where it grows tax-free. And then you get to spend it tax-free to pay for your child’s education. The law just got even more friendly toward parents choosing to make use of […]

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Travel expert Clark Howard holds deep convictions when it comes to travel. Shop the deal first and then figure out what you want to do at the destination. Be flexible on travel dates. Take early flights if you can. Never check a bag. You also won’t find Clark with designer luggage that costs hundreds of […]

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