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Die edge quality with its corresponding die strength are two important factors for excellent dicing quality especially for low-k wafers due to their weak mechanical properties and fragile structures. It is shown in past literatures that laser dicing or grooving does yield good dicing quality with the elimination of die mechanical properties. This is due to the excess heat energy that the die absorbs throughout the procedure. Within the internal structure, the mechanical properties of low-k wafers can be further enhanced by modification of the material. The purpose of this paper is to strengthen the mechanical properties of wafers through the heat-treatment process.

The methodology of this approach is by heat treating several low-k wafers that are scribed with different laser energy densities with different laser micromachining parameters, i.e. laser power, frequency, feed speed, defocus reading and single/multibeam setup. An Nd:YAG ultraviolet laser diode that is operating at 355 nm wavelength was used in this study. The die responses from each wafer are thoroughly visually inspected to identify any topside chipping and peeling. The laser grooving profile shape and deepest depth are analysed using a laser profiler, while the sidewalls are characterized by scanning electron microscopy (SEM) to detect cracks and voids. The mechanical strength of each wafer types then undergoes three-point bending test, and the performance data is analyzed using Weibull plot.

The result from the experiment shows that the standard wafers are most susceptible to physical defects as compared to the heat-treated wafers. There is improvement for heat-treated wafers in terms of die structural integrity and die strength performance, which revealed a 6% increase in single beam data group for wafers that is processed using high energy density laser output but remains the same for other laser grooving settings. Whereas for multibeam data group, all heat-treated wafer with different laser settings receives a slight increase at 4% in die strength.

Heat-treatment process can yield improved mechanical properties for laser grooved low-k wafers and thus provide better product reliability.

Companies allowing employees to self-report business expenses face the risk of expense fraud because some occasionally engage in dishonest behavior to receive reimbursements for their use. Drawing on the technology acceptance model, this study aims to investigate the effects of perceived usefulness, perceived ease of use and perceived security on the trust in e-reimbursement systems and the relationship with honest disclosure intention.

A self-administered questionnaire was distributed to 254 respondents, with the partial least squares structural equation modeling used to analyze the data.

The findings showed that perceived security and perceived usefulness explained trust in e-reimbursement systems, whereas perceived ease of use had no significant effect on it. Corporate governance and trust in e-reimbursement systems have a positive relationship with whistleblowing intention. At the same time, corporate governance mediates the relationship between trust in e-reimbursement systems and honest disclosure intention.

This study sheds light on using e-reimbursement systems within organizations to prevent fraudulent reimbursements and offers recommendations to management on enhancing employees’ intention to engage in honest disclosure behavior through e-reimbursement systems.

The research aims to provide a nuanced understanding of the complex social, psychological and organizational factors that serve as the foundation driving academics' knowledge-sharing behavior (KSB) within an academic enclave.

A cross-sectional research design using the partial least squares structural equation modeling (PLS-SEM) approach was employed to examine the determinants of personality traits among tertiary academics in Malaysia in relation to their KSB. To this end, a self-administered survey was distributed to a sample group of 526 respondents.

It is evident that conscientiousness and agreeableness are the personality traits that play a significant role in promoting KSB among academics in higher learning institutions (HLIs). These personality traits are positively linked with academics' willingness to transfer and receive knowledge. In contrast, the personality trait of openness to experience does not significantly influence KSB.

This study has employed a four-item measurement for evaluating the three distinct personality traits. Despite employing a brief measurement tool, the study has demonstrated significant reliability and validity, particularly in terms of convergent and discriminant validity.

The present study has revealed that conscientiousness in academics is intimately linked with their KSB, which is of paramount importance in the output-based education system. Notably, agreeableness among academics also conveys a positive effect on knowledge sharing (KS) in HLIs, as it cultivates trust and helpfulness among individuals and facilitates the exchange of valuable tacit knowledge.

This research explores the relationship between personality traits and KSB among Malaysian academics in HLIs. The study adopts the theories of planned behavior (TPB) and social capital theory (SCT) as theoretical ground, providing a nuanced understanding of the underlying motivations and mechanisms driving academics' knowledge-sharing behavior within the unique socio-cultural context of Southeast Asia.

This paper investigates the impact of strategic alliance network centrality and structural holes on firm innovation efficiency. In addition, the paper aims to explore the moderating effects of government R&D subsidies and firm technology standardization.

Based upon the literature on strategic alliance networks, this paper proposes a conceptual model with several hypotheses. The empirical analysis is based on a sample of 736 observations from 92 mechanical manufacturing firms in China from 2010 to 2017. We measured firm innovation efficiency via the DEA model and performed quantitative analysis with GMM estimation.

The results indicate that strategic alliance network centrality is positively related to firm innovation efficiency, and structural holes have a U-shaped relationship with firm innovation efficiency. Government R&D subsidies positively moderate the relationship between centrality and firm innovation efficiency. Firm technology standardization positively moderates the relationship between centrality and firm innovation efficiency and the U-shaped relationship between structural holes and firm innovation efficiency.

Firms should focus on improving innovation efficiency and maximizing innovation output under limited resources. Furthermore, managers ought to strengthen cooperation between firms and external alliances while promoting the utilization of strategic alliance network position resources to benefit innovation efficiency.

This paper considers that innovation efficiency, including input and output processes, is more representative than innovation performance, and few studies have focused on the relationship between strategic alliance networks and innovation efficiency. To fill this research gap, this paper explores the impact of strategic alliance network position embedding on innovation efficiency.

This study aims to integrate Delone and McLean’s information system success (DMISS) model with the innovation resistance model to evaluate the relationship between behavioural intention to use (BIU) and innovation resistance in the context of neo-banking. The primary objective of this study is to identify the drivers of neo-banking adoption and the barriers to its adoption and incorporate constructs such as e-trust (ETR) and personal innovativeness (PIV) to provide a more comprehensive understanding of the factors influencing neo-banking adoption.

A structured survey-based questionnaire was used to gather data from a diverse sample population in India. The Partial Least Squares Structural Equation Modeling (PLS-SEM) model was employed to further examine the adoption of neobanking services and users' intention to use neobanking services.

This study reveals a significant correlation between BIU and the uptake of neobanking services, demonstrating the value of consumers' readiness to embrace these offerings. However, resistance to usage has emerged as a major obstacle for consumers concerned about data security, technology reluctance and perceived risks associated with digital-only neobanks.

Analysing the driving and restraining factors will provide substantial information on the formation of consumers' decision-making processes in the Indian banking industry, which is undergoing rapid digital transformation. This information is of great importance to scholars, practitioners and policymakers, as it highlights the factors that may facilitate or impede the adoption of neobanking in India. The outcomes of this analysis will be of particular interest to researchers, experts and stakeholders in the field as they will provide valuable insights into the dynamics of consumer behaviour in the Indian banking sector.

This study represents an initial effort to examine BIUs and usage resistance within the rapidly developing neobanking sector in India. The findings of this study build on the existing research in this area and contribute to the ongoing discussion on the adoption of neo-banking.

This study aims to enhance the effectiveness of knowledge markets and overall knowledge management (KM) practices within organisations. By addressing the challenge of internal knowledge stickiness, it seeks to demonstrate how machine learning and AI approaches, specifically a text-based AI method for personality assessment and regression trees for behavioural analysis, can automate and personalise knowledge market incentivisation mechanisms.

The research employs a novel approach by integrating machine learning methodologies to overcome the limitations of traditional statistical methods. A natural language processing (NLP)-based AI tool is used to assess employees’ personalities, and regression tree analysis is applied to predict and categorise behavioural patterns in knowledge-sharing contexts. This approach is designed to capture the complex interplay between individual personality traits and environmental factors, which traditional methods often fail to adequately address.

Cognitive style was confirmed as a key predictor of knowledge-sharing, with extrinsic motivators outweighing intrinsic ones in market-based platforms. These findings underscore the significance of diverse combinations of environmental and individual factors in promoting knowledge sharing, offering key insights that can inform the automatic design of personalised interventions for community managers of such platforms.

This research stands out as it is the first to empirically explore the interaction between the individual and the environment in shaping actual knowledge-sharing behaviours, using advanced methodologies. The increased automation in the process extends the practical contribution of this study, enabling a more efficient, automated assessment process, and thus making critical theoretical and practical advancements in understanding and enhancing knowledge-sharing behaviours.

This paper provides a thorough examination of the advancements and impacts of artificial intelligence (AI) on construction management (CM) over the past five years, particularly focusing on its role in mitigating prevalent challenges such as inefficiency and ensuring quality. By methodically reviewing and synthesizing the body of research conducted in this period, it underscores key contributions and breakthroughs in the application of AI within construction management (AICM). Additionally, the study aims to shed light on emerging trends and forecast future directions for technological innovation in the construction management sector.

Guided by the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, this research conducts a bibliometric analysis of 176 relevant publications from the past five years. The analysis focuses on the adoption of AICM across three critical areas: construction equipment management, improvement of construction safety and construction cost optimization. Additionally, the study systematically identifies and examines 14 emerging themes within this domain, ensuring a comprehensive exploration aligned with PRISMA guidelines.

This manuscript summarizes recent research from the past five years in three key areas: construction equipment management, construction safety management and construction cost management within the realm of AICM. It identifies key gaps and outlines future research directions, including enhancing AI-driven equipment integration, developing sophisticated AI-based safety systems and optimizing cost management with advanced data analytics. These findings and directions are essential for steering the field toward greater digital innovation and sustainability.

This research provides a detailed analysis of the literature within the AICM domain, thoughtfully compiling significant findings and highlighting the importance of addressing user needs. The insights and recommendations shared aim to be beneficial for both academic researchers and industry professionals, contributing to the ongoing development of AICM as it moves toward a future characterized by digital innovation and sustainability.

This systematic review aims to identify the key determinants of knowledge transfer (KT) activities among researchers.

This study systematically reviewed KT literature in academic settings from 1995–2023. The authors searched Web of Science and Scopus using predefined keywords, following PRISMA guidelines for screening and eligibility assessment. From 158 selected articles, the authors extracted data and conducted a descriptive analysis to map KT activities’ evolution. A narrative synthesis approach categorized determinants of researchers’ KT activities.

The systematic review findings revealed a general conceptual framework that categorizes the identified determinants of KT into four categories. At the individual level, the factors are related to the sociodemographic characteristics of the researcher (e.g. gender, age, experience), their psychological aspects (e.g. attitude, intrinsic motivation, intention) and personal characteristics (e.g. self-efficacy, communication skills). At the research team level, leadership style and team dynamics. At the organizational level, the findings emphasize university characteristics (e.g. size, structure and ranking), KT culture installed and university resources. At the inter-organizational level, the key determinants were funding sources, network strength and trust.

The studies included in our database were different in terms of contexts, country of the study, the disciplines of KT and the types of KT activities examined. This variety restricts the direct comparison of research findings thus the generalizability of our conclusions. Future research should focus on specific contexts, disciplines, countries or types of KT activities to provide generalizable findings.

A better understanding of all the factors influencing KT among university researchers is essential for several reasons. First, it will enable the government to develop effective policies to promote KT ecosystems. Second, universities can create strategies, policies and programs to support researchers’ engagement in KT activities. Finally, researchers can be more strategic in their KT efforts.

This systematic review contributes to the literature by providing a comprehensive conceptual framework that identifies KT determinants at different levels and fills a gap in the existing literature that only addresses specific aspects of KT determinants. This framework can be a theoretical reference for future empirical studies. Furthermore, it practically provides recommendations for different actors including, government, universities and researchers.