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Drawing on the Job Demands-Resources (JD-R) theory, the primary goal of this study is to conceptualise and empirically validate a theoretical framework that explains the process by which digital job resources influence the innovative work behaviour of technological professionals. Specifically, this study aims to examine the impact of digital job resources, especially digital training, and digital communication, on employee digital engagement. Furthermore, it investigates the influence of digital engagement on digital leadership and the effect of digital leadership on innovative work behaviour. Lastly, the study examines whether digital engagement and digital leadership serially mediate the relationship between digital job resources and innovative work behaviour.

Data were collected from full-time technological professionals using multiple sampling techniques. A total of 307 samples were utilised for the final data analysis. Partial Least Squares Structural Equation Modelling (PLS-SEM), employing SmartPLS 4.0, was used to test the study hypotheses.

The findings of this study emphasize that digital engagement and digital leadership are pivotal in mediating the impact of digital communication on technological professionals' innovative work behaviour. Specifically, our results show that digital communication significantly shapes the digital engagement of these professionals. Digital engagement, in turn, positively influences digital leadership, which then fosters technological professionals’ innovative work behaviour. Notably, both digital engagement and digital leadership serve as mechanisms that link digital communication and innovative work behaviour. Contrary to our initial expectations, the study finds that digital training neither directly affects digital engagement nor has an indirect effect on innovative work behaviour.

The present study is distinct in offering a theoretical framework outlining the steps through which digital resources influence technological professionals' digital engagement, digital leadership capabilities, and their innovative work behaviour. Prior studies have predominantly focused on antecedents of innovative work behaviour, with an emphasis on individual characteristics and organisational environmental factors. There is limited research exploring how, or even if, digital job resources – such as digital training and digital communication – affect employees’ innovative work behaviour. Additionally, the examination of the interrelationship between digital engagement and digital leadership is notably lacking in existing literature. Much of the research has instead probed the converse relationship: how leadership styles impact employees' engagement. Lastly, this research is among the pioneering efforts to consider the serial mediating role of digital engagement and digital leadership between digital job resources and innovative work behaviour, a topic that remains underrepresented in academic discourse. This study addresses these gaps.

This study aims to examine the effects on student motivation and perception of technological interventions within undergraduate mechanical engineering and product design and manufacture programs at a Sino-foreign international university. The authors use an augmented reality game application within a class on Design for Manufacturing and Assembly (DfMA) that was developed using the approaches of microlearning and digital game-based learning (DGBL).

Structured as design-based research, the study reports on developing innovative educational interventions and provides an empirical investigation of their effectiveness. Data were collected using a mixed methods approach, using pre- and post-tests and questionnaires, together with researcher observations and participant interviews.

Through two rounds of playtests, the game positively affected intrinsic motivation and encouraged higher-order cognitive learning, critical thinking, communication and collaboration. Collaborative learning plays a significant role, DGBL is preferred over traditional methods and microlearning reduces information density and cognitive overload.

The study contributes to our understanding of digital game-based interventions on students’ intrinsic motivation and provides insights into effective ways to design instructional materials in similar teaching and learning settings.

This research introduces an innovation strategy aimed at bolstering the reliability of a renewable energy resource, which is hybrid energy systems, through the application of a metaheuristic algorithm. The growing need for sustainable energy solutions underscores the importance of integrating various energy sources effectively. Concentrating on the intermittent characteristics of renewable sources, this study seeks to create a highly reliable hybrid energy system by combining photovoltaic (PV) and wind power.

To obtain efficient renewable energy resources, system designers aim to enhance the system’s reliability. Generally, for this purpose, the reliability redundancy allocation problem (RRAP) method is utilized. The authors have also introduced a new methodology, named Reliability Redundancy Allocation Problem with Component Mixing (RRAP-CM), for optimizing systems’ reliability. This method incorporates heterogeneous components to create a nonlinear mixed-integer mathematical model, classified as NP-hard problems. We employ specially crafted metaheuristic algorithms as optimization strategies to address these challenges and boost the overall system performance.

The study introduces six newly designed metaheuristic algorithms. Solve the optimization problem. When comparing results between the traditional RRAP method and the innovative RRAP-CM method, enhanced reliability is achieved through the blending of diverse components. The use of metaheuristic algorithms proves advantageous in identifying optimal configurations, ensuring resource efficiency and maximizing energy output in a hybrid energy system.

The study’s findings have significant social implications because they contribute to the renewable energy field. The proposed methodologies offer a flexible and reliable mechanism for enhancing the efficiency of hybrid energy systems. By addressing the intermittent nature of renewable sources, this research promotes the design of highly reliable sustainable energy solutions, potentially influencing global efforts towards a more environmentally friendly and reliable energy landscape.

The research provides practical insights by delivering a comprehensive analysis of a hybrid energy system incorporating both PV and wind components. Also, the use of metaheuristic algorithms aids in identifying optimal configurations, promoting resource efficiency and maximizing reliability. These practical insights contribute to advancing sustainable energy solutions and designing efficient, reliable hybrid energy systems.

This work is original as it combines the RRAP-CM methodology with six new robust metaheuristics, involving the integration of diverse components to enhance system reliability. The formulation of a nonlinear mixed-integer mathematical model adds complexity, categorizing it as an NP-hard problem. We have developed six new metaheuristic algorithms. Designed specifically for optimization in hybrid energy systems, this further highlights the uniqueness of this approach to research.

The purpose of this study is to investigate the benefits and challenges of implementing circular economy (CE), as well as shed light on the influence of procurement strategy in CE implementation in construction projects.

A qualitative research approach with abductive reasoning was adopted. The empirical data were collected from the construction industry in the United Arab Emirates (UAE).

The results reveal that clients, consultants and contractors have limited awareness, knowledge and motivation to implement CE in construction projects. The absence of incentives to design projects following CE principles, lack of involvement of contractors and suppliers, non-use of materials that use CE principles and current procurement strategies are the main challenges for the implementation of CE in the UAE.

Previous research offers limited knowledge on CE and its implementation in construction projects particularly from a procurement strategy perspective. The findings of the study provide new knowledge of the benefits, challenges and role of procurement strategy for implementing CE. It is suggested that collaborative and partnering-based procurement methods are needed to facilitate the effective implementation of CE.

The purpose of this paper is to examine the impact of affiliate marketing strategies as a tool for increasing customers' engagement and vulnerability over financial services. This is attempted by examining the connection between affiliate marketing factors and customers' brand engagement and vulnerability metrics.

The authors developed a three-staged methodological context, based on the 7 most known centralized payment network (CPN) firms' website analytical data, which begins with linear regression analysis, followed by hybrid modeling (agent-based and dynamic models), so as to simulate brand engagement and vulnerability factors' variation in a 180-day period. The deployed context ends by applying the cognitive modeling method of producing heatmaps and facial analysis of CPN websites to the selected 47 vulnerable website customers, for gathering more insights into their brand engagement.

Throughout the simulation results of the study, it becomes clear that a higher number of backlinks and referral domains tend to increase CPN firms' brand-engaged and vulnerable customers.

From the simulation modeling process, the implication for backlinks and referral domains as factors that enhance website customers' brand engagement and vulnerability has been highlighted. A higher number of brand-engaged website customers could mean that vulnerable categories of customers would be impacted by CPNs' affiliate marketing. Improving those customers' knowledge of the financial services utility is of utmost importance.

The outcomes of the research indicate that online banking service providers can increase their customers' engagement with their brands by adopting affiliate marketing techniques. To avoid the increase in customers' vulnerability, marketers should aim to apply affiliate marketing strategies to domains relevant to the provided financial services.

The paper's outcomes provide a new approach to the literature, where the website customer's brand engagement comes out as a valuable metric for estimating online banking sector customers' vulnerability.

In this paper we ask: “What are the opportunities and challenges that creative methods pose in terms of conducting research processes with indigenous peoples impacted by emergencies and disasters?” To do that, we critically examine the creative and collaborative methodological approaches applied in the research project, Moving with Risk: Forced Displacement and Vulnerability in Colombia. This project sought to understand the trajectories of risk of families who were forcibly displaced as a result of the armed conflict in Colombia and resettled in areas at risk of disaster.

This article is intentionally written from the perspective of the researchers’ positionality. In doing so, we embrace writing that is situated and embodied in the researcher’s experiences and positionalities. This reflexive writing allows us to question the methodological experience of the research project we are analysing and, at the same time, ourselves, “the researchers” be questioned by it.

In this paper, we show how creative methods and participatory research can foster awareness and become the basis for inclusive and reciprocal research processes with indigenous communities in disaster studies. Specifically, we show that the use of creative methods helped us recognise that agency needs to be framed in collective spaces with the indigenous women we were working with and in relation to their livelihoods needs. We argue that finding spaces to conduct collaborative research and recognize agency is inextricably related to how the researchers reflect on their positionality.

This article contributes to critical perspectives in Disaster Studies and to an overall understanding of the role that creative methodologies play in research processes with people affected by disasters. It provides a novel perspective on the opportunities and challenges of applying arts-based methods in disaster risk studies with indigenous communities in Latin America.

This study aims to investigate the relationship between personality traits and innovative behaviour, using a mixed-methods approach to provide deeper insights into these dynamics.

The authors used a mixed-methods approach, integrating fuzzy set qualitative comparative analysis (fsQCA) with traditional multiple linear regression analysis. This study was conducted among 76 university graduates, using the Big Five personality model and the Innovator DNA model to assess innovative behaviour.

The findings reveal significant positive correlations between conscientiousness, extraversion and innovative behaviour. The inclusion of fsQCA allowed for a more nuanced understanding of the complex interactions between personality traits and innovative behaviour, highlighting configurations of traits that traditional methods may overlook.

This study's sample size and focus on university graduates may limit the generalisability of the findings. Future research should explore these relationships in more diverse populations and settings to enhance generalisability.

The insights gained from this study can inform the development of more effective talent management strategies, helping organisations to better align personality traits with roles that demand high innovation. This approach can optimise team composition and improve innovative output.

Understanding the configurations of personality traits that lead to innovative behaviour can help educational institutions and organisations foster environments that support diverse and innovative thinking, ultimately contributing to societal progress.

This research contributes to the literature by demonstrating the efficacy of fsQCA in capturing the complexities of human behaviour, particularly in the context of personality traits influencing innovation. By combining qualitative and quantitative analyses, this study provides a comprehensive perspective that enhances both methodological rigour and the depth of understanding in psychological and innovation studies.

This study aims to introduce a novel machine learning feature vector (MLFV) method to bring machine learning to overcome the time-consuming computational fluid dynamics (CFD) simulations for rapidly predicting turbulent flow characteristics with acceptable accuracy.

In this method, CFD snapshots are encoded in a tensor as the input training data. Then, the MLFV learns the relationship between data with a rod filter, which is named feature vector, to learn features by defining functions on it. To demonstrate the accuracy of the MLFV, this method is used to predict the velocity, temperature and turbulent kinetic energy fields of turbulent flow passing over an innovative nature-inspired Dolphin turbulator based on only ten CFD data.

The results indicate that MLFV and CFD contours alongside scatter plots have a good agreement between predicted and solved data with R² ≃ 1. Also, the error percentage contours and histograms reveal the high precisions of predictions with MAPE = 7.90E-02, 1.45E-02, 7.32E-02 and NRMSE = 1.30E-04, 1.61E-03, 4.54E-05 for prediction velocity, temperature, turbulent kinetic energy fields at Re = 20,000, respectively.

The method can have state-of-the-art applications in a wide range of CFD simulations with the ability to train based on small data, which is practical and logical regarding the number of required tests.

The paper introduces a novel, innovative and super-fast method named MLFV to address the time-consuming challenges associated with the traditional CFD approach to predict the physics of turbulent heat and fluid flow in real time with the superiority of training based on small data with acceptable accuracy.

The present research aimed to examine the role of FDP in enhancing the teachers' competencies through modern technological tools and innovative teaching methods. And also, to evaluate the impact of FDPs on teachers' attitudes and motivation to integrate modern teaching innovations into their classroom practices.

Using a pre-post study design, the study was conducted in two phases, Phase I was conducted one week before conducting FDP (14th to 20th August 2023) and Phase II was conducted after one week of FDP (15th to 21st September 2023). The data were gathered through structured questionnaires from the participants in FDP and collected data were processed and analyzed through a statistical package for social sciences.

The study found that undergoing FDP by teachers significantly influences the adoption of advanced pedagogical approaches and technology integration during their teaching practice. This indirectly helps in promoting quality education at large and contributes to the UNSDG-4. The study also revealed that the effective FDP not only helps in advancing teaching methods but also influences instructors' philosophies and attitudes towards students' overall learning and development.

These findings have several key implications for higher education institutions, policymakers and teachers at large. Firstly, HEIs should prioritize undertaking FDPs that promote in adopting advanced pedagogical approaches. This indirectly equips the instructors with the knowledge and skills necessary to integrate modern teaching methods into their real classroom practices. Further, by adopting FDP as a strategic initiative to improve the quality of education and work by aligning these programs with broader SDG goals.

Advancing education for enhancing the overall quality of learning depends on the competencies of instructors. The competencies of instructors rely upon the involvement of instructors in life-long learning, level of adoption of technology, innovative approaches used, etc. This study may help higher education institutions adopt FDP as a strategic initiative to improve the quality of education and work by aligning these programs with broader SDG goals.

The construction industry is a major contributor to global carbon emissions. This study investigates the role of procurement and contracting methods in carbon emission reduction (CER) in the construction industry. It also examines artificial intelligence’s (AI’s) potential to drive low-carbon practices, aiming to identify transformative policies and practices.

This study employed a qualitative methodology, engaging in semi-structured interviews with nine industry professionals alongside an innovative engagement with Generative Pre-trained Transformer (GPT) technology to gather insights into procurement and project delivery methods (PDM) role in CER. The study involved identifying patterns, organizing themes, and analyzing data to extract meaningful insights on effective policies and strategies for CER in the construction industry.

The results underscore the importance of early contractor involvement and integrated PDM for CER in construction. Results emphasize the pivotal role of project owners in directing projects toward sustainability, highlighting the need for client demand. The research identifies cost constraints, limited material availability, and human resource capacity as key barriers in the US. The study proposes innovative materials, financial incentives, education, and regulatory standards as effective interventions. It also explores the future use of AI in enhancing CER, suggesting new avenues for technological integration.

The study provides empirical insights into the role of procurement and PDM in CER within the US construction industry by using qualitative approach and use of a GPT. It underscores the interplay between contracting methods, stakeholder engagement, and AI’s emerging role, for enhancing policies and practices to decarbonize the US construction industry.