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In the digital era, the banking sector has transformed into a powerful intermediary, effectively connecting surplus and deficit units. This dynamic landscape empowers savers to secure their finances and generate returns, while simultaneously enabling businesses and individuals to access capital for investment and promoting economic growth. This study explores the relationships among banking development dimensions – represented by primary assets and liabilities, bank capital (core capital and required reserves) and economic growth as measured by components of gross domestic product (GDP).

The study consolidated monthly balance sheets from digital banks over a 20-year period, resulting in an aggregate monthly balance sheet that reflects the financial position of all digital banks in the Palestinian economy. The research employs both maximum likelihood and Bayesian structural equation modeling to measure the causal pathways of the consolidated balance sheet with the individual components of GDP.

The results revealed that bank main assets (investments and loans) and liabilities (deposits) collectively explain for 97% of bank capital. Investments and loans demonstrate significant negative correlations with bank capital, while deposits exhibit a positive impact. This leads to a fundamental conclusion that a substantial proportion of retained earnings within the banking sector is reinvested, fueling expansion and growth. Additionally, the results showed a significant relationship between bank capital and various GDP components, including private consumption, gross investment and net exports (p = 0.000). However, while the relationship between bank capital and government spending was insignificant in the maximum likelihood estimation, Bayesian estimation revealed a slight yet positive impact of bank capital on government spending.

This research stands out due to its unique exploration of the intricate relationship between bank sector development dimensions, primary assets and liabilities and their impact on bank capital in the digital era. It offers fresh insights by dividing this connection into specific dimensions and constructs, utilizing a comprehensive two-decade dataset covering the digital banks records.

The purpose of this study is to execute the application of confirmatory factor analysis in structural equation modeling, to investigate the reliability and validity of the proposed integrated digital library user success (IDLUS) scale in academic digital library computing information system in the area of information management and systems.

The study analyzed 355 valid responses (MPhil and PhD) from the largest and oldest public sector university in Pakistan. Sample selection was calculated through a stratified random sampling technique from the four faculties of the University of the Punjab. The instrument was constructed based on the available two digital library and information system success models. The first model is Jung’s (1997) digital library user success model that is further composed of flow model (1977), end user computing satisfaction model (1987) and Joshi’s overall user satisfaction model (1990). Similarly, the second model is DeLone and McLean’s reformulated information system success (2003) theory. The question items used a five-point Likert scale and executed regression weights, standardized regression weights, convergent validity, variance extracted, construct reliability and discriminant validity to infer results.

Findings show that the IDLUS scale has excellent validity and reliability estimates.

The study has theoretical implications for researchers and practical implications for information system developers.

To the best of the authors’ knowledge, this scale is the first-ever effort to measure the digital library user success in the context of Higher Education Commission-National Digital Library of Pakistan. Therefore, there was a dire need to conduct the psychometric analysis of the scale to examine the model fit statistics on the current sample in the particular cultural norms. Scale is based upon human factors taken from various Web success and information system success models from the fields of human–computer interaction information systems and computer-mediated communication.

This present study sheds light on how these issues affect police officers’ personal and professional lives. This research looks at how family support (FS) and workplace pressures affect police effectiveness.

The analysis of the measurement and structural model was conducted using AMOS version 26. To ensure the accuracy of the results, a two-stage analysis methodology (Anderson and Gerbing, 1988) was used. The first stage involved testing the measurement model using various validity indicators such as confirmatory factor analysis, comparative fit index, goodness of fit index and Tucker–Lewis index to evaluate the incremental compatibility of the model. Root-mean-square approximation error and CMIN/DF were also used to further assess the model’s validity. In the second stage of the analysis, the structural model was estimated using structural equation modeling technique (Hair et al., 1998). The data obtained from the measurement model was then used to determine the fit indices. Because the values were consistent with the criterion values, the model was deemed to be a good fit.

The study conducted sheds light on the crucial role that work stressors (WS) and FS play in determining job satisfaction (JS) among nongazetted officers (NGOs) and other ranks (ORs) police officials in the state of Punjab, India. The findings suggest that there exists a complex interdependence between WS and FS, and their correlation with regards to work, family and children’s evaluations. The first hypothesis (H1, Table 4) was found to be true (0.440, p = 0.001), indicating that more than 44 % of the job performance of police officials in Punjab is affected by stressors related to work. The study also identified eight WS that have a significant impact on the performance of police officials on the job. On the other hand, FS was found to have no influence on the job performance of police officials in Punjab, and hence, the second hypothesis was rejected. Further analysis showed that FS was more prevalent among NGOs as compared to ORs police personnel, but it did not directly influence their job performance. The benefits of FS may be moderated by the nature of the job; hence, FS may be more critical in stressful jobs such as police work than in less stressful jobs. The study emphasizes the importance of balancing work and family demands and highlights the need for family-friendly policies to achieve this balance. Future research should focus on the types of policies that should be offered to lessen stress and conflict and examine the benefits of merely offering programs versus requiring or encouraging their usage. The findings of this study could be useful for policymakers and organizations in designing policies that promote employee well-being and JS while balancing work and family demands.

As with any research endeavor, it is essential to interpret the findings of this study while considering its limitations. First, the study relied on a convenience sample drawn solely from one nation, namely, India, which may restrict the generalizability of the results to other countries or cultural contexts. Furthermore, it is important to note that this study exclusively explored the causal relationship between monetary compensation, intrinsic motivation and employee performance, without delving into the nuances of various forms or qualities of FS (e.g. emotional support, instrumental support, perceived vs actual support) and their impact on job performance among police officers. Additionally, the research did not investigate whether the influence of FS on job performance varies based on the specific WS encountered by police officers. These limitations highlight potential avenues for future research to explore in greater depth.

To enhance the well-being and JS of police officers and their families, a comprehensive set of tailored interventions and support programs can be implemented. Recognizing the unique stressors faced by officers and introducing critical incident debriefing sessions and counseling services to provide a confidential space for emotional processing. Offering flexible scheduling, exploring remote work options and developing workshops and resource programs to address the needs of officers’ families, including stress management and communication skills. Strengthening parental leave policies, incorporating extended paid leave and clear communication, to alleviate stress during significant family events. Establishing peer support networks within police departments to provide officers with a valuable avenue for sharing experiences and coping strategies. Collectively, these interventions aim to create a supportive and family-friendly environment within the police force, ultimately fostering improved work-family balance and enhanced well-being for police personnel. To enhance the well-being and JS of police officers and their families, a comprehensive set of tailored interventions and support programs can be implemented. Recognizing the unique stressors faced by officers, critical incident debriefing sessions and counseling services should be introduced to provide a confidential space for emotional processing. Given the demanding nature of police work, exploring flexible schedules and remote work options can assist officers in achieving a better work-life balance, particularly when dealing with family-related challenges. Workshops and resource programs specifically addressing the needs of officers’ families, including stress management and communication skills, can be developed to strengthen family connections. Strengthening parental leave policies, incorporating extended paid leave and clear communication, can alleviate stress during significant family events. Establishing peer support networks within police departments provides officers with a valuable avenue for sharing experiences and coping strategies. Regular mental health check-ins and screenings, as well as financial education workshops, acknowledge and address the unique challenges faced by officers and their families. Community engagement and recognition initiatives, along with specialized training on work-family balance, can foster positive morale. Finally, the establishment of a crisis intervention and FS hotline serves as a vital lifeline during critical situations, ensuring immediate assistance and resources for officers and their families in times of need. Collectively, these interventions aim to create a supportive and family-friendly environment within the police force.

The primary objective of this study is to assess the impact of occupational demands and familial support on the overall happiness levels of police officers stationed in Punjab, India. The research underscores the crucial importance of implementing family-friendly policies aimed at achieving a harmonious equilibrium between professional responsibilities and family life commitments. The findings unveil a multifaceted interconnection between occupational stress, familial support systems and individual assessments concerning career fulfillment, domestic life and parental responsibilities. Moreover, the study sheds light on various family-friendly initiatives, such as empowerment strategies and recognition programs, that have the potential to augment JS among police personnel. Furthermore, it suggests that future investigations delve deeper into the efficacy of implementing voluntary programs rather than mandating or promoting their usage in mitigating stress and resolving familial conflicts. The study establishes a clear correlation between JS, overall well-being and the interplay between occupational demands and familial encouragement. Policymakers and corporate entities are urged to take cognizance of these factors while formulating strategies aimed at enhancing the health and contentment levels of employees in the workplace. Additionally, the study underscores the significance of cultural factors, emphasizing the need to consider them in future research endeavors and policy formulations. Cultural factors such as societal norms, values and expectations can influence the way that police personnel perceive and use FS, as well as the impact that FS has on their job performance. By conducting cross-cultural studies, researchers can gain insights into how the relationship between FS and job performance may vary across different cultural contexts. This can help to identify universal factors as well as culturally specific influences on the interplay between FS and job performance among police personnel. Furthermore, investigating the impact of FS on job performance in diverse cultural contexts can contribute to the development of culturally sensitive support programs and policies for police officers. Understanding how cultural factors shape the experiences of police personnel and their families can inform the design of interventions that are tailored to the specific needs and challenges present in different cultural settings. In summary, expanding the research scope to include diverse cultural contexts can enhance the generalizability of the findings and contribute to the development of culturally sensitive support programs and policies for police officers.

The content of this paper is entirely original and has not been derived from any other published or unpublished documents. It has been created solely for the purpose of providing new and unique information for the readers.

Given that the current sustainability assessment in higher education institutions primarily relies on qualitative methods with relatively limited quantitative tools, the purpose of this study is to design a tool that could be used to comprehensively assess the overall state of higher education institutions’ sustainability.

The authors based the “Model to Assess the Sustainability of Higher Education Institutions” on the Triple Bottom Line (TBL) framework of economic, environmental and social factors, and established its primary dimensions as educational level, research capacity, community outreach, campus operations, campus experience and assessment reports. They designed the College Organisational Sustainability Scale (CO-SS) based on this research model, drawing their inspiration from the qualitative research tool, the Sustainability Assessment Questionnaire, and taking the following validation steps: expert review (n = 10), pilot testing (n = 150) and formal experiments (n = 1108). These steps were taken to optimise the scale items, test the model’s validity and assess its reliability.

After undergoing rigorous scientific validation, CO-SS was unequivocally confirmed as an effective and reliable tool, demonstrating its accurate reflection of the level of sustainability in higher education institutions.

The authors took an industry-specific approach by relying on the TBL and the Sustainability Assessment Questionnaire to construct and validate the CO-SS. Furthermore, the CO-SS has the potential to evolve into a self-assessment tool for higher education institutions, and a reliable foundation for data-driven decision-making in the realm of organisational sustainability at universities.

The occupational health risk associated with the production of prefabricated concrete components is often overlooked. This paper will use a damage assessment and cyclic mitigation (DACM) model to provide individualized exposure risk assessment and corresponding mitigation management measures for workers who are being exposed.

The DACM model is proposed based on the concept of life cycle assessment (LCA). The model uses Monte-Carlo simulation for uncertainty risk assessment, followed by quantitative damage assessment using disability-adjusted life year (DALY). Lastly, sensitivity analysis is used to identify the parameters with the greatest impact on health risks.

The results show that the dust concentration is centered around the mean, and the fitting results are close to normal distribution, so the mean value can be used to carry out the calculation of risk. However, calculations using the DACM model revealed that there are still some work areas at risk. DALY damage is most severe in concrete production area. Meanwhile, the inhalation rate (IR), exposure duration (ED), exposure frequency (EF) and average exposure time (AT) showed greater impacts based on the sensitivity analysis.

Based on the comparison, the DACM model can determine that the potential occupational health risk of prefabricated concrete component (PC) factory and the risk is less than that of on-site construction. It synthesizes field research and simulation to form the entire assessment process into a case-base system with the depth of the cycle, which allows the model to be continuously adjusted to reduce the occupational health damage caused by production pollution exposure.

Our proposed machine learning model contributes to improving the quality of Hire by providing a more nuanced and comprehensive analysis of candidate attributes. Instead of focusing solely on obvious factors, such as qualifications and experience, our model also considers various dimensions of fit, including person-job fit and person-organization fit. By integrating these dimensions of fit into the model, we can better predict a candidate’s potential contribution to the organization, hence enhancing the Quality of Hire.

Within the scope of the investigation, the competencies of the personnel working in the IT department of one in the largest state banks of the country were used. The entire data collection includes information on 1,850 individual employees as well as 13 different characteristics. For analysis, Python’s “keras” and “seaborn” modules were used. The Gower coefficient was used to determine the distance between different records.

The K-NN method resulted in the formation of five clusters, represented as a scatter plot. The axis illustrates the cohesion that exists between things (employees) that are similar to one another and the separateness that exists between things that have their own individual identities. This shows that the clustering process is effective in improving both the degree of similarity within each cluster and the degree of dissimilarity between clusters.

Employee competencies were evaluated within the scope of the investigation. Additionally, other criteria requested from the employee were not included in the application.

This study will be beneficial for academics, professionals, and researchers in their attempts to overcome the ongoing obstacles and challenges related to the securing the proper talent for an organization. In addition to creating a mechanism to use big data in the form of structured and unstructured data from multiple sources and deriving insights using ML algorithms, it contributes to the debates on the quality of hire in an entire organization. This is done in addition to developing a mechanism for using big data in the form of structured and unstructured data from multiple sources.

This paper aims to identify key factors influencing the development of advanced manufacturing clusters and propose governance pathways for their digital innovation ecosystems.

A quantitative analysis of the Tai-Xin Integrated Economic Zone in China is conducted using data collected through a questionnaire survey. An evaluation index for the development level of advanced manufacturing clusters is constructed, and a structural equation model is used to identify key influencing factors and governance pathways.

This paper reveals that factors such as industrial foundation, technological innovation capability, social institution environment and government policies have a significant positive impact on the development of digital innovation ecosystem in advanced manufacturing clusters. It constructs a governance model for the digital innovation ecosystem and proposes three major pathways: integration of heterogeneous innovation resources, enhancement of digital capabilities, and fostering digital collaborative governance. The crucial role of digital technology in improving data processing efficiency, optimizing resource allocation and promoting collaboration among entities is emphasized. These pathways can optimize resource allocation, boosting the competitiveness and innovation capacity of clusters.

By incorporating advanced manufacturing clusters into the digital innovation ecosystem framework, this paper enriches theoretical research on both fronts. It offers specific governance pathways and policy recommendations, providing valuable references and guidance for promoting the digital transformation and ecosystem construction of manufacturing clusters.

This study aims to evaluate the usage of partial least squares structural equation modeling (PLS-SEM) in journals related to logistics and supply chain management (LSCM).

Based on a structured literature review approach, the authors reviewed 401 articles in the field of LSCM applying PLS-SEM published in 15 major journals between 2014 and 2022. The analysis focused on reasons for using PLS-SEM, measurement model and structural model evaluation criteria, advanced analysis techniques and reporting practices.

LSCM researchers sometimes did not clarify the reasons for using PLS-SEM, such as sample size, complex models and non-normal distributions. Additionally, most articles exhibit limited use of measurement models and structural model evaluation techniques, leading to inappropriate use of assessment criteria. Furthermore, progress in the practical implementation of advanced analysis techniques is slow, and there is a need for improved transparency in reporting analysis algorithms.

This study contributes to the field of LSCM by providing clear criteria and steps for using PLS-SEM, enriching the understanding and advancement of research methodologies in this field.

This study evaluated the impact of a faculty training program on student assessment using the Kirkpatrick model.

A self-reported survey assessed 111 Saudi and non-Saudi participants' satisfaction. Subjective and objective measures (self-reported measures, assessment literacy inventory and performance-based assessment tasks) gauged participants' learning level. Pre- and post-training data were collected from 2020 to 2022.

A highly significant effect on satisfaction (>80%) and learning levels was observed, as manifested by workplace practices of student assessment (>70%, the cut-off score). Pre- and post-training comparisons of participants' satisfaction and assessment literacy scores showed significant improvements following training. Multiple regression analyses showed no significant effects for gender and educational attainment but a substantial impact of academic cluster on participants' student assessment skills.

Long-term effects of training faculty on assessment practices and student achievement will be studied at the institutional level in future research.

The current study contributes to human capital investment via faculty training on student assessment, helping them comply with assessment best practices. This assures the quality, fairness and consistency of assessment processes across disciplines in higher education institutions, enhances assessment validity and trust in educational services and may support institutional accreditation.

This study provides opportunities for sharing best practices and helps establish a community of practice. It enhances learning outcomes achievement and empowers higher education graduates with attributes necessary to succeed in the labor market. The human capital investment may have a long-term impact on overall higher education quality.

This study contributes to the scarce literature investigating the impact of training faculty from different clusters on student assessment using subjective and objective measures. It provides developing and evaluating a long-term student assessment program following the Kirkpatrick model.

In the context of rapid technological progress, this study investigates the factors that improve the academic performance of Saudi Arabian university students when they use ChatGPT. Using the technology-to-performance chain theory as a framework, this study identifies the variables that may affect the students' academic performance, thereby contributing to the discourse on the use of technology in education.

A survey is conducted on 257 respondents, and an online questionnaire is used to collect the data. Analysis of Moment Structures (AMOS) is employed to analyse the structural model to determine the direct connections between the different elements.

Findings reveal that task characteristics, technology characteristics and individual characteristics can significantly impact task-technology fit. Furthermore, task-technology fit can influence the utilisation of ChatGPT and students' academic performance. In addition, utilisation can significantly impact students' academic performance. Students are likely to utilise ChatGPT efficiently and demonstrate improved academic performance when they believe that the technology is a good fit for their tasks.

This study’s shortcoming is its exclusive focus on a single public university in Saudi Arabia, which limits its generalisability. Comparative studies among multiple universities in Saudi Arabia and in other Gulf nations should be conducted to enhance the generalisability of the results. In addition, diversifying the participants by including students from various universities and exploring the moderating variables would deepen our understanding of the utilisation of ChatGPT by students.

The practical implications of this study include the existence of a positive relationship between task characteristics and task-technology fit, which can guide organisations in aligning ChatGPT with specific activities for enhanced efficacy and workflow integration. In addition, understanding the association between technology characteristics and task-technology fit can help in selecting suitable technologies that will encourage user adoption and improve academic outcomes. Furthermore, the recognition of the impact of individual characteristics on task-technology fit and their utilisation can inform tailored support and training programmes to enhance user acceptance and utilisation of ChatGPT, particularly in educational settings such as those in Saudi Arabia, which will ultimately improve students’ academic performance.

This study’s focus on ChatGPT and how it affects the academic performance of Saudi Arabian university students distinguishes it from previous studies. This study provides insightful information on technology adoption in educational settings and contributes to our understanding of the factors that can impact academic performance through ChatGPT adoption by utilising technology-to-performance chain theory. Moreover, this study’s examination of task characteristics, technology characteristics and individual characteristics can significantly enrich discussions on optimal technology integration for educational objectives. This contribution is relevant in dynamic contexts, such as the rapidly evolving technological environment of Saudi Arabia.