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Building information modeling (BIM) is recognized as one of the technologies to upgrade the informatization level of the architecture engineering and construction (AEC) industry. However, the level of BIM implementation in the construction phase lags behind other phases of the project. Assessing the level of BIM implementation in the construction phase from a system dynamics (SD) perspective can comprehensively understand the interrelationship of factors in the BIM implementation system, thereby developing effective strategies to enhance BIM implementation during the construction phase. This study aims to develop a model to investigate the level of BIM implementation in the construction phase.

An SD model which covered technical subsystem, organizational subsystem, economic subsystem and environmental subsystem was developed based on questionnaire survey data and literature review. Data from China were used for model validation and simulation.

The simulation results highlight that, in China, from 2021 to 2035, the ratio of BIM implementation in the construction phase will rise from 48.8% to 83.8%, BIM model quality will be improved from 27.6% to 77.2%. The values for variables “BIM platform”, “organizational structure of BIM” and “workflow of BIM” at 2035 will reach 65.6%, 72.9% and 72.8%, respectively. And the total benefits will reach 336.5 billion yuan in 2035. Furthermore, the findings reveal five factors to effectively promote the level of BIM implementation in the construction phase, including: policy support, number of BIM standards, owners demand for BIM, investment in BIM and strategic support for BIM.

This study provides beneficial insights to effectively enhance the implementation level of BIM in the construction phase. Meanwhile, the model developed in this study can be used to dynamically and quantitatively assess the changes in the level of BIM implementation caused by a measure.

This study aims to develop and implement an initial framework for assessing progress in lean implementation within an higher education institution (HEI). It includes developing preliminary findings regarding the impacts of lean implementation in the HEI case organization and comparing outcomes from this assessment to outcomes from other published sources.

Existing literature on lean, particularly in an HEI context, was used to develop a preliminary lean assessment framework for HEIs. Quality and continuous improvement literature were also compared to the proposed assessment framework to further validate the approach. This assessment framework was then utilized to evaluate lean implementation at a large public university (LPU) in the U.S.

The paper presents the framework as well as the major findings from the Large Public University (LPU)’s assessment. The assessment findings are further compared to other HEI quality measures and lean assessments done in other industries. Overall, the findings suggest that the assessment framework provides valuable insight to HEI organizations implementing lean.

The research intends to support lean assessment standardization efforts by proposing a preliminary lean assessment framework for the HEI, grounded in research trends, research findings, identified gaps in the research, and case study outcomes. To the research team's knowledge, this is the first lean assessment framework proposed for HEIs and also contributes to research gaps related to service industry frameworks and those containing both practices and outcomes. The framework can be used by other researchers as a foundation for additional conceptual and empirical developments on the topic and by researchers and practitioners seeking to understand and assess lean implementation progress in the HEI.

Quality Assurance and Improvement Program (QAIP) is defined as “an ongoing and periodic assessment of the entire spectrum of audit and consulting work performed by the internal audit (IA) activity”. QAIP is an important component of internal auditors’ commitment to improve internal audit (IA) quality. The pressure towards improvement is urgent for central banks, in light of the vulnerabilities of their IA functions identified by the International Monetary Fund. The authors analyse the professional standards and the literature on IA and QAIP, aiming to propose general considerations to enhance IA quality and to develop and maintain a QAIP, with reference to central banks, also shedding light on the synergies among IA, QAIP and total quality management (TQM).

This paper reviews the most relevant professional standards in light of the professional and academic literature regarding IA quality, QAIP and their relationship with TQM. The analysis of these sources represents an important step to identify general measures to improve IA quality and develop effective QAIP in central banks.

This analysis shows that it is important to understand the rationale behind the development of an IA function and its theoretical and practical foundation, especially for complex organisations such as central banks. In addition, the authors show that QAIP represents an important tool to exploit the synergies between TQM and IA. These synergies could result in higher levels of quality for the IA function and more effective implementation of TQM within the whole organisation. Lastly, the authors provide practical suggestions to support the implementation of an effective QAIP in central banks and to spread TQM philosophy within the organisation.

The authors contribute to the scant literature on IA quality and QAIP by focusing on central banks and shedding light on the relationship with TQM. Regardless of their importance, these topics have been largely neglected by the extant literature.

The main purpose of this research is to (1) prioritise key determinants of Industry 4.0 (I4.0) readiness assessment and (2) evaluate causal relationships among those determinants and associated sub-criteria based on inputs from industry experts.

The methodology involved two phases: (1) an MCDM approach for determining causal relationships among determinants and (2) empirical validation of findings from the first phase using industry experts' inputs.

It was found that while the choice of I4.0 technologies is important, organisational factors are also critical, as evidenced by the ranking of the “Strategy and Organisation” determinant as the highest rank prominent determinant. Also, the ranking of the sub-criteria within each determinant shows the importance of several organisational influencing and resulting sub-criteria.

This research extends the existing literature on I4.0 by demonstrating the prioritisation of determinants and delineating causal relationships among them and associated sub-criteria as a basis for developing I4.0 adoption guidelines. This research is limited to the specific scope of determinants selected/considered and experts' inputs from the Sri Lankan manufacturing sector. Future studies could consider extending this research into a broader global manufacturing context.

Prioritisation and causal relationships of I4.0 readiness assessment determinants, supported with inputs from functional managers and industry experts, could be used to guide practitioners in developing guidelines for I4.0 adoption in a phased manner.

This research provides a re-evaluation and validation of a selected I4.0 readiness assessment framework from the perspectives of interdependencies and casual relationships among its determinants and sub-criteria, based on inputs from industry experts as a basis for developing guidelines for I4.0 adoption.

This study employs survey methods to statistically examine the internationalization of quality assurance (IQA) in Taiwanese higher education. The data collected were analyzed to assess the associations between administrators' opinions of the importance of IQA and their evaluations of its implementation, as well as the relationship between implementation and opinions on seven measures of international quality. The study also explores the mediating effect of implementation assessments on the relationship between opinions of the importance of IQA and opinions of international quality.

This study targeted higher education administrators from universities in Taiwan, including presidents, vice presidents, deans, section chiefs, directors, and heads of schools in various departments. Using systematic sampling methods, 80 universities were selected from a population of 159 higher education institutions in Taiwan, with 17-40 potential participants each in 2015. A total of 2,377 questionnaires were distributed to all the administrators of those institutions, and ultimately, 65 institutions and 337 valid questionnaires were analyzed.

The importance of IQA directly and positively influenced implementation of it on higher education institutions. The implementation directly and positively influenced the level of international quality of the institutions and the importance of IQA had an indirect positive influence on international quality through implementation. The aggregated institution-level results were similar to but much stronger than the individual-level results.

This study examined the IQA of higher education in Taiwan, which is increasingly important to institutions' competitiveness in the global higher education market. The data were analyzed using multilevel structural equation modeling at the individual-level and the aggregate-level. The analysis revealed direct and indirect associations between opinions about IQA and institutional quality. This study makes a significant contribution to the literature because it clarifies the role of administrators (individually and collectively) regarding their institutions' educational quality, and it provides useful information that institutions could apply to improve their international competitiveness.

This study aims to develop a comprehensive green supplier governance (GSG) concept and explore whether specific GSG approaches (green supplier assessment, green supplier assistance and green strategic partnership with suppliers (GSPS)) bring environmental and economic performance. Moreover, this study aims to reveal a synergistic effect of three GSG approaches on performance improvement.

Using data collected from 200 Chinese manufacturing firms, regression analysis was employed to reveal the relationship between specific GSG approaches and firm performance. Further, cluster analysis was used to identify groupings of firms regarding implementation levels of three GSG approaches and compare the performance of the firm groups.

Green supplier assessment (GSA) can bring environmental performance, but GSA is not associated with economic performance. Green supplier assistance is positively associated with economic performance, while green supplier assistance cannot improve environmental performance. Only GSPS leads to improvement for both environmental and economic performance. Furthermore, firms with high implementation levels of GSA and GSPS (whether with high or low implementation levels of GSAS) can achieve the best environmental and financial performance.

This study provides implications for firms to more strategically and comprehensively implement GSG approaches, which can be more effective in bringing environmental and economic performance.

The authors' study extends the GSG concept with two approaches by subdividing the collaborative approach into green supplier assistance and GSPS based on the collaboration levels. This study also sheds light on how to improve firm performance by different GSG approaches and reveals a synergistic effect of three GSG approaches on performance.

The study aims to explore the argument of implementing the lean method to part or whole of an operation by examining the moderating impact of varying levels of the extent of implementation of four different lean methods, along with their functionalities, in predicting productivity improvement (PI).

As the focus of understanding the efficacy of lean principles is shifting from process to industry level, this study tried to generalize the approach by gathering data from 132 large Indian auto component manufacturers. This involves an assessing/monitoring approach rather than measurement.

Results highlighted the interdependence or individuality of the extent of implementation of lean methods and their functionalities. Findings revealed a significant moderating effect in improving productivity to a greater extent of 50%.

Adopting an assessment approach to a measurement study provides a noteworthy contribution to bridging theory and practical consequences. The findings can be appropriately extrapolated to medium and small enterprises forming a critical connection in the entire automobile manufacturing ecosystem.

The study showed that even if a lean method is applied to a certain extent of operations the chances of PI are significant. This is important for decision makers as they confront problems of optimum resource allocation.

PI, reduced cost and generalization of results would enable the auto component industry to become more competitive.

The examination of the moderation effect of a lean principle implementation extent, along with that of its functionalities to predict the improvement in productivity from its existing level, is a major outcome of this study.

A maturity model for Industry 4.0 (I4.0 MM) with influencing factors is designed to address maturity issues in adopting Industry 4.0. Standardisation in I4.0 supports manufacturing industry transformation, forming reference architecture models (RAMs). This paper aligns key factors and maturity levels in I4.0 MMs with reputable I4.0 RAMs to enhance strategy for I4.0 transformation and implementation.

Three steps of alignment consist of the systematic literature review (SLR) method to study the current published high-quality I4.0 MMs, the taxonomy development of I4.0 influencing factors by adapting and implementing the categorisation of system theories and aligning I4.0 MMs with RAMs.

The study discovered that different I4.0 MMs lead to varied organisational interpretations. Challenges and insights arise when aligning I4.0 MMs with RAMs. Aligning MM levels with RAM stages is a crucial milestone in the journey toward I4.0 transformation. Evidence indicates that I4.0 MMs and RAMs often overlook the cultural domain.

Findings contribute to the literature on aligning capabilities with implementation strategies while employing I4.0 MMs and RAMs. We use five RAMs (RAMI4.0, NIST-SME, IMSA, IVRA and IIRA), and as a common limitation in SLR, there could be a subjective bias in reading and selecting literature.

To fully leverage the capabilities of RAMs as part of the I4.0 implementation strategy, companies should initiate the process by undertaking a thorough needs assessment using I4.0 MMs.

The novelty of this paper lies in being the first to examine the alignment of I4.0 MMs with established RAMs. It offers valuable insights for improving I4.0 implementation strategies, especially for companies using both MMs and RAMs in their transformation efforts.

This study aims to validate a model to assess the level of lean maturity of Brazilian hospitals based on the adaptation of the SAE J4000/2021 standard.

The methodology was divided: (1) adaptation of the standard to the health context, (2) application of the questionnaire through a survey, and (3) comparison of the results of the level of maturity in the standard and by the clustering technique, using Minitab.

The research presents two contributions: (1) validation of a model to assess the level of lean maturity based on the SAE J4000/2021; (2) insights into the level of maturity of Brazilian hospitals. It was observed that only 10% of the sample was classified at maturity level 3 and 75% at level 2. Private and large hospitals showed greater maturity compared to the others.

This includes the limited number of hospitals that participated in the survey, given the difficulty of adherence to due constraints of time and staffing from hospitals, during the COVID-19 pandemic.

This article presents a lean maturity assessment proposal, adapting a consolidated standard in the automobile industry to the health context. Insights on the lean maturity of Brazilian hospitals can contribute to the development of policies to encourage the implementation of the lean philosophy directed to each specific environment.

This study serves as a guide for public agents interested in monitoring the quality of hospital indicators through the SAE J4000/2021 standard. From this lean maturity analysis, hospital managers can understand their opportunities for improvement in both human and organizational aspects. This favors the improvement of service delivery to society that depends on health services.

Due to the lack of research that validates lean maturity level assessment models in Brazilian hospitals, this study can be considered a pioneer in this Brazilian research by validating the SAE J4000/2021 standard in its updated version in the health context.

This study aims to establish an assessment model to measure big data readiness in the public sector, specifically targeting local governments at the provincial and city/regency levels. Additionally, the study aims to gain valuable insights into the readiness of selected local governments in Indonesia by using the established assessment model.

This study uses a mixed-method approach, using focus group discussions (FGDs), surveys and exploratory factor analysis (EFA) to establish the assessment model. The FGDs involve gathering perspectives on readiness variables from experts in academia, government and practice, whereas the survey collects data from a sample of selected local governments using a questionnaire developed based on the variables obtained in FGDs. The EFA is used on survey data to condense the variables into a smaller set of dimensions or factors. Ultimately, the assessment model is applied to evaluate the level of big data readiness among the selected Indonesian local governments.

FGDs identify 32 essential variables for evaluating the readiness of local governments to adopt big data. Subsequently, EFA reduces this number by five and organizes the remaining variables into four factors: big data strategy, policy and collaboration, infrastructure and human resources and data collection and utilization. The application of the assessment model reveals that the overall readiness for big data in the selected local governments is primarily moderate, with those in the Java cluster displaying higher readiness. In addition, the data collection and utilization factor achieves the highest score among the four factors.

This study offers an assessment model for evaluating big data readiness within local governments by combining perspectives from big data experts in academia, government and practice.