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This study aims to integrate the resource-based view (RBV) with other theories that consider external factors necessary to respond successfully to dynamic and uncertain entrepreneurial business conditions.

The paper introduces an multi-criteria decision-making (MCDM) approach, utilizing the axial-distance-based aggregated measurement (ADAM) method with weights determined by the preference selection index (PSI) method, to rank eight European countries based on the Global Entrepreneurship Monitor (GEM) data. Additionally, the paper extends the existing entrepreneurial ecosystem taxonomy (EET), offering an additional classification.

The performed analysis emphasizes the importance and necessity of involving different dimensions of EE in assessing the countries' entrepreneurship performance, which facilitates creating adequate policy measures.

The crucial limitations are assessments based only on the GEM data from a particular period, possibly leading to a certain bias. Future research should involve data from various resources to increase the results' reliability.

The ranking results and country classification obtained using the ADAM-based approach and two distinct taxonomies served as the basis for formulating tailored policy recommendations, aiming to formulate tailored policy implications for increasing the number of new entrepreneurs and improving innovativeness, sustainability and internationalization of existing entrepreneurs for each group of countries.

The present study shows that the implementation and development of enterprise resource planning (ERP) systems as a technological innovation provide a competitive advantage that helps to improve the functionality of business intelligence (BI) systems in the digital transformation of manufacturing companies, in addition to improving overall firm performance.

The research uses the structural equation approach based on PLS-SEM technique with a sample of 120 firms in the manufacturing industry in Coahuila, Mexico.

The paper provides empirical insights into how the interaction of ERP systems and innovation significantly affects the functionality of BI Systems and has a substantial effect on overall firm performance.

Empirical evidence of how advanced digital management systems (ERP and BI) impact digitalization processes in organizations by improving performance is still scarce.

Industrialized construction has brought about expectations of improved productivity in the construction industry. However, the lack of a commonly accepted definition has created confusion regarding the types of development covered by the industrialized construction umbrella. These inconsistent definitions convoluted the discussion on this phenomenon. This study aims to clarify the definition of industrialized construction through a systematic literature review.

This systematic literature review was conducted according to PRISMA principles. Records were gathered from Scopus and Web of Science. Following the scientometric analysis, content analysis was conducted according to the template analysis approach.

The analysis of 121 articles revealed four main themes related to industrialized construction: 1) the construction concept, 2) construction methodologies, 3) systematization, rationalization and automatization and 4) societal and industrial change processes. Definitions of industrialized construction can be analyzed with seven clusters: 1) prefabrication, 2) standardization, 3) sector, 4) integration, 5) manufacturing practices, 6) technological investment and 7) none. Based on the content analysis, the proposed definition is: industrialized construction is the adoption of practices that minimize project-specific work in construction from the start of the design to the end of the building’s life cycle.

This study proposes a definition for industrialized construction following content analysis of broadly sampled literature. The proposed definition can provide a basis on which developments in the construction industry can be reflected.

Over the last 2 decades, supply chain sustainability research has become a highly dynamic and fruitful study area. This field has garnered significant attention due to its potential to reshape decision-making processes within supply chains. At the same time, the practical side of supply chain operations remains intensely competitive in today’s business landscape. Furthermore, the current academic research aims to outline effective strategies for achieving sustainability across supply chains, particularly in the manufacturing sector. In response to these challenges, this research has conducted an integrated multi-criteria decision-making approach to evaluate sustainable supply chain performance from the triple bottom line perspective, including financial, environmental, and social performance.

The initial stage involves selecting the crucial criteria (short-term and long-term) and alternatives for sustainable supply chain performance (SSCP) from experts and conducting an in-depth literature review. Initially, there were 17 criteria, but after a pilot test with co-authors and online discussions with experts, the number of criteria was subsequently reduced to 9. In the second phase, the Best-Worst Method (BWM) was applied to rank and prioritize the criteria. The third and final stage examined the causal relationship between the identified criteria, utilizing the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique.

Based on BWM analysis results, the top three criteria in terms of prominence are: (1) return on investment (ROI), (2) product quality, and (3) manufacturing lead time. Out of the three alternatives, financial performance (FP) is the most crucial dimension for SSCP, followed by environmental performance (ENP) and social performance (SP). On the other hand, the DEMATEL approach showed that work health and safety (short-term criterion), asset utilization (long-term criterion), energy consumption (long-term criterion), waste disposal (long-term criterion), manufacturing lead time (short-term criterion), and on-time delivery (short-term criterion) are categorized within the cause group, while criteria such as return on investment (ROI) (long-term criterion), customer-service level (short-term criterion), and product quality (long-term criterion) fall into the effect group.

The proposed study has certain drawbacks that pave the way for future research directions. First, it is worth noting the need for a larger sample size to ensure the reliability of results, the potential inclusion of additional criteria to enhance the assessment of sustainability performance, and the consideration of a qualitative approach to gain deeper insights into the outcomes. In addition, fuzziness in qualitative subjective perception could be imperative when collecting data to ensure its reliability, as translating experts’ perceptions into exact numerical values can be challenging because human perceptions often carry elements of uncertainty or vagueness. Therefore, fuzzy integrated MCDM frameworks are better suited for future research to handle the uncertainties involved in human perceptions, making it a more appropriate approach for decision-making in scenarios where traditional MCDM methods may prove insufficient.

The proposed framework will enable decision-makers to gain deeper insights into how various decision criteria impact SSCP, thus providing a comprehensive evaluation of SSCP that considers multiple dimensions, such as financial, environmental, and social performance within the manufacturing sector.

The proposed study is the first empirical study to integrate both BWM and DEMATEL approaches to evaluate sustainable supply chain performance in the manufacturing context.

The research investigates the impact of concrete design methods on performance, emphasizing environmental sustainability. The study compares the modified Bolomey method and Abrams’ law in designing concretes. Significant differences in cement consumption and subsequent CO2 emissions are revealed. The research advocates for a comprehensive life cycle assessment, considering factors like compressive strength, carbonation resistance, CO2 emissions, and cost. The analysis underscores the importance of evaluating concrete not solely based on strength but also environmental impact. The study concludes that a multicriteria approach, considering the entire life cycle, is essential for sustainable concrete design, addressing durability, environmental concerns, and economic factors.

The study employed a comprehensive design and methodology approach, involving the formulation and testing of 20 mixed concretes with strengths ranging from 25 MPa to 45 MPa. Two distinct design methods, the modified Bolomey method (three equations method) and Abrams’ law, were utilized to calculate concrete compositions. Laboratory experiments were conducted to validate the computational models, and subsequent analyses focused on assessing differences in cement consumption, compressive strength, CO2 emissions, and concrete resistance to carbonation. The research adopted a multidisciplinary perspective, integrating theoretical analysis, laboratory testing, and life cycle assessment to evaluate concrete performance and sustainability.

Conclusion from the study includes substantial variations (56%–112%) in cement content, depending on the calculation method. Abrams' law proves optimal for compressive strength (30 MPa–45 MPa), while the three equations method yields higher actual strength (30%–51%). Abrams' law demonstrates optimal cement use, but concrete designed with the three equations method exhibits superior resistance to aggressive environments. Cement content exceeding 450 kg/m³ is undesirable. Concrete designed with Abrams' law is economically favorable (12%–30% lower costs). The three equations method results in higher CO2 emissions (38–83%), emphasizing the need for life cycle assessment.

This study’s originality lies in its holistic evaluation of concrete design methods, considering environmental impact, compressive strength, and cost across a comprehensive life cycle. The comparison of the traditional Abrams' law and the three equations method, along with detailed laboratory tests, contributes novel insights into optimal cement use and concrete performance. The findings underscore the importance of a multicriteria approach, emphasizing sustainability and economic viability. The research provides valuable guidance for engineers and policymakers seeking environmentally conscious and economically efficient concrete design strategies, addressing a critical gap in the field of construction materials and contributing to sustainable infrastructure development.

The purpose of this study is to forecast the mechanical properties of ternary blended concrete (TBC) modified with oyster shell powder (OSP) and shea nutshell ash (SNA) using deep neural network (DNN) models.

DNN models with three hidden layers, each layer containing 5–30 nodes, were used to predict the target variables (compressive strength [CS], flexural strength [FS] and split tensile strength [STS]) for the eight input variables of concrete classes 25 and 30 MPa. The concrete samples were cured for 3–120 days. Levenberg−Marquardt's backpropagation learning technique trained the networks, and the model's precision was confirmed using the experimental data set.

The DNN model with a 25-node structure yielded a strong relation for training, validating and testing the input and output variables with the lowest mean squared error (MSE) and the highest correlation coefficient (R) values of 0.0099 and 99.91% for CS and 0.010 and 98.42% for FS compared to other architectures. However, the DNN model with a 20-node architecture yielded a strong correlation for STS, with the lowest MSE and the highest R values of 0.013 and 97.26%. Strong relationships were found between the developed models and raw experimental data sets, with R² values of 99.58%, 97.85% and 97.58% for CS, FS and STS, respectively.

To the best of the authors’ knowledge, this novel research establishes the prospects of replacing SNA and OSP with Portland limestone cement (PLC) to produce TBC. In addition, predicting the CS, FS and STS of TBC modified with OSP and SNA using DNN models is original, optimizing the time, cost and quality of concrete.

Women's empowerment plays a pivotal role in achieving sustainable and sustainable development in developed and developing contexts. The present paper explores the effect of technology orientation (TO), entrepreneurial orientation (EO), and digital technology self-efficacy (DTSE) on digital innovation (DI) and women's empowerment (WE) among Saudi women.

This is a cross-sectional study which applies a deductive approach. The study collected data from women in Saudi Arabia actively involved in entrepreneurship and utilizing digital technology. The survey questionnaire is used as a prevalent tool to get responses. Finally, the study concludes based on 316 valid samples.

The structural equation modeling through SmartPls4, the results exert an insignificant effect of TO on both DI and women empowerment. The study confirmed a positive significant impact of EO on DI but not on WE. Moreover, the DTSE construct is found to be a significant and robust analyst of DI and WE. With regard to mediating effects, DI mediates the relationship between EO, DTSE and WE, but not between TO and WE.

The study's findings contribute to more comprehensive and effective initiatives that foster innovation, gender equality, and WE in entrepreneurial networks. The study would assist policymakers and planners in developing robust strategies focusing on digitalization to boost DI and WE through enhanced DTSE. The study would also offer guidelines for policymakers to achieve sustainable development goals (SDGs) generally and specifically for Saudi Vision 2030, which is particularly ambitious to promote WE.

The study fills the gaps by offering a bunch of predictors, i.e., TO, EO, DTSE and DI, which predict WE in the Saudi context.

Freeways in Australia play a significant role in connecting distant communities, shifting freight and strengthening the country’s economy. To meet the growing needs of present and future generations, delivering a socially sustainable road infrastructure that creates generational benefits is essential. However, the existing literature reveals the lack of comprehensive indicators to assess the social sustainability performance of freeway projects. Therefore, this paper aims to identify a critical set of system-specific indicators to evaluate the life cycle social footprint of Australian freeways.

This study conducted 31 interview questionnaire surveys with actively engaged stakeholders involved in various freeway projects around Australia. The data collected was analysed using fuzzy set theory and other statistical approaches.

The study identified 42 critical indicators for assessing the social sustainability performance throughout the life cycle of freeways in the Australian context. For example, stakeholder involvement, reduction of casualty rate due to road accidents, fair remuneration to project workforce and improved accessibility to required services.

The context-specific opinions extracted from the industry experts and the comprehensive set of critical indicators identified would ensure that all the vital aspects of social sustainability are considered throughout the life cycle of Australian freeways in the future, assisting the decision-makers in enhancing the project’s social sustainability performance.

The linguistic explanations associated with the ratings given by the industry experts provide greater insight into the context of the life cycle social sustainability assessment of Australian freeways exclusively.

Blockchain technology can potentially address the challenges of information storage, sharing and management and improve them further in an organization and sector as a whole. This study aims to investigate the effects of technology, organization and environment on the behavioral intention of employees to adopt blockchain in the Indian insurance sector and the mediating role of knowledge management practices.

A structured questionnaire was used to collect a sample size of 390 responses based on convenience sampling. Partial least square structural equation modeling was used to analyze the data.

The findings highlighted that organizational factors, followed by technological factors, significantly impact employees' behavioral intentions. The results established that the impact of environmental factors is insignificant on blockchain adoption intention. Knowledge management practices significantly mediate the relationship between organizational factors, technological factors and behavioral intention.

The results indicate that organizations must prioritize organizational factors (technological competence, top management support and financial readiness) and knowledge management practices (knowledge creation, sharing and retention) to positively impact employees' behavioral intentions and ensure successful and effective technology adoption.

Using the Technology-Organization-Environment framework, the study tests the conceptual model, showing the relationship between technological, organizational and environmental factors, behavioral intention and knowledge management practices. The role of knowledge management practices in technology adoption within organizations has been scarcely explored. This study adds significant and novel contributions in this area.

This study aims to develop a production-oriented approach for optimal mass-customisation of floor panel layouts in cross-laminated timber (CLT) buildings. The study enables meeting building clients’ unique floor plan requirements at an optimal cost and simultaneously enhances manufacturers’ profit by minimising material and manufacturing process waste.

The present research uses a hybrid approach consisting of field data collection, mathematical modelling, development of a Genetic Algorithm (GA) and scenario analysis. Field data includes engineered timber production information, design data and building code requirements. The study adopts the Flexible Demand Assignment (FDA) technique to formulate a mathematical model for optimising the design of mass timber buildings and employs GA to identify optimal production solutions. Scenario analysis is performed to validate model outputs.

The proposed model successfully determines the load-bearing wall placement and building spans and specifications of floor panels that result in optimal production efficiency and the desired architectural layout. The results indicate that buildings made of a single category of thickness of panels but customised in various lengths to suit building layout are the most profitable scenario for CLT manufacturers and are a cost-effective option for clients.

The originality of the present study lies in its mathematical and model-driven approach towards implementing mass customisation in multi-storey buildings. The proposed model has been developed and validated based on a comprehensive set of real-world data and constraints.