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Previous studies have focused on explaining the developmental paths and the relevant skills necessary for smart factories, based on an extensive review of the literature. Unfortunately, there is a deficit of empirical analyses that present an in-depth overview of smart factory development. Although the literature supports the benefits of smart factories, it remains unclear whether there should be government intervention (GI) to facilitate or regulate such adoption. This study will provide an in-depth empirical analysis of smart factory adoption (SFA) and its role in manufacturing performance (MP) and sustainable manufacturing (SM).

This study used non-probability convenience and referral sampling techniques for data collection. This approach considered production managers from each company that participated in the survey questionnaire; thus, each production manager represented one firm. A total of 240 managers from several manufacturing companies participated in the study. This study employed direct and moderating hypotheses tested using PROCESS Macro, which Andrew Hayes developed for SPSS.

The study identified three fundamental elements of a smart factory: manufacturing big data (MBD), process automation (PA) and supply chain integration (SCI) and analyzed them individually to see how they affect MP. According to the results, building a smart factory has positive and significant impacts on MP and SM. Furthermore, this study explores the role of GI in promoting smart factory deployment for both production performance and sustainable production. The study found that GI did not have a significant moderating effect but did have a positive relationship with SM.

This study contributes to the literature on smart factories by examining the impact of SFA on MP and SM. This provides a more comprehensive overview of the potential benefits of smart factories across various aspects, such as the application of big data, adoption of automation technology and integration of the supply chain. This study suggests that managers and decision-makers consider investing in smart factory implementation to improve factory productivity and enhance sustainability. Policymakers and government officials can promote the adoption of smart factories by providing incentives, funding, and resources to manufacturing firms.

There is a scarcity of research measuring the actual performance of manufacturing firms that have already adopted smart factories, and this study seeks to address this gap in the literature. This study focuses on the implementation of manufacturing big data, process automation and supply chain integration and how the adoption of these technologies improves MP and provides a SM environment by conducting a real-time study of manufacturing organizations. This study presents an initial effort to explore the role of government involvement in promoting smart factories.

This study aims to proposed that blockchain helps the organization improve supply chain (SC) performance by improving integration, agility and security through real-time information sharing, end-to-end visibility, transparency, data management, immutability, irrevocable information and cyber-security platforms.

This study has made an initial effort toward proposing a framework that shows the problems and challenges for the O&G SC under its segments (upstream, midstream and downstream) and provides the interlink among blockchain properties for SCM problems. SC managers were selected for survey questionnaires from the Pakistan O&G industries.

This study analyzes the impact of blockchain-enabled SC on firm performance with an understanding of the SC robustness capabilities as a mediator. The result revealed that the SC manager believes that the blockchain-enabled SC has a positive and significant on firm performance and robustness capabilities.

Blockchain technology is reflected as high-tech to support the firm process, responses and methods. The technology helps eliminate bottlenecks, avoid uncertainties and improve decision-making, leading to improved SC functions. This study guides managers about the potential problems of existing SC and how blockchain solves SC problems more effectively.

The oil and gas (O&G) sectors are neglected by researchers, and there are limited studies on O&G supply chain management (SCM). Additionally, no empirical evidence suggests implementing blockchain for O&G as a solution for potential problems. Furthermore, present the roadmap to other industries those having complex SC networks for the implication of blockchain to improve the SC performance.

The objective of this study is to investigate the relationship between psychological empowerment and employee creativity with the mediating role of creative process engagement and intrinsic motivation by testing the research model.

Data were collected from in-depth interviews and surveys of 420 Vietnamese telecommunications enterprises employees. To test models and hypotheses, the collected data has been processed through Cronbach's alpha analysis, exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and structural equation modeling (SEM) using SPSS 22.0 and AMOS 24.0 tools.

The results confirm that psychological empowerment has a positive direct and indirect relationship through creative process engagement and intrinsic motivation with employee creativity.

The main limitation of this paper is that a full study of the impact of leadership style on psychological empowerment and psychological empowerment to employee creativity should be conducted in the future at Vietnamese telecommunications enterprises.

Telecommunications enterprises managers who want to stimulate employee creativity need to pay attention to empowering workers and creating conditions for them to participate in the creative process when doing work.

The main contribution of this research is to apply theories of psychological empowerment, self-determination and composition theory of creativity to understand the relationship between psychological empowerment, creative process engagement, intrinsic motivation and employee creativity of telecommunications enterprises employees in the context of a developing country in Asia.

The purpose of this paper is to investigate the relationship between firm value and cash holdings for the period 2003-2008. This study seeks to find if there are costs and benefits associated with holding too much or too little cash, then an optimal cash level exists where marginal benefits are offset by their costs. If this optimal point exists, then firm value will be maximized at that point and deviation from it will affect the firm value negatively.

Optimal cash level between firm value and cash holding is determined by investigating the concave relationship. If concave relationship exists then a residual term is included in the equation to see how deviations from the optimal level affect firm value. A two-step generalized method of moments (GMM) estimator is used in estimating all results. GMM controls for unobserved firm heterogeneity and endogeneity problems.

Results showed that a concave relationship exists between firm value and cash holdings, which confirmed that there is an optimal cash level that maximizes firm value. It was also found that deviations from the optimal level affect firm value negatively.

The paper provides the existence of an optimal point of cash between costs and benefits wherein firm value is maximized. It has implications for firms’ investment and financing decisions when there is limited access to external finance. At higher level of cash the study has implications for agency theory and governance practices.

The study establishes a conclusive relationship between firm value and cash holdings within the context of the Pakistani market.

The global construction industry faces both challenges and opportunities from electronic waste (e-waste). This study aims to present a bibliometric analysis and comprehensive literature assessment on e-waste in concrete construction materials.

This study studies 4,122 Scopus documents to examine garbage generation in different countries and inventive ways to integrate e-waste into construction as a sustainable strategy. This study lists famous researchers and their cooperation networks, demonstrating a robust and dynamic area with a surge in research output, notably from 2018 to 2022. Data is visually represented using VOS Viewer to show trends, patterns and study interests throughout time.

The findings imply that e-waste can improve construction materials’ mechanical characteristics and sustainability. The results are inconsistent and suggest further optimization. e-Waste into construction has garnered scientific interest for its environmental, life cycle, and economic impacts. This field has great potential for improving e-waste material use, developing sophisticated prediction models, studying environmental implications, economic analysis, policy formulation, novel construction methods, global cooperation and public awareness. This study shows that e-waste can be used in sustainable building. It stresses this area’s need for research and innovation. This lays the groundwork for using electronic trash in buildings, which promotes a circular economy and environmental sustainability.

The findings underscore the critical role of ongoing research and innovation in leveraging e-waste for sustainable building practices. This study lays the groundwork for integrating e-waste into construction, contributing to the advancement of a circular economy and environmental sustainability.

The social implications of integrating e-waste into construction are significant. Using e-waste not only addresses environmental concerns but also promotes social sustainability by creating new job opportunities in the recycling and construction sectors. It fosters community awareness and responsibility towards sustainable practices and waste management. Additionally, this approach can reduce construction costs, making building projects more accessible and potentially lowering housing prices.

This research contributes to the field by offering a bibliometric analysis and comprehensive assessment of e-waste in concrete construction materials, highlighting its global significance.

The purpose of this research is to address the existing gap in the study of construction and demolition waste (C&DW) by focusing on its impact on human health throughout the entire life cycle. And this research provides a comprehensive assessment model that incorporates the release of gaseous pollutants and particulate matter during the whole life cycle of C&DW, thereby contributing to a more holistic understanding of its impact on human health.

The research was conducted in two stages. Firstly, the quantitative model framework of pollutants emitted by C&DW was established. Three types of pollutants were considered, namely nitrogen dioxide (NO₂), sulfur dioxide (SO₂) and inhalable particulate matter (PM10). Second, disability-adjusted life year (DALY) and willingness to pay (WTP) assessments were used to provide a monetary quantified health impact for pollutants released by C&DW.

The results show that the WTP value of PM10 is the highest among all pollutants and 8.68E+07 dollars/a, while the WTP value in the disposal stage accounts for the largest proportion compared to the generation and transportation stage. These findings emphasize the importance of PM10 and C&DW treatment stage for pollutant treatment.

The results of this study are of great significance for the management department to optimize the construction management scheme to reduce the total amount of pollutants produced by C&DW and its harm to human health. Meanwhile, this study fills the gap in existing research on the impact assessment of C&DW on human health throughout the whole life cycle, and provides reference and basis for future research and policy formulation.

The purpose of this paper is to present an integrated data-driven framework for processing and analyzing large-scale vehicle maintenance records to get more comprehensive understanding on vehicle quality.

We propose a framework for vehicle quality analysis based on maintenance record mining and Bayesian Network. It includes the development of a comprehensive dictionary for efficient classification of maintenance items, and the establishment of a Bayesian Network model for vehicle quality evaluation. The vehicle design parameters, price and performance of functional systems are modeled as node variables in the Bayesian Network. Bayesian Network reasoning is then used to analyze the influence of these nodes on vehicle quality and their respective importance.

A case study using the maintenance records of 74 sport utility vehicle (SUV) models is presented to demonstrate the validity of the proposed framework. Our results reveal that factors such as vehicle size, chassis issues and engine displacement, can affect the chance of vehicle failures and accidents. The influence of factors such as price and performance of engine and chassis show explicit regional differences.

Previous research usually focuses on limited maintenance records from a single vehicle producer, while our proposed framework enables efficient and systematic processing of larger-scale maintenance records for vehicle quality analysis, which can support auto companies, consumers and regulators to make better decisions in purchase choice-making, vehicle design and market regulation.

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.

The purpose of this paper is to investigate the benefits of social media to enhance knowledge exchange in the organizations. Moreover, the current qualitative inquiry elaborates the orientation of doctors about social media applications and knowledge exchange in the workplace.

The constructionism approach based on grounded theory followed by qualitative design is used to investigate the exposition with the help of 22 non-directive and semi-structured interviews from the doctors of Bahawal Victoria Hospital. The cases are selected by using convenience sampling and thematic analysis is done using NVivo-11 plus.

The results end up with four major themes. The social media applications, at the first door, extend communication and relationship among employees. Moreover, these applications are equally beneficial for acquiring existing and new knowledge. Additionally, social media applications advance knowledge exchange by promoting knowledge sharing and transfer.

This study is equally beneficial for employees and management to promote knowledge exchange through social media applications. The effective and efficient use of social media applications helps organizations to boost knowledge strength among employees and can address various critical issues.

This is an attempt to sightsee the unattended dimension (i.e. knowledge exchange) in the context with social media. The social media applications are popular all over the world and pace of their usage is increasing day by day but their real contribution toward organizational well-being is still lacking in contemporary literature.

The emergence of the COVID-19 epidemic has considerably increased the intricacy of information, exacerbating the difficulties firms encounter in efficiently processing and understanding accurate data and knowledge. Consequently, the COVID-19 epidemic has profoundly exacerbated production ambiguity for firms, thereby disrupting their regular business operations and supply chain activities. Digital technologies (DTs) are essential tools for firms to process and interpret information and knowledge, thereby improving their resilience against supply chain interruptions.

This research investigates the effect of digital technologies on firm resilience throughout COVID-19, utilizing PLS-SEM and artificial neural networks (ANN) derived from a comprehensive survey of Pakistani manufacturing firms.

Our research assesses the mediating role of supply chain integration, memory, and absorptive capacity, as well as the moderating influence of information complexity. The outcomes demonstrate that supply chain integration (SCI), memory (SCM), and absorptive capacity (SCAC) mediate digital technologies’ influence on firm resilience. Moreover, in situations where information is highly complex, DTs have a greater effect on a firm’s resilience.

The results enhance our comprehension and awareness of the resilience-related effects of DTs and offer significant management insights for strengthening firm resilience in the setting of the COVID-19 pandemic.