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The present study uses dynamic capabilities and micro foundations theory to unearth the emergence of multilevel innovation in service sector organizations. Transformational leadership (TL) and corporate social responsibility (CSR) are proposed as human resource (HR) based antecedents. The study also aims to reveal the role of collective organizational engagement (COE) as a mediator in developing dynamic capabilities and contingent pay policy (CPP) as a moderator.

The research is a multilevel study where employees are nested within organizations. We used multilevel modeling in Mplus V. 8.7 to perform the analyses.

Results of multilevel structural equation modeling revealed that TL, CSR, and COE are vital microfoundations through which innovation emerges in an organization. COE acts as a partial mediator in the relationship between CSR and innovation. The level of CPP in organizations moderates these relationships at the employee level rather than at the firm level.

This research is one of the few multilevel and multisource studies to empirically test the impact of HR-based antecedents on innovative work behavior and firm innovativeness simultaneously. It also contributes to engagement literature by furthering an understanding of the nascent concept of COE.

The external business environment of the organization is always changing at a rapid pace. For a firm to adapt to changing client requirements, it must implement the right business procedures and strategies. To improve competitive advantage, this study investigates the roles that supply chain partnerships, cross-functional integration, responsiveness and resilience play in achieving competitive advantages in Palestine.

Industrial institutions in Palestine constitute the study population. Data are collected by distributing surveys via Google Forms linked to manufacturers in industries such as the Leather and shoe Industry, metal industries, chemical industries, construction industries, textile industries, stone and marble industries, pharmaceutical industry, veterinary industry, food industry, plastic industry, paper industry, major advantages and disadvantages. The SEM-PLS approach is used to analyze the data.

The findings demonstrate that supply chain responsiveness, resilience and cooperation are all improved by cross-functional integration in inventory data integration and immediate operation. Supply chain partnerships improve the supply chain’s responsiveness, resilience and competitive advantage by involving partners in work teams and exchanging best practices. The enhancement of supply chain resilience and competitive advantage is influenced by the company’s capacity to act promptly in response to variations in demands.

This paper faces some limitations and it can be drawn as follows: To enhance supply chain risk management, the study continues to concentrate on manufacturing organizations that have internal integration. It also emphasizes the necessity of supply chain integration, which establishes direct connections with outside partners.

The findings of this study suggest some policy implications, as follows: To provide the manufacturing sector with a competitive edge, operations supervisors must be able to track and assess processes to ensure they are meeting demand. Firms that possess the ability to adjust to novel procedures or advancements in technology gain a competitive edge by guaranteeing consistent and high-quality delivery of products.

By implementing IT integration, this study theoretically and practically advances the understanding of the resource-based view of competitive advantages. This study focuses on providing insights into the nature of the relationship between supply chain partnership, cross-functional integration, responsiveness and flexibility and competitive advantages in the manufacturing sector in the Palestinian market.

This study quantitatively investigates the impacts of digital and learning orientations on supply chain resilience (SCR) and firm performance (FP), aiming to fill the gaps in understanding their specific impacts in the context of Industry 4.0 developments and supply chain disruptions.

This study utilized survey techniques and structural equation modelling (SEM) to gather and analyse data through a questionnaire based on a seven-point Likert scale. Hypotheses were formulated based on an extensive literature review and tested using Amos software.

The study confirms SCR’s significant impact on FP, aligning with existing research on resilience’s role in organizational competitiveness. This study uncovers the nuanced impacts of digital and learning orientations on SCR and FP. Internal digital orientation (DOI) positively impacts SCR, while external digital orientation (DOE) does not. Specific dimensions of learning orientation – shared vision (LOS), open-mindedness (LOO) and intraorganizational knowledge sharing (LOI) – enhance SCR, while commitment to learning (LOC) does not. SCR mediates the relationship between DOI and FP but not between DOE and FP.

This research focuses on digital and learning orientations, recommending that future studies investigate other strategic orientations and examine the specific contributions of various digital technologies to SCR across diverse contexts.

The empirical findings emphasize the significance of developing internal digital capabilities and specific learning orientations to enhance SCR and FP, aligning these initiatives with resilience strategies.

This study advances knowledge by distinguishing the impacts of internal and external digital orientations and specific learning dimensions on SCR and FP, offering nuanced insights and empirical validation.

This article targeted to experimentally examine the impact of several considered process parameters namely, applied voltage (AV), tool feed rate, electrolyte concentration and pulse frequency (PF), on the material removal rate (MRR) and radial overcut (ROC) while performing shaped tube drilling of aviation grade Inconel 625 super alloy through electrochemical machining principle. Further, an attempt has also been made to develop mathematical models for the process responses along with advanced optimization with evolutionary methods.

The central composite rotatable design matrix was used to scheme out the experiments in the present study. The consistency and accuracy of the developed mathematical models were confirmed through statistical results. Additionally, a field emission scanning electron microscope analysis was conducted to assess and analyze the microstructure of the machined work samples. The study also seeks to optimize the selected process inputs for MRR and ROC through the implementation of the desirability method, particle swarm optimization (PSO) and Teaching Learning-Based Optimization (TLBO).

The ROC is significantly influenced by the input parameters, specifically the PF and AV. Less ROC values were observed when the high PF with moderate AV. The minimum and maximum values of ROC and MRR were obtained as; 135.128 µm and 380.720 µm; 1.37 mg/min and 2.3707 mg/min, correspondingly. The best optimized confirmatory results were obtained through the TLBO approach, with an MRR value of 3.1587 mg/min and a ROC of 71.9629 µm, in comparison to the PSO and desirability approaches.

The various challenges associated with the productive machining of aviation grade Inconel 625 superalloy have been explored experimentally. The conducted experimentation has been performed on the in-house fabricated micro-electrochemical setup capable of performing a variety of advanced machining operations at the miniaturized level. Further, the application of shaped tube drilling while processing aviation grade Inconel 625 superalloy has been explored with the developed micro-ECM set-up. Moreover, the performed microstructure analysis of the machined work samples has elaborated and explored the various associated surface integrity aspects which are quite crucial when it comes to real-life aerospace-related applications. The utility of designed experiments has further made the attempted experimental analysis more fruitful and qualitative too.

The purpose of this study is to investigate the usage of digital technologies (DTs) in improving the mental health of workers on construction sites.

A mixed research approach was used in the study, which comprised a questionnaire survey and two phases of semi-structured interviews. Purposive sampling was used to determine the interviewees and respondents of the questionnaire survey. Weighted mean rating (WMR) and manual content analysis were used to rank and evaluate the collected data.

The findings of this study revealed bipolar disorder, anxiety disorders, attention-deficit/hyperactivity disorder, obsessive-compulsive disorder, work-related stress and depression as the six most significant mental disorders (MDs) among the construction workforce and 30 causes for them. Moreover, 27 symptoms were related to the six most significant MDs, and sweating was the most significant symptom among them. Despite that, 16 DTs were found to be suitable in mitigating the causes for the most significant MDs.

There are numerous studies conducted on the application of DTs to construction operations. However, insufficient studies have been conducted focusing on the application of DTs in improving the mental health of workers at construction sites. This study can thus influence the use of DTs for tackling the common causes for MDs by bringing a new paradigm to the construction industry.

Currently, China’s manufacturing industry chain still faces the danger of chain breakage due to the persistent “lack of technology” issue. The definition and detection of key nodes in the industry chain are significant to the enhancement of the stability of the industry chain. Therefore, detecting the key nodes in the manufacturing industry chain is necessary.

A complex network based on the links amongst listed manufacturing enterprises is built, and the authors analyse the network’s basic characteristics and vulnerability, taking into account the impact of scientific and technological innovation on the stability of the industry chain.

It is found that the high structural characteristic of midstream nodes in the naval architecture and marine engineering equipment industry chain determines their importance to stability, and the key status of upstream nodes is reflected in the weakness of technological innovation. The upstream nodes should focus on improving their independent innovation and R&D capability, whilst the midstream nodes should maintain a close supply–demand cooperation relationship.

The key node detection model for industry chain stability is constructed by considering various factors from the perspective of network and technological innovation. Empirical study is conducted to verify effectiveness of proposed method.

This study investigates the influence of the wisdom of the crowd (WSC), trust and perceived value on consumer purchase intentions within social commerce (s-commerce) platforms. By constructing a novel theoretical model, the research aims to delineate the interrelationships among these variables, addressing an emerging area in social interactions and information technology.

To empirically test and validate the proposed model, the study collected data from 442 Facebook users familiar with online shopping. The analysis employed Structural Equation Modeling – Partial Least Squares (SEM-PLS) to test the hypotheses and examine the relationships between WSC, trust, perceived value and purchase intentions.

The findings confirm that WSC significantly influences trust, perceived value and the intention to purchase on s-commerce platforms. Both perceived value and trust are substantial determinants of purchase intentions. Notably, the PLS analysis revealed that WSC accounts for 36.8% of the variance in trust and 33.1% of the variance in perceived value related to purchasing decisions on s-commerce platforms.

This research contributes uniquely to the ongoing discourse on s-commerce adoption by integrating WSC as a pivotal factor in understanding perceived value and consumer adoption intentions. It provides a fresh perspective on how collective intelligence affects consumer behavior in digital marketplaces, enriching the theoretical and practical understanding of s-commerce dynamics.

The aim of the study is to understand the transformative impact of ChatGPT on artificial intelligence (AI) research, its applications, implications, challenges and potential to shape future AI trends. The study also seeks to assess the relevance and quality of research output through citation and bibliographic coupling analysis.

This study employed a comprehensive bibliometric analysis using Biblioshiny and VOSviewer to investigate the research trends, influential entities and leading contributors in the domain of AI, focusing on the ChatGPT model.

The analysis revealed a high prevalence of AI-related terms, indicating a significant interest in and engagement with ChatGPT in AI studies and applications. “Nature” and “Thorp H.H.” emerged as the most cited source and author, respectively, while the USA surfaced as the leading contributor in the field.

While the findings provide a comprehensive overview of the ChatGPT research landscape, it is important to note that the conclusions drawn are only as current as the data used.

The study highlights potential collaboration opportunities and signals areas of research that might benefit from increased focus or diversification. It serves as a valuable resource for researchers, practitioners and policymakers for strategic planning and decision-making in AI research, specifically in relation to ChatGPT.

This study is one of the first to provide a comprehensive bibliometric analysis of the ChatGPT research domain, its multidimensional impact and potential. It offers valuable insights for a range of stakeholders in understanding the current landscape and future directions of ChatGPT in AI.

The purpose of this study is to investigate the impact of Corona Virus Disease 2019 (COVID-19) on the outcome of construction projects and explore the moderating effects of emerging technologies on the relationship between COVID-19 and construction project outcomes.

Data for the study was collected through a Web-based, semistructured questionnaire. The responses of 62 construction practitioners were analyzed using a hierarchical linear regression model. The model consists of 16 independent variables, three control variables (organization size, organization type and project size), one moderator (adoption level of emerging technologies) and three dependent variables (project time, project cost and project quality).

The study confirms the negative significant impact of the COVID-19 pandemic on the performance of construction projects. It also identifies the significant moderating effects of emerging technologies in mitigating the impact of COVID-19 on construction projects. Further, it shows a significant increase in the application of emerging technologies in construction projects during the COVID-19 pandemic. Based on the findings related to the moderating impact of the technology, this study provides a clear set of recommendations for construction firms, public sector and research community in combating the unavoidable situation similar to the COVID-19 pandemic in the future.

To the best of the authors’ knowledge, this is the first study to identify the moderating role of technology on the impact of COVID-19 on the performance of the construction sector in Pakistan. The findings can also be used for the construction sectors of other developing countries.

Flood preparedness and response from the perspective of community engagement mechanisms have been studied in scholarly articles. However, the differences in flood mitigation may expose social and behavioural challenges to learn from. This study aimed to demonstrate how text mining can be applied in prioritising existing contexts in community-based and government flood mitigation and management strategies.

This investigation mined the semantics researchers ascribed to flood disasters and community responses from 2001 to 2022 peer-reviewed publications. Text mining was used to derive frequently used terms from over 15 publications in the Scopus database and Google Scholar search engine after an initial output of 268 peer-reviewed publications. The text-mining process applied the topic modelling analyses on the 15 publications using the R studio application.

Topic modelling applied through text mining clustered four (4) themes. The themes that emerged from the topic modelling process were building adaptation to flooding, climate change and resilient communities, urban infrastructure and community preparedness and research output for flood risk and community response. The themes were supported with geographical flood risk and community mitigation contexts from the USA, India and Nigeria to provide a broader perspective.

This study exposed the deficiency of “communication, teamwork, responsibility and lessons” as focal themes of flood disaster management and response research. The divergence in flood mitigation in developing nations as compared with developed nations can be bridged through improved government policies, technologies and community engagement.