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This study aims to examine how foreign and domestic firms react to policy uncertainty in an emerging economy. In addition, the study investigates if older foreign firms better adapt to policy uncertainty than newer entrants.

The study uses pharmaceutical sales data on India’s cardiovascular segment for January 2011–May 2016. The authors use fixed fixed-effects panel data regression to measure the market reactions of foreign and domestic firms faced with policy uncertainty.

While domestic and foreign firms react similarly to anticipated policy changes, foreign firms react more adversely to policy uncertainty. Among foreign firms, early entrants respond less adversely than new entrants.

Foreign firms are able to cope with anticipated policy changes in similar vein as the domestic firms by way of a priori reading of the host country’s regulatory landscape. The foreign firms’ response to policy uncertainty is significantly different from domestic firms. The difference between the market response of foreign and domestic firms decreases over time.

The authors' findings demonstrate that adaptability is the key for new foreign firms to face policy uncertainty. Foreign firms can respond to policy changes, especially the unanticipated ones by imbibing local practices.

The authors' findings suggest that enhanced policy uncertainty hurts foreign firms more adversely than domestic firms, and newer foreign firms are more hurt with policy uncertainty than the existing ones. Such uncertainty could also have unintended consequences for consumer welfare.

The authors' study uses two natural experiments in the same industry within short periods of time. The comparison offers key insights on the differences in domestic and foreign firm responses to the two types of policy uncertainty.

Seismic vulnerability evaluation of various public structures, especially school buildings, is very crucial for designing hazard mitigation initiatives in seismic prone areas. The city of Mymensingh is at great risk of earthquake because of its geographical location, geological structure and proximity to active faults. The city is famous for its ancient and renowned educational institutes that need to be evaluated for understanding the seismic performance of the building during an earthquake. This study aims to evaluate the seismic vulnerability of educational buildings of Mymensingh city using rapid visual screening (RVS) and index based approach.

RVS procedure includes field survey and secondary source assessment for evaluating structural vulnerability attributes. Analytical hierarchy process is applied to develop an index focusing on systematic attributes of vulnerability based on expert opinions. Then, a composite vulnerability map is developed combining both structural and systematic vulnerability score providing an equal weight.

This study evaluates the seismic vulnerability of 458 educational buildings of Mymensingh city and the result shows that 23.14% educational building has high, 46.29% has moderate and 26.86% has moderately low and only 3.71% buildings has the low seismic vulnerability. This study expected to be helpful in resource targeting and prioritizing seismic hazard mitigation activities for education buildings of Mymensingh city.

This study endeavors to present a comprehensive vulnerability assessment method by integrating RVS and index based approach that incorporates both structural and systematic dimensions of vulnerability. The result is expected to be helpful in the formulation of disaster prevention policy for vulnerable educational buildings and development of the earthquake-resistant building codes for the new building construction in Mymensingh city.

In this study, the authors investigate the effect of big data analytics capability (BDAC) on supply chain performance (SCP) to assess the mediating effect of supply chain innovation (SCI) and the moderating effect of a data-driven culture (DDC).

The authors collected the primary data through an online questionnaire survey from the manufacturing sector operating in Jordan. The authors used 420 samples for the final data analysis, which the authors performed via partial least squares structural equation modelling using SmartPLS 3.3.9 software.

The results indicate that BDAC has a strong relationship with SCI and SCP. SCI shows a positive relationship with SCP as well as a mediating effect on SCI. The authors confirmed that DDC moderated the relationship between SCI and SCP.

The authors developed a conceptual and empirical model to investigate the relationship between BDAC, SCI, DDC and SCP. The authors contributed new theoretical and managerial insights that add value to the supply chain management literature through testing the moderated-mediated model of these constructs in Jordan’s manufacturing sector.

Green innovation, digitization and sustainability have attracted considerable attention in recent years due to their transformative potential in organizations. This study, grounded in the resource-based view, explores the intricate relationship among green entrepreneurial orientations, sustainability-oriented innovation, and the circular economy, employing green knowledge management as a pivotal mechanism.

The proposed model and hypotheses were tested using Partial Least Squares (PLS) structural equation modeling (SEM) with a sample of 274 valid questionnaires collected from manufacturing firms in Saudi Arabia.

Results unveil positive relationships between green entrepreneurial orientations and sustainability-oriented innovation, as well as the circular economy, along with a positive mediation of green knowledge management in these relationships. In addition, these relationships exhibit heightened strength with increased levels of digital transformation.

The contributions of this research extend to both theoretical and practical realms, offering valuable insights for startups and traditional businesses as they explore the landscape of green innovation and digitization.

The purpose of this paper is to improve the lubrication and remove as much as possible of the heat generated in the bearing assembly, embedded in the jet engine.

To determine the necessary values of the air pressure and oil amount, an experimental approach is used. For that purpose, a custom made test rig is developed.

Less amount of oil makes better lubrication conditions, reflected in the smaller temperature of the bearings. Concerning the air pressure, too high and too low air pressure deteriorates the lubrication parameters. An optimum value should be determined experimentally. The influence of oil amount is remarkably bigger than the influence of air pressure.

This experimental investigation provides an easy and fast way to improve the high-speed bearings lubrication parameters.

This study investigates the impact of big data analytics capabilities on export performance. Moreover, it assesses the mediating effect of the supply chain innovation and moderating effect of supply chain agility.

This study is based on primary data that were collected from the manufacturing sector operating in Jordan. A total of 327 responses were used for the final data analysis. Data analysis was performed via a partial least square structural equation modeling (PLS-SEM) approach.

The results of the data analysis supported a positive relationship between big data analytics capabilities and the export performance as well as a mediating effect of supply chain innovation. It was confirmed that supply chain agility moderated the relationship of supply chain innovation and export performance.

This study developed a theoretical and empirical model to investigate the relationship between big data analytics capabilities, export performance, supply chain innovation and supply chain agility. This study offers new theoretical and managerial contributions that add value to the supply chain management literature by testing the moderated-mediated model of these constructs in the manufacturing sector in Jordan.

Integrating the Chat Generative Pre-Trained Transformer-type (ChatGPT-type) model with government services has great development prospects. Applying this model improves service efficiency but has certain risks, thus having a dual impact on the public. For a responsible and democratic government, it is necessary to fully understand the factors influencing public acceptance and their causal relationships to truly encourage the public to accept and use government ChatGPT-type services.

This study used the Latent Dirichlet allocation (LDA) model to analyze comment texts and summarize 15 factors that affect public acceptance. Multiple-related matrices were established using the grey decision-making trial and evaluation laboratory (grey-DEMATEL) method to reveal causal relationships among factors. From the two opposite extraction rules of result priority and cause priority, the authors obtained an antagonistic topological model with comprehensive influence values using the total adversarial interpretive structure model (TAISM).

Fifteen factors were categorized in terms of cause and effect, and the antagonistic topological model with comprehensive influence values was also analyzed. The analysis showed that perceived risk, trust and meeting demand were the three most critical factors of public acceptance. Meanwhile, perceived risk and trust directly affected public acceptance and were affected by other factors. Supervision and accountability had the highest driving power and acted as the causal factor to influence other factors.

This study identified the factors affecting public acceptance of integrating the ChatGPT-type model with government services. It analyzed the relationship between the factors to provide a reference for decision-makers. This study introduced TAISM to form the LDA-grey-DEMATEL-TAISM method to provide an analytical paradigm for studying similar influencing factors.

The study examines the key drivers of the adoption of disruptive technologies (DTs) in the digital supply chain (DSC) in developing nations.

The data were collected using well-established measures grounded in the diffusion of innovation (DOI) theory. The hypotheses were tested using the structural equation modeling (SEM) approach using SmartPLS. The authors control for the demographics and apply the required statistical diagnostics for robust findings.

The compatibility and IT expertise were the two key factors in adopting the DTs in DSC in developing nations. The organizations with higher compatibility and internal IT expertise and competence witness a higher level of adoption of DT. The perceived cost and complexity were not found statistically significant. This may be probably because developing nations such as India do not perceive the technology adoption complex.

The research enhances DTs adoption, assuming it is organizational innovation. This study makes a theoretical contribution to the DOI literature.

The practicing managers should pay attention to addressing the existing technology compatibility issues and spend efforts on training employees to increase the IT expertise to improve the adoption of DT.

The greater adoption of the DTs in DSCs can reduce wastages in supply chains by a faster sense and response and greater technological flexibility with transparency and information sharing.

The key antecedent to the acceptance of the DTs in developing nations is compatibility than complexity and IT expertise than the cost. The study's originality lies in the fact that most studies on technology adoption study a single technology, but this study captures a holistic view on a group of technologies under industry 4.0.

The present study aims to explore the concepts involved in the relationship between corporate social responsibility (CSR) and customer and employee satisfaction in service industries.

The research literature over the recent decade has been analyzed using a systematic review. Through thematic analysis and coding the findings of the final selected articles, the authors presented an integrative framework of the relationship between CSR and the satisfaction of critical stakeholders of service companies, namely, customers and employees.

The research framework encompasses six main categories called CSR, satisfaction, moderators, conditional variables, contextual variables and satisfaction outcomes. All categories but CSR are divided into customer and employee sections to make the research framework further comprehensible.

The results show that in service industries, employees need as much attention as customers, and CSR efforts to satisfy customers and employees can lead to several positive outputs for companies.

Failure of service companies to commit to their social responsibility may harm the environment, society’s ethics and laws and long-term corporate profitability. On the other hand, adherence to CSR can lead to social development and economic growth.

This study is one of the most comprehensive studies in the field of CSR and satisfaction, which simultaneously considers the two key stakeholders of a service company. In addition, it provides valuable avenues for further studies.