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This study aims to examine the mediating influence of organisational improvisation (OI) in the relationship between knowledge management (KM) processes and innovativeness. It explores the role of sharing, creating, acquiring and storing knowledge in managing uncertainties through developing improvisation capability. In addition, it examines whether KM–improvisation relationship contributes to higher innovativeness in information technology (IT) companies in India.

This study derived a conceptual framework based on a critical review of literature. The data were collected from 231 employees using an online questionnaire from listed Indian IT services and consulting companies. The data reliability, validity and biases were checked, and hypotheses were tested using path analysis in structural equation modelling using software such as SPSS and AMOS.

All KM processes, except acquisition, had a positive and significant relationship with innovativeness and OI. OI was positively related to innovativeness. Mediation results show that OI mediated the relationship between knowledge sharing–creation, knowledge base and innovativeness. OI did not mediate between knowledge acquisition and innovativeness. The results indicate that sharing, creating and storing knowledge builds an organisation’s ability to improvise and innovate in a dynamic environment.

A cross-sectional design limits its ability to derive a cause-effect relationship. Survey methods are prone to common method bias. Future studies can adopt a longitudinal approach with objective measures. Moreover, the impact of pertinent factors, such as experimentative culture; HR practices that reward and support improvisational behaviour and exposure to organisational routines/culture can be evaluated.

This study suggests that implementing KM processes of sharing, creating and storing knowledge are crucial to build improvisation and innovation capabilities to sustain in volatile market, and enhance employee innovative work performance and creativity, while gaining external knowledge is not beneficial in the moment of improvisation.

This study contributes to the literature being one of the first empirical research connecting KM processes, improvisation and innovation. This study adds to the knowledge-based innovation literature by presenting improvisation as a mediating link between KM processes and organisational innovativeness. This study extends the understanding of how organisations can harness knowledge to innovate in uncertain times. It provides evidence regarding the role of KM processes and capabilities to improvise innovation in the context of an emerging economy.

This systematic review comprehensively examines corporate political contributions (CPC), exploring their antecedents, evolving mechanisms and diverse organizational outcomes. It offers a holistic understanding of the business–politics relationship and proposes a managerial decision-making framework for strategic CPC engagement. The study also identifies gaps in the literature and suggests future research avenues.

This study employs a systematic review process to assess the CPC literature. Utilizing leading journals and databases like Web of Science, Scopus and EBSCO, we apply rigorous screening criteria to select 72 relevant papers critically analyzed using the “Antecedents-Phenomenon-Consequences” framework.

The research identifies two primary dynamics influencing CPC: “essential need” for firm survival and “elective choice.” It reveals that CPC strategies impact various firm performance metrics, including market returns, operational performance and policy outcomes. Research is concentrated in the US, with a limited focus on developing economies. Future research should focus on industry-specific studies, timing of contributions and cross-national comparisons.

This paper provides managers with a comprehensive framework for CPC engagement, helping them navigate political dynamics, optimize contributions and enhance firm performance while maintaining ethical and strategic considerations.

This paper systematically reviews the complex political strategy of CPC, providing a nuanced understanding of how CPC operates across different countries and contexts. It offers academics and professionals insights to develop robust theories and make informed decisions in a modern, complex business environment.

The rapid evolution of artificial intelligence (AI) is revolutionizing organizational operations and altering competitive landscapes. This study examines the influence of organizational resources on AI adoption in recruitment, focusing on their role in achieving competitive advantage through effective implementation.

This research utilizes a cross-sectional quantitative approach, applying partial least squares structural equation modeling (PLS-SEM) to data from 290 human resource (HR) professionals. It is grounded in the resource-based view (RBV) and dynamic capability framework (DCF).

The results reveal that HR competencies and open innovation significantly influence dynamic capabilities, which are essential for AI integration, supported by financial support and information technology (IT) infrastructure. These capabilities enable effective AI adoption, leading to a competitive advantage.

The cross-sectional data in this study captures the current landscape of AI adoption in recruitment, providing a snapshot of the present scenario in a rapidly evolving technological environment.

This study offers HR professionals and managers strategic guidance on effectively integrating AI into recruitment processes. By enhancing HR competencies, fostering collaboration and ensuring sufficient financial and infrastructural support, organizations can navigate AI adoption challenges and secure a competitive advantage in a rapidly evolving technological landscape.

The adoption of AI in recruitment can reduce biases, enhance diversity and improve fairness through standardized assessments. However, as AI technologies evolve, continuous human oversight is essential to ensure ethical use and to modify AI systems as needed, further reducing biases and addressing societal concerns in AI-driven recruitment processes.

This research introduces a novel framework that underscores the importance of integrating human expertise with advanced technological tools to ensure successful AI implementation. A key contribution is that HR professionals not only facilitate AI integration but also ensure accuracy, accountability and configure the most suitable AI tools for recruitment by collaborating with AI developers to meet the specific needs of the organization.

Generative artificial intelligence (GenAI) can potentially improve supply chain management (SCM) processes across levels and verticals. However, despite its promise, the implementation of GenAI for SCM remains challenging, mainly due to the lack of knowledge regarding its key drivers. To address this gap, this study examines the factors driving GenAI implementation in an SCM environment and how these factors optimize SCM performance.

A thorough literature review was followed to identify the drivers. The resultant model from the drivers was validated using a quantitative study based on partial least squares structural equation modeling (PLS-SEM) that used responses from 315 expert respondents from the field of SCM.

The results confirmed the positive effect of performance expectancy, output quality and reliability, organizational innovativeness and management commitment to GenAI usage. Further, they showed that successful GenAI usage improved SCM performance through improved transparency, better decision-making, innovative design, robust development and responsiveness.

This study reports the potential drivers for the contemporary development of GenAI in SCM and highlights an action plan for GenAI’s optimal performance. The findings suggest that by increasing the rate of GenAI implementation, organizations can continuously improve their strategies and practices for better SCM performance.

This study establishes the first step toward empirically testing and validating a theoretical model for GenAI implementation and its effect on SCM performance.

Nanofluids are used in technology, engineering processes and thermal exchanges. In thermal transfer processing, these are used for the smooth transportation of heat and mass through various mechanisms. In the current investigation, we have examined multiple effects like activation energy thermal radiation, magnetic field, external heat source and especially slippery effects on a bioconvective Casson nanofluid flow through a stretching cylinder.

Several studies used non-Newtonian fluid models to study blood flow in the cardiovascular system. In our research, Lewis numbers for bioconvection and the influence of important parameters, such as Brownian diffusion and thermophoresis effects, are also considered. This system is developed as a partial differential equation for the mathematical treatment. Well-defined similarity transformations convert partial differential equation systems into ordinary differential equations. The resultant system is then numerically solved using the bvp4c built-in function of MATLAB.

After utilizing the numerical approach to the system of ordinary differential equations (ODEs), the results are generated in the form of graphs and tables. These generated results show a suitable accuracy rate compared to the previous results. The consequence of various parameters under the assumed boundary conditions on the temperature, motile microorganisms, concentration and velocity profiles are discussed in detail. The velocity profile decreases as the Magnetic and Reynolds number increases. The temperature profile exhibits increasing behavior for the Brownian motion and thermal radiation count augmentation. The concentration profile decreased on greater inputs of the Schmidt number and magnetic effect. The density of motile microorganisms decreases for the increased value of the bio-convective Lewis number.

The numerical analysis of the flow problem is addressed using graphical results and tabular data; our reported results are refined and novel based on available literature. This method is useful for addressing such fluidic flow efficiently.

In the present-day highly customer-conscious service environment, supply chain management has become a critical component of health-care industry, helping in fulfilling patient expectation, optimizing inventory and automating departmental activities. Supplier selection is one of the crucial elements of health-care supplier chain, establishing mutually beneficial relationships with the reliable suppliers that provide the most value of money. Health-care supplier selection with feasible sets of alternatives and conflicting criteria can be treated as a multi-criteria decision making (MCDM) problem. Among the MCDM methods, grey relational analysis (GRA) appears as a potent tool due to its simple computational steps and ability to deal with imprecise data. The purpose of this paper is to explore the applicability of a newly developed MCDM tool for solving a health-care supplier selection problem.

In GRA, the distinguishing coefficient (ξ) plays a contributive role in final ranking of the alternative suppliers and almost all the past researchers have considered its value as 0.5. In this paper, a newly developed MCDM tool, i.e. dynamic GRA (DGRA), is adopted to evaluate the relative performance of 25 leading pharmaceutical suppliers for a health-care unit based on nine important financial metrics. Instead of static value of ξ, DGRA treats it as a dynamic variable dependent on grey relational variator and ranks the health-care suppliers using their computed rank product scores.

Based on rank product scores and developed exponential curve, DGRA classifies all the suppliers into reliable, moderately reliable and unreliable clusters, helping the health-care unit in identifying the best performing suppliers for subsequent order allocation. Among the reliable suppliers, alternatives A₂ and A₁₁ occupy the top two positions having almost the same performance with respect to the considered financial metrics.

Application of DGRA along with determination of the most reliable suppliers would help in effectively adopting multi-sourcing strategy to increase resilience while diversifying the supply portfolio, thereby enabling the health-care unit to minimize chances of sudden disruption in the supply chain. It can act as an intelligent decision-making framework aiding in solving health-care supplier selection problems considering perceived risks and dynamic input data.

The application of steel fiber reinforced concrete (SFRC) beams is limited in practice, partially due to the lack of accurate shear strength prediction models. This study aims to develop a reliable shear strength prediction model for SFRC beams.

In this study, an artificial neural network was employed to predict the shear strength of SFRC beams, utilizing a comprehensive database of 562 experimental studies. Multiple neural networks were established with varying hyperparameters, and their performance was evaluated using statistical parameters.

The neural network with 11 neurons showed superior results than other networks. The performance evaluation, efficiency and accuracy of the selected neural network were examined using margin of deviation, k-fold cross-validation, Shapley analysis, sensitivity analysis and parametric analysis. The proposed artificial neural network model accurately predicts the shear strength and outperforms other existing equations.

This research contributes to overcoming the limitations of existing prediction models for shear strength of SFRC beams without stirrups by developing a highly accurate model based on ANN. Utilizing a comprehensive database and rigorous evaluation techniques enhances the reliability and applicability of the proposed model in practical engineering applications.