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This study aims to respond to the latest research calls to precisely revisit the nexus between corporate green innovation (CGI) and financial decisions through deeply investigating the mediating effect of corporate environmental performance measured by the effectiveness of emission reduction.

This study analyzes nonfinancial-listed firms on the Australian Securities Exchange from 2002 to 2019 using multiple regression analysis on a panel data set. Initially, different static panel data approaches were used. To account for the potential endogeneity issue and generate robust outcomes, the authors apply the one-step system generalized method of moment, two-stage least squares and lagged model approaches.

The results provide a clear indication that the practices of green innovation can favorably contribute to the level of environmental performance, which in turn affect the firm’s ability in opening the new financial doors and shape solid capital structure. In this context, the effective environmental performance fully mediates the nexus between CGI and capital structure of a firm. More importantly, the outcomes are robust and coherent across different estimation techniques.

The originality of this study lies in its utilization of mediation analysis to explore the relationship between CGI and a firm's financial structure. This approach distinguishes it from previous research by offering a thorough and nuanced understanding of how green innovation practices influence the financing decisions of a firm.

This study aims to understand the relationship between family incivility and employees' organisational citizenship behaviour. It also explores the mediating role of negative rumination and the moderating roles of workplace friendship and optimism.

To test the hypothesised relationships, 381 responses were collected from employees at two-time points. The hypothesised relationships were tested using process macros.

The results indicated that family incivility is negatively related to organisational citizenship behaviour and increases negative rumination. The study also established the mediating role of negative rumination in the relationship between family incivility and organisational citizenship behaviour. The study also supported the buffering role of workplace friendship and optimism in the relationship between family incivility and negative rumination.

The study findings extend the understanding of how a non-work stressor can influence employee behaviour in the organisation. The study findings provide valuable directions to mitigate family incivility's adverse effects and extend the existing body of knowledge.

The study is unique as it links family events to work outcomes. Only a few scholarly attempts were undertaken to understand the effect of family incivility on employees' work behaviours. By explaining the mechanism and conditions, the study has a unique value to the scholarship.

Techniques for ingesting marketing data encompass systematically organizing, interpreting, and gathering information for practice in marketing analytics. This process starts with collecting data from numerous sources like sales records and customer interactions, followed by transforming and cleaning the data to guarantee accuracy and consistency. This chapter highlights the fundamental part of data ingestion in marketing analytics, underscoring its importance in transforming raw data into some useful insights. We study two types of data ingestion named batch ingestion and real-time ingestion and the practical implementation of ingestion in marketing analytics methods. By careful examination of these approaches, we aim to equip marketers with the tools and knowledge which is necessary for efficient utilization of data and authorize them to generate the decisions and implement that decisions in the data-centric business environment.

Small and medium enterprises (SMEs) significantly contribute to economic growth, development, exports and employment of the nations. To maintain competitiveness in today's market, SMEs must explore and identify enablers to enhance their digital transformation process. This paper aims to shed light on some essential enablers SMEs can use to implement digital resilience successfully.

The quantitative assessment and validation of the enablers have been done using powerful and novel techniques, namely, the Delphi method, “fuzzy interpretive structural modelling” (F-ISM) method and “cross-impact matrix multiplication applied to classification (MICMAC)” analysis. The F-ISM model is developed using the information drawn from digital transformation experts and practitioners involved in the digital transformation process for SMEs. Furthermore, the F-ISM model provides four paths to complete the pathway to digital resilience.

The F-ISM and MICMAC analysis revealed four ways to enhance the digital transformation process in SMEs. These enterprises can utilise these path assessments to become digitally resilient in the present dynamic scenario. To enhance digital resilience among SMEs, the study identified ten enablers. Among these, “management competencies” was the most crucial, followed by “knowledge management” and “monitoring and controlling”.

The present study is limited in that the data used to develop the models were collected from a small group of industry experts whose opinions may not exhibit the comprehensive views of the population.

The findings can help SMEs enhance the digital transformation process by taking up different pathways to integrate the various enablers of digital resilience depending on resource availability.

The results indicate the most critical and influential enablers for enhancing digital resilience among SMEs. This research can be valuable to academicians, industry practitioners and researchers for guiding their future work.

Small and medium-sized enterprises (SMEs) have contributed substantially to the global entrepreneurial environment and economic growth, particularly within emerging economies. However, the success rate of these enterprises is relatively low in most developing countries, particularly India. Therefore, this study aims to contribute towards creating a sustainable model for enhancing entrepreneurial performance by examining the impact of entrepreneur digital literacy, entrepreneurial attitude and innovative work behaviour on their sustainable performance. It also explores the role of innovative work behaviour as a mediator in the relationship between entrepreneurial digital literacy, attitude and SMEs’ sustainable performance.

Data was collected from a sample of 329 entrepreneurs registered with the Department for Promotion of Industry and Internal Trade portal in India using snowball random sampling and was analysed via the structural equation modelling technique.

The findings reveal that entrepreneurial digital literacy and attitude play a robust and critical role in supporting innovation and SMEs’ long-term success, respectively. Entrepreneurial digital literacy equips individuals with the skills to navigate digital tools effectively, fostering innovation within SMEs and enabling them to adapt to evolving market demands. Similarly, a positive entrepreneurial attitude, characterised by a proactive and risk-taking mindset, is essential for driving SMEs’ long-term success by facilitating growth and adaptation in competitive business environments. Moreover, the relationship between entrepreneurial digital literacy, attitude and sustainable performance was found to be partially mediated by innovative work behaviour. Innovative work behaviour, including creativity, proactivity and risk-taking, is a bridge, translating digital literacy and entrepreneurial mindset into tangible outcomes that contribute to sustainable entrepreneurial performance and resilience in dynamic market conditions.

The described research work offers a valuable contribution to the field by integrating digital literacy and entrepreneurial attitude within the context of entrepreneurship. Exploring how innovative work behaviour mediates the relationship between digital literacy, entrepreneurial attitude and sustainable entrepreneurial performance contributes to a deeper understanding of the mechanisms driving success in today’s digital economy.

The present paper aims to conduct a comprehensive scientific mapping of exchange rate forecasting, highlighting trends, developments, and methodological changes. This will provide research scholars, researchers, and policymakers valuable insights to facilitate predictions.

The researchers performed a bibliometric analysis of exchange rate forecasting using a scientific search method on the Scopus database from 1991 to 2022. They applied a web interface program called Biblioshiny, part of the Bibliometric package built in R by Aria and Cuccurullo (2017). VOSviewer was used to identify the most influential journals, authors, countries, articles, citations, and co-citations from 1,602 documents.

The conceptual and intellectual framework of the papers under review provided an in-depth and comprehensive analysis of the domain. The research outcomes provided essential information on the subject matter, highlighting the need for further investigation. The study’s findings demonstrate the evolution of the theory of forecasting exchange rates reflecting continuous developments in the methodologies applied to forecast the exchange rates.

The scientific mapping of the present study’s bibliometric analysis is limited to the Scopus database because of its comprehensive coverage of high-quality journals and predefined structures compatible with Bibliometrix software.

The study provides considerable insight into forecasting exchange rates and their interlinkages. By outlining the social and intellectual structure of the field, it directs upcoming scholars about the relevant topics, contexts and potential collaborations emerging in this field. The study also reveals the critical concerns in the area and leads to potential research opportunities.

The study sheds light on emerging trends and approaches to forecasting exchange rates and will provide valuable information and insights to policymakers, economists, investors and decision-makers in the financial sector. It will contribute to prioritising research in overlooked areas and support the formulation of effective policies.

This study contributes significantly by bringing together disparate literature on exchange rate forecasting, highlighting important journals, authors and documents, and examining the recent studies on the foreign exchange rate.

The current work investigates an unsteady squeezed flow of hybrid-nanofluid between two parallel plates in occurrence with a uniform transverse magnetic field. Water is used as base fluid mixed with Graphene Oxide (GO) and Copper (Cu) nanoparticles. The flow considered here is under slip boundary conditions.

The governing PDEs are transmuted into ODEs by applying an appropriate similarity transformation and then solved numerically using the 4th order R-K method with shooting technique. Graphical illustrations for velocity, temperature, entropy generation (NG), Bejan number (Be), streamline etc. are presented and discussed in detail from the physical point of view. The nature of the Nusselt number is also studied numerically through contour plots for different flow parameters.

There is no funding obtained for the research work.

This kind of study may be used in various fields including polymer processing, lubrication apparatus, compression including hydrodynamical machines compression, food processing etc.

It is observed that very little investigation has yet been made about the movement of hybrid nanofluid between two analogous plates.

In the digital marketing era, the strategic importance of data infrastructure has never been more pronounced. This chapter examines 34 case studies across various industries to uncover robust data infrastructures’ pivotal role in enhancing data-driven marketing campaigns. Among these, the selected five studies showcase organizations like Netflix, Amazon, Spotify, Airbnb, and Starbucks, demonstrating innovative uses of technology convergence, including artificial intelligence (AI), big data analytics, and cloud computing, to achieve marketing objectives. Their success stories are a testament to the power of robust data infrastructures, inspiring businesses to leverage their data assets more effectively. Through detailed analysis, this chapter identifies key challenges related to data quality, accessibility, and the integration of disparate technologies within marketing frameworks. It further explores solutions that have led to improved customer personalization, dynamic pricing models, and significant return on investment (ROI) enhancements. These findings provide practical insights that can be directly applied to your business. Additionally, this chapter highlights the emerging focus on sustainable infrastructure practices and the criticality of a unified data approach in optimizing marketing strategies. By synthesizing insights from leading market players, this study helps a comprehensive understanding of how modern data infrastructure underpins the success of data-driven marketing, offering valuable lessons for businesses aiming to leverage their data assets more effectively.