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Significant evidence in the literature points to money demand instability and therefore inaccurate forecasting. In view of this issue, this chapter seeks to use a method, innovative for money demand literature, that is, the machine learning model to predict money demand. Specifically, this chapter uses Random Forest Regression to predict money demand using monthly data in the Indian context over the period April-1996 to December-2018 using the variables usually used in literature. The chapter finds that in money demand prediction, the Random Forest Regression performs fairly well. The results are also compared to traditional models and it is found that the Random Forest Regression model has the potential to enhance the prediction of money demand over what traditional models predicts.

This paper aims to explore the causal relationship of user-generated content (UGC) on trust in UGC (TUGC), destination image (DI) and tourist visit intention (VI) guided by the stimulus-organism-response (SOR) theory. This work further examined the mediating effect of TUGC and DI between UGC and VI.

Data were collected through online and personal-administered surveys and randomized sampling. This work employed partial least squares structural equation modeling (PLS-SEM) to test the proposed model empirically.

In line with the SOR Theory, this work found UGCs that induce positive emotions and connection to the users motivate VI rather than UGCs that are predominantly factual. Furthermore, UGCs are considered reliable, authentic and less biased than brand-generated content. The findings of this work contribute to the theoretical understanding of UGC to VI in a destination.

This work proposes that destination marketers prioritize UGC that evokes positive emotions and connections with users, as it is more effective in encouraging VI. Strategies such as incentivizing content creators, improving online presence and engaging influencers can maximize UGC. Enhancing online traffic quality, visibility and interaction and implementing content policies are crucial for UGC's effectiveness. Marketers should align destination products with tourists' interests and collaborate with influencers for affiliate marketing to increase tourist-generated UGC. Furthermore, improved connectivity encourages UGCs about the destination.

In tourism marketing, UGC has become a valuable information source for tourists in making informed travel decisions. UGC is a tourist-generated content that offers factual information and authentic experiences through images, videos or text posted through social media platforms. UGC is considered more reputable than travel firms and the mainstream media as an information source. Due to the limited works on UGC in the literature, the influence of UGC on tourists' VIs has remained unexplored at the time of writing. This work bridges this gap by empirically examining the impact of UGC on Gen Z tourists' VI guided by the SOR theory.

Banks play a vital role in the economy. Investigating their competitive environment is crucial to ensuring economic stability and development. The FinTech disruption has risks and opportunities for incumbent banks, and it can be valuable to investigate its effects on banking performance. Therefore, the aim of this study is to assess whether investment in FinTech is associated with better performance of Indian banks during 2012–2018.

To do this, a sample of Indian banks was investigated between 2012 and 2018 using k-means and hierarchical cluster analysis, ANOVA, and pairwise comparison tests.

Results of the analysis strongly suggest that investment in FinTech is associated with better banking performance. Higher FinTech investments, represented by mobile transaction volume, are associated with higher efficiency scores and accounting-based performance. In particular, banks that invest in FinTech and have relatively low non-performing loans have a 7.7% higher Return on Employment (ROE) than banks with exceptionally low FinTech use and no significant investment in smart branches.

Therefore, it can be recommended that Indian banks adopt a forward-looking strategic approach when making investment decisions regarding new technologies. Failing to adapt to the FinTech disruption may result in poor value creation prospects in the long run.

To the best of the authors' knowledge, this is the first study that analyses. We are not aware of any similar study on whether investment in FinTech is associated with better performance of the Indian banks during 2012–2018.

The purpose of this paper is to analyze important theoretical work conducted in the research streams of coopetition dynamics and knowledge flows in the area of start-up entrepreneurship. The authors see in practice that venture capital (VC) firms are a highly essential component of the environment that gives birth to entrepreneurial ventures, helping them to grow profoundly. Interorganizational collaborations facilitate VC firms to be a beneficial partner because except for providing funding, they also possess knowledge-based resources to support the new business.

A systematic review of the literature was conducted, using relevant keywords and academic databases. Then, the backward search was implemented to examine the references of the selected papers, and finally, the forward search to explore the citations of the selected papers. After the selection of papers, they were classified according to their content. A thorough search of the extant literature was done in Scopus and Google Scholar using a combination of keywords such as coopetition, knowledge flows, VC firms, interorganizational and inter-firm knowledge dynamics.

This paper highlights the capability of venture capitalists and provides insights as to how knowledge transfer and sharing between VC firms affect new venture’s growth and prosperity.

This paper attempts to provide new perspectives and explore the significance of interorganizational coopetition and knowledge transfer and sharing between VC firms when they take part in the support and development of new ventures (e.g. start-ups). A theoretical model is proposed via the coopetition dynamics and inter-firm knowledge flows in the VC sector framework.

This paper adds to the existing theoretical knowledge and underlines the topic of interorganizational coopetition and knowledge flows between VC firms. This is the first attempt, on the one hand, to link inter-firm knowledge flows and new venture development, while on the other to examine the dynamics between VC firms and the collective contribution for the growth of start-ups.

By examining the impact of the day of the week during the COVID-19 pandemic and the subsequent economic recession, it is possible to provide insights into market behaviour during volatile times that can be furnished to investors and policymakers for informed decisions.

This study investigates the day-of-the-week effect on the Colombo Stock Exchange (CSE), with particular emphasis on the variations in this effect during the COVID-19 pandemic and the subsequent economic crisis.

The study applies the Exponential Generalised Autoregressive Conditional Heteroskedasticity (EGARCH) model, allowing for the evaluation of asymmetric responses to positive and negative shocks. The data span from January 2006 to December 2022 and are segmented into different periods: the entire sample, war and post-war periods, the COVID-19 pandemic and the economic crisis period, each reflecting distinct market conditions.

The study uncovers a significant day-of-the-week effect on the CSE. Mondays and Tuesdays typically show a negative effect, while Thursdays and Fridays display a positive impact. However, this pattern shifts notably during the COVID-19 pandemic, with all weekdays exhibiting significant positive impact, and varies further across different waves of the pandemic. The economic crisis period also shows unique weekday effects, particularly before and after an important political event.

A literature review or review article is an integral part of a scientific body of research which synthesizes prior knowledge and provides a holistic overview of a subject domain. While several studies emphasize the significance of literature reviews and include the guidelines for conducting a review, limited studies demonstrated different types of literature review methodologies in a comprehensive way. Accordingly, this chapter presents various types of review methodologies which includes narrative, descriptive, systematic, meta-analysis, hybrid, umbrella, scoping, theoretical, and critical reviews. In addition, the authors' skills including logical reasoning, content analysis, literature mapping, critical writing, and ethical consideration are presented. Further, quality aspects of the literature review are discussed such as the rigor and relevance of the selected studies. Overall, this chapter provides implications for researchers in understanding types of literature review methodologies along with their objectives, strengths, and weaknesses which can assist them in selecting a suitable methodology while conducting a review.

The aim of this review is to reflect the current state of Financial Technology (FinTech) research along with its journey of development. Further, a conceptual framework showing the interaction of independent, mediating, and moderating variables with dependent variables (acceptance of FinTech products and services) along with propositions is prepared to facilitate the future researchers. This systematic literature review consists of 110 articles from 78 journals indexed in two academic databases (Scopus and/or Web of Science), extracting facts and figures about FinTech during 2016–2021. Our findings contribute to the literature by exemplifying that FinTech is a mixed set of threats and opportunities. In the present review only 18 articles belong to 2016–2017 but 54 articles are considered from 2020–2021, the increasing number of FinTech articles in high-ranking journals indicate the speedily growing popularity of FinTech. Similarly, secondary data based articles are dominating the primary data based ones. Further, regression analysis and PLS-SEM are the most popular statistical techniques among the authors of FinTech articles. To the best of knowledge of the authors, this is a unique study in which the latest FinTech research findings are skimmed.

The purpose of this study is to examine characteristics of retracted publications from Indian authors and inspect a relationship between journal impact factor (JIF) and the number of authors (NoA).

The authors examined the general characteristics of retracted publications and investigated the correlation between JIF and NoA from Indian authors from January 1, 2017, to December 31, 2022. Data were mined from retraction watch http://retractiondatabase.org/ (n = 1,459) and determined the year of publication, year of retraction, authors, journals, publishers and causes of the retractions. A journal citation report was extracted to gather the JIFs.

About one-third of retracted papers were published in 2020; 2022 has the highest retraction rate (723); studies with two authors represent about one-third (476) of the published articles; Journal of Ambient Intelligence and Humanized Computing (354) has the highest number of retractions; Springer published the most retracted papers (674); and the majority of the journal (1,133) is indexed in journal citation reports, with impact factor extending from 0.504 to 43.474. Retraction due to legal reasons/legal threats was the most predominant reason for retraction.

This study reflects growth in author collaborations with a surge in the JIF. This study recommends that quick retraction is essential to reduce the adverse effects of faulty research.

Blockchain, in general, diversifies supply chain management in record-keeping and maintains authenticity. In addition, traditional issues and challenges like overflow and information overload press down mysteriously whenever the blockchain steering wheel of the supply chain turns. Factually, the miracle and twists in supply chain resilience have not been incorporated under systematic review homogeneously. As a result, this study reviews the potential impact of blockchain on logistics and supply chain (LSC) efficiency. The methodology of this study provides a subjective assessment of the utility of blockchain-based LSC performance.

On the other hand, the review reveals new insights on its current acceptance and applications, with a particular emphasis on the Limit Redundancy Mechanism and Core Information-based Direct Comparison. Prospectively, the identified facts under the research paradigm and extensive literature survey will subsidize the practices of blockchain technology and possible areas of extension in supply chain resilience in luminous fashion in the future. After all, this study materializes new solid magnitudes, adaptability, and a realistic overview of blockchain-based LSC movements.