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The purpose of this paper is to systematically analyze the literature around regulatory compliance and market manipulation in capital markets through the use of bibliometrics and propose future research directions. Under the domain of capital markets, this theme is a niche area of research where greater academic investigations are required. Most of the research is fragmented and limited to a few conventional aspects only. To address this gap, this study engages in a large-scale systematic literature review approach to collect and analyze the research corpus in the post-2000 era.

The big data corpus comprising research articles has been extracted from the scientific Scopus database and analyzed using the VoSviewer application. The literature around the subject has been presented using bibliometrics to give useful insights on the most popular research work and articles, top contributing journals, authors, institutions and countries leading to identification of gaps and potential research areas.

Based on the review, this study concludes that, even in an era of global market integration and disruptive technological advancements, many important aspects of this subject remain significantly underexplored. Over the past two decades, research has lagged behind the evolution of capital market crime and market regulations. Finally, based on the findings, the study suggests important future research directions as well as a few research questions. This includes market manipulation, market regulations and new-age technologies, all of which could be very useful to researchers in this field and generate key inputs for stock market regulators.

The limitation of this research is that it is based on Scopus database so the possibility of omission of some literature cannot be completely ruled out. More advanced machine learning techniques could be applied to decode the finer aspects of the studies undertaken so far.

Increased integration among global markets, fast-paced technological disruptions and complexity of financial crimes in stock markets have put immense pressure on market regulators. As economies and equity markets evolve, good research investigations can aid in a better understanding of market manipulation and regulatory compliance. The proposed research directions will be very useful to researchers in this field as well as generate key inputs for stock market regulators to deal with market misbehavior.

This study has adopted a period-wise broad-based scientific approach to identify some of the most pertinent gaps in the subject and has proposed practical areas of study to strengthen the literature in the said field.

This particular study examined the government bond price variations in order to determine the presence of excess volatility both at country and panel group level of BRICS countries context.

The study applied the autoregressive GARCH panel model approach proposed by Fakhry and Richter (2015) to evaluate the presence of excess volatility and then examined the diversification benefits. Further, the use of discrete wavelet transformation (DWT) has added the advantage to observe volatility across bonds along with potential diversification benefits by retaining information from the time and frequency domain perspective for both the maturities.

The main finding indicates that the excess volatility is present in BRICS countries at individual level i.e. in the case of Russia, India and China. However, the 10-year bond showing a less volatility compared to 5-year bond with the possibility of reaping out the benefits of diversification with international portfolio of sovereign bonds.

The main implication of the research is related to the non-perseverance of EMH as far sovereign bonds of BRICS countries are concerned as the results indicate presence of excess volatility in the 5-year and 10-year bond markets. However, the implicit behavior of 5-year bond could benefit the active fund managers and investors by taking an advantage of a reducing systemic risk through short-medium term investments.

This study contributes not only to the existing studies of similar nature by examining the excess volatility in bond markets but also taking account of co-moment of distinct maturities to confirm possible international diversification benefits for BRICS countries context.

This paper aims to provide a comprehensive conceptual framework and strong arguments with an intent to examine the stock market variables (predictors) indicating the money laundering (ML) and terrorism financing (FT) proceeds.

This paper provides a comprehensive review of ML/FT through the stock market across developed, developing and emerging jurisdictions, sheds light on the existing literature and critically evaluates the gap in the relevant studies. Moving forward, this paper develops the conceptual framework and formulates hypotheses to explore the empirical relationship.

This paper advocates and finds a basis to carry out much-needed empirical research between the ML/FT and stock market keeping in view the growing criminal cases in the developing countries. This paper suggests mining proxies from the publically available stock market data and the results of existing seminal research as variables of the study. These data and results carry information about the ML determinants. After developing hypothetical research providing concepts, this paper also finds that using a suitable methodology, preferable Bayesian logistic and linear regression models, it is possible to find the typologies and factors that can indicate and endorse the use of the stock market for ML/FT. Broadly, it is found that the significance of this study will be two-pronged: empirical development and policy implications.

This paper mainly focuses on the developing region, a newly emerging market and, peculiarly, a grey-listed region by the Financial Action Task Force (FATF).

In light of the existing literature and to the best of the researchers’ knowledge, this study will bring into focus the new age of the action research on the ML regime in the securities markets of the developing countries, hence, the emerging markets. Moreover, this research shall have a sheer significance for the policy measures on FATF recommendations on ML and FT, especially for the countries listed as “grey”.

The research based on comprehensive review will help in controlling the social behaviours aiding the proceeds of ML.

This research is extremely novel to the best of the researcher's knowledge.

This study aims to diagnose the global key contributors in the stock market manipulation studies during the past four decades.

The database search is based on the terms used in the existing body of knowledge. Using the bibliometric tools and techniques on the Scopus database, the study assessed and analysed the productivity of research studies, as well as the influence of the authors, publications, journals, affiliated institutions and countries.

This paper finds the USA as the leading country investigating this area, almost capturing 40% of the research studies in finance, moreover, a huge number of co-authors. Financial crises in the late 1990s and 2008 is observed as one of the main reasons for this intriguing research. The Journal of Finance is spotted as the most persuasive journal with the highest cite score and an unprecedented number of citations. The analysis of keywords engendered that most of the stock market manipulation studies are event-based studies. Seminally unique scientometric analysis revealed that the significance of stock market manipulation was mainly captured by event-based studies, insider trading and pump and dump schemes studies. However, much remained untapped to articulate the bridging scope of technology and media with stock market behaviour and manipulations.

The research only includes the Scopus database, however, incorporates 81% relevant study.

This study reckons that technology-based manipulations are emerging themes in this research field which invites the applied research to have productive outcomes.

The intriguing study incorporates a maximum number of the relevant literature and used a comprehensive technique for the selection of dataset in Scopus.

The present study investigates the moderating role of customer trust in customer relationship management (CRM) components and customer loyalty relationships in the context of the baking sector in Bangladesh.

Data were collected through a survey using a structured questionnaire from 350 customers of commercial banks in Bangladesh.

The key finding is that all CRM components (customer orientation, customer advocacy and customer knowledge) except customer engagement have positive impact on customer loyalty. Moreover, customer trust only moderates the relationship between customer knowledge and customer loyalty, whereas other CRM components and customer loyalty do not moderate by trust.

The findings of the study add to the substantial pool of knowledge on CRM components, customer trust and customer loyalty literature. More specifically, the moderating role of customer trust between customer knowledge and customer loyalty is the novel contribution of this research which will enrich the existing CRM literature particularly in the banking sector of Bangladesh.

An emerging body of literature has pinpointed the role of supply chain structure in influencing the extent to which supply chain members disclose information about their internal practices and performance. Nevertheless, empirical research investigating the effects of firm-level relational embeddedness on network-level transparency still lags. Drawing on social network analysis, this research examines the effect of relational embeddedness on supply chain transparency and the contingent role of digitalization in the context of environmental, social and governance (ESG) information disclosure.

In their empirical analysis, the authors collected secondary data from the Bloomberg database about 2,229 firms and 14,007 ties organized in 107 extended supply chains. The authors employed supplier and customer concentration metrics to measure relational embeddedness and performed multiple econometric models to test the hypothesis.

The authors found a positive effect of supplier concentration on supply chain transparency, but the effect of customer concentration was not significant. Additionally, the digitalization of focal firms reinforced the impact of supplier concentration on supply chain transparency.

The study findings contribute by underscoring the critical effect of relational embeddedness on supply chain transparency, extending prior literature on social network analysis, providing compelling evidence for the intersection of digitalization and supply chain management, and drawing important implications for practices.

This study aims to investigate the effect of post-treatment on anti-corrosion performance of Al coating on the surface of Ti-6Al-4V (TC4) fastener.

The Al coatings with different layer structures were prepared on TC4 by middle-frequency and direct-current combined magnetron sputtering. The cross-sectional morphology and surface roughness of coatings were characterized by scanning electron microscope and atomic force microscope. The corrosion resistance was evaluated by electrochemical method. The monolayer coating was post-treated by Alodine chemical conversion, Ar⁺ bombardment and a combination of two methods above.

The results show that the interfaces in bilayer and trilayer coatings reduce the defects. Ar⁺ bombardment reduces the corrosion current density, and Alodine chemical conversion leads to a higher pitting corrosion potential. The combined post-treatment has the highest polarization resistance.

The corrosion resistance of the Al coating is enhanced as the layer quantity increases. The combination of two post-treatments, Ar⁺ bombardment and Alodine chemical conversion, could achieve an overall improvement in corrosion resistance of Al coating.

This paper aims to discover and analyze the challenges hampering blockchain technology’s (BT’s) implementation in the Indian health-care sector. A total of 18 challenges have been prioritized and modeled based on an extensive literature search and professional views.

An integrated multi-criteria decision-making approach has been used in two phases. Best worst method (BWM) is used in the first phase to prioritize the challenges with sensitivity analysis to validate the findings and eliminate a few challenges. In the second phase, interpretive structural modeling is applied to the remaining 15 challenges to obtain relative relationships among them with cross-impact matrix multiplication applied to classification analysis for their categorization.

The study’s results reveal that limited knowledge and expertise, cost and risk involved, technical issues, lack of clear regulations, resistance to change and lack of top management support are the top-ranked or high-intensity challenges according to the BWM. Interpretive structural modelling findings suggest that the lack of government initiatives has been driving other challenges with the highest driving power.

This work has been conducted in the Indian context, so careful generalization of the results is needed.

This work will give health-care stakeholders a better perspective regarding blockchain’s adoption. It will help health-care stakeholders, service providers, researchers and policymakers get a glimpse of the strategies for eradicating mentioned challenges. The analysis will help reduce the challenges’ impact on blockchain’s adoption in the Indian health-care sector.

The adoption of BT is a novel concept, especially in developing countries such as India. This is one of the few works addressing the challenges to BT adoption in the Indian health-care sector.

This paper aims to conduct bibliometric analysis on the studies dealing with capital structure’s speed of adjustment (SoA) and identify the prominent themes while suggesting future research directions in the area. The existing reviews broadly focus on the capital structure, which provides the scope for conducting a review on this sub-aspect of capital structure.

This study uses a three-stage process to conduct this review: identification of academic journals, selection and analysis of target papers. This study uses a combination of bibliometric tools and a system thinking approach to assess the current status of publications and emerging themes within the literature.

This study has found a progressive evolution of SoA in capital structure research from 1984 to 2021. Studies largely focus on implementing the dynamic models to analyse the impact of adjustment costs, dynamic economic conditions, corporate governance practices and other variables on the firms’ adjustment speed and financial decisions. The network analysis of citations, keywords and clusters gives further knowledge on the intellectual structure of the data.

This study is highly dependent on the papers available within the SCOPUS database. Studies not included herein are not part of this analysis, which may or may not bear an effect on the study’s findings.

To the best of the authors’ knowledge, the application of systems engineering concept of “system thinking approach” to identify literature gap and suggest directions for forthcoming research is the first of its kind, thus adding a novel and multidisciplinary aspect to this study.

This paper aims to investigate the non-homogeneity in the speed of adjustment (SoA) of the capital structure of manufacturing companies. It also attempts to study the key determinants that accelerate the speed of adjustment towards the target leverage level.

Using the dynamic panel fraction (DPF) estimator on the partial adjustment model, the study captures the heterogeneous SoA of 2,866 firms across eight prominent sectors of the Indian manufacturing industry from 2009 to 2020. To ensure robustness, the empirical inferences of DPF are cross-verified with the estimates of panel-corrected standard errors (PCSE).

The authors find a combination of the capital structure's slow, moderate and rapid adjustment speed along with the relevance of trade-off theory. Interestingly, the lowest and fastest SoA is recorded by the dwindling textile sector and expanding food and agro sector, respectively. Profitability, firm size, asset tangibility and non-debt tax shields are the key firm-specific parameters that impact the SoA towards the target.

Availing the rarely employed estimator ‘DPF’ and the objective of documenting diverse and non-uniform adjustment speeds across the Indian manufacturing sectors marks a novel addition to capital structure literature.