Search results

This study aims to review financial inclusion as a catalyst to reduce financial scams and frauds faced by women in the five largest US states by population and proposed measures encouraging women’s financial safety.

Recognizing the unique socioeconomic landscape, the study seeks responses through a survey questionnaire from 4,113 women respondents analyzed by using a basic mixed-methods approach, including quantitative surveys analyzed through SPSS and qualitative short interviews thematically analyzed by using Nvivo.

The review results show that 94% of women believe that financial inclusion can protect them from scams and fraud. Also, it has been observed that financial crimes disproportionately affect women, often stemming from a desire to conceal such activities from close family members and partners. Older women, housewives and those from financially depressed areas need more financial inclusion plans to curb financial fraud.

The proposed measures may have positive social and economic implications on the females residing in the financially depressed areas.

The study represents the authors’ original contribution, examining the role of financial inclusion in preventing women from engaging in financial crimes.

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 purpose of this paper is to provide a systematic and comprehensive review of the existing literature on the determinants of firms reporting practices.

Following a systematic method, the sample literature of 135 studies was collected from the Scopus database. These studies were evaluated in terms of the theoretical lenses applied in the literature, yearly trend, regional distribution, research settings and prior studies finding to provide some recommendations for further research.

The investigation revealed that the literature was more interested in the agency theory in investigating the drivers of voluntary reporting such as company size, age, leverage, liquidity, profitability, corporate governance and ownership structure. Although firm-specific determinants were the most examined in the previous studies, however, the result is still inconclusive. Also, limited work was found on the country-related factors, while internal audit impact has yet to be explored.

Being the first of its kind, this research provides a comprehensive review of the current research landscape on the drivers of environmental or social disclosure and highlights several interesting opportunities for future research.

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 examine the existence of capital structure dynamics and speed of adjustment during different economic periods. This study adds to the existing body of literature by investigating the factors influencing adjustment process toward target debt in developing economies.

By employing two-step generalized method of moment (GMM) and sensitivity analysis, the study highlights critical factors which affect firms’ adjustment mechanism for target debt.

Dynamic GMM estimations confirm the substance of past leverage on current debt, which recognizes the existence of dynamic capital structure. The findings corroborate that adjustment process is subject to trade-off between convergence rate and cost of being off-target. The fraction of financing of Pakistani firms confirms the pattern of pecking order hypothesis. The outcome of study clearly validates the significance of dynamic trade-off modeling for optimal capital structure.

As more data become available, the authors would extend this study to investigate the sectoral analysis to find how capital structure dynamics are different across sectors and how distinctive behavior of each sector differently affects the adjustment process toward target debt across each sector. In addition, sector-level and macro-economic factors could be incorporated to examine how external factors affect the firm’s speed of adjustment across sectors.

The present study provides valuable insights for banking and corporate sector, mainly in Pakistan. The companies could take into consideration the firm-level factors which affect the adjustment process toward target debt. Likewise, the borrowing and lending procedures could be advanced by complying with dynamic mechanism of speed of adjustment. Furthermore, the findings of this research provide obstinate grounds for future research.

Both the use of dynamic GMM adjustment model and sensitivity analysis along with Sargan test validate the health of instruments and values.

The purpose of this paper is to examine the long-run relationship and short-term linkage between the Asian REIT markets and their respective macroeconomic variables.

The data collected comprised total return REIT Index from Japan, Hong Kong, Singapore, Malaysia, Thailand, Taiwan and South Korea and their macroeconomic variables from the date of availability of the data until December 2014. The macroeconomic variables are either available in monthly or quarterly basis, they will be separately tested with REIT Index respectively to their frequency. All the variables are tested for its stationarity prior to the investigation of their long-run relationship and short-term linkage using Johansen cointegration test and Granger causality test.

The results showed that certain of the emerging REIT markets show a higher degree of integration with macroeconomic variables in the long run. This implies that the emerging REIT markets are more sensitive towards the change in macroeconomic environment in relative to the developed REIT markets.

The paper implied that the distinction of each market structure and their unique way of policy implementation. The findings can assists policy makers to understand about the significance of policy implementation on the Asian REIT markets prior to decision making and also for the portfolio management my asset managers.

The paper is one of the few attempts at assessing the long-term relationship and short term linkage between the Asian REIT markets and the macroeconomic variables.

Brain tumor segmentation and classification is the interesting area for differentiating the tumorous and the non-tumorous cells in the brain and classifies the tumorous cells for identifying its level. The methods developed so far lack the automatic classification, consuming considerable time for the classification. In this work, a novel brain tumor classification approach, namely, harmony cuckoo search-based deep belief network (HCS-DBN) has been proposed. Here, the images present in the database are segmented based on the newly developed hybrid active contour (HAC) segmentation model, which is the integration of the Bayesian fuzzy clustering (BFC) and the active contour model. The proposed HCS-DBN algorithm is trained with the features obtained from the segmented images. Finally, the classifier provides the information about the tumor class in each slice available in the database. Experimentation of the proposed HAC and the HCS-DBN algorithm is done using the MRI image available in the BRATS database, and results are observed. The simulation results prove that the proposed HAC and the HCS-DBN algorithm have an overall better performance with the values of 0.945, 0.9695 and 0.99348 for accuracy, sensitivity and specificity, respectively.

The proposed HAC segmentation approach integrates the properties of the AC model and BFC. Initially, the brain image with different modalities is subjected to segmentation with the BFC and AC models. Then, the Laplacian correction is applied to fuse the segmented outputs from each model. Finally, the proposed HAC segmentation provides the error-free segments of the brain tumor regions prevailing in the MRI image. The next step is to extract the useful features, based on scattering transform, wavelet transform and local Gabor binary pattern, from the segmented brain image. Finally, the extracted features from each segment are provided to the DBN for the training, and the HCS algorithm chooses the optimal weights for DBN training.

The experimentation of the proposed HAC with the HCS-DBN algorithm is analyzed with the standard BRATS database, and its performance is evaluated based on metrics such as accuracy, sensitivity and specificity. The simulation results of the proposed HAC with the HCS-DBN algorithm are compared against existing works such as k-NN, NN, multi-SVM and multi-SVNN. The results achieved by the proposed HAC with the HCS-DBN algorithm are eventually higher than the existing works with the values of 0.945, 0.9695 and 0.99348 for accuracy, sensitivity and specificity, respectively.

This work presents the brain tumor segmentation and the classification scheme by introducing the HAC-based segmentation model. The proposed HAC model combines the BFC and the active contour model through a fusion process, using the Laplacian correction probability for segmenting the slices in the database.

Lean Six Sigma (LSS) is a widely accepted business improvement methodology in healthcare, which aims to improve operations and quality and reduce cost, medical errors and waiting time by combing the principles of lean thinking with Six Sigma methodologies. To implement LSS successfully in healthcare organizations it is necessary to know the readiness level before starting the change process. Thus, the purpose of this paper is to assess the readiness level for the implementation of LSS in healthcare using a fuzzy logic approach.

The current study uses a fuzzy logic approach to develop an assessment model for readiness to implement LSS. The conceptual model for readiness is developed with 5 enablers, 16 criteria and 48 attributes identified from the literature review. The current study does the study in a medium-size hospital from India.

The fuzzy readiness for implementation of LSS index (FRLSSI) and fuzzy performance importance index (FPII) are calculated to identify the readiness level for the implementation of LSS in the case hospital. The FRLSSI is computed as average ready with (3.30, 5.06 and 6.83) and the FPII computed helps to identify 15 weaker attributes from 48 attributes.

The current study uses only one hospital for study. In the future, the model can be tested in many hospitals.

The current study would be used by the managers of a healthcare organization to identify the readiness level of their organization to implement LSS. The proposed model is based on the identification of enablers, criteria and attributes to assess the readiness level of a healthcare organization and it helps to improve the readiness level to implement LSS effectively.

The present study contributes to the knowledge of readiness for the implementation of LSS in a healthcare organization. The conceptual model is developed for assessing the readiness level of a healthcare organization and it helps to improve the readiness level for successful implementation of LSS. Weaker attributes are identified and necessary corrective actions should be taken by the management to improve the readiness. The continuation of the assessment readiness model over a period of time would help to improve the readiness level of healthcare for the implementation of LSS.

This study aims to explore the relative efficiency of the working capital management (WCM) for Emirati firms before and during the coronavirus crisis. Next, this study explores the potential impact of WCM on the likelihood of financial distress.

A data envelopment analysis (DEA) was applied to assess the relative efficiency of the WCM. This study uses the emerging market Z-score model to predict the likelihood of financial distress. The logistic regression was applied to investigate the impact of the efficiency of WCM on firms’ financial distress.

The results of this study model showed a negative and significant influence of the efficiency of WCM on firms’ financial distress likelihood.

The findings have important implications for many stakeholders, including decision makers, WC managers, financiers, investors, financial consultants, researchers and others, in increasing their awareness of firms’ WCM performance before and during the crisis. Further, the results could have implications for trading strategies as investors seek attractive economic gains from their investment in firms that care about WCM.

The implications of WCM performance on social interests would cause firms’ decision makers to operate efficiently and achieve the best practices to minimise the probability of firms' financial distress.

This study advances a novel contribution to the literature by introducing a novel model to assess WCM based on DEA technology.