Search results

India has seven local library networks and a national library network (INFLIBNET). However, these networks are in differing stages of development. Most are at a preliminary stage although the earliest efforts in this area took place in 1989. This paper presents the results of a survey on factors affecting the development of local library networks. Participating libraries in four networks were administered a structured questionnaire. The study reveals that organisational factors such as planning, governance, funding communication and delivery, and administration are related to each other. Moreover, many factors are highly dependent on each other. Therefore, the library networks should try to raise the levels of awareness among participating libraries to develop the local library networks successfully.

The purpose of this study is to measure the availability, accessibility and usability of financial products and services in both rural and urban India from 1991 to 2014.

This paper uses principal component analysis (PCA) method to construct financial inclusion index that serves as a proxy variable for indicating the inclusiveness of financial products and services among the rural and urban people. To fulfill this objective, the study proposes separate indexes of financial inclusion for both rural and urban India from 1991 to 2014. The paper uses annual time series data from 1991 to 2014 to construct the rural-urban financial inclusion index. The used data have been collected from the basic statistical returns of Reserve Bank of India and Economic Political Weekly research foundation.

The study inferences that though there is a remarkable increase in financial inclusion in India from 1991 onwards, it does not result in sizeable growth of financial access to rural masses in comparison to urban masses. The rural India does not substantiate an equivalent growth to that of urban India, contrasting a perceptible increase in financial inclusion. The finding of this study will help the researchers and policymakers to understand the status of financial inclusion in the context of both rural and urban India. Furthermore, policymakers can take appropriate policy initiatives to fulfill the financial inclusion gap that exists between rural and urban people. Additionally, the proposed index is easy to compute and can be used to make comparison across countries for further studies.

The present paper attempts to include all possible dimensions (and indicators within a dimension) that have been considered so far by various authors. Therefore, the authors hope that this index will be more indicative and accurate than previous index. Again, the authors propose to use PCA for the first time to assign the weight of factors in the financial inclusion index for rural and urban India separately.

Fish are considered as one of the important aquatic animals in the planet. They play a vital role in the nutrient cycle. They can be considered as one of the healthy food for human beings. They can also act as a solution for some of the human health problems. If fish are affected by several diseases, they in turn provide an adverse effect on human health. Therefore, it is very much essential to protect fish from being affected by any diseases.

This paper is mainly focused on the identification of the red spot diseased area in fish. In this work, a fuzzy rule based method (FRBAM) and triangular membership function (TMFN) is used to identify the red spot disease (RSD) in the fish by analyzing several red spot diseased fish (RSDF) images. The canny edge detector is used for intermediate processing of RSDF images.

The proposed method is able to identify the red pixels over the fish by marking the affected area with red color by using a standard RGB model.

The proposed method follows FRBAM and TMFN in order to detect the RSD and canny edge detector for processing of RSDF images. Finally, it is tested using ten different image sizes and the results show its better performance in terms of detection of RSD affected regions of fish and execution time.

The purpose of this paper is to provide energy-efficient task scheduling and resource allocation (RA) in cloud data centers (CDC).

Task scheduling and RA is proposed in this paper for cloud environment, which schedules the user’s seasonal requests and allocates resources in an optimized manner. The proposed study does the following operations: data collection, feature extraction, feature reduction and RA. Initially, the online streaming data of seasonal requests of multiple users were gathered. After that, the features are extracted based on user requests along with the cloud server, and the extracted features are lessened using modified principal component analysis. For RA, the split data of the user request is identified and that data is pre-processed by computing closed frequent itemset along with entropy values. After that, the user requests are scheduled using the normalized K-means algorithm (NKMA) centered on the entropy values. Finally, the apt resources are allotted to that scheduled task using the Cauchy mutation-genetic algorithm (CM-GA). The investigational outcomes exhibit that the proposed study outruns other existing algorithms in respect to response time, execution time, clustering accuracy, precision and recall.

The proposed NKMA and CM-GA technique’s performance is analyzed by comparing them with the existing techniques. The NKMA performance is analyzed with KMA and Fuzzy C-means regarding P_rc (Precision), R_ca (Recall), F ms (f measure), Acr (Accuracy)and C_t (Clustering Time). The performance is compared to about 500 numbers of tasks. For all tasks, the NKMA provides the highest values for P_rc, R_ca, F_ms and A_cr, takes the lowest time (C_t) for clustering the data. Then, the CM-GA optimization for optimally allocating the resource in the cloud is contrasted with the GA and particle swarm optimization with respect to R_t (Response Time), P_t (Process Time), A_wt (Average Waiting Time), A_tat (Average Turnaround Time), L_cy (Latency) and T_p (Throughput). For all number of tasks, the proposed CM-GA gives the lowest values for R_t, P_t, A_wt, A_tat and L_cy and also provides the highest values for T_p. So, from the results, it is known that the proposed technique for seasonal requests RA works well and the method optimally allocates the resources in the cloud.

The proposed approach provides energy-efficient task scheduling and RA and it paves the way for the development of effective CDC.

This paper aims to analyze, using the bank-level data for India from 1991-2007, the effect of financial sector liberalization on the availability of credit to the private sector. The authors specifically ask whether public and private banks deployed resources freed up by reduced state preemption to increase credit to the private sector.

The authors use bank-level data for India from 1991-2007 and difference in difference estimates to analyze how state ownership of banks affected the allocation of credit to the private sector post liberalization, and additionally how the size of fiscal deficit affected this allocation.

The authors find that post liberalization, public banks continued to allocate a larger share of their assets to government securities, or held more cash, than private banks. Crucially, public banks allocated more resources to hold government securities when fiscal deficit was high. The authors rule out profit maximization, need to hold safer assets or the lack of demand for private credit as the possible reasons for the preference of the public banks to hold government securities. The authors suggest that moral suasion or “laziness” is consistent with this behavior.

Our findings suggest that in developing countries, with fewer alternative channels of financing, government ownership of banks, combined with high fiscal deficit, may limit the gains from financial liberalization.

The purpose of this paper is to identify and build a hierarchy of the factors influencing competitiveness of electronics manufacturing industry (EMI) at the industry level and apply the interpretive structural modeling, fuzzy Matriced’ Impacts Croisés Multiplication Appliquée á UN Classement (i.e. the cross-impact matrix multiplication applied to classification; MICMAC) and analytic hierarchy process (AHP) approaches. These factors have been explained with respect to managerial and government policymakers’ standpoint in Indian context.

This study presents a hierarchy and weight-based model that demonstrates mutual relationships among the significant factors of competitiveness of the Indian EMI.

This study covers a wide variety of factors that form the bedrock of the competitiveness of the EMI. Interpretive structural modeling and fuzzy MICMAC are used to cluster the influential factors of competitiveness considering the driving and dependence power. AHP is used to rank the factors on the basis of weights. Results show that the “government role” and “foreign exchange market” have a significantly high driving power. On the other hand, the “capital resource availability” and “productivity measures” come at the top of the interpretive structural modeling hierarchy, implying high dependence power.

The study has strong practical implications for both the manufacturers and the policymakers. The manufacturers need to focus on the factors of competitiveness to improve performance, and at the same time, the government should come forward to build a suitable environment for business in light of the huge demand and frame suitable policies.

The lackluster performance of the industry is because of the existing electronics policies and environmental conditions. The proposed interpretive structural modeling and fuzzy MICMAC and AHP frameworks suggest a better understanding of the key factors and their mutual relationship to analyze competitiveness of the electronics manufacturing industry in view of the Indian Government’s “Make in India” initiatives.

This paper contributes to the industry level competitiveness and dynamics of multi-factors approach and utilize the ISM–fuzzy MICMAC and AHP management decision tool in the identification and ranking of factors that influence the competitiveness of the EMI in the country.

This paper aims to examine whether board governance mechanisms and ownership structure play a role in foreign investors’ decisions when buying shares in Saudi listed companies

Foreign investment in the Saudi capital market started in 2015 and reached a peak in 2019, with corporate governance regulations having been updated in 2017. The authors tested the proposed relationships using hand collected data for all Saudi non-financial firms in 2019.

This study found that it does not play a role in attracting foreign investment in the Saudi capital market. Foreign investors also seem to avoid firms with concentrated ownership that either have high government or director ownership; however, accounting and market variables show significant impact on foreign investors' decisions. The outcomes of this study provide empirical evidence that current foreign investors in the Saudi stock market do not place enough merit on board governance and their investment decisions tend to depend on share performance. Thus, the results show that the current governance changes and capital market regulations in Saudi Arabia may not have been sufficient to stimulate the inflow of institutional foreign investment to the country to date, but rather they have attracted individual retail foreign investors.

This empirical study is one of only a small number of studies to investigate the impact of internal corporate governance on foreign ownership in developing countries and the first in the Saudi context. In fact, most previous governance research in Saudi Arabia focused on how board governance and ownership structure influences firm performance. A review of the prior studies found that only Badawi et al. (2019) examined the determinants of foreign ownership among Saudi listed firms. Thus, the present investigation extends that study by examining the role of board governance in attracting foreign investors.

The Zhang–Markusen (Z-M) inverse U-shape theory uses education as a human capital variable to investigate the impact of educational attainment on foreign direct investment (FDI) inflows to a country. The objective of this research is to empirically test this theory in a cross-country framework.

Fixed effect panel regression has been used to test the Z-M hypothesis for 172 countries for the period 1990–2015. For the purpose of this study, countries were divided into four groups as per the World Bank classification: Low-income economies, lower middle-income countries, upper middle-income economies and high-income economies.

The findings of this study reinforce the proposition that macroeconomic factors are the major determinants of FDI inflows into various countries. The authors find that the size of the market measured by gross domestic product (GDP), the growth potential of the market measured by real GDP growth rate and the availability of infrastructure are the major factors that enhance the attractiveness of a country as an FDI destination.

Though the Z-M theory has been empirically tested in cross-country frameworks, no consensus has been reached. Thus, it is interesting to look again at the validity of the Z-M hypothesis using data covering longer and more recent periods. The study includes both macroeconomic and human capital determinants of FDI, so as to arrive at a comprehensive model explaining the FDI flows into various countries.

This study aims to provide an overview of the development of corporate governance and ownership structure literature and offers a synopsis of the top contributors, influential articles, journals and potential research prospects on this subject.

This study used bibliometric analysis to review the literature. In all, 1,368 articles published between 1992 and 2022 in Scopus-indexed journals were considered.

This review reveals the top leading authors, institutions, countries and sources in the ownership structure research. Using bibliographic coupling, this study fetches four significant clusters. The theme of the first cluster revolved around cash holding. The second and third groups revealed how distinct characteristics of ownership impact the performance of the firm and disclosure decisions, respectively. The last and fourth cluster deals with risk-taking activities in financial institutions. Furthermore, this study suggests a road map in each cluster for future research.

Ownership structure plays a significant role in corporate governance by affecting manager incentives and determining the extent of monitoring. Previous studies have contributed to this field while focusing on the board of directors. However, no study synthesises the literature on ownership structure within corporate governance, which is the core element of the corporate governance system. Hence, this study gives a comprehensive overview and determines the latest and prominent research in ownership structure within corporate governance through bibliometric analysis.