Effects of Corporate Social Responsibility on Organizational Performance: A Conceptual and Literature Review

Sustainable development is considered as or can be one of the ways in achievement, progress, and benefit for organizations. Organizations use and utilize this source to require a system on which they can rely upon and keeping in mind the end goal to recognize openings and associated risks and to make, realize, control, and upgrade the sustainable goals of corporations which to be both progressively viable (for themselves and the overall population) and increasingly productive in terms of monetary. Based on the content analysis and literature review, this paper review and proposed the conceptual framework on the Corporate Social Responsibility (CSR) and its effect on Organizational Performance of Pakistan’s retail banks. It is conceptual type of research which possesses four hypotheses, first Independent Variable is CSR and Four Dependent Variables such as, Organizational Performance (OP), Financial Performance (FP), Employee Commitment (OC) and Organizational Reputation (OR) and these are not proven in this research. This paper suggests that by initiating the practices of corporate social responsibility can improve the performance of the organization and also increase its financial position, commitment level of the employees and improve the overall reputation or the company. Different literature suggests that this research could be done with primary as well as secondary sources sometimes the researchers have used mixed form of methodologies, this study is based on secondary data using internet which is one of the powerful tools to generate the data. Researchers conclude and give some suggestions for the future research work with the discussion on previous literature review.

Swims: A Sentiment Analysis Approach With Semi-Supervised Feature Weighting and Intelligent Model Selection

Sentiment Analysis is currently one of the most studied research fields. Its aim is to analyze people‟s sentiments, opinions, attitudes etc., towards different elements such as topics, products, individuals, organizations, and services. Sentiment analysis can be achieved by machine learning or lexical based methodologies or a combination of both. Recent research shows that domain specific lexicons perform better as compared to domain independent lexicons. In an effort to improve the performance of domain independent lexicons, this research incorporates machine learning with a lexical based approach, introducing a new approach called SWIMS, to determine the feature weight based on a well-known general-purpose sentiment lexicon, SentiWordNet. Support vector machine is used to learn the feature weights and an intelligent model selection approach is employed in SWIMS in order to enhance the classification performance. The features are selected based on their subjectivity and the effects of feature selection with respect to their part of speech information are studied extensively. Seven benchmark datasets have been used in this research, including large movie review dataset, multi-domain sentiment dataset and Cornell movie review dataset, all of which are freely available online for research purposes. In-depth performance comparison is conducted with the state of the art machine learning approaches, lexical based methodologies, and other tools used for sentiment detection. The proposed SWIMS approach attained accuracy, precision, recall and f-measure values of 83.06%, 83.30%, 82.83% and 83.03% respectively, averaged over the seven datasets. The evaluation of performance measures proves that the proposed approach outperforms other techniques for sentiment analysis.