There are many leadership styles, which have different impacts on employees' performance. In higher education, faculty performance depends on many factors including Leadership style & Organizational culture. This study aims to examine the effect of leadership styles on faculty performance (FP) and more specifically to examine the moderating effect of Organizational Culture in the association between leadership styles and faculty performance in higher education institutions (MUET, Jamshoro). This study used quantitative methodology to identify the leadership styles which exist in MUET, Jamshoro, and their impact on faculty performance with organizational culture as moderator. It used both the sampling techniques probability and non-probability, and the sample size was 384 and the data was analyzed in SmartPLS 3. For leadership style, Full Range Leadership Model was adopted and for organizational culture, Competing Value Framework (CVF) was used. This study found that Transformational (TF) leadership has a positive significant relation with faculty performance at MUET, Jamshoro. And Organizational Culture (OC) as moderator negatively moderates the relation between Laissez-faire (LF) leadership and faculty performance (FP). According to faculty, transformational leadership is best suited to promote their performance on account of giving them challenging work, autonomy, mutual trust, through supporting subordinates' creativity, improving their confidence, and maintaining collaborations. Laissez-faire leadership also exists in an academic institution and has a positive impact on faculty performance. However, Transactional leadership has a negative impact on faculty performance. The future study could be conducted in other universities, or a comparison of leadership styles can be made between public and private universities with different models of leadership style and with different organizational culture models.
The aim of current study is to investigate the impact of CSRRI on bank’s financial performance. For this purpose, ROA, EPS and PAT are taken as proxies for measuring bank’s financial performance by using time series and panel data. The time span is from 2004 to 2017. The current study used HBL and MCB bank for analysis. The dependent variables are ROA, EPS and PAT while independent variables are CSRRI and bank size. To estimate the model, the current study used quantitative data to analyse the results by using descriptive analysis, correlation analysis, and multiple regression analysis. The findings of the current study revealed that the slope coefficient of intercept and CSRRI are positive except bank size which is negative in three models. In short, the CSRRI can Further, CSR reporting may provide welfare for both banks and econometric models suggests that socially responsible banks can not only attract large numbers of customers but also increases profitability.
The scale of entrepreneurial ecosystems (EE) assesses the perceptions about entrepreneurial ecosystem domains, finances, capital finances, support, support professions, policies, markets, human resources, and culture. The scales are always error-prone—these scales must possess properties that enable them it to provide maximum information and validity reliability. Convenient sampling data from (n = 474) founders, co-founders, and entrepreneurs were collected. The IRT-GRM model is used to validate and test the instrument-based on polytomous scales. IRT yields discriminating power—the level of difficulty of the items of the scale. The scale consists of 48 items. The item Pol5 (4.13) was found to have the highest discriminating value (4.13), the item mar5 had the lowest discriminating value (1.57), and all items had discriminating values greater than the threshold value of 0.60. The EE Scale showed good reliability based on McDonald’s omega and Cronbach’s alpha (0.80 and 0.88). The parallel and factor analysis showed good agreement of the one-dimesnionality of the scale. The model goodness of fit statistics based on the comparative fit index (CFI) and the Tucker–Lewis index, (TLI) and the standardized root mean square residual (SRMR) showed a satisfactory level of fit; however, the root mean square error of approximation (RMSE) showed a poor fit. The item characteristic curves showed that the all item responses were properly ordered. The items of the scale showed a satisfactory level of discrimination power and level of difficulty, and it was found to have three levels of agreement about entrepreneurial ecosystem scale. It is concluded that the EE scale possesses good psychometric properties and that it is reliable and valid instrument to measure the entrepreneurial ecosystem of the given region.
The abundant resources of Sindh province for RE (Renewable Energy) such as wind, solar, etc. can be tapped through RETs (Renewable Energy Technologies) to fulfil energy needs. But RETs are still not able to make major breakthrough in individual's life to enhance it. Even though individuals began to use solar panels to conquer power deficiencies, more endeavours are required to diffuse RETs in Sindh. This research paper explores the present situation for the dissemination of RETs in masses. A survey is conducted to achieve the said task. It measures the opinion difference of respondents regarding awareness creation towards RETs, needs for funding, provision of incentives and role of community engagement required for promotion of RETs in Sindh. The opinion difference was measuredregarding stakeholders' individual perception and chances of occurring the same (societal perception). The outcome of the survey identifies an entirely opposite opinion of stakeholders regarding their individual and social perceptions. Thus, predicting the real situation for RETs diffusion in Sindh. It indicates that despite much enthusiasm for RETs, lesser possibilities are accessible for their fruitful dispersion in Sindh in current conditions. Lack of awareness regarding RETs, few funding opportunities and absence of incentives from government resulted in the low engagement of communities to utilise RETs. Hence, due to hurdles identified, RETs face hindrances in their popularisation, which can be addressed through appropriate policy decisions.
This paper investigates the performance of five supervised machine learning algorithms, including support vector machine (SVM), logistic regression (LogR), decision tree (DT), multiple perceptron neural network (MLP-NN), and K-nearest neighbours (KNN) for predicting the water quality index (WQI) and water quality class (WQC) in the coastal aquifer of the Gaza Strip. A total of 2,448 samples of groundwater were collected from the coastal aquifer of the Gaza Strip, and various physical and chemical parameters were measured to calculate the WQI based on weight. The prediction accuracy was evaluated using five error measures. The results showed that MLP-NN outperformed other models in terms of accuracy with an R value of 0.9945–0.9948, compared with 0.9897–0.9880 for SVM, 0.9784–0.9800 for LogR, 0.9464–0.9247 for KNN, and 0.9301–0.9064 for DT. SVM classification showed that 78.32% of the study area fell under poor to unsuitable water categories, while the north part of the region had good to excellent water quality. TDS was the most important parameter in WQI predictions while and were the least important. MLP-NN and SVM were the most accurate models for the WQI prediction and classification in the Gaza coastal aquifer.
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