Impact of motivation and job satisfaction on turnover: a case study of food additives producer company

In the current working environment, employee turnover focused as the main issue found in almost many organizations. Employees are the key to a company's productions and services. When employees are taken good care of by focusing on their needs, they will bring up the company’s performance. However, the increasing percentage of employee turnover makes the company pay more attention to motivation and job satisfaction. Thus, an organization strongly thinks that employee motivation and job satisfaction should reduce the employee turnover rate. This action research was conducted to analyze the current factors influencing motivation and job satisfaction that brings to turnover intention. This research also suggests professional leadership training as a planned intervention towards this company. This research was focused on estimating the outcome of the planned intervention to reduce employee turnover intention in the Company. This action research study was conducted using two frameworks which are Social Exchange Theory and Theory Planned Behaviors. The methodology used in this research was a mixed-method approach by taking two interviews with managers (Qualitative approach), while a questionnaire survey (Quantitative Approach) was conducted with 35 respondents. Thematic Analysis and Statistical Package for the Social Sciences (SPSS) were utilized to analyze the data. Before the intervention, motivation and job satisfaction among employees were evaluated as low, and turnover intention was high based on an interview with managers and pre-survey results. The positive effects of the professional leadership training as intervention organized in the Company initiated are notable in thematic analysis and post-training survey results retrieved from employees. Based on the research, professional leadership training will be recommended for future researchers

The impact of corporate social responsibility on financial performance: Evidence from Insurance firms

The field of Corporate Social Responsibility (CSR) has been growing very exponentially over the past decade. There are continuous opposing views of the role of the firms in society and disagree-ments as to whether wealth maximization should be the sole goal of any corporations out there. With Insurance companies facing and fulfilling in the intense demand of diverse stakeholders, this study explores the impact of CSR disclosures on Financial Performance among the listed domestic-owned companies in Malaysian insurance sector. Although CSR is a hot topic in Malaysia and throughout various industries, no detailed study has been conducted to ascertain whether Malaysian insurance companies derive any benefits therefrom. The study examines the impact of CSR on financial performance using an extensive content analysis method on annual reports from 13 domestic-owned Malaysian insurance companies over the past 9 years (2008-2017). The content analysis data is further transformed into GRI CSR Disclosure Index table before matching the findings against the Financial Performance indicators (return on assets (ROA), return on equities (ROE) and earnings per share (EPS)). The relationship between CSR and ROA, ROE and EPS is tested using correlation analysis. The results indicate significant relationship between CSR disclosure and Financial Performance; designates CSR has significant impact on ROA; whereas relationship between CSR and ROE & EPS is found to be insignificant. The study suggests and indicates that insurance companies in Malaysia ought to carry out efforts continually in a bigger scale so that their CSR activities are more aligned with the reporting regulatory standards as well as to bring a positive impact in the current prospect. In addition, the remedial action proposed by Bursa Malaysia from year 2016 is expected to improve the findings of this study and bring a tremendous improvement to the exiting regulatory guidelines

The investigation of sustainable environmental performance of manufacturing companies: mediating role of organizational support and moderating role of CSR

China is transitioning towards green and sustainable manufacturing, considering environmental measures as per external environment demands. This research investigates the impact of green entrepreneurial orientation, social entrepreneurship, and organizational ambidexterity on sustainable environmental performance. Besides, examining the mediating role of organizational support and moderating the role of corporate social responsibilities (CSR) are also included in the aim of this research. The present study has used quantitative data of 510 respondents from China’s manufacturing industry; further structural equation modelling (SEM) was employed to analyse the data. The results revealed that organizational ambidexterity, green entrepreneurial orientation, and social entrepreneurship are positively associated with sustainable environmental performance. Organizational support positively mediates among the nexus of green entrepreneurial orientation, social entrepreneurship, and sustainable environmental performance. The findings also showed that CSR significantly moderates the relationship between organizational support and sustainable environmental performance. These outcomes provide various implications for policymakers while making policies regarding CSR and sustainable environmental performance

Effect of personality traits and learning styles towards students' academic achievement in Johor Bahru

The purpose of this study is to investigate the effect of personality traits and learning styles towards the students' academic achievement in Johor Bahru. A total of 101 students from IPG Kampus Temenggong Ibrahim were chosen to be part of the respondents with the use of simple random sampling. The instrument Big Five Inventory (BFI), Kolb's Learning Style Inventory and The students' academic achievement is measured through the Cumulative Grade Point Average, also known as CGPA. Descriptive statistics, Chi-Square Test, Spearman's Correlation and Multiple Regression was used to anser research questions. The findings revealed that the most common per-sonality traits displayed by the students are Openness and Conscientiousness while the most common learning style displayed by the students is Converger. The research also revealed that there is no significant effect of the combination of both the personality traits and learning style towards the prediction of the academic achievement among school students. The same goes to the difference of personality traits and learning style between male and female students was not significant as well

The impact of CSR and green investment on stock return of Chinese export industry

A green and sustainable business environment has gained the attention of recent researchers and policymakers due to environmental and social issues globally. Therefore, the present research investigates the impact of corporate social responsibilities (CSR), green investment, green credit, and assets return on the stock return of the Chinese export industry. This study has taken the ten top export companies from China using the database of the Shanghai stock exchange. This study collected data from financial statements and stock exchange databases from 2009 to 2020. This study has used panel data analysis techniques such as robust standard error and fixed effect model (FEM) to examine the relations among the variables. The results revealed that CSR, green investment, green credit, and return on assets have a significant and positive association with the stock return of selected industries. These results imply that CSR instigates higher financial performance in the export industry; thus, improving CSR and sustainable financing promote socio-economic and societal development

Ecological innovation for environmental sustainability and human capital development: the role of environmental regulations and renewable energy in advanced economies

This study examines the trends in environmental sustainability and human well-being through green technologies, clean energy, and environmental taxes using panel data for the top eight advanced economies from 1990 to 2018. The study applies an advanced panel technique, cross-sectionally augmented distributed lags (CS-ARDL), to find long-run and short-run associations between these variables. Moreover, the role of foreign investment is added as a control variable. The CS-ARDL estimation confirms the productive impact of green technologies on environmental and human well-being, providing that it helps to reduce haze pollution while promoting human development. Moreover, clean energy and environmental taxes contribute toa sustainable environment and human development. Moreover, foreign investment is a direct source of haze pollution because of more industrialization and economic activities. The study finally recommends strengthening the promotion of green technology and clean energy to achieve both environmental and human well-being in the long run

Nurses’ perception of structural empowerment in commitment towards quality improvement sustainability

The purpose of this study is to examine the level of nurses’ perception on key predictors of structural empowerment that provided at workplace, which could influence the nurses’ commitment towards sustainability activities through QI in healthcare, Malaysia. The researcher has utilized the dominant elements of the Quality Improvement Framework (2016) developed by National Improvement (QI) team and relate to the conceptual links of Kanter’s Structural Theory of Power in Organizations (1977, 1993) to form the conceptual framework to support the current basic quantitative study. A total of 315 nurses from 5 private specialist hospitals in Johor, Malaysia was participated in the current study. Data were analyzed using descriptive statistics and structural equation modelling (SEM) analyses. Through descriptive statistics, results revealed that the nurses perceived themselves to empower with moderate to high level of powers gains through their existing formal and informal QI activities/system, as well as accesses to job-related empowerment structures in the workplace that could enhanced their commitment towards QI sustainability. SEM analysis indicated that a significant effect of job-related empowerment structures and the nurses’ commitment towards sustainable QI practices in local private healthcare sector in Johor, Malaysia. In conclusion, nurses can be empowered for sustainability activities through QI at all levels in the healthcare organization if they are empowering with sufficient and efficient of fundamental keys of job-related empowerment structures in workplace

Empirical linkages between ICT, tourism, and trade towards sustainable environment: evidence from BRICS countries

There is a growing utilisation of information and communication technologies (ICT) in the recent digital era. Trade and tourism have also attained attention as determinants of environmental sustainability. Therefore, this study investigates linkages between ICT, tourism, trade, economic growth, and environmental sustainability in BRICS economies. Advanced panel estimation entitled cross-sectionally augmented autoregressive distributed lags (CSARDL) was applied from 1990 to 2019. Findings suggest the adverse effect of tourism, trade, and growth factors on environmental sustainability, whereas ICT helps promote a sustainable environment among the targeted economies. Likewise, the shortrun results prove that economic growth and tourism are prone to ecological health, while trade possesses an insignificant influence on ecological sustainability. These results suggest the integration of ICT in trade and tourism sectors to mitigate their negative ecological consequences

Forecasting Malaysian stock price using artificial neural networks (ANN)

Predicting a stock price is a very difficult task because it is complex and involves many factors. This has led to drop in the investment level in the Malaysian stock market. It is difficult to predict the stock market because its environments are unstable and dynamic. Recently, the demand for neural network in the business arena is on the increase. It is need to analyze vast data in order to search for information and knowledge that do not exist by using traditional methods. This included stock market prediction that is a very significant research in business area. In regard to Bursa Malaysia, Artificial Neural Network (ANNs). ANNs was only used to predict main index, i.e. Kuala Lumpur Composite Index (KLCI), but no attempt to predict share price and in particular banking sector. Since ANN has potential to predict non-linear behavior, this research attempts the use of ANNs to predict banking sector stock price in FTSE Bursa Saham Malaysia Kuala Lumpur Composite Index (FBM KLCI). One of the interesting topics of stock-market research is stock market prediction. Precise stock forecasting becomes the greatest challenge in the investment industry because stock data distribution changes over time. This paper investigates the use of ANN to predict Malaysian stock price, in particular Maybank Berhad stock price. The feedforward neural back-propagation network with Training Function Gradient Decent Training Algorithm is used in this study. The outcome of selected stocks, namely Maybank, are modeled and simulated and the results show that ANN offers a very accurate stock model and also generates competitive systems using all four trading strategies. The results also show that, neural network is a good tool to predict stock price movement with accuracy higher than 95%. Closing price is a good input for neural network model for stock price prediction

Prediction of diabetic retinopathy using health records with machine learning classifiers and data Science

Diabetes is a rapidly spreading disease. It occurs when the pancreas produces insufficient insulin or the body cannot utilise it effectively. Diabetic retinopathy (DR) and blindness are two major issues for diabetics. Diabetes patients increase the amount of data collected about DR. To extract important information and undiscovered knowledge from data, data mining techniques are required. DM is necessary in DR to improve society's health. The study focuses on the early detection of diabetic retinopathy using patient information. DM approaches are used to extract information from these numeric records. The dataset was used to forecast DR using logistic regression, KNN, SVM, bagged tree, and boosted tree classifiers. Two cross-validations are used to find the best features and avoid overfitting. The dataset includes 900 diabetes patients. The boosted tree produced the best classification accuracy (90.1%) with 10% hold-out validation. KNN also achieved 88.9% accuracy, which is impressive. As a result, the research suggests that bagged trees and KNN are good classifiers for DR