Training and productivity have been found positively correlated in theory and practice. The pattern of the relationship, however, was not explored in pre - and post-regulation in the conceived sector. This paper aims to observe the effects of regulation and training expenses along with other covariates on employee productivity in the Microfinance sector of Bangladesh. Using annual data of MRA-licensed MFIs, we have estimated both panel and cross-sectional regression models. The regression results confirm the theoretical relationship between training expenses and employee productivity. The regulation also worked positively in enhancing employee productivity. However, in the early stage of regulation, the average productivity gain due to regulation was substantial and was showing an increasing trend but then it declined and reached a constant level of about 4% - 5% each year. Between 2008 and 2011, both regulation and training positively contributed to the gains in average productivity of the employees. After 2012, there was a positive trend of average productivity elasticity of training expenses but there was a flat effect of regulation after 2012. Regulation was found to short-run shifter in the average productivity of employees while training expenses had a positive trend effect.

The main focus of this research paper is to shed light on the challenges encountered by the chair production department employees when it comes to identifying and locating glue residue after the assembly process. This issue arises due to the transparent nature of the glue, making it difficult to detect and subsequently negatively impacting the overall quality of the chairs in the polishing department. To tackle this problem, various experiments and tests were conducted, which are thoroughly discussed in this paper. The findings of this study are not only applicable to the specific manufacturing company under investigation but also to other companies within the industry that face similar difficulties in detecting defects caused by transparent glue. As a result, a new method for detecting glue defects is proposed, which can be adopted by various industries encountering similar challenges.

Effective application of tools and techniques is critical for successful Total Quality Management (TQM) implementation. The influence of organization profile on the application of TQM tools and techniques and outcomes is investigated in Indian organizations. Correlation analysis and ANalysis Of VAriance (ANOVA) are utilized for the research. Tools and techniques are found having significant relation to the outcomes of implementation. Organization’s age, size, and turnover significantly influenced the TQM results. Organization’s size and turnover significantly influenced the application of tools and techniques. The study suggests taking the organization profile into consideration for effective implementation. Further research can be conducted in other countries.

The COVID-19 surge has mostly affected people and wreaked havoc on multiple sectors of the global economy. This study uses artificial neural networks (ANN) to develop COVID-19 prediction models to minimize the perilous situation. With positive infection data, these hybrid artificial neural network models looked at COVID-19 cases in Andhra Pradesh, India. Then, COVID-19 data were divided into training and testing for simulation. The developed model that takes the previous 14 days into account outperforms the others, depending on the results. According to the developed ANN models for Andhra Pradesh districts, the prediction model that works well and yields positive results is the one that receives lower values of error metrics like MSE, RMSE, MAE, and MAPE and higher values of R2. The hybrid neural network model that considers the previous 14 days for prophecy has suggested anticipating daily positive suffering, notably in areas of Andhra Pradesh, as a result of the collected data. Linear regression, ARIMA, and LSTM have been used for model assessment. The proposed 14-day model statistically surpasses all metrics in RMSE, MAE, MAPE, and R2. This study showed that an ANN-based model can predict the COVID-19 outbreak as well as other epidemics.

This study explores the impacts of participatory design (PD) on data-driven decision-making (DDDM) in organisations. Despite the extensive examination of PD and DDDM individually, there is a dearth of research in understanding their integration and their impact on decision-making processes in organisations. This research aims to fill this gap by investigating the potential impacts, challenges, benefits, and critical success factors associated with the incorporation of PD activities into DDDM. The study employs a systematic literature review methodology to provide a comprehensive understanding of the topic. The paper provides a research agenda for future researchers as well as discussing best practices for organizations seeking to optimise their data driven decision-making processes in a participatory manner. The research also discussed the ethical implications of data-driven decision-making. Ultimately, this research advances our understanding of how PD and DDDM can be effectively combined to achieve better decision-making outcomes.

Most small and medium scale enterprises in Ghana neglect very critical performance indicators in their bossiness. As a result most SMEs do not survive till their fifth anniversary. The purpose of this study is to examine the relationship between prevention focus, promotion focus, resilience, and SME performance in Ghana. Furthermore, we investigated the moderation effect of entrepreneurial learning and Entrepreneurial orientation on the relationship between entrepreneurial resilience and SME performance in Ghana. Our results show that both promotion- and prevention-focused increase entrepreneurial resilience and SME performance significantly. This is an indication that there is a strong positive correlation between entrepreneurial resilience and success in both the individual and firm levels. This inference can therefore assist policy makers, politicians and various industry players in designing training programs on how to improve the resilience of SME owners in Ghana. Statistically our analysis shows that prevention focus (PreV) has a positive effect on entrepreneurial resilience (EntR) (β = 0.250; p<0.001), and promotion focus (PreM) has a positive effect on entrepreneurial resilience (EntR) (β = 0.268; p<0.001). Additionally, entrepreneurial resilience (EntR) has a positive effect on innovative performance (IP) (β = 0.547; p<0.001). Furthermore, the mediation results has shown that entrepreneurial resilience (EntR) mediates the relationship between prevention focus (PreV) and innovative performance (IP) (β = 0.136; p<0.001); as well as that of promotion focus (PreM) and firm performance (IP) (β = 0.146; p<0.001). Finally, the results with regards to moderation effects has shown that entrepreneurial orientation (EntO) partially moderates the relationship between entrepreneurial resilience (EntR) and innovative performance (IP) (β = 0.109; p<0.05), however, the result established that entrepreneurial learning (EntL) does not moderate the relationship between entrepreneurial resilience (EntR) and firm performance (IP) (β = -0.031; p<0.05). Using the Resource Based View theory, we concluded that, SME’s needs to adopted prevention (vigilant/avoidant strategies) by minimizing losses, and taking calculated risks with motivation from mentors to avoid loss as this would achieve resilience as well as promotion of strategies (eager/approach strategies) to achieve gains for their SMEs during economic disruption and economic downturn such as COVID-19. We concluded that SME’s could also achieve resilience when managers or owners of SMEs inspire the employees to work harder, and think outside the box to identify latent ideas and opportunities within their SME business environment.

This study seeks to examine the various attributes that impact health and safety in construction (HSIC) across different companies, stakeholders, and nations. The objective is to identify these attributes and organize them within a framework to facilitate a clearer understanding. The research identified common characteristics that promote the adoption of HSIC, yielding advantages for governmental, private, and public entities. The United Kingdom, the United States, Canada, Australia, and Hong Kong are considered the leading countries in terms of conducting research on HSIC attributes. There exists significant potential for enhancing the contributions of developing countries. The proposed framework acknowledges a comprehensive set of 61 attributes, which are categorized into four distinct groups: Corporate regulatory, Employee's self-supportive, Workplace regulatory, and Federal regulatory attributes. These attributes function as a framework for clients and policymakers to enhance the quality of HSIC. In forthcoming periods, it is recommended to prioritize the utilization of empirical surveys conducted across diverse locations in order to ascertain the attributes that are deemed of utmost importance and necessitate significant attention.

This study chose Contingency Theory as a theoretical perspective to empirically investigate the role of transformational leadership, training, lecturer engagement, and compensation in improving lecturer performance. The research respondents were 166 lecturers at the College of Economics in Riau Province. The data was processed using PLS Structural Equation Modeling (SEM). This study proposes lecturer engagement and compensation as a strategy to improve lecturer performance. From the results, it was clear that transformational leadership and education and training affected lecturer performance, lecturer engagement played a role in mediating the effect of leadership and training on lecturer performance, and compensation moderated the effect of lecturer engagement on lecturer performance. These results reinforce the Contingency Theory which states that individual and organizational performance depends on the motivational system and the extent to which the leader has control and influence in certain situations.

This study took the Human Capital Theory as a theoretical perspective to empirically investigate the role of supervisors' supervision in moderating the effect of professionalism and creativity on the performance of public high school teachers in Pekanbaru. The research respondents were 144 teachers, the data were processed using the PLS Structural Equation Modeling (SEM). The study proposed supervisors' supervision as a strategy to improve teacher performance. The results of the study show that professionalism and creativity affect teacher performance—supervisors' supervision plays a role in strengthening teacher performance. These results strengthen the Human Capital Theory stating that a combination of knowledge, skills, innovation, and a person's ability to carry out their duties can create value to achieve goals.

One of the problems students often face is the lack of understanding of their interests and talents which will cause confusion in making future study choices and career plans. Career selection is an expression of personality into the world of work followed by identification of certain occupational stereotypes. Eight intelligences according to dr. Howard Gardner is linguistic, logical, mathematical, visual spatial, musical, kinesthetic, interpersonal, intrapersonal, and naturalist. This research purpose is to develop a system that produces information and professional recommendations that are in accordance with multiple intelligences of prospective high school students using a combination of Bayes' theorem and weighted product (WP) method. User’s preference value is calculated using the Bayes Theorem method to give each multiple intelligence value which is a criterion. WP method calculation to find professions that match user preferences. Weight of each criterion needed in WP method is calculated by Rank Order Centroid method. WP calculation will produce a ranking of 3 professions according to the input from the user. From system testing results that compared to results from experts, an accuracy of 67.33% is obtained. Based on the accuracy value, it can be said that the accuracy level system is quite good.