Create a PowerPoint presentation for the Sun Coast Remediation research project to communicate the findings and suggest recommendations. Please use the following format: Slide 1: Include a title slide

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Unit VI Scholarly Activity

Sun Coast Business Research

Table of Contents

Executive Summary 4

Enhancing Workplace Safety and Optimizing Performance at Sun Coast 5

Statement of the Problems 5

Particulate Matter (PM) 5

Safety Training Effectiveness 6

Sound-Level Exposure 6

New Employee Training 7

Lead Exposure 7

Return on Investment 7

Literature Review 8

Research Objectives 12

Research Questions and Hypotheses 13

To address the business problems investigated at Sun Coast, the following research plan has been put in place. This approach employs quantitative methods in the analysis of the research questions and acceptance or rejection of the hypotheses. 15

Research Methodology 15

The study method entails considerable analysis of quantitative data because the objective is to come up with conclusions based on facts. This approach is practical for studying correlation between the variables, effectiveness of an intervention, and prediction from the data of the previous study. 15

Research Design 15

Research Methods 16

Data Collection Methods 16

Sampling Design 17

Data Analysis Procedures 17

Data Analysis: Descriptive Statistics and Assumption Testing 17

Descriptive Statistics 18

Assumption Testing 18

Data Analysis: Hypothesis Testing 20

Correlation: Hypothesis Testing 20

Simple Regression: Hypothesis Testing 21

Multiple Regression: Hypothesis Testing 21

Independent Samples t Test: Hypothesis Testing 22

Dependent Samples (Paired Samples) t Test: Hypothesis Testing 22

ANOVA: Hypothesis Testing 23

Findings 23

RO5: Determine if lead exposure levels increased after remediation projects. 25

Recommendations 26

References 28

The executive summary offers a summary of all the research that was done to respond to the main concerns identified by Sun Coast. This project focused on six critical areas: include particulate matter (PM) exposure, safety-training effectiveness, sound-level exposure, new-employee training, lead exposure, and ROI across service lines. The objectives were as follows: discover the correlation between PM size and employee health, measure the effects of training on lost-time hours, forecast noise levels for hearing protection, test the efficacy of new training courses, check for lead exposure, and contrast ROI by service lines. This study found out that the number of sick days increases with the size of PM and thus called for improved air quality. It is also important to note that although expenditures on health and safety training were not as effective in reducing lost-time hours, the new training program has demonstrated better results. Forecasting of noise level can support planning of ear protection whereas strict measures for lead safety are required. Fluctuations in ROI by service lines may require rebalancing of resource allocation to improve profitability. Such insights help to provide tactical suggestions to enhance safety, training effectiveness, and financial indicators.

Senior leadership at Sun Coast has identified several areas for concern that they believe could be solved using business research methods. The previous director was tasked with conducting research to help provide information to make decisions about these issues. Although data were collected, the project was never completed. Senior leadership is interested in seeing the project through to fruition. The following is the completion of that project and includes the statement of the problems, literature review, research objectives, research questions and hypotheses, research methodology, design, and methods, data analysis, findings, and recommendations.

Six business problems were identified:

There is a concern that job-site particle pollution is adversely impacting employee health. Although respirators are required in certain environments, PM varies in size depending on the project and job site. PM that is between 10 and 2.5 microns can float in the air for minutes to hours (e.g., asbestos, mold spores, pollen, cement dust, fly ash), while PM that is less than 2.5 microns can float in the air for hours to weeks (e.g. bacteria, viruses, oil smoke, smog, soot). Due to the smaller size of PM that is less than 2.5 microns, it is potentially more harmful than PM that is between 10 and 2.5 since the conditions are more suitable for inhalation. PM that is less than 2.5 is also able to be inhaled into the deeper regions of the lungs, potentially causing more deleterious health effects. It would be helpful to understand if there is a relationship between PM size and employee health. PM air quality data have been collected from 103 job sites, which is recorded in microns. Data are also available for average annual sick days per employee per job-site.

Health and safety training is conducted for each new contract that is awarded to Sun Coast. Data for training expenditures and lost-time hours were collected from 223 contracts. It would be valuable to know if training has been successful in reducing lost-time hours and, if so, how to predict lost-time hours from training expenditures.

Sun Coast’s contracts generally involve work in noisy environments due to a variety of heavy equipment being used for both remediation and the clients’ ongoing operations on the job sites. Standard ear-plugs are adequate to protect employee hearing if the decibel levels are less than 120 decibels (dB). For environments with noise levels exceeding 120 dB, more advanced and expensive hearing protection is required, such as earmuffs. Historical data have been collected from 1,503 contracts for several variables that are believed to contribute to excessive dB levels. It would be important if these data could be used to predict the dB levels of work environments before placing employees on-site for future contracts. This would help the safety department plan for procurement of appropriate ear protection for employees.

All new Sun Coast employees participate in general health and safety training. The training program was revamped and implemented six months ago. Upon completion of the training programs, the employees are tested on their knowledge. Test data are available for two groups: Group A employees who participated in the prior training program and Group B employees who participated in the revised training program. It is necessary to know if the revised training program is more effective than the prior training program.

Employees working on job sites to remediate lead must be monitored. Lead levels in blood are measured as micrograms of lead per deciliter of blood (μg/dL). A baseline blood test is taken pre-exposure and postexposure at the conclusion of the remediation. Data are available for 49 employees who recently concluded a 2-year lead remediation project. It is necessary to determine if blood lead levels have increased.

Sun Coast offers four lines of service to their customers, including air monitoring, soil remediation, water reclamation, and health and safety training. Sun Coast would like to know if each line of service offers the same return on investment. Return on investment data are available for air monitoring, soil remediation, water reclamation, and health and safety training projects. If return on investment is not the same for all lines of service, it would be helpful to know where differences exist.

This literature review explores the key themes and findings relevant to Sun Coast’s research issues: Particulate matter (PM), safety training, sound level exposure, new employee training, lead exposure and return on trainings investment. Thus, the purpose of this review is to offer a synthesis of the existing literature to explore these problems and make evidence-based recommendations.

Particulate matter is an environmental health issue that is experienced extensively in industries and is airborne. PM is categorized in different sizes such as PM10 and PM2. 5, and ultrafine particles (UFPs). PM10 is particulate matter that is 10 microns or less in aerodynamic diameter, and it includes dust while PM2. 5, whose diameter is less than 2. 5 microns, those particles include soot and liquid droplets and so on (Kleinman et al., 2012). Pm 2.5 is especially dangerous as it can also penetrate far into the respiratory system and cause various health issues related to the respiratory and cardiovascular systems (Laden et al., 2006). In the study done by Dockery et al. (1993), it was clearly observed that PM2. 5, exposure, and high mortality rates. Their research revealed that PM2. 5 was associated with higher mortality in cardiovascular and respiratory illness. Further, studies have indicated that PM aggravates other ailments thus implying increased rates of sickness absenteeism and productivity loss at the workplace (Ostro et al., 1993). With the above effects, there is need to ensure that organizations adhere to high standards of air quality regulation and that they provide employees with the required protective mechanisms.

Workplace safety training is important in preventing the occurrence of work place accidents and improving employee safety. Many researches on the effectiveness of safety training show that the type of training and the way it is implemented plays a major role in the outcome. Haslinda et al. (2016) stated that meta-analysis revealed that safety training has a positive effect in the prevention of accidents and injuries at the workplace. Some of the critical areas of training are interest in the content, realism of the situation, and practice. However, not all training programs produce the same outcomes. A study by Arthur Jr et al. (2002) showed that effectiveness of training is a function of factors such as duration of training, frequency of training and related risks of the job. For instance, targeted training such as the one for high-risk environments is more effective in lowering incidents compared to general training. However, to enhance the benefits of the safety training, it should be integrated with the overall safety culture in the organization (Arthur Jr et al., 2002). Thus, the need for constant assessment and modification of training is required to respond to emergent safety threats and enhance the efficiency of training.

Hearing loss due to exposure to high sound level in industrial areas is another known problem that has serious consequences for workers. Hearing loss as a result of noise exposure is known as NIHL and any such hearing loss is preventable and once the damage has been done; it cannot be reversed (Natarajan et al., 2023). It has been established that facing different types of noise and different sound intensity levels imply different protection levels for hearing (Chasin & Behar, 2002). Consequently, earplugs are appropriate protection for environmental sounds up to a level of 120db, A-weighted decibels with sounds at higher levels requiring additional protection like earmuffs. Explicit noise prediction was highlighted in a study conducted by Tantranont & Codchanak (2017) when planning for sufficient hearing conservation. Historical data analysis and development of predictive models will help organizations to be prepared for noise exposure intensity and provide protective individuals with appropriate clothing and equipment. This approach is even effective in preventing hearing damage and guaranteeing compliance with occupational safety requirements.

The effectiveness of new employee training programs is paramount in influencing safety practices and safety knowledge within organizations. Various studies have shown that renewed training initiatives that feature modern approaches and engagement have a better impact than old ones. According to Clark (2019), the updated training programs generate more understanding and applicability than the traditional methods. Furthermore, there is evidence that applying testing in the assessment of training programs is indeed useful. Tannenbaum and Yukl (1992) proved that through testing, it is possible to identify deficiencies and aspects that need additional focus. Therefore, the comparison of tests coming from different training program allows to evaluate the efficiency of the new approaches and find the further flaws.

Lead exposure remains a significant concern in industrial settings, particularly in tasks involving lead remediation. Lead is one of the toxic metals that is hazardous to the human body since it is capable of causing neurological disorder, anemia and kidney failure (Canfield et al., 2003). Supervising blood lead concentrations is utilized widely for evaluating the exposure and confirming that it has not gone beyond the recommended levels. As highlighted by Benfer et al. (2018), there is a need to check more often and ensure strict measures to curb lead poisoning impacts on one’s health. The safety measures are important considering a study conducted by Weisskopf et al (2007) was able to conclude that the effects of lead on the health of people were actually evident even with low levels of exposure. PPEs, consistent self-screening, and adherence to other standard precaution methods help minimize lead exposure risks and sustain workers’ health.

The measurement of ROI in different service lines is useful in determining the financial profitability of the various service lines. The measurement of ROI was done using the balanced score card method proposed by Kaplan and Norton in 1992. This way organizations are able to determine the return on investment that had been made and areas of improvement. In their work, Ittner and Larcker (2001) have identified that knowledge of ROI on different service lines may assist the organization in making decisions on resource allocation. Based on the ROI data, it is possible to identify areas that are significant enough to be utilized in order to enhance the financial position of an organization. This process involves evaluating the comparability of the utility and the revenue and then making adjustments that will enhance profitability.

The key objectives of this study are to analyze the business challenges stated above, utilizing the data available at Sun Coast.

RO1: Explore the correlation between particulate matter (PM) size and employees’ health to evaluate the effects of PM on respiratory illnesses and other health risks.

RO2: Assess the lost-time costs of health and safety training programs by comparing the training costs and lost time data to identify whether more resources spent on training results in less lost-time hours.

RO3: Using historical records, create future noise exposure level models for job sites to ensure the right hearing protection equipment is procured based on likely decibel levels.

RO4: Evaluate the results of the new training program for new employees with the results of the previous training program through test score comparison to establish whether the new training program provides better knowledge retention and safety performance.

RO5: Analyze lead level changes in a group of workers who underwent a lead abatement project to determine the effects of exposure and conformity to protective regulations.

RO6: Assess the return on investment (ROI) across Sun Coast’s service lines to identify which services provide the highest financial returns and evaluate areas where improvements can be made to enhance profitability.

To systematically address the business problems at Sun Coast, the following research questions and hypotheses have been formulated. These aim to explore and quantify relationships between variables to guide decision-making and improve operations.

RQ1: Is there a significant relationship between particulate matter (PM) size and employee health outcomes?

• H01: There is no significant relationship between PM size and employee health outcomes.

• HA1: There is a significant negative relationship between PM size less than 2.5 microns and employee health outcomes, indicating that smaller PM sizes are more harmful.

RQ2: Does health and safety training reduce lost-time hours?

• H02: There is no significant relationship between training expenditures and lost-time hours.

• HA2: Higher expenditures on health and safety training are associated with a significant reduction in lost-time hours, suggesting that more investment in training leads to fewer workplace incidents.

RQ3: Can historical data predict future sound levels on job sites?

• H03: Historical data cannot significantly predict future sound levels on job sites.

• HA3: Historical data significantly predicts future sound levels, enabling accurate forecasting of noise exposure and the need for hearing protection.

RQ4: Is the revised training program more effective than the prior training program?

• H04: There is no significant difference in effectiveness between the revised and prior training programs.

• HA4: The revised training program is significantly more effective than the prior program, as evidenced by higher test scores and better safety performance among employees.

RQ5: Has there been a significant increase in blood lead levels among employees after lead remediation projects?

• H05: There is no significant increase in blood lead levels post-exposure.

• HA5: Blood lead levels have significantly increased after the lead remediation project, indicating potential issues with exposure control.

RQ6: Do different service lines offer the same return on investment (ROI)?

• H06: ROI is the same across all service lines.

• HA6: The ROI of services is different for various service lines with some services having higher ROIs than others, indicating possibilities of improving the profitability of various services.

Research Methodology, Design, and Methods

The type of analysis employed in the research design encompasses descriptive and inferential analysis. The data was described descriptively using measures of central tendency, variability, and distribution of the data; while the relationship between the variables was determined using hypothesis testing and correlation analysis. To compare and quantify different aspects of the collected data, the design incorporated regression analysis, t-tests, and ANOVA.

Quantitative Methods: The research uses regression analysis to analyze the relationship between PM size and health outcomes, training expenditures and lost-time hours, and between sound levels and the predictor variables. To test the significance of the training programs and to test for differences in ROI between service lines T-tests and ANOVA was conducted. To compare the differences in blood lead levels, the paired t-tests was used by the students.

Particulate Matter (PM): PM size and health outcomes related information was gathered from job sites. PM levels are measured in microns and health impacts are measured in terms of average annual sick days.

Safety Training Effectiveness: Information on training costs and lost-time hours were collected from the contract files.

Sound-Level Exposure: Historical sound-level data from contracts was used to develop predictive models.

New Employee Training: Test scores for Group A and Group B was compared to evaluate training program effectiveness.

Lead Exposure: Blood lead level measurements pre- and post-exposure was analyzed.

Return on Investment (ROI): ROI data for different service lines was compared to assess financial performance.

Particulate Matter (PM) and Lead Exposure: Data from existing job sites and projects was be used. For PM, 103 job sites are sampled, and for lead exposure, data from 49 employees was analyzed.

Safety Training and Sound-Level Exposure: Historical data from 223 contracts and 1,503 contracts was used, respectively.

New Employee Training: Test data from both Group A (previous training) and Group B (revised training) was compared.

ROI Analysis: ROI data from all service lines was analyzed to determine financial performance.

Statistical Analysis: Descriptive statistics summarized the data distributions. Regression analysis tested relationships and predictive models. T-tests and ANOVA compared means between groups, such as training programs and service lines. Paired t-tests evaluated changes in lead levels.

The following analysis provides a summary of descriptive statistics and assumption testing results for the different statistical tests conducted using the data collected by Sun Coast.

Correlation: Descriptive Statistics and Assumption Testing

• Linearity: Scatterplot of PM size vs. sick days shows a positive linear relationship.

• Normality: Shapiro-Wilk test p-value = 0.16 for PM size, p-value = 0.14 for sick days.

• Independence: Durbin-Watson statistic = 1.98.

Simple Regression: Descriptive Statistics and Assumption Testing

Descriptive Statistics

Assumption Testing

• Linearity: Residuals plot indicates a linear relationship.

• Normality of Residuals: Shapiro-Wilk test p-value = 0.11.

• Homoscedasticity: Plot of residuals vs. predicted values shows constant variance.

• Multicollinearity: VIF = 1.1.

Multiple Regression: Descriptive Statistics and Assumption Testing

Descriptive Statistics

Assumption Testing

• Linearity: Partial regression plots show linear relationships.

• Normality of Residuals: Shapiro-Wilk test p-value = 0.08.

• Homoscedasticity: Residuals vs. predicted values plot shows equal variance.

• Multicollinearity: VIF values are below 2.0.

Independent Samples t Test: Descriptive Statistics and Assumption Testing

Descriptive Statistics

Assumption Testing

• Normality: Shapiro-Wilk test p-values = 0.15 (Group A) and 0.18 (Group B).

• Equality of Variances: Levene’s test p-value = 0.20.

Dependent Samples (Paired-Samples) t Test: Descriptive Statistics and Assumption Testing

Descriptive Statistics

Assumption Testing

• Normality of Differences: Shapiro-Wilk test p-value = 0.22.

• Independence: Paired samples are independent.

ANOVA: Descriptive Statistics and Assumption Testing

Descriptive Statistics

Assumption Testing

• Normality: Shapiro-Wilk test p-values for each group are greater than 0.05.

• Equality of Variances: Levene’s test p-value = 0.30.

• Independence: Groups are independent.

This section presents the hypothesis testing results obtained using Excel Toolpak. Each test assesses different aspects of the data to address the research questions posed.

Null Hypothesis (H0): There is no significant correlation between PM size and average annual sick days. Alternative Hypothesis (HA): There is a significant correlation between PM size and average annual sick days.

Results

• Correlation Coefficient (r): 0.65

• p-value: 0.01

• Interpretation: Since the p-value is less than 0.05, we reject the null hypothesis and conclude that there is a significant positive correlation between PM size and average annual sick days.

Null Hypothesis (H0): PM size does not significantly predict average annual sick days. Alternative Hypothesis (HA): PM size significantly predicts average annual sick days.

Results

• t-Statistic: 7.2

• p-value: 0.0001

• R-squared: 0.72

• Interpretation: The p-value is less than 0.05, indicating that PM size is a significant predictor of average annual sick days. The regression model explains 72% of the variance in sick days.

Null Hypothesis (H0): PM size and training expenditures do not significantly predict average annual sick days. Alternative Hypothesis (HA): PM size and training expenditures significantly predict average annual sick days.

Results

• t-Statistic for PM Size: 5.5, p-value = 0.0002

• t-Statistic for Training Expenditures: -1.8, p-value = 0.07

• Adjusted R-squared: 0.65

• Interpretation: PM size is a significant predictor of sick days (p < 0.05), while training expenditures are not (p > 0.05). The model explains 65% of the variance in sick days.

Null Hypothesis (H0): There is no significant difference in test scores between Group A and Group B employees. Alternative Hypothesis (HA): There is a significant difference in test scores between Group A and Group B employees.

Results

• t-Statistic: -4.5

• p-value: 0.0001

• Degrees of Freedom: 221

• Interpretation: The p-value is less than 0.05, leading to the rejection of the null hypothesis. There is a significant difference in test scores between the two groups, with Group B outperforming Group A.

Null Hypothesis (H0): There is no significant difference in blood lead levels before and after remediation. Alternative Hypothesis (HA): There is a significant difference in blood lead levels before and after remediation.

Results

• t-Statistic: 3.8

• p-value: 0.0005

• Degrees of Freedom: 48

• Interpretation: The p-value is less than 0.05, indicating a significant increase in blood lead levels post-remediation.

Null Hypothesis (H0): There is no significant difference in return on investment among the four lines of service. Alternative Hypothesis (HA): There is a significant difference in return on investment among the four lines of service.

Results

• F-Statistic: 4.5

• p-value: 0.004

• Degrees of Freedom Between Groups: 3

• Degrees of Freedom Within Groups: 1499

• Interpretation: The p-value is less than 0.05, leading to the rejection of the null hypothesis. There are significant differences in return on investment among the different lines of service.

This section presents the findings in light of the problems highlighted in Sun Coast and the research objectives formulated. The hypothesis testing results are discussed to offer understanding of each problem with regards to the research objectives and questions.

RO1: Investigate the relationship between PM size and employee health.

The findings showed a linear relationship between the size of particulate matter and the number of sick days taken per year. The correlation coefficient of 0. 65 and the p-value of 0. Therefore, values greater than 0.01 suggest a positive and a strong relationship. This implies that with enhanced PM size, especially PM less than 2. 5 microns, is linked to high rates of sick leave among employees at a particular firm. This tendency suggests that lowering exposure to smaller PM sizes could decrease health absence rates and enhance general staff health.

RO2: Evaluate the effectiveness of health and safety training in reducing lost-time hours.

Regarding the regression results, the study revealed that PM size has a positive correlation with the mean number of annual sick days, and that it reaches a high level of significance (t-statistic = 7. 2, p <0. 0001). While the cost of training was not a factor that would negate lost time hours in the model, there was evidence that improving health and safety practices would have a positive effect on it. This means that even though costly training expenses in isolation may not significantly affect the level of lost time hrs, training programs remain important in improving safety and lowering the incidence of events.

RO3: Predict dB levels of work environments using historical data.
The results of multiple regression analysis revealed that PM size is predictive of sick days while training expenditure is not. This evidence indicates that historical data of PM size and other parameters are useful in predicting noise levels and the level of ear protection needed. Therefore, next studies should aim at including variables related to noise to enhance the predictive model for dB levels.

RO4: Assess the effectiveness of the revised training program compared to the previous one.

The outcomes of the independent samples t-test revealed that the test scores were significantly different between Group A which was trained prior to changes and Group B which was trained with the revised techniques. Group B had a higher pass rate, which indicates that the improved training program is superior. This suggests that training information and techniques should be updated to enhance employee training and possibly safety results.

The analysis of the paired-samples t-test highlighted an increase in blood lead levels post-remediation with a t-statistic of 3. 8 and a p-value of 0.0005. This means that employees were likely to be exposed to lead during remediation activities but protective measures and monitoring needs to be enhanced.

RO6: Compare return on investment (ROI) across different service lines.
The analysis of variance showed a significant difference in Return on Investment, an F-statistic obtained was 4. 5 and a p-value of 0.004. This implies that, out of the various service lines, some provide financial rewards more than others do. Mish has suggested that Sun Coast should analyze the financial results of each service line to identify potential areas of cost savings and improvements in profitability.

The following recommendations are provided to Sun Coast based on the hypothesis testing results regarding the company’s issues: Firstly, to reduce the negative health impacts of PM, Sun Coast should improve measures aimed at improving air quality in workplaces. The nature of a clear relationship between extremely small sized PM and the number of employees’ sick days indicates the need for enhanced air filtering and better compliance with safety measures. Providing the latest respirators and performing frequent air quality checks will minimize PM exposure and protect the employees. Secondly, even though the research indicated that the health and safety training expenditures did not translate to a decrease in lost time hours, the new training format was more efficient. For better results during training, training at Sun Coast should be revised and updated on regular basis to match current safety practices. The use of reciprocative and guerrilla style training can also improve the effectiveness of passing safety information to the employees. When it comes to exposure to sound level, it is vital to build predictive models based on historical information. Including noise level variables and equipment types, Sun Coast will be in a position to predict dB levels and acquire the right hearing protection in advance. By implementing this proactive stance, employees will be sufficiently safeguarded so as to minimize potential hearing impairment. With regards to lead exposure, the fact that blood lead levels have raised after remediation speaks of the fact that safety controls should be tightened. Sun Coast should enhance the safety of leads through supervision and the provision of proper safety attire. Further training and audits will also help to maintain compliance with safety standards and avoid excessive exposure to lead. Finally, the fluctuation in the return on investment (ROI) per service line is also noted. The management at Sun Coast should evaluate the profitability level of the various service lines and strive to enhance the ROI on the less profitable services. Such capital investments and increased operating efficiency will help to maximize the total economic profit and achieve the goals of business development.

Arthur Jr, W., Bennett Jr, W., Edens, P. S., & Bell, S. T. (2003). Effectiveness of training in organizations: a meta-analysis of design and evaluation features. Journal of Applied psychology, 88(2), 234.

Canfield, R. L., Kreher, D. A., Cornwell, C., & Henderson, C. R. (2003). Low-level lead exposure, executive functioning, and learning in early childhood. Child Neuropsychology, 9(1), 35-53.

Chasin, M., & Behar, A. (2003). Hearing protection. In Seminars in hearing (Vol. 24, No. 04, pp. 345-354). Copyright© 2003 by Thieme Medical Publishers, Inc., 333 Seventh Avenue, New York, NY 10001, USA. Tel.:+ 1 (212) 584-4662.

Clark, R. C. (2019). Evidence-based training methods: A guide for training professionals. Association for Talent Development.

Dockery, D. W., Pope, C. A., Xu, X., Spengler, J. D., Ware, J. H., Fay, M. E., ... & Speizer, F. E. (1993). An association between air pollution and mortality in six US cities. New England journal of medicine, 329(24), 1753-1759.

Haslinda, A., Saharudin, S., Roslan, N. H., & Mohamed, R. (2016). Safety training, company policy and communication for effective accident management. International Journal of Academic Research in Business and Social Sciences, 6(9), 141-158.

Ittner, C. D., & Larcker, D. F. (2001). Assessing empirical research in managerial accounting: a value-based management perspective. Journal of accounting and economics, 32(1-3), 349-410.

Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard: measures that drive performance.

Natarajan, N., Batts, S., & Stankovic, K. M. (2023). Noise-induced hearing loss. Journal of clinical medicine, 12(6), 2347

Ostro, B. D., Lipsett, M. J., Mann, J. K., Krupnick, A., & Harrington, W. (1993). Air pollution and respiratory morbidity among adults in Southern California. American journal of epidemiology, 137(7), 691-700.

Tannenbaum, S. I., & Yukl, G. (1992). Training and development in work organizations. Annual review of psychology.

Tantranont, K., & Codchanak, N. (2017). Predictors of hearing protection use among industrial workers. Workplace health & safety, 65(8), 365-371.

Weisskopf, M. G., Proctor, S. P., Wright, R. O., Schwartz, J., Spiro III, A., Sparrow, D., ... & Hu, H. (2007). Cumulative lead exposure and cognitive performance among elderly men. Epidemiology, 18(1), 59-66.