The usability analysis of software loading tools in a commercial airline.

Background: Aircraft software loading tools evolved as enhanced floppy disks and different types of portable data loaders emerged into the modern world. However, there was a lack of academic research centered on the usability of those tools. Objective: This study analyzed the usability of three aircraft software loading tools: floppy disks, Teledyne PMAT, and MBS mini PDL. Methods: A total of 20 avionics technicians answered 10 System Usability Scale (SUS) indicators and performed the software loading task. These professionals completed three sets of SUS questionnaires, one set for each software tool. Results: Analysis of Variance (ANOVA) indicated that there were statistical differences in SUS score and completion time. The comparable SUS score and completion time pertained to the following tools: floppy disks & MBS mini PDL and Teledyne PMAT & MBS mini PDL. Pearson correlation analysis noted a significant positive relationship between SUS score & software tool. Moreover, there was a significant negative relationship between SUS score & completion time and software tool & completion time. However, avionics technicians' years of experience lacked a significant impact on SUS score and completion time. Ultimately, it was recommended to utilize MBS mini PDL. This aircraft loading tool had the most significant impact, highest SUS score, and fastest completion time. Conclusions: The researchers presented managerial implications if MBS mini PDL was utilized, including sales increase and overhead cost savings. Finally, this research was the first study that analyzed the usability of the commonly used aircraft software loading tools.

Copywriters' preference evaluation on online copywriting course attributes during the COVID-19 pandemic.

Copywriting online course has become a famous online training over the past years and the reliance on online courses increased even during the COVID-19 pandemic. In recent years, online courses have become a popular training platform, especially for copywriting courses. The demand for online courses increased during the COVID-19 pandemic, prompting the need to optimize the learning experience of an online course's target audience. This study aimed to determine the combination of online course attributes most preferred by Filipino copywriters such as course style, payment method, course delivery, module duration, and course type. 292 Filipino copywriters from a leading Philippine-based copywriting group voluntarily participated in this study and answered an online questionnaire quantitative survey which was distributed using the purposive sampling method. Conjoint Analysis with an orthogonal design revealed that copywriters consider the course style attribute as the most important (46.007%), followed by payment method (18.236%), and course delivery (15.435%). Module duration (10.489%) and while the course type (9.833%) were was the least considered attribute of an online course. The result shows that Filipino copywriters prefer an intermediate-level video course on a Facebook group that lasts 1 to 3 hours per module and is paid per course for a total utility score of 0.281, while the least preferred combination was a beginner-level audiobook course that lasts less than 30 minutes per module, delivered via email, and paid per module, for a total utility score of -0.281. This study is the first study that analyzed the copywriters' preference for online copywriting course attributes during the COVID-19 pandemic. The findings of this study are beneficial to online course creators who are targeting copywriters. Finally, the result of this study can be expanded further to other online courses worldwide.

Indonesian martial artists' preferences in martial arts schools: Sustaining business competitiveness through conjoint analysis.

The popularity of martial arts in the present times has become apparent, therefore, it is necessary to explore martial artists' preferences and the schools' competitiveness. The purpose of this study was to assess martial artists' preferences concerning the services offered by martial arts schools. Conjoint analysis was utilized to analyze the responses of fifty-five (55) martial artists based on the seven (7) martial arts schools' attributes. The results showed that the type of martial arts was found to be the most important attribute (30.449%) followed by distance (27.970%), price range (22.706%), social environment (11.097%), class preference (5.080%), goal (1.562%), and schedule (1.135%). Furthermore, Muay Thai or Kickboxing was the most preferred martial art, Mixed Martial Arts (MMA) was the second priority, next was Taekwondo, then Karate, and finally Boxing. In addition, the martial artists' preferred distance was less than 8 km, and a monthly training cost of 150,000 to 450,000 IDR (10 to 20 USD). Martial artists liked attending open classes, treated martial arts as a hobby, and favored attending classes once or twice weekly. With the lack of conjoint-related studies in the martial arts industry, the findings contributed to academicians and addressed issues of inadequate studies. Most importantly, the researchers presented managerial implications to leverage marketing techniques and develop the business operations of martial arts schools.

Determination of loyalty among high school students to retain in the same university for higher education: An integration of Self-Determination Theory and Extended Theory of Planned Behavior.

Student loyalty generally refers to the formed bond between the student and a university. This relationship between a university and its students proves essential in a university's success in the competitive field of higher education institutions. The aim of this study was to determine the factors affecting students' loyalty among high school students to pursue their college or higher education in their current universities by utilizing Self-Determination Theory and Extended Theory of Planned Behavior. A total of 1224 high school students voluntarily participated and answered an online questionnaire that consist of 80 questions. Structural Equation Modeling (SEM) showed that competency had the highest direct significant effect on perceived behavioral control which subsequently led to student satisfaction, followed by relatedness and empathy. In addition, student satisfaction had the highest direct effect significant effect on student loyalty, followed by university image and effectiveness. Interestingly, university ranking, programs offered, and kinship patronage also had significant indirect effects on student loyalty. This new framework may be a theoretical foundation for universities to enhance student loyalty and student recruitment. Considering students as customers, the satisfaction of students would result in an increase in the application which would present an increase in population, sales, marketability, and profitability of the university.

Determinants of continuous visiting behavior to Palawan, Philippines: Integrating Uncertainty Reduction Theory and Expectation Confirmation Theory.

Palawan is a globally known island located in the Philippines. It has received several recognitions from a variety of international tourism organizations. The study aims to identify the factors affecting continuous visiting behavior to Palawan's travel accommodations. The study incorporated covariance-based structural equation modeling (CB-SEM) to apply ideologies of Uncertainty Reduction Theory (URT) and Expectation Confirmation Theory (ECT). 712 valid respondents answered an online questionnaire that was distributed to several social media platforms. Structural Equation Modeling (SEM) identified that interactive uncertainty significantly influenced perceived performance compared to passive uncertainty. Both the physical environment and attitude of employees significantly produced an impact on service experience and price acceptance. However, the physical environment negatively influenced price acceptance. Meanwhile, the attitude of employees was the sole exogenous variable that influenced price acceptance. Food and beverage didn't contribute a significant influence on service experience and price acceptance. Additionally, perceived performance and service experience contributed to positive confirmation. It was also revealed that confirmation was significant towards price acceptance, but was insignificant towards tourist satisfaction. Moreover, price acceptance significantly influenced tourist satisfaction. This study is the first comprehensive study that analyzed the visiting behavior in Palawan. Finally, integrating URT and ECT can be applied and extended, especially for analyzing the visiting behavior of different tourist destinations worldwide.

Factors Influencing Non-Fungible Tokens (NFT) Game Engagement during the COVID-19 pandemic: The Theory of Planned Behavior (TPB) and Hedonic Motivation System Adoption Model (HMSAM) Approach.

The prominent form of Non-Fungible Token (NFT) is found in the gaming industry. NFT games received immense attention during the COVID-19 pandemic because of their play-to-earn model. NFT gamers can enjoy and increase their finances in their spare time. Hence, the researchers utilized Structural Equation Modeling (SEM) to investigate the intention and immersive behaviors of 1082 respondents. The modified framework from the Theory of Planned Behavior (TPB) and Hedonic Motivation System Adoption Model (HMSAM) underwent SEM tests. These theories and methods were used to analyze relationships among hypotheses and assess factors influencing NFT game engagement. The results showed that hedonic motivation produced positive and significant influences on perceived usefulness, curiosity, joy, attitude, subjective norms, and perceived behavioral control. Subjective norms significantly influenced perceived ease of use. In due course, perceived ease of use yielded positive and significant effects on perceived usefulness, joy, attitude, and perceived behavioral control. Moreover, perceived usefulness, curiosity, joy, attitude, and perceived behavioral control had significant positive effects on behavioral intention. In addition, perceived usefulness, curiosity, joy, and attitude significantly and positively affected immersion. Meanwhile, only four hypotheses were not supported by the study. These findings were translated into theoretical and managerial implications to contribute to the academe given the strong the change of behavior of users towards NFT games during the pandemic; gaming industry since they will be able to develop, improve and create a new ecosystem in the gaming space, and NFT stakeholders since they will benefit from the development that will influence this study.

A machine learning ensemble approach to predicting factors affecting the intention and usage behavior towards online groceries applications in the Philippines.

The emergence of e-commerce platforms, especially online grocery shopping, is heightened by the COVID-19 pandemic. Filipino consumers started to adapt online due to the strict quarantine implementations in the country. This study intended to predict and evaluate factors influencing the intention and usage behavior towards online groceries incorporating the integrated Protection Motivation Theory and an extended Unified Theory of Acceptance and Use of Technology applying machine learning ensemble. A total of 373 Filipino consumers of online groceries responded to the survey and evaluated factors under the integrated framework. Artificial Neural Network that is 96.63 % accurate with aligned with the result of the Random Forest Classifier (96 % accuracy with 0.00 standard deviation) having Perceived Benefits as the most significant factor followed by Perceived Vulnerability, Behavioral Intention, Performance Expectancy, and Perceived. These factors will lead to very high usage of online grocery applications. It was established that machine learning algorithms can be used in predicting consumer behavior. These findings may be applied and extended to serve as a framework for government agencies and grocers to market convenient and safe grocery shopping globally.

Corrigendum to "Stock market optimization amidst the COVID-19 pandemic: Technical analysis, K-means algorithm, and mean-variance model (TAKMV) approach" [9(7) (July 2023) e17577].

[This corrects the article DOI: 10.1016/j.heliyon.2023.e17577.].

Employees' preference analysis on lean six sigma program coaching attributes using a conjoint analysis approach.

The purpose of this study was to determine the preference of employees for Lean Six Sigma project coaching attributes. Conjoint analysis with an orthogonal design was utilized and six attributes were considered: coaching style, frequency of coaching sessions, duration of coaching sessions, the turn-around time of feedback, documentation review, and mock defense. In addition, 4 different projects were also evaluated: Quick Win project, Yellow Belt project, Green Belt project, and Black Belt project. The results showed that for Quick Win projects, employees consider conducting a mock defense as the highest preference, followed by having a documentation review, and a democratic coaching style. For Yellow Belt projects, employees considered conducting a mock defense as the highest preference, followed by having a documentation review, and a weekly coaching session. For Green Belt projects, employees consider having a documentation review as the highest preference, followed by a transactional coaching style, and conducting a mock defense. Lastly, for Black Belt projects, employees consider having a documentation review as the highest preference, followed by conducting a mock defense, and a 1-week turn-around time for feedback. The results of this study will help companies to implement and to sustain better employee-oriented LSS programs.

Stock market optimization amidst the COVID-19 pandemic: Technical analysis, K-means algorithm, and mean-variance model (TAKMV) approach.

The Philippine stock market, just like most of its neighbors in the region, was seriously impacted by the global pandemic COVID-19. Investors remain hopeful while continuing to seek great ones in the damaged market. This paper developed a methodology for portfolio selection and optimization with the use of technical analysis, machine learning techniques, and portfolio optimization model. The combined methods of technical analysis, K-means clustering algorithm, and mean-variance portfolio optimization model will result in the development of the proposed TAKMV method. The study aims to integrate these three important analyses to identify portfolio investments. This paper uses the average annual risk and annual rate of return data for the years 2018 and 2020 to form the clusters and assessed the stocks that correspond to the investor's technical strategy such as Moving Average Convergence/Divergence (MACD) and Hybrid MACD with Arnaud Legoux Moving Average (ALMA). This paper solved the risk minimization problem on selected shares of the companies, based on the mean-variance portfolio optimization model. There are 230 and 239 companies for 2018 and 2020, respectively, listed in Philippine Stock Market, and all simulations were performed in MATLAB environment platform. Results showed that MACD strategy dominates the MACD-ALMA strategy in terms of the number of assets with a positive annual rate of return. The MACD works efficiently in the pre-COVID-19 condition while MACD-ALMA works efficiently during-COVID-19 condition, regardless of the number of assets with a positive annual rate of return. The results also show that the maximum expected portfolio return (RP) can be achieved using the MACD and MACD-ALMA in the pre-and during-COVID-19 conditions, respectively. The MACD-ALMA shows an advantage during high-risk market conditions and can also provide maximum RP. The performance of the TAKMV method was validated by applying its results and comparing it to the next year's historical price. The 2018 results were compared to 2019 data and the 2020 results were compared to 2021 data. For consistency, the comparison was applied to the same company per portfolio. Simulation results show that the MACD strategy is more effective compared to MACD-ALMA.

Evaluating factors influencing customers' intention to eat Korean cuisine "Samgyeopsal" in the Philippines: A structural equation model forest classifier approach.

Samgyeopsal has become a widely popular cuisine in the Philippines since 2014. The rise of Samgyeopsal is evident worldwide as it is available in countries such as the United States, Northern, and Southern Asia. This study aimed to explore the intention to eat Samgyeopsal during the COVID-19 pandemic utilizing structural equation modeling and random forest classifier. With a total of 1014 responses collected online, the result showed that utilitarian and hedonic motivation, Korean influence, and attitude led to very high actual behavior in east Samgyeopsal in the Philippines. Moreover, subjective norm, perceived behavioral control, and intention led to significant results influencing intention to actual behavior. Lastly, the COVID-19 safety protocol showed the least significant result. This study is the first study that evaluated the intention of consumers to eat Samgyeopsal in the Philippines during the COVID-19 pandemic. The results of this study would be beneficial to Korean BBQ restaurateurs and the further development of their marketing strategies even in other countries. Finally, the model construct of this study can be extended and applied in evaluating the consumers' eating intention toward other varieties of food or cuisines worldwide.

The effects of communication, empathy, encouragement, growth, and rewards on employee performance: A structural equation modeling approach.

BACKGROUND: Improving employee performance has been crucial in different organizations for it reflects the company's product and service quality. OBJECTIVE: The purpose of this study was to determine the factors affecting employee performance in the engineering and technology department in the Philippines during the COVID-19 pandemic. METHODS: The effects of these factors such as communication, empathy, encouragement, growth and rewards to employee performance were analyzed. RESULTS: Structural equation modeling (SEM) revealed that among the five factors, the encouragement, growth, and rewards were found to have a significant effect on employee performance in the engineering and technology department. Surprisingly, communication and empathy were found to have no significant effect to employee performance. CONCLUSIONS: This was the first study that investigated the factors affecting employee performance in the engineering and technology department in the Philippines during the COVID-19 pandemic. The results could serve as a guideline for the management under this type of department. The framework could also be helpful for sustaining the elevated performance of the employees in the engineering and technology department.

Determination of factors affecting customer satisfaction towards "maynilad" water utility company: A structural equation modeling-deep learning neural network hybrid approach.

The Maynilad Water Services Inc. (MWSI) is responsible for supplying water to the west zone of Metro Manila. The utility provides service to 17 cities and municipalities which frequently experience water interruptions and price hikes. This study aimed to identify the key factors affecting customer satisfaction toward MWSI by integrating the SERVQUAL dimensions and Expectation Confirmation Theory (ECT). An online questionnaire was disseminated to 725 MWSI customers using the snowball sampling method to obtain accurate data. Ten latent were analyzed using Structural Equation Modeling and Deep Learning Neural Network hybrid. It was found that Assurance, Tangibles, Empathy, Expectations, Confirmation, Performance, and Water consumption were all factors affecting MWSI customers' satisfaction. Results showed that having an affordable water service, providing accurate water bills, on-time completion of repairs and installations, intermittent water interruptions and professional employees contribute to the general satisfaction. MWSI officials may utilize this study's findings to assess further the quality of their services and design effective policies to improve. The employment of DLNN and SEM hybrid showed promising results when employed in human behavior. Thus, the results of this study would be beneficial when examining satisfaction to utilities and policies among service providers in different countries. Moreover, this study could be extended and applied among other customer and service-focused industries worldwide.

Determination of factors influencing young adults' intention to have COVID-19 vaccine in the Philippines: An integration of Health Belief Model and the Theory of Planned Behavior.

Objectives: The COVID-19 pandemic continues to increase around the world and businesses and markets across the world significantly decreased. The purpose of this study was to determine the factors that affect the intention to be vaccinated for the COVID-19 vaccine among young Filipino adults by integrating the Health Belief Model and Extended Theory of Planned Behavior. Study design: A cross-sectional study design was utilized. Methods: Factors such as understanding of the COVID-19 vaccine, self-efficacy, cues to action, perceived barriers, perceived benefits, perceived side effects, perceived behavioral control, attitude, subjective norm, and intention to be vaccinated were analyzed by utilizing Structural Equation Modeling (SEM). Results: With 865 young Filipino adults who answered a self-administered survey, it was seen that Understanding of the COVID-19 vaccine has the highest direct significant effect on cues to action, followed by perceived barriers, and perceived benefits. Interestingly, the primary factor was Understanding COVID-19 vaccines which had an indirect significant effect on the intention to get vaccinated. This is because knowing what the vaccine is for, its effects, and the application would lead to the acceptance of the COVID-19 vaccine. Moreover, the impact of being known to have the COVID-19 vaccine would lead to the intention to get vaccinated. Conclusions: The findings of this study can be utilized especially by the government in developing strategies for encouraging people to take the COVID-19 vaccine. Finally, the model construct of the study can be applied to explore more factors that can affect the intention to be vaccinated with the COVID-19 vaccine and other vaccines people worldwide.

Classification modeling of intention to donate for victims of Typhoon Odette using deep learning neural network.

The need for stability in the economy for world development has been a challenge due to the COVID-19 pandemic. In addition, the increase of natural disasters and their aftermath have been increasing causing damages to infrastructure, the economy, livelihood, and lives in general. This study aimed to determine factors affecting the intention to donate for victims of Typhoon Odette, a recent super typhoon that hit the Philippines leading to affect 38 out of 81 provinces of the most natural disaster-prone countries. Determining the most significant factor affecting the intention to donate may help in increasing the engagement of donations among other people to help establish a more stable economy to heighten world development. With the use of deep learning neural network, a 97.12% accuracy was obtained for the classification model. It could be deduced that when donors understand and perceive both severity and vulnerability to be massive and highly damaging, then a more positive intention to donate to victims of typhoons will be observed. In addition, the influence of other people, the holiday season when the typhoon happened, and the media as a platform have greatly contributed to heightening the intention to donate and control over the donor's behavior. The findings of this study could be applied and utilized by government agencies and donation platforms to help engage and promote communication among donors. Moreover, the framework and methodology considered in this study may be extended to evaluate intention, natural disasters, and behavioral studies worldwide.

Consumer preference analysis on the attributes of samgyeopsal Korean cuisine and its market segmentation: Integrating conjoint analysis and K-means clustering.

Samgyeopsal is a popular Korean grilled dish with increasing recognition in the Philippines as a result of the Hallyu. The aim of this study was to analyze the preferability of Samgyeopsal attributes which includes the main entree, cheese inclusion, cooking style, price, brand, and drinks using Conjoint Analysis and market segmentation using k-means clustering. A total of 1018 responses were collected online through social media platforms by utilizing a convenience sampling approach. The results showed that the main entrée (46.314%) was found to be the most important attribute, followed by cheese (33.087%), price (9.361%), drinks (6.603%), and style (3.349%). In addition, k-means clustering identified 3 different market segments: high-value, core, and low-value consumers. Furthermore, this study formulated a marketing strategy that focused on enhancing the choice of meat, cheese, and price based on these 3 market segments. This study has significant implications for enhancing Samgyeopsal chain businesses and helping entrepreneurs with consumer preference on Samgyeopsal attributes. Finally, conjoint analysis with k-means clustering can be utilized and extended for evaluating food preferences worldwide.

Determining the ergonomic risk factors affecting the musculoskeletal disorders of traffic enforcers in Manila City, Philippines.

BACKGROUND: Traffic enforcers are vulnerable to work accidents, injuries, and illnesses because they are commonly exposed to ergonomic risk factors while performing their tasks. OBJECTIVE: The purpose of this study is to determine the effects of environmental risk factors and postural risk factor to the prevalence of musculoskeletal disorders (MSDs) among traffic enforcers in Manila City, Philippines using binary logistic regression analysis. METHODS: A total of 120 participants were included in the study. The Nordic Musculoskeletal Questionnaire (NMQ) and Rapid Entire Body Assessment (REBA) were utilized. In addition, several devices such as a noise dosimeter, digital air thermometer, and IAQ sensors were also utilized to measure the environmental exposure of enforcers during their work shift. RESULTS: The prevalence of MSDs among traffic enforcers was high, with 71% of the respondents reporting symptoms of MSDs in more than one part of the body for the past 7 days. The body part that has highest prevalence was upper back, followed by lower back, and legs/ankles. Logistic regression analysis revealed that awkward work posture (OR = 4.61, 95% CI = 2.17, 9.83), noise exposure (OR = 1.42, 95% CI = 1.11, 1.82), heat exposure (OR = 0.53, 95% CI = 0.85, 1.05), and pollution exposure (OR = 0.94, 95% CI = 0.85, 1.05) were significant contributors for the prevalence of MSDs among traffic enforcers in Manila City. CONCLUSION: The prevalence of MSDs among traffic enforcers is caused by their work posture and exposure to psychosocial factors such as noise, heat, and poor air quality. Thus, to minimize the risk of MSDs, it is suggested to provide administrative controls, such as job rotation or shifting, and introduce frequent rest breaks. It is also recommended to provide enforcers with appropriate personal protective equipment, such as cooling vests, noise-canceling earplugs and N95 facemasks. This would help in uplifting musculoskeletal health for traffic enforcers and other workers in a similar field.

The effects of biomechanical risk factors on musculoskeletal disorders among baggers in the supermarket industry.

BACKGROUND: Baggers play a significant role in the customer service of supermarket stores. OBJECTIVE: The purpose of this study was to determine the effects of biomechanical risk factors on musculoskeletal disorders (MSD) experienced by baggers in the supermarket industry. METHODS: Forty baggers employed among five supermarket stores in the National Capital Region in the Philippines participated in the study. To examine the biomechanical risk factors of the baggers, this study used an observational approach as well as standard ergonomic tools like REBA and NIOSH. In addition, musculoskeletal discomfort was subjectively rated using the standardized Cornell Musculoskeletal Disorder Questionnaire at the end of the work shift. Furthermore, correlation and stepwise regression analysis were utilized to determine the association between biomechanical risk factors and MSD. RESULTS: Approximately 76% of study participants reported work-related MSD. The highest prevalence was in the foot area, followed by upper back, shoulder, lower leg, lower back, neck, and upper arm. Pearson correlation analysis had found a significant association between MSD and biomechanical risk factors. The highest correlation was observed between MSD and postural risk index (R2 = 0.863, p < 0.001), followed by weight lifted (R2 = 0.836, p < 0.001), task duration (R2 = 0.816, p < 0.001), task frequency (R2 = 0.756, p < 0.001), and lifting index (R2 = 0.661, p < 0.001). Furthermore, stepwise regression analysis revealed that the postural risk index (REBA) had the highest coefficient value (ß=16.99), indicating the strongest positive effect to MSD. CONCLUSION: This present study revealed that grocery baggers are frequently exposed to risk factors such as awkward posture, lifting heavy loads, extended task durations, and prolonged postures during repetitive tasks increased their risk of developing MSDs. Hence, preventive measures are suggested to minimize the risk of MSD, such as the implementation of intervention through ergonomic solutions like awareness and training on safe lifting practices, adjustment of workstations to fit the stature of workers, introducing rest breaks in between work shifts, use of floor mats, and use of compression stockings in conjunction with safety shoes.

Determining factors affecting customer satisfaction of the national electric power company (MERALCO) during the COVID-19 pandemic in the Phillippines.

This study aimed to determine factors affecting customer satisfaction of national electric power companies during the COVID-19 pandemic by integrating SERVQUAL and Expectation-Confirmation Theory approaches. A total of 529 participants voluntarily participated and answered an online questionnaire of 49 questions. Structural equation modeling indicated that Tangibility, Empathy, and Responsiveness were positively related to Service Quality which subsequently led to Customer Expectation, Energy Consumption, and Perceived Performance (PE). In addition, a higher PE was positively related to Confirmation, which eventually led to Customer Satisfaction. It was evident that integrating SERVQUAL and ECT could holistically measure customer satisfaction among electricity service providers.

The effect of positive reinforcement of behavioral-based safety on safety participation in Philippine coal-fired power plant workers: a partial least squares structural equation modeling approach.

The power plant accident is considered one of the man-made disasters in many developing countries, including the Philippines. This study aimed to identify the factors influencing behavioral-based safety (BBS) of coal-fired power plant workers which subsequently led to their active safety participation (SPT). In total, 201 coal-fired power plant workers in the Philippines answered the questionnaire consisting of 60 items using a purposive sampling approach. Partial least squares structural equation modeling (PLS-SEM) revealed that personal measures (PM) had significant effects on safety incentives (SI), safety management system (SMS) and safety perception (SP). SP also had significant effect on BBS which subsequently led to SPT. Finally, SMS had significant effect on safety culture (SC) which subsequently led to safety communication (SCM), BBS and SPT. This study is the first to explore BBS in the Philippine coal-fired power plant, and can positively reinforce SPT of workers in the coal-fired power plant industry.