Analysis of Actual Fitness Supplement Consumption among Health and Fitness Enthusiasts.

With the rising popularity of fitness supplements, understanding the factors driving their consumption behaviors is crucial. This study investigated the actual consumption of fitness supplements utilizing the Theory of Planned Behavior (TPB), Health Belief Model (HBM), and the Theory of Effort Minimization in Physical Activity (TEMPA) frameworks. The TPB provided insights into how attitudes, subjective norms, and perceived behavioral control influence individuals' intentions to consume fitness supplements. Additionally, the HBM sheds light on perceived effectiveness, benefits, barriers, and health motivation related to supplement consumption. Integrating the TEMPA framework further investigated the role of effort minimization in physical activity contexts. Through an online questionnaire, data were collected from a sample of 250 fitness supplement-consuming respondents. SEM analysis revealed significant associations between behavioral intentions and attitudes, perceived behavioral control, health motivation, and controlled precursors. However, it was seen that subjective norms, perceived effectiveness, perceived benefits, and automatic precursors were deemed insignificant. The findings contribute to a comprehensive understanding of the factors shaping actual consumption behaviors, offering valuable implications for marketers, health professionals, and policymakers seeking to promote informed and responsible supplement use among health and fitness enthusiasts.

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.

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.

Antecedents of behavioral intentions for purchasing safety tools among women.

The rise of crimes and violence against women are evident worldwide. The self-protection and behavioral aspects however are underexplored. The need for assessment for safety such as the purchasing intentions of safety tools should be considered to promote the mitigation and reduction of violence and crimes against women, may it be in households or outdoors. This study aimed to holistically analyze the determinants of behavioral intentions to purchase self-defense tools among women. Focusing on the Philippines, the structural equation modeling considering the extended theory of planned behavior (TPB) and protection motivation theory (PMT) integrated model was completely analyzed. A total of 553 valid respondents were purposively gathered to completely assess the behavioral domains and protective behaviors of women. From the results, self-efficacy showed the most significant factor affecting purchasing intentions, followed by behavioral intentions, and the behavioral domains under TPB. Moreover, indirect effects of PMT variables to purchasing intentions were seen; highest from understanding of safety, perceived risk, and then perceived severity. The extended latent variable such as perceived safety showed an indirect effect while buying impulse showed a direct significant effect on purchasing intentions. This is considered as the first study that assessed the different variables affecting purchasing intentions of self-defense tools. The findings of this study would provide a gateway as benchmark for how women feel, behave, and seeks protection. Implementing the suggestion with other aspects would end up promoting the overall safety of a country, not just for women, but for all victims against crime and violence.

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.

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.

Utilizing Structural Equation Modeling-Artificial Neural Network Hybrid Approach in Determining Factors Affecting Perceived Usability of Mobile Mental Health Application in the Philippines.

Mental health problems have emerged as one of the biggest problems in the world and one of the countries that has been seen to be highly impacted is the Philippines. Despite the increasing number of mentally ill Filipinos, it is one of the most neglected problems in the country. The purpose of this study was to determine the factors affecting the perceived usability of mobile mental health applications. A total of 251 respondents voluntarily participated in the online survey we conducted. A structural equation modeling and artificial neural network hybrid was applied to determine the perceived usability (PRU) such as the social influence (SI), service awareness (SA), technology self-efficacy (SE), perceived usefulness (PU), perceived ease of use (PEOU), convenience (CO), voluntariness (VO), user resistance (UR), intention to use (IU), and actual use (AU). Results indicate that VO had the highest score of importance, followed by CO, PEOU, SA, SE, SI, IU, PU, and ASU. Having the mobile application available and accessible made the users perceive it as highly beneficial and advantageous. This would lead to the continuous usage and patronage of the application. This result highlights the insignificance of UR. This study was the first study that considered the evaluation of mobile mental health applications. This study can be beneficial to people who have mental health disorders and symptoms, even to health government agencies. Finally, the results of this study could be applied and extended among other health-related mobile applications worldwide.

Factors Influencing the Perceived Effectiveness of COVID-19 Risk Assessment Mobile Application "MorChana" in Thailand: UTAUT2 Approach.

COVID-19 contact-tracing mobile applications have been some of the most important tools during the COVID-19 pandemic. One preventive measure that has been incorporated to help reduce the virus spread is the strict implementation of utilizing a COVID-19 tracing application, such as the MorChana mobile application of Thailand. This study aimed to evaluate the factors affecting the actual usage of the MorChana mobile application. Through the integration of Protection Motivation Theory (PMT) and Unified Theory of Acceptance and Use of Technology (UTAUT2), latent variables such as performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), hedonic motivation (HM), habit (HB), perceived risk (PCR), self-efficacy (SEF), privacy (PR), trust (TR), and understanding COVID-19 (U) were considered to measure the intention to use MorChana (IU) and the actual usage (AU) of the mobile application. This study considered 907 anonymous participants who voluntarily answered an online self-administered survey collected via convenience sampling. The results show that IU presented the highest significant effect on AU, followed by HB, HM, PR, FC, U, SEF, PE, EE, TR, and SI. This is evident due to the strict implementation of using mobile applications upon entering any area of the vicinity. Moreover, PCR was not seen to be a significant latent factor affecting AU. This study is the first to have evaluated mobile contact tracing in Thailand. The integrated framework can be applied and extended to determine factors affecting COVID-19 tracing applications in other countries. Moreover, the findings of this study could be applied to other health-related mobile applications worldwide.

Utilization of Random Forest and Deep Learning Neural Network for Predicting Factors Affecting Perceived Usability of a COVID-19 Contact Tracing Mobile Application in Thailand "ThaiChana".

The continuous rise of the COVID-19 Omicron cases despite the vaccination program available has been progressing worldwide. To mitigate the COVID-19 contraction, different contact tracing applications have been utilized such as Thai Chana from Thailand. This study aimed to predict factors affecting the perceived usability of Thai Chana by integrating the Protection Motivation Theory and Technology Acceptance Theory considering the System Usability Scale, utilizing deep learning neural network and random forest classifier. A total of 800 respondents were collected through convenience sampling to measure different factors such as understanding COVID-19, perceived severity, perceived vulnerability, perceived ease of use, perceived usefulness, attitude towards using, intention to use, actual system use, and perceived usability. In total, 97.32% of the deep learning neural network showed that understanding COVID-19 presented the most significant factor affecting perceived usability. In addition, random forest classifier produced a 92% accuracy with a 0.00 standard deviation indicating that understanding COVID-19 and perceived vulnerability led to a very high perceived usability while perceived severity and perceived ease of use also led to a high perceived usability. The findings of this study could be considered by the government to promote the usage of contact tracing applications even in other countries. Finally, deep learning neural network and random forest classifier as machine learning algorithms may be utilized for predicting factors affecting human behavior in technology or system acceptance worldwide.