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.

Determining Factors Influencing Collegiate Players' Intention to Pursue a Professional Career.

The rise of professional careers in playing sports has been seen in the recent generation. Ranging from traditional sports to recent e-games, it can be seen that student athletes are not only considering these as motivational factors for education, but also as future career paths. This study aims to fill the knowledge gap about the factors influencing college athletes' aspirations to play professionally. The study examines the complex web of athlete decision-making by utilizing the extended Theory of Planned Behavior (TPB) and analyzing the roles of motivational factors, self-efficacy, affective behavior, and behavioral domains. With the use of structural equation modeling, the study deciphers the intricate links, emphasizing the critical role that attitude plays in affective behavior. It emphasizes how much optimism and self-efficacy shape an athlete's behavior and subsequent pursuit of professional careers. The current research provides a benchmark for future studies exploring student athletes' career goals. This research adds understanding to the knowledge gap regarding the complex decision-making procedures of aspiring professional athletes. Both theoretical and practical implications are provided based on the results of the study, which stakeholders and institutions may consider for student athletes wanting to pursue a professional sports career.

Determining factors affecting Filipino consumers' behavioral intention to use cloud storage services: An extended technology acceptance model integrating valence framework.

Cloud Storage (CS) is a service that digitally stores, remotely manages, backs up, and renders internet-accessible data. However, despite its known benefits compared to traditional storage devices, this service is not widely used in developing nations such as the Philippines. This study integrated the Valence Theoretical Framework into the Extended Technology Acceptance Model (ETAM) to evaluate the influence of twelve variables on Filipino consumers' behavioral intention (BI) toward adopting CS services. The data is gathered through an online survey. Structural Equation Modeling was employed to examine the responses of 431 cloud users, mainly students and working professionals. Results showed that Perceived Benefit and Perceived Usefulness were the strongest determinants of BI. The Job Relevance was also found to be a significant factor. Therefore, CS providers should find additional ways to make their offerings more beneficial for the daily tasks of students and working individuals. Furthermore, considering the substantial influence of Perceived Risk and Subjective Norms on BI, CS providers must strengthen their security measures to boost users' trust in their services. Consumers who receive excellent service are likely to give positive reviews, which can be helpful to individuals who might also be considering purchasing CS for their data. Although the focus of this study is CS services, this can also serve as a reference when analyzing the BI of consumers concerning the adoption of other novel technologies applied in various sectors, including education, e-commerce, healthcare, and more.

Analyzing Preceding factors affecting behavioral intention on communicational artificial intelligence as an educational tool.

During the pandemic, artificial intelligence was employed and utilized by students around the globe. Students' conduct changed in a variety of ways when schooling returned to regular instruction. This study aimed to analyze the student's behavioral intention and actual academic use of communicational AI (CAI) as an educational tool. This study identified the variables by utilizing an integrated framework based on the Unified Theory of Acceptance and Use of Technology (UTAUT2) and self-determination theory. Through the use of an online survey and Structural Equation Modeling, data from 533 respondents were analyzed. The results showed that perceived relatedness has the most significant effect on the behavioral intention of students in using CAI as an educational tool, followed by perceived autonomy. It showed that students use CAI based on the objective and the possibility of increasing their productivity, rather than any other purpose in the education setting. Among the UTAUT2 domains, only facilitating conditions, habit, and performance expectancy provided a significant direct effect on behavioral intention and an indirect effect on actual academic use. Further implications were presented. Moreover, the methodology and framework of this study could be extended and applied to educational technology-related studies. Lastly, the outcome of this study may be considered in analyzing the behavioral intention of the students as the teaching-learning environment is still continuously expanding and developing.

The impact of green innovation initiatives on competitiveness and financial performance of the land transport industry.

The transportation sector is one of the primary contributors to greenhouse gas emissions that have deteriorating effects on the state of the environment. The implementation of sustainable practices has become one of the most challenging tasks of organizations at present. This study examined the effect of implementing green innovation initiatives on a firm's competitiveness and financial performance of motor vehicle companies in the Philippines. Data were gathered through an online survey questionnaire with a total of 206 respondents composed of employees of various ranks working in companies engaged in the manufacture, distribution, retail, and service of motor vehicles. The theoretical framework presented a hierarchical latent variable model which was validated using the partial least square structural equation modelling (PLS-SEM). The model fit, measurement, general construct fit, discriminant validity, and structural model parameters were examined and found to have acceptable values. The findings indicated that environmental regulations, market demand, government pressure, competitor pressure, corporate social responsibility, and employee conduct were the significant drivers of green innovation initiatives. The study also revealed that the implementation of green innovation initiatives positively affects the firm's competitiveness and financial performance. Motor vehicle companies and other types of organizations are encouraged to demonstrate not only their concern for society or community but also their concern for the environment to acquire better market leverage and financial position.

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.

Predicting factors affecting the intention to use a 3PL during the COVID-19 pandemic: A machine learning ensemble approach.

The COVID-19 pandemic had brought changes to individuals, especially in consumer behavior. As the government of different countries has been implementing safety protocols to mitigate the spread of the virus, people became apprehensive about traveling and going out. This paved way for the emergence of third-party logistics (3PL). Statistics have proven the rapid escalation regarding the use of 3PL in various countries. This study utilized Artificial Neural Network and Random Forest Classifier to validate and justify the factors that affect consumer intention in selecting a 3PL service provider during the COVID-19 pandemic integrating the Service Quality Dimensions and Pro-Environmental Theory of Planned Behavior. The findings of this study revealed that attitude is the most significant factor that affects the consumers' behavioral intention. Other factors such as customer satisfaction, customer perceived value, perceived environmental concern, assurance, responsiveness, empathy, reliability, tangibility, perceived behavioral control, subjective norm, and perceived authority support, are all contributing factors that affect behavioral intention. Machine learning algorithms, specifically ANN and RFC, resulted to be reliable in predicting factors as they obtained accuracy rates of 98.56% and 93%. Results presented that consumers' attitude, satisfaction, perceived value, assurance by the 3PL, and perceived environmental concerns were highly influential in choosing a 3PL package carrier. It was seen that people would be encouraged to use 3PL service providers if they demonstrate availability and environmental concerns in catering to the customers' needs. Subsequently, 3PL providers must assure safety and convenience before, during, and after providing the service to ensure continuous patronage of consumers. This is considered to be the first study that utilized a machine learning ensemble to measure behavioral intention for the logistic sector. The framework, analysis tools, and findings of this study could be extended and applied among other behavioral intentions regarding transportation worldwide. Managerial insights among service providers are discussed.

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.

Choosing a package carrier during COVID-19 pandemic: An integration of pro-environmental planned behavior (PEPB) theory and service quality (SERVQUAL).

Sustainability and safety have become the two most important considerations of consumers of the current century. The limited movement of consumers and significant shift to online business as effects of the current health pandemic led to an increased demand for package delivery services worldwide. This study investigated the factors that influence the intention of consumers in the Philippines to choose a package delivery or carrying service during the COVID-19 pandemic using the integrated pro-environmental planned behavior (PEPB) theory and service quality (SERVQUAL). An online questionnaire was distributed as the instrument data collection, and 400 respondents who have utilized the package delivery service during the pandemic participated in the study. The theoretical model was examined using the partial least square structural equation modeling (PLS-SEM) with higher-order constructs. Testing the different parameters of structural model, measurement model, and the model fit presented values within the threshold. Moreover, the HTMT and Fornell & Larcker discriminant validity analysis was performed to determine the overall fit of the constructs of the model. These tests demonstrated the acceptability of the model. The findings revealed that perceived environmental concern, perceived authority support, subjective norm, attitude, service quality, customer perceived value, and customer satisfaction significantly influence the consumer's intention to choose a package delivery service during the pandemic. The consumers' perception of pro-environmental activities was also an essential contributor since perceived environmental concern and perceived authority support indirectly affect behavior intention. The findings contribute to developing and validating an integrated model on sustainability and service quality in package delivery services. The study also provides suggestions to service providers to ensure quality and safety on package delivery during the pandemic.