Gym-Goers Preference Analysis of Fitness Centers during the COVID-19 Pandemic: A Conjoint Analysis Approach for Business Sustainability

The COVID-19 pandemic has had a great impact on the fitness centers industry. The purpose of this study is to analyze the preference of gym-goers on fitness centers in the Philippines during the COVID-19 pandemic by utilizing a conjoint analysis approach. One thousand gym-goers voluntarily participated in this study and answered 22 queries created from the orthogonal design. The results indicated that Price was the highest attribute considered (21.59%), followed by Ventilation (17.56%), Service (16.59%), Trainer (14.63%), Payment Method (11.95%), Operating Hours (8.90%), and Login (8.70%). The results also indicated that comfort, security, and fitness center services were the main aspects that gym-goers would consider as their main preference. The study highlighted how gym-goers are sensitive to the price set by the fitness centers. Moreover, due to the COVID-19 pandemic, ventilation and size are considered highly important attributes among gym-goers. Comfort, safety, and security are the main consideration to have sustainable fitness centers during and even after the COVID-19 pandemic. The outcome of this study may benefit fitness centers and increase their business market by considering the preference of customers. Finally, the result of this study can be utilized by fitness centers to promote a generalized fitness center for gym-goers of different generations, statuses, and even socioeconomic status during and even after the COVID-19 pandemic

Plantitas/Plantitos Preference Analysis on Succulents Attributes and Its Market Segmentation: Integrating Conjoint Analysis and K-means Clustering for Gardening Marketing Strategy

Many people have switched to gardening as their new hobby during the COVID-19 pandemic, including Filipinos. With its increasing popularity, Filipinos called the new hobbyists “plantitas” and “plantitos” instead of the old-fashioned term “plant people”. Among different plants, succulents are one of the most popular for plant lovers as they can thrive with even minimal care, making them suitable to be an indoor/outdoor plant. This study aims to determine the various preferences of plantitas and plantitos based on succulent attributes using a conjoint analysis approach, and to discover the market segments using a k-means clustering approach. The attributes presented in this study are the types of succulents, succulent variegation, price, size of the succulent (in terms of diameter), size of the pot, pot material, and payment method. The conjoint analysis results indicated that the price was the attribute that significantly affected consumer buying behavior, followed by the diameter size of the succulent. On the other hand, the k-means cluster analysis identified three customer segments based on the buying frequency of customers, namely high-value customers, core-value customers, and lower-value customers. A marketing strategy for succulent sellers was proposed based on these segmentations, particularly on how to gain and attract more customers. This study is one of the first studies that analyzed the preferences related to succulent attributes. Finally, the conjoint analysis approach and k-means clustering in this study can be utilized to analyze succulent preferences worldwide

Utilization of Random Forest Classifier and Artificial Neural Network for Predicting Factors Influencing the Perceived Usability of COVID-19 Contact Tracing “MorChana” in Thailand

With the constant mutation of COVID-19 variants, the need to reduce the spread should be explored. MorChana is a mobile application utilized in Thailand to help mitigate the spread of the virus. This study aimed to explore factors affecting the actual use (AU) of the application through the use of machine learning algorithms (MLA) such as Random Forest Classifier (RFC) and Artificial Neural Network (ANN). An integrated Protection Motivation Theory (PMT) and the Unified Theory of Acceptance and Use of Technology (UTAUT) were considered. Using convenience sampling, a total of 907 valid responses from those who answered the online survey were voluntarily gathered. With 93.00% and 98.12% accuracy from RFC and ANN, it was seen that hedonic motivation and facilitating conditions were seen to be factors affecting very high AU; while habit and understanding led to high AU. It was seen that when people understand the impact and causes of the COVID-19 pandemic’s aftermath, its severity, and also see a way to reduce it, it would lead to the actual usage of a system. The findings of this study could be used by developers, the government, and stakeholders to capitalize on using the health-related applications with the intention of increasing actual usage. The framework and methodology used presented a way to evaluate health-related technologies. Moreover, the developing trends of using MLA for evaluating human behavior-related studies were further justified in this study. It is suggested that MLA could be utilized to assess factors affecting human behavior and technology used worldwide

A Machine Learning Ensemble Approach for Predicting Factors Affecting STEM Students’ Future Intention to Enroll in Chemistry-Related Courses

The need for chemistry-related professionals has been evident with the rise of global issues such as the pandemic and global warming. Studies have indicated how an increase in the amount of professionals should start within the classroom setting, enhancing the interest and motivation of students to pursue higher education in the related field. This study aimed to evaluate and predict factors affecting STEM students’ future intention to enroll in chemistry-related courses. Through the use of machine learning algorithms such as a random forest classifier and an artificial neural network, a total of 40,782 datasets were analyzed. Results showed that attitude toward chemistry and perceived behavioral control represent the most influential factors, followed by autonomy and affective behavior. This demonstrated that students’ interest, application in real life, and the development of knowledge and skills are key indicators that would lead to a positive future intention for pursuing the course in higher education. This is the first study that has analyzed students’ future intentions using a machine learning algorithm ensemble. The methodology and results may be applied and extended among other human factor studies worldwide. Lastly, the presented discussion and analysis may be considered by other universities for their education strategies across different countries

Machine Learning Decision System on the Empirical Analysis of the Actual Usage of Interactive Entertainment: A Perspective of Sustainable Innovative Technology

This study focused on the impact of Netflix’s interactive entertainment on Filipino consumers, seamlessly combining vantage points from consumer behavior and employing data analytics. This underlines the revolutionary aspect of interactive entertainment in the quickly expanding digital media ecosystem, particularly as Netflix pioneers fresh content distribution techniques. The main objective of this study was to find the factors impacting the real usage of Netflix’s interactive entertainment among Filipino viewers, filling a critical gap in the existing literature. The major goal of using advanced data analytics techniques in this study was to understand the subtle dynamics affecting customer behavior in this setting. Specifically, the random forest classifier with hard and soft classifiers was assessed. The random forest compared to LightGBM was also employed, alongside the different algorithms of the artificial neural network. Purposive sampling was used to obtain responses from 258 people who had experienced Netflix’s interactive entertainment, resulting in a comprehensive dataset. The findings emphasized the importance of hedonic motivation, underlining the requirement for highly engaging and rewarding interactive material. Customer service and device compatibility, for example, have a significant impact on user uptake. Furthermore, behavioral intention and habit emerged as key drivers, revealing interactive entertainment’s long-term influence on user engagement. Practically, the research recommends strategic platform suggestions that emphasize continuous innovation, user-friendly interfaces, and user-centric methods. This study was able to fill in the gap in the literature on interactive entertainment, which contributes to a better understanding of consumer consumption and lays the groundwork for future research in the dynamic field of digital media. Moreover, this study offers essential insights into the intricate interaction of consumer preferences, technology breakthroughs, and societal influences in the ever-expanding environment of digital entertainment. Lastly, the comparative approach to the use of machine learning algorithms provides insights for future works to adopt and employ among human factors and consumer behavior-related studies

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

“The Big One” Earthquake Preparedness Assessment among Younger Filipinos Using a Random Forest Classifier and an Artificial Neural Network

Exploring the intention to prepare for mitigation among Filipinos should be considered as the Philippines is a country prone to natural calamities. With frequent earthquakes occurring in the country, “The Big One” has been predicted to damage the livelihood and infrastructure of the capital and surrounding cities. This study aimed to predict the intention to prepare for mitigation (IP) of “The Big One” based on several features using a machine learning algorithm ensemble. This study applied a decision tree, a random forest classifier, and artificial neural network algorithms to classify affecting factors. Data were collected using convenience sampling through a self-administered questionnaire with 683 valid responses. The results of this study and the proposed machine learning-based prediction model could be applied to predict the intention of younger Filipinos to prepare. The experimental results also revealed that the decision tree and the decision tree with random forest classifier showed understanding, perceived vulnerability, and perceived severity as factors highly affecting the IP of “The Big One”. The results of this study could be considered by the government to promote policies and guidelines to enhance the people’s IP for natural disasters. The algorithm could also be utilized and applied to determine factors affecting IP for other natural disasters, even in other countries

Cancel culture in a developing country: A belief in a just world behavioral analysis among generation Z

Cancel culture is a prevalent boycotting practice used to exert pressure, express disapproval, and enforce consequences online. While multiple studies have been done on cancel culture's history, evolution, and effects, none of them were focused on Cancel Culture for Gen Z, the most socially aware and digitally inclined generation. The study aimed to uncover the factors that influence Gen Z's intention to participate and actual participation in cancel culture by utilizing a newly established integrated framework of Belief in a Just World (BJW) and the Theory of Planned Behavior (TPB). A total of 677 valid survey responses from Gen Z respondents were collected to thoroughly evaluate the belief and behavioral dimensions of cancel culture through the utilization of Structural Equation Modeling (SEM). The study's results showed that attitude towards cancel culture, the subjective norm of cancel culture, and perceived behavioral control, are strong facilitating conditions that drive Gen Z's intent and actual participation in canceling behavior. It was seen that BJW has no effect on actual canceling behavior and a reverse effect on the intention to participate in canceling behavior. For the canceling methods, 97 % will unsubscribe or unfollow accounts and 94.68 % will block or mute accounts. It was also discovered that Facebook, Instagram, and YouTube are the top social media platforms used by Gen Zs in the Philippines with at least a 94 % usage rate. The findings of this study may be utilized by businesses and policymakers on how to reduce the incidence and impact of cancel culture

Online or Traditional Learning at the Near End of the Pandemic: Assessment of Students’ Intentions to Pursue Online Learning in the Philippines

Online learning has been utilized due to the sudden shift taken among educational institutions to continue students’ learning during the COVID-19 pandemic. Three years into the pandemic, universities now offer different modalities of education due to the establishment of online and modular learning modalities. Hence, the intention of students to adapt to online learning despite the availability of traditional learning is underexplored. With the limited availability of face-to-face learning at the near end of the epidemic in the Philippines, this study sought to analyze the factors that influenced behavioral intentions towards continuing online learning modalities. Five hundred students from different universities in the Philippines participated and answered 42 adapted questions in an online survey via Google Forms. Structural equation modeling (SEM) was used in this study, with factors such as an affective latent variable, attitude towards behavior, autonomy, relatedness, competency, expectation, confirmation, satisfaction, and behavioral intention. The study found that attitude towards behavior has the highest positive direct effect on students’ intentions to pursue online learning, followed by expectation and confirmation, satisfaction and behavioral intention, competence and behavioral intention, and the affective variable and satisfaction. The effect of expectations on satisfaction and the affective variable on behavioral intentions was seen to have no significance regarding students’ intentions. This also study integrated expectation–confirmation theory, the theory of planned behavior, and self-determination theory to holistically evaluate students’ intentions to pursue online learning despite the availability of traditional learning. The educational sector can utilize these findings to consider pursuing and offering online learning. Additionally, the study can help future researchers evaluate students’ behavioral intentions concerning online learning

Marketing Strategy and Preference Analysis of Electric Cars in a Developing Country: A Perspective from the Philippines

The wide-scale integration of electric vehicles (EVs) in developed countries represents a significant technological innovation and a step toward reducing carbon emissions from transportation. Conversely, in developing nations like the Philippines, the adoption and availability of EVs have not been as rapid or widespread compared to other countries. In identifying this gap, this study delved into the preferences and factors influencing Filipino consumers’ willingness to purchase EVs. The study gathered 311 valid responses utilizing conjoint analysis with an orthogonal approach to assess the attributes influencing customers’ purchase decisions. Conjoint analysis tools such as IBM SPSS v25 statistics were utilized to infer consumer preference. The results determined that cost is the primary concern for consumers by a considerable margin; followed by battery type and charging method; along with the type of EV, driving range, and charging speed; and most minor concern is regenerative brakes. Therefore, there is an apparent sensitivity to price and technology. This study is the first to apply conjoint analysis to the Philippine market, delivering in-depth consumer preference insights that can help manufacturers and policymakers customize their approach to making EVs more attractive and more viable in less developed markets. The results suggest that a targeted effort to overcome cost barriers and improve technological literacy among prospective buyers should be productive for speeding up EV adoption in the Philippines. The results could be extended in future research to a broader assessment of socioeconomic and environmental benefits, laying out a broader plan for promoting sustainable solutions in transportation