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.

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.

How important is the tuition fee during the COVID-19 pandemic in a developing country? Evaluation of filipinos' preferences on public university attributes using conjoint analysis.

In a developing country like the Philippines, it is critical to understand the important factors which lead college students to their current colleges and universities, especially during the COVID-19 pandemic. This study utilized the conjoint analysis approach with an orthogonal design for evaluating understudy's inclination in choosing a college with the various attributes such as the tuition fee, distance or location, employability, academic reputation, recommended by friends and peers, recommended by family or relatives, and the availability to transfer was assessed. A total of 518 Filipino students studying at public and state universities participated in answering the 16 combined attributions about university preference using purposive sampling approach. Based on the utilities estimate, the most important attribute was the tuition fee of the preferred university with an importance value of about 32.839%, followed by the employability rate of the university with about 6% gap difference. The mid-concerned attributes were the distance/location with an estimated of 11.139%, recommendation of friends or peers with approximately 11.689% tying together, and the academic reputation with an estimated of 10.638%. The two least important attributes were identified to be the availability to transfer, having with only about 2.713%, and the recommendation of parents with only 2% difference at approximately 4.453%. The outcomes of this study can aid college chairmen and enrolment specialists tweak their advertising procedures by giving significant data to the chief gatherings engaged with settling college decision choices.

Accident patterns and prevention measures for occupational injuries in the Philippine food and beverage manufacturing industry.

BACKGROUND: The manufacturing industry is one of the catalysts for the Philippines. However, this sector is one of the most dangerous industries in the Philippines considering the frequency of occupational injuries. OBJECTIVE: To determine the primary and root causes of recorded accidents, demographics of the person involved, and solutions to prevent the recurrence of certain accidents. METHODS: This study analyzed 185 occupational injury cases in a food and beverage manufacturing company in the Philippines from January to December 2018. A comprehensive classification system was established to examine and code each case in terms of age, gender, working shift, employee type, tenure, department, category, activity during the accident, root cause of injury, injury classification, direct cause of injury, type of injury, part of body injured, agent of injury, and location of the accident. Cramer's V analysis and Phi coefficient analyses were employed on the subject cases to determine the significant factors and the corresponding extent of significance. RESULTS: The results showed that the majority of the occupational injuries were caused by stepping on, striking against, or stuck by objects (77 cases, 41.6%), caught in between (34 cases, 18.4%), fall (34 cases, 18.4%), and exposure or contact with extreme temperatures (24 cases, 13%). Interestingly, female workers who had accidents were more likely due to inadequate hazard information or lack of procedures whereas male workers were more likely due to failure to secure. The prevention measures such as passive safeguards and personal protective equipment, pictograms, and regular safety audits were derived from the results of these analyses. CONCLUSIONS: This study is the first comprehensive analysis of occupational injuries in the food and beverage industry in the Philippines. The findings can be applied to positively influence the effectiveness of prevention and rehabilitation programs mitigating workplace injuries and illnesses.

Young adult preference analysis on the attributes of COVID-19 vaccine in the Philippines: A conjoint analysis approach.

Objective: Vaccines are utilized to prevent the severity of illnesses like the COVID-19 virus. Currently, there are a lot of COVID-19 vaccines available in the market like Pfizer, Moderna, AstraZeneca, Johnson and Johnson, and Sinovac. This research aimed to analyze the preference on the existing vaccine attributes of COVID-19. Study design: Specifically, this study considered 7 attributes such as cost, brand, recommendations, efficacy, side effects, vaccine type, and dose. Methods: A conjoint analysis with orthogonal design was utilized and 865 respondents were participated. Results: The result showed that consumers considered brand as the highest attribute, specifically Pfizer and Moderna among other brands. Moreover, the efficacy of 90% and higher were the preferred vaccine with 1 in 100 patient side effects reported. It was seen that safety and effectiveness is the priority in choosing a COVID-19 vaccine. Interestingly, the knowledge and understanding of the COVID-19 vaccine was found to drive consumer's preference for the vaccines available. Conclusions: The findings of this study could be utilized by the government to increase the willingness to be vaccinated. Lastly, the result of this study would pave a way to promote herd immunity to help fight the COVID-19 pandemic 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.

The perceived usability of the learning management system during the COVID-19 pandemic: Integrating system usability scale, technology acceptance model, and task-technology fit.

BACKGROUND: The education system has shifted from traditional to online during the COVID-19 pandemic. Thus, the Learning Management System (LMS) is one of the most important and comprehensive learning platforms that support and facilitate online learning during this pandemic. OBJECTIVE: This study explored the perceived system usability of LMS during the COVID-19 pandemic by utilizing Technology Acceptance Model (TAM), Task-Technology Fit (TTF), and System Usability Scale (SUS). METHODS: An online survey was utilized to collect data from 502 Filipino students from different academic institutions and different areas of study. RESULTS: Structural Equation Modeling (SEM) indicated that students' perceived ease of use of LMS had a significant effect on perceived usefulness which subsequently and positively led to students' perceived satisfaction of LMS. In addition, LMS technology characteristics was found to have a significant effect on LMS task-technology fit which subsequently led to perceived usefulness and perceived satisfaction. Interestingly, perceived satisfaction was found to have a significant effect on students' perceived system usability of LMS which was calculated using a System Usability Scale (SUS). CONCLUSION: The findings have implications within the education system globally, particularly in recognizing the relevance of LMS moving forward. Furthermore, since this is the first study that integrated SUS, TAM, and TTF, the conceptual framework can be utilized to evaluate the perceived usability of LMS worldwide.

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.

Factors Affecting the Perceived Usability of the COVID-19 Contact-Tracing Application "Thai Chana" during the Early COVID-19 Omicron Period.

Thai Chana is one of the mobile applications for COVID-19 disease-control tracking, especially among the Thais. The purpose of this study was to determine factors affecting the perceived usability of Thai Chana by integrating protection motivation theory, the extended technology acceptance model, and the system usability scale. In all, 800 Thais participated and filled an online questionnaire with 56 questions during the early COVID-19 omicron period (15 December 2021 to 14 January 2022). Structural equation modeling (SEM) showed that the understanding of COVID-19 has significant effects on perceived severity and perceived vulnerability, which subsequently leads to perceived usefulness. In addition, perceived usefulness and perceived ease of use have significant direct effects on attitude, which subsequently leads to the intention to use, actual use, and perceived usability. This study is one of the first studies that have analyzed the mobile application for COVID-19 disease-control tracking. The significant and substantial findings can be used for a theoretical foundation, particularly in designing a new mobile application for disease-control tracking worldwide. Finally, protection motivation theory, the extended technology acceptance model, and the system usability scale can be used for evaluating other disease-control tracking mobile applications worldwide.

Preference analysis on the online learning attributes among senior high school students during the COVID-19 pandemic: A conjoint analysis approach.

The COVID-19 pandemic has resulted in the shift from face-to-face to fully online learning. The purpose of this study was to evaluate the preference of senior high school students on online learning attributes during the COVID-19 pandemic by utilizing a conjoint analysis approach. Six attributes which consist of delivery type, assigned tasks, evaluation, virtual laboratory, interface layout, and delivery platform were simultaneously analyzed through orthogonal design. A total of 1189 senior high school students were collected via purposive sampling approach through the social media platform. The respondents voluntarily participated and answered 29 stimuli with 2 holdouts generated by using SPSS 25 utilizing a 7-point Likert scale. The results indicated that evaluation was found to be the most significant attribute and followed by virtual laboratory, delivery type, and delivery platform. Interestingly, multiple choice evaluation, not requiring virtual laboratories, mixed delivery type (synchronous with recorded lectures), and MS Teams as delivery platform were considered as the keys for the preference. This study is the first study that utilized a conjoint approach to analyze the senior high school students' preference on the online learning attributes during the COVID-19 pandemic. Finally, the conjoint approach can be applied and extended to evaluate the online learning attributes globally by utilizing the attributes and design created in this study.

Public utility vehicle service quality and customer satisfaction in the Philippines during the COVID-19 pandemic.

The implementation of lockdown due to the COVID-19 pandemic has affected most businesses worldwide. The transportation business, specifically in the Philippines, has been heavily affected since only the healthcare and essential workers were allowed to leave their homes during the early stage of the pandemic. This paper aimed to explore the service quality of Public Utility Vehicles (PUV) in the Philippines during the COVID-19 pandemic utilizing the SERVQUAL dimensions. A total of 564 participants answered an online questionnaire using the convenience sampling approach, consisting of 58 questions. Structural equation modelling (SEM) was applied to derive the causal relationships between SERVQUAL dimensions, COVID-19 safety protocol, and customer satisfaction simultaneously. Out of the six dimensions, the SEM indicated that COVID-19 protocols, tangibility, and assurance variables were found to significantly affect PUV service quality and thus, customer satisfaction. This study is one of the first complete studies that analyzed the PUV service quality during the COVID-19 pandemic. The findings could provide the government with an evaluation of the compliance of PUVs to the imposed COVID-19 protocols. Furthermore, the framework of this study could also be applied and extended in evaluating PUV worldwide.

The influence of organizational commitment on the perceived effectiveness of virtual meetings by Filipino professionals during the COVID-19 pandemic: A structural equation modeling approach.

BACKGROUND: Virtual meetings have been widely utilized during the COVID-19 pandemic. OBJECTIVE: The purpose of this study was to explore the influence of organizational commitment on the perceived effectiveness of virtual meeting by Filipino professionals during the COVID-19 pandemic. METHODS: A total of 513 Filipino professionals answered an online questionnaire which covered four latent variables: organizational commitment to virtual meetings, attitude toward virtual meetings, perceived effectiveness of virtual meeting as collaboration tool, and perceived effectiveness of virtual meeting as a social tool. Structural Equation Modeling (SEM) was utilized to analyze the causal relationships between the latent variables construct. RESULTS: SEM showed that organizational commitment to virtual meeting during the COVID-19 pandemic influenced the positive attitude of the employees which subsequently led to the perceived effectiveness of virtual meeting as a collaboration and social tool. CONCLUSIONS: This study is the first study that analyzed the influence of organizational commitment on the perceived effectiveness of virtual meeting during the COVID-19 pandemic in the Philippines. Our SEM construct can be applied and extended further, particularly in analyzing factors influencing the perceived effectiveness of virtual meeting during the COVID-19 pandemic.

Determining factors affecting the perceived usability of air pollution detection mobile application "AirVisual" in Thailand: A structural equation model forest classifier approach.

Air pollution has been evident worldwide. It presented numerous pieces of evidence that affect health-related adverse effects causing diseases and even death and the development of technology has helped monitor the exposure of people to air pollution. This research analyzed factors affecting the perceived usability of air pollution detection on the 'AirVisual' mobile application based on the integrated model of Protection Motivation Theory (PMT) and Unified Theory of Acceptance and Use of Technology (UTAUT2). A total of 416 participants voluntarily answered a self-administered survey consisting of adapted constructs covering factors such as Performance expectancy (PE), Effort expectancy (EE), Social influence (SI), Facilitating conditions (FC), Habit (HB), Perceived risk (PR), Perceived trust (PT), Intention to use (IU), and Perceived usability (PU). Structural Equation Modeling and Random Forest Classifier were utilized to determine factors affecting perceived usability of the 'AirVisual' mobile application. The results showed that PE, EE, SI, and FC were key factors leading to very high PU among users. Moreover, IU was seen to be the most significant factor affecting PU, followed by PT, PR, and HB. This study is one of the first studies that considered the evaluation of usability among health-related mobile applications covering air pollution. The results and the framework utilized in this model may be applied to evaluate other factors and applications related to health among people. Lastly, this study can also be extended to evaluate other mobile applications worldwide.

The Evaluation of Preference and Perceived Quality of Health Communication Icons Associated with COVID-19 Prevention Measures.

Icons have been widely utilized to describe and promote COVID-19 prevention measures. The purpose of this study was to analyze the preference and subjective design features of 133 existing icons associated with COVID-19 prevention measures published by the health and medical organizations of different countries. The 133 icons represent nineteen different function names, such as "Wash Hands" and "Wear Face Mask". A total of 57 participants were recruited to perform two different tests: ranking test and subjective rating test. The ranking test was conducted to elicit the preference ranking of seven icon designs representing each function name. It was followed by a subjective rating test using 13 semantic scales on the two most preferred icons to analyze their perceived quality. Spearmen correlation was applied to derive the possible correlations between users' rankings and the semantic scales, and Friedman's test was also performed to determine the true difference between ranking in terms of each semantic scale to provide a fully meaningful interpretation of the data. Generally, findings from the current study showed that the image presented in the icon is the key point that affects the icons' perceived quality. Interestingly, Spearman's correlation analysis between preference ranking and semantic scales showed that vague-clear, weak-strong, incompatible-compatible, and ineffective-effective were the four strongest semantic scales that highly correlated with the preference ranking. Considering the significant relationships between the semantic distances and the functions, images depicted in an icon should be realistic and as close as possible to its respected function to cater to users' preferences. In addition, the results of Spearman's correlation and Friedman's test also inferred that compatibility and clarity of icon elements are the main factors determining a particular icon's preferability. This study is the first comprehensive study to evaluate the icons associated with the COVID-19 prevention measures. The findings of this study can be utilized as the basis for redesigning icons, particularly for icons related to COVID-19 prevention measures. Furthermore, the approach can also be applied and extended for evaluating other medical icons.

Consumer Preference Analysis on Attributes of Milk Tea: A Conjoint Analysis Approach.

Milk tea is a famous drink that has been heavily consumed since 2011. This study aimed to determine the combination of milk tea attributes that were most preferred using a Conjoint Analysis Approach. Specifically, this study utilized different attributes such as the size of tapioca pearls, sugar level, price range, brands, type of milk tea, cream cheese inclusion, and the amount of ice. Conjoint analysis with the orthogonal design was utilized to evaluate the preference of milk tea among consumers. The results showed that pearl size was the attribute most considered by consumers (29.137%), followed by sugar level (17.373%), the amount of ice (17.190%), the type of drink (13.421%), price (11.207%), and the least considered were cream cheese inclusion (9.525%) and the brands (2.147%). The findings of this study will be beneficial to milk tea firms about consumer preferences regarding the various attributes of milk tea. Finally, the result of this study could be applicable to different beverage-focused studies worldwide.