Analysis of Factors Affecting Purchase of Self-Defense Tools among Women: A Machine Learning Ensemble Approach

Applied Sciences, Год журнала: 2023, Номер 13(5), С. 3003 - 3003

Опубликована: Фев. 26, 2023

Street crime is one of the world’s top concerns and a surge in cases has alarmed people, particularly women. Related studies recent news have provided proof that women are target for crimes violence at home, outdoors, even workplace. To guarantee protection, self-defense tools been developed sales on rise market. The current study aimed to determine factors influencing women’s intention purchase by utilizing Protection Motivation Theory (PMT) Planned Behavior (TPB). applied multiple data analyses, Machine Learning Algorithms (MLAs): Decision Tree (DT), Random Forest Classifier (RFC), Deep Neural Network (DLNN), predict purchasing consumer behavior. A total 553 Filipino female respondents voluntarily completed 46-item questionnaire which was distributed online, yielding 22,120 points. MLAs output showed attitude, perceived risk, subjective norm, behavioral control were most significant tools. Environment, hazardous surroundings, relatives peers, thinking control, all influenced buy RFC DLNN analyses proved effective, resulting 96% 97.70% accuracy rates, respectively. Finally, MLA analysis this research can be expanded assess affecting human behavior context safety.

Язык: Английский

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Determining the Factors Affecting a Career Shifter’s Use of Software Testing Tools amidst the COVID-19 Crisis in the Philippines: TTF-TAM Approach

Sustainability, Год журнала: 2022, Номер 14(17), С. 11084 - 11084

Опубликована: Сен. 5, 2022

The restrictions of the ongoing COVID-19 pandemic resulted in downturn various industries and contrast a massive growth information technology industry. Consequently, more Filipinos are considering career changes to earn living. However, people still need be upskilled. This study combines extended Technology Acceptance Model Task Fit framework determine factors affecting shifter’s use software testing tools its impact on perceived performance amidst Philippines. A total 150 testers voluntarily participated accomplished an online questionnaire consisting 39 questions. Structural Equation Modeling Deep Learning Neural Network indicated that had higher effect Perceived Performance Impact. Moreover, positively influenced Usefulness. Computer Self-Efficacy was strong predictor Ease Use. Use confirmed as Actual System Intention Use, Usefulness, Subjective Norm were also significant is first explore would very valuable enhancing government policies for workforce upskilling, improving private sector’s training development practices, developing competitive tool hasten users’ adaptability. Lastly, methodology, findings, could applied evaluate other adoption worldwide.

Язык: Английский

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Utilizing SEM-RFC to predict factors affecting online shopping cart abandonment during the COVID-19 pandemic

Heliyon, Год журнала: 2022, Номер 8(11), С. e11293 - e11293

Опубликована: Окт. 28, 2022

Online shopping has accelerated during to the pandemic and an increase in online cart abandonment (SCA) was also evident. The growth of is contributed by rising middle class, high consumer spending, millennials, a tech-savvy population which valuable e-commerce. This study aimed predict factors that affect SCA COVID-19 Pandemic utilizing SEM-RFC hybrid. Several such as self-efficacy, attribute conflicts, hesitation at checkout, emotional ambivalence, choice process satisfaction, attitude, subjective norms, perceived behavioral control were analyzed simultaneously. integrated cognition-affect-behavior paradigm with Theory Planned Behavior provide conceptual framework measured through survey questionnaire answered 1015 valid responses collected convenience sampling. Results showed Attitude, Attribute Conflict, Self-Efficacy, Emotional Ambivalence are primary significant affecting SCA. Amidst pandemic, consumers still value ease use, safety mobile applications they have, do not positively experience this time. findings may be applied extended researchers, retailers, businesses understand consumer's intentions. Moreover, results capitalized on business sector create marketing strategies develop models for sustainable worldwide.

Язык: Английский

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A Survey on Machine Learning Techniques for Stock Market Price Prediction

Опубликована: Янв. 28, 2024

In today's fiercely competitive business environment, industries aspire to grow rapidly. Achieving consistent expansion requires access substantial capital resources. Typically, there are three primary avenues for raising capital: Initial Public Offering (IPO), securing investments from angel investors, and obtaining loans. As companies beyond a certain size, it becomes increasingly challenging individual investors sustain operations solely their capital. Consequently, businesses require continuous influx of An IPO is prominent method capital, known elevate company's profile. It bolsters the credibility among suppliers, collaborators, customers. While motivation an infusion, may also offer founders early-stage opportunity partial divestment holdings. Following IPO, shares become publicly tradable. offers individuals invest in form stocks traded on open platforms. However, achieving optimal returns necessitates ability forecast stock prices accurately. This survey explores various techniques prediction prices, all rooted domain Machine Learning (ML). A comprehensive review existing research indicates that combining Random Forest with other models represent most effective approach price prediction. proposes potential enhancing future this domain.

Язык: Английский

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A comparative study of soil classification machine learning models for construction management

International Journal of Construction Management, Год журнала: 2024, Номер unknown, С. 1 - 10

Опубликована: Апрель 27, 2024

Язык: Английский

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