Determining factors influencing students’ future intentions to enroll in chemistry-related courses: integrating self-determination theory and theory of planned behavior

Ong, Ardvin Kester S.; Prasetyo, Yogi Tri; Pinugu, Jasmine Nadja J.; Chuenyindee, Thanatorn; Chin, Jacky; Nadlifatin, Reny

doi:10.1080/09500693.2022.2036857

Cited by 25 publications

(47 citation statements)

References 62 publications

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“…As seen in Figure 3 , only user resistance (H8) did not meet the criteria of being significant ( p -value > 0.05). In addition, SI5, SI6, PEOU2, PEOU3, and ASU5 indicators have values less than the 0.05 threshold [ 25 ]. Therefore, a revised model was constructed, omitting the insignificant variables to enhance the model fit of this study.…”

Section: Resultsmentioning

confidence: 99%

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

Yuduang

Ong

Vista

et al. 2022

IJERPH

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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.

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Section: Resultsmentioning

confidence: 99%

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

Yuduang

Ong

Vista

et al. 2022

IJERPH

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“…TPB's attitude construct has shown a reliable and robust influence on behavioral intent in several studies in various cultural settings (Almobaireek and Manolova 2012;Chaulagain et al 2021;Tommasetti et al 2018;Wu et al 2017). Ong et al (2022) also observed that attitude is the major predictor of intention. However, some studies reported contradictory results.…”

Section: Hypothesis Developmentmentioning

confidence: 87%

Investigating and Predicting Intentions to Continue Using Mobile Payment Platforms after the COVID-19 Pandemic: An Empirical Study among Retailers in India

Jena

2022

JRFM

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The Indian retail industry has registered tremendous growth recently. The sudden emergence of Coronavirus Disease 2019 (COVID-19) and the related measures that were taken by the authorities to curb the pandemic have compelled retailers and their consumers to transact using digital platforms. This study investigates the critical precursors to retailers’ behavioral intention to use mobile platforms for their business transactions in the post-pandemic era. This study adopted a framework that combined the theory of planned behavior (TPB) and self-determination theory (SDT) to predict behavioral intentions. A hybrid approach combining partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) techniques was used to test and validate the proposed framework. Four hundred and ninety-six participants from different central Indian cities participated in the study. PLS-SEM results confirmed that the motivational factors (need satisfaction [NS] and need frustration [NF]) significantly influence the attitude (AT), subjective norms (SN), perceived behavioral control (PBC), and behavioral intention (BI). Furthermore, the findings also established the partial mediating effect of AT, SN, and PBC on the relationship between motivational construct (NS and NF) and BI. Finally, the relationship established by SEM was successfully validated by ANN in the existence of a nonlinear relationship in the data. The findings may help retail stakeholders to support retail owners in their pursuit to continue using mobile payment systems in the post-COVID-19 world.

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“…The lean manufacturing concept is implemented in the automotive industry, Ford Motor in Taiwan to have an improvement on quality and cost aspects (4) (5) (6). In the automotive industry, particularly on the assembly field, lean manufacturing implementation starts with 5S, Value Stream Mapping, Kanban and Single Minute Exchange of Dies (7) (8) (9) (10) (11).…”

Section: Icasimentioning

confidence: 99%

Implementation of Lead Time Improvement in the Cutting Production Process using Clustering Data Mining and Lean Manufacturing

Steven

Chin

Nadlifatin

2022

KSS

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In steel companies, there are some processes in the production such as cutting, machining and heat treatment. The production process begins with cutting process and then machining process and the last one is the heat treatment process. It becomes a background that gives the challenge and needs continuous improvement on all aspects, mainly in the cutting process. The research is conducted at one of steel companies located in Pulogadung Industrial Estate. The purpose of the research is to know the factors which cause process lead time cannot be achieved. The methods used are clustering data mining and lean manufacturing. Clustering method can be used to focus on big data and find out clusters or the same pattern. Those clusters are processed with Weka software and using K-means algorithm. Improvement ideas will be implemented to the formed clusters using lean manufacturing such as Single Minute Exchange of Dies (SMED) which have been mapped through value stream mapping, 5S, and Kanban beforehand. The materials’ dimension on the production process is affecting the cutting process lead time. The thicker material diameters will need a longer time to process. With the methods used, lead time of cutting process increased from 3449 minutes to 2165 minutes (3 days to be 2 days). Meanwhile if the “SMED” activities are implemented, the cutting process lead time increased from 187 minutes to 136 minutes. Keywords: Clustering; SMED; Lean Manufacturing; Process Lead Time; Value Stream Mapping

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Determining factors influencing students’ future intentions to enroll in chemistry-related courses: integrating self-determination theory and theory of planned behavior

Cited by 25 publications

References 62 publications

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

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

Investigating and Predicting Intentions to Continue Using Mobile Payment Platforms after the COVID-19 Pandemic: An Empirical Study among Retailers in India

Implementation of Lead Time Improvement in the Cutting Production Process using Clustering Data Mining and Lean Manufacturing

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