Mediating role of self-efficacy on the relationship between conscientiousness and procrastination

Singh, Sandeep; Bala, Rajni

doi:10.1504/ijwoe.2020.109422

Cited by 10 publications

(5 citation statements)

References 39 publications

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“…The relevant LIWC categories are: work [67], tentative [68], positive emotion [48], and negative emotion [69]. Similarly, in our study, we also find a few traits have also correlations between self-efficacy and LIWC scores (Section IV) which include work [70], tentative [71], time [72], positive emotion [55], negative emotion [69] and affect [73]. We observe that a user with a high GSE score likely has positive correlations with tentative and positive emotion LIWC scores.…”

Section: Discussionsupporting

confidence: 80%

Attention and Meta-Heuristic Based General Self-Efficacy Prediction Model From Multimodal Social Media Dataset

Mukta,

Ahmad,

Zaman

et al. 2024

IEEE Access

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General Self-Efficacy (GSE) is a vital attribute of human psychology that describes one's belief about his own ability to succeed in specific situations. GSE is composed of cognitive, social, and behavioral skills of an individual. In this research, we first develop a GSE classification model by using Facebook content (i.e., profile photos and statuses). We collect data from a total of 435 Facebook users in an ethical data collection manner. Two hybrid machine learning methods are applied based on distinct feature extraction approaches: tool-based and deep learning-based. In our tool-based approach, we employ Linguistic Inquiry and Word Count (LIWC) and Bidirectional Encoder Representations from Transformers (BERT) for text and Mediapipe and DeepFace for image feature extraction. We apply Particle Swarm Optimization (PSO) for feature selection, resulting in a robust tabular dataset with high predictive performance for GSE scores. In the deep learning-based approach, we apply BERT and 1-dimensional convolutional neural network (1D-CNN) for text feature extraction, while UNet++ handles image segmentation, and VGG16 and ResNet-152 contribute image features, fused via Canonical Correlation Analysis (CCA). We also integrate a coattention model for image and text features. Traditional machine learning models, including Random Forest (RF), Xgboost, AdaBoost, and Stacking, are then trained on the feature set to predict GSE scores. This comprehensive model showcases a multifaceted approach to GSE prediction, combining tool-based and deep learning methodologies for enhanced accuracy and insights. Then, we develop a GSE prediction model by using the mentioned tool-based (i.e., LIWC, BERT, Mediapipe, and DeepFace) and deep learning-based feature extraction methods from both image and text datasets. The tool-based model achieves remarkable accuracy percentages of 85.80% (text), 91.06% (image), and an outstanding 93.25% for the hybrid model. The deep learning-based model exhibits competitive results, with accuracies of 64.80% (text), 73.06% (image), and 81.87% for the hybrid model.

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Section: Discussionsupporting

confidence: 80%

Attention and Meta-Heuristic Based General Self-Efficacy Prediction Model From Multimodal Social Media Dataset

Mukta,

Ahmad,

Zaman

et al. 2024

IEEE Access

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“…Since the errors are symmetrically distributed so, root mean square error values are examined to establish the predictive relevance of the proposed model. In terms of root mean square error (RMSE), all indicators yield lower prediction errors than the linear regression model resulting in higher predictive power of the model (Shmueli et al, 2019;Singh & Bala, 2020). All the values of PLS SEM_RMSE, LM-RMSE, or each indicator of endogenous variable (Purchase Intention) are reported in Table 7.…”

Section: Results and Findingsmentioning

confidence: 99%

Exploring the impact of beauty vloggers’ credible attributes, parasocial interaction, and trust on consumer purchase intention in influencer marketing

Garg,

Bakshi

2024

Humanit Soc Sci Commun

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The growing usage of digital platforms has made influencer marketing the most cost-effective marketing tool, especially for products consumed by young digital natives. Among other products, these include fashion and beauty-related items such as cosmetics, whose online marketing has created a particular category of influencers called ‘beauty vloggers.’ By applying the source credibility model, this study investigates whether beauty vloggers’ credible attributes (trustworthiness, expertise, and attractiveness) help consumers build trust towards them and whether followers’ trust leads to desirable marketing outcomes (increased purchase intention). Prior literature used the source credibility model in a fragmented manner. However, this study enhances the holistic framework of social media influencers’ impact on purchase intention by investigating all three credible attributes. It specifically addresses the significance of cultivating trust between beauty vloggers and followers, exploring the impact of vloggers’ credible attributes on consumer purchase intention. Additionally, beauty vloggers periodically engage in parasocial interactions to increase closeness with their audiences, affecting consumer behaviour. This study addresses the limited research on the consequences of parasocial interactions in an emerging market context, examining the impact of beauty vloggers’ repeated interactions on consumer behaviour, contributing to a comprehensive understanding of purchase intention using parasocial interaction as an antecedent. The research was conducted on consumers belonging to the age group of 16–45 who use social media platforms daily. Using structural equation modelling via SmartPLS software, the statistical analysis was done on 357 North Indian respondents. The study explores the influence of beauty vloggers on consumers’ purchasing behaviour in the Indian context. The results demonstrate that beauty vloggers’ credibility traits help in influencing consumer behaviour. The findings also revealed that trust partially mediates the impact of credible attributes of beauty vloggers and consumers’ purchase intention. Additionally, the results show that customers’ purchasing decisions might be influenced by repeated parasocial interactions done by beauty vloggers if they perceive them as credible information sources. The findings of the study contribute to help industrial marketers and practitioners of cosmetic companies use influencer marketing strategically in order to increase market sales.

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“…The severe group, characterized by high endorsement of procrastination items, likely represents the most serious procrastinators with potential issues in self-regulation and resistance to temptation. 24 These students might struggle with procrastination across various domains, exhibit lower conscientiousness, 35 and have difficulties in managing time effectively. Specifically, individuals in the severe group often struggle to meet deadlines for various tasks resulting in significant delays in both their daily activities and academic obligations.…”

Section: Discussionmentioning

confidence: 99%

Latent Profiles and Transitions of Bedtime Procrastination Among Chinese College Students: The Predictive Roles of Anxiety, Depression, Problematic Smartphone Use and Self-Control

Hong,

Xu,

Zheng

et al. 2024

NSS

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Mediating role of self-efficacy on the relationship between conscientiousness and procrastination

Cited by 10 publications

References 39 publications

Attention and Meta-Heuristic Based General Self-Efficacy Prediction Model From Multimodal Social Media Dataset

Attention and Meta-Heuristic Based General Self-Efficacy Prediction Model From Multimodal Social Media Dataset

Exploring the impact of beauty vloggers’ credible attributes, parasocial interaction, and trust on consumer purchase intention in influencer marketing

Latent Profiles and Transitions of Bedtime Procrastination Among Chinese College Students: The Predictive Roles of Anxiety, Depression, Problematic Smartphone Use and Self-Control

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