Generative AI-supported learning and Workplace Creativity in electronic retail
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Author(s)William WidjajaLink to ORCID Index: https://orcid.org/0000-0002-3851-9743
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Devi Rahnjen WijayadneLink to ORCID Index: https://orcid.org/0000-0001-5631-7670
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DOIhttp://dx.doi.org/10.21511/kpm.10(3).2026.11
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Article InfoVolume 10 2026, Issue #3, pp. 176-194
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This work is licensed under a
Creative Commons Attribution 4.0 International License
Type of the article: Research Article
This study examines the relationships among generative AI tool usage for Workplace Learning, Learning Agility, and Workplace Creativity among electronic retail employees in Indonesia. Learning Agility was modeled as a second-order construct comprising developing leadership or growth, seeking feedback, and developing systematically. Data were collected through a cross-sectional survey of 286 employees who actively used generative AI platforms for learning and work-related support. The hypotheses were tested using covariance-based structural equation modeling and bias-corrected bootstrap analysis. The results show that generative AI tool usage was positively associated with Learning Agility (β = 0.738, p < 0.001), while Learning Agility was positively associated with Workplace Creativity (β = 0.484, p < 0.001). Generative AI tool usage also maintained a positive direct relationship with Workplace Creativity (β = 0.408, p < 0.001). The indirect relationship through Learning Agility was statistically significant, with a bias-corrected 95% confidence interval ranging from 0.179 to 0.509. These findings indicate complementary partial mediation, suggesting that Generative AI Use is related to Workplace Creativity both directly and through employees’ adaptive learning capacity. The study applies Social Cognitive Theory to generative AI-supported workplace learning by distinguishing AI-enabled learning resources from employees’ behavioral engagement with those resources. The findings suggest that Generative AI Use is associated with Workplace Creativity both directly and indirectly through Learning Agility. Practically, electronic retail managers should combine AI adoption with employee development practices that encourage feedback seeking, reflection, experimentation, and systematic learning.
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JEL Classification (Paper profile tab)M53, M12, O33, D83, L81
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References45
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Tables8
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Figures2
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- Figure 1. Conceptual model
- Figure A1. Full model
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- Table 1. Variable measurement
- Table 2. Respondent characteristics
- Table 3. Convergent validity and reliability test
- Table 4. Discriminant validity of first-order constructs
- Table 5. Discriminant validity of second-order constructs
- Table 6. Goodness-of-fit
- Table 7. Direct relationships
- Table 8. Direct relationship
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Harnessing financial advice and literacy for financial well-being in the digital age
Anju Gupta ,
Shekhar Mishra ,
Deepak Kumar Behera ,
Abhilash Abhilash
doi: http://dx.doi.org/10.21511/imfi.22(1).2025.23
Investment Management and Financial Innovations Volume 22, 2025 Issue #1 pp. 299-310 Views: 2308 Downloads: 719 TO CITE АНОТАЦІЯUnder complex financial circumstances, individuals are empowered to improve financial decision-making by trusting financial advice and utilizing digital technology and resources. Though the extant research has explored numerous factors impacting financial well-being, the specific influence of financial advice and digital financial literacy remains underexamined in the Indian context. Thus, grounded on Social Cognitive theory, this study aimed to examine how insights gained from financial advice and digital financial literacy integrate into individual’s decision-making and, subsequently, influence their financial well-being. The data were collected using purposive sampling from Southern India, with 508 respondents recruited using social media platforms. The research hypotheses were empirically validated through hierarchical regression and mediation analysis using the Hayes Process Macro. The study’s findings reveal that financial advice positively predicted financial decision-making (β = 0.667; p < .000). Similarly, digital financial literacy has a positive impact on financial decision-making (β = 0.369; p < .000). Additionally, financial decision-making (β = 0.105; p < .065) positively predicted financial wellbeing. Thus, both factors emerged as transformative predictors of an individual’s financial well-being. Moreover, the findings reveal the mediating role of financial decision-making between financial advice, digital financial literacy, and financial well-being. Therefore, the study underscores that by leveraging the cumulative effect of professional financial advice and digital technologies, policymakers and government regulatory bodies can augment the critical ability of informed decision-making. Thus, these factors could navigate overcoming individual financial challenges and benefit the overall well-being of a diverse population.
