Financial Synergy for Sustainable Success: Optimizing Management, Investment, AI, and Risk SMEs in Bali

In the context of globalized and environmentally conscious business landscapes, this study investigates the intricate relationships between investment decisions, risk perception, artificial intelligence (AI), and sustainable success in small and medium enterprises (SMEs) in Bali. Employing a quantitative approach, the research explores the mediating role of investment decisions using theoretical frameworks such as the Triple Bottom Line, Pecking Order Theory, Trade-Off Theory, and Prospect Theory. Results from a sample of 150 SMEs reveal significant direct effects and correlations, with investment decisions mediating between AI, risk perception, and sustainability. Managerial implications emphasize strategic AI integration, robust risk management, and a balanced approach to investment decisions for sustainable success. While limitations include sample size constraints, future research directions suggest longitudinal and qualitative studies, exploration of sectoral variations, and assessing the impact of emerging technologies on sustainability decisions. This study provides actionable insights for companies navigating the complexities of sustainable decision-making and underscores the imperative for businesses to foster a culture of adaptability and innovation. By embracing these insights and cultivating a forward-thinking approach, companies can not only optimize their current sustainability strategies but also position themselves strategically to thrive in an ever-evolving business landscape.

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Publication Details

Journal
MAKSIMUM
Published
2024-09-26
DOI
https://doi.org/10.26714/mki.14.2.2024.204-219
Citations
1
Primary Topic
FinTech, Crowdfunding, Digital Finance
Type
article
Field-Weighted Citation Impact
0.53
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article

Financial Synergy for Sustainable Success: Optimizing Management, Investment, AI, and Risk SMEs in Bali

Ni Wayan Lasmi, Komang Widhya Sedana Putra P
1 citations
MAKSIMUM
FinTech, Crowdfunding, Digital Finance
0.53
article

Financial Synergy for Sustainable Success: Optimizing Management, Investment, AI, and Risk SMEs in Bali

Ni Wayan Lasmi, Komang Widhya Sedana Putra P
article en
1 citations

Abstract

In the context of globalized and environmentally conscious business landscapes, this study investigates the intricate relationships between investment decisions, risk perception, artificial intelligence (AI), and sustainable success in small and medium enterprises (SMEs) in Bali. Employing a quantitative approach, the research explores the mediating role of investment decisions using theoretical frameworks such as the Triple Bottom Line, Pecking Order Theory, Trade-Off Theory, and Prospect Theory. Results from a sample of 150 SMEs reveal significant direct effects and correlations, with investment decisions mediating between AI, risk perception, and sustainability. Managerial implications emphasize strategic AI integration, robust risk management, and a balanced approach to investment decisions for sustainable success. While limitations include sample size constraints, future research directions suggest longitudinal and qualitative studies, exploration of sectoral variations, and assessing the impact of emerging technologies on sustainability decisions. This study provides actionable insights for companies navigating the complexities of sustainable decision-making and underscores the imperative for businesses to foster a culture of adaptability and innovation. By embracing these insights and cultivating a forward-thinking approach, companies can not only optimize their current sustainability strategies but also position themselves strategically to thrive in an ever-evolving business landscape.

MAKSIMUMVol. 14(2)
University of National Education (ID)
Openalex Percentile: Top 25%
FinTech, Crowdfunding, Digital Finance
0.53
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Financial Synergy for Sustainable Success: Optimizing Management, Investment, AI, and Risk SMEs in Bali — Ni Wayan Lasmi, Komang Widhya Sedana Putra P · MAKSIMUM (2024) | TGRS Research Map | TGRS