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This paper aims to study the role of business intelligence, knowledge sharing and organisational innovation on gaining competitive advantage.

The statistical population of the study was the managers and the specialists of some export companies of which 213 persons participated in this research. Path analysis was carried out to analyse and interpret the data by Amos software.

The results showed that business intelligence has a positive and significant impact on knowledge sharing, organisational innovation and gaining competitive advantage. Further, business intelligence has a positive and significant effect on competitive advantage through knowledge sharing and organisational innovation. Knowledge sharing impacts gaining competitive advantage positively and significantly. Finally, organisational innovation impacts gaining competitive advantage positively and significantly.

This research highlights the role of business intelligence, knowledge sharing and organisational innovation on gaining competitive advantage in export companies.

The combination of strategic orientation and digitalization for sustainable competitive advantage among small businesses is still not given much attention in the literature. Therefore, this study aims to understand the influence of strategic orientation on sustainable competitive advantage while mediating the relationship with digitalization.

This study used a cross-sectional design. This design helped collect data from 234 small businesses in Arusha city, Tanzania. Since the study used latent variables, structural equation modeling (SEM) was used to analyze relationships and conduct confirmatory factor analysis. Through bootstrapping confidence intervals, Hayes's Process was also used to test how digitalization mediates the relationship between strategic orientations and sustainable competitive advantage.

The strategic orientation attributes that include market orientation, entrepreneurial orientation and learning orientation were positively and significantly related to digitalization. Furthermore, the results on digitalization and sustainable competitive advantage show a significant positive relationship. Finally, digitalization was analyzed to mediate the relationship between strategic orientation, market orientation, entrepreneurial orientation, learning orientation and sustainable competitive advantage. Hence, all hypotheses were supported.

This study adopted a cross-sectional design that helped to capture the quantitative information. In addition, the current study is limited to Tanzania's small businesses; thus, the findings cannot assure generalization of the conclusion to other countries because of the differences in social, cultural and technology across countries.

This study integrates the concepts of strategic orientation from the strategic management discipline and digitalization from a technology perspective. As a result, the study adds new knowledge about combining two aspects and determining whether they add value in terms of providing a sustainable competitive advantage. This knowledge comes from digitalization, which acts as a mediator between strategic orientation dimensions and a sustainable competitive advantage.

In today’s competitive business landscape, organizations are increasingly recognizing the strategic advantage of implementing sustainable practices to gain a competitive edge. This study aims to investigate the effect of green artificial intelligence (AI) on achieving a green competitive advantage, examining the mediating roles of green organizational learning, green product innovation and green process innovation. Additionally, the research explores the moderating role of perceived green climate in the relationship between green AI and these mediating factors.

This research examined companies in Isfahan, Iran, that have varying levels of artificial intelligence adoption within their business processes. The target population consisted of 148 senior managers from these companies. This study uses structural equation modeling to examine the proposed model.

Green AI positively impacted green organizational learning and green process innovation but not green product innovation. In addition, the results showed that green organizational learning, green product innovation and green process innovation had positive effects on green competitive advantage. Finally, the results showed that the perceived green climate did not play a moderating role in the relationship between green AI and these mediating factors.

Organizations should prioritize green AI initiatives, foster a culture of green learning and invest in green innovation to achieve sustainable growth and outpace competitors in the environmentally conscious marketplace.

This study positions itself at the forefront of research on green AI and green competitive advantage. It offers a unique framework by examining the combined effects of green AI, green learning and both product and process innovation on achieving a sustainable competitive advantage.

Investors' behavior in financial markets is often under the influence of various psychological and cognitive factors, as well as personality characteristics. This research explores which behavioral factors and personality traits affect investment decisions and, consequently, investment performance.

A survey analysis was conducted on a sample of 310 investors in Croatia. Partial least squares structural equation modeling was used to obtain the results.

Overconfidence heuristic, prospect theory elements, emotions and stability and plasticity (as big two personality dimensions) positively affect investment decisions, while herding has a negative effect. Investment decisions, observed through the preference for long-term investments, consequently have a positive effect on the investment performance satisfaction.

This research proposes a unique comprehensive model of the effect of numerous different cognitive and psychological behavioral factors on investment decisions. Furthermore, the influence of investment decisions on investment performance is observed simultaneously. Understanding human behavior based on their personal characteristics can help investors to make better investment decisions. Advisors can learn from human behavior and guide their clients in the right direction when it comes to stock investment. Scientists will be able to replicate the model with other data and make comparative analyses.