This research examines data from 1989 to 2022 across 48 Sub-Saharan African (SSA) countries using a novel panel data regression approach to uncover how conflict undermines economic stability. The study identifies the destruction of infrastructure, disruption of human capital development, and deterrence of investment as primary channels through which conflict negatively impacts economies. These findings support the hypothesis that armed conflict severely hampers economic performance in SSA, highlighting the urgency for effective conflict resolution strategies and robust institutional frameworks. The negative impacts extend beyond immediate losses, altering income growth trajectories and perpetuating poverty long after hostilities cease. Regional spillover effects emphasize the interconnectedness of SSA economies, where conflict in one country affects its neighbors. The research provides innovative insights by disaggregating impact pathways and employing a robust methodology, revealing the complexity of conflict's economic consequences. It underscores the need for comprehensive policy interventions to foster resilience and sustainable development in conflict-prone regions. While there is evidence of potential post-conflict growth, the overall net effect of armed conflict remains profoundly negative, diminishing economic prospects. Future research should focus on strengthening long-term resilience mechanisms and policy measures to enhance the peace dividend. Addressing the root causes of conflict and investing in peace-building efforts are essential for transforming SSA's economic landscape and ensuring sustainable growth and development.

The aim was to examine the relationships between selected demographic and psychographic factors and consumers' willingness to accept content generated by advanced technological innovations (AIGC) in social infrastructure. The sample consisted of 1,308 respondents. Spearman's correlation coefficient was used to examine the relationships between ordinal variables. To assess the differences between groups of respondents, a one-way analysis of variance was used, during which multiple linear regression analysis was used to confirm the predictive power of awareness and experience in relation to AI-generated content in relation to the tendency to accept such content. The study confirmed a statistically significant but weak negative relationship between the age of respondents and their willingness to accept AIGC, with younger age groups showing a slightly higher rate of acceptance. Respondents' attitudes toward the use of personal data through AI and their overall awareness of technological trends had a more significant impact on acceptance. The findings show that respondents who are open to data collection through AI technologies show a significantly higher level of acceptance of automatically generated content. Similarly, respondents who positively evaluate the current quality of AIGC have higher expectations for the future transformation of marketing strategies and media practices. The decisive factors in the social infrastructure for the acceptance of AIGC are not so much the age of the respondents, but rather their awareness, technological literacy, and level of trust in the technology itself. The study therefore recommends increasing transparency and public awareness about the use of AI in marketing and media practices in order to strengthen consumer confidence in automated content.