2692-2967ISSN (Online)
2770-0216ISSN (Print)
Digital transformation has fundamentally reshaped innovation dynamics in many parts of the global economy, and knowledge diffusion is no longer spatially bounded, as in large-scale innovation data collection, the density of collaborative ties and cross-border knowledge exchanges are increasing across institutional and technological domains. Due to structural changes in the daily organization of innovation activities, knowledge production has been reshaped by the expansion of digital infrastructures and the proliferation of networked research collaborations and innovation platforms. In this study, we aim to contribute to the understanding of global innovation systems by examining how patterns of knowledge diffusion are structured using network analysis in transnational innovation networks. This paper aims to identify structural configurations and relational mechanisms in innovation networks and how these contribute to theoretical understandings of knowledge diffusion. In this paper, we analyze the process of knowledge creation and diffusion as a networked system, using specific examples from our dataset of global innovation actors in order to examine their relational structures and positional roles of knowledge-producing entities. A sample of innovation network data from multiple sectors of global innovation systems took part in the empirical analysis, drawing from bibliometric indicators and the analysis of over large-scale relational linkages. We empirically found that we cannot assume uniformly that centrality or connectivity are either a prerequisite for innovation performance; a driver for diffusion of technological knowledge; a mechanism for individual learning; a mechanism for collective learning; and a determinant for accumulation of innovation capabilities. The findings indicate that actors adopt different strategies of using network positions in their learning: exploratory engagement or exploitative specialization. We argue for a more nuanced interpretation of innovation networks that acknowledges both its structural heterogeneity in shaping understandings of knowledge flows and providing policymakers with insights on organizations’ patterns of using digital infrastructures in other sectors and more complex configurations in the global system. The implications of this study could inform a policy framework in innovation governance on how actors can use their network resources for knowledge accumulation and coordination toward systemic innovation and that networks can function differently in alternative institutional contexts.
Read MoreDoi: https://doi.org/10.54216/AJBOR.130201
Vol. 13 Issue. 2 PP. 01-14, (2025)
In this study, we look at globalization processes over a longitudinal time horizon in the global system to reduce the fragmentation of analytical perspectives while integrating structural and relational dimensions. The analysis examines the dynamics of a complex network in global contexts, including economic, technological, institutional, and informational linkages, to identify systemic patterns that have implications for governance in the area of global integration. Based on a theoretical framework, we position this research to improve the understanding of globalization dynamics into empirically observable structures for the scholarly community. In this paper, we provide empirical insights into the structure of global networks by showing how connectivity and centrality have jointly shaped interaction patterns and asymmetries in the globalization process, affecting the stability of the system. Within each of these dimensions, we integrated observations into a multi-level repeated-measures analysis of network indicators (nodes × ties). Differences were assessed by use of a combination of correlation techniques and regression models, and network metrics within the global system that are relevant to these dynamics. Gephi-based visualization resulted in the exclusion of isolated components not being used for explanatory modeling and statistical testing. A significant main effect was found for network type and it influenced only the strength of associations and structural dependencies. The interaction of global actors of different system positions with other forms of global connectivity through network structures suggests that actors who are new to operating in a highly connected system may be at an increased risk of marginalization. Because increases in these structural imbalances have been associated with an increased likelihood of system-level instability, network-oriented analysis is an effective and integrative approach with potential to improve analytical rigor, policy relevance, and to inform globalization-related decision-making.
Read MoreDoi: https://doi.org/10.54216/AJBOR.130202
Vol. 13 Issue. 2 PP. 15-25, (2025)
As more companies position themselves to capitalize on becoming AI-driven innovators or market disruptors rather than traditional technology firms, this raises an important question for valuation research. The purpose of this study is to collect and analyze the various datasets, indicators, and patterns available in the current landscape of initial public offerings (IPOs) that are associated with artificial intelligence (AI). To (a) evaluate the effectiveness of econometric methods used within AI-related IPO analyses based primarily on narrative valuation and financial modeling, and (b) identify which industry indicators are the most predictive of pricing outcomes within these offerings. This paper then extends the existing literature by linking the narrative and quantitative dimensions of IPO valuation with the behavioral economics of investors and underwriters. Firms from AI-intensive sectors have a valuation premium and are relatively more appealing than non-AI peers in investor sentiment and pricing expectations. This results in a framework of factors defining AI association, valuation dynamics, and narrative influence that are considered relevant for the capital formation process. Within each model, results show differential effects for companies that belong to and do not belong to AI-related industries in price formation and fundraising outcomes. By bringing together descriptive insights and regression-based evidence on AI affiliation and IPO performance, this study reinforces the possibility of narrative bias and the symbolic influence of AI association through the combined analysis of market data from technology, financial, and innovation ecosystems. There is, however, a need for greater refinement concerning these classification measures to further improve the accuracy of IPO valuation models.
Read MoreDoi: https://doi.org/10.54216/AJBOR.130203
Vol. 13 Issue. 2 PP. 26-32, (2025)
The current swift advancement of Artificial Intelligence (AI) technologies is transforming operations management by integrating real-time data-driven insights for cost optimization and improved decision-making. In this paper, we explore the fusion of artificial intelligence (AI) technologies in call center operations management, focusing on how the integration of speech-to-text, text-to-speech, and speech analytics tools is revolutionizing customer interaction and decision-making. The fusion of real-time conversational data with advanced machine learning algorithms enables organizations to extract actionable insights, optimize key performance indicators (KPIs), and enhance customer satisfaction. Furthermore, in this research, we are estimating the approximate return on investment in the benchmarked private sectors of Uzbekistan, thus contributing to the future networks in the industry. Our research work bridges the gap between theoretical AI advancements and their practical applications, contributing to the growing body of knowledge on information fusion in intelligent systems in the emerging Uzbek market.
Read MoreDoi: https://doi.org/10.54216/AJBOR.130204
Vol. 13 Issue. 2 PP. 33-41, (2025)