Despite substantial investments in business intelligence and analytics (BIA), limited research examines its impact on firm risk and how BIA architectures should be configured to maximize value. Drawing on resource orchestration theory, this study investigates whether BIA mitigates performance risk (return on asset risk) and stock market risk (idiosyncratic risk), and how resource configurations—presence, scope, and breadth—shape these effects. Using panel data from 1,015 firms, the results show that BIA significantly reduces both types of risk, with stronger risk-mitigating effects emerging as resource scope and breadth increase. The findings also reveal meaningful differences across configurations, suggesting that not all BIA deployments yield equal benefits. Moreover, BIA's impact on risk reduction persists over the intermediate term. Overall, this research highlights the importance of considering firm risk and resource configuration when evaluating the business value of innovative technologies, offering implications for both scholarship and managerial practice.
The digital transformation has made information technology (IT) architecture a key factor in firm risk. This study examined how the strategic use of enterprise systems (ESs), particularly the balance between cloud-based software-as-a-service (SaaS) and on-premises solutions, affects financial stability. Introducing the SaaS ES configuration ratio (SaaS_ES_Balance), the research analyzed its impact on idiosyncratic risk-the firm-specific stock volatility-using data from 674 public companies over eight years. Findings indicate that a higher SaaS ratio significantly reduces risk, likely due to operational standardization, better governance, and increased financial flexibility from shifting expenses from capital expenditures to operational expenditures. The risk-reduction benefits are stronger for companies with low information risk, highlighting the importance of organizational readiness. This work contributes to the IT value literature by positioning ES configuration as a strategic risk management tool and provides guidance for managers on deploying SaaS solutions to bolster financial resilience.
Despite their potential, no significant scientific work has yet conceptually integrated AR and VR into digital twins, which presents a critical gap. A conceptual integration through the development of a meta-model would offer several key advantages. First, it would provide a unified framework that standardizes the interaction between physical systems, digital twins, and immersive technologies, ensuring consistency across various applications. Second, the meta-model would enable cross-domain scalability, allowing AR and VR-enhanced digital twins to be adapted more easily across different fields like manufacturing, healthcare, and mobility. Finally, a comprehensive meta-model would facilitate the systematic development and extension of digital twins, ensuring that AR/VR capabilities are seamlessly incorporated into existing and future DT systems. This foundational conceptual structure would drive innovation by offering a clear, modular approach to expanding DT functionalities without needing to reinvent frameworks for each new implementation.
Investigated in this study is the influence of information communication technology (ICT) on the exchange rate in Zambia from 2010 to 2021. The research methods employed involve using descriptive, exploratory, and experimental designs. The results are reported focusing on ICT acquisition, usage, and production in relation to the performance of the exchange rate: imports of ICTs from the Zambia Revenue Authority (ZRA) represented the uptake while the performance of the exchange rate from the Bank of Zambia represented actual volatilities. It was further discovered that the ICTs influence movements in financial transactions by increasing the quantum of transactions. The impact on the movements of currencies advertently affects the exchange rate causing volatility. The role of ICTs in the amalgamation of markets, trade linkages, and openness, is key. Conclusively, ICTs influence the exchange rate: inherent in productive means and permeate consumptive aspects of ICTs and financial transactions. The main limitation arose from data collection on ICT software and services due to lack of a consolidated data capturing information system.
Semantic segmentation was traditionally performed using primitive methods; however, in recent times, a significant growth in the advancement of deep learning techniques for the same is observed. In this paper, an extensive study and review of the existing deep learning (DL)-based techniques used for the purpose of semantic segmentation is carried out along with a summary of the datasets and evaluation metrics used for the same. The paper begins with a general and broader focus on semantic segmentation as a problem and further narrows its focus on existing DL-based approaches for this task. In addition to this, a summary of the traditional methods used for semantic segmentation is also presented towards the beginning. Since the problem of scene understanding is being vastly explored in the computer vision community, especially with the help of semantic segmentation, the authors believe that this paper will benefit active researchers in reviewing and studying the existing state-of-the-art as well as advanced methods for the same.
