Search Results (947)

State Departments of Transportation (DOTs) face challenges with traditional bridge inspections that are time-consuming, inconsistent, and paper-based. This study focused on an existing research gap regarding automated methods that streamline the bridge inspection process, prioritize maintenance effectively, and allocate resources efficiently. Thus, this paper introduces a digitalized bridge inspection framework by integrating Building Information Modeling (BIM) and Business Intelligence (BI) to enable near-real-time monitoring and digital documentation. This study adopts a Design Science Research (DSR) methodology, a recognized paradigm for developing and evaluating the innovative SmartBridge to address pressing bridge inspection problems. The method involved designing an Autodesk Revit-based plugin for data synchronization, element-specific comments, and interactive dashboards, demonstrated through an illustrative 3D bridge model. An illustrative example of the digitalized bridge inspection with the proposed framework is provided. The results show that SmartBridge streamlines data collection, reduces manual documentation, and enhances decision-making compared to conventional methods. This paper contributes to this body of knowledge by combining BIM and BI for digital visualization and predictive analytics in bridge inspections. The proposed framework has high potential for hybridizing digital technologies into bridge infrastructure engineering and management to assist transportation agencies in establishing a safer and efficient bridge inspection approach. Full article

Transition to electric vehicles has accelerated in diverse consumer sectors all over the world. Electric School Buses (ESBs) are a particular area of interest due to their environmental and financial potential benefits, including Vehicle-to-Grid (V2G) synergies. Storing electricity in times of lower demand to supply the grid at optimal times can provide significant sustainability benefits, among them a reduction in new generation capacity and financial revenue for battery owners. ESBs, with their high-capacity batteries, have significant potential to supply the grid in V2G systems. There are more than half a million school buses in the US, with a wide geographical distribution, which have significant idle times during school days and holidays. This presents very attractive investment possibilities, providing school districts with additional revenue and supplying local communities with sustainable electricity at high-demand times. This study develops a framework to financially evaluate sustainability of ESB V2G schemes in the US. It applies data analytics, GIS, and Business Intelligence to integrate and assess publicly available data to provide stakeholders with decision-making tools in selecting optimal locations and operation times for these projects. Results indicate that revenue for these projects is significant in most schools, with some locations generating very high revenue potential. Geospatial analysis for most locations and time frames indicates very promising results, with schools potentially receiving significant income from these systems. The framework provides, therefore, relevant information for stakeholders to make sustainable decisions on the development of these projects. Full article

Parking shortages and high costs in Chinese central business districts (CBDs) remain major urban challenges. Emerging automated vehicles (AVs) are expected to diversify parking options and mitigate these problems. However, AV users’ parking preferences and their influencing factors within existing urban zoning frameworks remain unclear. This study examines Nanjing as a representative case, proposing six distinct AV parking modes. Using survey data from 4644 responses collected from 1634 potential users, we employed nested logit models and random forest algorithms to analyze parking choice behavior. Results indicate that diversified AV parking modes would significantly reduce CBD parking demand. Users with medium- to long-term needs prefer home-parking, while short-term users favor CBD proximity. Key influencing factors include parking service satisfaction, duration, congestion time, AV punctuality, and individual characteristics, with satisfaction attributes showing the greatest impact across all modes. Comparative analysis reveals that random forest algorithms provide superior predictive accuracy for parking mode importance, while nested logit models better explain causal relationships between choices and influencing factors. This study establishes a dual analytical framework combining interpretability and predictive accuracy for urban AV parking research, providing valuable insights for transportation management and future metropolitan studies. Full article

Previous research has placed social innovation as a static outcome or single concept, thereby not effectively capturing the dynamism of innovation over time and changes in its purpose. This study attempts to develop an analytical framework which adopts dual axes of pathways of institutional change and levels of innovation for multidimensional analysis of social innovation. Drawing on this dual-axis framework, this study examined 121 social businesses in developing countries. These businesses were operated by social innovators who had been recognized as Ashoka Fellows between 2006 and 2025. Analysis of the cases revealed that the most prevalent type of early-stage social innovation was the peripheral-user type, inducing change at the user level from the periphery of the system. Moreover, the most frequently observed type of transition was from the peripheral-user type to the integrated-service wherein the innovation became partially integrated into the system and changes at the service level. What these findings suggest is that social innovations start at the user level, expand into services, and, in some cases, reach the system level. They move step by step into deeper forms of institutional integration. This study develops a conceptually grounded typology and empirically examines dynamic patterns of this process. Full article

Agroecological transition toward stronger sustainability demands systemic changes in various domains across farms, agroecosystem landscapes, and broader food systems. Business model innovation plays a critical enabling role, by aligning farming systems with agroecology. However, designing or transforming farming business models presents significant challenges, as it involves a radical rethinking of the foundational architecture of value creation, delivery, and capture. This study offers a structured and actionable approach to support this process, by developing a conceptual framework that systematically integrates the FAO’s 10 Elements of Agroecology into the Business Model Canvas, drawing on an exploratory literature review and following a five-stage process. The outcome is a prototype of an Agroecological Business Model Canvas (ABMC) that serves as both an analytical and strategic tool to support the design, evaluation, and improvement of agroecological business models. The proposed ABMC redefines conventional components and introduces additional ones to fully reflect agroecological principles and incorporate evaluation elements for assessing both the transition degree and multidimensional sustainability performance. By facilitating iterative reflection and co-design, the ABMC represents a practical device for advancing Agricultural Knowledge and Innovation Systems and supporting farmers in developing context-specific sustainable, resilient, and socially grounded agroecological business models. Full article

Objective: This study investigates how artificial intelligence (AI) research relates to firm value, focusing on dominant thematic trends, theoretical foundations, and global collaboration patterns. Methods: A PRISMA-guided systematic review was conducted on 219 peer-reviewed articles published between 2013 and May 2025 in the Web of Science Social Sciences Citation Index. Bibliometric techniques, including co-word, co-citation, and collaboration network analyses, were performed using the bibliometrix (version 4.2.3) in R (version 4.4.2) package to map key concepts, intellectual structures, and international research partnerships. Results: The literature is primarily grounded in strategic management theories such as the resource-based view (RBV) and dynamic capabilities. Emerging research streams emphasize digital transformation, big data analytics, and decision support systems. Frequently co-occurring terms include “firm performance,” “artificial intelligence,” “dynamic capabilities,” “information technology,” and “decision-making.” Collaboration mapping highlights the United States, United Kingdom, and China as leading hubs, with increasing contributions from emerging economies such as India, Malaysia, and Saudi Arabia. The alignment between co-word and co-citation structures reflects a shift from foundational theory to applied AI capabilities in firm-value creation. Implications: By integrating a systematic review with advanced bibliometric and science-mapping methods, this work establishes a structured foundation for theory development, empirical testing, and policy formulation in AI-driven business landscapes. Full article

Cognitive biases continue to pose significant challenges in executive decision-making, often leading to strategic inefficiencies, misallocation of resources, and flawed risk assessments. While traditional decision-making relies on intuition and experience, these methods are increasingly proving inadequate in addressing the complexity of modern business environments. Despite the growing integration of big data analytics into executive workflows, existing research lacks a comprehensive examination of how AI-driven methodologies can systematically mitigate biases while maintaining transparency and trust. This paper addresses these gaps by analyzing how big data analytics, artificial intelligence (AI), machine learning (ML), and explainable AI (XAI) contribute to reducing heuristic-driven errors in executive reasoning. Specifically, it explores the role of predictive modeling, real-time analytics, and decision intelligence systems in enhancing objectivity and decision accuracy. Furthermore, this study identifies key organizational and technical barriers—such as biases embedded in training data, model opacity, and resistance to AI adoption—that hinder the effectiveness of data-driven decision-making. By reviewing empirical findings from A/B testing, simulation experiments, and behavioral assessments, this research examines the applicability of AI-powered decision support systems in strategic management. The contributions of this paper include a detailed analysis of bias mitigation mechanisms, an evaluation of current limitations in AI-driven decision intelligence, and practical recommendations for fostering a more data-driven decision culture. By addressing these research gaps, this study advances the discourse on responsible AI adoption and provides actionable insights for organizations seeking to enhance executive decision-making through big data analytics. Full article

While integrated Artificial Intelligence and Business Analytics (AI-BA) represents a significant advancement in marketing analytics and greatly influences firms’ innovations, there is a considerable gap in current research regarding its impact on technological innovation. This study addresses this gap by exploring how AI-BA affects data-driven and technological innovation, considering the mediating roles of integration capabilities and digital platforms. A theoretical model has been developed based on the dynamic capability view (DCV) and organizational information processing theory (OIPT). The model has been validated using data from enterprises in Saudi Arabia, and Partial Least Squares Structural Equation Modeling (PLS-SEM) has been employed for analysis. The findings demonstrate that AI-BA directly enhances both technological and data-driven innovation. Additionally, it was discovered that data-driven innovation, integration capabilities, and digital platforms mediate these effects, thereby enhancing technological innovation within the respective industries. These findings provide both theoretical and practical insights into the relationship between AI-BA, data-driven innovation, and technological innovation. They enrich the existing literature and provide actionable guidance for practitioners aiming to align their AI-BA with improved technological innovation outcomes. Full article

In recent years, the field of European funds for business development has generated significant interest in the academic literature, stimulated by European Union (EU) regulations and the implementation of business financing programs. This context has led to an increase in research on the impact and use of European funds, particularly in terms of support for economic development and infrastructure. This paper presents a bibliometric analysis, using topic modeling, to examine academic publications on the use and absorption of European funds and how they influence the business environment. Using a dataset of 74 publications indexed in the Clarivate Analytics Web of Science Core Collection, covering the period 2005–2024, the present study aims to identify the main authors, institutions, journals, and collaboration networks involved. It also analyzes research trends, dominant themes, and the countries with the largest contributions in this field, using Latent Dirichlet Allocation (LDA) and BERTopic analysis as a complement to the classical bibliometric approach. The thematic analysis reveals a thematic cohesion around entrepreneurship, EU structural funds, regional development, and innovation. In addition, there has been a significant annual increase in publications in this field, and through the use of thematic maps, word clouds, and collaboration networks, this study provides an overview of the evolution of research on the absorption of European funds and its impact on the business environment. These findings contribute both to deepening academic knowledge and to formulating more effective European policies for optimizing fund absorption and supporting the sustainable development of the business environment. Full article

Digital Entrepreneurial Capability (DEC) is the integrated and learnable capacity that equips individuals, or founding teams, to sense, evaluate, and exploit entrepreneurial opportunities within digitally intermediated, platform-centric markets. The construct synthesises four interlocking elements. First, it requires technical dexterity: mastery of data engineering, AI-driven analytics, low-code development, cloud orchestration, and cybersecurity safeguards. Second, it draws on accumulated human capital—formal education, sector experience, and tacit managerial know-how that ground vision in operational reality. Third, DEC hinges on an opportunity-seeking mindset characterised by cognitive alertness, creative problem framing, a high need for achievement, and autonomous motivation. Finally, it depends on calculated risk tolerance, encompassing the ability to price and mitigate economic, technical, algorithmic, and competitive uncertainties endemic to platform economies. When these pillars operate synergistically, entrepreneurs translate digital affordances into scalable, resilient business models; when one pillar is weak, capability bottlenecks arise and ventures falter. Because each pillar can be intentionally developed through education, deliberate practice, and ecosystem support, DEC serves as a practical roadmap for stakeholders. It now informs scholarship across entrepreneurship, information systems, innovation management, and public-policy disciplines, and guides interventions ranging from curriculum design and accelerator programming to due-diligence heuristics and national digital literacy initiatives. Full article

The recent COVID-19 pandemic outbreak has demonstrated all the limitations of modern healthcare information systems in preventing and controlling pandemics, especially following an unexpected event. Existing approaches often fail to integrate real-time data and adaptive learning mechanisms, leading to inefficient response strategies and resource allocation challenges. To address this gap, in this paper, we propose PROTECTION, an innovative data-centric process-modeling-managing-and-mining framework for pandemic control and prevention that is based on the new paradigm that we name Knowledge-, Decision- and Data-Intensive (KDDI) processes. PROTECTION adopts Business Process Model and Notation (BPMN) as a standardized approach to model and manage complex healthcare workflows, enhancing interoperability and formal process representation. PROTECTION introduces a structured methodology that integrates Big Data Analytics, Process Mining and Adaptive Learning Mechanisms to dynamically update healthcare processes in response to evolving pandemic conditions. The framework enables real-time process optimization, predictive analytics for outbreak detection, and automated decision support for healthcare. Through case studies and experimental validation, we demonstrate how PROTECTION can effectively deal with the complex domain of pandemic control and prevention. Full article

The adoption of electric vehicles (EVs) has grown substantially in recent years, offering a cleaner and highly promising pathway toward the decarbonization of urban environments. However, this trend introduces new challenges in charging infrastructure and management. This paper proposes a synergistic integration of Business Intelligence (BI) and Artificial Intelligence (AI) techniques—including machine learning and data analytics—for solving the EV charging problem. We begin with an in-depth analysis of charging behaviors, leveraging extensive datasets from EVs, charging stations (CSs), and auxiliary sources. Based on this analysis, we introduce a BI framework utilizing advanced data mining methods to utilize large-scale data effectively. We then present a BI-based decision-making model that enables comprehensive analysis and optimized solutions for EV charge scheduling and the cooperation among different CS owners. The model is validated across multiple real-world scenarios and case studies, demonstrating significant improvements in charging efficiency, utilization, and reliability. By showcasing the practical applications of BI-driven analytics, our findings underscore the transformative impact of data-informed methodologies on EV charging operations. This paper concludes with a discussion of open research opportunities in AI- and BI-driven intelligent transportation—specifically in EV charging optimization, grid integration, and predictive analytics. Full article

The growing prevalence of digital labor platforms has fundamentally transformed business models by creating interconnected value systems that redefine how work is organized, delivered, and monetized in today’s digital economy. This study examines platform-based business model innovation through the lens of value co-creation processes, analyzing user-generated content from digital work platforms including Reddit, FlexJobs, Toptal, and Deel. Using Latent Dirichlet Allocation (LDA) topic modeling on 342 semantically filtered reviews from platform workers, we identified six key themes characterizing stakeholder experiences: User Experience and Platform Evaluation (23.77%), Financial Concerns and Time Management (18.49%), Platform Satisfaction and Recommendation System (16.60%), Paid Services and Investment Strategies (15.09%), Job Search Processes and Remote Work Alternatives (13.96%), and Overall Platform Performance and Account Management (12.08%). These findings reveal how digital platforms create value through complex interactions between technology infrastructure, governance mechanisms, and stakeholder experiences within interconnected ecosystems. The dominance of user experience concerns over purely economic considerations challenges traditional labor economics frameworks and highlights the critical role of platform design in worker satisfaction. Our analysis demonstrates that successful plsatform business models depend on balancing technological capabilities with human-centered value propositions, requiring innovative approaches to ecosystem orchestration, stakeholder engagement, and value distribution. The study contributes to understanding how digital business models can leverage interconnected value systems to drive sustainable innovation, offering strategic insights for platform design, ecosystem governance, and business model optimization in the digital era. Full article

Against the backdrop of accelerating global trade and rising consumer expectations, cross-border e-commerce must urgently increase consumers’ willingness to reuse them. This study uses social exchange theory (SET) and resource dependency theory (RDT) to look at how business process reengineering (BPR) in cross-border e-commerce logistics services helps with digitalising the services, resulting in consumers being more likely to keep using the platform. The study also investigates information sharing and supply chain integration (SCI) as variables. This study used a sample of Chinese cross-border e-commerce enterprises and employed confirmatory factor analysis and structural equation modelling (SEM) as analytical methods. The findings indicate a positive relationship between logistics service BPR and logistics service digitisation. Our results also show that SCI positively moderates the relationship between BPR and logistics service digitalization by enhancing cross-organizational collaboration and information flow. We further find that greater information sharing cross-border e-commerce platforms and logistics service providers strengthens SCI’s moderating effect, indicating a secondary moderating role of information sharing. This study proposes an innovative interactive perspective and, drawing on SET, constructs three models to identify the boundary conditions influencing the relationship. It provides a theoretical foundation and practical reference for cross-border e-commerce platforms seeking to optimize digital logistics services and enhance consumers’ willingness to reuse the platform. Full article

This study explores the innovative application of Artificial Intelligence (AI) in transforming data engineering practices, with a specific focus on optimizing data modeling and data warehouse automation for Business Intelligence (BI) systems. The proposed framework automates the creation of Data Vault models directly from raw source tables by leveraging the advanced capabilities of Large Language Models (LLMs). The approach involves multiple iterations and uses a set of LLMs from various providers to improve accuracy and adaptability. These models identify relevant entities, relationships, and historical attributes by analyzing the metadata, schema structures, and contextual relationships embedded within the source data. To ensure the generated models are valid and reliable, the study introduces a rigorous validation methodology that combines syntactic, structural, and semantic evaluations into a single comprehensive validity coefficient. This metric provides a quantifiable measure of model quality, facilitating both automated evaluation and human understanding. Through iterative refinement and multi-model experimentation, the system significantly reduces manual modeling efforts, enhances consistency, and accelerates the data warehouse development lifecycle. This exploration serves as a foundational step toward understanding the broader implications of AI-driven automation in advancing the state of modern Big Data warehousing and analytics. Full article