The comprehensive evaluation of urban business under deep learning and Siamese neural network.

As businesses and society navigate the potentials of generative artificial intelligence (GAI), the integration of these technologies introduces unique challenges and opportunities for human resources, requiring a re‐evaluation of human resource management (HRM) frameworks. The existing frameworks may often fall short of capturing the novel attributes, complexities and impacts of GAI on workforce dynamics and organizational operations. This paper proposes a strategic HRM framework, underpinned by the theory of institutional entrepreneurship for sustainable organizations, for integrating GAI within HRM practices to boost operational efficiency, foster innovation and secure a competitive advantage through responsible practices and workforce development. Central to this framework is the alignment with existing business objectives, seizing opportunities, strategic resource assessment and orchestration, re‐institutionalization, realignment and embracing a culture of continuous learning and adaptation. This approach provides a detailed roadmap for organizations to navigate successfully the complexities of a GAI‐enhanced business environment. Additionally, this paper significantly contributes to the theoretical discourse by bridging the gap between HRM and GAI adoption, the proposed framework accounting for GAI–human capital symbiosis, setting the stage for future research to empirically test its applicability, explore its implications on HRM practices and understand its broader economic and societal consequences through diverse multi‐disciplinary and multi‐level research methodologies.

Purpose The increasing popularity of generative artificial intelligence (GenAI) in higher education has raised question marks about its implications for skills including 21st-century skills. While 21st-century skills, particularly the “four Cs” (critical thinking, creativity, communication and collaboration), remain vital, the existing literature lacks a synthesis of how GenAI reshapes their relevance and assessment. Therefore, this study examines GenAI’s implications for higher education students' 21st-century skills’ relevance and assessment methods in evolving digital learning environments. Design/methodology/approach This study used a systematic review methodology and analyzed 62 publications from 2020 to 2024, sourced from Google Scholar and Web of Science. Preferred Reporting Items for Systematic Reviews and Meta-Analyses and the GenAI epistemology, pedagogy and assessment (GenAI EPA) analytical framework guided methodological processes including inclusion/exclusion, screening, coding and presentation of findings. Findings This study revealed three key findings. First, 21st-century skills, such as the four Cs, remain highly relevant in the GenAI era, although they have evolved to suit GenAI needs (e.g. critical evaluation of AI outputs). Second, traditional assessment methods (e.g. standardized tests) are inadequate in GenAI contexts, whereas alternative approaches, including digital portfolios, have proven to be more effective in capturing 21st-century skills. Third, stakeholders (especially educators) emphasize hybrid assessment models that combine process-oriented and outcome-oriented strategies to balance the disruption of GenAI integration with the maintenance of academic integrity. Practical implications This study highlights four key implications for higher education. First, institutions must integrate GenAI tools into curricula to develop and assess 21st-century skills. Second, ethical concerns (e.g. bias and privacy) necessitate clear AI use policies. Third, traditional assessments should shift toward dynamic, authentic methods (e.g. AI-assisted portfolios). Finally, educator-industry collaboration is vital, including co-designed curricula, workshops and internships for real-world AI readiness. Originality/value This study contributes to ongoing AI in education scholarship by mapping the interplay between GenAI and 21st-century skills, offering evidence-based recommendations for rethinking assessment paradigms. It highlights the urgency for institutional policies and practices that align pedagogical innovation with labor-market demands, ensuring graduates thrive in an AI-driven future.

The integration of hyperspectral imagery and LiDAR data offers promising potential for multimodal feature learning in urban land cover classification. However, effectively extracting and fusing these heterogeneous data sources to fully leverage their complementary strengths remains a challenging problem. To address this issue, we propose the Cascade Encoder–Decoder Fusion Network (CEDFNet), a novel multimodal framework designed for high‐precision urban land cover classification. CEDFNet employs two parallel Cascade Encoder–Decoder Networks (CEDNets) as its backbone, where the cascaded architecture enables progressive multi‐scale feature integration and improves the discrimination of land cover patterns across different spatial resolutions. In addition, the model incorporates two specialized modules: the Complementary Feature Focusing Module (CFFM), which enhances cross‐modal complementarity and produces high‐quality fused representations, and the Dense Attention Branch (DAB), which adaptively captures both low‐level and high‐level attentive cues to further strengthen feature expressiveness. Experimental evaluations on the Houston 2018 and MUUFL Gulfport datasets demonstrate that CEDFNet consistently outperforms state‐of‐the‐art baseline models, confirming its effectiveness and robustness in complex urban environments with diverse land cover distributions.

This paper deals with the application of generative artificial intelligence in business. With the introduction of information and communication technology, rapid changes are taking place in the business environment. A business organisation should have the capacity to adapt to these changes quickly to compete in the market and to justify its existence in the changing business world. Artificial Intelligence means the creation of computer systems that can works likes human being. (Kayid, 2020), conducted a study on the role of artificial intelligence in future technology. AI is presently used in various business fields like education, healthcare and business activities. Generative Artificial intelligence is a tool which replicates human capabilities and emulate human like functioning which helps the businessman to explore the unexplored portion of the market. (Feuerriegel et al., 2023) conducted a study on Generative Artificial Intelligence. Generative Artificial Intelligence has the capability of generating text, images and audio from trained data. Every business organisation can use GAI for managing their resources judiciously thereby reducing cost and wastage and increasing efficiency and profitability. Without adapting the changes that are taking place in the business environment there is limited chance for the business organisation for its development. The study is a descriptive study and examine the awareness of students regarding the use of GAI in packaged food business.

Purpose This study aims to investigate the impact of generative artificial intelligence (GenAI) on enterprise innovation performance, particularly from the perspective of knowledge management. It addresses key challenges in GenAI adoption – such as data biases, information overload and technological dependence – and proposes strategies to overcome these obstacles to enhance innovation. Design/methodology/approach Adopting a theoretical approach, this research analyzes the role of knowledge management in bridging the gap between GenAI and enterprise innovation. A structured framework based on four essential knowledge management processes – knowledge creation, retrieval and storage, transfer and sharing and application – is developed to tackle these challenges effectively. Findings The study reveals that while GenAI presents both opportunities and challenges for enterprise innovation, leveraging a structured knowledge management framework is key to unlocking its potential. It underscores the critical role of human–AI collaboration in mitigating issues such as data biases and integration challenges, ultimately improving innovation performance. The findings highlight the importance of complementing AI capabilities with human judgment to ensure successful outcomes in GenAI-driven innovation. Research limitations/implications This conceptual study calls for further empirical research to validate the findings and expand their generalizability. Future studies should explore contextual factors such as organizational characteristics, business environments and policy frameworks to refine the proposed framework. Originality/value This research offers novel insights into the intersection of GenAI, knowledge management and enterprise innovation. It stresses the importance of human involvement alongside GenAI, providing actionable recommendations for organizations navigating the complexities of AI adoption. In addition, it contributes to the evolving discourse on AI and innovation management, offering pathways for businesses to harness GenAI’s full potential and drive performance.

Purpose The rapid advancement of Generative Artificial Intelligence (Gen-AI) offers transformative opportunities for enhancing digital competence and management quality in small and medium-sized enterprises (SMEs). Given the challenges of a Volatile, Uncertain, Complex and Ambiguous (VUCA) business environment, SMEs face risks of losing competitive advantages to larger corporations. This study aims to explore Gen-AI trends and identify strategies that can support SMEs in building digital resilience and operational efficiency. Design/methodology/approach Through text analysis of patent data and using Gen-AI technology, this research examines potential AI applications to address SME challenges, such as labor shortages, productivity declines and operational inefficiencies. The study uses topic modeling and an OS-matrix framework to analyze trends in Gen-AI patents, aligning technological developments with SME-specific needs. Findings The study finds that Gen-AI technologies, such as automated content creation and predictive analytics, provide targeted solutions for key SME challenges. The OS-matrix framework reveals that specific Gen-AI applications can enhance SMEs’ adaptability and competitive positioning in dynamic markets. Research limitations/implications While this research underscores the potential of Gen-AI for SME digital transformation, limitations include a reliance on patent data and a lack of consideration of various industrial features of SMEs. Future research should expand data sources and apply findings across diverse SME sectors. Originality/value This study contributes insights by mapping Gen-AI advancements to SME needs under VUCA conditions. Therefore, integrating topic modeling with OS-matrix for aligning Gen-AI technologies to SME operational challenges, offers a strategic framework for digital adoption.

Generative artificial intelligence (GenAI) impacts higher education assessment and learning outcomes, which are closely related and intertwined. Literature suggests that educators and researchers have many varied concerns regarding student assessment in the higher education GenAI context, such as how to assess students’ learning and the new (refocused) learning outcomes that emerged in GenAI-facilitated learning environments. To provide evidence-based insights into and answers to these concerns, we conducted a scoping review by collating literature in relevant research areas. Following a five-stage scoping review framework, we collaboratively collected and coded 34 studies. The three assessment approaches identified in the review were traditional assessment, innovative and refocused assessment and GenAI-incorporated assessment. The new, refocused learning outcomes identified were career-driven competencies and lifelong learning skills. The review also revealed that most research designs were qualitatively oriented (e.g., with exploratory design, descriptive research, ethnographic research and phenomenological research). This study proposes a holistic diagram showing the current research status and trends. It suggests five future research directions: innovative assessment designs, collaborations among assessment approaches, new learning outcomes, relationships between assessment approaches and learning outcomes, and quantitative or mixed research studies. Implications for practice or policy: Traditional assessment methods in higher education do not operate effectively in the GenAI era. Innovative and refocused assessment and GenAI-incorporated assessment are promising strategies to assess student learning. Career-driven competencies and lifelong learning skills are new focused learning outcomes evolved from the use of GenAI. More quantitative and mixed research studies should be conducted to provide additional empirical evidence on the impact of GenAI on student assessment and learning outcomes.

Purpose As artificial intelligence (AI) technologies continue evolving, the transformation of knowledge and information paradigms offers new perspectives on comprehensive innovation in knowledge management (KM) within manufacturing firms. This study aims to explore the innovative application of generative artificial intelligence (GenAI) in the KM of manufacturing firms, to address challenges such as the acquisition of tacit knowledge, cross-departmental silos and dynamic knowledge optimization and to promote the effective use of knowledge resources and intelligent innovation. Design/methodology/approach This study combines literature analysis with Chinese manufacturing case studies to develop a five-phase GenAI-enhanced KM framework (acquisition, sharing, integration, application and optimization). Through empirical validation, this study establishes an intelligent KM innovation model integrating explicit-tacit knowledge dynamics and GenAI’s technical features. Findings This study constructs scenarios demonstrating how GenAI can facilitate intelligent KM in manufacturing firms. These scenarios broaden the channels for knowledge acquisition, sharing, integration and application, thereby contributing to the development of a logical model and a proposed operational architecture for intelligent KM within such firms. Research limitations/implications This study has limitations including GenAI implementation costs, data privacy concerns and industry-specific applicability. Future research should address cost-effective implementation, enhanced data privacy measures and cross-sector adaptation. Originality/value By proposing specific scenarios in which GenAI can be leveraged to enhance intelligent KM, this study refines a logical model and operational architecture that have the potential to significantly improve the efficiency of knowledge utilization. The findings of this study provide practical guidance and theoretical support for manufacturing firms aiming to leverage GenAI to enhance KM and foster innovation.

Quick and accurate structural damage detection is essential for maintaining the safety and integrity of infrastructure, especially following natural disasters. Traditional methods of damage assessment, which rely on manual inspections, can be labor-intensive and subject to human error. This paper introduces a hybrid deep learning model that combines the capabilities of ResNet50 and GoogLeNet, further enhanced by a convolutional block attention module (CBAM), proposed to improve both the accuracy and performance in detecting structural damage. For training purposes, a diverse dataset of images depicting both structural damage cases and undamaged cases was used. To further enhance the robustness, data augmentation techniques were also employed. In this research, precision, recall, F1-score, and accuracy were employed to evaluate the effectiveness of the introduced hybrid deep learning model. Our findings indicate that the hybrid deep neural network introduced in this study significantly outperformed standalone architectures such as ResNet50 and GoogLeNet, making it a highly effective solution for applications in disaster response and infrastructure maintenance.

Thematic analysis is a well known qualitative analytic method, usually driven by a human researcher to analyze qualitative data. However, in the current age of Generative Artificial Intelligence (GAI) technologies revolution, analyzing qualitative data is evolving. Many research studies have explored the potential of GAI to conduct qualitative data analysis. However, limited studies have explored the collaborative autoethnography qualitative approach in understanding the expectations, challenges and future insights based on two researchers’ personal reflections of using manual approach as well as obtaining support from GAI in analysing data using thematic approach. These reflections are not mutually exclusive but interplay to assist both researchers to understand the dynamics of analysing qualitative data. The study revealed that manual thematic analysis provided in-depth, context-rich insights, capturing cultural and contextual nuances, whereas the GAI-assisted approach offered efficiency and scalability but lacked interpretative depth. Additionally, challenges such as time constraints in manual analysis and prompt variability in GAI-assisted methods were identified, highlighting the need for hybrid approaches to enhance research efficacy. These findings contribute to the research methodologies literature in filling an empirical gap to elevate research efficacy and outcomes as well as present practical implications.

The aim of the article is to verify the convergence process of the Central and Eastern Europe (CEE) (CEE10) countries towards Western European countries (EU15) in years 1995–2016. Additionally, the paper aims to show the interaction between economic integration and convergence as well as business environment and growth. The study methods applied in in the article are analysis of the literature and wide range of quantitative methods (descriptive statistics. regression models (OLS and panel), the elements of taxonomic analysis (cluster analysis and Clark’s coefficient of divergence). In the study years, CEE10 and EU15 countries were developing in accordance with the convergence hypothesis. The impact of economic integration on convergence was confirmed as well as the dependence of growth from the business environment in EU10. The added value of the study is the combination of three important research problems: convergence, economic integration and business environment. In addition, the research area concerns the CEE countries, which is very desirable. Many prior studies suggested to elaborate development and business processes in emerging countries like CEE. Thus, the article tries to fulfill this research needs. It has not only cognitive but also utilitarian values. The research results can be taken into consideration by policy makers to create an appropriate development policy and a conducive business environment.

Latin America and the Caribbean Region experienced dramatic changes in the 1990s. Politically, all but one country, are governed by a democratically elected government. Economically, import substitution industrialization policies (ISI) followed in the past, were replaced by liberalization programs aimed at reducing inflationary pressures and creating a competitive environment. The significant increase in capital flows to Latin America in one single year, 1990, buried the 1980s as the “lost decade,” and the successful implementation of privatization programs region-wide prompted to affirm that the 1990s might constitute the “Latin America's decade.” Where does the euphoria come from? Is there any implicit promise to be derived from such international capital flows? Will the pattern be sustained? Has Latin America begun a new era? Are unfolding events on defiance of fundamentals? These and many other questions can be raised regarding the spectacular transformation of Latin America and the Caribbean, particu...

The integration of generative artificial intelligence (GenAI) in business education is reshaping data analysis and decision-making. However, concerns remain about potential over-reliance on GenAI at the expense of students’ critical thinking. This article presents an assessment designed to develop students’ technical proficiency in AI-supported data analysis while encouraging critical reflection on AI-generated outputs. Using a real-world-inspired case study, students collaborated with GenAI tools to conduct regression, forecasting, and hypothesis testing, and compared AI-generated results with manual analysis in Excel. Thematic analysis, aligned with Kolb’s experiential learning model, reveals stratified engagement: middle and strong performers demonstrated deeper reflection, especially on ethical implications and the importance of human oversight. Strong students further explored GenAI’s adaptability across different contexts. These findings contribute to the growing discourse on GenAI literacy in management education, underscoring the need for balanced pedagogical strategies that cultivate both AI skills and ethical and professional judgment essential for AI-augmented management practice.

This article explores the integration of MLOps pipelines with Generative Artificial Intelligence (GenAI) in renewable energy systems, aiming to enhance environmental efficiency and foster innovation. The objectives are to evaluate advancements in energy harvesting technologies for wireless sensor networks (WSNs), analyze the potential of GenAI for optimizing renewable energy operations, and address challenges in deploying MLOps frameworks in dynamic energy environments. The principal findings reveal that MLOps pipelines enable continuous model refinement, scalability, and efficient management of GenAI models, significantly improving renewable energy applications such as resource optimization, predictive maintenance, and energy storage. Energy harvesting technologies, coupled with GenAI, promise autonomous and sustainable solutions, reducing dependency on traditional power sources. Policy implications emphasize the need for standardized regulations, investments in computational infrastructure, and ethical guidelines for AI deployment in energy systems. By addressing current challenges, policymakers and researchers can unlock GenAI's full potential, advancing global sustainability goals.

Artificial intelligence (AI) has the potential to transform the way research is conducted, particularly through generative AI (GenAI) tools which can enhance written communication and foster innovation via knowledge development. This study focuses on the latter, examining the role of GenAI in specific knowledge development activities within literature reviews. Through an epistemological lens, we distinguish six key knowledge development activities: research synthesis, evidence aggregation, critique, theory building, research gap identification, and research agenda development. Our analysis demonstrates both the capabilities and limitations of GenAI in supporting these activities, highlighting how GenAI can assist in synthesizing previous work, discovering and integrating concepts, and advancing various knowledge domains. We emphasize a human-centered, synergistic approach where GenAI complements researchers’ efforts, rather than replacing them. Additionally, our activity-centric analysis provides insights into how different types of literature reviews can effectively benefit from GenAI support, thereby contributing to a broader understanding of AI integration in information systems research.