Integrating AI in Public Governance: A Systematic Review

This chapter introduces the ‘New Digital Era Governance’ as a new paradigm in public administration scholarship by compounding on the changing roles of digital technologies in public governance. Particularly, the chapter highlights the challenges inflicted upon public administration and society with the advancements of new digital technologies, such as big data, artificial intelligence, and blockchain. By comparing the changing role of digital technologies in government functions, public value creation, human resources, and governance, the chapter asserts that the theoretical and practical implications of ‘New Digital Era Governance’ differ from Dunleavy et al.’s ‘Digital Era Governance’.

In this paper, I provide an overview of digital government, covering its characteristics, scope, objectives, current status, and future potential. I believe digital government involves leveraging Information and Communication Technologies (ICT), particularly the internet, to transform the interaction between government and society positively. I briefly introduce two concurrent reform paradigms: the participatory and managerial approaches, striving to enhance the government's responsiveness, accessibility, transparency, responsibility, participation, shift, development, efficiency, and effectiveness. Additionally, I present digital government models that elucidate its development trends. I also outline the paper's target audience, structure, and educational objectives. Moreover, I introduce 'A Future of Governance in the Digital Era' as a novel paradigm in public administration studies, emphasizing the evolving role of digital technologies in public governance. I underscore the challenges posed to public administration and society by emerging digital technologies like big data, artificial intelligence, and blockchain. By contrasting the evolving functions of digital technologies in government, public value creation, human resources, and governance, I argue that the theoretical and practical implications of 'A Future of Governance in the Digital Era' diverge from those of Dunleavy et al.'s 'Digital Era Governance'.

Purpose Growing concerns about sustainability in food supply chains have prompted food processing small and medium-sized enterprises (SMEs) to adopt new technologies to improve their ecological performance. This study explores the integration of big data analytics capabilities powered by artificial intelligence (BDAC-AI) within green supply chain management (GSCM), examining the mediating roles of waste management (WM) and managerial environmental concern (MEC) in enhancing sustainable performance (SP). The research applies the technology acceptance model (TAM) and dynamic capability theory (DCT) as an integrated framework to understand these relationships. Design/methodology/approach The study employs a quantitative approach using partial least squares structural equation modeling (PLS-SEM) to examine direct and indirect pathways among the variables. The sample consists of 489 managers and directors from food processing SMEs, a sector vital for food security that faces significant waste management challenges. Data were collected via a structured survey and analyzed with SPSS and SmartPLS. Findings GSCM, BDAC-AI, WM and MEC each have a significant positive effect on SP. BDAC-AI and WM partially mediate the relationship between GSCM and SP, and MEC significantly moderates the impact of BDAC-AI on SP. These results provide a deeper understanding of how TAM and DCT together explain sustainable performance in food SMEs. Practical implications The findings offer guidance for managers and policymakers, showing that integrating big data analytics into GSCM strategies can yield improved sustainability outcomes and suggesting that supportive policies (e.g. incentives for green technology adoption) can amplify these benefits. Originality/value This study is novel in combining TAM and DCT to examine sustainability practices in food supply chains. It aligns GSCM with SP through the mediating roles of BDAC-AI and WM and the moderating role of MEC, thus filling a significant gap in literature. By benchmarking green supply chain best practices and leveraging BDAC-AI, food processing SMEs can enhance their sustainable performance.

The aim of the study was to analyze the role of artificial intelligence and machine learning in optimizing supply chain processes Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources, preferably because of its low-cost advantage as compared to field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Artificial intelligence (AI) and machine learning (ML) play a pivotal role in optimizing supply chain processes by enhancing demand forecasting accuracy through sophisticated algorithms. They streamline inventory management by predicting demand patterns and automating replenishment tasks, leading to reduced stockouts and excess inventory. AI- powered analytics enable real-time insights into supply chain performance, identifying bottlenecks and inefficiencies for proactive decision-making. Unique Contribution to Theory, Practice and Policy: Theory of technology acceptance model (TAM), resource-based view (RBV) theory & dynamic capabilities theory may be used to anchor future studies on analyzing the role of artificial intelligence and machine learning in optimizing supply chain processes. Encourage supply chain stakeholders to adopt blockchain solutions for enhanced transparency, traceability, and efficiency. Advocate for regulatory frameworks that promote the adoption of blockchain technology in supply chains while addressing concerns related to data privacy, interoperability, and standardization. Keywords Demand Forecasting, Resource utilization, Reduce costs, Minimize errors, and Increase Productivity, Artificial Intelligence, Machine Learning, Inventory, Transportation, Warehousing

Purpose: The aim of the study was to analyze the role of artificial intelligence and machine learning in optimizing supply chain processes
 Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.
 Findings: Artificial intelligence (AI) and machine learning (ML) play a pivotal role in optimizing supply chain processes by enhancing demand forecasting accuracy through sophisticated algorithms. They streamline inventory management by predicting demand patterns and automating replenishment tasks, leading to reduced stockouts and excess inventory. AI-powered analytics enable real-time insights into supply chain performance, identifying bottlenecks and inefficiencies for proactive decision-making.
 Unique Contribution to Theory, Practice and Policy: Theory of technology acceptance model (TAM), resource-based view (RBV) theory & dynamic capabilities theory may be used to anchor future studies on analyzing the role of artificial intelligence and machine learning in optimizing supply chain processes. Encourage supply chain stakeholders to adopt blockchain solutions for enhanced transparency, traceability, and efficiency. Advocate for regulatory frameworks that promote the adoption of blockchain technology in supply chains while addressing concerns related to data privacy, interoperability, and standardization.

The COVID-19 pandemic accelerated the global adoption of electronic health (e-health) tools for digital mental health services (DMHS) in South African higher education institutions (SA HEIs). However, the long-term sustainability of these innovations remains uncertain. This study employs an integrated Technology-Organisation-Environment (TOE) and Technology Acceptance Model (TAM) framework to investigate determinants of the sustainable adoption of e-health tools. A cross-sectional survey of 348 staff at a selected SA HEI was analysed using descriptive statistics and exploratory factor analysis. Three critical factors emerged: (1) University capacity to deliver DMHS, emphasising the significance of top management support, financial resources, and information and communications technology expertise; (2) Perceived benefits and importance of e-health tools, highlighting user perceptions of usefulness, ease of use, and behavioural intention; and (3) External support to enhance university capacity, including government policies, competitive pressures, and institutional partnerships. The study advances theory by synthesising TOE and TAM in a resource-constrained context, revealing how institutional readiness and user perceptions jointly influence the sustainable adoption of e-health tools for DMHS. Practical implications highlight the need for targeted investments in digital infrastructure, capacity-building, and policy alignment to strengthen DMHS sustainability. The study results are consistent with Sustainable Development Goal 3 (SDG 3), offering a roadmap for SA HEIs to leverage e-health tools for mental health resilience post-pandemic.

This study sought to determine the influence of Information and Communication Technology (ICT) adoption on the performance of Milimani Law Courts in Nairobi City County, Kenya. The specific objectives were to assess how the adoption of ICT has influenced the efficiency of judicial proceedings, judicial turnover rates, access to legal services, and judicial transparency and accountability. The research focused on four key ICT systems: the Electronic Case Management System (ECMS), SMS inquiry system, video conferencing, and digital recording of proceedings and transcription. The study was grounded on the Technology Acceptance Model, Disruptive Innovation Theory, and Dynamic Capabilities Theory. A mixed methods approach was employed, using both quantitative and qualitative research techniques. A sample of 270 respondents including judicial officers, lawyers, prosecutors, and litigants was selected through stratified simple random sampling. Data were collected using structured questionnaires and interview schedules, then analyzed using descriptive and inferential statistics via SPSS. The findings revealed that all four ICT systems positively and significantly influence the performance of Milimani Law Courts. ECMS had the highest influence on improving case management, efficiency, and record accessibility. The SMS inquiry system enhanced communication between the court and litigants, though with moderate influence compared to other technologies. Video conferencing emerged as a critical tool for enabling remote participation, especially during the COVID-19 pandemic, while digital recording and transcription significantly improved accuracy, transparency, and accountability in court proceedings. Overall, the adoption of ICT systems has contributed to faster case resolution, better access to justice, and enhanced transparency in legal processes. The study concludes that ICT adoption is instrumental in transforming judicial operations, and it recommends further investment in digital infrastructure, continuous user training, and policy reforms to support sustained implementation. It also calls for further research into the long-term effects of ICT adoption across various levels of Kenya’s judicial system.

Purpose: The ability of businesses to integrate information technology in their operations has been found to be an integral driver to their success. Through information technology, firms can utilize technological innovations to strengthen the uniqueness of their products in the market thus enhancing their competitive advantage. However, with limited evidence in the local context especially among the micro and small enterprises which continue to face a continuous decline, it warrants the study to assess how strategic technological innovations have been embraced among the small and micro enterprises and the role the innovations have played in enhancing the competitive advantage of the enterprises. Specifically, the study will examine the influence of enterprise’s IT Capabilities on competitive advantage among Micro and Small Enterprises (MSEs); determine the influence of Technological resources on competitive advantage among Micro and Small Enterprises (MSEs); explore the influence of adopted new technologies on competitive advantage among Micro and Small Enterprises; and establish the influence of Technological Processes and Products (TPP) on competitive advantage among Micro and Small Enterprises (MSEs) in Nairobi County, Kenya. The objectives will be anchored on dynamic capabilities theory, Schumpeter theory of innovation, technology acceptance model and diffusion theory of innovation. Methodology: Using a descriptive research approach, the study will collect and analyse both qualitative and quantitative data. This data was obtained from 386 micro and medium enterprises in Nairobi Central Business District, drawn from a population of 11,245 MSEs registered by the Nairobi City County in the CBD. A questionnaire was the main instrument of data collection, which was pilot-tested for validity and reliability. The collected data was analyzed using descriptive and inferential statistics. Findings: The findings were presented using frequency tables and graphs. In conclusion, the findings emphasize the critical role of technology in driving market expansion, customer engagement, and operational efficiency. The study highlights the importance of strategic support and intervention to address technological adoption challenges. By encouraging technological development and integration, policymakers and other stakeholders can assist MSEs in improving their operations, boosting the economy of Nairobi County. Investing in technological education and infrastructure is essential for MSEs to optimally benefit from TPP in the contemporary digital economy. Unique Contribution to theory, policy and practice: Training and development on how MSEs can use the platforms effectively, online marketing and selling needs to be promoted to help MSEs build their capacities. Therefore, when equipped with the appropriate knowledge and tools, the policymakers can help the MSEs to adopt e-commerce and increase their market access and productivity.

Purpose: The general objective of the study was to investigate the impact of Artificial Intelligence on supply chain optimization. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the impact of Artificial Intelligence on supply chain optimization. Preliminary empirical review revealed that AI significantly improved various aspects of supply chain management, including forecasting, inventory management, logistics, and risk management. It was found that AI technologies enhanced operational efficiency by providing more accurate demand predictions, optimizing logistics operations, and improving risk management capabilities. Additionally, AI contributed to greater sustainability in supply chains by reducing resource waste and supporting environmental goals, thus demonstrating its critical role in modernizing and optimizing supply chain practices. Unique Contribution to Theory, Practice and Policy: The Technology Acceptance Model (TAM), Resource-Based View (RBV) and Dynamic Capabilities Theory may be used to anchor future studies on Artificial Intelligence. The study recommended that future research should focus on developing theoretical models that integrate AI with traditional supply chain theories and that companies should adopt AI-driven tools for improved supply chain performance. It suggested that policymakers create guidelines for ethical AI use and data management to ensure responsible implementation. Additionally, it was recommended that collaboration between academia, industry, and technology providers be fostered to share best practices and address sector-specific needs. Lastly, it was advised that the long-term impacts and adaptability of AI technologies be evaluated to ensure their continued effectiveness and relevance.

The hospitality industry faces mounting pressure to transition towards sustainable practices. While knowledge assets such as human, structural, and relational capital are recognized as foundational for this shift, the precise mechanism through which they translate into superior sustainability performance remains underexplored. This study investigates the mediating role of adoption of green technology as a critical computational mechanism in this relationship. Drawing on resource-based view and dynamic capabilities theory, we propose a model where green technology acts as an active processing system, converting latent knowledge into actionable, sustainable outcomes. Using structural equation modeling (SEM) on survey data collected from hotel managers, we test this mediating model. The results confirm that green technology adoption fully mediates the relationship between knowledge assets and sustainability performance, measured through environmental, economic, and social dimensions. This research contributes to literature by moving beyond direct effects to model a more nuanced, systems-oriented pathway. The findings offer critical insights for hospitality managers, highlighting the strategic imperative of investing in integrated technological systems to leverage their intellectual capital for a sustainable future. The hospitality industry in China was forced to consider the importance of using green technology, training its employees and knowledge management. In this paper, the researcher explores the aspect of green technology as a mediator between training awareness and Knowledge Management Practices (KMP) and sustainable green practices in the hotel industry within Yunnan Province. Based on the Technology Acceptance Model (TAM) and Social Learning Theory, the study takes the form of a quantitative, cross-sectional and sample of 288 hotel employees and managers in Dali and Kunming. The results of Structural Equation Modeling (SEM) analysis indicate that training awareness and KMP are both significantly associated with determining the adoption of green technology, which has an impact on sustainable operational outcomes. Green technology has a partial mediating effect on the limb, but then it also has a greater effect when it is accompanied by strong organizational learning structures.

Objective: This study examines Business Process Automation (BPA) adoption in sustainability reporting within financial institutions, highlighting the lack of a structured framework guiding adoption across different stages. While BPA’s technological benefits are well-documented, the role of regulatory and stakeholder pressures—especially in emerging economies post-pandemic—remains underexplored. This study develops a structured framework integrating external pressures and internal capabilities to fill this gap. Theoretical Framework: This study integrates the Technology Acceptance Model, Innovation Diffusion Theory, Institutional Theory, Structuration Theory, Actor-Network Theory, and Dynamic Capabilities Theory to examine BPA adoption in sustainability reporting. Together, these perspectives explain the interplay of technological, organizational, and institutional factors shaping adoption in financial institutions.. Method: Using a mixed-methods approach, this study combines archival analysis, quantitative modeling (PLS-PM), and focus group discussions. It integrates Innovation Diffusion Theory, Technology Acceptance Model, Institutional Theory, Structuration Theory, Actor-Network Theory, and Dynamic Capabilities Theory for a holistic perspective on BPA adoption. Results and Discussion: BPA adoption follows a five-step framework, from assessment to full-scale integration in sustainability reporting. Regulatory pressures primarily drive initial adoption, but sustained integration depends on internal capabilities, governance structures, and strategic ESG alignment. Contrary to assumptions, financial institutions do not simply mimic industry peers in BPA adoption. Research Implications: A five-step framework is proposed to guide financial institutions in BPA adoption, emphasizing risk mitigation, stakeholder engagement, and strategic alignment with sustainability objectives Originality/Value: This research advances the literature by integrating qualitative and quantitative methods with multiple theoretical frameworks, offering a comprehensive perspective on BPA adoption and its strategic role in corporate governance and sustainability.

Purpose: The aim of the study was to assess the effect of data integration techniques on operational efficiency in manufacturing industries in Iran. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that by seamlessly amalgamating data from disparate sources across production lines, supply chains, and customer feedback systems, manufacturers have been able to streamline processes, optimize resource allocation, and achieve significant cost savings. For instance, real-time data integration facilitates timely decision-making, allowing for adaptive production planning and inventory management. Furthermore, the integration of advanced analytics and machine learning algorithms has enabled predictive maintenance strategies, reducing downtime and enhancing overall equipment effectiveness (OEE). These technological advancements not only bolster operational efficiency but also foster innovation, as companies leverage integrated data insights to drive continuous improvement initiatives and meet evolving consumer demands. Implications to Theory, Practice and Policy: Resource-based view (RBV), technology acceptance model and dynamic capabilities theory may be used to anchor future studies on assessing the effect of data integration techniques on operational efficiency in manufacturing industries in Iran. Manufacturing firms should prioritize strategic implementation plans for data integration technologies to ensure that investments in IoT, AI, ETL, data warehousing, data virtualization, and APIs are aligned with their overall business objectives. Policymakers should provide incentives and support for the adoption of advanced data integration technologies in the manufacturing sector.

Public administration and governance must adapt to the changing socio-economic environment, improving quality, process efficiency, and collaboration. Hence, public administration and public governance models have been significantly modified multiple times, resulting in differences in public governance practices. This paper examines different public governance models’ principles in Slovene and Japanese public administration. It quantifies elements based on the models' principles and applies them to an empirical case using a survey of 55 Slovene and 135 Japanese public managers. The independent samples t-test examines the differences in characteristics of public governance practices between state administration and local government in Slovenia and Japan. The results show that state administration institutions in both countries are strongly characterised by the (Neo)Weberian model’s principles, while Slovenia's local government leans towards Digital-era governance (DEG) and good governance (GG) principles. Japan's state and local administrations show equal presence of New public management (NPM), DEG, and GG models. The study aims to bridge a research gap by providing new findings on how different public governance models can be found at various Slovene and Japanese public administration levels and offers insights for public managers and policymakers for future public administration reforms.

Background This study aims to empirically test a comprehensive interrelationship between green supply chain management (GSCM), green technology innovation (GTI), waste management (WM), big data analytics capability powered by artificial intelligence (BDAC-AI), and their collective impact on sustainable performance (SP) in organizational contexts. Methods This study was conducted in Pakistan’s food processing sector. The respondents included 495 managers working in the food processing industry. A structural equation modelling (SEM) approach is used to examine direct and indirect relationships between the variables. The originality of this study lies in integration of the technology acceptance model (TAM) and dynamic capability theory (DCT) to understand sustainable practices in the context of the provided model. Results This study highlights that GSCM, GTI, WM, and BDAC-AI have positive, strong, and direct impacts on SP. Furthermore, GTI and WM only partially mediate the link between GSCM and SP, whereas the two moderate the link. In addition, BDAC-AI had a moderating effect on the relationship between GTI and SP. This study has managerial implications, including strategies that involve the use of theoretical frameworks for technological acceptance and dynamic capabilities to support sustainable initiatives. However, it is worth noting that the findings provide a practical contingency for managers and businesses interested in implementing green studies effectively, improving technologies, and strengthening sustainable performance capabilities. Conclusions The study extends the literature by establishing a model for operationalizing GSCM in the food processing sector. Furthermore, it adds value in that it first integrates TAM and DCT to explain sustainable operations and their impact on organizations. Furthermore, it extends the existing literature by establishing a relationship between GSCM and SC. It offers a model through which GSCM can be operationalized in the context of the FS sector.

BackgroundThis study aims to empirically test a comprehensive interrelationship between green supply chain management (GSCM), green technology innovation (GTI), waste management (WM), big data analytics capability powered by artificial intelligence (BDAC-AI), and their collective impact on sustainable performance (SP) in organizational contexts.MethodsThis study was conducted in Pakistan’s food processing sector. The respondents included 495 managers working in the food processing industry. A structural equation modelling (SEM) approach is used to examine direct and indirect relationships between the variables. The originality of this study lies in integration of the technology acceptance model (TAM) and dynamic capability theory (DCT) to understand sustainable practices in the context of the provided model.ResultsThis study highlights that GSCM, GTI, WM, and BDAC-AI have positive, strong, and direct impacts on SP. Furthermore, GTI and WM only partially mediate the link between GSCM and SP, whereas the two moderate the link. In addition, BDAC-AI had a moderating effect on the relationship between GTI and SP. This study has managerial implications, including strategies that involve the use of theoretical frameworks for technological acceptance and dynamic capabilities to support sustainable initiatives. However, it is worth noting that the findings provide a practical contingency for managers and businesses interested in implementing green studies effectively, improving technologies, and strengthening sustainable performance capabilities.ConclusionsThe study extends the literature by establishing a model for operationalizing GSCM in the food processing sector. Furthermore, it adds value in that it first integrates TAM and DCT to explain sustainable operations and their impact on organizations. Furthermore, it extends the existing literature by establishing a relationship between GSCM and SC. It offers a model through which GSCM can be operationalized in the context of the FS sector.