How to realize the full potentials of artificial intelligence (AI) in digital economy? A literature review

Purpose The purpose of this study is to enhance the understanding of management practices that expand artificial intelligence (AI) adoption in public organizations. Design/methodology/approach The research approach is an exploratory study. Research data were collected from informants representing various organizations working for public or government services in Finland. Findings The findings of this study indicate that large-scale AI adoption is a complex process in the public sector. This study identified three main practices of AI adoption: technological design practices, AI project design and management practices and networking practices. Additionally, this study shows that several value drivers and barriers to AI adoption are related to technological, organizational and environmental dimensions. This study emphasizes the importance of developing cross-functional AI capabilities and resources, which are crucial for expanding AI initiatives across organizations and networks. Practical implications Expanding and scaling AI adoption across organizational boundaries requires new management practices and multidisciplinary teamwork, from technological skills to AI governance practices and change management. Originality/value This study contributes to the emerging research on management practices involved in AI adoption in the public sector.

Purpose This study investigates the interplay between artificial intelligence (AI) adoption, ethical capitalistic orientation and the moderating role of human–AI synergy, a new construct developed via grounded theory. This study aims to understand how these variables influence organizational decision-making in the AI era, specifically promoting ethical practices and mitigating negative outcomes such as employee displacement. Design/methodology/approach Using both qualitative and quantitative regression analysis, this study examines the relationships among AI adoption, ethical capitalistic orientation and human–AI synergy. Statistical tools were used to test hypotheses, identify AI adoption’s impact on organizational ethics and assess human–AI synergy’s moderating role. This research also explores how combining AI and human collaboration can foster a more ethical, socially responsible business model. Findings Findings show AI adoption positively impacts ethical capitalistic orientation (β1 = 0.45, p = 0.0001). Human–AI synergy significantly enhances this effect (β2 = 0.30, p = 0.0005) and moderates the relationship between AI adoption and ethical practices (β4 = 0.15, p = 0.0030). The study emphasizes focusing on human–AI collaboration over workforce displacement to maintain ethical practices and achieve efficiency. These results highlight the importance of ethical AI adoption and social responsibility. Originality/value This study introduces human–AI synergy as a novel construct, demonstrating its critical moderating role in the relationship between AI adoption and ethical capitalistic orientation. It offers new insights into how businesses can leverage AI without workforce reduction or unethical practices. This research emphasizes building ethical frameworks for AI adoption, presenting a novel perspective integrating technology with human-centered decision-making.

Purpose The purpose of this paper is to identify and explain the organizational conditions under which artificial intelligence adoption in universities leads to structural change rather than incremental adaptation. By integrating Luhmann’s theory of decision premises with Argyris and Schön’s concept of organizational learning loops, the study conceptualizes artificial intelligence (AI) adoption as a process mediated by institutional structures and mechanisms of invisibilization and proposes strategies to foster double-loop learning that enable universities to surface and address organizational paradoxes, thereby creating the conditions for meaningful transformation in teaching, research and governance. Design/methodology/approach This conceptual study develops an analytical framework combining Luhmann’s theory of decision premises (programs, communication channels and personnel) with Argyris and Schön’s distinction between single-loop and double-loop learning to examine how universities process AI adoption. The approach synthesizes literature from organizational sociology, higher education studies and paradox theory to explain how contradictions are mediated by institutional structures and managed through mechanisms of invisibilization. The framework is applied analytically to the context of AI in teaching, research and governance, identifying conditions under which contradictions escalate into paradoxes that destabilize decision premises and create opportunities for structural change. Findings The study shows that universities often integrate AI within existing decision premises, containing contradictions through mechanisms of invisibilization, reframing contradictions as technical adjustments, recasting innovation as continuity and suspending role redefinitions, sustaining single-loop learning and organizational stability. Structural change through double-loop learning occurs when external pressures, such as regulatory mandates and funding constraints, converge with internal tensions in academic culture, governance and faculty roles, escalating contradictions into paradoxes that destabilize decision premises. The analysis posits that transformation depends on reconfiguring program premises toward reflexivity, redesigning communication channels for deliberative governance and redefining personnel premises to integrate AI-related expertise into formal authority structures. Research limitations/implications As a conceptual analysis, the study does not include empirical testing, which limits the ability to generalize findings across institutional contexts. Future research should apply and refine the proposed framework through comparative and longitudinal studies of AI adoption in universities, examining variations across governance models, regulatory environments and disciplinary cultures. The framework offers a basis for analyzing how decision premises mediate technological change, highlighting the need for research that investigates the interaction between external pressures, internal tensions and invisibilization mechanisms. Such work can inform both theory development in organizational change and the design of policies that foster reflexive, transformative AI integration. Practical implications The framework offers university leaders and policymakers strategies to foster transformative AI adoption by making organizational contradictions visible and actionable. Institutions can reconfigure program premises to align AI initiatives with mission and values, redesign communication channels to integrate AI within participatory governance and redefine personnel premises to incorporate AI-related expertise into formal authority structures. These interventions can help balance efficiency gains with academic autonomy, transparency and epistemic diversity. Policymakers can use the framework to design regulatory and funding mechanisms that incentivize reflexive adaptation rather than superficial compliance, thereby creating conditions for sustainable organizational change in teaching, research and governance. Social implications By framing AI adoption in universities as an organizational learning challenge, the study highlights its potential societal impact beyond technical efficiency. Universities play a central role in shaping knowledge production, professional formation, and public trust in expertise. AI integration that prioritizes reflexivity, inclusivity and participatory governance can strengthen these societal functions, fostering equitable access to high-quality education and preserving epistemic diversity. Conversely, uncritical adoption risks reinforcing managerial logics that marginalize academic voices and narrow the social purposes of higher education. The framework encourages institutions to engage with AI in ways that support democratic accountability and socially responsive knowledge systems. Originality/value This paper offers a novel conceptual framework linking Luhmann’s theory of decision premises with Argyris and Schön’s organizational learning loops to explain how AI adoption in universities is mediated by institutional structures. By introducing the concept of invisibilization mechanisms, reframing contradictions as technical adjustments, recasting innovation as continuity and suspending role redefinitions, the study advances understanding of why AI often reinforces stability rather than triggering structural change. It also extends organizational change theory in higher education by specifying conditions under which contradictions escalate into paradoxes and by proposing targeted strategies to foster double-loop learning that enable transformative, reflexive integration of AI technologies.

The AI-environment paradox: Unraveling the impact of artificial intelligence (AI) adoption on pro-environmental behavior through work overload and self-efficacy in AI learning.

Purpose The purpose of this study was to construct a mediated moderating model, based on transactional theory of stress and coping, examining how hotel artificial intelligence (AI) adoption affects employee service performance (in-role and extra-role service performance), with AI crafting as the mediator and appraisals toward AI (challenge appraisals and hindrance appraisals) as the moderators Design/methodology/approach This study used a sample of 334 frontline hotel service employees collected in a three-wave time-lagged design, using path analyses to test the conceptual model. Findings Results indicated that when challenge appraisals toward AI is high, hotel AI adoption and challenge appraisals toward AI interaction have a positive impact on AI crafting. However, when hindrance appraisals toward AI is high, hotel AI adoption and hindrance appraisals toward AI interaction have a negative impact on AI crafting; the interaction between hotel AI adoption and appraisals toward AI on employee service performance (in-role and extra-role service performance) was mediated by employee AI crafting. Practical implications Organizations should develop comprehensive AI adoption strategies that consider both opportunities and risks. Encouraging employee AI job crafting through training programs and knowledge sharing can enhance service performance. Managers should assess and actively shape employees’ cognitive appraisals of AI, promoting challenge rather than hindrance perceptions. Creating an inclusive organizational culture and open communication channels is crucial for fostering positive employee attitudes toward AI adoption. Originality/value This study contributes to the literature on AI adoption and employee service performance by examining when and how hotel AI adoption influences employee service performance.

The integration of Artificial Intelligence (AI) into personal finance and wealth management has fundamentally reshaped financial behaviors and decision-making processes. The primary objective of this study is to evaluate the role of AI in influencing personal financial behaviors and wealth management outcomes. Specifically, it aims to determine how AI adoption, investment, and usage impact personal savings and net worth. This study adopts a quantitative approach, utilizing secondary data from trusted sources such as Our World in Data and the Federal Reserve Bank of St. Louis. The dataset spans from 2010 to 2022, capturing trends over a significant period of AI development and adoption. A multivariate regression model is employed to examine the relationships between the dependent variables, Personal Savings Rate and Change in Net Worth, and independent variables such as AI adoption rate, AI investment, and household debt-to-income ratio. Descriptive statistics, correlation analysis, and stationarity tests are conducted to ensure data reliability and model validity. Diagnostic checks, including heteroskedasticity tests and Durbin-Watson statistics, further validate the robustness of the results. The study reveals that AI adoption positively influences personal savings by encouraging disciplined financial behaviors, consistent with the findings of prior research. However, its impact on wealth accumulation is less direct, with AI investment showing a surprising negative association with changes in net worth. This indicates inefficiencies in resource allocation or lag effects in the benefits of large-scale AI investments. Traditional economic factors, such as household debt and spending habits, continue to play significant roles in shaping financial outcomes, highlighting the enduring influence of non-technological determinants. The study also underscores the role of macroeconomic variables, such as unemployment, in moderating AI’s impact, with precautionary savings behaviors emerging during periods of economic uncertainty. Based on the findings, several actionable recommendations emerge. For individuals, the adoption of AI-driven tools that promote financial literacy and track spending can enhance savings and improve overall financial health. Financial institutions should prioritize user-centric designs in AI platforms, ensuring accessibility and functionality for diverse demographics. Policymakers are encouraged to support initiatives that bridge disparities in AI adoption, such as digital literacy programs and affordable access to financial technologies. Moreover, strategic investment in AI tools that address wealth management complexities, such as portfolio optimization and risk assessment, is critical for improving long-term financial outcomes. Originality This study contributes to the growing body of literature on AI in finance by offering a dual focus on personal savings and wealth management. Unlike previous studies that often treat these domains independently, this research provides an integrated perspective, highlighting both the synergies and divergences in AI’s impact. The findings on the nuanced relationship between AI investment and financial outcomes offer a fresh lens for evaluating the effectiveness of technological advancements. Furthermore, the study’s emphasis on traditional economic factors alongside AI-related variables underscores its originality in bridging the gap between technological innovation and foundational economic principles. This approach provides a robust framework for future research and practical applications in finance.

With the ever-growing adoption of artificial intelligence (AI), AI-based software and its negative impact on the environment are no longer negligible. Studying and mitigating this impact has become a critical area of research, as the adoption of AI technologies accelerates across various industries. However, it is currently unclear which role environmental sustainability plays during AI adoption in industry and which Green AI practices users of AI-based software apply. Moreover, little is known about how AI regulations influence Green AI practices and decision-making in industry.We therefore aim to investigate the Green AI perception and management of industry practitioners. To this end, we conducted a total of 11 interviews with participants from 10 different organizations that adopted AI-based software. The interviews explored three main themes: AI adoption, current efforts in mitigating the negative environmental impact of AI, and the influence of the EU AI Act and the Corporate Sustainability Reporting Directive (CSRD) on the first two themes.Our findings indicate that 9 of 11 participants prioritized business efficiency during AI adoption, with minimal consideration of environmental sustainability. Monitoring and mitigation of AI’s environmental impact were very limited. Only a single participant monitored negative environmental effects, with four others at least tracking model token usage. Regarding applied mitigation practices, six participants reported no actions, with the others sporadically mentioning techniques like prompt engineering, relying on smaller models, or not overusing AI. Awareness and compliance with the EU AI Act are low, with only one participant reporting on its influence, while the CSRD drove sustainability reporting efforts primarily in larger companies, as noted by six participants.All in all, our findings reflect a lack of urgency and priority for sustainable AI among these companies, with the main focus being the successful initial implementation and introduction of AI-based software. We suggest that current regulations are not very effective in this regard, which has implications for policymakers. Additionally, there is a need to raise industry awareness, but also to provide user-friendly techniques and tools to lower entry barriers for Green AI practices.

This paper investigates drivers of Artificial Intelligence (AI) adoption, specifically in rural small and medium-sized enterprises (SMEs). Research on AI has gained traction in recent times, however, remains an area in need of further investigation, notably the adoption of AI by SMEs, and particularly among rural SMEs. The focus on SMEs is important as they account for the majority of businesses worldwide, playing an important role in job creation and economic development. The research uses secondary data from the Longitudinal Survey for Small Business (LSBS), a large UK panel survey of SMEs, which provides a broad range of variables on a range of SMEs. Probit regression, using a series of environment, firm and network engagement factors as predictive variables, identifies drivers of AI adoption for rural SMEs. Among the numerous drivers of AI adoption in rural SMEs, networking, is identified as a key variable associated with adoption. This research contributes to the limited knowledge on this subject and more broadly to technology adoption in organisations. This leads to policy recommendations in promoting AI adoption among rural SMEs through better communication of the advantages of adoption among SMEs and network development. • A rural location is not a barrier to AI adoption for SMEs, with numerous rural SMEs adopting AI technology in their operations. • There are numerous antecedents to AI adoption for rural SMEs, including networking, prior innovative activity influence, and strategic planning. • Rural SMEs that adopt AI technology are more likely to have employees, tend to be larger, have more sites, and have a greater turnover than non-adopters. • Engagement in networks can facilitate the adoption of AI technology among rural SMEs, as well as enhance understanding of the benefits of AI technology to SMEs' operations. • Policy should seek to encourage increased levels of AI adoption among rural SMEs to support growth, and ensure that appropriate infrastructure exists to support the use of AI technology.

Background The integration of Artificial Intelligence (AI) in higher education has transformed teaching, learning, and administration, leading to the rise of smart universities. AI-powered tools enhance student engagement, knowledge retention, and administrative efficiency, offering personalized learning experiences and streamlining workflows. However, institutions face challenges related to faculty adaptation, ethical concerns, and data privacy risks. This study assesses the impact of AI adoption on student engagement, academic performance, and institutional challenges in higher education. Methods This cross-sectional quantitative study utilized structured surveys to assess AI awareness, perceived benefits, adoption levels, and challenges among 350 participants at Mangosuthu University of Technology (MUT), comprising 313 students and 37 lecturers. The collected data were analyzed using descriptive statistical methods, including mean percentages and frequency distributions, to identify key trends in AI adoption and its impact on student engagement, usage of AI tools, and academic outcomes. Results AI-powered learning tools significantly enhance student engagement (80%) and knowledge retention (75%), demonstrating their effectiveness in academic improvement. AI also increases administrative efficiency (70%) by automating enrolment, grading, and scheduling, reducing faculty workload. However, faculty adaptation (50%) remains a challenge due to limited training. Ethical concerns (40%), particularly regarding data privacy and algorithmic bias, necessitate greater transparency and oversight. The study found lecture capture systems (85%) and personalized content delivery (78%) to be the most widely used AI tools. Ethical dilemmas (80%), data privacy concerns (75%), and faculty resistance (60%) are key barriers to AI adoption. Additionally, a lack of resources (50%) limits access to AI-driven educational technologies. Conclusion and Recommendations While AI enhances student learning and institutional efficiency, faculty readiness, ethics, and infrastructure gaps remain challenges. Institutions must prioritize AI training, ethical policies, and infrastructure investment to ensure sustainable AI adoption. Encouraging faculty engagement, policy development, and continuous monitoring will maximize AI’s benefits and future-proof education.

This study investigates the relationship between artificial intelligence (AI) adoption, knowledge sharing, and employee performance in Malaysian small and medium-sized enterprises (SMEs), with knowledge sharing examined as a mediating mechanism. SMEs represent a vital component of Malaysia’s economy, yet many face resource limitations that affect their readiness to fully harness AI technologies. While AI is recognized for its potential to enhance efficiency and innovation, its impact on employee performance is not always straightforward. This research therefore, explores whether knowledge sharing acts as the bridge through which AI adoption translates into performance outcomes. Data were collected through a survey of SME employees across service and manufacturing sectors, and the responses were analysed using partial least squares structural equation modelling (PLS-SEM). Measurement model results confirmed strong reliability and validity for the constructs of AI adoption, knowledge sharing, and employee performance. Structural model assessment revealed that AI adoption significantly and positively influences knowledge sharing but does not directly affect employee performance. Meanwhile, knowledge sharing revealed a strong and significant relationship with employee performance and was also found to partially mediate the relationship between AI adoption and performance. The study findings highlight that AI’s value in SMEs lies not in the technology itself but in its ability to foster knowledge exchange, learning, and collaboration. In addition, employee performance improves when AI is embedded into organizational practices that encourage knowledge sharing, thereby complementing human creativity and expertise. Theoretically, this study integrated the Knowledge-Based View (KBV) and the Technology–Organization–Environment (TOE) framework to explain how AI adoption and knowledge sharing practices together influence employee performance. Practically, the results underscore the need for SME leaders to move beyond technology acquisition and focus on building collaborative cultures that enable knowledge sharing. Overall, this research contributes both theoretical and practical insights into how SMEs can strategically leverage AI adoption to enhance employee performance through the mediating mechanism of knowledge sharing.

Purpose The purpose of this study is to examine how AI adoption enhances operational performance in manufacturing firms and to investigate how variations in firms’ strategic focus moderate this relationship. Specifically, this study explores how AI adoption functions as a dynamic capability that enhances operational performance in manufacturing firms and investigates how different strategic orientations—exploration, exploitation, or ambidexterity—moderate this effect from the perspective of the Attention-Based View (ABV) of the firm. Design/methodology/approach This study employs a quantitative research method, using a sample of 426 Chinese manufacturing firms to examine the impact of artificial intelligence (AI) adoption on operational performance and to analyze the moderating role of firms’ strategic focus (exploration, exploitation, and ambidexterity). Data were collected through a structured questionnaire survey. To ensure the robustness of the findings and address potential endogeneity issues, hierarchical regression analysis and the two-stage least squares (2SLS) method were employed. Findings This study reveals that AI adoption significantly enhances the operational performance of manufacturing enterprises, but this effect is moderated by firms’ strategic focus. A high exploration tendency weakens the performance-enhancing effect of AI adoption due to implementation instability caused by excessive experimentation. A high exploitation tendency also reduces the positive impact of AI adoption, as over-reliance on existing processes constrains AI’s transformative potential. Furthermore, an ambidextrous strategy (coexistence of high exploration and high exploitation) further diminishes the positive effect of AI adoption, indicating that resource dispersion and increased coordination costs may offset its benefits. Originality/value From the perspective of Dynamic Capabilities Theory, this study empirically examines the impact of AI adoption—as a dynamic capability—on operational performance. Additionally, drawing on the Attention-Based View (ABV) of the firm, it uncovers the moderating role of firms’ strategic focus, addressing an existing research gap concerning strategic-level decision-making in AI adoption. The findings offer theoretical insights that guide enterprise managers in optimizing AI adoption strategies, helping them strike a balance between innovation and efficiency to maximize the benefits of digital transformation.

Purpose This study examines the effects of AI-driven tools on reference services and staff roles in academic libraries in Nigeria. It explores how AI technologies, such as chatbots and machine learning algorithms, enhance service efficiency, shift librarian responsibilities and impact user satisfaction. Design/methodology/approach A qualitative research approach was employed, utilizing semi-structured interviews with reference librarians, Information Technology support staff and administrators from six private universities in Nigeria. Participants were purposively selected for their experience with artificial intelligence (AI)-driven reference services. Thematic analysis was conducted to identify key patterns in AI adoption, staff adaptation and user engagement. Findings Findings indicate that AI tools such as Turnitin (plagiarism detection), Grammarly (writing assistance) and WhatsApp-based library chatbots (Meta AI) improve efficiency by automating routine tasks, reducing librarian workload and enhancing user experiences through personalized recommendations. However, challenges such as AI's limitations in handling complex inquiries, concerns about data privacy and the need for librarian oversight were identified. Additionally, AI adoption has reshaped librarian roles, necessitating continuous professional development in digital literacy and AI management. Originality/value This research provides insights into the evolving role of AI in Nigerian academic libraries, emphasizing the need for a hybrid approach that balances AI automation with human expertise. It highlights the importance of AI literacy training for both librarians and users to maximize the benefits of AI-driven reference services while maintaining service quality and ethical standards.

Background: With growth in Artificial Intelligence (AI) adoption, challenges and hurdles are also becoming evident. Organizations implementing AI are challenged to find ways to leverage AI to produce optimum results and benefits for the organization. Understanding other organizations’ AI implementation journeys will help them start and implement AI. By understanding the different facets of AI implementation, they can strategize AI to gain business value. Though several studies have examined AI adoption, there are few studies on how firms implement it. We close this gap by studying AI adoption and implementations in various firms. Method: Using a qualitative approach of semi-structured interviews, we studied twenty global organizations of various sizes that have implemented AI. Results: The study categorizes the results into four major themes – facilitators, barriers, trends, and strategies for implementing AI. Our study reinforces the relevance of the TOE framework and Roger’s DOI theory in studying AI adoption. Organizational factors such as top management support, strategic roadmap, availability of skilled resources, and corporate culture influenced AI adoption. Their lack of data or poor data quality is a primary challenge. The privacy laws concerning data, as well as regulatory bottlenecks, further exacerbate this problem. We also identified and mapped the standard AI implementations to their AI technologies. We found that most of them exploit AI’s image and natural language processing capabilities to automate their processes. Regarding implementation, firms work with partners to obtain customer data and use federated learning. Conclusion: Understanding firms’ AI implementation journey will help us promote further adoption and experimentation. Organizations can identify areas where they can leverage AI to enhance value, prepare themselves for the future, start and proceed with AI implementation efforts and overcome barriers they might encounter.

This research explores how social capital supports the adoption of artificial intelligence (AI)in developing countries, focusing on the role of "social brokers."A social broker is a trusted individual who occupies a unique position within a network, connecting individuals from different networks or maintaining connections with a larger number of individuals within the existing network.Based on input from the initial phase of the project, conducted in a developing country with high internet use but low AI adoption, we use qualitative research methods to better understand the practical aspects of AI adoption.Our early findings suggest that AI adoption goes beyond the right technology or skills and is strongly influenced by trusted communities and networks that shape decisions about AI adoption."Social brokers" play a key role in this process.They help close knowledge gaps, address concerns of people who have not adopted AI or have adopted it at a low level, and show how AI can be relevant and useful for specific jobs and tasks.These "social brokers" are often seen as trusted friends, technology influencers, former colleagues, or respected local industry experts.Their presence and activities in tightly connected social networks appear to be very important for reducing the gap in AI adoption.The next phase of this research will focus on identifying the aspects of social capital that influence AI adoption, understanding the relationships that help overcome resistance to adopting AI, and developing strategies that use social capital to encourage faster AI adoption in developing countries.

PurposeThis paper aims to explore factors impacting citizen intention toward artificial intelligence (AI) adoption, considering government regulation as a moderating variable. It focuses on the Palestinian Cellular Communications Sector in Gaza Strip, providing insights into the citizen-AI relationship dynamics. The research contributes to enhancing comprehension of AI technology from clients’ perspective.Design/methodology/approachTo test the hypotheses, a questionnaire was used in an empirical study to collect primary data. In total, 347 Palestinian citizens responded to the survey.FindingsThe findings of this paper reveal that perceived usefulness, perceived ease of use, perceived risks, social influence, user experience and privacy and security concerns significantly influence citizen intention toward AI adoption. Furthermore, government regulations as a moderating variable strengthen the impact of perceived usefulness, perceived ease of use, perceived risks, social influence, user experience and privacy and security concerns on citizen intention toward AI acceptance and adoption. Thus, further research should explore specific domains and cultural contexts to gain a more comprehensive understanding of the factors shaping acceptance and adoption.Research limitations/implicationsThe findings of the study should be understood in the context of their limitations. First, the study ignored cultural or domain-specific subtleties in favor of generic characteristics, which calls for more research in these particular circumstances. Second, relying on self-reported data might result in biases and limitations due to subjectivity in reporting, indicating the necessity for alternate data gathering methods and approaches in future research.Practical implicationsPolicymakers, developers and organizations working to promote the acceptability and implementation of AI applications should consider the practical implications of this study’s results. To secure the long-term use of AI technologies in a responsible and user-centric way, policymakers should give priority to public education and awareness, user-centered design and ethical AI development techniques. They should also stimulate partnerships and create monitoring systems.Originality/valueThis paper investigates the originality of factors that influence citizen intention toward AI acceptance and adoption. It uniquely examines the moderating role of government regulations in shaping this intention. By addressing this novel aspect, the paper contributes to advancing our understanding of the complex dynamics surrounding citizen intentions toward AI applications.