Lessons from the Public Sector Artificial Intelligence Adoption in Developing Countries: An AI Life Cycle Perspective

AI adoption will help Malaysian construction organisations, including Consulting Quantity Surveying (CQS) firms, cope with challenges and increase productivity. However, AI adoption by CQS firms faces adoption issues in Malaysia. Existing AI adoption studies discuss barriers in developed countries. Applying these studies' results in developing countries such as Malaysia is inappropriate, as construction industry practices and properties differ. Also, there is limited attention to factors affecting AI adoption. Existing studies on AI adoption in Malaysia are limited, and the stages of AI adoption have not been rigorously studied. The first objective of the study is to explore and categorise the factors of AI adoption and provide in-depth insights into the different AI adoption stages. The second objective is to identify 62 factors that affect the four stages of AI adoption and group them into four clusters. This study applied Systematic Literature Review (SLR) to identify and classify factor clusters based on the Diffusion of Innovation Theory and Technology Organisation Environment (TOE). The identified cluster of factors can be useful to decision-makers for conducting analyses of AI adoption stages and for formulating adoption strategies, by providing facts and observations within organisations. The review observes that factors affecting AI adoption stages vary across regions, due to governmental pressure, cultural differences, practices, and demographics.

This paper looks at public and business attitudes towards artificial intelligence, examining the main factors that influence them. The conceptual model is based on the technology–organization–environment (TOE) framework and was tested through analysis of qualitative and quantitative data. Primary data were collected by a public survey with a questionnaire specially developed for the study and by semi-structured interviews with experts in the artificial intelligence field and management representatives from various companies. This study aims to evaluate the current attitudes of the public and employees of various industries towards AI and investigate the factors that affect them. It was discovered that attitude towards AI differs significantly among industries. There is a significant difference in attitude towards AI between employees at organizations with already implemented AI solutions and employees at organizations with no intention to implement them in the near future. The three main factors which have an impact on AI adoption in an organization are top management’s attitude, competition and regulations. After determining the main factors that influence the attitudes of society and companies towards artificial intelligence, recommendations are provided for reducing various negative factors. The authors develop a proposition that justifies the activities needed for successful adoption of innovative technologies.

The research design used quantitative methods to study how AI awareness and adoption practices affect business efficiency for SMEs operating in Aba's Abia State Nigerian community. The structured survey questionnaire reached 370 randomly chosen SME owners and managers through stratified random sampling procedures that included manufacturing, retail, and services sectors. Researchers evaluated how familiar businesses were with AI along with the degree of implementation and the business effects resulting from AI solutions in efficiency and market competitiveness. Researchers performed statistical analyses through the combination of descriptive elements alongside inferential methods which incorporated multiple regression ANOVA and correlation coefficient testing. AI awareness creates positive effects on individuals' AI adoption decisions which in turn produces significant enhancements both in business efficiency and competitiveness. The evaluation reveals that business efficiency variations of 26.2% (R² = 0.262, p = 0.000) stem from AI awareness and adoption rates. Extensive adoption of AI remains restricted because of elevated implementation expenses and inadequate technical capabilities along with privacy-related obstacles. CEO of Trello highlighted the necessity for specific government policies along with educational initiatives and financial programs that would support SMEs' AI adoption to obtain sustainable business advantages.

Artificial intelligence (AI) is a promising generation of digital technologies. Recent applications and research suggest that AI can not only influence but also accelerate innovation in organizations. However, as the field is rapidly growing, a common understanding of the underlying theoretical capabilities has become increasingly vague and fraught with ambiguity. In view of the centrality of innovation capabilities in making innovation happen, we bring together these scattered perspectives in a systematic and multidisciplinary literature review. The aim of this literature review is to summarize the role of AI in influencing innovation capabilities and provide a taxonomy of AI applications based on empirical studies. Drawing on the technological–organizational–environmental (TOE) framework, our review condenses the research findings of 62 studies. The results of our study are twofold. First, we identify a dichotomous view of innovation capabilities triggered by AI adoption: enabling and enhancing. The enabling capabilities are those that research identifies as enablers of AI adoption, underscoring the competencies and routines needed to implement AI. The enhancing capabilities denote the role that AI adoption has in transforming or creating innovation capabilities in organizations. Second, we propose a taxonomy of AI applications that reflects the practical adoption of AI in relation to three underlying reasons: replace, reinforce, and reveal. Our study makes three main contributions. First, we identify the innovation capabilities that are either required for or generated by AI adoption. Second, we propose a taxonomy of AI applications. Third, we use the TOE framework to track trends in the theoretical contributions of recent articles and propose a research agenda.

Artificial Intelligence (AI) is viewed as having great potential for the public sector to improve the management of internal activities and the delivery of public services. However, realizing its potential depends on the proper implementation of the technology, which is characterized by unique factors, that afford or constrain its use. What these factors are and how they affect AI implementation is still poorly understood, and scholars call for studies to add empirical evidence to the existing knowledge. This study relies on a case study methodology and, by adopting an abductive approach, applies a double theoretical perspective: the Technology-Organization-Environment (TOE) framework and the Technology Affordances and Constraints Theory (TACT). Drawing on these combined lenses, we develop a conceptual framework that extends previous studies by showing how AI implementation is the result of a combination of contextual factors that are deeply interrelated and, specifically, how AI-related factors bring new affordances and constraints to the application domain.

Incorporating digital technologies, particularly artificial intelligence, into financial services operations is imperative for achieving critical sustainable development goals (SDGs) through digital financial inclusion. This paper examines the drivers behind AI adoption in South Africa's financial services landscape, given its highly advanced financial sector and rapidly evolving digitisation trends. Drawing on the Technological-Organizational-Environmental (TOE) framework, the study investigates the factors influencing AI adoption through a comprehensive analysis of existing literature, a survey of financial services professionals and binary logistic regression. The results of binary logistic regression indicated that technological, organisational and environmental improvements significantly enhance the likelihood of AI adoption in South Africa's financial services sector. Specifically, access to technological infrastructure, organisational leadership support, and regulatory clarity emerge as key determinants of AI adoption. Overall, this study underscores the need for companies in the financial sector to encourage a culture that welcomes innovation and the integration of AI technology, as well as the need for policymakers to develop comprehensive and unambiguous legislative frameworks that control AI use in financial services. Received: 13 March 2024 / Accepted: 31 August 2024 / Published: 05 September 2024

Urban solid waste management is an important component of urban sustainability. The concept of evaluating technologies from both life cycle and urban metabolism perspectives is proposed in this study. The analysis from life cycle perspective (using physical input-output life cycle assessment model) provides support for determining the priority of technologies, and the analysis from urban metabolism perspective (using physical input-output model) provides support for identifying the acceptability of technologies. Suzhou City in China is taken as an example. From urban metabolism perspective, sludge recycling is regarded as an accepted method, while current fly ash recycling method is unsatisfying. Technical levels of scrap tire recycling and food waste recycling should be improved to reduce their negative effects on Suzhou's urban metabolism. From life cycle perspective, sludge recycling has the smallest environmental impacts, and scrap tire recycling has larger environmental impacts than food waste recycling does. Thus, more concerns should be paid to technical improvements of scrap tire recycling than to that of food waste recycling. The concept of evaluating technologies from both life cycle and urban metabolism perspectives in this study provides foundations for evaluating technologies in other countries and cities.

<span id="docs-internal-guid-43377b33-7fff-4342-8e71-b985af862527"><span>Amidst the ongoing competitive challenges in the business environment, AI plays a fundamental role. Despite the pace of AI adoption has shown slow advancement in emerging markets, particularly in Pakistan. Forthcoming years will become essential for organisations to integrate AI into different facets of task management like recruitment and selection (R&S). The core purpose of the research is to address how numerous factors influence the adoption of AI under technology-organisation-environment (TOE) model. The study introduces a basis to examine the importance and interrelationship of key success factors in the adoption of AI. Six key factors influencing AI adoption were derived from a comprehensive review of the literature. The structural model was empirically validated using data collected via a Google-based mail survey conducted in Pakistan. The data is analysed through Structural Equation Modelling, revealing that factors such as relative advantage under Technology factor, technological competency, and support from top-management for Organisational factor, while competitive pressure and vendor support for Environment factor have a significant association with AI adoption regarding R&S in selected firms in Pakistan. The study’s findings offer valuable insights for organisations in Pakistan to refine their AI adoption strategies, particularly in R&S, helping them gain a competitive edge. It also highlights key factors specific to the Pakistani context, enhancing understanding of how local dynamics shape AI adoption in HR. These insights can guide organisations in overcoming challenges and optimising AI’s potential for organisation success.</span></span>

IntroductionThis paper examines the relationship between AI adoption and employees’ innovative work behavior (IWB), focusing on the mediating role of psychological capital and the moderating role of perceived error management culture.MethodsUsing two-wave survey data, we tested the hypothesized relationships with SPSS 27.0 and Mplus 8.0.ResultsThe results show that AI adoption is positively related to employees’ psychological capital, which in turn is positively related to IWB. In addition, perceived error management culture strengthens the positive relationship between AI adoption and psychological capital, thereby strengthening the indirect relationship between AI adoption and IWB through psychological capital.DiscussionBy conceptualizing AI as a new form of job resource, this study examines the internal mechanism and boundary condition underlying the AI-innovation link through a “technology-psychology-behavior” framework. Theoretically, we extend JD-R theory to the AI context and incorporate error management culture through trait activation theory. In practice, we provide empirical evidence on how AI adoption is associated with employee innovation through psychological capital.

Purpose: The study explores the key factors influencing AI adoption by public organizations, and sought to understand the dynamics of AI adoption, aiming to identify the potential challenges of integrating AI with ESG considerations.
 Originality/value: This research addresses the gap in understanding AI adoption in the public sector at the firm level, emphasizing the challenges and risks of technology integration. The study discuss how AI can be used effectively, contributing to societal appropriation of technological progress.
 Methods: Methodology employs a multi-stage analysis of literature, followed by ten interviews and a case study on Brazil's Federal Revenue Service. Empirical data was probed through rigorous coding and thematic analysis, selecting the most impactful factors influencing AI adoption.
 Results: The conclusions highlight the role of AI in elevating public services performance and reach. However, the deployment of AI calls for vigilant oversight to mitigate adverse effects and inequalities and demands a multidisciplinary strategy addressing an interplay of challenges.
 Conclusion: The study provides a framework for effective AI adoption, offering insights for decision-makers on strategizing AI adoption, emphasizing the importance of factoring ESG concerns into de decision to adopt this technology.

In the current digital era, digital technologies develop and emerge rapidly, businesses, especially the electronic sector more connected to information technology, facing challenges in the terms of its technology infrastructure and tactical directions. That’s why most of them adopt the latest digital technology (DT) and design novel business strategies and models. The growing significance of AI in the transformation of manufacturing operations and the demand for a thorough knowledge of the variables affecting its adoption serve as the driving forces behind the study. Several researchers have presented that digital technology can lead toward AI adoption. Though, previous studies lack an efficient transformation pathway. Therefore, this study establishes an inventive approach and aims to investigate the direct link between digital technology and AI adoption, the mediating function of knowledge sharing (KS) between them, and explore the moderating impact of privacy and security that assist in the acceleration of AI adoption in electronics manufacturing enterprises through the antecedent of digital technology. This study is quantitative in nature, random sampling method and questionnaire is used as a survey tool. Depending on 298 questionnaire data from electronic firms of Saudi Arabia, this study performs multi-level correlation and regression analysis to evaluate study hypotheses. Findings confirm that digital technology has a positive influence on AI adoption. In addition, outcomes corroborate that knowledge sharing mediates in the linkage between digital technology and AI adoption. The results also proved that privacy and security have a positive moderation impact on the association between digital technology and AI adoption. This study enlighten that the adoption of this framework enables electronic manufacturing companies to strategically integrate digital-technologies to promote effective AI adoption, increase its operational efficiency, and sustain a competitive advantage in the constantly evolving manufacturing landscape. The outcomes as well supplement the previous study on the linkage between digital technology and AI adoption, expand application space and theoretical boundary from the perspective of knowledge sharing, privacy and security at the managerial level, and give reference for AI adoption in, as electronics manufacturing firms.

AI adoption in education has attracted growing scholarly attention over the past decade. However, the specific mechanisms underlying AI adoption in multilingual educational contexts remain underexplored. To address this issue, this study drew on semi-structured interviews with Chinese ethnic minority students learning English as a third language (L3) to examine their adoption of AI-assisted English learning through an integrated UTAUT–SDT lens. Thematic analysis showed that, for minority L3 learners, facilitating conditions were not perceived as direct enablers of AI adoption, given the prior insufficient digital learning resource constrained their autonomous decision making in learning. Social influence manifests itself through co-ethnic peer affiliation and supportive teacher recognition, reinforcing learners’ sense of relatedness. Tibetan L3 learners perceived competence restoration, indicating performance expectancy, as AI makes English learning more accessible, manageable and academically supportive. At the same time, driven by necessity to adopt AI, L3 learners have been exposed to an increased level of vulnerability, including concerns about over-reliance, weakened critical awareness and risks to their multilingual cognitive development. These findings frame AI adoption in multilingual contexts as a complex socio-motivational process and offer implications for supporting minority students’ AI-assisted English learning.

Sustainability has now become the top priority in the global business environment. This study aims to comprehend and test the mediating effect of Artificial Intelligence Adoption (AIA) on Internal Environmental Dynamism (IED), Competitive Pressures (CP) and Business Sustainability (BS). The study used the Technology Organization Environment (TOE) framework and theories such as the Dynamic Capabilities View (DCV) to understand how AI adoption influences business sustainability. The research used primary data and the results were analyzed using partial least squares structural equation modelling (PLS-SEM) and artificial neural networks (ANN) as analytical methods. The result supported the hypothesized association between CP, IED, AIA and BS. The study found that AIA mediate the constructive relation between CP, IED, with BS. Competitive pressure emerged as the most significant predictor of business sustainability, with a normalized importance of 100%, followed by AI adoption (37.33%) and Internal Environmental Dynamism (36.10%). The results suggest some practical and theoretical implications for B2B firms. The study recommends that the CP can help firms adopt AI, which will ultimately impact the firm's sustainability goals in a dynamic market.

Artificial Intelligence is transforming how start-up companies conduct research and development (R&D) and generate value through innovation. This research investigates the impact of AI-based organizational capabilities on decision-making, problem-solving, productivity, customer satisfaction, and technology infrastructure. It also examines how these capabilities foster an innovative climate, enabling innovation in Saudi Arabian start-ups. Using a quantitative cross-sectional research approach that draws on the Technology–Organization–Environment (TOE) and Unified Theory of Acceptance and Use of Technology (UTAUT) frameworks, the present study analyzes survey data from 384 start-ups using Partial Least Squares structural equation modeling (PLS–SEM). As presented in the results section, all five AI-powered capabilities are positively and significantly related to the innovation ecosystem (R² = 0.685); further, technology infrastructure and decision-making have the most significant effect on the innovation ecosystem, and these results underline the importance of AI-aided organizational capabilities in preparing Saudi start-ups to innovate and collaborate in an ecosystem. The research extends the body of theoretical knowledge on AI adoption and guides how entrepreneurs and policymakers can strengthen the Kingdom’s innovation ecosystem in line with Vision 2030.

This study investigates the impact of AI-led innovation on competitive advantage in the United Arab Emirates (UAE) information technology sector. However, empirical studies examining how AI adoption contributes to firm-level competitiveness in emerging economies, particularly those in the Middle East, remain scarce. This study seeks to understand how AI-based innovation affects competitive advantage and the organizational mechanisms through which this impact occurs. The originality of the research lies in providing quantitative evidence from the UAE IT sector, thereby extending the strategic management and digital transformation literature beyond Western and advanced economies. The theoretical foundation of the study integrates the Resource-Based View, Dynamic Capabilities Theory, and the Technology–Organization–Environment (TOE) framework. A quantitative research design was employed, and data were collected from senior managers and decision-makers in IT firms operating in the UAE using a structured survey. Reliability, correlation, and regression analyses were conducted using SPSS to examine the relationships among AI-driven innovation, innovation capability, market adaptability, competitive advantage, business model transformation, and financial performance. The findings indicate that AI-driven innovation has a strong positive effect on competitive advantage. Firms that strategically apply AI exhibit higher levels of innovation capability and greater adaptability to market dynamics. Market adaptability is confirmed as a partial mediating mechanism through which AI-driven innovation enhances competitive advantage. Furthermore, business model transformation driven by AI adoption is found to positively influence financial performance. In addition, firm-level investment in AI-focused R&D strengthens the relationship between AI-driven innovation and competitive advantage, highlighting its moderating role. Competitive advantage, in turn, significantly contributes to improved financial performance. The study provides valuable implications for managers and policymakers by demonstrating how artificial intelligence can be leveraged as a strategic organizational resource to sustain competitive advantage and financial performance within IT firms in the UAE.