Harnessing artificial intelligence in the public sector: the critical role of strategic foresight in driving performance

This study examines how Artificial Intelligence (AI) capabilities influence financial performance through social innovation as a mediating mechanism in Malaysian Small and Medium Enterprises (SMEs). Drawing on data from 250 SMEs collected through judgmental sampling, the research investigates three key dimensions of AI capabilities: management capabilities (AIMC), personal expertise (AIPE), and infrastructure flexibility (AIIF). Using Structural Equation Modeling with Partial Least Squares (PLS-SEM), the study reveals that AI capabilities positively impact social innovation, with infrastructure flexibility showing the strongest effect (β = 0.381, p < 0.05), followed by personal expertise (β = 0.260, p < 0.05) and management capabilities (β = 0.146, p < 0.05). AI capabilities also demonstrate direct positive effects on financial performance, with AIIF having the most substantial impact (β = 0.207, p < 0.05). However, contrary to theoretical predictions, social innovation shows no significant mediating effect between AI capabilities and financial performance (β = 0.016, p > 0.05). These findings contribute to understanding the complex relationship between AI adoption and SMEs outcomes in emerging economies, suggesting that whilst AI capabilities can drive both social innovation and financial performance independently, the pathway from social innovation to financial performance requires additional supportive mechanisms.

In this study, artificial intelligence (AI) orientation, AI capabilities, as well as process-oriented dynamic capabilities (PDCs) within the realm of AI business value creation, are unpacked through multiple case studies in Hong Kong. We propose a conceptual framework suggesting that AI resources enable organizations to develop PDCs, manifesting in several abilities, thereby contributing to business value. In addition, the case study’s findings indicate that AI capabilities developed by organizations correlate with their AI orientation, which is their overall strategic direction and aspiration of employing AI technology. Apart from basic AI capabilities, AI-oriented organizations would develop advanced AI capabilities. The proposed conceptual framework and findings can guide and assist practitioners in utilizing AI resources and building AI capabilities. This study also enriches the growing body of research on AI and contributes to the limited understanding of AI capabilities in the extant literature.

Purpose This study aims to examine how big data analytical intelligence (BDAI) assimilation promotes new product performance (NPP) in business-to-business (B2B) manufacturing firms. This study tests the intermediary role of artificial intelligence (AI) capabilities and the contingency impact of electronic supply chain collaboration (ESCC) in the relationship between BDAI assimilation and NPP. Design/methodology/approach Drawing on the dynamic capabilities theory (DCT), this study tests the moderated-mediation model using multi-wave, multi-source data collected from 291 Chinese B2B manufacturing firms. Structural equation modeling was applied to test the proposed hypotheses. Findings The results demonstrate that BDAI assimilation significantly promotes NPP, and AI capabilities act as an intermediary bridge – empowering B2B firms to convert BDAI assimilation into enhanced NPP. This study further found that this mediation model is strengthened through the contingency impact of ESCC and increases its indirect effect on NPP. Practical implications This study suggests that B2B managers and policy architects should recognize that investment in BDAI assimilation is not sufficient. However, building AI capabilities might fully support BDAI assimilation to gain innovation outcomes such as NPP. This study further suggests the practical implications of ESCC in achieving higher returns through AI capabilities in the B2B context. Originality/value This study has threefold contributions to the existing literature on big data innovation and B2B firms. This study contributes to extend DCT by emphasizing AI capabilities as an intermediary channel and ESCC as a vital contingency – strengthening the relationship between BDAI assimilation and NPP. We contributed and understand how Chinese B2B firms strategically used big data and AI strategies to reach firms’ competitive advantage.

Purpose While the literature on artificial intelligence (AI) capability is expanding, gaps remain in understanding how this capability is internally developed in technology-based startups (TBS) across different life cycle phases. This study, grounded in the resource orchestration theory (ROT), investigates the pathway through which TBS use organizational creativity to build AI capability and achieve performance. Design/methodology/approach A conceptual framework based on ROT emphasizes the role of organizational creativity in the structuring and bundling processes. Data were collected through a survey of 166 managers and employees of TBS operating in Brazil and international markets, using multiple linear regressions and the Sobel test for analysis. The study validated the AI capability scale in the TBS context. Findings AI capability fully mediates the relationship between organizational creativity and performance, confirming that organizational creativity is a critical resource for AI capability development. These findings advance ROT by deepening the understanding of how AI capability is developed in TBS. The study offers a dynamic, process-based view of performance trajectories in TBS, demonstrating that the synchrony between creativity and AI capability creates a cyclical process, maximizing company performance. Originality/value This research identifies an alternative pathway for TBS to develop AI capability and achieve performance, highlighting the synchronization and co-evolution of resources and capabilities. It provides novel insights into AI capability’s mediating role and expands understanding of resource management in TBS across life cycle phases.

In today’s fast-changing business environment, artificial intelligence (AI) capability plays a critical role in fostering product innovation (PI). Resource-based theory (RBT) posits that resources and capabilities characterized as valuable, rare, inimitable, and non-substitutable can generate a sustained competitive advantage, providing an appropriate theoretical framework for this study. Using RBT this study examines how business intelligence transforming capability (BITC) mediates the relationship between AI capability and PI and how formal and informal knowledge governance mechanisms (FKGMs and IKGMs, respectively) moderate the effect of AI capability on BITC. Using partial least squares structural equation modeling on 516 Chinese manufacturing enterprises, we empirically test a mediated moderation model. The findings reveal that BITC significantly mediates the relationship between AI capability and PI. Both FKGMs and IKGMs strengthen the effect of AI capability on BITC (with IKGMs showing a stronger influence). This study theoretically contributes by identifying BITC’s mediating role, defining AI capability and BITC boundary conditions, revealing FKGMs’ and IKGMs’ asymmetries, and extending RBT. In terms of practical contributions, the findings emphasize the necessity of developing BITC and strategically applying both FKGMs and IKGMs to maximize AI capability-driven PI benefits.

Purpose Recent advancements in artificial intelligence (AI) have transformed it from a mere technological tool to a key strategic asset, able to enhance company value propositions by enabling deeper insights, improved decision-making and innovative business models. This study empirically examines how AI capabilities influence value definition, creation and capture in project-based organizations (PBOs) and evaluates the mediating role of organizational agility. Design/methodology/approach Drawing on Resource-Based View and Dynamic Capability View, we propose that AI capabilities constitute a unique type of organizational capability, enabling project-based organizations to utilize technological assets and other resources to boost productivity and generate economic value. The paper employs a survey instrument and a partial least squares structural equation modeling (PLS-SEM) to assess how AI capabilities impact project value processes and the mediating role of organizational agility in this relationship. Findings The results robustly support all proposed hypotheses concerning the direct effects. Additionally, organizational agility is identified as a mediator in the relationship between AI capabilities and project value processes. Our study confirms that developing robust AI capabilities necessitates strategic investment in core AI resources. This offers implications for managers and policymakers aiming to leverage AI for fostering competitive advantage. Originality/value This paper explores the role of AI capabilities in enhancing project value processes. It provides empirical evidence highlighting the significance of AI capabilities as essential organizational resources that enable the leveraging of AI to generate project value. The study supports the hypothesis that technology alone is insufficient for deriving value from it. This finding underscores the need for strategic investments in AI capabilities to fully capitalize on the potential of technological advancements.

Artificial Intelligence (AI) is increasingly discussed as a tool that can support speech and language therapy (SLT). However, clinical adoption of AI requires improved AI literacy among clinicians. AI is a rapidly evolving and often inconsistently defined field that can be difficult to navigate. Despite the definition provided by the EU AI Act, AI terminology can feel abstract for non-technical readers. To provide a foundational understanding of AI tailored for SLTs, by translating complex concepts into accessible language and organising them across three levels: (i) AI techniques (how AI works); (ii) AI capabilities (what AI can do) and (iii) clinical applications (how AI can support SLT). This tutorial is informed by foundational AI literature, established AI taxonomies, relevant SLT literature and regulatory and ethical guidelines. Clinical analogies are used to explain technical concepts, with additional technical detail signposted where relevant. Existing and conceptual examples illustrate the relevance of AI across paediatric SLT practice. This tutorial provides: (i) a clinician-focussed interpretation of the EU AI Act definition; (ii) an organisation of key AI concepts into techniques, capabilities and clinical applications; (iii) a production-line model for mapping clinical needs to AI design choices and (iv) a practice-focussed discussion of ethical and regulatory considerations. AI is best understood as a set of techniques that enable specific capabilities, which in turn support clinical applications. This tutorial promotes the safe, ethical and accountable use of AI as a tool that can support rather than replace clinicians. What is already known on this subject Current Artificial Intelligence (AI) literature is typically designed for technical audiences, making it difficult for clinicians to interpret. This can hinder the effective and responsible integration of AI into clinical practice. What this paper adds to the existing knowledge This tutorial provides a clinician-focussed explanation of AI, structured across three levels: (i) AI techniques (how AI works); (ii) AI capabilities (what AI can do) and (iii) clinical applications (how AI supports practice) in paediatric speech and language therapy. It also addresses key challenges, ethical considerations and regulatory requirements relevant to clinical contexts. What are the potential or actual clinical implications of this work? This tutorial lays the groundwork for informed engagement with emerging AI tools. It prepares clinicians to evaluate how different AI techniques and capabilities may support core clinical tasks (e.g., assessment, therapy planning and delivery).

Purpose Artificial intelligence (AI) is a significant trend in digital technology that is revolutionizing the field of global business and internationalization. Based on institutional theory and resource-based view, this study examines the intricate relationship between AI capabilities and export performance, taking into account the different province market development within a country and cultural distance. Design/methodology/approach This study gathered data from the websites of privately owned Chinese exporters and complemented it with a survey in 2023. In conducting the survey, we employed a simple random-sampling approach to select 1,500 exporters in China, with a focus on economic development indicators, particularly GDP contribution. We received 1,000 surveys, but only 749 were valid due to missing data. The study’s comprehensive coverage of regions ensured the inclusion of potential variations and subnational disparities within the country. This study conducted ordinary least squares (OLS) regression, and standardized variables before entering into the regressions. Findings The results demonstrate that AI capabilities have a significant positive impact on export performance. In addition, the influence of AI capabilities on export performance varies depending on the home-country and host-country institutional environment. The relationship between AI capabilities and export performance is strengthened by larger cultural distance, while province market development within a country has a negative moderating effect on this relationship. In less developed markets, the AI capabilities can drive significantly export performance. In developed markets with more advanced institutional development, the significance of AI capabilities in reducing transaction costs diminishes due to established institutions and market structures. AI capabilities serve as an intermediary institutional mechanism that connects the institutional context of the home country with the cultural environment of the host country. Originality/value While the impact of AI on international business and internationalization performance is a growing area of study, further exploration of the moderating factors that influence this relationship is needed. Organizations operating in diverse global markets are profoundly shaped by institutional contexts in their operational environments. This research addresses the relatively unexplored role of institutional factors within the home country (provincial market development) and host country (cultural distance) in moderating the effects of AI capabilities on export performance. This study illuminates the intricate dynamics underlying the relationship between AI capabilities and export performance, with a specific focus on province-level market development and cultural distance. Employing institutional theory as the overarching framework, this research sheds light on how AI serves as an intermediary institutional mechanism, bridging the gaps related to cultural differences and varying market development levels. In doing so, it contributes to academia by advancing our understanding of how AI is shaping internationalization dynamics and the interaction between AI capabilities and institutional factors. Additionally, it offers insights for business managers and policymakers to make informed strategic decisions regarding AI capabilities.

Purpose This study investigates the profound impact of artificial intelligence (AI) capabilities on decision-making processes and organizational performance, addressing a crucial gap in the literature by exploring the mediating role of decision-making speed and quality. Design/methodology/approach Drawing upon resource-based theory and prior research, this study constructs a comprehensive model and hypotheses to illuminate the influence of AI capabilities within organizations on decision-making speed, decision quality, and, ultimately, organizational performance. A dataset comprising 230 responses from diverse organizations forms the basis of the analysis, with the study employing a partial least squares structural equation model (PLS-SEM) for robust data examination. Findings The results demonstrate the pivotal role of AI capabilities in shaping organizational decision-making processes and performance. AI capability significantly and positively affects decision-making speed, decision quality, and overall organizational performance. Notably, decision-making speed is a critical factor contributing significantly to enhanced organizational performance. The study further uncovered partial mediation effects, suggesting that decision-making processes partially mediate the relationship between AI capabilities and organizational performance through decision-making speed. Originality/value This study contributes to the existing body of literature by providing empirical evidence of the multifaceted impact of AI capabilities on organizational decision-making and performance. Elucidating the mediating role of decision-making processes advances our understanding of the complex mechanisms through which AI capabilities drive organizational success.

Artificial Intelligence (AI) is gradually becoming an integral part of the digital strategy of organizations. Yet, the use of AI in public organizations in still lagging significantly compared to private organizations. Prior literature looking into aspects that facilitate adoption and use of AI has concentrated on challenges concerning technical aspects of AI technologies, providing little insight regarding the organizational deployment of AI, particularly in public organizations. Building on this gap, this study seeks to examine what aspects enable public organizations to develop AI capabilities. To answer this question, we built an integrated and extended model from the Technology-Organization-Environment framework (TOE) and asked high-level technology managers from municipalities in Europe about factors that influence their development of AI capabilities. We collected data from 91 municipalities from three European countries (i.e., Germany, Norway, and Finland) and analyzed responses by means of structural equation modeling. Our findings indicate that five factors – i.e. perceived financial costs, organizational innovativeness, perceived governmental pressure, government incentives, regulatory support – have an impact on the development of AI capabilities. We also find that perceived citizen pressure and perceived value of AI solutions are not important determinants of AI capability formation. Our findings bear the potential to stimulate a more reflected adoption of AI supporting managers in public organizations to develop AI capabilities.

PurposeThis study aims to evaluate an artificial intelligence (AI) capability scale using resource-based theory and tests its impact on dynamic capabilities and organizational creativity to influence the performance of public organizations.Design/methodology/approachThe study used qualitative and quantitative methods to develop and validate an AI capability scale using an integrative psychometric approach. An initial set of 26 items was selected from the literature for qualitative analysis. Self-reported data from 344 public managers in United Arab Emirates public organizations were used for scale refinement and validation. Hypotheses were tested against theoretically related constructs for nomological validation.FindingsA 23-item AI capability scale was developed. Nomological testing confirmed that AI capability positively and significantly enhances dynamic capabilities, which in turn boosts organizational creativity and improves organizational performance.Originality/valuePrevious information system literature has not sufficiently addressed the importance of organizational-level complementary resources in developing distinctive capabilities within public organizations. Grounded in resource-based theory and recent AI research, this study provides a framework for public sector organizations to assess their AI capabilities. The findings empirically support the proposed theoretical framework, showing that AI capability increases dynamic capabilities, organizational creativity and performance.

AI capability and green innovation impact on sustainable performance: Moderating role of big data and knowledge management

In the aftermath of the COVID-19 pandemic, college students have faced various challenges that could negatively impact their critical thinking abilities due to disruptions to education, increased stress and anxiety, less social interaction, and the advancement of distance learning relying more heavily on digital tools. With the increasing integration of AI technology across sectors, higher education institutions have deployed various AI capabilities for intelligent campuses and modernized teaching. However, how to fully utilize AI capabilities to promote students’ thinking awareness on learning effectiveness is still not clear, as critical thinking is an essential skill set holding significant implications for college students’ development. This research adopts the resource-based theory (RBT) to conceptualize the university as a unified entity of artificial intelligence (AI) resources. It aims to investigate whether AI capabilities can foster critical thinking awareness among students by enhancing general self-efficacy and learning motivation. In particular, it examines the causal relationships between AI capabilities, general self-efficacy, motivation and critical thinking awareness. Primary data was collected through a questionnaire administered to 637 college students. Structural equation modeling was employed to test hypotheses pertaining to causality. The results showed that AI capabilities could indirectly enhance students’ critical thinking awareness by strengthening general self-efficacy and learning motivation, but the effect on critical thinking awareness was not significant. Meanwhile, general self-efficacy significantly affected the formation of learning motivation and critical thinking awareness. This indicates that AI capabilities are able to reshape the cognitive learning process, but its direct influence on thinking awareness needs to be viewed with caution. This study explored the role of AI capabilities in education from the perspective of organizational capabilities. It not only proves how AI facilitates cognition, but also discovered the important mediating role of general self-efficacy and motivation in this process. This finding explains the inherent connections between the mechanism links. Furthermore, the study expands research on AI capabilities research from the technical level to the educational field. It provides a comprehensive and in-depth theoretical explanation theoretically, guiding the practice and application of AI in education. The study is of positive significance for understanding the need for the future development of the cultivation of critical thinking awareness talents needed for future development through AI capabilities in education.

PurposeThis study examines the impact of artificial intelligence (AI) capabilities on sustainability-oriented innovation performance. Furthermore, it explores the mediating role of green intellectual capital and the moderating role of learning orientation.Design/methodology/approachTo verify the hypothesised relationships, we conducted a hierarchical regression analysis and bootstrapping method with survey data collected from 355 Chinese firms.FindingsGrounded in organisational learning theory, the study found that AI capabilities have a positive influence on green intellectual capital (i.e. green human capital, green structural capital and green relational capital), and this connection is further reinforced by learning orientation. The analysis also reveals that green intellectual capital serves as a mediator in the relationship between AI capabilities and sustainability-oriented innovation performance.Originality/valueThis research explores the relationships among AI capabilities, green intellectual capital, learning orientation and sustainability-oriented innovation performance in a comprehensive model. This is the first known study to highlight that AI capabilities can improve sustainability-oriented innovation performance and gives managers implications on how to align AI capabilities while pursuing sustainability-oriented innovation performance.

The role of AI capabilities in environmental management: Evidence from USA firms