Developing industrial AI capabilities: An organisational learning perspective

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.

With the continuous development and widespread adoption of artificial intelligence (AI) technology, enterprises have realized the importance of possessing artificial intelligence capabilities to enhance organizational performance. However, there is still a relative lack of empirical research on how to build AI capabilities, and how AI capabilities impact organizational innovation performance. Based on the resource-based theory, this study constructs a conceptual model of “AI Capabilities - Organizational Response (Organizational Learning and Organizational Agility) - Ambidextrous Innovation”, exploring the direct impact of AI capabilities on ambidextrous innovation, and the mediating effects of organizational learning and organizational agility, also the moderating role of digital strategy in this linkage. With data from 253 survey questionnaires of Chinese enterprises, the results indicate that: first, AI capabilities have a significantly positive impact on organizational ambidextrous innovation; second, organizations with high levels of organizational learning ability and agility can better leverage AI capabilities to promote the development of ambidextrous innovation; Thirdly, digital strategy has played an active role in regulating the influence path of two-way innovation within the organization and provided sufficient boundary conditions; fourth, The data capability, technological capability, and fundamental resource capability of AI capabilities have differentiating effects on exploratory and exploitative innovations. The findings of this research hold significant theoretical and managerial value for understanding the driving mechanisms of organizational innovation performance in the era of AI.

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.

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.

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.

Tackling the grand challenges of 21st century agriculture (Ag) will require a fundamental shift in the way we envision the role of artificial intelligence (AI) technologies, and in the way we build agricultural AI systems. This shift is needed especially for complex, high-value agricultural ecosystems such as those in the Western U.S., where 300+ crops are grown. Farmers and policy makers in this region face variable profitability, major crop loss and poor crop quality owing to several challenges, including increased labor costs and shortages of skilled workers, weather and management uncertainties, and water scarcity. While AI is expected to be a significant tool for addressing these challenges, AI capabilities must be expanded and will need to account for human input and human behavior – calling for a strong AI-Ag coalition that also creates new opportunities to achieve sustained innovation. Accomplishing this goal goes well beyond the scope of any specific research project or disciplinary silo and requires a more holistic transdisciplinary effort in research, development, and training. To respond to this need, we initiated the AgAID Institute, a multi-institution, transdisciplinary National AI Research Institute that will build new public-private partnerships involving a diverse range of stakeholders in both agriculture and AI. The institute focuses its efforts on providing AI solutions to specialty crop agriculture where the challenges pertaining to water availability, climate variability and extreme weather, and labor shortages, are all significantly pronounced. Our approach to all AgAID Institute activities is being guided by three cross-cutting principles: (i) adoption as a first principle in AI design; (ii) adaptability to changing environments and scales, and (iii) amplification of human skills and machine efficiency. The AgAID Institute is conducting a range of activities including: using agricultural AI applications as testbeds for developing innovative AI technologies and workflows; laying the technological foundations for climate-smart agriculture; serving as a nexus for culturally inclusive collaborative and transdisciplinary learning and knowledge co-production; preparing the next generation workforce for careers at the intersection of Ag and AI technology; and facilitating technology adoption and transfer.

Purpose: This research explores the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in brand management. It aims to understand how AI technologies can optimize brand awareness, enhance personalized communication, and improve brand equity measurement, thereby redefining brand management strategies in the digital era. Design/methodology/approach: A systematic literature review was conducted, analyzing research studies published between 2014 and 2024. The methodology focused on articles that delve into the integration of AI and ML within brand management contexts. This approach was chosen to consolidate and synthesize a broad spectrum of findings on AI’s impact on brand management practices. Findings: The review identified significant enhancements in brand management facilitated by AI, particularly in consumer engagement and customization of consumer interactions through data-driven insights. AI's capability to analyze big data has enabled more precise consumer segmentation and targeting, thereby influencing brand loyalty and overall brand equity positively. Research limitations/implications: The primary limitation of this study is its reliance on data from articles indexed only in Scopus, potentially omitting relevant studies published in other languages or databases. Future research should expand the scope to include these sources and explore empirical validations of the proposed theoretical impacts of AI on brand management. Practical implications: This research highlights the practical applications of AI in improving brand management strategies, offering insights into effective AI integration into marketing practices. Businesses are encouraged to adopt AI-driven tools for better market segmentation, consumer behavior predictions, and enhanced customer relationship management. Social implications: The findings could influence public attitudes towards brand interaction by fostering greater acceptance of AI in consumer relations. The research supports enhanced corporate social responsibility through AI's ability to provide more targeted and meaningful consumer interactions. Originality/value: This paper contributes to the academic and practical understanding of AI’s role in brand management by synthesizing existing research and identifying future research directions. It is valuable to academicians, marketing professionals, and policymakers interested in the implications of AI technology in brand strategies. Keywords: Brand Management, Artificial Intelligence, Machine Learning, Consumer Engagement, Marketing Strategies. Category of the paper: Literature review.

Sustainable development with Artificial Intelligence: Examining the absorptive capacity pathways to green innovation.

As the Industry 4.0 paradigm accelerates, the importance of artificial intelligence (AI) in manufacturing industry is increasing. Manufacturing AI requires balanced capabilities between industry-specific domain knowledge and AI capability. Nevertheless, many manufacturing AI startups are mainly focusing on human capital based on AI or data science capability. This article focused on the importance of human capital based on domain knowledge for the success of manufacturing AI. In this article, the relationship between domain knowledge and corporate performance was analyzed for 127 global manufacturing AI startups. Furthermore, the moderating effects of educational level and cofounder size for domain knowledge were analyzed. In addition, an expanded analysis was conducted on effect between domain knowledge, educational level, cofounder size, and corporate performance by business model. This article has new implications for and provides practical contributions to the human capital strategy for manufacturing AI corporates.

In an era where artificial intelligence (AI) is revolutionizing business paradigms, this study delves into the intricacies of AI-driven business models, offering a nuanced understanding of their emergence, evolution, and impact on traditional business strategies. This scholarly inquiry aims to dissect the role of AI in reshaping business models, highlighting the interplay between technological innovation and business strategy. The study meticulously examines the integration of AI into various business facets by employing a systematic and thematic analysis of a diverse range of literature, including academic journals, industry reports, and case studies. This methodological approach facilitates a comprehensive understanding of AI's role in business innovation, addressing both the opportunities and challenges it presents. The findings reveal that AI-driven business models are characterized by enhanced operational efficiency, data-driven decision-making, and customer-centric approaches. These models signify a transformative shift from conventional business strategies, demanding a reevaluation of leadership roles and ethical considerations in the digital age. The study identifies key challenges in AI implementation, such as technical complexities and ethical dilemmas, while uncovering AI's vast opportunities for business growth and competitive advantage. Conclusively, the study recommends a balanced approach to AI integration, emphasizing the need for ethical AI practices, continuous adaptation, and a synergy between AI capabilities and human insights. It advocates for business leaders to embrace AI not just as a technological tool, but as a catalyst for sustainable and innovative business growth. This scholarly work contributes significantly to the discourse on AI in business, providing a foundational framework for future research and practical application in AI-driven business innovation. Keywords: Artificial Intelligence, Business Models, Digital Transformation, AI Integration, Leadership in AI, Ethical AI Practices.

Emerging economies confront the dual challenge of accelerating digital transformation while simultaneously mitigating environmental degradation under conditions of institutional and governance heterogeneity. In this context, this study examines how artificial intelligence (AI) capability influences green innovation efficiency (GIE) in emerging Asian economies and investigates whether environmental, social, and governance (ESG) performance conditions this relationship. Using an unbalanced panel of 59,112 firm-year observations from 4926 publicly listed firms across 15 emerging Asian economies over the period 2011–2022, we employ a comprehensive panel-data econometric framework that accounts for unobserved heterogeneity, dynamic effects, endogeneity, and potential self-selection bias. The empirical results indicate that AI capability is positively and significantly associated with higher green innovation efficiency. More importantly, ESG performance strengthens this relationship, suggesting that robust governance frameworks enhance firms’ ability to translate digital intelligence into environmentally efficient innovation outcomes. These findings underscore that AI adoption alone is insufficient to generate sustainable value; rather, its environmental effectiveness depends critically on complementary governance structures that promote transparency, accountability, and responsible risk management. The results remain robust after correcting for endogeneity concerns, alternative model specifications, and extensive sensitivity and heterogeneity analyses. Overall, this study contributes to the literature on digital transformation and sustainability by providing large-scale, multi-country evidence that highlights the pivotal role of ESG in shaping the sustainability returns to AI adoption in emerging economies.

PurposeThis paper presents pyBibX, a Python library devised to conduct comprehensive bibliometric and scientometric analyses on raw data files sourced from Scopus, Web of Science and PubMed, seamlessly integrating state-of-the-art artificial intelligence (AI) capabilities into its core functionality.Design/methodology/approachThe library executes a comprehensive exploratory data analysis (EDA), presenting outcomes via visually appealing graphical illustrations. Network capabilities have been deftly integrated, encompassing citation, collaboration and similarity analysis. Furthermore, the library incorporates AI capabilities, including embedding vectors, topic modeling, text summarization and other general natural language processing tasks, employing models such as sentence-BERT, BerTopic, BERT, chatGPT and PEGASUS.FindingsAs a demonstration, we have analyzed 184 documents associated with “multiple-criteria decision analysis” published between 1984 and 2023. The EDA emphasized a growing fascination with decision-making and fuzzy logic methodologies. Next, network analysis further accentuated the significance of central authors and intra-continental collaboration, identifying Canada and China as crucial collaboration hubs. Finally, AI analysis distinguished two primary topics and chatGPT’s preeminence in text summarization. It also proved to be an indispensable instrument for interpreting results, as our library enables researchers to pose inquiries to chatGPT regarding bibliometric outcomes. Even so, data homogeneity remains a daunting challenge due to database inconsistencies.Originality/valuePyBibX is the first application integrating cutting-edge AI capabilities for analyzing scientific publications, enabling researchers to examine and interpret these outcomes more effectively. pyBibX is freely available at https://bit.ly/442wD5z.

As Artificial Intelligence (AI) transforms various sectors of industry and all spheres of everyday life, there is more necessity to make intelligence relocate towards the place where data is being generated at the edge. This shift is termed Edge AI, and it allows gadgets, including smartphone, sensors, and cost-automated machines, to determine decisions on the ground, devoid of the continuously reliant connection to geographically distant distributed cloud servers. It is expected that it would augment response rates, data security as well as autonomy, but it also raises profound issues on how we would be able to construct resilient systems that are made of distributed agents with AI capabilities that could work effectively under real world conditions.In this paper, I will explore how deployment of AI at the edge may impact the topology, the resilience of AI fabrics, the complex interdependence of compute, data and learning systems that enable intelligent decisions. We revisit the rationale of Edge AI and how latency minimization and control of data sovereignty have led to it, and the issues of resource bottlenecks and latencies, synchronization and security in a distributed setting. We also make comparisons between the current solutions of data locality, collaborative intelligence, and federated learning and we also endeavor to find answers as to how an AI infrastructure can become flexible and dynamic in the processes of a decentralized world. We would like to establish a reputation of the principles of being smart, resilient, secure, and human aligned AI through this work.

This chapter explores the nature of intelligence itself, conceptualizing it as a massive operating system that functions on biological technology as human intelligence (HI), or digital technology as artificial intelligence (AI). Through the lens of biological technology, the operating system contains many different biological individuals, each with an evolved range of dimensional “capabilities” needed to exist in the world environment. The operating system itself can be considered a hyperselfish intelligence (HYPERINT) development system that is relentlessly evolving, and exploitatively optimizing the development of the highest performing technologies within the world environment, regardless of the needs of individuals or a species, in order to ruthlessly maximize its capabilities. This chapter argues that HYPERINT is driving humans towards a situation where AI technologies could rapidly lead to the development of artificial general intelligence (AGI) with super intelligence that vastly exceeds HI and human controllability. Strong global regulation of AI is needed to protect human civilization from the most extreme risks of AI and AGI. In conclusion, the chapter argues that the global community must make a transition out of Industry 4.0, which focuses on progressive total automation of human work, and move towards the goals of Industry 5.0, which are more human-centric and globally sustainable.

Background: The rapid advancement of artificial intelligence (AI) in various sectors has revolutionized problem-solving approaches, particularly in healthcare. Developed countries have invested significantly in AI research and applications in healthcare, while low-income countries such as Somalia lag due to various challenges. This study aimed to assess the knowledge, attitudes, and practices (KAP) of AI among healthcare professionals in Somalia and explore their familiarity with AI technologies and practices. Methods: A cross-sectional study was conducted from January 1, 2024, to March 15, 2024, among 441 healthcare professionals in Somalia, using an online questionnaire. The questionnaire assessed the participants' sociodemographic information, knowledge of AI applications in healthcare, attitudes towards AI capabilities, and practical experience with AI in healthcare. Results: Most participants demonstrated good knowledge of AI (67.6%) and a positive attitude towards its potential in healthcare (80.5%). However, a significant gap was observed in the practical application of AI, with 79.1% of the respondents reporting poor practice. The study also found that sociodemographic factors such as age, gender, and income level did not significantly influence knowledge or attitudes towards AI but did affect its practical use. Professionals in certain fields such as midwifery and public health are more likely to use AI in their work. Knowledge and attitude scores were also significant predictors of practice scores. Conclusion: Healthcare professionals in Somalia demonstrate a good understanding and positive attitudes towards AI but encounter challenges in its practical application. This study emphasizes the necessity of an enhanced infrastructure, technical expertise, and data access to fully utilize AI's potential in healthcare. It also highlights the significance of addressing ethical considerations and implementing regulations to ensure responsible use of AI in healthcare. Efforts are needed to translate awareness and receptiveness into effective practice, which could result in a better healthcare system.