Smart Business and the Social Value of AI

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 Despite the potential of artificial intelligence (AI) systems to increase revenue, reduce costs and enhance performance, their adoption by organisations has fallen short of expectations, leading to unsuccessful implementations. This paper aims to identify and elucidate the factors influencing AI adoption at both the organisational and individual levels. Developing a conceptual model, it contributes to understanding the underlying individual, social, technological, organisational and environmental factors and guides future research in this area. Design/methodology/approach The authors have conducted a systematic literature review to synthesise the literature on the determinants of AI adoption. In total, 90 papers published in the field of AI adoption in the organisational context were reviewed to identify a set of factors influencing AI adoption. Findings This study categorised the factors influencing AI system adoption into individual, social, organisational, environmental and technological factors. Firm-level factors were found to impact employee behaviour towards AI systems. Further research is needed to understand the effects of these factors on employee perceptions, emotions and behaviours towards new AI systems. These findings led to the proposal of a theory-based model illustrating the relationships between these factors, challenging the assumption of independence between adoption influencers at both the firm and employee levels. Originality/value This study is one of the first to synthesise current knowledge on determinants of AI adoption, serving as a theoretical foundation for further research in this emerging field. The adoption model developed integrates key factors from both the firm and individual levels, offering a holistic view of the interconnectedness of various AI adoption factors. This approach challenges the assumption that factors at the firm and individual levels operate independently. Through this study, information systems researchers and practitioners gain a deeper understanding of AI adoption, enhancing their insight into its potential impacts.

Application of artificial intelligence: benefits and limitations for human potential and labor-intensive economy – an empirical investigation into pandemic ridden Indian industry

Integrating artificial intelligence (AI) systems into education poses significant challenges for teachers during the transition phase of adapting technologies in lesson planning. We adopt a grounded theory approach to examine the characteristics, strategies, and outcomes of 51 K-12 teachers’ transition phases when utilising AI systems in China. Data including a 68,807-word transcription from two rounds of interviews with eight teachers revealed that K-12 teachers can be classified into technology followers, technology conservatives, technology pioneers, and technology disengagers. The study identifies three distinct phases of adaptation. The first phase is operation focusing on mastering AI system functionalities. The second is application integrating AI tools into pedagogical practices. The final is adaptation achieving stable and tailored usage. The outcomes are categorised into Basic Alignment meeting routine needs and Advanced Alignment enhancing instructional innovation. The outcome reflects different levels of openness, proactiveness, and effectiveness in developing strategies to overcome the challenges. The findings highlight that teachers’ perceptions of transition difficulties and external factors influence teachers’ AI adoption. Teachers’ perceptions of transition difficulties including valuing new methods and using AI tools matter. External factors such as training support, peer influence, and policy requirements significantly influence their strategies and outcomes. The study offers three recommendations on adapting policies to align with teachers’ stages of AI system adoption, balancing technical and pedagogical training, and fostering collaborative lesson planning through AI systems. Future research should explore the key metrics to quantify and track transition characteristics of the transition phase and long-term in-depth observation of K-12 teachers for a more comprehensive understanding.

Objective – This study investigates the readiness for artificial intelligence (AI) adoption in library and information centres of Pakistani universities. The projected outcomes of this study are expected to contribute to the development of best practices for effectively motivating university administrators and preparing librarians for adopting AI in library and information centres. Methods – A theoretical framework combining the technology-organization-environment (TOE) framework and the Technology Readiness Index (TRI) guided this qualitative study. Interviews were conducted with 27 senior representatives, including library managers and registrars, from 27 universities across four provinces and the capital city, Islamabad. A systematic approach was employed to analyze the data. Results – The findings indicate that the concept of AI adoption in Pakistani university libraries is new. The library and information sector of Pakistan is slow in adopting AI, which could have implications for its future competitiveness, despite the push for AI adoption by university librarians and administrators. The readiness for AI adoption in this sector is influenced by factors such as organizational technological practices, financial resources, university size, and data management and protection concerns. Conclusion – Library managers and researchers can implement the TOE framework and TRI scale to facilitate AI adoption in a manner that is relevant to library and information settings in Pakistan as well as other parts of the world. Our research indicates that most adoptions are still in their nascent phases, and numerous library managers feel uneasy due to either uncertainties about the precise benefits AI can bring to their libraries or a lack of knowledge and skills for its effective implementation. To manage the networks of internal and external stakeholders essential for successful AI adoption, universities should consider appointing individuals with a specialized knowledge of AI within their libraries.

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 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.

The Untapped Social Impact of Artificial Intelligence for Breast Cancer Screening in Developing Countries: A Critical Commentary of DeepMind

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.

Background and Purpose: Artificial Intelligence (AI) is transforming higher education by enhancing learning experiences through personalised instruction, automated assessments, and intelligent tutoring systems. In Malaysia, AI adoption among students is gaining momentum, and it is influenced by digital literacy, perceived usefulness, and social influence. This study examines the key factors influencing AI adoption among Malaysian students. Methodology: A survey research design was employed, utilising a structured questionnaire distributed to 286 students across four Malaysian universities. 224 valid responses were analysed using Partial Least Square (PLS-SEM) to test the hypothesised relationships among the variables. Findings: Results indicate that social influence has the most substantial effect on AI adoption (β = 0.503, p < 0.001), followed by perceived usefulness (β = 0.236, p < 0.001) and digital literacy (β = 0.188, p = 0.036). These findings suggest that students are more likely to adopt AI when they observe peers and educators using it effectively. Additionally, students who perceive AI as beneficial for academic performance are more willing to engage with AI technologies. Implication: The study contributes to understanding AI adoption in higher education; institutions can better prepare students for an AI-driven academic and professional landscape by addressing the identified factors. Keywords: AI adoption, digital literacy, Malaysian students, perceived usefulness, social influence.

As the adoption of artificial intelligence (AI) systems in radiology grows, the increase in demand for greater bandwidth and computational resources can lead to greater infrastructural costs for healthcare providers and AI vendors. To that end, we developed ISLE, an intelligent streaming framework to address inefficiencies in current imaging infrastructures. Our framework draws inspiration from video-on-demand platforms to intelligently stream medical images to AI vendors at an optimal resolution for inference from a single high-resolution copy using progressive encoding. We hypothesize that ISLE can dramatically reduce the bandwidth and computational requirements for AI inference, while increasing throughput (i.e., the number of scans processed by the AI system per second). We evaluate our framework by streaming chest X-rays for classification and abdomen CT scans for liver and spleen segmentation and comparing them with the original versions of each dataset. For classification, our results show that ISLE reduced data transmission and decoding time by at least 92% and 88%, respectively, while increasing throughput by more than 3.72 × . For both segmentation tasks, ISLE reduced data transmission and decoding time by at least 82% and 88%, respectively, while increasing throughput by more than 2.9 × . In all three tasks, the ISLE streamed data had no impact on the AI system's diagnostic performance (all P > 0.05). Therefore, our results indicate that our framework can address inefficiencies in current imaging infrastructures by improving data and computational efficiency of AI deployments in the clinical environment without impacting clinical decision-making using AI systems.

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.

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.

This paper pioneers the identification of artificial intelligence (AI) enablers like technology feasibility, sophistication, data integrity, interoperability and perceived benefits that can boost operational efficiency of firms in Indian food processing industry. With the food processing industry contributing significantly to do­mestic gross value added and generating an export earning of close to USD 40 billion from agricultural and processed food exports, the study examines the role of AI in overcoming the existing inefficiencies of firms, particularly the small and medium enterprises (SMEs) involved in food processing. For this, questionnaire wascirculated to 500 respondents comprising of IT and supply chain professionals, managers of food processing companies and academicians working in this do main, of which 341 complete responses were received. These responses were then analysed using PLS–SEM modeling, through which the relationship between AI adoption and operational efficiency of firm was established. The study found a significant relationship between AI adoption and operational efficiency. The R square and Q square values substantiate the predictive power of the model used in the study. The research has significant implications for supply chain professionals as technology adoption would boost resilience, integration and transparency of these firms. The study is also relevant for addressing issues pertaining to food security, employment generation, enhancing industrial output and export growth. Policy makers can also get perspectives on harnessing the benefits of AI technology while creating an enabling environment for different supply chain partners.

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.