Chatbots and team-based working dynamics: management decision implications

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.

This study examines the role of artificial intelligence (AI) adoption in enhancing innovation and competitiveness among small and medium-sized enterprises (SMEs) operating in the creative industries of the Special Region of Yogyakarta, Indonesia. Despite the growing strategic importance of AI for SMEs, empirical evidence from developing economies remains limited, particularly regarding how institutional and organizational factors jointly shape AI-driven competitiveness. To address this gap, this study proposes an integrative model that positions government support, organizational capacity, and inter-firm collaboration as antecedents of AI adoption. Data were collected from creative industry SMEs through a structured questionnaire. The measurement instrument captured organizational capacity, collaboration, government support, AI adoption, innovation, and competitiveness. Data analysis was conducted using Partial Least Squares–Structural Equation Modeling (PLS-SEM). The results indicate that both organizational capacity and collaboration play critical roles in facilitating AI adoption among creative SMEs. Government support emerges as a key enabling factor by strengthening internal capabilities and fostering collaborative networks. Furthermore, AI adoption is found to significantly stimulate product and process innovation, which in turn enhances SME competitiveness. Innovation functions as an essential mechanism through which AI adoption translates into superior competitive outcomes rather than acting as an isolated technological investment. The study concludes that AI adoption should be understood as a strategic capability embedded within a broader ecosystem of organizational readiness, collaboration, and policy support. For creative SMEs, effective AI utilization requires coordinated efforts across technological, organizational, and institutional dimensions to generate sustainable innovation and long-term competitive advantage.

Purpose This study investigates the complex interplay between technological and ethical factors influencing artificial intelligence (AI) adoption in entrepreneurship and startup ecosystems, with a particular focus on how these dynamics impact innovation outcomes and organizational performance. Design/methodology/approach Employing a comprehensive analytical framework, the research examines quantitative data to assess the relationships among technology related factors (such as interactivity, relative advantage, and perceived intelligence), ethical principles (including fairness, accountability, transparency, accuracy, and autonomy), ethical dilemma, and their collective influence on AI adoption and exploitative innovation within entrepreneurial contexts. Data was collected using a self-administrated questionnaire to 207 respondents, in the Iran entrepreneurship and startup ecosystem. The Partial Least Square-Structural Equation Modeling (PLS-SEM) technique was used to examine the proposed hypotheses of the study. Findings The findings reveal that technology related factors specifically interactivity, relative advantage, perceived intelligence, transparency, and accuracy significantly drive AI adoption among entrepreneurs. In contrast, ethical considerations such as fairness, accountability, and autonomy do not exhibit a direct influence on adoption decisions. Also, the moderating relationship of ethical dilemma between exploitative innovation and organizational performance by AI adaptation was rejected. Notably, the study highlights the pivotal mediating role of exploitative innovation, AI adoption enhances exploitative innovation, which in turn improves organizational performance; however, there is no direct relationship between AI adoption and organizational performance. Practical implications Entrepreneurs and startup leaders should prioritize AI technologies that offer clear interactive capabilities, relative advantages, and transparent, accurate operations to maximize adoption and performance benefits. While ethical principles remain important, their influence may be more pronounced at later stages of implementation or in highly regulated sectors. Policymakers and ecosystem builders are encouraged to focus on fostering environments that support the practical integration of AI, particularly in ways that enhance exploitative innovation and organizational scalability. Originality/value This research provides novel insights by disentangling the relative importance of technological versus ethical factors in AI adoption within entrepreneurial settings. It advances the literature by empirically demonstrating the limited direct impact of certain ethical considerations on adoption decisions and by highlighting the central role of exploitative innovation as a mediator between AI implementation and organizational outcomes.

Purpose This study aims to investigate the antecedents of artificial intelligence (AI) adoption and sustainable performance (SP) in the context of manufacturing small and medium-sized enterprises (SMEs) in Jordan. Design/methodology/approach To fill the gap in the literature and achieve the research work objectives, the technology–organization–environment (TOE) framework and the partial least squares structural equation modeling (PLS-SEM) approach were used to analyze the sample data collected (n = 190) from manufacturing SMEs. Findings The findings indicate that technological, organizational and environmental context significantly influence the manufacturing SMEs’ AI adoption. Furthermore, AI adoption positively associated with economic, environmental and social performance. Originality/value This paper explains the TOE theory in an innovative technological setting. The paper also provides knowledge about how AI adoption may influence sustainable performance. Overall, this study provides new investigation into the literature concerning AI technologies and sustainable performance through the TOE lens.

Purpose With the increasing adoption of artificial intelligence (AI), a critical gap persists in understanding how enabling factors, challenges and organizational outcome are interrelated. The current work addresses this need by examining how innovation – particularly through technological, environmental and organizational readiness – serves as a foundation for successful AI adoption. The study further examines the mediating role of innovative practices in AI adoption and how challenges moderate the impact of AI adoption on workforce productivity. Design/methodology/approach Data for the quantitative survey was collected from 490 employees working in telecommunication industry of Pakistan, using a convenience sampling technique. Partial least squares structural equation modeling (PLS-SEM) was employed to examine both the mediation and moderation effects within the proposed model. Findings Technological readiness (TR) and organizational readiness (OR) emerge as a significant internal driver influencing human resource productivity (HRP), with challenges in AI Adoption (CAA) serving as a positive moderator in these relationships. In contract, environmental readiness (ER), although important, its influence on HRP is comparatively weaker when moderated by CAA, suggesting that external challenges may have a limited impact on shaping internal HR outcomes. Practical implications Organizations can drive substantial value from this study by deepening their understanding of the essential prerequisites for effective AI adoption. The findings offer actionable guide for decision-makers to examine their environmental, technological and organizational readiness, assess potential challenges and craft informed strategies to maximize the integration of AI tools. This strategic alignment can enhance workforce productivity and ensure a more successful and sustainable AI implementation. Originality/value It reflects that prerequisites, innovative practices, challenges and outcomes are part of a dynamic process where readiness is necessary but not sufficient for the success of the organizations. Furthermore, the interplay between innovation and the ability to navigate challenges ultimately determines the effectiveness of AI adoption and its impact on organizational performance.

Purpose The current study examines the impact of artificial intelligence (AI) adoption on logistics sustainability and the energy efficiency of AI systems in North African nations. It also examines the role played by renewable energy infrastructure and the moderating effect exerted by the quality of institutions, government support and company size. Design/methodology/approach Based on partial least squares structural equation modeling (PLS-SEM) and multi-group analysis (MGA), this study examines secondary data from 2022 to 2024 in Morocco, Algeria, Tunisia and Egypt. Data are drawn from the World Bank’s GovTech Index, IEA, IRENA, and logistics performance indicators. Findings Results confirm that AI adoption has a strong impact on logistics sustainability (β = 0.48) and a modest impact on energy efficiency (β = 0.36). Renewable energy plays an important mediation role (indirect effect = 0.19), and these effects are moderated by institutional quality, governmental support and size of firm, and are significant. MGA reveals stronger AI–sustainability links in Morocco (Δβ = 0.27, p = 0.015) and greater benefits among large firms (Δβ = 0.22, p = 0.032). Practical implications The study emphasizes the significance of matching AI implementation with digital governance and renewable energy infrastructure. Low-carbon computing, energy monitoring, and policies catered to infrastructural gaps and corporate capacity take the stage. Originality/value The study makes an input in the area of energy management by positioning AI both as an optimizer of logistics and an energy user. It presents an innovative computational sustainability approach suited to developing economies with energy-constrained systems.

This study investigates the impact of Artificial Intelligence (AI) adoption on the sustainable performance of small and medium-sized enterprises (SMEs). Employing a hybrid quantitative approach, this research combines Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Networks (ANN) to examine the influence of various organizational, technological, and external factors on AI adoption. Key factors considered include top management support, employee capability, customer pressure, complexity, vendor support, and relative advantage. Data collected from 305 SMEs across multiple sectors were analyzed. The results reveal that all the proposed factors significantly and positively affect AI adoption, with top management support, employee capability, and relative advantage being the most influential predictors. Additionally, the adoption of AI technologies substantially enhances the economic, social, and environmental performance of SMEs, reflecting improvements in operational efficiency, cost reduction, and social value creation. The ANN results confirm the robustness of the SEM findings, highlighting the critical role of AI in driving sustainability outcomes. Furthermore, the study emphasizes the positive mediation effects of AI adoption on organizational performance, indicating that AI adoption serves as a key enabler in achieving both short-term operational gains and long-term sustainability objectives. This research contributes to the understanding of AI’s transformative role in enhancing the sustainable performance of SMEs in developing economies, offering strategic insights for both policymakers and business leaders.

PurposeThe rapid evolution of artificial intelligence (AI) is revolutionizing organizational operations and altering competitive landscapes. This study examines the influence of organizational resources on AI adoption in recruitment, focusing on their role in achieving competitive advantage through effective implementation.Design/methodology/approachThis research utilizes a cross-sectional quantitative approach, applying partial least squares structural equation modeling (PLS-SEM) to data from 290 human resource (HR) professionals. It is grounded in the resource-based view (RBV) and dynamic capability framework (DCF).FindingsThe results reveal that HR competencies and open innovation significantly influence dynamic capabilities, which are essential for AI integration, supported by financial support and information technology (IT) infrastructure. These capabilities enable effective AI adoption, leading to a competitive advantage.Research limitations/implicationsThe cross-sectional data in this study captures the current landscape of AI adoption in recruitment, providing a snapshot of the present scenario in a rapidly evolving technological environment.Practical implicationsThis study offers HR professionals and managers strategic guidance on effectively integrating AI into recruitment processes. By enhancing HR competencies, fostering collaboration and ensuring sufficient financial and infrastructural support, organizations can navigate AI adoption challenges and secure a competitive advantage in a rapidly evolving technological landscape.Social implicationsThe adoption of AI in recruitment can reduce biases, enhance diversity and improve fairness through standardized assessments. However, as AI technologies evolve, continuous human oversight is essential to ensure ethical use and to modify AI systems as needed, further reducing biases and addressing societal concerns in AI-driven recruitment processes.Originality/valueThis research introduces a novel framework that underscores the importance of integrating human expertise with advanced technological tools to ensure successful AI implementation. A key contribution is that HR professionals not only facilitate AI integration but also ensure accuracy, accountability and configure the most suitable AI tools for recruitment by collaborating with AI developers to meet the specific needs of the organization.

BackgroundIn recent years, the adoption of artificial intelligence (AI) has become increasingly relevant in various sectors, including higher education. This study investigates the psychosocial factors influencing AI adoption among Peruvian university students and uses an extended UTAUT2 model to examine various constructs that may impact AI acceptance and use.MethodThis study employed a quantitative approach with a survey-based design. A total of 482 students from public and private universities in Peru participated in the research. The study utilized partial least squares structural equation modeling (PLS-SEM) to analyze the data and test the hypothesized relationships between the constructs.ResultsThe findings revealed that three out of the six hypothesized factors significantly influenced AI adoption among Peruvian university students. Performance expectancy (β = 0.274), social influence (β = 0.355), and AI learning self-efficacy (β = 0.431) were found to have significant positive effects on AI adoption. In contrast to expectations, ethical awareness, perceived playfulness, AI readiness and AI anxiety did not have significant impacts on AI appropriation in this context.ConclusionThis study highlights the importance of practical benefits, the social context, and self-confidence in the adoption of AI within Peruvian higher education. These findings contribute to the understanding of AI adoption in diverse educational settings and provide a framework for developing effective AI implementation strategies in higher education institutions. The results can guide universities and policymakers in creating targeted approaches to enhance AI adoption and integration in academic environments, focusing on demonstrating the practical value of AI, leveraging social networks, and building students’ confidence in their ability to learn and use AI technologies.

Artificial Intelligence (AI) is transforming audit practice by redefining traditional frameworks and enabling the automation of data analysis, risk assessment, substantive testing, and continuous monitoring. This study investigates the effect of AI adoption by audit firms on enterprise risk management (ERM). It further assesses the mediating role of Information Technology (IT) infrastructure flexibility and the moderating roles of technology competencies and organizational culture in this relationship. Data were collected from 355 top managers in Ghana using a judgmental sampling technique based on predefined inclusion and exclusion criteria. The analysis was conducted using Partial Least Squares Structural Equation Modelling (PLS-SEM) with SmartPLS 4.1.1.7. The findings indicate that AI adoption positively and significantly influences ERM and IT infrastructure flexibility. IT infrastructure flexibility also has a positive effect on ERM and partially mediates the relationship between AI adoption and ERM. In addition, technology competencies significantly strengthen the relationship between AI adoption and ERM. Organizational culture positively moderates the relationship between IT infrastructure flexibility and ERM. These insights underscore the need for strategic alignment between AI investments and organizational capabilities. The study contributes to the limited empirical literature on AI-driven ERM in emerging economies and offers insights for policymakers and regulators seeking to promote technology-aided ERM.

Purpose This study investigates the individual-level determinants influencing the adoption of Artificial Intelligence (AI) in the accounting profession within an emerging economy context. It explores how perceptions such as usefulness, ease of use, trust, threat, and susceptibility shape AI adoption, and examines the mediating role of accounting profit. The research is grounded in an extended Technology Acceptance Model and contextualized for Bangladesh to address the gap in AI adoption literature in developing countries. Design/methodology/approach A structured online survey was administered to 478 accounting professionals across various sectors in Bangladesh. The questionnaire included established measures adapted from prior studies and was analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The model assessed both direct and mediated paths from individual perceptions to AI adoption, with accounting profit as the mediating variable. Bias testing, robustness checks, and validity assessments were also conducted to ensure reliability of the findings. Findings Perceived usefulness, trust in AI, and perceived threat significantly influenced AI adoption. Perceived ease of use and perceived susceptibility did not show direct significance. Accounting profit was found to mediate the relationships between perceived usefulness, perceived threat, and trust with AI adoption. These results indicate that while individual perceptions matter, financial feasibility plays a critical role in actual adoption decisions within accounting contexts in emerging economies. Practical implications Accounting firms and policymakers in emerging markets must prioritize financial readiness alongside technology training. To enhance AI adoption, strategies should focus on building trust, communicating the tangible value of AI, and ensuring that AI investments align with profit goals. The findings also suggest firms should promote perceived usefulness and address risk concerns when introducing AI into accounting systems. Social implications The study highlights the socio-economic barriers that affect technology diffusion in emerging economies. As accounting professionals face trust and threat perceptions, along with resource limitations, the integration of AI requires institutional support, inclusive digital training, and clear cost-benefit communication. Broader AI adoption could enhance transparency, efficiency, and job transformation in financial services if these conditions are addressed. Originality/value This study offers a novel integration of individual-level behavioural constructs and financial performance (accounting profit) to explain AI adoption in accounting. By focussing on Bangladesh, it extends Technology Acceptance Model literature into a highly relevant yet underexplored emerging market context. The research also contributes to accounting technology literature by showing how strategic perceptions and profitability jointly shape innovation decisions in the profession.

Aim/Purpose: This paper aims to investigate and understand the intentions of management undergraduate students in Hangzhou, China, regarding the adoption of Artificial Intelligence (AI) technologies in their education. It addresses the need to explore the factors influencing AI adoption in the educational context and contribute to the ongoing discourse on technology integration in higher education. Background: The paper addresses the problem by conducting a comprehensive investigation into the perceptions of management undergraduate students in Hangzhou, China, regarding the adoption of AI in education. The study explores various factors, including Perceived Relative Advantage and Trialability, to shed light on the nuanced dynamics influencing AI technology adoption in the context of higher education. Methodology: The study employs a quantitative research approach, utilizing the Confirmatory Tetrad Analysis (CTA) and Partial Least Squares Structural Equation Modeling (PLS-SEM) methodologies. The research sample consists of management undergraduate students in Hangzhou, China, and the methods include data screening, principal component analysis, confirmatory tetrad analysis, and evaluation of the measurement and structural models. We used a random sampling method to distribute 420 online, self-administered questionnaires among management students aged 18 to 21 at universities in Hangzhou. Contribution: This paper explores how management students in Hangzhou, China, perceive the adoption of AI in education. It identifies factors that influence AI adoption intention. Furthermore, the study emphasizes the complex nature of technology adoption in the changing educational technology landscape. It offers a thorough comprehension of this process while challenging and expanding the existing literature by revealing the insignificant impacts of certain factors. This highlights the need for an approach to AI integration in education that is context-specific and culturally sensitive. Findings: The study highlights students’ positive attitudes toward integrating AI in educational settings. Perceived relative advantage and trialability were found to impact AI adoption intention significantly. AI adoption is influenced by social and cultural contexts rather than factors like compatibility, complexity, and observability. Peer influence, instructor guidance, and the university environment were identified as pivotal in shaping students’ attitudes toward AI technologies. Recommendations for Practitioners: To promote the use of AI among management students in Hangzhou, practitioners should highlight the benefits and the ease of testing these technologies. It is essential to create communication strategies tailored to the student’s needs, consider cultural differences, and utilize the influence of peers and instructors. Establishing a supportive environment within the university that encourages innovation through policies and regulations is vital. Additionally, it is recommended that students’ attitudes towards AI be monitored constantly, and strategies adjusted accordingly to keep up with the changing technological landscape. Recommendation for Researchers: Researchers should conduct cross-disciplinary and cross-cultural studies with qualitative and longitudinal research designs to understand factors affecting AI adoption in education. It is essential to investigate compatibility, complexity, observability, individual attitudes, prior experience, and the evolving role of peers and instructors. Impact on Society: The study’s insights into the positive attitudes of management students in Hangzhou, China, toward AI adoption in education have broader societal implications. It reflects a readiness for transformative educational experiences in a region known for technological advancements. However, the study also underscores the importance of cautious integration, considering associated risks like data privacy and biases to ensure equitable benefits and uphold educational values. Future Research: Future research should delve into AI adoption in various academic disciplines and regions, employing longitudinal designs and qualitative methods to understand cultural influences and the roles of peers and instructors. Investigating moderating factors influencing specific factors’ relationship with AI adoption intention is essential for a comprehensive understanding.

The study aims to examine factors that influence the adoption-diffusion process of Artificial Intelligence (AI) in Supply Chain Risk Management (SCRM) across manufacturing, wholesale trade, retail trade, and transportation industries in India. As part of this study, eleven constructs that influence the adoption-diffusion stages of AI in SCRM were identified and examined. A survey was conducted to collect data from supply chain executives, risk professionals, and AI consultants across the manufacturing, wholesale trade, retail trade, and transportation industries in India. Partial least squares structural equation modeling (PLS-SEM) was used to study the data. Results show that these factors have varying degrees of influence and direction on the three stages of adoption of AI in SCRM. The study will enable the leadership team in the organizations to build a roadmap for the adoption, implementation, and routinization of AI in SCRM.

Artificial intelligence (AI) adoption is increasingly recognized as a source of competitiveness for small and medium-sized enterprises (SMEs). Yet, prior research has primarily treated structural constraints such as financial scarcity, skill shortages, and institutional weaknesses as mere barriers, leaving their post-adoption impact underexplored, particularly in emerging economy contexts. This study empirically examines a relationship that has been theoretically acknowledged but rarely tested in such settings. Drawing on the Resource-Based View, Contingency Theory, and Institutional Theory, we propose a multidimensional framework explaining why AI adoption does not uniformly translate into performance gains but depends on firms’ financial capacity, technical competencies, and institutional environment. Using survey data from 280 Tunisian SMEs analyzed with partial least squares structural equation modeling (PLS-SEM), results confirm that AI adoption significantly improves financial and operational performance. Financial and technical strengths amplify these effects, whereas institutional conditions exert no significant moderating influence, suggesting that firms compensate for institutional weaknesses through adaptive and informal mechanisms. By reconceptualizing structural constraints as post-adoption moderators rather than pre-adoption barriers, this study advances understanding of contextual contingencies shaping AI outcomes and provides insights for managers and policymakers in resource-limited economies.

This study examines the impact of AI adoption orientation on innovation performance in multinational corporations (MNCs), emphasizing team innovativeness as an intervening mechanism and technology orientation as a moderating factor. Using data from 410 respondents collected via a snowball sampling strategy and analyzed through partial least squares structural equation modeling (PLS-SEM), the findings reveal that artificial intelligence (AI) adoption orientation positively influences team innovativeness and innovation performance. Team innovativeness partially mediates this relationship, while technology orientation moderates the link between AI adoption and team innovativeness, underscoring the role of technological preparedness in enhancing innovation. The study contributes to theoretical understanding by integrating team dynamics and technology preparedness in AI-driven innovation. It provides practical insights for managers, policymakers, and organizational leaders on fostering an innovative culture and investing in technology skills to drive MNC competitiveness.