Harnessing Artificial Intelligence and Employee Resilience for Enhanced Business Performance: A Resource‐Based and Dynamic Capabilities Perspective

Purpose: The Jakarta Provincial Government aims for 80% of MSMEs to be digitalized by 2025; however,empirical evidence on whether the intensity of Artificial Intelligence (AI) use truly enhances business performance in developing economies remains inconclusive. This study examines the relationship between AI usage intensity and business performance among MSMEs in Jakarta, while accounting for firm size variations. Method: A cross-sectional survey was conducted among 300 MSME owners/managers in Jakarta’s five administrative regions who had used at least one AI tool in the past 3 months. The Technology Acceptance Model (TAM) was extended with a Resource-Based View (RBV) perspective and firm size variables (micro, small, medium). Data were analyzed using CB-SEM with Maximum Likelihood estimation and FIML to handle missing data. Results: The model demonstrated an excellent fit (χ2/df = 1.13; CFI = 0.993; RMSEA= 0.021; SRMR = 0.018). However, AI usage intensity did not have a significant direct effect on business performance (β = 0.085; p = 0.113). Firm size had a substantial direct effect on performance (small: β = 0.446, p < 0.001; medium: β = 0.548, p < 0.001). Small firms tended to have higher AI usage intensity (β = 0.269, p < 0.001). Nevertheless, mediation analysis confirmed that AI usage did not function as a significant mechanism for improving performance among small or medium firms. Implications: The findings indicate the presence of adoption without impact—access to and intensity of AI use alone are insufficient; business value emerges only when complementary resources (dynamic capabilities, data governance, and human resource skills) are available. Policy programs should therefore integrate managerial training and infrastructure financing rather than merely providing technology license subsidies. Originality/Value: This study is among the earliest quantitative examinations in the ASEAN context exploring the relationship between AI usage intensity and performance among MSMEs, using a SEM–TAM approach that incorporates firm size as a contingency variable.

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.

In today’s highly competitive business landscape, organizations are increasingly turning to data-driven analytics to enhance performance and inform strategic decision-making. This approach leverages vast amounts of data, transforming it into actionable insights that drive efficiency, innovation, and growth. The role of data-driven analytics is multifaceted, encompassing predictive, prescriptive, and descriptive analytics, each contributing uniquely to the decision-making process. Predictive analytics forecasts future trends and behaviors, enabling proactive strategies. Prescriptive analytics provides recommendations for optimal actions based on various scenarios, while descriptive analytics offers a clear view of past performance, helping to identify patterns and opportunities for improvement. The integration of advanced data analytics tools and techniques has revolutionized how businesses operate, particularly in areas such as marketing, finance, supply chain management, and customer relationship management. By utilizing data analytics, companies can identify market trends, understand customer preferences, optimize operations, and reduce costs. For example, in marketing, data-driven insights allow for targeted campaigns and personalized customer experiences, leading to higher conversion rates and customer loyalty. In supply chain management, analytics improve demand forecasting and inventory management, enhancing overall efficiency. Furthermore, the adoption of data-driven analytics fosters a culture of evidence-based decision-making within organizations. Leaders can make informed decisions quickly, reducing the risks associated with gut-feel approaches and ensuring that strategies are aligned with market realities and organizational goals. This shift towards analytics-based decision-making is supported by advancements in artificial intelligence and machine learning, which enhance the accuracy and depth of insights generated from data. However, the journey to becoming a data-driven organization is not without challenges. It requires significant investments in technology, talent, and change management. Organizations must also address data privacy and security concerns to maintain stakeholder trust. Despite these challenges, the benefits of data-driven analytics in strategic decision-making are undeniable. It empowers businesses to navigate complexities, seize opportunities, and achieve sustainable competitive advantages. In conclusion, data-driven analytics is a critical enabler of enhanced business performance. Its strategic application in decision-making processes not only drives operational excellence but also positions organizations for long-term success in a dynamic market environment. Keywords: Decision-Making, Data-Driven, Analytics, Business Performance, Strategic.

Purpose While existing literature acknowledges the importance of top management support for organisational success, the specific mechanisms through which it enhances business performance remain underexplored, particularly in emerging economy contexts. Prior studies have predominantly examined the direct relationship between top management support and business performance, overlooking the underlying pathways through which this relationship operates. Addressing this gap, this study investigates how continuous process improvement and workforce commitment mediate the relationship between top management support and business performance in Bangladesh's ready-made garments (RMG) sector. Design/methodology/approach This study adopts a quantitative, cross-sectional research design. A self-administered survey questionnaire was employed to gather data from employees working in various ready-made garment factories in Bangladesh. A total of 367 useable responses were collected from employees directly involved in the production process. Partial least squares structural equation modelling (PLS-SEM) was used to assess construct validity and reliability and to test the hypothesised direct and indirect relationships. Findings The findings indicate that top management support, continuous process improvement and workforce commitment positively influence business performance in the RMG sector. Among the three factors, workforce commitment has the highest effect on business performance (ß = 0.274), followed by continuous process improvement (ß = 0.248) and top management support (ß = 0.165). Additionally, the results suggest that continuous process improvement and workforce commitment partially mediate the relationship between top management support and business performance. The model explains 29.9% of the variance in business performance. Practical implications The study's findings provide valuable guidelines for practitioners and policymakers to implement necessary initiatives that enhance business performance in the RMG sector. Given that workforce commitment has the strongest influence on business performance, managers should prioritise employee engagement, motivation and retention strategies. Additionally, the findings are expected to assist industry actors in ensuring that management practices improve continuously and promote workforce commitment. Originality/value This study fills a critical gap in the literature by uncovering the mediating mechanisms, namely continuous process improvement and workforce commitment, through which top management support enhances business performance in the RMG sector. Through the extension of the application of the theory of constraints (TOC) to this context, the study develops and empirically tests a novel model that demonstrates how leadership actions are transmitted through process-level improvements and employee-level commitment to influence performance outcomes. This research offers industry leaders and policymakers a strategic framework to improve business performance and strengthen competitiveness in the global market.

The integration of artificial intelligence (AI) in fashion retail is reshaping industry dynamics and enhancing efficiency and innovation across product development, marketing, and supply chain management. This study examines how different capabilities of AI--operational, cognitive, and affective--influence employee creativity within the U.S. fashion retail industry. Guided by Dynamic Capability Theory, Social Cognitive Theory, and Conservation of Resources Theory, the study explores proactive and responsive creativity as critical outcomes of AI integration. Using a quantitative approach with Partial Least Squares Structural Equation Modeling (PLS-SEM), data from 302 fashion retail employees were analyzed. The results reveal that cognitive and affective AI capabilities significantly enhance both proactive and responsive creativity, with affective capabilities exhibiting the strongest influence. Operational AI capabilities, however, show no significant positive impact on employees' creative responses. Furthermore, creative self-efficacy positively moderates specific AI-creativity relationships, while AI-enhanced job replacement anxiety did not have a significant moderating effect. The findings offer key managerial implications, highlighting the critical role of selecting appropriate AI capabilities and developing formal training initiatives to maximize creative collaboration between employees and AI in fashion retail organizations.

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.

Supply chain management (SCM) has been increasingly recognized as a critical factor in improving bottom-line performances. In addition, more and more firms are making use of SCM to improve their performance. The study aims to understand and determine critical variables of SCM that would be able to enhance product quality and business performance in manufacturing companies in Malaysia. The study measures senior production or SCM managers' perceptions regarding SCM and level of performances in their companies. Two hundred and fifty responses are received and data are analyzed using SPSS. The study specifically investigates relationships between SCM, product quality and business performance and these associations are investigated utilizing parametric statistical analyses such as Pearson's correlations and structural equation modeling (SEM). The SEM result demonstrates that SCM dimensions namely 'lean production', 'new technology and innovation', 'strategic supplier partnership' and 'postponement concept' appear to be of primary importance and exhibit significant effects on product quality and business performance. Findings of the study provide a striking demonstration of the importance of SCM in enhancing bottom line performances of Malaysian manufacturing companies. The result indicates that manufacturing companies should emphasize greater attention to the waste elimination program through lean production as well as the technological aspects of SCM and a greater degree of management support for SCM implementation initiatives.

Research on firm internationalisation has tended to focus on Large Enterprises(LEs), with relatively less focus on Small Medium Enterprises (SMEs), despite the importance of SMEs in generating national employment and economic development. In many countries the government has acknowledged SMEs as a key source of domestic income growth and employment creation, most particularly from export activities. Studies on internationalisation of SMEs, however, have been predominantly focused on developed countries. This study therefore is focused on the internationalisation of SMEs from Indonesia, which represents developing countries. Most studies of exporting SMEs focus on macroeconomic factors such as regulation, infrastructure and programs which assist SME development. On the other hand, management literature has recognised the important roles of senior managers in terms of their management know‐how and international business skills as human capital which can influence the export activity of the firm. At the same time, social capital is also recognised a further resource needed for SME internationalisation. Therefore this research will address this gap in order to examine the role of entrepreneur’s human and social capital in internationalisation of the firm. Further to this, findings on the relationship between firm internationalisation and firm performance are mixed. More specifically, the effect of internationalisation of SMEs on business performance has not been seriously investigated. This study therefore seeks to fill this gap by examining the unique effect of different dimensions of internationalisation on business performance. Overall, this study has three purposes: the first is to measure company resources (Human and Social Capital) influencing Indonesia’s manufacturing exporting SMEs in their internationalisation process; the second is to examine the effect of human and social capital in relation to three dimensions of internationalisation (international performance, number of markets and time to internationalise); and the third is to assess the effect of the aforementioned internationalisation dimensions on firm financial performance. This study used a mixed method combining quantitative and qualitative approaches. The quantitative data was drawn from 241 SMEs’ managers and/or owners who have exporting activities from a total of approximately 3 million SMEs based on the Indonesian government database. The qualitative data was collected through interviews with ten SMEs owner‐managers who were participating in the survey. Using Structural Equation Modelling (SEM) technique, the findings suggest that Management Know‐How have no positive relationship with any dimension of internationalisation, but, interestingly, it has a negative relationship with time for internationalisation; suggesting that it delays the internationalisation of the firms. The findings also show that both International Business Skills and Social Capital skills have a positive relationship with international performance and number of exporting markets, but not with time for internationalisation. With regard to the effect of internationalisation on business performance, the findings show that international performance has no effect on business performance, but the number of export market has a positive relationship with business performance. On the other hand, the time for internalisation has a negative effect on business performance, meaning that the later the SMEs internationalise, the better their performance. Qualitative findings indicate that good networks with competitors, suppliers and government will help a firm to serve the international market successfully. From theoretical perspectives, the findings support the Resource‐Based View theory as international business skills have positive relationships with two dimensions of internationalisation. The findings of this study also support the traditional view of incremental internationalisation advanced by Stage Theory. Consequently, the findings imply that International New Venture Theory, which supports born‐global or leap‐frog approach in internationalising SMEs, is not appropriate for Indonesian context. Finally, this study also supports the Network Perspective which suggests that the relationships built by firms with other businesses, community leaders, and government have significant effect on internationalisation of SMEs. Overall, this study demonstrates the unique effect of different organisational capitals (i.e. human and social) as key resources for facilitating internationalisation.

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.

Purpose This study identifies the critical factors influencing the adoption of artificial intelligence in recruitment processes and examines how such adoption reconfigures the sources of competitive advantage. Design/methodology/approach Drawing on the resource-based view and dynamic managerial capabilities perspectives, we conceptualise managerial capabilities as the applied manifestation of the microfoundations of dynamic managerial capabilities, in the context of artificial intelligence implementation. Data from 245 HR professionals involved in artificial intelligence driven recruitment were analysed using structural equation modelling. Findings HR competency and open innovation, operating through the microfoundations of dynamic managerial capabilities and manifested in managerial capabilities, significantly drive artificial intelligence adoption. Top management support strengthens these relationships by ensuring strategic alignment and resource mobilisation. Artificial intelligence adoption enhances competitive advantage by integrating human expertise with artificial intelligence–enabled capabilities. Research limitations/implications The study does not capture longitudinal dynamics and their generalisability across different cultural and industrial contexts. Practical implications Organisations should build internal capabilities, foster external innovation partnerships and secure leadership commitment to ensure responsible and strategically aligned artificial intelligence adoption. Social implications The adoption of AI in recruitment processes underscores the pivotal role of human-machine collaboration, enhancing HR practices and promoting a competitive edge in the labour market. This shift necessitates a re-evaluation of workforce dynamics and the ethical considerations surrounding AI integration in human resource management. Originality/value This study offers a novel perspective on how intangible resources and managerial capabilities interact to drive artificial intelligence adoption and reshape competitive advantage in recruitment contexts.

Purpose: This study aims to examine the impact of customer orientation, competitor orientation, and brand commitment on the business performance of Micro, Small, and Medium Enterprises (MSMEs), focusing on the moderating role of artificial intelligence (AI) in enhancing strategic responsiveness and competitiveness in the digital transformation era.Method: A quantitative explanatory approach was used, involving structured questionnaires distributed to 373 MSME owners and managers in Lowokwaru District, Malang City. Data analysis was conducted using Partial Least Squares–Structural Equation Modeling (PLS-SEM) with SmartPLS software to assess both direct and moderating effects. Reliability and validity tests were performed through composite reliability and average variance extracted (AVE) analysis.Result: We found that customer orientation, competitor orientation, and brand commitment significantly and positively influenced MSME performance. However, AI did not significantly moderate these relationships, indicating that AI adoption among MSMEs is still in the early stages and has not yet shown measurable effects. Despite this, AI adopters exhibited improved operational efficiency and decision-making accuracy.Practical Implications for Economic Growth and Development: The study highlights the need for MSMEs to strengthen customer focus, competitor awareness, and brand commitment to enhance business performance. Although AI did not show a moderating effect, gradual adoption of AI technologies could improve efficiency, innovation, and competitiveness, fostering regional economic growth.Originality/Value: This research contributes to the literature by integrating AI as a moderating variable in the relationship between marketing orientation and business performance, offering empirical evidence that extends AI-based strategic capability models within the MSME context.

Purpose– Developing ambidexterity as a dynamic capability is important for firms to sustain their competitive advantage. Moreover, this capability allows firms to build the resiliency to mitigate enterprise risks. The purpose of this paper is to apply two main theoretical frames from the strategy literature, dynamic capabilities, and organizational ambidexterity, to supply chain management (SCM) to examine mitigation strategies for supply chain (SC) disruptions. The authors empirically investigated how the firm’s SC ambidexterity is developed through a dynamic capability-building process and how this, in turn, can mitigate the negative impact of SC disruptions and improve business performance.Design/methodology/approach– This study conducted a field survey to answer the research questions as there exists no archival database with detailed information on ambidextrous SC strategies and dynamic capability. A total of 316 usable responses were received from managers working in the SC area. Confirmatory factor analysis and structural equation modeling were run on SPSS (version 16.0) and AMOS (version 18.0) to test the hypotheses to answer research questions.Findings– Overall, the results of the study confirmed that a dynamic SC capability-building process is an antecedent of SC ambidexterity, and that SC ambidexterity is important to firms as it mitigate the negative impact of SC disruptions and enhance business performance. To take advantage of an ambidextrous SC, through minimizing the negative impact of SC disruptions and maximizing firm performance, firms should continually search for creative ways to satisfy new market needs and adapt to the fast changing business environment.Originality/value– This study applied a dynamic capability-building process and ambidexterity to SCM. From the resilient SC perspective, the study found that the ability to effectively utilize existing resources and create novel strategies for problem solving plays a critical role in addressing SC disruptions.

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.

The principal link between technology and competitive advantage is innovation. The quest for sustainable competitive advantage has caused firms to invest in innovation, consequently, innovation is responsible for new industries coming into being, and it is innovation, which allows some firms to dominate in that space. Thus, this study aimed to examine the critical supply chain (SC) innovation indicators that enhance business performance. This research study centred on the operational activities of the Weir Minerals Africa (WMA) Isando facility in Johannesburg, South Africa, including all elements associated with the supply chain activities of the plant. The study sample of 230 respondents was considered representative of the population. The philosophical worldview in this research is positivism. Positivism hinges on computable observations that lead to statistical interpretations. The study was quantitative, adopted the deductive research approach, and a set of hypotheses were formulated for the research to confirm or reject a theory. For this population, the researcher applied a census-based sampling approach. The results showed that there is a significant number of indicators that enhance business performance and these include a clear culture of continuous improvement, focusing on new markets both locally and globally, product portfolio changes, constant connection and engagement with customers and cost structures. In light of these findings, the study provided a set of recommendations. Weir Minerals has a significant number of supply chain innovation hubs and metrics that include improvement and innovation culture across the entire organisation, an aggressive drive to create new markets and a clear posture to enforce standards and consistent value to its customers. However, it has not made a significant and noticeable effort to connect regularly with its customers. It has to improve on its customer relationship management frontier. The study findings underscored the relevance of a holistic overview of the supply chain problems at Weir Minerals Isando (WMI) that may inform the supply chain management decisions and expand academic knowledge. Keywords: Supply Chain Innovation, Supply Chain Management, Sustainable Business Performance