Abstract
Orientation: The impact of artificial intelligence (AI) adoption and skills on organisational competitive advantage (CA) is examined through knowledge-sharing behaviour (KSB) in Indonesia’s creative industry.
Research purpose: This study investigates the impact of AI adoption and competence on organisational CA through KSB in the creative industry in Indonesia.
Motivation for the study: Indonesia’s creative industry is facing technological disruption that demands the use of AI. However, little research has demonstrated the role of a culture of knowledge sharing and individual competency in optimising CA.
Research approach/design and method: The study employed quantitative methods by distributing a survey to 225 individuals working in the creative industry. Structural equation modelling (SEM) was used for analysis.
Main findings: The results show that all independent variables, including AI adoption, competence, and KSB, significantly influence organisational CA. Furthermore, KSB was found to mediate the relationship between AI adoption and competence on CA.
Practical/managerial implications: These findings not only add to the literature on knowledge management but also provide practical guidance for managing organisations in the digital age. A culture of knowledge sharing must be fostered to maximise the benefits of AI adoption and competence in enhancing CA.
Contribution/value-add: This study demonstrates that to achieve CA, AI adoption, competencies and KSB are needed. These results demonstrate that technology alone is insufficient without structured teamwork behaviours.
Keywords: knowledge-sharing behaviour; competitive advantage; AI adoption; human resource competence; creative industry; structural equation modelling.
Introduction
Businesses nowadays must adapt to survive and expand because of environmental instability and heightened competition (Siahaan & Tan, 2022). The creative industry, particularly Indonesia’s batik and weaving sector, is under pressure to transform to remain relevant, become more competitive, and address the challenges of the evolving digital market. Conversely, strengthening human resource capabilities is crucial, as technology will only have a significant impact if industry players can utilise it effectively. Increasing competitive advantage (CA) does not always mean introducing more technology, especially for micro, small, and medium enterprises (MSME) that rely on cultural heritage such as weaving and batik.
Businesses cannot endure over time without a durable CA (Tong et al., 2022). Competitive advantage refers to a condition that places a business in a better or more profitable position (Wening et al., 2023). Competitive advantage in the batik and weaving creative industry depends on individual and organisational capabilities in production, management, and marketing. Further studies related to CA have been conducted by previous researchers across various fields, such as education (Zhang et al., 2022). Then, research by Tong et al. (2022) encourage advancement from the standpoint of bolstering company technologies and infrastructure to obtain a competitive edge.
In this study, one thing that researchers consider to have an influence on CA is knowledge-sharing behaviour (KSB). Knowledge-sharing behaviour is a crucial activity for businesses, as it enhances processes, employees, products, and overall performance (Alshebami et al., 2023). Knowledge sharing between partners can foster intercultural attitudes, competencies, and awareness, while also contributing to intercultural knowledge development (Shadiev et al., 2021). The purpose of knowledge sharing is to enable agents to balance the costs of sharing knowledge in different circumstances with the techniques they choose to use (Sarkadi et al., 2022). According to previous research (Wening et al., 2023), KSB has a significant effect on CA.
One of the factors that influences CA is artificial intelligence (AI) adoption (Penglong et al., 2024). Determining the use of AI technology is crucial because it has become a part of everyday life (Wang et al., 2023). By increasing productivity, accuracy, and decision-making, AI technologies seek to improve operations and radically alter how businesses handle their human resources (Ncube et al., 2025). Powerful AI systems are being developed that can communicate in natural language, understand user intent, and provide scalable, personalised, and interactive support (Ilieva et al., 2025). The AI-powered chatbots and virtual assistants can now respond to consumer requests 24/7 and much faster than humans (Khan et al., 2024). According to previous research (Gazi et al., 2024; Khan et al., 2024; Sarkadi et al., 2022), AI adoption has a significant effect on KSB. Similarly, AI adoption significantly affects CA (Abdelfattah et al., 2024; Climent et al., 2024; Kemp, 2023; Penglong et al., 2024; Wetering et al., 2023; Yoo et al., 2024).
To encourage more value-adding and business-centred services and processes, competency is one of the factors being prioritised (Coetzee et al., 2025). Competence frameworks outline the fundamental skills and abilities needed within organisations (Yang & Shen, 2025). These capabilities allow staff to align operational processes and evaluate project effectiveness, informing decisions about emerging trends (Joseph et al., 2021). According to previous studies (Smirnov et al., 2023), competence has a significant effect on KSB, and research by Sukoroto et al. (2023), Tong et al. (2022) and Zhang et al. (2022) confirms that competence has a significant effect on CA.
Based on the given explanation, the research questions we aim to address are: how do AI adoption and competence partially influence KSB and CA in the creative industry? And does KSB mediate the effect of AI adoption and competence on CA?
Accordingly, the research objectives are: to examine the partial influence of AI adoption and competence on KSB and CA in the creative industry and to analyse the mediating role of KSB in the relationship between AI adoption and competence on CA.
Literature review and hypothesis development
Artificial intelligence adoption
Organisations around the world are witnessing revolutionary shifts in the business landscape because of technological breakthroughs (Assounga & Sibassaha, 2024). Sustainable entrepreneurship strategies are increasingly incorporating AI (Ojong, 2025). An increasingly important technique is the application of AI, which facilitates the improvement of employee performance across a variety of tasks (Olan et al., 2024). Artificial intelligence can also help businesses to increase support and better allocate resources without requiring new licences, as automation reduces the workload on IT staff and minimises the rising costs of systems (Ganesan et al., 2025). Artificial intelligence is drastically transforming the commercial landscape and has become a crucial tool for improving global creativity, decision-making, and operational efficiency in the digital era (Herath & Mittal, 2022; Menzies et al., 2024). Further studies related to AI adoption have been conducted across fields: entrepreneurship (Ojong, 2025), education (Arya et al., 2025; Ilieva et al., 2025; Ma et al., 2025), development and services (Ahmad et al., 2025; Soleimani et al., 2022), and healthcare (Spatscheck et al., 2024; Yang et al., 2025).
AI-powered knowledge management is essential for promoting a culture of ongoing learning and collaboration within organisations by facilitating the exchange of knowledge and skills (Olan et al., 2024). According to previous research (Gazi et al., 2024; Khan et al., 2024; Sarkadi et al., 2022), AI adoption has a significant effect on KSB. Therefore, based on prior theories and research, the research hypothesis is:
H1a: AI adoption has a positive and significant effect on knowledge-sharing behaviour.
The adoption of AI contributes significantly to a company’s CA by fostering innovation, increasing operational efficiency, and facilitating the creation of dynamic capabilities (Abdelfattah et al., 2024). Artificial intelligence-powered business strategies play a critical role in achieving CA (Climent et al., 2024). According to previous research (Abdelfattah et al., 2024; Climent et al., 2024; Kemp, 2023; Penglong et al., 2024; Wetering et al., 2023; Yoo et al., 2024), AI adoption has a significant effect on CA. Therefore, the research hypothesis is:
H2a: AI adoption has a positive and significant effect on competitive advantage.
Human resource competence
Competence frameworks, which outline the fundamental skills, knowledge, and abilities required for job success, are used in competency forecasting (Yang & Shen, 2025). The ability to link staff operations to organisational processes allows employees to report on project effectiveness and make informed decisions based on emerging trends (Joseph et al., 2021). Competence is the ability to create, modify, or enhance outcomes in order to make one’s work more valuable (Kenedi et al., 2024). According to Braßler and Sprenger (2021), five important competencies were identified: (1) systems thinking, (2) anticipation or forward-thinking, (3) normative or value-based thinking, (4) strategic or action-oriented thinking, and (5) the ability to collaborate and interact with others. Studies on competence have also been carried out in SMEs (Chen et al., 2023), education (Arya et al., 2025; Ma et al., 2025; Rienda-Gómez et al., 2025; Xu & Liu, 2023), and the service sector (Assounga & Sibassaha, 2024; Lindawati & Wulani, 2021; Lubis et al., 2022).
An organisation’s most valuable resource is considered to be knowledge (Zhang, 2022). According to previous research (Smirnov et al., 2023), competence has a significant effect on KSB. Therefore, the research hypothesis is:
H1b: Competence has a positive and significant effect on knowledge-sharing behaviour.
Competence is the ability to apply logic, ideas, and imagination to accomplish, adapt, and improve outputs in order to perform meaningful work (Kenedi et al., 2024). According to previous research (Sukoroto et al., 2023; Tong et al., 2022; Zhang et al., 2022), competence has a significant effect on CA. Therefore, the research hypothesis is:
H2b: Competence has a positive and significant effect on competitive advantage.
Knowledge-sharing behaviour
Knowledge is a valuable resource for organisations, and encouraging knowledge sharing supports both organisational goals and employees’ interpersonal development (Zhang, 2022). Knowledge is generally divided into two types: explicit and tacit (Wening et al., 2023). Acquired knowledge can address a wide range of organisational challenges and concerns (Nayebpour & Sehhat, 2024). Further studies related to KSB have been conducted in various fields: in online platforms (Sun et al., 2024), education (Gebreyohans et al., 2022), and organisational settings (Thomas, 2025).
Knowledge is a crucial source of CA, and knowledge transfer is necessary to obtain a competitive edge. Individual human capital is an asset that is naturally transferable (Stadler et al., 2022). Knowledge sharing has become more efficient as a result of people’s increased ability to acquire and disseminate information more quickly and easily (Huang et al., 2023). Knowledge sharing is a critical activity for businesses, as it enhances four key areas: processes, employees, products, and overall enterprise performance (Alshebami et al., 2023).
Knowledge sharing between partners can foster intercultural attitudes, skills, and awareness while also contributing to intercultural knowledge development (Shadiev et al., 2021). The purpose of knowledge sharing is to enable agents to balance the costs of sharing knowledge in various contexts with the methods they choose to employ (Sarkadi et al., 2022). According to previous research by Wening et al. (2023), KSB has a significant effect on CA. Therefore, based on previous theories, the research hypothesis is:
H3: Knowledge-sharing behaviour has a positive and significant effect on competitive advantage.
Competitive advantage
Competitive advantage is a condition that places a business in a better or more profitable position (Wening et al., 2023). Artificial intelligence-powered solutions can further enhance inclusivity by utilising multimodal distribution, alternative formats (including audio), and simplified language to tailor content for users with varying needs. These features improve the accuracy of assessments and support the development of simulations, scenario-based tasks, and problem-solving environments that reflect professional contexts (Ilieva et al., 2025). By integrating AI into information systems, businesses can enhance customer engagement, optimise marketing strategies, and gain a competitive edge (Ganesan et al., 2025). Therefore, based on previous theories and research, the research hypothesis is:
H4a: AI adoption has a positive and significant effect on competitive advantage through knowledge-sharing behaviour.
Applying knowledge and practical skills to achieve optimal performance is the core of competency. Competency refers to the sum of a person’s skills, abilities, and other personal attributes that enable them to complete tasks successfully (Kenedi et al., 2024). It becomes easier to acquire complementary skills and knowledge when members of a company’s network collaborate and share information (Shen et al., 2025). Competitive advantage is a condition that places a business in a superior or more profitable position (Wening et al., 2023). Therefore, based on previous theories and research, the research hypothesis is:
H4b: Competence has a positive and significant effect on competitive advantage through knowledge-sharing behaviour.
Figure 1 depicts the conceptual model.
Research design
The quantitative process in this study used the partial least squares path modelling (SEM-PLS) method and SmartPLS 3.0 software to analyse the hypotheses and determine the outcomes of the human resource management strategy on organisational performance through innovation capability, with the research object being the creative industry. SmartPLS was used as the analysis tool, which is helpful in predicting correlations between components and validating hypotheses – to clarify whether or not there is a relationship between the factors (Hair et al., 2019).
Independent variables are elements that influence the dependent object or variable. Artificial intelligence adoption (X1) and competence (X2) are the independent variables in this research. In this study, CA (Y) is the dependent variable. Intervening variables are those that influence the relationship between the independent and dependent variables; in this research, KSB (Z) serves as the intervening variable
Researchers conducted a direct field survey in several batik and weaving industry centres in East Java. The survey included MSMEs, creative designers, community managers, and cooperatives associated with the industry. Researchers used quantitative methods by distributing questionnaires to determine the extent to which industry players have adopted AI technology, their level of capability, the intensity of their KSB, and their perceptions of business competitiveness. The initial results of this survey served as the basis for developing seven additional hypotheses to be tested to create relevant and applicable competitive strategies.
To collect data, 225 creative industry practitioners were given a questionnaire. The technique of purposive sampling was employed. Artificial intelligence adoption, competence, KSB and CA were measured using a five-point Likert scale that went from strongly disagree (1) to strongly agree (5). The development of the instrument was based on these well-established measuring scales. The SEM-PLS is an effective tool for analysing research data. The participants’ characteristics were as follows.
Based on Table 1, the majority of participants were female (56.9%), and most business owners were between 41 years and 50 years old (33.8%). The majority of respondents had a senior high school education and had been operating their businesses for 5–10 years. Most businesses (52.9%) employed between 1 and 4 people, indicating the micro-scale nature of the surveyed business units. In terms of revenue, 37.8% of respondents reported monthly sales between 10 million rupiah and 25 million rupiah. The variable measurements are presented in Table 2 below.
Ethical considerations
Ethical clearance to conduct this study was obtained from Jember University Ethics Committee of the Faculty of Dentistry (Reference number 3363/UN25.8/KEPK/DL/2025). All procedures performed in studies involving human participants were in accordance with the ethical standards.
Results
Measurement model
Partial least squares path modelling is a statistical method used in this study to simulate complex relationships between variables or elements that are not directly observable (Hair et al., 2019). While the structural model explains the relationships between variables, the measurement model explains how each construct is measured. The model measurement is presented in Figure 2.
To ensure the validity and reliability of the constructs, a comprehensive measurement technique was employed. A set of indicators was used to measure each construct. The primary statistical measures for these constructs are shown in the following Table 3. Partial Least Squares Structural Equation Modeling offers comprehensive hypothesis testing using methods such as bootstrapping, construct validity, and path analysis. The results are presented in Table 3.
| TABLE 3: Convergent validity, AVE, Cronbach’s alpha, and composite reliability test. |
PLS analysis is particularly important for SEM because of its practicality (Hair et al., 2019). The impact size and predictive relevance are as follows (see Table 4).
The R square (R2) values in Table 4 show that KSB has a value of 0.784 and CA has a value of 0.727, indicating that the variables AI adoption and competence are able to influence KSB by 78.4% and CA by 72.7%, while the remaining variance is affected by factors outside the model. The F square (f2) test is a measure of effect size in regression analysis that helps assess how much the independent variable contributes to explaining the model. This study identifies two strong relationships and three medium relationships.
Direct and indirect effects
Hypothesis testing is used to evaluate the significance of the impact of exogenous variables on endogenous variables using the bootstrap resampling approach. The significance threshold in this study is a p-value less than 5% or 0.05. The results are presented in Table 5.
Based on Table 5, both direct and indirect effects are presented in relation to the research hypotheses. Directly, AI adoption has a significant effect on both KSB and CA. Similarly, competence significantly affects both KSB and CA. In addition, KSB has a significant direct effect on CA. For the indirect effects, AI adoption and competence are shown to significantly impact CA through the mediation of KSB.
Discussion
The first hypothesis is that AI adoption has a significant effect on KSB in the creative industry. In addition to creating a unique database that enables it to apply AI and provide distinctive services, the company’s use of AI prevents clients, suppliers, and other stakeholders from switching to a rival’s business model (Climent et al., 2024). The batik and weaving creative industries will become more active in sharing knowledge through the use of AI technology. For example, artisans who use AI applications to create design patterns or digitally process fabrics are likely to share their benefits and methods with the community through online forums, local workshops, or social media. Businesses are becoming more open to discussions, sharing experiences, and providing technical solutions, thanks to the adoption of AI. This creates a collaborative work environment that was previously difficult to build when work was performed manually. Thus, AI is not just a tool for innovation; it is also changing the way the creative industry operates. This result is in accordance with Gazi et al. (2024), Khan et al. (2024) and Sarkadi et al. (2022), who found that AI adoption has a significant effect on KSB.
The second hypothesis is that competence has a significant effect on KSB in the creative industry. The competence level of entrepreneurs, particularly in management and technology, is crucial for encouraging knowledge sharing. Craftsmen or entrepreneurs in the batik and weaving industries who deeply understand the manufacturing process, digital marketing, or creative financial management tend to be more active participants in various discussion forums and community training sessions. Individuals with strong competencies have the confidence to share their knowledge and also understand the strategic value of information exchange. Therefore, knowledge sharing becomes more than just a social activity; it becomes a means of collaboration within the creative industry. This result is in accordance with Smirnov et al. (2023), who found that competence has a significant effect on KSB.
The third hypothesis is that AI adoption has a significant effect on CA in the creative industry. Artificial intelligence-enabled organisational capabilities provide a CA. For these capabilities to yield a competitive edge and facilitate value generation and use, they must have the proper level of specificity, development cost, and environmental fit (Climent et al., 2024). In the batik and weaving industry, the use of AI enhances CA through process automation, increased efficiency, and the creation of unique products. Businesses that use AI have a better chance of competing in a highly innovative and fast-paced market. The use of algorithms to reach specific audiences on social media is one example of how AI can optimise digital marketing. Therefore, AI-based facilitation programmes should encourage AI adoption, as it is a crucial part of long-term growth plans. This result is in accordance with Abdelfattah et al. (2024), Climent et al. (2024), Kemp (2023), Penglong et al. (2024), Wetering et al. (2023) and Yoo et al. (2024), who found that AI adoption has a significant effect on CA.
The fourth hypothesis is that competence has a significant effect on CA in the creative industry. Competitive advantage in the batik and weaving creative industry depends on individual and organisational capabilities in production, management, and marketing. To face the dynamics of competition, entrepreneurs with advanced technical skills, an understanding of design principles, the ability to read market trends, and the ability to manage resources efficiently will be better prepared. In fact, competency enhancement can be achieved through training programmes, certifications, and business incubation programmes specifically designed for the creative sector, which relies on cultural heritage. Batik and weaving artisans with superior skills can survive and become pioneers in markets. This result is in accordance with Sukoroto et al. (2023), Tong et al. (2022) and Zhang et al. (2022), who found that competence has a significant effect on CA.
The fifth hypothesis is that KSB has a significant effect on CA in the creative industry. The KS is essential to any organisation’s success and CA. Meanwhile, knowledge networks have emerged as a means of improving individual information sharing and as a useful instrument for promoting knowledge exchange in the domains of medicine, education, and business (Oskouei et al., 2024). Collective CA can be achieved through knowledge sharing within the batik and weaving community. When industry players share ideas about effective production strategies, environmentally friendly dyeing methods, or digital marketing techniques, this information spreads and results in equitable progress throughout the community. Creative collaboration centres, online community platforms, or peer-learning-based events are some ways to implement these activities. Therefore, a culture of sharing can be used to increase competitiveness among individuals and business groups in the batik and weaving creative industries. This result is in accordance with Wening et al. (2023), who found that KSB has a significant effect on CA.
The sixth hypothesis is that AI adoption has a significant effect on CA through KSB in the creative industry. The CA is a circumstance that puts the business in a better or more lucrative position (Wening et al., 2023). Therefore, to enhance AI’s CA, efforts must be focused directly on improving access to tools, technical training, and real-life project-based mentoring. This is because sharing behaviour cannot replace the fundamental need for technological skills and tools to gain CA. By integrating AI, businesses may increase customer engagement, optimise marketing strategies, and gain a competitive edge (Ganesan et al., 2025).
The seventh hypothesis is that competence has a significant effect on CA through KSB in the creative industry. Skilled entrepreneurs in the batik and weaving industry will have a greater CA if they engage in knowledge-sharing practices. This means that their skills not only benefit the individual but can also provide value to others when shared within the creative community. It is easier to acquire the complementary skills and knowledge that members of a company’s network possess when they collaborate and share information (Shen et al., 2025). Knowledge sharing facilitates the transition from competence to tangible market advantage. Therefore, in the batik and weaving creative industries, strengthening a culture of sharing should be a key strategy in human resource development policies. This will translate individual expertise into collective impact in industry competition. CA is a circumstance that puts the business in a better or more lucrative position (Wening et al., 2023).
Conclusion
The results show that all independent variables, including AI adoption, AI competency, and KSB, significantly influence organisational CA. Furthermore, KSB is proven to mediate the relationship between AI adoption and competence on CA. The current globalisation period makes rivalry fierce in the economic and industrial sectors. Every organisation wants every worker to be able to accomplish the organisation’s goals (Sasmita et al., 2023). These results suggest that, in the current era of digital transformation, synergy between technology and human resource capabilities is essential for enhancing a company’s long-term competitiveness, particularly in the creative industry. Therefore, to achieve sustainable excellence, the creative sector must pay close attention to the use of AI technology and the development of a knowledge-based collaborative culture.
To achieve real CA, the creative industry must recognise that the adoption of AI should be combined with employee competency development. Therefore, HR development strategies are a crucial part of digital transformation, not merely a complement. Instilling a culture of knowledge sharing is also essential. Overall, organisations must create a work environment that encourages both technology adoption and cross-functional collaboration; however, research shows that KSB can bridge the gap between competency and CA.
Practically, these findings show that businesses cannot just embrace AI; in order to fully capitalise on the advantages of this technology to boost competitiveness, they also need to establish a culture of information sharing. Organisations may turn the use of AI into real benefits through cooperation, creativity, and ongoing learning in the workplace by enhancing human resource competencies.
Limitations of the study and suggestions for further studies
While this research helps in understanding how knowledge and technology behaviour shape competitiveness, it has several limitations. Firstly, the data used are limited to a few creative industries and may not reflect the overall characteristics of the sector at the national level. Secondly, the quantitative approach does not examine the qualitative aspects of human-technology interactions as a whole, including cultural values regarding knowledge sharing or resistance to change. Thirdly, because this research is cross-sectional, it cannot reveal how organisational behaviour and technology adoption evolve over time.
Long-term research is recommended to examine how AI adoption and KSB change over time. To better align the findings with the diverse creative industries in Indonesia, the research should be expanded both sectorally and geographically. From a practical perspective, local governments and industry stakeholders should support AI skills training and develop digital-based knowledge collaboration platforms to accelerate innovation. To enhance the competitiveness of the creative industry at the regional and national levels, the combination of technological and human cognitive skills must serve as a strategic foundation.
Acknowledgements
The authors would like to express their gratitude to the respondents who participated in the survey and provided the data essential for this study.
Competing interests
The authors, Sri Wahyu Lelly Hana Setyanti, Khanifatul Khusna, Ni Ketut Seminari and Kamillaeni Jamillah received research funding from DPPM Kemendiktisaintek. The terms of this arrangement have been reviewed and approved by University of Jember in accordance with its policy on objectivity in research.
CRediT authorship contribution
Sri Wahyu Lelly Hana Setyanti: Conceptualisation, Methodology, Formal analysis, Investigation. Khanifatul Khusna: Software, Validation, Data curation, Resources. Ni Ketut Seminari: Software, Validation, Data curation, Resources, Writing – review & editing. Kamillaeni Jamillah: Resources, Writing – review-& editing, Supervision, Funding acquisition. All authors reviewed the article, contributed to the discussion of results, approved the final version for submission and publication, and take responsibility for the integrity of its findings.
Funding information
This work was supported by the DPPM Kemendiktisaintek (grant number: [2025.4571/UN25.3.1/LT/2025]).
Data availability
The data supporting the findings of this study are available from the corresponding author, Sri Wahyu Lelly Hana Setyanti, upon reasonable request.
Disclaimer
The views and opinions expressed in this article are those of the authors and are the product of professional research. They do not necessarily reflect the official policy or position of any affiliated institution, funder, agency, or that of the publisher. The authors are responsible for this article’s results, findings, and content.
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