Exploring the formation mechanism of technology standard competitiveness in artificial intelligence industry: A fuzzy-set qualitative comparative analysis

Liu, Siwei; Zhou, Lijun; Yang, Jing Article Exploring the formation mechanism of technology standard competitiveness in artificial intelligence industry: A fuzzy-set qualitative comparative analysis Journal of Business Economics and Management (JBEM) Provided in Cooperation with: Vilnius Gediminas Technical University (VILNIUS TECH) Suggested Citation: Liu, Siwei; Zhou, Lijun; Yang, Jing (2023) : Exploring the formation mechanism of technology standard competitiveness in artificial intelligence industry: A fuzzy-set qualitative comparative analysis, Journal of Business Economics and Management (JBEM), ISSN 2029-4433, Vilnius Gediminas Technical University, Vilnius, Vol. 24, Iss. 4, pp. 653-675, https://doi.org/10.3846/jbem.2023.18845 This Version is available at: https://hdl.handle.net/10419/317643 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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Published by Vilnius Gediminas Technical University *Corresponding author. E-mail: [email protected] This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Journal of Business Economics and Management ISSN 1611-1699 / eISSN 2029-4433 2023 Volume 24 Issue 4: 653–675 https://doi.org/10.3846/jbem.2023.18845 EXPLORING THE FORMATION MECHANISM OF TECHNOLOGY STANDARD COMPETITIVENESS IN ARTIFICIAL INTELLIGENCE INDUSTRY: A FUZZY-SET QUALITATIVE COMPARATIVE ANALYSIS Siwei LIU, Lijun ZHOU*, Jing YANG College of Economics and Management, China Jiliang University, Hangzhou, China Received 07 August 2022; accepted 02 March 2023 Abstract. This study aims to reveal the complex mechanism influencing technology standard competitiveness (TSC) in the artificial intelligence industry. Compared with research using traditional linear models, this research adopts the fuzzy-set qualitative comparative analysis (fsQCA) method to obtain the multiple equivalent paths for different factors that jointly produce TSC. The sample of this study involves 32 countries, and the research framework is constructed from the technological, organizational, and environmental aspects of the phenomenon. The fsQCA method was used to demonstrate the asymmetric relationship between cause and effect. The results indicate four configuration paths but no necessary conditions leading to TSC. Academic research intensity and market size play vital roles in developing TSC. Some logically complementary relationships exist between organizational participation, technological innovation ability, and international competitive pressure. These findings are helpful for policymakers in their formulation of artificial intelligence– related strategies. Keywords: technology standardization, artificial intelligence industry, fsQCA, technology–organization–environment (TOE) framework, technology standard competitiveness, configurations. JEL Classification: L15, O32, O57. Introduction Technology standards catalyze the development of cutting edge technology and the technology sector (Özsomer & Cavusgil, 2000; Jiang etal., 2018a; Blind & von Laer, 2022). The creation and development of intelligent technologies are inextricably linked to technology standards, especially in emerging industries. Absolute competitive advantages can be achieved through a mastery of the discursive power of technology standards (Lee & Oh, 2006; Gao etal., 2014; Narayanan & Chen, 2012; Jiang etal., 2020a). Technology standardization is crucial for preserving market stability, lowering market uncertainty, securing competitive 654 S. Liu, et al. Exploring the formation mechanism of technology standard competitiveness in artificial... advantage, and enhancing production effectiveness (Funk & Methe, 2001; Wakke etal., 2016; van de Kaa & Greeven, 2017; Blind & von Laer, 2022). It can promote industry-wide technological progress and technological dissemination and even affect a country or region’s economic growth (Jiang etal., 2016; Paik etal., 2017; Jiang etal., 2018a). Artificial intelligence (AI) has progressed from being computational to perceptual to cognitive (Liu etal., 2020; Margetis etal., 2021). AI has become a crucial driving force for industrial transformation in the fourth industrial revolution due to its wide applicability and the growing trend of data intelligence (Wu etal., 2020; af Malmborg & Trondal, 2021; Su etal., 2022). The direction of technical progress, the prospect of industrial development, and even the interests of countries are all significantly affected by ongoing developments in AI in which technology standards play a crucial role (Zielke, 2020). The United States, Germany, Japan, and other developed countries have adopted AI technology standards as a strategic tool to outperform its competitors (Fatima etal., 2020). In “US Leadership In AI: A Plan for Federal Engagement in Developing Technology Standards and Related Tools,” the National Institute of Standards and Technology [NIST] of the United States outlined nine areas of support (NIST, 2019). In addition, the Human Brain Project of the European Union and the AI/Big Data/Internet of Things/Network Security Integrated Project (AIP) of Japan prioritize standards and implementation specifications. After the conflict over information and communication technology (ICT) and 5G standards, the battle for AI technology standards, with all its discursive power, has taken center stage. It is a fundamental practical problem worth studying to explore the development path of national artificial intelligence technology standardization to clarify the formation mechanism of technology standard competitiveness (TSC). Studies have suggested that technology standardization is affected by a diverse and complex set of factors, including technical background, system design, market environment, andthe characteristics of the standard’s participants (Doganoglu & Wright, 2006; Blind, 2006; Gao, 2007; Brunsson etal., 2012; Li etal., 2019; Moon & Lee, 2021; Blind & von Laer, 2022; Malik etal., 2021). Additionally, the formulation and implementation of technology standardization development strategies in various countries exhibit path dependence (Kim, 1997; Kano, 2000; Jho, 2007; Lee & Oh, 2006, 2008; Lee etal., 2009; Choung etal., 2011). This study introduces the TOE framework based on previous research on technology standardization and constructs a research framework affecting the formation of TSC from three aspects: technology, organization, and environment. Furthermore, the research methods were primarily based on traditional net effects research models such as multiple regression (Paik etal., 2017; Blind & von Laer, 2022), structural equation modeling (Li etal., 2019; Jiang etal., 2020b), or case studies (Kwak etal., 2011; Choung etal., 2011; Kim etal., 2018). However, the traditional symmetry causal approach may not be suitable in a specific scenario and could not adequately predict actuality (Arrow etal., 2008; Papatheodorou & Pappas, 2017; Woodside, 2018), which is why complexity theory and configuration theory have gradually become popular (De Toni & Pessot, 2021; Jancenelle, 2021). In view of the complexity of technology standardization and the dynamic development of the artificial intelligence industry, it is necessary to explore the influence mechanism and path of TSC based on the configuration perspective and multi-dimensionality. Journal of Business Economics and Management, 2023, 24(4): 653–675 655 The main contribution of this study lies is in its construction of a framework of how technology standardization develops, from the perspective of technology–organization–environment (TOE) theory. In addition, to account for asymmetry, this study adopts the fuzzy-set qualitative comparative analysis (fsQCA) to obtain a variety of equivalent paths leading to TSC. The research results reveal the complexity of the path to technology standardization, constituting a novel contribution to the literature. The remaining parts of this study is structured as follows. The first section reviews the literature on the factors influencing technology standardization. The second section proposes a research framework based on TOE theory. The third section introduces the principles and advantages of the fsQCA method and each variable’s measurement methods and data sources. The fourth section presents results on descriptive statistics, on necessary analysis, and sufficient analysis. The discussion section emphasizes the key variables and supplementary logic of the antecedent variables in the configuration path. The last section summarizes this study’s research results and practical significance, discusses our study’s limitations, and outlines future research directions. 1. Literature review Technology standardization is a complex and dynamic process that includes licensing, standards development, and technological research and development, also can be regarded as the process of technology accumulation and development reaching a certain threshold, evolving into technical standards, and realizing comprehensive innovation (Paik etal., 2017; Jiang etal., 2018a). Uncertain factors are rife in the formation and industry-wide adoption of technology standards (Blind etal., 2017). First, the technical characteristics of the standard itself affects its scope of application and effectiveness (Doganoglu & Wright, 2006; Blind, 2006). Technical compatibility refers to the shared elements between various products and plays a vital role in restraining related subjects’ “multi-ownership” behavior from improving the market competitiveness of standards (Doganoglu & Wright, 2006). Standards with a higher level of technical compatibility are less costly to adopt and more easily promoted throughout an industry. Likewise, technology standards differ in their characteristics depending on the sophistication of the technology in question (Jiang etal., 2016). A more technologically sophisticated organization is more likely to develop and implement new technology standards to explore new markets (Blind & Mangelsdorf, 2016; De Vries etal., 2009). According to actor–network theory, technology standardization is a process featuring cooperation among various participants and involving technological and social factors that interweave to form a network (Gao, 2007). Many scholars have studied the behavioral motivations of participants in technology standardization (such as enterprises, suppliers, standard-setting organizations, users, and governments) in different industries and the interaction of these roles in technology standardization (Markard & Erlinghagen, 2017; Wiegmann etal., 2017; Kim etal., 2018). For example, to meet the needs of consumers, enterprises ensure product quality through compliance with technology standards (Moon etal., 2018; Fontagné etal., 2015; Moon & Lee, 2017). At times, these enterprises form or participate in standards alliances to expand the market and to ensure that technology standards are favor- 656 S. Liu, et al. Exploring the formation mechanism of technology standard competitiveness in artificial... able to their interests (Wang etal., 2016). Users, namely consumers and technology producers, contribute to the technology standardization process by participating in standardization and providing an on-the-ground perspective (De Vries & Slob, 2006; Jakobs etal., 1998). In addition, from the external context of standardization activities, market success opportunities are determined by social, institutional, and economic factors and by soft factors, such as political, social, and cultural factors, that influence the selection of technology. These factors are essential for standardization strategies (Hobday, 1995; Amsden, 2001; Choung etal., 2011). The formulation and development of technology standards is a complex process that involve a variety of factors and strategic trade-offs. By analyzing the implementation of Terrestrial Digital Technology in Latin America, Angulo etal. (2011) argued that technical characteristics, network externalities, and socioeconomic characteristics are key factors affecting the development of standardization. With regard to participation in international standardization, the models adopted in China and the United States are characterized by systematic participation in standardization and by decentralized standardization, respectively (Blind & von Laer, 2022). Countries differ in their location toward standardization activities. In South Korea, the development of technology standards is viewed as a strategic tool for catching up with technologically advanced countries (Lee etal., 2005). In Europe, regulatory governance plays the most prominent role in standardization activities (Egyedi, 2006). Furthermore, countries differ considerably in their institutions and these institutions’ effect on the market, level of technological development, and level of standardization (Whitley, 1999); in general, government policy is central to the ebb and flow of technology standardization in a country (Shin etal., 2015). Compared with those in developed countries, governments in developing countries tend to play a broader role in standardization, in relation to innovation, due to these countries’ low level of economic, human, and technological resources (Gao, 2014; Zoo etal., 2017; Dubé etal., 2012). In other words, the government not only directly invests in standards development and offer incentives but also coordinates the actions of standards stakeholders and balances stakeholder interests (Gao etal., 2014; Kshetri etal., 2011). Taking lightemitting diode (LED) technology standards as an example, van de Kaa and Greeven (2017) found that compared with those in other countries, China’s top-down economic institutions make greater use of standardization in the LED market. In particular, many studies focusing on the ICT industry have reported that countries greatly differ in their standardization development paths and strategies (Lee & Oh, 2006, 2008; Lee etal., 2009; Choung etal., 2011). For example, Kang etal. (2014) found that China prioritizes independent technological innovation to develop domestic standardization, whereas South Korea develops global standardization based on international standards established by local technology. In summary, the influencing factors of technological standardization of emerging industries include the technical characteristics of standards, standardization participants, and the external environment. Countries differ in their paths toward technology standardization due to differences in their economic climate, economic system, and standardization activity orientation. Based on the TOE theoretical framework, this study’s framework contains technological, organizational, and environmental factors influencing technology standardization. This study adopted configuration analysis to explore the path leading to technology standardization in the AI industry in various countries. Journal of Business Economics and Management, 2023, 24(4): 653–675 657 2. Research model construction TOE theory describes how technological innovation adoption and application at the organizational level are affected by technological, organizational, and environmental factors (Tornatzky & Fleischer, 1990). This theory mainly explains organizational technology integration and adoption behavior (Cruz-Jesus etal., 2019). TOE is used to evaluate the adoption of technological innovation, such as cloud computing adoption (Borgman etal., 2013), blockchain technology (Malik etal., 2021), and hospital information systems (Ahmadi etal., 2017). In TOE theory, technological factors pertain to the relationship between technology and organizations, such as technological innovation capability (Jiang etal., 2018a; Cruz-Jesus etal., 2019), characteristics of the technology itself (Cruz-Jesus etal., 2019; Malik etal., 2021) and technological advancement (Jiang etal., 2016; Blind & Mangelsdorf, 2016; De Vries etal., 2009). Organizational factors pertain to the organization’s characteristics, such as its size (Cho etal., 2022; Walker, 2014), resources, and structure (Chen etal., 2019; Pateli etal., 2020). Environmental factors pertain to the level of development and the organization’s industry (Borgman etal., 2013; Chen etal., 2019), market demand (Malik etal., 2021; Pateli etal., 2020), external pressure, and other factors (Pateli etal., 2020; Cruz-Jesus etal., 2019). Based on TOE theory and the characteristics of research objects, this study constructs a multivariate model describing the many factors driving TSC (see Figure 1). Organizational Context • Government Responsiveness •Organizational Participation Environmental Context • International Competitive Pressure •Market Size Technological Context •Technological Innovation Ability • Academic Research Intensity Linkage match Technology Standard Competitiveness Figure 1. Technology-Organization-Environment (TOE) framework 2.1. Technological context The previous technology standardization studies (Jakobs etal., 1998; Paik etal., 2017; Wiegmann etal., 2017; Blind & von Laer, 2022) indicated that standardization is part of the R&D process (Jiang etal., 2018b). Technical background in the development of standards is a crucial factor influencing technology standardization (Wiegmann etal., 2017), including technical compatibility (Doganoglu & Wright, 2006), technological innovation capability (Blind, 2006; Wen etal., 2020), technological advancement (Jiang etal., 2016; Blind & Mangelsdorf, 2016), technological uncertainty (Blind etal., 2017), academic research intensity (Choung etal., 2011) and other technological characteristics. This research considers two innovation characteristics within the technological context as the effect factors of TSC, namely, technological innovation ability and academic research intensity. 658 S. Liu, et al. Exploring the formation mechanism of technology standard competitiveness in artificial... Technology standardization is closely related to technological innovation (Blind, 2006). The upgrading of technology standards depends on the development of technological innovation, and standardization promotes technological innovation by influencing innovation performance and process innovation (Farrell & Saloner, 1985; Utterback, 1994; Blind, 2002; Wen etal., 2020). In other words, the standardization process is a continuation of R&D within an organization, and participation in standardization activities enables the organization to deliver products that consumers want and to obtain technologies that are suited to market conditions (Blind, 2006; Farrell & Saloner, 1985). Patent intensity and R&D intensity determine the technological standardization ability of enterprises (Blind & Thumm, 2004; Blind, 2006; Blind & Mangelsdorf, 2016). In addition, technology standardization requires both technical and nontechnical competence (Choung etal., 2011). For countries as a whole, the volume of scholarly publications can reflect its focus on a given set of emerging technologies. 2.2. Organizational context This study is based on the TOE theoretical framework to identify the organizational factors affecting the TSC, including technology standard alliance collaboration (Li etal., 2019; Jiang etal., 2020b), standardization “culture” (Wiegmann etal., 2017), standard participants (Ho & O’Sullivan, 2017; Markard & Erlinghagen, 2017), institutional and strategy context (Lee & Oh, 2006, 2008; Moon & Lee, 2021), government support (Gao, 2007; Kwak etal., 2011; Moon & Lee, 2021), organization participation (Blind, 2002; Blind & von Laer, 2022) and other organizational characteristics. This research examines two innovation characteristics within the organizational context to explain the influencing factors of TSC: government responsiveness and organizational participation. The process leading to the achievement of standards-based competitiveness and the diffusion of standards also involves government behavior, which is essential to standardization (Gao, 2007; Li etal., 2019). In addition to formulating policy, governments also deploy national resources (e.g., by investing in science parks) and set the country’s strategy (Blind & Thumm, 2004; Moon & Lee, 2021; Funk & Methe, 2001). Governments can also mandate standardization or intervene in the standards formulation process (Khemani, 1993), shape the market environment through regulatory mechanisms, and influence the behavior of technology standardization–related actors (Blind, 2012). However, the effect of such intervention on innovation depends on the uncertainty present in the market and the technological landscape (Blind etal., 2017). Blind noted that export ratio, market concentration, and the degree of participation in international competition are critical factors driving technology standardization (Blind, 2002; Blind & Thumm, 2004). Early participation in international standardization is crucial for a country’s commercial success in various industries (Blind & von Laer, 2022). In 2018, the International Organization for Standardization and the first joint technical committee of the International Electrotechnical Commission (ISO/IEC JTC 1) began focusing on information technology. The AI subtechnical committee (SC 42) of the organization is tasked with AI-related standardization, with a focus on basic commonalities, key general technologies, credibility, and the ethics of AI. The ISO/IEC JTC 1 has also focused on AI security and AI applications in key industries. Journal of Business Economics and Management, 2023, 24(4): 653–675 659 2.3. Environmental context The institutional theory holds that organizations must consider not only the influence of technological characteristics but also face the constraints of institutional factors such as market environment, beliefs, and social values (DiMaggio & Powell, 1983). Based on previous technology standardization studies and the definition of the environmental dimension of the TOE framework, it can include the installation base, namely the scale of users (De Vries & Slob, 2006; Zhang & He, 2015), market environment (Delcamp & Leiponen, 2014), public demand (Malik etal., 2021), institutional pressure (DiMaggio & Powell, 1983; Brunsson etal., 2012) and other environmental characteristics. This research examines two innovation characteristics within the environmental context to explain the influencing factors of TSC: international competitive pressure and market size. Institutional theorists emphasize the role of coercive, normative, and isomorphic pressures in the adoption and diffusion of standards (DiMaggio & Powell, 1983). Among these pressures, coercive pressure may emerge from international organizations’ assignment of tasks to member states and NGOs’ pressure on enterprises to comply with environmental standards (Brunsson etal., 2012). Normative pressures are more prevalent in professions with a joint knowledge base, emphasizing the positive effects of adopting standards. Isomorphic pressure refers to the pressure faced by an actor when another actor copies its strategy (e.g., the government of one city imitating the government of another city), giving rise to competition. Pressure from the external environment also drives the organization to reevaluate its allocation of and deficiencies in the resources at its disposal (García-Sánchez etal., 2018). A more turbulent competitive environment allows an organization to better self-renewal, reconfigure its resources to adapt to the environment, and leverage opportunities that may arise from an everchanging situation (Eisingerich etal., 2010). Ultimately, technology standardization is essential to organizing the market (Brunsson etal., 2012) and can reduce the inherent information asymmetry between producers and consumers (Akerlof, 1970). With the increase in turnover and the expansion of the emerging technology market, a lack of technology standards dampens the willingness of organizations to adopt new technologies (Malik etal., 2021). The formulation and implementation of technology standards are to meet the market demand, and the broader the market prospect of standards is more valuable (Delcamp & Leiponen, 2014). 3. Method 3.1. Fuzzy-set qualitative comparative analysis Ragin first proposed qualitative comparative analysis (QCA) in the 1980s (Ragin, 1987). This method combines case-oriented QCA with a variable-oriented quantitative comparative analysis to uncover the organic combination of qualitative and quantitative characteristics constituting a given phenomenon (Acquah etal., 2021). QCA has three main variations: crisp-set QCA (csQCA), multi-value QCA (mvQCA), and fuzzy-set QCA (fsQCA) (Pappas & Woodside, 2021). CsQCA is used to process complex binary data sets. The most significant limitation of this method is that binary variables cannot fully capture the complexity of cases 660 S. Liu, et al. Exploring the formation mechanism of technology standard competitiveness in artificial... that vary with level or degree (Ragin, 2008a). MvQCA is an extension of csQCA, preserving the idea of data set synthesis in csQCA. Unlike csQCA, mvQCA also allows the processing of multi-valued variables. Because the antecedent variables and outcome variables in this study were continuous, not dichotomous variables or multi-valued variables, it was more appropriate to adopt a fuzzy set for QCA, that is, to transform each variable into a fuzzy membership relationship between 0 and 1 (Abbott, 2001). This study chose fsQCA because it offered several advantages over its conventional counterparts. First, traditional symmetric empirical methods such as linear regression analysis and structural equation modeling can only identify one independent variable, and the prediction results are often unrealistic due to uncertainty in the market environment (Woodside, 2018; Papatheodorou & Pappas, 2017). Results in fsQCA are reflective of a combination of multiple conditions rather than a single factor. In fsQCA, Boolean algebra is used to compare and analyze each feature of multiple cases and to explore the combination of various configuration paths leading to the presence or absence of an outcome from a holistic perspective, allowing the method to capture asymmetry between antecedent variables and outcomes (Ragin & Fiss, 2008). Furthermore, fsQCA can be used to examine and combine multiple antecedent conditions to generate a result, and fsQCA can be used to determine numerous effective alternatives that produce the same equifinal outcome (Dahms, 2019; Russo etal., 2019; Witt etal., 2021; Fiss, 2011; Schneider & Wagemann, 2012). Finally, because fsQCA didn’t involve underlying hypothesis, correlation analysis, or single explanatory variable, thus this method was not subject to the endogenous influence caused by outliers or variable deviations (Witt etal., 2021; Fiss, 2011; Schneider & Wagemann, 2012). Therefore, fsQCA was used to examine how the degree of membership of cases (i.e., countries) in antecedent conditions (i.e., influence factors based on the TOE framework) is related to their degree of membership in the outcome (i.e., technology standard competitiveness). 3.2. Data collection and variables (1) Dependent variable Variable 1: Technology standard competitiveness This study measured the TSC from two aspects: the number of AI-related standards issued by the country and the number of associations participating in the formulation of standards. Data on this variable were obtained from the National Library of Standards, which has collected more than 1 million volumes of standards from 60 countries, over 70 international and regional standardization organizations, and more than 450 professional associations. The National Library of Standards collects data from standards databases, such as the Information Handling Services (IHS) database, the Perinorm database, the Korean standards database, the Taiwan standards database, and the Verein Deutscher Ingenieure (VDI) standards database. Using 24 keywords such as “artificial intelligence,” “wisdom city,” “intelligent manufacturing,” and “fingerprint identification” to determine the number of AI-related technology standards published in each country and the number of associations that participated in standards setting no later than March 30, 2022, 2253 standards and 33 associations were identified. Journal of Business Economics and Management, 2023, 24(4): 653–675 667 tion participation has also been verified at a government-department and enterprise level (Blind & Mangelsdorf, 2016; Blind, 2002). In all cases, Russia is the only country with a path where government responsiveness is missing as a core condition but still produces high-TSC. Studies have shown that due to the relatively weak strength of government departments in developing countries, a “small government” approach can mitigate the problem of improper coordination caused by decentralized institutions and provide more space for participants to act, thus facilitating standardization and innovation (Bekker etal., 2008; Zoo etal., 2017). Conclusions The results of this study can provide theoretical support and practical guidance for national governments in formulating and implementing AI technology standards strategy. Based on insights from the TOE theory, this study uses fsQCA to determine the configuration path that achieves high-TSC. The results suggest that different combinations of technology, organizational, and environmental factors can yield different pathways toward the same outcome. Our study provides several extensions and contributions to theory and practice. First, in view of the complexity and dynamics of technology standardization, this study combines the influencing factors of national artificial intelligence industry technology standardization based on the TOE theoretical framework, starting from multiple dimensions: technical background, organizational participation, government responsiveness, market demand, and development environment of the artificial intelligence industry, a comprehensive model of influencing factors of the whole process of technology standardization from the stage of technology patenting, patent standardization and standard industrialization is constructed. This study takes a novel perspective, expands the application range of the TOE theoretical model, and deepens the relevant research on the TSC. Second, this study breaks the traditional symmetry causal thinking and analyzes the causal complexity of multiple factors from the perspective of configuration theory. This study uses the fuzzy-set qualitative comparative analysis method to explore the different matching combinations of multiple variable conditions in producing high-TSC and analyzes the heterogeneity, causal asymmetric relationship between cases, and the equivalent path of producing the same result. The results of this study reveal the multi-driving path of the TSC of the artificial intelligence industry and open the black box of the complex interaction between the standard innovation endowment and the standard competitiveness of each country. Third, the results indicate no necessary conditions for high-TSC, which indicates that independent conditions cannot constitute the bottleneck of high-TSC. Governments should pay attention to the linkage and matching of multiple factors to improve the competitiveness of AI technology standards. The results also indicate four configuration paths serving sufficient conditions for high-TSC. Among them, high academic research intensity and high market size are the decisive factors forming the national high-TSC. In addition, two sets of potentially complementary logic in the path emerge when these two decisive factors exist. High participation of international standards organizations can supplement the lack of AI technological innovation ability, and high technological innovation ability can supplement 668 S. Liu, et al. Exploring the formation mechanism of technology standard competitiveness in artificial... the lack of government responsiveness to AI. These findings suggest that the government should first pursue high quality in the academic field and the large-scale development of the AI market when resources are insufficient. At the same time, governments should combine their existing foundations and conditions to optimize gradually. This research has some limitations, which future studies can address. Because data for some variables were unavailable, only 32 countries were included in the selected research samples. However, this small sample size made it difficult to uncover a more diverse set of paths. The AI industry was developing rapidly with the evolution and creation of organizations and continued technological and environmental innovation changing specific path dependence. Therefore, the future research framework can be improved according to the abundance of relevant data. The research method can combine dynamic QCA to analyze the path dependence during conditional or configuration evolution. Funding This work was supported by Zhejiang Provincial Science Technology Project of China under Grant [number 2022C25005]; The Key Project of National Social Science Fund of China under Grant [number 17AGL001]; The Project of National Social Fund of China under Grant [number 20BGL016] [number 21BGL004]. Author contributions Conceptualization and methodology, S. L., L. ZH and J.Y.; Collected data, data analysis and writing, S. L.; Revised advice, L. ZH and J.Y.; All authors have read and agreed to the published version of the manuscript. Disclosure statement The authors declare no conflict of interest. References Abbott, A. (2001). Review of Fuzzy-set social science, by Charles C. Ragin. Contemporary Sociology, 30(4), 330–331. https://doi.org/10.2307/3089735 Acquah, I. S. K., Naude, M. J., & Sendra-García, J. (2021). Supply chain collaboration in the petroleum sector of an emerging economy: Comparing results from symmetrical and asymmetrical approaches. 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