Research on the Influence of Firm Digital Intelligence Transformation and Management Innovation on Performance and Sustainable Development: Empirical Evidence from China
School of Economics and Management, Communication University of China, Beijing 100024, China
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Author to whom correspondence should be addressed.
Sustainability 2024, 16(17), 7578; https://doi.org/10.3390/su16177578 (registering DOI)
Submission received: 19 July 2024
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Revised: 23 August 2024
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Accepted: 29 August 2024
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Published: 1 September 2024
(This article belongs to the Special Issue Entrepreneurship, Innovation, and Regional and Firm Sustainable Development)
Abstract
:From the perspective of firm capability, this study explores the impact of digital intelligence transformation and management innovation on the performance and sustainable development of Chinese firms. On the basis of constructing an evaluation index system of firm digital intelligence transformation and management innovation, a comprehensive evaluation model of firm digital intelligence transformation and management innovation is constructed via the analytic network process (ANP) and fuzzy comprehensive evaluation method. Taking Chinese film and television firms as an example, this study selects 160 data samples from 85 firms and constructs a structural equation model to empirically analyze the impact of digital intelligence transformation and management innovation on the performance of firms. The study results show that, in the context of digitalization and intelligence, firm digital intelligence capability and dynamic capability have a significant positive effect on firm performance. Firm digital intelligence transformation and management innovation directly impact performance and can play a role through firm digital intelligence capability and dynamic capability as intermediaries, and the chain intermediary effect of digital intelligence capability and dynamic capability is significant. The research results show that firm digital intelligence and dynamic capabilities provide a functional path for firm digital intelligence transformation and management innovation to improve performance. Moreover, the innovative application of digital intelligence technology has an important impact on the sustainable development of firms. Digital transformation and management innovation can help firms enhance their ability to manage environmental uncertainties, improve firm performance and foster the achievement of sustainable development goals.
1. Introduction
Innovation is the core driving force of the sustainable development of firms. At present, data governance and digital intelligence empowerment are becoming the focus of digital economic development. In recent years, how firm digital intelligence transformation and innovation management improve firms’ performance and promote the development of the digital economy has become one of the emerging research frontiers regarding the digital economy [1]. “Digital intelligence” can be understood as a new generation of technological thinking and technology applications, such as big data, artificial intelligence, cloud computing, the Internet of Things, mobile internet, and blockchain. As a strategic resource and factor in production, “digital intelligence” profoundly influences economic, social, and national development; reshapes the behavior patterns of organizations and individuals; and plays a core driving role in enabling innovation [2]. The deep integration of digital intelligence technology and industry has led to the continuous emergence of new technologies, formats, and models; promoted innovation and changes in production methods and organizational methods; and effectively promoted the vigorous development of new industrial formats [3].
With the advent of the digital economy era, the use of digital intelligence technology to empower firms has become a general trend, and the construction of digital intelligence has become an important strategic goal and task for firms. Digital intelligence is the key to promoting firm innovation and change, improving quality, reducing cost, and increasing efficiency [4]. The application of digital intelligence technology to business can effectively promote the improvement of the efficiency of all aspects of a firm and achieve cost reduction and efficiency [5]. Moreover, it can precipitate the development of a wealth of digital assets, improve product quality and production efficiency [6], and promote innovation and value creation. The rapid development of digital intelligence technology creates supportive conditions for the digital intelligence transformation of firms, and digital intelligence transformation has important impacts on the performance and sustainable development of firms [7]. Digital intelligence transformation is the core force driving the innovation and development of firms. The construction of firm digital intelligence has become the key to promoting industrial transformation and upgrading, promoting changes in development quality and efficiency, and enhancing the vitality of industrial development. Thus, it is highly important to take advantage of the opportunities afforded by the digital intelligence revolution and use digital intelligence to empower firms to achieve sustainable development.
Currently, firms are gradually accelerating the process of digitalization. In this context, exploring how firm digital intelligence transformation and innovation management can improve firm performance to adapt to the development of digital intelligence, take advantage of the opportunities of digital intelligence, improve firm competitiveness, and help firms improve performance and achieve sustainable development goals is highly important [8,9]. On this basis, this study takes Chinese film and television firms as research objects, uses the data from 85 firms and, from the perspective of firm competence theory, empirically analyzes the impact of firm digital intelligence transformation and management innovation on firm performance with regard to digital intelligence strategy, digital intelligence technology innovation, digital intelligence business operation innovation, and digital intelligence organization management innovation. This study helps clarify the role of firm digital intelligence transformation and management innovation in firm performance and reveals the importance of firm digital intelligence transformation for sustainable development.
2. Literature Review
2.1. Digital Intelligence Transformation and Management Innovation
Digital intelligence involves the integration and application of digitalization and intelligence and is regarded as a new product in the development of the digital economy. Academic circles have conducted in-depth research on the concept connotation of digital intelligence. The connotation of digital intelligence is relatively rich, including not only the iterative evolution of different technology paradigms represented by digitalization, networking, and intelligence but also the extensive diffusion and application process of these emerging technologies in the industrial system [10,11]. Digitalization is the process of using big data, cloud computing, artificial intelligence, blockchain, and other technologies in the industry to trigger changes in the industry [12]. Digital intelligence is different from digitalization. Digital intelligence refers to the use of artificial intelligence, big data, virtual reality, augmented reality, cloud computing, 4K/8K, 5G, blockchain, and other digital intelligence technologies to empower all links in the value chain of firms and build digital intelligence capabilities to cope with dynamic changes in the environment. Some scholars believe that digital intelligence involves two aspects: internal intelligence and external digitalization [13]. On the one hand, internal intelligence refers to empowering an organization’s internal business process management, product technology, and service innovation to improve internal management efficiency, decision-making, and innovation capabilities [14]. External digitization, on the other hand, refers to empowering organizations to connect and interact with the external environment and customers to achieve efficient and accurate data sharing, information exchange, and personalized service delivery [15,16]. Digital intelligence can support data-based industry model innovation, improve the quality and function of existing products, and help firms develop new products to enter new fields [17]. On the one hand, firms implement digital intelligence to achieve cost reduction and efficiency; on the other hand, they pursue new revenue streams, new products and services, and new business models. Digital intelligence technology and network platforms have triggered profound changes in firm product operation and management, which provides new growth space for improving firm innovation performance [3].
Digital intelligence transformation refers to the process of using artificial intelligence, big data, cloud computing, and other digital intelligent technologies to promote the comprehensive transformation of firms’ organizational structure, business model, and corporate culture and then change or reshape value creation [18,19]. Whether firms have the ability to use digital intelligence technology to understand market changes and flexibly allocate various resources significantly affects the efficiency and effect of digital intelligence transformation [20]. Against the background of digital intelligence transformation, firms have increasing demand for digital intelligence capabilities. Some scholars believe that with the help of digital intelligence transformation, firms can overcome the technical bottleneck and physical time and space limitations of traditional knowledge absorption by cultivating digital intelligence capabilities, building digital intelligence architecture, selecting appropriate digital intelligence measures, and forming digital intelligence and component knowledge modules and cross-organizational knowledge links [21]. From the perspective of digital intelligence transformation and firm value, the core of digital intelligence capability is to interact with customers through perception and response mechanisms to create value jointly [22]. Digital intelligence capabilities provide opportunities for firms to achieve higher reliability, higher efficiency, and greater functionality, thereby exponentially increasing the value created by firms [23]. However, some studies have also shown that digital intelligence capability has little or no effect on firm performance [24].
2.2. Competence Theory of Firms
The competence theory of firms was conceived of in the 1980s on the basis of relevant economic research [25]. The theory was mainly developed based on resource-based theory, core capability theory, and dynamic capability theory. Scholars’ understanding of the connotation of firm capability theory has evolved from a static perspective to a dynamic perspective, from tangible resources to intangible resources, and from inside firms to outside firms [26,27,28]. In the field of firm innovation, the competence theory of firms has always been the focus of research. The resource-based view holds that only those resources with high value, rarity, inimitability, and irreplaceability can produce sustainable competitive advantages [29,30]. The theory of firm core capability holds that the organic combination of a firm’s resources, technology, and skills determines its competitiveness [28]. On this basis, scholars have subsequently defined the core capabilities of firms from different research backgrounds. Scholars agree that the core capability of firms emphasizes the rational allocation of resources and involves the coordination, interaction, and integration of resources and technologies, which can help firms improve their competitive advantages [31]. Dynamic capability theory belongs to the category of strategic management. Teece et al. (1997) proposed for the first time the concept of dynamic capability, pointing out that the theory of dynamic capability states that firms generate a dynamic comprehensive utilization capability for resources in the process of adapting to environmental changes [32]. This capability allows self-iteration and differentiation in the development of a firm’s coordinated competitive environment, which further establishes the competitive advantage of the firm [33]. Scholars agree that dynamic capability is the ability of an organization to change its resource base and cope with changes [34,35].
Firm competence theory provides a new perspective on this research. Whether a firm has unique capabilities affects the efficiency and effectiveness of the implementation of digital intelligence [36]. Competence theory can guide the construction of the logical framework of the digital intelligence capability and dynamic digital intelligence capability of film and television firms and provide a theoretical basis for exploring the mechanism through which digital intelligence transformation and management innovation affect firm performance.
On the basis of the above, scholars have studied firm digital intelligence transformation, management innovation, and firm performance from different perspectives. However, there are several features in the existing research. First, there are few studies involve a quantitative analysis of the relationships among digital intelligence transformation, management innovation, and performance at the firm level, and systematically explored the mechanism by which the digital intelligence revolution empowers firms and thus affects firm performance. Until now, research on the issues related to the transformation of firms and digital intelligence has focused mainly on the discussion on the mechanism underlying the digital intelligence of firms only from the perspective of internal factors of firms or certain environmental factors [15,37,38,39]. Empirical studies are relatively insufficient. With respect to the mechanism by which firm digital intelligence transformation and management innovation influence firm performance, a feasible comprehensive analytical system that combines qualitative and quantitative methods has not been established. Second, there are many studies on firm digital intelligence transformation, but few empirical studies directly related to this topic exist from the perspective of firm capability; only a few related studies on different aspects have been conducted [3,17,22]. With digital intelligence capability and dynamic capability as intermediary variables, this study takes Chinese film and television firms as the research objects. A total of 210 questionnaires were distributed to middle and senior managers of film and television firms. A structural equation model was used to empirically analyze the influence of digital intelligence transformation and management innovation on firm performance. These research results have important significance in the formulation of relevant policies for the intelligence transformation of firms, the guidance of firms to promote the intelligence transformation of firms, the implementation of innovation and reform, and the selection of the path of sustainable development, and provide an important reference to support the realization of the intelligence transformation of firms, improvements in firm performance, and sustainable development.
The rest of the article is divided into three sections. Section 3 provides a detailed description of the research methods, constructs the index system of digital intelligence transformation and management innovation, and establishes structural equation models to confirm the impact of digital intelligence transformation and management innovation on firm performance. In Section 4, through scale reliability and validity analysis, the model test, the intermediary effect test, and mechanism by which firm digital intelligence transformation and management innovation influence performance improvement are discussed. The results of the model are also discussed. The conclusions and suggestions for promoting the sustainable development of firms are summarized in Section 5.
3. Research Methods
3.1. Evaluation for Firm Digital Intelligence Transformation and Management Innovation
On the basis of expert research and a large number of literature analyses, this study presents an evaluation index system and measurement scale for digital intelligence transformation and management innovation and further designs a more reasonable comprehensive evaluation model of digital intelligence transformation and management innovation, which provides a tool for quantifying the level of digital intelligence transformation and management innovation.
On the basis of previous relevant research results, an evaluation index system to measure digital intelligence transformation and management innovation of film and television firms is constructed via grounded theory. Grounded theory is a research method that systematically collects, analyzes, and summarizes data for a new phenomenon or problem. It continuously compares, analyzes, revises, and improves categories with similarities and differences, and finally generates theories. This paper collects firm data from multiple channels and adopts the expert interview method and literature analysis method to further study the evaluation index system of digital intelligence transformation and management innovation of film and television firms. On the one hand, to ensure that the digital intelligence transformation and management innovation index system of film and television firms constructed in this study is in line with industry reality, more targeted firms are selected for interviews and research. A number of different types of film and television firms are investigated, interviewed, and analyzed, and 38 industry experts are interviewed with a semi-structured interview procedure to obtain first-hand information. Two main types of experts were interviewed. One group included 8 experts from industry authorities, whose work content is strongly related to this study, such as the department leader of the National Radio and Television Administration, the director of the China International Culture & Arts Company, president of the China Television Production Committee, the department leader of the Beijing Radio & Television Station, the director of the National Radio and Television Administration TV Drama production technology Innovation research and application Laboratory, and professors from the Communication University of China and the Chinese Academy of Arts. The other group included 30 senior managers of film and television firms who are very familiar with the development strategies and specific operations of firms. Specifically, the sample of this study selected the senior managers and technical leaders of Chinese film and television firms that have started to implement the development of digital intelligence, such as the senior executives of the listed companies: the CEO of iQiyi, the vice-president of Tencent video, the senior director of Youku operations, the department director of Mango Excellent Media Company, the technical director of Qingdao Oriental Movie Metropolis, and a number of the senior managers and technical directors of famous Chinese film and television firms. On the other hand, 110 secondary materials were collected from various authoritative sources as supplements. The method of collecting literature data was to search for keywords such as “film and television firms” and “digital intelligence transformation” and select literature that was highly relevant to the subject of this study from the past three years to ensure that the data could truly and accurately reflect the real situation of film and television firms. The diversity of data sources is also conducive to conducting data triangulation. The collection channels included domestic and foreign core database literature, books, and works on the digital intelligence of film and television firms; news reports and expert comments related to the digital intelligence of film and television firms; and a firm’s official website of public information descriptions and other text information. The collated text material is approximately 70,000 words. Three quarters of the data are randomly selected from the first-hand interview data and secondary materials for coding analysis, and the remaining quarter are used for theoretical saturation testing. Finally, this paper extracts 77 initial concepts, 28 initial categories, 8 main categories, and 4 core categories after classifying and summarizing the original data. Here, we take digital intelligence strategy and digital intelligence technology innovation as examples to show parts of the original statements: “In the past, a big data center has been positioned as a functional department, and now the level of the big data center is adjusted to a strategic department”; “Our design has a unique place in information strategic planning; we take the digital layout and use of high-tech cloud technology as standard”; “We should grasp trends and fully implement the key decisions of digital intelligence strategy”; “To increase investment in AIGC research and development and application and combine large numbers of film and television digital assets we have accumulated with large models for training”; “Our self-developed virtual production system is suitable for film and television shooting scenes”; and “Film and television companies must also build digital operating platforms.” To process and analyze the data, this study performs multiple steps, such as open coding, spindle coding, selective coding, and theoretical saturation testing, in accordance with the operating principles proposed by grounded theory. The evaluation dimensions of digital intelligence transformation and management innovation of Chinese film and television firms are explored in detail, and four dimensions of digital intelligence transformation and management innovation of film and television firms are identified: digital intelligence strategy, digital intelligence technology innovation, digital intelligence business operation innovation, and digital intelligence organization management innovation. An initial evaluation index system is further constructed. Two rounds of index screening were conducted through expert scoring and, finally, an evaluation index system of digital intelligence transformation and management innovation for film and television firms was determined, which was composed of 4 first-level indexes, 7 s-level indexes, and 21 third-level indexes, as shown in Table 1 [20,40,41,42,43].
In terms of questionnaire design, on the basis of the established evaluation index system via grounded theory, expert interview results were combined with the literature, and a five-level Likert scale was used to design the questionnaire. Each item was asked for a third-level index. SPSS 26.0 and AMOS 26.0 were used to conduct exploratory factor analysis and confirmatory factor analysis tests on the questionnaire. The results showed that the digital intelligence transformation and management innovation measurement scale developed in this research had a high level of reliability and validity and could more comprehensively reflect the composition dimension of the digital intelligence transformation and management innovation of Chinese film and television firms. This study improves upon the shortcomings of existing measurement scales for the digital intelligence transformation and management innovation of film and television firms and lays a foundation for subsequent research.
At present, firms’ digital intelligence transformation and management innovation are in the initial stage of exploration, and there are still some difficulties in the evaluation of firms’ digital intelligence transformation and management innovation. Considering that the digital intelligence transformation and management innovation evaluation of Chinese film and television firms is a complex systematic problem, the set of elements describing digital intelligence transformation and management innovation of film and television firms is an interdependent and mutually influencing organic whole, and the interdependence of evaluation indexes is unavoidable, so it is difficult to select completely independent evaluation indexes. To increase the objectivity and scientificity of evaluation, on the basis of the evaluation index system of firm digital intelligence transformation and management innovation, this study further proposes a method that combines the network analytic hierarchy process (ANP) and fuzzy comprehensive evaluation method and constructs a firm digital intelligence comprehensive evaluation model based on the Fuzzy-ANP. A comprehensive score of the digital intelligence transformation and management innovation of firms can be obtained, and a comprehensive evaluation of the digital intelligence transformation and management innovation of film and television firms can be carried out. The ANP method, which was developed on the basis of the analytic hierarchy process (AHP), has the ability to disassemble the hierarchy of evaluation objects, divide index categories in the form of element groups, and fully consider the correlations among indexes. Through expert opinions, guidelines are set up among indexes and element groups, and a judgment matrix is formed. The final index weight results reflect the correlation between evaluation contents to some extent [44].
In this study, on the basis of an evaluation index system of digital intelligence transformation and management innovation of film and television firms, the ANP is used for weight analysis, a judgment matrix is established under the guidance of experts, consistency tests and hypermatrix calculations are carried out, and the final global and grouped index weights are obtained after weighting and stabilizing processing. Second, because there are subjective indexes in the evaluation index system, the evaluation has certain fuzziness and uncertainty. To overcome the ambiguity and uncertainty of the evaluation criteria, this study introduces a fuzzy comprehensive evaluation method to construct a comprehensive evaluation model [45]. The combination of these two methods can eliminate subjective factors to a large extent and improve the objectivity and reliability of the evaluation results. This study takes three typical Chinese film and television firms as samples to test the comprehensive evaluation model of firm digital intelligence transformation and management innovation. The research shows that the evaluation model has high accuracy. Owing to its length, the fuzzy evaluation process is not presented in detail in this study.
3.2. Description of Variables
3.2.1. Explained Variable
Firm performance is the explained variable, which can be measured in two main ways: one is based on data from the public financial statements of firms, and the other is measured via a subjective questionnaire survey. Some scholars believe that the subjective questionnaire measurement method has the characteristics of strong operability and high correlation with subjective performance evaluation results [46,47]. In this study, the measurement of firm performance adopts the method of a subjective questionnaire survey so that respondents can judge their own performance connotations. In the scale setting of firm performance, this paper combined existing relevant research scales with the opinions of experts and the characteristics of the film and television industry to modify it. On the basis of the index system of results, project performance was measured from the two dimensions of economic benefit and social benefit, starting from the characteristics of the industry itself. The economic benefit dimension focuses mainly on the total income of film and television projects and the degree of improvement in fund recovery efficiency, whereas the social benefit dimension focuses mainly on the ideological value, artistic value, social reflection, and communication effects of film and television works [43].
3.2.2. Explanatory Variable
The explanatory variables are firms’ digital intelligence transformation and management innovation, measured by the degree of digital intelligence transformation and management innovation. On the basis of the research results of grounded theory and combined with expert opinions, this study measures the degree of digital intelligence transformation and management innovation of film and television firms in four dimensions: digital intelligence strategy, digital intelligence technology innovation, digital intelligence business operation innovation, and digital intelligence organization and management innovation. The digital intelligence strategy reflects the importance of digital intelligence construction and the implementation of the digital intelligence strategy of film and television firms. Digital intelligence technology innovation reflects the digital intelligence technology level and business process integration of film and television firms. Digital intelligence business operation innovation includes the application of digital intelligence technology in film and television project planning and development, project production, project marketing and other links. Digital intelligence organization management innovation refers to the transformation of the organizational management mode and cultural concept realized by film and television firms in the digital intelligent environment, which is manifested mainly in digital intelligence cooperation alliances, digital intelligence financial management, digital intelligence human resource management, digital intelligence leadership, digital intelligence talent construction, and corporate culture.
3.2.3. Intermediary Variables
Digital intelligence capability and dynamic capability are introduced into this study to explore the mediating effect of capabilities between firm digital intelligence transformation and management innovation and firm performance. Firm digital intelligence transformation and management innovation give a sustainable competitive advantage to firms and improve firms’ performance, and then have a positive impact on the sustainable development of firms [48]. In the long run, the core competence of a firm is an important source of sustainable competitive advantage. From the perspective of firm capability, improving the dynamic capability and digital intelligence capability of firms is actually improving the ability to maintain the sustainable competitive advantage and achieve sustainable development of the firms.
Dynamic capability refers to a firm’s ability to integrate, construct, and reconfigure internal and external resources to cope with a rapidly changing external environment, achieve new innovative outputs, and maintain sustainable competitive advantages [33]. Dynamic capability theory proposes that firms should break through path dependence and capability inertia, reconfigure firm resources, cultivate new capabilities, and match changes in the external environment to obtain sustainable competitive advantages [49]. Therefore, firm dynamic capability construction involves mainly the acquisition of external innovation resources, the reconfiguration and adjustment of internal resources, and the organic integration of internal and external resources to form new capabilities. The improvement of internal microelements of dynamic capability can enhance a firm’s insight into external market opportunities, promote the capture of opportunities, and restructure and allocate internal resources and capabilities, which helps to improve the firm’s adaptability to the external environment to further increase firm value and obtain competitive advantages and then improve firm performance [50,51]. Therefore, the digital intelligence dynamic capability of film and television firms can be regarded as the ability of film and television firms to use digital intelligence technology to integrate, construct, and reconfigure internal and external resources to cope with the rapidly changing external environment, better seize opportunities, and thus realize the improvement of innovation ability and maintain sustainable competitive advantages [52]. In the competitive environment, the positive effect of dynamic capabilities on firms’ continuous competitive advantage has been recognized. In the research on dynamic competence, most scholars emphasize the importance of opportunity identification, organizational learning and integrated innovation [53,54]. On the basis of the Teece framework and the characteristics of film and television production practices, this study divides the dimensionality of digital intelligence dynamic capabilities into digital intelligence perception capabilities, digital intelligence learning absorption capabilities, and digital intelligence resource integration capabilities.
The digital intelligence capability of film and television firms is a new capability formed in the process of digital intelligence construction. In this study, the digital intelligence capability of film and television firms is defined as the ability of firms to use digital intelligence technology to integrate digital intelligence resources widely in the value network with other organizational resources to promote the systematic digital intelligence reform of firms and realize digital intelligence value creation when the diffusion and embedding of digital intelligence technology in the daily value creation activities of firms is gradually increasing [55]. For firms, the key to obtaining a sustainable competitive advantage is to transform digital intelligence production factors into digital intelligence ability. In the process of digital intelligence development, the level of digital intelligence capability is the key for firms to realize digital intelligence and obtain higher performance levels. According to the firm competence theory, driven by the digital intelligence strategy, the digital intelligence capability possessed by film and television firms can help them combine the new generation of information technology resources with other existing resources and effectively improve their performance by strengthening the digital intelligence creative divergence capabilities, digital intelligence decision support capabilities, digital intelligence resource collaboration capabilities, and digital intelligence value creation capabilities [56,57,58].
The theoretical model is shown in Figure 1 [33,55,56,57,58]. As shown in the figure, the digital intelligence transformation and management innovation of film and television firms not only have a certain direct effect on firm performance but also have an indirect effect on the digital intelligence dynamic capability and digital intelligence capability, indirectly affecting firm performance. On the basis of the relevant theories, a series of theoretical hypotheses are proposed on the basis of the theoretical model in Figure 1 to reveal the internal mechanism through which digital intelligence transformation and the management innovation of film and television firms affect firm performance from a theoretical perspective, laying a foundation for subsequent empirical research.
3.3. Sample Selection and Data Sources
This study discusses the impact of digital intelligence transformation and management innovation on firm performance and sustainable development. Considering the availability of data, combined with personal learning and practical experience, this study selects the data of Chinese film and television firms as the research objects. Since the variables involved in this study make it difficult to obtain corresponding data from the publicly available materials of firms, this study uses previous research methods to collect data, namely by sending questionnaires to verify the conceptual model proposed in this paper. On the basis of previous relevant research results, this study targeted film and television firms for field interviews and surveys and, combined with the opinions of experts in the industry, constantly modified and improved the questionnaire. With the help of the structural equation model method, this paper studies the influence mechanism of firms’ digital intelligent transformation and management innovation on firm performance. In addition, we explore the mediating role of digital intelligence capability and dynamic capability.
For this study, data were collected in the form of a small-scale questionnaire, and the initial scale was pretested. All variables were collected via the 5-level Likert evaluation method. According to the pretest results, this study makes targeted adjustments to the initial scale, analyzes the reliability and validity of the scale, and finally forms a formal questionnaire for formal investigation. Because the questionnaire requires a certain understanding and knowledge of digital intelligence transformation and management innovation of film and television firms, the questionnaire was distributed to middle and senior managers of Chinese film and television firms who have been engaged in relevant management work for more than 5 years and have rich industry management experience. They are familiar with the digital intelligence transformation of film television firms, as well as the development, production, promotion and other work of film and television projects. The formal investigation was conducted from January to June 2024. In this study, 210 questionnaires were distributed to middle and senior managers of film and television firms, 171 questionnaires were recovered, and 160 valid questionnaires were finally recovered after screening out invalid questionnaires, with an effective questionnaire recovery rate of approximately 76.2%.
4. Results and Discussion
4.1. Reliability and Validity Analysis
4.1.1. Reliability Analysis of Scale
Reliability mainly evaluates the accuracy, stability, and consistency of the measurement results. The purpose of reliability analysis is to ensure that the measurement results can truly reflect the expected target and that the collected data have analytical value. Three indexes are generally used to test the reliability of a scale, namely, the Cronbach’s alpha value, the Cronbach’s alpha value of deleted items, and the component indicator of total credibility (CITC). Cronbach’s alpha is a measurement of the overall consistency of the questionnaire and the internal consistency of the items. If its value is greater than 0.7, it indicates that the scale has high internal consistency. The reliability of a single item is measured by the CITC value. In this research, items need to be checked one by one, and items that do not meet the requirements need to be deleted. In general, a CICT value should be greater than 0.4. If the Cronbach’s alpha value obtained after deleting one of the items is greater than the original Cronbach’s alpha value corresponding to the item, the item needs to be deleted.
In this study, SPSS 26.0 software was used to calculate the Cronbach’s alpha value of the obtained data and analyze each subdivision dimension. The reliability test results of the variables in this study are shown in Table 2. The reliability test revealed that the Cronbach’s alpha values corresponding to the seven dimensions of this study were all greater than 0.7 and were 0.885, 0.922, 0.955, 0.922, 0.927, 0.908, and 0.915, respectively, indicating good internal consistency among all the dimensions of the questionnaire. Therefore, the reliability of the results of this study was good. Moreover, the minimum CITC value of the scale items was 0.664, greater than 0.4, and the Cronbach’s alpha value after each item was deleted was no higher than the Cronbach’s alpha value of the corresponding factor, indicating that the internal consistency of each dimension of the questionnaire was good; that is, the scale passed the reliability test.
4.1.2. Validity Analysis of Scale
Validity refers to the degree to which a measuring tool can precisely measure a measured object. The more consistent the measured results are with the measured content, the greater the validity of the scale is. In existing research, validity mainly includes analysis content validity, discriminant validity, and convergent validity.
Content validity refers to whether the item design is reasonable. Content validity usually needs to be determined by expert evaluation or a theoretical basis. First, the questionnaire design of this study is based on the premise of combining the existing relevant research and referring to the relevant data in rooted theory and has been recognized by industry experts. The questionnaire has been revised according to expert opinions, thus ensuring the content validity of the scale.
Both convergent validity and discriminant validity were studied via confirmatory factor analysis. The main purpose of convergent validity analysis is to check the degree of correlation between the measures of the same measurement dimension. AMOS 26.0 software was used for confirmatory factor analysis, and the model fitting indexes were obtained as follows: chi-square freedom = 1.536, RESEA = 0.064, NFI = 0.913, and IFI = 0.944. In the convergent validity analysis, the mean average variance extraction (AVE) and composite reliability (CR) are used for analysis. If the AVE of each factor is greater than 0.5 and the CR is greater than 0.6, the convergent validity is good. Moreover, the standard load coefficient is used to judge convergent validity, and the factor loading value of each corresponding item is generally required to be greater than 0.5. After the convergent validity test, the CR of each factor is greater than 0.7, and the AVE is greater than 0.5. Therefore, the scale has high convergent validity.
Discriminant validity is used to test whether there is a significant difference between the various dimensions of a variable. In this study, the AVE square root judgment method was used to test discriminant validity. First, the correlation coefficient between the factors must be less than 0.85. Second, the size of the standardized correlation coefficient between the dimensions and the AVE square root value is compared. If the former is smaller than the latter, it indicates that there is good discriminant validity among the dimensions. According to the discriminant validity test, the AVE square root value of the factor digital intelligence strategy is greater than the maximum value of the absolute value of the correlation coefficient between the factor digital intelligence strategy and the other three factors, indicating that it has good discriminant validity. Similarly, the AVE square root values of the factors of intelligence technology innovation, intelligent business operation innovation, and intelligence organization management innovation are greater than the correlation coefficients between them and the other three factors, indicating that the scale has good discriminant validity.
4.2. Model Test
The core structure of this study concerns the relationships between digital intelligence transformation and the management innovation of film and television firms, digital intelligent capability, dynamic capability, and firm performance; however, these variables cannot be directly quantified to obtain data and must be reflected by many questionnaires. Therefore, in terms of research method selection, this study uses a structural equation model to study the mechanism of digital intelligence transformation and management innovation on the performance of film and television firms and attempts to explore the mediating effect of dynamic capability and digital intelligence capability. According to the hypothesis relationship and analysis, AMOS 26.0 was used to draw the structural equation model diagram in Figure 2, and the collected questionnaire data were input into the structural equation to test and analyze the model hypotheses.
The test results of the path coefficients are shown in Table 3. The structural equation model of the hypothesized relationship of each variable adopted in this study has a good fit effect on the sample data. According to the variable data in this study, digital intelligence strategy, digital intelligence technology innovation, digital intelligence business operation innovation, and digital intelligence organization management innovation have a significant positive impact on digital intelligence capability. The dynamic capability of digital intelligence has a significant positive effect on digital intelligence capability. With respect to the dynamic capabilities of firms, digital intelligence strategies, digital intelligence technology innovations, digital intelligence business operation innovations, and digital intelligence organization management innovations have significant positive effects on the dynamic capabilities of firms. For firm performance, digital intelligence capability and digital intelligence dynamic capability have a significant and positive effect on firm performance.
4.3. Mediation Effect Test
The structural equation model is suitable for mediating effect analysis of latent variables. To test whether the variables of dimensionally digital intelligence capability and dynamic capability play a transmitting role between intelligence transformation and management innovation and firm performance, this study further conducts a mediating test between digital intelligence capability and dynamic capability. In this study, AMOA26.0 was used to complete the mediation effect test, and bootstrap technology was used to test the mediating role of digital intelligence capability and dynamic capability. Using bias-corrected nonparametric percentiles, the 95% confidence interval was calculated for 2000 repeated samples. The confidence interval did not contain 0 and the mediating effect was significant. To obtain the impact of various dimensions of digital intelligence transformation and management innovation on intermediary variables and dependent variables more clearly, four dimensions of digital intelligence transformation and management innovation were used to test the intermediary variables and dependent variables.
Regarding the digital intelligence strategy of independent variables, in the process of mediating the test of digital intelligence capability and dynamic capability, the path relationship of each variable is shown in Table 4. The digital intelligence strategy of the independent variable has a significant positive correlation with the influence of the dependent variable on firm performance and has a significant positive correlation with digital intelligence capability and the dynamic capability of digital intelligence. The path model diagram is shown in Figure 3, which meets the basic conditions of the mediating effect test. Mediation tests can continue.
According to the analysis results in Table 5, the intermediary effects are 0.11, 0.217, and 0.206, respectively, and the 95% confidence interval does not contain 0, which indicates that the intermediary effect is valid. Therefore, digital intelligence capability and dynamic capability release a significant mediating effect in the model, and the confidence interval of the direct effect test is [0.173, 0.262]. The range does not contain 0, indicating that the direct effect is valid. Therefore, digital intelligence capability and digital intelligence dynamic capability play partial mediating roles in the model, and the chain mediating effect of digital intelligence capability and digital intelligence dynamic capability is significant.
As shown in Table 6, the path relationship of each variable is analyzed when the independent variable is digital intelligence technology innovation. The results of the mediating effect test are shown in Table 7.
5. Conclusions
The era of digital intelligence is quietly coming, and under the comprehensive empowerment of digital intelligence technologies represented by big data, artificial intelligence, cloud computing, and blockchain, the global industry has begun to enter the era of digital intelligence, and digitalization, networking, and intelligence have become the focus of the transformation and upgrading of all walks of life. First, on the basis of a large number of expert interviews and literature analysis, this study establishes an evaluation index system of firm digital intelligent transformation and management innovation and constructs a comprehensive evaluation model of firm digital intelligent transformation and management innovation on the basis of the Fuzzy-ANP. Second, this study takes 160 samples of data from Chinese firms as an example to model and verify the relationship between firm digital intelligence transformation and management innovation and firm performance and empirically tests the impact mechanism of firm digital intelligence transformation and management innovation on firm performance through an analysis of survey data. The quantitative relationships between firm digital intelligence transformation and management innovation and firm performance are clarified, and all the hypotheses are verified, as shown in Table 12. The research results show that against the background of digitalization, firm digital intelligence capability and dynamic capability have a significant positive effect on firm performance, and firm digital intelligence transformation and management innovation can directly affect performance. There are partial mediating effects between digital intelligence capability and dynamic capability in the model, and the chain mediating effects between digital intelligence capability and dynamic capability are significant. Digital intelligence transformation and management innovation can help firms better adapt to the external environment, promote value creation, improve performance, and achieve sustainable development. This paper explores the mechanisms by which firms’ digital intelligence transformation influence firm performance. However, for firms with different attributes, this influence shows heterogeneity. Due to time reasons, this study does not analyze the heterogeneity of the characteristics of different enterprises. Future researchers could conduct more research in this aspect.
With the advent of the digital economy era, digital intelligence has become an irreversible trend, and empowering digital intelligence to promote the sustainable development of firms is highly important. Therefore, this paper proposes the following suggestions:
- (1)
- Firms should actively deploy digital intelligence transformation strategies and pay attention to digital intelligence technology innovation, digital intelligence business operation innovation, and digital intelligence organization management innovation to accelerate the sustainable development of firms. The strategy of a firm controls the future development direction of the firm, and the strategic transformation of a firm is a process of replanning to help the firm overcome difficulties and gain competitive advantages according to changes in the macro environment, which is crucial for the sustainable development and innovation of the firm. Therefore, firms should carry out forward-looking layouts, vigorously develop industry-related digital intelligence technology, and promote the construction of firm digital intelligence from the aspects of digital intelligence business operations and organizational management.
- (2)
- Vigorously promoting the wide diffusion and application of digital intelligence technology in firms is an important way to promote the transformation of the digital intelligence and management innovation of firms. Firms should actively embrace digital intelligence technology, actively try new technologies, strengthen the integration of various business links and digital intelligence technology through digital intelligence technology and digital intelligence management, and promote the deep integration and application of a new generation of information technology and firm business. The use of digital intelligence technology to achieve internal business process management digital intelligence, the digital intelligence of each business link and service innovation applications to improve internal management efficiency, decision-making, and innovation capabilities.
- (3)
- Vigorously promoting digital intelligence technology to enable firms to connect and interact with the external environment and users to achieve efficient and accurate data sharing, information exchange, and personalized service provision. Digital intelligence technology service capabilities should be strengthened, new business models should be built, and ultimately, firms should be provided with technical environment support and value creation momentum, the reupgrading of firm economic value and social value should be realized, and improvements in firm performance and sustainable development should be promoted.
Author Contributions
Conceptualization, Y.L.; formal analysis, Y.L.; methodology, Y.L.; software, Y.L.; validation, Y.L.; investigation, Y.L. and P.S.; resources, Y.L. and P.S.; writing—original draft preparation, Y.L.; data curation, Y.L.; writing—review and editing, Y.L.; supervision, P.S.; funding acquisition, Y.L. and P.S. All authors have read and agreed to the published version of the manuscript.
Funding
This work was funded by the National Natural Science Foundation of China (Grant No. U21B20102) and funded by the Fundamental Research Funds for the Central Universities (Grant No. CUC230D018).
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Data are contained within the article.
Conflicts of Interest
The authors declare no conflicts of interest.
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Figure 1.
Theoretical model.
Figure 2.
Structural equation model. (path coefficient = 1).
Figure 3.
The path model diagram of digital intelligence strategy. (path coefficient = 1).
Table 1.
Indexes of digital intelligence transformation and management innovation.
| First-Level Indexes | Second-Level Indexes | Third-Level Indexes | Description |
|---|---|---|---|
| Digital intelligence strategy | Digital Intelligence strategy | Digital intelligence strategic thinking | Firms can deeply understand the significance of digital intelligent transformation from a strategic height |
| Digital intelligent strategic planning | Firm management has a clear vision of digital intelligence empowerment and includes digital intelligence transformation into strategic planning | ||
| Implementation of digital intelligence strategy | Firm management supports the necessary digital intelligence investment, supports use of digital and intelligent tools, and sets clear digital intelligence goals to ensure implementation of digital intelligence strategy | ||
| Digital intelligence technology innovation | Digital intelligence technology level | Digital intelligent technology and equipment development | Firms focus on research and development of digital intelligence technology and equipment |
| Digital intelligence technology and equipment introduced | Firms focus on introduction of digital intelligence technology and equipment | ||
| Digital intelligent platform construction | Firms pay attention to use of digital intelligence technology for platform construction | ||
| Digital intelligence business operation innovation | Film and television project planning and development | Digital intelligent script creation | Firms use digital intelligence technology to assist script creation |
| Digital intelligent script evaluation | Firms use digital intelligence technology to realize the digital intelligence of script evaluation | ||
| Film and television project production | Digital intelligent shooting | Firms use digital intelligence technology to realize the digital intelligence of the shooting process | |
| Intelligent production management | Firms use digital intelligence technology to realize digital intelligence in the production management process | ||
| Development and application of digital assets | Firms use digital intelligence technology to develop and apply digital assets | ||
| Digital postproduction | Firms use digital intelligence technology to realize digital intelligence of postproduction process | ||
| Digital intelligent review film | Firms use digital intelligence technology to realize digital intelligence of the film review process | ||
| Film and television project marketing | Digital intelligence marketing | Firms use digital intelligence technology to carry out fine propaganda and realize the intelligence of marketing tools and services | |
| Digital intelligence distribution | Firms often distribute film and television works through internet channels | ||
| Digital intelligence organization management innovation | Organization management mode | Digital intelligence cooperation alliance | Firm has established a digital intelligence cooperation alliance among firms |
| Digital intelligent financial management | Firms use the digital intelligence technology to realize the digital intelligence of financial management | ||
| Digital intelligent human resource management | Firms use digital intelligence technology to realize the digital intelligence of human resource management | ||
| Digital intelligence leadership | Business leaders have the ability to promote the development of digital intelligence | ||
| Digital intelligence talent construction | Firms pay attention to the construction of digital intelligence talents, and often carry out digital intelligence skills training for employees | ||
| Corporate culture | Firm culture conducive to the construction of digital intelligence | Firm has established and propagated the firm culture that is conducive to the construction of digital intelligence |
Table 2.
The reliability test results of the variables.
| Variable Dimension | Items | Component Indicator of Total Credibility (CITC) | Square Multiple Correlation | Cronbach’s Alpha Value of Deleted Items | Cronbach’s Alpha |
|---|---|---|---|---|---|
| Digital intelligence strategy | DSA1 | 0.756 | 0.573 | 0.863 | 0.885 |
| DSA2 | 0.792 | 0.630 | 0.831 | ||
| DSA3 | 0.803 | 0.646 | 0.812 | ||
| Digital intelligence technology innovation | DTA1 | 0.803 | 0.660 | 0.919 | 0.922 |
| DTA2 | 0.842 | 0.745 | 0.888 | ||
| DTA3 | 0.889 | 0.796 | 0.856 | ||
| Digital intelligence business operation innovation | DBA1 | 0.882 | 0.848 | 0.946 | 0.955 |
| DBA2 | 0.816 | 0.766 | 0.950 | ||
| DBB1 | 0.786 | 0.691 | 0.951 | ||
| DBB2 | 0.762 | 0.705 | 0.952 | ||
| DBB3 | 0.852 | 0.844 | 0.948 | ||
| DBB4 | 0.819 | 0.792 | 0.950 | ||
| DBB5 | 0.829 | 0.745 | 0.950 | ||
| DBC1 | 0.903 | 0.872 | 0.945 | ||
| DBC2 | 0.724 | 0.644 | 0.954 | ||
| Digital intelligence organization management innovation | DOA1 | 0.687 | 0.522 | 0.919 | 0.922 |
| DOA2 | 0.834 | 0.775 | 0.901 | ||
| DOA3 | 0.795 | 0.697 | 0.905 | ||
| DOA4 | 0.746 | 0.599 | 0.912 | ||
| DOA5 | 0.798 | 0.773 | 0.904 | ||
| DOB1 | 0.818 | 0.759 | 0.901 | ||
| Digital dynamic capability | DDP1 | 0.817 | 0.698 | 0.910 | 0.927 |
| DDP2 | 0.797 | 0.680 | 0.912 | ||
| DDS1 | 0.795 | 0.679 | 0.912 | ||
| DDS2 | 0.755 | 0.618 | 0.918 | ||
| DDI1 | 0.726 | 0.686 | 0.922 | ||
| DDI2 | 0.848 | 0.769 | 0.906 | ||
| Digital intelligence capability | DIC1 | 0.702 | 0.596 | 0.897 | 0.908 |
| DCI2 | 0.780 | 0.661 | 0.888 | ||
| DID1 | 0.794 | 0.698 | 0.885 | ||
| DIS1 | 0.685 | 0.577 | 0.900 | ||
| DIS2 | 0.818 | 0.766 | 0.886 | ||
| DIV1 | 0.748 | 0.755 | 0.891 | ||
| Firm performance | SP1 | 0.877 | 0.812 | 0.882 | 0.915 |
| SP2 | 0.767 | 0.664 | 0.901 | ||
| SP3 | 0.756 | 0.667 | 0.900 | ||
| SP4 | 0.783 | 0.689 | 0.896 | ||
| EP1 | 0.740 | 0.622 | 0.902 | ||
| EP2 | 0.664 | 0.545 | 0.913 |
Table 3.
Structural equation path test results.
| Path | Estimate | S.E. | C.R. | p | Test Results |
|---|---|---|---|---|---|
| Digital dynamic capability ← Digital intelligence strategy | 0.147 | 0.030 | 4.876 | *** | Support |
| Digital dynamic capability ← Digital intelligence technology innovation | 0.153 | 0.033 | 4.603 | *** | Support |
| Digital dynamic capability ← Digital intelligence business operation innovation | 0.343 | 0.065 | 5.298 | *** | Support |
| Digital dynamic capability ← Digital intelligence organization management innovation | 0.245 | 0.048 | 5.120 | *** | Support |
| Digital intelligence capability ← Digital intelligence technology innovation | 0.103 | 0.028 | 3.608 | *** | Support |
| Digital intelligence capability ← Digital intelligence business operation innovation | 0.187 | 0.055 | 3.365 | *** | Support |
| Digital intelligence capability ← Digital intelligence organization management innovation | 0.205 | 0.053 | 3.874 | *** | Support |
| Digital intelligence capability ← Digital dynamic capability | 0.276 | 0.055 | 5.054 | *** | Support |
| Digital intelligence capability ← Digital intelligence strategy | 0.102 | 0.025 | 4.013 | *** | Support |
| Firm performance ← Digital dynamic capability | 0.237 | 0.067 | 3.520 | *** | Support |
| Firm performance ← Digital intelligence capability | 0.317 | 0.087 | 5.308 | *** | Support |
*** p < 0.001.
Table 4.
Test result of path coefficient 1.
| Path | Estimate | S.E. | C.R. | p |
|---|---|---|---|---|
| Digital dynamic capability ← Digital intelligence strategy | 0.329 | 0.031 | 16.773 | *** |
| Digital intelligence capability ← Digital dynamic capability | 0.648 | 0.053 | 12.269 | *** |
| Digital intelligence capability ← Digital intelligence strategy | 0.357 | 0.033 | 10.775 | *** |
| Firm performance ← Digital dynamic capability | 0.211 | 0.024 | 8.801 | *** |
| Firm performance ← Digital intelligence capability | 0.308 | 0.029 | 10.679 | *** |
| Firm performance ← Digital intelligence strategy | 0.215 | 0.021 | 10.437 | *** |
*** p < 0.001.
Table 5.
Results of mediating effect test 1.
| Parameter | Estimate | Lower | Upper | p |
|---|---|---|---|---|
| Intermediary effect 1 | 0.110 | 0.089 | 0.131 | 0.001 |
| Intermediary effect 2 | 0.217 | 0.141 | 0.299 | 0.001 |
| Intermediary effect 3 | 0.206 | 0.145 | 0.279 | 0.001 |
| Direct effect | 0.215 | 0.173 | 0.262 | 0.001 |
| Total effect | 0.748 | 0.642 | 0.869 | 0.001 |
Table 6.
Test result of path coefficient 2.
| Path | Estimate | S.E. | C.R. | p |
|---|---|---|---|---|
| Digital dynamic capability < ---Digital intelligence technology innovation | 0.375 | 0.036 | 15.484 | *** |
| Digital intelligence capability < ---Digital dynamic capability | 0.665 | 0.053 | 12.514 | *** |
| Digital intelligence capability < ---Digital intelligence technology innovation | 0.327 | 0.031 | 10.625 | *** |
| Firm performance < ---Digital dynamic capability | 0.168 | 0.027 | 6.177 | *** |
| Firm performance < ---Digital intelligent capability | 0.267 | 0.025 | 10.791 | *** |
| Firm performance < ---Digital intelligence technology innovation | 0.237 | 0.025 | 9.619 | *** |
*** p < 0.001.
Table 7.
Results of mediating effect test 2.
| Parameter | Estimate | Lower | Upper | p |
|---|---|---|---|---|
| Intermediary effect 1 | 0.087 | 0.071 | 0.105 | 0.001 |
| Intermediary effect 2 | 0.147 | 0.043 | 0.231 | 0.006 |
| Intermediary effect 3 | 0.155 | 0.118 | 0.200 | 0.001 |
| Direct effect | 0.237 | 0.159 | 0.334 | 0.001 |
| Total effect | 0.626 | 0.554 | 0.702 | 0.001 |
Table 8.
Test result of path coefficient 3.
| Path | Estimate | S.E. | C.R. | p |
|---|---|---|---|---|
| Digital dynamic capability < ---Digital intelligence business operation innovation | 0.329 | 0.031 | 11.823 | *** |
| Digital intelligence capability < ---Digital dynamic capability | 0.383 | 0.033 | 11.609 | *** |
| Digital intelligence capability < ---Digital intelligence business operation innovation | 0.665 | 0.049 | 13.531 | *** |
| Firm performance < ---Digital dynamic capability | 0.199 | 0.018 | 11.117 | *** |
| Firm performance < ---Digital intelligence capability | 0.243 | 0.025 | 9.771 | *** |
| Firm performance < ---Digital intelligence business operation innovation | 0.307 | 0.026 | 11.737 | *** |
*** p < 0.001.
Table 9.
Results of mediating effect test 3.
| Parameter | Estimate | Lower | Upper | p |
|---|---|---|---|---|
| Intermediary effect 1 | 0.162 | 0.120 | 0.207 | 0.001 |
| Intermediary effect 2 | 0.206 | 0.167 | 0.250 | 0.001 |
| Intermediary effect 3 | 0.096 | 0.072 | 0.126 | 0.001 |
| Direct effect | 0.307 | 0.235 | 0.385 | 0.001 |
| Total effect | 0.770 | 0.671 | 0.881 | 0.001 |
Table 10.
Test result of path coefficient 4.
| Path | Estimate | S.E. | C.R. | p |
|---|---|---|---|---|
| Digital dynamic capability < ---Digital intelligence organization management innovation | 0.349 | 0.033 | 14.377 | *** |
| Digital intelligence capability < ---Digital dynamic capability | 0.450 | 0.044 | 10.317 | *** |
| Digital intelligence capability < ---Digital intelligence organization management innovation | 0.579 | 0.052 | 11.226 | *** |
| Firm performance < ---Digital dynamic capability | 0.137 | 0.025 | 5.483 | *** |
| Firm performance < ---Digital intelligence capability | 0.210 | 0.025 | 8.260 | *** |
| Firm performance < ---Digital intelligence organization management innovation | 0.392 | 0.043 | 9.140 | *** |
*** p < 0.001.
Table 11.
Results of mediating effect test 4.
| Parameter | Estimate | Lower | Upper | p |
|---|---|---|---|---|
| Intermediary effect 1 | 0.121 | 0.098 | 0.152 | 0.001 |
| Intermediary effect 2 | 0.144 | 0.059 | 0.227 | 0.003 |
| Intermediary effect 3 | 0.099 | 0.066 | 0.138 | 0.001 |
| Direct effect | 0.392 | 0.306 | 0.510 | 0.001 |
| Total effect | 0.757 | 0.654 | 0.890 | 0.001 |
Table 12.
Hypothesis test results.
| Hypothesis | Test Results |
|---|---|
| Digital intelligence strategy→ Digital intelligent capability | Support |
| Digital intelligence technology innovation→ Digital intelligent capability | Support |
| Digital intelligence business operation innovation→ Digital intelligent capability | Support |
| Digital intelligence organization management innovation→ Digital intelligent capability | Support |
| Digital intelligence strategy→ Digital dynamic capability | Support |
| Digital intelligence technology innovation→ Digital dynamic capability | Support |
| Digital intelligence business operation innovation→ Digital dynamic capability | Support |
| Digital intelligence organization management innovation→ Digital dynamic capability | Support |
| Digital intelligent capability→ Firm performance | Support |
| Digital dynamic capability→ Firm performance | Support |
| Digital intelligence strategy→ Digital intelligent capability, digital dynamic capability→ Firm performance | Support |
| Digital intelligence technology innovation→ Digital intelligent capability, digital dynamic capability→ Firm performance | Support |
| Digital intelligence business operation innovation→ Digital intelligent capability, digital dynamic capability→ Firm performance | Support |
| Digital intelligence organization management innovation→ Digital intelligent capability, digital dynamic capability→ Firm performance | Support |
| Digital intelligence strategy→ Digital dynamic capability→ Digital intelligent capability→ Firm performance | Support |
| Digital intelligence technology innovation→ Digital dynamic capability→ Digital intelligent capability→ Firm performance | Support |
| Digital intelligence business operation innovation→ Digital dynamic capability→ Digital intelligent capability→ Firm performance | Support |
| Digital intelligence organization management innovation→ Digital dynamic capability→ Digital intelligent capability→ Firm performance | Support |
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MDPI and ACS Style
Liu, Y.; Song, P. Research on the Influence of Firm Digital Intelligence Transformation and Management Innovation on Performance and Sustainable Development: Empirical Evidence from China. Sustainability 2024, 16, 7578. https://doi.org/10.3390/su16177578
AMA Style
Liu Y, Song P. Research on the Influence of Firm Digital Intelligence Transformation and Management Innovation on Performance and Sustainable Development: Empirical Evidence from China. Sustainability. 2024; 16(17):7578. https://doi.org/10.3390/su16177578
Chicago/Turabian StyleLiu, Yutong, and Peiyi Song. 2024. "Research on the Influence of Firm Digital Intelligence Transformation and Management Innovation on Performance and Sustainable Development: Empirical Evidence from China" Sustainability 16, no. 17: 7578. https://doi.org/10.3390/su16177578
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