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Article

Empirical Research on the Influence Mechanisms of Digital Resources Input on Service Innovation in China’s Finance Industry

by
Mingxia Liu
1,2 and
Wei Jiang
1,*
1
Economics and Management School, Wuhan University, Wuhan 430072, China
2
Institute of Strategic Management Research, Wuhan University, Wuhan 430072, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(12), 7143; https://doi.org/10.3390/su14127143
Submission received: 28 April 2022 / Revised: 26 May 2022 / Accepted: 8 June 2022 / Published: 10 June 2022
(This article belongs to the Special Issue Advances in Entrepreneurship and Innovation for Sustainable Management)
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Abstract

:
In today’s era, digital technology and the digital economy are the forerunners of the scientific and technological revolution and industrial transformation in the world. This paper chooses the organization and executive integration perspectives, studies the effects of relationship between digital resources input and service innovation in China’s finance industry, and the path and the mechanism of the transmission process. Through empirical research, we find that digital resource input has a significant positive impact on service innovation, and information sharing and value creation have significant mediating effects on the relationship between digital resource input and service innovation. Network openness significantly promotes the positive relationship between digital resource input and value creation, while big data technical ability significantly promotes the positive relationship between information sharing and service innovation. Our findings have some significant meanings for China’s financial enterprises.

1. Introduction

For the financial industry, the competitive advantage of an enterprise is the quality of service and the level of innovation, which helps the enterprise adapt to the digital change in the external environment and the change in customer demand brought by the change. Through service innovation, financial enterprises can improve their product advantage and service advantage, enhance customer satisfaction, and establish competitive advantages based on the changes in the digital competitive environment [1,2,3]. According to existing research results, although there are a number of studies on digital strategy and service innovation, there has been little research on the relationship between digitalization and service innovation, and most scholars have focused on digital strategy for enterprise innovation or influencing factors of the enterprise’s financial performance [4,5,6]. However, there are a small number of research papers on the influence mechanism and process of digitalization on service innovation. Therefore, it is necessary to address the influence of digitalization on the process and intermediate mechanism of service innovation. The research results can theoretically supplement the existing literature and has an important practical significance for enterprises in digital transformation [7,8,9].
Existing research analysis has examined the related factors influencing service innovation from the internal and external dimensions, respectively. The digital department setup and the knowledge transfer are the internal factors influencing service innovation performance, while the market assets, customer needs change, and the partners network are the external factors influencing service innovation [10,11,12,13,14,15]. This paper follows the existing research results and suggestions at the same time, combining with the grounded theory and qualitative research, to study the effects and mechanism of digitalization to service innovation, including information sharing and value creation that play an intermediary role; big data technology ability and network openness that play a moderating role during the relationship effects between digitalization and service innovation. This paper focuses on organizational and executive-level factors, which is consistent with the views of existing studies. Current scholars’ studies on strategic orientation and service innovation mainly explore the influence of the relationship between strategic orientation and service innovation from the dimensions of market orientation, entrepreneurship orientation, and technological innovation orientation [16].
Among the research on the mediating mechanism of digitalization influencing service innovation, the existing research focuses on the organizational and executive levels. Pasanen [17] explored the degree of influence on strategic orientation and service innovation from the age and occupation of the senior management team and individual seniors. Hughes and Morgan [18] found that in addition to senior executives’ professional and educational backgrounds, senior executives’ digitalization needs to be matched with digital resources, and the internal coordination of enterprises also plays a role in digital strategy implementation. At the organizational level, scholars have carried out relevant researches from the aspects of big data ability, value creation, organizational characteristics, and organizational resources. Scholars have found that data processing ability contributes to the improvement of service innovation quality, and the synergistic effect of digitalization and value creation can promote service innovation. An organizational innovation network plays a positive role in digital resource acquisition and digital strategy implementation. This paper ranges from the dimensions of organizational ability and senior managers to an analysis of the relationship between digitalization and service innovation.

2. Theoretical Model and Research Hypotheses

2.1. Digital Resource Input and Service Innovation

As for the research on the influence of digital resource input on service innovation, most scholars believe that enterprises can realize digital transformation by investing in digital software, hardware, and talents so as to enhance their advantages in service innovation [19]. Digital resources and an enterprise’s competitive advantage are not necessarily related but will promote enterprises to form ability establishment based on the digitalization [20,21,22,23,24,25], such as the internationally famous companies such as IBM and Philips enterprises that invest so much in technology innovation and accumulate digital resources. However, these resources will not necessarily produce immediate value, or form the competitive advantage of the enterprise, but these digital resources can be combined with the existing products and services to promote the recognition and acceptance in the market.
How to efficiently transform digital resources into digital capabilities is the strategic focus of service enterprises under the trend of deep digitalization [26]. Enterprises need to pay attention to which organizations are conducive to integrating external resources with operational resources such as digital technology rather than merely improving the allocation efficiency of internal resources. The pursuit of service innovation inevitably requires the input of internal resources and the integration of external resources [27]. The degree of digitalization investment varies with the cognition of enterprises and senior executives, and enterprises that invest a lot in digitalization are bound to improve and gain in digitalization transformation and service innovation. Based on the above analysis, hypothesis 1 is proposed in this paper.
Hypothesis 1 (H1).
Digital resource input of financial enterprises has a significant positive impact on service innovation.

2.2. Digital Resource Input, Information Sharing, and Service Innovation

In the process of digital transformation, sharing and communication inside and outside of the organization is a very important key factor for service innovation. Information is no longer a tool but can be regarded as a core resource with the same function as knowledge and skills [28]. Information sharing is through modern information technology, and data application management is used to implement the digital transformation of enterprise business processes, digital analysis of market changes and productivity, and the organization’s business model innovation. Digital information is the basis of information sharing by means of information to create online traditional trade and product information. Digital resource input is an important strategic element for enterprises to develop digital-oriented strategies and compete with digitalization, and plays a positive role in promoting service innovation in the financial industry [29,30,31,32,33]. Firstly, digital resource input can promote the realization of information sharing. United Nations Educational, Scientific and Cultural Organization (UNESCO) also defines information technology as that involving the application of production and life, as well as the interaction between machines and users based on information technology, so as to produce certain effects on the economy and society.
The digitalization of business and the digitalization and capitalization of enterprise assets can be realized through informationizing, thus providing the support of core data capabilities for the digital transformation of enterprises. Informatization is closely related to an enterprise’s ability to master data technology and big data technology and plays an important role in information sharing [34]. Digital construction is an important link in which enterprise service innovation and enterprise operation, scientific research, and other work are closely linked, promoted, and influenced by each other. It is the driving force and lifeline for enterprise survival and development and also the most powerful and active factor in promoting the development of enterprise digital transformation. At present, information sharing has entered the stage of rapid development of information and the gradual establishment of digitalization. Information sharing has become very important for the communication between enterprises and customers in the process of operation, which improves the current situation of high cost and low efficiency of traditional offline communication [35]. In the information age dominated by big data, the digital resource input plays a positive role in the information transmission and sharing between internal employees and external customers, thus improving the service innovation quality. The digital resources input improves the construction of the digital platform and digital system of enterprises, plays a positive role in the information construction and information interaction within enterprises, and thus improves the informatization degree of enterprises, the realization of information sharing, and the effective use value of information. Thus, the digitalization strategy can further have a positive impact on service innovation [36]. Based on the above analysis, hypothesis 2 is proposed in this paper.
Hypothesis 2 (H2).
Digital resource input of financial enterprises affects the information sharing degree of enterprises and improves service innovation to some extent; that is, information sharing plays an intermediary role between digital resource input and service innovation.

2.3. Digital Resource Input, Value Creation, and Service Innovation

Value creation, service innovation ecology and service platform are considered by scholars to be three important factors of service innovation under the service-oriented logic. The service platform is the core carrier of the service innovation ecosystem, and value creation is the prerequisite in realizing value interaction and resource sharing in the service innovation ecosystem. Digitalization makes the original value creator network realize decoupling and reconstruction in time and space, which will bring a new value creation mode [37]. The process and subject of value creation have changed in the logic of service dominance; the process of value creation has changed from linear generation to flow generation, and then to extinction to a more dynamic, networked, and systematic co-creation process. The subject of value creation has also changed from a limited and borderless B2B Network to a more universal and borderless A2A Network; digitization profoundly changes the co-creation of value in the network. First, the role of subjects is no longer fixed, and loose coupling relationship is formed between subjects through “soft contract” or “hard contract”. Secondly, subjects judge and respond spontaneously through their own “feelings”, and the depth of digitalization makes this perception more natural. Finally, each subject in the network does not directly create value for other subjects, but reflects the potential value of its existence through value proposition. In addition to the reshaping of the value creation network, the liquidation and flexible transfer of resources brought by digitization are another important prerequisite for the transformation of the value creation process. This makes network resources no longer bound by specific participants; in other words, based on the main participation of resources, value creation is no longer only having a single fixed resource, but together with the other participants, having more resources, so the role of the participants are not fixed, but with their own needs and resources of transfer and conversion.
As for value creation, scholars have also carried out relevant studies and analyzed the role of value creation in the relationship between digitalization and service innovation. Some scholars believe that value creation can be evaluated by users’ evaluation of products, services, and willingness to pay a certain cost [38], while others believe that users are an important part of enterprise value creation. Users’ consumption experience and satisfaction are ways to measure value creation, and enterprises can also achieve value creation by improving customer satisfaction and perception. Value creation in service innovation means that value creators use operational resources and object resources to create value through interaction, while digital construction and investment create value in networks, thus improving the quality of service innovation. On the one hand, digital resources not only bring new factors of production, but also make the network break the limitation of time and space and can be converted at any time. It changes the linear mode of transferring value from suppliers to users in traditional service innovation and promotes users’ participation and interaction in the process of value creation. It has formed a flexible value governance network with multi-party participation and overlapping interests of suppliers, platforms and users. At the same time, digital construction and resource input further accelerate the interaction and sharing of resources. Based on digitization, this bidirectional dynamic association mechanism can improve the innovation ability of enterprises through digital resource input, thus providing new value creation for customers, and then affecting the service innovation of enterprises. Based on the above analysis, hypothesis 3 is proposed in this paper.
Hypothesis 3 (H3).
Digital resource input of financial enterprises affects value creation and improves service innovation to some extent; that is, value creation plays an intermediary role between digital resource input and service innovation.

2.4. Moderating Roles of Network Openness

An enterprise’s developing digitalization strategy is bound to the innovation of the products and services through the digital resources layout; scholars have researched that digital resources, information sharing, the relationship between digital resources and information sharing is positive, and have put forward the theoretical construction of value-added information. It is believed that enterprises can increase their digital construction process and digital platform through a large number of digital resources, such as talents, finance, hardware and software, and on this basis, increase the scope and degree of information sharing. Through investigation and analysis of a large quantity of enterprise data, it has been shown that information sharing can improve the degree of enterprise information exchange, which can be achieved through relevant R&D investment in information technology, verifying this statement with data. Therefore, enterprises can improve the degree of information sharing based on digitization by investing in digital resources. Bharadwaj [39] and Mithas et al. [40] found that enterprises can reduce resource loss and costs in the process of information transmission through a higher level of digital investment, and by increasing the efficiency of information sharing and transmission while reducing costs. The enterprise’s digital information technology is rapidly transferred in the internal process and platform of the enterprise. Thu, the process of digital resources of the enterprise effectively improve the enterprise’s use of digital platforms for the construction of investment and technology to improve the degree of customer and market information exchange and sharing. If enterprises of information technology or digital building networks have a certain degree of opening to the outside world, they can promote more digital resources to enhance the degree of information sharing. Network openness was first proposed by Rusanen et al. [41] to illustrate the degree of external cooperation and communication of enterprises, and it also plays an important role in the concept of open innovation. In this process, it is very important that the data and network platform of digital investment has a certain openness, which will effectively improve the information-sharing ability of digital investment. Openness is the main characteristic of the open network platform or the acquisition of external resources to strengthen information collection ability, so that they can through open characteristics for enterprise strategy execution of the complementary strategic resources, network openness can reflect the enterprise internal information network degree of openness and innovation; therefore, it also plays a certain role in promoting information sharing and service innovation. Based on the above analysis, hypothesis 4a is proposed.
Hypothesis 4a (H4a).
Network openness has a positive moderating effect on the relationship between digital resource input and information sharing.
Barras [42] proposed reverse product cycle and product cycle concepts and theory, and proved that service innovation is different from traditional product innovation, explaining that the process of technological innovation spreads from manufacturing to service industries. Therefore, based on the relative theory of the value chain, value is created by the enterprise, the customer is the receiver of value creation, from this perspective, there is essentially no difference between product innovation and service innovation, but service-dominant logic states that service innovation and manufacturing innovation, in essence, is not much different, but they are different forms of knowledge.
Scholars mostly believe that digital resources used for the promotion of value creation is a very important behavior for the enterprise, and usually through a lot of digital resources such as personnel, finance, software, etc., can increase the enterprise’s digitalization construction process and digital platform on the basis of the digital platform construction increased improve satisfaction and create value for customers and value-added role. Digital resources are an important factor of production inputs; after the enterprise develops a digitalization strategy, investment by digital resources can help enterprises to establish digital technology and a digital platform based on customer value, increasing the frequency of interaction between businesses and their customers and also increasing the customer to get the efficiency of user value through the digital technology platform. Therefore, digital resource input can improve value creation [43].
At the same time, in the process of digital resource investment, enterprises can also increase their defense ability and resistance ability against competitors in the external environment, thus establishing industry entry barriers and maintaining sustainable competitive advantages of enterprises (Li Tang et al., 2020). Network openness plays a certain role in promoting the impact of digital resource input on value creation. To a certain extent, the openness of the network can make the effect of resource input be effectively utilized to the maximum extent [44]. When the digital resources construction of digital technology and platform, and when the enterprises of the construction of the digital platform has a certain openness, not only can this make the information move smoothly in the process of circulation and interaction, but also can let more external resources access the digital platform, and through the external network provide access to import more resources, so achieving higher network openness, creating a greater impact of digital resource input on value creation. Based on the above analysis, hypothesis 4b is proposed in this chapter.
Hypothesis 4b (H4b).
Network openness has a positive moderating effect on the relationship between digital resource input and value creation.

2.5. Moderating Roles of Big Data Technology Capability

Financial enterprises’ digital transformation in the process of information sharing is the construction of digital information resources for effective use and interaction with customers, which can improve the utility maximization of digital resources for certain roles in promoting service innovation, thus being conducive to enterprise upgrading service innovation on technology innovation and service.
Information sharing embodies the process of information transmission between the owner and user, based on the information itself as a kind of transmission and sharing a pattern on the basis of related research in the theory of limited resources. The enterprise not only uses its own resources to gain advantage with competitive market advantages and long-term benefits, but with other stakeholders can undertake related cooperation and information sharing, allowing unique and scarce resources of the enterprise can be constructed [45].
Information sharing can help enterprises realize the interactive use of digital technology between enterprise platforms and customers in the process of implementing digitalization, and help enterprises effectively obtain external resource information. Information sharing helps enterprises obtain more external resources and knowledge needed for their survival and development, especially in the era of rapid development and transformation of digital technology. Information sharing can provide necessary resource reserves and innovative knowledge for enterprises to carry out innovation, thus improving the service innovation level of enterprises. Through information sharing, innovative resources and knowledge can also be obtained, so as to gain first-mover advantage and core competitiveness for digital transformation enterprises. Financial enterprises are market-oriented, and information sharing can obtain relevant resources and information more in line with market and service innovation. The direction of service innovation needs to match the real needs of the market and customers, which is crucial for the content of service innovation to meet the changes in customer information and consumption trends in the market.
The meaning of big data technical ability refers to the ability to obtain value from massive big data. Compared with traditional data analysis ability, big data technical ability is characterized by more obvious features, higher timeliness, more accurate recording, and certain visualization features. As enterprises and efficient use of the advantage of big data technology, through the use of technology to use and thus able to provide the higher efficiency, innovation in financial service innovation of the enterprise, if the enterprise has the ability to support large data technology, in the process of information sharing and external knowledge and resources are the necessary resource effectively converted to service innovation, thus, the efficiency of service innovation can be improved. Therefore, for enterprises with strong technological capabilities of big data, the promotion of information sharing on service innovation is more obvious. In addition, big data technology capabilities can help enterprises obtain external resources and provide more strategic resources for enterprise innovation, thus improving the output and efficiency of technological innovation. Compared with financial enterprises with weak big data technology capabilities, financial enterprises with big data technology development and integration capabilities can promote service innovation to a greater extent through information sharing in the process of digital transformation. Based on the above analysis, this paper proposes hypothesis 5a.
Hypothesis 5a (H5a).
Big data technical capability has a positive moderating effect on the relationship between information sharing and service innovation.
Value creation originally refers to the process in which an enterprise produces and provides a series of products or services for target customers. The main factors affecting value creation include the rate of return on investment, the cost of capital use and the growth rate of investment. It is also an important factor in the formulation and implementation of strategic choices by an enterprise. Enterprises can enhance their value creation ability by means of digital technology innovation and R&D investment, so as to enhance their market added value and investment growth rate and enhance product competitiveness and service innovation through value creation. In the era of the digital economy, Maine and Garnsey [43] believe that financial enterprises can improve service innovation ability through digital technology research and development and investment. Financial enterprises interact frequently with market users and create more value for the market by obtaining resources through interaction with external enterprises and customers. For the nature of the financial industry, value creation is also related to the sense of experience and identity of market customers, as well as the value created by financial enterprises through digital technology.
Big data technical capability refers to the ability of enterprises to accurately acquire and analyze massive data. Big data plays a certain role in promoting the relationship between value creation and service innovation. First of all, it has been discussed above that the value creation influenced by the input of digital resources by financial enterprises will have a certain promotional effect on service innovation, and the embodiment of the value brought by digitalization in the improvement of service innovation can be influenced to a greater extent through the application of big data technology. Big data technology can make the value created digitally interact with customers to a greater extent and in a wider range to improve the effect. The capability of big data technology of financial enterprises, through the analysis of large amounts of data, can accurately model enterprise customers, suppliers, stakeholders, and other relevant aspects of value creation and the pattern way to create value, to improve the frequency and the effect of resource exchange, so as to make the value creation and service innovation to get a good match, Therefore, it promotes the relationship mechanism between value creation and service innovation. Based on the above analysis, hypothesis 5b is proposed in this paper.
Hypothesis 5b (H5b).
Big data technological capability has a positive moderating effect on the relationship between value creation and service innovation.
The theoretical model of this paper is shown in Figure 1, and the hypotheses of this chapter are summarized in Figure 1.

3. Methods

3.1. Sampling Methodology and Data Collection

3.1.1. Sampling Methodology

In this paper, the questionnaire survey method is the main method of data collection. In order to obtain the accuracy of information, the Exam star service is adopted to conduct research through the official agency. The item design of the questionnaire is based on the scale used by existing researchers in digital orientation, service innovation, and other related fields, and on this basis, situational adaptation adjustment is made. The specific design process of the questionnaire is as follows: the first step is to select the third-party research agency of the enterprise sample service of Exam Star to ensure the reliability of data acquisition and make it clear that the object of this study is the employees in China’s financial industry. The questions in the questionnaire are mainly about the measurement of variables such as digital orientation, service innovation, information sharing, value creation, big data technical ability, and network openness. The second step, centering on the research topic of this paper, combines the research and development of the maturity scale by domestic and foreign scholars, and pays attention to the further verification of the foreign scale by domestic scholars in the Chinese context. Thirdly, after the scale and item development, in order to ensure the completeness of information, a preliminary survey of small samples was carried out to correct the deviation of the survey of large samples.
Four hundred and ninety questionnaires were sent to financial industry employees through the enterprise sample service of Exam Star, and 326 valid questionnaires were recovered as the data basis of this chapter. STATA16 and SPSS were used to analyze the collected data and verify the relationship between digital orientation and digital resource input, as well as the relationship between digital resource input, value creation, information sharing, and service innovation, as well as the moderating effect of big data technical capability and network openness. On the basis of data processing, this chapter uses metering software STATA and a structural equation model to conduct statistical analysis.

3.1.2. Data Collection and Structure

This research is aimed at China’s financial industry, which is large and subdivided into four major categories: banking, insurance, trusts, and others, and securities. Among them, 122 are in the banking industry, 63 in the securities industry, and 56 in the insurance industry, accounting for 37.42%, 19.33% and 17.18% respectively. The research objects are all financial practitioners selected from the enterprise sample service of Exam Star, and questionnaires are issued and collected within the specified time and scope. The investigated objects cover four occupational types of general manager, senior executives, core members and ordinary members. Ordinary members and core members account for a large proportion, accounting for 30.06% and 41.41% respectively. Senior executives and general managers accounted for a relatively low proportion of the overall sample, accounting for 26.07% and 2.45% respectively. It can be seen that the implementation of digitalization and service innovation strategy is mostly carried out by middle and grassroots teams, while the strategy is formulated by senior management. As for the enterprises surveyed by the questionnaire, from the perspective of normal distribution, most enterprises are in the scale of 51~1000 employees, accounting for 34.66% of the total number. It shows that the enterprises that carry out service innovation and digital transformation generally reach a certain scale. In terms of the business operation years of respondents, most of them have been operating for more than 5 years, accounting for 63.50%. Moreover, 51.23% of these enterprises have been in the exploration period of digital transformation for more than 5 years.

3.1.3. Research Methods

Descriptive statistics, correlation analysis, and multiple regression analysis are used in this paper. Descriptive statistical analysis was conducted on the data variables collected from the questionnaire, including team role, number of employees, operating years, digital transformation years, digital capability, digital resource input, value creation, information sharing, and service innovation [46,47,48,49,50,51]. Correlation analysis was used to measure the pair correlation degree between variables. The sample data in this paper meet the requirements of continuous data, normal distribution, and linear relationships, so the Spearman correlation coefficient is adopted to analyze the correlation between variables. Multiple regression analysis is a predictive modeling technique that studies the relationship between dependent variables (targets) and independent variables (predictors). This technique is commonly used for predictive analysis, time series modeling, and finding causal relationships between variables.

3.2. Variable Measure and Regression Model

3.2.1. Explained Variables and Explanatory Variables

Service innovation (represented by SInno): generally, there are two methods of measurement, subjective evaluation and objective evaluation. It can be seen from the existing literature that the relevant measurement of objective evaluation occupies the majority, but the results of subjective evaluation also have reliability and validity. Digital resource input (represented by DRInv) is mainly based on [52,53,54,55], the main measure and digital resources can be divided into hardware and software equipment, talents construction investment mainly refers to the digital construction related to attracting and developing talents.

3.2.2. Intermediary Variables

Information sharing (represented by IShare): refers to the information exchange and interaction between the digitalized platform and the subject and recipient of digitalized technology. In the process of information sharing, it promotes the effective dissemination of information, the effective implementation of strategy, and the implementation of the digitalization strategy [56,57,58,59,60,61,62]. If anything is possessed and enjoyed by someone within a certain space and time range, others have no right to possess and enjoy it. If the possessor transfers what he owns or can to others, he himself loses the right to possess and enjoy these things. Value creation (represented by VCrea). The scholars’ study of value creation is analyzed from different angles, and the thought value created by users for evaluation of products and services, and willingness to pay a certain cost is used to assess the effect of the promotion of use value. In addition, the scholar thinks users are an important part of enterprise value creation, thus, users’ consumption experience and satisfaction are ways to measure value creation, and enterprises can also achieve value creation by improving customer satisfaction and perception.

3.2.3. Moderator Variables

Big data technology capabilities (represented by BDTech): the ability of enterprises promotes the innovation of new products and new processes through the integration of digital technology and physical components of products. Enterprises’ ability to collect technological and market information of stakeholders through the Internet, mobile phone, communication technology, and multimedia technology.
Network openness (Open): according to the description of the existing literature, can be divided into the center of the network of the core enterprise and other network members for its dependency; other members of the correlation between the network interconnected relations between businesses, often also referred to as the core of network-centric degrees or central enterprises; the center degree is higher, the greater number of its associated companies [63,64,65,66,67,68].

3.2.4. Control Variables

In the process of analyzing the relationship between digital resources input and service innovation, control variables are added in order to reduce the interference of other factors and effectively control the influence of other factors. In this paper, the number of employees (represented by Number), business operation years (represented by Time), and digital transformation years (represented by Optimize), are taken as control variables [69,70,71,72,73,74,75]. The number of employees reflects the number of employees in an enterprise to define the size of the enterprise. Company operation years (Time) refers to the age of the company since its establishment. Optimize is a measure of how long it takes an enterprise to implement a digital-oriented strategy and is measured in years. The industry (represented by Industry) of enterprises is divided into banking, insurance, securities, and trust categories [76,77,78,79,80,81].

3.2.5. Regression Model Selection

In terms of the multiple regression model, the following econometric model is set up in this paper to verify research Hypothesis 1, the influence of digital resource input (represented by DRInv) on enterprise service innovation (represented by SInno).
SInno = α + β1DRInv + β2Number + β3Time + β4Optimize + β5Industry + β6Role + ε
To test Hypothesis 2 and Hypothesis 3—whether information sharing and value creation play a mediating role in the impact of digital resource input on service innovation—this paper establishes a model based on the mediating effect test method. Firstly, models ①–③ is established to judge the mediating effects of information sharing (represented by IShare) and value creation (represented by VCrea) of the explanatory variable digital resource input (represented by DRInv) on the explained variable service innovation (represented by SInno). If DRInv is able to influence SInno by influencing IShare (or VCrea), then IShare (or VCrea) is called a mediator variable (Figure 2 is the corresponding path diagram). δ represents the total effect of DRInv on SInno, ab represents the mediating effect of DRInv through IShare, and δ ‘represents the direct effect of DRInv on SInno. If δ is significant and the coefficients a and b are significant, the mediating effect is significant [82,83,84,85]. If the coefficients δ, a and b are significant and the coefficients δ ‘is not significant, IShare has a complete mediating effect on SInno during DRInv’s influence. Similarly, the mediating effect model of value creation (VCrea) is similar.
In order to investigate the moderating effect of network openness (Open) and verify Hypothesis 4a and 4b, the interaction term DRInv × Open between digital resources input (DRInv) and network openness (Open) is introduced.
IShare = α + β1DRInv + β2Open + β3DRInv × Open + β4Number + β5Time
+ β6Optimize + β7Industry + β8 Role + ε
VCrea = α + β1DRInv + β2Open + β3DRInv × Open + β4Number + β5Time
+ β6Optimize + β7 Industry + β8 Role + ε
α is for the constant term, β1~β8 as for the coefficient of ε for the residual.
In order to investigate the moderating effect of big data technology capability (BDTech) and verify Hypothesis 5a and 5b, interaction item information sharing (IShare) × Big data technology capability (BDTech) and value creation (VCrea) × Big data technology capability (BDTech) are introduced.
SInno = α + β1IShare + β2BDTech + β3IShare × BDTech + β4Number + β5Time
+ β6Optimize + β7 Industry + β8 Role + ε
Sinno = α + β1VCrea + β2BDTech + β3VCrea × BDTech + β4Number + β5Time
+ β6 Optimize + β7 Industry + β8 Role + ε

4. Empirical Test and Results

The multiple regression analysis method was used to analyze the data comprehensively. STATA software was used to conduct multiple regression analysis to verify the results of theoretical assumptions in this paper. According to the theoretical model, using digital guide OLS multiple regression analysis, digital resources, information sharing, and value creation, big data, technical skills, the relationship between the network openness, and service innovation, which uses the method of descriptive statistics, correlation analysis, and multiple regression analysis.

4.1. Harman Single Factor Analysis

For the problem of possible common method deviation in the scale of the questionnaire, factor analysis can be carried out on all the scale items of the questionnaire, which is a common practice at present; that is, Harman’s single-factor test was used for analysis. The use of this test method should be based on a certain basic assumption; that is, if method variation exists, an unrotated common factor will be precipitated during exploratory factor analysis of all items covering all research constructs, and this common factor is particularly powerful in explaining most of the variation. The hypothesis that a single factor explains all variation can be accurately tested by looking at the variance explanation rate of the first common factor (the general critical standard is 40%). As can be seen from Table 1, the variance explanation rate of the first common factor is 20.78%, which is lower than the critical standard 40%, so it basically shows that there is no serious common method deviation.

4.2. Descriptive and Correlation Analysis

The descriptive statistical results of variables in this paper are shown in Table 2, below. (1) The mean value of variable digital resource input (DRInv) was 4.213, indicating that the interviewees agreed that their financial enterprises have invested in digital resources. (2) The average value of service innovation (SInno) variable was 4.154, indicating that most respondents agreed with the view that digital resource input can improve service innovation. (3) The mean value of information sharing (IShare) was 4.237, and the standard deviation was 0.528; The mean value creation (VCrea) was 4.125 and the standard deviation was 0.621. The mean value of big data technology capability (BDTech) was 4.064, and the standard deviation was 0.514, while the mean value of Network openness (Open) was 4.152, and the standard deviation was 0.511. This shows that enterprises differed greatly in information sharing, value creation, big data technology capability, and network openness. (4) Control variable: the mean value of number was 2.627, indicating that the number of employees in the company of the interviewees is about 200 or more; The mean value of the Time variable was 3.743, indicating that the business Time of the company interviewed was close to 4 years. The average time of optimize was 3.236, indicating that the average time of digital strategy implementation was about 3 years.
In this paper, the correlation between variables was preliminarily analyzed, and the Spearman correlation coefficient matrix was obtained, as shown in Table 3. Table 3 shows that: (1) digital resource input (DRInv) was significantly positively correlated with service innovation (SInno), indicating that digital resource input of financial enterprises contributes to the improvement of service innovation; (2) information sharing (IShare), value creation (VCrea) and big data technology capability (BDTech) are significantly positively correlated with service innovation (SInno), indicating that the information sharing, value creation, and big data technology capability generated by digital resource input of financial enterprises had a positive promoting effect on service innovation of enterprises; (3) big data technology capability (BDTech) was positively correlated with service innovation (SInno). Network openness was significantly positively correlated with information sharing and value creation, indicating that financial enterprises’ digital network openness contributes to the realization of information sharing and the improvement of value creation of digital platforms.

4.3. Analysis of Regression Results

In order to explore the research in a more comprehensive way, this paper tries to verify the research hypothesis with the multiple regression method. Based on the theoretical model mentioned above, OLS multiple regressions were used to analyze the relationship between digitalization resource input, information sharing, value creation, big data technical capability, network openness, and service innovation. First, the variance inflation factor test was carried out, and VIF values were all less than 2, far lower than the threshold of VIF = 10, indicating that the models had passed the multicollinearity test.

4.3.1. Main Effect: Digital Resource Input Has a Positive Impact on Service Innovation

In order to verify hypothesis 1 and build a regression model of digital resource input (DRInv) on service innovation, this paper obtained the regression results, shown in Table 4. There was a significant positive correlation between digital resource input (DRInv) and service innovation at the 1% level, with a coefficient of 0.61 (p < 0.01), indicating that the level of enterprise investment in digital resources effectively promotes the enterprise’s big data technology capabilities. Hypothesis 1 passed the empirical test.

4.3.2. Mediating Effect: The Mediating Effect of Information Sharing and Value Creation

The analysis results of the mediating effect of information sharing and value creation on the impact of digital resource input on service innovation are shown in Table 5.
(1)
Testing the mediating effect of digital resource input, information sharing, and service innovation
Table 5 shows the mediating effect of digital resource input → information sharing → service innovation path through a three-step regression test. Test model (1) tested the total effect of digital resource input of independent variable on dependent variable service innovation. The analysis results show that digital resource input had a significant positive total effect on service innovation (β = 0.610, p < 0.001). Model (2) was an analysis of the influence of digital resource input on information sharing of the intermediate variable. The analysis results showed that digital resource input also had a significant positive influence on information sharing (β = 0.602, p < 0.001). Model (3) is the regression analysis of digital resource input and information sharing on dependent variable service innovation. The results show that both digital resource input and information sharing had significant positive effects on service innovation (β1 = 0.385, p1 < 0.001; β2 = 0.374, p2 < 0.001). According to the discriminant method of Baron and Kenny (1986), information sharing has a significant mediating effect between digital resource input and service innovation. Meanwhile, the influence of digital resource input on service innovation in Model (3) was still significant, but the coefficient was 0.385, which was lower than the coefficient of 0.610 in Model (1). Therefore, this mediation is a partial mediation effect. Hypothesis 2 passed the empirical test.
(2)
Testing the mediating effect of digital resource input, value creation, and service innovation
Similarly, the mediating effect of “value creation” in the path of “digital resource input → value creation → service innovation” is tested by a three-step regression, and the results are shown in the middle part of Table 5. Model (4) tests the total effect of digital resource input of the independent variable on the service innovation of the dependent variable. The analysis results show that digital resource input had a significant positive total effect on service innovation (β = 0.610, p < 0.001). Model (5) was an analysis of the influence of digital resource input on the value creation of the intermediate variable. The analysis results show that digital resource input also had a significant positive impact on value creation (β = 0.544, p < 0.001). Model (6) is the regression analysis of digital resource input and value creation on dependent variable service innovation. The analysis results show that digital resource input and information sharing had significant positive effects on service innovation (β1 = 0.342, p1 < 0.001; β2 = 0.493, p2 < 0.001). According to the discriminant method of Baron and Kenny (1986), value creation plays a mediating role between digital resource input and service innovation. Meanwhile, the influence of digital resource input on service innovation in Model (6) was still significant, but the coefficient was 0.342, which was lower than the coefficient of 0.610 in Model (4). Therefore, this mediation is a partial mediation effect. Hypothesis 3 also passed the empirical test. The mediating effect are obtained in this chapter, as shown in Table 6.

4.3.3. Moderating Effect of Network Openness and Big Data Technology Capability

In order to verify the moderating effect of network openness (Open) and big data technology capability (BDTech) in Hypothesis 4a, 4b, and 5a, 5b, the analysis results of the moderating effect are obtained in this chapter, as shown in Table 7.
Model 1 and Model 2 are used to verify the influence of digital resource input on information sharing and the regulating mechanism of network openness on the relationship between them. (1) In Model 1, there was a significant positive correlation between digital resource input and information sharing at the 1% level (coefficient 0.602, p < 0.001), indicating that digital resource input was conducive to the improvement of enterprise information sharing level; (2) Model 2 introduces interaction terms on the basis of Model 1 and obtained the interaction term (DRInv × Open) coefficient of digital resource input (DRInv) and network openness (Open) was not statistically significant, but the coefficient was positive. The results of the data show that Hypothesis 4a was not fully validated.
Model 3 and Model 4 in Table 5 were used to verify the impact of digital resource input on value creation and the adjustment mechanism of network openness on the relationship between them. (1) In Model 3, digital resource input was significantly positively correlated with value creation at the 1% level (coefficient 0.544, p < 0.001), indicating that the degree of digital resource input is conducive to the increase of enterprise value creation; (2) On the basis of Model 3, the interaction term of digital resource input (DRInv) and network openness (DRInv × Open) was added into Model 4, which was significantly positive at the significance level of 5% (coefficient 0.193, p < 0.05). It shows that network openness had a positive moderating effect on the relationship between digital resource input and value creation; that is, network openness promotes the positive relationship between digital resource input and value creation, which verifies Hypothesis 4b.
Model 5 and Model 6 in Table 5 were used to verify the influence of information sharing on service innovation and the moderating mechanism of big data technology capability on the relationship between the two. (1) In Model 5, there was a significant positive correlation between information sharing and service innovation at the 1% level (coefficient 0.55, p < 0.001), indicating that the degree of information sharing was conducive to the improvement of service innovation level of enterprises; (2) on the basis of Model 5, Model 6 introduced the interaction term and concludes that the interaction term between information sharing (IShare) and big data technology capability (BDTech) (IShare × BDTech) was positively correlated with service innovation at the significance level of 5% (coefficient 0.169, p < 0.05). It showed that big data technical ability had a positive moderating effect on the relationship between information sharing and service innovation; that is, big data technical ability promoted the positive relationship between information sharing and service innovation, which verifies Hypothesis 5a.
Model 7 and Model 8 were used to verify the impact of value creation on service innovation and the adjustment mechanism of big data technology capability on the relationship between the two. There was a significant positive correlation between value creation and service innovation at the 1% level (coefficient 0.639, p < 0.001), indicating that the degree of value creation was conducive to the improvement of the service innovation level of enterprises; (2) on the basis of Model 7, Model 8 introduced the interaction term between value creation (VCrea) and big data technology capability (BDTech) (VCrea × BDTech). The results show that although the coefficient of interaction term was not statistically significant, the coefficient was positive. The data results show that Hypothesis 5b is not fully validated.

5. Discussions

The digital transformation of financial enterprises has been an important strategic choice rooted in the background of the rapid development of the global digital economy. Financial enterprises accelerate the process of digitalization, combine financial products and services with digitalization, and form a relatively competitive digital strategic advantage. In the practice of digital transformation, financial enterprises should promote online, digitalized and intelligent service transactions by means of digital platform construction, resource sharing, and cooperation with external organizations, so as to provide customers with better and more convenient experience and services. At present, networks such as cloud computing, big data, Internet of Things, and artificial intelligence have gradually become mature and have been used in various industries. The financial industry is an industry that can be well connected and embedded with digital technology. Digital transformation reflects the organic combination of digital technology and access technology. Therefore, in the era of the digital economy, financial enterprises need to actively adapt to the opportunities brought by the development and reform of digital technology, and apply financial technology to achieve digital strategic transformation.
Financial enterprises can increase the input of digital resources in information sharing and value creation. For example, China Merchants Securities has developed a digital platform that can be used by all employees. The functions of business opportunity release and business opportunity reply enable the front desk staff of the whole company to share all business clues and timely grasp the progress of all projects of the department. Financial enterprises can accurately capture customers’ investment and financing needs with the help of the Internet finance department’s big data technology capabilities. For example, when a customer clicks on a key product promoted by the company, the relevant department of Internet finance can know the customer who placed the order according to the operation track of the customer, and then send it to the service personnel for follow-up service. At the same time, we can make use of the existing Development platforms of Internet companies. For example, financial enterprises can make use of enterprise Wechat to develop digital mini-programs for “enterprise wealth + platform” to reach customers, and to develop a series of video content for service personnel to communicate with customers through vivid forms of mutual assistance.
The digital transformation of the financial industry and the improvement of service innovation ability need to inject digital resources; that is, enterprises can obtain effective resources to improve service innovation ability by investing in their own technology research and development or exchanging external resources. After the establishment of digitalization, financial enterprises should strengthen the digital input of financial products and services, so as to improve their digital capability and influence on enterprises. To serve the development of the digital economy, we need to continue to deepen the supply-side structural reform of the financial sector, guide more financial resources to flow into digital technological innovation and the development of the digital economy, and continuously meet the needs of economic and social development in the digital economy era with high-quality financial services. In today’s era, digital technology and the digital economy are the forerunners of the scientific and technological revolution and industrial transformation in the world.
The sound development of the digital economy is conducive to building a new development pattern, a modernized economic system and a new national competitive edge. Seize the opportunity to win the initiative, the financial sector to embrace the digital economic era, to grasp the development trend of digital economy and law, put more financial resources allocation to the frontier of the development of the digital economy, key link and important industry, to promote the healthy development of the digital economy at the same time, realize the transformation and upgrading, the innovation development of their own, and will continue to add new drivers and foster new strengths for high-quality development of the Chinese economy.

6. Conclusions and Future Work

6.1. Conclusions

This paper uses the quantitative analysis method, through the questionnaire star distribution and recovery of 326 data as the foundation, using the measurement software, through the data descriptive analysis, correlation analysis, regression analysis, and other methods, in order to verify the digital source resource input, information sharing, value creation, big data, technical capabilities, network openness, and other variables and the relationship between the service innovation. Through the research method of questionnaire survey, literature review in the fields of digital transformation, digital capability and service innovation, as well as the data and results obtained from interviews with enterprises in the early stage, the final questionnaire items are formed. Through various aspects of screening and sorting out the questionnaire data, 326 digital transformation enterprises were obtained. Statistical software STATA16 and SPSS were used to analyze the collected data and verify the relationship between digital resource input, value creation, information sharing and service innovation, as well as the moderating effect of big data technical capability and network openness. The empirical methods used in this study mainly include descriptive statistics and correlation analysis, analyzing the relevant indicators of empirical measures, and using regression analysis to analyze the relationship between research hypotheses and variables.
The results of regression analysis are as follows: most of the theoretical hypotheses have passed the test, and digital resource input has a significant positive correlation with service innovation and has passed the verification; Information sharing and value creation have significant mediating effects on the relationship between digital resource input and service innovation, which have been verified. The moderating effect of network openness on the relationship between digital resource input and value creation is significantly positive and verified. The moderating effect of network openness on the relationship between digital resource input and information sharing is partially supported, which has not been verified. The moderating effect of big data technical ability on information sharing and service innovation is significantly positive and verified, while the moderating effect of big data technical ability on the relationship between value creation and service innovation is significantly positive and not completely verified.

6.2. Possibilities of Future Work

There are a few elements that can be improved in future work. Firstly, this study takes service innovation as a dependent variable to discuss the influence of digital orientation, digital resource input, and other factors on service innovation. In fact, there are many other factors that affect enterprise service innovation, such as resource acquisition ability, innovation investment and cost, success rate of strategic decisions, etc., so the future research can enrich these factors. Secondly, the related index system of service innovation can be further expanded and concretized. According to product innovation, process innovation, market innovation, and organization innovation, the index system of service innovation is established in this paper. The setting of the scale refers to the dimensions and items of the existing research and the follow-up research can also focus on other dimensions or more dimensions to carry out more accurate dimensional index construction and data collection and analysis of service innovation. Thirdly, by summarizing the research on digitalization orientation, further research and exploration on digitalization orientation are needed to seek more scientific and effective temperature to analyze and verify the effect of the two dimensions on service innovation at the level of executives and organizations. Further research can be conducted from more dimensions.

Author Contributions

Conceptualization, M.L. and W.J.; methodology, M.L.; software, W.J.; validation, W.J.; formal analysis, W.J.; investigation, W.J.; writing—original draft preparation, W.J.; writing—review and editing, M.L. and W.J.; visualization, M.L. and W.J.; supervision, M.L. and W.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data was collected from Examination star of China.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Wang, Z.W.; Yin, H.L. Science and Technology Insurance and Regional Innovation: Evidence from Provincial Panel Data in China. Technol. Anal. Strateg. Manag. 2022, 34, 1054348. [Google Scholar] [CrossRef]
  2. Wang, X.; Wang, L.; Wang, S. Marketisation as a channel of international technology diffusion and green total factor productivity: Research on the spillover effect from China’s first-tier cities. Technol. Anal. Strat. Manag. 2020, 33, 491–504. [Google Scholar] [CrossRef]
  3. Liu, N.; Fan, F. Threshold effect of international technology spillovers on China’s regional economic growth. Technol. Anal. Strateg. Manag. 2020, 32, 923–935. [Google Scholar] [CrossRef]
  4. Chen, X.H.; Yan, D.; Chen, W. Can the digital economy promote FinTech development? Growth Change 2022, 53, 221–247. [Google Scholar] [CrossRef]
  5. Wang, J.; Xie, Z. Learning from institutional diversity to innovate: A study of Chinese exporters in digital industries. Int. J. Technol. Manag. 2021, 87, 384–411. [Google Scholar] [CrossRef]
  6. Vicente-Saez, R.; Gustafsson, R.; Martinez-Fuentes, C. Opening up science for a sustainable world: An expansive normative structure of open science in the digital era. Sci. Public Policy 2021, 48, 799–813. [Google Scholar] [CrossRef]
  7. Wang, J.; Li, X.; Wang, P.; Liu, Q. Bibliometric analysis of digital twin literature: A review of influencing factors and conceptual structure. Technol. Anal. Strat. Manag. 2022, 34, 2026320. [Google Scholar] [CrossRef]
  8. Xie, X.; Wu, Y.; Sendra Garcia, F.J. Gendered linguistic structures and the innovation performance of new ventures in emerging countries: The moderating effects of digitalisation and the entrepreneurial ecosystem. Int. J. Technol. Manag. 2021, 87, 46–77. [Google Scholar] [CrossRef]
  9. Sefyrin, J.; Gustafsson, M.; Wihlborg, E. Addressing digital diversity: Care matters in vulnerable digital relations in a Swedish library context. Sci. Public Policy 2021, 48, 841–848. [Google Scholar] [CrossRef]
  10. Vargo, S.L.; Lusch, R.F. Service dominant logic: Continuing the evolution. J. Acad. Mark. Sci. 2008, 25, 1–10. [Google Scholar] [CrossRef]
  11. Thanasopon, B.; Papadopoulos, T.; Vidgen, R. The role of openness in the fuzzy front-end of service innovation. Technovation 2015, 47, 32–46. [Google Scholar] [CrossRef]
  12. Cheng, C.C.J.; Krumwiede, D. What makes a manufacturing firm effective for service innovation: The role of intangible capital under strategic and environmental conditions. Int. J. Prod. Econ. 2017, 193, 113–122. [Google Scholar] [CrossRef]
  13. Pur, S.; Huesig, S.; Schmidhammer, C. Application and validation of a disruptive potential methodology for digital two-sided platforms-the case of marketplace lending in Germany. Int. J. Technol. Manag. 2022, 88, 205–246. [Google Scholar] [CrossRef]
  14. Wu, T.; Chen, B.; Shao, Y.; Lu, H. Enable digital transformation: Entrepreneurial leadership, ambidextrous learning and organisational performance. Technol. Anal. Strat. Manag. 2021, 33, 1389–1403. [Google Scholar] [CrossRef]
  15. Zhou, J.; Liu, C.; Xing, X.; Li, J. How can digital technology-related acquisitions affect a firm’s innovation performance? Int. J. Technol. Manag. 2021, 87, 254–283. [Google Scholar] [CrossRef]
  16. Hakala, H. Strategic Orientations in Management Literature: Three Approaches to Understanding the Interaction between Market, Technology, Entrepreneurial and Learning Orientations. Int. J. Manag. Rev. 2010, 13, 199–217. [Google Scholar] [CrossRef]
  17. Laukkanen, T.; Nagy, G.; Hirvonen, S.; Reijonen, H.; Pasanen, M. The effect of strategic orientations on business performance in SMEs. Int. Mark. Rev. 2013, 30, 510–535. [Google Scholar] [CrossRef]
  18. Hughes, P.; Morgan, R.E.; Kouropalatis, Y. Market knowledge diffusion and business performance. Eur. J. Mark. 2008, 42, 1372–1395. [Google Scholar] [CrossRef]
  19. Besson, P.; Rowe, F. Strategizing information systems-enabled organizational transformation: A transdisciplinary review and new directions. J. Strateg. Inf. Syst. 2012, 21, 103–124. [Google Scholar] [CrossRef]
  20. Wang, Y.; Su, X. Driving factors of digital transformation for manufacturing enterprises: A multi-case study from China. Int. J. Technol. Manag. 2021, 87, 229–253. [Google Scholar] [CrossRef]
  21. Arndt, F.; Ng, W.; Huang, T. Do-It-Yourself laboratories, communities of practice, and open innovation in a digitalised environment. Technol. Anal. Strat. Manag. 2021, 33, 1186–1197. [Google Scholar] [CrossRef]
  22. Pihlajamaa, M.; Malmelin, N.; Wallin, A. Competence combination for digital transformation: A study of manufacturing companies in Finland. Technol. Anal. Strat. Manag. 2021, 33, 2004111. [Google Scholar] [CrossRef]
  23. Zheng, Y.; Han, W. Does government behaviour or enterprise investment improve regional innovation performance?—Evidence from China. Int. J. Technol. Manag. 2021, 85, 274–296. [Google Scholar] [CrossRef]
  24. Garzella, S.; Fiorentino, R.; Caputo, A.; Lardo, A. Business model innovation in SMEs: The role of boundaries in the digital era. Technol. Anal. Strat. Manag. 2020, 33, 31–43. [Google Scholar] [CrossRef]
  25. Schillo, R.S.; Ebrahimi, H. Gender dimensions of digitalisation: A comparison of Venture Capital backed start-ups across fields. Technol. Anal. Strat. Manag. 2021, 33, 1918336. [Google Scholar] [CrossRef]
  26. Chu, W.; Kang, M. The effects of customers’ perceived relational benefits on the customer conception of service innovation at service centers for it products: The mediating role of customer participation. J. Adm. Sci. Technol. 2014, 35, 213–235. [Google Scholar]
  27. Witell, L.; Snyder, H.; Gustafsson, A. Defining service innovation: A review and synthesis. J. Bus. Res. 2016, 19, 2863–2872. [Google Scholar] [CrossRef] [Green Version]
  28. Hsu, T.T.; Tsai, K.H.; Hsieh, M.H. Strategic orientation and new product performance: The roles of technological capability. Can. J. Adm. Sci. 2014, 31, 44–58. [Google Scholar] [CrossRef]
  29. Yu, F.; Jiang, D.; Zhang, Y.; Du, H. Enterprise digitalisation and financial performance: The moderating role of dynamic capability. Technol. Anal. Strat. Manag. 2021, 33, 1980211. [Google Scholar] [CrossRef]
  30. Lemaire, S.L.L.; Bertrand, G.; Maalaoui, A.; Kraus, S.; Jones, P. How women entrepreneurs manage the digitalisation of their business initiating a dialogue between the entrepreneurship as practice approach and the theory of bricolage. Int. J. Technol. Manag. 2021, 87, 78–104. [Google Scholar] [CrossRef]
  31. Tsou, H.-T.; Chen, J.-S. How does digital technology usage benefit firm performance? Digital transformation strategy and organisational innovation as mediators. Technol. Anal. Strat. Manag. 2021, 33, 1991575. [Google Scholar] [CrossRef]
  32. Popkova, E.G.; Sergi, B.S.; Rezaei, M.; Ferraris, A. Digitalisation in transport and logistics: A roadmap for entrepreneurship in Russia. Int. J. Technol. Manag. 2021, 87, 7–28. [Google Scholar] [CrossRef]
  33. Margherita, A.; Nasiri, M.; Papadopoulos, T. The application of digital technologies in company responses to COVID-19: An integrative framework. Technol. Anal. Strat. Manag. 2021, 33, 1990255. [Google Scholar] [CrossRef]
  34. Nevo, S.; Wade, M. The formation and value of IT-enabled Resources: Antecedents and consequences of synergistic relationships. MIS Q. 2010, 34, 163–183. [Google Scholar] [CrossRef] [Green Version]
  35. Azma, F.; Mostafapour, M.A.; Rezaei, H. The application of information technology and its relationship with organizational intelligence. Procedia Technol. 2012, 1, 94–97. [Google Scholar] [CrossRef] [Green Version]
  36. Berman, S.J. Digital transformation: Opportunities to create new business models. Strategy Leadersh. 2012, 40, 16–24. [Google Scholar] [CrossRef]
  37. Helkkula, A.; Kowalkowski, C.; Tronvoll, B. Archetypes of service innovation: Implications for value cocreation. J. Serv. Res. 2018, 32, 284–301. [Google Scholar] [CrossRef] [Green Version]
  38. Lepak, D.P.; Smith, K.G. Value creation and value capture: A multilevel perspective. Acad. Manag. Rev. 2007, 32, 180–194. [Google Scholar] [CrossRef] [Green Version]
  39. Bharadwaj, A.A. Resource-based perspective on information technology: Technology capability and firm performance. MIS Q. 2000, 24, 169–196. [Google Scholar] [CrossRef]
  40. Mithas, S.; Tafti, A.; Mitchell, W. How a firm’s competitive environment and digital strategic posture influence digital business strategy. MIS Q. 2013, 37, 511–536. [Google Scholar] [CrossRef] [Green Version]
  41. Rusanen, H.; Halinen, A.; Jaakkola, E. Accessing resources for service innovation: The critical role of network relationships. J. Serv. Manag. 2014, 45, 223–225. [Google Scholar] [CrossRef]
  42. Barras, R. Towards a theory of innovation in services. Res. Policy 1986, 15, 161–173. [Google Scholar] [CrossRef]
  43. Maine, E.; Lubik, S.; Garnsey, E. Process-based vs. product-based innovation: Value creation by nanotech ventures. Tech-Novation. 2012, 32, 179–192. [Google Scholar] [CrossRef]
  44. Lu, H.; Du, D.; Qin, X. Assessing the Dual Innovation Capability of National Innovation System: Empirical Evidence from 65 Countries. Systems 2022, 10, 23. [Google Scholar] [CrossRef]
  45. Ryu, H.S.; Lee, J.N. Understanding the role of technology in service innovation: Comparison of three theoretical perspectives. Inf. Manag. 2018, 16, 294–307. [Google Scholar] [CrossRef] [Green Version]
  46. Sun, C.Z.; Yan, X.D.; Zhao, L.S. Coupling efficiency measurement and spatial correlation characteristic of water-energy-food nexus in China. Resour. Conserv. Recycl. 2021, 164, 105151. [Google Scholar] [CrossRef]
  47. Fan, F.; Du, D.B. The Measure and the Characteristics of Temporal-spatial Evolution of China Science and Technology Resource Allocation Efficiency. J. Geogr. Sci. 2014, 24, 492–508. [Google Scholar] [CrossRef]
  48. Wang, S.; Wang, X.L.; Lu, F. The impact of collaborative innovation on ecological efficiency—Empirical research based on China’s regions. Technol. Anal. Strateg. Manag. 2020, 32, 242–256. [Google Scholar] [CrossRef]
  49. Wang, S.; Zhang, J.Q. The symbiosis of scientific and technological innovation efficiency and economic efficiency in China—An analysis based on data envelopment analysis and logistic model. Technol. Anal. Strateg. Manag. 2019, 31, 67–80. [Google Scholar] [CrossRef]
  50. Xie, J.; Sun, Q.; Wang, S.; Li, X. Does Environmental Regulation Affect Export Quality? Theory and Evidence from China. Int. J. Environ. Res. Public Health 2020, 17, 8237. [Google Scholar] [CrossRef]
  51. Wang, X.; Wang, L.; Zhang, X. The spatiotemporal evolution of COVID-19 in China and its impact on urban economic resilience. China Econ. Rev. 2022, 74, 101806. [Google Scholar] [CrossRef]
  52. Yu, H.C.; Zhang, J.Q. Agglomeration and flow of innovation elements and the impact on regional innovation efficiency. Int. J. Technol. Manag. 2022, 28, 12564. [Google Scholar]
  53. Wang, Z.W.; Zong, Y.X.; Dan, Y.W.; Jiang, S.J. Country risk and international trade: Evidence from the China-B & R countries. Appl. Econ. Lett. 2021, 28, 1784–1788. [Google Scholar]
  54. Fan, F.; Zhang, X. Transformation effect of resource-based cities based on PSM-DID model: An empirical analysis from China. Environ. Impact Assess. Rev. 2021, 91, 106648. [Google Scholar] [CrossRef]
  55. Ke, H.; Dai, S. Does innovation efficiency inhibit the ecological footprint? An empirical study of China’s provincial regions. Technol. Anal. Strat. Manag. 2021, 33, 1959910. [Google Scholar] [CrossRef]
  56. Zhang, J.Q.; Chen, T.T. Empirical Research on Time-Varying Characteristics and Efficiency of the Chinese Economy and Monetary Policy: Evidence from the MI-TVP-VAR Model. Appl. Econ. 2018, 50, 3596–3613. [Google Scholar] [CrossRef]
  57. Fan, F.; Lian, H.; Liu, X.; Wang, X. Can environmental regulation promote urban green innovation Efficiency? An empirical study based on Chinese cities. J. Clean. Prod. 2020, 287, 125060. [Google Scholar] [CrossRef]
  58. Xiao, Z.L.; Du, X.Y. Convergence in China’s high-tech industry development performance: A spatial panel model. Appl. Econ. 2017, 49, 5296–5308. [Google Scholar]
  59. Ke, H.; Dai, S.; Yu, H. Spatial effect of innovation efficiency on ecological footprint: City-level empirical evidence from China. Environ. Technol. Innov. 2021, 22, 101536. [Google Scholar] [CrossRef]
  60. Wang, S.; Wang, J.; Wei, C.; Wang, X. Collaborative innovation efficiency: From within cities to between cities—Empirical analysis based on innovative cities in China. Growth Chang. 2021, 52, 1330–1360. [Google Scholar] [CrossRef]
  61. Yang, W.Y.; Fan, F.; Wang, X.L. Knowledge innovation network externalities in the Guangdong-Hong Kong-Macao Greater Bay Area: Borrowing size or agglomeration shadow? Technol. Anal. Strateg. Manag. 2021, 33, 1940922. [Google Scholar] [CrossRef]
  62. Wang, S.; Wang, J.; Wang, Y.; Wang, X. Spillover and Re-Spillover in China’s Collaborative Innovation. Int. Reg. Sci. Rev. 2022. [Google Scholar] [CrossRef]
  63. Yu, H.C.; Zhang, J.Q. Industrial collaborative agglomeration and green economic efficiency—Based on the intermediary effect of technical change. Growth Change 2022, 53, 578–596. [Google Scholar]
  64. Zhang, J.; Wang, S.; Yang, P. Analysis of Scale Factors on China’s Sustainable Development Efficiency Based on Three-Stage DEA and a Double Threshold Test. Sustainability 2020, 12, 2225. [Google Scholar] [CrossRef] [Green Version]
  65. Fan, F.; Lian, H.; Wang, S. Can regional collaborative innovation improve innovation efficiency? An empirical study of Chinese cities. Growth Chang. 2019, 51, 440–463. [Google Scholar] [CrossRef]
  66. Wang, S.; Wang, J. The hidden mediating role of innovation efficiency in coordinating development of economy and ecological environment: Evidence from 283 Chinese cities. Environ. Sci. Pollut. Res. 2021, 28, 47668–47684. [Google Scholar] [CrossRef]
  67. Wang, S.; Hou, D.L. The Mediation Effect of Innovation in the Domestic and International Economic Development Circulation. Technol. Anal. Strateg. Manag. 2022, 34, 1054535. [Google Scholar] [CrossRef]
  68. Fan, F.; Zhang, X.; Yang, W.; Liu, C. Spatiotemporal Evolution of China’s Ports in the International Container Transport Network under Upgraded Industrial Structure. Transp. J. 2021, 60, 43–69. [Google Scholar] [CrossRef]
  69. Zhang, H.; Lan, T.; Li, Z.L. Fractal evolution of urban street networks in form and structure: A case study of Hong Kong. Int. J. Geogr. Inf. Sci. 2022, 36, 1100–1118. [Google Scholar] [CrossRef]
  70. Fan, F.; Zhang, X.Y.; Wang, X.L. Are there political cycles hidden inside collaborative innovation efficiency? An empirical study based on Chinese cities. Sci. Public Policy 2022, 45, 101093005. [Google Scholar] [CrossRef]
  71. Zhao, L.S.; Hu, R.; Sun, C.Z. Analyzing the spatial-temporal characteristics of the marine economic efficiency of countries along the Maritime Silk Road and the influencing factors. Ocean. Coast. Manag. 2021, 204, 105517. [Google Scholar] [CrossRef]
  72. Fan, F.; Zhang, K.; Dai, S.; Wang, X. Decoupling analysis and rebound effect between China’s urban innovation capability and resource consumption. Technol. Anal. Strat. Manag. 2021, 33, 1979204. [Google Scholar] [CrossRef]
  73. Liu, S.; Fan, F.; Zhang, J. Are Small Cities More Environmentally Friendly? An Empirical Study from China. Int. J. Environ. Res. Public Health 2019, 16, 727. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  74. Wang, S.; Jia, M.; Zhou, Y. Impacts of changing urban form on ecological efficiency in China: A comparison between urban agglomerations and administrative areas. J. Environ. Plan. Manag. 2019, 63, 1834–1856. [Google Scholar] [CrossRef]
  75. Yu, H.C.; Liu, Y.; Liu, C.L. Spatiotemporal Variation and Inequality in China’s Economic Resilience across Cities and Urban Agglomerations. Sustainability 2018, 10, 4754. [Google Scholar] [CrossRef] [Green Version]
  76. Choi, Y.K. The impact of a platform company’s open innovation activities on its firm value: In the perspective of the venture ecosystem. Technol. Anal. Strateg. Manag. 2022, 34, 322–334. [Google Scholar] [CrossRef]
  77. Zhu, Q.Y.; Sun, C.Z.; Zhao, L.S. Effect of the marine system on the pressure of the food–energy–water nexus in the coastal regions of China. J. Clean. Prod. 2021, 319, 1–12. [Google Scholar] [CrossRef]
  78. Fan, F.; Dai, S.; Zhang, K.; Ke, H. Innovation agglomeration and urban hierarchy: Evidence from Chinese cities. Appl. Econ. 2021, 53, 6300–6318. [Google Scholar] [CrossRef]
  79. Thacher, T.D.; Pludowski, P.; Shaw, N.J.; Mughal, M.Z.; Munns, C.F.; Högler, W. Nutritional rickets in immigrant and refugee children. Public Health Rev. 2016, 37, 3. [Google Scholar] [CrossRef] [Green Version]
  80. Fan, F.; Cao, D.; Ma, N. Is Improvement of Innovation Efficiency Conducive to Haze Governance? Empirical Evidence from 283 Chinese Cities. Int. J. Environ. Res. Public Health 2020, 17, 6095. [Google Scholar] [CrossRef]
  81. Tang, H.; Zhang, J. High-speed rail, urban form, and regional innovation: A time-varying difference-in-differences approach. Technol. Anal. Strat. Manag. 2022, 34, 2026322. [Google Scholar] [CrossRef]
  82. Yu, H.; Zhang, J.; Zhang, M. Cross-national knowledge transfer, absorptive capacity, and total factor productivity: The intermediary effect test of international technology spillover. Technol. Anal. Strat. Manag. 2021, 34, 625–640. [Google Scholar] [CrossRef]
  83. Fichman, R.G.; Santos, B.; Zheng, Z. Digital innovation as a fundamental and powerful concept in the information systems curriculum. MIS Q. 2014, 38, 329–354. [Google Scholar] [CrossRef] [Green Version]
  84. Nambisan, S. Software firm evolution and innovation–orientation. J. Eng. Technol. Manag. 2002, 19, 141–165. [Google Scholar] [CrossRef]
  85. Stauss, B.; Hertog, P.D.; Wietze, V. Capabilities for managing service innovation: Towards a conceptual framework. J. Serv. Manag. 2010, 12, 490–514. [Google Scholar]
Sustainability 14 07143 g001 550
Figure 1. Theoretical model diagram of this paper.
Figure 1. Theoretical model diagram of this paper.
Sustainability 14 07143 g001
Sustainability 14 07143 g002 550
Figure 2. Diagram of the mediation model.
Figure 2. Diagram of the mediation model.
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Table
Table 1. Sample situation distribution table.
Table 1. Sample situation distribution table.
VariablesFrequencyPercentage
Type of industrybank12237.42%
insurance5617.18%
trust4614.11%
securities6319.33%
leasing3911.96%
Team rolegeneral manager82.45%
senior executive8526.07%
core member13541.41%
rank and file9830.06%
Number of employees1–505617.18%
51–20011334.66%
201–10009428.83%
more than 10006319.33%
Operations duration1 year195.83%
2–3 years3510.74%
4–5 years6519.94%
more than 5 years20763.50%
Digital transformation years1 year92.76%
2–3 years8325.46%
4–5 years6720.55%
more than 5 years16751.23%
Table
Table 2. Harman’s single-factor test.
Table 2. Harman’s single-factor test.
Total Variance Interpretation
Initial EigenvalueExtract the Sum of Squares of Loads
ComponentTotalVariance PercentageCumulative %TotalVariance PercentageCumulative %
114.7620.7820.7814.7620.7820.78
22.773.924.682.773.924.68
32.443.4428.122.443.4428.12
42.012.8330.952.012.8330.95
51.972.7733.721.972.7733.72
61.882.6436.361.882.6436.36
71.772.4938.851.772.4938.85
81.632.2941.141.632.2941.14
91.562.1943.331.562.1943.33
101.532.1545.481.532.1545.48
111.512.1347.611.512.1347.61
121.472.0749.691.472.0749.69
131.411.9851.671.411.9851.67
141.361.9253.591.361.9253.59
151.311.8455.421.311.8455.42
161.241.7557.171.241.7557.17
171.211.758.871.211.758.87
181.161.6360.51.161.6360.5
191.11.5562.051.11.5562.05
201.071.563.561.071.563.56
211.031.4565.011.031.4565.01
Table
Table 3. Descriptive statistics of variables.
Table 3. Descriptive statistics of variables.
VariableVariable SymbolObserved ValueMinimum ValueMaximum ValueMedianMeanStandard Deviation
Digital resource inputDRInv3261.1735.0004.3334.2130.481
Service innovationSInno3261.2865.0004.1434.1540.478
Information sharingIShare3261.4005.0004.4004.2370.528
Value creationVCrea3262.4005.0004.2124.1250.621
Big data technology capabilityBDTech3262.0005.0004.1674.0640.514
Network opennessOpen3261.1675.0004.3334.1520.511
Number of employeesNumber3261.0004.0003.0002.6270.989
Time of the companyTime3261.0004.0004.0003.7430.606
Average time of optimizeOptimize3261.0004.0004.0003.2360.918
Table
Table 4. Spearman correlation statistics of variables.
Table 4. Spearman correlation statistics of variables.
VariableDRInvSInnoIShareVCreaBDTechOpenNumberDRInvOptimize
DRInv1
SInno0.256 ***1
IShare0.325 ***0.427 ***1
VCrea0.310 ***0.326 ***0.198 ***1
BDTech0.287 ***0.279 ***0.324 ***0.323 ***1
Open0.239 ***0.215 ***0.237 ***0.255 ***0.322 ***1
Number0.0540.0490.060−0.0200.0360.0721
Time0.146 *0.158 **0.116 *0.0920.0060.0690.171 **1
Optimize0.329 ***0.227 ***0.219 ***0.129 *0.219 ***0.305 ***0.285 ***0.196 ***1
Notes: *, **, *** respectively stated for the significance at the level of 10, 5, and 1%.
Table
Table 5. Regression results of digital resource input to service innovation.
Table 5. Regression results of digital resource input to service innovation.
VariableCoefficientT ValueSignificant
constant1.173 ***3.6680.000
DRInv0.610 ***9.9500.000
Number0.0050.2850.861
Time0.0551.1940.234
Optimize−0.005−0.1830.851
Industrycontrol
Rolecontrol
R2 value0.438
Sample326
Notes: *** respectively stated for the significance at the level of 1%.
Table
Table 6. The mediating effects of information sharing and value creation.
Table 6. The mediating effects of information sharing and value creation.
ModelMediating Effect of Information SharingMediating Effect of Value Creation
VariableService Innovation
(1)		Information Share
(2)		Service Innovation
(3)		Service Innovation
(4)		Value Creation
(5)		Service Innovation
(6)
DRInv0.610 ***
(9.950)		0.602 ***
(8.842)		0.385 ***
(6.569)		0.610 ***
(9.950)		0.544 ***
(7.868)		0.342 ***
(6.748)
IShare 0.374 ***
(7.323)
VCrea 0.493 ***
(11.017)
BDTech
Number0.005
(0.285)		0.019
(0.583)		−0.002
(−0.101)		0.005
(0.285)		−0.019
(−0.580)		0.014
(0.665)
Time0.055
(1.194)		0.020
(0.356)		0.047
(1.157)		0.055
(1.194)		0.036
(0.639)		0.037
(1.012)
Optimize−0.005
(−0.183)		0.019
(0.491)		−0.012
(−0.428)		−0.005
(−0.183)		−0.027
(−0.684)		0.008
(0.325)
Intercept1.173 ***
(3.668)		1.423 ***
(3.653)		0.641 **
(2.176)		1.173 ***
(3.668)		1.781 ***
(4.507)		0.294
(1.104)
Industrycontrolcontrolcontrolcontrolcontrolcontrol
Rolecontrolcontrolcontrolcontrolcontrolcontrol
R2 value0.4380.3270.5540.4380.2960.647
VIF value<2<2<2<2<2<2
Sample326326326326326326
Notes: **, *** respectively stated for the significance at the level of 5, and 1%.
Table
Table 7. Results of moderating effect analysis.
Table 7. Results of moderating effect analysis.
Moderating Effect of Big Data
Technology Capability		Moderating Effect of Network Openness
VariableModel 1
Service Innovation		Model 2
Service Innovation		Model 3
Service Innovation		Model 4
Service Innovation		Model 5
Information Sharing		Model 6
Information Sharing		Model 7
Value Creation		Model 8
Value Creation
Intercept1.425 ***
(4.822)		3.309 ***
(3.123)		1.009 ***
(3.746)		0.299
(0.260)		1.423 ***
(3.653)		−0.165
(−0.128)		1.781 ***
(4.507)		3.658 ***
(2.787)
The main effect
DRInv 0.602 ***
(8.842)		0.466
(1.489)		0.544 ***
(7.868)		−0.432
(−1.358)
IShare0.550 ***
(11.524)		−0.286
(−1.146)
VCrea 0.639 ***
(14.747)		0.491
(1.769)
BDTech −0.292
(−1.023)		 0.380
(1.283)
Open 0.705 **
(2.023)		 −0.257
(−0.726)
Moderating effect
DRInv ×
Open		 0.039
(0.465)		 0.193 **
(2.270)
IShare ×
BDTech		 0.169 **
(2.526)
VCrea × BDTech 0.008
(0.109)
Control
Number−0.013
(−0.503)		−0.003
(−0.146)		0.010
(0.424)		0.008
(0.378)		0.019
(0.583)		0.017
(0.623)		−0.019
(−0.580)		−0.018
(−0.618)
Time0.044
(0.971)		0.081 *
(1.990)		0.031
(0.782)		0.072
(1.929)		0.020
(0.356)		0.055
(1.129)		0.036
(0.639)		0.077
(1.556)
Optimize0.033
(1.058)		0.004
(0.132)		0.058 *
(2.128)		0.021
(0.823)		0.019
(0.491)		−0.031
(−0.889)		−0.027
(−0.684)		−0.091 **
(−2.540)
Industrycontrolcontrolcontrolcontrolcontrolcontrolcontrolcontrol
Rolecontrolcontrolcontrolcontrolcontrolcontrolcontrolcontrol
R2 value0.4600.5770.5680.6460.3270.4990.2960.475
VIF value<2<2<2<2<2<2<2<2
Sample326326326326326326326326
Notes: *, **, *** respectively stated for the significance at the level of 10, 5, and 1%.
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Liu, M.; Jiang, W. Empirical Research on the Influence Mechanisms of Digital Resources Input on Service Innovation in China’s Finance Industry. Sustainability 2022, 14, 7143. https://doi.org/10.3390/su14127143

AMA Style

Liu M, Jiang W. Empirical Research on the Influence Mechanisms of Digital Resources Input on Service Innovation in China’s Finance Industry. Sustainability. 2022; 14(12):7143. https://doi.org/10.3390/su14127143

Chicago/Turabian Style

Liu, Mingxia, and Wei Jiang. 2022. "Empirical Research on the Influence Mechanisms of Digital Resources Input on Service Innovation in China’s Finance Industry" Sustainability 14, no. 12: 7143. https://doi.org/10.3390/su14127143

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