Abstract
The Internet of Things manufacturing technology is an important symbol for measuring the level of a country’s scientific and technological development. Enterprises that apply the Internet of Things manufacturing technology represent the level of a country’s industrial development to a large extent. However, the introduction of IoT manufacturing technology will not automatically generate benefits. It needs to be matched with a suitable organizational structure to maximize the advantages of IoT manufacturing technology. The enterprise organization structure has always been the focus of enterprise organization research and management research. From the linear system to the network organization, the innovation of enterprise organizational structure has never stopped. Therefore, the research on the organizational structure innovation of IoT manufacturing technology enterprises has strong theoretical and practical significance. This study conducted an empirical study on the impact of organizational innovation climate and individual innovation behavior on organizational structure innovation in IoT manufacturing technology enterprises in the form of a questionnaire survey. The structural equation model of organizational innovation climate and personal innovation behavior is proposed, the data and questionnaires are statistically and factorially analyzed by software such as SPSS 18.0 and AMOS 7.0, and the hypothesized structural equation model is verified.
1. Introduction
Since Schumpeter put forward the theory of technological innovation in 1912, innovation research has been a hot topic in academic circles. Early research on innovation was mainly carried out from a macro perspective, looking at innovation from a highly macro and abstract perspective, viewing enterprises as participants in the economic system, and focusing on the output of the economic system rather than the performance of the enterprise. With the change in environment and the rise of organizational theory, organizational structure innovation has become a research topic. In the late 1980s and early 1990s, the American manufacturing industry first proposed the concept of IoT manufacturing technology to enhance its own competitiveness and promote national economic growth based on the opportunities and challenges it faced. In the following years, there was a wave of applying IoT manufacturing technology around the world. New industrial countries such as Europe, America, Japan, and China have listed IoT manufacturing technology as a national high-tech and key development project. Manufacturing is the pillar industry of China’s national economy. About a quarter of the population is engaged in this industry, creating great material wealth. Therefore, it is the core of China’s national economy and the driving force of industrialization. China has introduced the Internet of Things manufacturing technology for more than 20 years, which has played a good role in promoting its economic growth because the theory of organizational innovation at home and abroad is not very unified and perfect. In addition, with the gradual formation of the global economic integration development model, for enterprises applying IoT manufacturing technology, the challenges faced by organizations in their operation are becoming more complex and diverse, which inevitably requires organizational theory. Keep up with the times. In particular, the global financial crisis in 2008 had a great impact on our country. The downward pressure on the domestic economy was increasing. The state proposed to strengthen technological transformation, accelerate the industrial adjustment of technological innovation, and put forward higher requirements for the application of Internet of Things manufacturing technology [1–5].
This paper studies and analyzes the dynamic system of enterprise organizational innovation applying IoT manufacturing technology, describes the entire emergent process of IoT manufacturing technology enterprise organizational innovation, and proposes an organizational innovation process model with adaptive robust control links according to the emergent characteristics. At the same time, a questionnaire survey was carried out on the employees of enterprises applying the Internet of Things manufacturing technology to investigate the organizational innovation atmosphere and personal innovation behavior, and empirical data were obtained. SPSS 18.0 was used to conduct a statistical analysis of the data and questionnaires. By using AMOS7.0 to verify the hypothetical structural equation model of organizational innovation climate and individual innovation behavior, the relationship between the two is obtained, which is a useful tool for IoT manufacturing technology enterprises. Achieving a higher degree of organizational structure innovation provides a valuable reference.
2. Related Work
At the end of the twentieth century, with the emergence of the new economy and the development of the theory of enterprise innovation, the theory of organizational innovation began to appear. There is no unified definition and classification of organizational innovation, and the similarities and differences between it and organizational structure innovation are unclear. For example, many scholars define organizational innovation as the organization adopting a new idea or behavior, where innovation refers to a new product, a new service, a new technology, or a new management practice. Alasoinni defined organizational innovation as a change in the division of labor and interactions within functions, between functions, and between organizations. Knight divided organizational innovation into four categories: product or service innovation, production process innovation, structural innovation, and people innovation. Michael Hammer and James Champy put forward the theory of process reengineering and enterprise reengineering. BPR became a new method of business organization that emerged in the United States in the early 1990s. They gave a precise definition of BPR: BPR is a fundamental rethinking of business processes and a complete overhaul to achieve dramatic improvements in business performance measures such as cost, quality, service, and speed. This definition contains four keywords: radical, thorough, dramatic, and flow. At the same time, Peter Senge, a professor at the Massachusetts Institute of Technology in the United States and a famous management scientist, pointed out the management concept of Learning Organization, stating that today’s society has entered the information age, and enterprises must remain in the social reform and market economy tide. Invincibly, becoming a learning enterprise organization is a development trend. Based on the basic principles of system dynamics, he specifically conceived some basic characteristics of future enterprises, including flat organizational structure, organizational informatization, more open organization, and the relationship between employees and managers gradually shifting from subordination to work. Correspondingly, for IoT manufacturing technology, in the 1960s, Joan Woodward studied the impact of technology on organizational structure. Lee and Leonard found that self-guided trolleys changed the nature of employees’ work in a low-volume manufacturing environment; scholars such as Samson believed that the successful implementation of IoT manufacturing technology should carefully consider organizational and human resource issues such as responsibilities, recognition of changes, positions, and skills. Ghani et al. proposed that necessary organizational changes must be made in the application of IoT manufacturing technology to obtain higher performance. Saraph and Sebastian reviewed numerous studies and concluded that the failure of IoT manufacturing technology is mainly due to the neglect of key human resource factors. Gerwin and Kolodny believed that IoT manufacturing technology has led to many changes in human resource management and practice and further suggested that human resource development should be integrated with the design of new technologies in manufacturing companies. Scholars such as Mital proposed that the purpose of enterprise application of IoT manufacturing technology is to enhance the reliability and flexibility of production and improve product quality and economy, pointing out that the key to the successful implementation of IoT manufacturing technology lies in the human factor. Mohammed Zhari published the paper “The Role of Total Quality Management on Organizational Innovation,” which provided new ideas for organizational structure innovation [6–11].
3. The Characteristics and Process of the Innovation Power of the Organizational Structure of the Internet of Things Manufacturing Technology Enterprises
3.1. IOT Manufacturing Technology Enterprise Organizational Structure Innovation Power Mode and Relationship
Foreign scholars attribute the power source of enterprise organizational structure innovation to technology power source, market power source, government power source, and transaction cost source. Chinese scholar Zhang Gang drew on the viewpoints of other scholars at home and abroad and summarized the main contents of six aspects of the dynamic mechanism of enterprise organizational structure innovation, that is, six possible main sources of power for organizational structure innovation: the introduction of new technologies in enterprises and the orientation of enterprise strategies. Changes in corporate value orientation and the company’s own development needs are the triggers for social, political, and economic changes. This paper believes in the innovation of enterprise organizational structure as a new organizational form for enterprises to adapt to environmental changes and productivity development requirements based on advanced manufacturing technology (Internet of Things manufacturing technology). The core innovation power of enterprises can be roughly divided into three modes. (1) Technology is the driving force of the organizational structure innovation model of the induced enterprise. From the perspective of the power source of innovation, the driving force of the technology-induced organizational structure innovation mainly comes from the development of new technologies for the enterprise. For enterprises in the fast “high-tech” industry, they have no choice but to innovate because to achieve market leadership, only continuous innovation and daring to take risks will enable enterprises to have better development opportunities. Therefore, the technology-induced enterprise organizational structure innovation model is the most important driving force for the organizational structure innovation of advanced manufacturing technology enterprises [12]. (2) The innovative power of the strategic-oriented organizational structure mainly comes from the change in the strategic orientation of the enterprise. For example, the senior leaders or managers of the enterprise make advance judgments on the changes in the internal and surrounding environment of the enterprise or make quick responses according to the existing changes. The specific performance is that according to the actual situation of the external environment and internal conditions, the corresponding material and organizational resources are concentrated to determine the organizational vision, clarify the goal of planning, adjust product structure, and realize strategic innovation. Subsequently, structural and cultural innovations are started along with strategic innovation and proceed simultaneously to realize the dynamic matching of the enterprise’s strategic, cultural, and structural innovation and achieve the purpose of promoting enterprise organizational structure innovation [13]. (3) The driving force of market pressure-oriented organizational structure innovation mainly comes from the pressure of market competition. The increasingly fierce competition environment pressure in the market makes enterprises have to consider innovative changes to deal with the problems of survival and development. The competition with competitors, the competition of products or services, the competition of product quality, the competition of capital strength, the competition of marketing and public relations require enterprises to use new technologies to develop and improve products and, at the same time, adjust their organizational structure to achieve the purpose of reducing enterprise costs and enhancing competitiveness [14].
Therefore, the market competition is more intense. Companies are more inclined to adopt new technologies and restructure efficient organizations to build competitive advantage. The development of new products and changes in organizational structure and systems, among others, aim to continuously provide high-quality goods and customer service. The combined effect of the three innovation power modes brings continuous power support to the innovation of organizational structure. The pulling effect caused by their interaction also provides favorable support and traction for their respective innovation models. Enterprises integrate the industrial structure through technological induction and accordingly need to adjust the department setting, resource allocation, and responsibility structure in the enterprise structure. The change in structure will inevitably lead to the innovation of structure, which will lead to the subtle change of corporate culture, that is, the change of corporate values and behavioral norms. Gradual changes in structure and culture, in turn, induce further changes in corporate strategy. Therefore, the innovation of the organizational structure of enterprises driven by technological induction is through structural innovation to cultural innovation and finally affects strategic innovation. The market pressure-type organizational structure innovation mainly promotes the change in enterprise strategy through changes in the external environment, thereby affecting the adjustment of the industrial structure of the enterprise or directly inducing the enterprise to enhance its competitiveness by introducing new technologies. It can also be seen from the innovation of strategic-led organizational structure that the changes in corporate culture and structure are carried out simultaneously and appear interactively under each innovation mode. Therefore, under specific circumstances, the various modes are mutually reinforcing and mutually inducing relationships, as shown in Figure 1.
3.2. General Innovation Process Model of IoT Manufacturing Technology Enterprise Organizational Structure
3.2.1. Process Model of General Organizational Structure Innovation
As an important content of modern enterprise management, organizational structure innovation is carried out based on the process. Lewin proposed a three-stage model of the organizational innovation process of unfreezing-change-refreezing. On the basis of Lewin’s three-stage model, Koter proposed an eight-stage model of the organizational innovation process (establishing crisis awareness, forming a guiding team, creating a vision, communicating between leaders and organizational members, empowering relevant personnel to make changes, planning goals to create short-term effects, consolidate achievements and deepen reforms, and institutionalize new achievements and new methods). Scheinti put forward the “adaptation cycle” theory of the organizational innovation process. He believed that organizational innovation is a continuous cycle process (insight into changes in the environment, research changes, implementation of changes, stability of changes measures, output changes results). The specific performance of organizational innovation activities is the orderly progress of each stage or the effective connection of each link. In other words, each stage and each link can fully explain that organizational innovation is a process composed of a series of activities with corresponding functions. Based on the viewpoints of scholars at home and abroad, we generally divide the specific implementation process of organizational structure innovation activities into two major stages. Action to make a choice the second stage is the organization implementation stage, which puts the selected action plan into practice to guide the organization and individual behavior [15]. The specific links included in these two stages are as follows: Figure 2.(1)Organizational structure innovation formulation stage. (1) The occurrence and determination of problems: at this stage, the managers of the organization need to consider the development and changing trends of the internal and external environment of the organization and find out the problems that the organization faces or will face at this stage according to the actual situation of the organization. For example, strong market pressure or the mismatch between organizational structure and product structure, according to which the goal of enterprise organizational structure innovation is formulated. (2) The problem is included in the organizational agenda. This stage is the feasibility analysis and demonstration stage of the innovation goal of the enterprise organizational structure. For all activities with the process, the process must be carried out under restricted conditions, so the process of organizational structure innovation must also be carried out in a specific organizational structure innovation environment. The problem that enterprises should consider is whether the goal of organizational structure innovation can be successfully achieved under the existing environmental conditions of the organization. If the result of the demonstration is that the goal of enterprise organizational structure innovation cannot be achieved under the existing organizational environment of the enterprise, then the enterprise needs to redefine the goal of organizational structure innovation according to the demonstration situation. Many enterprise organizational structure innovations end in failure, most of which are due to the lack of feasibility demonstrations of organizational structure innovation, which leads to the establishment of organizational structure innovation goals that are out of the actual situation of enterprises and organizations, resulting in increased risks of organizational structure innovation. (3) Organizational planning and preassessment: this stage is the design and selection stage of the company’s organizational structure innovation plan. Enterprises design several alternative organizational structure innovation plans according to the established organizational structure innovation goals and inherent resource conditions of the organization. Enterprise decision-makers select the best plan for implementation through a comprehensive evaluation of the alternative plans. In addition, the value tendency and risk preference of organizational decision-makers, the actual resource conditions possessed by the enterprise organization, and the coordination of various interests within the organization are also important factors that affect the choice of innovative plans for enterprise organizational structure.(2)Organization and implementation stage. (1) Organizational execution: the implementation of organizational structure innovation by the enterprise is the final confirmation of the innovation plan by the entire organization and is transferred to a controlled release. The specific content includes the acquisition of resources, the preparation of relevant documentation and manuals, and the training of organizational members. The final and crucial step is implementation because no matter how excellent an organization structure innovation plan an enterprise chooses, the implementation of the innovation plan largely determines whether the plan can achieve the ideal goal of organizational structure innovation. (2) Organizational evaluation: this stage evaluates the innovation effect of enterprise organizational structure. Enterprises need to generate a complete and effective evaluation index system according to the ultimate goal of organizational structure innovation, use this as a standard to make accurate value judgments on the effect evaluation of the organizational structure innovation process of enterprises, and provide feedback information according to the judgment for future improvement. The evaluation content in this process mainly includes the following aspects: suitability of organizational goals; input-output efficiency of organizational structure innovation; adequacy of organizational performance; actual performance of organizational structure innovation; resource input quality and allocation; the evaluation of the situation; the fairness of the organization’s formulation and implementation; the coordination of the internal interests of the organization; and the impact on the overall development of the enterprise. (3) Organization adjustment and improvement: this stage is the consolidation and adjustment of the innovation process of the enterprise organizational structure. Consolidate and adjust the organizational structure innovation of the enterprise according to the evaluation of the organizational structure innovation effect. If the effect of the organizational structure innovation is more significant and achieves the corporate organizational structure innovation goal set in advance, then the existing innovation achievements will be consolidated. If the organizational structure innovation effect is not very good, then the organizational structure innovation plan must be carried out. Improve and adjust, and then implement the improved and adjusted program. If the final organizational structure innovation result is far from the original target, it is impossible to achieve the goal of organizational structure innovation through timely improvement and adjustment of the organizational structure innovation plan. Implement new innovation activities under the newly formulated organizational structure innovation goals. (4) The end of the organization means the end of organizational structure innovation activities. To sum up, the general organizational structure innovation is divided into two stages, and each stage has four subprocesses. The first is the decision-maker’s process on what action to take for the organizational problem, which includes four subprocesses in the first stage: the occurrence and identification of the problem; the problem included in the organizational agenda; organizational planning and change; and organizational adoption and legalization change. Then, an idealized organizational structure innovation is formed through organizational formulation; then, it is transferred to the organizational execution stage of organizational structure innovation. The four subprocesses of this stage are organizational implementation, organizational evaluation, organizational adjustment and change, and organizational termination. The specific process is shown in Figure 2.
3.2.2. Characteristics of Organizational Structure Innovation Process
Through the general process model of organizational structure innovation, we can see that organizational structure innovation is a cycle with dynamic, systematic uncertainty and regular changes in the process of risk. Therefore, the process of organizational structure innovation has the following characteristics: (1) organizational structure innovation is a reciprocating and cyclical process. The organizational structure innovation process is different from the previous production process. It is often through repeated cycles between several steps in the innovation process to finally achieve the goal of improving organizational efficiency that the enterprise expects. (2) The process of organizational structure innovation is dynamic and systematic. The process of enterprise organizational structure innovation is dynamic and changes with time. Enterprises in different life cycle stages have different organizational characteristics. In different life cycle stages, the innovation of enterprise organizational structure needs to be adjusted accordingly. In addition, in the process of organizational structure innovation, members of the organization organically combine a series of resources in the enterprise and the organization to achieve the purpose of preorganizational structure innovation. This involves all aspects of the enterprise coorganization system. The process of structural innovation is an uncompromising complex system engineering. (3) The process of organizational structure innovation is uncertain and risky. Because, in most cases, the environment of organizational structure innovation is not static and there are many uncontrollable factors within the enterprise organization, such as the way of thinking and code of conduct of the people inside the organization, and because people’s cognitive ability to things is limited, we cannot fully understand and recognize the innovation process and cannot fully and effectively solve various problems in the innovation process. It has also been affected to varying degrees [16].
The process of organizational structure innovation is not only a simple change in the organizational structure or organizational procedures of an enterprise, but a reasonable and stable arrangement of human, material, and financial resources and their structure in the enterprise or organization, which means the combination of enterprise resources. The change of the method requires breaking the internal balance of the original organization, and the breaking of this balance can easily lead to the loss of control of enterprise management, coupled with the uncertainty of the process of organizational structure innovation itself, which means that the process of organizational structure innovation is full of risks for businesses.
4. An Empirical Study on the Impact of IoT Manufacturing Technology Companies on Organizational Structure Innovation
4.1. Study Design and Research Methods
This study used the empirical research method of a questionnaire survey. The content of the questionnaire is the organizational innovation climate scale and the personal innovation behavior scale. The organizational innovation climate scale is mainly based on the KEY scale of amabile and borrowed from the revision of the KEY scale based on the Chinese background by Jian Qiu Haozheng, Sun Rui, and Liu Yun, among others. The personal innovation behavior scale was designed based on Scott and Bruce’s three-stage model of innovation behavior and Janssen’s scale on innovation behavior. Questionnaire scores of the two scales are through the summary, induction, sorting, testing, item analysis, exploratory factor analysis, and confirmatory factor analysis. Among them, the organizational innovation climate scale contains six dimensions and 24 items, and the personal innovation behavior scale contains eight items. The formal measurement items passed the reliability and validity test, which proved that the organizational innovation climate in the background of advanced manufacturing technology enterprises could be improved by colleagues support (CS), supervisor support (SS), organizational philosophy (OV), resource provision (RS), task characteristics (TC), knowledge support (KS), and other six aspects are measured and evaluated. The personal innovation behavior of employees in IoT manufacturing technology enterprises can be measured and evaluated in two aspects: the generation of innovative ideas (F1) and the implementation of innovative ideas (F2). The research questionnaire showed good reliability and validity. Both the organizational innovation climate questionnaire and the personal innovation behavior questionnaire are designed in the form of a five-point Likert scale because, in most cases, the five-point scale is the most reliable, and if the options exceed five points, it is difficult for ordinary people to distinguish enough. The three-point scale limits the expression of moderate and strong opinions, and the five-point scale can express the difference between moderate and strong opinions. The respondents choose according to their actual situation in the enterprise and the degree of agreement for each item. In the innovation climate questionnaire, “1” means strongly disagree, “5” means strongly agree, “1” in the innovation behavior questionnaire means never innovating, and “5” means very frequent innovation. At the same time, the questionnaire adopts the method of filling in the blanks and selection and also collects the personal background information of the respondents, including age, gender, education, position, and tenure of service.
4.1.1. Sample Selection and Data Sources and Distribution
The research object of this research is knowledge workers in enterprises applying advanced manufacturing technology. According to the principle of improving the efficiency of questionnaire recovery as much as possible and meeting the required samples, the formal questionnaire survey was conducted by two methods. One is to distribute questionnaires to colleagues through familiar students working in companies that apply advanced manufacturing technology. Good relationships among colleagues and ease of contact ensured high questionnaire recovery rates and questionnaire validity. The second method is to collect questionnaires by sending web-mails to enterprises applying advanced manufacturing technology. This method is inefficient and has poor recovery. The validity of the questionnaire is also high. The questionnaire survey was carried out from August to September 2010. A total of 200 questionnaires were distributed by the first method, and 175 valid questionnaires were recovered. The effective recovery rate of the questionnaire was 87.5%. A total of 100 questionnaires were distributed by the second method, 34 valid questionnaires were recovered, and the effective recovery rate of the questionnaires was 34%. A total of 300 questionnaires were distributed, and 209 valid questionnaires were recovered, and the effective recovery rate was 70%.
4.1.2. Questionnaire Test Method
(1)Correlation Analysis of questionnaire items: each variable measurement item in this paper reflects related constructs and concepts, so item scores should be moderately related to each other. If an item has a very low correlation coefficient with all or most other items (often with < 0.03 as the criterion), then the item cannot be part of the scale. If one item is highly correlated with most other items (often by > 0.70), then it is questionable as an independent measure of the scale (Tang, 1999). In this study, 0.70 was used as the criterion for selecting topics. Item-total correlation analysis (Corrected Item-Total Correlation, CITC for short) is one of the methods to purify measurement questions. The CITC analysis follows the following principles: all the items whose CITC is less than 0.40 and which can increase Cronbach’s alpha coefficient should be deleted (Tian, Bearden, and Hunter, 2001). Therefore, we use 0.40 as the critical criterion to decide the choice of measurement items.(2)Reliability analysis of questionnaire items: the commonly used information test method in the Likert attitude scale is Cronbach’s alpha coefficient method. For item selection, the internal reliability of each subscale is mainly measured by the degree of change of the α coefficient before and after item deletion to test whether the items are really the theoretical constructs that are intended to measure the design and to what extent are all items that measure the same constructs internally consistent. The larger the Cronbach’s alpha value, the better the correlation between the questionnaire items and the higher the reliability of internal consistency. In general, Cronbach’s alpha values greater than 0.8 indicate excellent internal consistency, values between 0.6 and 0.8 indicate good internal consistency, and values below 0.6 indicate poor internal consistency. The scholar Gay (1992) believed that the reliability coefficient of any test or scale above 0.90 means the reliability is very good. Scholars have different opinions on the acceptable minimum reliability coefficient, such as 0.70 (De Vellis, 1991; Nunnally, 1978) and 0.80 (Bryman and Cramer, 1997). However, most believe that if the reliability is below 0.60, it is appropriate to revise the research tool. In this study, the standard of Cronbach’s alpha was set to 0.70.(3)Exploratory factor analysis of questionnaire items: exploratory factor analysis is to test whether all items theoretically belonging to the same dimension can clearly form a common factor with the largest extraction variance ratio so that the connotation of this dimension can be expressed and conceptualized. In this study, the KMO (Kaiser–Meyer–Olkin) measure and Bartlett’s sphericity test were used to judge whether the samples were suitable for factor analysis. The KMO value is used to judge the standard as shown in table 1, and the Bartlett sphericity test generally only needs to reach a significant level. Then, the principal component analysis method and the maximum variance method are used to rotate and solve the common factor, the eigenvalue is greater than 1, the gravel diagram determines the number of factor extractions, the factor loading is less than 0.50 as the topic selection scale, and the cumulative variance explanation rate of the extracted factors is not less than 50%. At the same time, the item with cross-factor loading exceeding 0.40 (Gorsuch, 1983) is not allowed. Otherwise, the item will be deleted, as shown in Table 1.(4)Questionnaire item validity analysis: the construct validity of the questionnaire was analyzed by confirmatory factor analysis. It can test the stability of a scale structure and can also simplify the items obtained by exploratory factor analysis. Judging whether a model and data are well fitted is usually judged by combining absolute fitting indicators and relative fitting indicators. Absolute fitting indicators include x2/df, GFI, AGFI, NCP, ECVI, RMR, and RMSEA; relative fitting indicators include CFI, NFI, IFI, TLI, and RFI. In this study, the indicators of x2/df, RMSEA, IFI, TLI, CFI, and GFI will be combined to judge the degree of model fit [17–20].(1)x2/df (view fit index): the fit index x2/df value refers to a statistic that directly tests the similarity between the sample covariance matrix and the estimated covariance matrix and can be used to measure the overall fit of the model and try to correct the model with a low degree of fit. It is generally believed that when x2/df < 3, the model fits better; the closer the x2/df value is to 1, the better the model fits, x2/df > 2 is an ideal result, 2 < x2/df < 5 indicates that the overall model is acceptable; x2/df > 5 indicates that the observed data do not fit the model well, x2/df > 10 indicates that the observed data does not fit the model, and the model is very poor.(2)Root Mean Square Error of Approximation (RMSEA) is more sensitive to the error model and easily explains the quality of the model; the RMSEA index is less affected by the sample size and is a widely used fitting index in recent years one. The variation interval of RMSEA is between 0 and 1, and it is generally believed that the closer to 0, the better. When RMSEA is less than 0.05, it indicates that the model fits very well; if it is greater than 0.05 and less than 0.08, it indicates that the model fits well; and if it is greater than 0.08 and less than 0.1, the model is acceptable.(3)Incremental Fit Index (IFI) is generally in the range of 0 to 1, and the closer to 1, the better the fit. It is generally believed that greater than 0.9 indicates a good fit, and if IFI is greater than 0.8, the model is considered acceptable.(4)Tucker–Lewis index (TLI), a nonstandard fitting index, is generally in the range of 0 to 1. The closer TLI is to 1, the better the fitting degree is. It is generally considered that a TLI greater than 0.9 indicates a good fit, and a TLI greater than 0.8 indicates that the model is acceptable.(5)Comparative Fit Index (CFI): the variation range of CFI is between 0 and 1. The closer it is to 1, the better the degree of fit is. If the CFI is greater than 0.9, it is considered that the model has a good fit. When the CFI is greater than 0.8, the model is considered acceptable.(6)Goodness of Fit Index (GFI): the variation interval of GFI is between 0 and 1. The closer it is to 1, the better the degree of fit is. If the GFI is greater than 0.9, it is considered that the model has obtained better results. If the GFI is greater than 0.8, the model is considered acceptable.
4.1.3. Structural Equation Model of Organizational Innovation Climate and Individual Innovation Behavior
Most studies believe that organizational innovation climate has a positive predictive effect on individual innovation behavior, whereas some studies believe that organizational innovation climate has no relationship with organizational innovation behavior. This paper focuses on the organizational innovation climate and organizational innovation behavior of IoT manufacturing technology enterprises. The relationship is further explored, and the following assumptions are made:
Hypothesis 1. Colleague support has a positive impact on individual innovative ideas.
Hypothesis 2. Colleague support has a positive effect on the implementation of individual innovative ideas.
Hypothesis 3. Supervisor support has a positive impact on individual innovative ideas.
Hypothesis 4. Supervisor support has a positive effect on the implementation of individual innovative ideas.
Hypothesis 5. Organizational philosophy has a positive impact on individual innovative ideas.
Hypothesis 6. Organizational philosophy has a positive impact on the implementation of individual innovative ideas.
Hypothesis 7. Resource supply has a positive impact on individual innovative ideas.
Hypothesis 8. Resource supply has a positive impact on the implementation of individual innovative ideas.
Hypothesis 9. Task characteristics have a positive impact on individual innovative ideas.
Hypothesis 10. Task characteristics have a positive impact on the implementation of individual innovative ideas.
Hypothesis 11. Knowledge support has a positive impact on individual innovative ideas.
Hypothesis 12. Knowledge support has a positive impact on the implementation of individual innovative ideas. By combining the above 12 assumptions, the structural equation model of the two groups of variables can be shown in Figure 3.
4.2. Questionnaire Test Results
4.2.1. Construct Validity Analysis of Organizational Innovation Climate Scale
According to the output results of the AMOS fitting model analysis, we summarize the main indicators in the validity analysis as shown in Table 2. The structural validity of the organizational innovation climate questionnaire is acceptable.
As shown in the results, after the confirmatory factor analysis of the factor structure model, the fitting indexes all reach the ideal level. Therefore, goal orientation can be divided into six clear dimensions: peer support, supervisor support, organizational philosophy, resource supply, task characteristics, and knowledge support, as shown in Figure 4.
4.2.2. Analysis of the Construct Validity of the Personal Innovation Behavior Atmosphere Scale
According to the output results of the AMOS fitting model analysis, we summarize the main indicators in the validity analysis as shown in Table 3. According to the above-mentioned main indicators and each indicator judgment standard, the RNSEA value reaches 0.100, and the minimum requirement is the personal innovation behavior questionnaire. Construct validity is okay.
As shown in the results, after the confirmatory factor analysis of the factor structure model, each fitting index has reached the ideal level. Therefore, goal orientation can be divided into two clear dimensions: the generation of innovative ideas and the implementation of innovative ideas, as shown in Figure 5.
4.3. Verification of the Organizational Innovation Atmosphere and Individual Innovation Relationship Structure Model of IoT Manufacturing Technology Enterprises
Based on the previous analysis, we use AMOS 7.0 to verify the hypothesis model of the relationship between organizational innovation climate and individual innovation behavior, as shown in Figure 6. The fitting index of the model is shown in Table 4. From the fitting index, it is assumed that the fitting degree of the model is good.
According to the verification results of the hypothesis structural equation model by the AMOS 7.0 software, as shown in Figure 6, in the IoT manufacturing technology enterprise, for hypothesis 1, the nonstandard regression coefficients of CS and F1 are 0.52, indicating that the support level of each colleague is 0.52. With an increase of one unit, the generation of individual innovative ideas will increase by 0.52 units; for hypothesis 2, when the nonstandard regression coefficients of parameters CS and F2 are 0.47, it indicates that for each unit increase in the support level of colleagues, the implementation of individual innovative ideas will increase by 0.47 units; for hypothesis 3, the nonstandard regression coefficients of parameters SS and F1 is 0.38, indicating that for each unit of supervisor support, the generation of individual innovative ideas will increase by 0.38 units; for hypothesis 4, the nonstandard regression coefficients of parameters SS and F2 are 0.25, indicating that each unit of supervisor support will increase individual innovative idea implementation students by 0.25 units; for hypothesis 5, the nonstandard regression coefficients of OV and F1 are 0.44, the generation of individual innovative ideas will increase by 0.44 units; for hypothesis 6, the standard regression coefficients of OV and F1 are 0.32, indicating that, for each unit increase in organizational concept level, the implementation of individual innovative ideas will increase by 0.32 units; for assuming hypothesis 7, the nonstandard regression coefficients of RS and F1 are 0.48, indicating that, for each unit increase in resource supply level, the generation of individual innovative ideas will increase by 0.48 units; for hypothesis 8, the nonstandard regression coefficients of RS and F2 is 0.50, indicating that, for each unit increase in resource supply level, the implementation of individual innovative ideas will increase by 0.50 units; for hypothesis 9, the nonstandard regression coefficients of TC and F1 are 0.43, indicating that, for each unit increase in task characteristic level, the generation of innovative ideas will increase by 0.43 units; for hypothesis 10, the nonstandard regression coefficients of TC and F2 are 0.52, indicating that, for each unit increase in the task characteristic level, the implementation of individual innovative ideas will increase by 0.52 units; for hypothesis 11, the nonstandard regression coefficients of KS and F1 are 0.40, indicating that, for each unit of knowledge support, the generation of individual innovative ideas will increase by 0.40 units; and for hypothesis 12, the nonstandard regression coefficients of KS and F2 are 0.36, indicating that, for each additional unit of knowledge support, the implementation of individual innovative ideas will increase by 0.36 units. [21–24].
To sum up, the organizational innovation climate has a significant positive impact on the innovation behavior of individuals in the organization. All 12 hypotheses have been verified, indicating that, in IoT manufacturing technology enterprises, people who are the main body of innovation, regardless of the factors that affect innovation in the organization, will have a certain impact on their innovation behavior. In particular, the stimulation, encouragement, and support of individuals’ innovative behaviors by enterprises, to a large extent, determines whether the organization can successfully develop its innovative ability and dynamic adaptability. Therefore, the higher the organizational innovation atmosphere, the greater the promotion of individual innovation behavior, and the improvement of individual innovation ability has a positive effect on organizational structure innovation through the function of seeking maximum consistency in organizational management. The greater the promotion effect, the more conducive to improving the organizational structure innovation of IoT manufacturing technology enterprises.
5. Conclusion
In recent years, there has been a wave of research and application of advanced manufacturing technology around the world. With the wide application of advanced manufacturing technology in China, many enterprises have obtained better benefits. However, many enterprises still fail when implementing IoT manufacturing technologies. The fundamental reason is that the traditional organizational management model of Chinese enterprises cannot well match the manufacturing technology of the Internet of Things, which restricts the development of the manufacturing technology of the Internet of Things. In order to maximize the benefits brought by IoT manufacturing technology, enterprises must learn to transform the traditional organizational management structure to adapt to the production requirements of advanced manufacturing technology. It is imperative to innovate the organizational structure of IoT manufacturing technology enterprises. The research of this paper not only has important theoretical significance for the development and enrichment of the theory of organizational innovation power and process theory of IoT manufacturing technology enterprises but also provides information on factors such as organizational innovation atmosphere and innovative behavior with others that affect the organizational innovation of IoT manufacturing technology enterprises. Empirical analysis research has strong practical application value. It provides a theoretical basis and useful reference for the organizational innovation practice activities of IoT manufacturing technology enterprises. The main points and conclusions are as follows.
This research conducted a questionnaire survey on the innovation atmosphere of enterprise organizations and individual innovation behavior among employees of enterprises applying advanced manufacturing technology, obtained empirical data, and used SPSS 18.0 to conduct statistical analysis on the data and questionnaire. Exploratory factor analysis, reliability analysis, and confirmatory factor analysis are mainly used to analyze the factor structure of the questionnaire, and each factor structure has good reliability and validity. Using AMOS 7.0 to verify the structural equation model of the hypothetical corporate organizational innovation climate and individual innovation behavior, it is concluded that the corporate organizational innovation climate has a significant positive impact on people’s innovation behavior in IoT manufacturing technology enterprises. Organizational innovation climate plays a role in organizational structure innovation through the positive influence of individual innovation behaviors in an organization. The positive relationship between organizational innovation climate and individual innovation behaviors means that IoT manufacturing technology enterprises can achieve a higher degree of organizational structure. Innovation provides a valuable reference.
Data Availability
The dataset can be accessed upon request.
Conflicts of Interest
The authors declare that they have no conflicts of interest.