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Article

Dynamic Capability Theory Based Study on Performance of Intelligent Manufacturing Enterprise under RFID Influence

by
Weibin Wang
* and
Caihong Liu
School of Business, Jiaxing University, Jiaxing 314001, China
*
Author to whom correspondence should be addressed.
Electronics 2023, 12(6), 1374; https://doi.org/10.3390/electronics12061374
Submission received: 14 February 2023 / Revised: 4 March 2023 / Accepted: 7 March 2023 / Published: 13 March 2023
(This article belongs to the Section Artificial Intelligence)
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Abstract

:
To help intelligent manufacturing enterprises lower their confusion in RFID investment and improve their performance through a prior understanding of the RFID influence, given the moderating of entrepreneurship, a dynamic capability theory-based conceptual model is proposed by us. Then, through a questionnaire survey, measurable indicators are effectively selected. Next, AMOS method-based modeling is put forward to analyze the moderating effect and mediating effect to test the research hypotheses. The results show, firstly, that the most significant variables are the “consistent investment” and the “management decision-making ability”. Besides, different from the view of cost performance, the best reflected factors of enterprise performance in “intelligent manufacturing” are “product innovation speed and product market share”. Secondly, proper RFID investment can have a positive impact on enterprise performance only if it is converted into intelligent manufacturing capability (mediating variable), which hints at the true mediating effect. Moreover, Entrepreneurship can positively moderate the RFID’s influence on an enterprise’s intelligent manufacturing capability, but not on the enterprise performance. Namely, the moderated mediating effect of enterprise’s intelligent manufacturing capability is tenable. Generally, reasonable RFID investment in Chinese enterprises can promote intelligent manufacturing capability to improve the “intelligent manufacturing” performance. All these influence relations also vary with different entrepreneurships.

1. Introduction

With the deep integration of production technology and advanced information technology (i.e., IT), “intelligent manufacturing” driven by advanced IT represented by Radio Frequency Identification (i.e., RFID) has become the revolutionary trend of global manufacturing. By means of mobile Internet, Internet of Things, big data, and other new information technology, intelligent manufacturing revolutionizes business process, improving the production efficiency and production quality with low consumption and low cost [1]. As a forward-looking and innovative Internet of Things technology, RFID technology, characterized by wireless sensing, interconnection, synchronous data processing and traceability, has a particularly direct impact on this “intelligent” revolution. It cannot only solve the problem of the automatic identification of raw materials, parts, and semi-finished products on the production line, but also monitors the error rate in real-time through traceability [2]. The RFID system can collect operational data in real-time so that the enterprise can make business decisions timely [3]. Its quality tracking of materials and products is more conducive to solving manufacturing problems in dynamic environments [4]. RFID technology with data service capabilities of high-precision real-time identification, non-contact synchronization, and visual management is especially suitable for discrete manufacturing, which requires high real-time and reliability requirements [5]. Undoubtedly, RFID can provide new ideas and methods for the transformation of Chinese manufacturing.
At present, some enterprises have begun to apply RFID technology, but due to RFID’s high cost and high integration requirements for information management systems, they are not optimistic about applying RFID. Although, while some enterprises are interested in RFID technology, due to its application mode, application method and application performance in the manufacturing industry being not very clear [6], most enterprises have not made enough scaled investment. At the same time, enterprises that are hesitant and partial to trying RFID technology will also miss the best time to invest in RFID. In this case, how the investment level of RFID in China’s intelligent manufacturing will affect the performance of enterprises, whether enterprises are worth investing in RFID, and how to make RFID investment decisions are crucial issues.
Under the intelligent manufacturing environment, to help Chinese manufacturing enterprises effectively make RFID investment decisions and give full play to the value and creativity of RFID technology in manufacturing, here, through the theoretical analysis, the paper will propose a conceptual model of the influence of RFID technology investment level on intelligent manufacturing capability and enterprise performance. Furthermore, based on the empirical data, the structural equation modeling method was utilized to verify the research hypothesis including the action path of each factor, clarifying the mechanism of action between related constructs, and discerning the core factors of constructs. As a result, some new findings can provide reference to the technology investment decision-making of enterprises.

2. Theoretical Basis and Research Hypotheses

2.1. Dynamic Capability Theory

Even successful enterprises lacking adaptability will have “core competence rigidity” [7]. Dynamic Capability Theory (i.e., DCT) originated from resource theory fully reflects the dynamics of the market environment. It refers to the potential to solve problems systematically, mainly including these capabilities of opportunity and threat awareness, decision-making, and the ability to allocate resources optimally [8]. This is fundamental to improving the company’s performance for competitive advantages with a rapidly changing environment and rebuilding the company’s ability [9]. It is composed of strategy formation, restructuring and transformation, cooperation and control, and coordination abilities [10]. Enterprise coordination, resource integration, heterogeneous resource sharing, organizational learning abilities and the enterprise’s continuous evolution ability jointly constitute the dynamic capability of an enterprise [11]. Although there is still inconsistent research on the construction of dynamic capability, from the concept of dynamic capability essentially, we think it is a flexible capability, including the perception ability, integration ability, and absorption ability [12], among which absorptive capability is the forecasting and decision-making control capability formed by knowledge learning. Its concept and composition are exactly the theoretical basis of this study.
In the current super-competitive environment, it is difficult for enterprises to rely on only one competitive advantage for a long time. In order to maintain the leading position, enterprises must rely on dynamic capabilities to adapt to changes in the environment, create a series of short-term competitive advantages, and obtain sustainable competitive advantages [13]. The environment enterprises face is becoming increasingly complex and high-tech is constantly upgrading, which makes the choice of enterprise development path and technology path more diverse. The core capabilities of enterprises need to upgrade and evolve with the change of environment. To this end, we will conduct theoretical analysis based on DCT.

2.2. RFID Investment from the Perspective of DCT

IT is often seen as a driving force for the continued growth and innovation of an enterprise. As an advanced IT, RFID has strong anti-interference, dynamic information perception, and information integration abilities, and its data tracking ability and seamless information acquisition ability are of great help to the dynamic management of the whole life cycle of the product [14]. Therefore, effective investment of RFID will be an important strategic resource to improve the dynamic capability of an enterprise.
As far as IT investment is concerned, it generally refers to financial disbursements in IT infrastructure, IT human resources, as well as IT intangible resources (such as organizational collaboration and knowledge management abilities), and other aspects to improve the operating environment [15]. Meanwhile, IT investment level is a relative concept. Different IT strategic goals lead to different levels of IT investment. Namely, organizations with operational and market-oriented IT investment goals have higher IT investment levels than those that do not pay much attention to IT investment goals [16]. RFID investments aimed at reducing corporate management deficiencies can have a considerable impact on productivity gains [17]. Especially in the uncertain and complex environment, enterprises should strengthen their “dynamic decisive capability”. As one of the information technologies with dynamic decision support, RFID investment is particularly important. Therefore, from the perspective of dynamic capability, RFID investment should not only be suitable for enterprises’ open competition strategy, but also support the transformation goal of intelligent manufacturing based on digitalization and network.
To sum up, the paper will take the basic investment of RFID and the matching investment of RFID technology and enterprise intelligent manufacturing strategy as indicators to evaluate the RFID investment level. These two indicators will respectively reflect the basic guarantee conditions of an enterprise’s intelligent manufacturing, and its different needs for basic support in different stages of “intelligent manufacturing” transformation.

2.3. Enterprise’s Intelligent Manufacturing Capability from the Perspective of DCT

Focusing on an enterprise’s manufacturing ability is more valuable than focusing on the manufacturing activity itself. Based on the current manufacturing environment, an enterprise needs to have the ability to keenly capture environmental changes and promote the formation of dynamic manufacturing capability through knowledge learning to respond to changes in the dynamic competitive environment [18]. Compared with conventional manufacturing environments, intelligent manufacturing faces more complex and dynamic customer needs, which means that the enterprise must dynamically moderate its existing abilities to adapt to environmental changes [19]. An enterprise’s intelligent manufacturing capabilities should mainly presented be manifested as in innovation ability, information service ability, intelligent and flexible management ability, advanced manufacturing system ability, business integration ability, and collaboration ability [20]. Therefore, the dynamic capability of an enterprise to achieve “intelligent manufacturing” should be manifested in: sensing and capturing market opportunities, integrating a larger range of information and resources, learning new knowledge, dynamic strategic decision-making, and exerting the interaction between these abilities.
Among them, perception and integration are the basic abilities for intelligent manufacturing of an enterprise, and the ability of absorbing knowledge to internalize it into decision-making ability is the high-level ability of an enterprise. The key to intelligent manufacturing lies in improving its dynamic market forecasting and management decision-making abilities.

2.4. Relationships among RFID Investment Level, Intelligent Manufacturing Capability and Enterprise Performance

2.4.1. Relationships between RFID Investment Level and Enterprise Performance

The relationship between IT investment and enterprise performance has always been the focus of academic attention. There are three main views. Firstly, the information productivity paradox holds that there is no significant relationship between IT investment and productivity or performance [21]. Secondly, the value-added theory believes that IT investment will significantly improve enterprise performance [22]; Thirdly, the hybrid theory believes that information technology investment and enterprise performance are not simply positive, negative, or unrelated, and different types of information technology investment show a nonlinear relationship or heterogeneity with enterprise performance [23]. Despite the ongoing debate, with the gradual utility expansion of advanced IT, in the long run, investment in advanced IT will have significant positive effects on economic growth and productivity [24]. IT investment is positively promoting enterprise innovation (such as product innovation, service innovation, process innovation, technology innovation, etc.) [25]. In view of the opportunities brought by new IT, suitable investment in RFID has become an important strategic resource to improve enterprise competitiveness. On the one hand, RFID technology can directly improve enterprise performance; on the other hand, RFID technology can indirectly improve the performance of enterprises through complementary organizational change (mainly logistics outsourcing, process restructuring, and adjusting organizational structure) [26,27].
Based on the above, the following research hypotheses present:
H1. 
RFID investment level has a direct positive impact on enterprise performance.
H1a. 
RFID’s fit with enterprise strategy is more important than RFID infrastructure investment.

2.4.2. Relationship between RFID Investment Level and Intelligent Manufacturing Capability of an Enterprise

Only when IT integrates with other complementary resources of enterprises into an enterprise capability, can it promote enterprises to obtain sustainable competitive advantages [28]. The process of transforming IT investment into performance is the process of forming and consolidating the enterprise’s IT ability and core competitiveness [29]. In other words, enterprise ability is an inevitable transformation of IT investment. In short, IT itself does not produce enterprise performance, and it will only promote enterprise performance after internalized into the enterprise’s management ability.
Hence, we put forward the following hypotheses:
H2. 
The RFID investment level positively affects an enterprise’s intelligent manufacturing capability.
H2a. 
An enterprise’s management decision-making ability is the factor most affected by RFID.
H2b. 
An enterprise’s strategic cooperation ability is the factor that is weakest affected by RFID.

2.4.3. Relationship between Intelligent Manufacturing Capability and Enterprise Performance

Enterprise performance, as a representative of an enterprise’s overall operating status and profitability, is an effective standard to measure the intelligent upgrading of manufacturing enterprises.
The prominent advantage of intelligent manufacturing is that it can greatly save manufacturing costs, especially labor cost and resource cost and improve their production and manufacturing efficiency, operational efficiency, resource utilization, production services, and so on [30]. Intelligent manufacturing can endow enterprises with dual capabilities, including development ability and exploration ability, which have a positive impact on enterprise performance [31]. The intelligent manufacturing capability of manufacturing enterprises is the fundamental guarantee to improve the comprehensive strength of the country. The basic path of implementation is to change the concept of “product as the center” to “intelligent service as the center” [32]. In view of the fact that intelligent manufacturing capability should be embodied in the process of product manufacturing and service activities, product quality, service quality and enterprise operating costs ultimately affect enterprise performance. Therefore, we believe that the performance of intelligent manufacturing enterprises lies in products quality, services quality and reduced operating costs. Moreover, the enterprise’s intelligent manufacturing capability affects its performance level, and has a greater positive impact on the improvement of the competitiveness of the entire manufacturing industry [33].
Thereby, the hypotheses are proposed:
H3. 
An enterprise’s intelligent manufacturing capability directly positively affects its performance.
H3a. 
The performance of an “intelligent manufacturing” enterprise is mainly reflected in the market share of enterprise products and the speed of product innovation.
Moreover, an enterprise’s dynamic capability provides an indirect transmission path in the formation of enterprise performance [34]. From this,
H4. 
The intelligent manufacturing capability is the mediating variable between RFID investment level and enterprise performance.

2.5. Moderating Role of Entrepreneurship

As a special spirit and thought to promote the development of enterprises, entrepreneurship is manifested as the characteristics of innovation, risk-taking, cooperation, persistence, and competition for the company [35]. RFID technology can accelerate the creation and distribution of knowledge, and is more likely to be actively adopted by new entrepreneurs with growth ambitions, new enterprise entry and high-growth enterprises [36]. The stronger the entrepreneurship of an enterprise is, the stronger its technological innovation ability will be [37].
According to hypothesis H2, we can know that the level of RFID investment has a positive impact on the enterprise’s intelligent manufacturing capability. Then further, will different entrepreneurship affect the effectiveness of RFID and affect an enterprise’s intelligent manufacturing capability? Can entrepreneurship significantly improve the production performance of enterprise R&D investment?
To this end, some hypotheses are raised:
H5. 
Entrepreneurship positively affects the RFID investment level.
H51. 
Entrepreneurship positively moderates the relationship between RFID and intelligent manufacturing capability of an enterprise.
H52. 
Based on the hypotheses of H2, H3 and H5, we also believe that entrepreneurship has a positive regulation effect on the relationship between RFID and enterprise performance.
In addition, the entrepreneurial spirit can create a higher performance for an enterprise only by the way of new impetus for enterprises [38] and taking the pioneering capability as the mediating variable [39].
Therefore, the following hypotheses are given:
H6. 
As a driving force of enterprise abilities, entrepreneurship has a moderating effect on the intelligent manufacturing abilities of enterprises.
H61. 
The higher the entrepreneurship is, the higher the moderating effect of mediating variables on the relationship between RFID investment level and enterprise manufacturing performance.

3. Materials and Methods

3.1. Conceptual Model

Based on the above analysis, the correlation dimension and hypothetical relationship are as shown in Figure 1.
The model mainly appears the influence of RFID investment on enterprise performance with the moderating effect of entrepreneurship and the mediating effect of intelligent manufacturing capability.

3.2. Index Design

According to the previous problem dimension analysis, the constructs (i.e., latent variables) and measurement items (i.e., explicit variables) in Figure 1 are put forward as shown in Table 1.

3.3. Sample Survey

Considering that manufacturing in the “Pearl River Delta Region” and the “Yangtze River Delta Region” in China relatively develop very fast, Some representative manufacturing companies in these two regions will be investigated. In addition, due to the impact of COVID-19 and the difficulty in carrying out field research, the investigation was entrusted to a third party company. Furthermore, most of the measurable indicators in this study belong to non-objective variables, so the variables were measured by the Likert seven-point scale, whose score of each measurable item was divided into five levels: 5 points, 4 points, 3 points, 2 points, and 1 point. The higher the number was, the higher the score for this item would be. Then, based on these data, some statistical analysis would be carried out by SPSS22 software.

3.4. Analysis of Reliability and Validity

Here, the effective sample size for this analysis is 230, which is 10 times more than the number of items, and the sample size is proper. through the confirmatory factor analysis (CFA) conducted for a total of 8 factors and 22 items, the data validity level by KMO value, commonality, variance interpretation rate, factor loading coefficient, and others are shown in Table 2.
It can be seen from Table 2 that the reliability coefficient of the data is greater than 0.9, and the absolute value of the standardized load system is greater than 0.6 and shows significance, which means that there is a good measurement relationship. In addition, the KMO value is greater than 0.8, indicating that the data had strong validity. Moreover, the AVE values corresponding to the factors are all greater than 0.5, and the CR values are all higher than 0.7, which means that the data in this analysis has a good aggregation (convergence) validity and reliable validity [55].

3.5. Data Discrimination Analysis

The discrimination validity between the concepts is shown in Table 3.
The minimum AVE square root value corresponding to each construct factor is 0.787, which is greater than the maximum 0.772 correlation coefficient between factors, indicating that all construct factors have good discriminative validity [56]. Namely, the sample data of this study has no serious common method bias [57], and the data quality and indicator constructs meet the research needs.

4. Structural Equation Modeling

4.1. Fitting of the Structural Equation Model

The Structural Equation Model (SEM) is an advanced statistical method developed on the basis of factor analysis and pathway analysis, which is widely used in various fields of social science research. It is also known as the covariance structural equation modeling method, which has the advantage of being graphical and intuitive [58]. In the paper, the AMOS 22.0 software would be employed to test the influence path, mediation and moderating effect of Figure 1.

4.1.1. Fitting Test

The initial structural model is tested by the Maximum Likelihood Estimation method. The test results of general indicators such as GFI, RMSEA, RMR, CFI, NFI, and NNFI are presented in Table 4.
In addition to the NFI approximate value, other indicators also meet the fitting standard, which means that the deviation between the actual observed value and the theoretical inference is small, and an effective structural model could be constructed.

4.1.2. Structural Equation Modeling

Given that some factors have little influence on each other, to improve the initial model, the correlation between residual variables was corrected with the M.i index value in the model operation results. After several adjustments, the structural equation model with good fitting is shown in Figure 2.
This model examined and demonstrated that the main relationship between RFID technology investment, enterprise intelligent manufacturing capability, and enterprise manufacturing performance existed significantly. Moreover, entrepreneurship has a positive influence on the RFID investment level. These main effects support the research hypotheses of H2, H3, H4, and H5. However, the direct influence of RFID on enterprise performance is not significant (p = 0.228), and the research results do not support H1. Obviously, L2 in L is slightly more important than L1 (H1a is established). Among the composition of C, C4 was the most important and C2 was the least important, and the conclusion supports H2a and H2b. At the same time, the importance of P2 indicates that H3a is also tenable.

4.2. Moderated Mediating Effect

4.2.1. Mediating Effect of Intelligent Manufacturing Capability

From Figure 1, it can be seen that an enterprise’s intelligent manufacturing capability is the mediation variable between RFID and enterprise performance, and this specific mediating effect can be expressed as shown in Table 5.
Except for C3, the indicators that measure an enterprise’s intelligent manufacturing capability are fully mediating.

4.2.2. Direct Moderating Effect of Entrepreneurship

The RFID investment level does not directly affect the enterprise performance, and by introducing the mediation variable—entrepreneurship (S), there is no significant direct relationship between L and P (H51 is not established). However, the S variable has a significant moderating effect on the relationship between L and C (t = 3.387, p = 0.001 < 0.05). This means that research hypothesis H52 is valid. Moreover, the significant difference in the moderating amplitude is as shown in Figure 3.
It can be seen that no matter how high or low S is, L positively affects C, merely that the slope when S is at a high level is significantly larger than the slope when S is at a low level. This shows that with the improvement of entrepreneurship, the influence of RFID investment level on the enterprise’s intelligent manufacturing capability will increase. In other words, for the same L, the higher the S is, the greater the influence of L on C will be.

4.2.3. Moderated Mediation Effect

We know that the direct effect of L on P is not significant, but under the mediation of C, there will be an indirect significant relationship between L and P. Further, whether the mediating effect of C will be moderated by S. To clarify this, first of all, the existence of the moderating effect of entrepreneurship on mediating variables can be tested in Table 6.
As can be seen from Table 6 that except for C2, other indicators in latent variable C could be positively moderated by S, that is, the latent variable S has a mediating moderating effect. Moreover, the moderating mediation effect involves 5 models, which are shown as follows:
P = 0.000 + 0.209 × L + 0.243 × C1 + 0.225 × C2 − 0.047 × C3 + 0.231 × C4
C1 = −0.123 + 0.585 × L + 0.227 × S + 0.206 × L × S
C2 = −0.011 + 0.587 × L + 0.037 × S + 0.018 × L × S
C3 = −0.093 + 0.556 × L + 0.202 × S + 0.155 × L × S
C4 = −0.094 + 0.746 × L + 0.094 × S + 0.158 × L × S
Based on the above, the moderated mediating effect is revealed as shown in Table 7.
According to Table 7, for C2, regardless of the level of entrepreneurship, the mediating effect of C2 is unchanged, which indicates that its mediating effect cannot be affected by S. Conversely, the mediating effect of C3, although not significant, will be moderated by S. For other mediation factors, regardless of the moderating level of S, the moderated mediation amplitude increases with the increase of the level of S. That is to say, the higher the entrepreneurship is, the greater the positive influence of RFID investment level on enterprise performance indirectly through the enterprise’s intelligent manufacturing capability will be.

5. Hypothesis Testing and Findings

5.1. Hypothesis Testing

Through the constructed Figure 2 and the analysis of some influence effects, the test results of research hypothesis are summarized as shown in Table 8.

5.2. Findings

According to Table 8 and the above analysis, the following findings are obtained.
  • Finding 1: H1, H2, H3 and H4 indicate that the higher the level of investment in RFID technology is, the greater the intelligent manufacturing capacity of a manufacturing enterprise will be, and correspondingly, the higher the enterprise performance will be. In response to the “intelligent manufacturing” transformation of China’s manufacturing industry, an enterprise should make full use of advanced information technology to assist it in building its “intelligent manufacturing” capability. Only RFID technology being integrated into the relevant aspects of manufacturing and internalized into the enterprise’s ability, can it drive the enterprise’s performance to grow. This also enlightens that China’s manufacturing enterprises shall not only invest heavily in technical infrastructure, but also pay more attention to the integration and application of new technologies to achieve the value creation of technology investment.
  • Finding 2: The test of the H1a hypothesis shows that the RFID investment level is not the investment in technology infrastructure that companies usually think. In fact, the effectiveness of limited technical investment made according to the company’s manufacturing strategy is the key element to reflect the level of technological investment.
  • Finding 3: It is feasible to use the four secondary latent variables of C to evaluate an enterprise’s intelligent manufacturing energy. Among them, the promotion of RFID technology to enterprise management and decision-making ability can best reflect the enterprise’s “intelligent manufacturing” capability. What is novel is that the enterprise’s vertical and horizontal business collaboration capability (C3), as a key factor in the enterprise’s “intelligent manufacturing” capability, does not have to mediate utility but can be moderated by S. In contrast, the willingness to cooperate (C2) has the least influence on the enterprise’s “intelligent manufacturing” capability, but it has a mediation effect but is not moderated by S. The reason may be that an enterprise’s business collaboration capability, as the core factor of the enterprise’s intelligent manufacturing capability, is more a reaction to the synergistic structural effect of the enterprise supply chain, and is not directly driven by RFID technology, so the mediation effect of C3 is not significant. However, driven by strong entrepreneurship, the RFID technology characteristics in business collaboration will be highlighted, and then C3 will tend to produce mediating utility under the mediation of S. The willingness of an enterprise to cooperate (C2) is the basis for it to carry out collaborative manufacturing, and should become a mediating variable, but willingness as a reflection of choice, will not change arbitrarily with the strength of entrepreneurship, otherwise, any development strategy of enterprises is difficult to implement.
  • Finding 4: H5 shows that the greater the innovation and risk-taking spirit of entrepreneurs are, the easier it will be for enterprises to increase investment in RFID, so under different entrepreneurships, enterprises have different awareness of RFID investment levels and investment strengths.
  • Finding 5: H51 indicates that the influence of RFID investment on an enterprise’s “intelligent manufacturing” capability is also affected by entrepreneurship. Under different entrepreneurial characteristics, the same RFID technology investment will produce different “intelligent manufacturing” capabilities. This is because companies with a high degree of pioneering and adventurous spirit are more courageous to absorb the energy of advanced technology and enhance their dynamic capability.
  • Finding 6: Even if entrepreneurship is high, there is no direct moderated relationship between L and P (H52). This further proves that RFID technology cannot have an influence on the enterprise “intelligent manufacturing” before it is transformed into a certain enterprise “intelligent manufacturing” capability. No matter how advanced the technology and how much investment, lack of effective application, it does not work as well as it should.
  • Finding 7: According to the test of H6 and H61, the stronger the entrepreneurship is, the stronger the influence of the enterprise’s intelligent manufacturing capability on the relationship between RFID investment level and enterprise performance will be. This modulated mediation effect also implies that a manufacturing enterprise should cultivate and give full play to entrepreneurship when using advanced information technology to carry out “intelligent manufacturing” transformation, to avoid being tied up in the process of change and inefficient or even failed “intelligent manufacturing” transformation.
  • Finding 8: For the evaluation of enterprise performance, many scholars give different indicators. Different from the conventional enterprise performance evaluation goals, the enterprise “intelligent manufacturing” performance indicators proposed in this paper based on the goal of “intelligent manufacturing” of manufacturing enterprises in China, are tested to be appropriate. Moreover, it is clear that an enterprise should pay the most attention to product innovation and product market share (H3a) in the “intelligent manufacturing” environment, and pay attention to whether the company’s intelligent manufacturing behavior improves or enhances customer service satisfaction. In addition, traditionally, people have used to emphasize the operating costs in the enterprise performance. However, they ignore the variability of enterprise performance indicators in different operating environments. The research in this paper shows that operating costs are still important, but less so than P2 and P3. The reason for this may be that enterprises that implement intelligent manufacturing pay more attention to the ultimate goal of “intelligent manufacturing”, so the cost of “intelligent manufacturing” within the affordable range is acceptable.
In response to the “intelligent manufacturing” transformation of China’s manufacturing industry, an enterprise should make full use of advanced information technology to assist it in building its “intelligent manufacturing” capability. Only RFID technology being integrated into the relevant aspects of manufacturing and internalized into the enterprise’s ability, can it drive the enterprise’s performance to grow. This also enlightens that China’s manufacturing enterprises shall not only invest heavily in technical infrastructure, but also pay more attention to the integration and application of new technologies to achieve the value creation of technology investment.

6. Conclusions

Based on the above findings, the components of our proposed conceptual model (i.e., Figure 1) are valid. Moreover, the force of factors, the influence relationship between dimensions and the mediating role of entrepreneurship have also been described above. The above findings mainly provide some theoretical reference and enlightenment for Chinese manufacturing enterprises so that they can make full use of the enabling function of RFID technology in enterprise intelligent manufacturing through reasonable RFID technology investment, promote the improvement of their “intelligent manufacturing” capacity, and continuously improve their performance.
The main contributions of this paper are: (1) A research model, which is mediated by enterprise intelligent manufacturing capability with entrepreneurship moderating has been proposed for the influence of RFID investment level on enterprise performance. (2) On the basis of the survey data, the construct, main path, and research hypothesis in structural modeling are effectively tested. (3) The significant new finding is that entrepreneurship can actively promote the influence of RFID on the intelligent manufacturing capability of enterprises, and can positively moderate the mediating variables. Therefore, RFID technology is more able to play an effective role in enterprises with a strong entrepreneurial spirit. (4) RFID technology itself cannot produce value, only after being integrated into the enterprise’s intelligent manufacturing capability, can it actively promote the improvement of enterprise performance. (5) The core factors in each dimension of the research model are identified for enterprises to strengthen management.
Inevitably, there are still some limitations in this study, for example, the number of samples used in this study is not very rich, and the study does not carry out comparative analysis for different manufacturing industries and enterprises of different natures in the same industry, etc. These need to be further discussed in the follow-up study.
In short, based on the perspective of the integration of RFID technology and the enterprise’s intelligent manufacturing capability, this paper discusses the influence of RFID investment level on enterprise performance based on the moderating variable and mediating variable, providing some useful references for the management decisions of industrial change in China’s manufacturing.

Author Contributions

Conceptualization, W.W. and C.L.; methodology, W.W.; software, C.L.; validation, W.W. and C.L.; formal analysis, W.W.; investigation, W.W. and C.L.; resources, C.L.; data curation, W.W.; writing—original draft preparation, W.W.; writing—review and editing, C.L.; visualization, W.W.; supervision, C.L.; project administration, C.L.; funding acquisition, W.W. and C.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Key Social Science Planning Projects in Zhejiang Province (Grantno. 22HQZZ602Z).

Data Availability Statement

The authors declare our research data is true, valid and reliable.

Conflicts of Interest

The authors declare no conflict of interest.

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Electronics 12 01374 g001 550
Figure 1. Conceptual Model.
Figure 1. Conceptual Model.
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Electronics 12 01374 g002 550
Figure 2. Structural Equation Model.
Figure 2. Structural Equation Model.
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Electronics 12 01374 g003 550
Figure 3. Simple Moderating of the Slope.
Figure 3. Simple Moderating of the Slope.
Electronics 12 01374 g003
Table
Table 1. Constructs (Latent Variables) and Measurement Items.
Table 1. Constructs (Latent Variables) and Measurement Items.
ConstructsMeasurement ItemsVariable ExplanationLiterature Sources
Investment level in RFID (L)RFID’ basic investment (L1)Investment in infrastructure (L11)Application scale of RFID technology[40,41,42,43]
Technicians (L12)Cultivate matching technicians
Consistent input with corporate strategy (L2)Input on adjusting enterprise strategy (L21)Investment in the internal consistency of RFID and enterprise management
Technical level of RFID (L22)RFID’s support for intelligent manufacturing
Entrepreneurship (S)Pioneering spirit (S1)Innovation of products and services[44,45,46]
Adventurous spirit (S2)The courage to innovate against existing conditions
Intelligent manufacturing capability of an enterprise (C)Information capability (C1)Information gathering capability (C11)Manufacturing data acquisition capability, that is, it can fully collect production site information such as manufacturing progress, on-site operation, quality inspection, equipment status, etc.[47,48]
Information visualization (C12)Ability to data visualization, information sharing and intelligent management in production based on industrial Internet
Strategic cooperation willingness (C2)Trust (C21)Trust promotion between parties in the new technological environment[49,50,51]
Initiative of cooperation (C22)Under the dynamic cooperation environment, the cooperation frequency and resource utilization rate between enterprises are increased by more than 10%
Business collaboration capability (C3)Vertical collaboration (C31)Product life-cycle collaboration throughout design, manufacturing, quality, logistics and other links
Horizontal collaboration (C32)Collaboration between manufacturing supply chain and productive service supply chain
Management decision-making ability (C4)Problem sensitivity (C41)Problem prediction and reasoning ability based on real-time data[47,52,53]
Business intelligence processing capability (C42)Business automation capability, such as automatic replenishment of inventory
Intelligent manufacturing performance of an enterprise (P)Operating cost (P1)In contrast, operating costs were reduced by more than 20 percent[47,54,18]
Product market share and product innovation rate (P2)The production cycle of the product is shortened by more than 30% and it occupies more than a third of the market
Service satisfaction (P3)The rate of defective products and customer complaints will be reduced by more than 20%
Table
Table 2. Reliability and validity.
Table 2. Reliability and validity.
ConstructsItemsFactor LoadingsCommon FactorStd. DeviCronbach’s αCRAVE
Factor1Factor2
SS10.0150.8170.6670.8960.9450.6420.779
S30.2630.7950.7010.856
L1L110.60.4110.5291.1430.620.765
L120.4880.610.6110.833
L2L210.50.5940.6021.0040.620.764
L220.4660.6610.6551.08
C2C210.590.4680.5670.7530.7450.853
C220.6470.3940.5740.884
C3C310.8160.1160.6790.9630.7360.847
C320.8170.1490.691.016
C4C410.7350.3270.6470.8770.6720.804
C420.8120.2220.7091.03
C5C520.7050.480.7270.9430.6490.844
C530.6270.4170.5670.95
PP120.520.4520.4751.1130.6610.793
P210.5740.4470.531.112
P220.5050.4960.5010.944
Cumulative variance interpretation rate (after rotation)36.358%61.368%−−−−−
KMO value0.909−−−−−
Bartlett’s spherical value2789.682−−−−−
df136−−−−−
p0−−−−−
Table
Table 3. Discrimination Validity.
Table 3. Discrimination Validity.
L1L2C1C2C3C4PS
L10.801−−−−−−−
L20.7330.788−−−−−−
C10.6260.6440.787−−−−−
C20.5740.560.5980.863−−−−
C30.6160.6010.6730.7160.858−−−
C40.6630.7720.7220.6030.7540.82−−
P0.5950.6310.6530.6020.5850.6670.806−
S0.4960.6110.4960.3820.4730.4810.5160.813
Table
Table 4. Model Fitting.
Table 4. Model Fitting.
Common Indicatorsχ2/dfGFIRMSEARMRCFINFINNFIIFI
Criteria<3>0.9<0.10<0.05>0.9>0.9>0.9>0.9
Value2.9570.9070.0940.0470.9020.8960.910.923
Table
Table 5. Mediating effect.
Table 5. Mediating effect.
ItemTotal EffectMediating Effect95% BootCIMediating
L1 ≥ C1 ≥ P0.294 **0.0870.037~0.148Full
L1 ≥ C2 ≥ P0.294 **0.0760.010~0.184Full
L1 ≥ C3 ≥ P0.294 **−0.016−0.088~0.036No
L1 ≥ C4 ≥ P0.294 **0.050.011~0.107Full
L2 ≥ C1 ≥ P0.414 **0.0920.034~0.162Full
L2 ≥ C2 ≥ P0.414 **0.070.012~0.144Full
L2 ≥ C3 ≥ P0.414 **−0.015−0.079~0.036No
L2 ≥ C4 ≥ P0.414 **0.1370.036~0.250Full
** p < 0.01.
Table
Table 6. Regression model.
Table 6. Regression model.
PC1C2C3C4
Constant0.000 (0.000)−0.123 * (−2.222)−0.011 (−0.170)−0.093 (−1.572)−0.094 (−1.917)
L0.209 ** (2.805)0.585 ** (10.082)0.587 ** (8.859)0.556 ** (9.016)0.746 ** (14.488)
S−0.227 ** (3.689)0.037 (0.531)0.202 ** (3.077)0.094 (1.712)
L*S−0.206 ** (4.003)0.018 (0.305)0.155 ** (2.833)0.158 ** (3.454)
C10.243 ** (3.481)−−−−
C20.225 ** (3.393)−−−−
C3−0.047 (−0.594)−−−−
C40.231 ** (2.716)−−−−
Sample size230230230230230
R20.560.5160.3680.4530.619
Adjusted R20.5480.5070.3570.4430.612
FF(5224) = 57.085, p = 0.000F(3226) = 80.279, p = 0.000F(3226) = 43.931, p = 0.000F(3226) = 62.407, p = 0.000F(3226) = 122.376, p = 0.000
* p < 0.05, ** p < 0.01.
Table
Table 7. The moderated mediating effect of intelligent manufacturing capability under different entrepreneurships.
Table 7. The moderated mediating effect of intelligent manufacturing capability under different entrepreneurships.
Mediating VariableLevelEffectBootSEBootLLCIBootULCI
C1+1SD0.0920.0390.0350.187
Mean0.1420.0410.0650.226
−1SD0.1920.0540.0860.296
C2−1SD0.1280.0520.030.237
Mean0.1320.050.0320.232
+1SD0.1360.0510.0340.239
C3−1SD−0.0190.037−0.1030.041
Mean−0.0260.048−0.130.057
+1SD−0.0330.06−0.1570.076
C4−1SD0.1360.0480.0440.237
Mean0.1720.0590.0550.29
+1SD0.2090.0740.0650.359
Table
Table 8. Hypothesis testing.
Table 8. Hypothesis testing.
HypothesesSupported?
H1L ≥ PNo
H2L ≥ CYes
H3C ≥ PYes
H4L ≥ C ≥ PYes
H5S ≥ LYes
H51The moderating effect of S on “L ≥ C”No
H52The moderating effect of S on “L ≥ P”Yes
H6Moderated mediating effect of C by SYes
H1aL2 is more important than L1Yes
H2aC4 is the most critical factor in C (i.e., most affected by RFID)Yes
H2bC2 is the least important in C (i.e., the least affected by RFID)Yes
H3aP2 is the most critical factor in P and is most affected by CYes
H61The higher S, the greater the performance of “L ≥ C ≥ P”Yes
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Wang, W.; Liu, C. Dynamic Capability Theory Based Study on Performance of Intelligent Manufacturing Enterprise under RFID Influence. Electronics 2023, 12, 1374. https://doi.org/10.3390/electronics12061374

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Wang W, Liu C. Dynamic Capability Theory Based Study on Performance of Intelligent Manufacturing Enterprise under RFID Influence. Electronics. 2023; 12(6):1374. https://doi.org/10.3390/electronics12061374

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Wang, Weibin, and Caihong Liu. 2023. "Dynamic Capability Theory Based Study on Performance of Intelligent Manufacturing Enterprise under RFID Influence" Electronics 12, no. 6: 1374. https://doi.org/10.3390/electronics12061374

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