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Article

Understanding the Efforts of Cross-Border Search and Knowledge Co-Creation on Manufacturing Enterprises’ Service Innovation Performance

by
Tu Lyu
*,
Qixiang Geng
and
Qiu Zhao
School of Business, Qingdao University, Qingdao 266071, China
*
Author to whom correspondence should be addressed.
Systems 2023, 11(1), 4; https://doi.org/10.3390/systems11010004
Submission received: 29 November 2022 / Revised: 20 December 2022 / Accepted: 21 December 2022 / Published: 22 December 2022
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Abstract

:
Based on the enterprise knowledge-based view, this study follows the basic logic of “knowledge acquisition-knowledge transformation-knowledge creation” to explore the effects of cross-border search and knowledge co-creation on the service innovation performance of manufacturing enterprises. Furthermore, compositional capability is introduced to investigate the moderator in the connection of knowledge co-recreation and service innovation performance. We collected 378 samples from the organizations that are taking servicizing transformation in China’s manufacturing industry. Then we applied structural equation modeling (SEM) to test our research model. The results reveal that both cross-border technological knowledge search and cross-border market knowledge search can significantly improve embedded knowledge co-creation and alliance-based knowledge creation of manufacturing enterprises, and then, directly and indirectly, boost service innovation performance. Compositional capability positively moderates the relationship between embedded knowledge co-creation and service innovation performance. This study provides theoretical and practical guidance for knowledge-based service innovation in China’s manufacturing industry.

1. Introduction

With the division of labor in the global value chain deepened and fragmented, the global manufacturing industry is transforming from being production-oriented to service-oriented. Promoting service innovation has become an effective way for manufacturing enterprises to expand the market value of products, thus extricating themselves from business difficulties and gaining competitive advantages [1].
Service innovation refers to the development of new or improved intangible offerings that provide value for customers [1]. Knowledge and knowledge-related practices are confirmed as fundamental to positive innovation performance [2]. Exploiting enterprise knowledge-based resources and cultivating enterprise knowledge capabilities have become an essential means to improve the service innovation performance of enterprises [3,4]. However, manufacturing enterprises are currently trapped in a dilemma that their innovation knowledge reserve is far from satisfying the demand for transformation and upgrading, so they must continuously acquire new knowledge, experience and skills to preserve their competitive advantages [5]. Due to the dynamic change in the market environment, enterprises face incredible difficulty in acquiring knowledge; thus, they lack insufficient information about the market environment and customer service demand and face service failure [6]. Meanwhile, the difficulty in acquiring technological knowledge restrains enterprises from understanding and utilizing emerging service technologies, thus triggering service backwardness and customer dissatisfaction [7]. Therefore, it has become the top priority to acquire new knowledge and effectively integrate knowledge resources into the knowledge resources that enterprises urgently need to enhance the performance of enterprise service innovation.
The view of customer-oriented logic argues that customers become the main body of value creation, and they help enterprises realize value co-creation by integrating and mobilizing products or services provided by enterprises [8]. Knowledge co-creation, in which manufacturing enterprises collaborate or interact with other members of the innovation network to produce new knowledge, is the key to acquiring strategic resources in the service innovation network [5]. Customer knowledge co-creation helps enterprises to implement service innovation by providing customers with higher-efficient creative solutions, shortening the development cycle of new products and services, and gratifying customers’ preferences and demands [9]. However, prior studies provide little evidence concerning the influence of customer knowledge co-creation on the service innovation performance of manufacturing enterprises. Thus, the first purpose of this study is to explore the critical path and mechanism of knowledge co-creation affecting service innovation performance.
Furthermore, whether enterprises can effectively accumulate and use the knowledge of external participants is the crucial premise for knowledge co-creation [10,11]. Discussing the key methods of enterprise knowledge discovery and acquisition becomes essential to achieve knowledge co-creation. Cross-border search (also called trans-boundary search or bounding-spanning search) is a crucial way to acquire external knowledge. It refers to the process that firms search for new knowledge across organizational and technological boundaries [12]. By analyzing the external competitive environment, it is feasible to search, identify and filter valuable new knowledge, which can not only help enterprises to deal with unconventional and complex problems and generate ideas more quickly, but also build an internal competitive atmosphere for enterprises [13] and spur the innovation activities of products and services, thus facilitating the sustained implementation of service innovation activities [14,15]. Therefore, acquiring knowledge resources via cross-border search activities may be a major cause for customer knowledge co-creation of manufacturing enterprises. However, prior studies rarely discuss the relationship between cross-border search and customer knowledge co-creation. Thus, the second task of this study is to understand the mediating effect of knowledge co-creation in the relationship between cross-border search and service innovation performance.
Moreover, compositional capability enables enterprises to integrate their internal and external resources to innovatively utilize them and make up for the deficiency in knowledge [16,17,18]. Enterprises with a brilliant compositional capability usually show a high sensitivity to knowledge resources in the external environment, integrate the external knowledge resources with their internal knowledge, innovative to develop the external knowledge resources and then build a knowledge system supporting their enterprise development. Thus, they are quickly able to respond to market demand, providing cost-effective service solutions and developing competitive advantages [19]. Therefore, compositional capability can determine the effect of transforming knowledge co-creation into service innovation performance. Therefore, the third goal of this study is to explore the moderating effect of compositional capability in the relationship between knowledge co-creation and service innovation performance.
Overall, based on the organizational knowledge view, our study aims to discuss the influence of cross-border search and knowledge co-creation on the service innovation performance of manufacturing enterprises. We built a research model following the logic of “cross-border search-knowledge co-creation-service innovation performance”. We also considered compositional capability as a boundary condition to explore its moderating impact. This research aims to enrich the theoretical discussion on cross-border search and service innovation performance and develop management strategies for manufacturing enterprises to improve their service innovation performance.

2. Theoretical Analysis and Research Hypotheses

Knowledge co-creation refers to a network relationship built by taking value co-creation as the basis, centering on participants, and co-creating new knowledge with other knowledge sources, or an open innovation behavior fully utilizing external resources [20]. According to the knowledge flow direction, knowledge co-creation can be further classified into embedded and alliance-based knowledge co-creation [21]. Embedded knowledge co-creation refers to the phenomenon that the external knowledge flows into an enterprise and integrates with the internal knowledge of the enterprise to co-create new knowledge, while alliance-based knowledge co-creation refers to the phenomenon that the internal knowledge flows out of an enterprise and integrates with the external knowledge to co-create new knowledge [11]. To achieve service innovation, manufacturing enterprises should gain professional market knowledge, besides advanced technological knowledge, so cross-border technological knowledge search and cross-border market knowledge search have the common focus of enterprises [22]. Enterprises pay more attention to the acquisition and reserve of diversified knowledge resources in service innovation, unlike traditional product innovation [23]. Cross-border search is an important channel to help enterprises effectively absorb new knowledge in an open innovation environment [15,24]. It can provide manufacturing enterprises with both market and technology support to timely adjust the knowledge structure in the service transformation.
The construction, improvement, and functioning of the enterprise knowledge base as a collection of knowledge are processes in which knowledge is acquired, transformed, and created. Cross-border search is an important way for enterprises to acquire knowledge; knowledge co-creation is a favorable means for enterprises to absorb and transform knowledge; service innovation is the fruit of knowledge creation. So, this research follows the logic of “knowledge acquisition-knowledge transformation-knowledge creation” to build a logical path of “cross-border search-knowledge co-creation-service innovation performance”. Beyond that, as compositional capability of the enterprise can affect the knowledge absorption and transformation effect [19], compositional capability is introduced and used as a moderating variable for the relationship between knowledge co-creation and service innovation performance. Figure 1 illustrates the theoretical framework of this research.

2.1. Cross-Border Search and Service Innovation Performance

Cross-border search helps enterprises identify opportunities and solve problems, reach across organizational boundaries to search for new knowledge and knowledge sources, update their existing knowledge structures continuously, and effectively respond to dynamic environmental changes. Prior studies have proven that cross-border search positively influences innovation [14,15,25].
Cross-border technological knowledge search is the enterprise behavior of going across existing knowledge boundaries to identify and search for new knowledge, such as new technologies and methods [22,26]. Enterprises adopt the method of cross-border search to break knowledge boundaries and acquire a broader range of technological knowledge; therefore, stimulating their innovative thinking, and boosting effective product improvement, extension, or creation of new products. Furthermore, a good mastery of heterogeneous technological knowledge can help enterprises to analyze effectively, integrate, and predict the technologies required for future industry development [27,28], forge a solid foundation for service technologies required for the transformation and upgrading of manufacturing enterprises, and provide a guarantee for significantly enhancing the service innovation performance [22]. Accordingly, we posited the following hypothesis:
Hypothesis 1 (H1a).
Cross-border technological knowledge search positively influences service innovation performance.
Effectively acquiring market demand information impels manufacturing enterprises to achieve service innovation [29]. Through cross-border market knowledge search, enterprises can quickly reach a wide range of knowledge about market demand, product design, and distribution channels, and accurately grasp customer preferences and market demand [22,26]. Based on that, cross-border market knowledge search can help manufacturing enterprises seize the opportunity to develop emerging markets, eliminate the current resource and experience dilemma, and enhance service innovation performance [22]. On this basis, the research presents the following hypothesis.
Hypothesis 1 (H1b).
Cross-border market knowledge search positively influences service innovation performance.

2.2. Cross-Border Search and Knowledge Co-Creation

According to the knowledge-based view, knowledge resources can help enterprises in their technological improvement and innovation to guarantee their market competitive advantage and take a core part in the steady innovation of enterprises [30,31]. Acquiring new knowledge, methods, and technologies across organizational boundaries effectively strengthens enterprises’ innovation capabilities [13,32]. Cross-border search, as a heterogeneous search behavior of external knowledge [24,33], enables enterprises to acquire diverse knowledge resources from different subjects in an open innovation environment and supports enterprises in timely updating their knowledge structure to gratify market demand [12].
Cross-border technological knowledge search provides enterprises with opportunities to connect with other stakeholders in the social network, facilitating cooperation with other enterprises and learning from their advanced technical experience. It is helpful to drive enterprises to optimize their production process to achieve product innovation or new product development [22,34], promoting the values of knowledge co-creation. Moreover, cross-border technological knowledge attracted enterprises to actively deepen knowledge exchange and interaction with other members to achieve technical reforms, optimization, and upgrading, heightening the trust between enterprises and other external innovation subjects [32], thus promoting the fulfillment of alliance-based knowledge co-creation. Therefore, we put forward the following two hypotheses.
Hypothesis 2 (H2a).
Cross-border search of technological knowledge positively influences alliance-based knowledge co-creation.
Hypothesis 2 (H2b).
Cross-border search of technological knowledge positively influences embedded knowledge co-creation.
Cross-border market knowledge search improves the sensitivity of enterprises to the external environment. It enables enterprises to keep abreast of the dynamic change of the market environment, the actual situation and demand of consumers, the dynamic condition of competitors, and the trend of market policies [22]. The active search for market knowledge stimulates enterprises to communicate internal market knowledge with external innovation subjects, deepen enterprises’ knowledge about consumer preference, substitute goods, and complementary products of their products [24,33,34], and promote enterprises to accurately set market goals and adjust and optimize products and services, thus enhancing the effect of alliance-based knowledge co-creation. Accordingly, the following hypotheses are proposed.
Hypothesis 2 (H2c).
Cross-border market knowledge search positively influences alliance-based knowledge co-creation.
Hypothesis 2 (H2d).
Cross-border market knowledge search positively influences embedded knowledge co-creation.

2.3. Knowledge Co-Creation and Service Innovation Performance

Service innovation of manufacturing enterprises is not the substitution of manufacturing with service, but the cross-border integration of manufacturing and service, which goes far beyond the original knowledge resource framework of enterprises [35]. As one of the core resources of enterprises, knowledge is the source that brings competitive advantages to enterprises to enhance their innovation performance [3,36]. Knowledge co-creation provides an effective way to enterprises to obtain new knowledge in the innovation network and thus strategically reserve resources [37], and spurs manufacturing enterprises to make breakthroughs in service innovation.
Knowledge co-creation subjects include participants in the product manufacturing process and stakeholders such as suppliers and users of product raw materials [38]. Enterprises built a close connection with these external innovation subjects, such as forming a knowledge alliance [11] by which the heterogeneous knowledge transferred and interacted with each other [39]. Therefore, knowledge co-creation subjects help enterprises to effectively expand the service innovation resource reservoir [21], thus providing manufacturing enterprises with knowledge elements required in service innovation. So, we develop the following hypothesis.
Hypothesis 3 (H3a).
Alliance-based knowledge co-creation produces a positive influence on service innovation performance.
In embedded knowledge co-creation, core enterprises serve as the main subject to integrate knowledge elements of external stakeholders with their internal knowledge structure to create new knowledge [11]. Integrating internal knowledge resources and external heterogeneous knowledge is necessary for enterprises to conduct innovation [40]. Furthermore, receiving external innovation knowledge can help to enhance the enterprises’ sensitivity to the external environment, and provide an impetus for product or process optimization and innovation to improve service innovation performance and consolidate the knowledge base [41,42]. Thus, the following hypothesis is developed.
Hypothesis 3 (H3b).
Embedded knowledge co-creation positively influences service innovation performance.

2.4. Mediating Effect of Knowledge Co-Creation

Enterprises’ internal resources and capabilities are limited, but the external innovation environment can bring infinite innovation opportunities and possibilities to enterprises [15,43]. Such an innovation environment can further stimulate enterprises to export some market knowledge and combine the market knowledge with other network members’ knowledge to promote service quality improvement and even form new service solutions. Specifically, cross-border market knowledge search helps enterprises quickly obtain information about potential consumers and market demands [22]. Based on this, enterprises will further process such market information to enhance their understanding of customer demands, form new knowledge, and create opportunities for their long-term development by improving product performance, service quality, and other relevant strategies [22,33]. Thus, service innovation performance is further enhanced accordingly [11,21]. Therefore, the following hypothesis is proposed in this study.
Hypothesis 4 (H4a)
Alliance-based knowledge co-creation produces a mediating effect on the influence of cross-border technological knowledge search on service innovation performance.
Hypothesis 4 (H4b).
Embedded knowledge co-creation produces a mediating effect on the influence of cross-border market knowledge search on service innovation performance.
Establishing external connections to search for and acquire new knowledge is another important way to reshape enterprise innovation performance [30,44,45]. Transboundary technology knowledge search lays the underlying technology foundation for enterprise knowledge co-creation. Integrating technical knowledge and enterprise internal knowledge creates opportunities for cross-innovation of related technologies [26] and promotes the expansion and improvement of service innovation performance of manufacturing enterprises [21]. In addition, cross-border technical knowledge search enables enterprises to establish a good interactive relationship with external stakeholders, creates a good atmosphere for knowledge interaction [22], and promotes member enterprises to export new technologies, new processes actively, and new methods. Moreover, this deepens each enterprise’s understanding of new technologies [27,28], reduces technology integration costs and improves service innovation efficiency. Accordingly, the following hypotheses are proposed:
Hypothesis 4 (H4c).
Embedded knowledge co-creation produces a mediating effect on the influence of cross-border technological knowledge search on service innovation performance.
Hypothesis 4 (H4d).
Alliance-based knowledge co-creation produces a mediating effect on the influence of cross-border market knowledge search on service innovation performance.

2.5. Moderating Effect of Compositional Capability

Compositional capability refers to the ability of an enterprise to integrate external and internal resources creatively, and its core is to improve the weak links by integration [16,17]. With regard to the time dimension, compositional capability can help to dynamically adjust the knowledge resources and abilities of enterprises. At the same time, it can assist enterprises in creatively reconstructing external core knowledge and internal knowledge base to cultivate new services or product service systems from the spatial system [18]. A more robust compositional capability enables an enterprise to integrate knowledge at a higher speed, bring the enterprise opportunities of integrating the new service innovation knowledge system learned in knowledge co-creation with its internal knowledge base, and drive the enterprise to build a solid knowledge foundation for the transformation and upgrading of service innovation. It is also worth noting that enterprises with prominent compositional capability can better match and recombine their internal knowledge with high potential and cost-effective external knowledge resources [46]. In this way, they can lower their R&D cost of service innovation and transform or develop new services or new product services according to an enterprise’s characteristics and market demand [18]. Therefore, with compositional capabilities, enterprises can deeply integrate their internal knowledge with the new knowledge acquired in knowledge co-creation and further improve service innovation performance. Based on the above analysis, the following hypotheses are proposed:
Hypothesis 5 (H5a).
Compositional capability positively moderates the relationship between alliance-based knowledge co-creation and service innovation performance.
Hypothesis 5 (H5b).
Compositional capability positively moderates the relationship between embedded knowledge co-creation and service innovation performance.

3. Research Methodology

3.1. Variable Measurement

The questionnaire survey method was adopted to acquire empirical data to verify the models and hypotheses proposed in this paper. The Likert five-point scale was used for the questionnaire. Relevant literature was based on finding the appropriate measurement items for the core concepts in the theoretical model of this research. The measurement items mainly came from the mature scales in the existing literature. Specifically, the measurement items of the cross-border technological knowledge search (TKS) and cross-border market knowledge search (MKS) were derived from the research findings of Sidhu et al. [34] and Martini et al. [47]; the measurement items of embedded knowledge co-creation (EKC) and alliance-based knowledge co-creation (AKC) were derived from the research results of Ali et al. [20] and Jiang et al. [11]; the measurement items of compositional capability (CCap) were derived from the research findings of Lu and Sun [29]; and the measurement items of service innovation performance (SIP) were derived from the research results of Avlonitis et al. [48]. Furthermore, the enterprise size, enterprise age and enterprise nature were taken as control variables.

3.2. Data Collection

This questionnaire survey took manufacturing enterprises engaged in service innovation activities as the respondents. 90 questionnaires were distributed for a pilot test. We adjusted the questionnaire based on the sample’s responses to avoid inexpressiveness, ambiguity, redundancy and other problems. Therefore, content validity is guaranteed to a certain extent. The final questionnaire was formed after the revision.
The questionnaires were distributed among middle and senior managers who knew well about the overall business running conditions and first-line managers who knew well about the overall service innovation condition of their enterprises in Shandong, Jiangsu, and Zhejiang provinces in China. In this questionnaire survey, a total of 378 questionnaires were collected, and 308 of them were considered valid after incomplete questionnaires and extreme regularization were eliminated. So, the valid recovery rate was 81.5%. The basic characteristics of the sample enterprises are shown in Table 1.

4. Data Analysis and Results

Common method bias (CMB) and multicollinearity may jeopardize the structural equation modeling (SEM) results. We tested the CMB using Harmon’s single-factor test method [49]. Principal component factor analysis showed that four factors explained 68.21% of the total variance. The variance interpretation rate of the first factor was 12.34%, less than 50%, indicating that there is no serious common method bias.

4.1. Reliability and Validity Test

We tested and exhibited the reliability and validity of the data in Table 2, the scores of Cronbach’s α and CR were higher than 0.7, and the AVE values were higher than 0.5 which met the requirements [50]. Thus, the data had good reliability and convergent validity. Moreover, Table 2 also indicated that the absolute values of the correlation coefficient between variables were less than the square root of AVE, which met the requirement [51]. All evidence indicates suitable reliability and validity.

4.2. Hypothesis Testing

4.2.1. Testing of Main Effect

We use the hierarchical regression analysis approach to examine the influence of cross-border search on SIP to construct the regression models of M3 and M4 with SIP as the dependent variable. Table 3 shows the testing results, indicating that TKS (β = 0.273, p < 0.001) and MKS (β = 0.288, p < 0.001) have a positive influence on SIP, supporting H1a and H1b.

4.2.2. Testing of Mediating Effect

To test the mediating role of knowledge co-creation in the relationship between cross-board search and SIP, we constructed the regression model of M9~M12 (in Table 4) by referencing the approach of Baron & Kenny [52]. Table 3 shows that in M1, TKS (β = 0.386, p < 0.001) and MKS (β = 0.404, p < 0.001) positively impact AKC, supporting H2a and H2b; in M2, TKS (β = 0.229, p < 0.001) and MKS (β = 0.222, p < 0.001) have a positive influence on EKC, thus supporting H2c and H2d; in M5, AKC (β = 0.332, p < 0.001) positively impact on SIP, therefore, H3a was supported; in M6, EKC (β = 0.296, p < 0.001) positively influence SIP, H3b was supported. Compared with M3 (in Table 3) and M9 (in Table 4), it is evident that the path coefficient value was declared from 0.273 (p < 0.001) to 0.171 (p < 0.001), meanwhile, AKC (β = 0.252, p < 0.001) positively impacted SIP, indicating that AKC is a partial mediator in the relationship between TKS and SIP. Similarly, AKC is also a partial mediator in the relationship between MKS and SIP, EKC is confirmed as a partial mediator in knowledge co-creation dimensions (TKS and MKS) and SIP. Accordingly, H4a~d were supported.

4.2.3. Testing of Moderating Effect

Considering EKC, AKC, CCap and SIP were continuous variables, we built a multiple regression model containing the product terms of knowledge co-creation and CCap to verify the moderating effect of CCap in dimensions of knowledge co-creation and SIP. We can determine whether the moderating effect of CCap is significant by checking the coefficient of the product term. Table 2 shows the testing results. In M7, the interaction coefficient of AKC and CCap was 0.071, which is not significant at the 95% confidence interval, therefore, H5a was not supported. In M8, the interaction coefficient of EKC and CCap (β = 0.268, p < 0.001) positively influences SIP, indicating that CCap plays a moderating role in the EKC-SIP link, thus, supporting H5b. We drew a graph to show the moderating relationship (Figure 2) using Cohen’s approach [53] to demonstrate the moderating effect. Based on the chart, with the increase of CCap (from −1std to +1 std), the line goes from gentle to steep, indicating that the relationship between EKC and SIP was intensified. This result showed that, compared with low CCap, EKC was highly correlated with SIP when CCap increased.
In order to present the research results of this paper more clearly, we summarized them in Table 5.

5. Discussion

5.1. Key Findings

This paper adopts the perspectives of the knowledge-based view to explore the effect of cross-border search on the service innovation performance of manufacturing enterprises. We also take knowledge co-creation as the mediator in the above relationship and set compositional capability as the moderator in the connection between knowledge co-creation and service innovation performance. The key findings are as follows.
Firstly, both cross-border technological knowledge search and cross-border market knowledge search increase service innovation performance. This research finds that cross-border search is crucial for organizations to absorb and internalize external heterogeneous knowledge. It can help cover the internal service knowledge shortage of enterprises and guarantee sufficient resources for service innovation. This finding is consistent with that of Wang et al. [24]. Meanwhile, the cross-border search can assist enterprises in improving and updating their internal knowledge structure, protect enterprises from depending on innovation paths and being restricted by resource endowment, and provide an opportunity for enterprises to build new service innovation channels to launch new service innovation businesses in the future.
Secondly, cross-border technological and market knowledge search produce a significantly positive influence on knowledge co-creation. The effective implementation of cross-border search provides an effective way for enterprises to acquire relevant knowledge resources [12]. A wealth of advanced knowledge resources can bring enterprises more opportunities to make knowledge co-creation in an open innovation environment. Therefore, enterprises can occupy the core position to acquire advanced knowledge and actively launch knowledge co-creation activities quickly.
Thirdly, by extending the research findings of Jiang et al. [11], this research finds that both alliance-based knowledge co-creation and embedded knowledge co-creation positively influence the service innovation performance of manufacturing enterprises. In an open innovation environment, alliance-based knowledge co-creation and embedded knowledge co-creation allow enterprises to maximize the opportunities of knowledge flow, create opportunities for enterprises to know, acquire and master knowledge. Moreover, it drives enterprises to keep abreast of external service innovation in a timely manner, so that enterprises can advance with the times, constantly optimize their internal service innovation structure and improve their service innovation performance.
Fourthly, knowledge co-creation has some mediating effects on the influence of cross-border technological knowledge search and cross-border market knowledge search on service innovation performance. Knowledge co-creation can boost the interaction between enterprises and external innovation subjects so that enterprises can better know about the service innovation characteristics and strategies of external innovation subjects [10], launch cross-border search activities more specifically and purposefully, save time in knowledge acquisition, internalize knowledge to the greatest extent and successfully fulfill service innovation.
Lastly, compositional capability positively moderates the relationship between embedded knowledge co-creation and service innovation performance. However, the positive moderating effect of compositional capability on the relationship between alliance knowledge co-creation and service innovation performance is not proven. This is perhaps because alliance-based knowledge co-creation requires enterprises to invest more funds, knowledge resources, time and energy [54]. In addition, enterprises should spend much time interacting with external knowledge sources and bear some risks to guarantee the successful implementation of alliance-based knowledge creation activities. Compositional capability performs its function mainly within the enterprise, so it cannot provide sufficient support to enterprises in their knowledge co-creation. Therefore, the moderating effect of compositional capability is insignificant.

5.2. Theoretical Contributions

This research makes theoretical contributions to the existing literature on service innovation and the knowledge-based view from the following aspects:
Firstly, this research constructs a framework model of cross-border search and knowledge co-creation on service innovation performance to demonstrate the logic of manufacturing enterprises in the process of “knowledge acquisition-knowledge transformation-knowledge creation”. Our study revealed the positive influence of cross-border search and knowledge co-creation on service innovation performance, thus, elaborating the relationship path of knowledge discovery, utilization and creation by enterprises.
Secondly, prior studies mainly focus on the direct impact of cross-border search on service innovation performance [30]. This research found that knowledge co-creation mediates the relationship between cross-border technological and market technology search and service innovation performance. Thus, this research expands and supplements the research on enterprise service innovation performance.
Lastly, prior studies mainly focused on discussing the relationship between knowledge co-creation and enterprise innovation performance [55] but ignored the boundary condition of knowledge co-creation works. This research concludes that the effect of embedded knowledge co-creation is determined by the compositional capability of the enterprise, which further deepens and extends the research on enterprise service innovation performance. Therefore, this research clarifies the differences in the influence mechanism of different knowledge co-creation modes and the effect of compositional capability as the boundary condition for the relationship between knowledge co-creation and service innovation. Therefore, our study provides new insights into the existing literature to understand knowledge co-creation.

5.3. Practical Implications

Firstly, enterprises should expand their channels of knowledge acquisition and actively implement cross-border search activities. In essence, cross-border search is to break through organizational boundaries and skillfully search for new knowledge from external partners and the external market, so enterprises should actively establish a favorable external relationship network, rapidly capture new technologies and new demand, and acquire more new knowledge.
Secondly, it is essential to build knowledge transformation paths and concentrate on boosting knowledge co-creation. Enterprises should highly value both external knowledge and internal knowledge. Moreover, enterprises should also pay great attention to the increase of the knowledge quantity and the integrated use of knowledge. To absorb external knowledge, enterprises should clear the bottleneck of their existing knowledge, properly integrate the internal and external knowledge, and build a new knowledge system promoting service innovation. Meanwhile, enterprises should actively form knowledge alliances with partners to create new knowledge and externalize knowledge efficacy jointly.
Lastly, enterprises should focus on cultivating knowledge capability and assisting in knowledge creation service performance. When embedding external knowledge, enterprises should develop compositional capability to enhance the knowledge transformation efficiency and effect. Therefore, it is necessary to cultivate the internal knowledge capability of enterprises, boost the use of knowledge to create service value, and strengthen the transformation of knowledge and the practical application of knowledge innovation findings.

5.4. Limitations and Future Study

This research inevitably has some limitations. First, policy factors are ignored in the discussion of cross-border searches. As Cao and Qiu [56] argued, policy knowledge is also important for service innovation. Therefore, the influence mechanism of cross-border policy knowledge search can be investigated in future research. Worse still, this research focuses on the influence of compositional capability (the internal knowledge capability of the enterprise). However, alliance-based knowledge creation requires enterprises and their external cooperative partners to work together to build knowledge systems. Therefore, future studies can introduce the external knowledge capability of enterprises into research to reveal the boundary conditions for alliance-based knowledge innovation to produce its effect. Third, this research is conducted specifically in the context of the Chinese manufacturing industry. In future research, relevant concepts can be verified and expanded in other cultural contexts. At last, this study only puts forward a general logical framework, suggesting that enterprises can improve performance through cross-organizational boundary knowledge search and knowledge co-creation process. Subsequent studies can further explore the key impacts of security and ethics on this process. These factors may hinder the success of knowledge co-creation. Moreover, enterprises need to enhance the ability of knowledge discrimination to identify wrong knowledge. Future research can also be further discussed on this point.

Author Contributions

T.L. was responsible for idea generation, manuscript writing, and revision. Q.G. was accountable for hypothesis development and data analysis. Q.Z. was responsible for the first draft writing. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Natural Science Foundation of Shandong Province, China (No. ZR2021QG007) and the Humanities and Social Science Fund of the Ministry of Education, China (No. 19YJC630118).

Institutional Review Board Statement

Ethical review and approval were not required for the study on human participants by the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with national legislation and institutional requirements. Written informed consent was obtained from the individual(s) to publish any potentially identifiable images or data included in this article.

Informed Consent Statement

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

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Systems 11 00004 g001 550
Figure 1. Research Model.
Figure 1. Research Model.
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Systems 11 00004 g002 550
Figure 2. The Moderation Effect of CCap in the relationship between EKC and SIP.
Figure 2. The Moderation Effect of CCap in the relationship between EKC and SIP.
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Table
Table 1. Sample demographics (N = 308).
Table 1. Sample demographics (N = 308).
VariableCategoryNumber%
Firm SizeLess than 50 people154.9
51–100 people6320.5
101–300 people10132.8
More than 300 people12941.9
Firm AgeLess than 1 year31
1–3 years103.2
3–5 years9731.5
More than 5 years19864.3
Firm propertyState-owned enterprise4614.9
Private enterprise10132.8
Sino-foreign joint venture12540.6
Wholly foreign-owned enterprise3511.4
Others10.3
IndustryComputer, communication, and other electronic equipment manufacturing industry5317.2
Chemical manufacturing industry3712
General equipment manufacturing industry3812.3
Electrical machine equipment
manufacturing industry		4013
Transportation equipment
manufacturing industry		206.5
Special equipment manufacturing industry206.5
Automotive manufacturing industry299.4
Textile and apparel industry4815.6
Furniture manufacturing134.2
Others103.2
Table
Table 2. Reliability and Validity Testing Results.
Table 2. Reliability and Validity Testing Results.
Cronbach’s αCRAVEMeanStd.TKSMKSAKCEKCCCapSIP
TKS0.7650.8200.5353.8530.5750.731
MKS0.8480.8720.5793.8140.5940.347 **0.761
AKC0.7780.8190.6033.8720.6310.373 **0.390 **0.776
EKC0.7920.8390.6353.8540.5610.237 **0.227 **0.155 **0.797
CCap0.8010.8130.5253.8760.5880.338 **0.302 **0.394 **0.447 **0.724
SIP0.7480.8120.5904.2270.5730.287 **0.290 **0.347 **0.298 **0.446 **0.768
** significant at the 0.01 level (two-tailed).
Table
Table 3. Results of Hierarchical Regression Analysis.
Table 3. Results of Hierarchical Regression Analysis.
VariablesAKCEKCSIP
M1M2M3M4M5M6M7M8
Firm Size−0.041−0.037−0.0060.0360.0270.0210.0510.034
Firm Age0.027−0.0060.0190.0080.0080.023−0.015−0.006
Firm property−0.082−0.127−0.155−0.172−0.142−0.139−0.114−0.125
TKS0.386 ***0.229 ***0.273 ***
MKS0.404 ***0.222 *** 0.288 ***
AKC 0.332 *** 0.196 ***
EKC 0.296 *** 0.107 *
CCap 0.377 ***0.436 ***
AKC*CCap 0.071
EKC*CCap 0.268 ***
R20.2250.0980.1060.1140.1410.1080.2520.247
adjusted R20.2120.0830.0940.1030.1290.0960.2380.232
F value17.5426.5488.9659.78512.4119.17716.93816.452
Note: * p < 0.05, *** p < 0.001.
Table
Table 4. Mediating Effect Testing Results.
Table 4. Mediating Effect Testing Results.
VariablesSIP
M9M10M11M12
Firm Size0.0090.0260.0050.027
Firm Age0.0070.0010.0180.009
Firm Property−0.087−0.094−0.082−0.091
TKS0.171 *** 0.217 ***
MKS 0.186 *** 0.235 ***
AKC0.252 ***0.244 ***
EKC 0.242 ***0.239 ***
R20.1670.1710.1540.161
Adjusted R20.1530.1570.1400.147
F value12.07412.42310.96711.623
Note: *** p < 0.001.
Table
Table 5. The Summarization of Results.
Table 5. The Summarization of Results.
HypothesisConclusion
H1a: Cross-border technological knowledge search positively influences service innovation performance.supported
H1b: Cross-border market knowledge search positively influences service innovation performance.supported
H2a: Cross-border search of technological knowledge positively influences alliance-based knowledge co-creation.supported
H2b: Cross-border search of technological knowledge positively influences embedded knowledge co-creation.supported
H2c: Cross-border market knowledge search positively influences alliance-based knowledge co-creation. supported
H2d: Cross-border market knowledge search positively influences embedded knowledge co-creation.supported
H3a: Alliance-based knowledge co-creation positively influences service innovation performance. supported
H3b: Embedded knowledge co-creation positively influences service innovation performance.supported
H4a: Alliance-based knowledge co-creation produces a mediating effect on the influence of cross-border technological knowledge search on service innovation performance. supported
H4b: Embedded knowledge co-creation produces a mediating effect on the influence of cross-border market knowledge search on service innovation performance.supported
H4c: Embedded knowledge co-creation produces a mediating effect on the influence of cross-border technological knowledge search on service innovation performance. supported
H4d: Alliance-based knowledge co-creation produces a mediating effect on the influence of cross-border market knowledge search on service innovation performance.supported
H5a: Compositional capability positively moderates the relationship between alliance-based knowledge co-creation and service innovation performance. unsupported
H5b: Compositional capability positively moderates the relationship between embedded knowledge co-creation and service innovation performance.supported
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Lyu, T.; Geng, Q.; Zhao, Q. Understanding the Efforts of Cross-Border Search and Knowledge Co-Creation on Manufacturing Enterprises’ Service Innovation Performance. Systems 2023, 11, 4. https://doi.org/10.3390/systems11010004

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Lyu T, Geng Q, Zhao Q. Understanding the Efforts of Cross-Border Search and Knowledge Co-Creation on Manufacturing Enterprises’ Service Innovation Performance. Systems. 2023; 11(1):4. https://doi.org/10.3390/systems11010004

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Lyu, Tu, Qixiang Geng, and Qiu Zhao. 2023. "Understanding the Efforts of Cross-Border Search and Knowledge Co-Creation on Manufacturing Enterprises’ Service Innovation Performance" Systems 11, no. 1: 4. https://doi.org/10.3390/systems11010004

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