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Article

The Mediating and Combined Effects of Trust and Satisfaction in the Relationship between Collaboration and the Performance of Innovation in Industry—Public Research Institute Partnerships

by
Kyung Yun Hwang
,
Eul Hyun Sung
and
Temitayo Shenkoya
*
Science & Technology Knowledge Research Institute, Chungnam National University, Daejeon 34134, Korea
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(4), 2128; https://doi.org/10.3390/su14042128
Submission received: 23 December 2021 / Revised: 26 January 2022 / Accepted: 4 February 2022 / Published: 13 February 2022
(This article belongs to the Section Economic and Business Aspects of Sustainability)
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Abstract

:
The purpose of this study was to analyze the mediating and combined effects of trust and satisfaction in a collaborative activity, while considering the effects of the performance of innovation in an Industry–Public Research Institute (I–PRI) collaboration. Data used in this study was collected through a survey targeting Small and Medium-Sized Enterprises (SMEs) in the INNOPOLIS Daedeok innovation cluster in Korea. PLS-SEM and fsQCA were used for the analysis of data collected. The results of this study show that trust mediates the relationship between collaboration and satisfaction, while satisfaction mediates the relationship between collaboration, and the performance of innovation, as well as the relationship between trust and the performance of innovation. In addition, it was confirmed that collaboration, trust, and satisfaction jointly affect the performance of innovation in I–PRI collaborations. Additionally, it was discovered that in-order to increase the performance of innovation - required to secure and maintain a competitive advantage; through I–PRI collaborations—SMEs need active collaboration with PRIs, and advanced management skills; to build mutual trust, and improve satisfaction. Finally, the theoretical implications of the factors that affect the performance of innovation in I–PRI collaborations were presented.

1. Introduction

In today’s fiercely competitive environment, innovation is important for a firm to secure a sustainable competitive advantage. A firm can innovate through its internal capabilities, but this can be limited, because of the cross-technical expertise and resources needed to excel in a competitive environment. As a result, open innovation through collaboration with external firms is attracting the attention of corporations. From the perspective of open innovation, firms need innovation from various sources—through collaboration with external organizations, as well as internal knowledge generation [1,2]. External institutions that firms can collaborate with are Public Research Institutes (PRIs)—including universities. Firms collaborate with PRIs in response to the rapid pace of technological change, high levels of competition, short product lifecycles, high R&D costs, and the complexity and uncertainty of innovation processes [3]. SMEs encounter difficulties in creating innovation that responds to changes in a competitive business environment with their own capabilities alone; due to the lack of dynamic capabilities. Since most SMEs do not have all the resources and capabilities needed to continuously develop new products, innovation-oriented SMEs seek collaboration with relevant institutions [4]. SMEs have been actively pursuing collaborations with external institutions, including universities—as a result, collaboration between SMEs and external institutions has been on the rise [5]. However, interorganizational collaboration does not always lead to a positive performance of innovation [6,7,8]. To exploit the positive potential of interorganizational collaboration, it is important to improve interorganizational trust and satisfaction [8]. Trust is an effective means of helping firms deal with uncertainties relating to their partners’ behavior [9]. Trust provides confidence that a firm will not exploit another firm’s vulnerability, while sharing tacit knowledge and hard-to-codify assets with its partners [6]. On the other hand, without satisfaction—such as mutual benefit—interorganizational collaboration is meaningless [8]. If active collaboration is performed to increase satisfaction between firms, the relationship between partners will continue to prosper, improving performance [10]. In this respect, trust and satisfaction will play an important mediating role in explaining the performance of innovation in an I–PRI collaboration. A firm’s innovative performance is a key factor in determining competitive advantage [11]. From an open innovation perspective, firms need to actively utilize collaboration with PRIs to create innovation. To maximize the performance of innovation in an I–PRI collaborations, it is very important to identify the factors and preconditions needed.
This study aims to analyze the mediating and combined effects of trust and satisfaction in the relationship between collaboration and the performance of innovation in an industry–public research institute partnership, from an open innovation perspective. In the past, the role of trust and satisfaction in the relationship between I–PRI collaboration and the performance of innovation was not clearly established. In interfirm relationships, collaboration improves trust [3], trust increases collaboration performance [12,13], and collaboration increases the performance of innovation [14]. However, empirical evidence that shows that trust alone, as a mediator of the relationship between collaboration and performance in interfirm relationships, is insufficient. Daniel [7] found that trust does not mediate between the relationship of collaboration and performance. Trust is one way to address the issues of uncertainty relating to knowledge leakage and opportunistic behavior of partners [15]. In I–PRI collaborations where R&D activities are at the core, it is necessary to confirm that trust is an important factor mediating between collaboration and the performance of innovation, because the leakage of partner knowledge and opportunistic behavior can act as a fatal threat to the firms involved. Meanwhile, in interfirm relationships, collaboration improves satisfaction [16], satisfaction increases collaboration performance [17,18], and collaboration improves innovative performance [19]. Based on existing literatures, in an I–PRI collaboration, satisfaction is expected to play a mediating role between the relationship of collaboration and performance, but empirical evidence shows this is insufficient. Kraus et al. [8] argued that cooperation is meaningless unless satisfaction, such as mutual benefit, is achieved in interorganizational relationships. On the other hand, Niranjan et al. [20] found empirical evidence that collaboration—such as information exchange—has a positive effect on satisfaction, and satisfaction has a positive effect on relationship performance. However, satisfaction does not mediate between collaboration and performance. As such, previous studies have produced confusing results, so it is necessary to clarify the mediating role of satisfaction between collaboration and the performance of innovation in I–PRI collaborations. Additionally, in previous studies, the combined effect of collaboration, trust, and satisfaction on the performance of innovation has not been analyzed. Previous studies simply analyzed the independent effects of collaboration, trust, and satisfaction on the performance of innovation; therefore, it is necessary to analyze the combined effect of collaboration, trust, and satisfaction on the performance of innovation. Because the relationship between variables is complex, when a specific causal factor is combined with another causal factor, the effect on the outcome may be greater [21]. Therefore, it is important that the combined effect of these factors is analyzed.
This study is unique in the following aspects: First, from a theoretical point of view, we analyzed the effect of collaborative activities between SMEs and PRIs on the performance of innovation, whereas, previous studies analyzed the performance of innovation in collaborations between firms [14,19,22]. We confirm that SMEs with insufficient resources and capabilities increase the performance of innovation—through collaborative activities with PRIs. Second, we focus on the role of trust and satisfaction between collaboration and the performance of innovation in I–PRI collaborations. I–PRI collaborations can create synergy by utilizing complementary resources between participating entities, thereby enhancing a firm’s performance of innovation [23]. However, I–PRI collaborative activities do not always lead to successful outcomes [24]. Through the study of I–PRI collaborations, we identified that the pathway to increasing the performance of innovation lies in increasing trust and improving satisfaction. Third, the combined effect of collaboration, trust, and satisfaction on the performance of innovation in interorganizational relationship was analyzed. Although three or more factors can influence the performance of innovation, there is no extant study analyzing this in an interorganizational relationship. We identified the combined effects of collaboration, trust, and satisfaction on the performance of innovation.
Our paper is structured as follows: Section 2 examines existing literatures on interorganizational relationships and checks how collaboration, trust, and satisfaction affect the performance of innovation. Data and methodology are presented in Section 3, and Section 4 contains the analysis of data. Finally, insights on increasing the performance of innovation of SMEs by I–PRI collaborations are derived from analyzing managers’ perceptions, in addition to managerial and academic contributions. These insights are presented in Section 5.

2. Literature Review and Hypotheses Development

2.1. Collaborative Activity

Collaboration suppresses opportunism, encourages cooperative behavior, and increases the potential value of relationships [25]. Collaborative activities include joint relationship effort, information sharing, and dedicated investments [26]. Knowledge exchange is actively conducted between SMEs and PRIs. It is defined as the sharing, transfer, accumulation, transformation, modification, and integration of knowledge—contributed by other organizations [27]. To respond to the present rapid technological and market changes, SMEs are required to collaborate with external organizations. SMEs usually do not like collaborating with potential competitors, rather they prefer collaborating with PRIs that are continuously generating new knowledge. In I–PRI collaborations, collaborative activity promotes interorganizational interaction, so it will have a positive effect on building trust between organizations. Bellini et al. [3] demonstrated the positive relationship that exists between collaborative experience and trust. On the other hand, Baah et al. [28] found that collaboration helps to form trust within a supply chain, and Hameed and Naveed [29] also showed that collaborative activity between firms increases trust between firms. However, Kraus et al. [8] found that trust between firms - promotes collaboration. As such, collaboration is closely related to trust, but the relationship between collaborative activity and trust is different. As emphasized by Bellini et al. [3], and Hameed and Naveed [29] - this study focuses on I–PRI collaboration, and established the hypothesis that collaborative activity has a positive effect on trust.
H1: 
Collaboration will have a positive effect on trust in an I–PRI collaboration.
In interorganizational collaboration, collaboration plays a role in enhancing satisfaction. Satisfaction is defined as the overall positive evaluation of the relationship of one organization with another [30]. It has a positive emotional state in which the expectations of the parties in a business relationship are satisfied. Collaborative behavior, such as information sharing, can be an important antecedent to satisfaction because it provides a means for partners to share goals, reconcile differences, and coordinate efforts to achieve common goals [26]. Selnes [31] found that communication is an important source of satisfaction. Dash et al. [32] suggested that interorganizational collaboration has a positive effect on satisfaction. While Agarwal and Narayana [16] showed that collaborative activities—such as sharing information between organizations—has a positive effect on satisfaction. On the other hand, Niranjan et al. [20] show that collaborative activities, such as information exchange, do not have a significant effect on satisfaction. Most interorganizational relationship studies show that collaborative activity improved satisfaction, while others show no significant relationship between them. Based on Agarwal and Narayana [16], and Niranjan et al. [20], we hypothesized that collaborative activity improves satisfaction in I–PRI collaborations, as follows:
H2: 
Collaboration will have a positive effect on satisfaction in an I–PRI collaboration.
Collaboration improves performance because interorganizational interactions can create synergies by supplementing scarce resources with each other [3,33]. Interorganizational collaboration increases performance [29]. When interorganizational collaboration is performed, it is possible to create an opportunity to achieve a common goal by transferring knowledge and skills between collaborating parties [33]. Cao and Zhang [33], and Pradabwong et al. [22] confirmed that the level of collaboration determines an organization’s performance in the supply chain. Schoenherr and Swink [34] confirmed that firms that collaborate show improved performance compared with those that do not. Hong et al. [15] argued that interorganizational collaboration has a positive effect on innovative performance within a supply chain. A firm’s external knowledge increases its innovativeness [35]. Therefore, if SMEs utilize external knowledge through collaborative activities—with external organizations, such as PRIs—then they can improve their performance. Todeva and Knoke [36] argued that a firm can achieve successful product innovation when it acquires skills, patent information, and a pool of distinctive resources through strategic technology collaboration. Bellini et al. [3] concluded that university–industry collaboration experience affects product and process innovations. Apa et al. [19] confirmed that formal collaboration between firms and universities improves a firm’s performance of innovation. Brettel and Cleven [37] showed that collaboration between universities and firms increased the performance of innovation in new products. As such, previous studies did not show the relationship between collaboration and the performance of innovation in I–PRI collaborations. Based on the evidence confirmed by Bellini et al. [3] and Apa et al. [19], this paper hypothesized that collaborative activity positively affects the performance of innovation in an I–PRI collaboration, as follows:
H3: 
Collaboration will have a positive effect on the performance of innovation in an I–PRI collaboration.

2.2. Trust

Interorganizational trust is defined as an organization’s belief that other organizations will perform actions that will be profitable to them [38]. Trust builds relationships by reducing the likelihood of opportunistic behavior and uncertainty [39]. Trust is an iterative process in which partners trust each other to perform specific tasks and act in the best interest of each other. The higher the level of interorganizational trust, the higher the interorganizational satisfaction [40]. Trust plays a role in increasing satisfaction, because it helps to overcome conflicts and ambiguities between partners in an interorganizational relationship [17]. Mungra and Yadav [17] confirmed that trust increases satisfaction in the relationship between manufacturers and suppliers. However, the relationship between trust and satisfaction in I–PRI collaborations has not been clearly identified. Based on existing interorganizational relationship studies, trust is expected to have a positive relationship with satisfaction in I–PRI collaborations. Therefore, we establish this relationship with the following hypothesis:
H4: 
Trust will have a positive effect on satisfaction in an I–PRI collaboration.
Trust promotes closer social relationships and reduces transaction uncertainty, thus enhancing the benefits of collaboration, such as product and process innovation, and creating knowledge [3,13]. Trust improves flexibility, lowers the cost of activity coordination, and increases the level of knowledge transfer in interorganizational collaborations [13,41]. Das and Teng [39] argued that one of the important factors that determines the performance of innovation - in interfirm strategic alliances - is the level of trust between partners. Bellini et al. [3] revealed that trust between partners has a positive effect on product and process innovations in university–industry collaborations. Hausman [40] found that a high level of interorganizational trust means high relationship strength, and a high relationship strength leads to high performance. Nyaga et al. [26] confirmed that trust improves performance in the relationship between buyer and supplier. Yang et al. [42] showed that trust between firms in the supply chain increases the overall level of information exchange, which increases the firm’s performance of innovation. Based on previous interorganizational relationship studies, we hypothesized that trust has a positive effect on the performance of innovation in I–PRI collaborations, as follows:
H5: 
Trust will have a positive effect on the performance of innovation in an I–PRI collaboration.

2.3. Satisfaction

Satisfaction is a positive emotional response to an association between partners [30]. Satisfaction is a key component of interorganizational relationship quality and is intended to maintain healthy and sustainable relationships [43]. Performance depends on the degree of satisfaction in interorganizational relationships [18]. The higher the satisfaction between organizations, the better the firm’s performance [44]. Mungra and Yadav [17] presented empirical evidence that satisfaction affects performance in interorganizational relationships. Al-Sabi et al. [45] showed the relationship between individual job satisfaction and the performance of innovation. Meanwhile, Gopalakrishnan and Zhang [46] presented evidence that interfirm satisfaction increases innovation. Empirical evidence showing a relationship between satisfaction and the performance of innovation has not been presented in I–PRI collaboration studies—so far. Based on Gopalakrishnan and Zhang [46], satisfaction was hypothesized to increase innovative performance in I–PRI collaborations, as follows:
H6: 
Satisfaction will have a positive effect on the performance of innovation in an I–PRI collaboration.

2.4. Mediating Effects

2.4.1. The Mediating Role of Trust

Firms and PRIs have different missions, organizational cultures, and project management models [47]. PRIs are interested in obtaining recognition within the scientific community by publishing and disseminating the results of collaboration [48], while firms are reluctant to disclose their results because of the risk that this information may-be misused by their competitors. Likewise, firms are oriented toward the commercialization of new technologies and knowledge [49]. If appropriate trust is not established between partners with different cultural characteristics, then the performance of innovation cannot be achieved [50] (p. 3). Davenport et al. [50] suggested that trust can be built only through iterative collaboration. Bellini et al. [3] concluded that collaboration affects trust, and trust has a positive effect on tangible and intangible benefits in university–industry collaborations. Hameed and Naveed [29] showed a relationship between collaboration and trust, and between trust and the performance of innovation in interfirm collaboration. However, the role of trust in mediating between the relationship of collaboration and the performance of innovation was not suggested in their study. In this study, based on the results of Bellini et al. [3] and Hameed and Naveed [29], we hypothesized that trust mediates the relationship between collaboration and the performance of innovation in I–PRI collaborations, as follows:
H7: 
Trust mediates the relationship between collaboration and the performance of innovation in an I–PRI collaboration.

2.4.2. The Mediating Role of Satisfaction

Collaboration increases satisfaction [16] (p. 29), and satisfaction improves performance [17,18]. In existing interorganizational relationship studies—such as those by Awan et al. [51] and Chen and Wei [52]—it was found that collaboration increases the performance of innovation. There are insufficient studies providing empirical analysis of the mediating role of satisfaction in the association between collaboration and the performance of innovation. Yu et al. [53] presented empirical evidence that interorganizational integration in the supply chain has a positive effect on satisfaction, and satisfaction improves performance. Considering the relationship between collaboration and satisfaction [16], satisfaction and performance [17], and the results of Yu et al. [53]; satisfaction is expected to mediate in the relationship between collaboration and the performance of innovation in I–PRI collaborations. Therefore, we established this relationship as the following hypothesis:
H8: 
Satisfaction mediates the relationship between collaboration and the performance of innovation in an I–PRI collaboration.
In an interorganizational collaboration, trust increases partner satisfaction [17,18], and satisfaction between collaborating partners contribute to improved performance of innovation [45,46]. Combining these results, satisfaction is expected to mediate the relationship between trust and the performance of innovation, but the empirical evidence to show this is insufficient. Mungra and Yadav [17] suggested that trust increases satisfaction and satisfaction improves performance in the relationship between firms, but the mediating effect of satisfaction on the relationship between trust and performance was not analyzed. Based on the relationship between trust and satisfaction [17], and the relationship between satisfaction and the performance of innovation [46], we hypothesized that satisfaction mediates between trust and the performance of innovation in I–PRI collaborations.
H9: 
Satisfaction mediates the relationship between trust and the performance of innovation in an I–PRI collaboration.
Figure 1 represents the research model, developed through a review of the litera-ture. This research model was tested using PLS-SEM.

3. Methodology

3.1. Sampling and Data Collection

To analyze the role of trust, and the relationship between collaboration and the performance of innovation in I–PRI collaborations, we conducted a survey targeting SMEs located in INNOPOLIS Daedeok, Korea. INNOPOLIS Daedeok is a global innovation cluster with 29 PRIs, 7 universities, more than 2000 firms, and more than 30,000 R&D workers. The sample framework was the list of move-in firms, supplied by the Korea Innovation Foundation. The survey was conducted for about two months, starting from May 1 2020. E-mail, web, and telephone calls were used as survey methods. Data from 137 SMEs, with complete questionnaire responses, were used for the analysis. The demographic distribution of the sample was as follows. Firms’ ages were as follows: less than 5 years (33.6%), 5–10 years (31.4%), and 10 years or more (35.0%). Firms with 5 or fewer employees accounted for 30.7%, those with 6–10 employees accounted for 30.7%, those with 11–20 employees accounted for 14.6%, those with 21–50 employees accounted for 12.4%, those with 51 or more employees accounted for 11.7%. Looking at the products manufactured by firms, single part manufacturing firms accounted for 13.1%, intermediate parts manufacturing firms accounted for 18.2%, finished product production firms accounted for 59.1%, and other firms accounted for 9.5%.

3.2. Measurement

Measures of each construct included in the research model were developed with reference to existing studies, and Likert-type scales (from 1 = “strongly disagree” to 5 = “strongly agree”) were used. For measures of I–PRI collaboration, 4 items were developed by modifying the measures used by Brettel and Cleven [37], Wilke et al. [54], and Martins et al. [55] (see Appendix). Measures of trust were developed based on the items used by Brown et al. [56], Yuan et al. [57], and Martins et al. [55], and 6 items were measured (see Appendix for more details). Measures of satisfaction were developed based on the items used by Høgevold et al. [58], Brown et al. [56], and Andaleeb [59], and 5 items were measured (see Appendix for more details). Measures for the performance of innovation were developed based on the items used by Prajogo and Ahmed [60], Gunday et al. [61], and Latan et al. [62], and 3 items were evaluated.

3.3. Method of Analysis

Two methods were used for data analysis. First, PLS-SEM was used to analyze the structural relationships between constructs. PLS-SEM is an analytical method focused on the analysis of variance and can be performed using SmartPLS. Second, fsQCA was used to analyze the data. Conventional correlational methods such as PLS-SEM rely on principles of linearity, additive effects, and unifinality [63], while fsQCA emphasizes the importance of nonlinearity, synergistic effects, and equifinality [64]. Since fsQCA can identify conditions that can affect the outcome, it can solve problems of linearity, additive effects, and unifinality more convincingly [65]. Therefore, in this study, we also used fsQCA to identify all conditions affecting the performance of innovation in I–PRI collaborations. For PLS analysis, SmartPLS version 3.0 of Ringle et al. [66] was used, and fsQCA 3.0 was used for fsQCA. This study was carried out in the order shown in Figure 2.

4. Results

4.1. Measurement Model

We first evaluated the measurement model in the PLS-SEM analysis. Reliability and convergence validity of the constructs were evaluated in the measurement model assessment. In this study, construct reliability was calculated using Composite Reliability (CR) and Cronbach’s alpha index. As shown in Table 1, Cronbach’s alpha of the constructs was 0.848~0.938, and composite reliability of the constructs was 0.907~0.951. CR and Cronbach’s alpha index exceeded the standard threshold of 0.7 [67]. The Average Variance Extracted (AVE) from each construct was assessed to test convergence validity, and all estimates exceeded the standard threshold of 0.5 (see Table 1).
Fornell and Larcker’s criterion was assessed to test for discriminant validity. If the square root of the AVE of each construct is higher than the correlation coefficients with other constructs, it can be said that discriminant validity is satisfied [68]. In Table 2, all constructs were found to satisfy this criterion. Additionally, the heterotrait–monotrait ratio of correlations (HMTM) was used to ascertain discriminant validity [69]. An HTMT value of 0.85 or less indicates that there is no problem with discriminant validity. In the result, the HTMT value was between 0.320 and 0.685, which was below 0.85. This means that discriminant validity was not a threat in this study. In summary, the results of the validity analysis in this study met both convergent and discriminant validity.
Table
Table 1. Measurement model evaluation results.
Table 1. Measurement model evaluation results.
ConstructsItems　Loadingst-Valuep-ValueComposite ReliabilityAverage Variance Extracted (AVE)Cronbach’s Alpha
Collaboration CA10.89538.0520.0000.9240.7520.890
CA20.89853.3100.000
CA30.84623.6780.000
CA40.82721.9010.000
TrustTR10.86328.6370.0000.9510.7640.938
TR20.88745.7900.000
TR30.86527.9710.000
TR40.90638.7870.000
TR50.88438.7980.000
TR60.83829.5510.000
Satisfaction SA10.8129.0560.0000.9070.7660.848
SA20.90961.8020.000
SA30.90146.3330.000
Performance of innovationIN10.90543.6050.0000.9190.7910.868
IN20.89839.2850.000
IN30.86421.8950.000
Table
Table 2. Comparison of the AVE square root and correlation between constructs.
Table 2. Comparison of the AVE square root and correlation between constructs.
1234
1. Collaboration0.867
2. Trust0.5840.874
3. Satisfaction 0.6100.6060.875
4. Performance of innovation0.4360.2910.4630.889
Note: Correlations are below the diagonal, and the squared roots of AVE are presented on the diagonal.

4.2. Structural Model Results

The absolute fit index is a value that indicates how well the model fits the sample data [70]. Standardized Root Mean Square Residual (SRMR) is one of the absolute fit indices. It is defined as the standardized difference between the observed correlation and the predicted correlation, and when this value is less than 0.10, the model has a good fit to the data [71]. In this study, the SRMR of the structural model was found to be 0.058. Additionally, the Normed Fit Index (NFI) was 0.869. According to Hair et al. [72], the closer the NFI is to 1, the better the fit [72]. Based on the SRMR and NFI values, this research model can be considered a well-fitted model. Meanwhile, in the PLS analysis, the coefficient of determination (R2) is a measure of the predictive accuracy of a model [73]. It is recommended that it exceeds at least 0.1 [74]. In PLS-SEM, if R2 is 0.60 or more, then it has a predictive accuracy of a substantial level, if it is 0.33, then it has a predictive accuracy of a moderate level, and if it is 0.19 or more, then it has a predictive accuracy of a weak level [75,76]. In the results obtained, all the R2 observed values exceed 0.1, the predictive accuracy of trust and satisfaction were at moderate levels, and the predictive accuracy of the performance of innovation was at a weak level. In I–PRI collaborations, the degree to which collaboration explains trust (R2 = 0.342) was 34.2%, and the degree to which collaboration and trust explains satisfaction (R2=0.466) was 46.6%. On the other hand, the degree to which collaboration, trust, and satisfaction explain the performance of innovation (R2 = 0.255) was 25.5%.
In I–PRI collaborations, the degree to which collaboration explains trust was 34.2%, and the degree to which collaboration and trust explain satisfaction was 46.69%. On the other hand, the degree to which collaboration, trust, and satisfaction explain the performance of innovation was 25.5%. The predictive relevance effect sizes were measured using cross-validated redundancy (Q2) [73]. If this value is greater than zero, it is considered that there is predictive relevance [77]. All Q2 values were above 0.193, which means that the predictability of the estimated model was high. The results of testing the path coefficient in the structural model were as follows: at the 5% significance level, collaborative activities were found to have a positive effect on trust (β = 0.584, t-value = 7.742, p-value = 0.000). These effects are like those of Bellini et al. [3] and Hameed and Naveed [29]. In addition, as shown by Agarwal and Narayana [16], it was found that I–PRI collaborative activities had a significant influence on satisfaction of I–PRI collaborations (β = 0.389, t-value = 4.810, p-value = 0.000). Additionally, as suggested by Hameed and Naveed [29], it was found that I–PRI collaborations significantly influenced the performance of innovation (β = 0.270, t-value = 2.737, p-value = 0.006). These results imply that H1, H2, and H3 are true.
Trust was found to have a positive effect on the satisfaction (β = 0.378, t-value = 4.405, p-value = 0.000) at the 5% significance level. This is similar to the results of Mungra and Yadav [17]. On the other hand, contrary to the results of Bellini et al. [3] and Yang et al. [42], it was found that trust did not significantly affect the performance of innovation (β = –0.075, t-value = 0.623, p-value = 0.534). These results imply that H4 is true, while H5 is false. Satisfaction was found to have a positive effect on the performance of innovation (β = 0.344, t-value = 3.434, p-value = 0.001) at the 5% significance level; therefore, H6 is valid (See Table 3 for more information). This is consistent with the results of Gopalakrishnan and Zhang [46].
We analyzed the mediating role of trust and satisfaction in the proposed I–PRI collaboration research model. To analyze the mediating effect, all direct, indirect, and total effects of the variables included in the model were estimated. In addition, the Variable Accounted For (VAF) index was calculated. VAF means the size of the indirect effect compared to the total effect. If this value is greater than 80%, mediation means full mediation [73]. As shown in Table 4, trust was found to partially mediate the relationship between collaboration and satisfaction in I–PRI collaborations. Additionally, satisfaction was found to partially mediate the relationship between collaboration and the performance of innovation. Also, satisfaction was found to fully mediate the relationship between trust and the performance of innovation. As such, H7 and H8 were partially supported, while H9 was fully supported.

4.3. Results of the Analysis of Data Using fsQCA

PLS-SEM analyzes the net effect between variables in a model, whereas fsQCA focuses on analyzing complex and asymmetric relationships between outcomes and its antecedents [21]. We performed additional analysis using fsQCA to overcome the limitations of PLS-SEM. fsQCA describes each case as a combination of a causal condition and an outcome. The outcome of our study was the performance of innovation by I–PRI collaborations, while the causal conditions are potential factors driving the performance of innovation such as - collaborative activity, trust, and satisfaction. fsQCA can be analyzed when causal conditions and outcomes are single items [78]. Therefore, each construct measured by multiple items must be converted into a single item. We converted them to a single item using the arithmetic mean. For fsQCA, it is important to first calibrate causal conditions and outcomes with a fussy set having a value between 0 and 1 [79]. We calibrated with a fussy set using the fsQCA software. At this time, since causal conditions and outcomes were measured on a 5-point Likert scale, 4, 3, and 2 were used as thresholds for the 5-point Likert scale [21]. Here, 4 was set as the full membership threshold, 2 was set as the full non-membership threshold, and 3 was set as the crossover point.
We generated a truth table after all variables were calibrated, and ordered this truth table according to frequency and consistency [21]. When generating the truth table, a frequency cutoff score was set so that configurations with few or no cases could be excluded from further analysis. In this study, a frequency cutoff suitable for the sample size was applied [63,79]. Therefore, structures of the truth table with less than one case were excluded from further analysis. When creating a truth table, a consistency cutoff is needed to determine the criteria for inclusion in the result set. The minimum recommended cutoff for consistency is 0.75 [80]. In this study, a solution consistency score of 0.85 was used. Table 5 shows that two configurations are sufficient to generate the performance of innovation by I–PRI collaborations. Here, large circles indicate core conditions, and small circles indicate peripheral conditions (See Table 5–for more information). Core conditions appear in both the intermediate and parsimonious solution, and peripheral conditions appear only in the parsimonious solution [63].
Based on the solution coverage, the two solutions were found to explain 94.30% of the performance of innovation by I–PRI collaborations. The overall solution coverage is similar to the R-square value in a regression analysis [81]. These solutions satisfy the minimum overall solution coverage and the overall solution consistency criteria proposed by Ragin [64]. When analyzing sufficient conditions, the coverage and consistency criteria should be 0.6 and 0.75, respectively [64]. The fsQCA results complement the results of the PLS-SEM analysis. Solution 1 shows the result that the performance of innovation occurs when collaboration is present, and trust is present in I–PRI collaborations. It was found that the core condition that determined the performance of innovation in this solution was the presence of collaboration. Solution 2 shows the result that the performance of innovation occurs when trust is present, and that satisfaction is present in I–PRI collaborations. In this solution, the core condition appeared to be the presence of satisfaction.
Table
Table 5. Sufficient conditions.
Table 5. Sufficient conditions.
ConfigurationSolution
12
Collaboration ●
Trust••
Satisfaction ●
Raw coverage0.91050.9159
Unique coverage0.02710.0325
Consistency0.87440.8682
Overall solution coverage: 0.9430
Overall solution consistency: 0.8471
Frequency cutoff—1; Consistency cutoff—0.8983
Note: Black circles indicate the presence of a condition—large circles indicate core conditions; small circles indicate peripheral conditions. Blank spaces indicate “Negligible”.

5. Discussion and Conclusions

To improve the performance of innovation in I–PRI collaborations, active collaborative activities between SMEs and PRIs should be performed, and these activities should be conducted in the direction of increasing trust and satisfaction. In the results analyzed using variance-based approaches (PLS-SEM), collaborations between SMEs and PRIs had a positive effect on building trust between collaborating parties. This is consistent with the research results of Bellini et al. [3] and Dash et al. [32], which revealed the relationship between collaboration and trust in interorganizational relationships. When knowledge sharing, transfer, accumulation, and transformation between SMEs and PRIs are actively carried out, trust between collaboration parties tends to increase. The trust built between SMEs and PRIs contributes to improved satisfaction. The relationship between trust and satisfaction—suggested in the research results of Mungra and Yadav [17], Anderson and Narus [38], and Nyaga et al. [26]—was also found in the collaborations between SMEs and PRIs. The higher the trust between the SMEs and PRIs, the higher the satisfaction. Satisfaction is affected by collaborative activities in I–PRI collaborations. This is consistent with the interorganizational relationships research results of Agarwal and Narayana [16], Dash et al. [32] and Anderson and Narus [38]. The higher the level of collaboration between SMEs and PRIs, the higher the satisfaction. The performance of innovation by I–PRI collaboration depends on collaboration and satisfaction. As suggested by the research results of Apa et al. [19], Bellini et al. [3], and Brettel and Cleven [37], it was found that the more active the collaboration between SMEs and PRIs, the better the performance of innovation. Additionally, in accordance with the relationship between satisfaction and performance that was suggested by Mungra and Yadav [17], our analyses found that the higher the satisfaction, the higher the performance of innovation in I–PRI collaborations.
We found that satisfaction mediates the relationship between collaboration and the performance of innovation, and the relationship between trust and the performance of innovation in I–PRI collaborations. This means that satisfaction between SMEs and PRIs is important in improving the performance of innovation in I–PRI collaborations. Contrary to the findings of Bellini et al. [3] and Nyaga et al. [26], trust between SMEs and PRIs did not have a significant effect on the performance of innovation. In the I–PRI collaborations, the buildup of trust between SMEs and PRIs does not directly lead to an increase in the performance of innovation. This result is consistent with the findings of Rindfleisch [82], which argue that there is no relationship between trust and collaboration. On the other hand, in I–PRI collaborations, trust did not directly affect the performance of innovation, but it was found to have an effect through satisfaction. Satisfaction was found to fully mediate between trust and the performance of innovation. This means that the hypothesis, established based on the research results of Mungra and Yadav [17], Bellini et al. [3], Susanty et al. [83], and Wong and Zhou [18], is valid. We found that satisfaction partially mediates between trust and the performance of innovation in I–PRI collaborations. Although trust does not directly affect the performance of innovation in I–PRI collaborations, it has an indirect positive influence through satisfaction. On the other hand, trust plays a partly mediating role between collaboration and satisfaction. The mediating role of trust in I–PRI collaborations is consistent with the results asserted in existing interorganizational relationship studies [16,26,84]. Additionally, satisfaction partially mediates the relationship between collaboration and the performance of innovation. This means that the hypothesis established based on the research results of Mungra and Yadav [17], Agarwal and Narayana [16], and Wong and Zhou [18] is valid. We found that satisfaction partially mediates between collaboration and the performance of innovation in I–PRI collaborations.
For SMEs to improve the performance of innovation through I–PRI collaborations, it is required that active collaborative activities be conducted based on trust between the collaborating parties, and that satisfaction is increased. As a result of fsQCA, in I–PRI collaborations, collaboration and satisfaction, combined with trust, increase the performance of innovation. The combination of collaboration and trust explains 91.05% of the performance of innovation. In this combination, collaboration is a core condition that determines the performance of innovation. The higher the trust and active collaboration between SMEs and PRIs, the better the performance of innovation. On the other hand, the combination of satisfaction and trust explains the performance of innovation by 91.59%. In this situation, satisfaction is a core condition that determines the performance of innovation. The higher the trust and satisfaction between SMEs and PRIs, the higher the performance of innovation. The core conditions that determine the performance of innovation in I–PRI collaborations are collaboration and satisfaction. When these factors are combined with trust, the performance of innovation increases.

5.1. Theoretical Implications

The contribution of this study is as follows: first, based on previous studies [16,17,19], we presented empirical evidence for factors that influence the performance of innovation in I–PRI collaborations. In today’s highly volatile business environment, it was confirmed that it is valuable for SMEs to utilize I–PRI collaborations to create innovative performance through collaboration. Second, unlike previous interorganizational studies [3,26], it was revealed that interorganizational trust does not directly affect the performance of innovation, but indirectly affects innovative performance through satisfaction in I–PRI collaborations. We presented empirical evidence that the performance of innovation does not improve simply because trust is high in I–PRI collaborations, but the performance of innovation increases when interorganizational trust is established, and satisfaction is high.
Third, unlike existing studies, we identified the conditions that the ensure that the combination of collaboration, trust, and satisfaction will improve the performance of innovation. We found that the performance of innovation is high when both collaboration and trust exist, and when satisfaction and trust exist simultaneously. We confirmed that collaboration is the most important core condition when collaboration and trust affect the performance of innovation through interaction. Additionally, it was discovered that satisfaction was an important core condition when satisfaction and trust combined to affect the performance of innovation.

5.2. Managerial Implications

This study provides the following practical implications. First, it is necessary for a firm to establish a high-trust relationship with PRIs in I–PRI collaborations. The combination of collaboration and trust, which are causal conditions, explains the performance of innovation to a high degree. Therefore, firms must manage collaborative activities well to increase PRIs’ trust in I–PRI collaborations. One of the important factors that enhances interorganizational trust is interorganizational communication [85,86]. To improve I–PRIs’ trust, a firm is required to actively perform collaborative activities and, at the same time, facilitate communication between collaboration entities. Active communication between firms and PRIs is an activity which is necessary to maintain close relationships and build trust.
Second, it is important for a firm to manage satisfaction in I–PRI collaborations. Satisfaction mediates the relationship between trust and the performance of innovation, and between collaboration and the performance of innovation. In addition, satisfaction plays an important role in directly determining the performance of innovation. Firms should manage I–PRI trust to improve satisfaction through active collaborative activities in I–PRI collaborations. According to the existing literature [17,56,57], relationship commitment has a positive effect on collaboration, trust, and satisfaction. Therefore, firms should increase their relationship commitment in I–PRI collaborations. Third, the combined effects of trust and satisfaction, as well as the combined effects of collaboration and trust, are important conditions for improving the performance of innovation. Therefore, firms should pay more attention to the combined effect of the three factors, rather than the role of just one factor. When collaboration and trust rise at the same time, or when trust and satisfaction increase at the same time, the performance of innovation also increases. Therefore, firms should conduct I–PRI collaborations with the intention of increasing collaboration and trust or trust and satisfaction—together. In I–PRI collaborations, if the commitment of researchers (firms and PRIs) is high, satisfaction will improve, and the performance of innovations will increase. To increase commitment, policies that strengthens institutions are needed in I–PRI collaborations.
Fourth, SMEs exposed to environmental uncertainty and lack of resources – should actively utilize their capabilities in I–PRI collaborations to secure and maintain their competitiveness. SMEs can deliver innovative performances and maintain their competitiveness by supplementing scarce resources and capabilities through I–PRI collaborations [87]. The Korean government has recognized the importance of industry–university–research cooperation as a national innovative growth engine, and has established and is operating the National Committee for Industry–Academia–Research Cooperation to establish and achieve their vision at a ministerial level [88]. In particular, the Korean government is actively recommending and supporting collaboration between SMEs and PRIs to strengthen the competitiveness of SMEs globally. When collaboration takes place in a high state of trust between firms and PRIs, commitment increases (through institutional support—such as incentives), satisfaction grows, and the performance of innovations will improve; this will enhance a firm’s competitiveness.

5.3. Limitations and Future Research

This study was conducted from the perspective of universities; however, future research should be conducted from the perspective of PRIs. Furthermore, this study was based in Korea, which is a developed country—it is necessary to analyze the differences between developed and developing countries. In countries with government-led economic growth, the role of PRIs is very important for the development of SMEs. However, in developed countries, PRIs appear to play a relatively less important role. Despite these limitations, this study shows the role of trust and satisfaction in collaborative relationships, and in the performance of innovation in I–PRI collaborations. Our findings add to our understanding of how to maximize the performance of innovation in I–PRI collaborations.

Author Contributions

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

Funding

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2019S1A5C2A03081332).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

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

Data Availability Statement

Data Availability at https://10.17605/OSF.IO/XQ5GF, accessed on 1 December 2021.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Collaboration:
(CA1) Our firm and collaboration partners actively participate in technology development.
(CA2) Our firm and collaboration partners share resources and expertise to reduce risk.
(CA3) Our firm and collaboration partners encourage the transfer of technology and innovation with each other.
(CA4) Our firm and collaboration partners exchange market information.
Trust:
(TR1) Our collaboration partners keep their promises faithfully.
(TR2) The actions of our collaboration partners give us trust.
(TR3) Our collaboration partners are honest and sincere.
(TR4) Our collaboration partners can be trusted.
(TR5) Our collaboration partners are willing to provide help and support to us.
(TR6) Our collaboration partners can be trusted because they share values.
Relationship satisfaction:
(SA1) If our firm collaborates again, we will do so with our current collaboration partner.
(SA2) Our firm is satisfied with collaboration with our I–PRI collaboration partners.
(SA3) Our firm is satisfied with the overall I–PRI collaboration relationship.
Performance of innovation:
(IN1) Our firm has improved existing products/services and processes through I–PRI collaborations.
(IN2) Our firm has developed new products/services and processes through I–PRI collaborations.
(IN3) Our firm’s technological capabilities have greatly improved through I–PRI collaborations.

References

  1. Enkel, E.; Gassman, O.; Chesbrough, H.W. Open R&D and open innovation: Exploring the phenomenon. RD Manag. 2009, 39, 311–316. [Google Scholar] [CrossRef]
  2. Trippl, M. Regional innovation systems and knowledge-sourcing activities in traditional industries—Evidence from the Vienna food sector. Environ. Plan A 2011, 43, 1599–1616. [Google Scholar] [CrossRef]
  3. Bellini, E.; Piroli, G.; Pennacchio, L. Collaborative know-how and trust in university–industry collaborations: Empirical evidence from ICT firms. J. Technol. Transf. 2019, 44, 1939–1963. [Google Scholar] [CrossRef]
  4. Shakeel, M.J.S.; Kannan, V.R.; Brah, S.A.; Hassan, S.Z. Linkages between firm innovation strategy, suppliers, product innovation, and business performance: Insights from resource dependence theory. Int. J. Oper. Prod. Manag. 2017, 37, 1054–1075. [Google Scholar] [CrossRef] [Green Version]
  5. Bellucci, A.; Pennacchio, L. University knowledge and firm innovation: Evidence from European countries. J. Technol. Transf. 2016, 41, 730–752. [Google Scholar] [CrossRef] [Green Version]
  6. Raza-Ullah, T.; Kostis, A. Do trust and distrust in coopetition matter to performance? Eur. Manag. J. 2020, 38, 367–376. [Google Scholar] [CrossRef]
  7. Daniel, J.L. Trust as a mediator of workplace spirituality and job performance. J. Bus. Behav. Sci. 2019, 31, 80–103. Available online: http://asbbs.org/files/2019/JBBS_Vol_31.2_Fall_2019.pdf#page=80 (accessed on 13 April 2021).
  8. Kraus, S.; Klimas, P.; Gast, J.; Stephan, T. Sleeping with competitors: Forms, antecedents and outcomes of coopetition of small and medium-sized craft beer breweries. Int. J. Entrep. Behav. Res. 2019, 25, 50–66. [Google Scholar] [CrossRef]
  9. Latusek, D.; Vlaar, P.W. Uncertainty in interorganizational collaboration and the dynamics of trust: A qualitative study. Eur. Manag. J. 2018, 36, 12–27. [Google Scholar] [CrossRef]
  10. Gibson, B.J.; Rutner, S.M.; Keller, S.B. Shipper-carrier partnership issues, rankings and satisfaction. Int. J. Phys. Distrib. Logist. Manag. 2002, 32, 669–681. [Google Scholar] [CrossRef]
  11. Kuncoro, W.; Suriani, W.O. Achieving sustainable competitive advantage through product innovation and market driving. Asia Pac. Manag. Rev. 2018, 23, 186–192. [Google Scholar] [CrossRef]
  12. Zhang, M.; Zhao, X.; Lyles, M. Effects of absorptive capacity, trust and information systems on product innovation. Int. J. Oper. Prod. Manag. 2018, 38, 493–512. [Google Scholar] [CrossRef] [Green Version]
  13. Nielsen, B.B. The role of knowledge embeddedness in the creation of synergies in strategic alliances. J. Bus. Res. 2005, 58, 1194–1204. [Google Scholar] [CrossRef]
  14. Hong, J.; Zheng, R.; Deng, H.; Zhou, Y. Green supply chain collaborative innovation, absorptive capacity and the performance of innovation: Evidence from China. J. Clean. Prod. 2019, 241, 118377. [Google Scholar] [CrossRef]
  15. Nikolova, N.; Mollering, G.; Reihlen, M. Trusting as a ‘leap of faith’: Trust-building practices in client-consultant relationships. Scand. J. Manag. 2015, 31, 232–245. [Google Scholar] [CrossRef]
  16. Agarwal, U.A.; Narayana, S.A. Impact of relational communication on buyer–supplier satisfaction: Role of trust and commitment. Benchmarking Int. J. 2020, 27, 2459–2496. [Google Scholar] [CrossRef]
  17. Mungra, Y.; Yadav, P.K. The mediating effect of satisfaction on trust-commitment and relational outcomes in manufacturer–supplier relationship. J. Bus. Ind. Mark. 2020, 35, 219–230. [Google Scholar] [CrossRef]
  18. Wong, A.; Zhou, L. Determinants and outcomes of relationship quality. J. Int. Consum. Mark. 2006, 18, 81–105. [Google Scholar] [CrossRef]
  19. Apa, R.; De Marchi, V.; Grandinetti, R.; Sedita, S.R. University-SME collaboration and the performance of innovation: The role of informal relationships and absorptive capacity. J. Technol. Transf. 2021, 46, 961–988. [Google Scholar] [CrossRef]
  20. Niranjan, S.; Spulick, S.R.; Savitskie, K. Mediating and moderating influencers of firm performance: Supply chain managers perspective. J. Enterp. Inf. Manag. 2018, 31, 38–63. [Google Scholar] [CrossRef]
  21. Pappas, I.O.; Woodside, A.G. Fuzzy-set Qualitative Comparative Analysis (fsQCA): Guidelines for Research Practice in Information Systems and Marketing. Int. J. Inf. Manag. 2021, 58, 102310. [Google Scholar] [CrossRef]
  22. Pradabwong, J.; Braziotis, C.; Tannock, J.D.T.; Pawar, K.S. Business process management and supply chain collaboration: Effects on performance and competitiveness. Supply Chain Manag. 2017, 22, 107–121. [Google Scholar] [CrossRef]
  23. George, G.; Zahra, S.A.; Wood, D.R. The effects of business-university alliances on innovative output and financial performance: A study of publicly traded biotechnology companies. J. Bus. Ventur. 2002, 17, 577–609. [Google Scholar] [CrossRef]
  24. Galán-Muros, V.; Plewa, C. What drives and inhibits university-business cooperation in Europe? A comprehensive assessment. RD Manag. 2016, 46, 369–382. [Google Scholar] [CrossRef]
  25. Srinivasan, R.; Brush, T.H. Supplier performance in vertical alliances: The effects of self-enforcing agreements and enforceable contracts. Organ. Sci. 2006, 17, 436–452. [Google Scholar] [CrossRef]
  26. Nyaga, G.N.; Whipple, J.M.; Lynch, D.F. Examining supply chain relationships: Do buyer and supplier perspectives on collaborative relationships differ? J. Oper. Manag. 2010, 28, 101–114. [Google Scholar] [CrossRef]
  27. Faraj, S.; Jarvenpaa, S.L.; Majchrzak, A. Knowledge collaboration in online communities. Organ. Sci. 2011, 22, 1224–1239. [Google Scholar] [CrossRef] [Green Version]
  28. Baah, C.; Acquah, I.S.K.; Ofori, D. Exploring the influence of supply chain collaboration on supply chain visibility, stakeholder trust, environmental and financial performances: A partial least square approach. Benchmarking: Int. J. 2021, 29, 172–193. [Google Scholar] [CrossRef]
  29. Hameed, W.U.; Naveed, F. Coopetition-based open-innovation and the performance of innovation: Role of trust and dependency evidence from Malaysian high-tech SMEs. Pak. J. Commer. Soc. Sci. 2019, 13, 209–230. [Google Scholar]
  30. Frazier, G.L.; James, D.G.; Sudhir, H.K. Dealer dependence levels and reciprocal actions in a channel of distribution in a developing country. J. Mark. 1989, 53, 50–69. [Google Scholar] [CrossRef]
  31. Selnes, F. Antecedents and consequences of trust and satisfaction in buyer-seller relationships. Eur. J. Mark. 1998, 32, 305–322. [Google Scholar] [CrossRef]
  32. Dash, S.; Bruning, E.D.; Guin, K.K. Antecedents of long-term buyer-seller relationships: A cross cultural integration. Acad. Mark. Sci. Rev. 2007, 11, 1–29. [Google Scholar] [CrossRef] [Green Version]
  33. Cao, M.; Zhang, Q. Supply chain collaboration: Impact on collaborative advantage and firm performance. J. Oper. Manag. 2011, 29, 163–180. [Google Scholar] [CrossRef]
  34. Schoenherr, T.; Swink, M. Revisiting the arcs of integration: Cross-validations and extensions. J. Oper. Manag. 2012, 30, 99–115. [Google Scholar] [CrossRef]
  35. Hameed, W.U.; Nisar, Q.A.; Wu, H.C. Relationships between external knowledge, internal innovation, firms’ open the performance of innovation, service innovation and business performance in the Pakistani hotel industry. Int. J. Hosp. Manag. 2021, 92, 102745. [Google Scholar] [CrossRef]
  36. Todeva, E.; Knoke, D. Strategic Alliances and Models of Collaboration. Manag. Decis. 2005, 43, 123–148. [Google Scholar] [CrossRef] [Green Version]
  37. Brettel, M.; Cleven, N.J. Innovation Culture, Collaboration with External Partners and NPD Performance. Creativity Innov. Manag. 2011, 20, 253–272. [Google Scholar] [CrossRef]
  38. Anderson, J.C.; Narus, J.A. A model of distributor firm and manufacturing firm working partnership. J. Mark. 1990, 54, 42–58. [Google Scholar] [CrossRef]
  39. Das, T.K.; Teng, B.S. Trust, control, and risk in strategic alliances: An integrated framework. Organ. Stud. 2001, 22, 251–283. [Google Scholar] [CrossRef] [Green Version]
  40. Hausman, A. Variations in relationship strength and its impact on performance and satisfaction in business relationships. J. Bus. Ind. Mark. 2001, 6, 600–616. [Google Scholar] [CrossRef]
  41. Smith, K.G.; Carroll, S.J.; Ashford, S.J. Intra- and interorganizational cooperation: Toward a research agenda. Acad. Manag. J. 1995, 38, 7–23. [Google Scholar] [CrossRef]
  42. Yang, H.; Chen, W.; Hao, Y. Supply chain partnership, inter-organizational knowledge trading and enterprise the performance of innovation: The theoretical and empirical research in project-based supply chain. Soft Comput. 2020, 24, 6433–6444. [Google Scholar] [CrossRef]
  43. Ping, R.A. Antecedents of satisfaction in a marketing channel. J. Retail. 2003, 79, 237–248. [Google Scholar] [CrossRef]
  44. Ghantous, N.; Das, S.S. International franchising and performance: A resource-based perspective. Int. J. Retail Distrib. Manag. 2018, 46, 744–763. [Google Scholar] [CrossRef]
  45. Al-Sabi, S.M.; Al-Ababneh, M.M.; Masadeh, M. The Role of Job Satisfaction on Employees’ The performance of innovation in the Jordanian Hospitality Industry. J. Tour. Hosp. Manag. 2019, 7, 186–201. [Google Scholar] [CrossRef]
  46. Gopalakrishnan, S.; Zhang, H. Client dependence: A boon or bane for vendor innovation? A competitive mediation framework in IT outsourcing. J. Bus. Res. 2019, 103, 407–416. [Google Scholar] [CrossRef]
  47. Veugelers, R.; Cassiman, B. R&D cooperation between firms and universities. Some empirical evidence from Belgian manufacturing. Int. J. Ind. Organ. 2005, 23, 355–379. [Google Scholar] [CrossRef]
  48. Link, A.N.; Siegel, D.S.; Bozeman, D. An empirical analysis of the propensity of academics to engage in informal university technology transfer. Ind. Corp. Change 2007, 16, 641–655. [Google Scholar] [CrossRef] [Green Version]
  49. D’Este, P.; Perkmann, M. Why do academics engage with industry? The entrepreneurial university and individual motivations. J. Technol. Transf. 2011, 36, 316–339. [Google Scholar] [CrossRef]
  50. Davenport, S.; Davies, J.; Grimes, C. Collaborative research programmes: Building trust from difference. Technovation 1999, 19, 31–40. [Google Scholar] [CrossRef]
  51. Awan, U.; Nauman, S.; Sroufe, R. Exploring the effect of buyer engagement on green product innovation: Empirical evidence from manufacturers. Bus. Strategy Environ. 2021, 30, 463–477. [Google Scholar] [CrossRef]
  52. Chen, J.; Wei, S. University—Industry Collaboration, Knowledge Management and Enterprise The performance of innovation: An Empirical Study. Ind. High. Educ. 2008, 22, 275–287. [Google Scholar] [CrossRef]
  53. Yu, W.; Jacobs, M.A.; Salisbury, W.D.; Enns, H. The effects of supply chain integration on customer satisfaction and financial performance: An organizational learning perspective. Int. J. Prod. Econ. 2013, 146, 346–358. [Google Scholar] [CrossRef]
  54. Wilke, E.P.; Costa, B.K.; Freire, O.B.D.L.; Ferreira, M.P. Interorganizational Cooperation in Tourist Destination: Building Performance in the Hotel Industry. Tour. Manag. 2019, 72, 340–351. [Google Scholar] [CrossRef]
  55. Martins, D.M.; De Faria, A.C.; Prearo, L.C.; Arruda, A.G.S. The Level of Influence of Trust, Commitment, Cooperation, and Power in the Inter-organizational Relationships of Brazilian Credit Cooperatives. Rev. Adm. 2017, 52, 47–58. [Google Scholar] [CrossRef] [Green Version]
  56. Brown, J.R.; Crosno, J.L.; Tong, P.Y. Is the Theory of Trust and Commitment in Marketing Relationships Incomplete? Ind. Mark. Manag. 2019, 77, 155–169. [Google Scholar] [CrossRef]
  57. Yuan, Y.; Feng, B.; Lai, F.; Collins, B.J. The Role of Trust, Commitment, and Learning Orientation on Logistic Service Effectiveness. J. Bus. Res. 2018, 93, 37–50. [Google Scholar] [CrossRef]
  58. Høgevold, N.; Svensson, G.; Otero-Neira, C. Trust and Commitment as Mediators Between Economic and Non-Economic Satisfaction in Business Relationships: A Sales Perspective. J. Bus. Ind. Mark. 2020, 35, 1685–1700. [Google Scholar] [CrossRef]
  59. Andaleeb, S.S. An Experimental Investigation of Satisfaction and Commitment in Marketing Channels: The Role of Trust and Dependence. J. Retail. 1996, 72, 77–93. [Google Scholar] [CrossRef]
  60. Prajogo, D.I.; Ahmed, P.K. Relationships between Innovation Stimulus, Innovation Capacity, and the performance of innovation. R&D Manag. 2006, 36, 499–515. [Google Scholar] [CrossRef]
  61. Gunday, G.; Ulusoy, G.; Kilic, K.; Alpkan, L. Effects of Innovation Types on Firm Performance. Int. J. Prod. Econ. 2011, 133, 662–676. [Google Scholar] [CrossRef] [Green Version]
  62. Latan, H.; Jabbour, C.J.C.; De Sousa Jabbour, A.B.L.; De Camargo Fiorini, P.; Foropon, C. Innovative Efforts of ISO 9001-certified Manufacturing Firms: Evidence of Links between Determinants of Innovation, Continuous Innovation and Firm Performance. Int. J. Prod. Econ. 2020, 223, 107526. [Google Scholar] [CrossRef]
  63. Fiss, P.C. Building better causal theories: A fuzzy set approach to typologies in organization research. Acad. Manag. J. 2011, 54, 393–420. [Google Scholar] [CrossRef] [Green Version]
  64. Ragin, C.C. Fuzzy-Set Social Science; The University of Chicago Press: Chicago, IL, USA, 2000. [Google Scholar]
  65. Skarmeas, D.; Lisboa, A.; Saridakis, C. Export performance as a function of market learning capabilities and intrapreneurship: SEM and FsQCA findings. J. Bus. Res. 2016, 69, 5342–5347. [Google Scholar] [CrossRef]
  66. Ringle, C.M.; Wende, S.; Becker, J.M. SmartPLS 3, Boenningstedt: SmartPLS GmbH. 2015. Available online: http://www.smartpls.com (accessed on 10 January 2021).
  67. Hair, J.F.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd ed.; Sage: Thousand Oaks, CA, USA, 2017. [Google Scholar]
  68. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  69. Henseler, J.; Ringle, C.M.; Sarstedt, M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 2015, 43, 115–135. [Google Scholar] [CrossRef] [Green Version]
  70. McDonald, R.P.; Ho, M.-H.R. Principles and practice in reporting structural equation analyses. Psychol. Methods 2002, 7, 64–82. [Google Scholar] [CrossRef]
  71. Hu, L.; Bentler, P.M. Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychol. Methods 1998, 3, 424–453. [Google Scholar] [CrossRef]
  72. Hair, J.F.J.; Black, W.C.; Babin, B.J.; Anderson, R.E. Multivariate Data Analysis, 7th ed.; Pearson Education Limited: Harlow, UK, 2010. [Google Scholar]
  73. Hair, J.F., Jr.; Sarstedt, M.; Hopkins, L.; Kuppelwieser, V.G. Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. Eur. Bus. Rev. 2014, 26, 106–121. [Google Scholar] [CrossRef]
  74. Falk, R.F.; Miller, N.B. A Primer for Soft Modeling; The University of Akron Press: Akron, OH, USA, 1992. [Google Scholar]
  75. Hair, J.F.; Ringle, C.M.; Sarstedt, M. PLS-SEM: Indeed a silver bullet. J. Mark. Theory Pract. 2011, 19, 139–151. [Google Scholar] [CrossRef]
  76. Chin, W.W. The partial least squares approach to structural equation modeling. Mod. Methods Bus. Res. 1998, 295, 295–336. [Google Scholar]
  77. Henseler, J.; Ringle, C.M.; Sinkovics, R.R. The use of partial least squares path modeling in international marketing. Adv. Int. Mark. 2009, 20, 277–320. [Google Scholar] [CrossRef]
  78. Cruz-Ros, S.; Guerrero-Sánchez, D.L.; Miquel-Romero, M.J. Absorptive capacity and its impact on innovation and performance: Findings from SEM and fsQCA. Rev. Manag. Sci. 2021, 15, 235–249. [Google Scholar] [CrossRef]
  79. Ragin, C.C. Redesigning Social Inquiry: Fuzzy Sets and Beyond; University of Chicago Press: Chicago, IL, USA, 2008. [Google Scholar]
  80. Rihoux, B.; Ragin, C.C. Configurational Comparative Methods: Qualitative Comparative Analysis (QCA) and Related Techniques; Sage Publications: Thousand Oaks, CA, USA, 2008. [Google Scholar]
  81. Woodside, A.G. Moving beyond multiple regression analysis to algorithms: Calling for adoption of a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory. J. Bus. Res. 2013, 66, 463–472. [Google Scholar] [CrossRef]
  82. Rindfleisch, A. Organizational trust and interfirm cooperation: An examination of horizontal versus vertical alliances. Mark. Lett. 2000, 11, 81–95. [Google Scholar] [CrossRef]
  83. Susanty, A.; Sirait, N.M.; Bakhtiar, A. The relationship between information sharing, informal contracts and trust on performance of supply chain management in the SMEs of batik. Meas. Bus. Excell. 2018, 22, 292–314. [Google Scholar] [CrossRef] [Green Version]
  84. Charterina, J.; Landeta, J.; Basterretxea, I. Mediation effects of trust and contracts on knowledge-sharing and product innovation. Eur. J. Innov. Manag. 2018, 45, 67–87. [Google Scholar] [CrossRef]
  85. Wang, X.; Tajvidi, M.; Lin, X.; Hajli, J. Towards an Ethical and Trustworthy Social Commerce Community for Brand Value Co-creation: A trust-Commitment Perspective. J. Bus. Ethics 2020, 167, 137–152. [Google Scholar] [CrossRef]
  86. Ahlf, H.; Horak, S.; Klein, A.; Yoon, S.-W. Demographic homophily, communication and trust in intra-organizational business relationships. J. Bus. Ind. Mark. 2019, 34, 474–487. [Google Scholar] [CrossRef]
  87. Tseng, F.C.; Huang, M.H.; Chen, D.Z. Factors of university–industry collaboration affecting university the performance of innovation. J. Technol. Transf. 2020, 45, 560–577. [Google Scholar] [CrossRef]
  88. Joint Ministries and Local Governments. Performance of Implementation Plan for 2020 of Industrial Education and Industry-University-Research Cooperation, and Implementation Plan for 2021; Draft; Ministry of Education: Seoul, Korea, 2021. Available online: https://www.moe.go.kr/boardCnts/view.do?boardID=337&lev=0&statusYN=W&s=moe&m=0303&opType=N&boardSeq=84351 (accessed on 30 December 2021).
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Figure 1. Research model.
Figure 1. Research model.
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Figure 2. Flowchart of research methodology.
Figure 2. Flowchart of research methodology.
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Table
Table 3. Structural model results.
Table 3. Structural model results.
Structural PathPath
Coefficients		t-Valuesp-ValuesConclusion
H1: Collaboration → Trust0.584 ***7.7420.000Supported
H2: Collaboration → Satisfaction 0.389 ***4.8100.000Supported
H3: Collaboration → Performance of innovation0.270 ***2.7370.006Supported
H4: Trust
→ Satisfaction 		0.378 ***4.8050.000Supported
H5: Trust
→ Performance of innovation		−0.075 ns0.6230.534Unsupported
H6: Satisfaction
→ Performance of innovation		0.344 ***3.4340.001Supported
R2 (Trust) = 0.342
R2 (Satisfaction) = 0.466
R2 (Performance of innovation) = 0.255
Note: ***—p < 0.01; ns—not significant.
Table
Table 4. Summary of mediating effect tests.
Table 4. Summary of mediating effect tests.
EffectTotal EffectIndirect EffectVAF
%		Mediation Strengths
ConstructsPath
Coefficient		t-Valuesp-ValuesPath Coefficientt-Valuesp-Values
H7: Collaboration→ Satisfaction
(via Trust)		0.61011.6480.0000.2214.5370.00036.3%Partial mediation
Collaboration
→ Performance of innovation
(Total)		0.4365.7560.0000.1662.2570.02438.1%Partial mediation
Collaboration
→ Performance of innovation
(via Trust)		 −0.0440.6130.540-10.1%No mediation
H8: Collaboration
→ Performance of innovation
(via Satisfaction)		 0.1342.7310.00730.7%Partial mediation
Collaboration
→ Performance of innovation
(via Trust + Satisfaction)		 0.0762.5880.01017.5%No mediation
H9: Trust
→ Performance of innovation
(via Satisfaction)		0.0550.4790.6320.1302.7680.006235.6%Full mediation
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Hwang, K.Y.; Sung, E.H.; Shenkoya, T. The Mediating and Combined Effects of Trust and Satisfaction in the Relationship between Collaboration and the Performance of Innovation in Industry—Public Research Institute Partnerships. Sustainability 2022, 14, 2128. https://doi.org/10.3390/su14042128

AMA Style

Hwang KY, Sung EH, Shenkoya T. The Mediating and Combined Effects of Trust and Satisfaction in the Relationship between Collaboration and the Performance of Innovation in Industry—Public Research Institute Partnerships. Sustainability. 2022; 14(4):2128. https://doi.org/10.3390/su14042128

Chicago/Turabian Style

Hwang, Kyung Yun, Eul Hyun Sung, and Temitayo Shenkoya. 2022. "The Mediating and Combined Effects of Trust and Satisfaction in the Relationship between Collaboration and the Performance of Innovation in Industry—Public Research Institute Partnerships" Sustainability 14, no. 4: 2128. https://doi.org/10.3390/su14042128

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