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Article

How Does Management Control Affect New Product Development Performance? A Research Methodology with OLS and fsQCA

by
Xingteng Li
1,2,* and
Yue-e Chen
3
1
Huaxin Consultation Design Research Institute Co., Ltd., Hangzhou 310051, China
2
School of Public Affairs, Zhejiang University, Hangzhou 310058, China
3
School of Foreign Languages, Anhui Jianzhu University, Hefei 230601, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(24), 10973; https://doi.org/10.3390/su162410973
Submission received: 1 November 2024 / Revised: 8 December 2024 / Accepted: 12 December 2024 / Published: 13 December 2024
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Abstract

:
High-quality development has become one of the important goals pursued by Chinese enterprises at present, and innovation is an important channel to realize high-quality development. Effective collaboration stands out as a pivotal element for the success of new product development (NPD). There are a variety of control mechanisms applied to mitigate uncertainty and foster cooperation. Despite the importance of these controls, the interplay between formal and informal management controls in NPD has been underexplored in prior research. The goal of this study is to validate the relationship between formal and informal control in a new product development scenario, responding to the debate about whether there is a substitution effect or a complementary effect between the two. This study addresses this gap by initially employing ordinary least squares (OLS) regression analysis to examine the role of each management control. Subsequently, fuzzy set qualitative comparative analysis (fsQCA) is employed to identify strategies for achieving high NPD performance in Chinese manufacturing enterprises. The results from the OLS analysis demonstrate that all forms of management control, especially trust, are effective in the improvement of NPD performance, while the results from fsQCA confirm that there is a complementary effect between formal and informal control, suggesting that informal control cannot function well without formal control. This research illuminates the synergistic dynamics of management controls within an open innovation context and emphasizes the importance of integrating both formal and informal controls to optimize NPD performance.

1. Introduction

High-quality development has become one of the important goals pursued by Chinese enterprises at present [1], and the high-quality development of manufacturing enterprises is the key to realize the transformation and development of China as a manufacturing country [2]. After more than 40 years of development, China has become the most complete and largest manufacturing industry in the world. However, behind the rapid expansion, the manufacturing industry shows the situation of “big but not strong, full but not excellent” [2]. Open innovation has become an important path to enhance the competitive advantage of organizations and promote rapid growth. Therefore, it has become increasingly important for organizational scholars to study how best to promote the practice of open innovation in the new situation [3].
Compared with the traditional model of innovation, open innovation is becoming the main trend of new product development (NPD) activities [4,5]. In open innovation, companies pay more attention to cooperation and connection with external organizations, such as suppliers, customers, research institutes, etc. [6,7]. NPD in open innovation offers a number of benefits, such as increasing product innovation output, reducing costs, enhancing flexibility, and expanding the market [8,9,10]. However, NPD in open innovation is also accompanied with some challenges of management [11], collaboration [12], and relational uncertainties [13]. In NPD collaborations, there are a series of control mechanisms to reduce uncertainty and promote cooperation in the process of development [8,14]. How to effectively implement the control mechanisms to reduce complexity is a major concern for NPD [15,16].
The management control in NPD is defined as the management of team members’ behavior and activity for the purpose of successful achievements within enterprises [17]. Generally, these are two types of control mechanisms; one mechanism refers to written documents (formal control) [18], and the other capitalizes on social norms and relations (informal control) [19], which are widely acknowledged in previous research [20,21]. There are at least two streams to generate insights into the joint effects of different control mechanisms: One is complementary, meaning various controls can be effectively combined to promote performance. The other is substitutionary, which refers to the negative combining effect of different controls [19]. Rijsdijk and van den Ende [22] proposed that the combination effect between different control mechanisms should be further explored.
Different research perspectives give different conclusions of joint effects, indicating that combinations of different control forms have different results. Adopting appropriate control forms is conducive to coordinating the interests among participants and improving the performance of NPD in open innovation [23]. However, few studies have explored the consequence of combining the formal and the informal control, let alone their different forms of combinations. Considering these research gaps, this study seeks to advance current understanding of different controls to answer the following question: How does management control affect NPD?
In the following parts, first, the related reviews and hypotheses are presented. Second, the data and methodology are described. Third, the results are obtained by necessity and sufficiency analysis. Then, some conclusions about empirical results were initiated. Finally, discussions and management implications are proposed, and the limitations are pointed out as well.

2. Literature Review

2.1. NPD in Open Innovation

Chesbrough [24] took the view that traditional innovation, without interaction and coordination with the external, occurs within the boundary of firms during the NPD process. Because of reasons like the shortening of innovation cycles, escalating of R&D costs, increasing of the technology rate, and evolving of consumer tastes, traditional innovation is no longer sustainable and open innovation is put forward sequentially [21]. Open innovation emphasizes the importance of coordination between the enterprise and its external as well as internal resources [25]. Outbound innovation was favorable for the long-term development of the enterprise’s future performance [26]. Developing new products in the context of open innovation was a fairly widespread phenomenon in different industries, including the manufacturing industry [8], low-tech industries [13], etc. NPD in open innovation is defined as “creating a new product through cooperation of different firms” [27]. The different firms involved in NPD may play respective roles, such as idea providing, creative screening, business analysis, R&D, and selling [28]. Lu, Yuan and Wu [8] proposed that appropriate organizational controls are essential in reconciling the interests of parties, which can foster coordination and cooperative behavior.

2.2. The Forms of Formal Control

The current research demonstrates the importance of formal control to promote cooperation and reduce uncertainty from the perspective of transaction [29,30], for the reason that formal control speaks of the action and obligations to perform in the future [31]. Formal control includes process descriptions, clear goals, and procedures in written format, by which firms can influence their partners to behave in accordance with specific requirements for the realization of organizational goals [32].
From the view of transaction, a cooperative NPD process can encounter many hazards, mainly including opportunism [33], measurement difficulty, and uncertainty [34], etc. For instance, collaborators with a high level of opportunism may deliberately hide development information for their own interest [35]. Uncertainty and measurement difficulty can limit efforts in cooperation, because they may not get the equal incentives [36]. To overcome such exchange hindrances, formal control is usually suggested to be employed in NPD [13].
To better understand the formal control in NPD, according to the research of Rijsdijk et al. (2011) [22] and Lu et al. (2015) [37], outcome control, process control, and contract control are three common types of formal control. Outcome control is the management of the project team’s NPD performance, such as monitoring, evaluating, and rewarding the achievement degree of the results. Process control includes ensuring the procedure of project activities, and conducting frequent observations and evaluations of project members’ behaviors. Contract control emphasizes the use of formal and legally binding agreements to clarify relationships between teams.

2.3. The Forms of Informal Control

The relational exchange theory strongly emphasizes that relationships can be used to favor exchange and mutual obligation [36,38]. Informal control uses social norms and relationships to improve NPD’s performance [19], which is essentially the same as transaction theory. In China, guanxi is a special relationship rooted in traditional Chinese culture, and is shaped by both cultural norms and the value system of Confucianism [39]. Favor and face are two important factors of guanxi. Favor sets an informal social obligation to maintain reciprocal relationships between collaborators [40]. Face refers to reputation and is used to cultivate and expand connections [41,42]. In addition, guanxi in China is a powerful control structure with unique expansion rules and impacts on firm behavior [43]. In addition to guanxi, trust as another form of informal control also plays an important role in NPD activities in China. Trust is the extent of recognition towards the reliability of NPD participants [8], which reduces costs and risks [44,45]. In the open innovation of NPD networks, trust is an essential component [46]. When the trust levels between enterprises and suppliers, and customers and third parties are improved, NPD performance can also be boosted sequentially [47]. As two forms of informal control, guanxi is based on reliance on interactions with other partners, while trust is based on the inner recognition of other team members.
From the perspective of relation, a firm’s success in NPD is largely influenced by information sharing and collaboration between firms [28]. The informal mechanisms of information sharing and cooperative mobilization are conducive to the success of NPD [13]. In the context of China, both guanxi and trust are important in NPD for two reasons: first, they can facilitate information sharing [48], and second, they can promote adaptation and collaboration.

2.4. Combination Between Management Controls

Generally, there are two conclusions about the combination of formal control and informal control in open innovation, namely the substitutionary effect and complementary effect. The substitutionary effect argues that the positive effects of formal control and informal control will offset each other. When used simultaneously, they will compromise each other’s strengths and even deteriorate performance [13,19]. Formal control and informal control take the same essential requirements for harmonious cooperation and information exchange. Therefore, the overlapping part of management control weakens their positive effects [31]. The other view of complementary effects suggests that formal control and informal control both contribute to positive outcomes in multi-party NPD. For example, Chonlatis [49] proposed that the managers can engage in both formal and informal activities to improve project efficiency during NPD projects. Monteiro, Lunkes and Rosa [21] also found that formal and informal controls facilitate social exchanges between managers by influencing trust. In view of the above-mentioned studies, we argue that there can be a complementary effect between formal control and informal control. For the purpose of success in NPD activities, enterprises need to adopt different management controls and their combination. In the following, we will discuss how to configure the form of management control in open innovation with survey data from two methodological perspectives, respectively.
In summary, prior research has focused on formal controls in innovation settings and revealed mixed results regarding their effectiveness. In case studies, informal control has been found to be generally important for innovation. Although there are also studies that focus on the joint role of formal and informal control, it remains unanswered the debate on whether the two are complementary or substitutes. Based on the literature analysis, our study contributes to the research on the role of managerial control in innovation, especially in new product development, based on the theory of the natural resource-based view (NRBV) of the firm [50], explaining how formal and informal control develops the necessary stakeholder integration and capacity for continuous improvement. We use different empirical research methods to further validate the mechanism of the role of firms’ use of formal and informal controls on new product development, which can both respond to the debate in the current literature and provide a reference path that can be used by firms to achieve high-quality development through new product development.

3. Data and OLS Analysis

3.1. Data Collection

The data of this paper were obtained by a questionnaire survey from manufacturing firms. Our study focuses on management control and new product development, so it puts higher requirements on the research sample. Specifically, the respondents of the questionnaire must meet the following requirements: First, the respondents must be the middle- and higher-level leaders of the enterprise, because only the middle- and higher-level leaders can be more aware of the status of the enterprise’s management and product development, and may also play a role in the management decision making. Second, the enterprise must have new product development projects in the last three years, which can exclude traditional manufacturing enterprises that do not pay attention to new product development. The questionnaire data are corresponding to the recently completed NPD projects which involve cooperation with other enterprises. Overall, 305 questionnaires were distributed and 219 of them were collected over 3 months. The effective response rate of the questionnaire was 71.80%. A large reason for the results of the questionnaire recovery is related to the relatively high requirements of the sample firms and, of course, to the amount of work involved in answering our questionnaires. Table 1 presents the statistics information for the samples. Varied ownership types are included in the current research samples, and the scale of manufacturing enterprises is relatively large, so they are suitable for this research. In addition, information about the distribution region of the sample firms and the segmentation of the sample firms was also counted, but is not reflected in Table 1, considering that it is not very relevant to the current study.

3.2. Measures

The way this study measures the variables is mainly from published academic papers in the field of management control and in the field of new product development, which will be described in the measurement method for each variable. The original questions were translated into Chinese by two professors with expertise in project management, and back-translated into English by two academic researchers in management. Each item was rated on a five-point Likert-type scale (ranging from 1, ‘strongly disagree’, to 5, ‘strongly agree’). All items are specified in Appendix A.
Outcome control. Four items are adapted from the study of Carbonell and Rodriguez-Escudero [18]. These items measure the reward and feedback mechanism for the product achievement of the product development team in the enterprise.
Process control. Based on the research of Carbonell and Rodriguez-Escudero [18], four items are measured to examine the degree of management control in the NPD process. The items referred to which degree management jobs put an emphasis on procedure activities when rewarding, evaluating, and supervising NPD teams.
Contract control. Three items, derived from Luo [51], were used to measure the binding force of the contract and determine whether subjects in open innovation receive the control of contract specifications.
Guanxi. The scale of guanxi, consisting of four items, is adapted from the research of Chu, et al. [52]. These items, based on the exchange of favors and personal relations, measure the extent of the connections with other companies.
Trust. Six items from the research of Lu, Yuan and Wu [8] are selected to measure trust. These items are concerned with the credence in other teams’ advantages, ability, and character, and faith in the promises of others.
NPD performance. The scale of NPD project performance is adapted from the study of Rijsdijk and van den Ende [22], which employs four items to measure the development results of the NPD. The items about the financial performance are removed from this questionnaire, because the subjective data from the questionnaire about the financial performance in the market are less convincing than the objective data [22].
In this research, SPSS26.0 was adopted to test the reliability and validity of variable structures. Specifically, the former was verified by Cronbach’s alpha and composite reliability [53]. According to Table 2, the Cronbach coefficients of all test items are greater than 0.70, and the value of composite reliability is also greater than 0.70, signifying that the scale is reliable. A validity test is made by CFA to calculate the aggregate validity and discrimination validity [53]. Specifically, convergent validity is calculated based on the factor loading coefficient and extracted variance value (AVE) [53]. It is shown in Table 2 that the factor loading values (p < 0.001) of all test items are higher than the benchmark value of 0.70, and the AVE values are also higher than 0.50, indicating that the results are of good convergent validity. The discriminative validity makes comparison through the AVE square root value and correlation analysis. According to Table 3, the square root value of each factor is greater than the “maximum value of correlation coefficient between this factor and other factors”. Therefore, this indicates the scale has good discrimination validity [53].

3.3. Regression Analysis

Using ordinary least squares (OLS), the survey data are analyzed to test the influence of formal control and informal control on NPD performance. The nature of ownership (dummy variable for foreign-funded enterprises), the number of employees, and the annual turnover are used as control variables. Models 1, 2, and 3 test the effects of three forms of formal control on new product development, models 4 and 5 test the effects of two forms of informal control on new product development, and model 6 considers the effects of both formal and informal control on new product development.
The specific results are shown in Table 4. It is evident that each form of control alone can play a positive role in promoting the performance of NPD. Compared with other forms of enterprises, foreign-funded enterprises have higher NPD performance. In Model 6, all forms of management controls are taken for regression. The results show that only trust makes a significant effect, while other forms of control are not significant, which shows that they are competitive explanatory variables among different management controls. In addition, there are some interesting results in the regression results, such as the very high significance of foreign firms as a control variable, which may indicate that the effectiveness of management control is better in foreign firms, which may be related to the corporate culture, and also suggests that the motivation for new product development in state-owned firms may come from other channels, such as government subsidies or policy incentives.
Then, we tested the variance inflation factor between the variables, and the results are shown in Table 5. There is no problem of collinearity between variables (VIF < 5). According to the results of regression analysis, the choice of any control form enables companies to promote NPD performance, and among them, trust is the most effective. However, the analysis method of OLS only provides a strategy of net benefits, demonstrating the effectiveness of management control, which is not necessarily helpful for a company to succeed in some new project. The following part will try to discuss how to achieve a high level of NPD performance from the perspective of configuration.

4. Qualitative Comparative Analysis

4.1. fsQCA

Qualitative comparative analysis (QCA) is a set analysis method based on Boolean algebra. It combines the advantages of qualitative research and quantitative research, which can provide solutions and causal recipes to the problem at issue [54]. A particularly useful feature of QCA is its ability to analyze complex complementarities between factors, defined as the fact that an outcome may result from several different combinations of causal conditions, i.e., different causal “recipes”. In addition, as a subset of QCA methods, fsQCA is able to use continuous or interval scaled variables [55]. Thus, fsQCA involves a more accurate and rigorous assessment of set-theoretic consistency than clear-set qualitative comparative analysis (csQCA), which can analyze only binary membership [56]. Since there are different combinations of management controls that contribute to a firm’s new product development process, we apply the QCA approach to explore the causal recipes between the role of these different management controls and new product development; in other words, fsQCA can provide a holistic view to analyze the relationship between management controls and new product development. The following equations demonstrate the fundamental algorithm of fsQCA:
○
μA∩B (x) = min(μA (x),μB (x)) (AND operation)
○
μA∪B (x) = max(μA (x),μB (x)) (OR operation)
○
μA′ (x) = 1 − μA (x) (NOT operation)
Note: μA (x), μB (x) are the membership functions representing the degree of membership of an element x in each fuzzy set.
By calibrating continuous variables, the fuzzy set qualitative comparative analysis (fsQCA) further expands the application scope of this methodology. Compared with the net effect analysis (e.g., structural equation model, multiple regression, analysis of variance), fsQCA can explore the influence of the combination effect among conditional variables on the results, rather than the single variable correspondence [57]. The characteristics of fsQCA can explore the complement and substitution relationship among independent variables, which helps enterprises to pin down an optimal management strategy according to enterprise resource endowment and organizational architecture [58], so fsQCA is a suitable empirical trial in this research. As for fsQCA, the usual steps include calibration, necessity analysis, and sufficiency analysis. To test the reliability of the results, two forms of robustness testing are also conducted.

4.2. Calibration

The process of calibration is to set the initial condition variable between 0 and 1, which indicates the degree from “non-membership” to “full membership” [59]. The key to this step is to find three anchor points, establishing the threshold for the full membership, the crossover point, and non-membership. Specifically, based on the results of the multi-item measurements, the mean of each item is calculated and three quantiles (5%, 50%, and 95%) are chosen [60]. It means that the original value that covers 5% of the survey data is the point of full non-membership (fuzzy score = 0.05); the original value that covers 50% of the survey data is the crossover point (fuzzy score = 0.5); and the original value that covers 95% of the survey data is the point of full membership (fuzzy score = 0.95). The calibration values of the conditional variables are shown in Table 3.

4.3. Necessity Analysis

We perform a necessity test for each individual form of control. We take the performance of NPD as an outcome, by analyzing the presence (or absence) of each form of management control. The necessity test can judge whether the condition improves the performance of NPD. Specifically, the consistency index is a measure of the degree of overlap between complex causal formulas and result conditions, which is similar to the correlation in statistical analysis. The coverage index indicates the percentage of results explained by the solution. In other words, the coverage value is similar to the R square in regression analysis [61]. As shown in Table 6, considering the fsQCA asymmetry, both high and low NPD performance situations are calculated. Neither the consistency nor coverage level of the present (or absence) is larger than the recommended threshold value of 0.9 [62], which indicates that a single conditional variable cannot provide a necessary explanation for the outcome variable. In other words, NPD is not encouraged when a single management control approach is adopted.

4.4. Sufficiency Analysis

After setting up the truth table, the following three solutions can be obtained by using fsQCA 3.0 software: a complex solution, a parsimonious solution, and an intermediate solution. We choose the intermediate solution to explain the final configuration. Because the intermediate solution simplifies the complex solution, the complexity is reduced without making unreasonable assumptions [55]. Consistency suggests the extent to which the causal combination represents a subset of the result. And the value of 0.75 was the minimum acceptable consistency for any solution [63]. Considering that this study uses the multi-case samples of objective data, we choose a higher consistency threshold to improve the explanatory power of the causal relationship of the solutions as much as possible. Referring to some previous studies, 0.80 is selected as the threshold value for consistency [64,65,66]. The configuration solutions larger than the consistency threshold (0.8) are sufficient.
Table 7 shows the results of the solution. The overall solution consistency level is 0.816, which means that the intermediate solution is of high reliability. The solution coverage level is 0.772, which indicates that 77.2% of the high NPD is provided by the intermediate solution. In general, the interpretation degree of the intermediate solution is ideal. According to the intermediate solution, eight paths can be obtained to achieve high NPD. Because there are three paths with the 0 value of unique coverage, i.e., they do not have the possibility of happening in our cases, five effective paths are obtained and grouped into three types of solutions. For a more intuitive observation, the control forms of the three types of solutions are described by a causal recipe in Table 8 [67].
The first kind of configuration solution is outcome-oriented. Both solutions of 1a and 1b are classified into the first type, and what they have in common is an emphasis on outcome control. In solution 1a, outcome control is a present condition, while contract control and trust are absent; In solution 1b, outcome control is also a present condition, while process control and guanxi are absent. But neither solution has a level of consistency greater than the threshold value of 0.80, so they were not sufficiently persuasive.
The second type of configuration solution is the joint of the contract and guanxi. In the configuration of solution 2, the condition of contract control and guanxi are present, while the outcome control and process control are absent. The consistency in solution 2 is greater than 0.80, which indicates that this is a feasible causal explanation path. Solution 2 illustrates the complementary effect of the contract control and guanxi, and the simultaneous configuration and collaboration of the two management controls can improve NPD performance.
The third type of configuration is the combination of formal controls and informal controls. In solution 3a, three formal controls (i.e., outcome control, process control, and contract control) are combined together with the guanxi; while in solution 3b, three formal controls are combined with trust. The consistency of solution 3a and 3b are greater than 0.80, indicating that they are both valid explanations. Compared with solution 2, solution 3a and 3b have higher raw coverage and consistency, indicating that these two configurations are more reliable and more likely to occur. The third type of configuration demonstrated that in order to achieve high NPD, enterprise managers should pay attention to NPD project results, processes, and contracts simultaneously, manage team members, and develop projects effectively. And it is necessary to choose the appropriate degree of informal controls to couple with formal controls.
Through the second and the third types of configurations, it can be found that NPD performance is improved through a combination of formal and informal controls. It testifies that the combination of management controls can change NPD performance, which signifies the importance of applying both formal and informal controls simultaneously. In addition, contract control is the most important form of management control, because it can substitute outcome control and process control in solution 2. We plot XY graphs about the three effective configurations of solution strategies and NPD performance to show asymmetric causal relationships between the portfolio path and output (see Figure 1). In fact, the consistency score is the proportion of the upper half of the triangle to the whole fuzzy subset [59].

4.5. Robustness Test

We refer to the research of Momparler, et al. [68] and analyze the necessary conditions of low NPD performance. The solutions obtained above achieve a high consistency, which indicates a strong correlation between conditions and results. Conversely, when the NPD condition is absent, the necessity result score should be low. As shown in Table 9, the consistency scores are quite poor.
The calibration of sample data is a critical step for fsQCA, and tiny changes may make a complex impact on the results [69]. We try to test robustness by setting the range of calibration anchor points as 0.90 or higher to represent the “full membership”; 0.10 or lower as the “non-membership”; and 0.50 as the crossover point [70]. Further, we also test the 75th percentile, the median, and the 25th percentile as the full membership, crossover point, and non-membership, respectively [71]. As shown in Table 10 and Table 11, two new sets of configuration solutions and results are similar to the previous scheme. The strategy to obtain high NPD performance is the combination of the formal control and informal control, and the contract control is the consistent core of solution selection. It demonstrates that the results of our findings are robust.

5. Discussion

Two methods (OLS and fsQCA) are employed to investigate the contribution of management control to the performance of NPD in open innovation. In fact, these two methodologies provide different perspectives, respectively. OLS is a symmetric causality analysis of the net benefit, which measures the impact of independent variables changing on the dependent variable. The results show that doing a good job of any single form of management control can improve the performance of NPD, which indicates that both the formal control and informal control investigated are effective. Judging from the results of our survey, trust among multi-parties for a project may be the most important. And fsQCA is an asymmetric causal relationship analysis—that is, whether a configuration condition has a substantial influence on the production of the result or not. The method of fsQCA can obtain combined paths in the complex NPD process, which is the major concern of managers, especially under the condition of limited resources. Therefore, this article contends that the conclusion of fsQCA has more practical meaning for the choice of control form in NPD.
In addition, the core principle of path selection based on the results of fsQCA analysis is the combined use of formal and informal control methods. Specifically, the simultaneous use of all three types of formal control and guanxi or trust can have a positive effect on new product development. If it is not possible to use multiple formal control methods at the same time, at least the combination of contract control and guanxi is preferred. Because these two can work together on the innovation behavior of the enterprise, and this idea has been verified by other scholars on the high quality development of the enterprise [72].

6. Conclusions

This study hopes to explore a new research strategy for NPD activities about management controls. To this end, we applied two methods of OLS and fsQCA in this study. Compared with OLS, fsQCA can identify the effects on an outcome due to the combination of causal conditions [73], which is a new trend in management scholarship and business practices in complex business scenarios [58]. The result of the combined path is to obtain a higher probability of success, rather than just considering a single influence, and has a more comprehensive management enlightenment. Through the objective data of Chinese manufacturing enterprises, this research demonstrates the causal relationship between the configuration scheme of management controls and NPD performance.
This study draws some interesting conclusions based on two research methods. First, the choice of a single formal or informal control does not lead to high new product development performance, but this does not mean that the role of formal and informal control in previous research is rejected, because this conclusion is based on a comparison between a single form of managerial control and a composite form of managerial control. This can provide a broader research perspective for scholars who choose different forms of single management control for in-depth research, and can be more in line with the real context of management practice, because new product development has to consider the complexity of the management environment [74]. Second, the use of a combination of formal and informal controls can facilitate new product development performance. Obviously, the findings of both the OLS and fsQCA research methods demonstrate the existence of complementary effects between formal and informal controls, which can both give a reasonable answer to the current debate on management control styles in new product development scenarios [13,19,31] and make up for the credibility of the previous findings on the effects of separate regression analyses. Third, in particular, the results based on fsQCA analysis show that in the specific implementation of management control, contract control is the most important means of formal control [75,76], which is consistent with the views of previous scholars, while relationship and trust as an informal control can be chosen alternatively, and it is important for the enterprise managers in the management practice to make more favorable choices based on the resources, because the enterprise, especially start-ups, has limited resources [77,78].

7. Theoretical Contributions and Managerial Implications

7.1. Theoretical Contributions

The theoretical contributions of this study are mainly in the form of useful additions to the current role of management control, especially its mechanism of action for new product development and innovation in enterprises. Specifically, first, this study complements the application of the NRBV theory in management control research, based on which it proposes the idea that the synergy between formal and informal controls is more effective for the enhancement of new product development capability. Second, it responds to the current academic debate on the alternative and complementary roles between formal and informal controls, especially exploring the effectiveness of management control in new product development scenarios of firms in the new era. Third, it proposes a methodology to jointly use two empirical methods, OLS and fsQCA, to jointly verify the mechanism of management control.

7.2. Managerial Implications

In this study, we overcome the limitations of traditional net effect analysis on enterprise NPD activities and obtain the combination of causal conditions by configuration solutions. Through the analysis of NPD path combination, we can obtain at least four meaningful management implications to help enterprises achieve a good performance of NPD in open innovation.
First, from the perspective of single management control form, the five forms surveyed in this article are all effective, and they can all improve the performance of NPD. Especially, trust makes the largest contribution to NPD performance, and is emphasized in both the internal and external cooperation of the enterprise. Therefore, the establishment of a trust relationship is extremely important. However, a single management control can only play a role in improving the performance of the NPD, which does not necessarily mean obtaining the success of NPD.
Second, we demonstrate the complementary effect of a formal control and informal control in a new way. Many papers explored the joint effect of a formal control and informal control from different perspectives in NPD [13,79,80]. There are two empirical effects, complement and substitution, between them, according to the prior literature. In this study, the complementary effect is confirmed between the two in NPD performance. In fact, the functional mechanisms of the formal control and the informal control are fundamentally divergent. The existence of the formal outcome, process, and contract control reduces risk in the NPD, so as to alleviate the transaction cost and enhance the performance from the economic perspective. In contrast, an effective informal control plays its role by facilitating information sharing and promoting adaptation and collaboration from the relational perspective. How to break through the boundary of an enterprise to seek win-win results by management and cooperation is the key to achieve a complementary effect.
Third, in formal control, the role of the contract control is superior to other forms of control. In view of the transaction cost, there will be uncertainty and opportunism in NPD’s cooperative team, which is effectively reduced by signing contracts or formal regulations. The contract control emphasizes the importance of the formal rules compliance among participants [81]. Contracts generate clear rights and obligations, which have been proved to promote corporate performance in some studies [75,76,82]. From the configuration obtained by fsQCA, the contract control is a stable and effective management control in this study. For enterprises operating NPD, the contract of participating teams should be clearly stipulated at the initial stage to avoid opportunism in the process of cooperation.
Finally, because of the unique cultural background, the informal control in China has long been a big concern. Some entrepreneurs are keen to build informal social networks [83]. However, in this paper, we chose guanxi and trust as two dimensions of informal control, and either of them is conducive to the high performance of NPD. According to the result of combination, although promoting information communication and building cooperative experience, the informal control cannot function well and independently without the formal control. This suggests that the informal control, as the management control or enterprise resources, should be properly stressed during NPD activities. According to NPD’s industry, scale, cycle, and other characteristics, senior managers need to allocate informal resources with participants and adapt them to the positive cooperation mechanism with the formal control.

8. Limitation and Future Research

There are many contributions, as well as limitations in the present study. First, the data in this paper are all subjective, although the results sometimes drawn from subjective data are quite similar to the objective data. If possible, the objective data should be collected to replicate the research. The objective data which can reflect the financial performance of new products in the market should be considered in the future research. Second, the data used in this study are collected from the manufacturing industry in China. Other industries like IT and the service industry should also get involved in the future research. Third, the management control is not only scientific but also artistic, and even culture-related, which is a multi-faceted and complex issue. In addition to the joint effects of various management configurations investigated, there may be other factors that affect NPD’s performance, such as, for example, the level of human capital, which has been widely argued to be a key factor in modern economic growth [84,85], among the firms’ employees and the particular sector as a whole. Future research could explore the relationship between potential influencing factors of NPD and management control configurations and apply specific cases to discuss the influence mechanism of partner characteristics on management control.

Author Contributions

X.L.: Conceptualization, Writing—original draft, Funding acquisition, Project administration. Y.-e.C.: Resources, Writing—review and editing, Methodology. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the China Postdoctoral Science Foundation (grant number: 2023M731171).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

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

Conflicts of Interest

Author Xingteng Li was employed by the company Huaxin Consultation Design Research Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Appendix A

List of items
All items were measured on a five-point scale, anchored by 1 = strongly disagree and 5 = strongly agree. The values of factor loading are in the corresponding bracket.
Outcome control.
During the NPD process:
The upper management develop specific targets for NPD performance (0.816).
The degree to which upper management supervises project performance (0.775).
The upper management provides feedback on the level of achievement of NPD goals (0.791).
The upper management rewards the product teams for goal attainment (0.787).
Process control.
During the NPD process:
The upper management specifies the processes and procedures in NPD for the team (0.843).
The upper management monitors the extent to which the team complies with established procedures (0.857).
The upper management modifies the program when the team does not achieve the expected results (0.654).
The upper management provides feedback according to the prescribed methods in the NPD process (0.611).
Contract control.
At the beginning of a new project:
We have a specific detailed agreement with customers (or suppliers) (0.854).
We have a formal agreement with customers (or suppliers), which specifies the responsibilities of both parties (0.903).
In general, contract is the basic mechanism to regulate the behavior of customers (or suppliers) in the process of product development (0.744).
Guanxi.
With other enterprises:
We have a good relationship with each other (0.760).
We share market information with each other (0.649).
We try to follow the principle of harmony in the relationship and work together to solve problems (0.870).
We try to protect each other’s “face” and avoid misunderstandings in cooperation (0.656).
Trust.
With other production development teams:
I believe we have the abilities to finish their respective tasks (0.769).
I believe we meet the requirements of the project in terms of technology and management (0.806).
I trust the ability of each other (0.830).
We are willing to help each other solve project problems (0.765).
We believe that each other’s commitment is credible (0.828).
The teams that work together are mostly upright and honest (0.778).
Performance.
In the recent NPD:
The production meets the quality requirements (0.891).
The production meets the time requirement of development (0.715).
The production meets the cost requirements (0.646).
The production meets customer expectations (0.886).

References

  1. Huang, H.; Qi, B.; Chen, L. Innovation and high-quality development of enterprises—Also on the effect of innovation driving the transformation of China’s economic development model. Sustainability 2022, 14, 8440. [Google Scholar] [CrossRef]
  2. Zhao, S.; Zhang, L.; Peng, L.; Zhou, H.; Hu, F. Enterprise pollution reduction through digital transformation? Evidence from Chinese manufacturing enterprises. Technol. Soc. 2024, 77, 102520. [Google Scholar] [CrossRef]
  3. Lyu, B.; Yi, R.; Fan, G.; Zhang, Y. Stakeholder network for developing open innovation practice of China’s manufacturing enterprises. Heliyon 2023, 9, e13192. [Google Scholar] [CrossRef] [PubMed]
  4. Dabić, M.; Posinković, T.O.; Vlačić, B.; Gonçalves, R. A configurational approach to new product development performance: The role of open innovation, digital transformation and absorptive capacity. Technol. Forecast. Soc. Change 2023, 194, 122720. [Google Scholar] [CrossRef]
  5. Jang, S.; von Zedtwitz, M. Opening up early or late? The effect of open innovation before and after product launch on new product market performance. Ind. Mark. Manag. 2023, 112, 113–127. [Google Scholar] [CrossRef]
  6. Ogink, R.H.; Goossen, M.C.; Romme, A.G.L.; Akkermans, H. Mechanisms in open innovation: A review and synthesis of the literature. Technovation 2023, 119, 102621. [Google Scholar] [CrossRef]
  7. Cui, N.; Wen, N.; Xu, L.; Qin, Y. Contingent effects of managerial guanxi on new product development success. J. Bus. Res. 2013, 66, 2522–2528. [Google Scholar] [CrossRef]
  8. Lu, P.; Yuan, S.; Wu, J. The interaction effect between intra-organizational and inter-organizational control on the project performance of new product development in open innovation. Int. J. Proj. Manag. 2017, 35, 1627–1638. [Google Scholar] [CrossRef]
  9. Ritala, P. Coopetition strategy—When is it successful? Empirical evidence on innovation and market performance. Br. J. Manag. 2012, 23, 307–324. [Google Scholar] [CrossRef]
  10. Luo, X.; Rindfleisch, A.; Tse, D.K. Working with rivals: The impact of competitor alliances on financial performance. J. Mark. Res. 2007, 44, 73–83. [Google Scholar] [CrossRef]
  11. Smets, L.P.M.; Langerak, F.; Tatikonda, M.V. Collaboration between competitors’ NPD teams: In search of effective modes of management control. R D Manag. 2016, 46, 244–260. [Google Scholar] [CrossRef]
  12. Mishra, A.A.; Shah, R. In union lies strength: Collaborative competence in new product development and its performance effects. J. Oper. Manag. 2009, 27, 324–338. [Google Scholar] [CrossRef]
  13. Bstieler, L.; Hemmert, M. The effectiveness of relational and contractual governance in new product development collaborations: Evidence from Korea. Technovation 2015, 45–46, 29–39. [Google Scholar] [CrossRef]
  14. Handley, S.M.; Benton, W.C., Jr. The influence of task- and location-specific complexity on the control and coordination costs in global outsourcing relationships. J. Oper. Manag. 2013, 31, 109–128. [Google Scholar] [CrossRef]
  15. Kim, J.; Wilemon, D. An empirical investigation of complexity and its management in new product development. Technol. Anal. Strateg. Manag. 2009, 21, 547–564. [Google Scholar] [CrossRef]
  16. Wang, X.; Um, K.-H.; Wang, S.; Kang, M. The mediating role of key supplier adaptability and involvement in the relationship between supplier relationship management and NPD project success. Oper. Manag. Res. 2023, 16, 794–807. [Google Scholar] [CrossRef]
  17. Netland, T.H.; Schloetzer, J.D.; Ferdows, K. Implementing corporate lean programs: The effect of management control practices. J. Oper. Manag. 2015, 36, 90–102. [Google Scholar] [CrossRef]
  18. Carbonell, P.; Rodriguez-Escudero, A.I. Management control, role expectations and job satisfaction of new product development teams: The moderating effect of participative decision-making. Ind. Mark. Manag. 2013, 42, 248–259. [Google Scholar] [CrossRef]
  19. Huang, M.-C.; Cheng, H.-L.; Tseng, C.-Y. Reexamining the direct and interactive effects of governance mechanisms upon buyer-supplier cooperative performance. Ind. Mark. Manag. 2014, 43, 704–716. [Google Scholar] [CrossRef]
  20. Ning, Y. Combining formal controls and trust to improve dwelling fit-out project performance: A configurational analysis. Int. J. Proj. Manag. 2017, 35, 1238–1252. [Google Scholar] [CrossRef]
  21. Monteiro, J.J.; Lunkes, R.J.; Rosa, F. Influence of formal and informal controls on trust and individual creativity. J. Account. Organ. Change 2023, 19, 689–705. [Google Scholar] [CrossRef]
  22. Rijsdijk, S.A.; van den Ende, J. Control Combinations in New Product Development Projects. J. Prod. Innov. Manag. 2011, 28, 868–880. [Google Scholar] [CrossRef]
  23. Gopal, A.; Gosain, S. The Role of Organizational Controls and Boundary Spanning in Software Development Outsourcing: Implications for Project Performance. Inf. Syst. Res. 2010, 21, 960–982. [Google Scholar] [CrossRef]
  24. Chesbrough, H.W. Open Innovation: The New Imperative for Creating and Profiting from Technology; Harvard Business Press: Boston, MA, USA, 2003. [Google Scholar]
  25. Appio, F.P.; Cacciatore, E.; Cesaroni, F.; Crupi, A.; Marozzo, V. Open innovation at the digital frontier: Unraveling the paradoxes and roadmaps for SMEs’ successful digital transformation. Eur. J. Innov. Manag. 2024, 27, 223–247. [Google Scholar] [CrossRef]
  26. Lu, C.; Qi, Y.; Hao, S. Enhancing innovation performance of SMEs through open innovation and absorptive capacity: The moderating effect of business model. Technol. Anal. Strateg. Manag. 2024, 36, 2907–2923. [Google Scholar] [CrossRef]
  27. Trott, P.; Hartmann, D. Why ‘open innovation’ is old wine in new bottles. Int. J. Innov. Manag. 2009, 13, 715–736. [Google Scholar] [CrossRef]
  28. Marzi, G.; Ciampi, F.; Dalli, D.; Dabic, M. New product development during the last ten years: The ongoing debate and future avenues. IEEE Trans. Eng. Manag. 2020, 68, 330–344. [Google Scholar] [CrossRef]
  29. Einhorn, S.; Fietz, B.; Guenther, T.W.; Guenther, E. The relationship of organizational culture with management control systems and environmental management control systems. Rev. Manag. Sci. 2024, 18, 2321–2371. [Google Scholar] [CrossRef]
  30. Gemente, G.B.; da Silva, A.L.; da Silva, E.M.; Costa, F.H. Pressures, power relationships and governance mechanisms: A multi-tier supply chain approach. Int. J. Logist. Manag. 2024, 35, 1–28. [Google Scholar] [CrossRef]
  31. Lee, G.; Shin, G.-c.; Haney, M.H.; Kang, M.; Li, S.; Ko, C. The impact of formal control and guanxi on task conflict in outsourcing relationships in China. Ind. Mark. Manag. 2017, 62, 128–136. [Google Scholar] [CrossRef]
  32. Zhang, L.; Fu, Y.; Lai, J.; Chen, Y. Complements or substitutes? Recipes of contract design, contract enforcement, and trust for enhanced project performance. Int. J. Proj. Manag. 2024, 42, 102587. [Google Scholar] [CrossRef]
  33. Um, K.-H.; Kim, S.-M. Collaboration and opportunism as mediators of the relationship between NPD project uncertainty and NPD project performance. Int. J. Proj. Manag. 2018, 36, 659–672. [Google Scholar] [CrossRef]
  34. Yan, T.; Dooley, K.J. Communication intensity, goal congruence, and uncertainty in buyer-supplier new product development. J. Oper. Manag. 2013, 31, 523–542. [Google Scholar] [CrossRef]
  35. Faems, D.; Janssens, M.; Madhok, A.; Van Looy, B. Toward an integrative perspective on alliance governance: Connecting contract design, trust dynamics, and contract application. Acad. Manag. J. 2008, 51, 1053–1078. [Google Scholar] [CrossRef]
  36. Mullins, R.; Chase, K.S.; Friend, S.B. Buyer-seller uncertainty: A systematic review and future research directions. J. Pers. Sell. Sales Manag. 2024, 44, 29–49. [Google Scholar] [CrossRef]
  37. Lu, P.; Guo, S.; Qian, L.; He, P.; Xu, X. The effectiveness of contractual and relational governances in construction projects in China. Int. J. Proj. Manag. 2015, 33, 212–222. [Google Scholar] [CrossRef]
  38. Wang, K.; Huang, H.; Yuan, Y. Can CEO’s professional connections drive green innovation of SMEs? Bus. Strategy Environ. 2024, 33, 5975–5995. [Google Scholar] [CrossRef]
  39. Cui, T.; Wu, Y.; Tong, Y. Exploring ideation and implementation openness in open innovation projects: IT-enabled absorptive capacity perspective. Inf. Manag. 2018, 55, 576–587. [Google Scholar] [CrossRef]
  40. Zhang, M.; Hartley, J.L.; Al-Husan, F.B.; ALHussan, F.B. Informal interorganizational business relationships and customer loyalty: Comparing Guanxi, Yongo, and Wasta. Int. Bus. Rev. 2021, 30, 101805. [Google Scholar] [CrossRef]
  41. Zhang, Y.; Zhang, Z. Guanxi and organizational dynamics in China: A link between individual and organizational levels. J. Bus. Ethics 2006, 67, 375–392. [Google Scholar] [CrossRef]
  42. Shen, L.; Zhou, K.Z.; Zhang, C. Is interpersonal guanxi beneficial in fostering interfirm trust? The contingent effect of institutional-and individual-level characteristics. J. Bus. Ethics 2022, 176, 575–592. [Google Scholar] [CrossRef]
  43. Dobrucalı, B. The role of Guanxi on international business-to-business relationships: A systematic review and future directions. J. Bus. Ind. Mark. 2020, 35, 1125–1140. [Google Scholar] [CrossRef]
  44. Dyer, J.H.; Chu, W.J. The role of trustworthiness in reducing transaction costs and improving performance: Empirical evidence from the United States, Japan, and Korea. Organ. Sci. 2003, 14, 57–68. [Google Scholar] [CrossRef]
  45. Lasrado, F.; Thaichon, P.; Nyadzayo, M.W. Exploring the role of relationship management and relationship quality in B2B: Empirical insights and future research directions. J. Bus. Ind. Mark. 2023, 38, 1055–1086. [Google Scholar] [CrossRef]
  46. Venesz, B.; Dőry, T.; Raišienė, A.G. Characteristics of lead users in different stages of the new product development process: A systematic review in the context of open innovation. J. Open Innov. Technol. Mark. Complex. 2022, 8, 24. [Google Scholar] [CrossRef]
  47. Lai, C.-S.; Chen, C.-S.; Chiu, C.-J.; Pai, D.-C. The impact of trust on the relationship between inter-organisational collaboration and product innovation performance. Technol. Anal. Strateg. Manag. 2011, 23, 65–74. [Google Scholar] [CrossRef]
  48. Sheng, S.; Zhou, K.Z.; Li, J.J. The Effects of Business and Political Ties on Firm Performance: Evidence from China. J. Mark. 2011, 75, 1–15. [Google Scholar] [CrossRef]
  49. Chonlatis. Dynamic capabilities of new product development teams in performing radical innovation projects. Int. J. Innov. Sci. 2018, 10, 333–349. [Google Scholar] [CrossRef]
  50. Hart, S.L. A natural-resource-based view of the firm. Acad. Manag. Rev. 1995, 20, 986–1014. [Google Scholar] [CrossRef]
  51. Luo, Y.D. Contract, cooperation, and performance in international joint ventures. Strateg. Manag. J. 2002, 23, 903–919. [Google Scholar] [CrossRef]
  52. Chu, Z.F.; Wang, Q.; Lai, F.J.; Collins, B.J. Managing interdependence: Using Guanxi to cope with supply chain dependency. J. Bus. Res. 2019, 103, 620–631. [Google Scholar] [CrossRef]
  53. 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]
  54. Park, Y.; Fiss, P.C.; El Sawy, O. Theorizing the Multiplicity of Digital Phenomena: The Ecology of Configurations, Causal Recipes, and Guidelines for Applying QCA. MIS Q. 2020, 44, 1493–1520. [Google Scholar] [CrossRef]
  55. Xie, X.; Wang, H. How can open innovation ecosystem modes push product innovation forward? An fsQCA analysis. J. Bus. Res. 2020, 108, 29–41. [Google Scholar] [CrossRef]
  56. Lou, Z.; Ye, A.; Mao, J.; Zhang, C. Supplier selection, control mechanisms, and firm innovation: Configuration analysis based on fsQCA. J. Bus. Res. 2022, 139, 81–89. [Google Scholar] [CrossRef]
  57. Ho, J.; Plewa, C.; Lu, V.N. Examining strategic orientation complementarity using multiple regression analysis and fuzzy set QCA. J. Bus. Res. 2016, 69, 2199–2205. [Google Scholar] [CrossRef]
  58. Lo, F.Y.; Rey-Marti, A.; Botella-Carrubi, D. Research methods in business: Quantitative and qualitative comparative analysis. J. Bus. Res. 2020, 115, 221–224. [Google Scholar] [CrossRef]
  59. Ragin, C.C. Redesigning Social inquiry: Fuzzy Sets and Beyond; University of Chicago Press: Chicago, IL, USA, 2009. [Google Scholar]
  60. Liao, S.Q.; Liu, Z.Y.; Ma, C.L. Direct and configurational paths of open innovation and organisational agility to business model innovation in SMEs. Technol. Anal. Strateg. Manag. 2019, 31, 1213–1228. [Google Scholar] [CrossRef]
  61. Berezan, O.; Krishen, A.S.; Agarwal, S.; Kachroo, P. Exploring loneliness and social networking: Recipes for hedonic well-being on Facebook. J. Bus. Res. 2020, 115, 258–265. [Google Scholar] [CrossRef]
  62. Zhang, M.; Li, B.; Yin, S. Configurational paths to regional innovation performance: The interplay of innovation elements based on a fuzzy-set qualitative comparative analysis approach. Technol. Anal. Strateg. Manag. 2020, 32, 1422–1435. [Google Scholar] [CrossRef]
  63. Ragin, C.C. Set relations in social research: Evaluating their consistency and coverage. Polit. Anal. 2006, 14, 291–310. [Google Scholar] [CrossRef]
  64. Jahanmir, S.F.; Silva, G.M.; Gomes, P.J.; Goncalves, H.M. Determinants of users’ continuance intention toward digital innovations: Are late adopters different? J. Bus. Res. 2020, 115, 225–233. [Google Scholar] [CrossRef]
  65. Bacon, E.; Williams, M.D.; Davies, G. Coopetition in innovation ecosystems: A comparative analysis of knowledge transfer configurations. J. Bus. Res. 2020, 115, 307–316. [Google Scholar] [CrossRef]
  66. Llanos-Contreras, O.; Alonso-Dos-Santos, M.; Ribeiro-Soriano, D. Entrepreneurship and risk-taking in a post-disaster scenario. Int. Entrep. Manag. J. 2020, 16, 221–237. [Google Scholar] [CrossRef]
  67. 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]
  68. Momparler, A.; Carmona, P.; Climent, F. Revisiting bank failure in the United States: A fuzzy-set analysis. Econ. Res.-Ekon. Istraz. 2020, 33, 3017–3033. [Google Scholar] [CrossRef]
  69. Goncalves, H.M.; Lourenco, T.F.; Silva, G.M. Green buying behavior and the theory of consumption values: A fuzzy-set approach. J. Bus. Res. 2016, 69, 1484–1491. [Google Scholar] [CrossRef]
  70. Alofan, F.; Chen, S.; Tan, H. National cultural distance, organizational culture, and adaptation of management innovations in foreign subsidiaries: A fuzzy set analysis of TQM implementation in Saudi Arabia. J. Bus. Res. 2020, 109, 184–199. [Google Scholar] [CrossRef]
  71. Pezeshkan, A.; Smith, A.; Fainshmidt, S.; Nair, A. A neo-configurational institutional analysis of international venture capital attractiveness and performance: Insights for Asia-Pacific. Asia Pac. J. Manag. 2022, 39, 365–393. [Google Scholar] [CrossRef]
  72. Lin, Y.-H.; Zhang, H. Impact of contractual governance and guanxi on contractors’ environmental behaviors: The mediating role of trust. J. Clean. Prod. 2023, 382, 135277. [Google Scholar] [CrossRef]
  73. Woodside, A.G.; Ko, E.; Huan, T. The new logic in building isomorphic theory of management decision realities. Manag. Decis. 2012, 50, 765–777. [Google Scholar] [CrossRef]
  74. Baxter, D.; Turner, N. Why Scrum works in new product development: The role of social capital in managing complexity. Prod. Plan. Control 2023, 34, 1248–1260. [Google Scholar] [CrossRef]
  75. Srivastava, S.C.; Teo, T.S.H. Contract Performance in Offshore Systems Development: Role of Control Mechanisms. J. Manag. Inform. Syst. 2012, 29, 115–158. [Google Scholar] [CrossRef]
  76. Ning, Y. Impact of quality performance ambiguity on contractor’s opportunistic behaviors in person-to-organization projects: The mediating roles of contract design and application. Int. J. Proj. Manag. 2018, 36, 640–649. [Google Scholar] [CrossRef]
  77. Audretsch, D.; Colombelli, A.; Grilli, L.; Minola, T.; Rasmussen, E. Innovative start-ups and policy initiatives. Res. Policy 2020, 49, 104027. [Google Scholar] [CrossRef]
  78. Jonek-Kowalska, I.; Wolniak, R. The influence of local economic conditions on start-ups and local open innovation system. J. Open Innov. Technol. Mark. Complex. 2021, 7, 110. [Google Scholar] [CrossRef]
  79. Bonner, J.M.; Ruekert, R.W.; Walker, O.C. Upper management control of new product development projects and project performance. J. Prod. Innov. Manag. 2002, 19, 233–245. [Google Scholar] [CrossRef]
  80. Wang, C.L. Guanxi vs. relationship marketing: Exploring underlying differences. Ind. Mark. Manag. 2007, 36, 81–86. [Google Scholar] [CrossRef]
  81. Lumineau, F.; Fréchet, M.; Puthod, D. An organizational learning perspective on the contracting process. Strateg. Organ. 2011, 9, 8–32. [Google Scholar] [CrossRef]
  82. Judge, W.Q.; Dooley, R. Strategic alliance outcomes: A transaction-cost economics perspective. Br. J. Manag. 2006, 17, 23–37. [Google Scholar] [CrossRef]
  83. Guo, C.; Miller, J.K. Guanxi Dynamics and Entrepreneurial Firm Creation and Development in China. Manag. Organ. Rev. 2010, 6, 267–291. [Google Scholar] [CrossRef]
  84. Azarnert, L.V. Is skilled immigration always good for growth in the receiving economy? Econ. Lett. 2010, 108, 116–118. [Google Scholar] [CrossRef]
  85. Azarnert, L.V. Transportation costs and the great divergence. Macroecon. Dyn. 2016, 20, 214–228. [Google Scholar] [CrossRef]
Sustainability 16 10973 g001
Figure 1. Fuzzy-set plots for solutions.
Figure 1. Fuzzy-set plots for solutions.
Sustainability 16 10973 g001
Table 1. Samples’ statistics information.
Table 1. Samples’ statistics information.
RangeNumberPercentage (%)
OwnershipState owned5123.28
Privately owned5022.83
Sino-foreign joint135.93
Foreign controlled9342.46
Others125.48
Number of employees<=3003716.89
301–9993315.07
1000–1999188.22
>=200013159.82
Annual turnover<320.91
(million)3–2094.11
20–100135.93
>10019589.04
Total 219100
Table 2. Cronbach’s alpha, composite reliability, and average variance extracted (AVE).
Table 2. Cronbach’s alpha, composite reliability, and average variance extracted (AVE).
VariableCronbach’s αComposite ReliabilityAVE
Formal controlsOutcome control (OC)0.8690.8710.628
Process control (PC)0.8290.8340.562
Contract control (CC)0.8710.8740.699
Informal controlsGuanxi (GX)0.8190.8260.547
Trust (TR)0.9100.9120.634
ResultThe performance of new production development (NPD)0.7890.7990.574
Note: All of the measures showed significance at 0.01 level (p< 0.01).
Table 3. Correlations, means, standard deviations, and calibration values.
Table 3. Correlations, means, standard deviations, and calibration values.
Variable(1)(2)(3)(4)(5)(6)MeanSDCalibration Values
Percentile 5MedianPercentile
95
(1) OC0.792 3.8630.7082.5004.0005.000
(2) PC0.725 ***0.750 3.8090.6392.5004.0005.000
(3) CC0.436 ***0.452 ***0.836 3.9270.6083.0004.0005.000
(4) GX0.476 **0.491 ***0.640 ***0.740 3.6120.6392.5003.7504.750
(5) TR0.556 ***0.592 **0.510 ***0.564 ***0.796 3.8180.5592.8334.0005.000
(6) NPD0.380 ***0.367 ***0.326 ***0.383 ***0.485 ***0.7583.6440.6242.2003.4004.400
Note: The diagonal represents the square root of AVE of each variable. ***, ** Represent significance at 1%, 5%, level, respectively.
Table 4. The impact of different forms of control on NPD performance in open innovation.
Table 4. The impact of different forms of control on NPD performance in open innovation.
Dependent Variable: NPD Performance
Model 1Model 2Model 3Model 4Model 5Model 6
OC0.291 *** 0.015
(0.052) (0.070)
PC 0.340 *** 0.094
(0.054) (0.076)
CC 0.362 *** 0.080
(0.061) (0.076)
GX 0.363 *** 0.106
(0.056) (0.074)
TR 0.495 ***0.306 ***
(0.061)(0.083)
Foreign0.237 ***0.243 ***0.257 ***0.229 ***0.191 ***0.204 ***
(0.073)(0.072)(0.072)(0.072)(0.069)(0.068)
Employees−0.009−0.006−0.045−0.028−0.004−0.024
(0.033)(0.032)(0.033)(0.032)(0.031)(0.031)
Turnover0.0730.1070.133 *0.135 *0.132 *0.126 *
(0.073)(0.072)(0.072)(0.071)(0.068)(0.068)
Constant2.137 ***1.894 ***1.710 ***1.776 ***1.150 ***0.856 **
(0.307)(0.315)(0.343)(0.321)(0.337)(0.347)
Observations219219219219219219
F-satistics11.9614.2512.8914.8821.3412.46
Prob > F000000
R20.1830.2100.1940.2180.2820.322
Note: Standard errors in brackets. ***, **, * Represent significance at 1%, 5%, and 10% level, respectively.
Table 5. Test of variance inflation factor.
Table 5. Test of variance inflation factor.
VariableOCPCCCGXTRForeignEmployeesTurnover
VIF2.252.321.952.051.971.031.231.17
Table 6. Necessity of the conditions relative to the occurrence of NPD.
Table 6. Necessity of the conditions relative to the occurrence of NPD.
Outcome Variable:NPD ~NPD
ConsistencyCoverageConsistencyCoverage
OC0.7590.7450.5950.651
~OC0.6440.5880.7660.780
PC0.7410.7760.5710.666
~PC0.6820.5880.8070.776
CC0.7230.7300.5960.671
~CC0.6740.6000.7600.753
GX0.7510.7690.5650.645
~GX0.6540.5740.7980.781
TR0.7150.7950.5470.677
~TR0.7100.5850.8340.766
Table 7. Results of the intermediate solution.
Table 7. Results of the intermediate solution.
Model: NPD = f (OC, PC, CC, GX, TR)
Raw CoverageUnqiue CoverageConsistency
1a. OC*~CC*~GX0.4850.0210.797
1b. OC*~PC*~GX0.4720.0040.769
2. ~OC*~PC*CC*GX0.4220.0030.807
3a. OC*PC*CC*GX0.5410.0720.859
3b. OC*CC*CC*TR0.5300.0510.876
~OC*CC*GX*~TR0.4120.0000.807
PC*CC*GX*~TR0.4440.0000.858
~PC*CC*GX*TR0.4240.0000.871
Solution coverage:0.772
Solution consistency:0.816
Table 8. Configurations for the achievement of high NPD.
Table 8. Configurations for the achievement of high NPD.
Outcome: NPD
Solution:1a1b23a3b
OCSustainability 16 10973 i001Sustainability 16 10973 i002⊗Sustainability 16 10973 i002Sustainability 16 10973 i002
PC ⊗⊗Sustainability 16 10973 i002Sustainability 16 10973 i002
CC⊗ Sustainability 16 10973 i002Sustainability 16 10973 i002Sustainability 16 10973 i002
GX ⊗Sustainability 16 10973 i002Sustainability 16 10973 i002
TR⊗ Sustainability 16 10973 i001
Raw coverage0.4850.4720.4220.5410.530
Unique coverage0.0210.0040.0030.0720.051
Consistency0.7970.7690.8070.8590.876
Note: Black circles (Sustainability 16 10973 i003) indicate that the condition is present, and the circles with “x” (⊗) indicate that the condition is absent. Large circles represent core conditions; small ones represent peripheral conditions. Blank spaces mean “don’t care”.
Table 9. Analysis of the necessity for low NPD performance.
Table 9. Analysis of the necessity for low NPD performance.
Outcome Variable:~NPD
ConsistencyCoverage
Solution 20.0990.509
Solution 3a0.1300.281
Solution 3b0.1210.270
Table 10. Calibrations by the 90th percentile, the median, and the 10th percentile.
Table 10. Calibrations by the 90th percentile, the median, and the 10th percentile.
Model: NPD = f (OC, PC, CC, GX, TR)
Raw CoverageUnique CoverageConsistency
PC*CC*GX*~TR0.3300.0090.812
~PC*CC*GX*TR0.3360.0140.818
~OC*CC*~GX*TR0.2930.0150.839
OC*PC*CC*GX0.4410.0280.836
OC*CC*GX*TR0.4420.0150.847
Solution coverage:0.571
Solution consistency:0.797
Table 11. Calibrations by the 75th percentile, the median, and the 25th percentile.
Table 11. Calibrations by the 75th percentile, the median, and the 25th percentile.
Model: NPD = f (OC, PC, CC, GX, TR)
Raw CoverageUnique CoverageConsistency
~OC*~PC*CC*GX0.1810.0730.829
~OC*CC*GX*TR0.1970.0520.849
PC*CC*GX*~TR0.2020.0270.836
OC*CC*GX*TR0.3190.1210.870
Solution coverage:0.501
Solution consistency:0.817
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Li, X.; Chen, Y.-e. How Does Management Control Affect New Product Development Performance? A Research Methodology with OLS and fsQCA. Sustainability 2024, 16, 10973. https://doi.org/10.3390/su162410973

AMA Style

Li X, Chen Y-e. How Does Management Control Affect New Product Development Performance? A Research Methodology with OLS and fsQCA. Sustainability. 2024; 16(24):10973. https://doi.org/10.3390/su162410973

Chicago/Turabian Style

Li, Xingteng, and Yue-e Chen. 2024. "How Does Management Control Affect New Product Development Performance? A Research Methodology with OLS and fsQCA" Sustainability 16, no. 24: 10973. https://doi.org/10.3390/su162410973

APA Style

Li, X., & Chen, Y.-e. (2024). How Does Management Control Affect New Product Development Performance? A Research Methodology with OLS and fsQCA. Sustainability, 16(24), 10973. https://doi.org/10.3390/su162410973

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