Content area
Purpose
The study aims to explore the conditional relationships between product modularization and new product development (NPD) efficiency. It is postulated that R&D outsourcing plays an important mediating role. Furthermore, the level of competency trust is considered an essential factor in moderating the indirect effect of product modularization on NPD efficiency via R&D outsourcing practices.
Design/methodology/approach
Drawing on transaction cost economics theory, this study suggests a moderated mediation model that addresses how product modularization effectively promotes NPD efficiency via outsourcing practices. The hierarchical regression and PROCESS macro model were conducted to test the hypotheses based on survey data from 273 manufacturing firms in China.
Findings
Product modularization enhances NPD efficiency directly and indirectly through the external collaboration of R&D outsourcing. Furthermore, the role of product modularization in R&D outsourcing practices is more effective when the competency trust in R&D outsourcing partners is high.
Originality/value
By showing the critical role of external collaboration, this study provides valuable insights into how manufacturing firms utilize product modularization to achieve desired NPD performance more effectively.
Quick value overview
Interesting because - As firms face various customer needs and requirements for personalized and interactive products in the era of mass customization and digital transformation, the concept of product modularization has gained attention. Product modularization provides many advantages, such as allowing firms to increase productivity and efficiency and provide more customized products to their customers. Studies have shown that new product development processes benefit from product modularization, but these studies are limited in explaining the mechanisms underlying it. This study explains the mechanisms that connect product modularization and new product development processes.
Theoretical value - The study found that R&D outsourcing, driven by product modularity, enhances the efficiency of new product development by leveraging the benefits of product modularization. The effectiveness of product modularization is heightened when coupled with a higher level of trust in suppliers' competency.
Practical value - Companies that strive to efficiently meet customer demands with new products must grasp their product architecture and proactively embrace modular strategies throughout the product development phase. Companies that possess high levels of product modularization can enhance new product development (NPD) efficiency by engaging in R&D outsourcing. They can leverage the specialization and economies of scale offered by dedicated suppliers. This not only reduces transaction costs but also provides greater flexibility. It is crucial for companies to establish trust-based relationships with their suppliers. This is essential for the effective implementation of modular strategies and the successful integration of R&D outsourcing into their NPD processes.
1. Introduction
The growing complexity of technologies and the shorter product life cycles, coupled with the globalization of markets and the presence of aggressive foreign competitors, are compelling firms to reassess their strategic thinking (Hsuan, 1999). The concept of product modularization has emerged and gained much attention from both academicians and practitioners as firms face various customer needs and requirements for personalized and interactive products in the era of mass customization and digital transformation (Asan et al., 2004; Baldwin and Clark, 2000; Helfat and Eisenhardt, 2004; Magnusson and Pasche, 2014; Sanchez and Mahoney, 1996; Ulrich and Tung, 1991; Wang et al., 2022). Product modularization creates product variety by flexibly recombining modules using standardized interfaces (Bask et al., 2010; Magnusson and Pasche, 2014), allowing firms to increase productivity and efficiency and provide more customized products to their customers (Gilmore and Pine, 2000; Piran et al., 2020; Shamsuzzoha and Helo, 2017). Product modularization is also known as a strategic approach to new product and process development (Brusoni et al., 2023). The modular product design approach enables the creation of a variety of product variations and also facilitates the design of product families (Shamsuzzoha et al., 2018). In NPD studies, several studies have shown that product modularization speeds up the product development process and reduces the NPD cost (Garud and Kumaraswamy, 1995). A high level of product variety and process flexibility in NPD can be achieved by product modularization with low coordination and integration costs (Magnusson and Pasche, 2014). Hence, modular product design reduces the transaction costs which occur in an exchange relationship and then facilitates and expedites product changes and updates without impacting the overall product architecture, resulting in NPD time savings (Danese and Filippini, 2010; Danese and Romano, 2004; Jacobs et al., 2007; Ulrich, 1995). Furthermore, it provides additional benefits to companies, including improved agility in supply chain management, promotion of product families, enhanced customer satisfaction and increased revenue (Jørgensen and Messner, 2009; Shamsuzzoha et al., 2018).
However, although previous studies have tested the benefits of product modularization in the NPD processes, they are limited to explaining the mechanism linking product modularization to NPD performance. It is a common practice for manufacturers to involve suppliers in NPD (Bao et al., 2017). Another stream of research has examined how R&D outsourcing plays a crucial role in enhancing NPD performance (Cassiman and Veugelers, 2006; DeSarbo et al., 2005; Howells, 1999; Pisano, 1990). Grimpe and Kaiser (2010) define R&D outsourcing as “the contractually agreed, the non-gratuitous and temporary performance of R&D tasks” (p. 1484). They posit that firms outsource their R&D activities to leverage the capabilities of external suppliers and contractors. R&D outsourcing practices as external acquisitions and contractual collaborations contribute to enhancing cost efficiency and innovation performance, especially when such practices are not available internally (Chesbrough, 2003; Cuervo-Cazurra et al., 2018; Frenz and Ietto-Gillies, 2009; Oke and Onwuegbuzie, 2013). R&D outsourcing can bring cost advantages through contractor specialization and cost-sharing in a joint commission, reducing fixed costs and providing greater control over R&D time and budget (Tapon and Thong, 1999). Thus, this study attempts to unveil an indirect mediating relationship through which product modularization influences NPD efficiency via R&D outsourcing and explores the following research question:
How does product modularization influence NPD efficiency through utilizing R&D outsourcing?
Additionally, this study examines the contingent impact of competence trust on the indirect relationship. While some emphasize the significance of early supplier involvement in product development, others concentrate on the relationship between the supplier and the buyer (Hsuan, 1999). Despite the many benefits such as in terms of speed, flexibility, variety and customization that product modular components can offer, there are risk factors identified in previous studies, which include opportunistic behavior and risk of imitation by suppliers. While working with suppliers for modular components of products and R&D outsourcing, product design changes, modifications or iterations may occur due to capacity and resource constraints, demand fluctuations and market conditions. If suppliers are opportunistic, they could be unwilling to embrace the changes and reluctant to deploy the best workforce at once. Eventually, they could look for other customers and markets by making imitation derivatives and adding more flexibility to the existing components to make them quickly adapt to other products (Baldwin and Clark, 2000; Leroy, 2009; Salvador and Villena, 2013; Schilling, 2000). Trust is a commonly utilized mechanism that represents both informal and formal approaches, respectively, aimed at countering opportunism and minimizing transaction costs (Bao et al., 2017). Thus, this study proposes competence trust as a moderating factor to not only suppress the risk factors of product modularization but to strengthen mutual collaboration between focal companies and their suppliers based on transaction cost economics (TCE) theory. It is expected that competence trust serves as a relational governance/capital to moderate the indirect meditated relationships between product modularization and NPD efficiency through R&D sourcing as trust lays the foundation for sharing knowledge and information in developing partnerships. Therefore, this study attempts to explore the following research question:
How does competence trust in outsourcing partners moderate the indirect effect of product modularization on NPD efficiency via R&D outsourcing?
This paper seeks to address the above two research questions and advance knowledge regarding the relationship between product modularization, competency trust, R&D outsourcing and NPD efficiency. Our study contributes to the current literature in two major ways. First, we identify the success factors of NPD in the context of R&D outsourcing. Second, we provide firms with insights and guidance on how to increase NPD efficiency more effectively.
This study is organized into five sections besides this introduction. Section 2 reviews the relevant literature and the hypotheses development. Then, Section 3 describes the methodological procedures that supported the conduction of this study, including sampling and data collection, questionnaire design and measurement validation. After, the hypotheses are tested and the results are presented in Section 4. Further discussion was provided in Section 5. Finally, Section 6 outlines conclusions.
2. Theoretical background and hypothesis development
2.1 The direct effect of product modularization and NPD efficiency
This study defines product modularization as the standardization of the core components that allow the reuse and sharing of the components across different products (Pil and Cohen, 2006; Stäblein et al., 2011). The module components can be recombined into new configurations with little loss of functionality, requiring few changes in the overall structure of the production system (Harmancioglu, 2009; Pil and Cohen, 2006). Thus, product modularization can maintain a low level of component variety and assembly complexity during production (Um et al., 2017) and enable decreases in production costs and mass customization (Lau et al., 2010; Stäblein et al., 2011). Such benefits of product modularization can be expanded into the NPD process, enhancing the firms' capabilities to develop new products in a speedy and cost-saving manner (Ray and Ray, 2011; Sanchez, 1996), which is understood as NPD efficiency in this study. NPD researchers argue that firms with superior module capabilities can speedily design and develop various new products by mixing and matching different combinations of existing module components (Sanchez, 1996). Moreover, according to TCE theory, product modularization reduces the transaction costs of information by providing the technology as transaction-specific assets, which promotes NPD efficiency (Griffith et al., 2009; Halldorsson et al., 2007). Also, product modularization helps firms manufacture existing module components on a large scale and reduce production costs in designing and developing new products by transferring or reusing the module components for new products, resulting in cost benefits in the NPD and numerous market-based innovation activities (Jacobides et al., 2018; Sanchez, 1996). Based on the arguments above, this study proposes that product modularization has a positive effect on NPD efficiency. Therefore, it is hypothesized as follows:
Product modularization is positively related to NPD efficiency
In addition to the benefits of product modularization in promoting NPD efficiency, this study argues that firms need to strategically outsource part of their R&D activities to suppliers (i.e. R&D outsourcing) to transfer product modularization into NPD efficiency indirectly (Wu and Park, 2009).
Firms have the opportunity to not only reap the advantages of cost savings and flexibility through product modularization but also potentially experience positive outcomes such as enhanced sustainability, improved digitalization, novel avenues for innovation and more effective collaboration (Mertens et al., 2023). Given that product modularization provides flexibility which enables assembling any required combination and reduces costly changes for the customized components, well-specified and standardized modularization promotes R&D outsourcing (Ernst and Kamrad, 2000). Moreover, according to TCE theory, modularization decreases transaction costs by providing the specified knowledge, then increases the degree of supplier–buyer interdependence in interfirm learning, which leads to more outsourcing decisions (Mikkola, 2003). Given that product modularization improves coordination efficiencies among all the components of the product and reduces the complexity of product designs (Ethiraj et al., 2008), such decomposability nature of product modularization correspondingly reduces coordination and communication costs (Contractor et al., 2010; Wang et al., 2018) and enables loosely coupled organizational structure, which can make it easier for firms to outsource their R&D activities externally (Harmancioglu, 2009; Wu and Park, 2009).
According to the resource-based view, one of the purposes of R&D outsourcing is to obtain technological resources at a lower cost from outsourced firms (Quinn, 1992). By acquiring and exploiting superior R&D capabilities of the outsourced firms, such as technologies and R&D personnel, the firms can augment their innovation capabilities regarding R&D costs and the speed of developing new products (Griffith et al., 2009; Huang et al., 2009). Existing research on the automotive industry underscores outsourcing as the primary catalyst for modularization (Jacobides et al., 2016; Pushpananthan and Elmquist, 2022). Although the coordination cost of R&D outsourcing for acquiring complex technologies occurs, if shared standards are provided, the cost and speed in NPD are optimized (Huang et al., 2009). Moreover, given that product modularization serves as a specific asset to R&D outsourcing, cospecialized investments reduce monitoring and enforcement costs, allowing firms to focus on NPD efficiency (Halldorsson et al., 2007). In other words, this study argues that R&D outsourcing plays a mediating role in sustaining R&D competencies and transforming product modularization into NPD efficiency. Therefore, it is hypothesized as follows:
Product modularization positively influences NPD efficiency indirectly through R&D outsourcing practices.
Although product modularization is a valuable organizational capability that provides opportunities to build outsourcing relationships and collaborate with suppliers (Harmancioglu, 2009) and reduces the transaction cost through information sharing in an exchange relationship, the decomposability of product modularization exposes the firms to the risk of the partners' opportunistic behavior such as knowledge leakage to competitors and threatening collaboration between them. This is because the process of modular systems is decomposable, wherein the module components of product designs are loosely coupled, and these components can be assembled into a wide range of end products (Sanchez and Mahoney, 1996; Zhang et al., 2014). In an extreme case, a perfectly modular product that is composed of components that perform entirely one or few functions (Cabigiosu et al., 2013) can increase the likelihood of imitation risk (Ethiraj et al., 2008). Such a dark side of product modularization can be complemented when the firms can curb the suppliers' opportunism and nurture a collaborative working relationship with them. From the perspective of TCE, this study examines competency trust as a contingent factor in the mediated pattern between product modularization and NPD efficiency.
Competency trust is referred to as firms' belief in the ability of their suppliers to perform tasks and activities at a set of levels (Dowell et al., 2015; Sako, 1992). It influences a firm's decisions to incorporate external competences into their modularization strategy (Zhou et al., 2023). Competency trust is developed based on firms' prior experience with their partner firms, such that the partner firms have demonstrated their ability to perform as expected by the firms (Curtis et al., 2010; Xue et al., 2018). Ha et al. (2011) defined trust in competency as the expectation of trustworthy behaviors as a result of conviction for the knowledge, know-how, business judgment and expertise of partners. When the firms believe that their partner firms have sufficient resources and competencies to fulfill their expectation, their potential opportunism is reduced (Xue et al., 2018), and the cooperation between them is boosted (Schiele, 2006). Drawing on the logic behind competency trust, this study argues that competency trust can offer a context of interfirm collaboration (Heffernan, 2004), suppressing the potential opportunism of the suppliers (Xue et al., 2018) and fostering their collaborative behavior (Schiele, 2006). In particular, how competency trust can be used to increase modularization production in an outsourcing relationship has not been investigated. When the firms build trust in the suppliers' competency, the firms with superior module capabilities can accentuate their strategic focus on R&D outsourcing and then realize higher levels of NPD efficiency. In other words, competency trust can enhance the effectiveness of product modularization on R&D outsourcing and then NPD efficiency, which is hypothesized as follows:
Competency trust in outsourcing partners moderates the indirect effect of product modularization on NPD efficiency via R&D outsourcing practices, such that the indirect effect is stronger as the level of competency trust increases.
Figure 1 illustrates the conceptual research model proposed in this study, showing hypothesized relationships between product modularization, R&D outsourcing, NPD efficiency and competency trust.
3. Method
3.1 Data sample
In this research, NPD efficiency has been chosen as the criterion variable, while product modularization has been presented as an independent variable. In addition, R&D outsourcing practices have been regarded as a mediating variable, while competency trust has been proposed as a moderating variable. To achieve the study objectives, a cross-sectional survey methodology was used.
We selected a random sample from a broad range of industries including electronics and telecommunications, biomedicine, chemicals, machinery, new materials, food, textiles and others. Data collection was subcontracted to a Chinese specialized survey company (https://www.wjx.cn, a website like SurveyMonkey) with access to 2.6 million panelists in China and a credible reputation for providing online services to more than 30,000 companies including many leading brands in various industries and 90% of Chinese colleges and universities.
All constructs were measured by referring to the R&D outsourcing situation in China and existing literature. The questionnaire was initially designed in English and was translated back into Chinese. A back-translation procedure was followed to guarantee conceptual equivalence. We required the survey company to only survey managers and engineers of Chinese manufacturing companies who had been engaged in R&D. The survey period was 2 weeks, from August 11, 2021, to August 25, 2021.
Recognizing that smaller firms might lack R&D activities, we specifically gathered data from companies with a minimum of 200 employees. Moreover, we stipulated that the survey company should secure at least 600 responses to accommodate the likelihood of receiving questionnaires that might not meet validity standards. In total, we amassed 779 responses; however, 465 were invalidated due to job positions that did not meet the required criteria. As a result, we were left with 314 viable responses. Furthermore, we eliminated 41 responses that were completed hastily (in under 240 s) or exhibited erratic answers. This meticulous process yielded a final count of 273 valid responses.
3.2 Measures
To develop measurement items of research variables, we relied primarily on the previous literature and adapted them to fit an outsourcing context. The content validity of each measure was checked upon the design of the project through expert interviews. A 1- to 5-point Likert scale was used for the survey constructs, and all measurement items are detailed in Appendix.
Adopting three items from the work of Parente et al. (2011), we measured R&D outsourcing to capture the extent to which the firm conducted its outsourced activities. The items were subsequently used by Kamuriwo and Baden-Fuller (2016) and Yamaguchi et al. (2021). Competency trust was measured with three items to evaluate the extent to which the firm trusted its outsourcing partners concerning their capabilities, resources and performance. The items were developed based on a review of the relevant literature (Lui and Ngo, 2004). NPD efficiency (NPDE) was measured with three items adopted from the previous work on NPD project performance (Cooper et al., 2004; Smith and Reinertsen, 1998; Swink et al., 2006) and assessed effective market achievement in an NPD project.
In addition, several control variables (i.e. firm size, market size, industry competition, and product substitutability) were included in our analysis, which may affect the dependent variable. Firm size has been a typical control in the literature on dynamic capabilities. It may affect governance mechanisms, supplier management practices and interfirm collaboration performance because large firms usually have greater business process capabilities and available resources (Lee et al., 2018). We calculated firm size as a natural logarithm of the number of employees. Market size, industry competition and product substitutability were included as control variables that reflect the operation environment and conditions (Millson and Wilemon, 2010). The descriptive statistics and inter-construct correlations of the variables are presented in Table 1.
3.3 Construct validity
We first tested the measurement model using exploratory factor analysis (EFA) to establish the validity and reliability of the scales used in our analysis. We used the principal component and rotated the factors using varimax rotation with Kaiser normalization. The Kaiser–Meyer–Olkin (KMO) value is 0.747, and the result of Bartlett's test of sphericity was significant (p < 0.000), indicating sampling adequacy for EFA. The results in Table 2 indicate that all items have higher loadings (>0.500) on their respective construct and lower loadings on other constructs, which suggests acceptable construct unidimensionality. Then, Cronbach's alpha values of each construct were calculated and ranged from 0.625 to 0.755. The composite reliability ranged from 0.675 to 0.797 (see Table 2). Both Cronbach's alpha and composite reliability for all the constructs were above the acceptable cutoff of 0.600. Churchill (1979) suggested that Cronbach's α value exceeding 0.6 was acceptable, which was confirmed by Rahimnia and Hassanzadeh (2013). All our scales met these standards for reliability, which minimizes the loss of information and ensures reliable scales (Cossío-Silva et al., 2016).
Then, we assessed convergent and discriminant validity by conducting confirmatory factor analysis (CFA) and calculating the average variance extracted (AVE). In the CFA model, the resulting model fit indices, χ2/df = 1.828, RMSEA = 0.055, CFI = 0.941, TLI = 0.919, IFI = 0.943, GFI = 0.950, and NFI = 0.882, were acceptable. In addition, the factor loadings were greater than 0.500 (i.e. ranging from 0.518 to 0.752). Moreover, it was found that the AVE values for three constructs (i.e. R&D outsourcing, competency trust, NPD efficiency) were higher than 0.500, except for product modularization with a value of 0.412. Nevertheless, we decided to include this variable as it is still larger than an acceptable level of 0.400 (Handley and Benton, 2009; Hatcher and O'Rourke, 2013; Menor et al., 2007; Yang et al., 2020). All of the square roots of the AVE value for each construct are greater than the corresponding correlation values. Taken together, we conclude that the research constructs have strong convergent and discriminant validity.
4. Analysis and results
To test the proposed hypotheses in this study, we used hierarchical regression. In Model 1, we included all of the control variables and product modularization into the regression model to examine its effect on NPD efficiency. Then, R&D outsourcing was added to Model 2 to examine their main effects on NPD efficiency. In Model 3, we entered all the control variables and product modularization to examine its effect on R&D outsourcing. Then, in Model 4, both competency trust and the interaction between product modularization and competency trust were added to examine the moderating effect of competency trust in the relationship between product modularization and R&D outsourcing. To reduce multicollinearity, the independent variables were mean-centered before the interaction terms were created.
The results are presented in Table 3. Model 1 reveals that product modularization positively affects NPD efficiency (B = 0.185, p < 0.01), providing support for H1. Model 3 shows that product modularization positively affects R&D outsourcing (B = 0.134, p < 0.05). Model 2 shows that R&D outsourcing positively affects NPD efficiency (B = 0.253, p < 0.001), and the effect of product modularization on NPD efficiency decreases after adding R&D outsourcing. Taken together, these results reveal a partial mediation effect of R&D outsourcing on the relationship between product modularization and NPD efficiency, supporting H2. Model 4 reveals that the moderating effect of competency trust on the relationship between product modularization and R&D outsourcing is significant and positive (B = 0.261, p < 0.05). Figure 2 also shows that product modularization is more positively associated with R&D outsourcing when competency trust is high. In addition, we used SPSS macro (i.e. PROCESS macro Model 7) to test the conditional indirect effect of competency trust. As shown in Table 4, the indirect effect of product modularization on NPD efficiency through R&D outsourcing is stronger (effect size = 0.061, 95% CI: [0.019, 0.112]) when the competency trust is higher. Thus, H3 is supported.
5. Discussion
As a major driver of firm growth and sustainable competitive advantage, NPD projects require efficient strategies for reducing time to market and responding faster and better to customer needs (Belbaly et al., 2007; Mu et al., 2009). Based on TCE theory and a resource-based view, this study proposes a moderated mediation model that addresses how product modularization influences NPD efficiency through R&D outsourcing under the circumstances of competency trust. Our findings highlight an underlying mechanism by which product modularization promotes NPD efficiency through R&D outsourcing. Moreover, the findings show that this mechanism is positively moderated by competency trust. Under a high level of competency trust, the indirect effect of product modularization on NPD efficiency via R&D outsourcing is significantly strengthened. Below, we discuss the implications of these findings for theory and management practice, identify our study's limitations and suggest directions for future research.
5.1 Theoretical implications
This study explores how and when product modularization contributes to NPD efficiency considering the synergism of R&D outsourcing and competency trust, which can provide important implications in the area of NPD performance. The first findings enrich our understanding of product modularization by showing its independent positive effect on NPD efficiency, which is consistent with previous studies (Parente et al., 2011; Ye et al., 2018). In this study, product modularization reflects the capability of reusing and sharing existing core components across different products without redesigning other module components, which can realize benefits in the NPD process and enhance the firm's performance in a speedy and cost-saving manner. This result aligns with the idea of most previous studies (Ray and Ray, 2011; Sanchez, 1996). According to TCE theory, since product modularization provides the information of technologies as specified assets, it is more likely to enhance NPD efficiency.
Second, this study deepens the research on product modularization by proposing the mediating role of R&D outsourcing. Previous studies on product modularization are mainly from a strategy integration perspective and view R&D outsourcing as the main driver of innovation (Jacobides et al., 2016; Pushpananthan and Elmquist, 2022). Product modularization breaks down a complex product system into independent modules and they can be recombined into new configurations with little loss of functionality, requiring few changes in the overall structure of the production system (Harmancioglu, 2009; Pil and Cohen, 2006). Our findings reveal that R&D outsourcing plays an important mediating role in the relationship between product modularization and NPD efficiency. However, some findings show non-significant or even weak outcomes of R&D outsourcing. Cuervo-Cazurra et al. (2018) emphasized that insourcing R&D contributes more to NPD than outsourcing R&D based on the idea of control of knowledge. R&D outsourcing does not always guarantee better NPD performance and even leads to negative effects when the product is complex (Lee et al., 2017). Kang and Um (2023) pointed out in their study that the firms may benefit from product modularity because the manufacturing firm and suppliers share standard components, standardized interfaces and communication standards, which means product modularity can increase the control of knowledge of suppliers. However, our findings reveal that R&D outsourcing driven by product modularity can result in efficient NPD by leveraging the benefits of modularity. Manufacturers are increasingly dependent on their suppliers in the current networked business environment to access external knowledge and use it to improve their NPD performance. Specifically, according to the resource-based view, a firm with high product modularization gains a better position in utilizing external resources (i.e. R&D outsourcing) to enhance NPD efficiency.
Lastly, this research also contributes to the understanding of competency trust by using a moderated mediation pattern, the results indicate that product modularization and competency trust influence R&D outsourcing synergistically and then indirectly affect NPD efficiency. In other words, the mediating effect of R&D outsourcing varies depending on the level of competency trust. Trust serves as a frequently employed mechanism that embodies both informal and formal approaches, working to counteract opportunism and reduce transaction costs (Bao et al., 2017; Dyer and Chu, 2003). The emerging research on competency trust mainly emphasized the benefits of trust in facilitating resource sharing (Curtis et al., 2010; Xue et al., 2018), while some research argued that a high level of trust reduces buyers' incentive and cognitive capabilities to search for and effectively process useful information from suppliers (Zhou et al., 2014). According to the TCE theory, outsourcing is prone to failure and would entail a variety of coordination costs associated with various aspects of interfirm transactions (e.g. search costs to find the right supplier, negotiation costs and coordination costs) (Díaz-Mora, 2008) and face various degrees of risks in managing the process of R&D (e.g. vendor selection, monitoring difficulty, expropriation of knowledge and shirking) (Hsuan and Mahnke, 2011). The effects of R&D outsourcing based on product modularization rely on the high level of competency trust in suppliers.
5.2 Managerial implications
Our findings provide fresh thinking on developing a modular manufacturing strategy and managing the process of R&D outsourcing in the NPD processes. First, product modularization is positively related to NPD efficiency. Thus, firms that require developing new products efficiently to meet their customer demand must understand their product architecture and actively adopt the modular strategy in product development.
Second, given that R&D resourcing could transform product modularization into NPD efficiency when a firm's products are characterized by a higher degree of modularization, the outsourcing strategy can be used more intensively to leverage external knowledge and resources. Firms with high product modularization can enhance their NPD efficiency through R&D outsourcing by taking full advantage of specialization and scale economies in components or other production tasks by specialized suppliers as well as reduce transaction costs and gain flexibility.
Our research shows that firms should heed the value of competency trust in adopting a modular strategy for achieving expected NPD performance. Decisions on supplier selection and evaluation can be made according to a firm's long-term strategic planning. Managers should realize that low-level competency trust in suppliers could weaken the indirect effect of product modularization on NPD efficiency through R&D outsourcing due to hidden transaction costs and risks in the R&D outsourcing process like vendor selection, uncertainty in delivery, quality, technology transfer risk, etc. As Arbaugh (2003) suggested, when the sourcing relationships are established, these relationships should be based on open communication, collaboration, and trust rather than mere cost efficiency so that all parties will learn and improve the performance result of the relationship. Firms must cultivate trustworthy relationships with their partners to ensure long-term relationships and continued competitiveness. Cooperating with those who have been receiving reputations could be a prudent tactic. Moreover, managers may achieve the goal of reducing negotiating costs and coordination costs according to past cooperation experiences with the suppliers. Thus, by evaluating suppliers' performance in terms of quality, delivery, cost and flexibility, firms need to develop competency trust relationships with suppliers to enhance the effectiveness of the modular strategy in exploiting R&D outsourcing in their NPD processes.
5.3 Limitations and future research directions
This study aims to understand the relationship between modular designs, competency trust and R&D outsourcing in the NPD process. Although there are valuable theoretical and managerial insights, some limitations remain to be addressed, opening up future directions for research.
First, multidimension measurements for NPD and trust are required. This study only focused on NPD efficiency. However, effectiveness is also an essential indicator of NPD performance. As another form of trust, goodwill trust is also developed in the course of a long-term relationship and plays an important role in the supply chain. Investigating the moderating effect of goodwill trust on the indirect effect of product modularization on NPD performance might be interesting.
Second, the current study is subject to the typical limitations associated with cross-sectional survey research. Due to the complexity of the trust relationship and the dynamic feature of product development, the moderating effect of competency trust might vary at different stages (e.g. early, middle, and late). Moreover, competency trust cultivation is a long-term collaborative relationship that generally requires time to develop, especially in China. Thus, further longitudinal case studies are required to understand the role of competency trust more comprehensively in the R&D outsourcing of modular products.
Lastly, our sample is from the Chinese manufacturing industry. China is a relationship-based society where interpersonal interaction and trust relationships, more commonly called “guanxi” influence every aspect of Chinese business transactions and performance (Gold et al., 2002; Lee et al., 2018). Li et al. (2017) noted that Chinese guanxi involves trust and leads indirectly to improved outsourcing performance. Although we explored the moderating mediation effect of competency trust in the current research mode, our findings do not necessarily generalize to other cultural contexts. Thus, it is interesting and meaningful to replicate this study in other countries.
6. Conclusions
In sum, based on TCE theory and a resource-based view, this study provides a moderated mediation model that addresses how product modularization influences NPD efficiency through R&D outsourcing under the circumstances of competency trust, which further enriches the extant research on the mechanism linking product modularization to NPD performance via R&D outsourcing. Our research offers useful theoretical and managerial implications. The decomposability nature of product modularization makes it easier for the firm to adopt R&D outsourcing and increase the intensity of its R&D outsourcing activities to suppliers for increasing NPD efficiency. The indirect positive effect of product modularization on NPD efficiency through R&D outsourcing is strengthened as the competency trust in suppliers increases. Suppliers with a good reputation, which also have sufficient capabilities and resources to complete the task, are more likely to decrease transaction costs and reduce outsourcing risks. The findings of this study also provide valuable insights into the ways that manufacturers in China can better implement outsourcing in the long run and achieve superior performance. Relaxing the limitations mentioned in the above discussions will be of our future interest.
Funding: This research was supported by the Science Foundation for Young Scholars of Zhejiang University of Science and Technology (#2023QN082) and the Scientific Research Foundation of Zhejiang University of Science and Technology (#F701118K01).
Figure 1
Conceptual research model
[Figure omitted. See PDF]
Figure 2
Moderation of the role of competency trust
[Figure omitted. See PDF]
Descriptive statistics and interconstruct correlations
| Mean | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | |
|---|---|---|---|---|---|---|---|---|---|
| 1. Firm size | 5.903 | 1.321 | 1 | ||||||
| 2. Market size | 3.600 | 0.690 | 0.331** | ||||||
| 3. Competition | 4.110 | 0.815 | 0.032 | 0.085 | |||||
| 4. Substitutability | 3.480 | 0.928 | −0.028 | −0.075 | 0.485** | ||||
| 5. Modularity | 3.614 | 0.704 | 0.029 | 0.199** | −0.011 | 0.048 | |||
| 6. R&D outsourcing | 3.865 | 0.621 | −0.052 | 0.105 | 0.153* | −0.002 | 0.213** | ||
| 7. Competency trust | 3.231 | 0.743 | 0.080 | 0.152* | 0.203** | 0.005 | 0.137* | 0.350** | |
| 8. NPD efficiency | 4.151 | 0.558 | 0.015 | 0.100 | 0.219** | 0.088 | 0.187** | 0.407** | 0.218** |
Note(s): N = 273; *p < 0.05, **p < 0.01
Source(s): Authors' work
Factor loadings and reliability
| Variables | Items | Factor loadings | Composite reliability | AVE | Cronbach's α |
|---|---|---|---|---|---|
| Product modularization | PM1 | 0.596 | 0.675 | 0.412 | 0.625 |
| PM2 | 0.705 | ||||
| PM3 | 0.518 | ||||
| R&D outsourcing | RDO1 | 0.679 | 0.790 | 0.556 | 0.755 |
| RDO2 | 0.752 | ||||
| RDO3 | 0.702 | ||||
| Competency trust | CT1 | 0.555 | 0.791 | 0.560 | 0.653 |
| CT2 | 0.621 | ||||
| CT3 | 0.707 | ||||
| NPD efficiency | NPDE1 | 0.679 | 0.797 | 0.568 | 0.701 |
| NPDE2 | 0.752 | ||||
| NPDE3 | 0.702 |
Note(s): N = 273; AVE stands for average variance extracted
Source(s): Authors' work
Hierarchical regression analyses
| NPDE | NPDE | R&D outsourcing | R&D outsourcing | |
|---|---|---|---|---|
| Model 1 | Model 2 | Model 3 | Model 4 | |
| Constant | 2.839 | 2.416 | 1.675 | 1.909 |
| Firm size | −0.042 | −0.048+ | 0.021 | 0.021 |
| Market size | 0.061 | 0.038 | 0.091 | 0.073 |
| Competition | 0.157** | 0.099+ | 0.230*** | 0.180 |
| Substitutability | −0.073 | −0.050 | −0.093+ | −0.087 |
| Product modularization | 0.185** | 0.151** | 0.134* | −0.134 |
| R&D outsourcing | 0.253*** | |||
| Trust | 0.231** | |||
| Product modularization × Trust | 0.261* | |||
| R2 | 0.088 | 0.172 | 0.084 | 0.126 |
| Adjusted R2 | 0.071 | 0.153 | 0.066 | 0.103 |
| F | 5.155*** | 9.192*** | 4.872*** | 5.463*** |
Note(s): N = 273; ***p < 0.001; **p < 0.01; *p < 0.05; +p < 0.10; NPDE stands for NPD Efficiency
Source(s): Authors' work
Bootstrap results for conditional indirect effect at CT = M±1 SD
| CT: Moderator | Indirect effect | SE | BC 95% CI | |
|---|---|---|---|---|
| Lower | Upper | |||
| −1 SD | −0.005 | 0.020 | −0.050 | 0.029 |
| M (0) | 0.039 | 0.018 | 0.006 | 0.077 |
| +1 SD | 0.061 | 0.024 | 0.019 | 0.112 |
Note(s): N = 273; Number of Bootstrap samples: 5,000, SE: Standard error, BC CI: Bias-corrected confidence intervals, CT: Competency Trust
Source(s): Authors' work
Survey items
| Product modularization | |
| PM1 | We can make changes in key components without having to redesign other components |
| PM2 | For our main product(s), we re-use components (carry-over) from previous product generations |
| PM3 | We have a high degree of component sharing between different products in our mainline |
| R&D outsourcing practices | |
| RDO1 | Percent of R&D budget spent on R&D outsourcing |
| RDO2 | The proportion of R&D outsourcing in your company's entire R&D activities |
| RDO3 | Our company actively adopts R&D outsourcing strategies when developing new products |
| Competency trust | |
| CT1 | The supplier has sufficient capabilities and resources to complete the task |
| CT2 | Outsourcing suppliers have a good reputation for performance |
| CT3 | Suppliers are worthy of our trust |
| New product development efficiency | |
| NPDE1 | Fast speed of new product introduction into the plant |
| NPDE2 | Timeliness of new product launch |
| NPDE3 | Cost-competitive of new product |
Source(s): Authors' work
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