Exhibit 7. Structural equation model results. Control Variables Firm size Firm age Technological turbulence Industry 1 (Petroleum, Gas and Petrochemical) Industry 2 (Automobile) Industry 3 (Home appliance) Independent Variable CP Moderators PDMP PDTP TU 4.5 4 3.5 3 2.5 2 1.5 1 4.5 4 3.5 3 2.5 Dependent variable (NPD Performance) Standard Error 2 B¹ 1.5 -0.017 -0.033 0.052 1 0.141 0.048 -0.051 -0.058 0.216** 0.303** 0.005 -0.018 0.279*** 0.273** y 0.628x+2.367 y=-0.836x+3.945 Low CP 0.146 0.135 0.059 y=0.696x +1.964 0.283 y=-0.904x+4.348 0.259 0.302 Interactions Effects CPx PDMP 0.087 -0.275 0.783 H2 (not supported) CP x PDTP 0.091 4.032 0.000 H3 (supported) CP × TU 0.101 3.951 0.000 H4 (supported) Model Fit Indices: x²/df = 1.703, GFI = 0.900, IFI = 0.900, CFI= 0.898, TLI = 0.883, RMSEA = 0.062 **p<1%; *p < 5%; Standardized coefficients are reported. Exhibit 8. Moderating effect of PDTP on CP-NPD performance association Low CP 0.062 0.059 0.076 24 0.135 t- High CP Value High CP P- Value -0.257 0.797 -0.531 0.596 0.807 0.420 1.678 Exhibit 9. Moderating effect of TU on CP-NPD performance associati 0.093 0.554 0.580 -0.627 0.531 -0.835 0.404 3.146 0.002 4.076 0.000 0.062 0.951 -Low PDTP -High PDTP -Low TU Corresponding hypothesis -High TU H1 (not supported) Exhibit 3. Sample demographics. Respondent tenure in current industry (years) <5 5-10 11-20 > 20 Respondent title CEO or general manager Senior R&D manager Senior marketing manager Senior project manager Firm size (number of employees) < 100 100-300 301-500 > 500 3% 15% 40% 42% 23% 16 13% 44% 32% 11% Firm age (years since incorporation) < 10 10-20 21-40 > 40 Firm industry Petroleum, Gas and Petrochemical (Parts) 30% Automobile (Automobile parts) 24% Home appliance 23% Textile 10% 21% 46% 23% 25% 38% 19% 18%
1. Were the data analyzed in a logical and scientific sound mannaer?
2. Does the paper provides a unique conntribiution to the engineering mngemnt disciplines?
3.How would you improve upon the article?
After the verification of reliability and validity of constructs, we constructed a Structural Equation Model (SEM) in AMOS 24.0 to examine the research hypotheses. The SEM fit indices fall within the thresholds described in the previous subsections (see Exhibit 7). Exhibit 7 presents the results of SEM. Consistent with prior literature, CP does not affect NPD performance significantly (), failing to provide support for H1. This effect is also under debate in
the literature. H2 predicts a positive moderating effect of PDMP on the association between CP and NPD performance. The interaction between CP and PDMP is not significant (), failing to provide support for H2. While we do not provide support for significant effect of CP on
NPD performance, the interaction between CP and PDTP is positive and significant ().
This finding suggests that CP improves NPD performance in the condition of high PDTP. Therefore, H3 is supported. Finally, the positive and significant interaction between CP and TU () supports the hypothesis that in the condition of high TU, CP improves NPD
performance (supporting H4).
For further exploration of the significant interaction effects of this research, we plot them in Exhibits 8 and 9 (we used Stats Tools Package developed by James Gaskin). Exhibit 8 depicts that CP improves NPD performance of firms when their PDTP is high (when PDTP is low, this effect is negative). Exhibit 9 shows that in the condition of low TU, the effect of CP on NPD performance is poor (negative). However, this effect becomes very significant and positive in the condition of high TU. This implies that TU positively moderates the association between CP and NPD performance.
In this study, we addressed a under debate research stream in the literature of NPD: Does CP hinders/promotes NPD performance?. The results of prior studies showed that CP does not always provide advantage to developer firms and it may hurt NPD performance. Based on contingency theory, our study tried to shed light on the CP-NPD performance association and to find conditions in which CP can be turned into a successful strategy. In this regard, we considered two types of contingency factors: organizational factor and environmental factor. For organizational factor, we viewed firms through the lens of RBV and considered PDP of developer firms as one of the critical resources that may have role in the CP-NPD performance association. For environmental factor, we concentrated on one of the most important characteristics of new products (i.e. TU) that may affect CP-NPD performance association. Therefore, we investigated the contingent role of PDMP, PDTP and TU in the CP-NPD performance association.
This study empirically tests a conceptual model of NPD performance and contributes to the literature in several aspects. First, according to the results, CP does not have significant effect on NPD performance. This may be caused by increased conflict between customer and developer firm (Wang et al., 2020). Indeed, this finding confirms inconsistency existing in the NPD literature (about the effect of CP on NPD performance) and enriches the literature by investigating this effect in the context of a developing country (i.e., Iran). Second, our finding showed that in the condition of high PDTP, CP has positive and significant effect on NPD performance. This finding highlights the importance of RBV of firms (VRIN resources) and indicates that firms with high level of PDTP are able to easily adapt themselves with customer and reduce conflict, which causes to higher NPD performance. Third, our investigations showed that PDMP has not any role in the association between CP and NPD performance. This finding was different from our expectations. We guess that PDMP provides rough information about customer needs and preferences that is not so useful in co-developing new product. It implies
that in spite of determinant role of marketing in the NPD process, it may not reduce conflict between customer and firm in co-developing new products, which causes to lower NPD performance. Finally, we found that in the condition of high TU, CP has positive and significant effect on NPD performance. High level of TU threatens success of NPDs (Um & Kim, 2018; Um & Oh, 2021). This finding reveals that when customer participates in the NPDs with high level of TU, it enables developer firm to overcome threatens caused by TU. Indeed, in this situation, the advantages of CP is more than its disadvantages.
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