Green market orientation, green innovation capability, green knowledge acquisition and green brand positioning as determinants of new product success

Abstract

PurposeThe purpose of this study is to assess if the mediating effect of green innovation capability (GIC) in the relationship between green market orientation (GMO) and new product success (NPS) was conditional on the moderating effects of green knowledge acquisition (GKA) and green brand positioning (GBP).Design/methodology/approachThe analysis was based on primary data gathered using a structured questionnaire, which was developed on a five-point Likert scale of 1-Strongly disagree to 5-Strongly agree. There were 259 manufacturing firms engaged in the study, with data analyzed using PROCESS macro (v.3.4) for SPSS (v.23).FindingsThe research revealed that GMO had no direct effect on NPS among manufacturing firms, the relationship was rather mediated by GIC of the firms. The effect of GMO on GIC was moderated by GKA, whereas the effect of GIC on NPS was moderated by GBP. Overall, the mediating effect of GIC in the relationship between GMO and NPS was conditional on the moderating effects of GKA and GBP.Research limitations/implicationsThe study focused on only knowledge acquisition (green), without recourse to assimilation, transformation and exploitation. These may, however, be very important in explaining the role of knowledge in green innovation.Practical implicationsGreen market-oriented manufacturing firms must seek to also make investments in GIC to transform those concepts into successful innovative products.Originality/valueDespite the increasing number of studies on GMO, very limited concentration has been paid to how firms could leverage on the potentials of GMO to enhance the success of new products introduced into the market. This study did not just establish the effect of GMO on the success of new products but also identified some intervening variables in this relationship.

Big data analytics capabilities and green supply chain performance: investigating the moderated mediation model for green innovation and technological intensity

“…Several studies in the literature have confirmed that the positive relationship between big data analytics capabilities and green innovation is still in its early stages (Mani et al, 2017). However, available empirical studies on this relationship have proven positive and direct effects of big data analytics capabilities on promoting green innovation (Imran et al, 2021;Waqas et al, 2021;Yaoteng and Xin, 2022) through using these technological and human capabilities in maximizing benefit and improving the efficiency of operational processes within factories and transportation methods, leading to reduced waste (Borah et al, 2021) and thus introducing more environmentally-related innovations (Mavi and Mavi, 2021). Tools used in big data analytics help to enhance green innovation capabilities within firms (Beier et al, 2022;Dong et al, 2022), and they provide accurate predictive tools that can be used in developing new environmental ideas that the firm may use in the future in developing green innovations (Bag et al, 2020;Meiyou and Ye, 2022).…”

Linkage of Green Brand Positioning and Green Customer Value With Green Purchase Intention: The Mediating and Moderating Role of Attitude Toward Green Brand and Green Trust

“…In many researches green brand positing is treated as uni-dimensional variable (Aulina & Yuliati, 2017; Baiquni & Ishak, 2019; Himawan, 2019; Tristiani et al, 2019). In recent years, this variable is still considered as a single construct instead of a multidimensional construct (Borah et al, 2021; Pebrianti & Aulia, 2021; Situmorang et al, 2021). The current study uses the green brand positioning as a uni-dimensional construct based on these facts.…”

Assessing the moderating role of organizational culture in the relationship between organizational leadership and organizational efficiency in the banking sector

“…When the correlation coefficients derived from both analysis (before and after controlling for the marker variable) are significantly different from each other, CMB is said to be present in the dataset. From Table 2, we conclude that there was no CMB in our dataset, as both the correlation scores from the restricted and unrestricted estimations were not significantly different from each other (Borah et al. , 2021).…”

“…That is, the square root of AVEs were larger than the inter-correlation coefficients. The study also assessed multicollinearity (Borah et al. , 2021) by assessing the correlation coefficients.…”

“…That is, the square root of AVEs were larger than the inter-correlation coefficients. The study also assessed multicollinearity (Borah et al, 2021) by assessing the correlation coefficients. Correlation coefficients greater than 0.7 is an indication of multicollinearity, which may cause a confounding effect (Tabachnick et al, 2007).…”