The impact of user innovator mindset on feedback volume, feedback diversity and new product development performance

PurposeThis study examines the influence of different user innovator mindsets on new product development (NPD) performance. The current research explores the relative impact of a product-focused user innovator mindset vs a customer-focused mindset on feedback volume and feedback diversity and investigates the effect of each type of feedback on product improvement and product diffusion.Design/methodology/approachThis study examines these relationships using two distinct types of data. Data on user innovator mindset, feedback characteristics and user innovator improvisation were obtained via an online survey. Archival data on NPD performance measures were acquired directly from an online research database, and results were obtained using confirmatory factor analysis.FindingsThe authors find that while neither type of user innovator mindset directly influences NPD performance, user innovators, who are highly customer-focused, have a significant advantage in sourcing knowledge from users in the form of a higher volume of feedback and more diverse feedback. In turn, feedback volume appears to positively influence product improvement, while feedback diversity positively influences product diffusion. Finally, the effect of both types of feedback on product improvement is enhanced for user innovators who are highly improvisational.Originality/valueThis research highlights the important role that customer focus plays in directly obtaining knowledge from customers (i.e. customer feedback) and the effects of that feedback on NPD performance. This study provides evidence that a user innovator's interest in accurately understanding the needs of their peers improves their access to external knowledge and enhances their innovation efforts.

Impact of Degradation and Material Crystallinity on the Mechanical Performance of a Bioresorbable Polymeric Stent

Bioresorbable polymeric stents (BPS) offer possibilities to help address the long-term complications associated with permanent vascular implants, however in-vivo degradation behaviour is not yet fully understood. Here, finite element analysis (FEA) techniques based on physio-chemical reaction diffusion equations are used to predict and analyse BPS degradation behaviour. Physio-chemical degradation models for polymers, both amorphous and semi-crystalline, are incorporated into the FEA software package Abaqus/Standard allowing for BPS degradation rate predictions to be made, with a focus on poly-L-lactide (PLLA). The outputs of the degradation models are linked to mechanical behaviour via three different damage models which couple the changes in molecular weight and crystallinity with a hyperelastic constitutive model for PLLA mechanical behaviour. A simplified representation of a PLLA BPS in an artery is used as a demonstration case. The effects of applied degradation product diffusion boundary conditions on the molecular weight and crystallinity of PLLA BPS under simulated degradation are examined, and the impact of material heterogeneities and mechanical load boundary condition on the scaffolding performance and elastic properties of the degrading stent are investigated. The results suggest that the BPS performance are strongly dependent on the assumed boundary conditions, both in terms of degradation product diffusion and mechanical loading.

Learning Dynamics in Social Networks

This paper proposes a tractable model of Bayesian learning on large random networks where agents choose whether to adopt an innovation. We study the impact of the network structure on learning dynamics and product diffusion. In directed networks, all direct and indirect links contribute to agents' learning. In comparison, learning and welfare are lower in undirected networks and networks with cliques. In a rich class of networks, behavior is described by a small number of differential equations, making the model useful for empirical work.

Consumer susceptibility to social influence in new product diffusion networks: how does network location matter?

Purpose This study aims to empirically investigate how susceptibility to social influence in new product adoption varies with one’s structural location in a social network. Design/methodology/approach The social network data were collected based on a sociometric network survey with 589 undergraduate students. Social network analysis and ordinary least squares regression analyses were used to test the hypotheses. Findings This study finds that consumers with high degree centrality (i.e. hubs) who have a large number of connections to others and consumers with high betweenness centrality (i.e. bridges) who connect otherwise distant groups in social networks are both less sensitive to informational influence from others. More importantly, the authors find evidence that consumers with moderate levels of degree/betweenness centrality are more susceptible to normative influence and status competition than those with low or high degree/betweenness centrality. The inverse-U patterns in the above relations are consistent with middle-status conformity and anxiety. Research limitations/implications This research complements social influence and new product diffusion research by documenting important contingencies (i.e. network locations) in consumer susceptibility to different types of social influence from a social network perspective. Practical implications The findings will assist marketers to leverage social influence by activating relevant social ties with effective messages in their network marketing strategies. Originality/value This research provides a better understanding of the mechanisms driving susceptibility to social influence in new product diffusion.

Cryptocurrency Adoption with Speculative Price Bubbles

This paper marries models of stochastic bubbles and the standard model of product diffusion to study the role of price bubbles in cryptocurrency adoption.

Nonlinear Dynamic Analysis of New Product Diffusion considering Consumer Heterogeneity

When a new product enters the market, individual consumers’ decision-making behavior and purchase time are uncertain. Based on the dynamics of epidemic transmission theory and agent modeling technology, this study proposes a new coupling model through the combination of the improved SEIR epidemic model and the heterogeneous agent model. This model considers consumer heterogeneity resulting from three aspects in consumers’ sensitivity, network topology, and considerations of information flow received. It aims to analyze how consumer heterogeneity affects the scale and speed of new product diffusion. The proposed model showed that consumers’ characteristics and behavior combination at the microlevel lead to the diversity of nonlinear diffusion curves at the macrolevel for new products. Moreover, a pilot study is conducted to simulate this model and examine how to estimate the model’s parameters using aggregated data about film products. The pilot study results suggested that different consumer characteristics and behavior combinations affect the scale and speed of new product diffusion to varying degrees. In different scenarios, there were significant differences in the influence of the degree of consumer heterogeneity on diffusion, accompanied by the occurrence of threshold. The results of the empirical analysis in this study are in line with reality.