The Effect of Negative Word-of-Mouth on Innovation Diffusion and the Performance of Marketing Strategies: an Agent Based Percolation Model

Abstract

Because real-world marketing experiments are costly, firms make use of diffusion models to decrease uncertainty. Over the last few years Agent Based Models of Percolation have received increased attention in the literature, in which awareness of an innovation diffuses through social contagion, and price and promotion (seeding) strategies can be experimented with (cf. Solomon et al., 2000).

A limitation of the basic percolation model such as Solomon et al. (2000) is that consumers in a social network only receive information about the existence of an innovation, but their own attitude towards the adoption of the innovation remains unaffected by that of their neighbors under the influence of Positive- and Negative Word-of-Mouth (PWOM and NWOM). Although the effects of PWOM and NWOM have been studied empirically, only few extensions on the basic percolation model have been made capturing this effect (e.g. the NWOM model by Erez et al. (2004), and the social reinforcement model by Mas Tur (2016)). This research addresses a gap in the literature studying the effect of NWOM on percolation size and exploring the performance of price and promotion (seeding) strategies given there is NWOM. The standard percolation model is extended with the effect of NWOM in the decision process of an actor, coming from rejecting neighboring actors.

It is found that, given there is NWOM, percolation size decreases and the steepness of the percolation threshold increases. This implies that percolation size, and therefore revenue, decreases and becomes more sensitive to price changes. Although the relation between network structure and social influence is studied by e.g. Mas Tur (2016), we have explored this relationship in depth and have identified new mechanisms which causes the relative decrease in percolation size, given there is NWOM, to be higher on clustered networks as opposed to random networks.

Regarding seeding strategies, it is found that increasing the number of seeds increases percolation size and that the relative decrease in percolation size given there is NWOM is lower for a high number of seeds as opposed to a low number of seeds. Furthermore, since promotion strategies can be targeted towards specific customers we have studied how percolation size differs by picking seeds with different network centralities. We have found that seeds which are placed far apart from each other and have a high degree centrality are particularly effective when aiming for high percolation size and that the relative decrease in percolation size given there is NWOM is lowest for seeds that have short path lengths to other agents in the network (betweenness and closeness centrality).