Predicting popular contributors in innovation crowds: the case of My Starbucks Ideas

Abstract

Purpose

This study aims to predict popular contributors through text representations of user-generated content in open crowds.

Design/methodology/approach

Three text representation approaches – count vector, Tf-Idf vector, word embedding and supervised machine learning techniques – are used to generate popular contributor predictions.

Findings

The results of the experiments demonstrate that popular contributor predictions are considered successful. The F1 scores are all higher than the baseline model. Popular contributors in open crowds can be predicted through user-generated content.

Research limitations/implications

This research presents brand new empirical evidence drawn from text representations of user-generated content that reveals why some contributors' ideas are more viral than others in open crowds.

Practical implications

This research suggests that companies can learn from popular contributors in ways that help them improve customer agility and better satisfy customers' needs. In addition to boosting customer engagement and triggering discussion, popular contributors' ideas provide insights into the latest trends and customer preferences. The results of this study will benefit marketing strategy, new product development, customer agility and management of information systems.

Originality/value

The paper provides new empirical evidence for popular contributor prediction in an innovation crowd through text representation approaches.

Citation

Hsiang, C.-Y. and Rayz, J.T. (2022), "Predicting popular contributors in innovation crowds: the case of My Starbucks Ideas", Information Technology & People, Vol. 35 No. 2, pp. 494-509. https://doi.org/10.1108/ITP-04-2019-0171