An iterative consumer-centric and technology-driven product innovation strategy based on selective and dynamic consumer attention

Consumer attention is a critical factor in driving consumer-centric innovation characterized by selectivity and dynamism. However, existing literature has emphasized the selectivity but overlooked the dynamism vital for frequent iterative innovation. This study addresses the gap by exploring innovation strategies with dynamic consumer attention. We constructed a data-driven agent-based model (ABM) with two decision-making entities: manufacturers and consumers. Aspect-based sentiment analysis (ABSA) results obtained from consumer reviews are used to shape consumer agents with selective and dynamic attention. A comparative experiment, utilizing manufacturers insights based on various consumer-generated knowledge, was devised to evaluate iterative innovation efficacy. The findings highlight benefits of innovation strategies considering consumer attention drivers, especially consumer self-learning and social influence behaviors. Additionally, we identified consumers' observation bias in product attributes exhibits a notable inverted U-shaped effect on new product adoption, and negatively impacting repeat purchases. Moreover, our results show that as technology for new products matures, incremental iteration based on consumer attention becomes the optimal strategy. However, rapid technological advancements or disruptive changes could make attention-focused strategies less reliable. These insights offer valuable implications for manufacturers to optimize product innovation strategies by understanding consumer characteristics, limited innovation resources, and current technological conditions.