Radical innovation has long dominated academic and industry discussions due to its potential to disrupt markets and establish long-term competitive advantages. However, many dominant firms in R&D-intensive industries increasingly struggle to develop entirely new technologies. In contrast, nondominant firms are turning to incremental innovations as a more feasible and cost-effective strategy. This study explores whether, and under what conditions, incremental innovations can capture market share compared to radical innovations. We use a comprehensive dataset from a major biochemical knowledge-intensive industry, encompassing over 700 brands and 19,000 customers across 72 months of sales, supplemented with government and field data. Our findings indicate that products developed through incremental innovations—particularly those launched by nondominant firms—can effectively capture more market share than those based on radical innovations. We also find that price-adjusted efficacy is positively associated with market share gains for incremental innovations, an effect not observed for radical ones. Additionally, incremental innovations tailored to local markets perform better, especially among informed customers. We conclude with strategic implications for nondominant manufacturers.

As more firms embrace ESG goals, a critical question emerges: does doing good help or hurt innovation? Drawing on the attention-based view of the firm, we argue that ESG performance can demand significant managerial attention, potentially crowding out focus on product innovation. We test this framework using a panel of 457 publicly listed U.S. firms over 15 years (2007–2021), comprising 19,340 firm-quarter observations. We find that ESG performance is negatively associated with product innovation, and that this relationship is moderated by three contextual factors. R&D intensity improves alignment and mitigates the negative association. Firms facing intense competition are more likely to sustain innovation efforts due to differentiation pressures. In contrast, firms with high ROA may become more risk-averse, reinforcing the negative effect. These findings highlight the unintended trade-offs of ESG commitment and offer guidance on aligning purpose with innovation goals.

This methodological paper presents a practical pipeline for matching company and product records across unstructured datasets. We propose techniques that combine heuristics, vector embeddings, and manual validation to reduce noise while preserving recall in large-scale marketing datasets.

This project explores how large language models and vector-based methods can classify customer activities and sentiment from online reviews. The goal is to automate the generation of marketing metrics—such as usage frequency, purchase drivers, and pain points—to support strategic decisions.

This study examines how the entry of generic competitors affects the effectiveness of pricing and distribution strategies in the crop protection market. Using a multi-year panel dataset, we estimate changes in elasticity and brand responsiveness to marketing efforts before and after generic entry events.