The food industry has recently experienced a rapid increase in uncertainty in new product development (NPD) due to the growing fragmentation of consumer needs, shortened product life cycles, and the diversification of distribution channels. In respons...
The food industry has recently experienced a rapid increase in uncertainty in new product development (NPD) due to the growing fragmentation of consumer needs, shortened product life cycles, and the diversification of distribution channels. In response to these environmental changes, firms have gradually moved away from traditional product development approaches based on intuition and experience, and have increasingly recognized the importance of data-driven decision-making grounded in market and consumer analytics. Advances in data technologies and the expansion of digital channels have generated diverse forms of consumer behavioral data, which can serve as critical strategic resources throughout the NPD process. Despite this growing importance, empirical research remains limited regarding how data are actually utilized across different stages of NPD at the firm level and how such differences in data utilization lead to variations in product performance.
Motivated by this research gap, this study examines data-driven NPD processes and outcomes in food manufacturing firms from the perspective of dynamic capability theory. Specifically, the study employs the Sensing–Seizing–Reconfiguring framework to investigate how data are utilized at different stages of the NPD process and how such utilization differs across firms.
To this end, a multi-case study was conducted on three representative firms in the Korean food industry: Lotte Wellfood, CJ CheilJedang, and Ourhome. Representative data-driven NPD cases were selected for each firm, namely Lotte Wellfood’s Chefood Whole-Cut Pork Cutlet, CJ CheilJedang’s Gourmet Sobaba Chicken, and Ourhome’s On The Go lunch box. Drawing on in-depth interviews and secondary data sources, this study analyzes how data were employed across the stages of market exploration, product planning and development, and post-launch management. In addition, the study examines how the presence and structural characteristics of dedicated data organizations influence NPD decision-making processes and performance outcomes.
The findings reveal that while all three firms utilize data to some extent in their NPD activities, there are clear differences in both the stages and the manner in which data are applied. Lotte Wellfood leveraged consumer reviews and online response data to precisely define consumer dissatisfaction at the market sensing stage, which contributed to the refinement of product concepts and resulted in stable post-launch performance. CJ CheilJedang extensively utilized domestic and international market data to identify global consumption trends, enabling the development of scalable product concepts suitable for mass production and overseas expansion. In contrast, Ourhome relied primarily on operational experience and internal data accumulated through its institutional food service business, which enhanced operational stability and feasibility but posed limitations in achieving differentiation in the consumer retail market.
Overall, this study demonstrates that the key determinant of NPD performance variation lies not in the mere presence of data utilization, but in the degree to which data are precisely leveraged at the market sensing stage. Furthermore, the structure of dedicated data organizations plays a critical organizational role in shaping NPD decision-making patterns and performance characteristics. By empirically analyzing data-driven NPD through the lens of dynamic capability theory, this study contributes both theoretical and practical implications for NPD strategy and organizational design in food manufacturing firms. The findings are expected to provide foundational insights for developing data-driven NPD strategies not only in the food industry but also across the broader consumer goods sector.