This study provides a systematic review of the literature on innovation research (IR) over the past two decades. We used data-driven approaches integrating network and natural language processing techniques on 41 inno-vation core and ancillary journals to characterize the IR landscape. Contrary to previous efforts, we explored knowledge in the whole IR field from general and specific patterns of growth and interaction using cluster-and term-based data and macro-and micro-level perspectives, respectively. Our results helped us uncover the changing features of the IR landscape in recent years: (i) a strong move into social-and sustainability-driven innovation; (ii) the merging of products and services into business model innovation; (iii) the more influential role of stakeholders such as the government and the general public; (iv) the use of global analytical perspectives while considering local contexts; (v) the importance of greater visions pulling innovation; (vi) the greater role of soft issues such as behaviors; and (vi) a shift into sectoral, geographical, and methodological diversification. Building on these aspects, we developed an emerging model for future innovation research and a series of IR propositions. Our findings help generate opportunities to build future innovation capabilities in research, practice, and education.