Selection bias of ideas for sustainability-oriented innovation in internal crowdsourcing

Decision biases reinforce firms' tendency to develop innovations based on narrow economic motivations. Consequently, sustainability-oriented ideas explicitly targeting social and environmental issues are easily discarded in idea selection when trade-offs between economic and sustainability values are faced. Given the so far limited knowledge about how sustainability-oriented ideas are developed and selected in organizations today, this research aims to explore how managerial biases affect selection of sustainability-oriented ideas in internal crowdsourcing. It does so through an empirical study drawing on data collected from a Swedish multinational company using internal crowdsourcing for different types of innovation ideas. The empirical study explicitly identifies sustainability-oriented ideas based on machine learning and captures managerial biases for ideas based on sentiment analysis. Regression analyses reveal that managerial biases potentially affect the selection of sustainability-oriented ideas through the mediating role of managerial attention in idea development. Furthermore, this mediating relationship is moderated by search pattern in terms of directed search. The study contributes to the literature on both innovation and sustainability, shedding new light on the effects of managerial bias, managerial attention, and innovation search for decision making and provides managerial implications enabling a fruitful adoption of sustainability-oriented innovation ideas.