Purpose: This paper aims to explore the effects of normative and hedonic motivations on continuous knowledge contribution, and how past contribution experience moderates the effects of the motivations on continuous knowledge contribution.Design/methodology/approach: Based on goal-framing theory, the present study proposes a comprehensive theoretical model by integrating normative and hedonic motivations, past contribution experience and continuous knowledge contribution. The data for virtual community members' activities were collected using the Python Scrapy crawler. Logit regression was used to validate the integrative model.Findings: The results show that both normative motivation (reflected by generalized reciprocity and social learning) and hedonic motivation (reflected by peer recognition and online attractiveness) are positively associated with continuous knowledge contribution. Moreover, these effects are found to be significantly influenced by members' past knowledge contribution experience. Specifically, the results suggest that past knowledge contribution experience undermines the influence of generalized reciprocity on continuous knowledge contribution but strengthens the effect of peer recognition and online attractiveness.Originality/value: Although the emerging literature on continuous knowledge contribution mainly focuses on motivations as antecedents that promote continuous knowledge contribution, most of these studies assume that the relationship between motivating mechanisms and continuous knowledge contribution does not change over time. The study is one of the initial studies to examine whether and how the influence of multiple motivations evolves relative to levels of past contribution experience.

Over recent years, companies have been increasingly adopting algorithmic decision systems (ADS) to replace humans. In this paper, we focus on how ADS facilitates human managers’ decision making rather than replacing humans altogether in the context of inventory ordering decisions, specifically on the effect of providing ADS performance information to human managers. Using a pair of field experiments, our results suggest that providing ADS performance information can improve their inventory ordering decisions. Interestingly, providing both positive and negative ADS performance information enhances human managers’ inventory ordering decisions, with the latter proving even more effective. Our analyses reveal that compliance and deliberation are two mechanisms through which ADS performance transparency influences inventory ordering decisions. Furthermore, we explore the heterogeneous effect and find that disclosing ADS performance information is more helpful for human managers overseeing products with lower sales and higher uncertainty, thus solving the pain points where human managers rely on algorithmic recommendations the most. Our results also show that ADS performance information serves as an equalizer that benefits low-performing managers more. These results demonstrate the importance of algorithm performance transparency in the human adoption of ADS and shed light on the managerial implications of using ADS within corporations.