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The purpose of this study is to investigate how incumbent firms’ marketing deterrence strategies, price-cuts and quality improvement, influence potential entrants’ (PEs) entry timing into incumbents’ markets and examine the moderating role of incumbents’ market-level resources and capabilities (R&Cs).

To test the hypotheses, an accelerated failure time model is applied to a rich data set of entry threats between 1997 and 2019 in the US airline industry.

The findings show that while quality improvements delay PEs’ entry, price-cuts expedite it. Furthermore, PEs are more likely to be deterred by price-cuts when incumbents possess high market-level R&Cs. However, such R&Cs do not moderate the link between incumbents’ quality improvement and PEs’ entry timing.

Market entry in this research is conceptualized and defined as a large resource deployment into a market and operationalized as a binary variable. However, PEs might rather choose a minor resource deployment instead to establish a foothold in new markets. It would be fruitful for future research to examine various levels of participation in a new market and examine how the incumbents’ marketing deterrence strategies affect PEs’ entry timing. Doing so would provide us with a deeper understanding of the difference between actual/full market entry and micro/minor market entry.

This research examines the impact of incumbents’ marketing deterrence strategies in a contingency framework and highlights the crucial role of R&Cs in implementing such deterrence strategies. Doing so provides actionable insights to managers who craft strategies to protect their markets against PEs.

Previous research illustrates that incumbent firms commonly respond to competitors’ entry threats by cutting prices and improving quality. While antecedents of these deterrence strategies have been extensively investigated, the consequences have yet to be empirically examined.

The study aims to investigate how recommender systems shape providers’ dynamics and content offerings on platforms, and to provide insights into algorithm designs for achieving better outcomes in platform design.

This study employs a multi-agent simulation framework coupled with reinforcement learning models to examine the influence of different recommender system designs on providers’ perception of demand and platform content.

The study reveals that recommender systems have the potential to introduce biases in providers’ understanding of user preferences, thereby impacting the variety of offerings on platforms. Moreover, it identifies algorithm design as a critical factor, with item-based collaborative filters showcasing superior performance in contexts where customers exhibit selectivity. Conversely, user-based models prove more effective in scenarios where recommendations significantly sway user decisions, ultimately boosting sales.

In practical terms, these insights can guide platform developers in making informed decisions regarding the selection and implementation of recommender system algorithms. By tailoring algorithm choices to specific contexts, platforms can enhance user welfare, ultimately leading to improved platform performance and profitability.

The findings underscore the importance of integrating provider dynamics and algorithmic biases into the design of recommender systems and platforms. This highlights avenues for future research to explore and refine our understanding of these dynamics.