Born-digital firms increasingly pursue virtual involvement, a digital market-entry strategy that prioritizes iterative data collection and algorithmic adaptation over physical expansion. We measure virtual involvement by the number of unique digital advertisements and define it as the intensity of a firm’s digital experimentation to collect user data, train algorithms, and enable continuous learning through a recursive feedback loop. Using a novel panel dataset covering 194 US and Chinese born-digital firms across 33 countries from 2016 to 2022, our empirical analysis finds that stringent host-country digital privacy regulation significantly reduces virtual involvement. This negative influence is weakened in larger digital markets, for firms from home countries with robust digital regulatory regimes, and when firms receive positive public sentiment. These results extend institutional theory and non-traditional entry mode research by introducing virtual involvement as a new dimension of international strategy under regulatory constraints. For policymakers and managers, our study offers actionable guidance: strict privacy rules protect user welfare, but supportive market conditions and regulatory experience are critical to sustaining data-driven innovation. Policymakers should balance user protection with market dynamism, while firms should leverage large markets and positive sentiment to maintain their virtual involvement.

We investigate whether FinTech can mitigate human biases in lending decisions using proprietary loan-level data from a Chinese auto equity lender. The lender first integrated big data credit scoring as an advisory tool to enhance its traditional lending model, subsequently transitioning to algorithmic decision-making with optional human override. Our findings reveal that cognitive biases decrease significantly when loan officers use algorithmic lending decisions, substantially reducing disparities in loan-to-value ratios between local and nonlocal borrowers without exacerbating default differentials. Notably, the discretionary adjustments made by loan officers remain modest. In contrast, advisory credit scores alone exhibit no discernible bias-reducing effects. Our study is among the first to demonstrate that automation and choice architecture – specifically, nudging via algorithmic defaults – is more effective than mere information provision in combating discrimination and promoting financial inclusion.

数字经济为内容市场注入庞大的流量，内容经常因其质量参差不齐引起极大争议。本文通过构建原创媒体与转载媒体竞争的理论框架，系统研究流量对内容质量的影响机制，探讨内容市场化量为质的规制思路。研究发现，相较于传统经济，数字经济下流量通过扩容效应、引流效应、覆盖效应这三类积极作用与挤占效应这一消极作用影响内容市场。当网络外部性较强时，引流效应占优，市场实现流量与质量的兼得，引流效应叠加扩容效应与覆盖效应也促使各方福利提升。进一步地，市场监管的加强能够改善网络外部性较弱的内容形式的质量。与自由转载相比，付费转载的治理方式能够提升原创媒体利润与社会总福利。本文为中国内容市场治理体系的完善提供了方向，对推动数字经济健康发展具有重要意义。

We consider the inference on individual and time specific treatment effects on the treated within the framework of panel data approach for programme evaluation. We formulate the target problem as constructing prediction intervals for high-dimensional linear regressions with weakly dependent data. Post-LASSO OLS is used for estimation, while dependent wild bootstrap and simple residual bootstrap are used for the construction of prediction intervals. The proposed prediction intervals are proved to have asymptotic validity as the number of pretreatment times goes to infinity. In the proof, we also establish the model selection consistency of LASSO for dependent data and under bootstrap measure, which may be of independent interest. Monte Carlo experiments illustrate that our method outperforms existing methods in finite samples under a wide variety of data generating processes except nonstationary data. Two empirical applications are also provided.