We study the value of commitment in a business environment that is both competitive and uncertain, in which two firms face stochastic demands and compete in positioning and repositioning. If the future demand tends to disperse or the demand uncertainty is sufficiently large, one firm chooses rigidity (i.e., commits not to change its positions), and the other chooses flexibility (i.e., to reposition freely). We find that a firm’s rigidity can benefit not only itself, but also its flexible rival. When uncertainty is larger, rigidity becomes more valuable relative to flexibility. These results arise because the asymmetric equilibrium generates two collective gains in addition to the usual individual gain (in terms of competitive advantages) accrued to the committing firm. A firm’s rigid repositioning can soften competition and generate a commitment value, and the other firm’s flexible repositioning generates an option value. Both values then spill over to competitors within the ecosystem. These results suggest that, when firms compete under uncertainty, commitment and options are valuable not only for the party that is making the choice, but also for all competing parties collectively. Commitment value and option value do not have to be mutually exclusive; they can coexist and even strengthen each other through unilateral commitment, which achieves the best of both strategies. This paper was accepted by Juanjuan Zhang, marketing.

We find that firms experiencing improvements in crowdsourced employer ratings significantly outperform firms with declines. The return effect is concentrated among reviews from current employees, stronger among early firm reviews, and also stronger when the employee works in the headquarters state. Decomposing employer ratings, we find the return effect is related to changing employee assessments of Career Opportunities and views of senior management. It is unrelated to work-life balance. Employer rating changes are associated with growth in sales and profitability and help forecast one-quarter-ahead earnings announcement surprises. The evidence is consistent with employee reviews revealing fundamental information about the firm.

The data-driven newsvendor problem with features has recently emerged as a significant area of research, driven by the proliferation of data across various sectors such as retail, supply chains, e-commerce, and healthcare. Given the sensitive nature of customer or organizational data often used in feature-based analysis, it is crucial to ensure individual privacy to uphold trust and confidence. Despite its importance, privacy preservation in the context of inventory planning remains unexplored. A key challenge is the nonsmoothness of the newsvendor loss function, which sets it apart from existing work on privacy-preserving algorithms in other settings. This paper introduces a novel approach to estimating a privacy-preserving optimal inventory policy within the f-differential privacy framework, an extension of the classical [Formula: see text]-differential privacy with several appealing properties. We develop a clipped noisy gradient descent algorithm based on convolution smoothing for optimal inventory estimation to simultaneously address three main challenges: (i) unknown demand distribution and nonsmooth loss function, (ii) provable privacy guarantees for individual-level data, and (iii) desirable statistical precision. We derive finite-sample high-probability bounds for optimal policy parameter estimation and regret analysis. By leveraging the structure of the newsvendor problem, we attain a faster excess population risk bound compared with that obtained from an indiscriminate application of existing results for general nonsmooth convex loss. Our bound aligns with that for strongly convex and smooth loss function. Our numerical experiments demonstrate that the proposed new method can achieve desirable privacy protection with a marginal increase in cost. This paper was accepted by J. George Shanthikumar, data science. Funding: This work was supported by the National Science Foundation [Grants DMS-2113409 and DMS 2401268 to W.-X. Zhou, and FRGMS-1952373 to L. Wang]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.01268 .