There is extant research on theorization, conceptualization, determinants, and consequences of corporate social responsibility (CSR). However, what firms include in their CSR or sustainability reports are much less covered and are predominantly covered in case studies of individual firms. In this paper, we instead take a holistic view and simultaneously explore what firms around the globe currently disclose in these reports, more specifically we investigate if firms are shareholder or stakeholder focused. In this investigation, we check the alignment of the reports to the materiality framework of Sustainability Accounting Standards Board (SASB) which was developed having shareholders as the intended user. To estimate what firms disclose in CSR reports we used the unsupervised Bayesian machine learning approach latent Dirichlet allocation (LDA) developed by Blei et al. We conclude that firms target shareholders as the intended users of these reports, even in environments where stakeholder approach of management is argued to be more dominant. Methodologically, we contribute by demonstrating that topic modeling can enhance the objectivity in reviewing CSR-reports.

A substantial literature indicates that competition on price comparison websites is fierce, leading to lower prices for products sold. As such, we want to answer the key research question: Why do firms compete on price comparison websites? Based on theory, we suggest that participation in these marketplaces leads to increased productivity, i.e., output increases when holding constant the level of inputs used. This, in turn, leads to increased profits, motivating firms to enter price comparison websites despite fierce competition. To find out if theory holds, we empirically investigate how firm entry into a price comparison website affects firm productivity, profits, and wages. Empirically investigating the impact of PriceSpy market participation on productivity, profits, and wages is not easy since firms are free to select whether and when to enter or exit the PriceSpy marketplace, and we use a two-step procedure to address this problem. In the first step, we control for differences in observables between entering firms and potential control-group firms. Then, in a second step, we use a within-firm difference-in-difference estimator on the matched data to investigate how entry into the PriceSpy marketplace affects output while holding inputs constant. Our results indicate that for the full sample of firms, PriceSpy participation increases output by almost 12% when holding the level of inputs constant. Also, an investigation of who gains from the increased productivity shows that, for entering firms, operating profits increase by 9% and gross wages by 14% when studying the full sample of firms. That labor gains more from PriceSpy participation is even clearer when studying the impact on wholesale and retail firms separately. For those firms, wages increased by 16–17% after entry, while no statistically significant impact was found regarding operating profits.

Theories of intertemporal price discrimination imply that prices must be chosen using mixed strategies, with retailers changing their prices randomly over time. Otherwise, consumers will learn which retailer has the lowest price, and eventually, all customers will patronize the lowest price retailer, or all retailers will charge the same price. We test whether price dispersion is explained by intertemporal price discrimination strategies using a dataset of identical products sold through the PriceSpy price comparison website. Our results show that there are clusters of retailers with similar pricing within each cluster, but with different price levels between clusters even after controlling for retailer heterogeneity. Retailers also remain in the same price cluster over time, suggesting that consumers have ample opportunities to learn which retailers belong to which price cluster. Intertemporal price discrimination is thus unlikely to have caused the observed price dispersion.

Existing methods for detecting algorithmic pricing rely on high-frequency data with sub-daily timestamps, yet academic researchers and competition authorities typically have access only to daily price snapshots. This paper develops a heuristic that identifies likely algorithmic pricers in such low-resolution data by combining two standardised metrics: the Average Rate of Price Changes (ARPC), capturing the frequency of price adjustments, and a Market Presence Ratio (MPR), capturing sustained retailer engagement. Requiring both metrics to exceed a threshold reduces false positives from promotional sellers and transient market participants. We apply the heuristic to a dataset of 10,365 unique retailer-category observations from the PriceSpy price comparison platform, covering seven national markets and 16 product categories over an 18-month period (July 2021–December 2022). Our results show that algorithmic pricing is prevalent across markets, with algorithmic pricers present in all 16 categories in the United Kingdom and in all seven countries for six categories. Prevalence is notably higher in consumer electronics than in household appliances. As a lower-bound estimate, approximately 4.5% of retailers are classified as algorithmic pricers at our baseline threshold. The heuristic provides a practical screening tool for competition authorities investigating algorithmic pricing adoption using commercially available data.