Many young firms use strategic actions to attract partners who help them increase the size of their operations quickly. This article examines the use of strategic actions to attract partners and increase system size in the context of franchising. We build on research in entrepreneurship, marketing, organization theory, strategic management, and finance to develop specific hypotheses about the influences of franchisor pricing policy and strategic control decisions on system size. We test these hypotheses empirically, using panel data on a sample of 1,292 business format franchise systems from 152 industries that were established in the United States between 1979 and 1996 and followed from their inception forward in time. Our model accounts for the endogeneity of strategic decisions, controls for unobserved firm and industry factors, and accounts for selection effects due to system failure. The results show that franchisors that grow larger (1) lower royalty rates as the systems age, (2) have low up-front franchise fees that rise over time, (3) own a small proportion of outlets and lower that percentage over time, (4) keep franchisees' initial investment low, and (5) finance their franchisees.firm size, franchising, pricing, ownership, econometric model

To contain the COVID-19 pandemic, governments introduced strict Non-Pharmaceutical Interventions (NPI) that restricted movement, public gatherings, national and international travel, and shut down large parts of the economy. Yet, the impact of the enforcement and subsequent loosening of these policies on the spread of COVID-19 is not well understood. Accordingly, we measure the impact of NPIs on mitigating disease spread by exploiting the spatio-temporal variations in policy measures across the 16 states of Germany. While this quasi-experiment does not allow for causal identification, each policy’s effect on reducing disease spread provides meaningful insights. We adapt the Susceptible–Exposed–Infected–Recovered model for disease propagation to include data on daily confirmed cases, interstate movement, and social distancing. By combining the model with measures of policy contributions on mobility reduction, we forecast scenarios for relaxing various types of NPIs. Our model finds that in Germany policies that mandated contact restrictions (e.g., movement in public space limited to two persons or people co-living), closure of educational institutions (e.g., schools), and retail outlet closures are associated with the sharpest drops in movement within and across states. Contact restrictions appear to be most effective at lowering COVID-19 cases, while border closures appear to have only minimal effects at mitigating the spread of the disease, even though cross-border travel might have played a role in seeding the disease in the population. We believe that a deeper understanding of the policy effects on mitigating the spread of COVID-19 allows a more accurate forecast of disease spread when NPIs are partially loosened and gives policymakers better data for making informed decisions.

Brands allocate their social media advertising across multiple platforms such as Facebook, Twitter, Instagram, and YouTube. Because consumers use multiple social media, brand communications on one platform could generate engagement within the same platform (direct effects) and potentially impact engagement with the brand on the other platforms (spillover effects). Additionally, past engagement with a post on a platform could sustain into the future, thereby improving the longevity of posts (carryover effects). These effects could also vary across platforms. Drawing on recent advertising literature, the authors propose and test differential carryover, spillover, and direct effects within and across social media. The empirical analysis indicates that these effects exist and are significant, supporting the propositions presented. The analysis provides generalizable guidelines to social media marketers on the effectiveness of the various platforms at sustaining a post and at creating direct and spillover effects across other platforms. Finally, the study also exemplifies a resource allocation model for brands to use when allocating their efforts across the various social media platforms to maximize both consumer engagement and the firm’s return on social media investment.

Extant models posit that awareness declines immediately and gradually after the cessation of advertising, whereas anecdotal evidence from managers suggests awareness stays constant for a while and then decays rapidly. This pattern arises because consumers remember advertisements for a finite time before they forget. Hence, we extend advertising models by incorporating the memory for ads. We conceptualize the role of memory as "delayed forgetting of ads" and capture it using delay differential equations, which exhibit richer dynamics and expand the class of dynamic models used in marketing. Analytically, we derive the 90% duration of advertising effects under various scenarios. Empirically, we analyze awareness evolution in the absence of advertising for the Peugeot car brand. We not only find strong support for the proposed model, but also estimate the memorability of Peugeot ads to be about 3 weeks. Moreover, if we ignore consumer memory as in the extant models, we would overstate the forgetting rate by 39%. Finally, we discuss managerial implications and identify new avenues for further research.

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