By contrast, Srivastava and co-authors develop an interactional language use model that overcomes these limitations and opens new avenues for theoretical development about the dynamics of organizational culture. They trace the enculturation trajectories of employees in a midsized technology firm based on analyses of 10.24 million internal emails. The language-based model of changing cultural fit (1) predicts individual attainment; (2) reveals distinct patterns of adaptation for employees who exit voluntarily, exit involuntarily, and remain employed; (3) demonstrates that rapid early cultural adaptation reduces the risk of involuntary, but not voluntary, exit; and (4) finds that a decline in cultural fit for individuals who had successfully enculturated portends voluntary departure.

The authors identify three types of transactions:(i) money transfers to others; (ii) short distance money self-transportation; and (iii) money storage for short to medium periods of time. The study takes advantage of a “natural experiment” – an unanticipated increase in transaction fees makes it is possible to examine how user behavior changes. The authors find that the willingness to pay to avoid walking with cash an extra mile is 2% of an average transaction. The same figure is 0.8% for storing money at home for an extra day. An important implication is that mobile money ameliorates significant amounts of crime-related risk related to handling cash.

In this platform, information is gradually delivered to consumers, which allows for decomposing the value of geolocation information in influencing consumer’s awareness, consideration, purchase intention and recommendation behavior. The results indicate that geolocation information makes a significant difference in early stages of the purchase process (opening rates), but not in the conditional likelihood of accepting or recommending a promotion. These results suggest that the value of geolocation is reflected primarily in increased consumer awareness, and not in changing consumer behavior in later stages of the conversion funnel. Further analysis reveals that the impact of design features of geolocated campaigns varies throughout the purchase funnel. For example, timing variables have an influence on opening rates, while value proposition attributes are the main drivers of acceptance rates. The paper also shows that the performance of geolocated campaigns is in-between social media (Facebook) and non-geolocated campaigns, suggesting that geolocation information can be useful in resolving the trade-off between reach and effectiveness.