Author: Svedic, Zorana
In six short years the mobile applications ("apps") market grew from only 500 apps to over a million available apps, which created a problem not only for customers who want to find the best apps but also for developers who want their apps downloaded. To support app discovery, app stores feature top–ranking charts that rank apps by number of downloads or by revenue generated, as well as customer feedback in the form of star ratings and written reviews. To better inform both customers and developers about how to locate or promote apps, this thesis extends the informational cascades model of popularity (ranks) by including elements of signaling theory, specifically customer–based signals (ratings and reviews)—hereby named the informational signals model. This longitudinal study uses Apple App Store daily data on the past sales ranks of iPhone Business apps, together with posted customer ratings and reviews, to provide market–based evidence of the effect of these signals on current sales ranks. Research findings confirm the importance of observable actions, as evident in the past ranks, for the current ranking of mobile apps. Moreover, the results also indicate that multiple aspects of observable feedback (customer ratings and reviews) must be considered when evaluating app quality, such as valence (average values or positive versus negative signals), volume (number of signals), and variance (overall agreement or disagreement on valence of signals). Additionally, to examine the effect of informational signals across the global mobile apps market, this study takes into account the moderating effect of country culture dimensions, such as individualism versus collectivism and low–context versus high–context communication styles. Overall, the findings based on 30 countries across the globe show that customers from different cultures vary in the way they share and receive informational signals. Hence the cross–cultural focus of this thesis contributes to the informational cascades model and the signaling theory.
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Thesis advisor: Cyr, Dianne
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