The combinatory role of online ratings and reviews in mobile app downloads: an empirical investigation of gaming and productivity apps from their initial app store launch (original) (raw)
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Mobile application software, or as it called in the era of smartphones “apps”, is a growing market, which has attracted the attention of both the entrepreneurs and academics. This relatively new product category is almost a decade old and is accessed mainly through the application distribution platforms that are called “app stores”. These platforms provide a sum of information about the app products that consist of internal information from the developers as well as information from users. This paper focuses on the latter, on the normative and informational characteristics of review comments, as they are filtered through positive and negative valence, to provide help to the customers on their process of purchase decision. For this purpose, a survey through an online questionnaire based on Dual Process Theory was conducted and its findings provide information for marketing related decisions about mobile app products.
THE RELEVANCE OF CUSTOMER REVIEWS IN THE MOBILE APPLICATION MARKETPLACES
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The focus of this paper is the design and player reception of a serious game called Missing released on Google Play with the intention of spreading awareness of trafficking and its impact on individuals and society. The aim of the paper is to investigate how the game has been received by its players, focusing on its trafficking theme, by analyzing player metrics and app store data available from the Google Play digital distribution system. The paper presents results focusing on three main knowledge contributions: the identification and characterization of the tension between the designer’s intention with a game’s mechanics and how they help to convey the message of the game, the identification of the complexity of finding relevant reviews relating to the serious theme of the game and the identification and characterization of the tension between the star rating and the content of the reviews. One of the conclusions is that even a negative review can mirror a positive result in terms...
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Smartphones have become popular in recent years; in turn, the number of application developers and publishers has grown rapidly. To understand users’ app preferences, many platforms such as Google Play provide different mechanism that allows users to rank apps. However, more detailed insights on user’s feelings, experiences, critiques, suggestions, or preferences are missing due to a lack of additional written comments. This research attempts to investigate the review analytics of Android games listed on Google Play using a proposed text analytic approach to extract all user reviews from game apps in Chinese. A total of 207,048 reviews of 4,268 free games from February to March 2013 are extracted and analyzed according to various metrics including game type and game attribute. The findings indicate there is high dependency between users’ gender and game type, males and females have differing opinions on game attributes. In particular, users of different game types prefer different g...
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Industry estimates suggests that smartphone gaming – playing video games on smartphone device, accessed via the device's app market – accounts for a growing segment of the entire video game play market. Yet, very little is known about the processes by which smartphone users search for and download these gaming apps. Exploratory data combining behavioral observation with post-behavior talk aloud sessions found that users tended to (1) evaluate only one game, (2) spend little time evaluating that game before downloading it, and (3) based this decision on familiarity or price considerations (with both implicitly based on rating). Privacy concerns were rarely mentioned, and classic motivations for video gameplay (such as challenge, competition, and socialization) were not represented. These data suggest that smartphone gaming might be a qualitatively different experience in terms of its uses and effects than other forms of gaming and mobile entertainment.
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Mobile apps are very popular. However, this is not true for every app, with some apps receiving millions of downloads, while other apps are mostly ignored. We investigate the popularity of apps in terms of downloads by focusing on two salient cues: (a) online recommendations (e.g., presence and valence of online reviews) and (b) visual characteristics of app icons (e.g., use of visual metaphors and anthropomorphism). Study 1 was a field study in which we content-analyzed 500 apps from the " transportation " subcategory of the Google Play Store. We found that the presence and valence of online reviews, as well as the presence of visual metaphors in app icons were positively related to the number of app downloads. Study 2 was an experiment in which we presented participants with different app icons containing different types of visual metaphors. We again found that app icons with visual metaphors led to more positive attitudes
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Critical acclaim is considered to be one of the main predictors of profitability of game products. Major game publishers face tremendous hurdles in order to fare well in different forums that review and rate their products. However, little evidence exists on the relationship between ratings and profitability beyond anecdotal assumptions. In this study we investigate the relationship between critical acclaim and commercial success in mobile free-to-play games via a mixed-method study. First we look at the correlation of reviews and profitability, and then present an exploratory qualitative inquiry, analyzing games with high Metascores and games with high grossing. The results reveal that the relationship between review ratings and profitability is even more problematic in mobile free-to-play games than in many other game categories. Games with high Metascores differ substantially from the top-grossing games, being closer to traditional single-player games than typical free-to-play games, with little emphasis on monetization mechanics.
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Research abounds on consumer price response in Western markets, but offers little guidance to managers marketing their products around the world. For instance, smartphone/tablet applications represent a global market for developers, who have yet to exploit their freedom in localizing marketing. This paper investigates how sales rank depends on app price, ratings, product complexity, product improvement and product labels for 1,800 apps across 60 countries. Beyond contrasting emerging and mature markets, we analyze how consumer response depends on cultural and economic factors. A hierarchical model distinguishes variation among apps in one country from country-level differences. We find that sales rank elasticities vary widely and systematically. The results support our hypotheses that price sensitivity is lower in countries with high uncertainty avoidance, while rating sensitivity is lower in countries with more income equality, higher individualism and higher masculinity. Product complexity hurts sales rank more in countries with high uncertainty avoidance. Finally, apps with age restriction labels score better in masculine countries with higher income and income inequality. Contrary to the common practice of global integration, we urge developers to adapt pricing and product characteristics. Economic and cultural communalities between countries allow 'local learning', as consumer sensitivities vary systematically in predictable patterns.
The Role of User Reviews in App Updates: A Preliminary Investigation on App Release Notes
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Release planning for mobile apps has recently become an area of active research. Prior research in this area concentrated on the analysis of release notes and on tracking user reviews to support app evolution with issue trackers. However, little is known about the impact of user reviews on the evolution of mobile apps. Our work explores the role of user reviews in app updates based on release notes. For this purpose, we collected user reviews and release notes of Spotify, the 'number one' app in the 'Music' category in Apple App Store, as the research data. Then, we manually removed non-informative parts of each release note, and manually determined the relevance of the app reviews with respect to the release notes. We did this by using Word2Vec calculation techniques based on the top 80 app release notes with the highest similarities. Our empirical results show that more than 60% of the matched reviews are actually irrelevant to the corresponding release notes. When zooming in at these relevant user reviews, we found that around half of them were posted before the new release and referred to requests, suggestions, and complaints. Whereas, the other half of the relevant user reviews were posted after updating the apps and concentrated more on bug reports and praise.
IRJET- Google Play Store Apps-Data Analysis and Ratings Prediction
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Google play store is engulfed with a few thousands of new applications regularly with a progressively huge number of designers working freely or on the other hand in a group to make them successful, with the enormous challenge from everywhere throughout the globe. Since most Play Store applications are free, the income model is very obscure and inaccessible regarding how the in-application buys, adverts and memberships add to the achievement of an application. In this way, an application's prosperity is normally dictated by the quantity of installation of the application and the client appraisals that it has gotten over its lifetime instead of the income is created. Application (App) ratings are feedback provided voluntarily by users and function important evaluation criteria for apps. However, these ratings can often be biased due to insufficient or missing votes. Additionally, significant differences are observed between numeric ratings and user reviews. This Study aims to predict the ratings of Google Play Store apps using machine learning Algorithms. I have tried to perform Data Analysis and prediction into the Google Play store application dataset that I have collected from Kaggle. Using Machine Learning Algorithms, I have tried to discover the relationships among various attributes present in my dataset such as which application is free or paid, about the user reviews, rating of the application.