Amazon Appstore

Andrei Hagiu, Tat-how Teh, Julian Wright

A growing number of intermediaries (e.g. Amazon, Apple's Appstore, and Walmart) act as resellers on their own marketplaces. We build a model of dual marketplace and reseller intermediation to explore the implications of this practice, and the call to ban it, taking into account an intermediary's optimal choice of mode. Our analysis shows that an outright ban tends to benefit third-party sellers at the expense of consumer surplus or welfare, even after allowing for innovation by third-party sellers. Rather than an outright ban, we show that policies that limit the imitation of highly innovative third-party products and prevent steering of buyers to the intermediary's own products would lead to preferable outcomes.

Hong Chen, Kevin Lachaud, Wenqi Zhou

Bader Althafairi, Noura Alhoumaida, Muskan Saxena, Zahraa Almsri

Amazon is the first large company that sells goods and services over the internet it was founded by jeff bezos in 1994. Amazon started out as an online book store then it grows quickly to add new items such as DVD’s, video games, electronics, clothing and more to the extent that the company logo symbolizes means that they sell all products from A to Z. Amazon.com try their best to get customer loyalty and trust. They offer state shipping service and they have many retail stores in different countries. It also purchases customer data and information to achieve customer needs and wants. Amazon is one of the first in the world to sell online and has many competitors like: ebay, rakuten and flipkart. Therefore, amazon has own over 40 subsidiaries includes: zappos, shopbop, IMDb, Amazon Prime, appstore, and amazon drive.

N. Vargas, Daria Hamrah, Marc M Serra

Artificial intelligence (AI) is increasingly applied across all fields of medicine, including plastic and cosmetic surgery, where it has become essential. Numerous mobile applications (apps) are available on various app store platforms, allowing patients to explore options for facial aesthetic modifications. Evidence suggests that AI tools can enhance surgeon-patient communication, assist patients in decision-making and managing surgical expectations, improve patient care, and streamline administrative, marketing, and logistical aspects. For surgical residents, AI tools and mobile apps also facilitate access to educational content, potentially improving surgical skills through virtual surgery simulations. Data on relevant online apps were gathered from official virtual app stores, including the Apple App Store, Google Play Store, Amazon Appstore, and Windows Store. App features were analyzed for compatibility with iOS, Android, and web-based platforms. Information on apps and online tools was sourced from the official websites of leading societies in aesthetic, cosmetic facial, and general plastic surgery. This article provides an overview of available online tools, detailing their focus, customer reviews, download size, and availability on app stores. It includes mobile apps launched by leading aesthetic/cosmetic surgery societies, which are utilized by members for networking, event information, practice management, and patient care. Trends in AI tools used by cosmetic plastic surgeons are also examined. A summary of the most popular mobile apps, both with and without AI technology and non-immersive virtual reality—recommended by prominent American and international aesthetic/cosmetic surgery societies confirms the growing significance and utilization of these tools in the field.

Nek Dil Khan, Javed Ali Khan, D. Khan, Jianqiang Li, Mumrez Khan, Shah Fahad Khan

In todays digital landscape, end-user feedback plays a crucial role in the evolution of software applications, particularly in addressing issues that hinder user experience. While much research has focused on high-rated applications, low-rated applications often remain unexplored, despite their potential to reveal valuable insights. This study introduces a novel dataset curated from 64 low-rated applications sourced from the Amazon Software Appstore (ASA), containing 79,821 user reviews. The dataset is designed to capture the most frequent issues identified by users, which are critical for improving software quality. To further enhance the dataset utility, a subset of 6000 reviews was manually annotated to classify them into six district issue categories: user interface (UI) and user experience (UX), functionality and features, compatibility and device specificity, performance and stability, customer support and responsiveness, and security and privacy issues. This annotated dataset is a valuable resource for developing machine learning-based approaches aiming to automate the classification of user feedback into various issue types. Making both the annotated and raw datasets publicly available provides researchers and developers with a crucial tool to understand common issues in low-rated apps and inform software improvements. The comprehensive analysis and availability of this dataset lay the groundwork for data-derived solutions to improve software quality based on user feedback. Additionally, the dataset can provide opportunities for software vendors and researchers to explore various software evolution-related activities, including frequently missing features, sarcasm, and associated emotions, which will help better understand the reasons for comparatively low app ratings.