Investigating the Impacts of AR, AI, and Website Optimization on Ecommerce Sales Growth

E-commerce has evolved into a vital element of modern life by giving customers a quick and easy way to buy products and services online. Businesses increasingly focus on building their online presence in order to remain competitive, which represents a huge change as a result of the growth of e-commerce. Utilizing artificial intelligence (AI), augmented reality (AR), and website optimization is one of the primary ways firms are aiming to improve their e-commerce operations at the moment. While AR can improve product recommendations and the visual component of online shopping by giving customers a more immersive experience, AI can be used to tailor the user experience and boost personalization. On the other side, website optimization can assist companies in enhancing the user experience and raising conversion rates. Businesses can make better choices about how to implement these variables into their operations by knowing how they affect e-commerce sales. This study used data from 190 global e-commerce sites to empirically examine the effects of using AI, AR, and website optimization on the increase of e-commerce sales. The study used a multiple regression analysis to look at how these factors and the rise of e-commerce relate to one another. The study's findings demonstrated that every element had a favorable and significant impact on the increase of e-commerce sales. This suggests that companies investing in artificial intelligence, augmented reality, and website optimization can anticipate a comparable rise in revenue. These results suggest that companies wishing to enhance their e-commerce operations should think about investing in AI, AR, and website optimization. They may improve client satisfaction this way, boost conversion rates, and eventually boost sales. &nbsp

The role of the distribution platform in price formation of paid apps

In this paper we study the role of the distribution platform as an important determinant of price of paid apps. We also examine how the distribution platform influences the price implications of important developers' app-level decisions. To these purposes, we construct a hierarchical model of price formation by using an ad-hoc panel dataset consisting of top paid apps from the two major app stores, namely Apple's App Store and Google Play. Our findings show that prices of paid apps strongly depend on the platform where the apps are marketed. Specifically, the App Store is associated with lower prices for paid apps than Google Play. We find evidence that this is because the impact of cross-store differences in developer competition prevails over the impact of cross-store differences in average consumer willingness to pay. We also find that the price premiums as a return to trialability are more likely to emerge in Google Play than in the App Store, and that developers are more likely to adopt a penetration price policy in Google Play, thus implying an influence of the distribution platform on the price implications of these app-level decisions. Finally, our evidence does not confirm the argument that a more marked price reduction for paid apps embedding ads or generating revenues from other interested third parties should be observed in Google Play

Market bundling strategies in the horizontal portal industry

The arrival of the Internet offers opportunities for both incremental efficiency gains and complete industry redefinition, presenting new value propositions and hence leading to the emergence of new businesses and industries. One particular case is that of the horizontal portal industry, such portals being consistently the most visited sites on the Web. Nevertheless, despite ongoing market concentration, overall profitability remains low. In this paper we contend that, although the industry has great potential for value creation, value appropriation in such information-based businesses remains problematic. The only way to achieve it is through cross-market bundling; that is, portals selling their products packaged with Internet access and proprietary content through system competition. We support our claims with theoretical argument and empirical evidence, analyzing the information distribution value chain in its entirety.Portals; information goods; Internet advertising; Internet service providers; content provider;

Unveiling the features of successful ebay sellers of smartphones: a data mining sales predictive model

JEL Classification guidelines (M310); (C380).EBay is one of the largest online retailing corporations worldwide, providing numerous ways for customer feedback on registered sellers. In accordance, with the advent of Web 2.0 and online shopping, an immensity of data is collected from manifold devices. This data is often unstructured, which inevitably asks for some form of further treatment that allows classification, discovery of patterns and trends or prediction of outcomes. That treatment implies the usage of increasingly complex and combined statistical tools as the size of datasets builds up. Nowadays, datasets may extend to several exabytes, which can be transformed into knowledge using adequate methods. The aim of the present study is to evaluate and analyse which and in what way seller and product attributes such as feedback ratings and price influence sales of smartphones on eBay using data mining framework and techniques. The methods used include SVM algorithms for modelling the sales of smartphones by eBay sellers combined with 10-fold cross-validation scheme which ensured model robustness and employment of metrics MAE, RAE and NMAE for the sake of gauging prediction accuracy followed by sensitivity analysis in order to assess the influence of individual features on sales. The methods were considered effective for both modelling evaluation and knowledge extraction reaching positive results although with some discrepancies between different prediction accuracy metrics. Lastly, it was discovered that the number of items in auction, average price and the variety of products available from a given seller were the most significant attributes, i.e., the largest contributors for sales.O EBay é uma das plataformas e retalho online de maior dimensão e abarca inúmeras oportunidades de extração de dados de feedback dos consumidores sobre vários vendedores. Em concordância, o advento da Web 2.0 e das compras online está fortemente associado à geração de dados em abundância e à possibilidade da sua respetiva recolha através de variados dispositivos e plataformas. Estes dados encontram-se, frequentemente, desestruturados o que inevitavelmente revela a necessidade da sua normalização e tratamento mais aprofundado de modo a possibilitar tarefas de classificação, descoberta de padrões e tendências ou de previsão. A complexidade dos métodos estatísticos aplicados para executar essas tarefas aumenta ao mesmo tempo que a dimensão das bases de dados. Atualmente, existem bases de dados que atingem vários exabytes e que se constituem como oportunidades para extração de conhecimento dado que métodos apropriados e particularizados sejam utilizados. Pretende-se, então, com o presente estudo quantificar e analisar quais e de que modo as características de vendedores e produtos influenciam as vendas de smartphones no eBay, recorrendo ao enquadramento conceptual e técnicas de mineração de dados. Os métodos utilizados incluem máquinas de vetores de suporte (SVMs) visando a modelação das vendas de smartphones por vendedores do eBay em combinação com validação cruzada 10-fold de modo a assegurar a robustez do modelo e com recurso às métricas de avaliação de desempenho erro absoluto médio (MAE), erro absoluto relativo (RAE) e erro absoluto médio normalizado (NMAE) para garantir a precisão do modelo preditivo. Seguidamente, é implementada a análise de sensibilidade para aferir a contribuição individual de cada atributo para as vendas. Os métodos são considerados eficazes tanto na avaliação do modelo como na extração de conhecimento visto que viabilizam resultados positivos ainda que sejam verificadas discrepâncias entre as estimativas para diferentes métricas de desempenho. Finalmente, foi possível descobrir que número de itens em leilão, o preço médio e a variedade de produtos disponibilizada por cada vendedor foram os atributos mais significantes, i.e., os que mais contribuíram para as vendas

The Economics of Internet Markets

The internet has facilitated the creation of new markets characterized by large scale, increased customization, rapid innovation and the collection and use of detailed consumer and market data. I describe these changes and some of the economic theory that has been useful for thinking about online advertising markets, retail and business-to-business e-commerce, internet job matching and financial exchanges, and other internet platforms. I also discuss the empirical evidence on competition and consumer behavior in internet markets and some directions for future research.internet, market, innovation, advertising, retail, e-commerce, financial exchanges

The Value of “Bespoke”: Demand Learning, Preference Learning, and Customer Behavior

“Bespoke,” or mass customization strategy, combines demand learning and preference learning. We develop an analytical framework to study the economic value of bespoke systems and investigate the interaction between demand learning and preference learning. We find that it is possible for demand learning and preference learning to be either complements or substitutes, depending on the customization cost and the demand uncertainty profile. They are generally complements when the personalization cost is low and the probability of having high demand is large. Contrary to usual belief, we show that higher demand uncertainty does not necessarily yield more complementarity benefits. Our numerical study shows that the complementarity benefit becomes weaker when customers are more strategic. Interestingly, the substitute loss can occur when the personalization cost is small and the probability of having high demand is large, when customers are strategic.postprin

Dark Sides of the Platform Economy: Market Power and Its Abuse by Platform Orchestrators

Platforms drive the 21st century with orchestrating gatekeepers dominating the digital economy. Simultaneously, IT policymakers are increasingly concerned with antitrust regulation in the digital realm, abuse of market power, and monopolization cases against the most prominent digital platforms. This paper contributes to this ongoing debate by presenting the Platform Power Abuse Taxonomy. We provide an overview of abusive behaviors, considering the market position of platform orchestrators and differentiating platform types and affected market participants. Adopting a cross-platform approach, we rigorously analyze orchestrators\u27 abuse vehicles and abuse tools to exploit their dominant position by incorporating findings from literature and real-world contexts

The Effects Of Recommendation Conflict On User’S Adoption Intention Toward Virtual Salespersons: A Principal-Agent Perspective

Virtual salesperson (VS) has been increasingly implemented on many Websites to provide online users with valuable shopping advice, because it has been proved to alleviate users’ cognitive overload and increase their decision quality. Thus, it has widely caught researchers’ attention to investigate what factors can increase user’s intention to adopt. However, there is little research examining the impact of another information resource on VS adoption intention when recommendation information conflict occurs. This study draws on principle-agent perspective to investigate whether online customer reviews have potential to arouse users’ concern about information asymmetry and the fear of VS opportunism. The research result should be of interest to academic researchers, developers of VSs, providers of VSs, and Webstores