Pricing and content development for online media platforms regarding consumer homing choices

Wu, Chung‐li; Chiu, Yun-Yao

doi:10.1016/j.ejor.2022.05.030

Cited by 24 publications

(9 citation statements)

References 27 publications

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“…Hotelling-type models are commonly adopted to investigate platform competition (e.g., Adner et al [24]; Bakos and Halaburda [25]; Chatterjee and Zhou [26]; Chiang and Jhang-Li [27]; Jiang et al [28]) and consumer choices (e.g., Wu et al [29]). See also Athey et al [30]), Bernstein et al [31], Chakraborty et al [32]), Wu and Chamnisampan [33] and Wu and Chiu [34].…”

Section: Notesmentioning

confidence: 98%

Ad-Valorem Taxes, Prices and Content Diversification in the News Market

Pires

2023

Games

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In this paper, we look at two research questions. First, can lower ad-valorem taxes, on the selling of news and on the selling of advertising, conduce to lower prices in the media sector? Second, can lower ad-valorem taxes stimulate firms to increase the diversity of content that they offer? The purpose of this work is to give tax political guidelines to policy makers for the media sector. This is important for a sector that has seen the reduction in payment subscriptions by readers (due to competition from free news from the Internet), and reduction of advertisement revenues due to competition from media giants like Google and Facebook. With this purpose we build on the Hoteling product competition model, which is the workhorse model in media economics. We show that ad-valorem taxes on the selling of advertising are preferable to ad-valorem taxes on the selling of news because the former conduce to reduction in prices of newspaper. However, both ad-valorem taxes on the selling of news and on the selling of advertisement reduces media diversity, because they reduce revenues that media firms can use to invest in media content.

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Section: Notesmentioning

confidence: 98%

Ad-Valorem Taxes, Prices and Content Diversification in the News Market

Pires

2023

Games

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“…In the context of previous research, several studies have delved into the intricacies of consumer behavior on various online shopping platforms. Relevant research explored the factors influencing consumers' platform choice and found that convenience and trustworthiness were pivotal factors affecting platform preferences [1]. Building on this, another study investigated the role of social media in shaping consumer decisions on platforms, highlighting the impact of peer recommendations on purchasing behavior [2].…”

Section: Literature Reviewmentioning

confidence: 99%

Consumer Behavior on Online Shopping Platforms in China

Dai

2024

HBEM

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Amidst the wave of digitization, online shopping has emerged as a pivotal avenue for individuals to purchase goods and services. Diverging from the conventional brick-and-mortar stores, online shopping platforms offer a more convenient and diversified shopping experience. However, despite their widespread popularity among consumers, investigating the variations in consumer behavior and the determinants across these platforms remains a worthy endeavor. This study zooms in on online shopping platforms such as Taobao, TikTok, and Pinduoduo, aiming to scrutinize the behavioral patterns, influencing factors, and improvement requisites of consumers on these platforms. Through conducting surveys, this research offers empirical evidence to deeply comprehend the behavioral models and determinants on various online shopping platforms. Valuable conclusions pertaining to platform selection, shopping preferences, and enhancement needs are drawn. These insights will serve as guiding principles for enterprises to formulate more targeted marketing strategies, while also offering substantial recommendations for platform operators to enhance user experiences and bolster platform competitiveness.

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“…i) Powered by deep learning algorithms, Netflix's recommendation engine, a complex matrix of collaborative filtering, content-based filtering, and hybrid approaches, has increased engagement by 20% (Wu et al, 2023). This results in an approximate annual increase of one billion hours of viewing, directly influencing revenue and customer retention.…”

Section: Strategies Employedmentioning

confidence: 99%

Revolutionising Conventional Marketing with AI: Leveraging Machine Learning for Marketing

Ramnani

2024

IJSREM

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Amidst an age characterized by rapid technological progress, this research paper investigates the revolutionary capacity of artificial intelligence (AI) and machine learning (ML) to alter traditional marketing methodologies fundamentally. In order to address the drawbacks of conventional marketing methods, this article develops a theoretical framework that emphasizes the critical importance of machine learning algorithms, such as supervised, unsupervised, and reinforcement learning, and AI customer relationship management (ACRM). The research explores pragmatic approaches, including meta-analysis, case studies, and A/B testing, to demonstrate how artificial intelligence (AI) can generate concrete outcomes and confer a competitive advantage. The paper provides an overview of several emerging trends, including Explainable AI (XAI), Edge AI, Fog Computing, Neuromorphic Computing, and Generative AI. It highlights their potential impact on the success of businesses. By means of perceptive case studies involving notable companies such as Spotify, Sephora, and Netflix, the article illustrates how ML and AI strategies have been customized to improve customer retention and develop efficacious marketing approaches. Among the methodologies that are deliberated upon are hyper-targeting and AI-powered segmentation employing LSTMs and dynamic learning. Moreover, the study investigates novel methodologies and their benefits for enhancing user involvement, including the integration of chatbots, Conversational AI, Augmented Reality (AR), Virtual Reality (VR), and attribution models tailored for multi-touch marketing initiatives. By incorporating these sophisticated methodologies, organizations can not only adjust to the ever-changing marketing environment but also generate customized and influential consumer encounters. This paper functions as an all-encompassing manual for marketing practitioners, researchers, and enterprises aiming to exploit the complete capabilities of AI and ML in order to drive their marketing endeavors to an unparalleled level of achievement.

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Pricing and content development for online media platforms regarding consumer homing choices

Cited by 24 publications

References 27 publications

Ad-Valorem Taxes, Prices and Content Diversification in the News Market

Ad-Valorem Taxes, Prices and Content Diversification in the News Market

Consumer Behavior on Online Shopping Platforms in China

Revolutionising Conventional Marketing with AI: Leveraging Machine Learning for Marketing

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