Social Influence on Digital Content Contribution and Consumption: Theories, Empirical Analyses, and Practices (Management for Professionals) 9819967368, 9789819967360

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Management for Professionals

Xuejing Ma

Social Influence on Digital Content Contribution and Consumption Theories, Empirical Analyses, and Practices

Management for Professionals

The Springer series “Management for Professionals” comprises high-level business and management books for executives, MBA students, and practice-oriented business researchers. The topics cover all themes relevant to businesses and the business ecosystem. The authors are experienced business professionals and renowned professors who combine scientific backgrounds, best practices, and entrepreneurial vision to provide powerful insights into achieving business excellence. The Series is SCOPUS-indexed.

Xuejing Ma

Social Influence on Digital Content Contribution and Consumption Theories, Empirical Analyses, and Practices

Xuejing Ma Asia Europe Business School East China Normal University Shanghai, China

ISSN 2192-8096 ISSN 2192-810X (electronic) Management for Professionals ISBN 978-981-99-6736-0 ISBN 978-981-99-6737-7 (eBook) https://doi.org/10.1007/978-981-99-6737-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Paper in this product is recyclable.

Preface

Digital content has become an integral part of our lives, from online texts we read to online videos we watch. As the digital content ecosystem continues to evolve and grow, it is essential to understand the factors that drive content consumption and contribution and their impact on users, content creators, and the wider industry. This book aims to provide an overview of digital content contribution and consumption from a social perspective, covering topics such as social incentives for content contribution, social motivations for content consumption, and their dynamics. We discuss the issue from three aspects: theories, empirical analyses, and practices. Utilizing real data from the industry, we provide valuable empirical evidence to understand the phenomenon and mechanisms of digital content contribution and consumption. This book is written for a broad audience, including researchers, industry practitioners, and students interested in digital content and its impact on society. It provides a deep dive into the various social, psychological, and economic factors that influence digital content consumption and contribution behavior, offering insights and recommendations for improving platform design, governance, and content creation. I wrote this book with the hope of stimulating interest in this important and rapidly evolving field. By exploring the social influence in digital content consumption and contribution, hope we can create a more wonderful and sustainable digital content ecosystem that benefits all stakeholders involved. Shanghai, China

Xuejing Ma

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Acknowledgments

I would like to express our sincere gratitude to everyone who contributed to the creation of this book. First and foremost, I would like to thank Professor Qiaowei Shen and Professor Hongju Liu from Peking University. They provided valuable insights and feedback throughout the writing process. Their input helped shape the structure and content of the book and enriched our understanding of digital content contribution and consumption. I would like to express my gratitude to the company that kindly shared their data with us. Without their help, it would not have been possible to conduct the empirical analysis that yielded valuable insights and advanced our understanding of digital content consumption and contribution. In addition, I would like to thank the National Natural Science Foundation of China (No. 72202069) and the Shanghai Philosophy and Social Science Project (No. 2022ECL005) for supporting me in writing this book. Finally, I would like to express our appreciation to Springer Nature to bring this book to fruition. Thank you for providing us with this excellent opportunity to share our research. Shanghai, China

Xuejing Ma

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Contents

Part I

Digital Content, PWYW Pricing, and Social Influence

1

Introduction to Digital Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 The Emergence of Digital Content . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Social Media as a Channel for Digital Content Distribution . . . . . 1.3 Digital Content in a Social World . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Organization of Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3 3 7 11 11 14

2

Incentives for Digital Content Contribution . . . . . . . . . . . . . . . . . . . . . . 2.1 Monetary Incentives for Digital Content Contribution . . . . . . . . . 2.2 Non-monetary Incentives for Digital Content Contribution . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

15 16 23 36

3

Motives for Digital Content Consumption . . . . . . . . . . . . . . . . . . . . . . . 3.1 Pay-What-You-Want Pricing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Social Aspects of Digital Content Consumption . . . . . . . . . . . . . . . 3.3 Motives for Digital Content Consumption for a Social Aspect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

41 42 52

4

Digital Content Contribution and Consumption in Live Streaming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Live Streaming Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Stakeholders in Live Streaming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Related Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

54 55 61 62 64 66 68 71 82 83

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Contents

Part II

Social Influence in Digital Content Contribution

5

Social Incentives and Digital Content Contribution . . . . . . . . . . . . . . . 87 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.2 Theoretical Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 5.3 Hypotheses Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 5.4 Empirical Background and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 5.5 Analysis and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

6

Dynamics of Digital Content Contribution, Monetary Incentive, and Social Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Theoretical Background and Hypotheses . . . . . . . . . . . . . . . . . . . . . 6.3 Empirical Background and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Analysis and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

115 115 117 118 120 128 131

Part III Social Influence in Digital Content Consumption 7

Social Interaction and Digital Content Consumption . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Theoretical Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Hypotheses Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Empirical Background and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Analysis and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

135 135 136 138 139 140 148 149

8

Dynamics of Digital Content Consumption and Social Norm . . . . . . 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Empirical Background and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Data Patterns and Potential Explanations . . . . . . . . . . . . . . . . . . . . 8.5 Analysis and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

151 151 153 156 158 164 172 179

Part IV Discussion and Conclusion 9

Conclusion Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1 Theoretical Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Empirical Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 Practical Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

185 185 186 188 189

Contents

10 Future Research Agenda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1 Recommendations for Research in Digital Content Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Recommendations for Research in Digital Content Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 Recommendations for Research in Live Streaming . . . . . . . . . . . . Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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191 191 192 193 196

Part I

Digital Content, PWYW Pricing, and Social Influence

Chapter 1

Introduction to Digital Content

In this Chapter, we aim to provide a thorough and comprehensive overview of digital content. To achieve this goal, we begin by introducing the concept of digital content and discussing its rising popularity in recent years. We also provide an overview of several types of digital content, including textual content, image content, audio content, video content, and games and interactive content, to give readers a broad understanding of the diverse forms that digital content can take. As digital content has become increasingly prevalent, social media platforms have emerged as one of the primary channels for content contribution and consumption. Therefore, we focus on providing detailed information about these platforms, including Facebook, Twitter, Instagram, YouTube, and TikTok. We delve into the unique features and tools offered by each platform and provide information on their user base to help readers understand the reach and impact of each platform. Finally, this Chapter also outlines the organization of the book, emphasizing our focus on exploring the role of social influence in digital content contribution and consumption. We discuss the importance of social influence in motivating individuals to produce and pay for digital content and highlight the various ways in which social influence can manifest in the digital content landscape. By providing this comprehensive overview, we hope to set the stage for a deeper exploration of these topics in subsequent chapters.

1.1 The Emergence of Digital Content With the internet’s continued growth, online platforms are seeing an increasing amount of digital content. Digital content refers to various types of information and media resources that are stored and transmitted in digital form. This includes but is not limited to textual content such as blog posts, news articles, e-books; images and photographs such as digital photos, illustrations, charts; audio content such as music, podcasts, radio programs; video content such as movies, TV shows, YouTube © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Ma, Social Influence on Digital Content Contribution and Consumption, Management for Professionals, https://doi.org/10.1007/978-981-99-6737-7_1

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videos; games and interactive media such as video games, virtual reality experiences, online interactive content. The emergence of digital content has transformed the way we create, consume, and share information and media. With the widespread availability of the internet, digital content has become more accessible than ever before. Today, individuals and organizations can easily produce and distribute various types of digital content, ranging from text-based articles and e-books to multimedia content such as videos and podcasts. The rise of digital content has also led to the emergence of new content platforms and business models. Content platforms such as YouTube, Instagram, and TikTok have provided individuals with powerful tools to create and distribute content to a global audience. Meanwhile, new business models have emerged that rely on digital content, such as subscription-based services like Netflix and Spotify, and e-commerce platforms like Amazon and Alibaba. The emergence of digital content has also had a significant impact on industries such as publishing, journalism, and entertainment. Traditional media outlets have had to adapt to the changing landscape by embracing digital platforms and experimenting with new business models. At the same time, new players have emerged that are disrupting established industries, such as independent content creators who are able to build large audiences and generate significant revenue through platforms like Patreon and OnlyFans. Overall, the emergence of digital content has had a profound impact on the way we create, consume, and share information and media. As technology continues to evolve, it will be interesting to see how digital content continues to shape our world and the industries that rely on it. Below, we will introduce some common forms of digital content. Textual Content Textual content is a type of digital content that primarily consists of written language in various forms. Some common examples of textual content include: Articles: Articles are a form of written content that provide information on a particular topic. They can be found in many forms such as news articles, blog posts, magazine features, and academic papers. E-books: E-books are digital versions of books that can be read on electronic devices such as e-readers, tablets, and smartphones. They are a popular way to distribute written content, especially for educational and instructional purposes. Social media posts: Social media platforms such as Facebook, Twitter, and LinkedIn are often used to share written content, such as short updates, status messages, and captions. Emails: Emails are a common form of written communication used for professional and personal purposes. They often contain written messages, attachments, and links to other types of digital content. Online reviews: Online reviews are a type of textual content that provide feedback on products, services, and experiences. They are often found on e-commerce platforms, travel websites, and review sites such as Yelp and TripAdvisor.

1.1 The Emergence of Digital Content

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Textual content is an important and versatile type of digital content that can be used for a wide range of purposes, including education, marketing, communication, and entertainment. Image Content Image content refers to digital content composed of visual elements, such as photographs, illustrations, graphics, and infographics. These visual elements can be used on their own or in combination with other types of digital content, such as textual content, to create a multimedia experience. Here are some common examples of image content: Photographs: Digital photographs are a popular type of image content that can be used for a wide range of purposes, such as news reporting, advertising, and personal expression. Illustrations and graphics: Illustrations and graphics, including cartoons, drawings, and charts, are often used in digital content to convey complex information in a visually appealing way. Infographics: Infographics are a type of image content that combines illustrations and graphics with text to present complex information in an easy-to-understand format. Memes: Memes are humorous images or videos that are often used to convey social commentary or cultural references. Social media posts: Image content is often used on social media platforms, such as Instagram and Pinterest, to share visual content with followers. Image content plays a crucial role in digital media, as it can be used to convey information, evoke emotions, and entertain audiences. With the increasing importance of visual communication in digital media, the use of high-quality image content is becoming more essential for businesses and content creators to effectively engage with their audiences. Audio Content Audio content is a type of digital content that primarily consists of sound recordings, such as music, podcasts, radio shows, and audiobooks. Audio content is becoming increasingly popular, with the growth of streaming services and the increasing prevalence of smart speakers and voice assistants. Here are some common examples of audio content: Music: Music is a popular form of audio content that can be streamed or downloaded from various platforms, such as Spotify, Apple Music, and Amazon Music. It is also used in other types of digital content, such as videos and podcasts. Podcasts: Podcasts are a type of audio content that allows individuals and organizations to create and share audio recordings on a wide range of topics, such as news, business, sports, and entertainment. Podcasts can be downloaded or streamed from various platforms, such as Apple Podcasts, Spotify, and Google Podcasts. Radio shows: Radio shows are a form of audio content that is broadcasted over the airwaves or online. They can be live or pre-recorded and cover a wide range of topics, such as news, music, talk shows, and sports.

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Audiobooks: Audiobooks are a type of audio content that allows individuals to listen to a book being read aloud. Audiobooks can be downloaded or streamed from various platforms, such as Audible and Google Play Books. Sound effects and samples: Sound effects and samples are used in various types of digital content, such as videos, podcasts, and music recordings, to add a unique audio element to the content. Audio content provides a unique and engaging way for individuals and organizations to connect with their audience. With the growing popularity of audio content, content creators are increasingly using audio as a way to engage with their audience and build their brand. Video Content Video content is a type of digital content that primarily consists of visual images and audio recordings. It is becoming increasingly popular as a means of communication, entertainment, and education. Here are some common examples of video content: Movies and TV shows: Movies and TV shows are popular forms of video content that can be streamed or downloaded from various platforms, such as Netflix, Hulu, and Amazon Prime Video. Videos: Videos are a long-form of digital content that typically range from a few minutes to several hours in length. They can cover a wide range of topics, including movies, TV shows, documentaries, educational content, product demos, and marketing videos, among others. Videos can be played on various platforms, such as YouTube, Facebook, Instagram, and Netflix, among others. Short videos: Short videos are a short-form of digital content that typically range from a few seconds to a few minutes in length. Short video platforms such as TikTok, Instagram Reels, Snapchat, and YouTube Shorts provide a short and snappy video creation and sharing platform. Short videos are often presented in a light-hearted, entertaining way to appeal to younger audiences and the fast-paced social media environment. Live streaming: Live streaming is a real-time form of digital content that allows content creators to transmit video and audio signals to an audience in real-time over the internet. Live streaming can be done on various platforms, including Facebook, YouTube, Instagram, and Twitch, among others. Live streaming can be used for many purposes, such as live news reporting, music performances, gaming competitions, education, and corporate events, among others. The advantages of live streaming are that it provides real-time interaction and immediate feedback, and builds a closer connection with the audience. Advertisements and promotional videos: Advertisements and promotional videos are used by businesses to promote their products or services. They can be found on various platforms, such as social media, websites, and streaming services. Video content provides a visual and engaging way for individuals and organizations to connect with their audience. With the growing popularity of video content, content creators are increasingly using video as a way to engage with their audience and build their brand. Video content creation requires skill, creativity, and resources, but it can be a powerful tool for storytelling and communication.

1.2 Social Media as a Channel for Digital Content Distribution

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Games and Interactive Content Games and interactive content are a type of digital content that allows users to actively engage with the content through various interactive elements. Here are some common examples of games and interactive content: Video games: Video games are a popular form of interactive content that allows users to control characters and actions within a virtual world. They can be played on various platforms, such as consoles, computers, and mobile devices. Interactive websites: Interactive websites use various interactive elements, such as animations, quizzes, and games, to engage with users and provide a unique browsing experience. Virtual and augmented reality: Virtual and augmented reality technologies allow users to interact with digital content in a more immersive way, such as through virtual simulations or overlaying digital elements onto the physical world. Interactive ads: Interactive ads use various interactive elements, such as quizzes, games, and polls, to engage with users and provide a more personalized advertising experience. Interactive videos: Interactive videos allow users to interact with the content by providing choices or allowing users to control the direction of the video. Games and interactive content provide a unique and engaging way for individuals and organizations to connect with their audience. They require a high level of creativity and technical skill to create, but they can be a powerful tool for storytelling, education, and entertainment. As technology continues to advance, the possibilities for games and interactive content will continue to expand, providing new and exciting ways for users to engage with digital content.

1.2 Social Media as a Channel for Digital Content Distribution Social media platforms such as Facebook, Twitter, Instagram, and LinkedIn have become powerful channels for the distribution of digital content. With billions of users worldwide, these platforms provide content creators and publishers with the ability to reach a massive and diverse audience. One of the key advantages of social media as a content distribution channel is its ability to facilitate virality (Berger & Milkman, 2012). Social media content has the potential to reach a large audience quickly and can spread rapidly through likes, shares, and comments. This can result in a significant increase in exposure and engagement for content creators, leading to increased visibility, brand awareness, and revenue. Social media platforms also provide content creators with a range of tools and features to optimize their content for distribution. For example, algorithms that prioritize content based on engagement and relevance can help content creators reach their

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target audience more effectively. Hashtags and keywords can also be used to increase the visibility of content and help users discover it more easily. Another advantage of social media as a content distribution channel is its ability to facilitate two-way communication between content creators and their audience. Social media platforms provide opportunities for content creators to engage with their audience through comments, direct messages, and live video sessions, among other features. This can help content creators build stronger relationships with their audience, receive feedback, and improve their content over time. Overall, social media platforms have become integral channels for the distribution of digital content. With their massive user bases and powerful distribution and engagement features, social media platforms provide content creators and publishers with the ability to reach and engage with their target audience on a global scale. Below, we will introduce some leading social media platforms for digital content distribution around the world. Facebook Facebook (https://www.facebook.com/) is a social media platform that was founded in 2004 by Mark Zuckerberg. It is one of the most popular and widely used social media platforms in the world, with over 2.99 billion monthly active users as of July 2023, 68.2% of them are daily users. Every day, the platform receives 2.04 billion unique visits from users.1 Facebook allows users to create a personal profile, connect with friends and family, join groups based on shared interests, and follow pages of organizations and public figures. Users can share text posts, photos, videos, and links on their profile and in groups and pages they are a part of. They can also react to posts with emojis and leave comments. Facebook offers various features and tools for users, such as Facebook Messenger for private messaging, Facebook Live for live streaming, and Facebook Marketplace for buying and selling goods and services. It also allows businesses to create pages and advertise to target audiences based on demographics and interests. As a social media, Facebook provides a platform for social connection, information sharing, and community building for individuals and businesses around the world. Twitter Twitter (https://www.twitter.com/) is a social media platform founded in 2006 that allows users to post short messages, called tweets, of up to 280 characters. It is a fastpaced platform that is used for real-time communication, news sharing, and social commentary. By July 2023, Twitter has 353.90 million users on the platform, with 330 million monthly active users.2 Users can follow other users and see their tweets in a chronological timeline. They can also like, retweet, and reply to tweets, as well as send direct messages to other users. 1 2

https://www.demandsage.com/facebook-statistics/. https://www.bankmycell.com/blog/how-many-users-does-twitter-have.

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Twitter is widely used by individuals, organizations, and businesses for various purposes, such as networking, marketing, and customer service. It is also used by journalists, politicians, and public figures to share their thoughts, opinions, and news updates. Twitter has introduced various features and tools over the years, including Twitter Moments, which curates tweets around a particular topic or event, and Twitter Spaces, a live audio chat feature. Although Facebook and Twitter are both social media platforms, they have distinct differences. Facebook is a more comprehensive platform, offering a wider range of features, such as groups, events, and marketplace, in addition to personal profiles and newsfeeds. It is a popular platform for personal and social networking, as well as for businesses to advertise and engage with customers. In contrast, Twitter is a more focused platform that is based on short-form text messages, or tweets, that are limited to 280 characters. Twitter is a dynamic platform that is frequently utilized for realtime communication, sharing news updates, and engaging in social commentary. The fast-paced nature of the platform allows users to stay informed and engaged with current events and trends. Users often use Twitter to express their thoughts and opinions on various topics, as well as to connect with other users who share similar interests. The platform’s trending hashtags and viral content make it a popular destination for users to discover and participate in ongoing conversations and debates. Instagram Instagram (https://www.instagram.com/) is a social media platform that was launched in 2010 and is now owned by Facebook. It is a visually-focused platform that allows users to share photos and videos with their followers. The platform is primarily used on mobile devices, but it can also be accessed through a desktop website. Instagram has over 2.35 billion users in 2023. On a daily basis, the typical Instagram user spends 24 min on the platform.3 Instagram is an application for sharing photos and videos. Users can upload their own photos or videos to the platform and share them with either a chosen circle of friends or their followers. Additionally, they can browse through and interact with posts uploaded by their friends, including commenting and liking. Instagram provides various features and tools for users, such as filters and editing tools to enhance their photos and videos, and the ability to tag other users and add hashtags to their posts to increase visibility. Instagram also offers features such as Instagram Reels for short-form video content, IGTV for longer videos, and Instagram Shopping for businesses to sell their products directly on the platform. As a popular social media platform that provides a visually-focused space for individuals and businesses, Instagram makes users connect with their followers and share their experiences and products. YouTube YouTube (https://www.youtube.com/) is a video-sharing platform founded in 2005 3

https://www.demandsage.com/instagram-statistics/.

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that allows users to upload, share, and view videos. It is one of the most popular and widely used video-sharing platforms in the world, with over 2.68 billion monthly active users as of 2023, 720,000 h of video are uploaded to YouTube every day.4 Users can create a personal channel and upload videos on various topics, such as music, entertainment, education, and vlogging. Users can also search for and watch videos posted by other users, as well as like, comment, and share videos. YouTube offers various features and tools for users, such as YouTube Live for live streaming, YouTube Shorts for short-form video content, and YouTube Premium for ad-free and offline viewing. It also allows businesses to create channels and advertise to target audiences based on demographics and interests. YouTube is widely used by individuals, businesses, and organizations for various purposes, such as entertainment, education, marketing, and news reporting. It is also used by content creators, such as YouTubers, to build a following and monetize their content. In 2022, the highest-earning Youtuber Mr. Beast earned $54 million. And in the first quarter of 2023, advertising on YouTube resulted in a revenue of $6.693 billion.5 TikTok TikTok (https://www.tiktok.com/) is a social media platform that was launched in 2016 and has rapidly gained popularity worldwide, especially among younger generations. It initially launched in China back when it was known as Douyin. The primary surge occurred in 2017, following Bytedance’s decision to expand the app beyond the Chinese market and serve other nations. As of 2023, TikTok’s user base has surpassed 1.677 billion, with 1.06 billion of those users being active on a monthly basis.6 The platform allows users to create and share short-form videos that are typically between 15 and 60 s long. TikTok’s content is often creative, humorous, and engaging, and ranges from lip-syncing to music to dance challenges, comedy skits, and cooking tutorials. Users can add music, text, and special effects to their videos, making them more visually appealing and entertaining. TikTok uses a recommendation algorithm that suggests content to users based on their viewing history and behavior on the platform. According to TikTok: “The system recommends content by ranking videos based on a combination of factors — starting from interests you express as a new user, and adjusting for things you indicate you’re not interested in.” The factors include: user activity, such as the videos you engage with through likes, shares, comments, and the content you produce as well as the accounts you follow; video information, which might consist of details such as captions, sounds, and hashtags; and, device and account settings, like your language preference, country setting, and device type.7 This algorithm has been

4

https://www.demandsage.com/youtube-stats/. https://www.demandsage.com/youtube-stats/. 6 https://www.demandsage.com/tiktok-user-statistics/. 7 https://www.bankmycell.com/blog/how-many-users-does-twitter-have. 5

1.4 Organization of Book

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highly effective in keeping users engaged, as well as in promoting viral trends and challenges.

1.3 Digital Content in a Social World As the world becomes increasingly connected through social media and digital platforms, the creation and sharing of digital content has become a ubiquitous part of our daily lives. From sharing photos and videos on social media to producing podcasts and live streams, digital content creation has become a powerful tool for self-expression, creativity, and community building. In this social world, the value of digital content lies not only in its intrinsic quality but also in its ability to connect people and foster social interaction. As such, social incentives have become a critical component in motivating individuals to produce and share digital content. These incentives can take a variety of forms, from monetary rewards and recognition to social validation and feedback. The impact of social incentives on digital content creation and sharing can be seen across a range of industries, from entertainment and media to education and marketing. In the entertainment industry, for example, social incentives such as live chat and virtual gifting have become integral to the success of live streaming platforms like Twitch and YouTube. In education, social incentives such as badges and leaderboards have been shown to increase student engagement and motivation in online learning environments. At the same time, the rise of social incentives has also raised important questions about the ethics of digital content creation and the potential risks associated with incentivizing certain types of content. As digital content continues to play an increasingly central role in our lives, it is essential that we continue to explore the complex interplay between social incentives and digital content creation, and work to develop ethical and effective incentive mechanisms that promote the creation of high-quality, socially valuable content.

1.4 Organization of Book Many content platforms rely on users to both consume and generate content, for example, reading Tweets on Twitter, watching videos on YouTube, and participating in live sessions on Twitch. Consequently, it is critical for these platforms to develop effective content monetization strategies to increase revenue from the consumption side and design incentive mechanisms to encourage higher quality content from the supply side. These strategies and mechanisms should be based on a clear understanding of both the motivations for content consumption and the incentives for content creation.

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In this book, our research encompasses both theoretical and empirical investigations into the topics of incentive mechanisms and social factors in digital content contribution and consumption. In terms of theoretical research, we begin by examining literature related to both monetary and non-monetary incentives for digital content provision in Chap. 2. This discussion provides a comprehensive overview of the different incentive mechanisms that have been used in the digital content industry to motivate content creators to produce high-quality content. In Chap. 3, we delve into the motives for digital content consumption. We introduce the concept of pay-what-you-want (PWYW) pricing, which is a prevalent method used by online platforms to generate revenue from digital content. We then discuss the motivations for digital content consumption from a social perspective, exploring the factors that drive individuals to seek out and engage with digital content. We separate these factors into four categories: social presence, social influence, social comparison, and social loafing. By examining these factors, we aim to provide a theoretical foundation for studying the impact of incentive mechanisms and social factors on digital content contribution and consumption. In Chap. 4, we focus on digital content contribution and consumption in the context of live streaming. Live streaming provides an ideal setting for us to study the incentives for digital content contribution and motivations for consumption. In this Chapter, we present an overview of the live streaming industry, review the relevant literature, and conduct a series of exploratory analyses. This Chapter provides a fundamental for the following empirical research in Part II and Part III. Part II and Part III explore the role of social influence from the perspectives of digital content contribution and consumption. Our research utilizes data from the live streaming industry, which is a novel form of streamed media that operates as a two-sided market, bringing together broadcasters and viewers. Broadcasters are the content providers on the platform and can perform a wide range of activities, from singing, dancing, and drawing to simply eating a meal, drinking alcohol, and playing video games, all in real time for online viewers. Viewers can participate in live sessions not only by watching but also by engaging in real-time interaction with broadcasters, such as sending “likes,” text messages, and virtual gifts during the live sessions. This unique dynamic of live streaming, with its immediate feedback and interactive environment, provides an ideal setting for studying the social influence on digital content contribution and consumption. Part II comprises two empirical studies centered around social influence in digital content contribution. The primary focus of Chap. 5 is on social incentives and their impact on digital content contribution. We empirically examine how social incentives like gift-giving and social interaction impact broadcasters’ behavior on live streaming platforms? Are there any differences in the incentive effects of gift-receiving and social interaction among broadcasters with different levels of experience? How can live streaming platforms leverage social incentives to encourage more digital content contributions from their users? The findings suggest that gift-receiving and social interaction both have a positive effect on broadcasters’ short-term activation and long-term retention. Specifically, broadcasters who receive more gifts and social

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interactions are less likely to leave the platform, and these effects become increasingly effective as broadcasters gain more experience on the platform. The results also show that both monetary (i.e., gift-receiving) and non-monetary (i.e., social interaction) incentives can motivate broadcasters to provide live content frequently. Overall, the study provides evidence that social incentives can enhance digital content contribution on online platforms. Chapter 6 discusses the dynamics of digital content contribution, monetary incentives, and social interaction. We focus on answering the question–can working harder leads to more monetary gaining or increased social popularity in the context of online content contribution? And how can digital content contribution, monetary earnings, and social earnings evolve over time? We find that in the context of online content contribution, working harder may not necessarily lead to higher monetary earnings, but it can attract more social value in the form of chat messages from viewers. Experienced content contributors tend to receive higher ratings and earnings. This Chapter challenges the assumption that traditional labor supply models can be applied to the context of online content contribution. It highlights the importance of social interaction and content quality in driving monetary gains for high-earning broadcasters. Part III consists of two empirical projects that focus on the impact of social influence on digital content consumption. Chapter 7 centers around the social motives behind digital content consumption. We empirically examine the effect of social interaction on gift-giving in live streaming, which is addressed using the Uses and Gratifications Theory (UGT) to formulate hypotheses. We also consider how the positive effect of viewer interaction on gift-receiving changes as broadcasters’ experience increases. One key finding is that social interaction has a positive effect on gift-giving in live streaming, which is supported by the empirical analysis of data from both novice and experienced users. The analysis also reveals that the positive effect of viewer interaction on gift-receiving decreases as broadcasters’ experience increases. Additionally, we discuss the implications of these findings for content creators and marketers in the digital space, highlighting the importance of social motivations and user engagement in driving digital content consumption. Chapter 8 delves into the dynamics of digital content consumption and examines the role of social norms in shaping consumer behavior. We explore the following questions: what are the dynamics of PWYW payment for digital content consumption? What are the underly mechanisms? And how do social norms influence the way consumers perceive and value PWYW pricing? We find a declining pattern in PWYW amount with the increase in individual tenure. We then propose and test several potential explanations, including variety seeking, alternative ways to interact, and substitution effect. The empirical results are consistent with the substitution effect that the cumulative spending in the past could crowd out the current PWYW amount, because viewers may believe that they have already contributed a significant amount. We further investigate how social norms can mitigate the decreasing pattern of digital content consumption amount over time. Presenting high-level payment amounts from others can drive one to pay more for digital content consumption. Our finding would help online content platforms understand the dynamics of individual

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PWYW behavior and design mechanisms to incentivize long-life customers to pay for digital content. By combining theoretical and empirical research, we aim to provide a comprehensive understanding of the role of incentives and social factors in digital content contribution and consumption. This understanding can be applied to a variety of industries and platforms, helping to inform the development of effective incentive mechanisms and social strategies for promoting high-quality and socially valuable digital content. Chapters 9 and 10 provide a comprehensive conclusion to the book, summarizing the key findings and insights gained from the theoretical and empirical research conducted throughout the preceding chapters. These concluding chapters also explore the theoretical contributions and practical implications of the research, discussing how the findings can inform future studies and the development of effective strategies for online platforms and content creators. Moreover, Chap. 10 outlines a research agenda for future studies on the topic of social influence in digital content contribution and consumption, highlighting potential avenues for further exploration and investigation in this dynamic and rapidly evolving field.

Reference Berger, J., & Milkman, K. L. (2012). What makes online content viral? Journal of Marketing Research, 49(2), 192–205. https://doi.org/10.1509/jmr.10.0353

Chapter 2

Incentives for Digital Content Contribution

In this chapter, we explore the topic of incentives for digital content contribution, focusing on the impact of both monetary and non-monetary incentives in motivating individuals to create and share digital content. Specifically, we examine the provision of four types of digital content, namely reviews, crowdsourcing, creative production, and social media, and identify the motivators that can be employed to incentivize the creation of each type of content. Our analysis reveals that while monetary incentives can be an effective motivator for some types of digital content, non-monetary incentives, such as social recognition and intrinsic motivation, may be more effective for other types. Additionally, we highlight the importance of aligning the type of incentive with the specific characteristics of the digital content being produced, as well as the motivations and preferences of the creators. By providing insights into the incentives that can drive digital content creation, this research contributes to a better understanding of the factors that drive online user participation, and can be valuable for platform managers seeking to encourage content contribution on their platforms. This Chapter provides a comprehensive review of literature related to incentives for digital content contribution, with a specific focus on the impact of monetary and non-monetary incentives. Firstly, we review the existing literature on the use of monetary incentives for digital content contribution, and highlight the potential drawbacks of using pecuniary rewards in certain circumstances. While it is generally believed that monetary rewards can motivate users to contribute content, our analysis shows that under certain conditions, they may diminish the intrinsic motivation that drives content provision, ultimately inhibiting users’ willingness to share their content. Secondly, we explore the role of non-monetary incentives, such as social ties, peer recognition, and self-presentation, in driving digital content contribution. Our literature review and analysis demonstrate that non-monetary incentives can be equally, if not more, effective in motivating users to contribute content, and may avoid the potential drawbacks associated with monetary incentives.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Ma, Social Influence on Digital Content Contribution and Consumption, Management for Professionals, https://doi.org/10.1007/978-981-99-6737-7_2

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The theoretical foundations laid out in this Chapter provide a basis for the empirical analyses presented in Part II of this book, which investigate the social incentives for digital content contribution, and the interplay between monetary incentives, social interaction, and content contribution dynamics.

2.1 Monetary Incentives for Digital Content Contribution A considerable body of literature suggests that offering monetary incentives can serve as a driving force for individuals to generate and disseminate digital content. Review Product reviews are assessments or evaluations of products, services, or businesses that are usually written and published on the Internet by consumers or users. These reviews can be found on various websites and platforms, such as e-commerce sites, social media, and review sites, and they can include written comments, ratings, or both (Li & Hitt, 2008). Online reviews can provide potential customers with valuable information about the quality, performance, and overall experience of a product or service, which can help them make more informed purchasing decisions (Woolley & Sharif, 2021). Online reviews play a significant role in influencing product sales. For example, products with a higher number of reviews are more likely to attract customers and drive sales (Chevalier & Mayzlin, 2006). 95% of customers read product reviews before making a purchase decision, and among them, 58% are willing to pay a premium for products with positive reviews.1 The rise of online reviews has transformed the way consumers make purchasing decisions, with numerous platforms now offering customers the ability to leave feedback on products and services. E-commerce giant, Amazon, is one of the most popular platforms for online reviews, where customers can leave feedback on products they have purchased. Yelp is a review website that focuses on local businesses, enabling users to leave reviews and ratings for restaurants, bars, and shops. TripAdvisor aggregates reviews and opinions from travelers on hotels, restaurants, and tourist attractions. Google Reviews is a feature of Google Maps that allows users to leave reviews and ratings for local businesses, while Facebook offers a similar service for businesses on their pages. These platforms provide customers with valuable feedback, which can help businesses improve their products or services and enhance their reputation. Providing monetary incentives is common for sellers and platforms to motivate users’ review writing. Li (2010) proposes it is a way to cover review posting costs, regardless of whether the content is positive or negative. Cabral and Li (2015) divide buyer behavior into two paradigms—homo economicus paradigm and homo reciprocus paradigm. The former implies that when feedback is incentivized, buyers are more inclined to provide it promptly, offer higher bids in expectation of a reward, and are more likely to leave feedback overall. While the latter refers to that 1

https://www.luisazhou.com/blog/online-review-statistics/.

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buyers are more likely to leave positive reviews when offered monetary incentives. They find evidence consistent with homo reciprocus behavior that when facing lowquality transaction, offering monetary compensation significantly decrease buyers’ likelihood of writing a negative review. Burtch et al. (2018) find that financial incentives, e.g., providing coupons, can motivate users to post larger volumes of reviews, but not lengthy reviews. The best strategy is to combine financial incentives and non-monetary incentives to induce more and longer reviews. However, monetary compensation is not always effective at inducing more reviews. Li and Xiao (2014) find that sellers’ rebate offers can only increase buyers’ feedback in good transactions but not in bad transactions. Sun et al. (2017) found that after introducing monetary rewards for posting reviews to an online review community, the less-connected members increased their contributions, whereas the moreconnected members decreased their contributions. Surprisingly, the total contributions unexpectedly decreased. Monetary rewards can have a negative impact on members who are motivated by a desire to maintain a prosocial image, as being paid for a review may diminish their reputation. This concern is becoming increasingly relevant for potential contributors in light of the Federal Trade Commission’s (FTC)2 heightened enforcement of its guidelines, which places greater public scrutiny on the “exchange” between monetary rewards and review contributions. Similarly, Khern-am-nuai et al. (2018) conduct a natural experiment to study the impact of monetary incentives on writing online reviews. Their findings suggest that the introduction of monetary incentives for reviews leads to a significant increase in positivity, but a decrease in overall quality. They also examin the impact of rewards on existing reviewers and found that while their level of participation decreased after monetary incentives were introduced, their quality of participation remained unchanged. Additionally, although the platform experienced an influx of new reviewers, they observed that a disproportionate amount of reviews were written for highly rated products. As for the valence of reviews, Woolley and Sharif (2021) found that financial incentives, both small guaranteed financial incentives and lottery-based incentives, can increase the relative positivity of review text. Specifically, the small financial incentive condition resulted in a 9.91% increase in the relative positivity of review text compared to the no incentive condition. The financial lottery incentive condition resulted in a 9.31% increase in the relative positivity of review text compared to the no incentive condition. The underlying mechanism is that incentives increase the enjoyment of review writing, which in turn mediates the effect of incentives on the relative positivity of review content. Their findings show that incentives are not only effective at increasing review volume, but also modify review content. Some people may believe that incentivized reviews are biased, while Qiao and Rui (2023)‘ research show that incentivized reviews may have higher quality. They 2

The FTC guideline states: “If there’s a connection between an endorser and the marketer of the product that consumers would not expect and it would affect how consumers evaluate the endorsement, that connection should be disclosed”.

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explore the question: does an incentive provision affect review text quality? Their research indicates that incentivized reviews are characterized by higher levels of coherence and detail. Unlike the findings in previous literature that incentivized reviews are usually shorter and use less complex words, their results show that, at least in terms of coherence and aspect richness, incentive reviews are higher in quality. Thus, they advocate for the inclusion of incentivized reviews on review platforms, provided that they are clearly labeled, as we believe they supplement organic reviews (Table 2.1). Crowdsourcing Crowdsourcing generally refers to the practice of openly soliciting the efforts of a community or “crowd” to complete a well-defined task and obtain a solution by a set deadline, typically through the Internet. This approach has gained popularity for a variety of tasks, including translation, programming, website design, and open innovation (Liu et al., 2014). This can involve soliciting ideas, feedback, or solutions to a problem from a broad and diverse audience, or outsourcing tasks to a distributed network of workers or volunteers. Crowdsourcing has become increasingly popular in recent years due to advances in technology and the proliferation of online platforms that facilitate collaboration and communication across large groups of people. Table 2.1 Monetary incentives for online review writing Research

Monetary incentives

Content contribution

Main findings

Burtch et al. (2018)

Coupons

Review volume and review length

Providing financial incentives is useful to motivate users contribute more reviews, but not lengthy reviews

Cabral and Li (2015)

Rebate

Negative review

Rebate help decrease the likelihood of negative feedback when the transaction quality is low

Khern-am-nuai et al. (2018)

Monetary rewards

Quantity and quality After introducing monetary rewards, of reviews reviews become more positive but the quality decreases

Li and Xiao (2014)

Rebate

Feedback reporting

Offering a rebate to buyers can lower the cost of providing feedback

Qiao and Rui (2023)

Monetary payment

Review quality

Better text quality serves to offset the reduced impartiality of incentivized reviews

Sun et al. (2017)

Monetary rewards

Product reviews posting

When facing monetary incentives, less-connected members increased their contributions, whereas the more-connected members decreased their contributions

Woolley and Sharif (2021)

Bonus & lottery

Positivity of review content

Financial incentives can increase the enjoyment of review writing, and then increase the positivity of review

2.1 Monetary Incentives for Digital Content Contribution

19

Here are some popular crowdsourcing platforms. Amazon Mechanical Turk is a popular micro-task platform that enables businesses to outsource small tasks to a large crowd of workers, while Kickstarter is a crowdfunding platform that allows entrepreneurs and creators to raise funds for their projects from a large number of supporters. Upwork is a freelancing platform that connects businesses with a global network of freelancers offering a range of services, while 99designs connects businesses with freelance graphic designers for their creative needs. InnoCentive and Topcoder are examples of platforms that enable businesses to engage with a global network of problem solvers, programmers, designers, and data scientists for idea generation, software development, and data analysis projects. Although crowdsourcing usually engages volunteers in online projects without monetary compensation (Alam & Campbell, 2017), a number of studies show that providing monetary incentives can help motivate users’ participation in crowdsourcing. Malone et al. (2010) argue that the promise of financial gaining can drive people’s participation in collective intelligence. On occasion, individuals receive immediate payment in the form of a salary for their work, while in other instances, they participate in an activity with the hope of earning future payments. This may involve performing a task to enhance their professional reputation or improve their skills, thereby increasing their chances of receiving future compensation. In a sequential all-pay auction, Liu et al. (2014) examine the effect of reward on the quantity and quality of users’ submissions. They obtain data from the crowdsourcing site Taskcn. On Taskcn, monetary rewards are provided by requesters. To initiate a request, the requester fills in an online form containing the task’s title, reward amount(s), submission deadline, and the number of submissions to be selected as winners. Upon reaching the deadline, the site notifies the requester to evaluate and choose the best solution(s) from all the submissions. The requester may also make a selection before the deadline, in which case users are notified of the solution being chosen and the task being closed. After the task is closed, the winner receives 80% of the reward, while the site keeps 20% as a transaction fee. Through a field experiment, they find that a higher reward can attract more participation and receive submissions with higher quality. Taking into account the presence of a reserve, which is a high-quality submission made early in the process, their study shows that it reduces the quality of subsequent submissions by discouraging experienced users from participating. Experienced users stand out from inexperienced ones by their preference for higher reward tasks over lower reward ones and their tendency to submit solutions later (Table 2.2). Creative Production Creative production refers to the process of generating original and innovative ideas and turning them into tangible works of art or content. This can include various forms of creativity such as music, film, literature, visual arts, design, and other forms of media. Advances in digital technology have simplified the process of digital publication and consumption of creative works (Li et al., 2021a, 2021b). Li et al. (2021a, 2021b) explore the impact of pecuniary incentives on book writers’ creative efforts and customer care efforts on online literature publishing

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Table 2.2 Monetary incentives for crowdsourcing participation Research

Monetary incentives

Content contribution

Main findings

Liu et al. (2014)

Rewards from requesters

Submission quantity and quality

A higher reward leads to more submissions and higher quality submission

Malone et al. (2010)

Promise of financial gaining

Collective innovation participation

Money can inspire the crowd to move more quickly

platforms. The creative effort comprises two dimensions—quantity and quality. The former is measured by the length of new chapters uploaded, and the latter is reflected in the average sentiment score of readers’ comments. The customer care effort refers to writers’ replies to readers’ comments to address their concerns, answer their questions, and acknowledge their support. They find that monetary incentives, including book sales and tips from readers, positively affect writers’ creative efforts in book writing, but not customer care efforts. Also in the context of online publishing, Li et al. (2021a, 2021b) focus on the incentive plan. Specifically, they study a serial publishing platform’s switching from a uniform commission plan to a quantity-based incentive plan. They find that chapters of a given book that were published during periods when writers achieved higher quantity brackets (and therefore higher commission rates) exhibited higher quality, as evidenced by their higher chapter-to-chapter customer retention rates. They use quantity-quality complementarity to explain the effect. The effect of monetary incentives on digital content contribution has also been examined in the context of knowledge sharing communities. Lin and Huang (2013) compare two Q&A online forums—Google Answers (with monetary incentives) and Yahoo! Kimo Knowledge + (without monetary incentives)—and conclude that Google Answers failed with its monetary incentives, while Knowledge + succeeded with its virtual rewarding mechanism. This suggests that the effectiveness of monetary incentives may depend on the specific design of the incentive scheme and the nature of the online community. Hsieh et al. (2010) focus on the pay-for-answers services on Q&A platforms. Their focal platform is Mahalo, on which askers can choose to pay for answers. Mahalo Answers offers the option for askers to pay for answers, with a minimum payment of one Mahalo Dollar, which can be obtained by purchasing one US Dollar. Paid questions are distinguished from free ones on the site’s homepage, with paid questions displayed above the screen fold and free questions below it. Their results show that higher rewards can expect more answers and longer answers. However, there is no significant effect of rewards on the quality of answers. Similarly, Chen et al. (2010a, 2010b) run a field experiment at Google Answers. They study the effect of monetary incentives, including price and tip on answer providers’ effort and answer quality. Their findings show that a higher price can not lead to longer or better answers. As for tipping from askers, it has no significant effect

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21

on the answer’s effort or the answer’s quality. A possible explanation is that tips are more likely to be viewed as the kindness of askers rather than the compensation for answering the question. Garnefeld et al. (2012) provide evidence that monetary rewards have different effects on short- and long-term intentions to post answers of users with different levels of activity. They show that, in the short term, monetary incentives can be employed by online community managers to boost the participation of both active and passive community members. While the effect is stronger for passive (vs. active) users. However, the hidden costs associated with these rewards are likely to surface in the long run as the motivation to participate among previously active members diminishes once the incentives are no longer in effect. For passive users, the positive effect of monetary incentives doesn’t diminish since there is no long-term crowding-out effect experienced by them (Table 2.3). Social Media Social media, also known as user-generated media, refers to online sources of information that are created, shared, and consumed by individuals with the intention of Table 2.3 Monetary incentives for online creative production Content contribution

Main findings

Chen et al. (2010a, Price and tip 2010b)

Answers’ effort and answer length and quality

Higher prices can not lead to longer answers or better answers

Garnefeld et al. (2012)

One-time monetary reward

Short-term and long-term intention to post an answer

In the short term, monetary incentives positively affect both active and passive users to post answers. But in the long term, monetary incentives are only useful for passive users not active users

Hsieh et al. (2010)

Financial rewards

Quantity and quality of answer provision

Higher rewards can induce a higher number of answers and longer answers, but not higher quality answers

Li et al. (2021a, 2021b)

Reader Writer efforts purchasing and and customer tipping care efforts

Book sales and tips positively affect writers’ creative production efforts but not customer care efforts

Li et al. (2021a, 2021b)

Uniform Book writer’s commission production plan vs. quantity-based incentive plan

When offered a quantity-based incentive plan, the chapters that are published during periods when writers achieved higher quantity brackets have better quality

Lin and Huang (2013)

Monetary rewards

The effect of monetary rewards on knowledge contribution depends on the context

Research

Monetary incentives

Knowledge contribution

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educating each other on various topics such as products, brands, services, personalities, and issues (Dellarocas et al., 2010; Mangold & Faulds, 2009). Social media platforms that leverage user-generated content (UGC) have transformed the way people interact, communicate, and share information online. Social media platforms that rely on UGC, such as Facebook, Instagram, and Twitter, have become a primary means of communication and information dissemination for billions of people worldwide. These platforms provide opportunities for users to express themselves, connect with others, and engage with a wide range of content. Online digital content platforms and providers may have misalignment incentives. Social media websites, such as YouTube, Facebook, and Twitter, aim to leverage user-generated content to attract advertisers. As a result, these websites prioritize the market potential of contributed content, while contributors may place less emphasis on this factor. Offering advertising-revenue sharing with content contributors could resolve this problem (Amaldoss et al., 2021). Tang et al. (2012) examine the effect of advertising-revenue sharing on digital content behavior on YouTube. Besides intrinsic motivations such as exposure and recognition, revenue sharing can be used to motivate video content contribution. Sun and Zhu (2013) explore the effect of the launch of an ad-revenue-sharing program initiated by a Chinese blog-hosting site in 2007. This program allowed bloggers to receive 50% of the ad revenue from advertisements on their blog pages, which is generated by the traffic they receive. They find that participants in the program experience a 13% increase in popularity compared to non-participants. This increase is due to a shift in content topics towards three domains: the stock market, salacious content, and celebrities, which account for 50% of the total increase. Moreover, the quality of participants’ content improves after the program takes effect, relative to non-participants. The program’s effects are more prominent among bloggers with moderate popularity and appear to persist even after enrollment in the program. Monetary incentives from other users can also drive providers to contribute more digital content and affect the quality of content. On some platforms, users can monetarily reward content creators for their contributions, which can incentivize creators to produce more and higher-quality content. For instance, on YouTube, content creators can monetize their videos through advertising revenue and receive direct financial support from their viewers through features such as “Super Chat.” Similarly, on TikTok, creators can earn money through the “TikTok Creator Fund,” which pays creators based on the views and engagement their content receives. On Twitch, viewers can purchase “Bits,” which are a form of virtual currency that they can use to tip content creators during live streams. Viewers on YouTube Live can purchase “Super Chat” messages, which are messages that stand out in the chat and can be seen by the content creator. Viewers can also send “Super Stickers,” which are animated images that can be purchased and sent as a form of support to the creator. Jain and Qian (2021) use a game-theoretic model to show that monetary incentives, including platform commission and consumer donations, can incentive users to provide better-quality digital content. They suggest that platforms should increase the commission to content providers to motivate higher-quality content, which in turn

2.2 Non-monetary Incentives for Digital Content Contribution

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could attract more demand to the platform. Consumers’ donation is not substituting for platform commission, it could also induce better content from producers. He et al. (2023) focus on the incentive structure. They find that after implementing completion-contingent monetary incentives with thresholds, users tend to reduce their contributions once they reach the thresholds. However, the negative impact can be mitigated by social approval. While the quality of reviews generally improves, the majority of the improvement is observed in reviews that initially had lower ratings. However, the relationship between a monetary incentive and contributor participation, as well as their total content volume, is a complex and nuanced one, and is not always straightforward. The impact of monetary incentives on users’ digital content contribution is not monotonic. Liu and Feng (2021) develop a theoretical model which divides content contributors into four types along two dimensions: whether they contribute without monetary incentive and whether they are effective at attracting the audience. They find two crowding out effects that result in a negative effect of monetary incentives. The first one is motivation crowding out, when a monetary incentive is introduced, it may actually reduce the motivation of contributors who are not driven by money to participate. For example, these non-monetary contributors may worry about being perceived as greedy and as a result, decrease their level of effort or even stop contributing altogether. The second one is competition crowding out, when a monetary incentive is increased, low-performing contributors may reduce their level of effort or even stop contributing altogether due to heightened competition in the platform. Furthermore, Bründl and Hess (2016) conduct a web survey of 543 broadcasters on social live streaming platforms and found that monetary incentives had a negative influence on contribution intentions. This suggests that factors beyond the incentive scheme itself, such as the social norms and motivations of the community members, may also play a role in determining the effectiveness of monetary incentives. In other words, monetary incentives may diminish intrinsic motivations (Deci & Ryan, 1985) (Table 2.4).

2.2 Non-monetary Incentives for Digital Content Contribution While monetary incentives have traditionally been used to motivate users to create content, research has shown that a variety of non-monetary factors can also be effective in promoting content generation. For example, social recognition and peer approval have been found to be powerful motivators for users to contribute to online communities and platforms. Additionally, the enjoyment and satisfaction derived from the act of content creation itself, as well as the opportunity to learn and develop new skills, have been identified as key non-monetary motivators. Furthermore, the sense of belonging to a community and the desire to help others have also been found to play important roles in motivating users to generate digital content. As such, it is

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Table 2.4 Monetary incentives for social medial content contribution Research

Monetary incentives

Content contribution

Main findings

Bründl and Hess (2016)

Payments and compensation

Live streaming content contribution

Monetary incentives negatively affect users’ live streaming content contribution

He et al. (2023)

Incentive structure

Post-treatment diary providing

Users become less willing to continue posting diary entries once the reward thresholds are reached

Jain and Qian (2021)

Commission and consumers’ donation

Content quality

Allowing consumers to donate can induce content with better quality

Liu and Feng (2021)

Whether users contribute without monetary incentive

UGC contribution

Content contributors may be negatively affect by monetary incentives because of motivation crowding out and competition crowding out

Sun and Zhu (2013)

Ad revenue sharing

Blog content and Participating in the ad-revenue-sharing quality program lead to more popular content and higher-quality content

Tang et al. (2012)

Ad revenue sharing

Video content contribution

Ad revenue sharing can help resolve the misalignment in incentives between digital content platform and contributions, which can motivate digital content contribution

important for platforms and communities to consider a range of motivational factors beyond just monetary incentives when seeking to encourage content generation and user participation. Reviews Non-monetary incentives can also affect the positivity of review content, Woolley and Sharif (2021) found that nonfinancial incentives, such as lottery entries, can increase the relative positivity of review text. Specifically, the nonfinancial lottery incentive condition resulted in a 9.47% increase in the relative positivity of review text compared to the no incentive condition. The results suggest that as monetary incentives, non-monetary incentives can also improve users’ review writing experience, and modify what reviews write. Burtch et al. (2018) explore the role of social norms in affecting users’ review writing behavior. Social norms pertain to the frequency of a particular behavior among a relevant group. In Burtch et al. (2018)’s study, it is the proportion of individuals who have already written reviews, which can be seen as a descriptive social norm (Cialdini et al., 1991). Their field experiment results show that social norms can be as useful as financial incentives to motivate users to provide reviews. While for review length, social norms are more effective at inducing lengthy reviews than financial incentives and a simple request. And it is best to adopt the combination of

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financial incentives and social norms to induce reviews in greater numbers and of greater length. Chen et al. (2010a, 2010b) study the effect of social comparison on users’ rating posting on MovieLens. They conduct a field experiment to send e-mail newsletters to let users know about other users’ movie ratings and benefits. Their results show that users who received information on the median user’s movie ratings significantly increased their monthly movie ratings by 530%, while those above the median decreased their ratings by 62%. Additionally, when provided with information on the average user’s net benefit score, above-average users tended to participate in activities that benefit others. The results are consistent with social preference theories. Goes et al. (2014) document a “popularity effect” in their research. They use data from epinions.com, a large product review platform. This platform allows one user to subscribe to another. They find that as users become more popular, i.e., get more subscriptions from other users, they produce more reviews and more objective reviews. However, their numeric ratings also systematically change and become more negative and more varied. Similarly, McIntyre et al. (2016) find that feedback from other users can motivate on to provide more reviews. While novice reviewers are more likely to continue producing reviews when they receive positive feedback, established reviewers do not show any noticeable increase in productivity as a result of such feedback. Lacan et al. (2022) explore what is the most efficient solicitation strategy to drive consumers’ willingness to answer an eWOM solicitation, in terms of timing, solicitation framing, and targeted individuals. Two experimental studies demonstrate that soliciting eWOM from engaged consumers or sending the solicitation close to the deadline leads to a higher number of eWOM communications. However, the results also reveal that framing the solicitation differently based on the target and the time remaining until the deadline creates a consistency effect across multiple construal levels. Consequently, a solicitation strategy that targets engaged individuals with gain-framed solicitation and non-engaged individuals with loss-framed solicitation is an effective way to increase the response to solicitation when it is most valuable (i.e., well in advance of the deadline). The effect of non-monetary incentives can change over time. Bhattacharyya et al. (2020) explore the temporal effects of repeated recognition and lack of recognition on users’ review posting behavior on Yelp.com with an annual Elite recognition system. Here, recognition refers to the “public appreciation of individuals’ efforts through non-monetary rewards” (Brun & Dugas, 2008). Their findings indicate that first-time recognized reviewers experience a notable surge in both the quantity and quality of their contributions. However, these positive outcomes exhibit a declining trend when the same reviewers receive the Elite badge in successive years. On the other hand, unrecognized reviewers who deserve recognition show a decrease in both their contribution effort and quantity. They use the reinforcement theory to explain the positive effect of first-time recognition on review contribution and the concept of satiation to explain the decline in contribution because of repeated recognition. The dynamics of reviews have also attracted attention from previous research. Liu (2006) collected movie review data from Yahoo Movies Web site, according to their

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findings, the primary predictor of word-of-mouth (WOM) after a movie is released is the amount of WOM from the previous week, which suggests a temporal dynamic. Regarding the sequential dynamics of online opinion, Wu and Huberman (2008) argue that reviewers are incentivized by the anticipated effect of their review on the overall rating and, by extension, on the decisions or preferences of other buyers. They suggest that buyers are more likely to post their reviews when they believe it will have a substantial impact, such as when there are fewer existing reviews or their experience significantly diverges from the prevailing average rating. Godes and Silva (2012) consider both sequential and temporal dynamics of online reviews. Their analysis reveals that, after controlling for calendar dates, the residual average temporal pattern of ratings is increasing. Regarding sequential dynamics, they observe a decline in ratings. Their results suggest that the ability to assess the diagnosticity of previous reviews may decrease over time, leading to more purchase errors and lower ratings when previous reviewers have diverse opinions (Table 2.5). Crowdsourcing The self-determination view is a framework that emphasizes the importance of intrinsic and extrinsic motivations in driving crowdsourcing activities (Deci & Ryan, 1985; Gagné & Deci, 2005). Intrinsic motivation emphasizes the inherent sources of satisfaction that arise from engaging in an activity, rather than its extrinsic rewards or consequences, while extrinsic motivation is oriented toward achieving a desired outcome, compensation, or reward. Studies have shown that both intrinsic and extrinsic motivations can play important roles in motivating individuals to participate in crowdsourcing activities. For example, individuals may be motivated intrinsically by the desire to learn new skills, contribute to a cause they believe in, or connect with like-minded individuals. On the other hand, extrinsic motivations such as recognition can also be powerful drivers of participation. Malone et al. (2010) propose that love and glory can motivate people to participate in systems for collective intelligence. Socializing with others and being recognized by peers are examples of love and glory genes that motivate users’ contribution. Brabham (2012) explores this question from the perspective of applied communication studies. Participants are motivated to take part in crowdsourcing due to various reasons, including the chance to advance their careers, to enjoy themselves, to express their ideas, to contribute to a joint effort, to receive recognition from their peers, and to acquire new skills and knowledge. Kaufmann et al. (2011) find that compared to monetary remuneration, intrinsic motivations are more important, especially enjoyment-based motivation, such as task autonomy and skill variety. Jiang et al. (2022) study the impact of performance feedback on crowdsourcing contest participants’ behavior. Crowdsourcing contests offer advantages over traditional innovation sourcing approaches. They enable innovation seekers to access and engage with a larger pool of innovators, evaluate a greater number of submissions, and only pay for successful ones. This novel approach to sourcing innovation leads to increased variety and novelty of innovations while reducing the risk of innovation failure. If the seeker’s sole concern is achieving the highest possible quality, both the full feedback, i.e., providing feedback throughout the contest, and late feedback,

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Table 2.5 Non-monetary incentives for online review writing Research

Non-monetary incentives

Content contribution

Bhattacharyya et al. (2020)

Recognition (i.e., badges)

Review After first recognition, the contribution effort contribution effort and quantity will and quantity increase. After repeated recognition, the contribution effort and quantity will be lower than that first recognition

Burtch et al. (2018)

Social norms

Review volume and length

For review volume, social norms are as useful as financial incentives. For review length, social norms are more effective than financial incentives and a simple request

Chen et al. (2010a, 2010b)

Social comparison

Ratings

Users below the median increase the number of their rating, while those above the median decrease their monthly ratings

Goes et al. (2014)

Users’ subscription

Volume and Online interactions encourage users content of reviews to write larger volume of reviews and more objective reviews

Lacan et al. (2022)

Solicitation strategy

Willingness to share the offer by eWOM

McIntyre et al. (2016)

Positive feedback Review writing

Positive feedback can motivate a novice reviewer to contribute reviews, but not a established reviewers

Woolley and Sharif (2021)

Lottery to receive Positivity of bonus gift review content

Non-financial incentives can increase the enjoyment of review writing, and then increase the positivity of review

Wu and Huberman (2008)

Expected impact of the review

Users are more likely to post reviews if they expect their reviews can have a significant impact

Review writing

Main findings

The most effective strategy is to solicit well in advance of the deadline and use a gain-framed solicitation for engaged individuals and a loss-framed solicitation for non-engaged individuals

i.e., providing feedback only in the second half of the contest, policies are effective. However, if the objective is to maximize the number of high-performing entries or the total number of participants, the late-feedback policy is the optimal choice. While feedback can guide creators in their exploration and exploitation decisions, it can also discourage entries and subsequent actions by incumbents. The late-feedback policy provides the benefits of feedback while mitigating this problem by delaying feedback until many creators have entered the contest.

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von Krogh et al. (2012a, 2012b) construct a motivation-practice framework to understand the relationship between users’ motivation and voluntary open source software development. Unlike the traditional self-determination view, their theoretical framework is based on a social practice view, in which the social practice and its supporting institutions act as mediations between motivation and contribution. Budhathoki and Haythornthwaite (2013) find that motivators associated with “personal but shared need” drive users to participate in open collaboration. Motivations for crowdsourcing can change over time. Alam and Campbell (2017) explored intrinsic and extrinsic motivations for crowdsourcing. As most previous studies have documented that both motivations are important (Krishnamurthy, 2006), they find that intrinsic motivation, such as self-interest, learning, and fun, as well as extrinsic motivation, such as indirect feedback, trust, and recognition, are both effective at driving users to participate in cultural crowdsourcing work. Volunteers typically demonstrate intrinsic motivations at the beginning, but both intrinsic and extrinsic motivations are vital in sustaining their long-term participation. Rotman et al. (2012, 2014) explore how motivation in online citizen science projects changes over time. Rotman et al. (2014) identify four initial motivations for citizen scientists to participate in collaborative projects: personal interest, selfpromotion, self-efficacy, and social responsibility. The authors also find that trust, communication, and recognition are important factors in motivating citizen scientists to continue contributing to collaborative projects. Rotman et al. (2012) find that volunteers’ motivation in citizen science projects is affected by a complex framework of factors that dynamically change throughout their cycle of work on scientific projects. These factors include personal interests, attribution, acknowledgment, and other external factors. Initially, volunteers are motivated by their curiosity to explore new opportunities and engage in enjoyable activities that broaden their horizons. At this stage, other motivational factors do not play a significant role. However, as volunteers reflect on their past experiences, they take into account the specific project, how well their various motivational needs were met, and how they felt during the project. During this reflection, they tend to focus on secondary motivational factors and the recognition they receive, which can be linked to egoism. Because many ecological scientific projects are lengthy and involve multiple stages, they can benefit from the cyclical engagement of volunteers. To ensure sustainable long-term volunteer participation, it is essential to repeatedly acknowledge and address their range of motivations throughout the project lifecycle (Table 2.6). Creative Production Li et al. (2021a, 2021b) find that on online publishing platforms, comments from readers can motivate book writers to put in more creative production efforts and customer care efforts. In the context of online knowledge exchange, Goes et al. (2016) explore the role of incentive hierarchies on answer providers’ efforts. They find that although these incentives based on glory may encourage users to contribute more before they reach their goals, their contribution levels drop significantly afterward and the positive

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Table 2.6 Non-monetary incentives for crowdsourcing participation Research

Non-monetary incentives

Content contribution

Alam and Campbell (2017)

Personal, collective, and Cultural crowdsourcing Both intrinsic and external factors participation extrinsic motivations play a vital role and the effectiveness of motivations change over time

Brabham (2012)

Advance career, have Participation in transit fun, express themselves, planning contribute effort, gain recognition, and learn skills and knowledge

Crowdsourcing participation will be motivated by a combination of intrinsic and extrinsic motivators, as well as rational, norm-based, and affective motivators

Budhathoki and Haythornthwaite (2013)

Personal but shared need

Open collaboration

“Personal but shared need” motivators drive open collaboration

Jiang et al. (2022) Performance feedback

Participation number and submission quality

Late feedback, rather than full feedback, leads to a better overall crowdsourcing contest outcome

Kaufmann et al. (2011)

Enjoyment based motivation, community based motivation, and social motivation

Low-paid crowdsourcing market participation

Intrinsic motivation is more effective than monetary payment

Malone et al. (2010)

Love and glory

Collective innovation participation

Love and glory could lower costs; glory can inspire the crowd to move more quickly

Rotman et al. (2014)

Personal interest, self-promotion, self-efficacy, and social responsibility; trust, communication, and recognition

Online citizen science projects participation

How motivations in online citizen science projects change over time

Rotman et al. (2012)

Personal interest as well Online citizen science as external factors such projects participation as attribution and acknowledgement

Propose a complex framework of factors that change over time and are cyclical

von Krogh et al. (2012a, 2012b)

Intrinsic and extrinsic motivations

Construct a motivation-practice framework based on a social practice view

Open source software development

Main findings

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effect appears to be only temporary. Furthermore, the impact of these incentives decreases as users achieve higher ranks. Lin and Huang (2013) propose that users’ intention to share online knowledge can be affected by a series of factors besides monetary incentives, including self-efficacy, i.e., a type of self-assessment that affects a person’s judgment about what course of action to pursue, altruism, i.e., a state where an individual is ready to provide aid to others without anticipating any reciprocation (Hsu & Lin, 2008), and sense of virtual community, which includes recognition and obligation, emotional and relationship, identification, and exchange of support. They find that self-efficacy and altruism can positively affect online knowledge contribution through attitude toward knowledge sharing. And some factors of a sense of virtual community drive users to share more on Q&A platforms because of the subjective norm. Garnefeld et al. (2012) focus on the role of normative pleas. Explicit normative incentives refer to incentives that are clearly stated and encourage individuals to act in accordance with community principles or norms, or towards achieving a shared objective (Schau et al., 2009). They observed that explicit normative incentives have a tendency to enhance the short-term eagerness of active community members to participate, but we did not notice a similar impact on their long-term posting intentions. Furthermore, when it comes to passive community members, explicit normative incentives are ineffective in encouraging them to contribute, either immediately or in the future. Chen et al. (2018) use a dynamic model to capture how the motivations to contribute in online communities change over time. They find that the relationship between motivating mechanisms and users’ online content contribution is not static. For example, their results demonstrate that reciprocity is only effective in shifting users from a low to a medium motivation state, while peer recognition, such as votes and acceptance from other users, can elevate all users to a high motivation state. Badges are effective in promoting a transition from a low or medium motivation state to a high motivation state, but interestingly, they have no impact on users who are already in the high motivation state. Jin et al. (2015) explore the social aspects of digital content contribution in on social Q&A community. Based on social capital theory, social exchange theory, and social learning theory, they find that contributors’ self-presentation (the number of personal information items for the contributor from registration to the current time period), other users’ recognition (the number of usefulness votes for the contributor’s answer during a certain period), and social learning opportunities (the number of followees of the contributor from registration to the current time period) all can positively drive users to contribute more knowledge on the platform. Chen et al. (2010a, 2010b) consider the reputation system on Q&A platforms. The reputation system can be observed as (1) the average rating (1 to 5 stars) of all their answers, which is based on the ratings given by the askers whose questions were answered by the Researcher; (2) the total number of questions they answered; (3) the number of refunds they issued; and (4) a list of all the questions they answered, along with their respective ratings. They find that an answer’s past reputation can positively affect both answer effort and answer quality. On an anonymous Q&A platform,

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reputation can be the most useful way to signal quality. Higher-reputation answerers may be in greater demand from askers. We even observed instances where askers specifically requested a particular answerer to answer their question. Additionally, in cases where quality may be uncertain, the outputs of high-reputation answerers are more likely to be viewed favorably by askers. As a result, answerers with high reputations are perceived as investing more time and effort into producing higherquality answers. These findings underscore the importance of having a reputationbuilding system in place for achieving high efficiency in knowledge markets. Ma and Agarwal (2007) investigate this question from the perspective of information system design. They propose that using community artifacts supporting virtual copresence, persistent labeling, self-presentation, and deep profiling, can positively affect perceived identity verification, and thus motivate users’ knowledge contribution. They find that virtual copresence, self-presentation, and deep profiling are effective at increasing users’ knowledge contribution and satisfaction through perceived identity verification. Chen and Yeckehzaare (2020) investigate how to use non-monetary incentives to motivate experts to contribute to Wikipedia. Their results indicate that mentioning private benefits leads to a 13% increase in experts’ interest in contributing. Moreover, they investigate the factors that predict the length and quality of expert contributions to a recommended Wikipedia article. Using a machine learning model, they find that a high level of matching accuracy between the recommended article and the expert’s expertise, combined with the expert’s reputation and the mention of public acknowledgment, are the most important predictors of both contribution length and quality. Their findings suggest that providing tailored recommendations and highlighting public acknowledgment can effectively motivate experts to contribute high-quality content to a platform. Zhang and Zhu (2011) examine the effect of group size on users’ content contribution behavior on Chinese Wikipedia. Contributing to Wikipedia can be regarded as a provision of public goods, which is related to the free-rider hypothesis. As suggested by prior literature, individual contribution levels decrease as the group size increases. However, Zhang and Zhu (2011)’s study presents empirical evidence that, in a setting with large group size, the influence of social effects exceeds that of free-riding incentives, which means there is a positive relationship between group size and users’ content contribution. Furthermore, their analysis reveals that individuals who place a greater value on social benefits exhibit a more pronounced response to the change than those who attach less importance to them. The findings emphasize the crucial role of social effects in the provision of public goods (Table 2.7).

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Table 2.7 Non-monetary incentives for online creative production Research

Non-monetary incentives

Content contribution

Main findings

Chen et al. (2010a, 2010b)

Reputation

Answers’ effort and answer quality

Reputation concern drives users to provide better answers

Chen et al. (2018)

Reciprocity, peer recognition, and self-image

Contribution to knowledge-sharing platform

A single motivating mechanism may operate differently across various states

Chen and Yeckehzaare Private benefit of (2020) contribution

Expert contribution length and quality

Experts’ reputations and public acknowledgment positively affect the quantity and quality of contributions on Wikipedia

Garnefeld et al. (2012) Normative pleas

Short-term and Normative pleas can long-term intention to only be used to post an answer motivate active users to provide answers in the short term

Goes et al. (2016)

Incentive hierarchies

Users’ answer contribution

Users contribute more before reaching their goals, but the positive effect disappears after that

Jin et al. (2015)

Self-presentation, peer recognition, and social learning

Knowledge contribution

Identity communication, peer recognition, and social learning all can motivate users to contribute more knowledge on social Q&A communities

Li et al. (2021a, 2021b)

Readers’ commenting Writer efforts and customer care effort

Reader comments positively affect writers’ creative production efforts and customer care efforts

Lin and Huang (2013)

Self-efficacy, altruism, and a sense of virtual community

Intention to share knowledge

Online knowledge sharing

(continued)

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Table 2.7 (continued) Research

Non-monetary incentives

Ma and Agarwal (2007)

Community artifacts Knowledge supporting virtual contribution copresence, persistent labeling, self-presentation, and deep profiling

Zhang and Zhu (2011) Group size

Content contribution

Content contribution

Main findings Community artifacts supporting identity communication can motivate users’ knowledge contribution Group size has a positive impact on driving users to contribute to online communities

Social Media Huang et al. (2019) focus on how to motivate users’ digital content contribution on social media platforms with performance feedback. Based on social value orientation theory, there are three types of orientations, including “cooperative”, i.e., maximizing the self and others’ gains, “individualist”, i.e., maximizing only the self’s gains, and “competitive”, i.e., maximizing gains of the self, relative to others. As for performance feedback, cooperatively framed feedback messages emphasize that the content benefited others; individualist-framed framed feedback messages express that the content was of high quality; and, competitively framed feedback messages show the content was better than others. By conducting field experiments on a large mobile recipe-sharing application, they find that in motivating users to post more content, individuals of different genders are affected differently by performance feedback that is framed in various ways. Their findings suggest that feedback framed cooperatively is more effective in motivating female subjects, while feedback framed competitively is more effective in motivating male subjects. Teichmann et al. (2015) focus on four motivational drivers—opinion leadership, self-presentation, enjoyment, and altruism. These drivers consist of four dimensions. The first driver, opinion leadership, pertains to self-oriented and extrinsic motives, whereby members participate in an online community to attain self-enhancement or other similar objectives. Given their expertise in a particular topic within the community, such as cycling or health, opinion leaders aim to disseminate their knowledge to influence other members. The second driver, self-presentation, is essentially otheroriented and extrinsic, as it involves using the community to showcase one’s sense of self and acquire positive feedback from other members, ultimately resulting in an improved community status. The third driver, enjoyment, is self-oriented and intrinsic, meaning that members participate in the community purely for the pleasure or satisfaction that it brings. The fourth driver is altruism, which is other-oriented and intrinsic, and involves striving towards ethical goals by aiding others, without expecting any recognition or reciprocal benefits in return. The findings demonstrate

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that opinion leadership, self-presentation, and enjoyment have a positive impact on content contribution, while altruism has a negative effect. Specifically, the positive drivers are more pronounced in company-hosted online communities than in consumer-hosted ones. To encourage members to contribute more actively, community managers should focus on attracting opinion leaders, creating opportunities for self-presentation, and fostering a sense of enjoyment and comfort among members. Toubia and Stephen (2013) study the intrinsic and image-related motivations to contribute social media content in the context of Twitter. They propose two distinct utilities that might be useful to motivate users to post content. The concept of intrinsic utility implies that users derive direct satisfaction from posting content, and are motivated to do so for the inherent pleasure of the activity itself, rather than for any external reward or outcome (Deci & Ryan, 1985). Image-related utility operates on the premise that users are driven by how others perceive them (Fehr & Falk, 2002), and is closely linked to a desire for status or prestige (Glazer & Konrad, 1996; Lampel & Bhalla, 2007). Using a dynamic discrete choice model, they provide evidence that non-commercial users on Twitter response to the addition of new followers or follow requests is not uniform; while some users increase their posting activities, others reduce their content output. However, the majority of non-commercial users undergo two distinct phases in their posting behavior, with intrinsic utility being the dominant factor when they have fewer followers, and image-related utility taking precedence as their follower count increases. Their study found that most non-commercial users on Twitter derive greater image-related utility from their posting activities than intrinsic utility. Jain and Qian (2021) find that a stronger network effect can motivate users to provide digital content with higher quality. When network effects are stronger, the market size tends to be larger, and content producers are typically offered a more generous compensation plan. Similarly, Shriver et al. (2013) explore the role of social networks in users’ blog posting on online social network platforms. Their empirical evidence also demonstrates that ties play a role in promoting content creation on a website, creating a connection between the formation of social ties, advertising revenues, and the generation of local network effects between content and ties. Zhang et al. (2012) have also examined network effects in a user-generated digital content environment. They adopt a vector autoregressive model to measure the interrelationship between digital content contributors and consumers. Their findings indicate that the acquisition of contributors (sellers) is the most financially valuable due to their significant impact on other contributors through strong network effects. Nonetheless, it takes longer for the financial value of contributors to fully manifest as the network effects require time to develop and reach their full potential. Thus, enhancing the network effect is a useful way to increase the benefits of acquisition, motivation, and retention of digital content providers.

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Tang et al. (2012) propose that the primary incentives for content contribution on social media are exposure and reputation. Using a dynamic structural model, they capture the dynamics of content contribution decisions on YouTube, taking providers’ forward-looking into consideration. They measure the content provider’s utility of exposure as a function of the number of video views and the number of new subscribers gained. As for reputation, they use the concept of ranking, a lower ranking number suggests a higher reputation. Results show that both exposure and reputation are useful motivators for video content contribution. In terms of social media contribution quality, Qiu and Kumar (2017) explore the role of social audience size and online endorsement. Based on image motivation theory and effort-performance theory, they propose that audience size and social endorsements can affect users’ contribution quality through image motivation and contribution effort. They examine this question in a social-media-based prediction market. Their findings reveal that prediction accuracy is enhanced by an increase in audience size and a higher level of online endorsement. Of note, users with intermediate prediction ability exhibit the strongest positive response to an increase in social audience size and online endorsement. As such, their results suggest that prediction markets may benefit from targeting individuals with intermediate abilities to achieve the greatest improvement in predictions. Non-monetary incentives that drive user-generated content contributions in the rapidly growing live streaming industry have been the subject of much research and analysis in the literature. Bründl and Hess (2016) divide individual motives for live streaming into enjoyment, self-expression and identity, information dissemination, monetary incentives, and taking structural dimension, i.e., social interaction ties, relational dimension, i.e., commitment, and cognitive dimension, i.e., share vision, into consideration. The results of the study indicate that the volume of contribution and continuance intention are influenced by distinct sets of factors. A broadcaster’s volume of contribution is mainly determined by a small number of individual motives and is significantly predicted only by the relational dimension of social capital. While enjoyment does not influence the volume of content contribution, monetary incentives are a crucial driver, implying that external rewards may be increasingly important in motivating content contribution. Conversely, a broadcaster’s social capital is the primary determinant of their continuance intention, with all three dimensions of social capital having an impact. Commitment is the most influential factor in determining continuance intention. Only enjoyment and information dissemination among individual motives has a positive effect on broadcasters’ continuance intention, while self-expression and identity do not significantly influence either the volume of content contribution or the intention to continue contributing (Table 2.8).

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Table 2.8 Non-monetary incentives for social medial content contribution Research

Non-monetary incentives

Content contribution

Main findings

Bründl and Hess (2016)

Individual motives, structural dimension, relational dimension, and cognitive dimension

Volume of content contribution and continuance intention

The constructs that influence the volume of contribution and continuance intention are significantly different from each other

Huang et al. (2019)

Performance feedback messages

Posting volumes

Feedback messages framed cooperatively are useful at motivating female users; while

Jain and Qian (2021)

Network effect

Content quality

A stronger network effect can induce content with better quality

Qiu and Kumar (2017)

Audience size and online endorsement

Quality of contribution

An increase in audience size and a higher level of online endorsement both can lead to contribution with higher quality

Shriver et al. (2013)

Social ties

Blog posting

Social ties have a positive effect on users’ content generation

Tang et al. (2012) Exposure and reputation

Video content contribution

Exposure and reputation are important incentives for users’ digital content contribution

Toubia and Stephen (2013)

Intrinsic and image-related motivation

Content posting on Twitter

Although both intrinsic and image-related motivations are useful to drive users to post more content, the effectiveness of image-related motivation is stronger

Teichmann et al. (2015)

Opinion leadership, self-presentation, enjoyment, and altruism

Content contribution to online communities

Opinion leadership, self-presentation, and enjoyment positively affect users’ content contribution to online communities; while the effect of altruism is negative

Zhang et al. (2012)

Network effect

Acquisition and retention of content contributors

Contributor acquisition is the most effective at acquiring and retention content contributors

References Alam, S. L., & Campbell, J. (2017). Temporal motivations of volunteers to participate in cultural crowdsourcing work. Information Systems Research, 28(4), 744–759. https://doi.org/10.1287/ isre.2017.0719 Amaldoss, W., Du, J., & Shin, W. (2021). Media Platforms’ content provision strategies and sources of profits. Marketing Science, 40(3), 527–547. https://doi.org/10.1287/mksc.2020.1248

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Chapter 3

Motives for Digital Content Consumption

Digital content platforms utilize two primary payment models for users, namely the merchant-set price mechanism and the pay-what-you-want mechanism. The former involves a fixed pricing structure for content, established by the platform or content creator, with users paying the designated price, such as membership or subscription fees for online video platforms and digital magazines. Under this model, consumers play a passive role in the pricing process. Conversely, the pay-whatyou-want (PWYW) model empowers consumers to actively engage in the pricing mechanism, allowing them to pay any amount, including zero, for the product or service. This book centers on the pay-what-you-want (PWYW) pricing strategy, a method that has gained popularity among online platforms, such as tipping, donation, and gift sending. PWYW pricing could be advantageous for digital product companies as it offers a cost-effective alternative to expensive anti-piracy measures (Kim et al., 2022). In this Chapter, we delve into the concept of PWYW, factors that could potentially affect PWYW behavior, and the implications of this pricing strategy. Additionally, we examine the social motivations that drive digital content consumption, and provide a comprehensive literature review and discussion that establishes a theoretical foundation for the empirical analysis presented in Part III. The analysis explores the social motivations for digital content consumption, the dynamics of digital content consumption, and the role of social norms in shaping consumer behavior. Overall, this Chapter aims to contribute to a better understanding of the factors that drive digital content consumption, and the ways in which PWYW and other pricing strategies can be leveraged to incentivize consumers to engage with digital content.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Ma, Social Influence on Digital Content Contribution and Consumption, Management for Professionals, https://doi.org/10.1007/978-981-99-6737-7_3

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3.1 Pay-What-You-Want Pricing Pay-what-you-want is a pricing strategy that involves buyer participation, whereby they are given the option to pay any amount they desire for a product or service, with the possibility of paying nothing at all (Kim et al., 2009). This pricing scheme has been adopted by many industries, such as music, restaurant, retail, and airlines Wang et al. (2022). In this chapter, several factors that may impact consumers’ payment behavior in the context of PWYW are examined. The first section concentrates on buyer-related factors, such as gender, loyalty, and need for cognition, as well as motivation-related factors like altruism, fairness concern, and satisfaction. The second section shifts the focus to seller-related factors, encompassing factors related to the seller and the buyer–seller relationship. Finally, context-related factors to PWYW are explored, with particular emphasis on the impact of external reference prices and payment timing.

3.1.1 Buyer-Related Factors to PWYW Buyer Characteristics Santana and Morwitz (2021) find that there exist gender differences in the context of PWYW. They find that compared to men, women would like to pay more when facing PWYW situations. It is because of the agentic versus communal orientation between males and females. Men tend to approach the payment decision from an agentic orientation, while women tend to approach it from a communal orientation, and these orientations affect consumers’ subsequent payment behavior. As agentic men tend to be more self-centered, their payment decisions are driven primarily by economic considerations, leading to lower payments. On the other hand, communal women tend to be more focused on others, and their payment decisions are influenced by both social and economic factors, resulting in higher payments. Stangl et al. (2017) show that the pay-what-you-want prices vary across three customer groups, namely potential, new, and repeat customers. When it comes to PWYW prices, service providers are likely to experience the most negligible loss with repeat customers and the greatest loss with potential customers. These findings are consistent with the research on traditional pricing methods. Studies show that loyal customers are willing to pay a higher price (Wieseke et al., 2014), which contradicts the notion that customers expect a reward, such as lower prices, for their loyalty (Reinartz & Kumar, 2002). As repeat customers result in the least loss for high-value services, the results suggest that PWYW prices should only be offered to them. By offering repeat customers a PWYW option, service providers can tap into their desire to feel rewarded for their loyalty (Wieseke et al., 2014) and potentially increase their level of engagement.

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Barone et al. (2017) demonstrate that low-power consumers show a greater inclination toward favorable purchase intentions in response to PWYW pricing as compared to fixed pricing, compared to high-power consumers. It is because PWYW’s ability to set prices is particularly appealing to those seeking to enhance their sense of power. PWYW fosters a feeling of personal control, which forms the basis for power elevation (Garbinsky et al., 2014). On the other hand, high-power consumers are less motivated by ego-based desires to restore their power, making them less likely to value the pricing control and regulatory capabilities of PWYW. Therefore, compared to high-power individuals, consumers who have a lower sense of power are expected to be more responsive to PWYW pricing as opposed to fixed pricing strategies. Rathore et al. (2022) investigate whether consumers with varied needs for cognition will respond differently to PWYW versus pick-your-price. The need for cognition is “a stable individual difference in people’s tendency to engage in and enjoy effortful cognitive activity” (Cacioppo et al., 1996). They find that consumers with a high need for cognition will exhibit a greater purchase intention when the product employs a PWYW strategy as compared to a pay-your-price strategy. Conversely, individuals with a low need for cognition will show a higher purchase intention when the product employs a pay-your-price strategy as compared to a PWYW strategy. It is because choosing a price can be a demanding task for consumers, as they have endless options to consider, leading to an increase in cognitive load. As the process of determining how much to pay for PWYW requires high cognitive effort (Wang et al., 2021), which is favored by individuals with a high need for cognition, consumers with a high need for cognition will exhibit a greater purchase intention toward PWYW compared to pay-your-price pricing (Wang et al., 2021). Motivation-Related Factors Kim et al. (2009) propose that fairness concern, altruism, satisfaction, and loyalty might drive consumers’ payment behavior in the context of PWYW. Their research indicates that consumers do not always behave in a rational manner as outlined in conventional economic theory, particularly in the context of PWYW pricing. However, not all factors are equally effective in motivating PWYW behavior. Factors such as fairness concern and satisfaction have been found to influence the amount that consumers are willing to pay, with consumers who perceive the transaction to be fair and satisfactory being more likely to pay a higher amount. On the other hand, factors such as altruism and loyalty have been observed to have no significant effect on PWYW payment amount. These findings suggest that businesses should consider the factors that drive PWYW behavior when developing pricing strategies, as this can help to optimize revenue and build stronger relationships with customers. In line with Kim et al. (2009)‘s findings, Kunter (2015)‘s research also documents the importance of fairness and customer satisfaction in the process of PWYW consumption. The study proposes an extension, positing that the influence of both fairness and customer satisfaction on PWYW behavior is contingent upon the social image of the service provider. Moreover, Kunter (2015) also identifies another factor

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that can affect PWYW behavior—feelings of guilty. People would have a guilty conscience if they don’t pay enough. Chen et al. (2017) also document the importance of consumers’ fairness concerns in PWYW payments. Their analytical models show that in industries with a substantial number of consumers who are fair-minded, where the distribution of consumers is biased towards lower willingness-to-pay levels, and where there is intense competition due to limited product differentiation, PWYW may prove to be a lucrative pricing strategy. Schons et al. (2014) examine the dynamics of consumers’ PWYW behavior. Their results show that over multiple transactions, consumers tend to pay less. The reason is that consumers’ internal reference prices decrease with the number of transactions. Furthermore, they suggest that for consumers with a higher fairness preference, the decline in PWYW amount tends to be slower. Roy et al. (2016) investigate how altruism, social desirability, and price sensitivity influence consumers’ payment intention in the context of PWYW. They find that altruism and social desirability have a positive impact on internal reference price, while price sensitivity has a negative impact on internal reference price. Internal reference price serves as a mediator between altruism, social desirability, price sensitivity, and consumers’ willingness to pay. Rabbanee et al. (2022) examine the moderating effect of price consciousness and social desirability on the relationship between internal reference price as well as fairness perception and consumers’ PWYW prices. They find that PWYW payments are positively affected by internal reference price and fairness perception. Price consciousness weakens the aforementioned effects, while social desirability strengthens them. The moderating impact of price consciousness is more pronounced in private settings, whereas the moderating impact of social desirability is more pronounced in public settings. Gneezy et al. (2010) create a shared social responsibility through the combination of PWYW pricing and corporate social responsibility. The proposed shared social responsibility approach is expected to enhance the efficacy of PWYW pricing. The effectiveness of PWYW pricing is thought to depend on the level of consumer demand for the product or service, as well as their willingness to support the company. With the shared social responsibility method, each purchase directly reflects the customer’s desire to support both the company and the charitable partner, thereby potentially increasing the effectiveness of the PWYW model. They find that when there is no charitable contribution, consumers’ purchase rates will be 4.49%. While when half of the revenue will go to charity, the purchase rates reach 8.39%. Also in the context of shared social responsibility, Jung et al. (2017) examine the effect of generosity. Through two field experiments, they find that consumers are sensitive to whether their spending will go to charity, but are insensitive to the proportion that will go to charity. Compared to a PWYW transaction without a charitable component, the inclusion of a 1% charitable contribution significantly alters behavior, while increasing the contribution from 1 to 100% has little impact. Therefore, when switching from PWYW to shared social responsibility, adopting

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the lowest possible charitable allocation could be the optimal strategy for firms to maximize their profit. Gneezy et al. (2012) explore the question of “what motivates people to behave non-selfishly in markets” in the context of PWYW pricing. They find that concerns related to self-image, at least partially, influence individuals’ non-selfish behavior. People pay a positive PWYW amount because they perceive paying for a good or service received is the right thing to do. The underlying argument is that people strive to maintain a sense of goodness and fairness, deriving utility from engaging in prosocial behavior as a signaling mechanism. By behaving in a prosocial manner, individuals are perceived positively by others as well as themselves. The evidence presented in this paper supports our assertion that self-image is a crucial factor in individuals’ payment decisions when using the PWYW model. A spotlight effect is found in Roy et al. (2021)‘s research. The spotlight effect refers to a tendency to have an egocentric bias when assessing the importance of one’s own behavior and external appearance. The study reveals that in a PWYW scenario, customers tend to focus more on themselves (rather than others) when making a payment, especially in the presence of distant (rather than close) others. This leads them to anchor the price they are willing to pay to their internal reference price initially. However, the presence of external reference prices reduces this anchoring effect. These findings provide valuable insights for managers to understand the factors that affect customers’ PWYW pricing decisions, based on both their internal and external reference prices. Social norms have also been found to play an important role in affecting consumers’ PWYW payment behavior. Riener and Traxler (2012) present evidence regarding the evolution of payments and revenues in a Vienna-based PWYW restaurant over two years, where customers are given the freedom to decide the price they pay for their meals. During the initial six-month period after the restaurant’s launch, the average payment amounted to EUR 5.65. Over the subsequent two-year period, payments exhibited a steady decline, albeit only by 12%, culminating in an average payment of EUR 5. The data indicate that the decline in payment decline implies a convergence of payments. The observed trend is in line with social norm models and aligns with the notion of norm convergence (Azar, 2007; Mengel, 2008). Narwal et al. (2022) examine how customers reduce their willingness to pay more for PWYW products by morally disengaging themselves from reciprocity concerns. According to the theory of moral disengagement, individuals use different cognitive processes to detach themselves from moral and social constraints, which allows them to engage in deviant behaviors (Bandura, 2002). In situations where customers perceive the responsibility for PWYW pricing offers to lie solely with the seller and not themselves, their willingness to pay is at its lowest. Involvement is also a primary factor that can influence consumers’ PWYW behavior. Sharma et al. (2020) explore two types of involvement—situational involvement and enduring involvement. The former is transient and determined by a temporary perception of product significance within particular purchasing contexts (Richins & Bloch, 1986). The latter is an intrinsic concern with the product category that is linked to an individual’s self-concept, values, and egos (Dholakia,

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2001). Results show that for different product categories, people may have different responses to situational involvement and enduring involvement. In one study, they find that situational involvement has both direct and indirect effects on RATIO, i.e., the ratio between the PWYW prices that consumers are willing to pay and their internal reference price. While enduring involvement has a positive but insignificant impact under low situational involvement and a significant negative impact under high situational involvement. In the other study, neither situational involvement nor enduring involvement has a significant effect on PWYW prices. However, time pressure and perceived crowding can moderate the effect of enduring involvement (Table 3.1).

3.1.2 Seller-Related Factors to PWYW Seller Characteristics Weisstein et al. (2016) focus on the familiarity of brands and virtual product experience in the context of PWYW. Based on communication and pricing theories, they propose that there is an interaction between brand familiarity and virtual product experience. Here, the virtual product experience refers to 3-D product experience Compared to a 2D product experience that only offers static product pictures, a 3D product experience that allows consumers to zoom in/out and rotate the product can enhance their perceived product knowledge, foster more positive brand attitudes, and lead to higher purchase intentions. Their results show that for unfamiliar brands, virtual product experience can increase consumers’ PWYW prices; while for familiar brands, virtual product experience has no significant effect. Weisstein et al. (2019) investigate the impact of external reference prices on PWYW payment across product types. They find that in the case of hedonic products, the lack of an external reference price results in higher perceived quality and PWYW payments compared to when an external reference price is present, while the opposite is true for utilitarian products. It is because that the evaluation of utilitarian products is based on their functional performance, and a utilitarian mindset is primarily influenced by the perceived quality of the product (Homer, 2008; Sen & Lerman, 2007). Consumers may encounter challenges in evaluating the quality of utilitarian products and establishing appropriate PWYW payment amounts before consumption. If an external reference price is present, it can act as a signal of product quality and enhance consumers’ PWYW payments. However, in the absence of an external reference price, consumers may experience uncertainty regarding product quality and be apprehensive about paying more than necessary for utilitarian products. This uncertainty often results in consumers opting for significantly lower PWYW payments, which can bring down the average PWYW payment well below the market price. This effect is particularly pronounced in the context of utilitarian products.

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Table 3.1 Buyer-related factors to PWYW Research

Factors

Main findings

Barone et al. (2017)

Power states

The impact of PWYW is mainly observed among low-power consumers who utilize the pricing control feature of PWYW to regulate their power status

Chen et al. (2017)

Fairness concerns

Consumers’ fairness consideration is an important driver for the profitability of PWYW strategy

Gneezy et al. (2010)

Charitable giving

In the context of PWYW, consumers significantly pay more if half of the revenue will go to charity

Gneezy et al. (2012)

Self-image concern

Individuals’ identity and self-image concerns drive their PWYW behavior

Jung et al. (2017)

Generosity

Charity contribution affect consumers’ payment behavior, but the proportion of payment that will go to charity has little effect

Kim et al. (2009)

Fairness concern, altruism, satisfaction, and loyalty

Fairness concern and satisfaction positively affect PWYW behavior, but altruism and loyalty have no significant effects

Kunter (2015)

Fairness, customer satisfactory, income, and feelings of guilty

Fairness, customer satisfactory, income, and feelings of guilty are all important in driving consumers to pay in the context of PWYW

Narwal et al. (2022)

Moral disengagement

Moral disengagement reduces consumers’ motivation to pay more in PWYW pricing

Rabbanee et al. (2022)

Price consciousness and social desirability

Price consciousness negatively moderate the positive effect of internal reference price and fairness perception, while social desirability acts as a positive moderator

Rathore et al. (2022)

Need for recognition

Consumers with a high need for cognition exhibit a greater purchase intention towards PWYW pricing

Riener and Social norms Traxler (2012)

Social norms can explain the long-term trends of PWYW amount evolution

Roy et al. (2016)

Altruism, social desirability, and price consciousness

Altruism, social desirability, and price consciousness can influence consumers’ PWYW payments by affecting the internal reference price

Roy et al. (2021)

Social presence

Social presence positively affects consumers’ PWYW payments. Social distance and external reference price can moderate this effect negatively

Santana and Morwitz (2021)

Gender

Compared to female, male typically pay less in the context of PWYW

Schons et al. (2014)

Internal reference price

Consumers’ internal reference price decreasing can explain the declining of PWYW amount over time (continued)

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Table 3.1 (continued) Research

Factors

Main findings

Sharma et al. (2020)

Situational involvement and enduring involvement

Situational involvement and enduring involvement affect PWYW payment behavior differently across varied product categories

Stangl et al. (2017)

Potential, new, and repeat customers

When comparing potential, new, and repeat customers, it is the latter who are willing to pay the highest prices

Buyer–Seller Relationship Kim et al. (2014a, 2014b) explore the social distance between buyers and sellers in the PWYW context. In their online study, they find that a decreasing social distance leads to an increase in PWYW prices. This observation may be attributed to the challenge of establishing an anonymous environment in a restaurant or cafeteria setting, where personal interactions between guests and waiters are inevitable and independent of the payment method used. Kim et al. (2014a, 2014b) also show that for new consumers, PWYW is useful to induce a high repeat purchase rate because of the entertaining and innovative character of PWYW. Mak et al. (2015) focus on the role of interaction between sellers and consumers. Then find that if there is repeated interaction between the seller and consumers, and whether PWYW will be offered in the future depends on whether the current revenue generated through PWYW is enough for the seller to meet their financial objectives, then paying under PWYW can be seen as paying for a public good that has a threshold requirement. Schmidt et al. (2015)‘s analytical finding indicates that as the buyer’s valuation increases and the seller’s cost rises, thus the PWYW amount paid by buyers also increases. This result aligns with outcome-based social preference models such as altruism or aversion to inequity. They also find that a considerable proportion of buyers make PWYW payments for strategic motives, as they want to ensure the seller’s continuity in business (Table 3.2).

3.1.3 Context-Related Factors to PWYW External Reference Prices In practice, platforms and sellers can provide information on reference prices, such as suggested or used fixed prices and amounts that are paid by other buyers. Basically, the external reference price can serve as an anchor, causing consumers to lean towards the external reference price when deciding on their chosen prices. According to the majority of existing literature, external reference prices serve as anchor points to decrease consumer uncertainty regarding a reasonable or equitable price, as they

3.1 Pay-What-You-Want Pricing

49

Table 3.2 Seller-related factors to PWYW Research

Factors

Main findings

Kim et al. (2014a, 2014b)

Social distance

Social distance has a negative effect on PWYW payments

Mak et al. (2015)

Communication between sellers and consumers

Consumers could come to a collective agreement and socially “contract” themselves to pay which leads to a PWYW equilibrium

Schmidt et al. (2015) Seller’s cost the keeping the seller in business

Consumers would pay more when seller’s cost is high and if they want to keep the seller in business

Weisstein et al. (2019)

Product type

For hedonic products, the absence of an external reference price leads to a higher PWYW price; while for utilitarian products, the results are the opposite

Weisstein et al. (2016)

Brand familiarity

For an unfamiliar brand, virtual product experience increase PWYW price; while for a familiar brand, virtual product experience has no effect

allow buyers to align their PWYW pricing decisions accordingly. Consequently, sellers providing higher levels of external reference prices are likely to receive higher payment amounts. Similarly, PWYW sales supplemented by external reference prices are expected to yield higher payments compared to offers without such references. These suppositions are supported by the majority of empirical studies on PWYW that examine external reference prices. Armstrong Soule and Madrigal (2015) find that firm-supplied numbers can positively affect consumers’ PWYW amount. Furthermore, the type of normative information is also important. If the information is presented as a descriptive norm, i.e., what others are doing, it will be more effective at affecting payment behavior than that represents an injunctive norm, i.e., what is the right thing to do. Gautier and Klaauw (2012) find the involuntary participants’ PWYW payments increase as the posted prices increase. Kim et al. (2014a, 2014b) show that consumers rely on external reference price when determining the PWYW amount. However, some studies have documented a negative effect of external reference prices on consumers’ PWWY amounts. Johnson and Cui (2013)‘s study results find not using external reference prices may be the most beneficial strategy for the firm. Both minimum and maximum prices exhibit a negative influence on consumers’ chosen prices in comparison to not offering an external reference price. But when the suggested price is close to consumers’ internal reference price, proposing a price results in participants selecting prices that are concentrated around the suggested price. Similarly, Gross et al. (2021) discuss three external reference price strategies— minimum price strategies, maximum price strategies, and suggested price strategies. Results show that for the external reference pricing in the form of a minimum price, the mean of PWYW payments decreases but is not significantly different from the

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control group. While in a maximum price condition, the anchoring effect occurs that the external maximum price decreases the mean paid price. Regarding the mixed results of the effect of external reference price, Jung et al. (2016) use sixteen field experiments and four hypothetical studies to examine how and why anchoring effects change. Initially, researchers on anchoring effects believed that larger anchor gaps would result in more significant effects. However, Jung et al. (2016) take into account both the absolute gap (the numeric difference between the anchor values) and the distributional gap (the difference in the percentile ranks of the anchors in the payment distribution). Their findings suggest that the latter is a better predictor of anchoring effects compared to the former. Furthermore, when extreme anchors are used, low anchors exert a stronger downward pull on payments than the upward inflationary pressure exerted by high anchors. Finally, when these same paradigms are tested in laboratory settings (where participants’ wallets are not affected), the range of payments in the distribution widens. As a result, previously irrelevant extremely high anchors become reasonable and influential. Regner (2015) explores the impact of a given price range in the online music industry. He finds that because of social norms, consumers tend to pay around the recommended price. Weisstein et al. (2016) examine the moderating effect of a seller-supplied anchor price on the relationship between brand familiarity and online PWYW price. They find that if the brand is unfamiliar to consumers, providing an external anchor price will have a negative effect on consumers’ PWYW prices. While if it is a familiar brand, a presence of an external anchor price will have no significant effect on the PWYW behavior. The reason is that if an unfamiliar brand makes an anchor price claim on the internet that is perceived as implausible, it is likely to increase uncertainty for consumers and lead to more price searches. In order to determine the appropriate PWYW payment, consumers may need to gather even more information. Therefore, the presence of an external anchor price may not enhance perceived product knowledge for unfamiliar brands, but instead, by increasing uncertainty and the need for additional price searches, it may actually diminish it. However, consumers who possess greater brand knowledge and have previous experience with the brand are more likely to have a pre-existing internal reference price, which they will mainly rely on to determine the price they are willing to pay. Timing of Payment In PWYW contexts, there is evidence from several studies suggesting that PWYW prices paid tend to be higher when buyers pay after consuming or trying out the goods, as opposed to when they first decide on a payment amount and then proceed to consume the service or receive the purchased product. This difference in payment amounts is attributed to the reduction of information asymmetries favoring sellers over buyers, particularly in the case of experience goods. Viglia et al. (2019) find that consumers would like to pay more if the PWWY payment is made after consumption. They explore the underlying psychological process and find that paying after consumption can help reduce uncertainty about the process and outcome of services.

3.1 Pay-What-You-Want Pricing

51

Christopher and Machado (2019) explore the impact of PWYW design. Specifically, they focus on four designs—payment visibility, information on payment recipients, timing of payment, and price recommendation. They find that by improving payment visibility or appealing to address consumers’ concerns about fairness, it is possible to increase payment amounts and decrease free riding. Adopting a price recommendation has a dual impact on sales: a positive effect on unit sales and a negative effect on average payments. Both effects are influenced by the magnitude of the recommendation. As for the timing of payment, the post-pay design leads to more revenue than the pre-pay design since consumers tend to take the quality consumption experience into consideration when deciding the PWYW price (Table 3.3). Table 3.3 Context-related factors to PWYW Research

Factors

Armstrong Soule and External reference Madrigal (2015) prices provided by sellers

Main findings The firm-supplied reference prices have a positive effect on individual PWYW amount. Descriptive framing information is more useful than injunctive framing norm

Christopher and Machado (2019)

Payment visibility, Various PWYW designs affect consumers’ PWYW information on behavior differently payment recipients, timing of payment, and explicit price recommendations

Gross et al. (2021)

External minimum Only the maximum price affects consumers’ price, external PWYW prices with a negative effect maximum price, and suggested price

Johnson and Cui (2013)

Minimum, maximum, and suggested prices provided by sellers

Compared to not providing an external reference price, giving a minimum or maximum price will decrease consumers’ chosen prices

Jung et al. (2016)

External reference prices in lab vs. field setting

When examined in the lab, anchoring effects demonstrate exceptional robustness and replicability. However, when applied to the field, particularly in the context of financial transactions, anchoring effects may become more delicate and vulnerable to external influences

Regner (2015)

Recommended price

A tendency to conform to social norms positively influences payments made around the suggested price

Viglia et al. (2019)

Payment timing

Paying after the consumption, compared to before consumption, make consumers pay more

Weisstein et al. (2016)

Seller-supplied anchor price

For an unfamiliar brand, providing an anchor price will decrease consumers’ PWYW prices. While for a familiar brand, the effect of anchor prices is not significant

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3.2 Social Aspects of Digital Content Consumption Digital content platforms offer a highly social context for users, thanks to the public nature of online participation (Zhang et al., 2016) and the traceability of online behavior (Trusov et al., 2009). Consequently, users frequently observe various behaviors of other users and engage with others while consuming digital content. In this Section, our focus is on the social aspects of digital content consumption. We delve into various concepts that are related to the social mechanisms influencing users’ consumption behavior, with particular emphasis on PWYW payments such as tipping, donations, and gift giving.

3.2.1 Social Presence The first concept we discuss is social presence. Social presence, also known as social existence or social presentation, refers to the degree to which users perceive the presence of other participants (Short et al., 1976). On internet platforms, although users cannot communicate face-to-face, the interactive features provided by network technology can still provide users with an interactive experience and a sense of social presence (Lee & Park, 2014), and this sense of social presence increases with the frequency and timeliness of interactions (Lee & Nass, 2005). Perceiving the presence of others directly affects the individual’s arousal level (Zajonc, 1965), which in turn affects consumer behavior, such as increasing hedonic consumption (Fedorikhin & Patrick, 2010) and promoting excessive consumption (Ku et al., 2005). Social presence plays an important role in digital content consumption on social media. Greater social presence leads to increased social influence that communication subjects have on each other’s behavior (Tang et al., 2012).

3.2.2 Social Influence Social influence refers to the impact of other people’s behavior, attitudes, and emotions on individual behavior, attitudes, and emotions. It mainly includes two types of influence: informative and normative (Deutsch & Gerard, 1955; Wood & Hayes, 2012). Informative influence refers to individuals using other people’s information to help them understand reality, while normative influence refers to individuals taking action to maintain consistency with others to obtain positive feedback. When voluntary payment is in a social context, the payment behavior of others that individuals observe may affect their payment decisions through both informative and normative pathways. Firstly, from an informative perspective, the payment amount of others may serve as a reference value and affect individuals’ payment decisions, acting as an external reference price. Shang and Croson (2009) confirmed the existence of this

3.2 Social Aspects of Digital Content Consumption

53

effect in the payment for public goods. They found that disclosing information on the 90th to 95th percentile of others’ payment amount helps to increase individuals’ voluntary payment amount. Additionally, the payment behavior of others may also bring social pressure on individuals to maintain consistency and encourage them to adopt similar payment behavior. Armstrong Soule and Madrigal (2015)verified the existence of this effect through experiments. Bapna and Umyarov (2015)used field experiments to find that an increase in the number of paying users among a user’s friends in an online music community positively influences the user’s willingness to purchase paid services.

3.2.3 Social Comparison Social comparison is the process in which individuals evaluate and understand themselves by comparing their behaviors with those of others (Festinger, 1954). People improve themselves by comparing with those in a superior position (upward comparison), enhance themselves by comparing with those in an inferior position (downward comparison), and evaluate themselves by comparing with those in an equal position (parallel comparison) (Suls et al., 2002). The upward comparison may trigger individuals’ perception of threat (Gilbert et al., 1995), leading to conspicuous consumption for the purpose of psychological compensation. When there is an upward social comparison in a consumer context, the utility that consumers derive from paying for a product or service includes not only the intrinsic value of the product or service but also the relative utility that comes from comparing themselves with others (Abel, 1990). Previous studies have found that individuals may use the payment to show their wealth (Glazer & Konrad, 1996) and generosity (Harbaugh, 1998), or to avoid revealing their poverty (Lynn, 1990). Compared to non-public consumption contexts, individuals are more likely to pay a higher amount to display their status and contribution when their payment information is publicly available and can be known by others (Ariely et al., 2009).

3.2.4 Social Loafing Social loafing refers to the phenomenon in which individuals contribute less when working on a task in a group than when working alone, resulting in a “free-rider” behavior (Jackson & Harkins, 1985). When a product or service has public goods properties, individual consumers’ payment behavior may be influenced by social loafing. This is because public goods do not have exclusivity, and regardless of whether consumers pay or how much they pay, they can receive the same product or service as others. The individual utility is positively correlated with the total payment amount of all users (Epple & Romano, 1996). When consumers observe that others have contributed a higher payment amount, they may think that the provider of the

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product or service has received sufficient compensation or reward, and thus decrease their willingness to pay or reduce the payment amount (Cornelli, 1996; Levitt, 2006; Mak et al., 2010; Romano, 1991). Warr (1982) and Roberts (1984) found in charitable donations that there is a complete substitution relationship between the payment amount of individuals and that of others, meaning that if others pay one dollar more, individuals will choose to pay one dollar less. Andreoni (1989, 1990) and Burtch et al. (2018) found incomplete substitution relationships, indicating that the decrease in the payment amount of individuals due to others paying one dollar more is less than one dollar. These studies all confirm the existence of social loafing in payment behavior.

3.3 Motives for Digital Content Consumption for a Social Aspect Typically, users consume digital content on a voluntary basis, often using a PWYW pricing model. Wan et al. (2017) propose that there are two distinct attributes of donation (consumption) behavior in social media. The first is the charitable attribute. Users usually do not receive direct commensurate tangible benefits when they make a payment or donation. The other is the consumptive attribute. Users pay for the consumption of goods or services provided by content contributors. Consumption on social media is akin to busking or street performance, where the performer or content creator offers a service or knowledge, and the audience then voluntarily contributes an amount. Given the two attributes of paying for digital consumption, Wan et al. (2017) develop a theoretical framework that focuses on the role of social and technological factors on users’ digital content consumption behavior. On the one hand, the social system consists of three elements—identification, interaction, and information value, it can affect users’ consumption intention through the emotional attachment to content providers. On the other hand, the technical system comprises competitiveness, sociability, and personalization, functional dependence on social media mediates the relationship between it and users’ willingness to pay for digital content. Consumers with heterogenous characteristics have different digital content consumption intentions. Punj (2015) finds that, besides income, education, and age, gender is also a factor influencing consumers’ willingness to pay. While males have a higher payment amount than females, the digital content consumption intention for females is significantly higher than for males. It is because females tend to prioritize the social aspect of information (Van Slyke et al., 2002). Borck et al. (2006) examine how others’ digital content payment behavior affects one’s own consumption decision. Unlike the prediction under the standard private provision model that users would like to contribute less if others contribute more, they find that for information good consumption, the more readers anticipate others contributing, the more likely they are to make a payment.

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Lu et al. (2021) investigate the influence of others on PWYW behavior in the context of live streaming. They propose that, on the one hand, the increasing audience size can boost voluntary payment, especially if social image concerns are significant, as larger audiences can magnify the perceived utility of social image. On the other hand, the increase in audience size may decrease tips if tipping motivation stems from the desire to receive reciprocal acts from the broadcaster, as larger audiences tend to intensify the competition for reciprocity. Their empirical analyses provide evidence for the former that as the audience size increases, the average tip per viewer becomes larger. The results emphasize the importance of social image in digital content consumption.

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Reinartz, W., & Kumar, V. (2002). The mismanagement of customer loyalty. Harvard Business Review, 80(7), 86–87. Scopus. Richins, M. L., & Bloch, P. H. (1986). After the new wears off: The temporal context of product involvement. Journal of Consumer Research, 13(2), 280. https://doi.org/10.1086/209067 Riener, G., & Traxler, C. (2012). Norms, moods, and free lunch: Longitudinal evidence on payments from a Pay-What-You-Want restaurant. The Journal of Socio-Economics, 41(4), 476–483. https://doi.org/10.1016/j.socec.2011.07.003 Roberts, R. D. (1984). A positive model of private charity and public transfers. Journal of Political Economy, 92(1), 136–148. Romano, R. (1991). When excessive consumption is rational. American Economic Review, 81(3), 553–564. Roy, R., Rabbanee, F. K., & Sharma, P. (2016). Antecedents, outcomes, and mediating role of internal reference prices in pay-what-you-want (PWYW) pricing. Marketing Intelligence & Planning, 34(1), 117–136. https://doi.org/10.1108/MIP-08-2015-0157 Roy, R., Sharma, P., Chan, R. Y. K., & Potdar, V. (2021). Exploring the role of spotlight effect in paywhat-you-want (PWYW) pricing—An anchoring and adjustment perspective. Psychology & Marketing, 38(5), 866–880. https://doi.org/10.1002/mar.21474 Santana, S., & Morwitz, V. G. (2021). The role of gender in pay-what-you-want contexts. Journal of Marketing Research, 58(2), 265–281. https://doi.org/10.1177/0022243721992056 Schons, L. M., Rese, M., Wieseke, J., Rasmussen, W., Weber, D., & Strotmann, W.-C. (2014). There is nothing permanent except change—Analyzing individual price dynamics in “paywhat-you-want” situations. Marketing Letters, 25(1), 25–36. https://doi.org/10.1007/s11002013-9237-2 Sen, S., & Lerman, D. (2007). Why are you telling me this? An examination into negative consumer reviews on the Web. Journal of Interactive Marketing, 21(4), 76–94. https://doi.org/10.1002/dir. 20090 Shang, J., & Croson, R. (2009). A field experiment in charitable contribution: The impact of social information on the voluntary provision of public goods. The Economic Journal, 119(540), 1422–1439. https://doi.org/10.1111/j.1468-0297.2009.02267.x Sharma, P., Roy, R., & Rabbanee, F. K. (2020). Interactive effects of situational and enduring involvement with perceived crowding and time pressure in pay-what-you-want (PWYW) pricing. Journal of Business Research, 109, 88–100. https://doi.org/10.1016/j.jbusres.2019.11.078 Short, J., Williams, E., & Christie, B. (1976). The social psychology of telecommunications. Wiley. Stangl, B., Kastner, M., & Prayag, G. (2017). Pay-what-you-want for high-value priced services: Differences between potential, new, and repeat customers. Journal of Business Research, 74, 168–174. https://doi.org/10.1016/j.jbusres.2016.10.030 Suls, J., Martin, R., & Wheeler, L. (2002). Social comparison: Why, with whom, and with what effect? Current Directions in Psychological Science, 11(5), 159–163. https://doi.org/10.1111/ 1467-8721.00191 Tang, Q., Gu, B., & Whinston, A. B. (2012). Content contribution for revenue sharing and reputation in social media: A dynamic structural model. Journal of Management Information Systems, 29(2), 41–76. https://doi.org/10.2753/MIS0742-1222290203 Trusov, M., Bucklin, R. E., & Pauwels, K. (2009). Effects of word-of-mouth versus traditional marketing: Findings from an internet social networking site. Journal of Marketing, 73(5), 90– 102. https://doi.org/10.1509/jmkg.73.5.90 Van Slyke, C., Comunale, C. L., & Belanger, F. (2002). Gender differences in perceptions of webbased shopping. Communications of the ACM, 45(8), 82–86. https://doi.org/10.1145/545151. 545155 Viglia, G., Maras, M., Schumann, J., & Navarro-Martinez, D. (2019). Paying before or paying after? Timing and uncertainty in pay-what-you-want pricing. Journal of Service Research, 22(3), 272–284. https://doi.org/10.1177/1094670519835308

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Wan, J., Lu, Y., Wang, B., & Zhao, L. (2017). How attachment influences users’ willingness to donate to content creators in social media: A socio-technical systems perspective. Information & Management, 54(7), 837–850. https://doi.org/10.1016/j.im.2016.12.007 Wang, C. X., Beck, J. T., & Yuan, H. (2021). The Control-effort trade-off in participative pricing: How easing pricing decisions enhances purchase outcomes. Journal of Marketing, 85(5), 145– 160. https://doi.org/10.1177/0022242921990351 Wang, C. X., Yuan, H., & Beck, J. T. (2022). Too tired for a good deal: How customer fatigue shapes the performance of Pay-What-You-Want pricing. Journal of Business Research, 144, 987–996. https://doi.org/10.1016/j.jbusres.2022.02.014 Warr, P. G. (1982). Pareto optimal redistribution and private charity. Journal of Public Economics, 19(1), 131–138. https://doi.org/10.1016/0047-2727(82)90056-1 Weisstein, F. L., Choi, P., & Andersen, P. (2019). The role of external reference price in pay-whatyou-want pricing: An empirical investigation across product types. Journal of Retailing and Consumer Services, 50, 170–178. https://doi.org/10.1016/j.jretconser.2019.05.017 Weisstein, F. L., Kukar-Kinney, M., & Monroe, K. B. (2016). Determinants of consumers’ response to pay-what-you-want pricing strategy on the Internet. Journal of Business Research, 69(10), 4313–4320. https://doi.org/10.1016/j.jbusres.2016.04.005 Wieseke, J., Alavi, S., & Habel, J. (2014). Willing to pay more, eager to pay less: The role of customer loyalty in price negotiations. Journal of Marketing, 78(6), 17–37. https://doi.org/10. 1509/jm.13.0104 Wood, W., & Hayes, T. (2012). Social Influence on consumer decisions: Motives, modes, and consequences. Journal of Consumer Psychology, 22(3), 324–328. https://doi.org/10.1016/j.jcps. 2012.05.003 Zajonc, R. B. (1965). Social Facilitation: A solution is suggested for an old unresolved social psychological problem. Science, 149(3681), 269–274. https://doi.org/10.1126/science.149.368 1.269 Zhang, L., Zhao, J., & Xu, K. (2016). Who creates trends in online social media: the crowd or opinion leaders?: Trends in online social media, the crowd, opinion leaders. Journal of ComputerMediated Communication, 21(1), 1–16. https://doi.org/10.1111/jcc4.12145

Chapter 4

Digital Content Contribution and Consumption in Live Streaming

This Chapter provides a broad overview of the live streaming industry and introduces three primary stakeholders of the industry, i.e., broadcasters, viewers, and platforms. As a novel form of streamed media, live streaming has been a booming industry that attracts hundreds of millions of broadcasters and viewers around the world. We provide a detailed introduction to the live streaming industry as it offers an excellent context for examining digital contribution and consumption behavior. Live streaming platforms allow broadcasters to present diverse content in real-time, while viewers can participate by not just watching, but also engaging in real-time interactions such as chatting and gifting. In China, gifting represents the primary source of revenue for most live streaming platforms. While gifting is completely voluntary, a surprisingly large number of viewers are willing to gift, which makes the live streaming industry a $1.23 billion market.1 In this Chapter, our empirical analyses revolve around the economics of live streaming and involve three main questions. We review relevant literature and specifically investigate what factors influence broadcasters’ receipt of gifts and what motivates viewers to send them. Additionally, we examine the role of gifting in the churn rate of both broadcasters and viewers, as well as the correlation between economic and geographic factors and the growth of the live streaming industry. Using a unique dataset from a popular Chinese live streaming platform, we conduct empirical analyses that provide valuable insights into the economics of live streaming in China. Our findings also raise additional questions that can be explored in future research. This book employs live streaming as our empirical context. In Parts II and III, we utilize data from live streaming to delve deeper into the social incentives for 1 https://www.globenewswire.com/en/news-release/2023/01/12/2587861/28124/en/2-6-BillionWorldwide-Live-Streaming-Industry-to-2031-Increase-in-Penetration-of-Mobile-Devices-and-Int ernet-Users-Drives-Growth.html.

This Chapter is derived, in part, from the article “Antecedents and Consequences of Gift-Receiving in Livestreaming: An Exploratory Study” published in Journal of Interactive Marketing on May 18, 2022, available online: https://journals.sagepub.com/doi/10.1177/10949968221095550. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Ma, Social Influence on Digital Content Contribution and Consumption, Management for Professionals, https://doi.org/10.1007/978-981-99-6737-7_4

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contributing digital content, the social motivations for consuming digital content, and the dynamics of both digital content contribution and consumption behavior, as well as their social aspects.

4.1 Live Streaming Industry In recent years, live streaming has witnessed a surge in popularity worldwide. For example, in 2017, 48% of US internet users watched live streaming content once a week, and 23% did so once a day.2 In China, the market size of live streaming was $7.5 billion in 2018, $10.6 billion in 2019, and is estimated to reach around $16.3 billion in 2020.3 Apart from professional live streaming platforms, such as Periscope, Twitch, and Panda TV, many social media platforms, such as Facebook, Twitter, and YouTube, have incorporated live streaming functions into their offerings. Live streaming is so popular that even before Facebook officially launched its live streaming service in April 2016, one-third of Facebook users had already watched at least one live video by celebrities on the platform.4 Live streaming was a $42.6 billion industry in 2019 and is expected to grow at a compound annual growth rate of 20.4% from 2020 to 2027.5 As a novel medium, live streaming is a typical two-sided market that connects broadcasters and viewers. Broadcasters are the content providers on live streaming platforms, they may sing, dance, play video games, and even sell products. Viewers can participate in the live sessions via real-time interactions, such as sending “likes”, text messages, and virtual gifts. According to the content produced, live streaming platforms can be classified into three major categories: showroom, gaming, and e-commerce (Chen & Xiong, 2019). Showroom platforms commonly feature realtime talent performances or everyday activities, such as talk shows and eating a meal. Gaming platforms broadcast video games. Game enthusiasts gather on such platforms to broadcast, watch, and communicate about video games. E-commerce live streaming is a rising star in the industry, which provides a new way to sell online. Given the COVID-19-induced lockdowns and isolations, the e-commerce live streaming industry has surged in popularity and is expected to reach more than 800 billion RMB by the end of 2020.6 Figure 4.1 presents snapshots of these live streaming platforms. Different live streaming platforms adopt different business models. For ecommerce live streaming platforms, advertising and commission fees are the main 2

https://www.emarketer.com/Article/Some-Live-Streaming-Video-Already-Constant/1016137. https://www.statista.com/statistics/874591/china-online-live-streaming-market-size/#statistic Container. 4 https://www.ericsson.com/assets/local/mobility-report/documents/2016/emr-november-2016live-streaming-joins-social-media.pdf. 5 https://www.grandviewresearch.com/industry-analysis/video-streaming-market. 6 https://technode.com/2020/06/12/livestreaming-in-china-only-for-sales-or-is-there-brand-value/. 3

4.1 Live Streaming Industry

(a) Showroom

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(b) Gaming

(c) E-Commerce

Fig. 4.1 a Showroom live streaming. Source https://www.6rooms.com. b Gaming live streaming. Source https://www.douyu.com. c E-Commerce live streaming. Source https://live.tmall.com

revenue sources. Showroom and gaming platforms employ an innovative giftingbased business model, which is among the main reasons behind their popularity, especially in China. Such platforms (e.g., Kuaishou and 6Rooms in China and TikTok and Twitch in the US) monetize live streaming content via viral gifts. Viewers can purchase virtual gifts from platforms and send them to broadcasters during live sessions. The monetary value of the received gifts will be shared between the platform and broadcasters. Even though platforms often take 60% to 70% of the gifts,7 they offer a convenient channel for broadcasters to capitalize on their talents. Notably, gifting during a live streaming event is a kind of “pay-what-you-want” pricing strategy. Gifting, which can be regarded as payment, is voluntary. In most instances, viewers can watch any live session or interact with any broadcaster irrespective of whether they pay or how much they pay. However, the wide practice among several live streaming platforms indicates that the gifting-based business model is viable. For example, in 2019, Kuaishou generated 31.4 billion RMB in revenue from gifting during live streaming events.8 Further, in the US, users have spent close to $41.3 million on TikTok by March 2019.9

7

http://mat1.gtimg.com/chinatechinsights/file/20160926/The_pulse_of_live_streaming_in_ China.pdf. 8 https://www.cbnweek.com/articles/normal/25317. 9 https://sensortower.com/blog/tiktok-revenue-75-million.

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Live streaming presents an ideal context for studying the social influence in digital content contribution and consumption. The real-time nature of live streaming allows for immediate feedback and interaction between broadcasters and viewers, creating a dynamic and interactive environment. This interactivity and social engagement can motivate individuals to produce and consume digital content, making live streaming a valuable platform for investigating the impact of social incentives on digital content contribution and consumption. Moreover, live streaming has become increasingly popular across a range of industries, from gaming and entertainment to e-commerce and education. This popularity has led to the emergence of a diverse range of live streaming platforms, each with their own incentive mechanisms and user communities. By examining the social influence in digital content contribution and consumption within the live streaming context, we can gain valuable insights into the broader role of social incentives in shaping online behavior and engagement.

4.2 Stakeholders in Live Streaming Broadcasters Broadcasters are the content providers in live streaming who host live sessions on the platform. There are some notable characteristics of broadcasters. In Chinese live streaming market, young female broadcasters are the majority, 73.80% of broadcasters were born after 1990 and 70.88% are female.10 The geographic distribution of broadcasters in China is quite uneven. Northeast provinces, namely Heilongjiang, Jilin, and Liaoning, exhibit a higher concentration of broadcasters partly due to the economic struggle and local culture in these regions. Data from multiple live streaming platforms indicate that more than or close to half of the top broadcasters are from Northeast China.11 Virtual gift is the dominant income source for broadcasters. Popular broadcasters can earn more than ¥10 million annually. The amount could be even higher if the broadcaster is a star or celebrity. However, the income distribution is pretty skewed. Only 15% of broadcasters can earn more than ¥5,000 per month.12 The potential to earn money attracts many people to join live streaming as broadcasters. Most of them are ordinary people with some talents, e.g., singing, dancing, drawing, and so on. Besides showing talents, broadcasters can also interact with viewers in real-time during live sessions. Interacting with viewers might be important for broadcasters to induce more virtual gifts. For example, viewers might be more likely to send gifts to broadcasters when broadcasters respond to their messages or perform as their request. 10

http://www.sohu.com/a/166236745_463963. http://tech.qq.com/a/20160825/003015.htm. 12 http://daxueconsulting.com/chinese-live-streaming-millionaires/. 11

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Broadcaster alliance is a featured organization in live streaming. As live streaming industry flourishes, broadcaster alliances come into being to support the development of broadcasters. Alliances usually consist of broadcasters and administrators, which provide broadcasters with a variety of resources, including training, equipment, facility, marketing, public relations, and so on. In return, a portion of broadcasters’ income will be shared with the alliances they belong to. Viewers On the viewer side, the user base of live streaming in China was 398 million in 2017, and is expected to reach 460 million in 2018.13 A number of industry reports and market surveys provide us some initial understanding of viewers’ characteristics and behavior on live streaming platforms. The gender distribution among viewers is contrary to that on the broadcaster side, more than 60% are males. While the gender ratio seems to becoming more and more balanced. Based on a report by iResearch, the proportion of female viewers increased from 30.5% to 35.6% in 2016, which could be credited to the diversification of live streaming content, like makeup, fashion and stars.14 Young generations account for the majority of the audience in live streaming. According to a survey conducted by Tencent in 2016, 83.1% of viewers are under the age of 30 and 42.7% are under 20. Moreover, viewers come from diverse geographic areas and socioeconomic background. 39.7% of viewers are from first- and secondtier cities, and 75.2% fall within the middle to high income range. Watching live streaming seems like a channel for viewers to seek companionship and interactions with others to fill their spare time. 67.8% of viewers engage in live streaming because of boredom according to the same survey by Tencent.15 Watching, gift sending (gifting), and text messages sending (chatting) are the three primary activities that viewers can engage in on live streaming platforms. Viewing and chatting are typically free. Viewers can start and stop watching any live session at any time, as well as communicate with broadcasters and other viewers through text-based instant messages. Besides these free functions, live streaming platforms usually provide a variety of well-designed virtual gifts, such as graphic representations of flowers, candies, and cars, whose prices range from less than ¥1 to more than ¥10,000. These virtual gifts need to be purchased with money, and such gifts can be regarded as a special type of payment. A surprisingly large number of viewers are willing to send gifts in live streaming. For example, YY Live, one of the leading live streaming platforms in China, reported 7.2 million paying users and $120.9 annual average revenue per paying user by 2015.16

13

http://www.iimedia.cn/61402.html. http://www.iresearchchina.com/content/details8_31752.html. 15 http://mat1.gtimg.com/chinatechinsights/file/20160926/The_pulse_of_live_streaming_in_ China.pdf. 16 From YY’s 2015 financial reports. 14

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Platforms According to the content, live streaming platforms in China can be broadly classified into three major categories, i.e., showroom, gaming, and pan-entertainment. The emergence of showroom platforms can be dated back to 2005 when 9158 was launched. 6Rooms and YY Live are also key players in this category and played an important role in establishing the business model for the live streaming industry. These platforms commonly feature real-time singing, dancing, and other talent performances. The “pay-what-you-want” pricing strategy works well for showroom streaming and leads to several highly profitable platforms. The leading players YY Live and 9158 went public on NASDAQ in 2012 and SEHK in 2014 respectively. 6Rooms was acquired by a public firm listed on SHE for a price tag of ¥2.6 billion in 2015. Gaming platform, as the name indicates, focuses on broadcasting video games. Game enthusiasts gather on gaming platforms to broadcast, watch and communicate about game playing. Video game streaming started its explosive growth in 2014 and attracted over 80 million viewers by the end of 2016.17 However, most gaming platforms struggle to earn any profit despite their popularity. Douyu, one of the market leaders, disclosed in November 2017 that is started to record profits.18 Pan-entertainment platforms provide contents that span a broad range of activities. These user generated contents may not be professional but emphasize self-expression. Among the three categories, pan-entertainment platforms emerged as the latest but grew the fastest. They account for 44.5% of the live streaming market according to a report released by Analysys in 2017.19 Live streaming platforms are typical two-sided markets. Viewers are more likely to stay on platforms with a large number of broadcasters and high-quality content; and broadcasters prefer platforms that are popular among viewers in order to attract more audience and virtual gifts. Concerning this indirect network effect (Katz & Shapiro, 1985; Rochet & Tirole, 2003), many platforms now offer popular broadcasters hefty salaries to prevent them from performing on other platforms. For example, a number of gaming platforms, such as Quanmin TV and Panda TV, signed with well-known game players at an annual salary of more than ¥30 million.20

4.3 Related Literature Gifting on live streaming platforms is a form of pay-what-you-want (PWYW) payment. Typically, viewers can watch any live session and interact with broadcasters (e.g., by sending text messages and "likes") on live streaming platforms, 17

http://report.iresearch.cn/report/201709/3058.shtml. https://technode.com/2017/11/21/chinas-version-of-twitch-secures-fresh-d-round-and-starts-tomake-profits/. 19 https://chozan.co/2017/07/05/marketing-prospects-live-streaming-china/. 20 daxueconsulting.com/chinese-live-streaming-millionaires/. 18

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regardless of whether or how much they pay. In contrast to traditional PWYW applications, where payment behavior of others is not observable, gifting in live streaming is publicly observable in real-time. This means that viewers can observe each other’s gifting behavior during their viewing period. Additionally, live streaming platforms highlight gifting information more prominently than other types of information, such as text messages. For instance, virtual gifts are designed with special colors, symbols, or animations, particularly for high-valued ones. As gifting is a novel pricing strategy and a primary revenue source for live streaming platforms, our focus is on factors associated with broadcasters’ gift attraction and viewers’ gift giving. Prior research has explored the motives for paying under PWYW pricing. For instance, studies Chen et al. (2017), Kim et al. (2009), and Schmidt et al. (2015) have found that altruism, satisfaction, loyalty, and fairness concerns have a positive impact on consumers’ payments. Mak et al. (2015) revealed that consumers are willing to pay to keep the business running under PWYW pricing. The social context of live streaming is linked to research on social influence. Armstrong Soule and Madrigal (2015) demonstrated that individuals are more likely to report higher payments when they see higher average payments from others. Lu et al. (2021) discovered that others’ payments have both a signaling effect and a competition effect on one’s payment in live streaming. The signaling effect motivates viewers to gift more than others to signal their economic status, while the competition effect makes viewers less likely to gift when facing a larger number of gifting instances and amounts from others. Romano (1991) theoretically proposed that an increase in payments from others would decrease one’s own incentives to pay. Then, this Chapter aims to investigate the role of gifting in user retention for both broadcasters and viewers. For broadcasters, receiving gifts from viewers serves as a form of monetary incentive that may drive individuals to continue working. Additionally, receiving interactions (such as text messages) from viewers can be seen as a non-monetary incentive that also motivates broadcasters to keep working (Cassar & Meier, 2018). We analyze the effects of both monetary and non-monetary incentives on broadcasters’ retention. For viewers, gifting represents not only a monetary expense but also a means of participating in live sessions and expressing satisfaction with the content. As participation and satisfaction have been identified as key drivers of consumer loyalty (Johnson et al., 2001; Mittal & Kamakura, 2001), we hypothesize that viewers who gift more are more likely to stay on the platform for longer periods. Finally, this Chapter also investigates the impact of economic and geographic factors on the growth and evolution of the live streaming industry. In the past, live streaming was thought to be more prevalent in underdeveloped regions with limited entertainment options. However, recent data suggests that the distribution of live streaming users is becoming more balanced, with the penetration index of live streaming being the highest in first-tier cities, according to Tencent MyApp Big Data. In the upcoming sections, we will present empirical evidence to address these three questions to provide a picture of live streaming economics.

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4.4 Data Description To investigate the economic behavior of both viewers and broadcasters in live streaming, we collected data from a major showroom live streaming platform in China. This platform had over 180,000 registered broadcasters and 100 million monthly active viewers as of June 2017. Our observation period spanned 200 days, from December 1, 2016 to June 18, 2017. We randomly sampled 10,000 broadcasters who had performed at least once and 10,000 viewers who had watched at least one session during this period. These individuals were selected as our focal broadcasters and viewers. For each broadcaster, we recorded their daily broadcasting length, as well as the gifts and text messages they received each day. We also obtained information about the broadcasters’ gender, performance type, alliance membership, geographic location, and other characteristics from the platform. For viewers, we tracked the duration of their viewing sessions, as well as the amount of gifts and number of text messages they sent during each session.

4.4.1 Broadcaster Side Patterns Among the 10,000 focal broadcasters who ever performed between December 1, 2016, and June 18, 2017, there are 92.62% female and 7.38% male. The gender distribution is extremely unbalanced. There are four primary types of broadcasters focusing on different performance content: i.e., singing, dancing, Chinese Hip-Hop, and chatting. 77.21% of focal broadcasters fall into the chatting category, singing, Chinese Hip-Hop, and dancing account for 14.80%, 4.71%, and 3.28% respectively. The content performed by male and female broadcasters is quite different. Chatting and singing are the most popular among female broadcasters while chatting and Chinese Hip-Hop are the top choices for male ones. As for alliance joining, 45.74% of focal broadcasters belong to an alliance and the largest broadcaster alliance consists of 944 broadcasters. These focal broadcasters performed 263,386 live sessions in total during the observational period. On average, a broadcaster performed 0.92 times, nearly once, a week. And the total length of broadcasting is 100.20 h on average and 14.83 h on median. 89.79% of focal broadcasters ever received virtual gifts from viewers. The distribution of the total amount of gifts received by these broadcasters is shown in Fig. 4.2. The top broadcaster gained ¥0.21 million in total during the observational period. However, the distribution is extremely skewed, the average amount was ¥10,390.24 and the median was ¥54.58, only 23.20% of broadcasters received more than ¥1,000. The total number of text messages received by each broadcaster is 10,971 on average and 465 on median. Broadcasters are free to choose how long to perform on each day. Daily broadcasting duration ranged from 0.08 to 24 h, with an average of 3.76 h and a median of

4.4 Data Description

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Fig. 4.2 Total amount of gifts received by broadcasters during the observational period

3.33 h. On each broadcasting day, there were 504.10 watching viewers on average. Only 0.43% of sessions attracted no audience at all. 88.17% of broadcasting days were gifted by at least one viewer. Figure 4.3a and b display the distribution of total gifting incidence and gifting amount on each broadcasting day. The average gifting incidence received per day is 11.19 and the median is 7.00, and the average gifting amount is ¥37.75 and the median is ¥28.90. As for interactions, viewers interact with broadcasters through text messages on 91.77% of broadcasting days, with 28.68 chatting viewers and 406.60 chatting messages per day on average.

4.4.2 Viewer Side Patterns In our viewer sample, there are 10,000 viewers who watched at least one live session in the observational period. 42.15% of them are paying viewers who ever sent virtual gifts during the 200 days, and the rest 57.85% are non-paying viewers who never gifted. We present a comparison of paying viewers and non-paying viewers on watching and chatting behavior. In general, paying viewers are much more active than non-paying viewers in watching live sessions and interacting with broadcasters on the platform. On average, a paying viewer spent 71.00 h on 267.40 live sessions in total during the observational period, which is about 7 times more than for non-paying viewers, who averagely watched 55.32 sessions totaling 9.86 h on the platform. For paying viewers, the total number of sessions in which the viewer sent messages to the broadcaster was 72.33, about 6 times more than that for non-paying viewers. We especially focus on viewers’ paying (gifting) behavior. During the observational period, the 4,215 paying viewers gifted ¥0.67 million in total, 82.81% of

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Fig. 4.3 Gifting incidence and the amount received by broadcasters per day

(a): Gifting Incidence

(b): Gifting Amount

which were gifted by the 5% top givers who contributed more than ¥4378.39 each. The extremely skewed distribution of paying viewers’ total incidence and amount of gifts are presented in Fig. 4.4a and b. While the top giver spent ¥0.11 million on the platform in total, the average total amount of gifts was ¥1,579.75 and the median is ¥30.70. The distribution of viewers’ amount of gifts sent in each paid session is presented in Fig. 4.5, which is ¥59.12 on average and ¥1.60 on median. During the whole observational period, viewers’ average number of gifted sessions was 26.72 and the median number was 7. Considering the proportion of paid sessions among watched sessions, paying viewers sent virtual gifts in 1 session for every 5.70 watched sessions on average.

4.5 Empirical Results

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Fig. 4.4 Total gifting incidence and amount sent by viewers during the observational period

(a): Gifting Incidence

(b): Gifting Amount

4.5 Empirical Results 4.5.1 What Factors Are Associated with Gifting? Broadcasters’ Gifting Receiving On the broadcaster side, we are interested in what factors lead to success in the sense of attracting virtual gifts. More specifically, we focus on what influences the number of paying viewers (Gift_Num_Day) and the total amount of gifts (Gift_Amt_ Day) the broadcaster received on each day. Under “pay-what-you-want” pricing, the quality of the product or service may increase the satisfaction of consumers, and thus increase their willingness to pay (Kim et al., 2009). Although we are not able to measure broadcasters’ performance quality directly, we could use the number of

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Fig. 4.5 Amount of gifts sent by viewers per session

watching viewers within the day (Watch_Num_Day) as a proxy since high-quality sessions are more likely to attract more viewers. As the number of watching viewers will increase naturally with the duration of the session, we control for the length of broadcasting on the day (Broad_Length_Day). The content type performed by the broadcaster may affect gifts received as well. As suggested by Kim et al. (2014), the interaction between buyers and sellers has a positive effect on price setting under the “pay-what-you-want” strategy. In the case of live streaming, interactions with viewers may help broadcasters induce virtual gifts, and more interactive formats can be more effective in doing so. For example, compared with dancers, it is more convenient for broadcasters in chatting, singing and MC categories to interact with viewers, since they could sit on broadcasting devices (e.g., laptops, tablets, and so on) and reply to viewers’ messages in real time. Variable Chat_Num_Day refers to the number of viewers who send text messages to the broadcaster in the session and Type refers to the content type (i.e., singing, dancing, chatting, and Chinese Hip-Hop) provided by the broadcaster. Broadcaster alliances may also play an important role in attracting gifts from viewers. Variable Alliance indicates whether the broadcaster joined an alliance or not. Broadcasters who join an alliance could enjoy some advantages over those who don’t. As we discussed in Sect. 4.2, alliances support broadcasters’ routine activities and provide training. Members in the alliance may even send virtual gifts to the broadcaster in order to encourage more gifts by following ordinary viewers.21

21

On the other hand, broadcasters who take live streaming seriously may be more likely to join an alliance, and alliances may be more likely to sign with high-quality performers. This selection could also contribute to a positive relationship between joining an alliance and attracting gifts.

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We regress the number of gifting incidence (Gift_Num_Day) and the amount of gifts (Gift_Amt_Day) a broadcaster receives on each broadcasting day on a set of independent variables. Results are presented in Table 4.1. All the continuous variables included in Tables 4.1, 4.2, 4.3, 4.4, 4.5 and 4.6 are transformed into logarithmic forms. We can see that the number of viewers (Watch_Num_Day) and the number of text messages (Chat_Num_Day) received on that day are positively related to both gifting incidence and amount, which suggest that the quality of content and interaction with viewers play important roles in gifts gaining in live streaming. Female broadcasters are better at attracting viewers to send gifts than males. Broadcasters who perform dancing are less effective in inducing virtual gifts than most other types of broadcasters, lacking interactions with viewers might be one of the reasons. For broadcasters who belong to an alliance, the number of gifting viewers per day is 2.4% and the total amount of gifts received per day is 24.1% more than independent ones. Joining an alliance seems to help broadcasters get more gifts, especially in terms of gifting amounts.

Table 4.1 Regressions on broadcasters’ gifting receiving

(1) Gift_Num_Day

(2) Gift_Amt_Day

Watch_Num_Day

0.037*** (0.001)

0.013*** (0.004)

Chat_Num_Day

0.533*** (0.002)

1.289*** (0.004)

Broad_Length_Day

0.307*** (0.003)

0.198*** (0.007)

Gender = Female

– (.)

– (.)

Gender = Male

−0.019*** (0.004)

−0.342*** (0.012)

Type = Singing

– (.)

– (.)

Type = Dancing

−0.132*** (0.006)

−0.260*** (0.016)

Type = Chatting

0.018*** (0.004)

−0.697*** (0.011)

Type = Chinese Hip-Hop

0.058*** (0.004)

0.274*** (0.011)

Alliance

0.024*** (0.003)

0.241*** (0.007)

Date fixed effects

Yes

Yes

Observations

267,828

267,828

Adjusted R-Square

0.7406

0.6764

Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01

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Table 4.2 Regressions on viewers’ gifting sending

(1) Gift

(2) Gift_Amt

Watch_Length

0.131*** (0.002)

0.085*** (0.004)

Chat_Times

0.372*** (0.002)

0.300*** (0.004)

Gift_Num_Other

0.247*** (0.004)

0.055*** (0.009)

Gift_Amt_Avg_Other

−0.012*** (0.002)

0.037*** (0.004)

Chat_Num_Other

−0.243*** (0.003)

−0.022** (0.010)

Watch_Num_Broadcaster

−0.133*** (0.003)

−0.016* (0.009)

Gift_Num_Broadcaster

0.487*** (0.006)

0.041*** (0.012)

Gift_Amt_Broadcaster

0.020*** (0.002)

0.089*** (0.004)

Watch_Num_Platform

−0.187*** (0.002)

−0.073*** (0.007)

Gift_Num_Platform

0.281*** (0.003)

– (.)

Gift_Amt_Platform

−0.028*** (0.001)

−0.020*** (0.004)

Gift_Ratio_Platform

1.212*** (0.017)

– (.)

IMR

– (.)

0.292*** (0.033)

Broadcaster fixed effects

Yes

Yes

Viewer fixed effects

Yes

Yes

Observations

1.447,197

112,633

Adjusted R-Square

0.4352

0.5818

Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01

Viewers’ Gifting Sending On the viewer side, we are interested in what motivates viewers to gifts, concerning viewers can watch any live session and communicate with any broadcaster regardless of their payments. Three sets of independent variables are considered: First, focal viewers’ watching and texting behavior in the session: Watch_Length, the length of watching of the session; Chat_Times, the number of text messages the viewer sent in the session. Second, other viewers’ behavior in the same session: Gift_Num_ Other and Chat_Num_Other refer to the number of other viewers who sent gifts and

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Table 4.3 Cox model for churning of broadcasters (1) Exit All

(2) Exit All

(3) Exit Top 25% Gift_Amt_ Cul

(4) Exit Top 25% Gift_Amt_ Cul

Broad_Length_ Cul

0.036 (0.064)

0.343*** (0.029)

−1.870*** (0.261)

−0.338*** (0.097)

Gift_Amt_Cul

−0.135*** (0.033)

−0.098*** (0.016)

−0.005 (0.165)

−0.035 (0.061)

Chat_Times_Cul

−0.376*** (0.039)

−0.116*** (0.020)

0.112 (0.223)

0.008 (0.072)

Gender = Female – (.)

– (.)

– (.)

– (.)

Gender = Male

– (.)

−0.028 (0.077)

– (.)

−0.036 (0.214)

Type = Singing

– (.)

−0.075 (0.128)

– (.)

−0.223 (0.275)

Type = Dancing

– (.)

−0.112 (0.165)

– (.)

−0.275 (0.360)

Type = Chatting

– (.)

0.044 (0.114)

– (.)

−0.181 (0.255)

Type = Chinese Hip-Hop

– (.)

– (.)

– (.)

– (.)

Alliance

– (.)

−0.257*** (0.034)

– (.)

−0.014 (0.110)

Broadcaster fixed effects

Yes

–

Yes

–

Observations

78,509

78,509

19,627

19,627

Adjusted R-Square

0.3829

0.0441

0.5046

0.2257

Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01

messages in the session respectively; Gift_Amt_Avg_Other indicates the average amount of gifts sent by other viewers in the session. Third, focal viewers’ historical behavior over the past 30 days: Watch_Num_Broadcaster refers to the number of times the focal viewer watched the current broadcaster’s live sessions; Gift_Num_ Broadcaster and Gift_Amt_Broadcaster refer to the number of times the focal viewer gifted and the amount he gifted to the current broadcaster respectively. We also control for focal viewers’ overall behavior on the live streaming platform in the past: Watch_Num_Platform measures the number of sessions the focal viewer watched on the platform in the past 30 days; Gift_Num_Platform and Gift_Amt_Platform indicate the gifting incidence and amount the focal viewer sent on the platform during 30 days before; Gift_Ratio_Platform is calculated as the number of gifted

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Table 4.4 Cox model for churning of viewers

(1) Exit

(2) Exit

Watch_Num_Cul

–0.585*** (0.037)

– (.)

Gift_Num_Cul

–0.424*** (0.055)

– (.)

Chat_Num_Cul

–0.140*** (0.051)

– (.)

Watch_Length_Cul

– (.)

–0.525*** (0.024)

Gift_Amt_Cul

– (.)

–0.103*** (0.028)

Chat_Times_Cul

– (.)

–0.029*** (0.028)

Viewer fixed effects

Yes

Yes

Observations

106,460

106,460

Adjusted R-Square

0.3819

0.3766

Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01

sessions (Gift_Num_Platform) divided by the number of watched sessions (Watch_ Num_Platform). Viewers’ payment decisions consist of two components: whether to gift (Gift) and how much to gift (Gift_Amt). We use a Heckman (1979) two-stage model to incorporate these two related decisions. Column (1) in Table 4.2 presents the results of the first stage of whether to gift, and Column (2) reports the second stage results of how much to gift. Since independent variables in the second stage should be a subset of those in the first stage to avoid multicollinearity, we exclude Gift_Num_Platform and Gift_Ratio_Platform in the second stage. Both Gift_Num_Platform and Gift_ Ratio_Platform reveal whether a viewer is likely to send a gift, which should be positively related to the probability of gifting rather than the amount of gifting. We find that a viewer’s engagement in the session, including watching and message interaction, is both positively associated with the gifting probability and amount. Others’ gifting behavior has significant effects on focal viewers’ gifting decisions. The number of other gift senders is positively related to focal viewers’ gifting incidence and amount. While the average amount of gifts from others is negatively associated with focal viewers’ gifting probability and positively associated with their gifting amount. It seems to be consistent with the signaling effect and competition effect discussed in Sect. 4.3 that viewers want to stand out by paying more than others but are constrained by competition from others. We also find a significant relationship between viewers’ past behavior and gifting decisions. Viewers’ past incidence and the amount of gifting to the current broadcaster are both positively related to the current gifting decisions, including whether to gift and how much to gift. However,

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Table 4.5 The effect of geographic and economic factors on broadcaster distribution (1) Broadcaster_Number

(2) Broadcaster_Density

Population

0.202*** (0.031)

– (.)

Province = Anhui

– (.)

– (.)

Province = Heilongjiang

198.956*** (40.981)

0.528*** (0.089)

Province = Jilin

194.718*** (45.339)

0.530*** (0.098)

Province = Liaoning

75.633* (38.704)

0.292*** (0.084)

Other Province Controls are Omitted

Yes

Yes

Tier of City = 1

– (.)

– (.)

Tier of City = 2

−379.648*** (82.400)

−0.950*** (0.179)

Tier of City = 3

−597.482*** (79.382)

−1.177*** (0.173)

Tier of City = 4

−767.170*** (78.430)

−1.464*** (0.170)

Tier of City = 5

−815.746*** (78.532)

−1.536*** (0.170)

Constant

825.142*** (84.564)

1.861*** (0.178)

Observations

290

290

Adjusted R-Square

0.7637

0.5707

Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01

the times watching the current broadcasters’ sessions in the past create fatigue and are negatively related to the gifting likelihood and amount currently. As for viewers’ overall past behavior on the platform, we find that the total amount of gifts sent in the past negatively relates to current gifting decisions which might be subject to a budget constraint. Finally, the coefficient of the inverse Mills ratio (IMR) is significant in the second stage, suggesting that the two decisions are correlated.

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Table 4.6 The effect of geographic and economic factors on viewer distribution

(1) Viewer_Number

(2) Viewer_Density

Population

0.020*** (0.004)

– (.)

Province Controls

Yes

Yes

Tier of City = 1

– (.)

– (.)

Tier of City = 2

−88.266*** (10.752)

−0.166*** (0.040)

Tier of City = 3

−125.745*** (10.358)

−0.225*** (0.039)

Tier of City = 4

−148.288*** (10.234)

−0.265*** (0.038)

Tier of City = 5

−153.589*** (10.247)

−0.272*** (0.038)

Constant

154.494*** (11.034)

0.309*** (0.040)

Observations

290

290

Adjusted R-Square

0.8482

0.4026

Standard errors in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01

4.5.2 What Is the Relationship Between Gifting and Retention? Broadcasters’ Gifting Receiving and Retention In this section, we want to explore the role of broadcasters’ gifting receiving in their retention. Gifting receiving, as a kind of monetary incentive, is the revenue source for broadcasters in live streaming. Prior research showed that monetary incentives could positively affect individuals’ willingness to work. For example, Lazear (2000) found that using piece-rate pay instead of hourly wage increased workers’ productivity by 44%. However, the positive effect might disappear when the amount of monetary gaining is large enough. Camerer et al. (1997) and Farber (2008) found evidence that taxi drivers would stop working after reaching the target income of that day. Thus, we propose that broadcasters who gained more virtual gifts would be more likely to keep working on the live streaming platform than those who got less. While for broadcasters who have already got large amounts of gifts, monetary incentives will no longer be an effective driver in motivating broadcasters to work. Among the 10,000 focal broadcasters in our data, there were 6,150 subjects who joint the live streaming platform during the observational period. We divide the 200day observational period into 29 weeks (with only four days in the last week). Since the data is right censored, we are not able to observe the exact date of churning

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79

of each broadcaster. We define churned broadcasters as those who had no broadcasting behavior within the last month of the observational period, and the rest are called retained broadcasters. Broadcaster’s monetary gaining is measured by the cumulative amount of gifts received since joining the platform (Gift_Amt_Cul), and non-monetary gaining is proxied by the cumulative number of text messages received (Chat_Times_Cul). In addition, we also include the cumulative length of broadcasting (Broad_Length_Cul) and a set of broadcasters’ characteristics (i.e., Gender, Type, and Alliance) as independent variables in our analyses. We use Cox regression models with time-varying coefficients to analyze broadcasters’ retention. Figure 4.6 displays the Kaplan–Meier curves showing broadcasters’ retention. About 35% of broadcasters were still active on the platform 20 weeks after joining. And broadcasters who never received any gift are at greater risk of churning than those who ever received gifts. Table 4.3 reports the results of Cox regressions. Column (1) of Table 4.3 presents the results of all broadcasters with individual fixed effects, while column (2) shows the results with broadcasters’ characteristics. We can see that broadcasters’ monetary gaining (Gift_Amt_Cul) and non-monetary gaining (Chat_Times_Cul) are both negatively related to broadcasters’ exiting, which means broadcasters who have got more money and interactions were less likely to churn. Broadcasters’ gender and type of performance have no significant relationship with churning, while broadcasters who belonged to an alliance would like to stay on the platform longer than those who were independent. Columns (3) and (4) of Table 4.3 focus on the top 25% of gifts receivers, we find that neither cumulative monetary gaining nor non-monetary gaining has a significant relationship with the decision of churning for broadcasters who have received large amounts of gifts. Viewers’ Gifting Sending and Retention For viewers, sending gifts to broadcasters is not only spending money but also a way to get involvement in live streaming. As we found in Sect. 4.5.1, viewers’ gifting decisions are strongly associated with their engagement in live sessions, and engagement could have a positive effect on users’ loyalty (Johnson et al., 2001; Mittal & Kamakura, 2001). Therefore, we propose that viewers who ever sent gifts on the platform would be more likely to be long-term users than those who never gifted. Similar to the analysis on the broadcaster side, we focus on the retention of 6,976 viewers who registered as new viewers during our observational period. The observational period is divided into weeks, and viewers who had no activity, including watching, gifting, and text sending behavior, on the platform were regarded as churned viewers. For each viewer, we can get information about how many sessions he has watched (Watch_Num_Cul), sent gifts in (Gift_Num_Cul) and sent text messages in (Chat_Num_Cul) in the past since joining the platform. And we also observe these viewers’ cumulative length of watching (Watch_Length_Cul), amount of gifting sent (Gift_Amt_Cul) and number of text messages sent (Chat_Times_Cul) since registration.

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Fig. 4.6 Kaplan–Meier survival estimates of broadcasters

(a): All the Broadcasters

(b): Broadcasters Grouped by Whether Ever Received Gifts

Figure 4.7 shows the Kaplan–Meier survival curves of viewers’ retention. We find that around 60% of viewers left the platform after 20 weeks, and non-paying viewers were more likely to exit than paying viewers. Table 4.4 reports the Cox regression results. Column (1) presents that the more sessions a viewer has engaged in, including watching, gifting, and chatting, the less likely he would churn. Column (2) tells us that viewers who have watched for a longer time sent a larger amount of gifts and more text messages and would like to stay longer on the platform.

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Fig. 4.7 Kaplan–Meier survival estimates of viewers

(a): All the Viewers

(b): Viewers Grouped by Whether Ever Sent Gifts

4.5.3 How is Live Streaming Development Influenced by Economic and Geographic Factors? In this Section, we explore how the development of live streaming is affected by geographic and economic factors. The development of live streaming is quite unbalanced in China. Geographically, northeast provinces (i.e., Heilongjiang, Jilin, and Liaoning) produce the majority of broadcasters and account for more than half of the top broadcasters according to a report by Tencent Technology.22 The culture in northeast provinces may play an important role in it. As the report summarized, broadcasters from Northeast China are more outspoken and talkative than those from 22

http://tech.qq.com/a/20160825/003015.htm.

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other areas. However, economic factors could matter even more than cultural factors. Most cities in northeast provinces have experienced an economic downturn in recent years. The GDP growth of northeast provinces ranked close to the bottom among all provinces in China in 2015. The lack of employment opportunities and low average salary have pushed residents in Northeast China to migrate away from those regions or to seek alternative ways to make a living. The average monthly income for an ordinary full-time broadcaster in live streaming could be more than ¥5,000, which is significantly higher than the monthly earning of an ordinary worker in Northeast China.23 This may help explain the high concentration of broadcasters in northeast provinces. As for economic factors, although the traditional view believed that live streaming is especially popular in less developed areas that lack entertainment options,24 it has moved to the mainstream and gained popularity in well-developed areas.25 We obtained data about broadcasters’ and viewers’ geographic distribution at the city level, including the number of broadcasters (Broadcaster_Number), the density of broadcasters (calculated as the number of broadcasters in the city divided by the population in the city, denoted with Broadcaster_Density), number of viewers (Viewer_Number) and density of viewers (Viewer_Density) in the city. Cities are classified into five tiers according to the classification system published by CBN in 2017.26 First-tier cities include the most developed areas in China, and fifth-tier cities are the least developed ones. From 1st-tier to 5th-tier, cities become less and less developed. And we also control province dummies. Regression results of broadcasters and viewers are reported in Tables 4.5 and 4.6 respectively. We find that, first, broadcasters are geographically concentrated in northeast provinces, namely Heilongjiang, Jilin, and Liaoning, which is consistent with our prediction that cultural and economic factors may provide favorable conditions for live streaming development. Second, the live streaming user number and user density are both positively related to the development of the city, which suggests that there are more broadcasters and viewers in better-developed areas.

4.6 Summary In this Chapter, we provide a broad overview of the live streaming industry and use a dataset from a Chinese platform to empirically explore several research questions related to the economics in this industry. In particular, we study the motives behind viewers’ gift sending behavior and factors leading to successful broadcasters in terms of gift receiving. We examine the role of gifting in user retention on both the broadcaster and viewer sides. And we also explore how the development of live streaming 23

http://www.sino-us.com/11/22014293338.html. https://www.economist.com/special-report/2017/02/09/chinas-new-craze-for-live-streaming. 25 https://techcrunch.com/2015/03/27/the-livestream-goes-mainstream/. 26 https://www.yicai.com/news/5293378.html. 24

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is influenced by geographic and economic factors. We view this Chapter as the first step in exploring the economics of the live streaming industry, which has become an important sector of the internet-based industry in China and is growing rapidly in the US as well as many other countries around the world. There are some limitations in this Chapter that offer opportunities for future extensions. First, we mainly focus on gifting in live streaming, but we also find that interactions play an important role in not only gifting receiving, and sending but also broadcaster and viewer retention. This research question can be further explored with appropriate data. For example, given data on text messages, one can use text analysis methods to extract relevant topics for each message and use them to predict viewers’ watching, gifting, and churning decisions. Such information will also be useful for live streaming platforms to recommend suitable broadcasters and improve viewers’ gifting and retention. Second, due to the fact that we don’t observe detailed information about each broadcaster and each live session, we can only investigate the effect of broadcasters’ gender, type of performance, alliance joining, and so on. One could use machine learning techniques to analyze recorded videos of live sessions and extract audio and visual cues to understand broadcasters’ and viewers’ real-time behavior. Finally, as an exploratory study, our empirical analyses are not sufficient to make causal inferences. In the future, more detailed analyses and experiments could be conducted to explore causal relationships. Such research could provide more insights into understanding the antecedents and consequences of broadcasters’ gifts gaining and viewers’ gifts giving, as well as user retention, which would help live streaming platforms design better pricing strategies and engagement mechanisms.

References Armstrong Soule, C. A., & Madrigal, R. (2015). Anchors and norms in anonymous pay-what-youwant pricing contexts. Journal of Behavioral and Experimental Economics, 57, 167–175. https:// doi.org/10.1016/j.socec.2014.10.001 Camerer, C., Babcock, L., Loewenstein, G., & Thaler, R. (1997). Labor supply of New York City cabdrivers: One day at a time. The Quarterly Journal of Economics, 112(2), 407–441. https:// doi.org/10.1162/003355397555244 Cassar, L., & Meier, S. (2018). Nonmonetary incentives and the implications of work as a source of meaning. Journal of Economic Perspectives, 32(3), 215–238. https://doi.org/10.1257/jep.32. 3.215 Chen, Y., Koenigsberg, O., & Zhang, Z. J. (2017). Pay-as-you-wish pricing. Marketing Science, 36(5), 780–791. https://doi.org/10.1287/mksc.2017.1032 Chen, Y., & Xiong, F. (2019). The business model of live streaming entertainment services in China and associated challenges for key stakeholders. IEEE Access, 7, 116321–116327. https://doi. org/10.1109/ACCESS.2019.2935005 Farber, H. S. (2008). Reference-dependent preferences and labor supply: The case of New York city taxi drivers. American Economic Review, 98(3), 1069–1082. https://doi.org/10.1257/aer.98. 3.1069 Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica, 47(1), 153–161.

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Johnson, M. D., Gustafsson, A., Andreassen, T. W., Lervik, L., & Cha, J. (2001). The evolution and future of national customer satisfaction index models. Journal of Economic Psychology, 22(2), 217–245. https://doi.org/10.1016/S0167-4870(01)00030-7 Katz, M., & Shapiro, C. (1985). Network externalities, competition, and compatibility. American Economic Review, 75(3), 424–440. Kim, J.-Y., Natter, M., & Spann, M. (2009). Pay what you want: A new participative pricing mechanism. Journal of Marketing, 73(1), 44–58. https://doi.org/10.1509/jmkg.73.1.044 Kim, J.-Y., Natter, M., & Spann, M. (2014). Sampling, discounts or pay-what-you-want: Two field experiments. International Journal of Research in Marketing, 31(3), 327–334. https://doi.org/ 10.1016/j.ijresmar.2014.03.005 Lazear, E. P. (2000). Performance pay and productivity. American Economic Review, 90(5), 1346– 1361. https://doi.org/10.1257/aer.90.5.1346 Lu, S., Yao, D., Chen, X., & Grewal, R. (2021). Do larger audiences generate greater revenues under pay what you want? Evidence from a live streaming platform. Marketing Science, 40(5), 964–984. https://doi.org/10.1287/mksc.2021.1292 Mak, V., Zwick, R., Rao, A. R., & Pattaratanakun, J. A. (2015). “Pay what you want” as threshold public good provision. Organizational Behavior and Human Decision Processes, 127, 30–43. https://doi.org/10.1016/j.obhdp.2014.11.004 Mittal, V., & Kamakura, W. A. (2001). Satisfaction, repurchase intent, and repurchase behavior: Investigating the moderating effect of customer characteristics. Journal of Marketing Research, 38(1), 131–142. https://doi.org/10.1509/jmkr.38.1.131.18832 Rochet, J.-C., & Tirole, J. (2003). Platform competition in two-sided markets. Journal of the European Economic Association, 1(4), 990–1029. https://doi.org/10.1162/154247603322 493212 Romano, R. (1991). When excessive consumption is rational. American Economic Review, 81(3), 553–564. Schmidt, K. M., Spann, M., & Zeithammer, R. (2015). Pay what you want as a marketing strategy in monopolistic and competitive markets. Management Science, 61(6), 1217–1236. https://doi. org/10.1287/mnsc.2014.1946

Part II

Social Influence in Digital Content Contribution

Chapter 5

Social Incentives and Digital Content Contribution

In this chapter, we conduct an empirical study using data from a live streaming platform to examine the impact of social incentives on users’ digital content contribution behavior. Our findings indicate that both social interaction and monetary rewards can increase users’ short-term frequency of content contribution and long-term retention on the platform. Furthermore, we observe that the effect of social interaction varies among users with different levels of experience on the platform.

5.1 Introduction In this Chapter, we examine the consequences of social incentives, including social interaction and gift-giving, on users’ digital content contribution. Specifically, using data from live streaming, we test the role of social interaction and gift-receiving in driving broadcasters to provide more live content in the short term and stay longer on the platform in the long term. The results show that the more gifts broadcasters receive, the sooner they start the next live session and the less likely for them to leave the platform. They suggest that gift-receiving plays a positive role in broadcasters’ short-term activation and long-term retention. In addition, the relationship between broadcasters’ received social interaction and their live streaming behavior is also explored. Results are similar to those of gift-giving in that social interaction is positively related to broadcasters’ broadcasting frequency and retention. We also find the effects to be heterogeneous among broadcasters with different experience levels. The incentive effect of gift-receiving on broadcasters’ long-term retention becomes stronger as their experience increases, while there is no difference between more- and less-experienced broadcasters on short-term activation. As for the effect This Chapter is derived, in part, from the article “Antecedents and Consequences of Gift-Receiving in Livestreaming: An Exploratory Study” published in Journal of Interactive Marketing on May 18, 2022, available online: https://journals.sagepub.com/doi/10.1177/10949968221095550. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Ma, Social Influence on Digital Content Contribution and Consumption, Management for Professionals, https://doi.org/10.1007/978-981-99-6737-7_5

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of social interaction, we find it becomes less useful to drive broadcasters to start a new live session quickly, but tends to be more effective at retaining broadcasters on the platform as their experience increases.

5.2 Theoretical Background The empirical analysis in this chapter mainly relates to three streams of literature— incentives for content contribution, social interaction, and live streaming. The first is the literature on incentives for content provision, and specifically focuses on the effects of monetary rewards and social interaction. Monetary rewards have been proven to be useful to motivate users to provide more content on online platforms (e.g., Garnefeld et al., 2012; Roberts et al., 2006). Liu et al. (2014) explored the effect of monetary rewards on task submission on a crowdsourcing platform and found that the users’ participation level is positively affected by monetary incentives. Sun and Zhu (2013) showed that the quantity of content provision in blog posting increases after introducing an ad revenue-sharing program. As for social factors, audience size (Zhang & Zhu, 2011), social ties (Shriver et al., 2013), and self-image (Chen et al., 2018; Toubia & Stephen, 2013) are all found to play a significant role in stimulating people to provide online content. Goes et al. (2014) research confirmed a “popularity effect” in users’ review provision. They found that users write more reviews and more objective reviews when the number of followers increases. McIntyre et al. (2016) studied users’ review writing behavior on Yelp and found that receiving positive feedback increases users’ probability to continue providing reviews. Past research also shows the heterogeneous effects of different motivations on content provision. For example, Garnefeld et al. (2012) confirmed that monetary incentives are useful in improving users’ participation in the short run, while explicit normative pleas are only effective for active users. The second stream of literature is related to social interaction. While traditional tipping is a norm-driven behavior (Azar, 2007), especially in a private context, online tipping has a stronger social basis (Hilvert-Bruce et al., 2018). In this research, we specifically focus on social interaction in live streaming. Previous literature suggests that social interaction plays an important role in driving individuals to give gifts and tips in several aspects. Zhou et al. (2019) separated social interaction in broadcast media into two categories: broadcaster-viewer interaction and viewer-viewer interaction. They focused on the latter one and argued that viewers’ gifting can be driven by the presence of others, social competition, and emotional stimuli through affecting viewers’ arousal levels. Wan et al. (2017) proposed that responses from content creators make people feel socially connected, create an emotional attachment to a content creator, and thus increase the intent to donate to content creators on social media. Li et al. (2018) explored the relationship between interactivity, social presence, and gift giving intention based on flow theory. The online environment with a high level of interactivity and social presence makes people immersed and leads to a flow state, which is useful to drive users to send virtual gifts to broadcasters.

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Finally, this study contributes to the emerging literature on live streaming. Several factors have been found to be useful in driving viewers to send gifts in live streaming. For example, Lu et al. (2021) find a concave relationship between audience size and broadcasters’ gift-receiving, which supports the explanation of social image rather than reciprocity seeking. Lin et al. (2021) examine the role of emotion in inducing gift-sending and find that a happier broadcaster is likely to receive more tips from viewers. Li and Peng (2021) explore the effect of live streamer characteristics (e.g., trustworthiness and expertise) and live scene characteristics (e.g., telepresence and instant feedback) on viewers’ gift-giving intention. Li et al., (2021a, 2021b) find that identity-based motivation can drive viewers to send paid and free gifts in distinctive ways. This paper differs from previous research by focusing on the role of interactivity, which is a salient feature of live streaming. Specifically, in this chapter, we examine the relationship between social interaction initiated by viewers and broadcasters’ live streaming behavior, which provides new insights into our understanding of social incentives and digital content contribution.

5.3 Hypotheses Development In this section, we first elaborate on the theoretical foundations of this study. Specifically, we adopt the uses and gratification theory (UGT) as the overarching theory to explain the consequences of incentives on digital content contribution in the context of live streaming. UGT proposes that people adopt a certain media to satisfy their desires and needs to achieve gratification (Katz et al., 1973). Previous research has documented that the need for entertainment, social interaction, and tension release all can drive people to use certain social media (Wei & Lu, 2014; Zolkepli & Kamarulzaman, 2015). In this Chapter, we examine the relationship between social incentives and digital content provision by exploring the role of gift-receiving and social interaction on broadcasters’ live streaming behavior, including their short-term live content provision and long-term retention. UGT has also been used to explain why users would like to contribute social media content. Gratification factors for providing live streaming performance cover both intrinsic motivations, such as challenge seeking, enjoyment, and self-presentation, and extrinsic motivation, such as monetary reward, feedback, and social benefits (Sjöblom & Hamari, 2017; Zhao et al., 2018). Based on these findings in UGT, we further explore how broadcasters’ short- and long-term content provision behaviors are associated with gratification factors—including gift and social interaction receiving. We formally propose our hypotheses in the following. Previous research in UGT has documented that users’ content contribution can be driven by various motivations, such as enjoyment (Kaufmann et al., 2011), monetary payoffs (Chen et al., 2010), and social motives (Goes et al., 2014). This study investigates the effects of two gratification factors on broadcasters’ live streaming behavior. One is gift-receiving, which satisfies broadcasters’ needs for monetary compensation; the other is message-receiving, which provides broadcasters with social benefits.

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Gift receiving, as a kind of monetary incentive, is the dominant revenue source for broadcasters. Popular broadcasters can earn millions of dollars from live streaming (Forbes, 2020). The potential to earn money attracts many people to become broadcasters to monetize their talents. Prior research has shown that monetary incentives could positively affect individuals’ willingness to work. For example, Hammermann and Mohnen (2014) found that monetary prizes lead to better performance. DellaVigna and Pope (2018) also showed that monetary payoffs (even low rates) are highly effective in motivating people to complete tasks. In the online context, several studies found that monetary reward positively affects users’ intention to provide content, such as blogs (Sun & Zhu, 2013), reviews (Khern-am-nuai et al., 2018), and crowdsourcing tasks (Liu et al., 2014). As for the effect of social interactions, Li et al., (2021a, b) found that readers’ positive feedback affects writers’ output. McIntyre et al. (2016) showed that receiving positive feedback can drive Yelp review writers to continue to produce reviews. Accordingly, on live streaming platforms, we expect that broadcasters who receive more virtual gifts and social interactions are likely to provide live sessions more frequently. Thus, we propose the following hypotheses: H1a: Gift-Receiving is Positively Related to Broadcasters’ Short-Term Broadcasting Frequency. H1b: Social Interaction is Positively Related to Broadcasters’ Short-Term Broadcasting Frequency.

However, prior research also found that people would work less if they feel they are compensated enough. For example, Lazear (2000) found that using piece-rate pay instead of hourly wage increases workers’ productivity by 44%. But, the positive effect might disappear when the amount of monetary gain is adequate. Camerer et al. (1997) and Farber (2008) found evidence that taxi drivers would stop working after reaching their day’s target income. Thus, monetary compensation would lead to reduced work supply after a certain point. Similar evidence of non-monetary incentives is found in prior research. In the context of online content provision, users may feel satisfied with their current state and discontinue contribution (Chen et al., 2018). Goes et al. (2016) found that the positive effect of glory-based incentives is increasingly smaller for higher-rank users. In our context of live streaming, experienced broadcasters are more likely to have received a considerable amount of gifts and social interactions in the past. Accordingly, we propose that the impact of both monetary and non-monetary incentives on broadcasters’ content provision will become weaker with the increase of their experience. In other words, the marginal effects of gift-receiving and social interaction on the frequency of performing diminish over time, more experienced broadcasters’ content provision behavior is less affected by extrinsic rewards. Hence, we hypothesize the following: H2a: The positive relationship between gift-receiving and short-term broadcasting frequency is weaker for more experienced broadcasters than less experienced ones. H2b: The positive relationship between social interaction and short-term broadcasting frequency is weaker for more experienced broadcasters than less experienced ones.

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Regarding broadcasters’ long-term retaining behavior, the relationship between monetary incentive and long-term retention has been documented by many economic, human resource, and psychological studies (e.g., Kryscynski, 2021; Trevor et al., 1997). For example, Gardner et al. (2004) found that high pay levels will maintain and enhance employees’ future performance. Besides monetary incentives, the nonpecuniary utility also plays an important role in keeping users on platforms. Bode et al. (2015) presented empirical evidence of a positive retention effect associated with participation in corporate social initiatives. Wang and Chiang (2009) explored the effect of social interaction on users’ continuance usage in the context of online auctions. They found that social interaction positively affects users’ auction continuance intention. Zhao et al. (2018) surveyed broadcasters in live streaming, and found that extrinsic rewards, social benefits, and feedback all can motivate broadcasters to continue providing live streaming content. Accordingly, we propose that broadcasters with higher gift-receiving and more social interactions are less likely to leave the platform. H3a: Gift-receiving is positively related to broadcasters’ long-term retention. H3b: Social interaction is positively related to broadcasters’ long-term retention.

Regarding the moderating effect of broadcasters’ experience on the platform, we expect it to enhance the positive relationship between broadcasters’ gift- and social interaction-receiving and long-term retention. Trevor et al. (1997) showed that the effect of salary growth on decreasing turnover was greatest for high performers. In our context of live streaming, experienced broadcasters are like the high performers who are more likely to receive a larger amount of gifts. Thus, we expect the increase in gift-receiving should have a stronger positive effect on retaining them. Moreover, broadcasters with more experience tend to build a stronger connection with viewers and acquire more loyal followers. Keeping in interacting with existing viewers and followers can also be a reason for experienced broadcasters to stay on the platform. Hence, we predict that experienced broadcasters who receive a large amount of virtual gifts and social interactions are the most likely to stay on the platform. Therefore, the following hypotheses are proposed: H4a: The positive relationship between gift-receiving and long-term retention is stronger for more experienced broadcasters than less experienced ones. H4b: The positive relationship between social interaction and long-term retention is stronger for more experienced broadcasters than less experienced ones.

5.4 Empirical Background and Data This study collected data from a major showroom live streaming platform in China, which featured more than 180,000 registered broadcasters and 100 million monthly active users by the end of June 2017. Figure 5.1 presents a snapshot of a live session on the focal platform. Both the gift-sending and message-sending activities are socially

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Fig. 5.1 Snapshot of a live session on the focal platform

visible to everyone in the live session. The observation period spans from November 14, 2016 to June 18, 2017, with 217 days in total. 46,457 registered broadcasters performed at least once during the observational period. 63.41% of them are newly registered broadcasters who joined the platform during the observational period. We observe the date and length of their broadcasting sessions as well as viewers’ gifting and messaging behavior in each session. For our analysis in the following, we aggregate the observations into the broadcaster-day level. We also observe broadcasters’ characteristics, such as gender, performance type, and experience of broadcasting on this platform. We will explain in detail each of the variables in the following. Table 5.1 lists the key variable definitions and Table 5.2 presents the summary statistics. Table 5.2a shows the descriptions for the dummy and categorical variables and Table 5.2b shows the summary statistics of broadcasters’ and viewers’ behavior on each broadcasting day. There are 6,912,771 observations on the broadcasterday level in total, 1,324,126 (19.15%) of which comprise broadcaster live streaming behavior, i.e., broadcasters choose to perform on these days. Table 5.2b only includes the 1,324,126 observations with broadcasting behavior. Gift-receiving. In this research, we specifically focus on broadcasters’ giftreceiving. We generate three measures related to it.1 The first one is Gift_Amt_Sum, which refers to the total monetary amount of virtual gifts a broadcaster receives on a certain day. The second one is Gift_Num, which refers to the number of viewers who send gifts to a broadcaster on a certain day. We also care about how much money each viewer gifts and construct the third measure Gift_Amt_Avg, which refers to the average monetary amount of gifts each gifting viewer sends to a broadcaster on a certain day. The virtual gift is the major revenue source for showroom broadcasters in live streaming. On our focal platform, examples of virtual gifts include flowers, claps, and luxury cars with animation effects, sold from 0.05 to 10,000 RMB.2 On broadcasting days, a broadcaster receives 437.84 RMB a day on average with substantial variation, ranging from 0 to 2,353,584.75 RMB. The distribution of gift-receiving is extremely skewed. On 12.06% of performing days, broadcasters don’t receive any gifts. The average number of viewers who send gifts is 11.20 for each broadcaster on each performing day, and the median is 7. As for the average amount of gifts each 1

Since broadcasters’ gift-receiving can only be observed on performing days, these three measures are missing on non-performing days. 2 US Dollar to RMB Yuan exchange rate was around 6.5 in 2017.

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Table 5.1 Variable definitions Variable

Definition

Gift_Amt_Sum

Total monetary amount of gifts a broadcaster receives on a certain day (in ¥)

Gift_Num

Number of viewers who send gifts to a broadcaster on a certain day

Gift_Amt_Avg

Average monetary amount of gifts each gifting viewer sends to a broadcaster on a certain day (in ¥)

Chat_Msg_Sum

Total number of messages a broadcaster receives on a certain day

Chat_Num

Number of viewers who send gifts to a broadcaster on a certain day

Chat_Msg_Avg

Average number of messages each chatting viewer sends to a broadcaster on a certain day

View_Num

Number of views for a broadcaster on a certain day

Broad

Whether a broadcaster performs on a certain day

Broad_Length

Length of broadcasting for a broadcaster on a certain day (in hour)

Broad_Length_ Cul

Cumulative length of broadcasting since joining the platform (in hour)

Last_Broad_Diff Number of days between the last live session and a certain day Next_Broad_ Diff

Number of days between the next live session and a certain day

Gender

A dummy variable that equals 1 for male and 0 for female

Type

A categorical variable referring to the type of a broadcaster, includes sing, dance, talk show, and hip-hop

Union

A dummy variable that equals 1 if a broadcaster belongs to a union and 0 if not

Leave

A dummy variable that equals 1 if a broadcaster doesn’t perform during the last month of the observational period

Table 5.2 Summary statistics

Variable

Freq

Percent (%)

Gender = female

43,273

93.15

Gender = male Type = talk show Type = dance

3,184

6.85

34,602

74.48

1,635

3.52

Type = hip-hop

2,372

5.11

Type = sing

7,848

16.89

Union = 0

29,131

62.71

Union = 1

17,326

37.29

Leave = 0

16,481

35.48

Leave = 1

29,976

64.52

Note We report broadcasters’ union belonging in the middle of the observational period, i.e., March 2, 2017

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gifting viewer sends, the average is 25.54 RMB and the median is 4.46 RMB, which suggests that small amounts of tips are more common. Social interaction. This research explores the effect of social interaction on broadcasters’ gift receiving. We observe viewers’ chatting behavior in each broadcaster’s live session. Chat_Msg_Sum is the total number of chatting messages received by a broadcaster on a performing day. Broadcasters receive 413.72 messages on each broadcasting day on average, with a median of 214. The distribution of messagereceiving is also highly skewed that most broadcasters receive less than 40 chatting messages during live streaming. Chat_Num and Chat_Msg_Avg refer to the number of chatting viewers and the average number of messages sent by each chatting viewer for each broadcaster on a performing day, respectively. On average, 30.35 viewers interact with the broadcaster in each live session, and each sends 14.00 messages on average in a live session. Control variables. We consider three sets of control variables in the empirical analysis. The first set is broadcasters’ characteristics, including gender, type of performance, and union status. The distribution of gender is significantly unbalanced, with 93.15% female and 6.85% male. In terms of broadcaster type or live-streaming content, there are four main types including chatting or talk show, dancing, hiphop, and singing, which is denoted by Type. Talk-show is the largest category which attracts 74.48% of the broadcasters, while dancing, hip-hop, and singing account for 3.52%, 5.11%, and 16.89%, respectively. Broadcasters can choose to join (or exit) a broadcaster union on the platform. The variable Union refers to whether a broadcaster belongs to a union at a given point of time. A broadcaster union is a featured organization in the live streaming industry. In broadcaster unions, professional managers and administrators oversee unions and provide broadcasters with a variety of resources, including training, equipment, facility, marketing, and public relations. In return, a portion of broadcaster income is shared with the unions to which they belong. In our data, 9,385 broadcasters were already in a union at the start of the observational period. We observe 22,036 broadcasters joined a union, 14,657 broadcasters chose to quit the union, and 4,987 broadcasters switched from one union to another one during the observational period. The second set is viewers’ viewing behavior in live sessions. View_Num is the total number of viewers who watch the broadcaster’s live performance on each broadcasting day. The number of viewers varies from 0 to 73,954, with a mean of 531.84 and a median of 116. The third set of control variables includes broadcasters’ behavior. Broadcasters are free to choose whether to perform and how long to perform on each day. On average, broadcasters performed 28.5 days during the 217-day observational period. The length of broadcasting on a day, Broad_Length, ranges from 0.08 h to 24 h3 ; the average and median are 3.73 and 3.33 h, respectively. Broad_Length_Cul captures 3

There are 20 observations (18 unique broadcasters) with 24-h broadcasting behavior in our dataset. It is possible for some broadcasters to continuously perform for 24 h. Some specific content, such as traveling, fishing, and sleeping, can last for a long time. We also conduct robustness checks by excluding the broadcasters who ever continuously performed for 24 h, and find that our results still hold.

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broadcasters’ live streaming experience, which is the cumulative length of broadcasting hours since joining the platform. It ranges from 0 (new broadcasters) to 12,551.92 h, with an average of 966.44 h. Last_Broad_Diff refers to the number of days between the last live session and the current session. We are also interested in broadcasters’ short-term activation and long-term retention. The former is measured by Next_Broad_Diff , which refers to the number of days between the next live session and the current one. From these two measures, we find that broadcasters perform roughly every two days on average. The latter considers broadcasters’ churning. Since our data is right-censored, we cannot observe each broadcaster’s exact churning date. We define churned broadcasters as those who have no broadcasting behavior in the last month of the observational period. The 99 percentile of the time interval between two performing days of a broadcaster is 24 days. It suggests that the probability that a broadcaster would broadcast again after one month of no activity is low. Leave records whether the broadcaster leaves the platform, based on the definition of churning above, which is equal to 1 if the broadcaster churns and 0 otherwise. 64.52% of broadcasters churned by the end of the observational period, and the number is 65.23% for broadcasters who newly joint the platform during the observational period. The distributions of key variables are presented in Fig. 5.2 and the Pearson correlations of all continuous variables are reported in Table 5.3.

5.5 Analysis and Results 5.5.1 Short-Term Consequence of Social Incentives This section focuses on the short-term consequence of broadcaster gift-receiving and social interaction to explore how these two factors affect broadcasters’ decision to provide the next live performance. We especially focus on the effect of social incentive, i.e., social interaction, on digital content contribution in the short run. Next_Broad_Diff refers to the number of days between the broadcaster’s next and the current broadcasting day. We present the relationship between the amount of gifts received on the current performing day and the number of days to start the next live session in Fig. 5.3a, and the relationship between the number of messages received on the current performing day and the number of days between the next and the current session in Fig. 5.3b. The figures show that broadcasters who receive more virtual gifts and chat messages on the current performing day are more likely to start the next session sooner. We use the Heckman two-stage model to address the selection issue of whether a broadcaster chooses to perform on a certain day, and we rely on observations on broadcasters’ performing days to examine how their current receiving of gifts and social interactions influence their broadcasting decision in the future. The first-stage Probit model captures individual broadcaster’s decision of whether to broadcast:

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Fig. 5.2 Variable distributions

(a) Gift_Amt_Sum

(b) Gift_Num

(c) Gift_Amt_Avg

Br oadit∗ = Z i α1 + X itB α2 + X itV α3 + νt + ∈it ,  Br oadit =

1, i f Br oadit∗ ≥ 0 , 0, i f Br oadit∗ < 0

(5.1)

(5.2)

where Br oadit∗ is the latent variable that represents broadcaster i’s propensity to broadcast on day t. Br oadit is 1 if broadcaster i broadcasts on that day and 0 otherwise. The model incorporates three sets of variables that could influence broadcasting decisions (see Table 5.1). The first set Z i refers to time-invariant broadcaster characteristics including Gender and Type. X itB and X itV refer to broadcaster and viewer behaviors, respectively. X itB includes Union, which refers to whether the broadcaster

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Fig. 5.2 (continued)

(d) Chat_Msg_Sum

(e) Chat_Num

(f) Chat_Msg_Avg

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Fig. 5.2 (continued)

(g) View_Num

(h) Broad_Length

(i) Broad_Length_Cul

(j) Last_Broad_Diff

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99

Table 5.3 Correlation log(Gift_ Amt_Sum)

log(Gift_ Num)

log(Gift_ Amt_Avg)

log(Chat_ Msg_Sum)

log(Chat_ Num)

log(Chat_ Msg_Avg)

log(Gift_ Amt_Sum)

1.000 (0.000)

log(Gift_ Num)

0.795 (0.000)

1.000 (0.000)

log(Gift_ Amt_Avg)

0.940 (0.000)

0.567 (0.000)

1.000

log(Chat_ Msg_Sum)

0.767 (0.000)

0.792 (0.000)

0.630 (0.000)

1.000

log(Chat_ Num)

0.793 (0.000)

0.840 (0.000)

0.628 (0.000)

0.913 (0.000)

1.000

log(Chat_ Msg_Avg)

0.488 (0.000)

0.486 (0.000)

0.433 (0.000)

0.822 (0.000)

0.527 (0.000)

1.000

log(View_ Num)

0.679 (0.000)

0.716 (0.000)

0.545 (0.000)

0.685 (0.000)

0.830 (0.000)

0.294 (0.000)

log(Broad_ Length)

0.562 (0.000)

0.664 (0.000)

0.427 (0.000)

0.675 (0.000)

0.687 (0.000)

0.461 (0.000)

log(Broad_ Length_ Cul)

0.492 (0.000)

0.330 (0.000)

0.480 (0.000)

0.428 (0.000)

0.465 (0.000)

0.249 (0.000)

log(Last_ −0.203 Broad_Diff) (0.000)

−0.236 (0.000)

−0.164 (0.000)

−0.263 (0.000)

−0.245 (0.000)

−0.214 (0.000)

log(Next_ −0.251 Broad_Diff) (0.000)

−0.287 (0.000)

−0.206 (0.000)

−0.306 (0.000)

−0.285 (0.000)

−0.249 (0.000)

log(Broad_ Length)

log(Broad_ Length_ Cul)

log(Last_ Broad_Diff)

log(Next_Broad_Diff)

log(View_ Num) log(View_ Num)

1.000 (0.000)

log(Broad_ Length)

0.627 (0.000)

1.000 (0.000)

log(Broad_ Length_ Cul)

0.398 (0.000)

0.371 (0.000)

1.000 (0.000)

log(Last_ −0.225 Broad_Diff) (0.000)

−0.306 (0.000)

−0.132 (0.000)

1.000 (0.000)

log(Next_ −0.247 Broad_Diff) (0.000)

−0.351 (0.000)

−0.149 (0.000)

0.355 (0.000)

1.000 (0.000)

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5 Social Incentives and Digital Content Contribution (a)

Fig. 5.3 Relationship between monetary incentive, social interaction, and broadcasting interval

(b)

belongs to a union at time t, and Broad_Length_Cul, which refers to one’s cumulative length of broadcasting since joining the platform. Broad_Length_Last refers to the broadcaster’s performance duration (in hours) on their last broadcasting day, and Last_Broad_Diff refers to the number of days from the last broadcasting day to date. X itV includes the number of viewers (View_Num_Last), total amount of gifts received (Gift_Amt_Sum_Last), and total number of messages received (Chat_Msg_Sum_ Last) on the last broadcasting day. We take the log transformation of the continuous variables in the analysis as they are highly skewed. The vector νt includes calendar week fixed effects for the time trend and day-of-week fixed effects for the weekday and weekend pattern. ∈it is the random error term. In the second stage, we estimate a model of broadcasters’ short-term live streaming behavior, conditional on the decision to broadcast with Br oadit = 1. Thus, to correct for the selection bias, the inverse Mills ratio (I M Rit ) is calculated and incorporated into the second-stage model: 

  φ Br oadit . I M Rit =   Br oadit 





(5.3)

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101

A problem of simultaneity may occur that broadcasters’ receiving of gifts and social interactions can affect their frequency of performing, and the live streaming frequency can also influence how many gifts and how many messages broadcasters can receive. The generalized method of moments (GMM) is a proper approach to deal with the simultaneity (Ullah et al., 2018). Following Arellano and Bond (1991) and Blundell and Bond (1998), we propose the dynamic GMM model: N ext_Br oad_Di f f it =γ1 N ext_Br oad_Di f f i,t−1 + Z i γ2 

+X itB γ3 + X itV γ4 + γ5 I M Rit + νt + ξit ,

(5.4)

where N ext_Br oad_Di f f i,t−1 is the one period lagged dependent variable. Z i includes a set of broadcaster characteristics. X itB includes broadcasters’ union belonging (Union), length of broadcasting (Broad_Length), and their cumulative length of broadcasting (Broad_Length_Cul). Regarding viewer behavior, X itV includes Viewer_Num, Gift_Amt_Sum, and Chat_Msg_Sum. We also include the inverse Mills ratio to control for the selection bias, and week and day-of-week fixed effects to control for the time patterns. The lagged dependent variable N ext_Br oad_ Di f f i,t−1 , Gift_Amt_Sum, and Chat_Msg_Sum are regarded as endogenous variables and are instrumented with their lagged levels to address the simultaneity and dynamic endogeneity. We use the serial correlation test and Hansen overidentification test to check the validity of the dynamic GMM model. Table 5.4 presents the first-stage estimation results. Column (1) in Table 5.4 shows that broadcasters who received more gifts and more chatting messages in the past are more likely to provide live streaming content. It reveals that both monetary and non-monetary gaining can drive broadcasters to provide live content. Results also show that relative to females, males are less likely to broadcast. Broadcasters who perform hip-hop and talk-show are more likely to broadcast than dancers and singers. Being in a union is positively related to one’s broadcasting probability. Experienced broadcasters perform more frequently than inexperienced ones. Broadcasters’ decisions to broadcast can also be influenced by their past broadcasting behavior. Broadcasters who performed longer in the last session and whose last session is more recent are more likely to initiate a live session on a certain day. Moreover, to explore how the effects of viewer behavior change over time with broadcaster experience, we introduce two interactions: (1) the interaction between viewers’ gift-sending (Gift_Amt_Sum) and broadcaster experience (Broad_Length_ Cul) and (2) the one between viewer message-sending (Chat_Msg_Sum) and broadcaster experience (Broad_Length_Cul). Column (2) shows that the effect of viewers’ gifts and messages on broadcaster’s content provision enhances as the broadcasting experience increases. Table 5.5 reports the main results. Column (1) presents the baseline model, and Column (2) displays the results after adding two interactions between viewer behavior and broadcaster experience. First, in Column (1), the coefficients of Gift_Amt_Sum and Chat_Msg_Sum are negative and significant. A 1% increase in broadcasters’ giftreceiving reduces the number of days between the next live session and the current

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Table 5.4 First stage results

(1) Broad

(2) Broad

log(Gift_Amt_Sum_Last)

0.057*** (0.000)

0.012*** (0.000)

log(Chat_Msg_Sum_Last)

0.031*** (0.000)

0.008*** (0.000)

log(Gift_Amt_Sum_Last) *

0.006***

log(Broad_Length_Cul)

(0.000)

log(Gift_Amt_Sum_Last) *

0.006***

log(Broad_Length_Cul)

(0.000) (0.000)

−0.035*** (0.000)

log(Broad_Length_Last)

0.543*** (0.000)

0.553*** (0.000)

Gender = male

−0.076*** (0.000)

−0.074*** (0.000)

Type = dance

−0.017*** (0.000)

−0.008* (0.084)

Type = hip-hop

0.037*** (0.000)

0.036*** (0.000)

Type = sing

−0.051*** (0.000)

−0.051*** (0.000)

Union = 1

0.081*** (0.000)

0.090*** (0.000)

log(Broad_Length_Cul)

0.039*** (0.000)

0.006*** (0.000)

log(Last_Broad_Diff)

−1.027*** (0.000)

−1.023*** (0.000)

Week FE

Yes

Yes

Day-of-Week

Yes

Yes

Observations

6,349,175

6,349,175

Pseudo R2

0.623

0.623

Log likelihood

−1.202e + 06

−1.201e + 06

log(View_Num_Last)

−0.033***

p-values in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01

one by 0.009%. And a 1% increase in the number of received messages decreases the number of days between two live performances by 0.025%. The results suggest that both monetary (i.e., gift-receiving) and non-monetary (i.e., social interaction) incentives can motivate broadcasters to provide live content frequently, which supports H1a and H1b. Regarding the moderating effect, Column (2) shows that the interaction between Gift_Amt_Sum and Broad_Length_Cul has no significant effect on broadcasters’

5.5 Analysis and Results

103

Table 5.5 Short-term consequence of gift receiving and social interaction (1) log(Next_Broad_Diff)

(2) log(Next_Broad_Diff)

Lag.log(Next_Broad_Diff)

0.030*** (0.000)

0.029*** (0.000)

log(Gift_Amt_Sum)

−0.009*** (0.000)

−0.010*** (0.000)

log(Chat_Msg_Sum)

−0.025*** (0.000)

−0.036*** (0.000)

log(Gift_Amt_Sum) *

0.000

log(Broad_Length_Cul)

(0.641)

log(Chat_Msg_Sum) *

0.002***

log(Broad_Length_Cul)

(0.000)

log(View_Num)

0.000 (0.394)

0.001 (0.108)

log(Broad_Length)

−0.112*** (0.000)

−0.114*** (0.000)

Gender = male

0.064*** (0.000)

0.065*** (0.000)

Type = dance

−0.016 (0.129)

−0.011 (0.292)

Type = hip-hop

−0.045*** (0.000)

−0.040*** (0.000)

Type = sing

−0.024*** (0.000)

−0.018*** (0.001)

Union = 1

−0.008*** (0.000)

−0.007*** (0.000)

log(Broad_Length_Cul)

0.030*** (0.000)

0.020*** (0.000)

IMR

0.147*** (0.000) 0.148*** (0.000)

IMR_Interact Week FE

Yes

Yes

Day-of-Week

Yes

Yes

Observations

1,235,223

1,235,223

AR(1) P-Value

0.000

0.000

AR(2) P-Value

0.877

0.605

Hansen Test P-Value

0.879

0.998

p-values in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01

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5 Social Incentives and Digital Content Contribution

frequency of broadcasting, which means H2a is not supported. One of the plausible explanations is that many broadcasters regard live streaming as an important source of revenue rather than an entertainment channel. As reported in the data description section, many broadcasters spend considerable time on live streaming, which is equivalent to a half-time job with 3.73 h every two days on average. Therefore, monetary incentives can be an important driver for broadcasters to perform frequently regardless of their experience level. As for the effect of social interaction, we find that the interaction between Chat_Msg_Sum and Broad_Length_Cul is positive and significant. It supports H2b that the positive effect of social interaction on shortening the time interval between two live sessions is weakened by broadcasters’ experience. Other factors, including broadcasters’ characteristics, behavior, and viewer participation, are related to broadcaster decisions about when to initiate the next live session. Relative to female broadcasters, the number of days between two performances is about 6% longer for males. Broadcasters who hip-hop and sing perform more frequently than those who chat and dance. Being in a union decreases the number of days between two live sessions, possibly due to the arrangement of unions. Broadcasters with longer cumulative broadcasting experience are less likely to perform frequently, but those who stream longer during the current broadcasting day are more likely to perform sooner the next time. The number of viewers in the current live session does not affect broadcasters’ streaming frequency, after controlling for gifting and social interaction. Finally, the Arrelano-Bond serial correlation tests show that the second-order serial correlation of the residuals is not significant for both regressions, 0.15 (p = 0.877) and −0.52 (p = 0.605), respectively. The Hansen tests of overidentification restrictions are also insignificant. Consequently, the dynamic GMM models appear valid.

5.5.2 Long-Term Consequence of Social Incentives Apart from the short-term consequence of monetary and social incentives, this study also analyzes the decision by broadcasters to leave the live streaming platform, which can be regarded as the long-term consequence of gift-receiving and social interaction on digital content contribution. Figure 5.4a shows the probability of leaving on a certain day since registration. We also compare the leaving probability between two groups of broadcasters with above-average versus below-average gift receiving (message receiving), as presented in Fig. 5.4b and c. The patterns show that broadcasters who receive more gifts and messages are less likely to leave the platform. On average, a broadcaster performed for 127.92 h on 13.20 days in total before leaving the platform, and the medians are 21.50 h and 6 days, respectively.

5.5 Analysis and Results Fig. 5.4 Kaplan–Meier curves

105

(a)

(b)

(c)

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5 Social Incentives and Digital Content Contribution

We use the Cox proportional hazard model to predict the probability of leaving. Since the data is right-censored, we cannot observe the exact leaving time for each broadcaster. Instead, we labeled broadcasters who ever performed in the last month of the observational period as retaining broadcasters and those who were inactive during that period as churning ones. In the Cox proportional hazard model, the hazard rate h i (t) for individual i on day t (i.e., the rate of leaving after the performance on day t for individual i) is assumed to take the following form:   h i (t) = h 0 (t) exp Z i δ1 + X itB δ2 + X itV δ3 + δ4 I M Rit + νt , 

(5.5)

where h 0 (t) is the baseline hazard rate. Z i , X itB , and X itV include the same independent variable as listed in Sect. 5.5.1. I M Rit refers to the inverse Mills ratio, which is calculated from Eq. (5.3), and νt includes the week and day-of-week fixed effects. Table 5.6 presents the Cox regression results. Column (1) affirms the importance of gift-receiving in broadcasters’ long-term retention. The coefficient of Gift_Amt_ Sum is significant and negative, which suggests that broadcasters who receive more gifts are less likely to leave the platform. The result is similar for Chat_Msg_Sum that receiving more social interactions can also increase the probability for broadcasters to retain. These findings provide support for H3a and H3b. We also focus on the moderating effect of broadcasters’ experience. In Column (2), as the cumulative length of broadcasting increases, gift-receiving becomes increasingly effective in reducing broadcasters’ churning, which supports H4a. We also find evidence to support H4b that viewers’ message-sending, i.e., Chat_Msg_Sum, is useful to keep broadcasters staying on the platform, especially for those who have long cumulative broadcasting hours. These empirical results show that the timevarying effects of gift-receiving and social interaction are different regarding the short-term activation and long-term retention. For example, as a broadcaster’s experience accumulates, the positive effect of social interaction on broadcasting frequency attenuates, while its positive effect on long-term retention increases. Regarding other controls, most factors affect broadcasters’ long-term behavior in the same way as short-term behavior. One exception is broadcasters’ experience. We find that experienced broadcasters are more likely to stay on the platform, but they are less likely to perform sooner after the current live session. 

5.5 Analysis and Results

107

Table 5.6 Long-term consequence of gift receiving and social interaction

(1) Leave

(2) Leave

log(Gift_Amt_Sum)

−0.210*** (0.000)

−0.069*** (0.000)

log(Chat_Msg_Sum)

−0.030*** (0.000)

0.024*** (0.004)

log(Gift_Amt_Sum) *

−0.025***

log(Broad_Length_Cul)

(0.000)

log(Chat_Msg_Sum) *

−0.020***

log(Broad_Length_Cul)

(0.000) (0.000)

0.062*** (0.000)

log(Broad_Length)

−0.939*** (0.000)

−0.939*** (0.000)

Gender = male

0.161*** (0.000)

0.141*** (0.000)

Type = dance

−0.011 (0.780)

−0.009 (0.817)

Type = hip-hop

−0.128*** (0.001)

−0.114*** (0.002)

Type = sing

0.141*** (0.000)

0.152*** (0.000)

Union = 1

0.003 (0.814)

−0.019 (0.164)

log(Broad_Length_Cul)

−0.305*** (0.000)

−0.252*** (0.000)

IMR

0.496*** (0.000)

log(View_Num)

0.065***

0.485*** (0.000)

IMR_Interact Week FE

Yes

Yes

Day-of-Week

Yes

Yes

Observations

1,277,669

1,277,669

Pseudo R2

0.124

0.126

Log likelihood

−1.943e + 05

−1.939e + 05

p-values in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01

5.5.3 Robustness Checks In our dataset, the maximum length of broadcasting is 24 h, which raises the question of whether it is practical for broadcasters to perform continuously for such a long

108

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duration, or if they simply leave their feed switched on without actually performing during the entire time. To address this concern, we carefully examine the data and find that it is true that there are 20 observations (18 unique broadcasters) with 24-h broadcasting behavior in our dataset. First, it is possible for some broadcasters to continuously perform for 24 h. For example, some broadcasters stream outdoor activities, such as traveling and fishing (Lu et al., 2019), and some broadcasters focus on streaming daily life, such as sleeping.4 These live performances can last for a long time. In addition, to test the robustness of our results, we repeat the empirical analysis after excluding the broadcasters who ever continuously performed for 24 h. Tables 5.7 and 5.8 report the results of the consequences of gift receiving and social interaction on digital content contribution without broadcasters who ever performed for 24 h on a day, respectively. Results are very similar to those in Tables 5.5 and 5.6.

5.6 Summary In this chapter, we undertake an empirical investigation into the impact of social incentives on digital content contribution, with a particular emphasis on live streaming across various industries. Our aim is to shed light on the role of both monetary rewards and social interaction in stimulating users’ participation levels in online platforms. We will also delve into the various social factors that motivate individuals to provide online content. By analyzing these factors, we hope to gain a deeper understanding of how social incentives can be leveraged to increase user engagement and content contribution, ultimately leading to a more vibrant and dynamic online ecosystem. In this section, we dive deeper into the crucial role that social incentives play in digital content contribution and explore the short-term and long-term consequences of gift-receiving and social interaction. Our analysis reveals that both factors are powerful motivators for broadcasters to perform more frequently in the short run and stay on the platform in the long run. However, we also discovered that the effect of these incentives varies significantly among broadcasters with different levels of experience. While gift-receiving has been found to be a consistent motivator for broadcasters to provide live streaming content frequently, the positive effect of social interaction tends to be weaker as one’s experience accumulates. This suggests that social incentives may be more effective for attracting and retaining novice broadcasters, while monetary rewards may be more effective for experienced broadcasters. Over time, as broadcasters gain more experience on the platform, the effects of both gift-receiving and social interaction become stronger in keeping them engaged and committed to the platform. Our findings highlight the importance of understanding the interplay between social incentives and digital content contribution, and the need 4

https://www.businessinsider.com/influencer-instagram-youtube-twitch-alexa-recognition-spe ech-disturb-money-subscribe-donate-view.

5.6 Summary

109

Table 5.7 Short-term consequence of gift receiving and social interaction (exclude broadcasters with 24-h performance) (1) log(Next_Broad_Diff)

(2) log(Next_Broad_Diff)

Lag.log(Next_Broad_Diff)

0.030*** (0.000)

0.029*** (0.000)

log(Gift_Amt_Sum)

−0.009*** (0.000)

−0.010*** (0.000)

log(Chat_Msg_Sum)

−0.025*** (0.000)

−0.036*** (0.000)

log(Gift_Amt_Sum) *

0.000

log(Broad_Length_Cul)

(0.638)

log(Chat_Msg_Sum) *

0.002*** (0.000)

log(Broad_Length_Cul) log(View_Num)

0.000 (0.426)

0.001 (0.122)

log(Broad_Length)

−0.112*** (0.000)

−0.114*** (0.000)

Gender = male

0.064*** (0.000)

0.064*** (0.000)

Type = dance

−0.016 (0.130)

−0.011 (0.292)

Type = hip-hop

−0.045*** (0.000)

−0.040*** (0.000)

Type = sing

−0.024*** (0.000)

−0.018*** (0.001)

Union = 1

−0.008*** (0.000)

−0.007*** (0.000)

log(Broad_Length_Cul)

0.030*** (0.000)

0.020*** (0.000)

IMR

0.148*** (0.000) 0.148*** (0.000)

IMR_Interact Week FE

Yes

Yes

Day-of-Week

Yes

Yes

Observations

1,233,578

1,233,578

AR(1) P-Value

0.000

0.000

AR(2) P-Value

0.475

0.660

Hansen Test P-Value

0.902

0.998

p-values in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01

110 Table 5.8 Long-term consequence of gift receiving and social interaction (exclude broadcasters with 24-h performance)

5 Social Incentives and Digital Content Contribution

(1) Leave

(2) Leave

log(Gift_Amt_Sum)

−0.210*** (0.000)

−0.069***

log(Chat_Msg_Sum)

−0.030*** (0.000)

0.024*** (0.004)

(0.000)

log(Gift_Amt_Sum) *

−0.025***

log(Broad_Length_Cul)

(0.000)

log(Chat_Msg_Sum) *

−0.020***

log(Broad_Length_Cul)

(0.000)

log(View_Num)

0.065*** (0.000)

0.061*** (0.000)

log(Broad_Length)

−0.940*** (0.000)

−0.939*** (0.000)

Gender = male

0.161*** (0.000)

0.141*** (0.000)

Type = dance

−0.011 (0.771)

−0.009 (0.807)

Type = hip-hop

−0.130*** (0.000)

−0.116*** (0.002)

Type = sing

0.141*** (0.000)

0.152*** (0.000)

Union = 1

0.003 (0.801)

−0.019 (0.170)

log(Broad_Length_Cul)

−0.305*** (0.000)

−0.252*** (0.000)

IMR

0.496*** (0.000) 0.485*** (0.000)

IMR_Interact Week FE

Yes

Yes

Day-of-Week

Yes

Yes

Observations

1,276,006

1,276,006

Pseudo R2

0.124

0.126

Log likelihood

−1.942e + 05

−1.938e + 05

p-values in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01

References

111

for platforms to develop effective incentive mechanisms that cater to broadcasters with varying levels of experience. This research makes several theoretical contributions. On the one hand, we extend current gifting and tipping literature by focusing on the effect of gift-receiving on individuals’ short-term activation and long-term retention. We consider multiple aspects of live streaming content provision behavior, including the short-term behavior, i.e., when to initiate the next live session, and the long-term behavior, i.e., whether to stay on the platform. We find that gift-receiving and social interaction have different impacts on broadcasters’ short- and long-run behavior. The results provide insights into the underlying motivation of multiple aspects of content provision. On the other hand, past research hardly focused on how the effects of gift-receiving and social interaction change with time, and we add to the literature by emphasizing the role of individual experience. Although prior research found that individuals’ motivation can change over time (Goes et al., 2016), the time-varying effects of gift-receiving and social interaction are still unclear. We focus on how the effects change with the increase of individual experience and verify the importance of the heterogeneity of individuals. Our findings also have important practical implications for live streaming platforms as well as for broadcasters. the empirical evidence on the short- and longterm consequences of gift-receiving suggests that monetary incentive can always be regarded as an effective way to activate and retain broadcasters. As for non-monetary incentives, with the increase in broadcasters’ experience, the effect of social interaction on increasing performing frequency tends to be weaker, while it turns to be more effective at retaining them on the platform. These findings suggest that platforms should consider both broadcasters’ experience levels and outcome goals when recommending live sessions to viewers. For example, if platforms want to retain less-experienced broadcasters, recommending their live sessions to viewers who are likely to send gifts rather than messages could be an effective way. While for moreexperienced broadcasters, leading viewers who would like to interact in their live sessions can be useful to keep the broadcasters on the platform.

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Chapter 6

Dynamics of Digital Content Contribution, Monetary Incentive, and Social Interaction

This chapter centers on the interplay between digital content contribution, monetary incentives, and social interaction. In particular, unlike Chap. 5, which examines the impact of social incentives on content contribution, this chapter also explores the potential effects of content contribution on providers’ social and monetary gains. Through our empirical analyses, we uncover evidence that challenges the traditional labor supply theory.

6.1 Introduction In this Chapter, we examine how users’ digital content contribution and the monetary incentive and social interaction evolve over time. More generally, we explore whether working hard can make one rich or popular in the context of online content contribution. Working hard is defined as the overall amount of effort individuals devote to their work (Sujan et al., 1994; Weiner, 1980). It is generally accepted that those who work harder should have better performance, such as earning higher salaries (Altonji et al., 2013), receiving career promotions (O’Reilly & Chatman, 1994), and increasing their creativity (Suh & Shin, 2008). However, the empirical contexts in previous studies are mainly traditional working situations. Little is known about the relationship between working hard and the rate of earning in the context of online content contribution. Online content contribution differs from traditional labor supply in several ways. First, unlike workers who are usually asked to follow a fixed schedule of working hours, users are free to choose how much content to contribute and how much effort

This Chapter is derived, in part, from the article “Can working hard make one rich? Evidence from online content contributors” published in Applied Economics Letters on Nov 15, 2021, available online: https://www.tandfonline.com/doi/abs/10.1080/13504851.2021.1998319. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Ma, Social Influence on Digital Content Contribution and Consumption, Management for Professionals, https://doi.org/10.1007/978-981-99-6737-7_6

115

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6 Dynamics of Digital Content Contribution, Monetary Incentive …

to put on online platforms. Second, different from the contract-based salary in traditional working situations, the earning of online content contributors depends on the response of content consumers. The indirect monetizing model involves advertising and the direct model involves charging the users are the two primary revenue sources for online content contributors (Lu et al., 2020), both are related to the behavior of the demand side. Third, content contributors’ hard working may not have an immediate impact on content consumers’ responses. It may take a long time for content contributors to improve their ability to provide high-quality content and accumulate followers. Thus, the findings in traditional working situations may not be suitable for the context of online content contribution. The key purpose of our study is to investigate the impact of working hard on online contributors’ earnings, including monetary earnings (i.e., virtual gift receiving) and non-monetary earnings (i.e., chat message receiving), over time. Observations of broadcasters in live streaming provide an ideal context for us to address this question. Live streaming is a novel form of “online streaming media simultaneously recorded and broadcast in real time”.1 Two primary stakeholders on live streaming platforms are broadcasters and viewers. Broadcasters are content contributors who are free to decide how long to perform and how much effort to exert in creating live content. Viewers are content consumers who can watch live videos in real time as well as interact with broadcasters in several ways, such as sending virtual gifts and text messages. Gifting from viewers is the main revenue source for broadcasters, who in turn can redeem it for cash. Figure 6.1 shows a snapshot of a live streaming session from our focal platform, both gift-sending and message-sending information are displayed on the screen. We track a panel of 157 broadcasters’ behavior for nearly one year on a gamecentric live streaming platform. On this platform, most broadcasters are gamers who play video games in real time in their live sessions. In this setting, whether a broadcaster works hard is measured by the hours of live content he provides during a certain period. And the earning rate is defined as the hourly monetary amount of gifts one receives. We develop a PVAR (panel vector autoregression) model to capture both the short- and long-term effect of the length of performance on the hourly earning. Results show that working hard has no significant impact on broadcasters’ monetary earnings in general, which contrasts with the common findings in traditional working situations. However, it does positively affect broadcasters’ social earnings, which brings them more social interactions from the viewer side. In addition to examining the relationship between social interaction and giftreceiving, we have also investigated the impact of working hours on broadcasters’ hourly monetary earnings. Our analysis has revealed that the effect of working hard differs among broadcasters with heterogeneous earning powers. Specifically, our findings indicate that for broadcasters whose earning rate is above the average, working hard does not appear to induce a higher hourly monetary gain. In contrast, for broadcasters who are less well-paid, we have found a positive relationship between the hours of performing and the hourly earning. 1

https://en.wikipedia.org/wiki/livestreaming.

6.2 Theoretical Background and Hypotheses

117

Fig. 6.1 A snapshot of a live session

These results suggest that the impact of working hard on hourly monetary earnings may vary depending on the individual broadcaster’s earning power. For high-earning broadcasters, other factors such as audience engagement and content quality may play a more significant role in driving their monetary gains. On the other hand, for less well-paid broadcasters, working hard and increasing their performance hours may be a more effective strategy for increasing their monetary earnings.

6.2 Theoretical Background and Hypotheses First, our research is related to the literature on online content contribution. In this field, most prior studies focus on the incentives for content contribution. Monetary incentives, such as ad sharing (Sun & Zhu, 2013), subscription revenue (Li et al., 2021), and tipping (Lin et al., 2021), and non-monetary incentives, such as status seeking (Lampel & Bhalla, 2007), social norms (Burtch et al., 2018), and exposure (Tang et al., 2012), are both effective at motivating users’ content contribution. However, little attention is paid to the effect of content contribution. In this study,

118

6 Dynamics of Digital Content Contribution, Monetary Incentive …

we investigate the impact of users’ amount of content contribution on their earning rates, and, in further, explore the dynamics of the effect over time. Second, this study is also relevant to the literature in labor economics on working hours, wages, and earnings. Previous research mainly documents a positive relationship between working hours and earnings. Pencavel (2015) finds that the relationship is nonlinear that workers earning is proportional to hours below an hours threshold, but rises at a decreasing rate above the threshold. Holmes and Srivastava (2002), Fang et al. (2004), and Suh and Shin (2008) examine this question in the context of salespeople, they all find that working harder, i.e., working longer hours, can lead to better performance and a higher income level. Our work is relevant to labor supply by examining the relationship between working hours and earning rate in the context of online content contribution. Last, our work is linked to studies that focus on individuals’ content contribution behavior at different stages. Edelman (2012) focus on content contributors’ behavior on Google Answers, he finds that more experienced answerers receive higher asker ratings and higher rate of earnings. Lin (2020) shows that with the increase in experience, sellers’ sales revenue increases but not for their sales order. The reason is that experienced individuals learn to seek higher order value and improve product quality. Our research also takes users’ heterogeneity into consideration. We examine whether the effect of working hard differs among content contributors with different levels of earing rate.

6.3 Empirical Background and Data Live streaming provides an ideal context for us to address the research question. As a novel form of online streamed media, live streaming lets users create, watch, and engage in live videos in real time. Live streaming has rapidly become widespread in recent years. In 2020, the global live streaming market size was valued at $50.11 billion, and is expected to expand at a CAGR (compound annual growth rate) of 21% from 2021 to 2028.2 Live streaming platforms connect users from two sides, one is the broadcaster side, and the other is the viewer side. Broadcasters provide a variety of content, such as singing, dancing, and gaming, to viewers in real time. Viewers can join broadcasters’ live sessions to watch their performances as well as engage in live sessions in several ways simultaneously. Common engagement activities in live streaming are message-sending and giftsending, as shown in Fig. 6.1. Message-sending is a free way for viewers to interact with broadcasters and other viewers. As we can see in Fig. 6.1, the text messages are displayed on the screen in a rolling manner. Unlike messages-sending which is free of charge, gift-sending is a paid engagement activity. Viewers can purchase virtual gifts from the live streaming platform, and then send these gifts to their favorite broadcasters within live sessions. Gift-sending from viewers is the main 2

https://www.grandviewresearch.com/industry-analysis/video-streaming-market.

6.3 Empirical Background and Data

119

Table 6.1 Descriptive statistics Variable

N

PerformHour (in hours)

8,164

Mean 6.929

SD 25.232

Min

Max

0.000

168.000

GiftHourly (in s¥)

2,551

47.983

921.211

0.000

45,956.297

MessageHourly

2,551

98.266

474.658

0.000

8,063.056

revenue source for broadcasters on most live streaming platforms, especially for those focus on gaming and showroom performance. The gifting-based business model is indicated to be viable by the practice among a large number of platforms. For example, Kuaishou, one of the largest live streaming platforms in China, received more than ¥3.14 billion in revenue from viewers’ gift-sending in 2019.3 The revenue from gift-sending for TikTok in the US was $41.3 million by 2019.4 We collect data from a major gaming live streaming platform in China. The platform initiated a live streaming service in 2014 and has attracted 174 million monthly active users and 7.6 million paying users by the end of 2020.5 The data tracks the broadcasting, gift-receiving, and message-receiving behavior of 157 broadcasters during 52 weeks, from June 1, 2020, to May 28, 2021. These broadcasters performed at least once during the observational period. There are 8,164 observations in the dataset, each records a broadcaster’s length of live performing, amount of gifts received, and number of messages received in each week. Broadcasters are free to choose whether to broadcast on a given day. In our dataset, there are 2,551 observations with a positive length of broadcasting, that is, broadcasters choose to perform in these weeks. Table 6.1 presents descriptive statistics of three key variables used in the empirical analysis: PerformHour, denoting the total hours of broadcasting for each broadcaster in each week. We find that broadcasters provide 6.929 h of live content in each week on average. GiftHourly, refers to the hourly amount of virtual gifts a broadcaster receives in a given week. This measure is calculated by dividing the total monetary values received during a week by the hours performed in the week, which is similar to the concept of the hourly wage rate for jobs. The average hourly amount of gifts received is ¥47.983, and the highest amount is ¥45,956.297. MessageHourly, referring to the number of chatting messages a broadcaster receives during a week, which is also measured on hourly based as GiftHourly. In a broadcasting week, broadcasters receive 98.266 messages per hour on average. Table 6.2 reports the correlations among the key variables. All the correlations are positive and significant, suggesting that these variables are highly correlated and could affect each other. To further explore the temporal dynamics of the key variables, we present the evolution processes of two sample broadcasters in Fig. 6.2. The broadcaster in Fig. 6.2a is a high-frequency performer who performed in every week during the 3

https://www.cbnweek.com/articles/normal/25317. https://sensortower.com/blog/tiktok-revenue-75-million. 5 https://www.chinanewsweb.com/index.php/2021/03/23/douyu-has-174-million-active-users-inqapril-and-its-community-content-ecology-continues-to-flourish/. 4

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6 Dynamics of Digital Content Contribution, Monetary Incentive …

Table 6.2 Correlation log(PerformHour)

log(GiftHourly)

log(PerformHour)

1.000 (0.000)

log(GiftHourly)

0.625 (0.000)

1.000 (0.000)

log(MessageHourly)

0.769 (0.000)

0.703 (0.000)

log(MessageHourly)

1.000 (0.000)

observational period, while the one in Fig. 6.2b has a declining trend of broadcasting likelihood. Some initial evidence can be found in these two figures. First, there is a positive relationship between broadcasters’ gift- and message-sending. Second, the correlation between broadcasters’ length of live streaming and the gift-(message-) received in the same week is unclear. For the broadcaster in Fig. 6.2a, the correlation seems to be negative in that when the broadcaster chooses to broadcast longer, the hourly amount of gifts and the number of messages he receives both decrease. While for the broadcaster in Fig. 6.2b, the correlation seems to be positive. This model-free evidence motivates us to test the interrelationships in a more suitable way. In the next section, we will develop a PVAR model to study the effect of one variable on the others simultaneously.

6.4 Analysis and Results 6.4.1 Empirical Model We adopt a PVAR to capture the interrelationship between the length of broadcasting, the hourly amount of gifts received, and the hourly number of messages received. Specifically, we are interested in the effect of the length of working (i.e., the hours of broadcasting) on the hourly monetary gaining (i.e., the hourly monetary amount of gifts received) and the hourly social gaining (i.e., the hourly number of messages recved) to examine whether working hard can make an individual rich and popular. PVAR model is an extension of the VAR (vector autoregression) model, which can be used in panel data setting (Holtz-Eakin et al., 1988). The PVAR model with these three endogenous variables of interest and j time lags can be specified as follows:

6.4 Analysis and Results Fig. 6.2 Evolution of key variables

121

(a)

(b)

⎡ ⎤⎡ ⎤ ⎤ j j j β11 , β12 , β13 Per f or m H ourit Per f or m H ouri,t− j ⎢ ⎥ j j j J ⎣ Gi f t H ourlyit ⎦ =  j=1 ⎣ β21 , β22 , β23 ⎦⎣ Gi f t H ourlyi,t− j ⎦ j j j MessageH ourlyit MessageH ourlyi,t− j β ,β ,β ⎡ 31 ⎤32 33 ∈1it +μi + ⎣ ∈2it ⎦ ∈3it (6.1) ⎡

where i is the broadcaster index and t is the week index. The coefficient matrix of β captures the effects of lagged endogenous variables. μi is the broadcaster fixed effect which controls the unobserved individual effects. it is a vector of random errors which assumed to follow a normal distribution.

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6 Dynamics of Digital Content Contribution, Monetary Incentive …

6.4.2 Main Results We present the main results in the following order: (1) stationarity and unit root tests; (2) determining the optimal number of lags; (3) PVAR results; and, (4) IRF results. Stationarity and Unit Root Tests We first check the stationarity condition of the PVAR model by calculating the modulus of each eigenvalue. The GMM estimation of PVAR suffers from the weak instruments problems if the variables are near unit root (Abrigo & Love, 2016). A PVAR model is stable if all moduli of the companion matrix are strictly less than one. Checks of stability are reported in Table 6.3. A graph of the stability check is displayed in Fig. 6.3. We can see that all the eigenvalues lie inside the unit circle, which suggest the PVAR model satisfies stability condition. Determining the Optimal Number of Lags Before running the formal PVAR model, we first use the moment and model selection criteria to determine the optimal number of lags, i.e., the optimal j in the Eq. (6.1) Table 6.3 Checks of stability

Variable

Eigenvalue Real

Imaginary

PerformHour

0.7962559

0

Modulus 0.7962559

GiftHourly

0.6003529

0

0.6003529

MessageHourly

0.3890757

0

0.3890757

Fig. 6.3 Roots of the companion matrix

6.4 Analysis and Results Table 6.4 The optimal number of lags

123

Lag

MBIC

MAIC

MQIC

1

−267.834

−23.253

−107.786

2

−221.350

−37.914

−101.313

3

−149.132

−26.842

−69.108

4

−77.859

−16.714

−37.847

(Abrigo & Love, 2016). Based on the three selection criteria proposed by Andrews and Lu (2001), the optimal number of lags should be the one that has the smallest MAIC, MBIC, and MQIC. Table 6.4 presents the results from the first-, second-, third-, and fourth-order PVAR model using the first five lags of the endogenous variables as instruments. We can see that one lag should be selected according to MBIC and MQIC, while two lags should be the optimal one as suggested by MAIC. Thus, in the main analysis, we run a first-order PVAR model. And in the robustness check, we fit a second-order PVAR model to verify the main results. PVAR Results Our analysis has utilized a first-order PVAR model to estimate the relationship between broadcasting length and gift-receiving as well as message-receiving in the live streaming context. The results of this analysis are presented in Table 6.5. Our findings indicate that the broadcasting length in the previous period does not have a significant effect on the hourly monetary amount of gifts received by the broadcaster in the current period. However, we have found a significantly positive effect of broadcasting length on the hourly number of chat messages. This suggests that working hard and increasing the length of one’s broadcasting sessions may not directly enhance their monetary earning power, but it can increase their social value by attracting more engagement and interaction from their viewers. Table 6.5 Main results

PerformHour (t−1) GiftHourly (t−1) MessageHourly (t−1)

(1)

(2)

(3)

PerformHour (t)

GiftHourly (t)

MessageHourly (t)

0.779***

0.011

0.328***

(0.000)

(0.176)

(0.000)

0.215***

0.614***

0.397***

(0.000)

(0.000)

(0.000)

−0.004

0.007

0.393***

(0.785)

(0.178)

(0.000)

Broadcaster Fixed Effects

Yes

N

7850

p-values in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01

124

6 Dynamics of Digital Content Contribution, Monetary Incentive …

In addition to the findings regarding the relationship between broadcasting length and gift-receiving, our analysis has also revealed some other noteworthy effects. Firstly, our results suggest that individuals’ content contribution behavior is primarily driven by monetary incentives rather than non-monetary ones. In Column (1) of Table 6.5, we have found that the hourly amount of gifts received has a positive and significant effect on the length of broadcasting, while the impact of the hourly number of messages received is not significant. This indicates that broadcasters are more motivated by the monetary rewards they receive rather than the social interaction they have with their viewers. Secondly, we have found that past monetary gains can positively influence the number of messages received, while the opposite is not true. This suggests that broadcasters who have received higher monetary rewards in the past may be more likely to attract greater social interaction and engagement from their viewers in the future. Overall, our findings provide valuable insights into the drivers of content contribution behavior and audience engagement in the live streaming context. By understanding the importance of monetary incentives and the role of past performance in driving social interaction and engagement, broadcasters and live streaming platforms can develop strategies to optimize their performance and achieve greater success on the platform. IRF Results In our analysis, we have employed an impulse response function (IRF) to describe how the variation of one variable affects another variable over time. An IRF is a statistical tool that allows us to measure the dynamic response of a variable of interest to a shock or impulse in another variable. By using an IRF, we are able to estimate the magnitude and duration of the effect of a shock in one variable on another variable, providing valuable insights into the underlying dynamics of the system. This allows us to better understand how different variables are related to each other and how they influence each other over time. Figure 6.4a presents the simple IRFs, while Fig. 6.4b displays the cumulative IRFs that capture the cumulative effect of the shock over time We are particularly interested in the effect of the length of performance on hourly monetary and non-monetary earnings. Our analysis finds that the hours of broadcasting have no significant immediate effect on the hourly monetary earnings. However, over time, the effect tends to be weakly positive. The shock on the length of performing (i.e., 25.232) increases the hourly amount of gifts received by around 0.015 after 3 weeks. Although the cumulative effect increases to about 0.1 after 10 weeks, it is overall insignificant, suggesting that the long-term effect of the length of performance on hourly monetary earnings is limited. Regarding the effect of broadcasting hours on received social interaction, we find that it changes over time. The positive effect becomes stronger initially but tends to decrease after 3 weeks, exhibiting an inverted U-shape pattern. This suggests that the length of performance can positively affect social interaction in the short term, but the effect may diminish over time.

6.4 Analysis and Results Fig. 6.4 IRF

125

(a)

(b)

The analysis of IRFs provides valuable insights into the dynamics of the live streaming context. Our findings indicate that working hard to provide more live content may not be effective in attracting more gifts from viewers. In other words, working hard alone may not be sufficient to increase broadcasters’ monetary earnings. However, our analysis does show that working hard can help attract more chat messages from viewers, which can bring social value to broadcasters. By understanding the complex relationships between different variables and their impact over time, broadcasters and live streaming platforms can develop strategies to optimize their performance and achieve greater success on the platform. These insights can help broadcasters tailor their content and engagement strategies to maximize their social value and potentially increase their monetary earnings. Likewise, live streaming platforms can use these insights to improve their platforms and provide better support to broadcasters, ultimately leading to a more thriving and successful live streaming ecosystem. In the following section, we will conduct two additional analyses to confirm the robustness of the main results.

126

6 Dynamics of Digital Content Contribution, Monetary Incentive …

6.4.3 Robustness Checks To ensure the robustness of our main results, we conducted two additional analyses as robustness checks. Firstly, we examined the effect of using an alternative number of lags in the PVAR model. Table 6.4 reports three criteria used to determine the optimal number of lags in the model, with two of them suggesting using a first-order PVAR model, while the third criterion, MAIC, indicating the use of a second-order model. In the main analysis, we reported the results of the first-order PVAR model. For the robustness check, we used a second-order PVAR model. The results of this robustness check are presented in Table 6.6a. Column (2) shows that the length of broadcasting has no significant immediate effect on the amount of gifts received per hour in one week, but has a weakly positive effect in two weeks. As for social interaction, Column (3) shows that broadcasting hour has a significant and positive effect on the messages received in one week, but the effect disappears in two weeks. Secondly, we examined the effect of using an alternative measure of monetary earnings. In the main analysis, we used the hourly amount of gifts (hourly number of messages) received as the measure of monetary (social) earnings. For the robustness check, we used the total number of gifts (messages) received instead. The results of this robustness check are presented in Table 6.6b. Our analysis has consistently shown that the length of performance in the previous week has no significant effect on the total amount of gifts received in the current week, but has a positive effect on the total number of messages received. These results again highlight the importance of working hard to attract social engagement and create social value on the live streaming platform. While working hard may not necessarily result in higher monetary earnings, our findings suggest that it can still be valuable in terms of building a loyal and engaged audience. By consistently providing high-quality content and engaging with their viewers, broadcasters can build a strong social presence that can be leveraged in various ways, such as through sponsorships or collaborations with other broadcasters.

6.4.4 Heterogeneity Analysis Empirical results above consistently imply that the length of performance has no significant effect on the monetary earning, but a positive effect on the social earning. In this section, we further explore whether this effect varies across individuals with heterogenous earning power. On live streaming platforms, broadcasters’ ability to attract gifts from viewers is dramatically different. According to a report released by China Association of Performing Arts, broadcasters earn from ¥3,000 to ¥5,000 per month on average, while the revenue for top broadcasters can reach up to tens of millions of RMB.6 We split the broadcasters in our dataset into two groups according 6

https://www.globaltimes.cn/page/202105/1223886.shtml.

6.4 Analysis and Results

127

Table 6.6 Robustness checks (1)

(2)

(3)

PerformHour (t)

GiftHourly (t)

MessageHourly (t)

0.693***

-0.006

0.288***

(0.000)

(0.508)

(0.000)

0.122***

0.016*

-0.039

(0.000)

(0.067)

(0.187)

0.174***

0.426***

0.228***

(0.000)

(0.000)

(0.004)

0.111**

0.268***

0.155**

(0.011)

(0.000)

(0.048)

−0.005

0.003

0.331***

(0.755)

(0.474)

(0.000)

−0.010

−0.006

0.169***

(0.456)

(0.167)

(0.000)

0.760***

0.019

0.531***

(0.000)

(0.416)

(0.000)

0.115***

0.675***

0.322***

(0.000)

(0.000)

(0.000)

−0.002

0.014

0.414***

(0.885)

(0.187)

(0.000)

(a) PerformHour (t−1) PerformHour (t−2) GiftHourly (t−1) GiftHourly (t−2) MessageHourly (t−1) MessageHourly (t−2) Broadcaster Fixed Effects

Yes

N

7693

(b) PerformHour (t−1) GiftTotal (t−1) MessageTotal (t−1) Broadcaster Fixed Effects

Yes

N

7850

p-values in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01

to the average hourly amount of gifts received. The more group includes broadcasters who earn above the average, and the less group includes those with below-average earnings. Table 6.7 shows the heterogeneity analysis results. We compare the results in Columns (3) and (4) to understand the different effects of the length of performance on the hourly earnings. For the more group, we find that the effect of performing hours is not significant, while it is positive for the less group. Figures 6.5 and 6.6 display the IRFs for the more and less group, respectively. In Fig. 6.5, for broadcasters who receive a more-than-average earning, neither the simple IRF nor the cumulative IRF for the effect of the PerformHour on GiftHourly is significant. While in Fig. 6.6, for broadcasters in the less group, the response of broadcasters’ monetary earnings to the length of the performance is overall significant and positive. As shown in Fig. 6.6a,

128

6 Dynamics of Digital Content Contribution, Monetary Incentive …

Table 6.7 Heterogeneity results (1)

(2)

(3)

(4)

(5)

(6)

PerformHour (t)

GiftHourly (t)

MessageHourly (t)

More

Less

More

Less

More

Less

0.793***

0.782***

0.098

0.016**

0.557**

0.337***

(0.000)

(0.000)

(0.608)

(0.013)

(0.019)

(0.000)

0.181

0.148***

0.452**

0.486***

0.506**

0.134

(0.142)

(0.005)

(0.013)

(0.000)

(0.022)

(0.103)

MessageHourly (t−1)

0.053

−0.006

0.161

0.001

0.382**

0.389***

(0.659)

(0.661)

(0.212)

(0.861)

(0.038)

(0.000)

Broadcaster Fixed Effects

Yes

Yes

Yes

Yes

Yes

Yes

N

400

7450

400

7450

400

7450

PerformHour (t−1) GiftHourly (t−1)

p-values in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01

the carryover effect changes temporally that it first increases and reaches the peak in 3 weeks, and then decreases over time. As displayed in Fig. 6.6b, the cumulative effect increases gradually and is not satiated. Our results suggest that although working hard may not be effective at inducing more monetary earnings for individuals with high earning power, it can still be a useful way for less-paid individuals to enhance their earning abilities. These findings can be rationalized by considering the role of experience in live streaming. Compared to more highly paid broadcasters, less paid individuals are more likely to be those with less experience in live streaming. By performing for longer periods, broadcasters, especially those with less experience, have the opportunity to improve their skills in live streaming and interacting with viewers. Thus, less-paid individuals are more likely to benefit from working hard to provide more live content. Furthermore, working hard to provide more live content can also help less-paid individuals to build a loyal and engaged audience, which can be leveraged in various ways to drive higher earnings, such as through sponsorships or collaborations with other broadcasters. As such, working hard can be a valuable strategy for less-paid individuals to enhance their earning potential and achieve greater success on the platform. Thus, less-paid individuals are more likely to benefit from working hard to provide more live content.

6.5 Summary In this Chapter, we focus on the impact of working hard on monetary and social earnings in the context of online content contribution. In the empirical context of live streaming, we define working hard as providing long hours of live content, monetary

6.5 Summary Fig. 6.5 IRF for the more group

129

(a)

(b)

earning as the gifts a broadcaster receives from viewers, and social earnings as the chat messages a broadcaster receives. We track 157 broadcasters’ content provision and earning behavior spanning 52 weeks, and adopt a PVAR model to quantify the interrelationship between the hours of performing and the hourly earnings. We find a robust and striking result that providing more content has no significant effect on content contributors’ monetary earnings, but a significant and positive effect on their social earnings. The results suggest that although working hard cannot make one rich on online content platforms, it can help one get more social value. Furthermore, we also find the effect of working hard differs between content contributors with aboveand below-average hourly earnings. Well-paid users cannot benefit from working hard, but less-paid users can expect to receive more revenue from providing more online content. Our research contributes to the literature in the following ways. First, we contribute to the literature on online content contribution by examining the effect of working

130 Fig. 6.6 IRF for the less group

6 Dynamics of Digital Content Contribution, Monetary Incentive …

(a)

(b)

hard on earnings. Prior research mainly focuses on the factors, such as monetary and non-monetary incentives (e.g., Burtch et al., 2018; Lampel & Bhalla, 2007), that can affect users’ content provision behavior, but neglect the effect of content contribution on outcomes. In this study, we consider how content contributors’ hourly monetary and social earnings are affected by their quantity of content provision, and find a striking result that is different from traditional labor supply. Second, our paper is one of the rare studies that provide empirical evidence on the interrelationship between working hard and earning. In traditional working situations, individuals are usually asked to follow a certain working schedule and the salary is relatively fixed. Broadcasters’ behavior in live streaming provides an ideal empirical setting to address this question. Broadcasters are free to choose the quantity of live content to provide and their earnings, i.e., gift-sending from viewers, have considerable variation. Thus, we utilize observations from a live streaming platform to provide empirical insights. Third, we explore not only the short-term effect of working hard but also how the

References

131

effect evolves over time. In this study, a PVAR model is adopted to quantify both the immediate and carryover impacts of hours of performing on hourly monetary gaining and social interaction. Our findings have significant managerial implications for both online content contributors and platforms. For online content contributors, our results suggest two key implications. Firstly, although working harder may not lead to higher monetary earnings for individuals who have already established a high earning rate, it can be an effective way for less-paid individuals to enhance their earning abilities. Therefore, content contributors in the initial stages of their careers should work harder to increase their earning potential. Secondly, providing more digital content can help individuals receive more social interactions, which can bring social value and benefit their overall development on the platform. For platforms, our findings suggest that it is essential to provide support to less experienced and less paid broadcasters, as they are more likely to benefit from working hard and providing more live content. Additionally, platforms should consider ways to promote social engagement and interaction among broadcasters and viewers, as this is an effective way to build a loyal and engaged audience and drive success on the platform.

References Abrigo, M. R. M., & Love, I. (2016). Estimation of panel vector autoregression in stata. The Stata Journal,16(3), 778–804. https://doi.org/10.1177/1536867X1601600314 Altonji, J. G., Smith, A. A., Jr., & Vidangos, I. (2013). Modeling earnings dynamics. Econometrica,81(4), 1395–1454. https://doi.org/10.3982/ECTA8415 Andrews, D. W. K., & Lu, B. (2001). Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models. Journal of Econometrics,101(1), 123–164. https://doi.org/10.1016/S0304-4076(00)00077-4 Burtch, G., Hong, Y., Bapna, R., & Griskevicius, V. (2018). Stimulating online reviews by combining financial incentives and social norms. Management Science,64(5), 2065–2082. https://doi.org/ 10.1287/mnsc.2016.2715 Edelman, B. (2012). Earnings and ratings at google answers. Economic Inquiry,50(2), 309–320. https://doi.org/10.1111/j.1465-7295.2011.00414.x Fang, E., Palmatier, R. W., & Evans, K. R. (2004). Goal-setting paradoxes? Trade-offs between working hard and working Smart: The United States versus China. Journal of the Academy of Marketing Science,32(2), 188–202. https://doi.org/10.1177/0092070303261413 Holmes, T. L., & Srivastava, R. (2002). Effects of job perceptions on job behaviors. Industrial Marketing Management,31(5), 421–428. https://doi.org/10.1016/S0019-8501(01)00158-4 Holtz-Eakin, D., Newey, W., & Rosen, H. S. (1988). Estimating vector autoregressions with panel data. Econometrica, 56(6), 1371–1395. JSTOR. https://doi.org/10.2307/1913103 Lampel, J., & Bhalla, A. (2007). The role of status seeking in online communities: Giving the gift of experience. Journal of Computer-Mediated Communication,12(2), 434–455. https://doi.org/ 10.1111/j.1083-6101.2007.00332.x Li, X., Liao, C., & Xie, Y. (2021). Digital piracy, creative productivity, and customer care effort: evidence from the digital publishing industry. Marketing Science:1275. https://doi.org/10.1287/ mksc.2020.1275

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Lin, Z. (2020). Earning more by selling less in the sharing economy: The secret of provider experience. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3558310 Lin, Y., Yao, D., & Chen, X. (2021). Happiness begets money: emotion and engagement in live streaming. Journal of Marketing Research, 002224372110024. https://doi.org/10.1177/002224 37211002477 Lu, S., Yao, D., Chen, X., & Grewal, R. (2020). Do larger audiences generate greater revenues under pay what you want? Evidence from a live streaming platform. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3516777 O’Reilly, C. A., & Chatman, J. A. (1994). Working smarter and harder: A longitudinal study of managerial success. Administrative Science Quarterly,39(4), 603–627. Pencavel, J. (2015). The productivity of working hours. The Economic Journal,125(589), 2052– 2076. https://doi.org/10.1111/ecoj.12166 Suh, T., & Shin, H. (2008). When working hard pays off: Testing creativity hypotheses. Corporate Communications: An International Journal,13(4), 407–417. https://doi.org/10.1108/135632808 10914838 Sujan, H., Weitz, B. A., & Kumar, N. (1994). Learning orientation, working smart, and effective selling. Journal of Marketing,58(3), 39–52. https://doi.org/10.1177/002224299405800303 Sun, M., & Zhu, F. (2013). Ad revenue and content commercialization: Evidence from blogs. Management Science,59(10), 2314–2331. https://doi.org/10.1287/mnsc.1120.1704 Tang, Q., Gu, B., & Whinston, A. B. (2012). Content contribution for revenue sharing and reputation in social media: A dynamic structural model. Journal of Management Information Systems,29(2), 41–76. https://doi.org/10.2753/MIS0742-1222290203 Weiner, B. (1980). Human motivation. Holt, Rinehart, and Winston.

Part III

Social Influence in Digital Content Consumption

Chapter 7

Social Interaction and Digital Content Consumption

In this Part, our attention shifts to the subject of digital content consumption. This Chapter explores how social interaction influences users’ content consumption behavior and examines how this effect varies based on the contributors’ level of experience. Our findings reveal a positive correlation between users’ social interaction and gift sending, particularly among contributors with high levels of content provision experience. This research underscores the significance of social influence in digital content consumption.

7.1 Introduction This Chapter delves into examining the relationship between social influence and digital content consumption. Our analysis is based on data collected from a major live streaming platform in China, and our primary focus is on exploring the relationship between social interaction initiated by viewers and broadcasters’ gift-receiving. Social interaction, as defined by Varey (2008), refers to “an interpersonal action or relations between self and other.” In the context of live streaming, viewers engage in social interaction by sending text messages to broadcasters and other viewers. As such, in our empirical analysis, we have characterized social interaction initiated by viewers as viewers’ message sending. Our empirical results demonstrate that within-session social interaction initiated by viewers is positively associated with broadcasters’ gift-receiving. Furthermore, we have found that the positive effect of viewer interaction on gift-receiving becomes even stronger as broadcasters’ experience increases. This suggests that broadcasters

This Chapter is derived, in part, from the article “Antecedents and Consequences of Gift-Receiving in Livestreaming: An Exploratory Study” published in Journal of Interactive Marketing on May 18, 2022, available online: https://journals.sagepub.com/doi/10.1177/10949968221095550. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Ma, Social Influence on Digital Content Contribution and Consumption, Management for Professionals, https://doi.org/10.1007/978-981-99-6737-7_7

135

136

7 Social Interaction and Digital Content Consumption

who have more experience in the live streaming platform are better able to leverage social interaction to increase their audience engagement and gift-receiving. Overall, our findings shed light on the importance of social interaction in the live streaming context and its impact on content consumption. They highlight the need for broadcasters to prioritize social interaction with their viewers and for platforms to provide tools and features that facilitate such interactions.

7.2 Theoretical Background Our research mainly relates to gifting and tipping. In these studies, gifting and tipping behavior are documented to be affected by both individuals’ intrinsic and extrinsic motivations. Intrinsic motivation is the value of giving per se, derived from individuals’ private preferences, such as altruism (Andreoni, 1990), reputational concerns (Cappellari et al., 2011), and prestige (Harbaugh, 1998). Extrinsic motivation is driven by external benefits, such as social status (Goode et al., 2014), social image (Ariely et al., 2009), and social interaction (Wan et al., 2017). Since this study focuses on the gift-sending in live streaming—a novel online medium—we summarize the recent literature on the gifting and tipping behavior in the online context in Table 7.1. Social signaling has been found to be an important factor affecting individuals’ gifting and tipping. Lu et al. (2021) explored viewers’ gift giving in live streaming. They conducted a field experiment and found that an increase in audience size leads to an increase in broadcasters’ revenue because of viewers’ social image concerns. Hou et al. (2019) and Li et al. (2021) verified the role of status seeking in gifting and tipping. Users’ gifting behavior is always positively related to their social status motivation. Interactivity can also drive viewers to gift and tip. The presence of others (Zhou et al., 2019), social visibility (Shmargad & Watts, 2016), and social identification (Wan et al., 2017) all have a positive effect on gifting and tipping behavior on online platforms. Kim et al. (2018) used social exchange theory to analyze the antecedents of gifting on social network services (SNS) platforms. Their findings show that users’ frequency of SNS gifting is affected by perceived worth, gifting experience, and the number of friends. Our research on digital content consumption in the context of live streaming makes contributions to the literature in two key areas. Firstly, prior research has not thoroughly explored the effect of social interaction on gift-giving. As summarized in Table 7.1, the social aspect of gifting and tipping is crucial in the online context. Social factors, such as social status (Goode et al., 2014; Lampel & Bhalla, 2007), social exchange (Kim et al., 2018), and social visibility (Shmargad & Watts, 2016), have all been documented to have a positive effect on gift-giving, but little research has been conducted on how social interaction affects gift-sending behavior. Our study addresses this research gap and sheds light on the role of social interaction in gift-giving behavior. Secondly, there is limited research on the dynamic effects of gift-giving and social interaction, and our study provides valuable insights into how these effects change

7.2 Theoretical Background

137

Table 7.1 Summary of selected literature on gifting and tipping in the online context Research

Factors Research influencing gifting context and tipping

Method

Main findings

Goode et al. (2014)

– Social status

Virtual worlds

Empirical

Gifting is associated with future enhancements of the gift giver’s social status

Hou et al. (2019)

– Sex and humor appeals – Social status display

Live streaming

Survey

Sex and humor appeals and social status display play considerable roles in viewers’ consumption intention

Kim et al. (2018)

– Social exchange Social network

Survey

Perceived worth, gifting experience, and the number of friends all have positive effect on the frequency of gifting

Lampel and Bhalla 2007

– Social status

Virtual community

Survey

Informational gift giving is strongly driven by status and status seeking

Li et al. (2021) – Identity

Live streaming

Observational data

Users’ gifting behavior is affected by class identity and relational identity

Lu et al. (2021) – Audience size

Live streaming

Field experiment

The relationship between audience size and average tip per viewer is positive

Shmargad and Watts (2016)

– Social visibility

Social network

Observational data

Gift rates increased with the number of ties the user kept on the network, but decreased with the density of these ties

Wan et al. (2017)

– Social factors – Technical factors

Social media

Survey

Donation intention is determined by the emotional attachment to the content creator and functional dependence on social media

Yang et al. (2021)

– Pride-tagged Online money tipping – Surprise-tagged money

Experiment

Pride-tagged (vs. surprise-tagged) money leads to an increased willingness to tip online

Zhou et al. (2019)

– Social interaction

Observational data

Gifting behavior is affected by social factors, include presence of others, social competition, and emotional stimuli

Live streaming

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7 Social Interaction and Digital Content Consumption

with individual experience. By analyzing data from both novice and experienced users, we are able to examine how gift-giving behavior and social interaction evolve as individuals gain more experience with the live streaming platform. This analysis provides a nuanced understanding of the complex interplay between social influence and digital content consumption in the context of live streaming.

7.3 Hypotheses Development We examine the impact of social interaction on digital content consumption, i.e., viewers’ gift-sending in live streaming. Similar to our approach in Sect. 5.3, we utilize the Uses and Gratifications Theory (UGT) to formulate our hypotheses in this section. Viewers’ motivation to watch and engage in live streaming has a strong social basis. UGT assumes that people use a certain media to satisfy their desires and needs to achieve gratification (Katz et al., 1973). In the context of our study, UGT helps address our primary question—the effect of social interaction on gift-giving in live streaming—by clarifying the gratification factors for consuming live content. Hilvert-Bruce et al. (2018) find that social motivations, such as meeting new people and interacting with others, are key reasons for viewers’ live streaming engagement. Sjöblom and Hamari’s (2017) findings also show that social integrative motivation drives people to spend a longer time watching streams. These findings suggest that social interaction is an important factor that contributes to users’ gratification with live streaming. Thus, we expect that social interaction should be positively associated with viewers’ gift sending, i.e., broadcasters’ gift receiving. The interactive format is one of the most notable features of live streaming. Relative to traditional media, live streaming provides viewers with a real-time and engaging experience. Viewers can interact with broadcasters and other viewers during live sessions via sending text messages or comments. All the messages are visible to everyone in the live session. In previous findings in UGT, social motivation plays a vital role in driving viewers to engage in live streaming (Hilvert-Bruce et al., 2018; Hou et al., 2019). We propose that social interaction can affect viewers’ gift-sending in several ways. First, social interaction is a way for viewers to engage during live sessions, which can increase their involvement levels (Wongkitrungrueng & Assarut, 2020). On social media, user engagement and interaction have a positive effect on elements such as participation (So et al., 2021), repurchase intention (Wu et al., 2020), and retailer sales (Kumar et al., 2016). Broadcaster-viewer interactions can be vital in inducing virtual gifts. For example, viewers might be more likely to send gifts to broadcasters when broadcasters respond to their messages or address their requests.1 Kang et al. (2021) find that broadcaster responsiveness (i.e., the response rate for customers’ questions in unit time) positively affects broadcasters’ gift-receiving.

1

http://mat1.gtimg.com/chinatechinsights/file/20160926/The_pulse_of_live_streaming_in_ China.pdf.

7.4 Empirical Background and Data

139

Second, social interactions can be effective in promoting viewers’ gift-sending by increasing the level of arousal and social presence. An individual’s arousal level can be affected by many factors, such as social color (Batra & Ghoshal, 2017), physical exertion, and emotionally-charged stimuli (Sanbonmatsu & Kardes, 1988). In the context of live streaming, viewer interactions can be regarded as sensory stimuli (Zhou et al., 2019). High levels of arousal can be positively associated with customer purchase intentions (Yoon et al., 1998) and willingness-to-pay (Bagchi & Cheema, 2013). Regarding live streaming, Zhou et al. (2019) showed that the more viewerviewer interactions, the higher the arousal level, which induces viewers’ gift sending. Besides, even for viewers who don’t actively participate by themselves, observing interactions from others would make individuals be aware of the social closeness. Since viewers’ social interactions would increase their levels of involvement and feelings of social presence and arousal level, we hypothesize the following: H1: Social interactions are positively related to broadcasters’ gift-receiving. We also explore how the effect of social interaction changes with the increase of broadcasters’ experience. We propose that relative to less experienced broadcasters, experienced ones can benefit more from viewers’ social interaction. Prior qualitative research on live streaming found that viewers would like to send messages to broadcasters in exchange for broadcasters’ responses (McIntyre et al., 2016), and receiving feedback will increase viewers’ willingness to pay (Hilvert-Bruce et al., 2018). Broadcasters with more experience are better able to interact with viewers which involves the knowledge, capabilities, and understanding of live streaming (Li & Peng, 2021). Thus, experienced broadcasters are more likely to recognize valuable interactions from users, respond to these interactions, and monetize social interactions. Therefore, we hypothesize the following: H2: The positive relationship between social interaction and gift-receiving is stronger for more experienced broadcasters than less experienced ones.

7.4 Empirical Background and Data To investigate the relationship between social interaction and digital content consumption, we have utilized the same dataset as in Chap. 5. However, in this section, we have shifted our focus from the relationship between gift-sending and social interaction on broadcasters’ live streaming content contribution to the relationship between viewers’ social interaction, specifically their message-sending behavior, and digital content consumption, specifically their gift-giving behavior.

140

7 Social Interaction and Digital Content Consumption

7.5 Analysis and Results 7.5.1 Main Analysis In this study, we seek to understand the factors that drive gift-receiving during live streaming events, with a particular focus on the role of social interaction. To this end, we have analyzed a large dataset from a major live streaming platform in China and examined the relationship between social interaction and gift-receiving. Figure 7.1 illustrates the positive relationship between the number of messages received and the amount of gifts received by broadcasters on a performing day. This suggests that social interaction, as measured by the number of messages received, is an important antecedent of gift-receiving in the live streaming context. To further investigate this relationship, we have used a formal model that incorporates various measures of social interaction, such as the number of chat messages, the number of chatting viewers, and the average number of messages sent by each chatting viewer. Since broadcasters can only receive virtual gifts from viewers in their live sessions, and no gift-receiving is observed if they don’t perform live on a given day, there exists a sample selection issue. We address this issue using the Heckman two-stage model to incorporate a selection correction term in the main equations (Heckman, 1979). The first-stage model is identical to the one presented in Sect. 5.5.1, and we will not reiterate it here. In the second stage, we use three measures of broadcasters’ gift-receiving as dependent variables, i.e., Gift_Amt_Sum, Gift_Num, and Gift_Amt_Avg, referring to the total amount of gifts received, the number of gifting viewers, and the average

Fig. 7.1 Relationship between social interaction and gift receiving

7.5 Analysis and Results

141

amount of gifts sent by each gifting viewer for each broadcaster on a performing day. Since all the three dependent variables are continuous over strictly positive values but take zero with positive probability, we use a Tobit model for the corner solution response. 

Gi f t_Recei ving it∗ = Z i β1 + X itB β2 + X itV β3 + β4 I M Rit + νt + ξit , Gi f t_Recei ving it = max{0, Gi f t_Recei ving it∗ }.

(7.1) (7.2)

Independent variables in Eq. (7.1) are very similar to those in the first stage. Z i includes the broadcaster’s characteristics that do not vary with time (i.e., Gender and Type). X itB and X itV capture broadcaster behavior (i.e., Union, Broad_Length, and Broad_Length_Cul) and viewer behavior (i.e., Viewer_num and Chat_Msg_ Sum), respectively. Notice that the set of independent variables in the second stage is a subset of those in the first stage. The variable Last_Broad_Diff is excluded. While it captures one’s tendency to broadcast, it is not necessarily related to the amount of gifts the broadcaster receives, conditional on the broadcast decision. We also check the non-normality of residuals in Tobit regression and find that we can not reject the null hypothesis that the residuals are normal. As for the concern of heteroscedasticity, we solve it by bootstrapping standard errors (Amore & Murtinu, 2019; Jain & Thietart, 2014). The first-stage estimation results are presented in Fig. 5.4 in Sect. 5.5.1. Table 7.2 reports the estimation results of the second-stage model. Our primary interest is the effect of viewers’ social interaction on broadcasters’ gift-receiving. We report the results using Gift_Amt_Sum, the total amount of gifts received on a certain day, as the dependent variable with three measures of social interactions, i.e., Chat_Msg_Sum, Chat_Num, and Chat_Msg_Avg, which refer to the number of chatting messages, chatting viewers, and the average number of messages sent by each chatting viewer, respectively. Columns (1), (3), and (5) in Table 7.2 report the main effect of social interaction. We find that a 1% increase in the number of chatting messages received increases the broadcaster’s total amount of gift receiving by 0.718%. And the numbers are 1.348% and 0.856% for a 1% increase in the number of chatting viewers and the average number of messages sent by each chatting viewer, respectively. The empirical results support H1. Columns (2), (4), and (6) incorporate the interaction between viewer chats and broadcaster experience. The significant and positive coefficients of interaction terms reveal that as the broadcaster experience increases, viewer interactions become increasingly important in inducing broadcasters’ gift-receiving. The results provide strong support for H2. Regarding the effects of other control variables, we find that broadcasters’ giftreceiving is positively related to the number of viewers in their live sessions, and being in a union can help broadcasters get more gifts. Longer live sessions tend to have a larger total amount of gifts. Finally, the inverse Mills ratio coefficients are significant, which justifies the joint estimations of equations (3) and (4) as we

142

7 Social Interaction and Digital Content Consumption

Table 7.2 Effect of social interaction on digital content consumption

log (Chat_ Msg_Sum)

(1)

(2)

(3)

(4)

(5)

(6)

log (Gift_ Amt_Sum)

log (Gift_ Amt_Sum)

log (Gift_ Amt_Sum)

log (Gift_ Amt_Sum)

log (Gift_ Amt_Sum)

log (Gift_ Amt_Sum)

0.718***

0.418***

0.856***

0.356***

(0.000)

(0.000)

log (Chat_ Msg_Sum) *

0.059***

log (Broad_ Length_Cul)

(0.000)

log (Chat_ Num)

1.348***

1.088***

(0.000)

(0.000)

log (Chat_ Num) *

0.047***

log (Broad_ Length_Cul)

(0.000)

log (Chat_ Msg_Avg)

(0.000)

(0.000)

log (Chat_ Msg_Avg) *

0.099***

log (Broad_ Length_Cul)

(0.000)

log (View_ Num) log (Broad_ Length) Gender = male Type = dance

0.369***

0.332***

0.023***

0.000

0.760***

0.769***

(0.000)

(0.000)

(0.000)

(0.910)

(0.000)

(0.000)

0.090***

0.129***

0.247***

0.284***

0.396***

0.399***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

−0.078***

−0.088***

−0.230***

−0.247***

0.001

0.023***

(0.000)

(0.000)

(0.000)

(0.000)

(0.863)

(0.000)

0.324***

0.410***

0.388***

0.431***

0.139***

0.177***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

0.492***

0.501***

0.550***

0.561***

0.570***

0.565***

Type = hip-hop

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

Type = sing

0.794***

0.809***

0.901***

0.921***

0.892***

0.879***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

Union = 1

0.202***

0.224***

0.232***

0.247***

0.250***

0.253***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

log (Broad_ Length_Cul)

0.144***

−0.127***

0.119***

0.002

0.189***

−0.043***

(0.000)

(0.000)

(0.000)

(0.184)

(0.000)

(0.000)

IMR

−0.174***

−0.292***

(0.000) IMR_ Interact

−0.270***

(0.000)

(0.000)

−0.179***

−0.303***

−0.269***

(0.000)

(0.000)

(0.000) (continued)

7.5 Analysis and Results

143

Table 7.2 (continued) (1)

(2)

(3)

(4)

(5)

(6)

log (Gift_ Amt_Sum)

log (Gift_ Amt_Sum)

log (Gift_ Amt_Sum)

log (Gift_ Amt_Sum)

log (Gift_ Amt_Sum)

log (Gift_ Amt_Sum)

Yes

Yes

Yes

Yes

Yes

Yes

Day-of-Week Yes

Yes

Yes

Yes

Yes

Yes

Week FE

Observations 1277669

1277669

1277669

1277669

1277669

1277669

0.275

0.261

0.262

0.238

0.241

Pseudo R2

0.271

Log likelihood

−2.146e+06 −2.132e+06 −2.175e+06 −2.171e+06 −2.241e+06 −2.233e+06

p-values in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01

did. And the negative coefficients imply that estimates would be downward biased without the correction.

7.5.2 Robustness Checks In order to verify the validity and reliability of the primary empirical findings, we have created two alternative measures of gift receiving, namely Gift_Num and Gift_ Amt_Avg. These measures are used to capture the number of gifting viewers and the average amount of gifts sent by each gifting viewer, respectively. Our estimation results are presented in Table 7.3, which continue to support the validity of both H1 and H2. Table 7.3 (a) displays the results of our analysis using Gift_Num as the dependent variable. We have discovered that receiving more social interaction, such as chat messages, is positively associated with an increase in the number of virtual gifts received from viewers’ live streaming content consumption. Furthermore, this effect is even more pronounced among broadcasters with a higher level of experience. Table 7.3 (b) shows the results obtained by using Gift_Amt_Avg as the dependent variable, which demonstrates a positive association between social interaction and gift-sending. Moreover, this relationship becomes more significant as broadcasters gain more experience. In addition, we have conducted further robustness checks by excluding broadcasters who have ever continuously performed for 24 h. As mentioned in Sect. 5.5.3, the concern is whether these broadcasters actually perform continuously for the full 24-h duration. By excluding these broadcasters from our analysis, we can ensure that our results are not driven by any outliers or extreme cases. Our estimation results, which are reported in Table 7.4, remain robust and consistent with our primary findings, providing further evidence for the validity and reliability of our results.

144

7 Social Interaction and Digital Content Consumption

Table 7.3 Effect of social interaction on digital content consumption (with alternative dependent variables) (a)

log (Chat_ Msg_Sum)

(1)

(2)

(3)

(4)

(5)

(6)

log (Gift_ Num)

log (Gift_ Num)

log (Gift_ Num)

log (Gift_ Num)

log (Gift_ Num)

log (Gift_ Num)

0.289***

0.174***

(0.000)

(0.000)

0.631***

0.489***

0.300***

0.197***

log (Chat_ Msg_Sum) *

0.023***

log (Broad_ Length_Cul)

(0.000)

log (Chat_ Num)

(0.000)

(0.000)

log (Chat_ Num) *

0.026***

log (Broad_ Length_Cul)

(0.000)

log (Chat_ Msg_Avg)

(0.000)

(0.000)

log (Chat_ Msg_Avg) *

0.021***

log (Broad_ Length_Cul)

(0.000)

log (View_ Num) log (Broad_ Length)

0.198***

0.184***

0.015***

0.002***

0.354***

0.356***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

0.317***

0.335***

0.341***

0.363***

0.467***

0.468***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

0.086***

0.084***

0.017***

0.009***

0.112***

0.118***

Gender = male

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

Type = dance

−0.121***

−0.089***

−0.075***

−0.053***

−0.199***

−0.191***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

0.004*

0.005**

0.017***

0.022***

0.043***

0.041***

Type = hip-hop

(0.077)

(0.012)

(0.000)

(0.000)

(0.000)

(0.000)

Type = sing

0.070***

0.073***

0.100***

0.110***

0.122***

0.118***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

Union = 1

0.082***

0.093***

0.089***

0.099***

0.106***

0.107***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

−0.051***

−0.155***

−0.066***

−0.129***

−0.033***

−0.081***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

log (Broad_ Length_Cul)

(continued)

7.5 Analysis and Results

145

Table 7.3 (continued) (a) IMR

−0.071***

−0.106***

(0.000) IMR_Interact

−0.119***

(0.000)

(0.000)

−0.078***

−0.116***

−0.126***

(0.000)

(0.000)

(0.000)

Week FE

Yes

Yes

Yes

Yes

Yes

Yes

Day-of-Week

Yes

Yes

Yes

Yes

Yes

Yes

Observations

1,277,669

1,277,669

1,277,669

1,277,669

1,277,669

1,277,669

Pseudo R2

0.425

0.432

0.447

0.451

0.362

0.363

Log likelihood −1.140e + −1.125e + −1.096e + −1.088e + −1.265e + −1.263e + 06 06 06 06 06 06 p-values in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01 (b)

log (Chat_ Msg_Sum)

(1)

(2)

(3)

(4)

(5)

(6)

Log (Gift_ Amt_Avg)

Log (Gift_ Amt_Avg)

Log (Gift_ Amt_Avg)

Log (Gift_ Amt_Avg)

log (Gift_ Amt_Avg)

log (Gift_ Amt_Avg)

0.397***

0.279***

(0.000)

(0.000)

0.623***

0.598***

0.550***

0.260***

log (Chat_ Msg_Sum) *

0.023***

log (Broad_ Length_Cul)

(0.000)

log (Chat_ Num)

(0.000)

(0.000)

log (Chat_ Num) *

0.005***

log (Broad_ Length_Cul)

(0.000)

log (Chat_ Msg_Avg)

(0.000)

(0.000)

log (Chat_ Msg_Avg) *

0.058***

log (Broad_ Length_Cul)

(0.000)

log (View_ Num) log (Broad_ Length)

0.146***

0.132***

0.021***

0.019***

0.364***

0.369***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

−0.111***

−0.094***

0.039***

0.043***

0.012***

0.014***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000) (continued)

146

7 Social Interaction and Digital Content Consumption

Table 7.3 (continued) (a) −0.138***

−0.142***

−0.209***

−0.210***

−0.086***

−0.073***

Gender = male

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

Type = dance

0.331***

0.365***

0.331***

0.336***

0.241***

0.263***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

0.364***

0.367***

0.408***

0.410***

0.396***

0.392***

Type = hip-hop

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

Type = sing

0.574***

0.579***

0.650***

0.653***

0.611***

0.602***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

Union = 1

0.123***

0.132***

0.150***

0.151***

0.140***

0.143***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

log (Broad_ Length_Cul)

0.141***

0.034***

0.134***

0.122***

0.165***

0.030***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

IMR

−0.136***

−0.223***

(0.000) IMR_Interact

−0.162***

(0.000)

(0.000)

−0.136***

−0.224***

−0.158***

(0.000)

(0.000)

(0.000)

Week FE

Yes

Yes

Yes

Yes

Yes

Yes

Day-of-Week

Yes

Yes

Yes

Yes

Yes

Yes

Observations

1,277,669

1,277,669

1,277,669

1,277,669

1,277,669

1,277,669

Pseudo R2

0.212

0.214

0.194

0.194

0.201

0.203

Log likelihood −1.885e + −1.881e + −1.928e + −1.928e + −1.911e + −1.906e + 06 06 06 06 06 06 p-values in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01 Table 7.4 Effect of social interaction on digital content consumption (exclude broadcasters with 24-h performance)

log (Chat_ Msg_Sum)

(1)

(2)

(3)

(4)

(5)

(6)

log (Gift_ Amt_Sum)

log (Gift_ Amt_Sum)

log (Gift_ Amt_Sum)

log (Gift_ Amt_Sum)

log (Gift_ Amt_Sum)

log (Gift_ Amt_Sum)

0.717***

0.417***

(0.000)

(0.000)

log (Chat_ Msg_Sum) *

0.059***

log (Broad_ Length_Cul)

(0.000)

log (Chat_ Num)

1.345***

1.086***

(0.000)

(0.000) (continued)

7.5 Analysis and Results

147

Table 7.4 (continued) (1)

(2)

(3)

(4)

(5)

(6)

log (Gift_ Amt_Sum)

log (Gift_ Amt_Sum)

log (Gift_ Amt_Sum)

log (Gift_ Amt_Sum)

log (Gift_ Amt_Sum)

log (Gift_ Amt_Sum)

0.854***

0.356***

log (Chat_ Num) *

0.047***

log (Broad_ Length_Cul)

(0.000)

log (Chat_ Msg_Avg)

(0.000)

(0.000)

log (Chat_ Msg_Avg) *

0.098***

log (Broad_ Length_Cul)

(0.000)

log (View_ Num) log (Broad_ Length) Gender = male Type = dance

0.369***

0.333***

0.024***

0.001

0.760***

0.768***

(0.000)

(0.000)

(0.000)

(0.558)

(0.000)

(0.000)

0.094***

0.132***

0.251***

0.288***

0.401***

0.403***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

−0.078***

−0.088***

−0.230***

−0.247***

−0.000

0.022***

(0.000)

(0.000)

(0.000)

(0.000)

(0.961)

(0.000)

0.323***

0.410***

0.386***

0.429***

0.137***

0.176***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

Type = hip-hop

0.491***

0.501***

0.548***

0.560***

0.568***

0.563***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

Type = sing

0.795***

0.809***

0.901***

0.921***

0.891***

0.878***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

Union = 1

0.202***

0.224***

0.232***

0.248***

0.250***

0.253***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

log (Broad_ Length_Cul)

0.145***

−0.127***

0.119***

0.002

0.190***

−0.042***

(0.000)

(0.000)

(0.000)

(0.127)

(0.000)

(0.000)

IMR

−0.174***

−0.291***

(0.000)

Week FE

(0.000) −0.178***

IMR_ Interact

Yes

(0.000) −0.302***

(0.000) Yes

−0.269*** −0.269***

(0.000) Yes

Yes

(0.000) Yes

Yes

Day-of-Week Yes

Yes

Yes

Yes

Yes

Yes

Observations 1276006

1276006

1276006

1276006

1276006

1276006

0.275

0.261

0.262

0.239

0.241

Pseudo R2

0.271

Log likelihood

−2.143e+06 −2.129e+06 −2.171e+06 −2.168e+06 −2.237e+06 −2.229e+06

p-values in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01

148

7 Social Interaction and Digital Content Consumption

7.6 Summary In this chapter, we have conducted a comprehensive analysis of the relationship between social interaction and digital content consumption. Utilizing a unique dataset from live streaming, we have explored the impact of social interaction on broadcaster gift-receiving. Our results reveal that various forms of social interactions from viewers, including the number of chat messages, the number of chatting viewers, and the average number of messages sent by each chatting viewer, all have a positive effect on broadcasters’ gift-receiving. Furthermore, our analysis has shown that the positive effect of viewers’ social interaction on broadcasters’ gift-receiving is further enhanced by the broadcaster’s experience. This suggests that broadcasters who have more experience in the live streaming platform are better able to leverage social interaction to boost their audience engagement and increase their gift-receiving. We contribute to the literature on gifting and tipping by considering from a social interaction perspective. Existing literature has explored the effects of several social factors, such as audience size (Lu et al., 2021), social status (Goode et al., 2014; Lampel & Bhalla, 2007), social exchange (Kim et al., 2018), and social visibility (Shmargad & Watts, 2016), on gifting and tipping behavior. However, there is hardly any research that focuses on the effect of social interaction, which is one of the most notable features of live streaming. Compare to Lu et al.’s (2021) study which investigates the role of audience size in gift receiving in live streaming, we control for the audience size, and focus on the effect of social interactions initiated by viewers on broadcasters’ gift receiving. Our findings suggest that gift receiving increases for a fixed audience size when the number of social interactions increases. Our findings have important practical implications for both live streaming platforms and broadcasters. Our empirical results demonstrate that social interaction is positively associated with broadcasters’ gift-receiving, and this relationship is even stronger for more experienced broadcasters. Based on our findings, we would encourage broadcasters to prioritize social interactions with their viewers. This can be achieved by directly engaging with viewers through chat messages or by creating opportunities for viewers to interact with each other, such as by raising interesting topics for discussion. By fostering social interaction, broadcasters can increase their audience engagement and potentially attract more gift-sending from their viewers. Furthermore, our findings suggest that live streaming platforms could benefit from highlighting social interaction in their platform design. Platforms can consider enlarging the message area or placing it in an eye-catching position to make social interaction more prominent and encourage viewers to engage with broadcasters and each other. By emphasizing social interaction, platforms can potentially increase the overall engagement and gift-sending of their viewers, ultimately leading to a more successful live streaming experience for both broadcasters and viewers.

References

149

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Chapter 8

Dynamics of Digital Content Consumption and Social Norm

This Chapter examines the evolution of users’ digital content consumption behavior over time. Our empirical analysis indicates a decreasing trend in users’ payment for digital content. We delve into the underlying mechanism and confirm the explanation of the substitution effect. We also explore the potential use of social influence to alleviate the decline in content payments and discover that social norms can serve as a tool to mitigate the decrease in digital content consumption over time, as users tend to increase their payments when they observe others making large payments.

8.1 Introduction This Chapter investigates the dynamics of digital content consumption, and the role of social norms. Digital content consumption typically adopts Pay-what-you-want (PWYW) pricing strategy. Pay-what-you-want (PWYW) is a participative pricing strategy in which buyers are allowed to pay any intended price for a product or service, including zero (Kim et al., 2009). This pricing strategy has been put into practice in many industries. For example, in 2007, the band Radiohead allowed users to download its album “In Rainbows” at any price they wanted, even for free. The strategy was so successful that it generated $3 million in sales in total.1 PWYW pricing has also been adopted by museums (e.g., American Museum of Natural History), restaurants (e.g., Der Wiener Deewan), and online platforms (e.g., Twitch). However, there have been some comments about PWYW pricing indicating that it only works for a limited time and would not be an ideal marketing strategy in the 1

https://www.bbc.com/news/uk-england-33609867.

This Chapter is derived, in part, from the article “Do long-life customers pay more in pay-what-you-want pricing? Evidence from live streaming” published in Journal of Business Research on January 22, 2022, available online: https://www.sciencedirect.com/science/article/ abs/pii/S0148296322000431. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Ma, Social Influence on Digital Content Contribution and Consumption, Management for Professionals, https://doi.org/10.1007/978-981-99-6737-7_8

151

152

8 Dynamics of Digital Content Consumption and Social Norm

long run.2 This assertion contrasts with the experience of many firms that have used PWYW as the dominant pricing strategy for an extended period of time. For example, in 2019 the total value of virtual gifts reached 140 billion RMB on Kuaishou, a leading live streaming platform in China. The company received a commission of 31.4 billion RMB, accounting for 80.4% of its total revenue.3 The goal of this paper is to study how an individual’s PWYW amount for digital content consumption changes over time and explain the long-term trend in PWYW payment behavior. Existing literature has documented that PWYW amount could be affected by a number of factors, such as fairness concerns (Chen et al., 2017; Kim et al., 2009), social norms (Riener & Traxler, 2012), altruism (Schmidt et al., 2015), and feelings of control (Wang et al., 2021; Warren et al., 2021). These studies mainly focus on consumers’ incentive to pay in one-shot settings, and there is still limited knowledge about the dynamics of prices paid over time in PWYW and its underlying mechanism. In this research stream, Viglia et al. (2019) find that compared with paying before consumption, paying after consumption can increase PWYW amount due to consumers’ uncertainty deduction. Both Riener and Traxler (2012) and Gneezy et al. (2012) present variations in customers’ average payment in a PWYW restaurant over two years. Empirical evidence shows that the average PWYW amount decreased slightly over the two years and converged to a positive amount of e5. Levitt (2006) also finds a similar pattern that the voluntary payment rates for bagels and donuts decreased by about 5% over a period of three to four years. In contrast to most previous studies in which the long-term trend of PWYW amount is not the primary research question, Riener and Traxler (2012) seek to explain the declining pattern through social norms. They postulate that customers learned about others’ payments over time and established the norm in their minds. However, their analyses rely on the aggregate information of all customers rather than individual payment dynamics, and hence are not able to test the interpretation directly because of the data limitation. Here, we conduct a longitudinal analysis of the evolution of individual digital content consumption, i.e., PWYW behavior, and empirically test several possible explanations for the trend. The empirical context of our research is live streaming. Live streaming is a novel media platform where broadcasters perform in real time and viewers watch live content simultaneously. Many live streaming platforms, such as Twitch, YouNow, and TikTok, have emerged in recent years worldwide, which attracted millions of individual users to provide and consume live streaming content. PWYW has been adopted as the main pricing strategy for most live streaming platforms. Viewers can voluntarily send virtual gifts and tips to their favorite broadcasters. We utilize the feature of payment in live streaming to study the dynamics of PWYW amount over the viewer’s tenure, through which we can know the evolution of users’ digital content consumption. 2

https://medium.com/@nidhi.titus/radiohead-adopting-pay-what-you-want-pricing-fdee50871 72a#:~:text=PWYW%20pricing%20strategy%20would%20not,the%20offer%20while%20it%20l asts. 3 https://www1.hkexnews.hk/listedco/listconews/sehk/2021/0205/sehk21011501424.pdf.

8.2 Literature Review

153

We track a panel of more than 60,000 viewers during a period of 165 days since their registration. Our empirical analysis reveals a robust pattern: a viewer’s PWYW digital content consumption amount decreases over his tenure. This finding contrasts with the trend commonly documented in fixed pricing, where customers would pay more as their tenure increases (e.g., Morgan & Hunt, 1994). We propose several possible explanations for the decline in PWYW payment over time, including variety seeking, alternative ways of interacting, and substitution between payment in the past and at present. We empirically test each interpretation and find that the substitution effect can explain why longer-life viewers tend to pay less. Furthermore, we also explore heterogeneity in the negative effect of cumulative payment in the past on the current PWYW amount for digital content consumption. We finding that the dampening effect of past payments is weaker for viewers who currently have access to more broadcasters and who observe higher payments from others underscores the crucial role of social norms in digital content consumption. Social norms can influence consumers’ payment behavior by shaping their perceptions of what is fair and acceptable. For example, if viewers perceive that others are paying higher amounts, they may feel compelled to do the same to conform to the norm. On the other hand, if viewers perceive that others are paying lower amounts, they may adjust their payment behavior accordingly to avoid standing out or being perceived as unfair. The discovery emphasizes the significance of social norms in the realm of digital content consumption. Our research contributes to the literature through an in-depth analysis of the dynamics of PWYW payment. Using data from live streaming, we find that individual payment amount tends to decline over time under PWYW, and the mechanism is the substitution effect between individual payment in the past and at present. Our research suggests that the temporal trend of payment under PWYW pricing is different from that under fixed pricing. Moreover, from a managerial perspective, our heterogeneity analysis implies that when using PWYW pricing, firms could mitigate the negative effect of past cumulative payments by providing more diversified products or publicizing large payment amounts of other customers. Understanding the dynamics of social norms in digital content consumption is essential for firms to design effective pricing strategies and maximize revenue.

8.2 Literature Review First, our research is related to the literature on PWYW pricing. PWYW pricing allows customers to choose the price to pay on their own. Although individuals can pay nothing to get the product or service, PWYW always leads to positive payment amounts (Natter & Kaufmann, 2015; Sharma et al., 2020). A number of factors have been found to have an impact on individual PWYW payments. Kim et al. (2009) propose that PWYW payment behavior is mainly driven by customers’ fairness, satisfaction, price consciousness, and income. Furthermore, the importance of fairness in PWYW has been documented by Chen et al. (2017), Kunter (2015),

154

8 Dynamics of Digital Content Consumption and Social Norm

and Jang and Chu (2012). In addition, self-image (Lampel & Bhalla, 2007), selfidentity (Gneezy et al., 2012), and charitable contribution (Gneezy et al., 2010; Jung et al., 2017) are also important drivers for individuals to pay a positive amount under PWYW. In live streaming, audience size (Lu et al., 2021), emotion (Lin et al., 2021), and identities (Li et al., 2021) are all vital in encouraging viewers to send virtual gifts on the platform. Another stream of literature focuses on the role of outside information, such as reference prices and the payment behavior of others. Regner and Barria (2009) and (Gautier & Klaauw, 2012) both find that stating a reference price can increase voluntary payments. However, Johnson and Cui (2013) show that external minimum and maximum reference prices have negative effects on consumers’ PWYW payments. They suggest that the most beneficial strategy is to provide no reference price and give consumers the freedom to decide on their own. As for others’ payment behavior, Shang and Croson (2009) find that observing larger payment amounts from others can increase one’s own amount of donation. Soule and Madrigal (2015) show that knowing a higher average amount of payment from others leads to a higher reported payment amount under PWYW. There also exists a body of literature focusing on investigating boundary conditions for PWYW. Roy et al. (2021) show the role of spotlight effect in PWYW pricing. Moreover, the spotlight effect is stronger when people are with distant (vs. close) others and when the external reference price is absent (vs. present). Christopher and Machado (2019) find that the positive relationship between the consumption experience and PWYW amount is stronger under a post-pay (vs. pre-pay) design. Roy et al. (2016) argue that the effect of social visibility on PWYW payment varies with the different purchase motivations. Under the influence of extrinsic and altruistic motivations (rather than intrinsic motivation), consumers would pay more in public than in private. Compared with previous literature, which mainly focuses on the one-shot scenario under PWYW, our research explores how individual payment behavior changes over time. Under fixed pricing, most literature finds that a customer’s payment increases as his lifetime increases. For example, Best (2013) find that long-life customers produce higher revenue than newer ones; thus, the total profits increase over customers’ length of tenure. Morgan and Hunt’s (1994) empirical evidence also show that the benefits from long-term customers are more than those from short-term ones. Moreover, Umashankar et al. (2017) distinguish attitudinal loyalty from behavioral loyalty. They find that behaviorally loyal customers are more sensitive to price and tend to spend less, whereas attitudinal loyalty can mitigate the positive relationship between behavioral loyalty and price sensitivity. Under PWYW pricing, Stangl et al. (2017) compare the voluntary payment amounts among potential, new, and repeat customers, the latter is found to pay the highest prices. Their findings suggest a positive relationship between customers’ PWYW amount and tenure, which is consistent with that under fixed pricing. Additionally, Machado and Sinha (2013) also show that retained customers tend to pay more under PWYW. However, Schons et al. (2014) show that PWYW prices paid decrease with the number of transactions as the internal reference prices decrease. By tracking payments in a PWYW restaurant over a period of two years, both Riener and Traxler (2012) and Gneezy et al. (2012) show that the average

8.2 Literature Review

155

payment amount decreases slightly. Riener and Traxler (2012) propose a potential explanation for the decreasing trend, in which individuals establish their payment norms by learning from peers during the period and their payments converge to the average payment of others. Although it is difficult for Riener and Traxler (2012) to verify the explanation directly, they derive and test several predictions to show the possibility of the interpretation. Here, our findings are consistent with the data patterns in Riener and Traxler’s (2012) and Gneezy et al.’s (2012), and in addition, our detailed individual-level data allow us to empirically test several possible explanations for the declining trend. Table 8.1 presents how our study extends the literature on payment dynamics in PWYW pricing. Last, our work is related to the substitution effect in PWYW. Substitution effect has been found in PWYW payment for some goods which have the similar properties as public goods that everyone can have access to no matter whether he pays or how Table 8.1 Literature on payment dynamics in PWYW pricing Levitt (2006)

Gneezy et al. (2012)

Riener and Traxler (2012)

Schons et al. (2014)

This study

Overall objective

– Understand – Investigate – Study the – Examine the – Identify the the extent to the role of distribution dynamics in dynamics of which identity and and evolution prices paid in payment individuals self-image of payments PWYW over behavior behave consideration in PWYW multiple over time honestly and under customerunder the factors PWYW seller PWYW that influence pricing transactions – Test potential explanations the level of – Investigate honest boundary conditions

Data

Aggregate-level Aggregate-level Aggregate-level Individual-level Individual-level

Pattern of payment dynamics

Payment rates on the honor system decrease over time

The average PWYW payment decreases over time

The average PWYW payment declines over time, converging at a positive level

Underlying No mechanism

No

Payment norms No by learning from others

Substitution effect

Direct test No of the underlying mechanism

No

No

No

Yes

No

No

No

– Individual preference – Overall satisfaction

– Variety seeking – Payment norms

Boundary condition

Prices paid in PWYW decrease with the number of transactions

A declining pattern in PWYW amount with the increase in individual tenure

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8 Dynamics of Digital Content Consumption and Social Norm

much he pays. The substitution between others’ payments and one’s own payment has been documented by many studies. Andreoni (1989) documents a crowd-out effect in charity donation: the more others pay, the less one would like to spend. Burtch et al. (2013) find that in crowd-funded markets, prior contributions from others could crowd out one’s contribution. The reason is that an individual’s marginal benefit decreases as others have paid enough to meet the need of the public goods. Under PWYW pricing, the substitution effect could be explained by altruism and warm glow (Andreoni, 1989, 1990). For example, individuals may voluntarily pay to cover the cost of the business (Cornelli, 1996; Levitt, 2006) or keep the business running (Mak et al., 2010; Romano, 1991). From this point, the substitution effect should exist not only between the payments from others and one’s own but also between one’s own spending in different periods. Individuals who have spent a lot in the past may lower their current spending as the sellers have already been well compensated. Here we focus on the substitution between individual spending in the past and at present and explore whether it can explain the decline in PWYW amount for digital content consumption over tenure.

8.3 Empirical Background and Data 8.3.1 Empirical Background The empirical context of our research is live streaming, which is a novel form of online streamed media simultaneously recorded and broadcasted in real time. Live streaming has rapidly gained popularity worldwide in recent years, which was valued at USD 50.11 billion in 2020.4 Twitch, as one of the biggest live streaming platforms in the United States, has attracted around 2.91 million average concurrent viewers and 9.28 million monthly broadcasters in 2021. In the United States, 85% of people have watched a live video over the past year according to a survey conducted in May 2021.5 Live streaming platform is a typical two-sided market that connects broadcasters and viewers. Broadcasters are the content providers in live streaming, and viewers can watch the live video and pay for it voluntarily by sending virtual gifts to broadcasters. As the main revenue source for both broadcasters and platforms, the monetary value of gifts from viewers will be shared between the two stakeholders. Two features of the live streaming industry make it an ideal setting to study the dynamics of digital content consumption behavior in PWYW. First, payment for live streaming is voluntary for viewers. Viewers can access live videos and chat with broadcasters within live sessions regardless of whether they pay or how much they pay. Such PWYW pricing strategy has been widely used by live streaming platforms 4

https://www.grandviewresearch.com/industry-analysis/video-streaming-market#:~:text=The% 20global%20video%20streaming%20market,to%20boost%20the%20market%20growth. 5 https://www.thinkwithgoogle.com/consumer-insights/consumer-trends/the-future-of-video-vie wing/.

8.3 Empirical Background and Data

157

to monetize content provided by broadcasters (Lu et al., 2021). Second, it is possible for us to track a user’s multiple payment transactions during a period. In the traditional PWYW situations, such as museum tickets,6 album selling,7 and restaurants,8 consumers’ payment histories are difficult to record because the transactions do not happen frequently or the methods to identify the user are lacking. While, on live streaming platforms, viewers are likely to watch live sessions frequently, and each viewer is assigned a unique identity number, which allows us to track the viewer’s behavior over a period.

8.3.2 Data Description We collect data from a major live streaming platform in China. As a showroom platform, broadcasters on it mainly conduct talent-show performances, such as singing, dancing, and talk shows. Viewers on this platform can watch live videos for free as well as interact with broadcasters by sending virtual gifts and chatting messages. Figure 8.1 presents a snapshot of a live session on the platform. We tracked a panel of 64,039 viewers who registered between October 21, 2016, and November 3, 2016, and watched at least one live session during the observational period. For each viewer, we have complete information about their behavior within 165 days since their initial registration on the platform. The dataset contains 10,566,435 observations, each recording a viewer’s watching, gift-sending, and

Fig. 8.1 A Snapshot of a live session

6

https://www.nyc-arts.org/collections/35/free-museum-days-or-pay-what-you-wish. http://content.time.com/time/arts/article/0,8599,1666973,00.html. 8 https://www.theguardian.com/travel/2014/jan/28/pay-what-you-want-restaurants-around-world. 7

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8 Dynamics of Digital Content Consumption and Social Norm

message-sending behavior on a certain day.9 Table 8.2 presents the summary statistics of the 64,039 focal viewers’ overall behavior by the end of the 165th day after their registration. Panel A reports the statistics of watching behavior. During the 165 days, viewers watched live videos for 6.359 days on average. The average number of watching sessions and the average length of watching on each watching day are 5.534 and 14.723 min,10 respectively. Figure 8.2 shows the number of watching viewers over time, which dropped dramatically at the initial stage and became stable after that. In the dataset, 39,313 focal viewers watched only once. Panel B of Table 8.2 reports the statistics of gifting behavior for viewers who sent gifts at least once during the 165 days. 6,709 viewers ever paid, accounting for 10.476% of all the focal viewers. These viewers sent gifts to broadcasters on 10.028 days on average, with a median of 3 days. On each watching day, they spent RMB 43.522 in 0.700 sessions on average, and on each gifting day, they spent RMB 69.940 in 1.429 sessions on average. Panel C of Table 8.2 reports the statistics of chatting behavior for viewers who sent messages at least once during the 165 days. 31,333 viewers (48.930%) interacted with broadcasters by sending text messages in live sessions. The average number of chatting days is 5.645; on each chatting day, they sent 12.875 messages in 1.719 sessions on average.

8.4 Data Patterns and Potential Explanations 8.4.1 Data Patterns In this research, we focus on how individuals’ digital content consumption behavior changes over the length of their tenure. We present several temporal patterns of giftand message-sending behavior in Fig. 8.3.11 Surprisingly, as shown in Fig. 8.3a, we find that the total amount of gifts a viewer sends on each watching day declines over time. Subsequently, we graph two patterns of the average amount of gifts sent in each session. Figure 8.3b displays how the average amount of gifts a viewer sends in each watched session changes with the increase in lifetime duration. Figure 8.3c presents the pattern of the conditional average gifting amount, that is, the average amount of gifts a viewer sends in each gifted session. These two patterns are consistent with the pattern of Fig. 8.3a that viewers’ average spending in each session decreases over time. To alleviate the concern that the declining trend in PWYW payments may be caused by the declining novelty of the platform for users, we also analyze the change in the number of chatting messages for long term users. The pattern plotted in the 9

In the main analysis, we provide both day-level and session-level results, which are presented in the Sects. 5.2 and 5.3, respectively. 10 Watching behavior that lasts less than one minute is excluded in the analysis. 11 To present the long-term dynamics of viewers’ behavior patterns, Fig. 8.3 focuses on viewers who were still active, i.e., ever watched live videos, during the last 30 days in the observational period.

8.4 Data Patterns and Potential Explanations

159

Table 8.2 Summary statistics Min

Median

Max

1

1

165

6.359

17.421

Average number of 64,039 watching sessions on each watching day

1.000

2.000

270.222

5.534

9.226

64,039

1.000

4.650

1,440.000

14.723

34.924

10.028

18.281

0.700

0.768

43.522

226.472

1.429

0.920

69.940

292.228

Statistic

N

Mean

St. Dev

Panel A: watching behavior Total number of watching days

Average length of watching on each watching day (in minutes)

64,039

Panel B: gifting behavior Total number of gifting days

6,709

1

3

161

Average number of gifting sessions on each watching day

6,709

0.006

0.500

14.194

Average amount of gifts sent on each watching day (in ¥)

6,709

0.000

4.000

11,479.990

Average number of gifting sessions on each gifting day

6,709

1.000

1.000

19.000

Average amount of gifts sent on each gifting day (in ¥)

6,709

0.050

10.000

11,479.990

1

1

165

5.645

14.588

Average number of 31,333 chatting sessions on each watching day

0.006

1.000

85.750

1.369

1.760

Average number of 31,333 messages sent on each watching day

0.006

3.000

1,560.276

10.472

26.934

Average number of 31,333 chatting sessions on each chatting day

1.000

1.000

114.333

1.719

1.894

Average number of 31,333 messages sent on each chatting day

1.000

4.000

1,991.210

12.875

30.181

Panel C: chatting behavior Total number of chatting days

31,333

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8 Dynamics of Digital Content Consumption and Social Norm

Fig. 8.2 Number of watching viewers against tenure

new Fig. 8.3d shows that, for users that remained active on the platform during the last 30 days, the number of messages sent on each day was non-decreasing, and by contrast, the payment amount for these users declined over time as depicted in the existing Fig. 8.3a. In other words, these users were still engaged with the platform but reduced their payments over time.

8.4.2 Potential Explanations The patterns we find in our data challenge the expectations derived from most previous literature. Contrary to the basic tenet of relationship marketing that lifetime increases profitability (Morgan & Hunt, 1994), our descriptive analysis shows that individuals’ digital content consumption amount decreases over time under PWYW pricing. To verify the existence of the negative effect of tenure, we run two simple regressions with tenure (Tenure) as the independent variable and average spending in each session on each watching day (GiftAmtAvg) and total spending on each watching day (GiftAmtSum) as the dependent variables, respectively. Moreover, we incorporate viewer fixed effects and day-of-week controls to capture unobserved individual and time heterogeneity. Results are reported in Columns (1) and (2) of Table 8.3. Viewers’ tenure has a significant and negative effect on both average gifting amount and total gifting amount, which is consistent with the descriptive evidence. Here, we propose several potential explanations for the decline in spending over time. First, one possible explanation is variety seeking. Previous research finds that long-life customers tend to try different products or brands they have not purchased

8.4 Data Patterns and Potential Explanations Fig. 8.3 Gift- and message-sending on each day against tenure

161 (a) Total Amount of Gifts Sent on Each Day

(b) Average Amount of Gifts Sent in Each Watched Session on Each Day

(c) Conditional Average Amount of Gifts Sent in Each Gifted Session on Each Day

(d) Total Number of Messages Sent on Each Day

407,192

0.456

0.355

Num. obs

R2

Adj. R2 0.387

0.483

407,192

Yes Yes

Yes

Yes

(0.002)

(0.001)

Day-of-week controls

log(GiftAmtSum) −0.041***

log(GiftAmtAvg)

−0.020***

Viewer fixed effects

log (GiftAmtSumPast)

log (ChatMsgAvg)

log (WatchLengthAvg)

log (WatchSession)

log (Tenure)

(2)

(1)

Table 8.3 Preliminary analysis

0.445

0.533

407,192

Yes

Yes

0.476

0.559

407,192

Yes

Yes

(0.002)

(0.001)

(0.002)

(0.002) 0.456***

(0.002)

0.024***

0.030*** 0.331***

0.032***

(0.002)

(0.001)

(0.001)

0.446

0.533

407,192

Yes

0.477

0.559

407,192

Yes

Yes

(0.001)

(0.001) Yes

(0.002) −0.013***

(0.001)

0.456***

(0.002)

0.026***

(0.002)

0.186***

(0.002)

0.010***

log(GiftAmtSum)

(6)

−0.021***

0.331***

(0.001)

−0.005**

(0.002) 0.185***

0.006***

log(GiftAmtAvg)

(5)

(0.001)

0.001

log(GiftAmtSum)

(4)

−0.005***

−0.007***

log(GiftAmtAvg)

(3)

162 8 Dynamics of Digital Content Consumption and Social Norm

8.4 Data Patterns and Potential Explanations

163

before, namely, variety seeking (Givon & Horsky, 1990). It is one of the reasons why the repeat-buying rate declines over the long term (Dawes et al., 2021). In live streaming, if viewers become more variety seeking over time, long-life viewers’ attention can be distracted by the diversity of broadcasters, which could lead to a reduction in payment. Another potential explanation is that long-life viewers may use message-sending as a substitute for gift-sending to interact with broadcasters. In our empirical setting, as shown in Fig. 8.1, viewers can interact with broadcasters by paying (i.e., sending gifts) or for free (i.e., sending messages). Previous research has documented that nonmonetary exchanges are more valuable than monetary transfers in social relationships (Prendergast & Stole, 2001). Thus, experienced viewers may be more likely to interact by sending messages rather than sending gifts, and we may also observe a decreasing pattern of paying over time. To test these two possible explanations, we run two regressions using the average and total spending on each watching day, GiftAmtAvg and GiftAmtSum as the dependent variables, respectively. In these two regressions, we control for the number of live sessions the viewer watches (WatchSession),12 the average time spent in each session (WatchLengthAvg), and the average number of chatting messages sent by the viewer in each session (ChatMsgAvg) on each watching day. Results are reported in Columns (3) and (4) of Table 8.3. After controlling for watching and chatting behavior, viewers’ tenure has no significant effect on the total spending but still has a significant and negative effect on the average spending. This suggests that the decline in spending over time cannot be fully explained by variety seeking and inclination to sending messages. Second, an alternative potential explanation is that spending in the past could dampen current payments. Under PWYW pricing, individuals may care about the total amount of money received by the firm or seller. They provide monetary support to keep the firm in business (Mak et al., 2010; Romano, 1991) or cover the seller’s cost (Cornelli, 1996; Levitt, 2006). Burtch et al. (2013) find a substitution effect in voluntary contribution in crowd-funded markets. They find that prior contributions from others could crowd out one’s contribution amount because the cumulative contribution in the past makes the current contribution become less important to the recipient. In our empirical context, high-spending viewers may think they have given a lot of money to the platform and that they do not need to spend more on it, which suggests a substitution between one’s prior and current payment. Viewers with longer tenure are more likely to have spent more in the past because they stayed longer and watched more live videos on the platform. Thus, we may find that the viewers’ daily spending decreases over time because of the dampening effect of cumulative spending in the past. To examine this explanation, we construct a variable GiftAmtSumPast to measure the total monetary amount of gifts sent by a viewer since the registration (in ¥). Columns (5) and (6) of Table 8.3 report the results incorporating GiftAmtSumPast as an independent variable. We find that the total spending 12

Our data are aggregated by day, and thus, a viewer’ multiple viewing sessions of the same broadcaster on the same day will be aggregated into one observation. Therefore, the variable WatchSession, measuring the number of live sessions watched by a viewer on a certain day, is actually the number of broadcasters watched by a viewer on a certain day. As a result, we think this variable can test for variety seeking.

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8 Dynamics of Digital Content Consumption and Social Norm

in the past does have a significant and negative effect on the current total and average spending and the effect of tenure turns out to be positive after controlling for the spending in the past. The results provide initial support for this explanation. In the following section, we will conduct further analysis to verify the explanation and explore the heterogeneity among viewers.

8.5 Analysis and Results 8.5.1 Empirical Model To examine the relationship between individuals’ digital content consumption behavior and their tenure, we estimate a model of viewers’ gifting decisions using data from live streaming. Note that we only observe viewers’ gifting behavior if they choose to watch live videos on a certain day. This means that the truncation of giftsending is incidental because it depends on watching or not. The Heckman two-stage model is the usual approach to address such selection bias issues (Heckman, 1979). The first stage Probit model captures the individual viewer’s decision whether to watch or not: W atch it∗ = α Z it + μi + νt + εit ∫ W atch it =

1, i f W atch it∗ > 0 0, i f W atch it∗ ≤ 0

(8.1)

(8.2)

where W atch it∗ is the latent variable that represents viewer i’s propensity to watch live videos on day t. W atch it denotes the observed watching behavior, which equals 1 if viewer i watches on day t and 0 otherwise. Z it includes a set of variables that could affect a viewer’s decision to watch or not on that day: Tenure, the number of days since registration; WatchLengthSumPast, the cumulative time spent on watching live videos since registration till the end of day t–1 (in minutes); GiftAmtSumPast, the cumulative monetary amount of gifts sent by viewer i since registration till the end of day t–1 (in ¥); ChatMsgSumPast, the cumulative number of messages sent by viewer i since registration till the end of day t–1; WatchLengthSumPast30Days, GiftAmtSumPast30Days, and ChatMsgSumPast30Days, which denote the total time spending (in minutes), amount of gift-sending (in ¥), and number of message-sending within the 30 days before day t, respectively; and LastWatchDays, the number of days since the last watching day till day t. We include a viewer fixed-effect, μi , and a dayof-week fixed-effect, νt , to capture unobserved individual and time effects. ∈it is the random error that follows a normal distribution.

8.5 Analysis and Results

165

In the second stage, we estimate a fixed-effect linear model to examine the effect of tenure on the amount of gifting conditional on watching, that is, conditional on W atch it = 1. To correct for the selection bias, we compute the inverse Mills ratio, I M Rit = φ(α Z it ) , and incorporate it into the second stage model: Φ(α Z it ) Ʌ

Ʌ

Ʌ

Ʌ

Yit = β X it + γ I M Rit + μi + νt + ξit

(8.3)

where Yit is the dependent variable of interest. Two dependent variables related to PWYW amount for digital content consumption are taken into consideration: GiftAmtSum, the total monetary amount of gifts sent by viewer i on day t (in ¥), and GiftAmtAvg, the average monetary amount of gifts sent by viewer i in each watched live session on day t (in ¥). X it is the vector of independent variables capture viewer i’s behavior on day t and historical behavior before day t. The two key independent variables of interest are Tenure and GiftAmtSumPast, which refer to a viewer’s lifetime duration and the cumulative spending since registration (in ¥), respectively. We focus on the impact of tenure (Tenure) since our descriptive evidence shows its striking negative effect on individuals’ PWYW amount for digital content consumption, namely, the average and total amount of gifts sent on each day in our empirical setting. This negative effect contrasts with most previous research that longlife customers are likely to pay more than short-time ones (e.g., Morgan & Hunt, 1994). We propose an explanation that the longer a viewer stays, the more likely he has spent a lot on the platform, and the cumulative spending in the past could dampen viewers’ current payment. To examine this explanation, we compare the models with and without controlling for the cumulative spending (GiftAmtSumPast) to see whether the effect of Tenure changes and whether GiftAmtSumPast does have a negative effect on the amount of payment. Other independent variables in X it capture viewer i’s watching and chatting behavior on day t and in the past: WatchSession, the number of sessions watched by viewer i on day t; WatchLengthAvg, the average length of time spent in each watched session (in minutes); ChatMsgAvg, the average number of chatting messages sent in each watched session; WatchLengthSumPast, the total length of time spent on watching live videos in the past (in minutes); and ChatMsgSumPast, the total number of messages sent in the past. As in the first stage model, we also control for the individual and time fixed effects here. It is important to control for the unobserved individual heterogeneity with fixed effects. For instance, some viewers may never pay for live streaming because of budget constraint.

8.5.2 Day-Level Results Table 8.4 reports the first-stage estimation results. We find that tenure (Tenure) negatively affects viewers’ propensity to watch; in other words, viewers are less likely to watch live videos over time. Furthermore, the number of days since the last watching day (LastWatchDasy) has a negative effect on viewers’ probability of watching, which

166

8 Dynamics of Digital Content Consumption and Social Norm

Table 8.4 First stage results

Watch log (Tenure)

−0.267*** (0.002)

log (WatchLengthSumPast)

0.327*** (0.002)

log (GiftAmtSumPast)

−0.013*** (0.002)

log (ChatMsgSumPast)

−0.070*** (0.003)

log (WatchLengthSumPast30Days)

−0.023*** (0.002)

log (GiftAmtSumPast30Days)

0.038*** (0.002)

log (ChatMsgSumPast30Days)

0.069*** (0.002)

log (LastWatchDays)

−0.690*** (0.002)

Viewer fixed effects

Yes

Day-of-Week Controls

Yes

Log Likelihood

−726,773.895

Num. obs

10,563,795

***

p < 0.001; ** p < 0.01; * p < 0.05

suggests that if a viewer has not watched live videos for a long time, he is less likely to come back to watch in the future. Moreover, viewers’ historical watching, gifting, and chatting behavior since registration and within the 30 days before that day all have a significant impact on their current decision to watch or not. We particularly focus on the second stage estimation results as reported in Table 8.5. Table 8.5 (a) presents the results with the average spending in each watched session (GiftAmtAvg) as the dependent variable. In Column (1), we only include one independent variable: viewers’ lifetime duration (Tenure). Results show that tenure has a significant and negative effect on average spending, which is consistent with the descriptive findings in Chapter 8.4. In Column (2), we add a set of control variables related to viewers’ behavior on the same day. We find that the more sessions watched on a certain day, the less spent in each session on average. However, the average length of time spent and the average amount of message-sending in each watched session both positively affect the average amount of gift-sending. It is worth noting that the effect of tenure is still significantly negative even after controlling for viewers’ current watching and chatting behavior, which confirms the declining pattern in PWYW amount over time. Next, we examine whether the cumulative spending in the past can explain the dampening effect of tenure on the PWYW

8.5 Analysis and Results

167

amount for digital content consumption. We add the variable GiftAmtSumPast, the total monetary amount of gifts sent since registration till now (in ¥), in the model. The results are reported in Column (3) of Table 8.5. As expected, the effect of GiftAmtSumPast is negative, and after including GiftAmtSumPast, the effect of Tenure turns out to be positive. We continue to control for viewers’ past watching and chatting behavior by including WatchLengthSumPast, the total length of watching in the past (in minutes), and ChatMsgSumPast, the total number of messages sent in the past, in the model. As shown in Column (4) of Table 8.5, the cumulative spending in the past still has a significant and negative effect on the average gifting amount, and the effect of tenure is no longer negative but becomes insignificant. These empirical findings, first, confirm the patterns that with the increase in lifetime duration, individuals’ PWYW amount for digital content consumption decreases over time. Then, these results provide evidence for the explanation that the dampening effect of tenure is because of the cumulative spending in the past. A larger total amount of payment in the past decreases an individual’s subsequent payment amount under PWYW. Additionally, we explore the effect of tenure and cumulative spending in the past on the total spending on each day. Table 8.5 (b) reports the results with the total amount of gifting on each watching day (in ¥), GiftAmtSum, as the dependent variable. Results are similar to those in Table 8.5 (a). In Column (1), without other control variables, we find that tenure has a significant and negative effect on total spending, which suggests that long-life viewers are likely to spend less than shortlife ones. In Column (2) to Column (4), we add control variables stepwise. The full model as reported in Column (4) shows that regarding daily spending, the effect of cumulative spending in the past is significant and negative, whereas the effect of tenure is no longer significant. The findings, again, confirm the negative effect of the total spending in the past on the PWYW amount for digital content consumption.

8.5.3 Session-Level Results In this Chapter, rather than analyzing viewers’ gifting behavior on each day, we now focus on their gifting behavior in each live session. In the day-level analysis, we examine how viewers’ total spending and average spending in each session on each watching day are affected by their tenure and cumulative spending in the past. It is straightforward to analyze the temporal change of PWYW amount. However, since the data used in the previous analysis is aggregated at the day level, some information, such as the viewer’s preference for a certain broadcaster and the cumulative spending on a certain broadcaster, might be omitted. Therefore, we restructure the data, and each observation in the restructured data represents a viewer’s behavior in a live session. Using the restructured data, we perform several additional analyses to confirm our results.

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8 Dynamics of Digital Content Consumption and Social Norm

Table 8.5 Day-level results (a) (1)

(2)

(3)

(4)

−0.021***

−0.008***

0.005***

0.002

(0.001)

(0.001)

(0.001)

(0.002)

log (WatchSession)

−0.005***

−0.005***

−0.005***

(0.001)

(0.001)

(0.001)

log (WatchLengthAvg)

0.031***

0.032***

0.032***

(0.002)

(0.002)

(0.002)

log (ChatMsgAvg)

0.331***

0.331***

0.331***

(0.001)

(0.001)

(0.001)

−0.020***

−0.022***

log(GiftAmtAvg) log (Tenure)

log (GiftAmtSumPast)

(0.001)

(0.001)

log (WatchLengthSumPast)

−0.000

log (ChatMsgSumPast)

0.004***

(0.001) (0.002) IMR

0.004* (0.002)

(0.002)

(0.002)

(0.003)

Viewer fixed effects

Yes

Yes

Yes

Yes

Day-of-week controls

Yes

Yes

Yes

Yes

Num. obs.

407192

407192

407192

407192

R2

0.456

0.533

0.533

0.533

0.355

0.446

0.446

0.446

−0.039***

0.002

0.012***

0.002

(0.002)

(0.002)

(0.002)

(0.003)

log (WatchSession)

0.185***

0.185***

0.186***

(0.002)

(0.002)

(0.002)

log (WatchLengthAvg)

0.024***

0.025***

0.025***

(0.002)

(0.002)

(0.002)

0.456***

0.456***

0.456***

Adj.

R2

*** p

< 0.01; ** p < 0.05; * p < 0.1

0.018***

0.005**

0.008***

(b) log (Tenure)

log (ChatMsgAvg)

(0.002) log (GiftAmtSumPast)

(0.002)

(0.002)

−0.015***

−0.020***

(0.002) log (WatchLengthSumPast)

(0.002) 0.002 (0.002) (continued)

8.5 Analysis and Results

169

Table 8.5 (continued) (a) (1)

(2)

(3)

(4)

log(GiftAmtAvg) 0.009***

log (ChatMsgSumPast)

(0.002) −0.026***

−0.004

−0.014***

(0.004)

(0.003)

(0.003)

(0.004)

Viewer fixed effects

Yes

Yes

Yes

Yes

Day-of-week controls

Yes

Yes

Yes

Yes

Num. obs.

407192

407192

407192

407192

R2

0.483

0.559

0.559

0.559

0.387

0.476

0.477

0.477

IMR

Adj.

R2

*** p

< 0.01; ** p < 0.05; * p < 0.1

−0.005

The results are reported in Table 8.6. Two within-session gifting behavior are considered. The first one is Gift, indicating whether the viewer sends gifts to the broadcaster in the live session. The second one is GiftAmt, referring to the conditional amount of gifts (¥), that is, how much the viewer spends if he decides to pay in the session, which is strictly positive. Columns (1) and (2) report the estimation results while controlling for viewers’ behavior on the same day. Columns (3) and (4) present the result after adding variables related to viewers’ watching, gifting, and chatting behavior in the past. We compare the results in Columns (1) and (2) with Columns (3) and (4) to examine whether the cumulative spending in the past can explain the dampening effect of tenure on PWYW amount for digital content consumption. First, the results in Columns (1) and (2) in Table 8.6 show a declining pattern in gifting likelihood and amount over time and that the tenure has a significant and negative effect on both Gift and GiftAmt. The findings confirm our previous results that individuals tend to spend less as their tenure increases under PWYW. In addition, the length of time spent in this session (WatchLength) and the number of chatting messages sent in this session (ChatMsg) both affect viewers’ gifting behavior positively. Furthermore, viewers’ gifting behavior in other sessions on the same day is related to their paying in this session. GiftSessionDay refers to the number of other sessions gifted by the viewer on the same day, and GiftAmtAvgDay refers to the average amount of money spent on each other session on the same day. The more sessions the viewer sends gifts to, the more likely he sends gifts to the focal session but the amount of gifts tends to be smaller. However, the average spending in other sessions has a positive effect on both gifting incidence and amount. OthGiftNum and OthGiftAmtAvg denote other viewers’ gifting behavior in the live session, the former one is the number of other viewers who send gifts in the session and the latter one is their average amount of gifting. We find that observing more gifting incidences from others leads to an increase in one’s gifting probability and amount. Others’ average

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8 Dynamics of Digital Content Consumption and Social Norm

Table 8.6 Session-level results (1)

(2)

(3)

(4)

Gift

log(GiftAmt)

Gift

log(GiftAmt)

log (Tenure)

−0.015***

−0.087***

0.054***

0.089***

(0.003)

(0.005)

(0.005)

(0.009)

log (WatchLength)

0.197***

0.110***

0.196***

0.104***

(0.003)

(0.004)

(0.003)

(0.004)

log (ChatMsg)

0.612***

0.304***

0.612***

0.301***

(0.002)

(0.003)

(0.002)

(0.003)

log (GiftSessionDay)

0.333***

−0.036***

0.337***

−0.030***

(0.004)

(0.007)

(0.004)

(0.007)

log (GiftAmtAvgDay)

0.109***

0.121***

0.110***

0.125***

(0.002)

(0.003)

(0.002)

(0.003)

log (OthGiftNum)

0.021***

0.011**

0.021***

0.012***

(0.002)

(0.005)

(0.002)

(0.005)

log (OthGiftAmtAvg)

−0.042***

0.064***

−0.042***

0.063***

(0.002)

(0.003)

(0.002)

(0.003)

log (WatchSessionCul)

0.210***

−0.087***

0.215***

−0.082***

(0.005)

(0.009)

(0.005)

(0.009)

log (WatchSessionCul)^2

−0.050***

0.005**

−0.052***

0.000

(0.001)

(0.002)

(0.001)

(0.002)

0.182***

0.171***

0.186***

0.188***

(0.002)

(0.002)

log (GiftAmtSumCul)

(0.002)

(0.002)

log (GiftAmtSumPast)

−0.028***

−0.099***

(0.003)

(0.005)

log (WatchLengthSumPast)

−0.003

−0.006

(0.004)

(0.007)

−0.021***

−0.018***

log (ChatMsgSumPast) 0.262***

(0.004)

(0.007)

0.032***

0.075***

IMR

0.105*** (0.006)

(0.011)

(0.009)

(0.014)

Viewer fixed effects

Yes

Yes

Yes

Yes

Day-of-week controls

Yes

Yes

Yes

Yes

Log Likelihood

−236050.136

−235879.376

R2 (full model)

0.597

0.600

Adj. R2 (full model)

0.574

0.577

Num. obs. *** p

2044818

< 0.01; ** p < 0.05; * p < 0.1

124538

2044818

124538

8.5 Analysis and Results

171

spending has a negative effect on viewers’ decision to pay but has a positive effect on their conditional payment amount. Additionally, we control for how many times the viewer watched the broadcaster’s performance in the past (WatchSessionCul) and how much money he spent on the broadcaster in the past (GiftAmtSumCul). Results show that viewers who have already spent a lot on the broadcaster are more likely to send gifts to the broadcaster in the future, which may be because of their preference for the broadcaster. Then, we turn to focus on the results in Columns (3) and (4) in Table 8.6, which control for the effect of viewers’ behavior in the past. Three variables related to viewers’ overall behavior on the platform in the past are included in the main analysis: WatchLengthSumPast, the total length of time spent on watching live videos since registration (in minutes); GiftAmtSumPast, the total monetary amount of gifts sent by the viewer since registration (in ¥); and ChatMsgSumPast, the total number of chat messages sent by the viewer since registration. As expected, the effect of cumulative spending is significant and negative on both gifting probability and conditional gifting amount, and more importantly, the effect of tenure is no longer negative after controlling for viewers’ past behavior. The effects of the other control variables are almost qualitatively the same as those in Columns (1) and (2). The results based on the session-level data are consistent with the findings and explanations based on the day-level analysis.

8.5.4 The Role of Social Norms In the previous analysis, we find that cumulative spending has a negative effect on the PWYW amount for digital content consumption. In this Chapter, we study how the negative effect varies across heterogeneous individuals because of social norms. In other words, we investigate whether manipulating social norms can mitigate the negative effect. We first explore the role of the number of broadcasters that the viewer is interested in. In the previous analysis, we propose that cumulative spending in the past negatively affects viewers’ gifting behavior since they may think the platform and broadcasters have been well compensated. Given a certain amount of cumulative spending, as the number of broadcasters watched and gifted by the viewer increases, the average spending on each broadcaster decreases. As the compensation for each broadcaster is diluted, the viewer may feel that each individual broadcaster is not compensated enough. Additionally, previous research finds that variety seekers are less price sensitive and tend to spend more on purchasing (Feinberg et al., 1992). Thus, we expect that the number of broadcasters that are watched and gifted by the viewer will weaken the negative effect of cumulative spending on PWYW amount. To test this proposition, we introduce two interaction terms between the cumulative spending (GiftAmtSumPast) and two measures of the number of broadcasters of interest. The two measures are WatchNumPast30Days and GiftNumPast30Days, referring to the number of unique broadcasters the viewer watched and gifted in

172

8 Dynamics of Digital Content Consumption and Social Norm

the past 30 days, respectively. We only consider the number of broadcasters being watched and gifted in recent days since viewers’ preferences and behavior patterns could change dynamically. The empirical results are reported in Table 8.7. We find that the interaction terms involving the number of watched and gifted broadcasters are significant and positive for both the total and average amount of spending on each day. This implies that viewers who watched or gifted more broadcasters are less negatively affected by their cumulative spending in the past, which is consistent with our expectations. We then study how the observed others’ payment behavior moderates the effect of cumulative spending on amount of payment. Under PWYW, individuals’ payment behavior can be affected by the payment norm, namely, the payment behavior of others (Agerström et al., 2016; Alpizar et al., 2008). Feldhaus et al. (2019) find that compared with a low payment norm, revealing a high payment norm increases individual payment by 27%. In our empirical setting, viewers can learn the payment norm by observing others’ payment behavior. Accordingly, we expect that the greater the observed payment incidences and amount of others, the more one would like to pay. To test this proposition empirically, we construct two measures to capture information about other viewers’ payment behavior that can be observed by a focal viewer. The first one is OthGiftAmtSession, which denotes the average monetary amount of gifts sent by other viewers in each session watched by the focal viewer. The second one is OthGiftNumSession, which refers to the average number of other viewers who send gifts to the broadcaster in each session watched by the focal viewer. We include the interactions between the cumulative amount of payment in the past (GiftAmtSumPast) and these two variables OthGiftAmtSession and OthGiftNumSession in the model. Table 8.8 reports the results. We find that all the coefficients of interaction terms are significant and positive, which suggests that observing payment from others can weaken the negative effect of cumulative spending on the current PWYW amount for digital content consumption.

8.6 Summary In this Chapter, we focus on the dynamics of payment behavior under PWYW pricing. Specifically, we study how individual PWYW amount for digital content consumption changes over time and the underlying reason for the trend. By tracking the payment behavior of more than 60,000 viewers over 165 days since their registration on a live streaming platform, we find that viewers’ PWYW amount on each day declines over time. This finding is in contrast with the positive relationship between customers’ lifetime and the amount of payment often documented in the context of fixed pricing (e.g., Morgan & Hunt, 1994). We propose three potential explanations for this intriguing trend, namely, variety seeking, alternative ways to interact, and the substitution effect of prior spending. The empirical results consistently suggest that cumulative spending in the past has a significant and negative effect on one’s

(0.000) 0.015*** (0.000)

(0.000)

log(WatchNumPast30Days)

−0.009***

−0.004***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000) 0.003***

(0.000) 0.004***

(0.000)

0.000***

(0.000)

(0.000) −0.001***

−0.039***

0.002***

(0.000) 0.001***

−0.022***

(0.000)

−0.023***

log(GiftAmtSumPast) *

−0.071*** (0.001) 0.041***

(0.000) 0.023***

(0.000)

0.005***

(0.000)

−0.037***

(0.000)

−0.059***

(0.000)

0.003***

(0.000)

(0.000) 0.461***

(0.000)

(0.000) 0.333***

(0.000) 0.464***

(0.000)

−0.027***

0.334***

(0.000)

(0.000)

(0.000)

0.008*** 0.079***

0.023***

0.007***

log(GiftNumPast30Days)

(4)

(continued)

log(GiftAmtSum)

−0.011***

(0.000)

(0.000)

0.081***

−0.010*** −0.028***

(0.000)

(0.000)

0.003***

(0.000)

0.004***

0.001***

(3) log(GiftAmtAvg)

0.022***

(2) log(GiftAmtSum)

(1)

log(GiftAmtAvg)

log(GiftAmtSumPast) *

log(WatchNumPast30Days)

log(ChatMsgSumPast)

log(WatchLengthSumPast)

log(GiftAmtSumPast)

log(ChatMsgAvg)

log(WatchLengthAvg)

log(WatchSession)

log(Tenure)

Table 8.7 Heterogeneity analysis 1

8.6 Summary 173

Adj. R2 (full model)

p < 0.01; ** p < 0.05; * p < 0.1

0.371

0.367

(full model)

R2

***

10,566,435

Num. obs 0.428

0.432

10,566,435

Yes

Yes

Yes

Yes

(0.000)

(0.000)

Viewer Fixed Effects

−0.003***

(2)

−0.001***

(1)

Day-of-Week Controls

IMR

log(GiftNumPast30Days)

Table 8.7 (continued)

0.369

0.373

10,566,435

Yes

Yes

(0.000)

0.431

0.435

10,566,435

Yes

Yes

(0.000)

−0.003***

(0.000)

(0.000) −0.001***

(4)

(3)

174 8 Dynamics of Digital Content Consumption and Social Norm

(0.002) −0.143*** (0.003) 0.002 (0.002) 0.010*** (0.002) −0.021***

(0.001)

−0.069***

(0.002)

−0.000

(0.001)

0.005***

(0.002)

−0.005***

log(OthGiftNumSession)

(0.000)

0.453***

0.330***

(0.000)

(0.002)

(0.002)

log(OthGiftAmtSession)

0.026***

0.032***

0.017***

(0.002)

(0.001)

(0.001)

0.173***

−0.011***

0.007***

(0.003)

(0.002)

(0.001)

0.002

−0.000

0.001

−0.026*** (0.002)

(0.002)

(0.002)

0.010***

(0.002)

0.002

(0.003)

−0.102***

(0.002)

0.455***

(0.002)

0.025***

(0.002)

0.180***

(0.003)

0.001

(continued)

log(GiftAmtSum)

(4)

−0.015***

(0.002)

0.005***

(0.001)

−0.000

(0.002)

−0.063***

(0.001)

0.331***

(0.002)

0.032***

(0.001)

−0.007***

(0.002)

log(GiftAmtAvg)

log(GiftAmtSum)

log(GiftAmtAvg)

(3)

(2)

(1)

log(GiftAmtSumPast) *

log(OthGiftAmtSession)

log(ChatMsgSumPast)

log(WatchLengthSumPast)

log(GiftAmtSumPast)

log(ChatMsgAvg)

log(WatchLengthAvg)

log(WatchSession)

log(Tenure)

Table 8.8 Heterogeneity analysis 2

8.6 Summary 175

*

p < 0.01;

p < 0.05; p < 0.1

0.448

Adj. R2 (full model)

**

0.535

R2 (full model)

***

407,192

407,192

Num. obs 0.482

0.563

Yes

Yes

Day-of-Week Controls

Yes

(0.004)

Yes

(0.003)

Viewer Fixed Effects

0.447

0.534

407,192

Yes

Yes

(0.003)

0.009***

0.479

0.561

407,192

Yes

Yes

(0.004)

−0.003

(0.001)

(0.000) −0.001

log(OthGiftNumSession)

0.010***

0.025***

IMR

(4)

0.012***

(2)

(3)

(1)

log(GiftAmtSumPast) *

Table 8.8 (continued)

176 8 Dynamics of Digital Content Consumption and Social Norm

8.6 Summary

177

current payment, lending support to the explanation of the substitution effect. Additionally, we find that after controlling for the cumulative spending in the past, the effect of tenure is no longer significantly negative, which implies that the decline in PWYW amount for digital content consumption over time is mainly because of the substitution effect of spending in the past. Of particular significance is our examination of the function of social norms in ameliorating the adverse impact of users’ tenure on Pay-what-you-want (PWYW) payment behavior. Our empirical results indicate that exposure to high levels of consumption by others can induce viewers to pay higher amounts for digital content, thereby mitigating the negative effect of tenure on PWYW payment. This finding highlights the importance of social norms in shaping consumer behavior and has significant implications for the design of pricing strategies in digital content consumption.

8.6.1 Theoretical Contribution This research contributes to the PWYW literature by highlighting the dynamics of individual digital content consumption behavior. Previous studies have found opposite trends in the evolution of individual payment over time. Some of them, such as Levitt (2006) and Gneezy et al. (2012), have found a declining trend in PWYW amount over time. As the evolution of PWYW amount is not the primary research question in their research, they only descriptively present the variations in the average payment amount over a period without further investigation. Riener and Traxler (2012) use the same data as Gneezy et al. (2012) and seek to interpret the declining pattern from the perspective of a social norm framework. However, due to data limitations, it is difficult for Riener and Traxler to come up with a direct test of their interpretation, and instead, they derive several testable predictions. Our empirical analysis in the online content context confirms that individuals tend to pay less as their tenure increases. But unlike existing studies in which individuallevel payment behavior over time is unavailable, we are able to track individual payment behavior over an extended period of time, allowing us to empirically test several potential explanations for the individual payment evolution. Evidence reveals that the main reason for long-life individuals to spend less is because they have already spent a lot in the past, suggesting a substitution effect between one’s spending in the past and at present. Our research also contributes to the emerging literature on live streaming. Existing research has explored the drivers for viewers’ gift-sending in live streaming. For example, Lu et al. (2021) explore the effect of audience size on broadcasters’ giftreceiving using data from field experiments. They find that a larger audience size would lead to a higher average amount of tipping, which supports the explanation of social image rather than reciprocity seeking. Lin et al. (2021) examine the role of emotion in inducing gift-sending. The results show that a happier broadcaster is likely to receive more tips from viewers. This study extends this research stream

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8 Dynamics of Digital Content Consumption and Social Norm

by focusing on how viewers’ gift-sending behavior changes over time. Instead of investigating the factors that could affect viewers’ payment behavior in the current session, we investigate the evolution of their payment behavior in the long run.

8.6.2 Managerial Implication Our findings have important managerial implications for firms and sellers that adopt PWYW pricing. Although firms often expect that they can get more profits from repeat customers rather than new ones, our empirical results show that repeat customers’ PWYW amount for digital content consumption could be crowded out by their cumulative spending in the past. Therefore, under PWYW pricing, firms should reconsider the profitability of different customers and the tradeoffs between managing existing customers and attracting new ones. Moreover, the heterogeneity analysis shows that the dampening effect of prior spending can be mitigated as customers encounter more diverse products and observe higher payments from others. These findings suggest a more careful design of recommendation and information disclosure. For example, firms could try to reduce the negative effect of the total spending in the past by recommending a higher variety of products or making the information about higher payment amount more salient.

8.6.3 Limitation and Future Research Agenda Limitations in our research offer opportunities for future extension. First, future research can develop structural models to separate out the loyalty and substitution effect. With the increase in tenure, both the loyalty of the customer and the cumulative spending in the past increased. Structural measures can help capture these two effects simultaneously. Second, as pointed out by prior studies (e.g.,Hüttel et al., 2018; Lee, 2019; Niemand et al., 2019), price claims and especially zero prices can lead consumers to make price-quality inferences, which in turn affect their behavior. In the context of PWYW, if in the long term, an increasing number of consumers make poor price-quality inferences, their payments will decline over time. With appropriate measurement of consumer perceptions, future research can explore the effects of such price-quality inferences on PWYW pricing. Third, we examine two heterogeneous factors, that is, the diversity of consumption and the observed payments from others. Future research can explore more moderators that can help firms mitigate the dampening effect of cumulative spending. For example, heterogeneous characteristics of individuals and specific attributes of products may serve as alternative boundary conditions to the relationship between the cumulative and the current spending in PWYW.

References

179

Finally, our findings are based on observational data from a live streaming platform. As live streaming is mostly used in entertainment, future research can explore whether our results apply to other contexts. Although we believe that the substitution effect is not restricted to the live streaming industry, it is possible that different markets might exhibit different mechanisms and even different trends. Further examination of other industries may shed more insights into the dynamics of individual payment behavior under PWYW pricing.

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Part IV

Discussion and Conclusion

Chapter 9

Conclusion Summary

This chapter provides a summary of the book’s contents along with a discussion and conclusion. The book revolves around the topic of social influence in digital content contribution and consumption. We have discussed this issue from three aspects: theories, empirical analyses, and practices. This chapter is organized according to this logic, where we sequentially discuss theoretical, empirical, and practical aspects. We provide an overview of the relevant theories, concepts, and literature covered in this book, starting with Chap. 2 and concluding in Chap. 4. Following that, we summarize the empirical analyses presented in Chaps. 5 through 8. Finally, we explore the theoretical insights and practical implications of the research and how the findings can inform future studies and the development of effective strategies for online platforms and content creators.

9.1 Theoretical Discussion The theoretical section consists of two folds. The first component pertains to digital content contribution and explores the impact of monetary and non-monetary incentives on motivating users to provide and share different types of digital content. This section covers a wide range of topics related to digital user-generated content, including the effects of financial incentives and social norms on content creation and participation, the role of feedback and recognition in motivating users, and the impact of intrinsic and extrinsic motivation on user behavior. It also covers a variety of platforms and contexts, including crowdsourcing, social media, and online reviews. The research is drawn from a diverse set of disciplines, including information systems, marketing, economics, and psychology. The second component focuses on digital content consumption and covers the PWYW pricing strategy, which is commonly utilized on platforms that rely on usergenerated content. Additionally, we introduce relevant concepts and theories related to users’ consumption behavior for digital content and discuss various motives for © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Ma, Social Influence on Digital Content Contribution and Consumption, Management for Professionals, https://doi.org/10.1007/978-981-99-6737-7_9

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consuming such content. The theoretical part of this book establishes the theoretical framework that underpins the subsequent empirical analyses, which form the core of this book.

9.2 Empirical Conclusion To investigate the digital content contribution and consumption behavior empirically, we employ data from live streaming platforms. As a representative example of a twosided market, live streaming platforms involve two key stakeholders: broadcasters as content providers and viewers as content consumers. This setting offers an ideal context for examining the incentives for content contribution, the motivations behind consumption, and the associated dynamics. The empirical section of this book is structured around two main topics, each of which comprises two major research questions. The topic I focus on concerns digital content contribution, with the first empirical analysis exploring the social incentives for digital content contribution. More specifically, we investigate how social incentives such as gift-giving and social interaction affect the behavior of broadcasters on live streaming platforms. We also analyze whether there are differences in the incentive effects of gift-receiving and social interaction among broadcasters with varying levels of experience. The conclusion is as follows. Conclusion 1: Both social interaction and monetary earning positively affect the short-term activation and long-term retention of digital content contributors. As content contributor experience increases, monetary earning has a stronger incentive effect on long-term retention, while social interaction becomes less effective at promoting content provision frequency but more effective at retaining contributors on the platform. The second empirical analysis explores the dynamics of digital content contribution, monetary incentive, and social interaction. Our focus is on addressing the question of whether generating digital content more frequently can result in increased monetary gain or social popularity. Additionally, we analyze how digital content contribution, monetary earnings, and social earnings can evolve over time across contributors with heterogenous monetary earning power. The following is the conclusion. Conclusion 2: Contributing digital content frequently cannot lead to more monetary gaining but can induce more social interaction. For content contributors whose monetary earning rate is above the average, the effect of contribution frequency on the monetary earning rate is insignificant. While for those below the average, there is a positive relationship between contribution frequency and monetary gaining. Our empirical research on digital content contribution has the following contributions. The research makes several theoretical contributions to the literature on digital content contribution. Firstly, it extends the existing literature on gifting and tipping by examining the effect of gift-receiving on individuals’ short-term activation and

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long-term retention. While prior research has explored the impact of monetary and non-monetary incentives on users’ content provision behavior (e.g., Khern-am-nuai et al., 2018; Qiao & Rui, 2023), this study considers multiple aspects of digital content provision behavior, including when to initiate the next live session and whether to stay on the platform. The results highlight that gift-receiving and social interaction have different impacts on broadcasters’ short- and long-run behavior, providing insights into the underlying motivation for multiple aspects of content provision. Secondly, the studies emphasize the role of individual experience in the timevarying effects of gift-receiving and social interaction. While prior research has shown that individuals’ motivation can change over time (e.g., Alam & Campbell, 2017; Rotman et al., 2012), this study focuses on how the effects change with the increase of individual experience, highlighting the importance of heterogeneity. By considering how the effects of gift-receiving and social interaction change over time, the study offers a more nuanced understanding of the underlying mechanisms driving digital content contribution behavior. Thirdly, the analyses examine the effect of working hard on earnings in the context of online content contribution. While prior research has explored the factors that can affect users’ content provision behavior, such as monetary and non-monetary incentives (e.g., Chen et al., 2018; Liu & Feng, 2021), this study considers how content contributors’ hourly monetary and social earnings are affected by their quantity of content provision. The study finds a striking result that is different from traditional labor supply; specifically, it shows that working harder does not necessarily lead to higher earnings. This finding provides new insights into the underlying mechanisms driving digital content contribution behavior, and suggests that traditional labor supply models may not be applicable in the context of online content contribution. Topic II examines the consumption of digital content, specifically focusing on the social aspects. Firstly, we investigate the social motives behind people’s consumption of digital content. Drawing upon the Uses and Gratifications Theory, we analyze how social interaction influences users’ willingness to pay for digital content, and how this influence varies depending on the content provider’s level of experience. Our conclusions are summarized as follows. Conclusion 3: Social interaction has a positive effect on digital content consumption. As content contributor experience increases, the positive effect diminishes. Next, we delve into the dynamics of digital content consumption behavior. Our focus is on the evolution of users’ payments for content and the underlying mechanism driving it. With our findings, we further investigate how social norms play a role in impeding the decline of users’ digital content consumption. The conclusions are as follows. Conclusion 4: Users’ payments for digital content tend to decrease over time, largely due to the substitution effect, which means that the cumulative spending in the past could crowd out current payments. Moreover, social norms can help moderate this declining trend, for instance, by presenting one with others’ significant payments for digital content.

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The empirical analyses on digital content consumption contribute to the existing literature in the following ways. First, we examine the effect of social interaction on gifting and tipping behavior. While prior research has explored various social factors that can influence gifting and tipping behavior, such as audience size (Lu et al., 2021), social norms (Azar, 2007), and altruism (Andreoni, 1990), there is a lack of focus on the effect of social interaction, which is a notable feature of digital content consumption. The empirical analysis controls for audience size and focuses on the effect of social interactions initiated by viewers on broadcasters’ gift receiving behavior. The results suggest that gift receiving increases for a fixed audience size when the number of social interactions increases, offering new insights into the underlying mechanisms driving gifting and tipping behavior. Second, our research highlights the dynamics of individual digital content consumption behavior. Previous studies have found opposing trends in the evolution of individual payment over time, with some showing a declining trend in PWYW amount over time (e.g., Gneezy et al., 2012; Riener & Traxler, 2012). The study tracks individual payment behavior over an extended period, allowing for an empirical investigation of several potential explanations for individual payment evolution. The evidence reveals that the main reason for long-time users spending less is due to a substitution effect between past and present spending, contributing to a better understanding of the dynamics of digital content consumption behavior. Last, we extend the emerging literature on live streaming by focusing on viewers’ gift-sending behavior over time. While prior research has examined the drivers of viewers’ gift-sending behavior, such as audience size (Lu et al., 2021) and emotion (Lin et al., 2021), the study investigates the evolution of viewers’ payment behavior in the long run. By examining how viewers’ gift-sending behavior changes over time, the study offers new insights into the underlying motivations driving digital content consumption behavior in the context of live streaming.

9.3 Practical Implications Empirical findings in this book have several practical implications for online content platforms, online content contributors, and firms and sellers that adopt PWYW pricing. Firstly, the study’s practical implications are relevant for digital content platforms in general, as they highlight the importance of both monetary and non-monetary incentives for content contributors. Digital content platforms can benefit from understanding the dynamics of individual digital content consumption behavior and the drivers of gifting and tipping behavior. Platforms can consider providing a range of incentives that go beyond monetary compensation, such as social interaction, to attract and retain content contributors with different outcome goals and experience levels. For example, less-experienced content contributors may be more motivated by monetary incentives, while more experienced ones may be more motivated by social recognition and status.

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Secondly, we directly provide empirical evidence for content contribution and consumption on live streaming platforms. The study’s empirical evidence on the short- and long-term consequences of gift-receiving suggests that monetary incentive can always be regarded as an effective way to activate and retain broadcasters. However, the study also found that with the increase in broadcasters’ experience, the effect of social interaction on increasing performing frequency tends to be weaker. Still, social interaction turns out to be more effective at retaining broadcasters on the platform. Therefore, live streaming platforms should consider broadcasters’ experience levels and outcome goals when recommending live sessions to viewers. For example, if platforms want to retain less-experienced broadcasters, recommending their live sessions to viewers who are likely to send gifts rather than messages could be an effective way. While for more-experienced broadcasters, leading viewers who would like to interact in their live sessions can be useful to keep the broadcasters on the platform. Thirdly, for online content contributors, the study’s results suggest two key implications. Firstly, working harder may not lead to higher monetary earnings for individuals who have already established a high earning rate. Still, it can be an effective way for less-paid individuals to enhance their earning abilities. Therefore, content contributors in the initial stages of their careers should work harder to increase their earning potential. Secondly, providing more digital content can help individuals receive more social interactions, which can bring social value and benefit their overall development on the platform. Lastly, firms and sellers that adopt PWYW pricing should reconsider the profitability of different customers and the tradeoffs between managing existing customers and attracting new ones. The study suggests that repeat customers’ PWYW amount for digital content consumption could be crowded out by their cumulative spending in the past. Therefore, under PWYW pricing, firms should design more diverse products and make the information about higher payment amount more salient to reduce the negative effect of total spending in the past. Moreover, the study’s heterogeneity analysis shows that the dampening effect of prior spending can be mitigated as customers encounter more diverse products and observe higher payments from others. These findings suggest a more careful design of recommendation and information disclosure. For example, firms could try to reduce the negative effect of the total spending in the past by recommending a higher variety of products or making the information about higher payment amount more salient.

References Alam, S. L., & Campbell, J. (2017). Temporal motivations of volunteers to participate in cultural crowdsourcing work. Information Systems Research, 28(4), 744–759. https://doi.org/10.1287/ isre.2017.0719 Andreoni, J. (1990). Impure altruism and donations to public goods: A theory of warm-glow giving. The Economic Journal, 100(401), 464. https://doi.org/10.2307/2234133

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Azar, O. H. (2007). Why pay extra? Tipping and the importance of social norms and feelings in economic theory. The Journal of Socio-Economics, 36(2), 250–265. https://doi.org/10.1016/j. socec.2005.11.046 Chen, W., Wei, X., & Zhu, K. X. (2018). Engaging voluntary contributions in online communities: A hidden Markov model. MIS Quarterly, 42(1), 83–100. https://doi.org/10.25300/MISQ/2018/ 14196 Gneezy, A., Gneezy, U., Riener, G., & Nelson, L. D. (2012). Pay-what-you-want, identity, and selfsignaling in markets. Proceedings of the National Academy of Sciences, 109(19), 7236–7240. https://doi.org/10.1073/pnas.1120893109 Khern-am-nuai, W., Kannan, K., & Ghasemkhani, H. (2018). Extrinsic versus intrinsic rewards for contributing reviews in an online platform. Information Systems Research, 29(4), 871–892. https://doi.org/10.1287/isre.2017.0750 Lin, Y., Yao, D., & Chen, X. (2021). Happiness begets money: Emotion and engagement in live streaming. Journal of Marketing Research, 58(3), 417–438. https://doi.org/10.1177/002224372 11002477 Liu, Y., & Feng, J. (2021). Does money talk? The impact of monetary incentives on user-generated content contributions. Information Systems Research, 32(2), 394–409. https://doi.org/10.1287/ isre.2020.0971 Lu, S., Yao, D., Chen, X., & Grewal, R. (2021). Do larger audiences generate greater revenues under pay what you want? Evidence from a live streaming platform. Marketing Science, 40(5), 964–984. https://doi.org/10.1287/mksc.2021.1292 Qiao, D., & Rui, H. (2023). Text performance on the vine stage? The effect of incentive on product review text quality. Information Systems Research, 34(2), 676–697. https://doi.org/10.1287/isre. 2022.1146 Riener, G., & Traxler, C. (2012). Norms, moods, and free lunch: Longitudinal evidence on payments from a Pay-What-You-Want restaurant. The Journal of Socio-Economics, 41(4), 476–483. https://doi.org/10.1016/j.socec.2011.07.003 Rotman, D., Preece, J., Hammock, J., Procita, K., Hansen, D., Parr, C., Lewis, D., & Jacobs, D. (2012). Dynamic changes in motivation in collaborative citizen-science projects. Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work, 217–226. https://doi. org/10.1145/2145204.2145238

Chapter 10

Future Research Agenda

This chapter serves as an extension to the primary content of this book by highlighting areas for future research that can build upon the topics covered in the book and expand our understanding of digital content consumption and its impact on users and the wider digital content ecosystem. In addition to offering research recommendations for digital content contribution and consumption, we have a separate section that specifically focuses on live streaming and discusses relevant topics that can be explored in this area.

10.1 Recommendations for Research in Digital Content Contribution This book explores the social motivations that drive digital content contribution and how the provision of content, social interaction, and monetary incentives have evolved over time. However, there is still a need for further research to deepen our understanding of this topic. Future research can explore the complex interplay between social motives, content provision, and monetary incentives in digital content contribution. For example, research can investigate how social recognition and status affect content creators’ motivations to produce high-quality content and engage with their audience. Additionally, research can examine the impact of platform design and policies on content creators’ behaviors and experiences, as well as the implications of these factors for platform governance and regulation. Furthermore, research can explore how emerging technologies, such as virtual reality and augmented reality, are shaping the future of digital content contribution. For instance, research can investigate how these technologies influence content creators’ behaviors and motivations, as well as the implications of these technologies for the monetization of digital content and the future of digital content platforms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Ma, Social Influence on Digital Content Contribution and Consumption, Management for Professionals, https://doi.org/10.1007/978-981-99-6737-7_10

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Moreover, incorporating new data and methods, such as NLP, experimental methods, machine learning, longitudinal studies, and social network analysis, can help deepen our understanding of the factors that drive digital content contribution and the implications of these behaviors for platform governance, regulation, and innovation. For example, NLP techniques can be used to analyze user-generated content on digital content platforms, such as product reviews, social media posts, and online forums. This approach can provide insights into the language and sentiment used by content creators and consumers, which can shed light on the underlying motivations and behaviors of these individuals. Machine learning algorithms can be used to predict content creators’ behaviors, such as the likelihood of producing highquality content or engaging with their audience. This approach can help identify the key factors that drive digital content contribution and provide insights into how these behaviors can be encouraged and supported.

10.2 Recommendations for Research in Digital Content Consumption This book delves into the motivations underlying digital content consumption, the dynamics of content consumption behavior, and the role of social norms in shaping these behaviors. Drawing from our findings, we suggest that there is an opportunity for future research to build upon this work and further explore these topics. Firstly, in this book, the social factors we mainly focus on are social interaction and social norms. However, other social factors, such as social comparison, social status, and social loafing, can also affect users’ digital content contribution and consumption behavior. Combining a set of social factors can be an effective way to maximize the effectiveness of motivating users to pay more for digital content. One potential approach to combining social factors is to incorporate social norms and social comparison mechanisms into platform design. For example, digital content platforms could highlight the popularity or positive reviews of certain content in order to create social norms that encourage users to view and engage with this content. Additionally, social comparison mechanisms could be used to show users how their consumption behaviors compare to those of their peers, providing feedback and incentives to increase engagement and spending. Future research can further explore how to combine social factors to maximize the effectiveness of motivating users to pay more for digital content. Secondly, future research can explore how to improve market design on digital content platforms to encourage users to pay more for content. One potential research idea is gamification, which involves incorporating game-like elements such as points, badges, and leaderboards to incentivize user engagement and retention. Gamification can be a valuable mechanism for improving platform market design as it has been shown to increase user engagement, retention, and revenue in other contexts. By applying gamification techniques to digital content platforms, researchers can explore

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the impact of these mechanisms on content creation and consumption behaviors and identify ways to incentivize users to pay more for content. For example, gamification techniques could be used to incentivize users to pay for premium content by awarding them points or other benefits for purchasing content or subscribing to premium services. While gamification is a promising approach to improving platform market design, there are also potential challenges and limitations to consider. For example, gamification mechanisms may not be equally effective for all users and may lead to unintended consequences such as the production of low-quality or clickbait content. Future research can examine the effectiveness of gamification mechanisms on different types of users and content and identify ways to mitigate potential negative consequences. Finally, there is a possibility for research to explore how emerging technologies, like virtual reality and augmented reality, can be utilized to generate novel social mechanisms that impact users’ content consumption behavior. These technologies offer immersive social experiences that have the potential to influence users’ content preferences and behaviors in innovative ways. For example, virtual reality and augmented reality offer a unique opportunity to create social environments that can motivate users to consume specific types of content or engage with particular creators or communities. For instance, virtual reality can be used to create fully immersive environments where users can interact with digital content in a more engaging and dynamic way. This type of experience can increase user engagement and retention by providing users with a more personalized and interactive content experience. Similarly, augmented reality can be used to create social experiences that blend the physical and digital worlds, allowing users to engage with content in a more contextual way. For example, augmented reality can be used to create content that is specifically tailored to a user’s location or environment, providing users with a more personalized and relevant content experience. It is important to note that there are also potential challenges and limitations to consider when it comes to the use of virtual reality and augmented reality in shaping social mechanisms for content consumption. For example, not all users may have access to the necessary hardware or software to participate in these types of experiences, which could limit their impact. Additionally, there may be concerns around privacy and data security that need to be addressed when creating social experiences in virtual and augmented reality.

10.3 Recommendations for Research in Live Streaming This book’s empirical context is centered on live streaming, where we delve into the social dynamics of digital content contribution and consumption. Future research can study additional angles of gifting, the primary business model, in live streaming by using more transactional and viewer-broadcaster relational data. Figure 10.1 presents an overall conceptual framework to help understand gifting and its consequences in live streaming. Regarding the antecedents of gifting, we differentiate between the

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characteristics and behavior of viewers and broadcasters. In particular, first, on the broadcaster side, future research can focus on the effects of broadcasters’ demographics, characteristics, performance content, and individual motives. It is especially worthwhile to investigate the type, content, and quality of live shows. Given that previous research, mainly codes live content manually but lack automatic analysis (Hu et al., 2021), we encourage future researchers to collect video data and use machine learning models to facilitate content analysis. Second, on the viewer side, future research can consider the role of viewers’ demographics, social interaction, and intrinsic and social motivation. In this study, we find a positive relationship between the number of social interactions initiated by viewers and gift sending. Other dimensions of social interaction, such as the type and quality of interaction, also deserve investigation. What kind of social interaction can foster more gift sending? How do the effects vary with live content? These questions may be addressed by analyzing social interaction texts by future researchers. The consequences of gift receiving in live steaming can be discussed from two aspects—the short- and long-term behavior of broadcasters. In this paper, we study the effect of gift receiving on broadcasters’ frequency of performing in the short run and their retention in the long run. Besides these two outcomes, other short-term behavior, such as the quantity and quality of live content provision, and long-term behavior, such as broadcasters’ popularity and content choice, can also be taken into consideration. We encourage future research to further investigate the impact of gift receiving on broadcasters’ behavior in live streaming. In addition to the relevant constructs and main effects, we also propose several potential moderators to enrich the framework. As depicted in Fig. 10.1, the heterogeneity of viewers, broadcasters, and platforms can either amplify or mitigate the direct relationships described in the framework. For example, a viewer with higher social status in the live session could be more strongly driven by social motivation to send gifts. And, as we have investigated in this study, broadcasters’ experience of live streaming can also moderate the relationship between social interaction and gift receiving, and the effect of gifting receiving on broadcasters’ future behavior. Some relevant theories can be used to explain the relationships in the framework. Uses and gratification theory is the overarching theory used in this research. It explains why viewers’ gift sending is motivated by social interaction, and why broadcasters’ live streaming behavior is affected by both gift receiving and social interaction from a perspective of need satisfaction. Other related theories, such as media richness theory, warm-glow theory, and incentive theory, can also help understand viewers’ gift sending behavior. For example, based on media richness theory, perceived media richness would positively affect users’ gratifications, and thus influence their gifting behavior. Warm-glow theory suggests that the feeling of a “warm glow” may drive viewers to tip voluntarily. In conclusion, given live streaming’s growing popularity, it is worth studying the gifting-based business model in this industry. Our research presents an initial step toward understanding the antecedents and consequences of gift receiving in live streaming, there remain many avenues for future research. We hope this work can stimulate more studies in this area to help move the research forward.

Fig. 10.1 Overall conceptual model of gifting in live streaming

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