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YouTube allows its users to upload and view videos on its platform. YouTube provides notification to the subscribers whenever a channel uploads a new video thereby making the channel subscribers the potential viewers of the video. And thus, they are the first to come to know about any new offering. But later on, the view count also increases due to virality, that is, mass sharing of the content by the users on different social media platforms similar to word-of-mouth in the field of marketing. Therefore, the purpose of this paper is to examine different diffusion patterns as they can help to inflate traffic and generate revenue.

YouTube's view count grows majorly through virality. The pattern of view count growth has generally been considered unimodal in most of the available research in the field of YouTube. In the present work, the growth process due to views through the subscribers and views due to word-of-mouth (virality) is presented. Considering that the impact of virality in view count growth comes later in the video life cycle; the viewing patterns of both the segments have been mathematically modeled; independently.

Different models have been proposed to capture the view count growth pattern and how the impact of virality changes the view count growth curve and thereby results in a multimodal curve structure. The proposed models have been verified on various view count data sets of YouTube videos using SPSS (Statistical Package for the Social Sciences), and their ranks have been determined using a weighted criteria–based approach. The results obtained clearly depict the presence of many modes in the life cycle of view counts.

Till now, the literature is evident of the video life cycle following a bell shape curve. This study claims that the initial thrust is by subscribers and then the contribution in the view count by people watching via word-of-mouth comes into picture and brings in another hump in the growth curve.

The purpose of this study/paper is to propose a mathematical model that is able to predict the future popularity based on the view count of a particular YouTube video. Since the emergence of video-sharing sites from early 2005, YouTube has been pioneering in its performance and holds the largest share of internet traffic. YouTube plays a significant role in popularizing information on social network. For all social media sites, viewership is an important and vital component to measure diffusion on a video-sharing site, which is defined in terms of the number of view counts. In the era of social media marketing, companies demand an efficient system that can predict the popularity of video in advance. Diffusion prediction of video can help marketing firms and brand companies to inflate traffic and help the firms in generating revenue.

In the present work, viewership is studied as an important diffusion-affecting parameter pertaining to YouTube videos. Primarily, a mathematical diffusion model is proposed to predict YouTube video diffusion based on the varying situations of viewership. The proposal segregates the total number of viewers into two classes – neoterics viewers, i.e. viewers those viewing a video on a direct basis, and followers, i.e. viewers those watching under the influence.

The approach is supplemented with numerical illustration done on the real YouTube data set. Results prove that the proposed approach contributes significantly to predict viewership of video. The proposed model brings predicted viewership and its classification highly close to the true value.

Thereby, a behavioral rationale for the modeling and quantification is offered in terms of the two varied and yet connected classes of viewers – “neoterics” and “followers.”

Recently, analyses of the characteristics of viral content in the social media field have attracted considerable attention. However, the influence of instant videos has grown significantly, and most social media platforms have begun to introduce them.

The authors conducted a series of independent-samples t-tests using a large-scale data set collected from the YouTube Shorts platform to identify the characteristics of popular instant videos and discussions surrounding them. The authors further analyzed how they differ from other viral content.

The results indicate that viewers leave varied variety of comments based on the topic of conversation in the community, rather than on the video itself. Furthermore, video producers and viewers attempt to reach a consensus in a straightforward and intuitive manner. All analyzed texts contained appropriate attitudes and tendencies according to their roles on the platform.

This study aimed to discover and understand the video and conversational characteristics of popular instant videos, which differ from the existing widely known viral content.

Experiential learning focuses on learners, their activities, experiences, and how they process and reflect upon them. Experiential learning about sustainable development in general and about Sustainable Development Goals (SDGs) in particular has to consider specific challenges of sustainable development, such as interdisciplinarity, trade-offs, conflicts of interests, ambiguities, as well as the need for critical thinking, creativity, and communication skills. Although experiential learning offers a broad range of benefits, well-documented course designs are rare, especially in business education. The purpose of this chapter, therefore, is to present a course design that we have implemented more than 50 times over the last 10 years. By providing insights into the course design, its main components, and the experiences we have gathered from it, we hope to inspire and motivate other educators to apply similar methods of experiential learning in relation to the SDGs in business education. The modular course concept consists of a kickoff meeting, three thematic trainings, three academic assignments, and two days of simulation games, role-playing, group exercises, and several rounds of reflection and evaluation. In the following sections, we describe the core elements of our course design, provide insights into success conditions and potential pitfalls, explain how the course can be adapted and replicated, and discuss the requirements for experiential learning.

We examine the role of financial and nonfinancial performance measures in managing revenues derived from life cycles of a type of intellectual property products − motion pictures.

Our study focuses on the first two markets in which audiences can watch a motion picture – the upstream theatrical market and the downstream home video market. We combine data collected from numerous public and proprietary sources and form a final sample of 654 motion pictures. Then we perform regression analysis on the data.

First, three measures of a movie’s performance in the theatrical market, opening box office revenue, peak rank, and weeks at the peak rank, have positive effects on subsequent revenues in the home video market. Second, the same set of performance measures also predicts the motion picture’s life span in the theatrical market. Third, when the actual life span of a motion picture in the theatrical market deviates from its predicted value, the total return on investment in the motion picture decreases.

We do not have data on other downstream markets related to motion pictures, such as pay-per-view and online video streaming.

This study suggests that the public and proprietary data can be used to inform managerial decisions regarding intellectual property product life cycles.

This is the first accounting study that directly examines life cycle revenues of intellectual property products. We also extend literature on revenue driver and revenue management research to the product level.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.