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The phenomenon of sponsored search advertising—where advertisers pay a fee to Internet search engines to be displayed alongside organic (nonsponsored) Web search results—is gaining ground as the largest source of revenues for search engines. Using a unique six-month panel data set of several hundred keywords collected from a large nationwide retailer that advertises on Google, we empirically model the relationship between different sponsored search metrics such as click-through rates, conversion rates, cost per click, and ranking of advertisements. Our paper proposes a novel framework to better understand the factors that drive differences in these metrics. We use a hierarchical Bayesian modeling framework and estimate the model using Markov Chain Monte Carlo methods. Using a simultaneous equations model, we quantify the relationship between various keyword characteristics, position of the advertisement, and the landing page quality score on consumer search and purchase behavior as well as on advertiser's cost per click and the search engine's ranking decision. Specifically, we find that the monetary value of a click is not uniform across all positions because conversion rates are highest at the top and decrease with rank as one goes down the search engine results page. Though search engines take into account the current period's bid as well as prior click-through rates before deciding the final rank of an advertisement in the current period, the current bid has a larger effect than prior click-through rates. We also find that an increase in landing page quality scores is associated with an increase in conversion rates and a decrease in advertiser's cost per click. Furthermore, our analysis shows that keywords that have more prominent positions on the search engine results page, and thus experience higher click-through or conversion rates, are not necessarily the most profitable ones—profits are often higher at the middle positions than at the top or the bottom ones. Besides providing managerial insights into search engine advertising, these results shed light on some key assumptions made in the theoretical modeling literature in sponsored search.

The fraction of a user population willing to tolerate nuisances of size x is summarized in the survivor curve S ( x ); its shape is crucial in economic decisions such as pricing and advertising. We report a laboratory experiment that, for the first time, estimates the shape of survivor curves in several different settings. Laboratory subjects engage in a series of six desirable activities, e.g., playing a video game, viewing a chosen video clip, or earning money by answering questions. For each activity and each subject we introduce a chosen level x ∈ [ x min , x max ] of a particular nuisance, and the subject chooses whether to tolerate the nuisance or to switch to a bland activity for the remaining time. New non-parametric techniques provide bounds on the empirical survivor curves for each activity. Parametric fits of the classic Weibull distribution provide estimates of the survivor curves’ shapes. The fitted shape parameter depends on the activity and nuisance, but overall the estimated survivor curves tend to be log-convex. An implication, given the model of Aperjis and Huberman (SSRN, doi: 10.2139/ssrn.1672820 , 2011 ), is that introducing nuisances all at once will generally be more profitable than introducing them gradually.

Driven by data proliferation, digital technologies have transformed the marketing landscape. In parallel, significant privacy concerns have shaken consumer–firm relationships, prompting changes in both regulatory interventions and people’s own privacy-protective behaviors. With a comprehensive analysis of digital technologies and data strategy informed by structuration theory and privacy literature, the authors consider privacy tensions as the product of firm–consumer interactions, facilitated by digital technologies. This perspective in turn implies distinct consumer, regulatory, and firm responses related to data protection. By consolidating various perspectives, the authors propose three tenets and seven propositions, supported by interview insights from senior managers and consumer informants, that create a foundation for understanding the digital technology implications for firm performance in contexts marked by growing privacy worries and legal ramifications. On the basis of this conceptual framework, they also propose a data strategy typology across two main strategic functions of digital technologies: data monetization and data sharing. The result is four distinct types of firms, which engage in disparate behaviors in the broader ecosystem pertaining to privacy issues. This article also provides directions for research, according to a synthesis of findings from both academic and practical perspectives.

This article explores monetization and networking strategies within the consolidating creator economy. Through a large-scale study of linking practices on YouTube, we investigate how creators seek to build their online presence across multiple platforms and widen their income streams. In particular, we build on a near-complete sample of 153,000 “elite” YouTube channels with at least 100,000 subscribers, retrieved at the end of 2019, and investigate the URLs found in 137 million video descriptions to analyze traces of these strategies. We first situate our study within relevant literature around the creator economy, the role of platforms, and issues such as social capital building and economic precarity. We then outline our data and analytical approach, followed by a presentation of our findings. The article finishes with a discussion on how monetization and networking strategies via placing URLs in video descriptions have become more important over time, but also differ substantially between channel sizes, content categories, and geographic locations. Our empirical analysis shows that YouTube, as a highly unequal platformed media system, thrives on the economic pressures it exerts on its creators.

The proliferation of online gambling platforms has heightened concerns over their potential to intensify problematic gambling behaviors. While previous research has examined various risk factors, the influence of prior online gaming experience on gambling transitions remains underexplored. This study investigates whether and how engagement with online gaming facilitates the migration from offline to online gambling. Using survey data from 742 adults in South Korea, the analysis demonstrates that individuals with prior online gaming experience are significantly more likely to engage in online gambling, place bets more frequently, and spend larger amounts on real-money online betting. Among this group, higher expenditures on in-game purchases are positively associated with increased gambling frequency and spending, indicating a behavioral link between financial investment in online gaming and subsequent online gambling behavior. The study further identifies two prominent features of online gambling—platform anonymity and the perceived advantage of data-driven decision-making—as especially appealing to experienced online gamers. These findings reveal key psychological and behavioral mechanisms that connect online gaming to online gambling engagement. They also underscore the need for regulatory frameworks that address the evolving risks posed by digitally mediated gambling environments.

The phenomenon of paid search advertising has now become the most predominant form of online advertising in the marketing world. However, we have little understanding of the impact of search engine advertising on consumers' responses in the presence of organic listings of the same firms. In this paper, we model and estimate the interrelationship between organic search listings and paid search advertisements. We use a unique panel data set based on aggregate consumer response to several hundred keywords over a three-month period collected from a major nationwide retailer store chain that advertises on Google. In particular, we focus on understanding whether the presence of organic listings on a search engine is associated with a positive, a negative, or no effect on the click-through rates of paid search advertisements, and vice versa for a given firm. We first build an integrated model to estimate the relationship between different metrics such as search volume, click-through rates, conversion rates, cost per click, and keyword ranks. A hierarchical Bayesian modeling framework is used and the model is estimated using Markov chain Monte Carlo methods. Our empirical findings suggest that click-throughs on organic listings have a positive interdependence with click-throughs on paid listings, and vice versa. We also find that this positive interdependence is asymmetric such that the impact of organic clicks on increases in utility from paid clicks is 3.5 times stronger than the impact of paid clicks on increases in utility from organic clicks. Using counterfactual experiments, we show that on an average this positive interdependence leads to an increase in expected profits for the firm ranging from 4.2% to 6.15% when compared to profits in the absence of this interdependence. To further validate our empirical results, we also conduct and present the results from a controlled field experiment. This experiment shows that total click-through rates, conversions rates, and revenues in the presence of both paid and organic search listings are significantly higher than those in the absence of paid search advertisements. The results predicted by the econometric model are also corroborated in this field experiment, which suggests a causal interpretation to the positive interdependence between paid and organic search listings. Given the increased spending on search engine-based advertising, our analysis provides critical insights to managers in both traditional and Internet firms.

In contemporary data-driven economies, data has become a valuable digital asset that is eligible for trading and monetization. Peer-to-peer (P2P) marketplaces play a crucial role in establishing direct connections between data providers and consumers. However, traditional data marketplaces exhibit inadequacies. Functioning as centralized platforms, they suffer from issues such as insufficient trust, transparency, fairness, accountability, and security. Moreover, users lack consent and ownership control over their data. To address these issues, we propose DataMesh+, an innovative blockchain-powered, decentralized P2P data exchange model for self-sovereign data marketplaces. This user-centric decentralized approach leverages blockchain-based smart contracts to enable fair, transparent, reliable, and secure data trading marketplaces, empowering users to retain full sovereignty and control over their data. In this article, we describe the design and implementation of our approach, which was developed to demonstrate its feasibility. We evaluated the model’s acceptability and reliability through experimental testing and validation. Furthermore, we assessed the security and performance in terms of smart contract deployment and transaction execution costs, as well as the blockchain and storage network performance.

This article analyzes the business ethics of digital games, using Ayn Rand's philosophy of Objectivism. It identifies different types of monetization options as virtuous or nonvirtuous, based on Rand's views on rational self-interest. It divides the options into ethical Mover and unethical Looter designs, presents those logics in relation to an illustrative case example, Zynga, and then discusses a view on the role of players in relation to game monetization designs. Through our analysis of monetization options in the context of Objectivist ethics, the article contributes to discussions on game revenue ethics. It also expands the still understudied area of applying Rand's ethics to business, in the context of a new sector, game development, and business. This research enables ethicists to apply a wider-than-before perspective on virtue ethics to online business, and helps game developers act in a virtuous manner, which provides them with a long-term business advantage.

Purpose of Review With its inclusion in the International Statistical Classification of Diseases and Related Health Problems, 11th Revision, gaming disorder (GD) has been officially recognized as a behavioral addiction. Etiological models of GD refer to the interaction of personal, environmental, and game-related risk factors. However, the role of video game characteristics in the development of problem gaming and GD is not well understood. This systematic review summarizes the literature that examines the association between video game genres and structural characteristics and GD symptoms. Recent Findings Recent studies report that playing massively multiplayer online role-playing games, first-person shooters, and real-time strategy games/Multiplayer Online Battle Arena (MOBA) is associated with more time spent gaming and higher endorsement of GD symptoms. Reward and punishment features (reinforcement), social features, and monetization features have been associated with more frequent gaming and higher scores on self-report measures of GD. The literature is limited due to inconsistencies in measurement of game features, as well as other methodological weaknesses, demonstrating the need for higher quality studies, including studies of individuals with verified gaming disorders to understand their use of specific games. Summary Current research suggests that some game types containing certain structural game characteristics are positively related to problem gaming. We propose that future studies are guided by theory-based taxonomies of structural game characteristics and examine game influences using experimental designs. For this purpose, a measurement tool to aid the investigation of diverse elements of video game characteristics would be valuable.

This paper summarizes the discussion in a panel session on the Internet of things (IoT) at the 2017 International Conference on Information Systems (ICIS) in Seoul, Korea. The panel explored a research agenda on IoT technology and its interaction with business and society. IoT refers to the ever-growing number of numerous physical devices that feature software and location-based technologies that connect together in a network and exchange data with one another. IoT has garnered significant attention in information systems due to its rapidly expanding market and demand from a wide range of stakeholders such as consumers, businesses organizations, and government agencies. The IoT may be the next industrial revolution in which interconnected physical devices will automate skills and tasks. In today’s hyper-connected economy, IoT can radically transform businesses and society through increased transparency, optimized production processes, and decreased operating expenses. Overall, the panel identified a six-pronged IS research agenda for IoT that comprises the IoT’s impact on business and society, IoT monetization and end-user services, the IoT as a distributed platform, the convergence of the IoT and blockchain, security concerns and solutions, and the IoT and ethics. The paper concludes with a future direction for IoT.