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AbstractIn contrast with traditional forms of entertainment media (e.g., movies, novels, and television), video games are unique in their ability to provide immersion, agency, and transformation (IAT) during the consumptive experience. As the video game medium has evolved over generations of consoles, the experience of IAT has become increasingly complex from the perspective of consumers. To better understand this phenomenon, this research presents a framework for understanding the consumption of video games by examining the intersection of player, narrative, and gameplay. Our findings suggest that advancements in video game technology and design have gradually increased the degree of integration among these domains. Although the subjective experience of IAT has generally improved as a function of greater integration, various conflicts arise from the tensions that exist between player, narrative, and gameplay. Consequently, this research explores the specific nature of such conflicts to provide a richer understanding of video game consumption and the impact of its evolution on consumers. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract
This work describes and illustrates a free and easy-to-use online text-analysis tool for understanding how consumer word use varies across contexts. The tool, Wordify, uses randomized logistic regression (RLR) to identify the words that best discriminate texts drawn from different pre-classified corpora, such as posts written by men versus women, or texts containing mostly negative versus positive valence. We present illustrative examples to show how the tool can be used for such diverse purposes as (1) uncovering the distinctive vocabularies that consumers use when writing reviews on smartphones versus PCs, (2) discovering how the words used in Tweets differ between presumed supporters and opponents of a controversial ad, and (3) expanding the dictionaries of dictionary-based sentiment-measurement tools. We show empirically that Wordify's RLR algorithm performs better at discriminating vocabularies than support vector machines and chi-square selectors, while offering significant advantages in computing time. A discussion is also provided on the use of Wordify in conjunction with other text-analysis tools, such as probabilistic topic modeling and sentiment analysis, to gain more profound knowledge of the role of language in consumer behavior.

Abstract
Previous research has shown that consumers respond differently to decisions made by humans versus algorithms. Many tasks, however, are not performed by humans anymore but entirely by algorithms. In fact, consumers increasingly encounter algorithm-controlled products, such as robotic vacuum cleaners or smart refrigerators, which are steered by different types of algorithms. Building on insights from computer science and consumer research on algorithm perception, this research investigates how consumers respond to different types of algorithms within these products. This research compares high-adaptivity algorithms, which can learn and adapt, versus low-adaptivity algorithms, which are entirely pre-programmed, and explore their impact on consumers' product preferences. Six empirical studies show that, in general, consumers prefer products with high-adaptivity algorithms. However, this preference depends on the desired level of product outcome range—the number of solutions a product is expected to provide within a task or across tasks. The findings also demonstrate that perceived algorithm creativity and predictability drive the observed effects. This research highlights the distinctive role of algorithm types in the perception of consumer goods and reveals the consequences of unveiling the mind of the machine to consumers.

Abstract
This research shows that AI-based conversational interfaces can have a profound impact on consumer–brand relationships. We develop a conceptual model of verbal embodiment in technology-mediated communication that integrates three key properties of human-to-human dialogue—(1) turn-taking (i.e., alternating contributions by the two parties), (2) turn initiation (i.e., the act of initiating the next turn in a sequence), and (3) grounding between turns (i.e., acknowledging the other party's contribution by restating or rephrasing it). These fundamental conversational properties systematically shape consumers' perception of an AI-based conversational interface, their perception of the brand that the interface represents, and their behavior in connection with that brand. Converging evidence from four studies shows that these dialogue properties enhance the perceived humanness of the interface, which in turn promotes more intimate consumer–brand relationships and more favorable behavioral brand outcomes (greater recommendation acceptance, willingness to pay a price premium, brand advocacy, and brand loyalty). Moreover, we show that these effects are reduced in contexts requiring less mutual understanding between the consumer and the brand. This research highlights how fundamental principles of human-to-human communication can be harnessed to design more intimate consumer–brand interactions in an increasingly AI-driven marketplace.