A duration model analysis of consumer preferences and determinants of video game consumption

Abstract

Despite the enormous revenues generated by the video game industry in recent years, relatively little research has been undertaken into consumer preferences and the determinants of video game consumption. This study addresses this deficiency through the analysis of data from a popular online video game that includes historic behavioural information for 1,408 consumers participating in 728,811 unique rounds of gameplay. We analyse these data with the goal of estimating determinants of the aggregate amount of time that a consumer spends playing the game. Through the estimation of duration models, we show that less experienced consumers are less likely to continue playing the game at any given point, possibly due to having achieved mastery and becoming bored. However, we also find that consumers tend to play the game for longer periods when using a wider range of character roles and vehicles, implying that a consumer's interest can be maintained through exposure to greater variety. Our results represent the first such evidence on in‐game consumer preferences, which has important implications for video game consumption through optimisation of gameplay experiences to satisfy these preferences.

Impact of perceived value on casual mobile game loyalty: The moderating effect of intensity of playing

“…In accordance with the payment equity concept, when a player spends a lot of time playing CMGs, (s)he has a higher perception of the costs (economic and noneconomic) associated with the behaviour than the benefits obtained, and is more likely to try other games and make comparisons between them. Thus, (s)he becomes more demanding in his/her evaluation of the utility and fun that games provide, and games perhaps become more boring after the challenge to learn them passes, which is consistent with previous research in the context of video games (e.g., Kaimann, Stroh-Maraun, & Cox, 2018). This study is an important contribution to the consumer behaviour literature because, first, it provides empirical support for the payment equity concept (Bolton & Lemon, 1999) and, second, it extends the findings of Shankar et al (2003), who argued that high frequency of service use negatively affects user satisfaction, by demonstrating it also has a negative effect on perceived value.…”

“…Game developers should focus strongly on ways to entertain players throughout games, for example, by introducing social network interaction or by facilitating competition or cooperation between players as they level up (Li et al, 2015;Liu, 2016). Equally, they should introduce design modifications that produce surprises for players as they advance in the game (Kaimann et al, 2018;Su et al, 2016). Players' loyalty will be reinforced if games are useful for improving their quality of life, for example, by fighting loneliness or boredom, bodily involvement, promoting participation in sports, skills development, obtaining recognition from other players, winning prizes, among others (Besombes & Maillot, 2018;Chang et al, 2014).…”

“I will survive”: Online streaming and the chart survival of music tracks

“…where x is a vector of covariates, β is a vector of regression coefficients, and ε is the error with density f(). Walls, 1997;Deuchert, Adjamah, & Pauly, 2005;Kaimann, Stroh-Maraun, & Cox, 2018) or the exponential function (e.g., Kaimann et al, 2018;McKenzie, 2009). However, Clement, Fabel, and Schmidt-Stolting (2006) suggest that the diffusion of music over the product lifecycle necessitates the use of more flexible survival models and instead recommend the use of log-logistic models.…”

“…The distribution of the error term defines the regression model, with f () fitted to normal, logistic or extreme‐value densities corresponding to lognormal, log‐logistic, or exponential/Weibull regressions, respectively. Previous studies adopting survival analysis in the context of entertainment industries have tended to use either the Weibull distribution (e.g., De Vany & Walls, 1997; Deuchert, Adjamah, & Pauly, 2005; Kaimann, Stroh‐Maraun, & Cox, 2018) or the exponential function (e.g., Kaimann et al, 2018; McKenzie, 2009). However, Clement, Fabel, and Schmidt‐Stolting (2006) suggest that the diffusion of music over the product lifecycle necessitates the use of more flexible survival models and instead recommend the use of log‐logistic models.…”

Les composantes de l’expérience de consommation des joueurs en ligne : de la réalisation virtuelle à la virtualisation réelle

“…Peu de recherches ont abordé le sujet des MMORPG et des jeux en ligne, notamment en tant qu’expérience de consommation (Kaimann et al, 2018). L’expérience de jeu est « un ensemble composé des sensations, des pensées, des sentiments, des actions et de la construction de sens du joueur dans un cadre de jeu » (Ermi et Mäyrä, 2005 : 91).…”