3 Results
The UPEQ questionnaire showed reliability (between-item reliability α = 0.930, main factors α = 0.930 and between subfactors α = 0.915) and all its main and subfactors significantly correlated with the rating of the game. Although rating itself showed a negative correlation (τ = − 0.033) with ‘playtime hrs’ and ‘daysplayed’ (τ = − 0.043), it is assumed that higher rating as well as higher scores on factors and subfactors of UPEQ indicate a higher satisfaction with the game in general.
Over 200 pairs of Kruskal–Wallis H tests were conducted; therefore, we only report the differences that are shown to reject the null hypothesis—0.05 significance level, the distributions of the testing variable’s two samples (generations in this case) are the same.
Among multiple factors of motivation, there were significant differences regarding Playstyle between Millennials (M = 3.82, SD = 1.03) and both Generation Z (M = 3.91, SD = 1.02) and Baby Boomers (M = 4.01, SD = 0.91).
Agency displayed the greatest number of significant pairwise comparisons across the generations. Although the difference between Generation Z (M = 3.19, SD = 1.13) and Millennials (M = 3.14, SD = 1.0) did not reject the null hypothesis, all other comparisons, including with Baby Boomers (M = 3.63, SD = 0.98) and Generation X (M = 3.35, SD = 1.01), were significant.
For Growth, only Baby Boomers (M = 4.33, SD = 0.86) showed a significant difference across the four generations. While Generation X showed (M = 4.18, SD = 0.85) similar means to Millennials (M = 4.12, SD = 0.91) and Generation Z, (M = 4.17, SD = 0.91).
Mastery saw significant differences between Generation X (M = 3.97, SD = 0.8), and both Millennials (M = 4.03, SD = 0.85) and Generation Z (M = 4.08, SD = 0.84).
Closeness displayed one significant difference, namely between Baby Boomers (M = 3.62, SD = 1.2) and Generation X (M = 3.48, SD = 1.17).
The distribution of ranks in Interdependence was the same across the different generations. While Closeness to non-playable characters (NPC Closeness) was not significantly different between Baby Boomers (M = 3.15, SD = 1.11),Generation X (M = 3.07, SD = 1.07) or between Millennials (M = 2.83, SD = 1.17) and Generation Z (M = 2.85, SD = 1.22), it was significant for all other comparisons.
For measures of Presence, only narrative presence showed a significant difference in three degrees. Baby Boomers (M = 3.76, SD = 0.85) had significant mean differences with both Millennials (M = 3.55, SD = 1.00) and Generation Z (M = 3.54, SD = 0.99). The third significant mean difference was between Generation X (M = 3.65, SD = 0.9) and Millennials (M = 3.55, SD = 1.0).
A variety of the
Behavioral measures were more prominent, such as ‘daysplayed’, ‘playtime hrs’,’prelvl30 playtime’, rejecting the null hypothesis for all six pairwise comparisons and only Baby Boomers and Generation X being the same for ‘daysingroup’, ‘playtime group hrs’, ‘prelvl30 playtimegroup’ and ‘max gearscore’. ‘Max level’ was the only variable that showed significant differences between Generation Z and other groups as well as between Millennials and Generation X. Means and standard deviations for these measures are presented in Table
1.
Table 1Behavioral measures, means and standard deviation (SD)
daysplayed | 102.23 | 48.93 | 81.13 | 49.36 | 50.89 | 40.42 | 34.99 | 33.06 | 56.46 | 45.42 |
playtime_hrs | 398.60 | 297.02 | 292.92 | 246.14 | 172.38 | 178.15 | 107.06 | 125.77 | 194.99 | 206.77 |
prelvl30_playtime_hrs | 416.61 | 293.59 | 307.15 | 245.08 | 186.40 | 180.13 | 124.52 | 130.40 | 212.53 | 209.27 |
days_in_group | 62.26 | 50.85 | 52.60 | 45.70 | 35.96 | 35.29 | 23.94 | 27.81 | 38.29 | 38.77 |
playtimegroup_hrs | 139.18 | 174.67 | 114.99 | 137.30 | 79.92 | 102.98 | 51.72 | 81.02 | 84.37 | 113.81 |
prelvl30_playtimegroup_hrs | 146.14 | 176.40 | 120.94 | 138.55 | 86.85 | 105.11 | 60.70 | 85.67 | 92.36 | 116.55 |
max_gearscore | 460.62 | 107.66 | 455.19 | 118.81 | 429.74 | 147.11 | 378.20 | 184.03 | 426.15 | 150.86 |
max_level | 29.54 | 2.76 | 29.42 | 3.07 | 29.06 | 3.71 | 27.93 | 5.43 | 28.93 | 4.01 |
Based on the self-reported measures, Rating of the game only differed between Baby Boomers (M = 8.16, SD = 1.81) and Millennials (M = 7.91, SD = 1.79); Players self-reported the number of hours per week which were spent on playing video games, and the sole difference identified for this measure was for Generation Z (Median = 21–30 h per week) compared to the other generations (Median = 11–20 h per week).
Additionally, analyses of self-reporting based on the Money spent on video games per month resulted in a decreasing trend of reporting with age, displaying the lowest amount for Baby Boomers (Median = 11–30 $) in comparison with Generation Z (Median = 31–60 $).
Kendall’s tau for non-parametric correlations with age and generation index (categorical number associated with each generation) were particularly high. Table
2 presents Kendall’s tau values of Age and Generation index correlates with measured variables. Among those, strongest significant correlation was observed between generation index and ‘prelvl30 playtime hrs’ at τ = 0.326, which is significant at the 0.01 level (2-tailed).
Table 2Kendall's tau (correlation coefficient)
daysplayed | .314** | − .324** |
daysingroup | .205** | − .209** |
playtime_hrs | .313** | − .324** |
playtimegroup_hrs | .149** | − .153** |
prelvl30_playtime_hrs | .317** | − .326** |
prelvl30_playtimegroup_hrs | .138** | − .139** |
max_gearscore | .171** | − .177** |
max_level | .115** | − .121** |
Self-reported hrs/week | − .079** | .084** |
Self-reported $/month | − .045** | .041** |
Rating | 0.01 | − 0.01 |
Playstyle | − 0.01 | 0.01 |
Agency | .053** | − .055** |
Growth | 0.01 | − 0.01 |
Mastery | − .049** | .050** |
Closeness | − 0.01 | 0.01 |
Interdependence | 0.00 | 0.00 |
NPCCloseness | .060** | − .057** |
Narrative | .035** | − .033** |
Emotional | 0.01 | − 0.01 |
Physical | 0.00 | 0.00 |
Autonomy | .031** | − .034** |
Competence | − .022** | .023** |
Relatedness | 0.00 | 0.00 |
Age | 1.00 | − .802** |
Generation | − .802** | 1.00 |
Correlations with generation index were also significant (2-tailed at the 0.01 level) for the number of days played (‘daysplayed’) and the amount of time (hrs) played (‘playtime hrs’) (both τ = 0.324), as well as ‘days in group’ (τ = 0.209), while ‘max gear score’ (τ = 0.177), ‘playtime group hrs’ (τ = 0.153), ‘prelvl30 playtime group hrs’ (τ = 0.139), ‘max level’(τ = 0.121), self-reported hours per week (τ = − 0.084), NPC Closeness (τ = 0.057), Agency (τ = 0.055), and Mastery (τ = − 0.050).
Age showed similar power in correlations with ‘daysplayed’ (τ = 0.317) ‘playtime hrs’ (τ = 0.314), ‘daysingroup’ (τ = 0.205), ‘max gear score’ (τ = 0.171), ‘playtime group hrs’ (τ = 0.149), ‘prelvl30 playtime group hrs’ (τ = 0.138), ‘max level’(τ = 0.115), self-reported hours per week (τ = − 0.079), NPCCloseness (τ = 0.060), Agency (τ = 0.053), and Mastery (τ = − 0.049).
3.1 Correlations for Age and Generation Index
We employed a series of machine learning techniques to further examine the significance of behavioral and self-reported measures in prediction of Age and generational index. As this testing conditions include 64 classes of age in 4 classes of generations, a random attempt at predicting age and generation will have baseline accuracies of 1.56% and 25% respectively.
First, we used a step forward neural network, which is a logistic regression classifier where the input is first transformed using a learnt non-linear transformation. This transformation projects the input data into a space where it becomes linearly separable. This intermediate layer is referred to as a hidden layer (Rumelhart et al.
1986).
In this experiment, a multilayer perceptron with 13 neurons in the hidden layer yielded a 30.00% accurate model in predicting age based on the input variables, including ‘playtime hrs’ (Predictor Importance = 0.11), Competence (PI = 0.10), ‘prelvl30 playtime hrs’ (PI = 0.08), Relatedness (PI = 0.07), ‘playtimegroup hrs’ (PI = 0.06), Growth and ‘daysplayed’ (both PI = 0.05), and Closeness (PI = 0.04). For the prediction of generation, a hidden layer consisting of 5 neurons reached the accuracy of 24.10% with the variables of ‘playtime hrs’ (PI = 0.13), ‘prelvl30 playtime hrs’ (PI = 0.09), ‘daysplayed’ and ‘max level’ (both PI = 0.07), as well as ‘prelvl30 playtime group hrs’ (PI = 0.05).
The support vector machine regression module, which aims to fit the best regression line within a margin of error, was fed with all aforementioned input variables, predicted the age of our participants with a 26.05% success rate, compared to prediction of generation, which was accurate at 58.31% level. This difference in accuracy gains might be due to the number of categories for prediction (4 classes of generation and 64 classes of age in this study).
The linear model with a forward stepwise selection produced a 25.40% accurate model in predicting age, with predictors such as ‘playtime hrs’ (PI = 0.28), ‘playtime group hrs’ (PI = 0.27), Agency and ‘daysplayed’ (both PI = 0.08), self-reported hours per week (PI = 0.07), Emotional Presence (PI = 0.05), as well as Growth (PI = 0.04). For generation, the accuracy was at a 30.70% range, with the predictors ‘playtime hrs’ (PI = 0.28), self-reported hours per week (PI = 0.11), ‘playtime group hrs’, Agency and ‘daysplayed’ (all PI = 0.10), Emotional Presence (PI = 0.06), as well as Growth and Competence (both PI = 0.05).
Finally, a multimodal ensemble, which uses a supervised learning algorithm that combines a set of classifiers into a meta-classifier by taking weighted voting of their prediction for the final forecast, for age and generation prediction respectively produced 55.30% and 58.70% accurate results which were the highest rates among all tested models. Please refer to “
Appendix 2” for graphs showing model accuracy gain for prediction of age and generation.
4 Discussion
This paper has presented the results from a study focusing on two large scale surveys deployed to players of
Tom Clancy’s The Division 2 (Ubisoft
2019), an online multiplayer shooter played by millions of players of different ages, and provides insight into the various factors relating to four different generational cohorts: Baby Boomers, Generation X, Millennials and Generation Z.
In summary, older players feel more agentic in the game, less masterful, closer to non-playable characters, and feel more present in the narrative than the younger generations. Additionally, even if older players perceive the game to provide them with more meaningful and believable options, the feedback provided by the game does not make them feel competent and effective.
The results from this study have shown that Agency, Mastery and feeling close to fantasy elements of the game, are the most discriminating measures of need satisfaction scores among generations. As a Previous study (Salmon et al.
2017) has shown, older adults prefer games that are easier to learn, challenging and single player. Our results confirm this finding by reporting a positive correlation between age and players’ perceived agency and closeness to non-playable characters and narrative presence in the game. On the other hand, the reported negative correlation of age with the perceived Mastery suggests that the older players consider themselves less competent in the game because of either an age-related decay in game performance or one’s perception of it. Future studies could examine the relationship between game performance metrics and age. We also showed that duration of play and playing in groups correlates positively with age. Playtime is among the most popular measures of engagement (Wirth et al.
2013) as well as player churn prediction (Bertens et al.
2017). More playtime for older adults other than more available free time for the retired individuals, could be connected to the trend of lower perception of Mastery among older generations. Older generations may be compensating for their lower perception of Mastery by spending more time in the game, specifically when they reportedly feel more agentic and present in the game narrative.
Connecting measures of Need satisfaction (UPEQ), playtime behavior and self-reported gaming habits of the players of different age groups, gives this study a unique perspective into generational differences in expectation, approach and perception that varies as video game audiences mature. This leads to various suggestions for the industry, academics and policy makers.
For game developers, this study argues that playtime and number of days played are not necessarily due to higher engagement, but perhaps the generational index of that population (see Sect.
4.1). Although, future research should consider further investigation relating to higher playtimes of both the Baby Boomers and Generation X cohort and their respective potential obsession with this medium as discussed by Przybylski et al. (
2009).
For game designers, the results show that there are significant differences in perceived need satisfaction among generations, suggesting that certain generations may assess the game differently (which is not reflected in the single measure of rating) or have different aptitudes for subfactors of UPEQ. Future studies may explore multiple games between and within subjects.
For researchers and policy makers, the current study has established that age and generational index is indeed a factor of importance in the study and rapport of online gaming populations and their corresponding perception and behavior, and further investigation into socio-political factors related to this phenomenon is therefore advised.
We will now discuss Play time (behavioral) and UPEQ (need satisfaction) separately in the proceeding sections to outline the implications of our results.
4.1 Play Time
Among the behavioral measures, the majority of the cross-generational pairwise comparisons were significant (amounts to 78% of the comparisons), with predominantly Baby Boomers having the highest and Generation Z the lowest averages, and other generations following the trend. The positive correlation of age with most of the behavioral measures confirms this claim.
The results described in the results section, such as significant differences in playtime means across generation, correlations between measures and predictor importance of them when using machine learning techniques, suggest that both age and generation index are in fact indicators of the amount of time players spent in
TD2. Playtime is considered to be among the most important factors in the game analytics and player churn prediction (Sifa et al.
2014). The ‘Playtime’ data from this study highly correlates with, and is the most important predictive factor of, age and generation in multiple methods.
Although the power of this impact is not overwhelming (correlation coefficient for generation and ‘prelvl30 playtime hrs’ is τ = 0.326; with ‘playtime hrs’ and ‘days played’ is both τ = 0.324), it pertains to three distinct aspects of time spent in the game. The data analysis showed that the more mature audience generally engage with the game more frequently, spend more time in the game, take more time to complete the game (playtime per level) and stay in the game for additional content, consistent with findings of Salmon et al. (
2017).
Frequency of engagement, which was measured by counting the number of days in which the player started at least one game session, is an important factor for game developers since it impacts server populations, loading and login times on the game side (Suznjevic et al.
2014). Furthermore, the more frequent interaction a player has with the game can also help the player to remember the often-complex control scheme and economic logic of the game (Jakesch and Carbon
2012). Spending more time in the game is a major goal pursued by the industry (Canossa et al.
2019) and sometimes deemed as a potential for obsessive attachment (Przybylski et al.
2009), which in turn has also been connected to a better sense of achievement and success among boys (Hamlen
2010). Moreover, although previous studies have shown that playtime is related to rating of the game and player engagement (Azadvar and Canossa
2018; Wirth et al.
2013) in this study, rating was not a factor of age, generation or playtime. The only significant difference in averages for rating was that Baby Boomers’ rating of the game was higher than Millennials’ by 2.75%.
While the Baby Boomers, on average, logged into the game 67 more days than the average Generation Z player (35 days), Baby Boomers also stayed in the game on average for 50+ min more per day, playing more than the average Generation Z player. The higher game engagement amounts to an average of 400 h spent in the game by Baby Boomers compared to 100 h for Generation Z and 200 h overall average.
Previous research has shown that older adult gamers not only play longer, but also pay more for games and are more willing to pay (Schultheiss
2012). Results of the current study confirm that Baby Boomers play significantly more and share with Generation X the highest frequency in purchasing additional content, though in self-reported measures they reported the most modest amount of money spent in a month on video games compared to the other generations (Millennials reported the highest amount spent). We suggested that this trend may be a sign or a consequence of more income and available leisure time for the aging population, according to the existing literature (Pearce
2008; Marston
2012).
4.2 Need Satisfaction (UPEQ)
In the perceived need satisfaction (UPEQ scores), there were significant differences in 33% of the comparisons between the 4 generations. On these measures, Baby Boomers tend to score the highest, while Millennials score the lowest. Baby Boomers felt significantly closer to the non-playable characters and felt more present in the game narrative than other generations, supporting a previous study (Pearce
2008) suggesting that Baby Boomers prefer games with a story.
Given the measures of Autonomy, according to KW H-tests, scores on Playstyle and Agency differed significantly between Millennials (lowest scores) and other generations, with Baby Boomers standing out with the highest score. This might be related to Baby Boomers’ higher scores for fantasy elements of the game (NPC Closeness and Narrative Presence) due to the high correlation with the sense of autonomy (agency and playstyle). This relationship implies that by investing more in the fantasy one may perceive game options to be more meaningful (Deci and Ryan
2012).
For Mastery (including items like “I felt competent at the game.”), Generation X scores were significantly lower than those of the other generations. This finding emphasizes that Generation X players perceive themselves to be less competent though the measure of game performance (max_gearscore) was significantly higher for them compared to Millennials and Generation Z players. The mismatch observed in this case could be a result of designing games with the younger audience as a target and therefore missing nuances of how older adults interact with this medium (De Schutter and Vanden Abeele
2010).
Another point worthy of discussion is how Baby Boomers have both the highest overall playtime and group playtime; however, the ratio, which gives ‘group playtime percent’ is the lowest for them (at 34.92%) and is the highest for Generation Z players (48.31%). The same argument is valid when we look at pre-level 30 group playtime percent. In other words, the percentage of social play is decreasing with age. However, there were no significant differences in UPEQ’s Relatedness score (perception of social aspects of the game) among generations. As we have shown, higher scores of narrative presence and NPC Closeness may be a signal that the older adults prefer story-based games which are usually played individually. Another explanation might be the lack of technological familiarity (IJsselsteijn et al.
2007) or reluctance to engage with online communications due to safety risks (Agosto and Abbas
2017). Future studies may explore this emerging pattern to examine its generalizability to other games and what it implies for game development targeting older adults.
4.3 Limitations
The limitations of this study include different forms of respondents’ selection for our surveying tool. Participants had to sign up for Ubisoft email communications and fulfill a playtime requirement and completion of the survey to be included in our study. Participants who chose to respond to the survey specified their age and other self-reported data without any form of validation.
This study only included respondents from one commercial video game, Tom Clancy’s The Division 2, 4 months after its release. The nature of the game, the type of audience it attracts and the time frame for data collection may have skewed our dataset and emphasizes the need for further investigation of a more diverse products, samples and time frames.
The behavioral measures could consist of a more granular set, in order to connect closer to need satisfaction types and to build better predictors. Current variables largely revolve around playtime and progression, while they could include performance metrics, activity types and sequence of player actions, something that could potentially reinforce player modeling attempts.
This study took an exploratory approach to available data sets, future studies may test the claims of this article as hypothesis in more controlled testing environments.
5 Conclusions
The current study employs a self-determination based model (UPEQ) to study a large number of players of a commercial online video game, across multiple generations. Multimodal data (self-reported habits, need satisfaction according to UPEQ, and in-game behavior) was used to increase reliability of the tested concepts.
Although rarely attempted, Marston and colleagues studied engagement among the aging population for an exercise game (Marston et al.
2016). However, we expanded on that premise by evaluating different aspects of player need satisfaction in a larger scale and by adding data from other generations for meaningful comparisons. The results present a significant difference in the mean values of the behavioral and need satisfaction measures. Significant correlations and predictive importance of these measures also facilitated our exploration in the effects of age and generation on play behavior and perception of need satisfaction.
Our data showed a trend that significantly higher playtime, days played and group playtime was correlated positively and significantly more for older adults. We conclude that older generations take more time going through the content and that they play more on average although their tendency to play with others decreases with age according to the presented data.
Intergenerational comparison of perceived need satisfaction also found that older gamers feel more agentic, present in the narrative, closer to non-playable characters but feel less competent at the game. These findings may imply an aptitude for a more narrative driven experience for older players who also prefer to take more time to play the same amount of content compared to younger audiences.
Significant differences between generations when evaluating the game in regard with Need Satisfaction, also implies that single measures of player engagement (e.g. Ratings or Playtime) are not able to capture the depth of experience and psychological need satisfaction of multiple generations playing a commercial video game. While this study provides insight into generational differences of players of a single commercial video game, investigation of between-and-within subjects as well as between-and-within video games could shed more light on the nature and causality of the explored correlations.