main-content

## Swipe to navigate through the articles of this issue

Published in:

19-05-2022 | Research Article

# The Impact of New Content and User Community Membership on Usage of Online Games

Authors: Paulo Albuquerque, Yulia Nevskaya

Published in: Customer Needs and Solutions | Issue 1-2/2022

## Abstract

We investigate the motivations behind product usage in categories characterized by frequent product updates and social interactions between users. The proposed approach builds on theoretical work on experiential products to define consumer utility as a function of intrinsic preferences, social interactions, the match of content with user experience, and future benefits. We empirically test our model using an individual data set from the online gaming industry on daily content consumption, product innovation, and group membership. The results show that usage of simpler features is primarily motivated by intrinsic preferences, while group interactions and future benefits of learning about the product are relatively more important to explain consumption of more complex content. We find that an early innovation schedule and lowering content complexity can motivate engagement in initial stages of the product lifecycle, while providing incentives to social interactions is useful to increase content consumption in later stages. Our approach can be used to optimize the schedule and content of new product updates.
Footnotes
1
An anecdotal example of how important the relation between the purchase (subscription) decision and product usage is to managers and consumers alike was provided in May of 2011 at the earnings call of Activision Blizzard, one of the major developers of computer games. At the call, the discussion revolved around one of their main products, the popular online game World of Warcraft, which contributed a large percentage of the firm’s profits. Subscriptions declined from 12 million paying customers at the end of 2010 to 11.4 million at the end of March of 2011 and continued to rapidly drop until May. In response to questions about this decline, the company’s CEO, Mike Morhaime, said that “subscriber base does not change linearly. It fluctuates based on content consumption, which players seem to be doing a whole lot of - at a more rapid pace,” and continued by promising “faster release of new content” to respond to the demand decline.

2
We opt to model the consumer decision to join any group, instead of a specific group, in part due to data limitations and what can be identified in the empirical application. The model could be made to accommodate the consumer decision of choosing a specific group by changing the choice set at this decision stage and adding elements to the utility such as group characteristics.

3
It is possible to also include a more permanent satiation directly dependent on product usage, but we found that the one-day state dependence combined with content aging to explain the data well.

4
Although it is possible to have a more complex function for consumer expertise that accounts for both the quantity and level of past content consumed, we find that the maximum level of past content complexity matches a wide range of applications. For example, in computer games, a player’s level is usually defined as a function of most complex completed content; in TV series, viewership of the latest episode shown is also a good representation of the most useful knowledge about the storyline events. In other applications, that might not be true. For example, in educational products, the amount and level of content absorbed are both important to measure consumer progression in learning. In that case, a more complex expertise function is necessary. We note that both $$l_{it}$$ and $$l_{t}$$ are discretized for the estimation algorithm.

5
The probability $$Pr(\tilde{p}_{t+1}=\tilde{p_{t}}+1|\tau _{\widetilde{p}t})$$ reflects the firm’s propensity to invest in resources to generate more content. From the consumer’s point of view, this propensity is assumed to be exogenous; each individual consumer believes that her actions will not influence the firm’s decision and timing of product updates. In our application, the firm appears to have invested a certain amount of resources in the product (e.g., hired programmers) to support a fairly stable schedule of content introduction; according to announcements from the firm, new content is launched whenever ready and stable for usage, not before and not after. This decision resulted in several updates in the first half of the product lifecycle, followed by a longer period of time without additional introductions before the next version of the product. A similar schedule had also happened before our analysis period and supports the assumption that the schedule of product updates is an exogenous decision by the firm and not directly influenced by a consumer’s usage decisions. In addition, in our application, the transition to a new product update can occur only on Tuesdays, when server maintenance is performed, which makes $$Pr(\tilde{p}_{t+1}=\tilde{p_{t}}+1|\tau _{\widetilde{p}t},\varvec{X}_{t})=0$$ except when $$X_{t}^{Monday}=1$$. For the estimation of the duration model, $$\tau _{\widetilde{p}t}$$ is measured in weeks. The model was estimated by maximum likelihood and the estimated parameters with standard errors in parentheses are $$\chi _{1}=-2.885$$ (0.738) and $$\chi _{2}=0.228$$ (0.126).

6
Given that $$w_{ijt}$$ are known, we estimate the vector of parameters $$\varvec{\omega }$$ using a logit model, where the dependent variable is the competitive position in period $$t+1$$ and independent variables represent expectation relevant information available to consumer in period t that we collect in vector $$\varvec{Y}_{t}$$. The estimates for the intercept, current level of expertise, age of content, and update-specific effects, and current competitive position, with standard errors in parentheses, are $$\omega _{0}=-33.998$$ (0.809), $$w_{max(a,l)}=2.311$$ (0.061), $$\omega _{\tilde{p}\,age}=-1.825$$ (0.052), $$\omega _{p=1}=22.743$$ (0.623), $$\omega _{p=2}=19.787$$ (0.567), $$\omega _{p=3}=13.002$$ (0.432), and $$\omega _{\check{l}}=9.402$$ (0.099).

7
We estimated our model using both finite and infinite horizons, and the results do not change significantly between these two cases. Our results are based on the infinite horizon formulation. Since we use the infinite horizon approach, we need to cap the time variable for content aging. We choose a large number — 210 days — from the introduction of an update, after which the content does not age.

8

9
In our application, we set the time discount rate to 0.975 per day, which corresponds to 0.84 per week. This time discount rate is in line with values used in the literature on entertainment and experiential products. For example, [18] use a similar value, while [25] use a discount rate of 0.86 per week for video games. See [54] for the discussion of consumer time discount rates. We tested other discount rates and got similar substantive results.

11

12
Given that the objective of the paper is to measure the impact of innovation on product usage, the data includes only content related to the game’s main storyline. There are other unrelated tasks that we do not include in our analysis.

13
We note that our data is more detailed than the patterns presented in the figure, since it includes the transition of players at the daily level and not just when product updates are introduced. On the rare occasions when we observed a user do multiple tasks within the same day, we chose the higher level task to code participation on that day.

14
In practice, if this is a serious concern in other applications, the following change in choice probabilities can be implemented to account for this limitation: every time a consumer completes one achievement included in alternative j, subtract $$\frac{1}{C_{j}}Pr(j)$$ from the probability of choosing action j and add it to the outside good probability, where term $$C_{j}$$ is the number of tasks included in choice set j.

15
We note that more challenging content that demands cooperation of multiple players can still be performed by individuals who are not part of a game community. The difference is that to perform these tasks, users form temporary groups just before attempting a task using an option called “looking for group.”

16
After reading multiple user forums discussions, the decision of changing a group seems not to be primarily driven by prestige. Instead, it appears that switches are due to better matches between the user and the group characteristics (e.g., time of day available for playing) or because of a personal connection to the group. Unfortunately, we do not have information about group characteristics or objectives and hence we do not model this social aspect of match between group and individual members.

18
Although the temporal patterns allow for partial identification of content aging, the (quadratic) functional form assumption contributes to its identification, by giving a specific pattern to aging that separates it from other components, such as forward-looking behavior.

19
From the technical point of view, our formulation can be seen as a regular infinite horizon formulation, with the evolution of the membership state from period t to period t+1 being stochastic.

20
We solve for the value function at the points that are multiples of 42 days and use linear approximation of the value function in the objective function.

21
We found the unobserved heterogeneity to be insignificant for the social interaction decisions and present the simpler results with observed heterogeneity across the ability dimension only. We allow for both unobserved and observed heterogeneity for the content choice stage.

22
For clarity of exposition, we do not present the $$J-1$$ intercepts of each choice $$(J-1=19)$$ nor the $$X_{t}$$ coefficients for each weekday in Table 3. For the alternative intercepts, they vary between 0.16 and 1.27, with the second product update having the most positive intercepts. Before our analysis period, and looking at past records of the game, we found that content at the middle of each expansion is usually the most valuable to users, by providing a strong progress or completion of the storyline behind the game, which justifies the knowledge of quality of content by consumers before launch. For the weekday intercepts, they vary between −0.13 and 0.33. The higher estimates are for Saturday and Sunday, when users have more time to play the game.

23
The probability of belonging to one of the three segments is above 85% for most individuals.

24
In the actual scenario, we use the stochastic belief about the timing of introduction, as in the estimation.

25
An alternative way to implement an increase in content difficulty would be the change the success rates of completion.

Literature
1.
Ajzen I, Driver BL (1992) Application of the theory of planned behavior to leisure choice. J Leis Res 24:207–224 CrossRef
2.
Algesheimer R, Dholakia UM, Hermann A (2005) The social influence of brand community: evidence from European car clubs. J Mark 69:19–34 CrossRef
3.
Arcidiacono P, Jones JB (2003) Finite mixture distributions, sequential likelihood and the EM algorithm, econometrica. Econom Soc 71(3):933–946 CrossRef
4.
Arndt J (1967) Role of product-related conversations in the diffusion of a new product. J Mark Res 4(3):291–295 CrossRef
5.
Berlyne DE (1970) Novelty, complexity, and hedonic value. Percept Psychophys 8(5–A):279–286 CrossRef
6.
Bonfield EH (1974) Attitude, social influence, personal norms, and intention interactions as related to brand purchase behavior. J Mark Res 11:379–389 CrossRef
7.
Blizzard Entertainment (2008) World of Warcraft subscriber base reaches 11.5 million worldwide. available at http://​us.​blizzard.​com/​en-us/​company/​press/​pressreleases.​html?​id=​2847816
8.
Chung D, Steenburgh T, Sudhir K (2013) Do bonuses enhance sales productivity? A dynamic structural analysis of bonus-based compensation plans. Mark Sci 33(2)
9.
Celsi RL, Rose RL, Leigh TW (1993) An exploration of high-risk leisure consumption through skydiving. J Consum Res 20(1):1–23 CrossRef
10.
Coombs CH, Avrunin GS (1977) Single-peaked functions and the theory of preference. Psychol Rev 84(2):216–30 CrossRef
11.
Druehl CT, Schmidt GM, Souza GC (2009) The optimal pace of product updates. Eur J Oper Res 192(2):621–633 CrossRef
12.
Entertainment Software Association (2011) Top 10 industry facts, October 10th
13.
Entertainment Software Association (2019) 2019 Essential facts about the computer and video game industry
14.
Fuller J, Bartl M, Ernst H, Muhlbacher H (2004) Community based innovation: a method to utilize the innovative potential of online communities. 37th Annual Hawaii International Conference on System Sciences, Proceedings of the, Big Island, HI, 2004, p. 10
15.
GlobalWebIndex (2020) The latest social media trends to know for 2020. https://​www.​globalwebindex.​com/​reports/​social. Accessed 30 May 2020
16.
Golder PN, Tellis GJ (2004) Growing, growing, gone: Cascades, diffusion, and turning points in the product life cycle. Mark Sci 23(2), 207–218 Spring
17.
Gordon BR (2009) A dynamic model of consumer replacement cycles in the PC processor industry. Mark Sci 28(5):846–867 CrossRef
18.
Hartmann W, Viard V (2008) Do frequency reward programs create switching costs? A dynamic structural analysis of demand in a reward program. Quant Mark Econ 6(2):109–137 CrossRef
19.
Hirschman EC, Holbrook MB (1982) Hedonic consumption: emerging concepts, methods, and propositions. J Mark 46(3), 92–101 (Summer)
20.
Holbrook MB, Hirschman EC (1982) The experiential aspects of consumption: consumer fantasies, feelings, and fun. J Consum Res 9(2):132–140 CrossRef
21.
Holbrook MB, Chestnut RW, Oliva TA, Greenleaf EA (1984) Play as a consumption experience: the roles of emotions, performance, and personality in the enjoyment of games. J Consum Res 11(2):728–739 CrossRef
22.
Holt DB (1995) How consumers consume: a typology of consumption practices. J Consum Res 22(1):1–16 CrossRef
23.
Huang G, Khwaja A, Sudhir K (2015) Short-run needs and long-term goals: a dynamic model of thirst management. Mark Sci 34(5):702–721 CrossRef
24.
Huh YE, Kim S-H (2008) Do early adopters upgrade early? Role of post-adoption behavior in the purchase of next-generation products. J Bus Res 61(1):40–46 CrossRef
25.
Ishihara M, Ching A (2019) Dynamic demand for new and used durable goods without physical depreciation: the case of Japanese video games. Mark Sci 38(3):392–416 CrossRef
26.
Kopalle PK, Neslin SA, Sun B, Sun Y, Swaminathan V (2012) The joint sales impact of frequency reward and customer tier components of loyalty programs. Mark Sci 31(2):216–35 CrossRef
27.
Lattin JM, McAlister L (1985) Using a variety-seeking model to identify substitute and complementary relationships among competing products. J Mark Res 22(4):330–339 CrossRef
28.
Leibenstein H (1950) Bandwagon, snob, and Veblen effects in the theory of consumers’ demand. Quart J Econ 64:183–207 CrossRef
29.
Luo L, Ratchford B, Yang B (2013) Why we do what we do: a model of activity consumption. J Mark Res 50(1):24–43 CrossRef
30.
Luo Z-Q, Pang J-S, Ralph D (1996) Mathematical programs with equilibrium constraints. Cambridge University Press
31.
Mahajan V, Muller E, Bass F (1990) New product diffusion models in marketing: a review and directions for research. J Mark 54(1):1–26 CrossRef
32.
Mahajan V, Muller E, Bass F (1995) Diffusion of new products: empirical generalizations and managerial uses. Mark Sci 14(3), G79–G88. Part 2 of 2: Special Issue on Empirical Generalizations in Marketing
33.
McAlister L (1982) A dynamic attribute satiation model of variety-seeking behavior. J Consum Res 9:141–49 CrossRef
34.
Mojir N, Sudhir K (2021) A model of multipass search: price search across stores and time. Manag Sci 67(4)
35.
Muller E (2014) Innovation diffusion. The history of marketing science, edited by Winer, Russell S., Neslin, Scott A., page 91
36.
Muniz AM Jr, O’Guinn TC (2001) Brand community. J Consum Res 27(4):412–432 CrossRef
37.
Muñiz AM, Schau H (2005) Religiosity in the abandoned Apple Newton brand community. J Consum Res 31(4):737–747 CrossRef
38.
Nielsen (2011) State of media: U.S. Digital Consumer Report, Q3-Q4 2011
40.
Norton JA, Bass FM (1987) A diffusion theory model of adoption and substitution for successive generations of high-technology products. Manag Sci 33(9):1069–1086 CrossRef
41.
Pollak RA (1970) Habit formation and dynamic demand functions. J Polit Econ 78(4):745–763 CrossRef
42.
Raju PS (1984) Exploratory brand switching: an empirical examination of its determinants. J Econ Psychol 5:202–221 CrossRef
43.
Ram S, Jung H-S (1990) The conceptualization and measurement of product usage. J Acad Mark Sci 18(1):67–76 CrossRef
44.
Rogers EM (2003) Diffusion of innovations, 5th edn. Free Press, New York, NY
45.
Schau HJ, Muñiz AM, Arnould EJ (2009) How brand community practices create value. J Mark 73(5):30–51 CrossRef
46.
Shih C-F, Venkatesh A (2004) Beyond adoption: development and application of a use-diffusion model. J Mark 68(1):59–72 CrossRef
47.
Simonov A, Ursu R, Zheng C (2022) Do suspense and surprise drive entertainment demand? Evidence from Twitch.tv, working paper
48.
Spinnewyn F (1981) Rational habit formation. Euro Econ Rev 15:99–109 CrossRef
49.
Srinivasan S, Pauwels K, Silva-Risso J, Hanssens DM (2009) Product innovations, advertising, and stock returns. J Mark 73(1):24–43 CrossRef
50.
Su C-L, Judd K (2012) Constrained optimization approaches to estimation of structural models. Econometrica 80(5):2213–2230 CrossRef
51.
Unger L, Kernan J (1983) On the meaning of leisure: an investigation of some determinants of the subjective experience. J Consum Res 9(4):381–392 CrossRef
52.
Vigneron F, Johnson LW (1999) A review and a conceptual framework of prestige-seeking consumer behavior. Acad Mark Sci Rev 1:1–15
53.
Williams D, Yee N, Caplan S (2008) Who plays, how much, and why? Debunking the stereotypical gamer profile. J Comput Mediated Comm 13:993–1018 CrossRef
54.
Yao S, Mela CF, Chiang J, Chen Y (2012) Determining consumers’ discount rates with field studies. J Mark Res 49(6):822–841 CrossRef
55.
Zhao Y, Yang S, Shum M, Dutta S (2022) A dynamic model of player level-progression decisions in online gaming. Manag Sci forthcoming
56.
Craft and Hobby Association, Industry Report (2016)
Title
The Impact of New Content and User Community Membership on Usage of Online Games
Authors
Paulo Albuquerque
Yulia Nevskaya
Publication date
19-05-2022
Publisher
Springer US
Published in
Customer Needs and Solutions / Issue 1-2/2022
Print ISSN: 2196-291X
Electronic ISSN: 2196-2928
DOI
https://doi.org/10.1007/s40547-022-00127-2