Pricing Of Virtual Goods And Designing Game Challenge .
Proceedings of the 51st Hawaii International Conference on System Sciences 2018Pricing of Virtual Goods and Designing Game Challenge Level for Free-toPlay Mobile Games in the Presence of Copycat CompetitorsIsmail CivelekWestern Kentucky Universityismail.civelek@wku.eduYipeng LiuNorthern Illinois Universityyliu@niu.eduAbstractIn-game purchases, virtual currency, contentdesign for heterogeneous consumers and strongcompetition are key challenges for mobile gameproviders. This study addresses determination ofoptimal game design strategies for game providers inthe presence of heterogeneous players and copycatcompetitors. Moreover, this paper incorporatespricing of virtual goods/currency into the Free-toPlay (F2P) mobile game design via a duopoly modeland characterizes the optimal strategies for gameproviders in terms of pricing of virtualgoods/currency and the game challenge level.1. IntroductionThe digital game industry is growing at a veryfast rate bringing in 83.6 Billion in 2014 and wasexpected to increase by 19.1% to 99.6 Billion in2016 [6]. The way digital games are played hasevolved during the past few years as the sales ofmobile devices have increased dramatically. WhilePC gaming and console gaming are still leaders in theway that digital games are played, the mobile gamingsegment has been steadily increasing with 966million players worldwide [12], bringing in revenueof 34.8 billion. Top mobile games can be verysuccessful in generating significant revenue,Supercells games’ Clash of Clans and Hay Daygenerated 2.4 million a day [17]. The dominant wayfor mobile games to earn revenue has been shiftingfrom the traditional pay outright for the game to afree to play model, which earns revenue throughmicro transactions.The main difference between mobile F2P andconsole games is that there is no free trial for players;instead players download the entire game for free [3].Thus, there are no barriers for players to downloadand start playing an F2P mobile game. Players canstill play the entire game without spending realmoney; however, many players are willing to buyvirtual goods/currency to speed up their gameprogression. In today’s mobile game market, F2PURI: http://hdl.handle.net/10125/50053ISBN: 978-0-9981331-1-9(CC BY-NC-ND 4.0)Sean R. MarstonWestern Kentucky Universitysean.marston@wku.edugame developers prefer as many players as possiblepresent in the game due to network effect, becausemost F2P games are massively multiplayer onlinegames. Therefore, these mobile games are totally freeto download without any free-trial period.This study focuses on F2P mobile gamesgenerating revenue from selling virtual currency (i.e.diamonds, crystals that can be used to purchasearmor, equipment or faster leveling in game) orvirtual goods. In-app purchases will be the numberone revenue source for mobile apps while paid appswill account for 37.8% and ad based revenue willaccount for 14% at the end of 2017 [11]. Therefore,our study only focuses on revenue generated byselling virtual currency/goods in F2P mobile games.This study’s main contribution is providing aframework for a game provider facing strongcompetition and heterogeneous players.This study incorporates pricing of virtualgoods/currency into the F2P mobile game design viaa duopoly model and characterizes the optimalstrategies for game providers in terms of pricing ofvirtual goods/currency and the game challenge level.The purpose of our monopoly and duopoly models isto explore strategies and provide managerial insightsfor the original game providers in pricing of virtualgoods and setting challenge level of the F2P mobilegames. In the duopoly model, we investigate acommonly observed practice, where the copycatgame providers compete against the original gameproviders through duplicating the game mechanismof the original game. We reveal the optimal pricingand game design strategies for both the original andcopycat game providers engaged in a duopolycompetition. In addition, we show that there shouldbe a reasonable first-mover advantage for the originalgame provider to create original games.We review the literature in Section 2; then, ourmodeling approach and results are presented inSections 3. We conclude our study with a discussion,managerial implications for game providers andfuture research directions in Section 4.Page 1350
2. Literature ReviewRegarding current research in digital games, mostof the literature focuses on traditional computer andconsole games, studies embracing virtual currency,game design and competition among F2P gameproviders are very limited. The goal of this literaturereview is to show that this paper fills a gap in mobilegame (specifically F2P games) design incorporatingpricing of virtual goods/currency for both monopolyand duopoly cases. In a recent article, Guo et al. [7]study a monopoly game provider’s problem of sellingvirtual currency to players who enjoy leisure and earnvirtual currency. They conclude that decreasing thevirtual currency price and increasing the number ofvirtual goods would improve game providers’revenue. Finneran and Zhang [6] provide a review ofpromises and challenges of studying flow, s and cautions researchers to investigatehidden assumptions of theories in other disciplines.Agarwal and Karahanna [1] use a structuralequation analysis to examine cognitive absorption ofinformation technology use based on joyment, control, and curiosity. They propose thatplayfulness and personal innovativeness are keydeterminates of cognitive absorption. Liu et al. [13]argue that competition is the key factor of gamedesign that should be incorporated into organizationalactivity games such as employee training games.This paper only focuses on F2P games on mobiledevices. Regarding game satisfaction and virtualcurrency in F2P games, researchers have beenstudying motivations of playing the games andimpacts of virtual currency and promotions. Yee [18]presents that achievement, social environment andimmersion components are main reasons for playingvideo games. Moreover, Ryan et al. [15] demonstratethat game enjoyment; autonomy, competence andrelatedness are important factors for intentions toplay video games. Besides players’ intentions to playthese video games, Moon et al. [14] proposeownership-enhancing and socialization-enhancingstrategies to improve player commitment in playingthe game.Considering virtual currency and promotions invideo games, Guo and Barnes [8] model consumer’sbehavior buying virtual currency via a mixture ofnew constructs and established theories, includingtheory of planned behavior, technology acceptancemodel, trust theory and unified theory of acceptanceand use of technology. Additionally, Hamari andLehdonvirta [10] focus on marketing of virtual goodsfor F2Ps due to untapped potential for marketing ofvirtual goods and Hamari [9] investigates purchasebehavior for virtual goods in three F2P gameenvironments: social networking, first-person shooterand social virtual world games.This paper examines micro transactions in digitalgames, specifically in F2P mobile games. This studyis positioned in the interface of information systems,e-commerce, and economics literature. This studyalso characterizes the optimal strategies for gameproviders in terms of pricing of virtualgoods/currency, game challenge level and threat fromcopycat game providers.3. Duopoly ModelCloning is as old as the video game industryitself. In the mobile game market, the problem ofcopycat games is an even more staggering problemdue to familiarity with the popular games and anincreased chance of capturing the attention of players(e.g., Pokémon Go vs. Citymon Go, Clash of Clansvs. Game of War, Super Mario Bros vs Super Max,2048 vs. Threes etc.) [2]. With a seemingly infinitenumber of games on mobile platforms, it is quitecommon to see many games which are extremelysimilar to each other, particularly when you considerthe features of the gameplay mechanics. Analyzing aduopoly case in our problem coincides with thecopycat problem of the F2P mobile game market. Inour model, there are two game providers, A and B,whose F2P games are competing for the samemarket. Thus, the game providers need to make theirdecisions not only to capture market share andgenerate revenue from players buying virtualgoods/currency, but also to consider the strategy ofthe other game providers.In mobile games, we observe that the amount ofvirtual goods purchased by consumers vary fromplayer to player. To model the “free to play” virtualgame scenario, we first require that consumers beheterogeneous in their gaming challenge levelpreferences. Let θ represents individual player’spreference about the challenge level of the game,which is assumed to be distributed uniformlybetween 0 and 1. Hence, the population density isnormalized at 1. Players are heterogeneous in θ, thisfollows from the classic Shapiro’s [16] treatment ofconsumers’ heterogeneous tastes for productqualities. Players may consume different amount ofvirtual goods/currency depending on the actual gamechallenge level relative to individual player’spersonal preference. This is also the departing pointfor our model from the existing literature (e.g., [7,8]), where players are often assumed to consumePage 1351
either zero or one quantity of the game’s virtualgoods/currency. A mobile game player may not playthe game at all if the game is deemed as too difficultfor a beginner or too easy for a more experiencedplayer. Hence, the actual game challenge level has animpact on the potential market size for the game.Therefore, we treat the actual game challenge level γas one of the two decision variables of the gameproviders in addition to the unit price p for the virtualgoods/currency.The game challenge level will cause disutility to aplayer in both directions when the game challengelevel set by the provider and player’s preference onthe game challenge level are not equal to each other.When a game fails to meet a player’s preconception,either by being too easy or too challenging for thatplayer, it often causes frustration to the player’s gameexperience. Lowering the challenge level to “Easy”can feel humiliating to a self-titled “hardcore” player,as raising the challenge level to “Hard” would beunthinkable to a “casual” one. When the gamechallenge level is higher than a player’s preferredchallenge level, the player may choose to purchase acertain amount of virtual goods/currency to align thegame’s challenge level with the player’s preferredchallenge level and therefore improve the player’sutility. Meanwhile, if the game challenge level islower than a player’s preferred challenge level, thenthe player will not purchase any virtualgoods/currency as doing so will only decrease aplayer’s utility.Without loss of generality, we assume gameprovider B creates a copycat game of provider A;thus, the problem is modeled as a three-stageStackelberg game. In Stage 1, the original gameprovider A announces the game challenge level γAand the unit price for the virtual goods/currency pAfor its game. In stage 2, the copycat announces gamechallenge level γB and the unit price for the virtualgoods/currency pB for its game observing thestrategies adopted by provider A. In stage 3, playersdecide which game to play and choose the amount ofvirtual goods/currency GA and GB to purchase. Theduopoly case is solved by backward induction.3.1. Player’s Decision in Stage 3By backward induction, first we solve the player’sdecision problem in Stage 3 assuming the gamechallenge levels (both original and copycat games)and the unit prices for the virtual goods/currency arealready observed by players. A player’s utilityfunction has the following forms depending on thechoice of the F2P game, UA V-c(γA-θ-GA)2-pAGA orUB V-s-c(γB-θ-GB)2-pBGB, as s is the discountedutility for the copycat game.We assume a penalty s 0 for the gross utility ofthe copycat game. This is because the original gameoften offers a larger player base and is deemed byplayers as more valuable due to positive networkeffects. Furthermore, most F2P mobile games aremultiplayer, there are already more players playingthe original game when the copycat game isintroduced. Hence, the copycat game provider willsuffer for not being first to market. In our analysis,we also choose to focus on the duopoly competitionbetween the original game and the copycat when themarket is fully covered. An original game with largegross utility often enjoys an initial release success,which not only attracts a lot of players but also drawscopycat competitors due to its popularity. Hence, inF2P games if an original game is indeed very popularthen a copycat game is almost guaranteed to show up,hence a duopoly competitor.Since UA and UB are concave in GA and GBrespectively, the optimal amount of virtualgoods/currency purchased by the player is found bysolving the first order conditions and the optimalsolutions are GA* γA-θ-(pA/2c) and GB* γB-θ-(pB/2c).Setting GA* 0 and GB* 0, we find the indifferencepoints (θUA and θUB) for players purchasing virtualgoods/currency as θUA γA-(pA/2c) and θUB γB-(pB/2c).3.2. Copycat’s Decision in Stage 2The copycat game provider’s objective is tooptimize its revenue using both its game’s challengelevel and the pricing of the game’s virtualgoods/currency. If the copycat provider sets the unitprice for its virtual goods higher than the originalgame provider, then the duopoly game is expected tobe dominated by the original game provider. This isbecause players already perceive the copycat game asthe less valuable product (due to the discount factors), if the virtual goods in copycat game are moreexpensive, players would have no incentive to playthe copycat game. Therefore, we choose to focus onthe case where pB pA.Considering to ensure that the copycat gameprovider’s revenue is positive, there exists an upperbound such that pB 2cγB- (pA2-4cpA γA 4c(s cγB2 )).Note that this upper bound on pB is smaller than thelower bound on pA. Therefore, the price set on thevirtual goods/currency sold by the original gameprovider is always going to be greater than thecopycat game provider i.e., pA pB, which isconsistent with our previous assumption. The copycatgame must offer virtual goods/currency at a cheaperprice than the original game to have positive revenue.Page 1352
Failing to provide cheaper virtual goods/currencywill result in players choosing to participate only inthe original game.According to our analysis, the optimal price andchallenge level for the copycat provider depends onthe original game provider’s challenge level andprice. The copycat provider’s optimal price will belower than the original game provider’s price; hence,this will allow the copycat to attract players. Thereexists an optimal game challenge level for thecopycat provider in response to the original gameproviders’ strategies set in Stage 1.3.3. Original Game Provider’s Decision inStage 1In Stage 1, the original game provider A sets itsprice and challenge level with the expectation that acopycat game will show up in Stage 2. The revenuefunction of the original game provider is derivedbased on the targeted market between [θi, θUA]. Theoriginal game provider’s optimal pA and γA can befound by substituting the optimal pB* and γB* (foundin Stage 2) into RA and then we solve the revenuemaximization problem for the original gameprovider.The upper bound of pA is derived based on theconstraint that 0 θi 1 and the lower bound of pA isderived based on the constraint that θUA θi. Solvingthe above maximization problem, we obtain theoptimal unit price for the original game provider suchthat pA* (2/15) (7c-2 (c2 15cs)). Since the revenuefunction RA is convex in γA, the interior optimalsolution of γA* do not exist. As a result, we analyzethe optimal γA* as well as the optimal unit price pA*numerically.𝑅"RApAFigure 2. The Change of the Orginal GameProvider’s Revenue Function with respect to pAFigures 1 and 2 illustrate the change of theoriginal game provider’s revenue function withrespect to γA and pA. We set c 0.35, pA 0.11 s 0.1 inFigure 1 and c 0.35, γA 1, s 0.1 in Figure 2. Therevenue function RA as shown in Figure 1 is convexin γA. Starting approximately at γA 0.33, the originalgame provider may choose to raise the gamechallenge level to increase its overall revenue.Moreover, raising the game challenge level to thehighest value leads to the possible maximizedrevenue. This indicates the optimal solution for thegame challenge level is found at γA* 1. In anotherword, it is in the original game provider’s bestinterest to offer the most challenging game in theduopoly setting. Furthermore, if the original gameprovider does not set γA to the upper bound value, thecopycat provider will steal market share by cuttingthe price of its virtual goods/currency. That is not adesirable situation for the original game provider;thus, the challenge level γA should be the maximumfeasible value based on previously set parameters.Also illustrated in Figure 2, although the revenuefunction RA is not strictly concave in pA, the optimalvalue for the unit price pA that maximizes the originalgame provider’s revenue does exist (approximately atpA 0.11) in the shown example, which is consistentwith the closed form solution presented previously,pA* (2/15) (7c-2 (c2 15cs)).3.4. Impact of First-Mover Advantage, s𝛾"Figure 1. The Change of the Orginal GameProvider’s Revenue Function with respect to γAFigure 3 illustrates the impact of the discountfactor s on the revenues of the two game providers. InFigure 3, we set c 0.35, pA 0.11 and γA 1, the redline represents the original game provider’s revenueand the black line represents the copycat gameprovider’s revenue.Page 1353
𝑅𝑅&𝑅"RB RA, game providershave no incentive tobecome the first mover instage 1, hence there will beno game for players toplay.RA RB, the original gameprovider now has incentiveto become the first mover.Copycat provider thereforehas a game to “copy” instage 2𝑠̂𝑠Figure 3. The Critical Threshold Value of theDiscount factor s that Provide First Mover AdvantageAs shown in Figure 3, when the first-moveradvantage is small, the revenue of the copycat gameprovider’s revenue is higher than the original gameprovider. If the discount factor value is known to theoriginal game provider beforehand, then the originalgame provider is better off as a copycat. Thus, nooriginal game would be introduced to the market atall. The discount factor or penalty s reflects a firstmover advantage for the original game provider. Toensure that there is a healthy market in which originalgames will continue to be produced, the industry canexamine the use of regulations. For example, byregulating the release time of games, the industry canpotentially ensure that a reasonable high first-moveradvantage and prevent market failure.While this study does not recommend regulation,it’s potentially in the industries’ best interest toexamine possible use of regulations to sustain ahealthy mobile game market. The larger the firstmover advantage the more incentive for the originalgame provider to develop brand new games. Theoriginal game provider may pursue legal methodsthrough copyright or patent protection to secure apenalty from the copycat provider so that thediscount factor is big enough to provide necessaryincentives. But often, the copycat game only clonesthe gameplay and mechanics of the original game,which is not copyrightable or enforceable. Thus, theoriginal game provider should consider investingmore in the content of the game so that it will takelonger and make it harder for the copycat provider toclone. By releasing the game first into the market, theoriginal game provider also enjoys the first moveradvantage in building up its play base. In choosingF2P mobile games, players often favor the game withbigger player base. From the copycat gameproviders’ perspective, it is in their best interest toreduce the first mover advantage received by theoriginal game provider. Copycat game provider mayachieve this by releasing the cloning version of thegame soon after the release of the original gamebefore the original game builds up dominated playerbase. This practice is often observed in real world,when a popular mobile game is released a copycatgame tends to follow quickly. For example, Clash ofClans was released in August 2012 and less than ayear later Game of War was released in July 2013.When comparing the revenue of the two games in2015, Clash of Clans came out ahead with 1.345billion while Game of Ware made 799 million [4].4. ConclusionRevenue generation in F2P games is achallenging task for game providers due toheterogeneous consumers and strong competitionfrom copycat games. In this study, we characterizeoptimal strategies for a monopoly game provider andfor both the original and copycat game providers in aduopoly case.In the duopoly model, we focus on popular F2Pgames with rich content. We show that there exists apair of optimal solutions for the copycat provider’sdecision problem. To maximize revenue, both theoptimal game challenge level and the optimal unitprice for the virtual goods/currency should be set bycopycat provider in observation of original gameprovider’s strategy. It is also shown that there is anupper bound on the price of the copycat gameprovider, which regulates the unit price for virtualgoods/currency set by copycat provider should becheaper than that of the original game. Regarding theoriginal game provider’s strategy, we show that anoptimal unit price for the virtual goods/currency doesexist for the original game and the original gameprovider should set the challenge level of the game tothe highest value possible to maximize its expectedrevenue anticipating copycat provider will cut itsprice in the duopoly setting. Moreover, we concludethat there should be a reasonable first moveradvantage (in the form of discounted value to copycatgames) for the original providers in order for them tocreate original games.This study is not without its limitation. Althoughit integrates pricing decisions with F2P game designand heterogonous players, our model does not takeplayers adaptive behaviors into consideration. Playersmay improve their playing skills hence modify theirpreferred game challenge levels as they become moreskilled with gameplay mechanics, which in turnPage 1354
could affect game providers’ strategies. Moreover,our model assumes the discount factor is the same forall players, but in actuality it might be different. Inaddition to this assumption, the duopoly case analysisis limited to popular F2P games with rich content.For future research, it will be interesting to expandthe model to include players with multiple accountsfor the same game and players playing both games inthe duopoly case.5. References[1] Agarwal, R. and E. Karahanna. Time flies when you'rehaving fun: Cognitive absorption and beliefs aboutinformation technology usage. MIS Quarterly, 24, 4 (2000),665-694.[2] App Annie & IDC Portable Gaming Report rtablegaming-report-2014-review/ , Accessed on 2/2/2017.[3] Cheng, H. K. and Liu, Y. Optimal software free trialstrategy: the impact of network externalities and consumeruncertainty. Information Systems Research, 23, 2, (2012),488-504.[4] Crawley, D. Rise of the clones: why it pays (sometimes)to be a copycat mobile t-pays-tobe-copycat-mobile-game/, Accessed on 05/26/2016.[5] Finneran, C. M. and P. Zhang. Flow in computermediated environments: Promises and challenges.Communications of the Association for InformationSystems, 15, 1 (2005), 82-101.[6] Global Games Market Report Infographics ting-37/, Accessedon 08/20/2017.[7] Guo, H., Hao, L., Mukhopadhyay, T., & Sun, D. Sellingvirtual currency in digital games: implications on gameplayand social welfare. In Theory in Economics of InformationSystems, Information Systems Society 2015 pp. 1-28.[8] Guo, Y. and Barnes, S. Why people buy virtual items invirtual worlds with real money. ACM SIGMIS Database,38, 4 (2007), 69-76.[9] Hamari, J. Why do people buy virtual goods? Attitudetoward virtual good purchases versus game enjoyment.International Journal of Information Management, 35, 3(2015), 299-308.[10] Hamari, J. and Lehdonvirta, V. Game design asmarketing: How game mechanics create demand for virtualgoods. International Journal of Business Science & AppliedManagement, 5, 1(2010), 14-29.[11] Kearl, M. 30 Essential Stats on in-app Purchases purchase-stats/, Accessed on 02/01/2017.[12] Keating, L. Gaming on-the-go: the future of mobilegaming vs. 0150604/gaming-go-future-mobile-vs-consoles.htm, Accessed on05/26/2016.[13] Liu, D., Li, X., and Santhanam, R. Digital games andbeyond: What happens when players compete? MISQuarterly, 37, 1 (2013), 111-124.[14] Moon, J., Hossain, Md. D., Sanders, G. L., Garrity, E.J., and Jo, S. Player commitment to massively multiplayeronline role-playing games (MMORPGs): an integratedModel. International Journal of Electronic Commerce, 17, 4(2013), 7-38.[15] Ryan, R. M., Rigby, C. S., and Przybylski, A. Themotivational pull of video games: A self-determinationtheory approach. Motivation and Emotion, 30, 4 (2006),344-360.[16] Shapiro, C. Optimal pricing of Experience Goods, BellJournal of economics, 14, 2 (1983), 497-507.[17] Strauss, K. The 2.4 Million-Per-Day ny-supercell/Accessedon05/18/2016.[18] Yee, N. Motivations for play in online games.CyberPsychology & Behavior, 9, 6 (2006), 772-775.Page 1355
most F2P games are massively multiplayer online games. Therefore, these mobile games are totally free to download without any free-trial period. This study focuses on F2P mobile games generating revenue from selling virtual currency (i.e. diamonds, crystals that can be used to pur
Subject matter of contract of sale-Goods. Goods may be classified as :-2. Existing Goods- a) specific goods, b) ascertained goods, c) unascertained goods. 3. Future goods- which do not exist with the seller at the time of sale. the contract thus is an agreement to sell. 4. Contingent goods - a type of future goods, the acquisition of which .
4355 Peavey Road Chaska, MN 55318 Phone: 952.448.6539 Fax: 952.448.7950 Call For Pricing Call For Pricing Call For Pricing Call For Pricing Call For Pricing Call For Pricing Call For Pricing Call For Pricing Call For Pricing. Sales Service Rentals Southwest Rental & Sales :: Equipment :: ELECTRIC TOOLS
The airline industry is developing new mechanisms for pricing and revenue management to improve an airline's capabilities for dynamic pricing. ATPCO has worked with the industry to identify and define three dynamic pricing mechanisms: Optimized Pricing, Adjusted Pricing, and Continuous Pricing. The agreed-
Pricing The categories for pricing air freight are ULD Pricing Tariff Pricing Contract Pricing Spot Bid Pricing Air freight pricing is very fluid and is quickly affected by capacity and market conditions. ULD pricing is based on the type of ULD. Each device w
Price is simple, but pricing is challenging Price is the amount of money charged for a product or service. Pricing is the determination of prices. Pricing can be monetary or non‐ monetary. Pricing decisions will lead to specific pricing strategies and tactics. Price seems to be simple, but pricing is
5 Pricing Strategies Everyday Low Pricing High/Low Pricing Odd Pricing Leader Pricing Multiple Unit Pricing/Price Bundling Price Lining One-Price Policy Markdowns Reduction in the initial retail price Markdown as % of net sales amount of markdown net sales X 100 Ex. You bought 100 sweaters and 80% sell at 50 each while
Analysis in Pricing Decisions; Monopoly Pricing vs. Competitive Pricing; Bottom Line Pricing (d) Costing of Service Sector – methods, pricing, performance measurement (e) Transfer Pricing - Objectives, Methods ( Cost Based, Market Price Based, Negotiated Pricing),
Sale of Goods Act 1979 c. 54 3 (3) Where by a contract of sale the seller purports to effect a PART U present sale of future goods, the contract operates as an agree- ment to sell the goods. 6. Where there is a contract for the sale of specific goods, and Goods the goods without the knowledge of the seller have perished at which have the time when the contract is made, the contract is void.
