現今工業國家的玩家10%的空閒時間都被各種遊戲佔據了，且在過去十年中，透過強大的智慧行動裝置，遊戲逐漸巧妙地融入了玩家的日常生活。策略類手遊一直廣受玩家們的喜愛，其中，具代表性的策略類手遊─部落衝突─更是排行全球前十大最高獲利的手遊之一。此研究試圖探討策略類手遊玩家之偏好，分別利用聯合分析法找出影響玩家願付價格之主要因素，並利用基於隨機效用理論的多元羅吉特計算出玩家的願付價格。研究對象鎖定為部落衝突之付費玩家。首先，策略類手遊之關鍵屬性包含互動性、挑戰性、角色多元性、使用者介面與價格，我們可透過聯合分析法分析這些策略類手遊特性是如何影響玩家的購買決策的。五個屬性的分層皆被定義後，模擬情境也會由各屬性中的不同分層組合而成的替選方案建構完成。接著運用多元羅吉特分析玩家對策略類手遊之偏好，並透過校估實驗對象針對每項屬性之邊際願付價格(MWTP)與針對策略類手遊之總願付價格(TWTP)。
本研究於2018年三、四月在中國、美國和台灣知名的遊戲論壇─百度、Reddit、巴哈姆特發放網路問卷，並收集到317份有效樣本。敘述性統計提供了關於填答者的社經背景、遊戲使用經驗與各變數的特性。根據多元羅吉特校估結果顯示，互動性和使用者介面對玩策略類手遊之效用有顯著的正向關係，而高挑戰性和價格對玩策略類手遊之效用有顯著的負向關係。其中，屬性係數和邊際願付價格的比較發現影響玩家對策略類手遊之偏好與願付價格最大的是高挑戰性(MWTPCHc = -NTD 3,019)，接著依序為簡單的使用者介面(MWTPUIb = NTD 2,033)和高互動性(MWTPINb = NTD 1,734)，此外研究也發現由各顯著屬性之邊際願付價格加總得知的玩家總願付價格為NTD 748。 最後，根據實證數據分析的結果，本研究提出實用且使用者導向的遊戲設計給遊戲開發商，以幫助他們設計出更具吸引力之策略類手遊。

Nowadays, games of all kinds take up about 10% of the total leisure time of gamers in industrialized countries, and the ability to take gaming on the road in daily routines via smartphone devices has significantly influenced the gaming industry over the past decade. Mobile strategy games (STG) are being widely played by gamers, and clash of clans (COC) is listed among the top ten most profitable mobile games. The present study is intended to investigate gamer preferences for mobile STG and to measure their willingness to pay (WTP) using conjoint analysis and a multinomial logit analysis incorporating random utility theory. The research object is gamers who have spent money on COC. First, the key attributes of mobile STG, including interaction, challenge, character diversity, user interface, and price considered in the conjoint analysis are adopted to analyze how they impact gamer purchase decisions. The levels of attributes are defined, and the choice sets are composed of alternatives with combinations of various levels across attributes. Afterwards, a multinomial logit analysis model is adopted to describe the preference of the gamers toward the game, and the estimated results derived from the multinomial logit model are measured as their marginal willingness to pay (MWTP) for each attribute of mobile STG and total willingness to pay (TWTP) for mobile STG.
A total of 317 effective samples were collected using online questionnaires distributed to the well-known game Baidu forums in China, Reddit in the U.S., and Bahamut in Taiwan during March and April of 2018. A descriptive statistics analysis was conducted to provide the demographic background of the respondents, game usage experience, and the characteristics of each variable. The results of the MNL estimation indicated that at statistical significance, interaction and user interface positively influence the utility of playing mobile STG, while “a bit too difficult” level of challenge and price negatively influence the utility of playing mobile STG. In particular, the comparison of the coefficients and MWTP indicated that “a bit too difficult” level of challenge (MWTPCHc = -NTD 3,019) has the strongest impact on gamers’ preference and WTP for mobile STG, followed by the “user-friendly” level of user interface (MWTPUIb = NTD 2,033) and “high” level of interaction (MWTPINb = NTD 1,734). Further, gamers’ TWTP for mobile STG derived from the sum of each attributes’ MWTP was NTD 748. Ultimately, in the light of the estimation results, the degree of the influence of each attribute and a practical user-centered game design will be provided for the purpose of developing a more attractive mobile STG.

Abdel-Aty, M. (2003). Analysis of driver injury severity levels at multiple locations using ordered probit models. Journal of Safety Research, 34(5), 597-603.
Ainslie, A., Drèze, X., & Zufryden, F. (2005). Modeling movie life cycles and market share. Marketing Science, 24(3), 508-517.
Aldanondo-Ochoa, A. M., & Almansa-Sáez, C. (2009). The private provision of public environment: Consumer preferences for organic production systems. Land Use Policy, 26(3), 669-682.
Alexa. (2018a). Top sites in China. Retrived from https://www.alexa.com/to psites/coun tries/CN
Alexa. (2018b). Top Sites in United States. Retrived from https://www.alexa.com/top sites/countries/US
Alexa. (2018c). Top Sites in Taiwan. Retrived from https://www.alexa.com/to psites/cou ntries/TW
App Store. (2018). App Store Review. Retrives from https://itunes.apple.Com/cn/app/%E7%99%BE%E5%BA%A6%E8%B4%B4%E5%90%A7-%E5%85%A8%E7%90%83%E6%9C%80%E5%A4%A7%E4%B8%AD%E6%96%87%E5%85%B4%E8%B6%A3%E7%A4%BE%E5%8 C%BA/id477927812?mt=8
Ashford, N., & Benchemam, M. (1987). Passengers' choice of airport: An application of the multinomial logit model. Washington, DC: Science Engineering Medicine.
Baek, S. (2005). Exploring customer preferences for online games. International Journal of Advanced Media and Communication, 1(1), 26-40.
Baker, G. A. (1999). Consumer preferences for food safety attributes in fresh apples: Market segments, consumer characteristics, and marketing opportunities. Journal of Agricultural and Resource Economics, 24(1), 80-97.
Basuroy, S., & Nguyen, D. (1998). Multinomial logit market share models: Equilibrium characteristics and strategic implications. Management Science, 44(10), 1396-1408.
Ben-Akiva, M. E., Lerman, S. R., & Lerman, S. R. (1985). Discrete choice analysis: theory and application to travel deman. London, England: The MIT Press.
Bharati, P., & Chaudhury, A. (2006). Studying the current status of technology adoption. Communications of the ACM, 49(10), 88-93.
Bhat, C. R., & Pulugurta, V. (1998). A comparison of two alternative behavioral choice mechanisms for household auto ownership decisions. Transportation Research Part B: Methodological, 32(1), 61-75.
Bloomberg. (2017). China Just Became the Game Industry Capital of the World. Retrived from https://www.bloomberg.com/news/articles/2017-06-01/china–just-became-the-games-industry-capital-of-the-world
Boccaletti, S., & Moro, D. (2000). Consumer willingness-to-pay for GM food products in Italy. AgBioForum, 3(4), 259-267.
Brown, T. C., & Gregory, R. (1999). Why the WTA–WTP disparity matters. Ecological Economics, 28(3), 323-335.
Campbell, D., Hutchinson, W. G., & Scarpa, R. (2006). Using discrete choice experiments to derive individual-specific WTP estimates for landscape improvements under agri-environmental schemes: Evidence from the rural environment protection scheme in Ireland. FEEM Working Paper, 26(1), 2-24.
Carlsson, F., & Martinsson, P. (2001). Do hypothetical and actual marginal willingness to pay differ in choice experiments?: Application to the valuation of the environment. Journal of Environmental Economics and Management, 41(2), 179-192.
Cattin, P., & Wittink, D. R. (1982). Commercial use of conjoint analysis: A survey. The Journal of Marketing, 28(3), 44-53.
Chen, Q., Anders, S., & An, H. (2013). Measuring consumer resistance to a new food technology: A choice experiment in meat packaging. Food Quality and Preference, 28(2), 419-428.
Cho, V., Cheng, T. E., & Lai, W. J. (2009). The role of perceived user-interface design in continued usage intention of self-paced e-learning tools. Computers and Education, 53(2), 216-227.
Chou, W. S., & Tseng, Y. N. (2011). The evaluation and analysis of enjoyment for digital casual games. Journal of Design Science, 14(2), 47-70.
Choi, J. H., & Lee, H. J. (2012). Facets of simplicity for the smartphone interface: A structural model. International Journal of Human-Computer Studies, 70(2), 129-142.
Cohen, M. A., Rust, R. T., Steen, S., & Tidd, S. T. (2004). Willingness‐to‐pay for crime control programs. Criminology, 42(1), 89-110.
Crawford, C. (1997). The art of computer game design. New York, NY: McGraw-Hill.
Davis, G. (1997). Are Internet surveys ready for prime time? Marketing News, 31(8), 31-31.
Dommeyer, C. J., & Moriarty, E. (2000). Comparing two forms of an e-mail survey: Embedded vs attached. International Journal of Market Research, 42(1), 1-10.
Dubin, J. A. (2007). Valuing intangible assets with a nested logit market share model. Journal of Econometrics, 139(2), 285-302.
Gil, J. M., Gracia, A., & Sanchez, M. (2000). Market segmentation and willingness to pay for organic products in Spain. The International Food and Agribusiness Management Review, 3(2), 207-226.
Green, P. E., Carroll, J. D., & Goldberg, S. M. (1981). A general approach to product design optimization via conjoint analysis. The Journal of Marketing, 45(3), 17-37.
Green, P. E., & Krieger, A. M. (1991). Segmenting markets with conjoint analysis. The Journal of Marketing, 55(4), 20-31.
Green, P. E., & Rao, V. R. (1971). Conjoint measurement for quantifying judgmental data. Journal of Marketing Research, 8(3), 355-363.
Green, P. E., & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5(2), 103-123.
Green, P. E., & Srinivasan, V. (1990). Conjoint analysis in marketing: New developments with implications for research and practice. The Journal of Marketing, 1(1), 3-19.
Greene, G., Moss, C. B., & Spreen, T. H. (1997). Demand for recreational fishing in Tampa Bay, Florida: A random utility approach. Marine Resource Economics, 12(4), 293-305.
Guadagni, P. M., & Little, J. D. (1983). A logit model of brand choice calibrated on scanner data. Marketing Science, 2(3), 203-238.
Gupta, S., & Chintagunta, P. K. (1994). On using demographic variables to determine segment membership in logit mixture models. Journal of Marketing Research, 31(1), 128-136.
Guide, Jr., Daniel, R., & Li, J. (2010). The potential for cannibalization of new products sales by remanufactured products. Decision Sciences, 41(3), 547-572.
Hoffman, D. L., & Novak, T. P.. (2000). The evolution of the digital divide: How gaps in Internet access may impact electronic commerce. Journal of Computer-Mediated Communication, 5(3), 34-46.
Hsu, C. L., & Lu, H. P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information and Management, 41(7), 853-868.
Huizenga, J., Admiraal, W., Akkerman, S., & Dam, G. T. (2009). Mobile game‐based learning in secondary education: Engagement, motivation and learning in a mobile city game. Journal of Computer Assisted Learning, 25(4), 332-344.
Ida, T., & Sato, M. (2006). Conjoint analysis of consumer preferences for broadband services in Japan. The Kyoto Economic Review, 75(2), 115-127.
Jeong, G., Koh, D., & Lee, J. (2008). Analysis of the competitiveness of broadband over power line communication in Korea. ETRI Journal, 30(3), 469-479.
Johnson, F. R., Lancsar, E., Marshall, D., Kilambi, V., Mühlbacher, A., Regier, D. A., Bresnahan, B. W., Kanninen, B., & Bridges, J. F. (2013). Constructing experimental designs for discrete-choice experiments: Report of the ISPOR conjoint analysis experimental design good research practices task force. Value in Health, 16(1), 3-13.
Kamakura, W. A., & Russell, G. J. (1989). A probabilistic choice model for market segmentation and elasticity structure. Journal of Marketing Research, 26(4), 379-390.
Kim, J. S., & Han, S. P. (2009). A study on measuring receptors’ potential effect of public-service advertising: Concentrated on the results of WTP (willingness to pay) methodology in 2006 and 2008. Journal of Media Gender and Culture, 12(1), 253-260.
Kim, M. S., Kim, E., Hwang, S., Kim, J., & Kim, S. (2017). Willingness to pay for over-the-top services in China and Korea. Telecommunications Policy, 41(3), 197-207.
Kolstoe, S., & Cameron, T. A. (2017). The non-market value of birding sites and the marginal value of additional species: Biodiversity in a random utility model of site choice by eBird members. Ecological Economics, 137(1), 1-12.
Krishnamurthi, L., & Raj, S. P. (1991). An empirical analysis of the relationship between brand loyalty and consumer price elasticity. Marketing Science, 10(2), 172-183.
Kroes, E. P., & Sheldon, R. J. (1988). Stated preference methods: An introduction. Journal of Transport Economics and Policy,22(1), 11-25.
Kurkovsky, S. (2009). Engaging students through mobile game development. ACM SIGCSE Bulletin, 41(1), 44-48.
Laarman, J. G., & Gregersen, H. M. (1996). Pricing policy in nature-based tourism. Tourism Management, 17(4), 247-254.
Lee, J., Kim, Y., Lee, J. D., & Park, Y. (2006). Estimating the extent of potential competition in the Korean mobile telecommunications market: Switching costs and number portability. International Journal of Industrial Organization, 24(1), 107-124.
Liu, I. F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C. H. (2010). Extending the TAM model to explore the factors that affect intention to use an online learning community. Computers and Education, 54(2), 600-610.
Liu, S. Y., Yeh, R. C., Wang, T. L, & Huang, H. L. (2011). A study on the factors influencing online game players' behaviors. Journal of Meiho University, 30(1), 147-172.
Loureiro, M. L., & Hine, S. (2002). Discovering niche markets: A comparison of consumer willingness to pay for local (Colorado grown), organic, and GMO-free products. Journal of Agricultural and Applied Economics, 34(3), 477-487.
Luce, R. D., & Tukey, J. W. (1964). Simultaneous conjoint measurement: A new type of fundamental measurement. Journal of Mmathematical Psychology, 1(1), 1-27.
McFadden, D. (1975). The revealed preferences of a government bureaucracy: Theory. The Bell Journal of Economics,6(2), 401-416.
McFadden, D. (1978). Modeling the choice of residential location. Transportation Research Record, 673(1), 72-77.
Market Intelligence & Consulting Institute [MIC], (2018). Game Economics of IP Rise: Almost 40% Gamers Are Paid Gamers. Retrived from https://mic.iii.org.tw/I ndustryObservations_PressRelease02.aspx?sqno=495
Mobile Marketing. (2017). 2.4BN Smartphone Users in 2017, Says Emarketer. Retrived from http://mobilemarketingmagazine.com/24bn-smartphone-users-in-2017-s ays-emarketer
Newzoo. (2017a). Top 50 Countries by Smartphone Users and Penetration. Retrived from https://newzoo.com/insights/rankings/top-50-countries-by-smartphone-p enetr ation-and-users/
Newzoo. (2017b). The Global Games Market Will Reach $108.9 Billion in 2017 with Mobile Taking 42%. Retrived from https://newzoo.com/insights/articles/the-gl obal-games-market-will-reach-108-9-billion-in-2017-with-mobile-taking-42/
Newzoo. (2017c). Game Revenues of Top 25 Public Companies up 17% in 2016, Top 10 Take More than Half of Global Market. Retrived from https://newzoo.com/insights/articles/game-revenues-of-top-25-public-companies-up-17-in-2016/
Newzoo. (2018). Top 100 Countries by Game Revenues. Retrieved from https://newzoo.com/insights/rankings/top-100-countries-by-game-revenues/
Nielson. (2016). Games Account for 10% of Gamers' Leusure Time. Retrives from https://venturebeat.com/2016/05/28/nielsen-says-games-take-up-about-10-of-our-leisure-time/
Olsen, J. A., & Smith, R. D. (2001). Theory versus practice: A review of ‘willingness‐to‐pay’in health and health care. Health Economics, 10(1), 39-52.
Open Signal. (2017). Global State of Mobile Networks. Rtrived from https://opensignal.com/reports/2017/02/global-state-of-the-mobile-network
Park, E., Baek, S., Ohm, J., & Chang, H. J. (2014). Determinants of player acceptance of mobile social network games: An application of extended technology acceptance model. Telematics and Informatics, 31(1), 3-15.
Park, H. J., & Kim, S. H. (2013). A Bayesian network approach to examining key success factors of mobile games. Journal of Business Research, 66(9), 1353-1359.
Pitkow, J. E., & Recker, M. M. (1995). Results from the first World-Wide Web user survey. Computer Networks and ISDN Systems, 27(2), 243-254.
Rezaei, S., & Ghodsi, S. S. (2014). Does value matters in playing online game? An empirical study among massively multiplayer online role-playing games (MMORPGs). Computers in Human Behavior, 35(1), 252-266.
Rhyne, T. M. (2002). Computer games and scientific visualization. Communications of the ACM, 45(7), 40-44.
Rollings, A., & Adams, E. (2003). Andrew Rollings and Ernest Adams on game design. London, England: Macmillan Computer Pub.
Scarpa, R., & Del Giudice, T. (2004). Market segmentation via mixed logit: Extra-virgin olive oil in urban Italy. Journal of Agricultural and Food Industrial Organization, 2(1), 154-163.
Schwabe, G., & Göth, C. (2005). Mobile learning with a mobile game: Design and motivational effects. Journal of Computer Assisted Learning, 21(3), 204-216.
Sharp, C. E., & Rowe, M. (2006). Online games and e-business: Architecture for integrating business models and services into online games. IBM Systems Journal, 45(1), 161-179.
Shin, J., Park, Y., & Lee, D. (2016). Strategic management of over-the-top services: Focusing on Korean consumer adoption behavior. Technological Forecasting and Social Change, 112(1), 329-337.
Shiroiwa, T., Sung, Y. K., Fukuda, T., Lang, H. C., Bae, S. C., & Tsutani, K. (2010). International survey on willingness‐to‐pay (WTP) for one additional QALY gained: what is the threshold of cost effectiveness?. Health Economics, 19(4), 422-437.
Statista. (2017). Mobile Gaming Industry: Statistics and Facts. Retrived from https://www.statista.com/topics/1906/mobile-gaming/
Statista. (2018). Combined Desktop and Mobile Visits to Reddit.com from April 2017 to March 2018 (in millions). Retrived from https://www.statista.com/statistics/443332/reddit-monthly-visitors/
Superdata. (2017). Worlwide Digital Games Market: September 2017. Retrived from https://www.superdataresearch.com/us-digital-games-market/
Sweetser, P., & Wyeth., P. (2005). GameFlow: A model for evaluating player enjoyment in games. Theoretical and Practical Computer, 3(3), 3-26.
Taguchi, G., & Konishi, S. (1987). Taguchi methods: Orthogonal arrays and linear graphs: Tools for quality engineering. Dearbon, MI: American Supplier Institute.
TechAdvisor. (2018). The Best Android Games of All Time. Retrived from https://www.techadvisor.co.uk/test-centre/game/best-android-games-of-all-tim e-3625745/
Tellis, G. J. (1988). The price elasticity of selective demand: A meta-analysis of econometric models of sales. Journal of Marketing Research, 25(4), 331-341.
ThinkGaming. (2018). Top Grossing iPhone - Games. Retrived from https://thinkgam ing.com/app-sales-data/
Tse, A. C. (1998). Comparing response rate, response speed and response quality of two methods of sending questionnaires: E-mail vs. mail. Market Research Society Journal, 40(4), 1-12.
Voelckner, F. (2006). An empirical comparison of methods for measuring consumers’ willingness to pay. Marketing Letters, 17(2), 137-149.
Voiskounsky, E., Mitina, O. V., & Avetisova, A. A. (2004), Playing online games: Flow experience. Psych Nology Journal, 2(3), 259-281.
Walsh, J. P., Kiesler, S., Sproull, L. S., & Hesse, B. W. (1992). Self-selected and randomly selected respondents in a computer network survey. The Public Opinion Quarterly, 56(2), 241-244.
Wang, H., Xie, J., & Li, H. (2010). Water pricing with household surveys: A study of acceptability and willingness to pay in Chongqing, China. China Economic Review, 21(1), 136-149.
Watt, J. (1997). Using the Internet for Quantitative Survey Research. Retrived from http://www.swiftinteractive.com/whitel.asp
Weng, H. T., Chang, S. T., & Chang, T. C. (2010). Creative element analysis of single player action game. Journal of Commercial Design, 14(1), 83-96.
Wertenbroch, K., & Skiera, B. (2002). Measuring consumers’ willingness to pay at the point of purchase. Journal of Marketing Research, 39(2), 228-241.
Whittington, D., Briscoe, J., Mu, X., & Barron, W. (1990). Estimating the willingness to pay for water services in developing countries: A case study of the use of contingent valuation surveys in southern Haiti. Economic Development and Cultural Change, 38(2), 293-311.
Witte, J. C., Amoroso, L. M., & Howard, P. E. (2000). Research methodology: Method and representation in Internet-based survey tools mobility, community, and cultural identity in Survey2000. Social Science Computer Review, 18(2), 179-195.
Yee, N. (2006). Motivations for play in online games. Cyber Psychology and Behavior, 9(6), 772-775.