由於全球在2020年開始受到COVID-19疫情的影響，為暸解決實體學習的問題，全球各地也都積極的將研發經費投入智慧學習產業中。因此，也讓智慧學習服務的企業面臨許多競爭與挑戰。然而，臺灣在智慧學習產業中企業間的競爭力高。以企業的角度，光有好的學習系統輔助教學是不夠的。企業須結合數位行銷能力以增加產品的曝光度，讓使用者能持續使用企業的智慧學習服務，才能在競爭激烈的智慧學習產業中嶄露頭角。因此，本研究基於“學習者的觀點探討開發適性化商業模擬系統之功能特性”。以此更全面性瞭解適性化商業模擬系統建構之基礎與未來的發展。
為了深度瞭解學習者在使用適性化商業模擬系統的多元觀點與學習價值。本研究採用Beer Game作為研究標的，以方法目的鏈理論完成兩階段研究探究學習者在遊玩時所著重的因素。第一階段為透過“既有屬性”建構的“既有系統屬性-學習結果-最終價值”之心理階層結構。研究結果顯示“資訊提供-體驗長鞭效應-成就感”、“供應鏈角色-訓練組織性思考-提升經營績效-成就感”、“團隊合作-促進合作互動-溫暖的人際關係”，此三條路徑是學習者著重的心理階層結構。
第二階段則以學習者期望所增加的“新創屬性”建構“新創屬性-預期學習結果-期望價值”之心理階層結構。研究發現，大部分的學習者期望在供應鏈學習系統中獲得“加入立體動態影像-增進遊玩樂趣-享樂人生”、“設置限時回合制-強化時間管理-冒險刺激”、“建立對話框-促進有效決策-成就感”、“增加關鍵事件&增加教學回饋-訓練獨立思考-成就感”等重要路徑。
最後，在性別與不同教育背景的交叉分析中發現，協同合作對於不同族群而言具有多重含義，皆各自發展出不一樣的學習路徑與價值。

The COVID-19 pandemic, which started in 2020, caused many major countries to invest massive amounts in the intelli gent learning industry specifically for research into the study of physical learning related issues. There is now a tremendous amount of competition and many challenges being encountered by all the enterprises engaged in intelligent learning services. In the intelligent learning industry, however, vigorous competition is seen among the enterprises in Taiwan. The introduction of well-organized auxiliary teaching in the learning system is no longer enough and enterprises need to incorporate digital marketing ability to build sufficient product exposure while at the same time making users willing to continue to use the company’s intelligent learning services. This helps them to become more competitive in a most intensive and vigorous intelligent learning industry. For this reason, the concept of “The study on developing the functional characteristics of the Adaptive Commercial Simulation System from the viewpoint of the learner”. This provides a more comprehensive basis of knowledge about the fundamentals needed for the implementation of the Adaptive Commercial Simulation System and its future development.
To gain deeper insight into more diversified viewpoints and the learning value of the Adaptive Commercial Simulation System to be employed by the user, the Beer Game has been selected as the target of this research. The Means-end Chain theory has been employed to complete two-stage research into the factors to be emphasized by a learner playing the game. The first stage focuses on a hierarchical mental structure of the “Existing System Attributes – Learning Consequence – Terminal Value” implemented through the “Existing Attributes”. Research results have shown that the most likely hierarchical mental structure routes that will be followed by a learner are “Information Supply – Experiencing the Bullwhip Effect – Sense of Accomplishment”, “Role of the Supply Chain – Training Organizational Thinking – Enhancing Operation Performance - Sense of Accomplishment”, and “Team Work – Promoting Cooperation and Interaction – Warm Relationships with Others”.
In the second stage focus is on the “Innovative Attributes – Anticipated Learning Consequence – Expected Value” being implemented through the “Innovative Attributes” that should be increased by the learner. Research also shows that most of the learners are expected to add the following crucial routes in the Supply Learning System: “Dimensional Dynamic Image – Improving the Fun of the Game – Fun and Enjoyment Life”, “Setting Up Term-based Limited Time – Strengthening Time Management – Excitement”, “Establishing Dialogue Frame – Promoting Effective Decision Making – Sense of Accomplishment”, and “Increasing Critical Event & More Teaching Feedback – Training Independent Thinking – Sense of Accomplishment”.
A cross analysis of gender and differing educational background, showed that multiple meanings were achieved by subjects from different groups through collaborative cooperation where different learning routes and values were developed by each group.

經濟部(2020)。智慧學習產業產值調查報告。https://www.epark.org.tw/download/109%E5%B9%B4%E5%BA%A6%E6%99%BA%E6%85%A7%E5%AD%B8%E7%BF%92%E7%94%A2%E5%80%BC%E8%AA%BF%E6%9F%A5%E6%9C%9F%E6%9C%AB%E5%A0%B1%E5%91%8A_v5.pdf
Abubakre, M., Faik, I., & Mkansi, M. (2021). Digital entrepreneurship and indigenous value systems: An Ubuntu perspective. Information Systems Journal, 31(6), 838-862.
Agudo-Peregrina, A. F., Iglesias-Pradas, S., Conde-González, M. Á., & Hernández-García, Á. (2014). Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior, 31, 542-550.
Álvarez-González, P., López-Miguens, M. J., & Caballero, G. (2017). Perceived employability in university students: Developing an integrated model. Career Development International, 22(3), 280-299.
Annabi, C. A., & Muller, M. (2016). Learning from the adoption of MOOCs in two international branch campuses in the UAE. Journal of Studies in International Education, 20(3), 260-281.
Arcand, M., & Nantel, J. (2012). Uncovering the nature of information processing of men and women online: The comparison of two models using the think-aloud method. Journal of Theoretical and Applied Electronic Commerce Research, 7(2), 106-120.
Arnab, S., Lim, T., Carvalho, M. B., Bellotti, F., de Freitas, S., Louchart, S., …De Gloria, A. (2015). Mapping learning and game mechanics for serious games analysis. British Journal of Educational Technology, 46(2), 391-411.
Arquero, J. L., Donoso, J. A., Jiménez, S., & González, J. M. (2009). Exploratory analysis of the demanded professional profile on business administration degree: Implications for the accounting area. Spanish Accounting Review, 12(1), 43-66.
Baldissin, N., Bettiol, S., Magrin, S., & Nonino, F. (2013). Business game-based learning in management education. Udine, Italy: The Business Game s.r.l..
Barari, S., Agarwal, G., Zhang, W. C., Mahanty, B., & Tiwari, M. (2012). A decision framework for the analysis of green supply chain contracts: An evolutionary game approach. Expert Systems with Applications, 39(3), 2965-2976.
Bellotti, F., Berta, R., & De Gloria, A. (2010). Designing effective serious games: Opportunities and challenges for research. International Journal of Emerging Technologies in Learning, 5(3), 22-35.
Brem, A., Viardot, E., & Nylund, P. A. (2021). Implications of the coronavirus (COVID-19) outbreak for innovation: which technologies will improve our lives? Technological Forecasting and Social Change, 163, 120451.
Burgers, C., Eden, A., van Engelenburg, M. D., & Buningh, S. (2015). How feedback boosts motivation and play in a brain-training game. Computers in Human Behavior, 48, 94-103.
Cachon, G. P., Randall, T., & Schmidt, G. M. (2007). In search of the bullwhip effect manufacturing & service. Journal of Operations Management, 9(4), 457-479.
Cantor, D. E., & Macdonald, J. R. (2009). Decision-making in the supply chain: Examining problem solving approaches and information availability. Journal of Operations Management, 27(3), 220-232.
Chaharsooghi, S. K., Heydari, J., & Zegordi, S. H. (2008). A reinforcement learning model for supply chain ordering management: An application to the beer game. Decision Support Systems, 45(4), 949-959.
Chang, C. C., Liang, C., Chou, P. N., & Lin, G. Y. (2017). Is game-based learning better in flow experience and various types of cognitive load than non-game-based learning? Perspective from multimedia and media richness. Computers in Human Behavior, 71, 218-227.
Chen, F., & Samroengraja, R. (2000). The stationary beer game. Production and Operations Management, 9(1), 19-30.
Chen, F., Drezner, Z., Ryan, J. K., & Simchi-Levi, D. (2000). Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information. Management Science 46(3), 436-443.
Chen, H. R., & Lin, Y. S. (2016). An examination of digital game-based situated learning applied to Chinese language poetry education. Technology Pedagogy and Education, 25(2), 171-186.
Cheng, Y., & Wang, S. H. (2011). Applying a 3D virtual learning environment to facilitate student’s application ability -- The case of marketing. Computers in Human Behavior, 27(1), 576-584.
Chuang, T. Y., & Chen, W. F. (2009). Effect of computer-based video games on children: An experimental study. Educational Technology & Society, 12(2), 1-10.
Chung, L. Y., & Chang, R. C. (2017). The effect of gender on motivation and student achievement in digital game-based learning: A case study of a contented-based classroom. Eurasia Journal of Mathematics, Science and Technology Education, 13(6), 2309-2327.
Coppini, M., Rossignoli, C., Rossi, T., & Strozzi, F. (2010). Bullwhip effect and inventory oscillations analysis using the beer game model. International Journal of Production Research, 48(13), 3943-3956.
D'Amours, S., Marier, P., Rönnqvist, M., Azouzi, R. & Fjeld, D. (2017). The online woodsupply game. INFORMS Transactions on Education, 18(1), 71-87.
Dennis, C., Morgan, A., Wright, L. T., & Jayawardhena, C. (2010). The influence of social e-shopping in enhancing young women’s online shopping behavior. Journal of Customer Behaviour, 9(2), 151-174.
Diana, Y. W., & Elena, K. (2006) Learning, communication, and the bullwhip effect. Journal of Operations Management, 24(6), 839-850.
Edali, M., & Yasarcan, H. (2014). A mathematical model of the beer game. Journal of Artificial Societies and Social Simulation, 17(4), 2.
Ferguson, M. E., & Drake, M. J. (2021). Teaching supply chain risk management in the COVID-19 Age: A review and classroom exercise. Decision Sciences Journal of Innovative Education, 19(1), 5-14.
Fetter, G., & Shockley, J. (2014). Developing students’ understanding of co-opetitionand multilevel inventory management strategies in supply chains: An in-classspreadsheet simulation exercise. Decision Sciences Journal of Innovative Education, 12(2), 79-89.
Fito-Bertran, A., Hernandez-Lara, A. B., & Serradell-Lopez, E. (2014). Comparing student competences in a face-to-face and online business game. Computers in Human Behavior, 30, 452-459.
Forrester, J. W. (1961). Industrial dynamics. Cambridge, MA: MIT Press.
Franzosi, R. P. (2008). Content analysis. Thousand Oaks, CA: Sage Publications.
Fu, F. L., Su, R. C., & Yu, S. C. (2009). EGameFlow: A scale to measure learners’ enjoyment of e-learning games. Computers & Education, 52(1), 101-112.
Garrido, A., Morales, L., & Serina, I. (2016). On the use of case-based planning for e-learning personalization. Expert Systems with Applications, 60(C), 1-15.
Garris, R., Ahlers, R., & Driskell, J. (2002). Games, motivation, and learning: A research and practice model. Simulation & Gaming, 33(4), 441-467.
Geary, S., Disney, S. M., & Towill, D. R. (2006). On bullwhip in supply chains -historical review, present practice and expected future impact. International Journal of Production Economics, 101(1), 2-18.
Giannoccaro, I., Pontrandolfo, P., & Scozzi, B. (2003). A fuzzy echelon approach for inventory management in supply chains. European Journal of Operational Research, 149(1), 185-196.
Graesser, A., Chipman, P., Leeming, F., & Biedenbach, S. (2009). Deep learning and emotion in serious games. In: Ritterfeld, U., Cody, M., Vorderer, P. (Eds.), Serious Games, Mechanisms and Effects. Routledge, New York and London.
Größler, A., Thun, J. H., & Milling, P. M. (2008). System dynamics as a structural theory of strategic issues in operations management. Production and Operations Management, 17(3), 373-384.
Grunert, K. G., & Grunert, S. C. (1995). Measuring subjective meaning structures by the laddering method: Theoretical considerations and methodological problems. International Journal of Research in Marketing, 12(3), 209-225.
Gutman, J. (1982). A means-end chain model based on consumer categorization processes. Journal of Marketing, 46(2), 60-72.
Gutman, J. (1997). Means-end chains as goal hierarchies. Psychology & Marketing, 14(6), 545-560.
Hannah, B., & Lybecker, K. M. (2010). Determinants of recent online purchasing and the percentage of income spent online. International Business Research, 3(4), 60-71.
Hernández, A. B., Gorjup, M. T., & Cascón, R. (2010). The role of the instructor in business games: A comparison of face-to-face and online instruction. International Journal of Training and Development, 14(3), 169-179.
Hernández-Lara, A. B., Perera-Lluna, A., & Serradell-López, E. (2019). Applying learning analytics to students’ interaction in business simulation games. The usefulness of learning analytics to know what students really learn. Computers in Human Behavior, 92, 600-612.
Hernández-Lara, A. B., Serradell-López, E., & Fitó-Bertran, À. (2019). Students’ perception of the impact of competences on learning: An analysis with business simulations. Computers in Human Behavior, 101, 311-31.
Hieber, R., & Hartel, I. (2003). Impacts of SCM order strategies evaluated by simulation-based ‘beer game’ approach: The model, concept, and initial experiences. Produclion Plonning & Conlrol, 14(2), 122- 134.
Hsieh, T. L. (2014). Motivation matters? The relationship among different types of learning motivation, engagement behaviors and learning outcomes of undergraduate students in Taiwan. Higher Education, 68(3), 417-433.
Huang, W. H. (2011). Evaluating learners’ motivational and cognitive processing in an online game-based learning environment. Computers in Human Behavior, 27(2), 694-704.
Hwang, G. J., Wu, P. H., & Chen, C. C. (2012). An online game approach for improving students’ learning performance in web-based problem-solving activities. Computers & Education, 59(4), 1246-1256.
Hwang, G. J., Yang, L. H., & Wang, S. Y. (2013). A concept map-embedded educational computer game for improving students’ learning performance in natural science courses. Computers & Education, 69, 121-130.
Im, S., Bhat, S., & Lee, Y. (2015). Consumer perceptions of product creativity, coolness, value and attitude. Journal of Business Research, 68(1), 166-172.
Isabelle, D. A. (2020). Gamification of entrepreneurship education. Decision Sciences Journal of Innovative Education, 18(2), 203-223.
Jafari-Sadeghi, V., Garcia-Perez, A., Candelo, E., & Couturier, J. (2021). Exploring the impact of digital transformation on technology entrepreneurship and technological market expansion: The role of technology readiness, exploration and exploitation. Journal of Business Research, 124, 100-111.
Jung, Y., & Kang, H. (2010). User goals in social virtual worlds: A means-end chain approach. Computers in Human Behavior, 26(2), 218-225.
Jurgelaitis, M., Čeponienė, L., Čeponis, J., & Drungilas, V. (2019). Implementing gamification in a university-level UML modeling course: A case study. Computer applications in Engineering Education, 27(2), 332-343.
Kahle, L. R. (1983). Social values and social changes: Adaption to life in America. New York, NY: Praeger.
Kaliappen, N. (2019). Educational benefits of using business strategy game (BSG) in teaching and learning strategic management. International Journal of Emerging Technologies in Learning, 14(7), 209-215.
Kao, G. Y. M., Tsai, C. C., Liu, C. Y., & Yang, C. H. (2016). The effects of high/low interactive electronic storybooks on elementary school students’ reading motivation, story comprehension and chromatics concepts. Computers & Education, 100, 56-70.
Katsaliaki, K., Mustafee, N., & Kumar, S. (2014). A game-based approach towards facilitating decision making for perishable products: An example of blood supply chain. Expert Systems with Applications, 41(9), 4043-4059.
Kent, C., Laslo, E., & Rafaeli, S. (2016). Interactivity in online discussions and learning outcomes. Computers & Education, 97, 116-128.
Kiili, K., de Freitas, S., Arnab, S., & Lainema, T. (2012). The design principles for flow experience in educational games. Procedia Computer Science, 15, 78-91.
Kimbrough, S. O., Wu, D. J., & Zhong, F. (2002). Computers play the Beer Game: Can artificial agents manage supply chains? Decision Support Systems, 33(3), 323-333.
Klenosky, D. B. (2002). The “Pull” of tourism destinations: A means-end investigation. Journal of Travel Research, 40(4), 385-395.
Kolbe, R. H., & Burnett, M. S. (1991). Content-analysis research: An examination of applications with directives for improving research reliability and objectivity. Journal of Consumer Research, 18(2), 243-250.
Korat, O., & Shamir, A. (2008). The educational electronic book as a tool for supporting children’s emergent literacy in low versus middle SES Groups. Computers & Education, 50(1), 110-124.
Korat, O., Levin, I., Atishkin, S., & Turgeman, M. (2014). E-book as facilitator of vocabulary acquisition: Support of adults, dynamic dictionary and static. Reading and Writing, 27(4), 613-629.
Krippendorff, K. (2012). Commentary: A dissenting view on so-called paradoxes of reliability coefficients. In C. T. Salmon (eds.), Communication Yearbook 36 (pp. 481- 499). New York: Routledge.
Lai, K. P., Chong, S. C., Ismail, H. B., & Tong, D. Y. K. (2014). An explorative study of shopper-based salient e-servicescape attributes: A Means-end chain approach. International Journal of Information Management, 34(4), 517-532.
Lam, P., McNaught, C., Lee, J., & Chan, M. (2014). Disciplinary difference in students’ use of technology, experience in using eLearning strategies and perceptions towards eLearning. Computers & Education, 73, 111-120.
Lau, A. K. W. (2015). Teaching supply chain management using a modified beer game: An action learning approach. International Journal of Logistics Research and Applications, 18(1), 62-81.
Lau, K., & Gardner, D. (2019). Disciplinary variations in learning styles and preferences: Implications for the provision of academic English. System, 80, 257-268.
Lee, H. T., & Wu, J. C. (2006). A study on inventory replenishment policies in a two echelon supply chain system. Computers & Industrial Engineering, 51(2), 257-263.
Lee, H., Padmanabhan, V., & Whang, S. (2015). The bullwhip effect in supply chains. IEEE Engineering Management Review, 43(2), 108-117.
Lengyel, P. S (2020). Can the game-based learning come? Virtual classroom in higher education of 21st century. International Journal of Emerging Technologies in Learning, 15(2), 112-126.
Leppard, P., Russell, C. G., & Cox, D. N. (2004). Improving means-end-chain studies by a ranking method to determine hierarchical value maps. Food Quality & Preference, 15(5), 489-497.
Lewis, J. M., Ross, S., & Holden, T. (2012). The how and why of academic collaboration: Disciplinary differences and policy implications. Higher Education, 64(5), 693-708.
Li, Q. (2010). Digital game building: Learning in a participatory culture. Educational Research, 52(4), 427-443.
Li, S., & Lin, B. (2006). Accessing information sharing and information quality in supply chain management. Decision Support Systems, 42(3), 1641-1656.
Liao, Y. W., Huang, Y. M., & Wang, Y. S. (2015). Factors affecting students’ continued usage intention toward business simulation games: An empirical study. Journal of Educational Computing Research, 53(2), 260-283.
Lim, S., & Reeves, B. (2010). Computer agents versus avatars: Responses to interactive game characters controlled by a computer or other player. International Journal of Human-Computer Studies, 68(1-2), 57-68.
Lin, H. H., Yen, W. C., & Wang, Y. S. (2018). Investigating the effect of learning method and motivation on learning performance in a business simulation system context: An experimental study. Computers & Education, 127, 30-40.
Lin, H. W., & Lin, Y. L. (2014). Digital educational game value hierarchy from a learners’ perspective. Computers in Human Behavior, 30, 1-12.
Lin, Y. L., & Tu, Y. Z. (2012). The values of college students in business simulation game: A means-end chain approach. Computers & Education, 58(4), 1160-1170.
Lin, Y. L., Lin, H. W., Yang, Y. T., & Jhan, Y. C. (2014). Console game value hierarchy: A means-end chain approach. European Journal of Social Sciences, 44(1), 58-77.
Liu, C. L. (2017). Using a video game to teach supply chain and logistics management. Interactive Learning Environments, 25(8), 1009-1024.
Liu, D., Ma, S., Ru, Q., Guo, Z., & Ma, S. (2009). Design of multi-strategic learning environment based on constructivism. First International Workshop on Education Technology and Computer Science, 2009. ETCS’09. 3, 226-228.
López-Mosquera, N., & Sánchez, M. (2011). The influence of personal values in the economic-use valuation of peri-urban green spaces: An application of the means-end chain theory. Tourism Management, 32(4), 875-889.
Luarn, P., Jhan, Y. C., & Lin, H. W. (2022). A contemporary research on learners’ expectations: Innovative attributes on beer game with means-end chains theory. International Journal of Game-Based Learning, 12(1), 1-22.
Luarn, P., Jhan, Y. C., & Lin, H. W. (in press). Have you spotted where I am? Exploring the impact of different forms of native advertising. International Journal of Mobile Communications.
Macdonald, J. R., Frommer, I. D., & Karaesmen, I. Z. (2013). Decision making in the beer game and supply chain performance. Operations Management Research, 6(3), 119-126.
McColgan, M., Colesante, R., & Andrade, A. (2018). Pre-service teachers learn to teach with serious games. Journal of STEM Education: Innovations and Research, 19(2), 19-25.
Mendoza, G. A. G., & Jung, I. (2021). Understanding immersion in 2D platform-based online collaborative learning environments. Australasian Journal of Educational Technology, 37(1), 57-67.
Miller, L. M., & Cassady, D. L. (2012). Making healthy food choices using nutrition facts panels: The roles of knowledge, motivation, dietary modifications goals, and age. Appetite, 59(1), 129-139.
Mitchell, A., & Savill-Smith, C. (2004). The use of computer and video games for learning: A review of the literature. London, UK: Learning and Skills Development Agency.
Moore, T., & Morton, J. (2017). The myth of job readiness? Written communication, employability, and the ‘skills gap’ in higher education. Studies in Higher Education, 42(3), 591-609.
Morecroft, J. D., & Sterman, J. D. (2000). Modeling for learning organizations. Portland, OR: Productivity Press.
Nacke, L., & Lindley, C. A. (2008). Flow and immersion in first-person shooters: Measuring the p1ayer’s gamep1ay experience. In Proceedings of the Conference on Future Play (pp. 81-88). Toronto, Ontario, Canada: ACM.
Nelson, B., & Erlandson, B. (2008). Managing cognitive load in educational multiuser virtual environments: Reflection on design practice. Educational Technology Research and Development, 56(5-6), 619-641.
Neuendorf, K. A. (2002). The content analysis guidebook. Thousand Oaks, CA: Sage.
Nienhaus, J., Ziegenbein, A., & Schoensleben, P. (2006). How human behaviour amplifies the bullwhip effect. Production Planning & Control: The Management of Operations, 17(6), 547-557.
OECD (2019). Economic outlook for Southeast Asia, China and India 2020: Rethinking education for the digital era. https://read.oecd-ilibrary.org/development/economic-outlook-for-southeast-asia-china-and-india-2020_ff309743-en#page1
Okay-Somerville, B., & Scholarios, D. (2017). Position, possession or process? Understanding objective and subjective employability during university-to-work transitions. Studies in Higher Education, 42(7), 1275-1291.
Olson, J. C., & Reynolds, T. J. (1983). Understanding consumers’ cognitive structures: Implications for marketing strategy, in advertising and consumer psychology. LL. Percy and A. G. Woodside (eds.), Lexington, Mass: Lexington Books.
Ortiz-Martinez, E., Santos-Jaen, J. M., & Palacios-Manzano, M. (2022). Games in the classroom? Analysis of their effects on financial accounting marks in higher education. The International Journal of Management Education, 20(1), 100584.
Padgett, D. K. (2008). Qualitative methods in social work research (2nd Eds.). Los Angeles, CA: SAGE.
Pai, P., & Arnott, D. C. (2013). User adoption of social networking sites: Eliciting uses and gratifications through a means-end approach. Computers in Human Behavior, 29(3), 1039-1053.
Pando-Garcia, J., Periañez-Cañadillas, I., & Charterina, J. (2016). Business simulation games with and without supervision: An analysis based on the TAM model. Journal of Business Research, 69(5), 1731-1736.
Papadimitriou, S., Kamitsios, M., Chrysafiadi, K., & Virvou, M. (2021). Learn-and-play personalised reasoning from point-and-click to virtual reality mobile educational games. IIntelligent Decision Technologies-Netherlands, 15(2), 321-332.
Park, J., Yoon, Y., & Lee, B. (2009). The effect of gender and product categories on consumer online information search. Advances in Consumer Research, 36, 362-366.
Peter, J. P., & Olson, J. C. (2010). Consumer behavior & marketing strategy (9th Eds.). New York, NY: McGraw-Hill.
Peter, J., & Lauf, E. (2002). Reliability in cross-national content analysis. Journalism & Mass Communication Quarterly, 79(4), 815-832.
Pike, S. (2012). Destination positioning opportunities using personal values elicited through the repertory test and laddering analysis. Tourism Management, 33(1), 100-107.
Prensky, M. (2007). Digital game-based learning. New York, NY: McGraw-Hill.
Prensky, M. (2008). Students as designers and creators of educational computer games: Who else? British Journal of Educational Technology, 39(6), 1004-1019.
Proserpio, L., & Gioia, D. A. (2007). Teaching the virtual generation. Academy of Management Learning & Education, 6(1), 69-80.
Rao, Z. (2005). Gender, academic major, and Chinese students’ use of language learning strategies: Social and educational perspectives. The Journal of Asia TEFL, 2(3), 115-138.
Reynolds, T. J., & Gutman, J. (1988). Laddering theory, method, analysis and interpretation. Journal of Advertising Research, 28(1), 11-31.
Reynolds, T. J., Dethloff, C., & Westberg, S. J. (2001). “Advancements in laddering”, understanding consumer decision making: The means-end approach to marketing and advertising strategy. Mahwah, NJ: Lawrence Erlbaum Associates.
Reynolds, T. J., Gengler, C. E., & Howard, D. (1995). A means-end analysis of brand persuasion through advertising. International Journal of Research in Marketing 12(3), 257-266.
Robbins, S. P., & Coulter, M. A. (2021). Management (15th Eds.). New Jersey, NJ: Pearson.
Roderer, T., & Roebers, C. M. (2010). Explicit and implicit confidence judgments and developmental differences in meta-memory: An eye-tracking approach. Metacognition and Learning, 5(3), 229-250.
Rokeach, M. (1968). Beliefs, attitudes, and values: A theory of organization and change. San Francisco: Jossey-Bass.
Rokeach, M. (1973). The nature of human values. New York, NY: The Free Press.
Rosenheck, L., Cheng, M. T., Lin, C. Y., & Klopfer, E. (2021). Approaches to illuminate content-specific gameplay decisions using open-ended game data. Educational Technology Research and Development, 69(2), 1135-1154.
Rubera, G., Ordanini, A., & Mazursky, D. (2010). Toward a contingency view of new product creativity: Assessing the interactive effects of consumers. Marketing Letters, 21(2), 191-206.
Ruiz, J. G., Mintzer, M. J., & Leipzig, R. M. (2006). The impact of e-learning in medical education. Academic Medicine, 81(3), 207-212.
Russo, K., Donnelly, M., & Reid, A. J. M. (2006). Segregation - the perspectives of young patients and their parents. Journal of Cystic Fibrosis, 5(2), 93-99.
Ryu, S. W., & Lee, K. K. (2003). A stochastic inventory model of dual sourced supply chain with lead-time reduction. International Journal of Production Economics, 81-2, 513-524.
Sarkar, B. B., & Chaki, N. (2012). A distributed retail beer game for decision support system. International Congress on Interdisciplinary Business and Social Sciences (ICIBSoS). Symposium conducted at the meeting of Jakarta, INDONESIA.
Schwartz, J. D., Wang, W., & Rivera, D. E. (2006). Simulation-based optimization of process control policies for inventory management in supply chains. Automatica, 42(8), 1311-1320.
Secundo, G., Mele, G., Vecchio, P. D., Elia, G., Margherita, A., & Ndou, V. (2021). Threat or opportunity? A case study of digital-enabled redesign of entrepreneurship education in the COVID-19 emergency. Technological Forecasting and Social Change, 166, 120565
Sermersheim, K. L., & Keim, M. C. (2005). Mid-level student affairs managers: Skill Importance and need for continued professional development. College Student Affairs Journal, 25(1), 36-49.
Shaltayev, D. (2021). Mixed-integer linear programming optimization for the supply chain game. Decision Sciences Journal of Innovative Education, 19(4), 250-264.
Sheu, J. B. (2005). A multi-layer demand-responsive logistics control methodology for alleviating the bullwhip effect of supply chains. European Journal of Operational Research, 161(3), 797-811.
Shih, B. Y., Chen, T. H., Cheng, M. H., Chen, C. Y., & Chen, B. W. (2013). How to manipulate interactive E-book on learning natural catastrophe-An example of structural mechanics using power machine. Natural Hazards, 65(3), 1637-1652.
Shin, D. (2019). How does immersion work in augmented reality games? A user-centric view of immersion and engagement. Information, Communication & Society, 22(9), 1212-1229.
Shovityakool, P., Jittam, P., Sriwattanarothai, N., & Laosinchai, P. (2019). A flexible supply chain management game. Simulation & Gaming, 50(4), 461-482.
Siddiqui, A., Khan, M., & Akhtar, S. (2008). Supply chain simulator: A scenario-based educational tool to enhance student learning. Computers & Education, 51(1), 252-261.
Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2000). Designing and managing the supply chain. Concepts, strategies, and case studies. New York, NY: McGraw-Hill.
Sitzmann, T. (2011). A meta-analytic examination of the instructional effectiveness of computer-based simulation games. Personnel Psychology, 64(2), 489-528.
Smeets, D. J. H., & Bus, A. G. (2015). The interactive animated e-book as a word learning device for kindergartners. Applied Psycholinguistics, 36(4), 899-920.
Smyth, L., Mavor, K. I., & Platow, M. J. (2017). Learning behaviour and learning outcomes: The roles for social influence and field of study. Social Psychology of Education: International Journal, 20(1), 69-95.
Song, J. M., Park, A., & Zhao, Y. (2021). The Hunger Chain: A competitive simulation for teaching supplychain management. Decision Sciences Journal of Innovative Education, 19(3), 204-213.
Srinivasan, A., & Venkatraman, N. (2018). Entrepreneurship in digital platforms: A network centric view. Strategic Entrepreneurship Journal, 12(1), 54-71.
Stanton, W. J., Etzel, M. J., & Walker B. J. (1991). Fundamentals of marketing. New York, NY: McGraw-Hill.
Sterman, J. D. (1989). Modeling managerial behaviour: Misperceptions of feedback in a dynamic decision making experiment. Management Science, 35(3), 321-339.
Stiller, S., Falk, B., Philipsen, R., Brauner, P., Schmitt, R., & Ziefle, M. (2014). A game-based approach to understand human factors in supply chains and quality management. 2nd International Conference on Ramp-Up Management (ICRM), Aachen, GERMANY.
Stouten, H., Heene, A., Gellynck, X., & Polet, H. (2012). Learning from playing with microworlds in policy making: An experimental evaluation in fisheries management. Computers in Human Behavior, 28(2), 757-770.
Strozzi, F., Bosch, J., & Zaldivar, J. M. (2007). Beer game order policy optimization under changing customer demand. Decision Support Systems, 42(2), 2153-2163.
Stummer, C., & Kiesling, E. (2021). An agent-basedmarket simulation for enriching innovation management education. Central European Journal of Operations Research, 29, 143-161.
Surti, C., & Celani, A. (2019). Newsvendor game: A behavioral exercise in decisionmaking under risk. Decision Sciences Journal of Innovative Education, 17(4), 324-343.
Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. New York, NY: Springer.
Tao, Y. H., Yeh, C. R., & Hung, K. C. (2012). Effects of the heterogeneity of game complexity and user population in learning performance of business simulation games. Computers & Education, 59(4), 1350-1360.
Thompson, D., Bhatt, R., Lazarus, M., Cullen, K., Baranowski, J., & Baranowski, T. (2012). A serious video game to increase fruit and vegetable consumption among elementary aged youth (Squire's Quest! II): Rationale, design, and methods. JMIR Research Protocols, 1(2), e19.
Triantafyllakos, G., Palaigeorgiou, G., & Tsoukalas, I. A. (2011). Designing educational software with students through collaborative design games: The We! Design&Play framework. Computers & Education, 56(1), 227-242.
Valette-Florence, P. (1998). A causal analysis of means-end hierarchies in a cross-cultural context: Methodological refinements. Journal of Business Research, 42(2), 161-166.
Valette-Florence, P., & Rapacchi, P. B. (1991). Improvements in means-end chain analysis. Journal of Advertising Research, 31, 30-45.
van den Berg, M., Voordijk, H., Adriaanse, A., & Hartmann, T. (2017). Experiencing supply chain optimizations: A serious gaming approach. Journal of Construction Engineering and Management, 143(11), 04017082.
Vanany, I., & Syamil, A. (2016). Teaching supply chain management using an innovative practical game. International Journal of Information Systems and Supply Chain Management, 9(4), 82-99.
Viberg, O., & Gronlund, A. (2013). Cross-cultural analysis of users’ attitudes toward the use of mobile devices in second and foreign language learning in higher education: A case from Sweden and China. Computers & Education, 69, 169-180.
Villalta, M., Gajardo, I., Nussbaum, M., Andreu, J. J., Echeverría, A., & Plass, J. L. (2011). Design guidelines for classroom multiplayer presential games. Computers & Education, 57(3), 2039-2053.
Wadhwa, S., Bibhushan, & Chan, F. T. S. (2009). Inventory performance of some supply chain inventory policies under impulse demands. International Journal of Production Research, 47(12), 3307-3332.
Walker, B. A., & Olson, J. C. (1991). Means-end chains: Connecting products with self. Journal of Business Research, 22(2), 111-118.
Wang, C., Teo, T. S., Dwivedi, Y., & Janssen, M. (2021). Mobile services use and citizen satisfaction in government: Integrating social benefits and uses and gratifications theory. Information Technology & People, 34(4), 1313-1337.
Woodside, A. G. (2004). Advancing means-end chains by incorporating Heider’s balance theory and Fournier’s consumer-brand relationship typology. Psychology & Marketing, 21(4), 279-294.
Wouters, P., Van Nimwegen, C., Van Oostendorp, H., & Van Der Spek, E. D. (2013). A meta-analysis of the cognitive and motivational effects of serious games. Journal of Educational Psychology, 105(2), 249-265.
Yen, W. C., & Lin, H. H. (2020). Investigating the effect of flow experience on learning performance and entrepreneurial self-efficacy in a business simulation systems context. Interactive Learning Environments.
Yin, R. K. (2015). Qualitative research from start to finish (2nd eds.). New York, NY: The Guilford Press.
Ylonen, A., Gil lespie, H., & Green, A. (2018). Disciplinary differences and other variations in assessment cultures in higher education: Exploring variability and inconsistencies in one university in England. Assessment & Evaluation in Higher Education, 43(6), 1009-1017.
Zaman, B., Geurden, K., De Cock, R., De Schutter, B., & Vanden Abeele, V. (2014). Motivation profiles of online Poker players and the role of interface preferences: A laddering study among amateur and (semi-) professionals. Computers in Human Behavior, 39, 154-164.
Zulfiqar, S., Sarwar, B., Aziz, S., Chandia, K. E., & Khan, M. K. (2018). An analysis of influence of business simulation games on business school students’ attitude and intention toward entrepreneurial activities. Journal of Educational Computing, 57(1), 1-25.