스마트팩토리 개인 맞춤형 제품의 가격, 품질, 서비스의 소비자 인식 및 선호도에 관한 탐색적 연구
Copyright 2011 THE KOREAN ACADEMIC SOCIETY OF BUSINESS ADMINISTRATION
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
In addition to technological development, the key to the success of personalized production is to increase customer acceptance by considering them as key strategic factors. However, there is a lack of attempts to approach smart factory from the end-user's perspective, particularly with regard to personalized products that may be more expensive, of lower quality, and have lower customer service levels than existing products. These practical limitations of personalized products may be perceived as a risk by consumers. This study examines consumers' perception of price, quality, and consumer service, as well as their preference for personalized products and types of customization. The results indicate that consumers' willingness to pay a premium for personalized products was found, while their tolerance for quality reduction was extremely low, and their tolerance for service degradation was low. In addition, it was found that there was a preference for personalized products, and among the three customized types, option selection customized types were the most preferred. This study provides practical implications by analyzing realistic factors that companies should consider when supplying personalized products.
Keywords:
Smart Factory, Personalized Products, Consumer Perceptions, PreferenceAcknowledgments
This research is supported by the Institute of Management Research at Seoul National University.
References
- Bei, L. and Chiao, Y. (2006), “The determinants of customer loyalty: An analysis of intangible factors in three service industries,” International Journal of Commerce and Management, 16(3&4), pp.162-177. [https://doi.org/10.1108/10569210680000215]
- Büchi, G., Cugno, M., and Castagnoli, R. (2020), “Smart factory performance and Industry 4.0,” Technological Forecasting and Social Change, 150, pp.119790 [https://doi.org/10.1016/j.techfore.2019.119790]
- Chen, B., Wan, J., Shu, L., Li, P., Mukherjee, M. and Yin, B. (2018), “Smart factory of industry 4.0: Key technologies, application case, and challenges,” IEEE Access, 6, pp.6505-6519. [https://doi.org/10.1109/ACCESS.2017.2783682]
- Cho, E. and Sundar, S. S. (2022), “How do we like our online dates—customized or personalized? The differential effects of user vs. system tailoring on date preferences,” Computers in Human Behavior, 127, pp.107037. [https://doi.org/10.1016/j.chb.2021.107037]
- Choi, Y.H. and S. H. Choi (2017), “A Study on the Factors Influencing the Competitiveness of Small and Medium Companies Applied with Smart Factory System,” Information Systems Review, 19(2), pp.95-113. [https://doi.org/10.14329/isr.2017.19.2.095]
- Chong, H. R., K.. H. Bae, M. K. Lee, H. M. Kown, and S. H. Hong (2020), “Quality Strategy for Building a Smart Factory in the Fourth Industrial Revolution,” Journal of the Korean Society for Quality Management, 48(1), pp.87-105.
- Conchar, M. P., Zinkhan, G. M., Peters, C. and Olavarrieta, S. (2004), “An integrated framework for the conceptualization of consumers’ perceived-risk processing,” Journal of the Academy of Marketing Science, 32(4), pp.418-436. [https://doi.org/10.1177/0092070304267551]
- Dayal, S., Landesberg, H. and Zeisser, M. (1999), “How to build trust online,” Marketing Management, 8(3), pp.64-71.
- Doug, R. (2000), “Personalized Views of Personalization,” Communications of the ACM, 43(8), pp.26-158. [https://doi.org/10.1145/345124.345133]
- Fei, T., Jiangfeng, C., Qinglin, Q., Zhang, M., Zhang, H. and Fangyuan, S. (2018), “Digital twin-driven product design, manufacturing and service with big data,” The International Journal of Advanced Manufacturing Technology, 94(9-12), pp.3563-3576. [https://doi.org/10.1007/s00170-017-0233-1]
- Fornell, C., and Larcker, D. F.(1981), “Evaluating Structural Equation Models with Unobservable Variables and Measurement Error,” Journal of Marketing Research, 18(1), pp.39-50. [https://doi.org/10.1177/002224378101800104]
- Gu, X. and Koren, Y. (2018), “Manufacturing system architecture for cost-effective mass-individualization,” Manufacturing letters, 16(11), pp.44-48. [https://doi.org/10.1016/j.mfglet.2018.04.002]
- HARMAN, H. H. (1967). Modern factor analysis (2nd ed.). Chicago: University of Chicago Press.
- Henseler, J., Ringle, C. M. and Sarstedt, M. (2015), “A new criterion for assessing discriminant validity in variance-based structural equation modeling,” Journal of the Academy of Marketing Science, 43(1), pp.115-135. [https://doi.org/10.1007/s11747-014-0403-8]
- Hoh, J. and A. R. Lee (2020), “Investigating Key Security Factors in Smart Factory: Focusing on Priority Analysis Using AHP Method,”, Information Systems Review, 22(4), pp.185-203. [https://doi.org/10.14329/isr.2020.22.4.185]
- Huang, X., and Zhang, D. (2020), “Service product design and consumer refund policies,” Marketing Science, 39(2), pp.366-381. [https://doi.org/10.1287/mksc.2019.1204]
- Kim, G. M., and M. J. Nam (2021), “The Success of Smart Factory Adoption: Firm’s Dynamic Capability Perspective,” Journal of Information Technology Applications and Management, 28(4), pp.45-57.
- Kim, H. R., and M. K. Lee (2002), “Consumer Reactions to the Internet Service Personalization,” Yonsei Business Review, 39(2), pp.153-180.
- Kim, J. Y., and E. Y. Rhee (2004), “ The influence of Service Quality, Product Quality, Price on Store Patronage for Apparel Stores,” Journal of the Korean Society of Clothing and Textiles, 28(1), pp.12-21.
- Kim, T. J. and J. Y. Kang (2021), “A Study on the Strategic Factors of Speed Factory Robot Cafe Service Development: Evaluation by Unmanned Level,” Korean Management Review, 50(1), pp.53-80. [https://doi.org/10.17287/kmr.2021.50.1.53]
- Ko, K. S., J. J. Huh, and J. I. Oh (2021), “A Study on the Factors that Affect the Adoption of a Smart Factory - Focusing on the Comparison between Customers and Suppliers -,” Korea Business Review, 25(3), pp.129-151. [https://doi.org/10.17287/kbr.2021.25.3.129]
- Kontos, A. P. (2004), “Perceived risk, risk taking, estimation of ability and injury among adolescent sport participants,” Journal of Pediatric Psychology, 29(6), pp.447-455. [https://doi.org/10.1093/jpepsy/jsh048]
- Koren, Y. (2010), The global manufacturing revolution: product-process-business integration and reconfigurable systems, John Wiley & Sons, New Jersey, NY. [https://doi.org/10.1002/9780470618813]
- Lampel, J. and Mintzberg, H. (1996), “Customizing customization,” Sloan Management Review, 38(1), pp.21-30.
- Laplume, A., Anzalone, G. C., and Pearce, J. M. (2016), “Open-source, self-replicating 3-D printer factory for small-business manufacturing,” The International Journal of Advanced Manufacturing Technology, 85(1), pp.633-642. [https://doi.org/10.1007/s00170-015-7970-9]
- Lee, D. Y., J. S. Yun, S. G. Lee (2017), “Value and utilization of manufacturing data,” Journal of the KSME, 57(8), pp.49-53.
- Lee, S. J, B. K. Lim, K. R. Park, and J. C. Park (2018), Smart Factory Operation and Strategy, Seoul, Hanol Press.
- Li, L., Liu, F., and Li, C. (2014), “Customer satisfaction evaluation method for customized product development using Entropy weight and Analytic Hierarchy Process,” Computers & Industrial Engineering, 77, pp.80-87. [https://doi.org/10.1016/j.cie.2014.09.009]
- Li, Q., and Zhou, W. (2017), “The joint decisions of modularity level design and refund price in a two-tier supply chain,” 2017 IEEE International Conference on Industrial Engineering and Engineering Management, pp.1437-1440. [https://doi.org/10.1109/IEEM.2017.8290130]
- Liu, Q., Zhang, H., Leng, J., and Chen, X. (2019), “Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system,” International Journal of Production Research, 57(12), pp.3903-3919. [https://doi.org/10.1080/00207543.2018.1471243]
- Luo, X., Li, H., Zhang, J., & Shim, J. P. (2010), “Examining multi-dimensional trust and multi-faceted risk in initial acceptance of emerging technologies: An empirical study of mobile banking services,” Decision Support Systems, 49(2), pp.222-234. [https://doi.org/10.1016/j.dss.2010.02.008]
- Mabkhot, M. M., Al-Ahmari, A. M., Salah, B., and Alkhalefah, H. (2018), “Requirements of the smart factory system: A survey and perspective,” Machines, 6(2), pp.23. [https://doi.org/10.3390/machines6020023]
- Mittal, B. and Lassar, W. M. (1996), “The role of personalization in service encounters,” Journal of Retailing, 72(1), pp.95-109. [https://doi.org/10.1016/S0022-4359(96)90007-X]
- Moon, J., Chadee, D., and Tikoo, S. (2008), “Culture, product type, and price influences on consumer purchase intention to buy personalized products online,” Journal of Business Research, 61(1), pp.31-39. [https://doi.org/10.1016/j.jbusres.2006.05.012]
- Netemeyer RG, Krishnan B, Pullig C, Wang G, Yagci M, Dean D, Ricks J, Wirth F. (2004), “Developing and validating measures of facets of customer-based brand equity,” Journal of Business Research, 57(2), pp.209-224. [https://doi.org/10.1016/S0148-2963(01)00303-4]
- Oh, J. H., and J. D. Kim (2019), “A Study on Strategic Utilization of Smart Factory: Effects of Building Purposes and Contents on Continuous Utilization,” Asia Pacific Journal of Samall Business, 41(4), pp.1-36. [https://doi.org/10.36491/APJSB.41.4.1]
- Osterrieder, P., Budde, L., and Friedli, T. (2020), “The smart factory as a key construct of industry 4.0: A systematic literature review,” International Journal of Production Economics, 221, pp.107476. [https://doi.org/10.1016/j.ijpe.2019.08.011]
- Parasuraman, A., Zeithaml, V. A. and Berry, L. L. (1994), “Reassessment of expectations as a comparison standard in measuring service quality: Implications for further research,” Journal of Marketing, 58(1), pp.111-124. [https://doi.org/10.1177/002224299405800109]
- Park, J. S., and J. W. Kang (2020), “Smart Factory Policy Measures for Promoting Manufacturing Innovation,” Asia Pacific Journal of Small Business, 42(2), pp.117-137. [https://doi.org/10.36491/APJSB.42.2.6]
- Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., and Podsakoff, N. P. (2003), “Common method biases in behavioral research: a critical review of the literature and recommended remedies,” Journal of Applied Psychology, 88(5), 879-903. [https://doi.org/10.1037/0021-9010.88.5.879]
- Rezaei, M., Shirazi, M. A. and Karimi, B. (2017), “IoT-based framework for performance measurement: A real-time supply chain decision alignment,” Industrial Management & Data Systems, 117(4), pp.688-712 [https://doi.org/10.1108/IMDS-08-2016-0331]
- Roselius, T. (1971), “Consumer rankings of risk reduction methods,” Journal of Marketing, 35(1), pp.56-61. [https://doi.org/10.1177/002224297103500110]
- Rosenbaum, M. S., Ramirez, G. C., Campbell, J. and Klaus, P. (2019), “The product is me: Hyper-personalized consumer goods as unconventional luxury,” Journal of Business Research, 129(40), pp.446-454. [https://doi.org/10.1016/j.jbusres.2019.05.017]
- Sevilla, J., and Townsend, C. (2016), “The space-to-product ratio effect: How interstitial space influences product aesthetic appeal, store perceptions, and product preference,” Journal of Marketing Research, 53(5), pp.665-681. [https://doi.org/10.1509/jmr.13.0601]
- Shi, Z., Xie, Y., Xue, W., Chen, Y., Fu, L., and Xu, X. (2020), “Smart factory in Industry 4.0,” Systems Research and Behavioral Science, 37(4), pp.607-617. [https://doi.org/10.1002/sres.2704]
- Shih, H. P. (2004), “An empirical study on predicting user acceptance of e-shopping on the Web,” Information & Management, 41(3), pp.351-368. [https://doi.org/10.1016/S0378-7206(03)00079-X]
- Song, Z., Sun, Y., Wan, J., and Liang, P. (2017), “Data quality management for service-oriented manufacturing cyber-physical systems,” Computers and Electrical Engineering, 64, pp.34-44. [https://doi.org/10.1016/j.compeleceng.2016.08.010]
- Suh, W. J., C. S. Seo, J. W. Hong, and Z. L. Su (2007), “An Empirical Study on the Mediation Effects of Satisfaction and Trust between Quality and Purchasing Intention in Chinese Internet Shopping Malls,” The e-Business Studies, 8(2), pp.33-59. [https://doi.org/10.15719/geba.8.2.200706.33]
- Sundar, S. S. and Marathe, S. S. (2010), “Personalization versus customization: The importance of agency, privacy, and power usage,” Human Communication Research, 36(3), pp.298-322. [https://doi.org/10.1111/j.1468-2958.2010.01377.x]
- Surprenant, C. F. and Solomon, M. R. (1987), “Predictability and personalization in the service encounter,” Journal of Marketing, 51(2), pp.86-96. [https://doi.org/10.1177/002224298705100207]
- Tam, K. Y., and Ho, S. Y. (2006), “Understanding the impact of web personalization on user information processing and decision outcomes,” MIS Quarterly, 30(4), pp.865-890. [https://doi.org/10.2307/25148757]
- Trenz, M., Veit, D. J., and Tan, C. W. (2020), “Disentangling the impact of omnichannel integration services on consumer behavior in integrated sales channels,” MIS Quarterly, 44(3), pp.1207-1258 [https://doi.org/10.25300/MISQ/2020/14121]
- Wang, S., Ouyang, J., Li, D., and Liu, C. (2017), “An integrated industrial ethernet solution for the implementation of smart factory,” IEEE Access, 5, 25455-25462 [https://doi.org/10.1109/ACCESS.2017.2770180]
- Wang, S., Wan, J., Li, D. and Zhang, C. (2016), “Implementing smart factory of industrie 4.0: an outlook,” International Journal of Distributed Sensor Networks, 12(1), pp.1-10. [https://doi.org/10.1155/2016/3159805]
- Wang, S., Wan, J., Zhang, D., Li, D. and Zhang, C. (2016), “Towards smart factory for industry 4.0: A self-organized multi-agent system with big data based feedback and coordination,” Computer Networks, 101(13), pp.158-168. [https://doi.org/10.1016/j.comnet.2015.12.017]
- Weller, C., Kleer, R. and Piller, F. T. (2015), “Economic implications of 3D printing: Market structure models in light of additive manufacturing revisited,” International Journal of Production Economics, 164(4), pp.43-56. [https://doi.org/10.1016/j.ijpe.2015.02.020]
- Woo, S. H., and S. D. Kwon (2019), “A Study on Personalized Product Demand Manufactured by Smart Factory,” Management & Information Systems Review, 38(1), pp.23-41. [https://doi.org/10.29214/damis.2019.38.1.002]
- Wuest, T., Irgens, C. and Thoben, K. D. (2014), “An approach to monitoring quality in manufacturing using supervised machine learning on product state data,” Journal of Intelligent Manufacturing, 25(5), pp.1167-1180. [https://doi.org/10.1007/s10845-013-0761-y]
- Xu, X. and Jackson, J. E. (2019), “Investigating the influential factors of return channel loyalty in omni-channel retailing,” International Journal of Production Economics, 216, pp.118-132. [https://doi.org/10.1016/j.ijpe.2019.03.011]
- Xu, X., and Hua, Q. (2017), “Industrial big data analysis in smart factory: Current status and research strategies,” IEEE Access, 5, 17543-17551. [https://doi.org/10.1109/ACCESS.2017.2741105]
- Yoon, Y. S., J. H. Lee., H. W. Oh., and K. R. Park (2020), “Development Direction of Smart Factory for Mass Customization Based On Investigating Adidas SpeedFactory Closure,” The Journal of Korean Institute of Communications and Information Sciences, 45(11), pp.1980-1993. [https://doi.org/10.7840/kics.2020.45.11.1980]
- Yunita, D. and Ali, H. (2017), “Model of purchasing decision (renting) of generator set: Analysis of product quality, price an service at PT. Hartekprima Listrindo,” Scholars Journal of Economics, Business and Management, 4(11), pp.833-841.
- Zeithaml, V. A., Berry, L. L., and Parasuraman, A. (1996), “The behavioral consequences of service quality,” Journal of marketing, 60(2), pp.31-46 [https://doi.org/10.1177/002224299606000203]
- Zhang, R., Li, J., Huang, Z., and Liu, B. (2019), “Return strategies and online product customization in a dual-channel supply chain,” Sustainability, 11(12), 3482. [https://doi.org/10.3390/su11123482]
∙ The author Suhan Woo is a Ph.D. candidate in Management Information Systems at Seoul National University. His research interests focus on digital business strategy and digital transformation.
∙ The author Sundong Kwon is a professor in the Department of Management Information Systems at Chungbuk National University. He received his Ph.D. in MIS major from Seoul National University. He has published papers in journals such as British Journal of Management, Asia Pacific Journal of Information Systems, Information Systems Review, Journal of Information Technology Application and Management, and Korean Management Review. His interests include Smart Factory and Machine Learning/Deep Learning-based data management.
∙ The author JungJoo (JJ) Jahng is a professor of Information Systems at the College of Business School, Seoul National University. He received a B.S. degree in business administration and Master of Business Administration (MBA) from Seoul National University, and a Ph.D. degree in management information systems from the University of Wisconsin-Milwaukee. His research interests are in the domains of digital business strategy, and digital transformation. His research has appeared in a number of journals such as IEEE Transactions on Systems, Man, and Cybernetics, the European Journal of Information Systems, the Journal of Information Technology, and the E-Service Journal.