近年來，消費者已經將其對產品功能的關注轉移到可以從產品中獲得的價值。由於這種趨勢，許多企業開始開發產品服務系統（Product Service System, PSS），該系統不僅為客戶提供有形產品，而且還提供無形服務。此外，隨著智慧型手機的日益普及，服務提供者可以使用智慧化產品服務系統（Smart PSS）根據使用者產生的資料向其提供客製化服務。
儘管許多研究對智慧化產品服務系統框架進行了廣泛的討論，但很少有研究將客戶視為主動的資料生產者，也就是說客戶主動提供資料給產品服務系統。且大部分研究都是提出通用的智慧化產品服務系統而不是個人化的服務。為了縮小研究差距，本研究提出了一個智慧化產品服務系統框架，該框架包括：（1）用於判斷客戶需求的自然語言處理（Natural Language Processing, NLP）方法。 （2）具備機器學習的推薦系統。因此，客戶已不只是個服務接受者，更是資料生產者，且其將與服務提供者形成價值共創過程。這項研究的主要貢獻是一個個人化的智慧化產品服務系統設計框架，它可以為計程車業者，遊客和景點帶來雙贏的局面。此外，為驗證所提出的框架，本研究提供了詳細的案例研究。

Nowadays, consumers have shifted their focus on product functionality to the value that can be derived from products. Due to this trend, companies start to develop a product service system (PSS), which not only provides customers with tangible products, but also intangible services. Additionally, with the increasing popularity of smart phones, service providers can provide customized services to customers based on user-generated data with Smart Product Service System (Smart PSS).
Despite extensive research on Smart PSS framework, few of these frameworks treated customer as an active data producer. Additionally, most of them proposed a general solution instead of a personalized one. To bridge the research gap, this study proposed a framework that includes: (1) natural language processing (NLP) methods to analyze user-provided data. (2) a recommendation system integrating deep learning to offer customers with personalized solutions. The main contribution of this research is a personalized smart PSS design framework which could make a win-win situation for taxi operators, tourists and attractions. Additionally, to validate the proposed framework, this study provided a detailed case study.

Abramovici, M., Aidi, Y., Quezada, A., & Schindler, T. (2014). PSS sustainability assessment and monitoring framework (PSS-SAM)–case study of a multi-module PSS solution. Procedia CIRP, 16, 140-145.
Baines, T. S., Lightfoot, H. W., Evans, S., Neely, A., Greenough, R., Peppard, J., ... & Alcock, J. R. (2007). State-of-the-art in product-service systems. Proceedings of the Institution of Mechanical Engineers, Part B: journal of engineering manufacture, 221(10), 1543-1552.
Bangor, A., Kortum, P., & Miller, J. (2009). Determining what individual SUS scores mean: Adding an adjective rating scale. Journal of usability studies, 4(3), 114-123.
Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. Jama, 319(13), 1317-1318.
Chang, D., Gu, Z., Li, F., & Jiang, R. (2019). A user-centric smart product-service system development approach: A case study on medication management for the elderly. Advanced Engineering Informatics, 42, 100979.
Chen, X. W., & Lin, X. (2014). Big data deep learning: challenges and perspectives. IEEE access, 2, 514-525.
Chiu, M. C., Chu, C. Y., & Chen, C. C. (2018). An integrated product service system modelling methodology with a case study of clothing industry. International Journal of Production Research, 56(6), 2388-2409.
Chiu, M. C., Chu, C. Y., & Kuo, T. C. (2019). Product service system transition method: building firm’s core competence of enterprise. International Journal of Production Research, 1-21.
Chiu, M-C., Kuo, M-Y., and Kuo, T-C. (2015) A Systematic Methodology To Develop Business Model of a Product Service System. International Journal of Industrial Engineering: Theory, Applications and Practice 22(3), 369-381.
Chiu, M. C., Tsai, C. D., & Li, T. L. (2020). An Integrative Machine Learning Method to Improve Fault Detection and Productivity Performance in a Cyber-Physical System. Journal of Computing and Information Science in Engineering, 20(2).
Chiu, M. C., & Tsai, C. H. (2020). Design a personalised product service system utilising a multi-agent system. Advanced Engineering Informatics, 43, 101036.
Chowdhury, S., Haftor, D., & Pashkevich, N. (2018). Smart Product-Service Systems (Smart PSS) in Industrial Firms: A Literature Review. In 10th CIRP Conference on Industrial Product-Service Systems, IPS2 2018, 29-31 May 2018, Linköping, Sweden (Vol. 73, pp. 26-31).
Demirkan, H., Bess, C., Spohrer, J., Rayes, A., Allen, D., & Moghaddam, Y. (2015). Innovations with Smart Service Systems: Analytics, Big Data, Cognitive Assistance, and the Internet of Everything. CAIS, 37, 35.
Deng, L., & Yu, D. (2014). Deep learning: methods and applications. Foundations and Trends® in Signal Processing, 7(3–4), 197-387.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... & Dean, J. (2019). A guide to deep learning in healthcare. Nature medicine, 25(1), 24.
Fargnoli, M., & Haber, N. (2019). A practical ANP-QFD methodology for dealing with requirements’ inner dependency in PSS development. Computers & Industrial Engineering, 127, 536-548.
Fayers, P.M. and Machin, D. (2007) Quality of Life, the Assessment, Analysis and Interpretation of Patient-Reported Outcomes. 2nd Edition, Wiley, Chichester.
Geum, Y., Lee, S., Kang, D., & Park, Y. (2011). Technology roadmapping for technology-based product–service integration: A case study. Journal of Engineering and Technology management, 28(3), 128-146.
Goedkoop, M. J., Van Halen, C. J., Te Riele, H. R., & Rommens, P. J. (1999). Product service systems, ecological and economic basics. Report for Dutch Ministries of environment (VROM) and economic affairs (EZ), 36(1), 1-122.
Guillon, D., Ayachi, R., Vareilles, É., Aldanondo, M., Villeneuve, É., & Merlo, C. (2020). Product⋎ service system configuration: a generic knowledge-based model for commercial offers. International Journal of Production Research, 1-20. doi: 10.1080/00207543.2020.1714090
Hussain, R., Lockett, H., & Vasantha, G. V. A. (2012). A framework to inform PSS Conceptual Design by using system-in-use data. Computers in Industry, 63(4), 319-327.
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.
Kasey M. Lobaugh, Bobby Stephens, Jeff Simpson. (2019). The consumer is changing, but perhaps not how you think. Retrieved from Deloitte website: https://www2.deloitte.com/us/en/insights/industry/retail-distribution/the-consumer-is-changing.html, viewed on 2020/01/14
Kim, M. J., Lim, C., & Kim, K. J. (2018). A data-driven approach to designing new services for vehicle operations management. International Journal of Industrial Engineering, 25(5).
Kuhlenkötter, B., Wilkens, U., Bender, B., Abramovici, M., Süße, T., Göbel, J., Michael Herzog, Alfred Hypki & Lenkenhoff, K. (2017). New Perspectives for Generating Smart PSS Solutions–Life Cycle, Methodologies and Transformation. Procedia CIRP, 64, 217-222.
Lau, J. H., & Baldwin, T. (2016). An empirical evaluation of doc2vec with practical insights into document embedding generation. arXiv preprint arXiv:1607.05368. Retrieved from: https://arxiv.org/abs/1607.05368, viewed on 2020/01/14
Le, Q., & Mikolov, T. (2014, January). Distributed representations of sentences and documents. In International conference on machine learning (pp. 1188-1196).
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436.
Lee, C. H., Chen, C. H., & Trappey, A. J. (2019). A structural service innovation approach for designing smart product service systems: Case study of smart beauty service. Advanced Engineering Informatics, 40, 154-167.
Lerch, C., & Gotsch, M. (2015). Digitalized product-service systems in manufacturing firms: A case study analysis. Research-Technology Management, 58(5), 45-52.
Lim, C., & Maglio, P. P. (2018). Data-driven understanding of smart service systems through text mining. Service Science, 10(2), 154-180.
Liu, Z., Ming, X., Qiu, S., Qu, Y., & Zhang, X. (2020). A framework with hybrid approach to analyse system requirements of smart PSS toward customer needs and co-creative value propositions. Computers & Industrial Engineering, 139, 105776.
Liu, Z., Ming, X., & Song, W. (2019). A framework integrating interval-valued hesitant fuzzy DEMATEL method to capture and evaluate co-creative value propositions for smart PSS. Journal of cleaner production, 215, 611-625.
Long, H. J., Wang, L. Y., Shen, J., Wu, M. X., & Jiang, Z. B. (2013). Product service system configuration based on support vector machine considering customer perception. International journal of production research, 51(18), 5450-5468.
Längkvist, M., Karlsson, L., & Loutfi, A. (2014). A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognition Letters, 42, 11-24.
Maglio, P. P., & Lim, C. H. (2016). Innovation and big data in smart service systems. Journal of Innovation Management, 4(1), 11-21.
Manzini, E., & Vezzoli, C. (2003). A strategic design approach to develop sustainable product service systems: examples taken from the ‘environmentally friendly innovation’Italian prize. Journal of cleaner production, 11(8), 851-857.
Mont, O. K. (2002). Clarifying the concept of product–service system. Journal of cleaner production, 10(3), 237-245.
Muñoz López, N., Santolaya Sáenz, J. L., Biedermann, A., & Serrano Tierz, A. (2020). Sustainability assessment of product–service systems using flows between systems approach. Sustainability, 12(8), 3415.
Nunnally, J.C. (1978) Psychometric theory. 2nd Edition, McGraw-Hill, New York.
Porter, M. E., & Heppelmann, J. E. (2015). How smart, connected products are transforming companies. Harvard business review, 93(10), 96-114.
Ramos, J. (2003). Using tf-idf to determine word relevance in document queries. In Proceedings of the first instructional conference on machine learning (Vol. 242, pp. 133-142).
Roy, R. (2000). Sustainable product-service systems. Futures, 32(3-4), 289-299.
Song, W., & Cao, J. (2017). A rough DEMATEL-based approach for evaluating interaction between requirements of product-service system. Computers & Industrial Engineering, 110, 353-363.
Tukker, A. (2004). Eight types of product–service system: eight ways to sustainability? Experiences from SusProNet. Business strategy and the environment, 13(4), 246-260.
Valencia, A., Mugge, R., Schoormans, J. P., & Schifferstein, H. N. (2013). Characteristics of Smart PSSs: Design Considerations for Value Creation. In 2nd Cambridge academic design management conference (pp. 351-364).
Valencia Cardona, A. M., Mugge, R., Schoormans, J. P., & Schifferstein, H. N. (2015). The design of smart product-service systems (PSSs): An exploration of design characteristics. International Journal of Design, 9 (1), 2015.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998-6008).
Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018). Recent trends in deep learning based natural language processing. ieee Computational intelligenCe magazine, 13(3), 55-75.
Zheng, P., Lin, T. J., Chen, C. H., & Xu, X. (2018). A systematic design approach for service innovation of smart product-service systems. Journal of Cleaner Production, 201, 657-667.
Zheng, P., Wang, Z., & Chen, C. H. (2019). Industrial smart product-service systems solution design via hybrid concerns. Procedia CIRP, 83, 187-192.
Zheng, P., Wang, Z., Chen, C. H., & Khoo, L. P. (2019). A survey of smart product-service systems: Key aspects, challenges and future perspectives. Advanced Engineering Informatics, 42, 100973.