近年來，全球主要國家力推機場智慧化及自動化。為了提升旅客及航空公司於機場之營運效率，大部份機場已經設置自助報到專區，更積極推動使用自助行李托運櫃檯專區等自助服務科技，以求優化旅客報到流程、提昇通關速率、強化服務品質、滿足機場智能應用需求。然而，旅客對於自助行李托運的接受度和使用頻率，仍有待提昇。接受度與使用頻率過低，勢必影響機場及航空公司未來是否持續建置自助行李托運櫃台。本議題至關重要，但少有研究探討。
因此，本研究擬探討影響旅客使用機場自助行李托運系統之關鍵因素。首先，透過文字探勘技術，蒐集「PPT論壇」相關貼文等，經斷詞後，提取詞彙，再以隱含狄利克雷分佈 (Latent Dirichelet Allocation，LDA) 擷取主題，作為影響旅客使用機場自助行李托運服務之關鍵要素。之後，以技術接受模式為基礎，將關鍵要素歸入理論模型各構面中，並使用偏最小平方法結構方程 (Partial Least Squares Structural Equation Modeling，PLS-SEM)驗證技術接受模式各路徑是否顯著。依據分析結果，感覺有用、感覺易用均為顯著影響旅客使用自助行李托運服務之關鍵要素，本研究結果可供機場或航空公司於規劃設置自助行李托運服務、訂定推廣策略之用，分析架構也可作探討影響消費者接受其他新興運輸科技意圖之用。

In the era of continuous technological advancement, airports around the world are fully promoting airport intelligence and automation services. In order to raise the efficiency of the airport, the self-service check-in kiosks area have been generally set up. Now, airports are actively building self-service technologies such as self-service bag drop and automatic boarding gates. All in order to optimize passenger check-in and customs clearance rates, and strengthen service quality to enhance airport intelligence services. However, passengers' acceptance of self-service bag drop and frequency of use will inevitably affect whether airports and airlines’ implement a large number of self-service bag drop counters in the future.
This research intends to explore the analysis of the key factors of passengers using airport self-service bag drop, and its purpose is to understand the behavioral intention and acceptance of passengers. Use text mining technology to search for related articles and other information in “PPT BBS”. After using JIEBA for word segmentation and extracting keywords, the part-of-speech tagging can make emotional words more quickly found in the analysis process. And performing topic modeling to build themes, the key factors that affect travelers' use of the self-service bag drop are obtained.
After that, the results of the previous analysis are inferred as hypotheses. The text of the proposal is quantified in the form of topic distribution. The statistical method of Partial Least Squares Structural Equation Modeling (PLS-SEM) is used for data analysis. The key elements that affect passengers' use of self-service bag drop services at the airport are obtained, and whether the impact relationship is significant and the relative weight between the evaluation criteria are verified. The research results of this study can be used as a reference for airports or airlines to consider when planning to set up self-service bag drop.

Albers S. (2010). PLS and Success Factor Studies in Marketing. In Esposito Vinzi V., Chin W., Henseler J., Wang H. (Eds), Handbook of Partial Least Squares. Springer Handbooks of Computational Statistics. (Chap. 8, pp. 409-425). Berlin, Germany: Springer
Alghamdi, R., and Alfalqi, K. (2015). A Survey of Topic Modeling in Text Mining. (IJACSA) International Journal of Advanced Computer Science and Applications, 6(1), 147-153
Ali, F., Rasoolimanesh, S.M., Sarstedt, M., Ringle, C.M. and Ryu, K. (2018), An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research. International Journal of ontemporary Hospitality Management, 30(1), 514-538.
Avkiran, N.K. and Ringle, C.M. (2018), Partial Least Squares Structural Equation Modeling: Recent Advances in Banking and Finance. Berlin, CA: Springer.
Bagozzi, R.P., Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Sciences, 16, 74–94.
Bao, Y., Xiong, T., Hu, Z., & Kibelloh, M. (2013). Exploring gender differences on general and specific computer self-efficacy in mobile learning adoption. Journal of Educational Computing Research, 49(1), 111-132.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3(1), 993-1022.
Chakraborty, G., Pagolu, M., & Garla, S. (2014). Text mining and analysis: Practical methods, examples, and case studies using SAS. Cary, NC: SAS Institute.
Chen, J. V., Yen, D. C., & Chen, K. (2009). The acceptance and diffusion of the innovative smart phone use: A case study of a delivery service company in logistics. Information & Management, 46(4), 241-248.
Chen, Y., Zhang, H., Liu, R., Ye, Z., & Lin, J. (2019). Experimental Explorations on Short Text Topic Mining Between LDA and NMF Based Schemes. Knowledge-Based Systems, 163, 1-13.
Cheong, J. H., Park, M. C. (2005). Mobile internet acceptance in Korea. Internet research, 15 (2), 125-140
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295-336.
Chin, W. W. (2010). How to write up and report PLS analyses. In Handbook of Partial Least Squares (pp. 655–690). Berlin, Germany: Springer
Ding, X., Liu, B., & Yu, P. S. (2008). A Holistic Lexicon-based Approach to Opinion Mining. Paper presented at the Proceedings of the 2008 international conference on web search and data mining, New York, United States.
Dijkstra, T. K., Henseler, J. (2015a). Consistent and asymptotically normal PLS estimators for linear structural equations. Computational Statistics & Data Analysis, 81(1), 10-23.
Dijkstra, T. K., Henseler, J. (2015b). Consistent partial least squares pathmodeling. MIS Quarterly, 39(2), 297-316.
Garson, G. D. (2016). Partial Least Squares: Regression and Structural Equation Models. Asheboro, NC: Statistical Associates Publishers.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152.
Hair, J.F., Sarstedt, M., Pieper, T.M. and Ringle, C.M. (2012), The use of partial least squares structural equation modeling in strategic management research: a review of past practices and recommendations for future applications. Long Range Planning, 45(5/6), 320-340.
Hair, J.F.; Sarstedt, M.; Ringle, C.M.; Mena, J.A. (2012), An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40, 414–433.
Hatzivassiloglou, V., & Wiebe, J. M. (2000). Effects of adjective orientation and gradability on sentence subjectivity. In COLING 2000 Volume 1: The 18th International Conference on Computational Linguistics (pp. 299–305). Saarbrucken, Germany: Morgan Kaufmann.
Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems, 116(1), 2-20.
International Air Transport Association. (2019). Report of Air Transport IT Insights. Retrieved from https://www.iata.org/en/publications/store
/global-passenger-survey/
Islam, Z., Kim Cheng Low, P. and Hasan, I. (2013). Intention to use advanced mobile phone services (AMPS). Management Decision, 51(4), 824-838.
Johnson, R. A., & Wichern, D. W. (2002). Applied Multivariate Statistical Analysis (Vol. 6). Upper Saddle River, NJ: Prentice hall.
Karami, A., & Collins, M. (2018). What do the US West Coast public libraries post on Twitter? In Proceedings of the Association for Information Science and Technology (Vol.55, pp. 216-225). Vancouver, Canada: Asis&t.
Kaufmann, L. and Gaeckler, J. (2015). A structured review of partial least squares in supply chain management research. Journal of Purchasing and Supply Management, 21(4), 259-272.
Kay, R. H., & Lauricella, S. (2011). Gender differences in the use of laptops in higher education: A formative analysis. Journal of Educational Computing Research, 44(3), 361-380.
Kibelloh, M., & Bao, Y. (2014). Can online MBA programmes allow professional working mothers to balance work, family, and career progression? A case study in China. The Asia-Pacific Education Researcher, 23(2), 249-259.
Kibelloh, M., & Bao, Y. (2014). Perceptions of international female students toward e-learning in resolving high education and family role strain. Journal of educational computing research, 50(4), 467-487.
Kim, S. G., & Kang, J. (2018). Analyzing the Discriminative Attributes of Products Using Text Mining Focused on Cosmetic Reviews. Information Processing & Management, 54(6), 938-957.
Landis, R. S., Beal, D. J., & Tesluk, P. E. (2000). A comparison of approaches to forming composite measures in structural equation models. Organizational Research Methods, 3(2), 186-207.
McDonald, R. P. (1999). Test Theory: A Unified Treatment. New York, NY: Psychology Press.
Medhat, W., Hassan, A. and Korashy, H. (2014), Sentiment analysis algorithms andapplications. A survey, Ain Shams Engineering Journal, 5(4), 1093-1113.
Montgomery, D.C.; Peck, E.A.; Vining, G.G. (2012). Introduction to Linear Regression Analysis (4th ed., Vol. 821). Hoboken, NJ: John Wiley & Sons.
Ng, C. Y., & Law, K. M. Y. (2020). Investigating Consumer Preferences on Product Designs by Analyzing Opinions from Social Networks Using Evidential Reasoning. Computers & Industrial Engineering, 139, 106-180.
Nitzl, C. (2016), The use of partial least squares structural equation modelling (PLS-SEM) in management accounting research: Directions for future theory development. Journal of Accounting Literature, 37(12), 19-35.
Nolasco, D., & Oliveira, J. (2019). Subevents Detection Through Topic Modeling in Social Media posts. Future Generation Computer Systems, 93, 290-303.
O’brien, R.M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality and Quantity, 41, 673–690.
Peng, D.X. and Lai, F. (2012). Using partial least squares in operations management research: a practical guideline and summary of past research. Journal of Operations Management, 30(6), 467-480.
Pradhan, M. K., Oh, J., & Lee H. (2018). Understanding Travelers' Behavior for Sustainable Smart Tourism: A Technology Readiness Perspective. Journal of Sustainability, 3-15.
Radnan, P., & Purba, J. (2018). The Use of Information Communication Technology (ICT) As The Technology Acceptance Model (TAM) of Mobile Banking. Jurnal Manajemen dan Pemasaran Jasa, 9(2), 283-298.
Richter, N.F., Sinkovics, R.R., Ringle, C.M. and Schlägel, C.M. (2015). A critical look at the use of SEMin international business research. International Marketing Review, 33(3), 376-404.
Rigdon, E. E. (2012). Rethinking partial least squares path modeling: In praise of simple methods. Long Range Planning, 45(5-6), 341-358.
Rigdon, E. E. (2014). Rethinking partial least squares path modeling: breaking chains and forging ahead. Long Range Planning, 47(3), 161-167.
Rindskopf, D. (1984). Using phantom and imaginary latent variables to parameterize constraints in linear structural models. Psychometrika, 49(1), 37-47.
Ringle, C.M., Sarstedt, M., Mitchell, R. and Gudergan, S.P. (2020). Partial least squares structural equation modeling in HRM research. The International Journal of Human Resource Management, 31(12), 1617-1643.
Ringle, C.M., Sarstedt, M. and Straub, D.W. (2012). A critical look at the use of PLS-SEM in MIS quarterly. MIS Quarterly, 36(1), iii-xiv.
Saaty, T., & Vargas, L. (2006). Decision making with the analytic network process: Economic, Political, Social and Technological Applications with Benefits, Opportunities, Costs and Risks. New York, NY: Springer.
Sanchez-Franco, M. J., Navarro-García, A., & Rondán-Cataluña, F. J. (2019). A naive Bayes strategy for classifying customer satisfaction: A study based on online reviews of hospitality services. Journal of Business Research, 101, 499-506. Retrieved from https://doi.org/10.1016/j.jbusres.2018.12.
051
Sanchez-Franco, M., Cepeda-Carrion, G., & Roldán, J. (2019). Understanding relationship quality in hospitality services: A study based on text analytics and partial least squares. Internet Research, 29(3), 478–503. Retrieved from https://doi.org/10.1108/IntR-12-2017-0531
Shen, C. W., Min, C., & Wang, C. C. (2019). Analyzing the trend of O2O commerce by bilingual text mining on social media. Computers in Human Behavior, 101, 474-483. Retrieved from https://doi.org/10. 1016/j.chb.
2018.09.031.
Shiau, S., Huang, C.-Y., Yang, C.-L., & Juang, J.-N. (2018). A derivation of factors influencing the innovation diffusion of the OpenStreetMap in STEM education. Sustainability, 10(10), 34-47.
Shiau, W.-L.; Chau, P.Y. Management. (2016) Understanding Behavioral Intention to Use A Cloud Computing Classroom: A Multiple Model Comparison Approach. Information & Management, 53, 355–365.
SITA. (2019). Passenger Services. Air Transport IT Insights 2019. Retrieved from https://www.sita.aero/resources/surveys-reports/air-transport-it-insights-2019/
Sosik, J.J., Kahai, S.S. and Piovoso, M.J. (2009). “Silver bullet or voodoo statistics? A primer for using thepartial least squares data analytic technique in Group and Organization Research”. Group and Organization Management, 34(1), 5-36.
Taherdoost, H. (2018). A review of technology acceptance and adoption models and theories. Procedia manufacturing, 22, 960-967.
Tenenhaus, M. (2008). Component-based structural equation modelling. Total quality management, 19(7-8), 871-886.
Tzeng, G.-H., & Huang, C.-Y. (2012). Combined DEMATEL technique with hybrid MCDM methods for creating the aspired intelligent global manufacturing & logistics systems. Annals of Operations Research, 197(1), 159-190.
Walczuch, R., Lemmink, J., & Streukens, S. (2007). The effect of service employees’ technology readiness on technology acceptance. Information & Management, 44(2), 206-215.
Walker, R.H., Craing-lee, M., Hecker, R. and Francis, H. (2002), Technology-Enable Service Delivery: An Investigation of Reasons Affecting Customer Adoption and Rejection. International Journals of Service IndustryManagement, 13(1) , 91-106.
Yang, C.-L., Yuan, B. J., & Huang, C.-Y. (2015). Key determinant derivations for information technology disaster recovery site selection by the multi-criterion decision making method. Sustainability, 7(5), 6149-6188.