近年來，由於預防醫療、健康管理意識抬頭，加上物聯網和雲端運算技術進步，智慧穿戴裝置之市場隨之崛起，並且發展蓬勃。科技產品是否能廣普及，取決於消費者對該產品、技術的接受程度，因此，許多學者提出有關消費者行為之理論。近三十年來，技術接受模式已廣泛運用於分析、解釋消費者對新科技之接受行為，但現有研究受到樣本及取樣的限制無法針對母體分析，對消費者行為的探討，仍多所限制。此外，雖然運用文字探勘分析社群之研究日增，但關於智慧穿戴裝置的研究非常稀有，現有之研究，仍局限於歐美國家之少數個案，且少有以理論研證者。為解決此研究缺口，本研究擬定義一新穎的分析架構，使用網路爬蟲技術萃取社群網站有關智慧手環之貼文，並以現有情感詞典詞彙作為基礎，然後使用Jieba進行斷詞、提取特徵詞及意見詞，接著進行情感分析和主題建模，得出影響消費者接受智慧手環之關鍵因素。之後依據技術接受模式提出假設，再以偏最小平方法結構方程（PLS-SEM）驗證影響關係是否顯著。本研究以探勘 PTT 社群網站驗證分析架構之可行性，結果顯示除知覺易用性（Perceived Ease of Use, PEU）、知覺有用性（Perceived Usefulness, PU）會影響消費者接受、使用智慧手環外，情緒（Sentiment）於知覺易用性（Perceived Ease of Use, PEU）對知覺有用性（Perceived Usefulness, PU）之調節作用亦為顯著，負面情緒尤其如此。未來，本研究所定義，且驗證完善之分析架構，可用於探勘影響其他產品消費行為之因素，亦可做為產品行銷與設計次世代消費電子產品之依據。

In recent years, due to the rising awareness of preventive medical treatment and health management, coupled with the advancement of the Internet of Things and cloud computing technology, the market for smart wearable devices has risen and developed rapidly. The vigorous development of the smart wearable device market depends on consumers' acceptance of the product and technology, and many scholars have put forward theories about technology adoption behavior. During the past decades, the technology acceptance model has been widely used to analyze and explain consumers' acceptance behavior of new technologies. However, existing research is limited by samples and sampling, and therefore cannot represent the population and fully reflect consumer behavior. Also, although the literature on the use of text mining and analysis of social platforms is increasing due to the flourishing of social media, research on the topic of smart wearable devices and smart bracelets is very rare, and the existing research is still limited to a minority. Among them, most of the areas analyzed by text mining are a few cases in European and American countries, and there are few theoretical kinds of research. Therefore, this research aims to propose a novel analysis framework to cross the research gap. The web crawler technology will be adopted to extract posts on social media websites related to smart bracelets based on the vocabularies of existing sentiment dictionaries. Then use the Jieba to segment words, extract feature words and opinion words, then perform sentiment analysis and topic modeling, and get the key factors that affect consumers' acceptance of smart bracelets. After that, a theoretic framework based on the technology acceptance model will be verified by using, the Partial Least Squares-Structural Equation Model (PLS-SEM). This study is to explore the feasibility of the analysis framework by exploring the PTT social website. The results demonstrate that Perceived Ease of Use (PEU) and Perceived Usefulness (PU) affect consumers’ acceptance and use of smart bracelets. Further, sentiment mediates the correlation relationship between the PEU and PU; the effects of negative sentiments are especially significant. In the future, based on the results of empirical studies, the analytical framework proposed and verified in this research can be used to mine factoring influencing consumers’ acceptance of novel technologies, as well as the basis for marketing and design of next-generation consumer electronics.

Adapa, S., Fazal-e-Hasan, S. M., Makam, S. B., Azeem, M. M., & Mortimer, G. (2020). Examining the antecedents and consequences of perceived shopping value through smart retail technology. Journal of Retailing and Consumer Services, 52, 101901. Retrieved from https://doi.org/10.1016/j.jretconser.2019.101901.
Ahuja, R., Chug, A., Gupta, S., Ahuja, P., & Kohli, S. (2020). Classification and clustering algorithms of machine learning with their applications. In X. S. Yang & X. S. He (Eds.), Nature-Inspired Computation in Data Mining and Machine Learning (Vol. 855, pp. 225-248). Cham, Switzerland: Springer.
Akyol, S., & Alatas, B. (2020). Sentiment classification within online social media using whale optimization algorithm and social impact theory based optimization. Physica A: Statistical Mechanics and Its Applications, 540, 123094. Retrieved from https://doi.org/10.1016/j.physa.2019.123094.
Al-Rawi, A. (2019). Gatekeeping fake news discourses on mainstream media versus social media. Social Science Computer Review, 37(6), 687-704.
Alalwan, A. A. (2018). Investigating the impact of social media advertising features on customer purchase intention. International Journal of Information Management, 42, 65-77. Retrieved from https://doi.org/10.1016/j.ijinfomgt.2018.06.001.
Alalwan, A. A., Rana, N. P., Dwivedi, Y. K., & Algharabat, R. (2017). Social media in marketing: A review and analysis of the existing literature. Telematics and Informatics, 34(7), 1177-1190. Retrieved from https://doi.org/10.1016/j.tele.2017.05.008.
AlAwadhi, S., & Al-Daihani, S. M. (2019). Marketing academic library information services using social media. Library Management, 40(3/4), 228-239.
Allahyari, M., Pouriyeh, S., Assefi, M., Safaei, S., Trippe, E. D., Gutierrez, J. B., & Kochut, K. (2017). A brief survey of text mining: Classification, clustering and extraction techniques. Retrieved from https://arxiv.org/abs/1707.02919.
Amado, A., Cortez, P., Rita, P., & Moro, S. (2018). Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis. European Research on Management and Business Economics, 24(1), 1-7. Retrieved from https://doi.org/10.1016/j.iedeen.2017.06.002.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411.
Android board of PTT, https://www.ptt.cc/bbs/Android/index.html, accessed 15 October 2019.
Anggraini, N., Kaburuan, E. R., Wang, G., & Jayadi, R. (2019). Usability Study and Users’ Perception of Smartwatch: Study on Indonesian Customer. Procedia Computer Science, 161, 1266-1274. Retrieved from https://doi.org/10.1016/j.procs.2019.11.241.
Anstead, N., & O'Loughlin, B. (2015). Social media analysis and public opinion: the 2010 UK general election. Journal of Computer-Mediated Communication, 20(2), 204-220. doi:10.1111/jcc4.12102.
Araque, O., Zhu, G., & Iglesias, C. A. (2019). A semantic similarity-based perspective of affect lexicons for sentiment analysis. Knowledge-Based Systems, 165, 346-359. Retrieved from https://doi.org/10.1016/j.knosys.2018.12.005.
Balahur, A., & Perea-Ortega, J. M. (2015). Sentiment analysis system adaptation for multilingual processing: The case of tweets. Information Processing & Management, 51(4), 547-556. Retrieved from https://doi.org/10.1016/j.ipm.2014.10.004
Balogh, Z., Magdin, M., & Molnár, G. (2019). Motion detection and face recognition using Raspberry Pi, as a part of, the Internet of Things. Acta Polytechnica Hungarica, 16(3), 167-185.
Banyai, F., Zsila, A., Kiraly, O., Maraz, A., Elekes, Z., Griffiths, M. D., Demetrovics, Z. (2017). Problematic social media use: results from a large-scale nationally representative adolescent sample. PLoS One, 12(1), e0169839. doi:10.1371/journal.pone.0169839.
Beecham Research. (2016). Wearable technology application chart. Retrieved December 10, 2019, from http://www.beechamresearch.com/download.aspx?id=36.
Binali, H., Wu, C., & Potdar, V. (2010, April). Computational approaches for emotion detection in text. In 4th IEEE International Conference on Digital Ecosystems and Technologies (pp. 172-177). Dubai, United Arab Emirates.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993-1022.
Çakıt, E., Olak, A. J., Karwowski, W., Marek, T., Hejduk, I., & Taiar, R. (2020). Assessing safety at work using an adaptive neuro-fuzzy inference system (ANFIS) approach aided by partial least squares structural equation modeling (PLS-SEM). International Journal of Industrial Ergonomics, 76, 102925. Retrieved from https://doi.org/10.1016/j.ergon.2020.102925.
Camacho, D., Luzón, M. V., & Cambria, E. (2021). New research methods & algorithms in social network analysis. Future Generation Computer Systems, 114, 290-293. Retrieved from https://doi.org/10.1016/j.future.2020.08.006.
Cambria, E. (2016). Affective computing and sentiment analysis. IEEE Intelligent Systems, 31(2), 102-107. doi:10.1109/MIS.2016.31.
Carr, C. T., & Hayes, R. A. (2015). Social media: Defining, developing, and divining. Atlantic Journal of Communication, 23(1), 46-65. doi:10.1080/15456870.2015.972282.
Cecchinato, M. E., Cox, A. L., & Bird, J. (2015). Smartwatches: the good, the bad and the ugly? In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems (pp. 2133-2138). Seoul, Korea.
Chang, C. C., Hung, S. W., Cheng, M. J., & Wu, C. Y. (2015). Exploring the intention to continue using social networking sites: The case of Facebook. Technological Forecasting and Social Change, 95, 48-56. Retrieved from https://doi.org/10.1016/j.techfore.2014.03.012.
Chen, M. Y., Liao, C. H., & Hsieh, R. P. (2019). Modeling public mood and emotion: Stock market trend prediction with anticipatory computing approach. Computers in Human Behavior, 101, 402-408. Retrieved from https://doi.org/10.1016/j.chb.2019.03.021.
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. Retrieved from https://doi.org/10.1016/j.knosys.2018.08.011.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295-336.
Choi, J., & Kim, S. (2016). Is the smartwatch an IT product or a fashion product? a study on factors affecting the intention to use smartwatches. Computers in Human Behavior, 63, 777-786. Retrieved from https://doi.org/10.1016/j.chb.2016.06.007.
Chuah, S. H. W. (2019). You inspire me and make my life better: Investigating a multiple sequential mediation model of smartwatch continuance intention. Telematics and Informatics, 43, 101245. Retrieved from https://doi.org/10.1016/j.tele.2019.101245.
Chuah, S. H. W., Rauschnabel, P. A., Krey, N., Nguyen, B., Ramayah, T., & Lade, S. (2016). Wearable technologies: The role of usefulness and visibility in smartwatch adoption. Computers in Human Behavior, 65, 276-284. Retrieved from https://doi.org/10.1016/j.chb.2016.07.047.
Clayton, K., Blair, S., Busam, J. A., Forstner, S., Glance, J., Green, G., Nyhan, B. (2019). Real solutions for fake news? measuring the effectiveness of general warnings and fact-check tags in reducing belief in false stories on social media. Political Behavior. doi:10.1007/s11109-019-09533-0.
Costello, K. (2019). Gartner says worldwide wearable device sales to grow 26 percent in 2019. Retrieved October 4, 2019, from https://www.gartner.com/en/newsroom/press-releases/2018-11-29-gartner-says-worldwide-wearable-device-sales-to-grow-.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.
Dehghani, M., Abubakar, A. M., & Pashna, M. (2018). Market-driven management of start-ups: The case of wearable technology. Applied Computing and Informatics, ahead-of-print(ahead-of-print). Retrieved from https://doi.org/10.1016/j.aci.2018.11.002.
Dehghani, M., Kim, K. J., & Dangelico, R. M. (2018). Will smartwatches last? factors contributing to intention to keep using smart wearable technology. Telematics and Informatics, 35(2), 480-490. doi:10.1016/j.tele.2018.01.007.
Ding, X., Liu, B., & Yu, P. S. (2008). A holistic lexicon-based approach to opinion mining. In Proceedings of the 2008 International Conference on Web Search and Data Mining (pp. 231-240). New York, United States.
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3), 719-734. doi:10.1007/s10796-017-9774-y.
Eason, J., & Kumar, S. (2019). Evaluation of text mining Techniques Using Twitter Data for Hurricane Disaster Resilience. Earth and Space Science Open Archive. doi:10.1002/essoar.10501407.1.
e-shopping board of PTT, https://www.ptt.cc/bbs/e-shopping/index.html, accessed 15 October 2019.
Fang, Y. M., & Chang, C. C. (2016). Users' psychological perception and perceived readability of wearable devices for elderly people. Behaviour & Information Technology, 35(3), 225-232.
Fitness board of PTT, https://www.ptt.cc/bbs/Fitness/index.html, accessed 15 October 2019.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
Ghiassi, M., & Lee, S. (2018). A domain transferable lexicon set for Twitter sentiment analysis using a supervised machine learning approach. Expert Systems with Applications, 106, 197-216. Retrieved from https://doi.org/10.1016/j.eswa.2018.04.006.
Gossiping board of PTT, https://www.ptt.cc/bbs/Gossiping/index.html, accessed 15 October 2019.
Hair, J., Hult, G. T. M., Ringle, C., Sarstedt, M., & Thiele, K. (2017). Mirror, mirror on the wall: A comparative evaluation of composite-based structural equation modeling methods. Journal of the Academy of Marketing Science, 45(5), 616-632. doi:10.1007/s11747-017-0517-x.
Hair, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101-110. Retrieved from https://doi.org/10.1016/j.jbusres.2019.11.069.
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. doi:10.2753/MTP1069-6679190202.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24.
Hashimi, H., Hafez, A., & Mathkour, H. (2015). Selection criteria for text mining approaches. Computers in Human Behavior, 51, 729-733.
He, W., Zha, S., & Li, L. (2013). Social media competitive analysis and text mining: A case study in the pizza industry. International Journal of Information Management, 33(3), 464-472. doi:10.1016/j.ijinfomgt.2013.01.001.
Henseler, J. (2017). Partial least squares path modeling. In H. Latan & R. Noonan (Eds.), Advanced Methods for Modeling Markets (pp. 361-381). New York: Springer.
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.
Hongbing, G. (2019). Research on the construction of early warning index system of network public opinion emergency based on computer simulation. IOP Conference Series: Earth and Environmental Science, 310(5), 052044. doi:10.1088/1755-1315/310/5/052044.
Hwang, B. N., Huang, C. Y., & Yang, C. L. (2016). Determinants and their causal relationships affecting the adoption of cloud computing in science and technology institutions. Innovation, 18(2), 164-190.
Ibrahim, N. F., & Wang, X. (2019). Decoding the sentiment dynamics of online retailing customers: Time series analysis of social media. Computers in Human Behavior, 96, 32-45. Retrieved from https://doi.org/10.1016/j.chb.2019.02.004.
Im, I., Kim, Y., & Han, H.-J. (2008). The effects of perceived risk and technology type on users’ acceptance of technologies. Information and Management, 45(1), 1-9. Retrieved from https://doi.org/10.1016/j.im.2007.03.005.
Irfan, R., King, C. K., Grages, D., Ewen, S., Khan, S. U., Madani, S. A., Li, H. (2015). A survey on text mining in social networks. The Knowledge Engineering Review, 30(2), 157-170. doi:10.1017/s0269888914000277.
Jay, C., Ramon, L., & Ubrani, J. (2020). Earwear and wristbands drive first quarter growth in the worldwide wearables market, says IDC. Retrieved February 10, 2020, from https://www.idc.com/promo/wearablevendor.
Jeong, B., Yoon, J., & Lee, J. M. (2019). Social media mining for product planning: A product opportunity mining approach based on topic modeling and sentiment analysis. International Journal of Information Management, 48, 280-290. Retrieved from https://doi.org/10.1016/j.ijinfomgt.2017.09.009.
Jieba, https://github.com/fxsjy/jieba, accessed 8 February 2020.
Jones, M. J., & Harvey, M. (2019). Library 2.0: The effectiveness of social media as a marketing tool for libraries in educational institutions. Journal of Librarianship and Information Science, 51(1), 3-19.
Joo, J., & Sang, Y. (2013). Exploring Koreans’ smartphone usage: An integrated model of the technology acceptance model and uses and gratifications theory. Computers in Human Behavior, 29(6), 2512-2518.
Joo, S., Choi, N., & Baek, T. H. (2018). Library marketing via social media: The relationships between Facebook content and user engagement in public libraries. Online Information Review, 42(6), 940-955.
Jung, H., & Gyou Lee, B. (2020). Library marketing via social media: The relationships between Facebook content and user engagement in public libraries. Expert Systems with Applications, 113851. Retrieved from https://doi.org/10.1016/j.eswa.2020.113851.
Jung, Y., Kim, S., & Choi, B. (2016). Consumer valuation of the wearables: The case of smartwatches. Computers in Human Behavior, 63, 899-905. Retrieved from https://doi.org/10.1016/j.chb.2016.06.040.
Kamboj, S., Sarmah, B., Gupta, S., & Dwivedi, Y. (2018). Examining branding co-creation in brand communities on social media: Applying the paradigm of stimulus-organism-response. International Journal of Information Management, 39, 169-185. Retrieved from https://doi.org/10.1016/j.ijinfomgt.2017.12.001.
Kanapala, A., Pal, S., & Pamula, R. (2019). Text summarization from legal documents: a survey. Artificial Intelligence Review, 51(3), 371-402.
Kao, Y. S., Nawata, K., & Huang, C. Y. (2019). An exploration and confirmation of the factors influencing adoption of IoT-based wearable fitness trackers. Int J Environ Res Public Health, 16(18), 3227. doi:10.3390/ijerph16183227.
Kapoor, K. K., Tamilmani, K., Rana, N. P., Patil, P., Dwivedi, Y. K., & Nerur, S. (2017). Advances in social media research: Past, present and future. Information Systems Frontiers, 20(3), 531-558. doi:10.1007/s10796-017-9810-y.
Karami, A., Bennett, L. S., & He, X. (2018). Mining public opinion about economic issues: Twitter and the us presidential election. International Journal of Strategic Decision Sciences (IJSDS), 9(1), 18-28.
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.
Karjaluoto, H., Riquelme, H. E., & Rios, R. E. (2010). The moderating effect of gender in the adoption of mobile banking. International Journal of Bank Marketing, 28(5), 328-341.
Kauffmann, E., Peral, J., Gil, D., Ferrández, A., Sellers, R., & Mora, H. (2019). A framework for big data analytics in commercial social networks: A case study on sentiment analysis and fake review detection for marketing decision-making. Industrial Marketing Management. Retrieved from https://doi.org/10.1016/j.indmarman.2019.08.003.
Kaur, K. (2009). Marketing the academic library on the web. Library Management, 30(6/7), 454-468. doi:10.1108/01435120910982140.
Kayser, V., & Blind, K. (2017). Extending the knowledge base of foresight: The contribution of text mining. Technological Forecasting and Social Change, 116, 208-215. doi:10.1016/j.techfore.2016.10.017.
Khatoon, M., Banu, W. A., Zohra, A. A., & Chinthamani, S. (2019). Sentiment analysis on tweets. In Software Engineering (pp. 717-724). Singapore: Springer.
Kietzmann, J. H., Hermkens, K., McCarthy, I. P., & Silvestre, B. S. (2011). Social media? Get serious! Understanding the functional building blocks of social media. Business Horizons, 54(3), 241-251. doi:10.1016/j.bushor.2011.01.005.
Kim, K., Park, O. j., Yun, S., & Yun, H. (2017). What makes tourists feel negatively about tourism destinations? Application of hybrid text mining methodology to smart destination management. Technological Forecasting and Social Change, 123, 362-369.
Kim, K. J., & Shin, D.-H. (2015). An acceptance model for smart watches: implications for the adoption of future wearable technology. Internet Research, 25(4), 527-541.
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. Retrieved from https://doi.org/10.1016/j.ipm.2018.06.003.
Kobayashi, V. B., Mol, S. T., Berkers, H. A., Kismihok, G., & Den Hartog, D. N. (2018). Text mining in organizational research. Organ Res Methods, 21(3), 733-765. doi:10.1177/1094428117722619.
Kovalev, M. S., & Utkin, L. V. (2020). A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov–Smirnov bounds. Neural Networks, 132, 1-18. Retrieved from https://doi.org/10.1016/j.neunet.2020.08.007.
Krey, N., Chuah, S. H.-W., Ramayah, T., & Rauschnabel, P. A. (2019). How functional and emotional ads drive smartwatch adoption. Internet Research , 29(3), 578-602.
Ku, L. W. & Chen, H. H. (2007). National taiwan university semantic dictionary (NTUSD). Retrieved January 25, 2020, from http://nlg18.csie.ntu.edu.tw:8080/lwku/pub1.html.
Ku, L. W., Lo, Y. S., & Chen, H. H. (2007). Using polarity scores of words for sentence-level opinion extraction. In Proceedings of NTCIR-6 Workshop Meeting (pp. 316-322). Tokyo, Japan.
Kuder, G. F., & Richardson, M. W. (1937). The theory of the estimation of test reliability. Psychometrika, 2(3), 151-160. doi:10.1007/BF02288391
Li, J., Ma, Q., Chan, A. H. S., & Man, S. S. (2019). Health monitoring through wearable technologies for older adults: Smart wearables acceptance model. Applied Ergonomics, 75, 162-169. Retrieved from https://doi.org/10.1016/j.apergo.2018.10.006.
Li, S., Jiao, F., Zhang, Y., & Xu, X. (2019). Problems and changes in digital libraries in the age of big data from the perspective of user services. The Journal of Academic Librarianship, 45(1), 22-30. Retrieved from https://doi.org/10.1016/j.acalib.2018.11.012.
Liu, B., & Zhang, L. (2012). A survey of opinion mining and sentiment analysis. In Mining Text Data (pp. 415-463). Boston, MA: Springer.
Liu, G., Xu, X., Deng, B., Chen, S., & Li, L. (2016, May). A hybrid method for bilingual text sentiment classification based on deep learning. In 2016 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) (pp. 93-98). Shanghai, China.
Liu, Y., Bi, J. W., & Fan, Z. P. (2017). Ranking products through online reviews: A method based on sentiment analysis technique and intuitionistic fuzzy set theory. Information Fusion, 36, 149-161. Retrieved from https://doi.org/10.1016/j.inffus.2016.11.012.
Liu, Y., Jiang, C., & Zhao, H. (2019). Assessing product competitive advantages from the perspective of customers by mining user-generated content on social media. Decision Support Systems, 123, 113079. Retrieved from https://doi.org/10.1016/j.dss.2019.113079.
Llamas, R., Shirer, M., & Ubrani, J. (2020). Earwear and wristbands drive first quarter growth in the worldwide wearables market, says idc. Retrieved February 10, 2020, from https://www.idc.com/getdoc.jsp?containerId=prUS46432620.
Makki, I., Alhalabi, W., & Adham, R. S. (2019). Using emotion analysis to define human factors of virtual reality wearables. Procedia Computer Science, 163, 154-164. Retrieved from https://doi.org/10.1016/j.procs.2019.12.097.
Mayfield, A. (2008). What is social media? International Journal of Advanced Computer Science and Applications, 7(11), 414-418.
Maynard, D., & Funk, A. (2011). Automatic detection of political opinions in tweets. In Extended Semantic Web Conference (pp. 88-99). Berlin, Heidelberg: Springer.
Mishra, P., Pandey, C. M., Singh, U., Gupta, A., Sahu, C., & Keshri, A. (2019). Descriptive statistics and normality tests for statistical data. Annals of Cardiac Anaesthesia, 22(1), 67-72. doi:10.4103/aca.ACA_157_18.
MobileComm board of PTT, https://www.ptt.cc/bbs/MobileComm/index.html, accessed 15 October 2019.
MobilePay board of PTT, https://www.ptt.cc/bbs/MobilePay/index.html, accessed 15 October 2019.
Moore, S. (2016). Gartner survey shows wearable devices need to be more useful. Retrieved November 16, 2019, from https://www.gartner.com/en/newsroom/press-releases/2016-12-07-gartner-survey-shows-wearable-devices-need-to-be-more-useful.
Mostafa, M. M. (2013). More than words: Social networks’ text mining for consumer brand sentiments. Expert Systems with Applications, 40(10), 4241-4251. doi:10.1016/j.eswa.2013.01.019.
Mun, Y. Y., & Hwang, Y. (2003). Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human-Computer Studies, 59(4), 431-449. Retrieved from https://doi.org/10.1016/S1071-5819(03)00114-9.
Murphy, J., Link, M. W., Childs, J. H., Tesfaye, C. L., Dean, E., Stern, M., Harwood, P. (2014). Social media in public opinion research: executive summary of the aapor task force on emerging technologies in public opinion research. Public Opinion Quarterly, 78(4), 788-794. doi:10.1093/poq/nfu053.
Nascimento, B., Oliveira, T., & Tam, C. (2018). Wearable technology: What explains continuance intention in smartwatches? Journal of Retailing and Consumer Services, 43, 157-169. Retrieved from https://doi.org/10.1016/j.jretconser.2018.03.017.
Nassirtoussi, A. K., Aghabozorgi, S., Wah, T. Y., & Ngo, D. C. L. (2015). Text mining of news-headlines for FOREX market prediction: A multi-layer dimension reduction algorithm with semantics and sentiment. Expert Systems with Applications, 42(1), 306-324.
Nenkova, A., & McKeown, K. (2012). A survey of text summarization techniques. In C. C. Aggarwal & C. Zhai (Eds), Mining Text Data (pp. 43-76). Boston, MA: Springer.
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, 106180. Retrieved from https://doi.org/10.1016/j.cie.2019.106180.
Ngai, E. W., Tao, S. S., & Moon, K. K. (2015). Social media research: Theories, constructs, and conceptual frameworks. International Journal of Information Management, 35(1), 33-44. Retrieved from https://doi.org/10.1016/j.ijinfomgt.2014.09.004.
Nieroda, M. E., Mrad, M., & Solomon, M. R. (2018). How do consumers think about hybrid products? computer wearables have an identity problem. Journal of Business Research, 89, 159-170. Retrieved from https://doi.org/10.1016/j.jbusres.2018.04.024
Niknejad, N., Ismail, W. B., Mardani, A., Liao, H., & Ghani, I. (2020). A comprehensive overview of smart wearables: The state of the art literature, recent advances, and future challenges. Engineering Applications of Artificial Intelligence, 90, 103529. Retrieved from https://doi.org/10.1016/j.engappai.2020.103529.
Nisbet, R., Miner, G., & Yale, K. (2018). IBM watson. In Handbook of Statistical Analysis and Data Mining Applications (pp. 773-781). Boston: Academic Press.
Nolasco, D., & Oliveira, J. (2019). Subevents detection through topic modeling in social media posts. Future Generation Computer Systems, 93, 290-303. Retrieved from https://doi.org/10.1016/j.future.2018.09.008.
Novak, T. P., & Hoffman, D. L. (2019). Relationship journeys in the internet of things: a new framework for understanding interactions between consumers and smart objects. Journal of the Academy of Marketing Science, 47(2), 216-237. doi:10.1007/s11747-018-0608-3.
Nunnally, J. C. (1978). Psychometric theory. New York: McGraw-Hill.
Orabi, M., Mouheb, D., Al Aghbari, Z., & Kamel, I. (2020). Detection of bots in social media: A systematic review. Information Processing & Management, 57(4), 102250. Retrieved from https://doi.org/10.1016/j.ipm.2020.102250.
Organization, W. H. (2010). Global recommendations on physical activity for health 2010. Retrieved November 5, 2019, from chrome-extension://oemmndcbldboiebfnladdacbdfmadadm/https://www.who.int/dietphysicalactivity/global-PA-recs-2010.pdf.
Ozbay, F. A., & Alatas, B. (2020). Fake news detection within online social media using supervised artificial intelligence algorithms. Physica A: Statistical Mechanics and Its Applications, 540, 123174. Retrieved from https://doi.org/10.1016/j.physa.2019.123174
Öztürk, N., & Ayvaz, S. (2018). Sentiment analysis on Twitter: A text mining approach to the Syrian refugee crisis. Telematics and Informatics, 35(1), 136-147.
Pandey, A. C., Rajpoot, S. D., & Saraswat, M. (2017). Twitter sentiment analysis using hybrid cuckoo search method. Information Processing & Management, 53(4), 764-779. Retrieved from https://doi.org/10.1016/j.ipm.2017.02.004.
Park, E. (2020). User acceptance of smart wearable devices: An expectation-confirmation model approach. Telematics and Informatics, 47, 101318. Retrieved from https://doi.org/10.1016/j.tele.2019.101318.
Park, S. B., Kim, J., Lee, Y. K., & Ok, C. M. (2020). Visualizing theme park visitors’ emotions using social media analytics and geospatial analytics. Tourism Management, 80, 104127. doi:10.1016/j.tourman.2020.104127.
Pasupa, K., & Ayutthaya, T. S. N. (2019). Thai sentiment analysis with deep learning techniques: A comparative study based on word embedding, POS-tag, and sentic features. Sustainable Cities and Society, 50, 101615. Retrieved from https://doi.org/10.1016/j.scs.2019.101615.
Peña-García, N., Gil-Saura, I., Rodríguez-Orejuela, A., & Siqueira-Junior, J. R. (2020). Purchase intention and purchase behavior online: A cross-cultural approach. Heliyon, 6(6), e04284. Retrieved from https://doi.org/10.1016/j.heliyon.2020.e04284.
PTT, https://term.ptt.cc/, accessed 15 October 2019.
Rafique, H., Almagrabi, A. O., Shamim, A., Anwar, F., & Bashir, A. K. (2020). Investigating the acceptance of mobile library applications with an extended technology acceptance model (TAM). Computers & Education, 145, 103732. Retrieved from https://doi.org/10.1016/j.compedu.2019.103732.
Rajanen, D., & Weng, M. (2017, September). Digitization for fun or reward? a study of acceptance of wearable devices for personal healthcare. In Proceedings of the 21st International Academic Mindtrek Conference (pp. 154-163). Tampere, Finland.
Rambaccussing, D., & Kwiatkowski, A. (2020). Forecasting with news sentiment: Evidence with UK newspapers. International Journal of Forecasting. Retrieved from https://doi.org/10.1016/j.ijforecast.2020.04.002.
Rasool, A., Tao, R., Marjan, K., & Naveed, T. (2019). Twitter sentiment analysis: A case study for apparel brands. Journal of Physics: Conference Series, 1176(2), 022015. doi:10.1088/1742-6596/1176/2/022015.
Rathore, A. K., & Ilavarasan, P. V. (2020). Pre- and post-launch emotions in new product development: Insights from twitter analytics of three products. International Journal of Information Management, 50, 111-127. Retrieved from https://doi.org/10.1016/j.ijinfomgt.2019.05.015.
Razali, N. M., & Wah, Y. B. (2011). Power comparisons of shapiro-wilk, kolmogorov-smirnov, lilliefors and anderson-darling tests. Journal of Statistical Modeling and Analytics, 2(1), 21-33.
Richter, N., Sinkovics, R., Ringle, C., & Schlaegel, C. (2016). A critical look at the use of SEM in international business research. International Marketing Review, 33(3), 376-404. doi:10.1108/IMR-04-2014-0148.
Road_Running board of PTT, https://www.ptt.cc/bbs/Road_Running/index.html, accessed 15 October 2019.
Russey, C. (2018). New PwC study reveals consumers use wearables to stay fit and live longer. Retrieved October 3, 2019, from https://www.wearable-technologies.com/2018/12/new-pwc-study-reveals-consumers-use-wearables-to-stay-fit-and-live-longer/.
Sailunaz, K., & Alhajj, R. (2019). Emotion and sentiment analysis from Twitter text. Journal of Computational Science, 36, 101003. Retrieved from https://doi.org/10.1016/j.jocs.2019.05.009.
Salloum, S. A., Al-Emran, M., & Shaalan, K. (2017). Mining social media text: extracting knowledge from Facebook. International Journal of Computing and Digital Systems, 6(2), 73-81.
Sanchez-Franco, M. J., Cepeda-Carrion, G., & Roldán, J. L. (2019). Understanding relationship quality in hospitality services: A study based on text analytics and partial least squares. Internet Research, 29(3), 478-503.
Seo, W., Yoon, J., Park, H., Coh, B.-y., Lee, J. M., & Kwon, O. J. (2016). Product opportunity identification based on internal capabilities using text mining and association rule mining. Technological Forecasting and Social Change, 105, 94-104.
Sergueeva, K., Shaw, N., & Lee, S. H. M. (2020). Understanding the barriers and factors associated with consumer adoption of wearable technology devices in managing personal health. Canadian Journal of Administrative Sciences/Revue Canadienne des Sciences de l'Administration, 37(1), 45–60.
Sezgen, E., Mason, K. J., & Mayer, R. (2019). Voice of airline passenger: A text mining approach to understand customer satisfaction. Journal of Air Transport Management, 77, 65-74.
Sharma, D., Sabharwal, M., Goyal, V., & Vij, M. (2020). Sentiment analysis techniques for social media data: A review. In A. K. Luhach, J. A. Kosa, R. C. Poonia, X. Z. Gao & D.S. Jat (Eds.), First International Conference on Sustainable Technologies for Computational Intelligence (pp. 75-90). Singapore: Springer.
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), 3447.
Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017). Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter, 19(1), 22-36. doi:10.1145/3137597.3137600.
Silge, J., & Robinson, D. (2017). Text mining with R: A tidy approach." O'Reilly Media, Inc.".
Singh, N., Roy, N., & Gangopadhyay, A. (2019). Analyzing the emotions of crowd for improving the emergency response services. Pervasive and Mobile Computing, 58, 101018. Retrieved from https://doi.org/10.1016/j.pmcj.2019.04.009
Sánchez-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
Sánchez, R. A., & Hueros, A. D. (2010). Motivational factors that influence the acceptance of moodle using TAM. Computers in Human Behavior, 26(6), 1632-1640. Retrieved from https://doi.org/10.1016/j.chb.2010.06.011.
Sodhi, P., Awasthi, N., & Sharma, V. (2019). Introduction to machine learning and its basic application in Python. Available at SSRN 3323796.
Sohn, K., & Kwon, O. (2020). Technology acceptance theories and factors influencing artificial intelligence-based intelligent products. Telematics and Informatics, 47, 101324. Retrieved from https://doi.org/10.1016/j.tele.2019.101324.
Sony-style board of PTT, https://www.ptt.cc/bbs/Sony-style/index.html, accessed 15 October 2019.
Sun, Y., Liang, C., & Chang, C. C. (2020). Online social construction of Taiwan's rural image: Comparison between Taiwanese self-representation and Chinese perception. Tourism Management, 76, 103968. Retrieved from https://doi.org/10.1016/j.tourman.2019.103968.
Talib, R., Hanif, M. K., Ayesha, S., & Fatima, F. (2016). Text mining: techniques, applications and issues. International Journal of Advanced Computer Science and Applications, 7(11), 414-418.
Teso, E., Olmedilla, M., Martínez-Torres, M. R., & Toral, S. L. (2018). Application of text mining techniques to the analysis of discourse in eWOM communications from a gender perspective. Technological Forecasting and Social Change, 129, 131-142. Retrieved from https://doi.org/10.1016/j.techfore.2017.12.018.
Turcotte, J., York, C., Irving, J., Scholl, R. M., & Pingree, R. J. (2015). News recommendations from social media opinion leaders: effects on media trust and information seeking. Journal of Computer-Mediated Communication, 20(5), 520-535. doi:10.1111/jcc4.12127.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
Verkijika, S. F. (2020). An affective response model for understanding the acceptance of mobile payment systems. Electronic Commerce Research and Applications, 39, 100905. Retrieved from https://doi.org/10.1016/j.elerap.2019.100905.
Wang, H., Can, D., Kazemzadeh, A., Bar, F., & Narayanan, S. (2012). A system for real-time twitter sentiment analysis of 2012 us presidential election cycle. In Proceedings of the ACL 2012 System Demonstrations (pp. 115-120). Jeju Island, Korea.
Wang, L., Wang, X. K., Peng, J. J., & Wang, J. Q. (2020). The differences in hotel selection among various types of travellers: A comparative analysis with a useful bounded rationality behavioural decision support model. Tourism Management, 76, 103961. Retrieved from https://doi.org/10.1016/j.tourman.2019.103961.
Wang, Y., Li, H., & Wu, Z. (2019). Attitude of the Chinese public toward off-site construction: A text mining study. Journal of Cleaner Production, 238, 117926. Retrieved from https://doi.org/10.1016/j.jclepro.2019.117926.
Williams, L. J., & Hazer, J. T. (1986). Antecedents and consequences of satisfaction and commitment in turnover models: A reanalysis using latent variable structural equation methods. Journal of Applied Psychology, 71(2), 219.
Witten, I. H., Don, K. J., Dewsnip, M., & Tablan, V. (2004). Text mining in a digital library. International Journal on Digital Libraries, 4(1), 56-59. doi:10.1007/s00799-003-0066-4.
Xiang, Z., Du, Q., Ma, Y., & Fan, W. (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58, 51-65. Retrieved from https://doi.org/10.1016/j.tourman.2016.10.001.
Xu, X. (2020). Examining the role of emotion in online consumer reviews of various attributes in the surprise box shopping model. Decision Support Systems, 136, 113344. Retrieved from https://doi.org/10.1016/j.dss.2020.113344.
Yang, B., Liu, Y., Liang, Y., & Tang, M. (2019). Exploiting user experience from online customer reviews for product design. International Journal of Information Management, 46, 173-186. Retrieved from https://doi.org/10.1016/j.ijinfomgt.2018.12.006.
Yang, S., & Zhang, H. (2018). Text mining of Twitter data using a latent Dirichlet allocation topic model and sentiment analysis. International Journal of Computer and Information Engineering, 12(7), 525-529.
Zeng, D., Chen, H., Lusch, R., & Li, S. H. (2010). Social media analytics and intelligence. IEEE Intelligent Systems, 25(6), 13-16. doi:10.1109/mis.2010.151.
Zhang, M., Luo, M., Nie, R., & Zhang, Y. (2017). Technical attributes, health attribute, consumer attributes and their roles in adoption intention of healthcare wearable technology. International Journal of Medical Informatics, 108, 97-109. Retrieved from https://doi.org/10.1016/j.ijmedinf.2017.09.016.
Zhang, S., Wei, Z., Wang, Y., & Liao, T. (2018). Sentiment analysis of Chinese micro-blog text based on extended sentiment dictionary. Future Generation Computer Systems, 81, 395-403. Retrieved from https://doi.org/10.1016/j.future.2017.09.048.
Zhang, Y., Zhang, C., & Liu, X. (2017, August). Dynamic scholarly collaborator recommendation via competitive multi-agent reinforcement learning. In Proceedings of the Eleventh ACM Conference on Recommender Systems (pp. 331-335). Como, Italy.
Zhang, Y., Zhang, D., Vance, N., & Wang, D. (2019). An online reinforcement learning approach to quality-cost-aware task allocation for multi-attribute social sensing. Pervasive and Mobile Computing, 60, 101086. Retrieved from https://doi.org/10.1016/j.pmcj.2019.101086.
Zhou, X., Tao, X., Yong, J., & Yang, Z. (2013, June). Sentiment analysis on tweets for social events. In Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD) (pp. 557-562). Whistler, BC, Canada.