نوع مقاله : مقاله پژوهشی
نویسندگان
1 کارشناسی ارشد رشته مدیریت منابع اطلاعاتی، دانشگاه الزهرا ، تهران، ایران
2 دانشیار گروه مدیریت، دانشکده علوم اجتماعی و اقتصاد، دانشگاه الزهرا، تهران، ایران نویسنده مسئول: a.khadivar@alzahra.ac.ir
3 استادیار گروه مدیریت صنعتی و فناوری اطلاعات ، دانشگاه شهید بهشتی، تهران، ایران
چکیده
بررسی‌ها نشان داده است که بسیاری از سازمان‌ها برای ایجاد دانش و بهبود تصمیم‌گیری از تحلیل نظرات و محتوای تولید شده توسط کاربران در شبکه‌های اجتماعی بهره‌برداری کرده‌اند. در این پژوهش، به تحلیل عملکرد مالی سازمان‌های غیرانتفاعی با استفاده از تحلیل نظرات کاربران پرداخته شده است. مجموعه داده‌ی استفاده شده در این پژوهش شامل 26714 توییت کاربران از جمعا 23 حساب توییتری در سراسر جهان می‌باشد و ده سال داده‌ی مالی شامل سال‌های 2010-2020 این سازمان‌ها از 5 سازمان غیرانتفاعی منتخب جمع‌آوری شده است. نتایج حاصل از روش‌های مدل‌سازی موضوع و تحلیل احساسات به داده‌های پانلی تبدیل شده‌اند. نتایج مدل‌سازی موضوعی و تحلیل احساسات به داده‌های پنلی تبدیل و با استفاده از روش‌های حداقل مربعات معمولی و حداقل مربعات معمولی پویا تحلیل شده‌اند. نتایج نشان می‌دهند که محتوای تولید شده توسط کاربران و عملکرد مالی سازمان‌های غیرانتفاعی رابطه معنی داری دارند. در حالی که احساسات مثبت تأثیر معنی‌داری بر عملکرد مالی این سازمان‌ها ندارند، احساسات منفی و بازتوئیت‌ها تأثیر منفی و علاقمندی‌ها رابطه ی مثبتی با عملکرد مالی را نشان می دهند.
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
Performance Investigation of Nonprofit Organizations by Sentiment Analysis on Social Networks
نویسندگان [English]
- Elnaz Valizadeh Hamzekolaei 1
- Ameneh khadivar 2
- Fatemeh Abbasi 3
1 Master of Management of Information Technology, Alzahra University, Tehran, Iran.
2 Associate Professor, Management Department, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran Corresponding Author: a.khadivar@alzahra.ac.ir
3 Assistant Professor, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran
چکیده [English]
Researches have demonstrated that many organizations leverage the analysis of user-generated opinions and content on social networks to cultivate knowledge and enhance decision-making. This study aims to analyze the financial performance of non-profit organizations by examining user-generated opinions. The dataset comprises 26,714 user tweets from 23 global Twitter accounts and ten years of financial data (2010-2020) from five selected non-profit organizations. The results of topic modeling and sentiment analysis were converted into panel data. These results were analyzed using ordinary least squares (OLS) and dynamic ordinary least squares (DOLS) methods.Findings indicate a significant relationship between user-generated content and the financial performance of non-profit organizations. While positive sentiments did not exhibit a significant impact on financial performance, negative sentiments and retweets demonstrated a negative relationship, whereas likes showed a positive correlation with financial performance. By harnessing insights from user-generated content, non-profit organizations can optimize their content strategies to improve financial outcomes. Nevertheless, it is imperative to consider the adverse effects of negative sentiments and retweets to achieve substantial improvements in financial performance.
کلیدواژه‌ها [English]
- sentiment analysis
- nonprofit organizations
- topic modeling
- social network
- panel data
- Alarifi, A., Alsaleh, M., & Al-Salman, A. (2016). Twitter turing test: Identifying social machines. Information Sciences, 372, 332–346. https://doi.org/10.1016/j.ins.2016.08.036
- Butcher, L. (2009). Nonprofit organizations outpace businesses in use of social media
- Canada boasts second largest nonprofit sector in the world [Internet]. CharityVillage. 2005 [cited 2023 Sep 24]. Available from: https://charityvillage.com/canada_boasts_second_largest_nonprofit_sector_in_the_world/#:~:text=Although%20the%20United%20States%20has
- Charity Navigator [Internet]. Charity Navigator. 2017. Available from: https://www.charitynavigator.org
- Chianese, A., & Piccialli, F. (2016b). A service oriented framework for analysing social network activities. Procedia Computer Science, 98, 509–514. https://doi.org/10.1016/j.procs.2016.09.087
- Epstein, M. J., & McFarlan, F. W. (2011). Measuring the efficiency and effectiveness of a nonprofit's performance. Strategic finance, 93(4), 27.
- Goh, K., Heng, C., & Lin, Z. (2013). Social Media Brand Community and Consumer Behavior: Quantifying the Relative impact of User- and Marketer-Generated content. Information Systems Research, 24(1), 88–107. https://doi.org/10.1287/isre.1120.0469
- Goldberg, K. L. (2020). Adopting Analytics: A Framework for Nonprofit Organizations(Doctoral dissertation, University of Maryland University College).
- González-Rodríguez, M. R., Díaz-Fernández, M. C., Bilgihan, A., Shi, F., & Okumus, F. (2021). UGC involvement, motivation and personality: Comparison between China and Spain. Journal of Destination Marketing & Management, 19, 100543. https://doi.org/10.1016/j.jdmm.2020.100543
- Hewett, K., Rand, W., Rust, R. T., & Van Heerde, H. J. (2016). Brand buzz in the echoverse. Journal of Marketing, 80(3), 1–24. https://doi.org/10.1509/jm.15.0033
- Kaplan, R. S. (2001). Strategic performance measurement and management in nonprofit organizations. Nonprofit Management and Leadership, 11(3), 353–370. https://doi.org/10.1002/nml.11308
- Lai, C., She, B., & Tao, C. (2017). Connecting the dots: A longitudinal observation of relief organizations’ representational networks on social media. Computers in Human Behavior, 74, 224–234. https://doi.org/10.1016/j.chb.2017.04.037
- Lee, C., & Nowell, B. (2014). A framework for assessing the performance of nonprofit organizations. American Journal of Evaluation, 36(3), 299–319. https://doi.org/10.1177/1098214014545828
- Li, S. (2018). Topic modeling and latent dirichlet allocation (lda) in python. Towards Data Science, 31.
- Liu, X. (2020). Analyzing the impact of user-generated content on B2B Firms’ stock performance: Big data analysis with machine learning methods. Industrial Marketing Management, 86, 30–39. https://doi.org/10.1016/j.indmarman.2019.02.021
- Liu, X., Singh, P. V., & Srinivasan, K. (2016). A structured analysis of unstructured big data by leveraging cloud computing. Marketing Science, 35(3), 363–388. https://doi.org/10.1287/mksc.2015.0972
- Miller, D. (2011). Miller (1983) revisited: A reflection on EO research and some suggestions for the future. Entrepreneurship Theory and Practice, 35(5), 873–894. https://doi.org/10.1111/j.1540-6520.2011.00457.x
- Palomino, M., Taylor, T., Göker, A., Isaacs, J., & Warber, S. (2016). The Online Dissemination of Nature–Health Concepts: Lessons from Sentiment Analysis of Social Media Relating to “Nature-Deficit Disorder.” International Journal of Environmental Research and Public Health, 13(1), 142. https://doi.org/10.3390/ijerph13010142
- Rathi, D., Given, L., Forcier, E., & Vela, S. (2016). Every Task its Tool, Every Tool its Task: Social Media Use in Canadian Non-Profit Organizations. Proceedings of the Annual Conference of CAIS / Actes Du Congrès Annuel De L ACSI. https://doi.org/10.29173/cais866
- Razi, M., Tarn, J. M., & Mumuni, A. G. (2019). Effectiveness of incentivized social media campaigns for a Fortune 500 company’s brand. American Journal of Business, 34(1), 37–56. https://doi.org/10.1108/ajb-07-2017-0019
- Rui, H., Liu, Y., & Whinston, A. (2013). Whose and what chatter matters? The effect of tweets on movie sales. Decision Support Systems, 55(4), 863–870. https://doi.org/10.1016/j.dss.2012.12.022
- Salamon, L. M., Anheier, H. K., Studies, J. H. I. F. P., & Project, J. H. C. N. S. (1996). The International Classification of Nonprofit Organizations - ICNPO Revision 1.0.
- Saura, J. R., Debasa, F., & Reyes-Menendez, A. (2019). Does User Generated Content Characterize Millennials’ Generation Behavior? Discussing the Relation between SNS and Open Innovation. Journal of Open Innovation Technology Market and Complexity, 5(4), 96. https://doi.org/10.3390/joitmc5040096
- Saxton, G. D., & Waters, R. D. (2014). What do StakeholdersLikeon Facebook? Examining Public Reactions to Nonprofit Organizations’ Informational, Promotional, and Community-Building Messages. Journal of Public Relations Research, 26(3), 280–299. https://doi.org/10.1080/1062726x.2014.908721
- Tayal, D. K., & Yadav, S. K. (2016). Sentiment analysis on social campaign “Swachh Bharat Abhiyan” using unigram method. AI & Society, 32(4), 633–645. https://doi.org/10.1007/s00146-016-0672-5
- Waters, R. D., & Lo, K. D. (2012). Exploring the impact of culture in the social media sphere: A content analysis of nonprofit organizations’ use of Facebook. Journal of Intercultural Communication Research, 41(3), 297–319. https://doi.org/10.1080/17475759.2012.728772
- Weisbrod, B. A. (1975). Research in Health Economics: A survey. International Journal of Health Services, 5(4), 643–661. https://doi.org/10.2190/kvhp-37n5-f6cd-rl6a
- Xu, X., Wang, X., Li, Y., & Haghighi, M. (2017). Business intelligence in online customer textual reviews: Understanding consumer perceptions and influential factors. International Journal of Information Management, 37(6), 673–683. https://doi.org/10.1016/j.ijinfomgt.2017.06.004
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