The Voice of the Consumer on sVoD Systems During Covid-19: A Service Opportunity Mining Approach

Tuğçe Ozansoy Çadırcı, Ayşegül Sağkaya Güngör, Sena Kılıç

Abstract


Abstract

Electronic word of mouth (e-WOM) is a vital channel for the exchange of customer-generated content. As the e-WOM messages created by consumers pile all around the Web, they generate an unbiased voice about products and services. With their high-level production in online environments, e-WOM message contributions go far beyond consumer decision-making. They become a vital source for gaining insights on designing and improving marketing offerings. The purpose of the study is to analyze service improvement opportunities for subscription-based video-on-demand (sVoD) services by exploring customer-generated eWOM messages. In addition to this, the study aims to comprehend the effects of the COVID-19 pandemic on expectations, real feelings, and attitudes of customers towards its subscription-based video-on-demand services and to compare these emotions with those in the pre-pandemic period. Acting on customer-generated e-WOM messages for sVoD services, the paper provides a real-time analysis of monitoring customer needs and wants in a fast-moving service environment with topic-based sentiment analysis. The main procedures include data extraction and pre-processing, topic modeling, sentiment analysis, and opportunity analysis. When pre-covid and post-covid sentiments are compared, it is found that all sentiment scores have decreased, except for content diversity. The rich content offered by Amazon has led subscribers to take step to post positive comments about the platform. Addtionally, the results show possible service improvement opportunities in streaming quality, TV series content selection, use of commercials, and customer value generation in sVoD service encounters. The study identifies service improvement opportunities using data mining technology, which can provide a more in-depth understanding of consumer perceptions of marketing offerings and service quality. In addition, it analyzes the perceptions of consumers toward sVoD services in times of the Covid-19 pandemic.

DOI: https://doi.org/10.54663/2182-9306.2022.sn11.5-29



Keywords


Video-on-demand services; topic modeling; sentiment analysis; Covid-19; customer reviews

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References


Abdullah, M. S. F., & Artanti, Y. (2021). The Effect of Situational Factor, Visual Mer chandising, and Electronic Word of Mouth on Impulsive Buying Behavior on Video on Demand Services Current The Covid-19 Pandemic Crisis. Journal of Business and Behavioural Entrepreneurship, 5(1), 78-91.

Alhidari, A., Iyer, P., & Paswan, A. (2015). Personal level antecedents of eWOM and purchase intention, on social networking sites. Journal of Customer Behaviour, 14(2), 107-125.

Amazon. (2019). https://press.aboutamazon.com/news-releases/news-release details/amazoncom-announces-fourth-quarter-sales-21-874-billion/

Anaya-Sánchez, R., Molinillo, S., Japutra, A., & Millán, A. (2016). Exploring the Sense of Belonging, Participation and Trust in Online Communities: A comparison between Spain and United States. International Journal of Marketing, Communication and New Media, 4(6).

Bickart, B., & Schindler, R. M. (2001). Internet forums as influential sources of consumer information. Journal of interactive marketing, 15(3), 31-40.

Blei, D. M. (2012), Probabilistic Topic Models. Communications of the ACM, 55(4), 77-84.

Blei, D and Lafferty, J.D. (2009). Topic Models. In A.Srivstava and M. Shahami(Eds.), Text Mining Classification, Clustering, and Applications (pp.101-124). Boca Raton: Chapman and Hall/CRC.

Blei, D., Ng, A.Y and Jordan, M.I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Approach, 3, 993-1022.

Bouma, G.(2009). Normalized (pointwise) Mutual Information in Collacation Extraction. In Proceedings of GSCL, 31-40.

Brindha, D., Jayaseelan, R., & Kadeswaran, S. (2020). Covid-19 Lockdown, entertainment and paid ott video-streaming platforms: A qualitative study of audience preferences. Mass Communicator: International Journal of Communication Studies, 14(4), 12-16.

Chang, J. (2015). lda: Collapsed Gibbs Sampling Method for Topic Models. R package version 1.4.2, https://cran.r-project.org/web/packages/lda/lda.pdf.

Collier, J. E., and Kimes, S. E. (2013). Only if it is convenient: Understanding how convenience influences self-service technology evaluation. Journal of Service Research, 16(1), 39-51.

Constantinides, E., & Fountain, S. J. (2008). Web 2.0: Conceptual foundations and marketing issues. Journal of direct, data and digital marketing practice, 9(3), 231-244. Cruz, M., e Silva, S. C., & Machado, J. C. (2017). The influence of WOM and Peer Interaction in the Decision-Making Process of Generation Z within the family. International Journal of Marketing, Communication and New Media, (2).

Çadırcı Ozansoy, Tuğçe; Sağkaya Güngör, Ayşegül (2015). Electronic Word-of-Mouth Communication in Online Social Networks: The Motivational Antecedents of Electronic Word-of-Mouth (eWOM) Engagement in Online Social Networks in Capturing, Analyzing, and Managing Word-of-Mouth in the Digital Marketplace (pp. 77-102), Hersey, PA, IGI-Global Publications.

Dixit, A., Marthoenis, M., Arafat, S. Y., Sharma, P., & Kar, S. K. (2020). Binge watching behavior during COVID 19 pandemic: a cross-sectional, cross-national online survey. Psychiatry research, 289, 113089.

Doh, S. J., & Hwang, J. S. (2009). How consumers evaluate eWOM (electronic word- of-mouth) messages. Cyberpsychology & behavior, 12(2), 193-197.

Dogruel, L. (2018). Cross-Cultural differences in movie selection. decision-making of German, US, and Singaporean media users for video-on-demand movies. Journal of International Consumer Marketing, 30(2), 115-127.

Elwalda, A., Lü, K., & Ali, M. (2016). Perceived derived attributes of online customer reviews. Computers in Human Behavior, 56, 306-319.

Erkan, I., & Elwalda, A. (2018). Your comments are important to me! The impacts of online customer reviews in shopping websites. International Journal of Internet Marketing and Advertising, 12(1), 1-18.

Feinerer, I. and Hornik, K. (2019). tm:Text Mining Package, R package version 0.7-7, https://cran.r-project.org/web/packages/tm/tm.pdf.

Gimpel, G. (2015). The future of video platforms: Key questions shaping the TV and video industry. International journal on media management, 17(1), 25-46.

Godes, D., & Mayzlin, D. (2004). Using online conversations to study word-of-mouth communication. Marketing science, 23(4), 545-560.

Godes, D., & Mayzlin, D. (2004). Using online conversations to study word-of-mouth communication. Marketing science, 23(4), 545-560.

Godinho de Matos, M., and Ferreira, P. (2020). The Effect of Binge-Watching on the Subscription of Video on Demand: Results from Randomized Experiments. Information Systems Research, 31(4), 1337-1360.

Güngör, A. S., and Çadırcı, T. O. (2013). Segmenting eWOM engagers on online social networks based on personal characteristics and behaviour. Ekev Academic Review, 17(57), 33-50.

Hannigan, T. R., Haans, R. F., Vakili, K., Tchalian, H., Glaser, V. L., Wang, M., Kaplan, S. and Jennings, P. D. (2019). Topic modeling in management research: Rendering new theory from textual data. Academy of Management Annals, 13 (2), 586- 632.

Heng, Y., Gao, Z., Jiang, Y., and Chen, X. (2018). Exploring hidden factors behind online food shopping from Amazon reviews: A topic mining approach. Journal of Retailing and Consumer Services, 42, 161-168.

Huang, Y., Liu, H., Li, W., Wang, Z., Hu, X., and Wang, W. (2020). Lifestyles in Amazon: Evidence from online reviews enhanced recommender system. International Journal of Market Research, 62(6), 689-706.

Jockers, M. (2020). syuzhet: Extracts Sentiment and Sentiment Derived Plot Arcs from Text, R package version 1.0-6 https://cran.r-project.org/web/packages/syuzhet/syuzhet.pdf.

Kim, K., Yoon, S., and Choi, Y. K. (2019). The effects of eWOM volume and valence on product sales–an empirical examination of the movie industry. International Journal of Advertising, 38(3), 471-488.

Köster, A., Matt, C., & Hess, T. (2021). Do All Roads Lead to Rome? Exploring the Relationship Between Social Referrals, Referral Propensity and Stickiness to Video-on- Demand Websites. Business & Information Systems Engineering, 63(4), 349-366. Kübler, R., Seifert, R., and Kandziora, M. (2020). Content valuation strategies for digital subscription platforms. Journal of Cultural Economics, 1-32.

Leichtman Research Group, Inc. (2020). https://www.leichtmanresearch.com/wp content/uploads/2020/08/LRG-Press-Release-08-28-20.pdf

Li, X., and Hitt, L. M. (2010). Price effects in online product reviews: An analytical model and empirical analysis. MIS quarterly, 809-831.

Lin, T. M., Lu, K. Y., & Wu, J. J. (2012). The effects of visual information in eWOM communication. Journal of Research in Interactive Marketing. 6(1), 7-26.

Lindlahr, S. (2020). Forecast of Video-on-Demand users by segment worldwide from 2017 to 2025(in million). Statista. https://www.statista.com/forecasts/456771/video- on- demand-users-worldwide-forecast.

Littleton, C. (2014). Linear TV Watching Down, Digital Viewing Up in Nielsen’s Q3 Report. Variety, (December 3),[available at http://variety. com/2014/tv/news/linear-tv- watching-down-digital-viewing-up-in-nielsens-q3-report-1201369665/].

Luo, Y. (2020) The Streaming War During the Covid-19 Pandemic.

Mohammad, S., and Turney, P. (2010, June). Emotions evoked by common words and phrases: Using mechanical turk to create an emotion lexicon. In Proceedings of the NAACL HLT 2010 workshop on computational approaches to analysis and generation of emotion in text (pp. 26-34).

Mikos, L. (2020). Film and Television Production and Consumption in Times of the COVID-19 Pandemic–The Case of Germany. Baltic Screen Media Review, 8(1), 30-34. Mohd-Any, A. A., Winklhofer, H., and Ennew, C. (2015). Measuring users' value experience on a travel website (e-value) what value is cocreated by the user?. Journal of Travel Research, 54(4), 496-510.

Naratthawan, T. (2021). Factors Affecting Online Media Streaming Service Subscriptions Behaviors During The Covid-19 Pandemic In Thailand (Doctoral dissertation, Mahidol University).

Nielsen Report on "COVID-19: TRACKING THE IMPACT ON MEDIA CONSUMPTION"https://www.nielsen.com/us/en/insights/article/2020/covid-19- tracking-the-impact-on-media-consumption/

Newman, D., Lau, J.H., Gieser, K. and Baldwin, T. (2010). Automatic Evaluation of Topic Coherence. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics , 100-108. Noah, S. (2020). Dual portfolio management strategies of online subscription video on demand (SVOD) companies: a genre perspective. Journal of Media Business Studies, 1- 22.

Oyedele, A., and Simpson, P. M. (2018). Streaming apps: What consumers value. Journal of Retailing and Consumer Services, 41, 296-304.

Riekkinen, J. (2018). Piracy versus netflix: Subscription video on demand dissatisfaction as an antecedent of piracy. In Proceedings of the Annual Hawaii International Conference on System Sciences;. University of Hawai'i at Manoa. Rinker,T. (2019). “sentimentr:Calculate Text Polarity Sentiment”, R package version 2.7-1, https://cran.r-project.org/web/packages/sentimentr/sentimentr.pdf.

Sankar, H., Subramaniyaswamy, V., Vijayakumar, V., Arun Kumar, S., Logesh, R., and Umamakeswari, A. J. S. P. (2020). Intelligent sentiment analysis approach using edge computing‐based deep learning technique. Software: Practice and Experience, 50 (5), 645-657.

Sievert, C. and Shirley, K. (2014). LDAvis: A Method for Visualizing and Interpreting Topic Models. In Proceedings of the Workshop on Interactive Language Learning, Visualization, and Inferences, 63-70.

Sijoria, C., Mukherjee, S., & Datta, B. (2018). Impact of the antecedents of eWOM on CBBE. Marketing Intelligence & Planning. 36 (5), 528-542.

Statista, Digital Market Outlook (2020) - Segment Report- Digital Media Report 2020 - Video-on-Demand.

Susanno, R., Phedra, R., and Murwani, I. A. (2019). The determinant factors of the intention to spend more time binge-watching for Netflix subscriber in Jakarta. Journal of Research in Marketing, 10(3), 807-812.

Statista, number of movies and TV shows subscribers get per dollar on major SVoD platforms in the United States as of September 2020. https://www.statista.com/statistics/1110891/svod-content-value-for-money-us/ Tashanova, D., Sekerbay, A., Chen, D., Luo, Y., Zhao, S., and Zhang, T. (2020). Investment Opportunities and Strategies in an Era of Coronavirus Pandemic. SSRN 3567445.

Thakur, R. (2018). Customer engagement and online reviews. Journal of Retailing and Consumer Services, 41, 48-59.

Trenz, M. and Berger, B. (2013). "Analyzing Online Customer Reviews - An Interdisciplinary Literature Review and Research Agenda". ECIS 2013 Completed Research. 83. https://aisel.aisnet.org/ecis2013_cr/83.

Ulwick, A. W. (2002). Turn customer input into innovation. Harvard business review, 80(1), 91-98.

Vega, M. T., Perra, C., De Turck, F., and Liotta, A. (2018). A review of predictive quality of experience management in video streaming services. IEEE Transactions on Broadcasting, 64(2), 432-445.

Wang, C. C., & Wang, Y. T. (2010). Persuasion effect of e-WOM: The impact of involvement and ambiguity tolerance. Journal of Global Academy of Marketing, 20(4), 281-293.

Wang, Y., Wang, Z., Zhang, D., and Zhang, R. (2019). Discovering cultural differences in online consumer product reviews. Journal of Electronic Commerce Research, 20(3), 169-183.

Wayne, M. L. (2018). Netflix, Amazon, and branded television content in subscription video on-demand portals. Media, Culture & Society, 40(5), 725-741.

Wenzel, P., Mahle, I., and Pätzmann, J. U. (2016). Streaming Services and Service Design: An Analysis of Netflix and Amazon Video Based on the Gap Model by Parasuraman, Berry and Zeithaml. Markenbrand, (5/2016), 20-31.

Westbrook, R.A. (1987), "Product/consumption-based affective responses and post purchase processes", Journal of Marketing Research, Vol. 24 No. 3, pp. 258-70.

Wu, T. (2013). Netflix's war on Mass Culture. New Republic, 4.

Yaylı, A., and Bayram, M. (2012). E-WOM: The effects of online consumer reviews on purchasing decisions. International Journal of Internet Marketing and Advertising, 7(1), 51-64.


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International Journal of Marketing, Communication and New Media

ISSN: 2182-9306

DOI: 10.54663/2182-9306

Qualis Periódicos - CAPES: B2

REBIB: Q2


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