Recommended for You: Explicit Motivations and Recommender Systems Influence Users’ Media Engagement and Well-Being

Abstract

People are inundated with readily accessible entertainment media choices. Recommender systems are frequently utilized to support people's media selection and engagement. The psychological factors that influence people's engagement with recommender systems and entertainment content is an under-explored area. This paper reports the findings of an exploratory study where users’ explicit experiences of audiovisual media services (e.g., Netflix, TikTok, YouTube, HBO Max) and their recommender systems were examined through an online survey (n = 339). Thematic analysis, factor analysis, and path analysis were used to evaluate the data. The results indicate that (1) recommender systems influence users’ emotions and media use, (2) people prefer personalized compared to generic recommendation lists, (3) the temporal length of recommended content influences people's interest, and (4) perceived utility leads to the endorsement of recommender systems and may influence users’ well-being. The findings point to wider psychological implications associated with user awareness of recommender systems.

Keywords

entertainment preferences media psychology choice personalization aesthetic experience well-being

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References

Adamowicz

Swait

J. D.

(2012). Are food choices really habitual? Integrating habits, variety-seeking, and compensatory choice in a utility-maximizing framework. American Journal of Agricultural Economics, 95(1), 17–41. https://doi.org/10.1093/ajae/aas078

Arun

T. M.

Signh

Khan

S. J.

Akram

M. U.

Chauhan

(2021). Just one more episode: Exploring consumer motivations for adoption of streaming services. Asia Pacific Journal of Information Systems, 31(1), 17–42. https://doi.org/10.14329/apjis.2021.31.1.17

Bartsch

(2012). Emotional gratification in entertainment experience. Why viewers of movies and television series find it rewarding to experience emotions. Media Psychology, 15(3), 267–302. https://doi.org/10.1080/15213269.2012.693811

Birney

(2024). What is the maximum length for a tiktik video in 2024? A look at how tiktik’s time lengths have changed and why. Android Authority. https://www.androidauthority.com/how-long-are-tiktok-videos-3163309/

Bogost

(2014). Reverse engineering Netflix. Open Transcripts. http://opentranscripts.org/transcript/reverse-engineering-netflix/

Bondad-Brown

B. A.

Rice

R. E.

Pearce

K. E.

(2012). Influences on tv viewing and online user-shared video use: Demographics, generations, contextual age, media use, motivations, and audience activity. Journal of Broadcasting & Electronic Media, 56(4), 471–493. https://doi.org/10.1080/08838151.2012.732139

Braun

Clarke

(2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

Camilleri

M. A.

Falzon

(2021). Understanding motivations to use online streaming services integrating the technology acceptance model (TAM) and the uses and gratifications theory (UGT). Spanish Journal of Marketing, 25(2), 216–236. https://doi.org/10.1108/SJME-04-2020-0074

Cardario

Morewedge

C. K.

(2022). Why do people eat the same breakfast every day? Goals and circadian rhythms of variety seeking in meals. Appetite, 168(2022), 105716. https://doi.org/10.1016/j.appet.2021.105716

10.

Castro

Rigby

J. M.

Cabral

Nisi

(2021). The binge-watcher’s journey: Investigating motivations, contexts, and affective states surrounding netflix viewing. Convergence: The International Journal of Research into New Media Technologies, 27(1), 3–20. httpsss://doi.org/10.1177/1354856519890856

11.

Ceci

(2024). Most popular streaming services worldwide as of January 2024, by number of monthly visits. Statista. https://www.statista.com/statistics/1461063/svod-video-platforms-traffic-global/

12.

Chakraborty

Siddiqui

Paul

Dash

Mas

F. D.

(2023). Watching is valuable: Consumer views – content consumption on OTT platforms. Journal of Retailing and Consumer Services, 70(2023), 103148. https://doi.org/10.1016/j.jretconser.2022.103148

13.

Cotter

K. N.

Pawelski

J. O.

(2022). Art museums as institutions for human flourishing. The Journal of Positive Psychology, 17(2), 288–302. https://doi.org/10.1080/17439760.2021.2016911

14.

Derrick

J. L.

Gabriel

Hugenberg

(2009). Social surrogacy: How favored television programs provide the experience of belonging. Journal of Experimental Social Psychology, 45(2), 352–362. https://doi.org/10.1016/j.jesp.2008.12.003

15.

Diener

Wirtz

Tov

Kim-Prieto

Choi

Oishi

Biswas-Diener

(2009). New well-being measures: Short scales to assess flourishing and positive and negative feelings. Social Indicator Research, 97(2010), 143–156. https://doi.org/10.1007/s11205-009-9493-y

16.

Dixon

S. J.

(2024). Most popular social networks worldwide as of April 2024, ranked by number of monthly active users. Statista. https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/

17.

Eklund

(2022). Custom thumbnails: The changing face of personalization strategies on netflix. Convergence: The International Journal of Research into New Media Technologies, 28(3), 737–760. https://doi.org/10.1177/1354856521064520

18.

Flayelle

Maurage

Di Lorenzo

K. R.

Vögele

Gainsbury

S. M.

Billieux

(2020). Binge-watching: What do we know so far? A first systematic review of the evidence. Current Addiction Reports, 7(2020), 44–60. https://doi.org/10.1007/s40429-020-00299-8

19.

Galderisi

Heinz

Kastrup

Beezhold

Sartorius

(2015). Toward a new definition of mental health. World Psychiatry, 14(2), 231–233. https://doi.org/10.1002/wps/2-231

20.

Goldbeck

Norris

(2013). Personality, preferences, and recommendations. 2013 IEEE/ACM International Conference on Advances In Social Networks Analysis and Mining, 1414–1215. https://doi.org/10.1145/2492517.2492572

21.

Goldbeck

Norris

(2013). Proceedings of the 2013 IEEE/ACM internation conference on advances in social network analysis and mining (pp. 1414–1415). Association for Computing Machinery. https://doi.org/10.1145/2492517.2492572

22.

Gomez-Uribe

C. A.

Hunt

(2015). The netflix recommender systems: Algorithms, business value, and innovation. ACM Transaction on Management Information Systems, 6(4), 1–19. https://doi.org/10.1145/2843948

23.

Guo

(2022). The impacts of service quality, perceived value, and social influences on video streaming service subscription. International Journal of Media Management, 24(2), 65–86. https://doi.org/10.1080/14241277.2022.2089991

24.

Guttmann

(2023, August 29). Time spent with digital vs. Traditional Media in the U.S. 2025. Statista. https://www.statista.com/statistics/565628/time-spent-digital-traditional-media-usa/

25.

Hasan

M. R.

Jha

A. K.

Liu

(2018). Excessive use of online video streaming services: Impact of recommender system use, psychological factors, and motives. Computers in Human Behavior, 80(2018), 220–228. https://doi.org/10.1016/j.chb.2017.11.020

26.

Higgs

Ruddock

(2020). Social influences on eating. In Meiselman

(Ed.), Handbook of eating and drinking (pp. 277–291). Springer. https://doi.org/10.1007/978-3-030-14504-0_27.

27.

Hildén

(2022). The public service approach to recommender systems: Filtering to cultivate. Television & New Media, 23(7), 777–796. https://doi.org/10.117/1527464211020106

28.

Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

29.

Hutmacher

Appel

(2022). The psychology of personalization in digital environments: From motivation to well-being – A theoretical integration. Review of General Psychology, 27(1), 26–40. https://doi.org/10.1177_10892680221105663

30.

Janicke-Bowles

S. H.

Raney

A. A.

Oliver

M. B.

Dale

K. R.

Zhao

Neumann

Clayton

R. B.

Hendry

A. A.

(2022). Inspiration on social media: Applying an entertainment perspective to longitudinally explore mental health and well-being. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 16(2), Article 1. https://doi.org/10.5817/CP2022-2-1

31.

Joo

Sang

(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. https://doi.org/10.1016/j.chb.2013.06.002

32.

Kang

Lou

(2022). AI Agency vs. Human agency: Understanding human-ai interaction on TikTok and their implications for user engagement. Journal of Computer-Mediated Communication, 27(5), 1–13. https://doi.org/10.1093/jcmc/zmac014

33.

Kline

R. B.

(2011). Principles and practice of structural equation modeling (3rd ed.). Guilford Press.

34.

Koo

(2022). Picturing diversity: Netflix’s inclusion strategy and the netflix recommender algorithm (NRA). Television & New Media, 24(3), 1–17. https://doi.org/10.1177/15274764221102864

35.

LaRose

Eastin

M. S.

(2004). A social cognitive theory of internet uses and gratifications: Toward a new model of media attendance. Journal of Broadcasting and Electronic Media, 48(3), 358–377. https://doi.org/10.1207/s15506878jobem4803_2

36.

Little

R. J. A.

(1988). A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association, 83(404), 1198–1202. https://doi.org/10.1080/01621459.1988.10478722

37.

Lobato

(2019). Netflix nations: The geography of digital distribution. New York University Press.

38.

Lumivero (2020). NVivo (Version 13, 2020 R1), www.lumivero.com.

39.

Madrigal

(2014). How Netflix reverse-engineered Hollywood. The Atlantic. https://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/

40.

Matassi

Boczkowski

P. J.

(2021). An agenda for comparative social media studies: The value of understanding practices from cross-national, cross-media, and cross-platform perspectives. International Journal of Communication, 15(22), 207–228. https://doi.org/https://ijoc.org/index.php/ijoc/article/view/15042

41.

Melchionne

(2017). Aesthetic choice. British Journal of Aesthetics, 57(3), 283–298. https://doi.org/10.1093/aesthj/ayx019

42.

Meyerend

(2022). The algorithm knows i’m black: From users to subjects. Media, Culture & Society, 45(3), 1–17. https://doi.org/10.1177/01634437221140539

43.

Nielsen (2023). Streaming grabs a record 38.7% of total TV usage in July, with acquired titles outpacing New Originals. Retrieved August 1, 2023. https://www.nielsen.com/insights/2023/streaming-grabs-a-record-38-7-of-total-tv-usage-in-july-with-acquired-titles-outpacing-new-originals/.

44.

Oliver

M. B.

(2022). Social media use and eudaimonic well-being. Current Opinion in Psychology, 45(2022), 101307. https://doi.org/10.1016/j.copsyc.2022.101307

45.

Pajkovic

(2022). Algorithms and taste-making: Exposing the netflix recommender system’s operational logics. Convergence: The International Journal of Research into New Media Technologies, 28(1), 214–235. https://doi.org/10.1177/13548565211014464

46.

Peters

Calvo

R. A.

Ryan

R. M.

(2018). Designing for motivation, engagement and wellbeing in digital experience. Frontiers in Psychology, 9(2018), 1-15. https://doi.org/10.3389/fpsyg.2018.00797

47.

Raza

S. H.

Yousaf

Sohail

Munawar

Ogadimma

E. C.

Siang

J. M. L. D.

(2021). Investigating binge-watching adverse mental health outcomes during COVID-19 pandemic: Moderating role of screen time for web series using online streaming. Psychology Research and Behavior Management, 14(2021), 1615–1629. https://doi.org/10.2147/PRBM.S328416

48.

R Core Team (2022). R: A Language and environment for statistical processing. R Foundation for Statistical Computing. https://www.R-project.org/

49.

Reinecke

Kreling

R. J.

(2022). The longitudinal influence of hedonic and eudaimonic entertainment preferences or psychological resilience and wellbeing. Frontiers in Communication, 7(2022), 991458. https://doi.org/10.3389/fcomm.2022.991458

50.

Revelle

(2023). psych: Procedures for Psychological, Psychometric, and Personality Research. Northwestern University. R package version 2.3.6, https://CRAN.R-project.org/package=psych.

51.

Rosseel

(2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. https://doi.org/10.18637/jss.v048.i02

52.

Rubin

A. M.

(1981). An examination of television viewing motivations. Communication Research, 8(2), 141–165. https://doi.org/10.1177/009365028100800201

53.

Ryan

R. M.

Deci

E. L.

(2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68

54.

Shin

(2020). User perceptions of algorithmic decision in the personalized AI system: Perceptual evaluation of fairness, accountability, transparency, and explainability. Journal of Broadcasting & Electronic Media, 64(4), 541–565. https://doi.org/10.1080/08838151.2020.1843357

55.

Shin

(2021). The effects of exaplainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human – Computer Studies, 146(2021), 102551. https://doi.org/10.1016/j.ijhcs.2020.102551

56.

Sigre-Leirós

Billieux

Mohr

Maurage

King

D. L.

Schimmenti

Flayelle

(2022). Binge-watching in times of COVID-19: A longitudinal examination of changes in affect and TV series consumption patterns during lockdown. Psychology of Popular Media, Advance online publication, 12(2), 173-185. https://doi.org/10.1037/ppm0000390

57.

Siles

Espinoza-Rojas

Naranjo

Tristán

M. F.

(2019). The mutual domestication of users and algorithmic recommendations on netflix. Communication, Culture & Critique, 12(4), 1–20. https://doi.org/10.1093/ccc/tcz025

58.

Srivastava

(2011). Positive mental health and its relationship with resilience. Industrial Psychiatry Journal, 20(2), 75–76. https://doi.org/10.4103/0972-6748.102469

59.

Starke

Baleis

Keller

Marcinkowski

(2022). Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature. Big Data & Society. 9(2), 1–16. https://doi.org/10.1177/20539517221115189

60.

Tay

Diener

(2014). The development and validation of the comprehensive inventory of thriving (CIT) and the brief inventory of thriving (BIT). Applied Psychology: Health and Well-Being, 6(3), 251–380. https://doi.org/10.1111/aphw.12027

61.

Sundar

S. S.

(2020). Rise of machine agency: A framework for studying the psychology of human-AI interaction (HAII). Journal of Computer-Mediated Communication, 25(1), 74–88. https://doi.org/10.1093/jcmc/zmz026

62.

Thakker

Patel

Shah

(2021). The comprehensive analysis on movie recommendation system employing collaborative filtering. Multimedia Tools and Applications, 80(2021), 2864–28672. https://doi.org/10.1007/s11042-021-10965-2

63.

Dilley

A. E.

Kaufman

J. C.

(2015). Do we create what we watch? Creativity and entertainment preferences. Psychology of Aesthetics, Creativity, and the Arts, 9(4), 394–404. https://doi.org/10.1037/aca0000032

64.

Ullmann-Margalit

(2006). Big decisions: Opting, converting, drifting. Royal Institute of Philosophy Supplements, 58(2006), 157–172. https://doi.org/10.1017/S1358246100009358

65.

Varela

Kaun

(2019). The netflix experience: A user-focused approach to the netflix recommendation algorithm. In Plothe

Buck

A. M.

(Eds.), Netflix at the nexus: Content, practice, and production in the age of streaming television (pp. 197–211). Peter Lang Publishing.

66.

Venkatesh

Morris

M. G.

Davis

G. B.

Davis

F. D.

(2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

67.

Venkatesh

Thong

J. Y. L.

(2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/https://ssrn.com/absract=2002388

68.

Vorderer

Halfmann

(2019). Why do we entertain ourselves with media narratives? A theory of resonance perspective on entertainment experiences. Annals of the International Communication Association, 43(2), 79–96. https://doi.org/10.1080/23808985.2019.1599298

69.

Vorderer

Reinecke

(2015). From mood to meaning: The changing model of the user in entertainment research. Communication Theory, 25(4), 447–453. https://doi.org/10.1111/comt.12082

70.

Wickham

(2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.

71.

Worthington

R. L.

Whittaker

T. A.

(2006). Scale development research: A content analysis and recommendations for best practices. The Counseling Psychologist, 34(6), 806–838. https://doi.org/10.1177/0011000006288127

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