Understanding users’ negative responses to recommendation algorithms in short-video platforms: a perspective based on the Stressor-Strain-Outcome (SSO) framework

Adolescents’ Algorithmic Resistance to Short Video APP’s Recommendation: The Dual Mediating Role of Resistance Willingness and Resistance Intention

“…Therefore, this study attempts to propose an explicit research model that can integrate the above-mentioned factors that affect algorithmic resistance. Third, some studies have made an ambiguous distinction between algorithmic recommendation content, a particular feature of the algorithm (i.e., greedy), and the recommendation system itself ( Ma et al, 2021 ), which makes it impossible to distinguish what users’ resistance intentions and behaviors are targeted, and this study points to the recommendation system of short video platforms. Furthermore, although several prior studies have attempted to “decode algorithms” from a user perspective ( Lomborg and Kapsch, 2020 ), most of them have been conducted through qualitative methods such as semi-structured interviews and self-reports ( Koenig, 2020 ; Lomborg and Kapsch, 2020 ; Swart, 2021 ).…”

“…This dependency on recommendation algorithms is likely to lead adolescent users to view them as a “necessity” when using social media, ultimately reducing their fatigue and resistance to recommendation algorithms. Some studies also have found that immersion and mind flow intensify adolescents’ negative reactions to recommendation algorithms ( Lin et al, 2020 ; Ma et al, 2021 ). In summary, the following two hypotheses are considered reasonable:…”

“…Social media users’ affective attitudes and willingness to act toward algorithms influence their eventual behavior of interacting with (resisting) algorithms. Previous studies concerning user responses to social media and recommendation algorithms have been conducted under the S-O-R framework ( Lin et al, 2020 ; Ma et al, 2021 ), which regard users as an object that receive a stimulation, generate psychological reactions, and makes corresponding behavioral responses, without making a clear distinction between users’ behavioral awareness and behavioral willingness. However, not all social media behaviors are logical or rational ( Li and Ye, 2022 ).…”

“…Compared with the active but time-consuming video searching behavior, recommendation algorithms make it more efficient for social media users to obtain the diverse and abundant content they are interested in through accurate recommendations based on big data, completing the transformation from “I look for information” to “information finds me” ( Park and Kaye, 2020 ). Despite the potential advantages that recommendation algorithms provide to users, they also have the “dark side” ( Salmela-Aro et al, 2017 ; Springer and Whittaker, 2018 ; Ma et al, 2021 ). Previous research has shown that as a structural power that constrains users’ agency ( Reviglio and Agosti, 2020 ; Schwartz and Mahnke, 2021 ), recommendation algorithms pose a range of problems such as visibility hegemony ( Swart, 2021 ), operational opacity ( Kulshrestha et al, 2017 ), bias and discrimination ( Kulshrestha et al, 2017 ), information overload ( Lin et al, 2020 ), disinformation and misinformation ( Clark, 2020 ) and privacy breaches ( Lam and Hsu, 2006 ), which may lead to negative psychological and behavioral responses from platform users, such as social media fatigue ( Bright et al, 2015 ; Dhir et al, 2019 ; Whelan et al, 2020 ; Fan et al, 2021 ; Pang, 2021 ), fear of missing out ( Roberts and David, 2020 ; Tandon et al, 2021 ), and forced disconnection ( Nguyen, 2021 ; Vanden Abeele et al, 2022 ), platform migration ( Maier et al, 2015 ; Luqman et al, 2017 ), etc.…”

“…Second, existing research on users’ negative reactions to platform recommendation algorithms has focused more on the influence of users’ psychological factors on resistance ( Velkova and Kaun, 2021 ). For example, most studies have used the S-O-R framework ( Lin et al, 2020 ; Ma et al, 2021 ) while lacking consideration of individual ability factors as well as interpersonal factors. Therefore, this study attempts to propose an explicit research model that can integrate the above-mentioned factors that affect algorithmic resistance.…”