Fun or Frustration: Modelling Discontinuance Intention of Social Media Users

Abstract

Excessive usage of social network services (SNS) can lead to exhaustion and frustration, which, in turn, can induce SNS discontinuance intention. In this study, the negative factors when using SNS are empirically tested. We constructed a research model that explores the effects of social overload, disclosure, and pattern of SNS use on SNS-induced exhaustion and frustration. We also examined the influence of exhaustion and frustration on SNS discontinuance intention. An empirical study of 170 SNS users from Bangladesh provides support for our hypotheses. Partial least squares algorithm is implemented to test the hypotheses. The result reveals significant influence of frustration on SNS discontinuance intention. The paper contributes by identifying several factors. Both theoretical and practical implications are discussed.

Social media discontinuation: A systematic literature review on drivers and inhibitors

“…Our results show that the existing SMD studies are grounded in different social media platforms (e.g., Facebook, WeChat) (e.g. Mahmud et al, 2020;Lin et al, 2020) and were conducted in different countries (e.g., Australia, China, Finland, Germany, India, Pakistan, and the USA) (e.g. Wang et al, 2020;Wu et al, 2019;Cao et al, 2020), but these platform and cultural differences were not accounted for.…”

“…Further emotional factors studied so far are dissatisfaction (Wu et al, 2019;Zhang et al, 2016;Zhou et al, 2018;Zhang et al, 2015;Nawaz et al, 2018), frustration (Mahmud et al, 2020;Maier, 2020), distress (Cao et al, 2020), strain (Luqman et al, 2020) towards SMD (Luqman et al, 2018), and stress (Maier, 2020). One paper examined the case of COVID-19 and identified fear of COVID-19, due to the abundance of information on social media on the pandemic, as a factor that positively influences SMD (Liu et al, 2021).…”

“…Social overload, defined as the increased level of social support one is expected to provide to others through social media, is the most prominent factor (Maier et al, 2012). Social overload is positively related to regret (Cao and Sun, 2018;Wang et al, 2020), social media exhaustion (Cao and Sun, 2018;Cao et al, 2020), distress (Cao et al, 2020), frustration (Mahmud et al, 2020), social media fatigue (Zhang et al, 2015;Zhang et al, 2016;Lin et al, 2020), and dissatisfaction (Zhang et al, 2015;Nawaz et al, 2018). Other relational factors that have an indirect impact on SMD are communication overload (Cao and Sun, 2018;Luqman et al, 2020;Mahmud et al, 2020), cyberbullying (Cao et al, 2020), technology-family friction (Luqman et al, 2020) and extrinsic motivation to abandon social media (Maier, 2020).…”

Modelling consumer’s intention to use iot devices: role of technophilia

“…Table 3 shows that the square root of AVE is more prominent than the corresponding construct correlation, which demonstrates that our construct is particular from different develops [36]. The correlation values should be greater than among other latent variables [37]. Table 4 shows these values.…”

Mathematical Modelling of Teachers’ Intention to Participate in Online Training During Covid-19 Lockdown: Evidence from Emerging Economy

“…For the demographic data analysis, we used SPSS version 21. In the second part, we used partial least squares regression testing using SMARTPLS 3.0 [20] [21]. In smartPLS 3.0 we followed two types of results following the research of [22].…”