Online disinhibition: conceptualization, measurement, and implications for online deviant behavior

Abstract

PurposeOnline disinhibition is one of the key factors leading to the occurrence of cyberaggression, cyberbullying and various forms of deviant behaviors in the online environment. To understand the composition of online disinhibition, this study aims to conceptualize online disinhibition and develop a measurement instrument for online disinhibition.Design/methodology/approachWe followed a rigorous procedure to develop and validate the multidimensional instrument of online disinhibition in three phases: item generation, measurement development and instrument testing.FindingsWe developed a 23-item online disinhibition scale and identified six key dimensions: dissociative anonymity, invisibility, asynchronicity, solipsistic introjections, dissociative imagination and minimization of authority.Practical implicationsThe online disinhibition instrument is an accessible and easily administered measure that can be used as a checklist for systems designers and administrators to evaluate the level of online disinhibition among users. It offers systems design information on how to prevent and combat online deviant behaviors on platforms.Originality/valueThis work provides a rich conceptualization of an online disinhibition instrument that can serve as a springboard for future work to understand online deviant behaviors. The newly developed measurement instrument of online disinhibition also adds to the repository of rigorous research scales in this area.

Curing toxicity – developing design principles to buffer toxic behaviour in massive multiplayer online games

“…Embedded in theories of aggressive behaviour on the internet, we propose the concept of the online disinhibition effect (ODE) to offer a chance to theoretically address TB. The ODE already showed its explanatory potential in neighbouring contexts of cyberbullying (CB) (Cheung et al, 2016;Lowry et al, 2017;Udris, 2014) and explained substantial shares of variance in a unified theory of TB (Kordyaka et al, 2020). Additionally, previous research identified different antecedent variables of the ODE (i.e.…”

“…"I feel that my game related life is separated from the offline world"; M = 4.93, SD = 1.23, a = 0.86) and minimization of authority (e.g. "I feel that I can get rid of authority"; M = 4.79, SD = 1.38, a = 0.93)(Cheung et al, 2016).3.1.3.4 Control variables. Additionally, we measured hours of play per week (M = 8.57, SD = 8.98), experience of play in years (M = 4.32, SD = 5.62) and primary-used platform (15% mobile, 58% personal computers and 27% consoles) as control variables to have the chance to prevent unwanted confounding effects on the results.…”

For whom does flow not enhance online gamer loyalty?

“…We computed the loadings and cross-loadings. This is common practice in the literature (Banks, 2020; Cheung et al , 2021; Goode, 2012, 2019). Table 1 shows that the cross-loadings are small, indicating acceptable validity.…”

Examining Relationship between Emotional Intelligence and Counterproductive Work Behavior: A Moderated Mediated Model

“…Counterproductive work behavior (CWB) represents the deliberate conduct by employees that harms an organization, its associates, or both (Spector et al, 2006). CWB is a distinct voluntary action that disrupts substantial organizational rules and well-being of organization, employees or both (Gruys & Sackett, 2003;Robinson & Bennett, 1995;Skarlicki & Folger, 1997 (Godinet & Vakalahi, 2009), antisocial behavior (Burt, 2012), organizational aggression (LeBlanc & Barling, 2004), mobbing/bullying (Sperry, 2009), deviant behaviors (Cheung, Wong, & Chan, 2020), noncompliant behavior (Kleinsinger, 2003), workplace retaliation (Solano & Kleiner, 2003), and revenge (Bies & Tripp, 2005). Counterproductive work behavior's severity ranges from making fun of someone and absenteeism (Spector et al, 2006) to theft and fraud (Wimbush & Dalton, 1997).…”