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Purpose
Drawing on the control agency theory and the network effect theory, the purpose of this paper is to examine the effect of privacy assurance approaches, network externality and technology complementarity on consumers' self-disclosure in mobile payment (MP) applications. The authors identify four types of privacy assurance approaches: perceived effectiveness of privacy setting, perceived effectiveness of privacy policy, perceived effectiveness of industry self-regulation and perceived effectiveness of government legislation. The research model considers how these privacy assurance approaches influence privacy concerns and consumers' self-disclosure in MP applications under boundary conditions of network externality and technology complementarity.


Design/methodology/approach
An online survey with 647 sample users was conducted to empirically validate the model. The target respondents were current consumers of a popular MP application. The empirical data were analyzed by a structural equation modeling approach.


Findings
The empirical results reveal several major findings. First, privacy assurance approaches can effectively decrease privacy concerns, which ultimately formulates consumers' self-disclosure in MP applications. Second, network externality and technology complementarity weaken the effect of perceived effectiveness of privacy setting on privacy concerns. Third, network externality and technology complementarity strengthen the relationship between perceived effectiveness of government legislation and privacy concerns, while they have non-significant interaction effect with perceived effectiveness of privacy policy and industry self-regulation on privacy concerns.


Practical implications
MP providers and stakeholders can harness the efficacy of privacy assurance approaches in alleviating privacy concerns and promoting consumers' self-disclosure in MP applications.


Originality/value
The authors' work contributes to the information privacy literature by identifying effective privacy assurance approaches in promoting consumers' self-disclosure in MP applications, and by highlighting boundary conditions of these privacy assurance approaches.

Purpose
Drawing on the social learning theory, the purpose of this paper is to examine the antecedents and consequences of users' excessive online social gaming. Specifically, the authors develop a model to propose that observational learning and reinforcement learning mechanisms together determine excessive online social gaming, which further foster adverse consequences.


Design/methodology/approach
The model is empirically validated by a longitudinal survey among users of a popular online social game: Arena of Valor. The empirical data are analyzed using component-based structural equation modeling approach.


Findings
The empirical results offer two key findings. First, excessive online social gaming is determined by observational learning factors, i.e. social frequency and social norm, and reinforcement learning factors, i.e. perceived enjoyment and perceived escapism. Second, excessive online social gaming leads to three categories of adverse consequences: technology-family conflict, technology-work conflict and technology-person conflict. Meanwhile, technology-family conflict and technology-work conflict further foster technology-person conflict.


Originality/value
This study contributes to the literature by developing a nomological framework of excessive online social gaming and by extending the social learning theory to excessive technology use.