Suchergebnisse

Purpose
Peer-to-peer (P2P) accommodation has become increasingly popular in recent years, and hotels are facing unprecedented impacts. Attracting new consumers and retaining existing ones are critical to the success of P2P accommodation and hotels. The purpose of this paper is to examine three categories of antecedents for hotels consumers' switching intention: push (i.e. satiation), pull (i.e. perceived value) and mooring (i.e. optimal stimulation level) factors using push–pull–mooring (PPM) model.


Design/methodology/approach
Airbnb was chosen as the research context. An online survey was conducted to examine the proposed research model and hypotheses. A total of 292 valid data were collected from Airbnb users through a survey.


Findings
The findings show that the three categories of factors have positive and significant effects on switching intention. Additionally, the mooring factor has a significant moderating effect on the relationship between pull factors and switching intention. Furthermore, the mooring factor affects both pull and push factors.


Originality/value
First, this is one of the early studies to pay attention to switching intention from hotels to P2P accommodation. Second, to provide a comprehensive understanding of consumers' switching intention, the authors use PPM model to establish the research framework. This research improves the understanding of consumer's switching intention by identifying the push and pull factors based on the differences between hotels and P2P accommodation in accordance with optimal stimulation level theory and consumer value theory.

Purpose– With the prevailing microblogging phenomenon, many marketers have created their microblog accounts to promote products, advertise brands, and attract consumers. The purpose of this paper is to examine the effects of consumers' community commitment, information technology (IT) habit, and participation on their brand loyalty in the context of enterprise microblogs.Design/methodology/approach– A total of 364 valid data were collected from fans or followers of enterprise microblogs through a survey onWeibo.com. The data were analyzed with smart partial least squares.Findings– Community commitment and IT habit directly lead to brand loyalty and indirectly affect brand loyalty through the mediating influence of participation. Information quality, perceived expertise, and social interaction are antecedents of community commitment. Satisfaction, importance, and social interaction positively affect IT habit.Research limitations/implications– To enhance the generalizability of the study, future studies may examine findings in other platforms (e.g. Twitter) with a large sample size.Practical implications– Practitioners can increase consumers' brand loyalty through enterprise microblogs. Considerable attention should be directed to facilitating members' community commitment and IT habit. In addition, the study points out significant antecedents of community commitment and IT habit for researchers, managers, and companies.Originality/value– To uncover the marketing potential of microblogging technology, the authors highlight the need for investigating how enterprise microblogs generate promising marketing outcomes. The present study examines key determinants of brand loyalty. The model empirically shows the significant effects of community commitment, IT habit, and participation.

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.