Search results

A wealth of studies have identified numerous antecedents to online self-disclosure. However, the number of competing theoretical perspectives and inconsistent findings have hampered efforts to obtain a clear understanding of what truly influences online self-disclosure. To address this gap, this study draws on the antecedent-privacy concern-outcome (APCO) framework in a one-stage meta-analytical structural equation modeling (one-stage MASEM) study to test a nomological online self-disclosure model that assesses the factors affecting online self-disclosure.

Using the one-stage MASEM technique, this study conducts a meta-analysis of online self-disclosure literature that comprises 130 independent samples extracted from 110 articles reported by 53,024 individuals.

The results reveal that trust, privacy concern, privacy risk and privacy benefit are the important antecedents of online self-disclosure. Privacy concern can be influenced by general privacy concern, privacy experience and privacy control. Furthermore, moderator analysis indicates that technology type has moderating effects on the links between online self-disclosure and some of its drivers.

First, with the guidance of the APCO framework, this study provides a comprehensive framework that connects the most relevant antecedents underlying online self-disclosure using one-stage MASEM. Second, this study identifies the contextual factors that influence the effectiveness of the antecedents of online self-disclosure.

Building upon uncertainty reduction theory, this work aims to explore how four uncertainty reduction factors (i.e. online property review, online textual description, online visual description and online instant messenger) mitigate property quality uncertainty and property fit uncertainty, which further influence Airbnb use intention.

This work tests the proposed research model using a structural equation modeling approach with 335 Airbnb users.

The findings reveal that the online property review, online textual description, online visual description and online instant messenger can efficiently mitigate property quality uncertainty and property fit uncertainty, which ultimately influence Airbnb use intention.

This study provides useful insights on mitigating property uncertainty in the peer-to-peer (P2P) accommodation platforms. Researchers are encouraged to investigate the boundary conditions that influence the effectiveness of uncertainty reduction strategies in alleviating property uncertainty.

P2P accommodation service providers are suggested to take actionable uncertainty reduction strategies to mitigate property uncertainty in online P2P accommodation platforms.

First, this study advances research on P2P accommodation by identifying two key types of property uncertainty, namely, property quality uncertainty and property fit uncertainty. Second, this study extends research on P2P accommodation by proposing contextualized passive, active and interactive uncertainty reduction strategies in mitigating property uncertainty. Third, this study extends uncertainty reduction theory to the P2P accommodation context. Fourth, this study enriches uncertainty reduction theory by verifying the mediating effects of property quality uncertainty and property fit uncertainty.

Listing popularity indicates the public’s interest in a listing on peer-to-peer (P2P) accommodation platforms. Although listing popularity is crucial to the survival and development of the P2P accommodation platform, this issue has received limited attention in the tourism management discipline. Drawing upon the heuristic-systematic model and uncertainty reduction theory, this study aims to examine the impacts of host and property attributes on listing popularity.

The model was empirically validated using a data set of 6,828 listings on a popular P2P accommodation platform called Airbnb. This study chooses a hierarchical regression analysis to perform the model validation.

The findings reveal that host self-disclosure, host reputation and host identity verification are key host attributes in promoting listing popularity. Meanwhile, property visual description, property photo verification and property visual appeal are important property attributes in facilitating listing popularity.

The study adds useful insights on understanding on determinants of listing popularity. Future researchers are recommended to empirically verify the underlying psychological mechanism by which host attributes and property attributes influence listing popularity.

The P2P accommodation platform should promote the listing popularity by taking advantage of the host attributes and providing property attributes.

First, to the best of the authors’ knowledge, this study is one of the few studies to explore the formation of the listing popularity. Second, this study examines how the host and property attributes promote the listing popularity through the heuristic and systematic information processing modes.

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.

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.

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.

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.