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From a relational maintenance perspective, this study explores the impact of being envied (benignly vs maliciously) on consumers' feelings of social anxiety and its influence on their tendencies toward inconspicuous consumption, based on the resource conservation theory and the model of “Sensitivity about Being the Target of a Threatening Upward Comparison.” (STTUC)

Four studies were conducted in this paper. Studies 1a and 1b tested the main hypothesis that being maliciously envied (vs benignly) can increase consumers' inconspicuous consumption of luxury products and luxury hotel experiences. Study 2 replicated this finding and examined the mediating role of social anxiety. Study 3 investigated the moderating effect of ideal self-congruity (low vs high).

The findings reveal that being maliciously envied (vs benignly) is associated with higher levels of inconspicuous consumption and social anxiety acts as a mediating role. Moreover, when individuals have a strong sense of ideal self-congruity, the positive impact of being maliciously envied (vs benignly) on inconspicuous consumption is further amplified, confirming the moderating role of ideal self-congruity.

This study sheds light on a novel mechanism that elucidates how different types of being envied influence consumers' inconspicuous consumption and the conditions under which this impact is heightened.

This study aims to explore how perceived usefulness and perceived ease of use affect users' sensory experience and behavioral experience in the metaverse context, and explore the significance of building a metaverse platform for companies, and provide management enlightenment for corporate development.

Data were collected from 329 online participants and analyzed using structural equation modeling and hierarchical regression.

Perceived usefulness and ease of use have a significant influence on customer sensory experience and behavioral experience in the metaverse. Sensory experience mediates the influence of perceived usefulness and availability on behavioral experience. Social presence positively moderates the influence of both usefulness and ease of use on sensory and behavioral experience. In other words, perceived usefulness and perceived ease of use in the metaverse situation affect sensory experience through social presence, and then affect behavioral experience.

This research clarifies the effect of usefulness and ease of use on customer sensory and behavioral experience. It confirms the positive moderating role of social presence and the mediating role of sensory experience in the influence pathway between perceived usefulness, ease of use, and behavioral experience. From the perspective of management, this study provides theoretical support for the positive impact of corporate metaverse platform building on customer brand experience in the future, and provides impetus for companies to pay attention to improving the usefulness and ease of use of their metaverse platform.

The crowdsourcing software development platforms organize geographically distributed developers to complete various developing tasks, bringing convenience and efficiency to users. However, with the increasing number of both developers and tasks, it becomes more and more challenging to match tasks and suitable developers, especially for imbalanced data. The purpose of this paper is to propose an accurate and diverse recommendation model for crowdsourcing tasks.

A revised circle loss function is applied to achieve a certain adaptive ability, which is critical for imbalanced data, it guarantees diversity by maximizing the target label score and leveraging mathematical approximation to automatically balance the weights. Besides, the authors leverage the capsule network to obtain the semantic feature of tasks’ descriptions, modify the dynamic routing mechanism to better learn users’ preferences and improve the recommendation accuracy.

The comprehensive experiments conducted on real crowdsourcing platform data demonstrate that the proposed Crowd-CapsNet model can achieve high recommendation accuracy with a certain diversity. It improves around 1% accuracy with only 37% training time of the LSFA approach.

This paper proposes Crowd-CapsNet, an adaptive crowdsourcing task recommendation model. A relatively general feature pre-processing method describes crowd-sourcing tasks and the modified capsule network further obtains the semantic features to improve the recommendation accuracy and diversity.