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Social media addiction and stress are global phenomena, but little is known about how Facebook (Gen Y) and TikTok (Gen Z) users in the US experience these issues. For marketers, understanding social media stress sources is essential for effective marketing. Grounded in several theories, this study aims to test a model of psychological motivations – the need to belong and fear of missing out (FOMO) – and two moderators – gender and social media self-control – of addiction and stress.

This study used an online survey to collect data from Facebook (n = 320 Gen Y) and TikTok (n = 230 Gen Z) users. Data analysis entailed statistical analysis: structural equation modeling, nonparametric tests and hierarchical multiple regression.

Results show the need to belong increases stress among Facebook and TikTok users and social media addiction among Facebook users. On both platforms, consumers’ FOMO increases both addiction and stress. Facebook and TikTok use increases addiction. However, while TikTok does not directly increase stress, Facebook addiction does. Two moderators were found for Facebook users: gender and social media self-control. First, the need to belong-addiction link is stronger for male Facebook users. Second, consumers with a low need to belong and FOMO are less addicted to Facebook with high (vs low) self-control.

Understanding the detrimental effects of social media on consumers’ mental health in the form of social media stress and addiction requires examining specific platforms like Facebook and TikTok. This study addresses three gaps in the literature on social media: (1) psychological factors, (2) the role of gender and self-control as moderators and (3) the association between Facebook and TikTok use and stress among Gen Y and Gen Z consumers.

A major portion of our social interaction now occurs online, facilitated by social networking sites (SNSs) that enable people to connect and communicate at will. However, the characteristics of SNS communication can introduce problematic outcomes on otherwise healthy processes, one of which is social comparison. In this work, we investigate whether compulsive SNS use could be driven by two phenomena related to social comparison: the fear of missing out (FoMO) and envy.

Using the stimulus-organism-response framework, we developed a model that was tested with data from a sample of SNS users (N = 330) based in the United States. The analysis was done through partial least squares structural equation modeling.

Our findings show FoMO's association with the two forms of dispositional experienced envy, benign and malicious, as well as expected envy of others (expected envy). Interestingly, benign and expected envy were associated with SNS stalking and self-disclosure, but malicious envy had non-significant associations. Finally, both SNS stalking and self-disclosure were linked to compulsive SNS use.

We study the nuanced ways in which the two forms of experienced envy and expected envy can be triggered by FoMO and result in users' engagement with problematic SNS use. Our research provides evidence that, in addition to benign envy being an antecedent of compulsive SNS use, the wish to invoke envious feelings in others can also significantly drive compulsive use.

This paper aims to investigate the impact of a company’s decision to turn organic. Specifically, it examines the effect of such a decision on brand/product outcomes, and the role that organic market penetration plays in these effects.

Two experiments were conducted using two different food product categories. Data were analyzed using mean comparison tests, serial mediation and moderation analyses.

Results from Study 1 show that turning organic serially leads to increased perceptions of brand adaptability (mediator 1) and a positive effect on consumers’ perceived product quality (mediator 2), thus leading to stronger purchase intentions. Study 2 replicates and highlights the importance of market characteristics, showing that in markets with low organic market penetration rate (OMPR), brands turning organic are seen as challenging market norms, serially increasing (1) brand innovativeness (an additional mediator), (2) adaptability, (3) product quality perceptions, and then purchase intentions.

This research used online experiments, but the analysis of actual consumer decisions would bring further insight into the effects of turning organic. Moreover, the experiments involved only food products, while other fast-growing organic product categories – like organic cosmetics – could be examined for replication purposes.

By turning organic, brands can position themselves as adaptable and responsive to changing market trends, which – in turn – positively influences how consumers perceive product quality. Companies can thus leverage this positioning by emphasizing their transition to organic in marketing campaigns, framing it as a response to evolving consumer values. Further, turning organic is more beneficial in markets with low OMPRs, which indicates that brands should consider turning organic primarily in such markets.

Unlike previous studies that focused on the static fact of being organic, this research adopts a dynamic view by showing that turning organic affects both product and brand outcomes. It also examines the specific market conditions under which turning organic is the most favorable for brands.

Aqueous zinc-ion battery has broad application prospects in smart grid energy storage, power tools and other fields. Co₃O₄ is one of the ideal cathode materials for water zinc-ion batteries due to their high theoretical capacity, simple synthesis, low cost and environmental friendliness. Many studies were concentrated on the synthesis, design and doping of cathodes, but the effect of process parameters on morphology and performance was rarely reported.

Herein, Co₃O₄ cathode material based on carbon cloth (Co₃O₄/CC) was prepared by different temperatures hydrothermal synthesis method. The temperatures of hydrothermal reaction are 100°C, 120°C, 130°C and 140°C, respectively. The influence of temperatures on the microstructures of the cathodes and electrochemical performance of zinc ion batteries were investigated by X-ray diffraction analysis, scanning electron microscopy, cyclic voltammetry curve, electrochemical charging and discharging behavior and electrochemical impedance spectroscopy test.

The results show that the Co₃O₄/CC material synthesized at 120°C has good performance. Co₃O₄/CC nanowire has a uniform distribution, regular surface and small size on carbon cloth. The zinc-ion battery has excellent rate performance and low reaction resistance. In the voltage range of 0.01–2.2 V, when the current density is 1 A/g, the specific capacity of the battery is 108.2 mAh/g for the first discharge and the specific capacity of the battery is 142.6 mAh/g after 60 charge and discharge cycles.

The study aims to investigate the effect of process parameters on the performance of zinc-ion batteries systematically and optimized applicable reaction temperature.

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.