Search results

Paramedics are responsible for managing emergencies, caring for patients and performing life-saving procedures under heavy workloads, which can have a significant negative effect on their emotional eating and food addiction (FA) behaviors. Thus, this cross-sectional study aims to shed light on the relationship between emotional eating tendencies and FA in paramedics by considering their food preferences, sex, and body mass index (BMI) factors.

The questionnaire consisted of Yale Food Addiction Scale and Emotional Eating Questionnaire (EEQ), as well as sex, age, weight, height and number of snacks and main meals collected face-to-face from the 196 paramedics.

The FA prevalence was 14.9%, and more than half of the paramedics were emotional eaters. The total score of the EEQ was significantly higher in the FA diagnosed group than in the group FA not diagnosed (p < 0.001). The food preferences of the paramedics were found to differ significantly depending on whether they were diagnosed with FA or emotional eating. Being a food addict or emotional eater significantly increases the odds of consuming chocolate-wafer, pie-cake, chips, pastries, pasta and fries (p < 0.05), and participants with FA diagnosis and emotional eaters were more likely to prefer these foods than those with nondiagnosis and nonemotional eaters (p < 0.05).

Findings highlighted the connection between FA and the emotional eating behavior of paramedics, indicating that they attempt to compensate for their emotional ups and downs through eating. The job-related stress and emotional eating behaviors of paramedics may increase their BMI and susceptibility to FA.

This study aims to focus on how smartphone addiction impacts young consumer behavior related to mobile technology (i.e. the compulsive app downloading tendency). After a thorough literature review and following the risk and protective factors framework, this study explores factors that could mitigate its effects (resilience, family harmony, perceived social support and social capital).

The study used the covariance-based structural equation modeling approach to analyze data collected from 275 Generation Z (Gen Z) smartphone users in Spain.

Results suggest that resilience is a critical factor in preventing smartphone addiction, and smartphone addiction boosts the compulsive app downloading tendency, a relevant downside for younger Gen Z consumers.

Through the lens of the risk and protective factors framework, this study focuses on protective factors to prevent smartphone addiction and its negative side effects on app consumption. It also offers evidence of younger consumers’ vulnerability to smartphone addiction, not because of the device itself but because of app-consumption-related behaviors.

This study aims to fill various gaps detected in the literature on mass prestige (hereafter referred to as masstige) theory. The originality of the work stems from the multidimensional application of Paul’s (2015) model, the introduction of brand addiction as a construct from the consumer-brand relationship (CBR) theory within the context of wines and the exploration of a new and less studied sector in masstige strategies.

A structured questionnaire was distributed to collect data from masstige wine brand buyers in Portugal, of whom 166 completed the questionnaire correctly. A conceptual model was developed and tested using partial least squares structural equation modelling.

The findings include that only two dimensions of Paul’s (2015) masstige scale affect brand addiction: brand knowledge and excitement and status. Brand addiction has a positive effect on brand loyalty and electronic word of mouth (eWOM), and brand loyalty has a positive impact on eWOM. Theoretical and managerial implications were explored.

This research added a CBR perspective to masstige theory and applied masstige theory to wine brands for the first time. These three distinctive aspects collectively contribute to the novelty and significance of the research, opening up exciting possibilities for future investigations and providing a valuable contribution to the academic community and the wine industry alike.

This study explores the determinants of police officer support for pre-arrest/booking deflection programs that divert people presenting with substance use and/or mental health disorder symptoms out of the criminal justice system and connect them to supportive services.

This study analyzes responses from 254 surveys fielded to police officers in Delaware. Questionnaires asked about views on leadership, approaches toward crime, training, occupational experience and officer’s personal characteristics. The study applies a new machine learning method called kernel-based regularized least squares (KRLS) for non-linearities and interactions among independent variables. Estimates from a KRLS model are compared with those from an ordinary least square regression (OLS) model.

Support for diversion is positively associated with leadership endorsing diversion and thinking of new ways to solve problems. Tough-on-crime attitudes diminish programmatic support. Tenure becomes less predictive of police attitudes in the KRLS model, suggesting interactions with other factors. The KRLS model explains a larger proportion of the variance in officer attitudes than the traditional OLS model.

The study demonstrates the usefulness of the KRLS method for practitioners and scholars seeking to illuminate patterns in police attitudes. It further underscores the importance of agency leadership in legitimizing deflection as a pathway to addressing behavioral health challenges in communities.

This study is a radical interactionist analysis of family conflict. Drawing on both a negotiated order perspective and Athen's theory of complex dominative encounters, this study analyzes the role that domination plays in conflicts among intimates. As the family engages in repeated conflicts over roles, the family also engages in negotiations over the family order, what role each party should play, interpretations of past events, and plans for the future. These conflicts take place against a backdrop of patriarchy that asymmetrically distributes power in the family to determine the family order. The data from this study come from a content analysis of mothers with substance use problems as depicted in the reality television show Intervention. The conflicts in these families reveal that these families develop a grinding family order in which families engaged in repeated conflict but also continued to operate as and identify as a family. These conflicts are shaped by and reinforce patriarchal expectations that mothers are central to family operation. The intervention at the end of each episode offered an opportunity for the family to engage in a concerted campaign to try to force the mother into treatment and reestablish the family order.

The rise and popularity of digitalization have made the addictive use in the virtual world more common, which has aroused wide attention from academia and public. Uncovering the underlying mechanism of addictive use is essential to address this serious issue.

By utilizing the context of massively multiplayer online games (MMOGs), this study developed virtual-domain perfectionism of seeking excellence and avoiding failure from the dual process model of perfectionism and identified four affordances in MMOGs from the perspective of technology affordance. The authors surveyed 302 valid samples in MMOGs to empirically test the research model.

The results demonstrate that two processes of virtual-domain perfectionism influence addictive use positively in MMOGs. Technology affordances perform as the antecedents of virtual-domain perfectionism and conduct distinct impacts in MMOGs. Specifically, affordances of interaction and identity are positively related to virtual-domain perfectionism, while achievement affordance is unrelated to virtual-domain perfectionism. Immersion affordance is positively related to virtual-domain perfectionism of seeking excellence and negatively associated with virtual-domain perfectionism of avoiding failure.

This study identified virtual-domain perfectionism and specific MMOGs affordances. The research model provides insights into addictive use in MMOGs by leveraging context and combining lenses. Research findings help elucidate the role of virtual-domain perfectionism on the addictive use from MMOGs affordances with the corresponding technical features.

Reverse logistics services are designed to move goods from their point of consumption to an endpoint to capture value or properly dispose of products and materials. Artificial intelligence (AI)-based reverse logistics will help Micro, Small, and medium Enterprises (MSMEs) adequately recycle and reuse the materials in the firms. This research aims to measure the adoption of AI-based reverse logistics to improve circular economy (CE) performance.

In this study, we proposed ten hypotheses using the theory of natural resource-based view and technology, organizational and environmental framework. Data are collected from 363 Indian MSMEs as they are the backbone of the Indian economy, and there is a need for digital transformation in MSMEs. A structural equation modeling approach is applied to analyze and test the hypothesis.

Nine of the ten proposed hypotheses were accepted, and one was rejected. The results revealed that the relative advantage (RA), trust (TR), top management support (TMS), environmental regulations, industry dynamism (ID), compatibility, technology readiness and government support (GS) positively relate to AI-based reverse logistics adoption. AI-based reverse logistics indicated a positive relationship with CE performance. For mediation analysis, the results revealed that RA, TR, TMS and technological readiness are complementary mediation. Still, GS, ID, organizational flexibility, environmental uncertainty and technical capability have no mediation.

The study contributed to the CE performance and AI-based reverse logistics literature. The study will help managers understand the importance of AI-based reverse logistics for improving the performance of the CE in MSMEs. This study will help firms reduce their carbon footprint and achieve sustainable development goals.

Few studies focused on CE performance, but none measured the adoption of AI-based reverse logistics to enhance MSMEs’ CE performance.

To extend research into sport consumer behaviors related to online sports betting, this study is designed to identify and examine the relationship between online sports betting motivations and online sports betting intentions. By applying a push-pull framework from online sport consumption and gambling studies, nine motivations to engage in online sports betting were identified. These motivations were hypothesized to motivate online sports betting intentions.

A quota sampling technique based on the sports bettor demographics available in the American Gaming Association (AGA, 2019) and the Pew Research Center (2022) obtained a total of 550 completed surveys that met the inclusion and exclusion criteria. For data analyses, confirmatory factor analysis (CFA) and structural equation modeling (SEM) were used to examine the measurement model and the hypothesized model, respectively.

The results revealed that four motivations (i.e. monetary gain, excitement, convenience and negative technology-readiness) were related to online sports betting intention, while five motivations (i.e. sport fandom, positive technology-readiness, impulsivity, socialization and promotion) were not.

The results provide foundational theoretical knowledge of what motivates sports fans to participate in online sports betting. Furthermore, the findings assist practitioners in their allocation of resources by enhancing their understanding of online sports betting motivations.

On the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data.

First, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption.

Compared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability.

This paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability.

1. The stability of electricity consumption is important to the stable operation of the grid.

2. An improved FP-growth algorithm is employed to explore the influencing factors.

3. The improved algorithm enables the mining of rules containing specific attribute labels.

4. Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

The stability of electricity consumption is important to the stable operation of the grid.

An improved FP-growth algorithm is employed to explore the influencing factors.

The improved algorithm enables the mining of rules containing specific attribute labels.

Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.