Advancing on weighted PLS-SEM in examining the trust-based recommendation system in pioneering product promotion effectiveness

Perception of stigma across diagnostic models of personality pathology

Stigmatizing views surrounding mental illness are widespread. Personality disorders (PDs) are among the most stigmatized mental illnesses, as individuals with PDs are often described using pejorative terms, which might impact clinicians' a priori expectations and increase the likelihood of stigmatization, discrimination, or early termination from treatment. The degree to which the terms used in any diagnostic classification systems are stigmatizing has never been examined. The current study aims to explore the level of stigma perceived in diagnostic terms used and to compare which systems of classification (the Diagnostic and Statistical Manual of Mental Disorders, fifth edition [DSM-5] Section II, DSM-5 Alternative Model of Personality Disorder, and Hierarchical Taxonomy of Psychopathology [HiTOP]) are reported as less stigmatizing. The current study consisted of three samples. Individuals with lived experience of personality pathology (n = 218) completed an online survey examining the level of stigma perceived in diagnostic terms; mental health care providers (n = 75) and undergraduate psychology students (n = 732) also completed online surveys examining their perceptions of stigma within diagnostic terms. We examined differences in perceived stigma between the three classification systems across the three samples. Among mental health care providers, the HiTOP was rated as the least stigmatizing while DSM-5 categorical labels were rated as the most stigmatizing. There were no significant differences found among individuals with lived experience or undergraduate students. Understanding the degree to which the terms used to describe personality pathology contributes to reducing stigma has potentially important repercussions for research and clinical practice. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Designing an intervention to help the quitters quit: A qualitative, intervention co-design study

Objective

E-cigarettes are increasingly being provided by publicly funded stop smoking services. Our objectives were to understand the challenges and establish the means by which services could best support the use and subsequent discontinuation of e-cigarettes for this purpose.

Methods

Semi-structured interviews and co-design workshops with service users and providers of a stop smoking service.

Results

Thematic analysis was conducted. Interviews identified: 1. a reluctance to use e-cigarettes for cessation, 2. struggle to quit e-cigarettes (dependency, fear of relapse, compensatory "puffing") and 3. service development needs (consistency of approach). Co-design workshops suggested: 1. facilitation of e-cigarette use through understanding previous failed attempts, 2. offering a longer, two-staged approach to tobacco then e-cigarette cessation, careful timing of behavioural strategies and 3. enhanced communication between providers.

Conclusions

Our study suggests additional modifications to smoking cessation support measures when e-cigarettes are used for smoking cessation to address the challenges posed by public health guidance: "smokers should switch to vaping and vapers should stop smoking completely".

Innovation

Our study is the first to consider experiences of service users and providers about the challenges of using e-cigarettes for cessation; our co-design group of providers informed nine strategies needed to support this approach in practice.

Symbolic consumption as a non-traditional predictor of brand loyalty in the sports industry, football club segment

Brand loyalty is one of the main objectives of marketing, due to its impact on the sustainability of organisations; however, the explanation of how this type of loyalty is built in the sports industry is complex, due to the way it is organised. Traditionally, brand loyalty in this industry has been explained by satisfaction, service quality, commitment and trust, thus other variables that have been analysed from the marketing field have not been included in the models developed for the sports industry. The aim of this article was to explore the relationship between symbolic consumption as a non-traditional predictor of brand loyalty in the sports industry, more specifically in the football club segment. For this purpose, a quantitative cross-sectional study was defined, with a non-probabilistic sampling. To test the proposed relationship, modelling by means of partial least squares structural equations was chosen. The results showed that symbolic consumption is positively related to brand loyalty in the sports industry in the football club segment, with a large effect size. This study becomes a starting point for scholars who wish to further explore the relationship between symbolic consumption and brand loyalty in the context of sport. Likewise, for marketing professionals in the sports industry, it suggests the importance of symbolic consumption for the design of strategies that enable the creation of brand loyalty in consumers in the case of football clubs. In this sense, marketing professionals should seek to create advertising and promotional campaigns that appeal to strengthen the relationship of the sports consumer with their social groups based on the actual and ideal self-concept, as lifestyle.

Technology Behavior Model—Impact of Extended Reality on Patient Surgery

Smart surgery is a new way to utilize smart devices to change existing surgeries. Smart glasses can enhance the surgical procedure so that the patient can understand the procedure more intuitively. Surgery is for patients, and patient acceptance of extended reality surgery is the purpose of this study. This study uses the technology behavior model, which is more in line with the user’s assessment of the acceptance behavior of the new technology. A triangulated research approach was used, which applies to this study for a specific patient population. Primary data were collected from hospitals through questionnaires and were statistically analyzed by CB&PLS-SEM multimodel using SmartPLS software. It was concluded that patients were influenced by operational emotional factors in undergoing extended reality surgery. The study provides a basis for future research related to the practical application of smart surgery from the patient’s perspective in viewing and accepting surgery.

Combining rank-size and k-means for clustering countries over the COVID-19 new deaths per million

This paper deals with the cluster analysis of selected countries based on COVID-19 new deaths per million data. We implement a statistical procedure that combines a rank-size exploration and a k-means approach for clustering. Specifically, we first carry out a best-fit exercise on a suitable polynomial rank-size law at an individual country level; then, we cluster the considered countries by adopting a k-means clustering procedure based on the calibrated best-fit parameters. The investigated countries are selected considering those with a high value for the Healthcare Access and Quality Index to make a consistent analysis and reduce biases from the data collection phase. Interesting results emerge from the meaningful interpretation of the parameters of the best-fit curves; in particular, we show some relevant properties of the considered countries when dealing with the days with the highest number of new daily deaths per million and waves. Moreover, the exploration of the obtained clusters allows explaining some common countries' features.

A Novel Big Data Approach for Record and Represent Compliance in the Covid-19 Era

The present work shows the importance of the Big Data Analytics (Anisetti et al., 2018) scenario applied to corporate COVID-19 compliance, following the concept of “accountability” related privacy issues introduced by the new General Data Protection Regulation. This compliance approach is very useful for territorial security, environmental monitoring and safety prevention (in today pandemic context) and it is developed by the use of technologies (RFID, QR-CODE, NFC, blockchain) and HMI (Human Machine Interface) that it is fundamental core for data monitoring and information representation. The paper shows some new approaches about user HMI interface developed to collect big data about compliance and an innovative data representation approach using audio spectrum waves to record, share and evaluate compliance through an audio representation following a new process mapping approach.