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This study aims to describe and analyze the factors associated with dependence and motivation to stop smoking in patients with schizophrenia.

This descriptive, analytical study was conducted between October 2021 and April 2023 at two psychiatric centers in Morocco. The study population consisted of 274 smokers diagnosed with schizophrenia, who were examined just before their discharge. In addition to sociodemographic and economic data, tobacco use status and clinical information, the authors assessed dependence with Fagerström Test for Nicotine Dependence (FTND), motivation to quit and depression.

Around three-quarters (74%) smoked more than 10 cigarettes a day, with a mean FTND score of 5.61 (±1.94). Dependence was reported in 76% of smokers. More than two-thirds (69%) had made at least one attempt to quit, and almost all participants (99%) had done so without medical assistance. Nicotine dependence was associated with income, illness duration, motivation to stop smoking and depression. In addition, lower income, level of education, number of hospitalizations, attempts to stop smoking and nicotine dependence were associated with motivation to quit tobacco use. However, depression was not associated with motivation to stop smoking.

The present study has the following limitations: the cross-sectional nature of the study does not allow for temporal evaluation, the sampling technique does not allow for generalization of the results, participants’ responses may be subjective despite the use of validated psychometric scales.

The results of this research have important public health implications: Duration of schizophrenia progression was associated with nicotine dependence – highlighting the need to offer help as soon as possible after diagnosis, as a preventative measure; Calgary depression score was a factor associated with increased dependence – suggesting that screening and additional help for people with co-existing mental health problems could be important. Similarly, the onset of depression after the development of schizophrenia should be monitored.

The authors have further searched the literature and have not found similar studies. The absence of such studies justifies the significance of this research, and its results will be valuable for publication to guide researchers in the treatment of tobacco dependence and, furthermore, to guide the preventive efforts of health authorities in Morocco. Additionally, to the best of the authors’ knowledge, this study is the first of its kind in Morocco and among the few in North Africa.

Research on training and/or L&D effectiveness is predominantly conducted in a traditional L&D context. Little research is conducted on training and/or L&D in the context of artificial intelligence (AI)-based learning. The present study aims to investigate the relationship between the adoption of AI-based learning systems and learners’ behavior. Drawing from the theory of planned behavior, the research examines the impact of attitude (ATT), subjective norm (SN) and perceived behavioral control (PBC) as AI-based learning intention (ALI) factors relate to changes in learners' behavior. Additionally, inspired by the self-determination theory by Deci and Ryan, the study further examines the mediating role of learner engagement between ALI and behavioral change.

Following a theoretical framework and using a systematic literature review method, the survey research has been planned by considering a sample from Indian industries. The collected data have been analyzed using SPSS-AMOS 27. While path analysis has been conducted to analyze the direct impact of ALI on learners' behavior, Hay’s PROCESS macro has been used to check the mediating impact of learner engagement between ALI and learners' behavior.

The results proved a significant and positive impact of all ALI factors such as ATT, SN and PBC on learners’ behavioral change. Further, the research found that learning engagement (LE) successfully mediates between AI learning intention and behavioral change.

In the absence of any empirical study in identifying the relationship among learning intention, LE and behavioral outcome, the result of this study may provide useful insights to researchers and practitioners.

This paper aims to capture the complex interdependences between supply chain disruptions (SCDs), SC risk mitigation strategies and firm performance in the context of disruptive events to enhance resilience for medium-sized and large firms coping with complex supply chain networks. The roles of digitalization, insurance and government support have also been addressed as potential strategies to counteract the impacts of disruptions on supply chains.

This study is based on an empirical investigation in an FMCG company – using a hybrid causal mapping technique based on the frameworks of interpretive structural modeling (ISM) and Bayesian networks (BN) – of 11 levels of relationships between SCDs (in supply, production, logistics, demand and finance), SC risk mitigation strategies (flexibility, efficiency, agility and responsiveness), insurance, government support, information and knowledge sharing, digitalization and finally the key firm performance measures (continuity, quality and financial performance).

The results of the empirical investigation reveal and describe: (1) the nature and probabilistic quantification of the lower-level relationships among the four SCDs, among the mitigation strategies and the three firm performance measures; (2) the nature and probabilistic quantification of the higher-level relationships among the impacts of SCDs, SC risk mitigation strategies and firm performance and (3) how to model and quantify the complex interdependences in single firms and their supply chains.

Our results can support managers in developing more effective decision-making models to assess and manage unfavorable events and cascade effects among different functions and processes in the context of risks and disruptions.

This study aims to develop cause-and-effect relationships among the critical success factors (CSFs) of fintech adoption and rank these CSFs based on their importance in the model.

To achieve the objectives of the study, the Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) approach was used. The data was collected from 16 experts using a questionnaire.

The findings demonstrated the interrelationships among the CSFs. In total, 16 critical factors were recognized as causal factors, and the remaining eight were considered effect factors. The CSFs were ranked based on their importance in fintech adoption.

This study is novel as it investigates CSFs of fintech adoption using FDEMATEL, and it contributes to understanding the nature of these factors and how they affect fintech adoption. The findings propose a significant basis to deepen fintech adoption and deliver a clue to design a practical framework for fintech adoption.