Purpose This study examines the mediating and moderating roles of information security in information systems, focusing on the integration of the Technology Acceptance Model (TAM), Interpretive Structural Model (ISM), and the Health Belief Model (HBM). It investigates how technical, administrative, and human‐centric measures influence organizational information security performance. Design/Methodology/Approach The research adopts a theoretical and empirical approach to analyze the impact of systems quality, security policies, and training on performance outcomes. The sample size comprised 301 individuals, satisfying the minimum need for structural equation modeling and augmenting the statistical power of the study conducted using R 4.4.2 version software. This modeling is employed to test the hypothesized relationship and explore mediating and moderating effects. Findings The results highlight that information security training significantly enhances awareness, which mediates improvements in performance systems quality is a key determinant of resilience, while management systems and polices show limited direct effects unless integrated with other factors. Strict policies, combined with high systems quality, can hinder performance because of inefficiencies. Practical Implications Organizations might balance robust systems, strategic training, and adaptable polices to cultivate a resilient security culture. The findings provide actionable insights for mitigating threats and optimizing resources. Originality/value This study offers a comprehensive framework for aligning technical and human‐centric strategies, contributing to a deeper understanding of holistic information security management.

The study investigates the phenomenon of cancel culture within social sciences from 2016 to 2023. Utilizing a scientometric perspective, it analyzes the evolution, themes, and visibility of academic publications on cancel culture. The research employs cocitation and keyword co‐occurrence analyses using CiteSpace and VOSviewer based on data extracted from the Scopus database. The main findings reveal a significant increase in research volume starting in 2021, particularly influenced by the COVID‐19 pandemic. Five major thematic clusters are identified: deplatforming, cultural conflicts, intersections (politics, philosophy, and popular culture), racism and repercussions, and celebrities. Key influential works and authors, such as Rogers and Gillespie, are highlighted for their substantial citation impact. The study concludes that cancel culture is a complex, interdisciplinary field, continually evolving with significant scholarly interest and diverse research areas.

Behavioral science confronts the issue of how people’s behaviors differ from what they intend to do. However, current models, such as the theory of planned behavior, are insufficient to account for contextual influences and interdisciplinary effects, especially in the case of modern social phenomena. The majority of studies concentrate on single domains (e.g., health and consumer behavior) and employ manual coding schemes, overlooking essential thematic relationships. This research highlights the necessity for integrative frameworks that attempt to analyze why intentions fail to be realized in complex settings such as climate change and digitalization. The primary objectives of this research are to identify and operate dominant and emerging thematic trends in intention–behavior literature in a time series from 1979 to 2025 and to analyze and investigate the effects of publication index status and citation patterns on scholarly impact. This study uses structural topic modeling (STM) alongside bibliometric analyses to identify themes and correlations in intention–behavior research. STM employs generalized linear models to include document‐level metadata, allowing for the discovery of related topics and the key factors influencing the development of the literature. Data collection was initially performed on February 20, 2025, through the Web of Science database, using studies that were identified following PRISMA guidelines, reviewed, and considered relevant. The initial records numbered 5350. Significant thematic trends were found to define, and key psychological mechanisms to explain the intention–behavior gap were identified. The study also found that the determinants of publication index status and citation trends play important roles in establishing the discipline’s fate and the impact of intention–behavior literature. Based on these findings, the study highlights how strong thematic links in intention–behavior research can inform cross‐domain interventions—such as integrating physical activity and organic food campaigns or leveraging sustainable tourism to promote ethical consumption—by targeting shared psychological drivers like health identity and self‐image. In future research, the intention–behavior gap should be investigated across different disciplines and contexts and with longitudinal and experimental designs to take advantage of the psychological and contextual factors that affect behavior.

Given the growing recognition of digital financial inclusion (DFI) and governance (GVN) systems in ensuring the proper dissemination of public funds, both variables can play a transformative role in enhancing the proper delivery of public health facilities. This study investigates the role of DFI and GVN in determining the health spending (HSP) across 22 Arab League countries over the period 1999 to 2023. The cross‐sectionally augmented autoregressive distributed lag (CS‐ARDL) model was employed for empirical estimation, and robustness of findings was performed through FMOLS and 2SLS approaches. The empirical findings demonstrate that a 1% increase in DFI is associated with an estimated 0.11% rise in health expenditure, while a 1% improvement in GVN leads to a 1.19% increase in HSP in the long run. These results underscore the significant role that both factors play: better DFI enables faster and broader access to financial resources, supporting greater allocations toward healthcare, whereas improved GVN ensures effective policy execution and optimal resource utilization. The consistency of the finding across alternative estimation techniques and the inclusion of several control variables suggest that strengthening digital financial infrastructure and GVN mechanisms can significantly boost HSP in Arab countries. The study offers novel insights into the integration of DFI and GVN for a unified empirical framework to explain variations in health expenditure.

Digital technologies are an integral part of everyday life, making a balanced digital life essential to avoid their negative impact on well‐being. This study is aimed at validating the Digital Life Balance (DLB) Scale in Urdu and examining its relationship with social media addiction, Internet addiction, and life satisfaction among Urdu‐speaking individuals. A sample of 332 participants (Mage: 26.75 years; SD = 7.12; 41.9% female) completed the DLB Scale along with the Social Media Addiction Scale (SMA), Internet Addiction Test (IAT), and Satisfaction With Life Scale (SWLS). The results demonstrated satisfactory to acceptable internal consistency for the DLB Scale (α = 0.72), consistent with previous validations. Confirmatory factor analysis supported the unidimensionality of the scale. Correlation analysis revealed that higher DLB is positively associated with life satisfaction and negatively correlated with social media and internet addiction, particularly with avoidance behaviors and problematic social media use. Stepwise regression identified life satisfaction as the strongest predictor of DLB, followed by internet avoidance and problematic social media use. These findings underscore the importance of balancing digital and nondigital activities for maintaining psychological well‐being. The study highlights the need for culturally adapted tools to assess digital behaviors and provides critical insights for developing interventions aimed at promoting digital well‐being in diverse populations.

The adoption of artificial intelligence (AI) based marketing systems by companies is increasing. These systems can help companies improve their marketing performance, increase their market share, and reduce their marketing costs. Few researchers, in this regard, have sought to investigate the causes of the nonacceptance of marketing systems based on AI. This article uses the qualitative research method to identify the effective factors in the adoption of marketing systems based on AI. The current study is practical in aim and qualitative in essence, utilizing an exploratory methodology. The statistical population of the research includes 238 studies including articles on the factors of acceptance of marketing systems based on AI between 2019 and 2024. The data collection tool was selected in the form of a systematic review and library studies of literature and previous researches, and the research method is the meta‐synthesis of Sandelowski and Barroso. The sampling method is also selected based on the entry and exit criteria of the PRISMA (preferred reporting items for systematic reviews and meta‐analyses) method. PRISMA is a framework for evaluating and enhancing the quality of review articles and scientific studies through systematic review and meta‐synthesis. The findings of this research show that the four factors of functional expectations, usage expectations, organizational factors, and user intent have a significant effect on the acceptance of these systems. Companies that promote a culture of learning and embracing innovation are more likely to adopt these systems. These findings can help companies to increase the adoption of AI‐based marketing systems in their organization.

This paper aims to examine the effectiveness of machine learning classification algorithms as a strategy to overcome the limitations associated with traditional methods for developing computerized adaptive versions of the Minnesota Multiphasic Personality Inventory‐2 (MMPI‐2). The focus is on the three scales in the neurotic area of the instrument, namely, hypochondria, depression, and hysteria, which were administered electronically to a nonclinical sample of 383 participants. The findings indicate that a machine learning classifier based on a model tree (ML‐MT) algorithm effectively handled the complex MMPI‐2 scales, yielding accurate scores while noticeably reducing item administration. In particular, the ML‐MT algorithm achieved item savings between 85.99% and 93.78% and produced scores that differed from those of the full‐length scales by only 2.5–3.3 points. Compared to the countdown algorithm, the ML‐MT algorithm proved to be significantly more efficient and accurate. Furthermore, the ML‐MT scores retained their validity, as indicated by correlations with other MMPI‐2 scales that were comparable to those obtained with the full‐length scales (the average difference between the correlations was less than 0.10). These findings support the potential of the ML‐MT algorithm as an effective method for adaptive assessment in the context of the MMPI instruments and other psychometric tools.

This study is aimed at determining the primary factors influencing students’ selection of higher education institutions in the relatively newly formed region of Kurdistan Region of Iraq (KRI). It investigates how students’ characteristics moderate the selection process and explores the mediating effect of digital versus traditional marketing communication tools employed by higher education institutions in shaping students’ choices. Three main hypotheses were formulated based on a literature review, and data were collected online from a convenience snowball sample of 1058 freshman undergraduate and postgraduate students enrolled in higher education institutions in the KRI at the time of the data collection. The study mainly found that academic quality and reputation, variety of programs, and social benefits have statistically significant direct impacts on the selection of higher education institutions. The study also found that, among the student characteristics variables, only gender and family income had a significant direct moderating effect. All the marketing communication mix tools, digital and traditional, have a significant positive mediating effect. However, digital advertisement has the greatest mediating effect in comparison to other tools. Higher education institutions should take the results of this study into consideration when developing their positioning and communication strategies.

Telepractice in neuropsychology has become increasingly prevalent in recent years due to its ability to provide accessible and convenient care to patients regardless of their location. However, the validation of many neuropsychological tools for distance assessments remains limited, and there is a particular lack of remotely administered assessment tests with alternate forms, which are crucial for monitoring symptoms and performance in clinical contexts and for minimizing practice effects in research practice. Consequently, the present study was aimed at evaluating the consistency of the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) scores across videoconference and face‐to‐face administrations and to examine whether the scores obtained via videoconference support interpretations similar to those obtained via face‐to‐face administration. A total of 185 participants aged between 20 and 79 years (M = 46.24, SD = 19.63) underwent RBANS testing twice: once in person using the standard pen‐and‐paper modality and once remotely via videoconference, using Alternate Forms A and B to mitigate the learning effects. Results from the linear mixed models revealed no significant differences between remote and face‐to‐face administrations based on the modality of administration (p > 0.05). Bayes factors supported the null hypothesis, suggesting that RBANS performance is consistent across the two modalities of administration. However, discrepancies were observed in certain subtests between alternate forms of the RBANS, highlighting the need for standardization. In conclusion, findings suggested that the same norms that are used to interpret the RBANS scores obtained via face‐to‐face administration may be employed when administered remotely through videoconferencing. Accordingly, the study provides valuable insights into the feasibility of remote neuropsychological assessment and underscores the potential utility of videoconference technology in clinical and research settings.