Browsing by Subject "correlation"

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    Examining personality assessment in asynchronous video interviews (AVI): convergence between human personality judgements and AI/ML scoring
    (2025) Cronje, Jacobus Fouche; de Kock, Francois
    The assessment of personality is an essential component of personnel selection due to its validity in predicting job performance. To assess personality, asynchronous video interviews (AVIs) scored using artificial intelligence (AI) algorithms are increasingly used, allowing candidates to record responses to interview prompts that are subsequently evaluated automatically by AI algorithms and/or human raters. As questions remain about the validity of AI-based AVI scoring approaches, this study examines the convergence between human-and AI-scored personality assessments. To measure personality, the study focuses on the HEXACO model, which measures Honesty-Humility, Emotionality, Extraversion, Agreeableness, Conscientiousness, and Openness to Experience. Verbal responses were transcribed from videotaped AVIs of 161 mock interview candidates who answered five AVI questions. Responses were scored by 15 trained human raters and a closed-dictionary text-analysis keyword-counting AI algorithm developed for this study, respectively. The correlation between trait-level scores produced by human judges and AI scoring was tested both across traits and within traits (trait-level) to assess scoring convergence. Moreover, in addition to comparing score levels produced by the two scoring methods (AI vs. human raters), score spread (i.e., variability), rank-order stability, and rating reliability were evaluated. The findings revealed a moderately positive and significant overall convergence (r = .29, p < .001) across traits between human and AI evaluations, which suggests that AI scoring may potentially be useful as a replacement of human evaluations when general screening is desired. Trait-level convergence varied between scoring methods, with the scoring consensus between human raters and AI being higher for some traits than for others, suggesting that these methods rely on different information and/or may interpret interview responses differently. The research highlights the potential of AI to complement human- based scoring of AVIs used in recruitment, selection, and assessment while also identifying the limitations of algorithm-based scoring in capturing complex human behaviour in interviews. The findings may further contribute to understanding the role of AI in personality assessment and implications for organisational practices.
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    Open Access
    Interannual memory effects for spring NDVI in semi-arid South Africa
    (2008) Richard, Yves; Martiny, Nadège; Fauchereau, Nicolas; Reason, Chris; Rouault, Mathieu; Vigaud, Nicolas; Tracol, Yann
    Almost 20 years of Normalized Difference Vegetative Index (NDVI) and precipitation (PPT) data are analysed to better understand the interannual memory effects on vegetation dynamics observed at regional scales in Southern Africa (SA). The study focuses on a semi-arid region (25°S–31°S; 21°E–26°E) during the austral early summer (September–December). The memory effects are examined using simple statistical approaches (linear correlations and regressions) which require the definition of an early summer vegetation predictand (December NDVI minus September NDVI) and a consistent set of potential predictors (rainfall amount, number of rainy days, rainfall intensity, NDVI and Rain-Use-Efficiency) considered with 4 to 15-month time-lag. An analysis over six SA sub-regions, corresponding to the six major land-cover types of the area reveals two distinct memory effects. A “negative” memory effect (with both rainfall and vegetation) is detected at 7 to 10-month time-lag while a “positive” memory effect (with vegetation only) is observed at 12 to 14-month time-lag. These results suggest that interannual memory effects in early summer vegetation dynamics of semi-arid South Africa may preferably be driven by biological rather than hydrological mechanisms.