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The purpose of this study is to revisit the measures of internal consistency for multi-item scales in hospitality research and compare the performance of Cronbach’s α, omega total (ω_Total), omega hierarchical (ω_H), Revelle’s omega total (ω_RT), Minimum Rank Factor Analysis (GLB_fa) and GLB algebraic (GLB_a).

A Monte Carlo simulation was conducted to compare the performance of the six reliability estimators under different conditions common in hospitality research. Second, this study analyzed a data set to complement the simulation study.

Overall, ω_Total was the best-performing estimator across all conditions, whereas ω_H performed the poorest. α performed well when factor loadings were high with low variability (high/low) and large sample sizes. Similarly, ω_RT, GLB_fa and GLB_a performed consistently well when loadings were high and less variable as well as the sample size and the number of scale items increased. Of the two GLB estimators, GLB_a consistently outperformed GLB_fa.

This study provides hospitality managers with a better understanding of what reliability is and the various reliability estimators. Using reliable instruments ensures that organizations draw accurate conclusions that help them move closer to realizing their visions.

Though popular in other fields, reliability discussions have not yet received substantial attention in hospitality. This study raises these discussions in the context of hospitality research to promote better practices for assessing the reliability of scales used within the hospitality domain.

This study aims to recognize the challenge of identifying deceptive information and provides a framework for thinking about how we as humans negotiate the current media environment filled with misinformation and disinformation.

This study reviews the influence of Wilson’s (2016) General Theory of Information Behavior (IB) in the field of information science (IS) before introducing Levine’s Truth-Default Theory (TDT) as a method of deception detection. By aligning Levine’s findings with published scholarship on IB, this study illustrates the fundamental similarities between TDT and existing research in IS.

This study introduces a modification of Wilson’s work which incorporates truth-default, translating terms to apply this theory to the broader area of IB rather than Levine’s original face-to-face deception detection.

False information, particularly online, continues to be an increasing problem for both individuals and society, yet existing IB models cannot not account for the necessary step of determining the truth or falsehood of consumed information. It is critical to integrate this crucial decision point in this study’s IB models (e.g. Wilson’s model) to acknowledge the human tendency to default to truth and thus providing a basis for studying the twin phenomena of misinformation and disinformation from an IS perspective. Moreover, this updated model for IB contributes the Truth Default Framework for studying how people approach the daunting task of determining truth, reliability and validity in the immense number of news items, social media posts and other sources of information they encounter daily. By understanding and recognizing our human default to truth/trust, we can start to understand more about our vulnerability to misinformation and disinformation and be more prepared to guard against it.

The purpose of this paper is to assess the level of nurses’ competencies while providing care to COVID-19 patients.

A descriptive, correlational design was used to collect data from nurses who were providing care to COVID-19 patients at four public hospitals.

A total of 377 nurses (64.5% females) aged 23–50 consented to participate and completed the survey. The mean score of nurses’ competencies in providing care to COVID-19 patients was 2.5 (SD = 0.81). The results of correlation coefficient tests disclosed a significant positive correlation between reported competence level and sex r_pb (377) = 0.18, p < 0.01; working area r_pb (377) = 0.2, p < 0.01; disaster experience r_pb (377) = 0.16, p < 0.01; disaster education r_pb (377) = 0.25, p < 0.01; and disaster training r_pb (377) = 0.31, p < 0.01.

The COVID-19 pandemic response heavily relied on nurses. However, they had a gap in clinical competencies that indicates an urgent need to incorporate disaster management courses in basic nursing education and to update training in hospitals based on nurses’ needs to improve their capabilities in dealing with COVID-19 pandemic.

To the best of the authors’ knowledge, this is the first study that investigated the perceived level of Jordanian nurses’ competencies in providing care to COVID-19.