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Previous research has demonstrated strong relations between work characteristics (e.g. job demands and job resources) and work outcomes such as work performance and work engagement. So far, little attention has been given to the role of authenticity (i.e. employees’ ability to experience their true selves) in these relations. The purpose of this paper is to explore the relationship of state authenticity at work with job demands and resources on the one hand and work engagement, job satisfaction, and subjective performance on the other hand.

In total, 680 Dutch bank employees participated to the study. Structural equation modelling was used to test the goodness-of-fit of the hypothesized model. Bootstrapping (Preacher and Hayes, 2008) was used to examine the meditative effect of state authenticity.

Results showed that job resources were positively associated with authenticity and, in turn, that authenticity was positively related to work engagement, job satisfaction, and performance. Moreover, state authenticity partially mediated the relationship between job resources and three occupational outcomes.

Main limitations to this study were the application of self-report questionnaires, utilization of cross-sectional design, and participation of a homogeneous sample. However, significant relationship between workplace characteristics, occupational outcomes, and state authenticity enhances our current understanding of the JD-R Model.

Managers might consider enhancing state authenticity of employees by investing in job resources, since high levels of authenticity was found to be strongly linked to positive occupational outcomes.

This study is among the first to examine the role of authenticity at workplace and highlights the importance of state authenticity for work-related outcomes.

This study aims to identify leather type and authenticity through optical coherence tomography.

Optical coherence tomography images taken from genuine and faux leather samples were used to create an image dataset, and automated machine learning algorithms were also used to distinguish leather types.

The optical coherence tomography scan results in a different image based on leather type. This information was used to determine the leather type correctly by optical coherence tomography and automatic machine learning algorithms. Please note that this system also recognized whether the leather was genuine or synthetic. Hence, this demonstrates that optical coherence tomography and automatic machine learning can be used to distinguish leather type and determine whether it is genuine.

For the first time to the best of the authors' knowledge, spectral-domain optical coherence tomography and automated machine learning algorithms were applied to identify leather authenticity in a noncontact and non-invasive manner. Since this model runs online, it can readily be employed in automated quality monitoring systems in the leather industry. With recent technological progress, optical coherence tomography combined with automated machine learning algorithms will be used more frequently in automatic authentication and identification systems.

With the advent of the COVID-19 and increased access to Internet technologies, students tend to use e-learning technologies for improved academic results, and since then, improving student engagement in a virtual space became a difficult task for educational institutions globally. The present study aims to examine the effect of key e-learning factors (i.e. perception, hedonic motivation (HM), usefulness, empowerment and attitude) on student engagement among open and distance learners (ODLs) of the central region of India.

Simple random sampling was applied to collect data from ODLs of Chhattisgarh. ODLs who are using e-learning platforms for at least one year were chosen to be the participant in the study. The authors collected 1,137 responses using both online and offline modes of collection.

The test results indicated that usefulness, HM and attitude factors were found to have a significant relationship with student engagement in e-learning, while perception and empowerment variables did not contribute to the engagement of students.

The present study is novel in its approach. It clarifies the key factors to student engagement which might increase the students' level of involvement in e-learning technologies, if these factors are addressed tactfully by the educational institutions or concerned administration.