Academic Employees’ Workplace Deception Detection Knowledge

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{"abstract":"Humans are known as fallible lie detectors, often considered inept and nescient in unmasking deceit. To unravel the truth behind this speculation, this organizational communication study assessed the academic employees' workplace deception (WD) detection knowledge when using Facial micro-expressions (FME), context, verbal, vocal, and non-verbal cues. It determined the significant difference in their WD knowledge after exposure to the treatments, investigated their ways in cognitive and psychological processing of cues when detecting WD, and examined how their WD detection knowledge influenced their communication behavior in the organization. Non-equivalent Control Group pretest-posttest quasi-experimental design was employed for this purpose. Results indicate that both control and treatment groups were much knowledgeable when using FME before and after exposure to Facial Micro-Expression Training (FMET). While findings reveal no significant difference in the treatment group's WD detection knowledge after FMET, the said group shows an increase from less to moderate knowledge after Online Deception Detection Training (OODT). The researchers believe that much is yet to be discovered in the field of detecting deceptive communication involving academic employees. The outbreak of the Corona Virus Disease 19 (COVID-19) in the Philippines might have caused disruptions and disadvantages, but these limitations may be turned into a light that brings in fresh perspectives. Hence, it is recommended that more studies in deception detection be conducted in the Philippines.","abstract_source":"original","authors":["Apple Grace Bonhoc","Dr. Maico Demi B. Aperocho"],"date":"2023-01-04","doi":"10.5281/zenodo.7502766","id":934,"issue":8,"journal":{"issn":"2822-4353","name":"Psychology and Education: A Multidisciplinary Journal"},"pdf_sha256":"7c780cb099b68c037f6aeacee64c707b30d317bb467cd673e5d311ee84e32211","schema":"scimatic-article/1","title":"Academic Employees’ Workplace Deception Detection Knowledge","volume":6}

Check it yourself

1. The record, from the token's own metadata:

printf '%s' "$(curl -s https://scimatic.org/nft/articles/934.json | jq -r .canonical_record)" | sha256sum

2. The hash stored on chain (contentHash(934)):

curl -s https://rpc.scimatic.net -H 'content-type: application/json' \
  -d '{"jsonrpc":"2.0","id":1,"method":"eth_call","params":[{"to":"0x0E596B4bb924937aA35D9b342A90347ed6F05eF8","data":"0xf3e0c29000000000000000000000000000000000000000000000000000000000000003a6"},"latest"]}'

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