J
Jingguo Wang
Researcher at University of Texas at Arlington
Publications - 62
Citations - 2017
Jingguo Wang is an academic researcher from University of Texas at Arlington. The author has contributed to research in topics: Information security & Music piracy. The author has an hindex of 19, co-authored 56 publications receiving 1588 citations. Previous affiliations of Jingguo Wang include University at Buffalo.
Papers
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Consumer environmental awareness and channel coordination with two substitutable products
TL;DR: This paper focuses on the impact of consumer environmental awareness (CEA) on order quantities and channel coordination within a one-manufacturer and one-retailer supply chain.
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Why do people get phished? Testing individual differences in phishing vulnerability within an integrated, information processing model
TL;DR: The results indicate that most phishing emails are peripherally processed and individuals make decisions based on simple cues embedded in the email, and computer self-efficacy was found to significantly influence elaboration, but its influence was diminished by domain specific-knowledge.
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Research Article Phishing Susceptibility: An Investigation Into the Processing of a Targeted Spear Phishing Email
TL;DR: This study examines how users' attention to “visual triggers” and “phishing deception indicators” influence their decision-making processes and consequently their decisions, and suggests that overall cognitive effort expended in email processing decreases with attention to visual triggers and phishing deceived indicators.
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Security services as coping mechanisms: an investigation into user intention to adopt an email authentication service
TL;DR: The results show that user intention to adopt an email security service is contingent upon users' perception of risk and evaluation of both internal and external coping strategies.
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Research Note---A Value-at-Risk Approach to Information Security Investment
TL;DR: The concept of value-at-risk is introduced to measure the risk of daily losses an organization faces due to security exploits and use extreme value analysis to quantitatively estimate the value at risk.