Stay Away From Me Baek, Tae Hyun; Morimoto, Mariko
Journal of advertising,
04/2012, Volume:
41, Issue:
1
Journal Article
Peer reviewed
This study attempts to identify the potential determinants of advertising avoidance in the context of personalized advertising media, including unsolicited commercial e-mail, postal direct mail, ...telemarketing, and text messaging. Using a self-administered survey (n = 442), the proposed model is tested with structural equation modeling analysis. The findings indicate that while ad skepticism partially mediates the relationship between ad avoidance and its three determinants (perceived personalization, privacy concerns, and ad irritation), both privacy concerns and ad irritation have a direct positive effect on ad avoidance. However, increased perceived personalization leads directly to decreased ad avoidance.
We studied people’s success on the detection of phishing emails after they were trained under one of three phishing frequency conditions, where the proportion of the phishing emails during training ...varied as: low frequency (25% phishing emails), medium frequency (50% phishing emails) and high frequency (75% phishing emails). Individual base susceptibility to phishing emails was measured in a pre-training phase in which 20% of the emails were phishing; this performance was then compared to a post-training phase in which participants aimed at detecting new rare phishing emails (20% were phishing emails). The Hit rates, False Alarm rates, sensitivities and response criterion were analyzed. Results revealed that participants receiving higher frequency of phishing emails had a higher hit rate but also higher false alarm rate at detecting phishing emails at post-training compared to participants encountering lower frequency levels during training. These results have implications for designing new training protocols for improving detection of phishing emails.
Dr. George Papageorgiou, former Research Director of the Laboratory of Membrane Biophysics and Biotechnology at the National Centre for Scientific Research 'Demokritos', Athens, Greece, passed away ...on November 21, 2020.
We collected research articles from Retraction Watch database, Scopus, and a major retraction announcement by Springer, to identify emails used by authors. Authors' emails can be institutional emails ...and noninstitutional emails. Data suggest that retracted articles are more likely to use noninstitutional emails, but it is difficult to generalize. The study put some focus on authors from China.
We explain how to determine automatically the e-mail address of the corresponding author in a Web of Science record. Next, we distinguish two types of e-mails used by corresponding authors of ...academic papers: institutional e-mails and non-institutional ones. We investigate differences between papers with an institutional e-mail address and those with a non-institutional one. It is found that, on average, papers with an institutional e-mail address receive more citations than other ones.
Being constantly connected to others via e-mail and other online messages is increasingly typical for many employees. In this paper, we develop and test a model that specifies how interruptions by ...online messages relate to negative and positive affect. We hypothesize that perceived interruptions by online messages predict state negative affect via time pressure and that perceived interruptions predict state positive affect via responsiveness to these online messages and perceived task accomplishment. A daily survey study with 174 employees (a total of 811 day-level observations) provided support for our hypotheses at the between-person and within-person level. In addition, perceived interruptions showed a negative direct association with perceived task accomplishment. Our study highlights the importance of being responsive to online messages and shows that addressing only the negative effects of perceived interruptions does not suffice to understand the full impact of interruptions by online messages in modern jobs.
•We show the prevalence of email tracking in marketing communication.•We propose features that facilitate tracking detection using machine learning.•The new features are resilient against ...manipulation by trackers.•We assess the detection model through out-of-time-and-universe validation.•Tree learning algorithms achieve high detection rates and few false alarms.
Email tracking allows email senders to collect fine-grained behavior and location data on email recipients, who are uniquely identifiable via their email address. Such tracking invades user privacy in that email tracking techniques gather data without user consent or awareness. Striving to increase privacy in email communication, this paper develops a detection engine to be the core of a selective tracking blocking mechanism in the form of three contributions. First, a large collection of email newsletters is analyzed to show the wide usage of tracking over different countries, industries and time. Second, we propose a set of features geared towards the identification of tracking images under real-world conditions. Novel features are devised to be computationally feasible and efficient, generalizable and resilient towards changes in tracking infrastructure. Third, we test the predictive power of these features in a benchmarking experiment using a selection of state-of-the-art classifiers to clarify the effectiveness of model-based tracking identification. We evaluate the expected accuracy of the approach on out-of-sample data, over increasing periods of time, and when faced with unknown senders.
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