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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.

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.

Handling huge volume of emails is a very challenging task in the customer support applications and an automated email responding system will be of great help. In this paper, an intelligent email autoresponder system is developed which either attempts to respond to the incoming emails from various category of customers or generate token for service request to address the issue manually by an expert member. First, based on the content the system has to predict whether the mail belong to the category of auto responding or to invoke a service request. This classification of email is carried out using long short term (LSTM) and bi-directional LSTM networks (Bi-LSTM) networks and the classification performance is analyzed. The results presented in this work show that the Bi-LSTM classifier outperforms LSTM network.

With the influx of technological advancements and the increased simplicity in communication, especially through emails, the upsurge in the volume of unsolicited bulk emails (UBEs) has become a severe threat to global security and economy. Spam emails not only waste users’ time, but also consume a lot of network bandwidth, and may also include malware as executable files. Alternatively, phishing emails falsely claim users’ personal information to facilitate identity theft and are comparatively more dangerous. Thus, there is an intrinsic need for the development of more robust and dependable UBE filters that facilitate automatic detection of such emails. There are several countermeasures to spam and phishing, including blacklisting and content-based filtering. However, in addition to content-based features, behavior-based features are well-suited in the detection of UBEs. Machine learning models are being extensively used by leading internet service providers like Yahoo, Gmail, and Outlook, to filter and classify UBEs successfully. There are far too many options to consider, owing to the need to facilitate UBE detection and the recent advances in this domain. In this paper, we aim at elucidating on the way of extracting email content and behavior-based features, what features are appropriate in the detection of UBEs, and the selection of the most discriminating feature set. Furthermore, to accurately handle the menace of UBEs, we facilitate an exhaustive comparative study using several state-of-the-art machine learning algorithms. Our proposed models resulted in an overall accuracy of 99% in the classification of UBEs. The text is accompanied by snippets of Python code, to enable the reader to implement the approaches elucidated in this paper.

In collaboration with three companies selling a diverse set of products, we conducted randomized field experiments in which experimentally tailored email ads were sent to millions of individuals. We found consistently that personalizing the emails by adding consumer-specific information (e.g., recipient’s name) benefited the advertisers. Importantly, such content is not likely to be informative about the advertised product or the company. In our main experiment, we found that adding the name of the message recipient to the email’s subject line increased the probability of the recipient opening it by 20% (from 9.05% to 10.80%), which translated to an increase in sales’ leads by 31% (from 0.39% to 0.51%) and a reduction in the number of individuals unsubscribing from the email campaign by 17% (from 1.2% to 1.0%). We present similar experiments conducted with other companies, which show that the effects we document extend from objectives ranging from acquiring new customers to retaining customers who have purchased from the company in the past. Our investigation of several possible mechanisms suggests that such content increases the effort consumers make in processing the other content in the rest of the advertising message. Our paper quantifies the benefits from personalization and sheds light on the role of noninformative advertising content by analyzing several detailed measures of recipient’s interaction with the message. It provides external validity to psychological mechanisms and has clear implications for the firms that are designing their advertising campaigns. Data and the online appendix are available at https://doi.org/10.1287/mksc.2017.1066 .

The paper examines the influence of opt-in e-mail marketing on consumer behaviour. The study attempts to extend the Stimuli-Organism-Response (S-O-R) theory that has been broadly explored in consumer research. Following a critical review of the literature organisation approach, a hypothetical model has been proposed for this study, based on identified factors, such as, informational value, entertainment-based message content, layout, visual appeal, attitude toward e-mail advertising and intention towards the sender in the context of opt-in email marketing. Data were collected in South Africa through an online survey of 436 opt-in e-mail marketing subscribers. Structural equation modelling (SEM) was employed to measure the proposed hypotheses of the study. The research results suggest that even during a pandemic, e-mail marketers could employ certain features in promotional and informational e-mail marketing communication, particularly informational value, entertainment-based message content, layout, visual appeal, as a means to design their e-mail marketing messages and plan e-mail advertising campaigns. The findings of the study are intended to advance the e-mail marketing knowledge base to help marketers during a pandemic, such as COVID-19. The paper provides marketers with relevant insights on how to effectively engage with e-mail subscribers.

Rubrics are defined as coherent sets of criteria for students' work that include detailed descriptions of quality for a specific learning task. They are used in different subjects to guide student learning and assess its results. This study demonstrates the design and development of an assessment rubric for writing emails in English as a foreign language (EFL). As English emails are a key form of communication in the globalised world, it is essential to have a rubric which is both fit for classroom use and empirically validated. In our study, six raters were trained to assess a sample of N = 1017 emails from learners at lower secondary level in Switzerland. We evaluate the reliability of the ratings by assessing interrater agreement after a period of training and by comparing their scores to those of an expert rater. Further, we analyse the linguistic quality of learner texts by focusing on four key markers of emails and compare this analysis to our rubric based scores to analyse its face validity. Our results show high reliability and validity of the rubric. We discuss the potential of such rubrics to improve teaching quality in various subjects and learning domains.

We propose various self-exciting point process models for the times when e-mails are sent between individuals in a social network. Using an expectation-maximization (EM)-type approach, we fit these models to an e-mail network dataset from West Point Military Academy and the Enron e-mail dataset. We argue that the self-exciting models adequately capture major temporal clustering features in the data and perform better than traditional stationary Poisson models. We also investigate how accounting for diurnal and weekly trends in e-mail activity improves the overall fit to the observed network data. A motivation and application for fitting these self-exciting models is to use parameter estimates to characterize important e-mail communication behaviors such as the baseline sending rates, average reply rates, and average response times. A primary goal is to use these features, estimated from the self-exciting models, to infer the underlying leadership status of users in the West Point and Enron networks. Supplementary materials for this article are available online.

Multichannel retailers need to understand how to allocate marketing budgets to customer segments and online and offline sales channels. We propose an integrated methodological approach to assess how email and direct mail effectiveness vary by channel and customer value segment. We apply this approach to an international beauty retailer in six countries and to an apparel retailer in the United States. We estimate multi-equation hierarchical linear models and find that sales responsiveness to email and direct mail varies by customer value segment. Specifically, direct mail drives customer acquisition in the offline channel, while email drives sales for both online and offline channels for current customer segments. A randomized field experiment with the beauty retailer provides causal support for the findings. The proposed reallocation of marketing resources would yield a revenue lift of 13.5% for the beauty retailer and 9.3% for the apparel retailer, compared with the 6.5% actual increase in the field experiment.

The Wilson Journal of Ornithology recognizes the importance of the current ornithological literature to our science. This is why we publish a book reviews section to let colleagues know of the publication of important works in ornithology. We are looking for reviewers willing to write for this section of the WJO. If you are interested in signing up to receive review assignments, or if you are eager to write a review of a particular book that has excited your imagination, please let the Book Review Editor know by email. We cannot have a Reviews section without your participation! Also, WJO is looking for a future Book Review editor, who will train with Bruce Beehler for 12 months and then take over, assuming the role is found suitable and of interest. Please contact Bruce Beehler, Book Review Editor, email: brucebeehler@gmail.com.