The Influence of Self-Esteem and Locus of Control on Perceived Email Overload

As email use becomes more ubiquitous in organisations, negative effects that stem from its use are becoming more prevalent. This study considers Email Overload as a negative product of email use. It explores the link between the personality traits of Self-esteem and Locus of Control and Email Overload. Furthermore it proposes a link between the level of perceived Email Overload and individual productivity in the work place. A sample of 239 respondents from an engineering organisation was collected for this study. Using Partial Least Squares (PLS) results suggest a strong negative relationship between Email Overload and productivity, indicating that as perceived Email Overload increases, a person’s productivity decreases. Weaker links were formed with Self-esteem and Locus of Control to Email Overload

Ansiedad por Covid - 19 y salud mental en estudiantes universitarios

The objective of the present study is to determine the relationship between anxiety by Covid - 19 and mental health in 356 university students (227 women and 129 men, average age = 22.36 years, Standard Deviation = 2.46). It also has as specific objectives to compare the anxiety by Covid - 19 and mental health according to sociodemographic variables. To whom it was applied the Coronavirus Anxiety Scale in Spanish and the Mental Health Inventory-5 (MHI). The study confirms that there is a statistically significant correlation between anxiety by Covid – 19 and mental health (ρ = −.67, p <.01). Also, regarding the comparisons made statistically significant differences are evidenced according to the variables sociodemographic previously mentioned. The study confirms that the more anxiety by Covid - 19 the lower mental health in a sample of Peruvians university students.El objetivo de la presente investigación fue determinar la relación entre ansiedad por Covid - 19 y salud mental en 356 estudiantes universitarios (227 mujeres y 129 hombres, Medad = 22.36 años, DE = 2.46). Asimismo, se comparó la ansiedad por Covid - 19 y salud mental entre algunas variables sociodemográficas. Se aplicó la versión en español de la Coronavirus Anxiety Scale (CAS) y el Mental Health Inventory-5 (MHI). Los resultados muestran que una mayor ansiedad por COVID – 19 se relaciona con una disminución de la salud mental (ρ = −.67, p <.01). Asimismo, respecto a las comparaciones realizadas se evidencian diferencias estadísticamente significativas en función a las variables sociodemográficas previamente mencionadas. El estudio confirma que a mayor ansiedad por COVID – 19 menor salud mental en una muestra de estudiantes universitarios peruanos

Afrontamiento del estrés y su relación con el bienestar psicológico a causa del estado de emergencia generado por el Covid-19 en estudiantes de la carrera de psicología de una universidad privada de Lima este

El propósito del presente estudio es analizar la relación existente entre las estrategias de afrontamiento al estrés y bienestar psicológico en estudiantes del tercer año de una universidad de Lima este. Participaron 68 estudiantes universitarios entre 18 y 23 años. Se usaron los instrumentos de Escala de Modos de Afrontamiento al Estrés (COPE) diseñado por Carver, Scheir y Weintraub (1989) adaptado al contexto peruano y la Escala de Bienestar Psicológico (SPWB), propuesta por Carol Ryff (1989), adaptada al contexto peruano. Los resultados muestran que existe una relación altamente significativa y positiva entre el bienestar psicológico con el estilo de afrontamiento enfocado en el problema, así como el estilo enfocado en la emoción.LIMAEscuela Profesional de PsicologíaPsicología educativ

Personality differences as predictors of action-goal relationships in work-email activity

Email is a ubiquitous and work-critical tool for many people at work today. Research suggests that people engage a range of actions to deal with work-email, with the same email action (e.g. turning off email) facilitating some goals (e.g. for well-being) but hindering others (e.g. for being helpful). Using mixed-methods across two studies with knowledge workers who use work-email, we examined whether individual differences in personality can explain why there is a goal paradox of work-email actions. The theory of purposeful work behavior (TPWB) informs our approach. In Study 1, semi-structured interviews (N=28) uncovered (using thematic analysis) a comprehensive list of 72 work-email actions that differently impact goals related to Work, Well-being, Control and Concern. Study 2 then addressed whether personality traits could predict work-email activity directed towards these four goals. A multi-level survey (N=341; n = 5575) of work-email activity was analyzed using cross-level hierarchical linear modelling. We found that action-goal relationships in dealing with work-email, could be predicted by people’s trait-relevant goal striving. This advances understanding of why work-email actions can be both beneficial and problematic for people. Use of habitual actions also interacted with personality to strengthen action-goal relationships, except for those with low Emotional Stability. Findings are discussed in terms of their implications for theory, policy and practice

Detecting the Intent of Email Using Embeddings, Deep Learning and Transfer Learning

Throughout the years\u27 several strategies and tools were proposed and developed to help the users cope with the problem of email overload, but each of these solutions had its own limitations and, in some cases, contribute to further problems. One major theme that encapsulates many of these solutions is automatically classifying emails into predefined categories (ex: Finance, Sport, Promotion, etc.) then move/tag the incoming email to that particular category. In general, these solutions have two main limitations: 1) they need to adapt to changing user’s behavior. 2) they require handcrafted features engineering which in turn need a lot of time, effort, and domain knowledge to produce acceptable performance.This dissertation aims to explore the email phenomenon and provide a scalable solution that addresses the above limitations. Our proposed system requires no handcrafted features engineering and utilizes the Speech Act Theory to design a classification system that detects whether an email required an action (i.e. to do) or no action (i.e. to read). We can automate both the features extraction and the classification phases by using our own word embeddings, trained on the entire Enron Email dataset, to represent the input. Then, we use a convolutional layer to capture local tri-gram features, followed by an LSTM layer to consider the meaning of a given feature (trigrams) concerning some “memory” of words that could occur much earlier in the email. Our system detects the email intent with 89% accuracy outperforming other related works. In developing this system, we followed the concept of Occam’s razor (i.e. law of parsimony). It is a problem-solving principle stating that entities should not be multiplied without necessity. Chapter four present our efforts to simplify the above-proposed model by dropping the use of the CNN layer and showing that fine-tuning a pre-trained Language Model on the Enron email dataset can achieve comparable results. To the best of our knowledge, this is the first attempt of using transfer learning to develop a deep learning model in the email domain. Finally, we showed that we could even drop the LSTM layer by representing each email’s sentences using contextual word/sentence embeddings. Our experimental results using three different types of embeddings: context-free word embeddings (word2vec and GloVe), contextual word embeddings (ELMo and BERT), and sentence embeddings (DAN-based Universal Sentence Encoder and Transformer-based Universal Sentence Encoder) suggest that using ELMo embeddings produce the best result. We achieved an accuracy of 90.10%, comparing with word2vec (82.02%), BERT (58.08%), DAN-based USE (86.66%), and Transformer-based USE (88.16%)