The impact of communication technologies on life and relationship satisfaction

Abstract

a b s t r a c tPrevious studies have shown a relationship between the use of communications technology and wellbeing, particularly mediated through its effect on personal relationships. However, there is some debate over whether this effect is positive or negative. The present study explored this issue further, examining whether the effect varies depending on the type of communications technology, and the nature of the personal relationship. An online survey was conducted with 3421 participants in three countries (Australia, UK and US). It examined the use of ten communication methods, overall satisfaction with life and satisfaction with four different kinds of relationships (close and extended family, and close and distant friends).Results indicate that richer communication methods, which include non-verbal cues, were positively associated with both overall satisfaction with life and satisfaction with relationships. These methods included face-to-face communication, and phone and video calls. Conversely, more restricted methods, such as text messaging and instant messaging, were negatively associated with both variables. Social networking was negatively associated with overall satisfaction, but not with satisfaction with relationships. The strength of the association between a communications method and satisfaction with a relationship varied depending on the type of relationship, but whether it was positive or negative did not change.

On Developing an Integrated Family Mobile Application

“…Franzen found that internet and e-mail has a positive effect on conserving family social ties [13]. Richer communication methods that include face-toface communication, phone and video calls showed a positive relationship satisfaction as mentioned by Goodman-Deane et al [14]. Yet, text messaging and instant messaging were negatively associated.…”

Development and validation of the WhatsApp Negative Impact scale (WANIS)

“…The purpose of this study was to create a scale to evaluate the negative impact of WhatsApp on users (WANIS), since there are no tools available at the moment, and it is becoming increasingly necessary to have a tool to know the possible negative consequences derived from the continued use of this instant messaging application. To this end, a list of items was compiled based on the observation of the behaviours (individual and group) of WhatsApp users and of previous studies on the impact of instant messaging in different areas of everyday life (Bhatt & Arshad, 2016;Goodman-Deane et al, 2016;Grover et al, 2016;Lin, 2012). The EFA disclosed that the three-factor solution was most appropriate for interpreting the scale.…”

“…An initial list of 69 items was compiled to assess instant messaging addiction based on observation of behaviours (individual and group) and research on instant messaging usage and its impact on different areas of everyday life (Bhatt & Arshad, 2016;Goodman-Deane, Mieczakowski, Johnson, Goldhaber, & Clarkson, 2016;Grover et al, 2016;Lin, 2012). The items were grouped into five categories: abuse, social consequences, family consequences, effects on the couple, academic consequences.…”

Text and phone calls: user behaviour and dual-channel communication prediction

“…This is a relatively small dataset (31, 515 calls and 135, 474 text messages) of 111 users from an emerging user base for exploring dual-channel communication traits of smartphone users. Big data analytics have been used to explore many aspects of human life; but despite having many capabilities [23], its unstructured and complex nature causes many problems [24]. In contrast, small data is often structured, easy to access, and easy to manage.…”

“…In contrast, small data is often structured, easy to access, and easy to manage. Hence, small data has been successfully used to solve problems surrounding individuals like impact of communication technologies on relationships [24,25], communication behaviour of smartphone users [10,[13][14][15], user-centric context aware models for prediction [26], and call prediction [2,11].…”