Procrastination on social media: predictors of types, triggers and acceptance of countermeasures

Procrastination has been described as the voluntary delay of urgent tasks, which may result in negative consequences (Klingsieck 2013), such as higher stress and poor health (Tice and Baumeister 1997; Sirois et al. 2003). It is viewed within psychological research as a self-regulation failure (van Eerde and Klingsieck 2018). Self-regulation refers to our ability to make use of our cognitive, emotional and behavioural resources to achieve a goal or outcome (Baumeister and Heatherton 1996). Procrastination is a phenomenon that has been argued as being especially relevant in student populations (van Eerde and Klingsieck 2018), where it has been shown it can have severe consequences on academic achievement (Kim and Seo 2015).

Procrastination may be exacerbated by technologies such as social media and smartphones (Rozgonjuk, Kattago and Täht 2018). This possibility was first raised in early research into the internet, where it was found that people who perceive the internet as enjoyable are more likely to report higher levels of online procrastination (Lavoie and Pychyl 2001). There are several forms of procrastination in the context of SNSs, such as cyberslacking and personal web usage. Cyberslacking is defining as an employee who uses the Internet for a non-work related task during working hours (Bock and Ho 2009). It can create various issues for the employee related to work performance and can also disrupt the work environment (O’Neill, Hambley and Bercovich 2014). In academia, cyberslacking behaviour affects the students' educational performance and damages their cognitive and retention abilities, mainly due to the surfing of unrelated digital media during class time (McKeachie & Svinicki 2013). Personal web usage is any voluntary act of using the Internet for personal use during working hours such as gambling, online shopping, or news surfing, which can cause procrastination, time wasting, and reduce work quality and productivity (Coker 2013).

If online technologies do increase procrastination, then this is a matter of concern, especially in relation to young adult and student populations. There has been an increase in the amount of time that young adults spend online, with 16–24 year olds in the UK spending on average 27 h a week online in 2018; three times the amount than a decade before (OFCOM 2018). In students this behaviour may overlap with offline activities and commitments, with research suggesting that most students spend up to 40% of their time in class on social media (Ravizza et al, 2016). The need for students to balance social networking site (SNS) use with their academic studies has been identified as a new form of the classic self-control dilemma, defined as competition between an immediate low priority impulse and a distal high priority goal (Reinecke and Hofmann 2016). Indeed, using social media at the cost of performing goal related activities has been argued to be a specific type of procrastination (Meier et al 2016). It is possible that there are unique characteristics of SNS related procrastination. For example, it has been noted that procrastination facilitated through Facebook appears to be associated with increased anxiety on the part of the procrastinator (Sternberg et al, 2020).

Alblwi (2020) have discuss several psychological theories related to behavioural change that can be used to reason about the factors that contribute towards procrastination. As they note self-determination theory (SDT) focuses on human motivation and personality, addressing people’s inherent growth tendencies, innate, and psychological needs, which are formed by combining cognitive evaluation theory and organismic integration theory (OIT) (Ryan and Deci 2000). SDT identifies motivational factors that trigger people to make certain choices without an external stimulus and interference. Cognitive evaluation theory explains the effects of external factors on internal motivation. It addresses the common psychological health needs, including competence, autonomy, and relatedness, which are essential to gain intrinsic and extrinsic motivation based on getting rewards or outcomes inherent in the task. For example, people may use SNS for entertainment or gaining popularity by trying to increase their social circle. On the other hand, OIT theory discusses the different types of motivations which can regulate behaviour. OIT defines six types of regulations (amotivation, external, introjected, identified regulation, integrated, intrinsic) that vary according to the amount of autonomy available to the participant and the motivational value of the activity, to them (Ryan and Deci 2000). For example, a person driven by external regulation may be motivated to satisfy an external demand and always maintains a good relationship with their peers. This could result increased SNS use.

Research has identified several individual factors that relate to procrastination. Özer et al. (2009) found that over half of their sample of 784 participants self-reported frequent academic procrastination, with male students reporting much more frequent procrastination than female students. There were also reported differences in why females and males procrastinate. Female students reported procrastinating more because of their fear of failure and fear of appearing lazy. Males, on the other hand, reported procrastination because of taking risks and rebelling against control. Kaya et al. (2012) replicated this finding, observing that female students were able to manage time better than male students. It was also found that the time management skills of the students decreased as their anxiety level increased. Steel et al. (2001) conducted a meta-analysis of the association between personality traits and procrastination. The results of the review suggested a weak connection between neuroticism, rebelliousness and sensation seeking. Strong and consistent predictors of procrastination were task aversiveness, task delay, self-efficacy, and impulsiveness, as well as conscientiousness and its facets of self-control, distractibility, organisation and achievement motivation. It has also been observed that procrastination shares many features with the Dark Triad of personality (narcissism, Machiavellianism and psychopathy), such as high impulsivity and low conscientiousness (Lyons and Rice 2014). In contrast to studies linking procrastination to anxiety Martincekova and Enright (2020) found no relationship between procrastination and individuals’ proneness to guilt. Overall, the research literature on individual predictors of procrastination is varied, and as commented on elsewhere (van Eerde and Klingsieck 2018), lacks consistency.

It has been argued that procrastination also varies between cultures (Triandis 1989; Eskin 2003; Nair 2017). Beswick et al. (1988) for example found that Asian students, compared to Western students, scored higher on hypervigilance (a panicky style of decision making) and procrastination (avoidant styles of decision making). This phenomenon was further analysed by Klassen et al. (2010) across Canadian and Singaporean students. Singaporean students were more likely to be negative procrastinators and spent more time procrastinating than Canadian students and were also found to demonstrate lower self-regulation and self-efficacy. This would appear to be consistent with the cultural dimensions of each country, as reported by Hofstede Insights (2020), which is based on original research conducted by Hofstede et al. (2010). Under these ratings, Singapore is reported to be a markedly more collectivistic culture than Canada, which means that individuals are more reluctant to take individual actions that could result in a negative outcome (for example failing an exam), and in turn more likely to use procrastination as an avoidance strategy. Conversely Singapore is rated by Hofstede Insights (2020) as having a lower level of uncertainty avoidance than Canada, which could result in feelings of less pressure to plan and complete tasks on time. This also supports the work of Beswick et al (1988) who found that Asian students procrastinate more than Western students in university settings. It must be acknowledged that models of cultural dimensions have been criticised for being overly simplistic (Vignoles et al. 2016), and as such caution should be taken when interpreting the research literature on this topic.

Interventions have been proposed to reduce procrastination. In a meta-analysis of studies van Eerde and Klingsieck (2018) identify several therapeutic techniques that appear to be effective, with cognitive behavioural therapy being the most effective. Therapeutic approaches of this type are typically time and resource intensive and require one to one communication between the therapist and the patient, often done in-person. However, whilst SNS and related technologies may contribute towards procrastination, they can also be used to deliver countermeasures. This is because the technologies allow for real time, intelligent, data driven prevention and intervention strategies to be delivered, including the use of gamification (Alblwi et al. 2019a). This has been explored in relation to other online behaviours including digital addiction (Alrobai et al. 2019) and fear of missing out (Alutaybi et al. 2019a, b). It contrasts with traditional methods of prevention and intervention campaigns used within social psychology and behaviour change research. These traditional approaches rely on mass media campaigns and other related approaches to present information to the target population in a one-sided manner (Caraban et al. 2019).

In addition to therapeutic interventions, recently, different design techniques have been suggested to reduce procrastination. Redesigning websites by removing extra distracting content, visualising the usage time and rewarding self-control related behaviour are a few of these techniques discussed in HCI research to reduce time spent in procrastinating and support productivity (Lyngs et al. 2019; Lyngs et al. 2019). Design interventions in social media, such as hiding likes count (Grosser 2019), removing news feed and setting goal reminders (Kovacs et al. 2019; Lyngs et al. 2020), have been examined. Blocking continuous online news feed has been shown to reduce the time spent on social media, especially with individuals who are more vulnerable to social media distractions (Mark et al. 2018). While several researchers have examined removing or blocking content, others investigated adding a visual cue to boost productivity and reduce interruption time. Liu et al. (2014) presented users with a visual representation of the time spent when a task is interrupted. Compared to the control group, the subject presented with this visual cue spent less time procrastinating and completed the job more efficiently. Persuasive design techniques have been used as countermeasures. Foulonneau et al. (2016) used context-aware persuasive messages to limit users' screen time. Despite the power that persuasive techniques and gamification tools have on altering human behaviour, they are rarely adapted in this context (Lukas and Berking 2018).

The data produced by SNS also provides opportunities for a deeper understanding of how procrastination is created and viewed, such as the attitudes towards procrastination identified in large scale sentiment analysis of social media posts by Chen et al. (2020). Appropriate use of SNS technologies may facilitate the application of techniques developed in other domains to reduce rates of other problematic behaviour by increasing knowledge and shifting decision making. For example, it been suggested that one reason students procrastinate is due of their lack of awareness on how to schedule time properly (Nair 2017). Using intelligent, real-time systems that delivers advice based on the SNS behaviour of that individual and steers them towards effective time management may prevent them from developing problematic procrastination in the first place. This is consistent with the nudge approach to behaviour change (Thaler and Sunstein 2009), which is increasingly used in conjunction with technology to address health and wellbeing issues (Caraban et al. 2019). It should be noted that it is within the realm of the designers of SNS to embed such techniques. As we observe in our research SNS are already set-up in ways that encourage immersion (Alutaybi et al. 2019a, b). There is potential for the same techniques and systems to be used to reduce SNS facilitated procrastination.

To do so we need to first understand how social media may trigger different types of procrastination and what countermeasures could be implemented that would be acceptable to users of these systems. In previous research, (Alblwi et al. 2019a, 2019b, 2020), we identified different types of procrastination and SNS features which may trigger it. The work also identified technical countermeasures that can be integrated to social media design to help users have more control over their procrastination. In this paper, we study predictors of the types of procrastination and the acceptance of these countermeasures. The predictors include gender, culture, self-control, number of procrastination hours per day, personality traits and the types of procrastination participants declared themselves to experience.

A two-phase study was conducted that consisted of an initial qualitative phase followed by a quantitative phase. This approach is consistent with the model of generalisation, as identified by Mayring (2007). The approach was chosen due to the lack of research literature on social media facilitated procrastination. As noted in the previous section, the role of social media in the experience of procrastination remains relatively unexplored, with potential countermeasures being largely based on research on procrastination in other domains and mainly concerned with time management tools neglecting the new elements social media add such as the continuous peer pressure, notifications, immersive designs, and personalised content. Similarly, there is a lack of theoretical understanding of procrastination in relation to social media. The mixed methods approach has been argued to contribute towards theory (Coxon, 2005). We adopted an exploratory sequential mixed methods approach (Creswell and Plano Clark 2017) where the initial qualitative data collection and analysis was used to develop a deeper understanding of the phenomenon as experienced by participants and provide a basis for the quantitative part that was meant for confirming the findings, discovering trends and inferring prediction relations amongst procrastination components. Data were gathered in the qualitative phase through two stages, an exploration stage followed by a co-design stage. Data were then gathered in the quantitative phase through use of an online survey, which sought to determine how the factors identified in the qualitative phase could be applied to a larger population. As part of quantitative phase regression analysis was used to identify which variables are predictive of procrastination and acceptance of procrastination countermeasures. The phases were conducted in sequence, meaning that the findings from the qualitative phase informed those of the quantitative phase. The materials used in these phases can be found at https://​eprints.​bournemouth.​ac.​uk/​34402/​.

In the following subsections, we briefly present the procrastination types which were identified during the qualitative phase of the study, which are published in Alblwi et al. (2019b). We then present the SNS features that were identified to trigger procrastination in Alblwi et al. (2019b) and, also, the suggested countermeasures for each of these SNS features. The whole set of suggested countermeasures can be found in Alblwi et al (2019a) and the pairing between these countermeasures and SNS features can be found in Alblwi et al. (2020).

Descriptive statistics about the preferred countermeasures for combating procrastinating arising from the identity features of SNSs are discussed in Alblwi et al. (2020). The survey design can be found in Appendices 5 and 6 of Alblwi 2020. The questions are built on the qualitative phase findings, i.e. the resulted procrastination types, triggers and countermeasures. The questionnaire also included demographics, personality (Rammstedt and John, 2007) and self-control questions (Tangney et al 2004). The focus of this paper is on using inferential analysis to explore how the factors of gender, personality, self-control and culture can be used to predict levels of agreement towards types and triggers of procrastination; and acceptance of proposed countermeasures for procrastination.

This analysis was restricted to participants from the UK and the Kingdom of Saudi Arabia (KSA), as these were the countries which jointly provided most of the participants. A total of 288 surveys were returned, 123 (42.7%) from KSA and 165 (57.3%) from the UK. The sample from KSA consisted of 72.4% male and 27.5% female respondents, with a mean age of 31.6 (s.d. 5.8). The sample from the UK consisted of 46.1% male respondents and 53.9% female respondents, with a mean age of 24.7 (s.d. 7.4).

A series of regressions were conducted for the degree to which participants agreed with the procrastination types and triggers, and the acceptance of countermeasures, as determined by the statements in the quantitative survey.

The second set of regression models also used multiple regression, with each outcome variable being how much participants agreed that they were prone to each of the SNS features as triggers of their procrastination. The predictors were the scores on the five personality scales, the total self-control score, gender (male/ female), the self-reported number of hours per day spent procrastinating on social media, and country (KSA/ UK) and finally the agreement scores on each of the four types of procrastination (i.e. the four outcome measures in the first set of regression models).

The third set of regression models used binary logistic regression. The outcome variable was acceptance of potential countermeasures, operationalised as a categorical response (no/yes) as to whether the participant would accept the proposed countermeasure. The predictors in all the models were again were the scores on the five personality scales, the total self-control score, gender (male/ female), and country (KSA/ UK) and finally the agreement scores on each of the four types of procrastination.

As demonstrated in our previous research individuals appear to agree that the features of SNS may trigger procrastination; agree that this procrastination can take on different forms; and agree that countermeasures can be used to mitigate this (Alblwi et al. 2019a, 2019b, 2020). The main finding of this paper is that, overall, factors such as gender, personality, hours of SNS use and culture (UK/ KSA) do not seem to be predictive of the types of procrastination experienced on SNS; triggers of procrastination due to SNS features or perceived acceptability of SNS facilitated countermeasure This is notable given the previously discussed research which suggests that there is an association between these factors and the experience of procrastination in non-SNS related procrastination (Steel et al. 2001; Özer et al. 2009; Nair 2017). Perhaps not surprisingly self-control was the most consistently significant predictor for procrastination related outcomes in the regression models, although again even then it was only a significant predictor in some of the models, which themselves predicted relatively little of the variance in the outcome variable. Self-control has been found in other studies to be a determinant of procrastination in relation to social media addiction (Eksi et al 2019). The procrastination type of mood modification was also a significant predictor in several models. This is consistent with previous research that has identified that procrastinated related tasks such as coursework are often identified as by individuals as stressful, boring or frustrating (Pychyl et al. 2000). We also noted in our previous research that some individuals use SNS to experience positive emotions of acceptance and appreciation at times when they are feeling lonely or isolated from their social group (Alblwi et al 2019b). This could highlight one of the key challenges in designing SNS platforms in way that does not encourage procrastination, namely that by their nature SNS cater to the fundamental human need for socialisation.

Culture was a significant predictor in several of the regression models, but to a relatively limited extent. This is of interest, given the different cultures of KSA and the UK. As reported by Hofstede Insights (2020) KSA is estimated to score substantially higher on the power distance cultural dimension than the UK. Power distance refers to the degree in which power is spread throughout a society, such as for example whether there exists a strict hierarchy within a culture. This could be expected to impact on social interactions in several ways. For example, one type of procrastination that was identified was avoidance (procrastinating to avoid difficult or unpleasant tasks). Culture was found to be a significant predictor within the relevant regression model, with participants from KSA significantly less likely to agree that they experienced this type of procrastination. This is consistent with the power distance cultural dimension, as individuals in cultures with a higher power distance may feel less able to avoid difficult or unpleasant tasks. In contrast, KSA is estimated to have a higher degree of uncertainty avoidance as compared to the UK (Hofstede Insights 2020). This refers the degree to which individuals are willing to tolerate ambiguity. However, culture was not a significant predictor for the emergence type of procrastination (checking a notification as soon as it is received), which it could have been expected to be as unread notification could be considered a form of ambiguity. UK culture is estimated to also display higher levels of individualism than KSA. Individualism refers to the degree of interdependence between members of a society, characterised by Hofstede Insights (2020) as whether an individual’s self-image defined by ‘I’ or ‘we’. KSA is considered a collectivistic culture on this dimension, where individuals are driven towards maintaining strong relationships and considering the welfare of their wider group. As such this is factor that could be expected to be linked to social media and in turn SNS facilitated procrastination. Nevertheless, there was again a lack of strong or notable relationships between culture and acceptance of procrastination countermeasures. Overall, the relative lack of on an impact of culture on SNS related procrastination may be an indication that behaviour on SNS transcends geographical and cultural norms of behaviour. This reflects one of the fundamental questions posed within the field of cyberpsychology, which is whether the internet enhances or transforms behaviour (Suler 2004). In the case of SNS related procrastination it may be that the norms of SNS use are greater and more persuasive than the cultural norm that the individual operates within.

Some of the inconsistencies within the research literature may relate to methodological issues. As was the case in this study procrastination is usually a self-reported measure. It has been noted that self-reported procrastination can differ from observed procrastination, which may indicate that self-assessment of procrastination is influenced by self-concept (Steel et al 2001). In the study by Chen et al. (2020) it was demonstrated through sentiment analysis of social media that procrastination is viewed negatively. It is possible that this creates a social desirability bias when self-report is used, leading individuals to under-report their levels of procrastination. This is where technology may be able to contribute by enabling the collection of the objective data, such as for example use of social media apps by students around the time of assignment submission. Further, the types of social media facilitated procrastination that were used in the analysis were derived from the participants themselves, although the method through which these types were elicited was based our review of the research literature. As discussed elsewhere (van Eerde and Klingsieck 2018) procrastination is a complex and under-researched topic. There may be conflation with other areas such as digital addiction, although there is also a lack of consensus and clear conceptualisation within that area as well (Almourad et al 2020). We do not claim that the measure of procrastination that we used to elicit the attitudes towards procrastination countermeasures are equivalent to fully developed and tested psychometric measures of procrastination.

Only two cultures are were compared in the analysis. Including a greater range of cultures in the future research may identify cultural factors predict types, triggers, and acceptable countermeasures of procrastination. If cultural is a relevant factor, then this is something that online systems to counter procrastination can be designed to account for. Similarly, if future research identifies that individual factors such as gender and personality are relevant to procrastination prevention and intervention strategies then online systems can be designed to provide tailored messages. In addition to these limitations, it should be acknowledged that basing the types of procrastination used as predictors on the opinions of participants may have some bias. Nevertheless, prompting individuals to consider their own patterns and styles of procrastination may have benefits. This type of meta-cognition is the basis of cognitive behavioural therapy, which has been identified as one of the most effective treatments for procrastination in offline therapeutic sessions (van Eerde and Klingsieck 2018).

The results of this study demonstrate new avenues through which procrastination can be addressed. As van Eerde and Klingsieck (2018) note that there is mixed evidence over whether procrastination can be changed, with some research suggesting that it has a genetic component (Gustavson et al 2014) and elements of a stable traits (Steel 2007). However, studies have also documented what appears to be changes in procrastination over time (van Eerde and Klingsieck 2018), which would suggest there is a degree of malleability involved. Use of SNS analysis may provide the additional data needed to provide a better understanding of procrastination, and in turn more evidence-based prevention and intervention approaches. Whilst we did not find any factors that were strongly predictive of SNS related procrastination or acceptance of countermeasures it is possible that future research could identify other relevant factors. One advantage to studying SNS related procrastination is that the medium provides high volumes of data about the behaviour of the individual, through which procrastination may be identified. Changes within the design of SNS may also provide natural experiments than can be used to better determine the relationship between design features and the triggering of procrastination. Overall, better use needs to be made of the data available. As commented by van Eerde and Klingsieck (2018) procrastination is a topic often covered by self-help books, which may not be based on scientific evidence. It has also been observed that managed procrastination may be used to relieve stress, improve mood and increase work efficiency (Ivarsson and Larsson 2011). This suggests that a more nuanced approach to the management of procrastination may be needed, where individuals can experience the benefits of procrastination whilst mitigating the harmful consequences. In the case of SNS facilitated procrastination these strategies are not limited to being implemented after the problem has developed, instead using appropriate design and intelligent monitoring problematic procrastination may be prevented.

In this paper, we studied predictors between various personal factors and proneness to procrastination on SNS sites and also acceptance of a new range of SNS features to combat it. We argued the dual role of SNS in both triggering and, potentially, mitigating procrastination. Our findings showed that users who self-declared to have frequent procrastination on SNS are generally welcoming to having new SNS features to help them mitigate it. They also agreed that their procrastination falls into one or more of the four types of procrastination that we identified. They also agreed on the role of different social media features in triggering their procrastination. There did not appear to be any strong relationships between the type of procrastination experienced and the acceptance of the different types of countermeasure. This may suggest that, despite there being different manifestations of procrastination, potential countermeasures may act upon shared, underlying characteristics of procrastination. However, as SNS become increasingly targeted and personalised it is possible that specific, future design features may become more salient to individuals who experience specific type of procrastination. Neuroticism and self-control were significantly able to predict the type of procrastination. This would mean the ability to personalise SNS to avoid triggers, for example, the emergence type of procrastination for users with some personality traits, through reducing notifications or providing users with advanced ways to filter and customise them. According to Alutaybi et al. (2019a, 2019b) and also Alblwi et al. (2020), the simple ways offered by commercial social networks and digital wellbeing tools of muting notification all together or encouraging a time off can create anxiety, fear of missing out and passive procrastination. In our future work, we will focus on the design methods of countermeasures in a user-centred style catering to the actual user experience with such countermeasures in a real-world context. Given the innate conflict between the need to procrastinate, e.g. to change mood, and the serious task being delayed, our tests and design methods for such countermeasures shall also strive at minimising reactance to them in users and at the same time making them visible and perceived to be useful and worth a try. Applying AI and machine learning can help drawing a rich user model derived through previous interactions with and on social networks. With additional input from tools like calendars, timers, fitness apps and the like, such AI can draw a better picture of user habits, lifestyle and types of procrastination and preferences towards it countermeasure. The use of intelligent recommendations, i.e. our proposed countermeasure of Suggestions and Goal Setting, is also a promising direction for future work.