The role of personality in motivational regulation and academic procrastination

Abstract

This study explores the role of the motivational self-regulation in academic procrastination under the personality framework. Therefore, the aims of the study were to investigate: (a) the role of personality dimensions in the self-regulation of motivation; (b) the role of self-regulation of motivation in procrastination; and (c) the mediating role of the self-regulation of motivation. The participants were 274 university students (M = 21 years). The Big Five traits explained from 6% to 17% variance of the individual motivational regulation strategies (MRSs). Both personality (conscientiousness) and the MRS (environmental control) were significant predictors of academic procrastination. Conscientiousness, agreeableness, and intellect showed an indirect effect on reducing academic procrastination, mediated through the strategy of environmental control, thus additionally suggesting the important role of this motivational strategy. Since this strategy can be taught, these findings have a strong practical value.

Keywords:

• Personality
• motivation
• motivational regulation
• self-regulation
• procrastination

Introduction

In the current understanding of student’s academic engagement and performance, models of self-regulated learning provide an explanation for how students take an active and reflective role in their own learning (Puustinen & Pulkkinen, 2001). They can also provide further comprehension of the mechanisms of self-regulation underlying detrimental students’ behaviours like academic procrastination which may be regarded as a failure in self-regulation (Park & Sperling, 2012; Steel, 2007). The central process of self-regulated learning is self-regulation of motivation (Wolters, 2011), and it may be especially valuable to understand how the motivational self-regulation is related to self-regulation deficits as shown by academic procrastination.

Currently, there is no comprehensive picture about the role of motivational self-regulation in academic procrastination, and especially not under the personality framework. Therefore, this study aims to deepen our understanding of the relationship between personality, motivational self-regulation, and academic procrastination. We propose that Big Five personality traits may, in a meaningful way, explain individual variations in motivational regulations to some extent and that motivational regulation may be one of the mechanisms that explain how personality translates into undermining behaviour as indexed by academic procrastination. In addition, given prior research linking academic procrastination both to personality and motivational variables, we investigated whether motivational regulation will increment the Big Five traits and emerge as a unique predictor of academic procrastination.

Self-regulation of motivation

Self-regulated learning is defined as an active and constructive process through which students set learning goals, strive to monitor the goals, and regulate and control their own cognition, motivation and behaviour, guided and constrained by their own objectives and characteristics of the environment (Pintrich, 2004). A core part of self-regulated learning is the self-regulation of motivation (Wolters, 2011), which is defined as more or less conscious control of one’s own motivation, serving mainly to increase effort and persistence (Wolters, 2003a). In order to self-regulate motivation, students use various motivational regulation strategies (MRSs; Schwinger, von der Laden, & Spinath, 2007; Wolters, 1998, 2003a) which may be conceptually related to different motivational mechanisms and theories, as suggested previously (Schwinger & Otterpohl, 2017; Wolters & Benzon, 2013). Two of these strategies are value enhancement strategies, aiming at imaginative modification of a boring activity to make it more exciting (the enhancement of the situational interest strategy), or increasing the personal relevance of material (enhancement of the personal significance strategy). The effectiveness of these two strategies can be theoretically linked to the expectancy-value theory (Eccles & Wigfield, 1995), which posits that the more useful, more important, or more interesting the learning materials are, the more likely students are to engage in a task and to persist longer. Three motivational strategies are self-talk strategies based on the achievement goal theory (e.g. Dweck & Leggett, 1988; Elliot, 1999), each regulating motivation with different kinds of self-talk, using different reasons to pursue learning. These are the mastery self-talk strategy, which refers to motivating oneself with the goal of extending competence and mastering the subject; the performance-approach self-talk with the goal of achieving good performance; and the performance-avoidance self-talk strategy, highlighting the goal to avoid failure as a motivational force. The self-consequating strategy refers to rewarding oneself for persistence in an activity and may be associated with the behavioural theory of learning (Skinner, 1938), although, as pointed out by Wolters and Benzon (2013), the initiation of this strategy is autonomous and driven by volition. The proximal goal setting strategy consists of breaking a long-term goal into smaller subgoals which are more easily achieved and may be theoretically understood under the goal-setting theory (Locke, 1996). Finally, students also reported using the environmental control strategy (Schwinger et al., 2007) which includes choosing beneficial physical arrangements of the working environment conducive to learning. This strategy may be associated with planned behaviour aimed at a mind and behaviour setting and focusing attention on the task ahead and is also driven by volition.

Since the self-regulation of motivation is posited as a core process of self-regulated learning (for a review see Wolters, 2011), an understanding of both its predictors and correlates is important. Previous research suggested that extrinsic self-regulation strategies (performance self-talk and self-consequating) had a direct, but weak, effect on students’ grades (Wolters, 1998, 1999). More recent work has found that the effect of the frequent use of motivational regulation strategies on higher academic success is indirect through higher effort (Schwinger, Steinmayr, & Spinath, 2009; Schwinger & Stiensmeier-Pelster, 2012). Using students’ self-reported effort as a criterion, a recent study also found that the mastery self-talk strategy was the most effective strategy, followed by the proximal goal setting and performance approach self-talk (Schwinger & Otterpohl, 2017). Additionally, an analysis of different individual profiles of motivational regulation found that profiles emphasising the mastery and/or performance-approach self-talk were most related to academic success (Schwinger, Steinmayr, & Spinath, 2012). The use of motivational strategies is also found to be negatively related to academic procrastination and positively to well-being (Grunschel, Schwinger, Steinmayr, & Fries, 2016).

Self-regulation of motivation and personality

Self-regulated motivation, just like self-regulated learning is, in theory, contextual and task specific (Zimmerman, 2008). Accordingly, students’ use of MRSs varies across different situations (Wolters, 1998). However, it may also be assumed that there are also some stable individual differences in self-regulation across a class of learning situations. The currently proposed overall and dominant model for researching individual differences in stable dispositions is the Big Five personality model, and these dimensions repeatedly appear across cultures (McCrae & Allik, 2002). The Big Five dimensions of personality are: agreeableness (e.g. forgiving, trusting, cooperative, friendly, concerned with others’ needs), conscientiousness (e.g. persistent, disciplined, efficient), extraversion (e.g. warm, sociable, active, talkative), neuroticism (e.g. worries a lot, poor impulse control, emotional instability), and intellect/openness (e.g. curious about many different things). Previous studies found that the Big Five personality model, and especially conscientiousness, overlaps with academic motivation conceived under the self-determination theory, as shown by the 11–17% of variance in academic motivation explained by the Big Five dimensions (Komarraju, Karau, & Schmeck, 2009). Several studies (e.g. Bipp, Steinmayr, & Spinath, 2008; Zweig & Webster, 2004) also revealed the significant relationships between personality and achievement goals, thus also suggesting the role of personality in academic motivation. However, to the best of our knowledge, there is no study investigating the role of personality dimensions in the self-regulation of motivation. This was, therefore, the first aim of this study.

Self-regulation of motivation, personality, and academic procrastination

According to Steel (2007), academic procrastination involves delaying beginning or completing an intended course of action despite the expectation of being worse off for the delay. Academic procrastination (AP) can be considered a self-handicapping strategy that occurs in an academic setting, referring to the postponement of academic goals to the point where optimal performance becomes highly unlikely, resulting in psychological distress (Ferrari, Johnson, & McCown, 1995; Steel, 2007) and lower academic achievement (e.g. Schraw, Wadkins, & Olafson, 2007; Senécal, Koestner, & Vallerand, 1995). It is highly related to some personality traits, mostly with a lack of conscientiousness, but some relations were also found with neuroticism (Klingsieck, 2013; Schouwenburg & Lay, 1995; Steel, 2007). As suggested by several authors, AP appears to contradict the characterisation of self-regulated, organised, and motivated students and may be regarded as a failure in self-regulation (Park & Sperling, 2012; Steel, 2007; Wolters, 2003b). Procrastination was found to be negatively correlated with self-efficacy for self-regulated learning, which refers to students’ beliefs in their capacity to implement academic self-regulated learning strategies (Klassen, Krawchuk, & Rajani, 2008; Klassen et al., 2010). High procrastinators reported less use of learning and regulatory strategies, including rehearsal, time and study environment management, and effort regulation (Park & Sperling, 2012), and less metacognitive strategies (Wolters, 2003b). With regard to motivational regulation, it was found that MRSs are negatively associated with procrastination (Grunschel et al., 2016; Wolters & Benzon, 2013). Students who reported delaying their academic work tended to report the less frequent use of five out of the six motivational regulation strategies (Wolters & Benzon, 2013). Furthermore, Grunschel et al. (2016) found that the use of most of the individual MRSs had positive indirect effects on students’ academic performance through (lower) academic procrastination.

However, given the overlap between personality and procrastination (Schouwenburg & Lay, 1995), as well as the overlap between personality and motivation (Komarraju et al., 2009), a study is needed to investigate the effects of motivational strategies on academic procrastination while controlling for personality variables. Therefore, the second aim of this study was to investigate the relationship between self-regulation of motivation and procrastination, while controlling for personality variables.

As a third aim of the study, we investigate the role of the self-regulation of motivation as a potential mediator of the effect of personality on procrastination. As noted earlier, personality and especially high conscientiousness and high emotional stability are shown to be associated with lower procrastination. However, the mechanism through which personality exerts this influence is not yet clear. Based on studies showing the link between personality and motivation (Komarraju et al., 2009), and the link between MRSs and procrastination (Wolters & Benzon, 2013), we assume that one of the possible mechanisms may be through higher motivational regulation. However, there is no study investigating the mediational role of MRSs in the link between personality and academic procrastination.

Hypotheses

Seven distinct yet related hypotheses rooted in the conceptual framework that address important research questions regarding the relationship of motivational regulation with personality and academic procrastination were put forward. The Hypotheses 1–5 were related to the association of each personality dimension and motivational regulation strategies. Hypothesis 6 was related to the incremental value of MRSs above personality in explaining academic procrastination, and Hypothesis 7 was related to the mediational role of motivational regulation in the relationship between personality and academic procrastination.

Based on the description of personality dimensions, specific relations between each personality dimension and motivational regulation strategies may be expected. Higher conscientiousness may be expected to associate with higher overall use of MRSs because conscientious students are persistent and will, therefore, put more effort into maintaining their motivation. More specifically, because they try to accomplish tasks, are self-oriented perfectionists and try to do their best (Stoeber, Otto, & Dalbert, 2009), they may also use more self-talk to remind themselves to put additional effort into learning in order to master the learning material and to get good grades. Additionally, given that conscientious students are organised and like order, they may have a greater tendency to arrange their learning environment and to eliminate distractions; because they are self-disciplined, goal oriented, and like planned behaviour, they may be inclined to set goals and sub-goals. Therefore, we expected that conscientiousness is positively associated with the total use of motivational regulation strategies, and especially with the frequent use of the strategies of mastery self-talk, performance-approach self-talk, environmental control, and proximal goal setting (Hypothesis 1).

Given that individuals with high intellect/openness are intellectually curious, prone to adopt learning-goal orientation (Day, Radosevich, & Chasteen, 2003), and are more likely to enjoy learning, we expected them to score highly on the use of MRSs, especially those related to regulation through enhancement of intrinsic motivation. Therefore, we hypothesised that intellect would be positively associated with the total use of MRSs, and with the enhancement of the personal significance and the mastery self-talk strategies (Hypothesis 2).

Based on the fact that extraverts are inclined to greater excitation and an arousing context, are more often goal pursuing and have high external motivation (Komarraju et al., 2009), and have more social needs (Engeser & Langens, 2010), extraversion was expected to be positively related to the overall use of MRSs, and especially with the strategy of the enhancement of situational interest, performance-approach self-talk, and self-consequating (Hypothesis 3).

Next, individuals with high levels of agreeableness demonstrate highly cooperative behaviour and compliance with teacher instruction and therefore they make more effort and stay focused on learning tasks (Vermetten, Lodewijks, & Vermunt, 2001). In other words, they try to maintain their positive academic behaviour and to maintain their academic motivation. Therefore, we hypothesised that agreeableness is positively related to the overall use of MRSs (Hypothesis 4).

Finally, Bidjerano and Yun Dai (2007) suggested that neurotic students (the opposite of emotional stable) cope with their anxiety about failure by intensifying their efforts. In other words, emotionally unstable students use more, and emotional stable students use fewer MRSs. Therefore, we hypothesised that emotional stability is negatively associated with the use of MRSs, and is especially strongly associated with the performance-avoidance self-talk strategy because this strategy strives to avoid failure (Hypothesis 5).

The second aim of the study was to investigate the relationship between motivational regulation strategies and procrastination, while controlling for personality variables. Based on the presumption that procrastination may represent a failure in self-regulation and in line with the results of previous research (Park & Sperling, 2012; Wolters & Benzon, 2013), we predicted that lower use of MRSs will increment the prediction of academic procrastination, above and beyond personality variables (Hypothesis 6).

With regard to the mediational role of MRSs, we were especially interested in exploring which motivational regulation strategies (one or several) represent a mechanism of the effect that personality dimensions have on (decreasing) procrastination. We predicted that lower conscientiousness would lead to lower use of MRSs, which in turn would lead to higher academic procrastination (Hypothesis 7).

Materials and method

Participants and procedure

The convenience sample consisted of 274 undergraduate students in their first year of study during the academic years 2012–2013 and 2013–2014, mostly female (71%), enrolled in the large public university situated in Zagreb, the capital of Croatia. All students were majoring in science education, most of them in mathematics (86%), and the rest in geography (14%). The average age was 21 years (M = 21.10; SD = 2.18), ranging from 19 to 35. Students completed a questionnaire during the regular psychology course. Students participated in the research voluntarily and anonymously. The questionnaire consisted of a wide array of scales assessing demographic, personality, motivation, and procrastination variables.

Measures

Personality

Personality was assessed with the Croatian version of the International Personality Item Pool 50 (IPIP 50; Goldberg, 1999; Mlačić & Goldberg, 2007). IPIP 50 is a shorter version of the Goldberg (1992) IPIP, in which each of the Big Five dimensions of personality (Extraversion, Agreeableness, Conscientiousness, Emotional Stability, and Intellect) is assessed by 10 items, all using a five-point Likert scale, ranging from 1 (very inaccurate) to 5 (very accurate). A higher result indicates a more expressed dimension. The questionnaire was previously demonstrated to have good psychometric characteristics (Mlačić & Goldberg, 2007).

Motivational regulation strategies

MRSs were assessed with the 30-item Motivational Regulation Questionnaire (MRQ, Schwinger et al., 2007) which is an extended version of Wolters’s (1998, 1999) questionnaire. The MRQ yields a total score and scores on eight subscales operationalising eight motivational strategies: (1) Enhancement of situational interest, (2) Enhancement of personal significance, (3) Mastery self-talk, (4) Performance-approach self-talk, (5) Performance-avoidance self-talk, (6) Environmental control, (7) Self-consequating, and (8) Proximal goal setting. Each MRQ item is rated on a five-point Likert scale, which ranged from 1 (very rarely/never) to 5 (very often/always). Previous research showed adequate psychometric characteristics of the MRQ (Schwinger et al., 2007, 2009). For the purpose of this study, the MRQ was translated into Croatian using a standard back-translation procedure. Internal validity was explored through an exploratory factor analysis with a principal component analysis. The results showed that an eight-factor model accounted for 59.07% of the variance, with all items having loadings on the expected factors. Cronbach alpha coefficients for MRQ subscales ranged from 0.66 to 0.86, similar to German version in which it ranged from 0.68 to 0.86 (Schwinger et al., 2009).

Academic procrastination

Academic procrastination was measured using a 3-item scale constructed to assess students’ tendency to postpone completing their study obligations (e.g. ‘I postpone most of my student obligations until the last moment’). All items were rated on a five-point Likert type scale, ranging from 1 (does not refer to me at all) to 5 (refers to me very often/always). The scale demonstrated a clear one-factor structure with item loadings of 0.95, 0.97, and 0.97, respectively.

All measures demonstrated acceptable or even high Cronbach alpha reliability (Nunnally & Bernstein, 1994) (see Table 1).

Table 1. Means, standard deviations, and correlations between the Big Five personality traits, motivational regulation strategies and academic procrastination (n = 274).

Scale, variables (number of items)	1.	2.	3.	4.	5.	6.	7.	8.	9.	10.	11.	12.	13.	14.	15.
1. IPIP extraversion (10)	(.86)	.05	.02	.21**	.31**	.18**	.28**	.16*	.09	−.06	.10	.12*	.19**	.20**	−.08
2. IPIP agreeableness (10)		(.83)	.37**	−.11	.15*	.19**	.20**	.26**	.26**	.03	.36**	.29**	.29**	.35**	−.14*
3. IPIP conscientiousness (10)		–	(.81)	.19**	.09	.09	.09	.26**	.17**	−.08	.30**	.12	.25**	.22**	−.46**
4. IPIP emotional stability (10)			–	(.87)	.10	−.05	.04	−.06	−.21**	−.27**	−.01	−.14*	−.05	−.15*	−.11
5. IPIP intellect (10)			–	–	(.81)	.21**	.34**	.21**	.03	−.02	.14*	.16**	.22**	.24**	−.14*
6. MRQ enhancement of situational interest (5)				–	–	(.86)	.58**	.46**	.31**	.17**	.21**	.36**	.40**	.67**	−.11
7. MRQ enhancement of personal significance (3)				–	–	–	(.79)	.42**	.27**	.11	.15*	.24**	.35**	.59**	−.12
8. MRQ mastery self-talk (4)				–	–	–	–	(.81)	.61**	.28**	.43**	.46**	.58**	.81**	−.25**
9. MRQ performance-approach self-talk (5)				–	–	–	–	–	(.86)	.37**	.35**	.34**	.44**	.71**	−.15*
10. MRQ performance-avoidance self-talk (3)				–	–	–	–	–	–	(.82)	.11	.15*	.14*	.47**	.01
11. MRQ environmental control (3)				–	–	–	–	–	–	–	(.66)	.41**	.43**	.57**	−.32**
12. MRQ self-consequating (4)				–	–	–	–	–	–	–	–	(.85)	.50**	.66**	−.17**
13. MRQ proximal goal setting (3)				–	–	–	–	–	–	–	–	–	(.70)	.73**	−.27**
14. MRQ overall use of MRS (30)				–	–	–	–	–	–	–	–	–	–	(.92)	−.26**
15. Academic procrastination (3)				–	–	–	–	–	–	–	–	–	–	–	(.96)
Mean	3.37	3.89	3.60	3.23	3.62	2.82	3.43	3.19	3.45	2.34	3.55	3.77	3.53	3.25	3.26
SD	.66	.55	.61	.68	.54	.84	.81	.82	.84	.93	.74	.82	.77	.55	1.09
Range	1.5−4.9	1.6−5	1.6−4.8	1.5−5	2.2−5	1−4.8	1−5	1−5	1−5	1−5	1.33−5	1−5	1−5	1.40−4.70	1−5
Skewness	−.23	−1.03	−.59	.21	−.03	−.09	−.18	−.12	−.36	.54	−.15	−.62	−.30	−.31	−.14
Kurtosis	−.19	1.93	.16	−.04	−.27	−.47	−.29	−.13	−.18	−.12	−.21	.42	.06	.85	−.72

Note. IPIP = International Personality Item Pool 50; MRQ = Motivational Regulation Questionnaire. Internal reliability coefficients (α) appear in parentheses along the main diagonal. *p < .05, **p < .01 (two-tailed tests). Maximal range of response scale: 1–5.

Data analysis

First, we present descriptive statistics and zero-order correlations (Pearson’s coefficients) for all the variables. We performed a preliminary analysis in order to determine whether the association between personality and motivational strategies varies across gender. In these hierarchical linear regression models with each MRS as criteria, gender and Big Five personality variables were entered at Step 1, and the five Gender × Personality interactions were entered at Step 2. An increase in R² at the second step was not significant for any of the strategies, indicating that gender did not interact with personality in predicting motivational strategies. Therefore, we did the main analyses of male and female students together.

In order to investigate the relationship between the Big Five dimensions and each MRS, nine multiple regression analyses were performed, with the Big Five variables entered together as predictors and each of the MRS (plus the overall MRSs) score used as criteria. To assess the incremental value of MRSs in academic procrastination, we used a multiple regression analysis with the Big Five variables controlled for in the first step of the analysis. To investigate whether motivation regulation has a mediation role in the relationship between personality and academic procrastination, we performed five mediation analyses by means of PROCESS macro (Hayes, 2012), with each of the Big Five dimensions used as predictors, eight MRSs entered as parallel mediators (multiple mediator model, MMM), and academic procrastination as a criterion. Since the focus of this research was on exploring mediational mechanisms, we chose PROCESS procedure (Hayes, 2012). While structural equation modeling has apparent benefits, especially for estimating model fit, for research designs such as this, the results obtained by PROCESS procedure and SEM are largely identical (see Hayes, Montoya, & Rockwood, 2017). The MMM was used since it enables us to understand not only whether there is an indirect effect, but also which MRS accounts for the relationship between a certain personality trait and AP. The macro uses the resampling method of bootstrapping, which gives an estimate of the indirect effect by resampling the dataset k times (we used 1.000). An indirect effect is considered statistically significant if its 95% confidence interval (CI) does not include zero.

Results

Descriptive statistics and correlation analysis

Descriptive statistics and the correlation matrix are reported in Table 1. Motivational regulation strategies were mostly low-to-moderate intercorrelated, with the exception of two non-significant correlations. A higher level of academic procrastination was associated with lower levels of agreeableness, conscientiousness, and intellect, and five out of eight motivational regulation strategies.

Regression analyses

The results of the regression analyses aimed at testing Hypotheses 1 to 5 are presented in Table 2. Overall, Big Five personality factors together explained 21% of the variance in the total use of MRSs (R = 0.47, F(5, 268) = 15.43, p < .001). Personality traits taken together explained from 6% (the performance-avoidance self-talk strategy) to 16% (enhancement of the personal significance and environmental control strategies) of the variance in the individual use of specific MRSs. Only the performance-avoidance self-talk strategy was explained by one personality variable (low emotional stability, aka neuroticism), while all other strategies were explained by several personality dimensions. Extraversion, agreeableness, conscientiousness, and intellect all have significant β predictors in a positive direction, while for emotional stability all significant β coefficients were in a negative direction.

Table 2. Multiple regression analyses with the Big Five traits regressed on each of the eight motivational regulation strategies (n = 274).

Factor: Motivational regulation strategy	Predictor	β	R²	Adjusted R²
Enhancement of situational interest	Extraversion	.15*
	Agreeableness	.13*	.09**	.08**
	Intellect	.15*
Enhancement of personal significance	Extraversion	.20**
	Agreeableness	.15*	.18**	.16**
	Intellect	.26**
Mastery self-talk	Extraversion	.13*
	Agreeableness	.13*	.15**	.13**
	Conscientiousness	.22**
	Emotional stability	−.13*
	Intellect	.15*
Performance-approach self-talk	Extraversion	.14*
	Agreeableness	.17**	.13**	.12**
	Conscientiousness	.15*
	Emotional stability	−.25**
Performance-avoidance self-talk	Emotional stability	−.26**	.07**	.06**
Environmental control	Agreeableness	.26**	.17**	.16**
	Conscientiousness	.20**
Self-consequating	Agreeableness	.23**	.13**	.11**
	Emotional stability	−.16**
Proximal goal setting	Extraversion	.16**
	Agreeableness	.17**	.16**	.15**
	Conscientiousness	.19**
	Intellect	.13*
Total use of motivational strategies	Extraversion	.18**
	Agreeableness	.24**	.22**	.21**
	Conscientiousness	.15*
	Emotional stability	−.21**
	Intellect	.16**

Note. *p < .05, **p < .01. Nonsignificant beta coefficients are not shown for the sake of simplicity.

In support of Hypothesis 1, conscientiousness was found to be positively associated with the total use of MRSs (β = 0.15, p < .05), and with the strategies of mastery self-talk (β = 0.22, p < .01), performance-approach self-talk (β = 0.15, p < .05), environmental control (β = 0.20, p < .01), and proximal goal setting (β = 0.19, p < .01). Support was obtained also for Hypothesis 2. Intellect was positively associated with the total use of MRSs (β = 0.16, p < .01), and with the use of the enhancement of personal significance strategy (β = 0.26, p < .01) and the strategy of mastery self-talk (β = 0.15, p < .05).

In support of Hypothesis 3, extraversion was found to be positively associated with the overall use of MRSs (β = 0.18, p < .01), the strategy of the enhancement of situational interest (β = 0.15, p < .05), and the strategy of performance-approach self-talk (β = 0.14, p < .05), while, contrary to Hypothesis 3, it was not associated with the strategy of self-consequating (β = 0.11, p > .05). Thus, Hypothesis 3 was partially supported.

Support was also obtained for Hypothesis 4. As predicted, agreeableness was found to be positively associated with the total use of MRSs (β = 0.24, p < .01), and with all types of MRSs, except with the strategy performance-avoidance self-talk strategy.

The results were also in line with Hypothesis 5. Emotional stability was, as predicted, negatively associated with four out of eight strategies, with standardised β coefficients ranging from −0.13 (mastery self-talk) to −0.26 (performance-avoidance self-talk). Also, as predicted, emotional stability had the highest beta coefficient for performance-avoidance self-talk. In fact, this strategy was explained only by emotional stability, which accounted for 6% of the variance in performance-avoidance self-talk (R = 0.27, F(5, 268) = 4.15, p < .01).

The results of the separate hierarchical regression analysis showed that the use of MRSs was negatively associated with academic procrastination, even after controlling for the Big Five variables, thus confirming Hypothesis 6 (see Table 3). The Big Five personality variables explained 21% of the individual variance in AP (ΔF(5, 268) = 15.57, p < .001), with conscientiousness being the only significant predictor (β = −0.47, p < .001). MRSs explained an additional 5% of the variance in AP (ΔF(8, 260) = 2.22, p = .026). Taken together, personality variables and MRSs explained 24% of the variance in AP, F(13, 260) = 7.58, p < .001), with conscientiousness and the environmental control motivational regulation strategy being the only significant predictors of AP in the final equation (β = −0.41 and −0.18, respectively, both p < .01).

Table 3. Results of the hierarchical regression analysis of academic procrastination (n = 274).

	Step1	Step2
Step 1: Main effect of the Big Five personality traits
Extraversion	−0.04	−0.01
Agreeableness	0.05	0.12
Conscientiousness	−0.47**	−0.41**
Emotional stability	0.00	−0.02
Intellect	−0.09	−0.06
Step 2: Main effects of motivational regulation strategies
Enhancement of situational interest	–	0.02
Enhancement of personal significance	–	−0.02
Mastery self-talk	–	−0.04
Performance-approach self-talk	–	0.02
Performance-avoidance self-talk	–	0.01
Environmental control	–	−0.18**
Self-consequating	–	−0.02
Proximal goal setting	–	−0.09
ΔR^2	0.23**	0.05*
Adjusted R^2	0.21**	0.24*

Note. The standardised regression coefficients are presented. *p < .05, **p < .01

Mediation analysis

The results of multiple mediation analysis with MRSs as mediators in the link between personality and academic procrastination supported Hypothesis 7. Indirect effects emerged for conscientiousness on AP (β = −0.04), 95% CI [−0.0745, −0.0156], for agreeableness on AP (β = −0.08), 95% CI [−0.1250, −0.0437] and for intellect on AP (β = −0.05), 95% CI [−0.0862, −0.0081]. The strategy of environmental control was the only significant mediator in all these relationships. Effect sizes for indirect effects of environmental control, in the form of standardised indirect effects, were: −0.09 (95% CI [−0.1490, −0.0319]); −0.05 (CI [−0.0990, −0.0077]), and −0.03 (CI [−0.0835, −0.0059]) for agreeableness, conscientiousness, and intellect, respectively. These effects sizes are considered to be small (Cohen, 1988; Lachowicz, Preacher, & Kelley, 2018).

Since there was still a significant direct effect of conscientiousness on AP (β = −0.20, p < .05), the mediation of conscientiousness on AP through motivational regulation was partial (see Table 4).

Table 4. Mediation analyses: summary of total, direct, and indirect effects in the relationship between the Big Five traits and academic procrastination as criteria (n = 274).

Predictors	Extraversion		Agreeableness		Conscientiousness		Emotional Stability		Intellect
	Effect	SE	Effect	SE	Effect	SE	Effect	SE	Effect	SE
Total effect	−0.04	0.03	−0.08*	0.04	−0.25*	0.03	−0.05	0.03	−0.08*	0.04
Direct effect	−0.01	0.03	−0.00	0.04	−0.20*	0.03	−0.05	0.03	−0.04	0.04
Indirect effect
Total	−0.03*	0.02	−0.08*	0.02	−0.04*	0.01	−0.00	0.02	−0.05*	0.02
Enhancement of situational interest	0.00	0.01	0.00	0.01	0.00	0.00	−0.00	0.00	0.00	0.01
Enhancement of personal significance	−0.00	0.01	−0.00	0.01	−0.00	0.00	−0.00	0.00	−0.00	0.02
Mastery self-talk	−0.01	0.01	−0.02	0.02	−0.01	0.01	0.00	0.01	−0.02	0.01
Performance-approach self-talk	0.00	0.00	0.01	0.01	0.00	0.01	−0.00	0.01	0.00	0.00
Performance-avoidance self-talk	−0.00	0.00	0.00	0.00	−0.00	0.00	−0.01	0.01	−0.00	0.00
Environmental control	−0.01	0.01	−0.05*	0.02	−0.03*	−0.01	0.00	0.01	−0.02*	0.01
Self-consequating	0.00	0.01	0.00	0.01	−0.00	0.00	−0.00	0.01	0.00	0.01
Proximal goal setting	−0.01	0.01	−0.02	0.02	−0.01	0.01	0.00	0.00	−0.02	0.01
	R^2 = 0.14**F(9, 264) = 4.69, p < .01		R^2 = 0.14**F(9, 264) = 4.68, p < .01		R^2 = 0.26**F(9, 264) = 10.34, p < .01		R^2 = 0.15**F(9, 264) = 5.11, p < .01		R^2 = 0.14**F(9, 264) = 4.80, p < .01

Note. SE - Standard Error. *p < .05, **p < .01. R² refers to the model with a specific Big Five trait and eight motivational regulation strategies entered as predictors, and academic procrastination as a criterion.

Discussions

This study was conducted to enhance our knowledge of the personality mechanisms underlying self-regulation of motivation, and the assumed mediational and incremental role of the self-regulation of motivation in decreasing procrastination. First, it was shown that the Big Five personality factors together explained 21% of the variance in the overall use of MRSs (accounting for 6% to 16% of the variance in specific motivational strategies). Thus, the results suggest that besides the role of context as posited in self-regulation theory (e.g. Zimmerman, 1998), personality is one of the important mechanisms underlying the individual differences in the regulation of motivation. The higher motivational regulation, as indicated by the higher overall use of motivational regulation strategies, was associated with the pattern of higher extraversion, agreeableness, conscientiousness, intellect, and neuroticism (low emotional stability).

The use of specific strategies was associated with personality variables in a meaningful, hypothesised direction. Students with high extraversion reported higher use of regulation by trying to make learning more pleasant (enhancement of the situational interest strategy) and by reminding themselves of how important it is to get good grades (the performance-approach self-talk strategy). Extraverted students also more often used the regulation through enhancing intrinsic interest (the enhancement of personal significance strategy) and by setting subgoals (proximal goal setting strategy), probably due to their high-performance motivation (Judge & Ilies, 2002). Conscientiousness was positively associated with the use of mastery self-talk, performance-approach self-talk, environmental control, and proximal goal setting strategies for the regulation of motivation. Highly conscientious students more frequently pay attention to how important it is to work intensely to learn the material for the sake of mastery and to obtain good grades, more often eliminate distractions and use a step-by-step approach to the workload. Students with high agreeableness try to sustain their effort and regulate motivation with a higher use of all but one (performance-avoidance self-talk) of the motivational regulation strategies, indicating the higher engagement of these students, at least partly because of their cooperativeness and willingness to meet demands. Intellect was found to be positively associated specifically with motivational regulation strategies operating through the enhancement of intrinsic motivation, i.e. enhancement of personal significance and mastery self-talk. These results are in line with the findings that students with high intellect are inclined to more autonomous regulation (Komarraju et al., 2009). However, intellect was also related to the enhancement of the situational interest strategy. Students with high intellect are also creative and probably use more imagination and maybe, therefore, are prone to look for ways to bring more meaning and enjoyment to tasks. Neurotic students in order to cope with their anxiety about failure, also put more effort into keeping up their motivation as indexed by higher use of MRSs and especially used the performance-avoidance self-talk strategy whose goal is to avoid failure, e.g. not to make a fool of themselves or not to perform worse than others. The use of this motivational strategy is associated with negative and conflicting emotions, and is found to be associated with lower well-being (Grunschel et al., 2016) and academic burnout (Ljubin-Golub 2016), suggesting that the use of this strategy has an ambiguous or even negative value in learning, especially in the long run.

The study also showed that motivational regulation has the mediational role in the relationship between personality and academic procrastination. More specifically, conscientiousness, agreeableness, and intellect had an indirect effect on (reducing) procrastination, through the higher use of the environmental control strategy. In other words, students who are conscientious, agreeable and have a high intellect, because of their personality characteristics, are more likely to choose and arrange an adequate environment and context for their learning. This study broadens knowledge about the role of personality in academic procrastination by showing that some personality traits may have an indirect effect on procrastination.

Out of personality variables, consciousness was most strongly associated with academic procrastination, in line with previous studies (Steel, 2007). Consciousness had both direct link with academic procrastination and an indirect link through the environmental control strategy. Since mediation was partial, some other processes, besides environmental control, may also be relevant as potential mediating mechanisms in the relationship between conscientiousness and AP. One of the possible candidates for such a mechanism may be better time management skills since these skills are related both to academic procrastination (Lay & Schouwenburg, 1993), and to conscientiousness (Liu, Rijmen, MacCann, & Roberts, 2009). Conscientious individuals are prone to approach demanding tasks with planning and to work against deadlines in a timely fashion (Liu et al., 2009), and these heightened time management skills, in turn, may reduce the postponing of academic obligations.

The study showed that motivational regulation, through environmental control, also had the unique role of incrementing the Big five traits in the explanation of academic procrastination. The finding of a negative relationship between motivational regulation and procrastination is in line with Wolters and Benzon (2013) and Grunschel et al. (2016) studies. However, in Wolters and Benzon’s (2013) study, the strongest relationship with procrastination had the strategy of mastery self-talk, and in Grunschel et al’s (2016) study, it was the strategy of goal setting. In this study, the strategy of environmental control explained an additional 5% of the variance in AP, besides the effect of conscientiousness. The results suggest that this strategy has specific importance in reducing academic procrastination, after controlling for personality.

Thus, this study found the central role of environmental control (arranging environment that facilitates learning, avoiding distractions) as a regulatory mechanism for academic success, as indexed by lower academic procrastination. This finding is in line with the research showing that distracting oneself by multitasking (e.g. learning and at the same time sending messages through cell phones) undermines the perceived efficacy in learning (Ljubin-Golub & Miloloža, 2010). In contrast, the study of Schwinger and Otterpohl (2017) using self-reported effort as criteria, suggested that the most effective strategy was mastery self-talk, followed by proximal goal setting and performance self-talk. Besides using different criteria (academic procrastination vs. effort), the sample in this study consisted of science education students, while the sample in the study done by Schwinger and Otterpohl (2017) consisted of students with various majors, with only 35% of them enrolled in science. It may well be that students majoring in science are highly motivated by mastery goals and other intrinsic forms of motivation and therefore have a higher level and reduced variance of the mastery self-talk strategy, leading to the non-significant effect of mastery self-talk strategy. Although we consider this interpretation plausible, it should be checked in further research. However, this study clearly points to the fact that the relative importance of an individual strategy may be different in some groups of students.

The environmental control strategy is considered unique, because the items emphasise preventing problems and not increasing students’ willingness to complete the task, as suggested by Wolters and Benzon (2013). In light of the dispute about whether this strategy might better be viewed as a strategy for the regulation of context (Pintrich, 2004), or a strategy for the regulation of motivation (Wolters & Benzon, 2013), this study indicates that effective environmental structuring leads to decreased academic procrastination, i.e. sustained effort in completing academic activities, and is therefore more consistent with the view of environmental control as a proper motivational regulation strategy.

This study also provides some evidence for the reliability and validity of a Croatian version of a Schwinger et al. (2007) instrument for assessing motivational regulation strategies. Results of an exploratory factor analysis provided strong support for the existence of the same eight different types of strategies as in the study conducted with German samples. The results also support Wolter and Benzon’s (2013) suggestion that college students are able to differentiate between concepts of importance and interest which were in the earlier work of Wolters (1999), with younger students subsumed under the strategy of interest enhancement.

Practical implications

The study has some important preliminary implications for educational practice. First, efforts to increase students’ ability to regulate their study motivation may reduce procrastination. One suggestion on how to increase students’ motivation regulation is to teach students about a variety of motivational regulation strategies and by creating learning environments that facilitate the use of different MRSs. This study also suggests that teachers should be aware of the differences in students’ personality traits and encourage them to choose MRSs in line with their personality-based preferences. In addition, teachers should discourage the use of the performance-avoidance self-talk strategy and give support to emotionally unstable students using this strategy, to focus more on individual progress, rather than comparing themselves to others.

Second, this study points to the particular value of environmental control strategy for reducing procrastination. Thus, it is especially important to teach students about the value of this strategy. Environment management can be taught and modified since it is a specific and not complex behaviour (e.g. Cleary & Zimmerman, 2004). Teachers can identify students with poor environment management control and encourage them to form good environment control habits. Students may be first taught about the importance of this strategy and then reinforced to maintain environmental control by eliminating distractions before and during learning (such as TV, cell phones, the internet, etc.), as well as to choose a learning time when they can concentrate especially well.

Limitations and future directions

First, the sample comprised university students, with the majority of them majoring in mathematics education. Our sample may well differ in the Big Five factors in comparison with the population norms because it has been shown that personality influences the choice of study in higher education (Boone, van Olffen, & Roijakkers, 2004). Further research is needed to determine whether the results may be generalised to other student populations. Second, the study did not address students’ age, study grade level, prior achievement, parent education level or socio-economic status, while such variables may also be important for motivation (Froiland & Oros, 2014). Third, the study is of a cross-sectional type and no causal effects are implied. Also, it is important to note that we used a self-constructed scale for measuring academic procrastination. We wanted our measure to be short, in line with Steel’s (2007) definition, and to tap the extent to which students unnecessarily delay their academic activities, while most of the procrastination scales measure general and not academic procrastination. However, for that reason, caution is required when comparing our results to the results of the other studies.

This study demonstrated the role of Big Five personality model in motivational regulation. The role of some other personality variables may be also promising and even more relevant to self-regulated learning, such as the motivational traits from the Reiss personality profile of fundamental goals and motivation sensitivities (Havercamp & Reiss, 2003). The traits from a lower order position in the hierarchy, such as perfectionism, may also be interesting for further study, as antecedents and a steady inclination toward the use of specific motivational regulation strategies.

Conclusions

This study makes both theoretical and practical contributions. First, this study extends self-regulated motivation research by showing that students’ individual differences in personality are associated in a meaningful way with their self-regulation of motivation. Second, the study shows the theoretically expected but under-researched, mediational role of motivational regulation in the link between personality and procrastination. Third, our findings suggest that the link between the self-regulation of motivation (through environmental control) and academic procrastination exists even after controlling for personality variables. Thus, this study shows that personality should be taken into account when investigating motivation regulation, and it also shows the unique and important value of the environmental control strategy for lowering academic procrastination. Besides this theoretical value, a practical value of the study is that isolating a specific behaviour, such as environment management, for its relationship to academic procrastination, may be useful.

Disclosure statement

No potential conflict of interest was reported by the authors.