The Impact of Regulatory Fit on Experienced Autonomy

Abstract

This research sought to test the hypothesis that regulatory fit enhances people’s feelings of autonomy. Regulatory fit can be created by prompting people to execute a task using means of task execution that fit (vs. do not fit) their preferred means of goal-pursuit. Assigning people to do a task using a particular means implies they do not exercise choice in applying their preferred means of goal-pursuit. Nevertheless, we reasoned that fitting task means would lead to higher feelings of autonomy while working on a task because, under conditions of regulatory fit, people are using the means that they would have chosen if they had been given choice. We conducted 10 experiments (total N = 3,124) to test the effect of regulatory fit versus regulatory non-fit on experienced autonomy and a meta-analysis of the effects supported our hypothesis for both promotion focus-based fit and prevention focus-based fit.

Keywords

autonomy regulatory fit regulatory focus

When people in groups and organizations have decision-making freedom—job autonomy—it generally increases their functioning and well-being (Parker et al., 2017; Van den Broeck et al., 2016). Consequently, much literature has focused on choice freedom as an antecedent of human functioning and well-being in groups and organizations (Morgeson & Humphrey, 2006; Parker, 2014; Parker et al., 2017; Van den Broeck et al., 2010). However, motivation researchers rightfully note that it is rarely possible to substantially improve choice freedom in jobs, and that even within jobs that offer little latitude, individuals can, given the right conditions, experience a great deal of autonomy (Deci et al., 2017). More than 40% of the global workforce holds an occupation low in choice freedom (e.g., assembly line workers, service staff, nurses, emergency responders, call center employees; Deci et al., 2017). Surprisingly, research on antecedents of experienced autonomy in low autonomy conditions is sparse (Botti & Iyengar, 2004, 2006; Deci et al., 2017; Gagné & Deci, 2005; Van den Broeck et al., 2016).

We theorize that regulatory fit—the congruence between individuals’ motivational orientation and the strategic means they are prompted to use to execute a task (Higgins, 2000, 2005)—can enhance their experienced autonomy even when they do not choose these strategic means themselves (Pierro et al., 2009). Autonomy research showed that individuals’ preferences and their ability to pursue those preferences are an important precursor to their experienced autonomy (Deci & Ryan, 2000; Dysvik & Kuvaas, 2010; Greguras & Diefendorff, 2009; Sheldon, 2014). Similarly, we expect that regulatory fit (relative to non-fit) positively affects individuals’ experienced autonomy because individuals engage in a task in the manner that they would have chosen had they been given a choice (Dan-Cohen, 1992; Gagné & Deci, 2005; Higgins, 1997).

Theoretical Background

Most contemporary job design models (Demerouti et al., 2001; Hacker, 2003; Hackman & Oldham, 1975; Karasek, 1979; LePine et al., 2005; Rudolph et al., 2017) consider autonomy as an objective property of the job, yet it is well recognized that individuals’ functioning and well-being are determined mostly by how they appraise their job (LePine et al., 2005; Webster et al., 2011). A similar point is made by self-determination theory, which emphasizes the importance of people’s subjectively experienced autonomy for their well-being and performance (Baard et al., 2004; Deci et al., 2017; Deci & Ryan, 2000; Gagné & Deci, 2005; Morgeson & Humphrey, 2006; Parker, 2014). An important part of people’s experienced autonomy is the extent to which they feel that their activities align with their core selves, values, and interests (Amabile et al., 1976; Csikszentmihalyi, 1990; Deci & Ryan, 2000; Spreitzer, 1996). Thus, although job autonomy is considered a work characteristic, self-determination theory maintains that experienced autonomy is the driver of autonomy’s beneficial work outcomes (Deci et al., 2017; Dysvik & Kuvaas, 2013; Ryan et al., 2019; Van den Broeck et al., 2010). So far, the relationship between freedom of choice and experienced autonomy has mostly been examined in situations where individuals have a great deal of freedom of choice. Yet, it has been argued that—under the right conditions—people may experience autonomy even though they have little or no freedom of choice in their work (Deci et al., 2017). Ryan and Deci (2006) stated that, “one could have only one option (which functionally means no choice) and yet feel quite autonomous as long as one truly endorses that option” (p. 1,577). It follows that even without choosing themselves, people may experience relatively greater autonomy, if the course of action they are prompted to apply aligns with their goals and preferences.

We suggest that individuals’ approach to task accomplishment—the strategies they are prompted to use—influence their experienced autonomy. That is, we propose that assigning individuals to conditions in which they are prompted to use goal-pursuit strategies they prefer causes higher experienced autonomy compared to strategies they do not prefer, even when they do not have a choice to work under other conditions. We draw upon regulatory focus theory (Higgins, 1997) to study whether individuals’ experienced autonomy is higher when their goal-pursuit strategies—which are elicited through the strategic outcome framing of their tasks—fit (vs. do not fit) with their preferred strategies.

Regulatory focus theory (Higgins, 1997) distinguishes two orientations that influence how people typically prefer to make decisions. Those who are promotion-focused pursue ambitions, aspirations, and growth. Their strategic means of choice in pursuing those goals is eagerness. Eagerness entails a preference to choose acting upon opportunities to advance (Crowe & Higgins, 1997). This goal-driven preference arises because advancement typically benefits from action. For example, they tend to choose opportunities and they tend to choose speed over accuracy in task performance (Förster et al., 2003; Hamstra et al., 2011). Those who are prevention-focused pursue responsibilities, obligations, and security. Their strategic means of choice in pursuing those goals is vigilance. Vigilance entails a preference to choose to refrain from action (Crowe & Higgins, 1997). This goal-driven preference arises because maintaining security mostly requires not acting. They tend to make decisions to prevent potential wrong actions (Crowe & Higgins, 1997) and tend to choose accuracy over speed in task performance (Förster et al., 2003; Hamstra et al., 2011).

Those who repeatedly choose a strategy have become practiced in that strategy and applying this “choice” becomes normal (Higgins et al., 2001; Kruglanski et al., 2002; Shah & Kruglanski, 2000). They thus come to idiosyncratically prefer those means of goal-pursuit and keep choosing them across tasks when they have the freedom to do so. In short, regulatory focus research indicates that people tend to choose how they approach an instance of goal-pursuit in alignment with their own goals; in other words, they prefer to use means that fit with their goals. We propose that this same fit can also affect individuals’ experienced autonomy, even when they do not choose these strategic means, so that, even when these strategies are assigned to them through the way in which their outcomes are framed. Indeed, in real life situations people often do not have the freedom to choose their preferred means of task execution. Rather, they are often assigned to work tasks and strategies (Humphrey et al., 2007; Parker, 2014). Similarly, regulatory fit between goals and means has been shown to affect people’s experiences of a task when individuals did not choose a task or a strategic orientation to the task freely (Freitas & Higgins, 2002). When people choose in line with their preferences, they feel relatively autonomous. This is the way they would ordinarily act or choose; it is what they would normally do. When individuals are assigned to their preferred means, their task experience is similar to the task experience they would have had if they had self-chosen these means; more similar, that is, compared with if they are assigned to non-preferred means of task execution. In other words, fit implies a task experience that is similar to the experience the individual would have had they chosen it themselves.

Therefore, it seems plausible that individuals’ experienced autonomy is comparatively higher when the means of task execution they are assigned to fit (vs. do not fit) what they would choose if they had a choice, even when they are instructed to do an activity using a particular means. Consequently, we theorize that fitting instructions lead to relatively greater autonomy experiences compared with non-fitting instructions. We hypothesize that individuals experience greater autonomy under conditions of regulatory fit, compared with regulatory non-fit and we test this regarding both the fit between promotion focus and eager means and prevention focus and vigilant means.

Method

We conducted 10 experiments that all have nearly identical procedures and only slight variations, and we therefore describe their method and procedure as one experiment. We explain all variations, and we focus on the meta-analytic results (total N = 3,124). A discussion of the sample size determination is given in a separate Supplemental Methods document. Tables 1 and 2 provide details on the variations in the studies in terms of sample characteristics—statistics, such as sample age and gender—and methodological characteristics that varied across the experiments. The data are available upon reasonable request from the first author.

Table 1

Experimental Design Characteristics of the Ten Experiments

Sample no.	Language	Task name	No. of trials	Required response	Time pressure (seconds per trial)	Framing manipulation
1	English	Snowy pictures	10	Enter word	Yes (10 s)	Seven or more correct vs. no more than three incorrect
2	English	Lexical decision	40	Click button: yes vs. no	Yes (1.5 s)	Ninety percent or more correct vs. not more than 10% incorrect
3	English	Numerical series	10	Enter number	No (unlimited)	Seven or more correct vs. no more than three incorrect
4	Dutch	Lexical decision	40	Click button: yes vs. no	Yes (1.5 s)	Ninety percent or more correct vs. not more than 10% incorrect
5	French	Numerical series	10	Enter number	Yes (15 s)	More than seven correct vs. no more than two incorrect
6	Dutch	Numerical series	10	Enter number	Yes (20 s)	More than seven correct vs. no more than two incorrect
7	English	Lexical decision	40	Click button: yes vs. no	Yes (1.5 s)	Ninety percent or more correct vs. not more than 10% incorrect
8	English	Numerical series	10	Enter number	Yes (20 s)	Seven or more correct vs. no more than three incorrect
9	English	Snowy pictures	10	Enter word	Yes (10 s)	Seven or more correct vs. no more than three incorrect
10	English	Numerical series	10	Enter number	No (unlimited)	Seven or more correct vs. no more than three incorrect

Table 2

Descriptive Statistics of the Samples and the Study Variables and Correlations Between the Theoretical Study Variables

Sample no.	N	Gender (% F)	Experiment language	Participant language (%)^a	Participant age		(1) Promotion focus			(2) Prevention focus			(3) Autonomy			Correlations
					M	SD	M	SD	α	M	SD	α	M	SD	α	1, 2	1, 3	2, 3
1	106	66.0	English	E: 100	27.71	3.87	3.79	0.63	.73	3.34	0.82	.76	3.00	0.86	.84	.14	.11	.03*
2	133	60.2	English	D: 57.9^b	24.38	5.16	3.73	0.51	.58	3.46	0.77	.75	3.26	0.83	.87	.12	.01	–.07*
3	142	51.4	English	D: 81.7^c	24.49	5.21	3.60	0.54	.49	3.42	0.71	.60	3.46	0.85	.86	.09	.20*	–.03*
4	157	64.3	Dutch	D: 100	41.79	13.96	3.95	0.53	.69	3.63	0.78	.77	3.76	0.92	.89	.12	.17*	.10*
5	118	54.2	French	F: 100	25.34	8.71	3.81	0.56	.56	3.60	0.50	.54	2.52	0.95	.86	.10	.05	.13*
6	117	44.4	Dutch	D: 100	30.06	10.73	3.83	0.54	.71	3.38	0.76	.79	3.04	0.89	.88	.15	.17	–.00*
7	560	48.9	English	E: 100	32.78	10.94	3.48	0.65	.69	3.08	0.86	.80	3.03	0.94	.85	.13*	.13*	–.07*
8	714	55.0	English	E: 100	31.71	9.53	3.46	0.67	.68	3.14	0.90	.82	2.42	0.98	.86	.10*	.04	–.12*
9	619	56.1	English	E: 100	31.21	9.09	3.57	0.66	.69	3.16	0.90	.81	2.81	0.98	.87	.24	.02	–.06*
10	458	56.1	English	E: 100	31.76	9.59	3.49	0.66	.68	3.18	0.86	.79	3.23	0.95	.88	.10*	.13*	.04*

E = English, D = Dutch, G = German, F = French, O = Other. ^bG: 15.8; E: 9.0; O: 17.3. ^cG: 6.3; E: 2.1; O: 9.9.

p < .05.

Regulatory fit (vs. non-fit) in this research is created by the interplay of two factors. The first factor is the chronic promotion or prevention orientation that people have toward their goals and the second factor is the eager versus vigilant means applied in a particular context or on a task. In our studies, we used a questionnaire measure of participants’ dispositional promotion focus and prevention focus, and an experimental manipulation of the eager versus vigilant means that is created through the framing of the task’s target.

Participants

Samples 1 to 6 were collected by student research assistants working on their master theses and these samples, in most cases, consist of peers (fellow students at an international business school) or others from their own social networks. The majority of experiments were conducted in English (see Table 1). Two experiments were conducted in Dutch (with only Dutch speaking participants), and one was done in French (with only French speaking participants). Samples 7 through 10 were collected via Amazon’s Mechanical Turk, with only native English speakers.

We used various checks to protect data quality. We included several attention checks to which participants need to give the correct answer, we asked participants to self-report whether they took part in the study seriously, and we made sure all participants followed the instructions. For example, in most studies, we instructed participants not to do the study on a smartphone because this would make it unrealistic to validly engage in the task and we checked this after study completion. Furthermore, we also checked whether participants were native speakers of the study language, and we excluded a small set of statistical outliers (details about all quality checks and exclusions are provided in the Supplementary Materials).

Experimental Procedure

Regulatory fit was created through the interplay of individuals’ dispositional regulatory focus and the means of task execution they were instructed to use. All 10 experiments featured the following same four key elements. First, after having given informed consent, participants completed a questionnaire measuring dispositional regulatory focus. Second, participants received task instructions. Third, the framing conditions were embedded in these task instructions. That is, these instructions included an eager or a vigilant framing, based on random assignment. Fourth, after completing the task under one of these framing instructions—eager or vigilant –, participants completed our primary dependent variable of interest—experienced autonomy, which was identical across all samples. Furthermore, all 10 experiments featured three elements that varied: the regulatory focus measure, task type, and the framing manipulation. These four constant elements and three varying elements that we used across the 10 experiments are outlined in Table 1. Table 2 provides descriptive statistics, correlations, and Cronbach’s alphas of the individual samples and measures.

Measures and Manipulations

Regulatory Focus

In all but one of the experiments, we measured regulatory focus using the Regulatory Focus Questionnaire (RFQ) developed by Higgins et al. (2001). Sample 5 used a different measure of regulatory focus (validated in French; Faur et al., 2017) because we were unable to obtain a valid French version of the Higgins questionnaire. The French regulatory focus measure that we used asks participants to rate their agreement with proverbs or proverbial mottos that reflect a promotion focus or prevention focus. For promotion focus, we used six items, such as “Nothing ventured, nothing gained.” For prevention focus, we used eight items, such as “Better safe than sorry.” Response options ranged from 1 (strongly disagree) to 5 (strongly agree).

Tasks

As the list of tasks in Table 1 indicates, across the 10 experiments, the tasks varied in several ways, such as the type of task, the number of trials, and the timing of the trials. The snowy pictures task is a task in which a drawn picture of an object is obscured by snow. The task is to identify the hard-to-see object within the larger image. Participants are asked to type their response in a box next to the image and are given 10 s to do so. Lexical decision tasks require participants to respond yes or no whether a letter string is an existing word. In our studies, the letter strings or words were five characters each and participants had 1.5 s to respond. In numerical series tasks, participants see a series of three or four numbers that they are asked to complete, and they need to type in the solution. Numerical series tasks were done in two variations: easy trials with a time-limit per trial and difficult trials without a time-limit per trial. In implementing these tasks, the most important criterion for us was that people would choose between giving versus not giving an answer, a key difference in eagerness versus vigilance (see Crowe & Higgins, 1997).

Framing Manipulations

In the vigilant framing condition, participants read a variant of the following:

Your score is determined as follows: You start with 10 points and there is a total of 10 points that you can lose. For each incorrect answer, you lose one point. The goal for this task is to not incorrectly complete more than 3 numerical series. With this type of goal, research suggests that the most effective way to attain it is to go about it very carefully and try to not incorrectly complete any series.

In the eager framing condition, participants read a variant of the following:

Your score is determined as follows: You start with 0 points and there is a total of 10 points that you can gain. For each correct answer, you gain one point. The goal for this task is to correctly complete 7 or more numerical series. With this type of goal, research suggests that the most effective way to attain it is to go about it very enthusiastically and try to correctly complete as many series as possible.

We added emphasis by using bolded or underlined text, to the elements in the manipulations that differ between the conditions. We constructed these framing manipulations based on a combination of manipulations used in prior research (Renkema & Van Yperen, 2008; Shah et al., 1998). As Table 1 indicates, in some samples, we used percentages exactly as in the work of Shah et al. (1998). In other samples, we used exact numbers, and the exact numbers varied slightly between samples. What is important to note about these framing manipulations is that the objective target is identical in both conditions, so that, people are pursuing the same goal, but the framing, which is created by the scoring instructions over which they have no choice, prompts them to apply an eager or vigilant means in goal-pursuit (Shah et al., 1998).

Experienced Autonomy

After carrying out the task, we asked participants to rate the extent to which they had felt autonomous during the task. This measure was based on other adjective-type self-report experienced autonomy measures (e.g., Radel et al., 2011) and was identical across all the samples, though language of the experiments varied (English, Dutch, and French). Specifically, they were asked to rate a set of six words or phrases, such as “free” and “autonomous,” preceded by the preamble “During the task, I felt . . .” on a scale ranging from 1 (strongly disagree) to 5 (strongly agree).

Results

We conducted the same moderated multiple regression analysis across all 10 experiments. The results of all these analyses are presented in Table 3. Each analysis included the following five regression parameters: a parameter for promotion focus, a parameter for prevention focus, a dichotomous, dummy coded variable parameter for the two experimental conditions (0 = vigilance, 1 = eagerness), a parameter for the interaction of promotion focus and the dummy variable, and a parameter for the interaction of prevention focus and the dummy variable.

Table 3

Moderated Multiple Regression Results for the Individual Samples. T-Values for Condition by Focus Interactions Form the Basis for the Meta-Analysis

Predictors	Sample 1 (N = 106)				Sample 2 (N = 133)				Sample 3 (N = 142)				Sample 4 (N = 157)				Sample 5 (N = 118)
	B	SEb	t	p	B	SEb	t	p	B	SEb	t	p	B	SEb	t	p	B	SEb	t	p
Promotion	–.10	.13	–0.73	.469	.12	.14	0.88	.383	.09	.12	0.82	.414	.03	.11	0.29	.769	–.01	.13	–0.08	.939
Prevention	.05	.13	0.41	.685	–.14	.13	–1.10	.272	.12	.12	1.18	.239	.16	.10	1.52	.130	.36	.14	2.52	.013
Condition	.35	.19	1.83	.070	.20	.18	1.11	.270	–.04	.14	–0.26	.792	.17	.15	1.17	.246	.12	.18	0.68	.500
Cond. × prom.	.44	.19	2.27	.025	–.20	.18	–1.09	.279	.08	.15	0.54	.593	.20	.15	1.36	.176	.08	.19	0.42	.678
Cond. × prev.	–.12	.19	–0.61	.547	.13	.18	0.71	.479	–.32	.14	–2.22	.028	–.13	.15	–0.86	.391	–.41	.19	–2.19	.031
Predictors	Sample 6 (N = 117)				Sample 7 (N = 560)				Sample 8 (N = 714)				Sample 9 (N = 619)				Sample 10 (N = 458)
	B	SE_b	t	p	B	SE_b	t	p	B	SE_b	t	p	B	SE_b	t	p	B	SE_b	t	p
Promotion	.10	.13	0.80	.428	.06	.06	0.98	.328	.00	.05	0.02	.983	.01	.06	0.16	.871	.02	.06	0.37	.712
Prevention	.02	.13	0.16	.873	.01	.06	0.09	.931	–.16	.05	–2.99	.003	–.06	.06	–1.01	.314	.10	.07	1.51	.131
Condition	.17	.19	0.90	.373	.09	.08	1.12	.263	.21	.07	2.89	.004	.21	.08	2.67	.008	.02	.09	0.21	.838
Cond. × prom.	.13	.19	0.66	.512	.19	.08	2.25	.025	.08	.08	1.13	.259	.03	.08	0.39	.694	.22	.09	2.37	.018
Cond. × prev.	–.06	.19	–0.32	.754	–.19	.08	–2.29	.022	.06	.08	0.76	.448	–.03	.08	–0.30	.767	–.16	.09	–1.74	.083

Note. Condition is coded 0 for vigilant framing, and 1 for eager framing.

To summarize the 10 experiments, we standardized all variables prior to the analyses. In line with Goh et al. (2016), we conducted meta-analytic tests of the conditions by promotion interaction and the conditions by prevention interaction that model the hypothesis. Hence, the inputs for these meta-analytic tests are the interaction terms in Table 3. We converted the t-values of the interactions in Table 3 for the samples to r-values (equivalent to Pearson’s correlation coefficients).

As Figure 1 illustrates, r-values for the promotion fit interaction ranged from −.10 to .22. The mean r was .062 (Z = 3.458, p [one-tailed] = .000272) and the 95% confidence interval ranged from .027 to .097. As Figure 2 illustrates, r-values for the prevention fit interaction ranged from −.19 to .06. The mean r was −.045 (Z = −2.514, p = .005969) and the 95% confidence interval ranged from –.080 to −.010.

Figure 1

Individual Sample Effect Sizes (r) for Promotion Fit and Meta-Analytic r

Figure 2

Individual Sample Effect Sizes (r) for Prevention Fit and Meta-Analytic r

In line with our prediction, these findings support the effect of regulatory fit on experienced autonomy for both the prevention fit effect and the promotion fit effect. We conducted several additional analyses to shed further light on the effects we found, first on moderating task features and second on alternative explanations.

Secondary Analyses on Moderating Task Features

We conducted moderation analyses on the combined data of all samples using various study features and the only factor we found to show any indication to moderate the fit effects was a five-category factor with the different “time limit” variations (see Table 1). That is, we observed a marginal interaction between the framing-conditions, prevention focus, and this method factor, F (4, 3094) = 2.04, p = .086. Further exploration showed the following. First, the prevention fit effect, B = −0.20, SE = 0.08, t(3,094) = −2.54, p = .011, and the promotion fit effect, B = 0.18, SE = 0.08, t(3,094) = 2.33, p = .020, were significant in the “no time limit” category. Second, the prevention fit effect was significant in the 1.5-s time category, B = −0.15, SE = 0.07, t(3,094) = −2.23, p = .026, where the promotion fit effect was marginally significant, B = 0.12, SE = 0.07, t(3,094) = 1.77, p = .076. We exclude Category 3 from these conclusions because it is only based on one sample (so that, the conclusions are identical to those from the individual sample analyses). However, neither of the other categories on the time limit factor showed any significant fit effects, all ps > .220.

In the eager condition, promotion focus was positively related to experienced autonomy in both the no time limit category and the 1.5-s time limit category, B = 0.22, SE = 0.06, t(3,094) = 3.93, p < .001 (Figure 3A), and, B = 0.17, SE = 0.05, t(3,094) = 3.69, p < .001 (Figure 3B), respectively. In the vigilant condition, promotion focus was unrelated to experienced autonomy for both these methodological categories, B = 0.04, SE = 0.06, t(3,094) = 0.65 (Figure 3A), p = .516, and B = 0.06, SE = 0.05, t(3,094) = 1.21, p = .225 (Figure 3B), respectively. Prevention focus, in the vigilant condition, was positively related to experienced autonomy, but this effect was only close to significant in the no time limit category, B = 0.11, SE = 0.06, t(3,094) = 1.93, p = .054 (Figure 3C), and not in the 1.5-s time category, B = 0.03, SE = 0.05, t(3,094) = 0.57, p = .572 (Figure 3D). Prevention’s effect in the eager condition, however, was significantly negative in the 1.5-s category, B = −0.12, SE = 0.05, t(3,094) = −2.59, p = .0.10 (Figure 3D), and marginally significant in the no time limit category, B = −0.09, SE = 0.05, t(3,094) = −1.66, p = .096 (Figure 3C).

Figure 3

(A) The Promotion Fit Interaction in the “No Time Limit” Category. (B) The Promotion Fit Interaction in the “1.5-s” Category. (C) The Prevention Fit Interaction in the “No Time Limit” Category. (D) The Prevention Fit Interaction in the “1.5-s” Category

Secondary Analyses on Task Enjoyment

The effect that we observed might be due to a general positivity effect caused by the enjoyment of the task (Freitas & Higgins, 2002), rather than something about regulatory fit that uniquely affects experienced autonomy. Finally, therefore, we sought to examine whether the effect of regulatory fit remains while controlling for task enjoyment. To show that our observed effect still holds, we used the samples in which the effect of regulatory fit on experienced autonomy was the strongest and most consistent, a combined set of Samples 7 and 10 (N = 1,018). The measurement of task enjoyment was a three-item scale from Van Yperen (2003), and the items were (M = 3.59, SD = 1.13; α = .91): “I enjoyed doing this task,” “I thought the task was fun,” and “I did not like doing this task” (reversed item) and participants responded on a scale from 1 (strongly disagree) to 5 (strongly agree).

First, the zero-order correlations between experienced autonomy and task enjoyment was quite strong, r(1,018) = 0.52, p < .001. Second, in a repetition of our previous analysis on this combined sample, the effects on experienced autonomy were (as is to be expected) stronger than in the separate samples, for prevention fit, B = −0.18, SE = 0.06, t(1,012) = −2.85, p = .005, and for promotion fit, B = 0.20, SE = 0.06, t(1,012) = 3.25, p = .001. Third, the effects of on task enjoyment were less strong than those on experienced autonomy and were only marginally significant for prevention fit, B = −0.11, SE = 0.06, t(1,012) = −1.70, p = .090, and for promotion fit, B = 0.10, SE = 0.06, t(1,012) = 1.59, p = .112. Fourth, and most importantly, the effects of prevention fit, B = −0.12, SE = 0.05, t(1,011) = −2.30, p = .022, and promotion fit, B = 0.15, SE = 0.05, t(1,011) = 2.83, p = .005, on experienced autonomy remained significant when task enjoyment was included in the analysis, whereas in that same analysis, task enjoyment was significantly related to experienced autonomy as well, B = 0.50, SE = 0.03, t(1,011) = 18.53, p < .001.

Discussion

The aim of this research was to test whether people may experience greater autonomy when the strategies they are instructed to use fit (vs. do not fit) their motivational orientation. In line with our predictions, the meta-analysis of these 10 experiments indicated that regulatory fit (compared with non-fit) positively impacted participants’ experienced autonomy. It supported this fit effect both for promotion-based fit and for prevention-based fit. Exploration of task characteristics that moderated the presence of the fit effects suggested that the effects occurred when the time pressure “matched” the level of task complexity. When the numerical series task was used in a difficult version, the fit effects were seen when there was no time limit on the trials. When the lexical decision task was used, the fit effects were seen as well, and here the task contains a strict, short time limit but the trials are very simple. These findings suggest that the fit effect on autonomy might not occur, or might be overruled, by other task features, such as excessive time pressure, which could be perceived as controlling in its own right (Burgess et al., 2004; Radel et al., 2011).

Contributions and Implications

Autonomy research typically maintains that freedom of choice is required to affect experienced autonomy (e.g., Baard et al., 2004; Gagné & Deci, 2005; Humphrey et al., 2007; Morgeson & Humphrey, 2006; Parker, 2014) or that reducing freedom of choice actively undermines individuals’ experienced autonomy (e.g., Bartholomew et al., 2011a, 2011b; Radel et al., 2011). One important theoretical implication of our research, in contrast, is that we show that experienced autonomy may vary under the right conditions even when there is no choice involved. The important boundary condition, however, is that individuals work in line with their personal strategy preferences, and we relied on regulatory fit to demonstrate this.

This research also contributes to regulatory fit research. Freitas and Higgins (2002), in one of the classic regulatory fit studies, found that regulatory fit affects people’s enjoyment of a task regardless of whether they had freedom of choice. Our findings take this one step further: we show that regulatory fit can affect people’s experienced autonomy even when people do not exercise choice.

Limitations

A limitation of this research, one might argue, is the use of experimental methodologies, which can yield lower ecological validity. First, we would argue that the experimental methods we used are, in fact, an important asset in this research. Being able to give participants the exact same task, the exact same objective, but experimentally creating conditions of regulatory fit versus non-fit, enables us to rule out alternative explanations and confounds. This is important because to test our hypothesis, a requirement is that we rule out any possibility for people to make actual choices. Conversely, a non-experimental methodology would make it essentially impossible to rule this out. For example, people may select into jobs that fit their regulatory focus (Sassenberg & Scholl, 2013) and, as a consequence, they may experience relatively more autonomy; however, since the selection probably involved some level of autonomous choice, the study would inherently be confounded. It would not be able to discern whether the autonomy resulted from the use of fitting means or from people’s own choice of the use of fitting means or the selection of a fitting job.

Although we found overall evidence for the impact of regulatory fit on experienced autonomy, the sizes of these effects are, when present, quite small. In relatively controlled settings, with homogeneous samples, such as students, we could observe small to medium effect sizes of r = .20, but in other, larger samples, we found smaller effect sizes and the overall meta-analytic rs for both promotion and prevention fit were smaller than .10. Hence, the effects are small and further research in field settings is needed to determine the practical impact that, for example, an intervention could be expected to have.

A last limitation is that we did not measure whether participants actually applied the eager or vigilant strategy we instructed them to use. In the case of framing manipulations, the possible outcomes that participants face are theoretically considered to cause the application of eager or vigilant means, but research typically does not measure whether participants actually do apply these means (for an exception, see Hamstra & Schreurs, 2018). Future research on the effect of fit on autonomy experiences should take this into account.

Future Research

Interestingly, research on how experienced autonomy can be systematically improved by factors other than freedom of choice is still scant (Humphrey et al., 2007; Van den Broeck et al., 2016). Some variables are linked to, for instance, autonomy satisfaction and intrinsic motivation—which reflect experienced autonomy—but this is typically not the focus of autonomy research (see Howard et al., 2017; Parker, 2014). Consequently, future research should theorize and test alternative ways that experienced autonomy may be improved.

Our experiments do not show that the experience of fit (e.g., feeling right, task enjoyment) needs to precede the effect on experienced autonomy. In fact, we believe that regulatory fit is just one example of a situation in which doing something according to one’s preferences is relatively beneficial for feelings of autonomy. Our data on task enjoyment suggest that the positive experience on the task is not the entire explanation for the effect of regulatory fit on experienced autonomy. While this provides tentative support that this effect is not unique to regulatory fit, further research to tease apart different ways in which people may experience relatively more autonomy while not having choice is warranted.

In relation to regulatory fit, it might also be worthwhile to provide a deeper exploration of differences in tasks. Time pressure in combination with vigilant framing led to relatively lower autonomy experiences, as a main effect of framing conditions was weakest (entirely absent) in the studies that did not employ time pressure or time limits. In addition, the effect of prevention on experienced autonomy under eager framing became negative when the task required fast responses, while there was no significant positive effect in the vigilant condition with such tasks. This finding suggests that the precise effects (positive experience of fit, negative of misfit) could depend on a more complex interaction of factors than has so far been considered in regulatory fit theory. Researchers seeking to examine regulatory fit effects might need to explicitly take time pressure into account to yield valid models.

Concluding Remarks

Whereas autonomy research typically maintains that freedom of choice is required to affect individuals’ experienced autonomy our research proposed and found that autonomy can be influenced even in contexts where there is no objective freedom of choice and regulatory fit between goals and strategic means may be one such context opening up various interesting new areas of research.

Supplemental Material

sj-docx-1-spp-10.1177_19485506231168522 – Supplemental material for The Impact of Regulatory Fit on Experienced Autonomy

Supplemental material, sj-docx-1-spp-10.1177_19485506231168522 for The Impact of Regulatory Fit on Experienced Autonomy by Melvyn R. W. Hamstra, L. Maxim Laurijssen and Bert Schreurs in Social Psychological and Personality Science

Footnotes

Handling Editor: Veronika Job

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Availability of Data and Material

For data collection, the authors acknowledge the assistance of Dave Van Delden, Lydia Nellen, Mats Wolterink, Ainsley Loudoun, Pim Lambregts, Pauline Kairis, and Arno van Nieuwenborgh. The data that support the findings of this study are available from the corresponding author upon reasonable request.

ORCID iD

Melvyn R. W. Hamstra

Supplemental Material

The supplemental material is available in the online version of the article.

Author Biographies

Melvyn Hamstra is a professor at IESEG School of Management (France). He conducts research and teaches on leadership, motivation, and learning processes in organizations.

Maxim Laurijssen is a researcher and business consultant. In his research, he focuses on the fundamental role of motivation, autonomy, and control in applied contexts, such as organizations.

Bert Schreurs is a professor of human resource management at the Faculty of Social Sciences and Solvay Business School, Vrije Universiteit Brussel (Belgium). His research focuses on work stress and motivation, and proactive work and career behavior.

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