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Self-efficacy is a well-known psychological resource, being positively associated with increased performance. Furthermore, results from field studies suggest a positive impact of self-efficacy on flow experience, which has not yet been tested experimentally. In this study, we manipulated self-efficacy by means of positive feedback and investigated whether self-efficacy serves as a mediator in the relationship between positive feedback and flow and in the relationship between positive feedback and performance. Our sample consisted of 102 participants (63 female, 39 male). The experimental group received positive feedback after completing 5 min of mental arithmetic tasks on a computer, whereas the control group received no feedback. A second session of a mental arithmetic task was then completed for 5 min. Mediation analyses confirmed that specific self-efficacy mediated a positive effect of positive feedback on flow as well as on both performance measures (quality and quantity) in a subsequent task. However, direct effects of feedback on flow and on performance were not significant, which suggests the presence of other mechanisms that remain to be investigated.
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ORIGINAL RESEARCH
published: 10 June 2020
doi: 10.3389/fpsyg.2020.01008
Edited by:
Stephen Fairclough,
Liverpool John Moores University,
United Kingdom
Reviewed by:
Osman Titrek,
Sakarya University, Turkey
Nicola Baumann,
University of Trier, Germany
*Correspondence:
Corinna Peifer
corinna.peifer@rub.de
Specialty section:
This article was submitted to
Performance Science,
a section of the journal
Frontiers in Psychology
Received: 25 July 2019
Accepted: 22 April 2020
Published: 10 June 2020
Citation:
Peifer C, Schönfeld P, Wolters G,
Aust F and Margraf J (2020) Well
Done! Effects of Positive Feedback on
Perceived Self-Efficacy, Flow
and Performance in a Mental
Arithmetic Task.
Front. Psychol. 11:1008.
doi: 10.3389/fpsyg.2020.01008
Well Done! Effects of Positive
Feedback on Perceived Self-Efficacy,
Flow and Performance in a Mental
Arithmetic Task
Corinna Peifer1*, Pia Schönfeld2, Gina Wolters1, Fabienne Aust1and Jürgen Margraf2
1Faculty of Psychology, Applied Psychology in Work, Health, and Development, Ruhr University Bochum, Bochum,
Germany, 2Faculty of Psychology, Mental Health Research and Treatment Center, Ruhr University Bochum, Bochum,
Germany
Self-efficacy is a well-known psychological resource, being positively associated with
increased performance. Furthermore, results from field studies suggest a positive impact
of self-efficacy on flow experience, which has not yet been tested experimentally. In this
study, we manipulated self-efficacy by means of positive feedback and investigated
whether self-efficacy serves as a mediator in the relationship between positive feedback
and flow and in the relationship between positive feedback and performance. Our
sample consisted of 102 participants (63 female, 39 male). The experimental group
received positive feedback after completing 5 min of mental arithmetic tasks on a
computer, whereas the control group received no feedback. A second session of a
mental arithmetic task was then completed for 5 min. Mediation analyses confirmed that
specific self-efficacy mediated a positive effect of positive feedback on flow as well as on
both performance measures (quality and quantity) in a subsequent task. However, direct
effects of feedback on flow and on performance were not significant, which suggests
the presence of other mechanisms that remain to be investigated.
Keywords: feedback, self-efficacy, flow, performance, mental arithmetic
INTRODUCTION
“Well done!” – Positive feedback has been found not only to enhance performance (e.g., Kluger and
DeNisi, 1996;Hattie and Timperley, 2007), but also to be an efficient intervention to manipulate
perceived self-efficacy (e.g., Brown et al., 2012). Self-efficacy refers to the judgment of one’s own
abilities to successfully cope with future demands (Bandura, 1983). It can either refer to a general
judgment, called general self-efficacy, or it can refer to more specific domains, such as mathematical
skills, then called specific self-efficacy. Self-efficacy is well-known as a psychological resource
protecting mental health and buffering the negative effects of stress (Bandura, 1977, 1986;Schönfeld
et al., 2016). In line with Bandura’s social cognitive theory (SCT), there is a broad basis for higher
self-efficacy being associated with lower symptoms of depression and anxiety as well as with higher
optimism and emotional well-being (Benight et al., 1999;Rottmann et al., 2010;Singh and Bussey,
2011;Wang et al., 2014).
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Peifer et al. Well Done
While self-efficacy has been found to enhance performance
(e.g., Bandura and Locke, 2003), contradictory findings also
exist, suggesting that effects may differ with respect to different
performance outcomes such as performance quality and quantity
(Vancouver et al., 2014). The first part of our study contributes
to the answer to this yet open research question by examining
the effects of positive feedback as an intervention to manipulate
self-efficacy and we test how self-efficacy affects performance
quality and quantity. Further, we contribute to research on
the mechanisms via which the effects of positive feedback are
transmitted to performance by using self-efficacy as a mediator
in the feedback-performance relationship.
The second part of this study examines flow – the experience
of being fully absorbed in a task (Csikszentmihalyi, 1975) –
in relation to positive feedback and self-efficacy. Feedback has
been described as a central antecedent of flow (e.g., Landhäußer
and Keller, 2012), but effects of positive feedback on flow have
not yet been investigated experimentally. Another antecedent
of flow as identified in field studies (e.g., Zubair and Kamal,
2015a,b) is self-efficacy. This study aims to replicate the positive
relationship between self-efficacy and flow in an experimental
setting, in which self-efficacy is manipulated using positive
feedback. Finally, and adding to the existing research, we aim to
test whether self-efficacy transmits effects of positive feedback on
flow experience.
Positive Feedback and Performance
Feedback can be defined as the “provision of information
regarding some aspect(s) of one’s task performance” (Kluger
and DeNisi, 1996, p. 255). Meta-analyses show impressive
effects of feedback on increased performance, with average
effect sizes of d= 0.40 (Kluger and DeNisi, 1996) and
d= 0.79 (Hattie and Timperley, 2007). Research has identified
moderators of the feedback-performance relationship, with
findings suggesting that positive feedback is more efficient
than negative feedback. For example, Arbel et al. (2014) found
that positive feedback improved learning performance more
than negative feedback. Furthermore, it has been found that
feedback after good trials enhanced learning in comparison to
feedback after poor trials (Chiviacowsky and Wulf, 2007). In
line with these findings, the meta-analysis of Kluger and DeNisi
(1996) found that feedback following correct results, that is,
positive feedback, was more effective than feedback following
incorrect results. Furthermore, feedback was more effective when
it was provided by a computer (d = 0.41) vs. not (d= 0.23;
Kluger and DeNisi, 1996).
One particular type of feedback is normative feedback,
which refers to information on one’s performance compared
to referenced others, allowing comparative inferences (Hartwell
and Campion, 2016). Accordingly, positive normative feedback
is the information that one’s performance was better than that
of referenced others, such as feedback indicating above-average
performance. Studies suggest that such positive normative
feedback – even if it is false feedback – leads to increased
performance compared to negative normative feedback (i.e.,
indicating below-average performance; Bandura and Jourden,
1991;Wulf et al., 2010). Based on these findings, we expect to
replicate earlier studies by finding positive effects of positive
normative feedback on performance.
Positive Feedback as an Intervention to
Increase Self-Efficacy
Previous research has shown that false normative positive
feedback not only affects performance, but also self-efficacy,
and such feedback has been successfully applied to manipulate
self-efficacy (e.g., Reynolds, 2006;Beattie et al., 2016;Brown
et al., 2016;Dimotakis et al., 2017). This has also been used
in experiments with mental arithmetic tasks (Weinberg et al.,
1979;Wright and Gregorich, 1989;Eden and Zuk, 1995;
Brown et al., 2012). The approach is in line with Bandura’s
SCT (Bandura, 1977). Bandura (1977) pointed out that there
are different kinds of information that lead to expectations
about personal efficacy: performance accomplishment, vicarious
experience, verbal persuasion and psychological states. In line
with that, external persuasion through positive feedback to
induce an experience of success should be an effective strategy
to manipulate self-efficacy (Achterkamp et al., 2015). In line with
this, we expect to replicate positive effects of positive normative
feedback on self-efficacy.
Self-Efficacy and Performance
Successful mastery experiences contribute to the development
of efficacy beliefs and increase the investment of effort and the
level of performance (Bandura, 1997). Perceived self-efficacy is
a key dynamic and malleable factor affecting behavior (Gist and
Mitchell, 1992;Hardy, 2014), and some evidence indicates that
higher self-efficacy leads to better performance in cognitive and
sports tasks (e.g., Beattie et al., 2014;Niemiec and Lachowicz-
Tabaczek, 2015). At the same time, divergences in social
cognitive and control theories lead to different assumptions
about the effects of self-efficacy (see Bandura and Locke,
2003;Bandura, 2012;Schönfeld et al., 2017). For example,
Powers’ (1973, 1991) perceptual control theory assumes that
the discrepancy between one’s personal goal and one’s perceived
progress in handling a situation successfully regulates the
performed action (e.g., Vancouver et al., 2002). In case of
a low discrepancy, the person will invest fewer resources in
achieving the goal, and successful performance is assumed to
be easy. In situations in which perceived progress is ambiguous,
perceived capabilities (i.e., self-efficacy) can be used as an
indicator of progress. As a consequence, high perceived skills
will lead to a decreased perceived discrepancy between goal and
progress. Thus, according to perceptual control theory, high self-
efficacy would undermine performance and motivation. Initial
empirical findings support these assumptions (e.g., Vancouver
et al., 2001, 2002, 2008, 2014;Vancouver and Kendall, 2006;
Vancouver, 2012;Beattie et al., 2014). A study by Vancouver
et al. (2014), for example, found that self-efficacy was negatively
related to performance quality, while it was positively related
to performance quantity. They assumed that individuals with
high self-efficacy allocate less effort per task, which leads to
faster progress (performance quantity), but lower quality of
results in the form of more mistakes. Yet, more research
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Peifer et al. Well Done
is needed to disentangle effects of self-efficacy on different
performance measures.
In accordance with the findings of Vancouver et al. (2014),
our study differentiates between performance quantity and
performance quality in that we assume that self-efficacy has
positive effects on performance quantity, but negative effects on
performance quality.
A general limitation of research on self-efficacy and
performance is that it is largely based on observational
rather than experimental designs, so no conclusions can be
drawn about the direction of effects. While most studies assume
positive effects of self-efficacy on performance, Bandura (1977)
in fact already identified performance accomplishment as an
antecedent of self-efficacy. Accordingly, experimental research
on the relationship between self-efficacy and performance is
necessary to disentangle potential bidirectional effects.
Consequently, by differentiating between performance
quantity and quality, and by applying an experimental design in
which we manipulate self-efficacy by means of positive feedback,
we aim to contribute to a better understanding of the relationship
between self-efficacy and performance.
Self-Efficacy as a Mechanism That
Transmits Effects of Positive Feedback
on Performance
As mentioned above, the induction of positive feedback including
a favorable comparison to others has been found to be a
suitable method to enhance the level of self-efficacy, which in
turn, affects performance (see Zinken et al., 2008). Based on
a comparative appraisal, the individual is persuaded that he
or she has performed successfully, which is in line with SCT.
Using feedback-manipulation as a strategy to increase a person’s
appraisal of his or her capabilities, beneficial effects have also
been demonstrated in the context of emotional learning processes
(Zlomuzica et al., 2015). However, to the best of our knowledge,
the mediation hypothesis of self-efficacy has not yet been tested.
Integrating the described relationships, we propose that self-
efficacy acts as a mediator, transmitting positive effects of positive
feedback on performance. Taking the differential expectations for
the relationship of self-efficacy with performance quantity and
performance quality into account, we expect that self-efficacy acts
as a mediator, transmitting positive effects of positive feedback on
performance quantity (Hypothesis 1a), but transmitting negative
effects of positive feedback on performance quality (Hypothesis 1b).
Effects of Feedback on Flow-Experience
Flow is the positive experience of being fully absorbed in
an optimally challenging task. While in flow, individuals
are completely concentrated on the task at hand, which is
experienced as rewarding in itself. Individuals perceive clear goals
and feedback and a high level of control over the demands,
thereby experiencing a merging of action and awareness, and a
loss of self-consciousness, along with a distorted sense of time
(Csikszentmihalyi, 1975). Flow can be experienced in different
activities and tasks, among them cognitive tasks such as solving
math calculations – even under laboratory conditions (e.g.,
Harmat et al., 2015;Ulrich et al., 2016).
Feedback has been described as one of the core antecedents
fostering flow-experience (Bakker, 2005;Demerouti, 2006;
Landhäußer and Keller, 2012;Nakamura and Csikszentmihalyi,
2014). This conceptualization is in line with findings based on
the job characteristics model (Hackman et al., 1975), showing
that feedback along with four other core job characteristics is
positively related to flow experience (Bakker, 2005;Demerouti,
2006;Maeran and Cangiano, 2013). Studies that have specifically
examined the relationship between feedback and flow have
confirmed a positive link between the two (Rau and Riedel,
2004;Maeran and Cangiano, 2013). However, these studies have
focused on feedback in general, and the effects of specifically
positive feedback on flow have not yet been studied using
quantitative research. Qualitative research has provided first
indications that positive feedback – but not negative feedback –
is an antecedent of flow (Jackson, 1995;Swann et al., 2015).
In line with this, positive normative feedback has been found
to have positive effects on positive affect during a challenging
task (Hutchinson et al., 2008) – which is often linked to flow
experience. Furthermore, positive normative feedback has been
suggested to have energizing and reinforcing effects (Kühn
et al., 2008) – both typical characteristics of the experience of
flow. Bringing together theoretical and empirical evidence, we
expect to find positive effects of positive normative feedback on
flow experience.
Self-Efficacy and Flow-Experience
A central component of flow is the perceived balance between
the demands of the task and the individual’s skills (e.g.,
Csikszentmihalyi, 1975;Landhäußer and Keller, 2012). The level
of self-efficacy is an individuals’ evaluation of his/her skills
and therefore has a substantial impact on how the balance
between skills and task demands is perceived. High levels
of self-efficacy should thus positively impact flow. Empirical
studies support this assumption: For example, Zubair and Kamal
(2015a,b) investigated the relationship between the dimensions of
psychological capital (Luthans et al., 2004) and flow experience
and found that all dimensions, including self-efficacy, were
positively related to flow. In a two-wave longitudinal study
design, Salanova et al. (2006) found that work-specific self-
efficacy beliefs facilitated the experience of work-related flow.
In another longitudinal study, Rodríguez-Sánchez et al. (2011)
found that teachers’ work-related self-efficacy positively affected
their flow experience. Furthermore, collective efficacy beliefs are
associated with higher flow. In a longitudinal study with small
groups, it was found that collective efficacy beliefs can lead to
higher collective flow, which in turn leads to higher collective
self-efficacy in the future, forming a reciprocal relationship
(Salanova et al., 2014). Furthermore, Pineau et al. (2014) found
that self-efficacy as well as team-efficacy are significantly related
to dispositional flow. All in all, abundant research supports the
hypothesis that self-efficacy beliefs are positively associated with
flow experience. Following the existing literature, we postulate
that self-efficacy facilitates flow experience.
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Self-Efficacy as a Mechanism That
Transmits Effects of Positive Feedback
on Flow
While results from field studies, including long-term studies,
suggest a reciprocal and positive relationship between self-
efficacy and flow, this relationship has not yet been tested
experimentally. As outlined above, positive feedback is an
established intervention to positively affect self-efficacy. Thus,
we use positive feedback to manipulate self-efficacy with the
aim to test the effects of self-efficacy on flow experimentally.
Furthermore, and as described above, theoretical considerations
and qualitative research suggest also direct effects of positive
feedback on flow. We suggest that these effects of positive
feedback on flow can be explained at least partially by increased
self-efficacy. Accordingly, we propose that self-efficacy acts as a
mediator, transmitting positive effects of positive feedback on flow
experience (Hypothesis 2).
MATERIALS AND METHODS
Participants and Design
The sample was recruited at Ruhr University Bochum (Germany)
through postings in social media networks, such as student
groups in Facebook or via announcements on notice boards. The
total sample consisted of 134 subjects (82 females, 52 males).
Due to missing values, data from 23 subjects were excluded from
the analyses: Eighteen participants did not complete the Flow-
Short-Scale, one participant did not complete the self-efficacy
scale and four participants did not perform the mental arithmetic
task. The data was z-transformed and due to outliers on the
study variables (flow, specific self-efficacy, performance quantity,
and performance quality) another nine subjects were excluded1.
The final analysis included data from 102 participants, of which
63 were female and 39 were male, with a mean age of 22.51
(SDage = 3.13). Participants were mainly undergraduate students
(76.5%). Another 20.6% were students with a bachelor’s degree
and 2.9% held a secondary school degree. Participants rated their
ability in mental arithmetic on a 100-point scale on average at
M= 54.22 (SD = 17.32). Furthermore, they rated the difficulty of
the experimental task on an 8-point Likert Scale from 0 = “not
difficult at all” to 7 = “very difficult” to be at an average level,
with the second task being slightly more difficult than the first
(Mtask1 = 3.87, SDtask1 = 1.60; Mtask2 = 4.43, SDtask2 = 1.63).
Participants were randomly assigned to one of two conditions:
the positive feedback condition (n= 53, 33 female, Mage = 22.43,
SDage = 3.17) or the non-feedback condition (n= 49, 30 female,
Mage = 22.59, SDage = 3.12). All participants provided written
informed consent and received course credit for participation.
The study was approved by the local Ethics Committee of the
Faculty of Psychology at Ruhr University Bochum, Germany.
Task
The experimental task was a computer-based mental arithmetic
task, which lasted 5 min per block, with the participant sitting
1We performed all analyses with and without outliers and results did not change.
alone in the laboratory in front of a computer screen. A computer
program written in VB.NET (Microsoft Visual Studio [Software],
2015) was used to generate the task on the screen. Participants
typed the calculated numbers into the computer and pressed
“enter” after each calculation. All the previously calculated
numbers could be seen on the screen while working on the
task. After 5 min, the task stopped automatically. In the first
block of the mental arithmetic task, participants were asked to
subtract the number 12, starting at 2000, consecutively with
maximal accuracy and rapidness for 5 min. In the second block
of the mental arithmetic task, participants were asked to subtract
the number 17, starting at 2043, consecutively with maximal
accuracy and rapidness for 5 min. The mental arithmetic task was
constructed based on the Trier Social Stress Test (Kirschbaum
et al., 1993), which uses a similar mental arithmetic task as part
of the protocol. Importantly, and in contrast to the Trier Social
Stress Test, there was no social stress component in our mental
arithmetic task.
Feedback Manipulation
After the first block of the mental arithmetic task, the feedback
group saw a note on their computer screen stating that their
performance had been evaluated in terms of accuracy and
rapidness. According to this analysis, he or she had performed
better than the average of the previous participants, and that
compared to the average participant, he or she was better able to
follow new instructions and to manage mathematical problems
spontaneously. The control group also saw a note on their screen,
simply stating that time was up.
Procedure
The experiment was conducted in a laboratory room of Ruhr
University Bochum. The participant was seated in front of a
computer and asked to read and sign the informed consent
form. Self-report measures and subsequent instructions were
presented on the computer screen (see Figure 1). After a baseline
measure of self-efficacy, the participant was asked to complete the
first block of the mental arithmetic task (2000–12). Participants
in the feedback group received positive normative feedback
after the task was accomplished, while participants in the no-
feedback group received no feedback. Right after this feedback,
specific self-efficacy was assessed. Participants then completed
the second block of mental arithmetic tasks (2043–17) for 5
min and finally were asked to answer questionnaires on flow
and specific self-efficacy with respect to that task. Participants
were then debriefed regarding the purpose of the study and the
feedback manipulation.
Measures
Specific Self-Efficacy
Based on a guide for constructing self-efficacy scales developed
by Bandura (2006), participants rated their ability to complete
mental arithmetic tasks on a 10-point scale from 0 = “cannot
do at all” to 100 = “can do very well” to measure their level
of specific self-efficacy (Mt1 = 52.84, SDt 1 = 18.21) for mental
arithmetic tasks. This item was assessed at baseline level (t0),
after the first task following the feedback manipulation (t1), and
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FIGURE 1 | Study procedure.
after the second task (compare Figure 1). The three measurement
points t0, t1, and t2 were used for the manipulation check, that
is, to test if the feedback manipulation increased specific self-
efficacy over time. Specific self-efficacy at t1 was used to test the
hypothesized mediation effects.
Performance
Performance quantity (MQN = 28.33, SDQN = 9.28) was assessed
using the number of calculated results in the given time (5 min).
Performance quality (MQL = 0.90, SDQL = 0.10) was assessed
using the ratio between the number of correctly calculated results
and the total number of calculated results.
Flow
Flow (Mt2 = 4.50, SDt 2 = 1.18) was measured with the Flow-
Short-Scale (Rheinberg et al., 2003), which comprises ten items
measuring absorption (“I did not notice time passing”) and
fluency (“My thoughts/activities ran fluidly and smoothly”) as
experienced during the task on a 7-point Likert Scale. The
reliability of the scale was very good with a Cronbach’s Alpha
of 0.92. The scale was administered after the second task (at t2;
compare Figure 1).
Data Analysis
Data were analyzed with the IBM SPSS statistics package.
To analyze the efficiency of our manipulation, we tested
whether participants’ specific self-efficacy increased over
time in the feedback group using a repeated measures
ANOVA. As the Mauchly test of sphericity was significant,
we used the Greenhouse-Geisser correction procedure. The
mediation analyses were conducted with the SPSS macro Process
(Hayes, 2013). For the mediation analyses, all variables were
z-standardized. To estimate if self-efficacy served as the mediator,
the indirect effect ab was estimated (Preacher and Hayes, 2008).
We report 95% confidence bootstrap intervals for the indirect
effect (nbootstrap = 5000).
RESULTS
Table 1 shows means, standard deviations and correlations of all
study variables.
Manipulation Check
With regard to the experimental manipulation of specific self-
efficacy, a significant main effect of time [F(1.82,181.59 )= 4.97,
p= 0.010, ηp2= 0.047] and an interaction effect [F(1.82,
181.59 )= 6.23, p= 0.003, ηp2= 0.059] were found for specific
self-efficacy for mental arithmetic tasks. As can be seen in
Figure 2, in contrast to the feedback group, the control group
showed a decrease in specific self-efficacy for mental arithmetic
tasks after Task 1.
FIGURE 2 | Manipulation Check: Level of specific self-efficacy regarding
mental arithmetic tasks at measurement points t0, t1, and t2 for the control
group (no feedback) and the experimental group (positive feedback).
TABLE 1 | Shows means, standard deviations and correlations of all study variables.
Variable MW SD MWCG SDCG MWEG SDEG 123456
1 Group – – – – – – 1
2 Specific SE (t0) 54.22 17.32 54.69 16.85 53.77 17.89 0.03 1
3 Specific SE (t1) 52.84 18.21 48.78 17.75 56.60 17.96 0.22* 0.76** 1
4 Specific SE (t2) 50.29 19.47 49.39 19.08 51.13 19.97 0.05 0.69** 0.79* 1
5 Flow 4.50 1.18 4.43 1.14 4.57 1.23 0.06 0.46** 0.56** 0.67** 1
6 Performance QL 0.90 0.10 0.90 0.09 0.90 0.11 0.00 0.29** 0.30** 0.45** 0.40** 1
7 Performance QN 28.33 9.28 28.69 9.70 28.00 8.96 0.04 0.39** 0.37** 0.45** 0.20* 0.32**
p<0.05; ∗∗ p<0.01.
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FIGURE 3 | Mediation models of the effect of positive feedback on
performance quantity (A) and performance quality (B) via specific self-efficacy.
The arrows represent direct effects. The indirect effects were (A) β= 0.17
(SE=0.10, 0.01 <CI <0.39) for performance quantity and (B) β= 0.13
(SE=0.08, 0.00 <CI <0.32) for performance quality. *p<0.05; **p<0.01;
***p<0.001.
Testing of Hypotheses
To test Hypothesis 1a, we z-standardized all study variables
and performed a mediation procedure with feedback as the
independent variable, specific self-efficacy as the mediator, and
performance quantity as the dependent variable. The a- (β= 0.43,
SE = 0.19, p = 0.029) and b-path (β= 0.40, SE = 0.09, p<0.001)
were significant. The indirect effect was β= 0.17 (SE = 0.10,
0.01 <CI <0.39) and significant (compare Figure 3A). The
total effect was β=0.07 (SE = 0.20, t=0.38, p = 0.708)
and unexpectedly not significant. The direct effect was also
not significant (β=0.25, SE = 0.19, t=1.30, p = 0.195)
when controlling for the indirect effect. However, as the indirect
effect was significant, specific self-efficacy appeared to transmit
a positive effect of positive feedback on performance quantity
as hypothesized.
Regarding Hypothesis 1b, the a- (β= 0.43, SE = 0.19, p = 0.029)
and b-path (β= 0.31, SE = 0.10, p = 0.002) of the model with
specific self-efficacy as the mediator, group as the independent
variable, and performance quality as the dependent variable were
both significant. The indirect effect was β= 0.13 (SE = 0.08,
0.00 <CI <0.32) and significant (compare Figure 3B). The
total effect was β=0.00 (SE = 0.20, t=0.02, p = 0.988) and
not significant. The direct effect was not significant (β=0.14,
SE = 0.20, t=0.70, p = 0.483) when controlling for the indirect
effect. As the b-path in this model was significantly positive, these
results run counter to our expectation to find negative effects of
increased self-efficacy on performance quality. We further found
that self-efficacy appeared to transmit a positive effect of positive
feedback on performance quality – while we had expected that a
negative effect would be transmitted.
To conclude – in line with Hypothesis 1a, but in conflict with
Hypothesis 1b – specific self-efficacy mediated a positive effect
FIGURE 4 | Mediation model of the effect of positive feedback on flow via
specific self-efficacy. The arrows represent direct effects. The indirect effect
was β= 0.25 (SE=0.11, 0.02 <CI <0.47). *p<0.05; ***p<0.001.
of positive feedback on both performance quantity and quality.
Contrary to our expectations, however, the total effect of positive
feedback on both performance measures was not significant.
Regarding Hypothesis 2 the a- (β= 0.43, SE = 0.19, p = 0.029)
and b-path (β= 0.57, SE = 0.09, p<0.001) of the z-standardized
mediation model with specific self-efficacy as the mediator,
feedback as the independent variable, and flow as the dependent
variable were significant. In support of Hypothesis 2, the indirect
effect was β= 0.25 (SE = 0.11, 0.02 <CI <0.47) and significant.
The total effect was β= 0.12 (SE = 0.20, t= 0.61,p=0.544) and
not significant. The direct effect was not significant (β=0.13,
SE = 0.17, t=0.74, p = 0.461) when controlling for the indirect
effect (compare Figure 4). The significance of the indirect effect
confirms Hypothesis 2, that specific self-efficacy would transmit
a positive effect of positive feedback on flow. However, and
contrary to our expectations, the total effect of positive feedback
on flow was not significant.
DISCUSSION
In this study we aimed to test the postulated effect of self-
efficacy on flow-experience and performance experimentally. To
manipulate self-efficacy, we used a well-established paradigm,
that is, false normative positive feedback about performance on a
mental arithmetic task. Using a bootstrap procedure to conduct
mediation analyses, we found that positive feedback enhances
specific self-efficacy, which, in turn, enhances performance
(quality and quantity) and flow experience in a subsequent task.
In the following we discuss our results in more detail:
First of all, given our successful manipulation check, we could
replicate earlier studies showing that false positive normative
feedback is an efficient intervention to promote self-efficacy
(Weinberg et al., 1979;Wright and Gregorich, 1989;Eden and
Zuk, 1995;Brown et al., 2012). It can therefore be recommended
as an experimental manipulation of self-efficacy in future studies.
Furthermore, Hypothesis 1a was confirmed: The results
suggested that positive feedback has an indirect positive effect on
performance quantity via self-efficacy. Considering the positive
b-path in our model, that is, the positive effect of self-efficacy
on performance quantity, our results replicate earlier studies
that also found that performance increases with increased self-
efficacy (e.g., Bandura and Locke, 2003;Vancouver et al., 2014).
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Peifer et al. Well Done
As previous research on the relationship between self-efficacy and
performance is largely based on observational data, a strength of
our study is the experimental approach to manipulate self-efficacy
using positive feedback.
By differentiating between performance quantity and
quality, our study further contributes to the debate regarding
whether – and for which performance parameters – self-efficacy
has positive or maybe even negative effects on performance:
some researchers argue that high self-efficacy might undermine
motivation, as a person might believe that effort is not necessary
to successfully cope with low demands compared to high
abilities, which leads to an increase in performance quantity
(as also found in our study), but to a decrease in performance
quality (see, e.g., Vancouver et al., 2001, 2002, 2014;Vancouver,
2012). That is why we assumed in Hypothesis 1b that we would
find a negative indirect effect of positive feedback via self-efficacy
on performance quality. However, contrary to Hypothesis 1b,
we found this indirect effect to be positive, with a positive effect
of self-efficacy on performance quality. A possible explanation
for the contradictory findings in the literature could be that
the undermining effect of self-efficacy on performance quality
only occurs if self-efficacy is very high. In our case, we told
participants in the feedback condition that their performance
was “above average”, which is a relatively moderate manipulation.
Thus, while we successfully increased self-efficacy levels, they
only increased to a moderate but not to a very high level. This
explanation would be in line with the proposed inverted u-shaped
relationship between self-efficacy and performance (cf. Schönfeld
et al., 2017): there could be an increase in performance until
self-efficacy is moderately high and a decrease in performance if
self-efficacy further increases.
Hypothesis 2 was also confirmed, with our results suggesting
that positive feedback increases flow-experience via increased
self-efficacy. The finding that experimentally induced self-efficacy
increased flow-experience supports earlier cross-sectional field
studies that have found positive associations between the two
(Zubair and Kamal, 2015a,b) and longitudinal studies (Salanova
et al., 2006;Rodríguez-Sánchez et al., 2011) with first evidence
for a causal effect of self-efficacy on flow experience. By using
an experimental manipulation to increase self-efficacy, we add
further evidence to the existing literature that self-efficacy can
causally increase flow-experience.
However, our study did not test the opposite causal direction,
that experiencing flow would increase self-efficacy. Flow provides
an enjoyable feeling of control over the activity at hand, while
applying one’s skills. Thus, it is likely that flow also enhances self-
efficacy, and that the effects are bidirectional. This reciprocity
suggests that an upward spiral of self-efficacy and flow can
occur – as supported by earlier results from field studies
(Salanova et al., 2006, 2014). Future experimental studies should
look at both causal directions, replicating and complementing
previous results.
Our results further showed an indirect effect of positive
feedback on flow. This is in line with earlier theory and research:
In his nine components of flow-experience, Csikszentmihalyi
(1990) had already named “clear goals and feedback” as one of
the components. Later operationalizations of flow distinguished
between antecedents, characteristics and consequences of flow
and considered feedback as an antecedent (Nakamura and
Csikszentmihalyi, 2002;Landhäußer and Keller, 2012). Cross-
sectional field studies on feedback and flow supported this
assumption (Rau and Riedel, 2004;Maeran and Cangiano, 2013).
To the best of our knowledge, ours is the first study to show an
indirect effect of positive feedback on flow in an experimental
design, thereby providing insights into a mechanism that
can transmit the effects of positive feedback to flow: self-
efficacy. Accordingly, providing positive feedback enhances self-
efficacy, which presumably enhances the feeling of competency
and control in the respective task – two characteristics of
flow experience.
However, it needs to be stated that while the indirect effect
of positive feedback via self-efficacy on flow was significant, the
total effect was not significant, and neither was the direct effect
of positive feedback on flow when self-efficacy was included
as a mediator. According to Hayes (2009, 2013), finding an
indirect effect confirms mediation, while a missing total effect
does not contradict mediation: “A failure to test for indirect
effects in the absence of a total effect can lead to you miss
some potentially interesting, important, or useful mechanisms
by which X exerts some kind of effect on Y (Hayes, 2009;
p. 415).” In cases in which the total effect is not significant, it is
likely that different mechanisms play a role in the relationship
between independent and dependent variable. This means that
in addition to a positive effect of positive feedback on flow via
enhanced self-efficacy, it is likely that there are counteracting
mediators in this process, transmitting negative effects of positive
feedback on flow. Such possible mediators should be investigated
in future studies.
Similarly, the indirect effect of positive feedback via self-
efficacy on performance quantity and quality were significant,
while the total and direct effects were not significant. This again
underlines the possibility of counteracting mechanisms between
positive feedback and performance. While positive feedback via
self-efficacy positively impacts performance, positive feedback
might lead to the assumption that less effort is necessary, which
could have a counteracting negative impact on performance (e.g.,
Vancouver et al., 2014). Such counteracting mechanisms of action
should be examined in future studies, for example with the use of
physiological indicators of mental effort, such as high frequency
heart rate variability. Not only short-term but also long-term
perspectives are relevant: an increase in self-efficacy through
regular positive feedback might have long-term consequences on
flow, and – through extensive application – on skill acquisition
and future performance.
Another possible explanation for the lack of a total or
direct effect of positive feedback on flow is that the feedback
manipulation was potentially not salient enough: We told
participants that they were better than average – which is
what most people would assume anyway: the “better-than-
average” effect is a robust finding in research on social
comparisons (compare Alicke and Govorun, 2005). Accordingly,
our positive feedback was potentially a confirmation of an
existing assumption (i.e., neutral feedback) rather than a
positive deviation. Future studies should use stronger positive
feedback manipulations in order to investigate its effects on flow
and performance.
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Peifer et al. Well Done
Yet another explanation for the missing findings could be the
kind of feedback that we used: Prior studies that addressed the
relationship between flow mostly referred to either task-inherent
feedback or supervisor feedback (mostly according to the Job
Characteristics Model, Hackman et al., 1975). In their feedback
measure, these studies did not differentiate between a normative
vs. an individual reference norm (e.g., Bakker, 2005;Demerouti,
2006). Regarding performance there is research differentiating
between normative and individual feedback: Brunstein and
Hoyer (2002), for example, investigated the effects of positive
vs. negative feedback with normative vs. individual reference
norm on performance. They did not find any main effects
of positive normative feedback on performance (i.e., d2-test
for concentration). Descriptively, positive normative feedback
was even associated with lower performance (higher reaction
times). In addition, Brunstein and Hoyer (2002) found significant
interactions with the achievement motive: A higher implicit (but
not explicit) achievement motive was associated with higher
performance after feedback. Interestingly, it was not positive
normative feedback but negative individual feedback that spurred
achievement-motivated individuals’ performance. These results
show that future studies should explicitly differentiate between
normative and individual feedback to get deeper insights into
the mechanisms that facilitate or hinder flow experience and
performance. As moderating variables, implicit and explicit
achievement motives should be controlled, as differential effects
might occur. Furthermore, feedback with an individual reference
norm may be more appropriate to foster performance and flow
(Brunstein and Hoyer, 2002;Brunstein and Maier, 2005).
While Brunstein and Hoyer found that performance was
enhanced by negative feedback at least for individuals scoring
high on the implicit achievement motive, we do not know how
negative feedback impacts flow. In theoretical models (Nakamura
and Csikszentmihalyi, 2002;Landhäußer and Keller, 2012) as well
as in previous (field) studies (Rau and Riedel, 2004;Maeran and
Cangiano, 2013) on the relationship between feedback and flow,
positive and negative feedback were not distinguished. However,
there is evidence from qualitative research that positive feedback
is especially beneficial for flow (Jackson, 1995;Swann et al.,
2015). Thus, it will be interesting for future studies to look at the
differential effects of positive vs. negative feedback, differentiating
between task-inherent, normative and individual feedback, and
controlling for an individual’s achievement motive.
A potential limitation of our results refers to the fact that
we did not use change scores of performance in the mental
arithmetic tasks to control for baseline scores. However, based
on the design of our study, the interpretation of change scores
is problematic: The two tasks differed very much in difficulty:
while the first task was very easy (continuously subtracting 12
from 2000), the second task was more difficult (continuously
subtracting 17 from 2043). Our results clearly support this, as
both performance indicators decreased significantly from t1 to
t2. Furthermore, the low difficulty in the first task likely led
to a ceiling effect of performance in both groups, reducing the
systematic variance in the data. These circumstances make the
change scores very hard to interpret. At the same time, as we
had randomly assigned our participants to the experimental
conditions, we believe that mental arithmetic skills are equally
distributed between groups. Accordingly, we refrained from
using change scores in our data analysis. Detailed results
using change scores in the analyses can be found in the
Supplementary Material.
One more possible limitation of our study refers to the
measurement of flow: By using the flow short scale (Engeser
and Rheinberg, 2008), we applied a widely used componential
approach to assess flow (compare Moneta, 2012). This scale
measures flow as a continuous phenomenon: the more its
components are pronounced, the higher flow values. The
components used in the flow short scale reflect those proposed
by Csikszentmihalyi (1975, see flow definition above). However,
an ongoing discussion in flow research is whether flow is
a continuous phenomenon or if it is rather a yes-or-no
phenomenon (compare Engeser, 2012;Peifer and Engeser,
in press), with an individual either being in flow or not. A cut-off-
value for flow when measured with the componential approach
has not yet been identified and is a challenge for future research.
With what we know today, the current study is limited insofar
as it cannot differentiate between flow or not-flow, but rather
measures more or less pronounced flow components.
Practical Implications
Based on our results, we can recommend positive feedback as
an intervention to enhance self-efficacy. This recommendation
can be applied to different contexts such as work or schools.
In the work context, positive feedback could be given by
supervisors in annual performance reviews and in regular
meetings. A precondition to providing clear feedback is goal
setting, as expectations are made transparent to the employee.
Goals should be realistic and achievable in due time in order
to provide the opportunity for positive feedback on a regular
basis. In general, positive feedback can also come from sources
other than just the supervisor – for example from customers
or colleagues. Positive feedback has further been found to be
positively related to job satisfaction (Yukl et al., 2002) and well-
being (Stocker et al., 2014). Thus, an organizational climate of
mutual appreciation is a good basis for employees’ self-efficacy,
satisfaction and well-being. As shown in the current study, self-
efficacy is also positively related to flow and performance and acts
as a mediator transmitting positive effects of positive feedback to
flow and performance.
While we manipulated self-efficacy in this study using
positive feedback, there are other interventions that have
been successfully used to enhance self-efficacy, for example
interventions to increase psychological capital (Luthans et al.,
2006, 2008, 2010). These interventions could therefore also be
used to increase self-efficacy, and, in turn, performance and
flow-experience.
In this study, we found short-term and immediate effects of
self-efficacy on flow-experience. Future studies should have a
look at spillover effects and at long-term effects of self-efficacy
interventions: Enhancing flow-experience has been found to
affect future flow and performance in similar tasks (Christandl
et al., 2018). Furthermore, flow has been found to lead to
long-term increased performance via increased motivation to
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Peifer et al. Well Done
practice (Engeser et al., 2005;Schüler, 2007;Schüler and Brunner,
2009). Thus, interventions to foster the pleasant experience of
flow are a valuable endeavor for institutions (e.g., organizations,
schools) as well as for individuals (e.g., employees, students).
CONCLUSION
“Well done?” – our study investigated the relationships between
positive normative feedback, self-efficacy, performance, and
flow experience. Our results provide experimental evidence
that positive feedback enhances self-efficacy. Further, we found
an indirect effect of feedback via self-efficacy on performance
quantity and quality, as well as on flow experience. However,
mutually opposing counter-mechanisms were potentially
also active as we did not find a total effect of positive
normative feedback on performance and flow, calling for further
research on this issue.
DATA AVAILABILITY STATEMENT
The datasets generated for this study are available on request to
the corresponding author.
ETHICS STATEMENT
All procedures performed in studies involving human
participants were in accordance with the ethical standards of
the institutional and/or national research committee and with
the 1964 Helsinki declaration and its later amendments or
comparable ethical standards.
INFORMED CONSENT
Informed consent was obtained from all individual participants
included in the study.
AUTHOR CONTRIBUTIONS
CP and PS conceived of the presented idea. PS carried out the
experiment. CP, PS, and FA developed the theory. PS and GW
wrote the methods. GW and FA performed the computations and
wrote the results part. CP wrote the discussion. JM supervised
the concept and findings of this work. All authors discussed the
results and contributed to the final manuscript.
ACKNOWLEDGMENTS
We acknowledge support by the DFG Open Access Publication
Funds of the Ruhr University Bochum.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fpsyg.
2020.01008/full#supplementary-material
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Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Copyright © 2020 Peifer, Schönfeld, Wolters, Aust and Margraf. This is an open-
access article distributed under the terms of the Creative Commons Attribution
License (CC BY). The use, distribution or reproduction in other forums is permitted,
provided the original author(s) and the copyright owner(s) are credited and that the
original publication in this journal is cited, in accordance with accepted academic
practice. No use, distribution or reproduction is permitted which does not comply
with these terms.
Frontiers in Psychology | www.frontiersin.org 11 June 2020 | Volume 11 | Article 1008
... Studies that implemented normative feedback administered various kinds of cognitive tasks (e.g., arithmetic tasks, name recall). They demonstrated that participants who received positive normative feedback reported higher self-efficacy and performed better compared to those who did not receive feedback [18,19]. These results seem to suggest that fictitious positive normative feedback is an effective intervention to promote self-efficacy [18]. ...
... They demonstrated that participants who received positive normative feedback reported higher self-efficacy and performed better compared to those who did not receive feedback [18,19]. These results seem to suggest that fictitious positive normative feedback is an effective intervention to promote self-efficacy [18]. However, to our knowledge, no studies have manipulated self-efficacy in the spatial cognition domain. ...
... One recent study on arithmetic tasks [18] suggested that the effect of positive feedback on spatial learning is mediated by increasing self-efficacy. Our second aim was to investigate whether positive feedback could increase spatial self-efficacy in spatial-recall tasks. ...
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We examined the roles self-efficacy plays in environmental learning in terms of self-efficacy feedback and task-specific (navigation-based) self-efficacy. We manipulated self-efficacy using positive and neutral feedback to investigate the relationship between receiving positive feedback and environmental learning performance and subsequent recall. A total of 231 participants were administered visuospatial tasks, where 117 received positive feedback, and 114 received neutral feedback. Then, we tested environmental learning using route retracing, pointing, and map-completion tasks. Before each environmental task, participants evaluated their task-specific self-efficacy. A series of spatial self-reported preferences were gathered as well. Mediation models showed that receiving positive feedback after a visuospatial task influences environmental recall performance through the mediation of task-specific self-efficacy. Moreover, after accounting for experimental manipulation and gender, we found that task-specific self-efficacy, sense of direction, and visuospatial abilities influence spatial-recall task performance, even with some differences as a function of the specific recall tasks considered. Overall, our findings suggest that among individual characteristics, task-specific self-efficacy can sustain environmental learning. Furthermore, giving positive feedback can improve spatial self-efficacy before conducting spatial-recall tasks.
... The main critique was that the nine dimensions, identified as flow state in the measures, are inherently different concept types (Quinn, 2005). For example, the balance between skill and challenge and goal clarity are structural features of a task (Buil et al., 2018;Fullagar et al., 2013;Peifer et al., 2020;Tan & Miksza, 2018). However, feedback is the set of cues that one extracts from a task (Quinn, 2005). ...
... The findings of this study indicated that Skills was the strongest predictor of flow in performing. Thus, the current results corroborated the findings of previous researchers who reported that a balance between skills and challenge may associate with flow in performing (Peifer et al., 2020;Tan & Miksza, 2018). Both Skill and Challenge explained a larger proportion of variance in performing than teaching in the statistical analysis (Table 2), and significantly more Skill-and Challenge-related codes were found in the participants' open-ended descriptions of performing compared to teaching. ...
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The purpose of this study was to examine music teachers’ experiences with flow while performing and teaching music. A model with four flow antecedents (Challenge, Skills, Goal Clarity, and Feedback) and three dimensions of flow state (Absorption, Enjoyment, and Intrinsic Motivation) was adopted to investigate music teachers’ flow experiences in performing and teaching. Two hundred twenty-five music teachers completed the Flow in Music Performing and Teaching Scale, modified from Buil et al. The analyses revealed that the four flow antecedents in the proposed model explained a substantial amount of variance in music teachers’ flow state for both performing and teaching music settings (54.0% in performing, 34.7% in teaching). Participants’ open-ended descriptions of flow were also collected to further explore music teachers’ personal experiences with flow. The open-ended descriptions revealed that music teachers cited topics pertaining to Challenge, Skills, Absorption, Enjoyment, and Flow Disruptor when describing flow in performance. In contrast, they tended to cite topics pertaining to Feedback, Goal Clarity, and Group Flow when describing flow in teaching. Because Feedback and Goal Clarity appeared to be more closely associated with flow in teaching, executing lesson plans to accomplish goals while flexibly responding to students’ spontaneous feedback may elicit flow for teaching.
... Previous research shows that social norms, (alongside self-e cacy, response e cacy, and negative emotions), play a crucial role in regulating climate-related behavior 15 . Properly administered normative feedback (i.e., about one's performance relative to signi cant others) has been demonstrated to be useful for motivating behavior change 34 , and for supporting individual feelings of self-e cacy 35 , across a variety of domains, including the environment. Furthermore, the perception that climate change is distant from the individual -that is, happening to people who are far away from both in time and space -is often argued to be the leading barrier of climate change inaction 36,37 . ...
... Contrasting with our predictions, the VR intervention, in general, did not result in a larger increase in selfe cacy beliefs compared to the control condition. However, we did nd that the VR intervention had a signi cantly more positive impact on self-e cacy beliefs when normative feedback was included compared to generic feedback, which is in line with research indicating that relevant normative feedback can increase self-e cacy beliefs 35,47 . Our sample reported generally high pre-treatment self-e cacy beliefs (4.2 on a ve-point scale), and there is some evidence that initially high self-e cacy can decrease in the rst phases of acquiring a new skill 48 . ...
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As the food industry significantly contributes to global carbon emissions, studying new behavioral interventions promoting plant-based diets is crucial for mitigating climate change. This study investigates the impact of an efficacy-focused virtual reality (VR) intervention designed according to instructional design principles on eating behavior. In the preregistered intervention study, psychology students randomly assigned to nine blocks (analyzed n = 123, follow-up n = 90) were allocated to either the VR intervention (four groups) or the control (notreatment) condition (five groups). The study employed a parallel design, and the primary outcome was the effect of the VR simulation on dietary footprint measured from one week before to one week after the intervention. The VR intervention decreased individual dietary footprints (d = 0.4) significantly more than the control condition. Similarly, response efficacy and knowledge increased to a larger extent in the VR condition compared to the control. For knowledge, the effect persisted for one week. The VR intervention had no impact on intentions, self-efficacy, or psychological distance. Additional manipulation of normative feedback enhanced self-efficacy; however, manipulation of geographical framing did not influence psychological distance. This research received no financial support from any funding agency and was registered on 15/09/2021 at Open Science Foundation with the number 10.17605/OSF.IO/2AXF3.
... It is the balance between the task demands, social expectations, personal competencies and achievement goals that leads toward an autotelic optimal learning experience. As an agentic variable in the self-determination theory, self-efficacy has also been found to be predictive of flow (Mahdi Hosseini & Fattahi, 2014;Rodríguez-Sánchez et al., 2011;Peifer, Schönfeld, Wolters, Aust and Margraf, 2020). For instance, Hong, Tsai, Hsiao, Chen, Chu, Gu and Sitthiworachart (2019) found that flow mediates between self-efficacy and learning continuance intention in a game-based learning context. ...
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We focus on the predictors of persistence and achievement in online learning by studying the students’ learning intentions and their psychological states during learning activities. Flow/autotelic experience is a powerful predictor of engagement in MOOCs and online learning in general and relates to the deep involvement and sense of absorption during learning activities. Both theory and empirical evidence propose that predictors of flow in an educational setting include the need for belonging to a group of learners. Using path analyses and structural equation modeling, we verify the causal links between social intentions, autotelic experience and MOOC learning outcomes such as final grade and dropout. Using the Online Learning Enrollment Intentions (OLEI) scale, we find that in total six OLEI items predict MOOC success and dropout, with flow as a mediating effect. In two models, we verify “Autotelic experience” as a mediator between enrollment intentions and MOOC final grade and dropout. Our results highlight socially driven intentions as major factors to be considered in online learning environments. We draw theoretical and practical implications for MOOC design, considering explicit communication about the provided learning environment and tools towards a socially shared learning experience.
... Last but not least, the results indicate that normative feedback significantly increased participants' selfefficacy. According to previous findings, positive normative feedback can increase the feeling of self-efficacy, which can indirectly impact performance (Dimotakis, Mitchell, & Maurer, 2017;Peifer, Schönfeld, Wolters, Aust, & Margraf, 2020). In our study, the participants received normative feedback after the first selection and second selection; thus, as we found a significant decrease from first to second selection and the mean value was under population average, the second feedback was positive for the majority of the participants. ...
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Full-text available
We developed and tested a Virtual Reality (VR) intervention that allowed users (1) to visualize the consequences of food behavior and (2) to revise their food choices and see how this would alter future climate scenarios. In this pre-registered study, using a 2x2 design, we investigated whether the intervention can be enhanced using normative feedback (normative vs. generic) and by varying the degree of immersion (head-mounted display vs. desktop). The intervention advertised online was self-administered by 122 participants residing in the USA. We observed a large decline in dietary carbon footprint one week after the intervention regardless of experimental condition (d = -0.63). This change was mediated by increased intentions, self-efficacy, risk perception, and emotional reactions. In addition, normative feedback increased self-efficacy, and changes in response efficacy separately predicted pro-environmental donations. Our findings show how psychologically informed VR interventions might be used to stimulate action against climate change. These findings have possible implications for the Metaverse as a platform to communicate environmental issues.
... As shown in a meta-analysis, feedback has positive effects on performance, and this was especially the case with positive feedback [55]. One possible mechanism is the increase in self-efficacy [56]. Hence, our hypothesis is that appreciation can serve as a buffer which reduces the negative effects of the COVID-19 pandemic on perceived stress (Hypothesis 6a), frequency of flow experience (Hypothesis 6b), satisfaction with work (Hypothesis 6c), satisfaction with life (Hypothesis 6d), satisfaction with work performance (Hypothesis 6e), and satisfaction with wellbeing (Hypothesis 6f). ...
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Background The first analyses of the various consequences of the COVID-19 pandemic show that the risk to nurses’ psychological well-being is particularly high. As the pandemic and the demands imposed on nurses are not yet fully understood, there is a need to seek buffering factors to protect nurses’ psychological health. In line with the earliest evidence, we hypothesize pandemic-related increases in perceived stress and decreases in the frequency of flow experiences, likewise in satisfaction with work, life, work performance, and well-being. As protective factors while dealing with pandemic-related stress, we suggest an individual’s sense of humor and perceived appreciation. Methods In June/July 2020 – during the first lockdown in Germany – participants completed an online-survey in which they were asked to rate their situation before the pandemic (retrospectively) and during the pandemic. Our sample consisted of 174 registered nurses (161 females, 13 males, Mage = 40.52), of whom 85 worked as public health nurses and 89 as geriatric nurses. Results During the pandemic, nurses felt more stressed, had fewer flow experiences, and were less satisfied with their work, life, work-performance, and well-being than before the pandemic. In addition, nurses felt more appreciation from society but less from their patients. Sense of humor and the perceived appreciation of society and patients were confirmed as buffers of negative pandemic-related effects. Conclusion Our study contributes to the so far scarce knowledge on nurses’ pandemic-related stress and well-being in combination with their resources. Moreover, we were able to identify sense of humor and appreciation as protective factors.
... Although early research on feedback interventions showed that positive and negative feedback are differentially related to motivation and performance (e.g., Cusella, 1982), most studies have considered feedback as a unidimensional process, thereby neglecting its valence (e. g., Nakamura and Csikszentmihalyi, 2014;Peifer, Schönfeld, Zipp, Aust, & Margraf, 2020). Nevertheless, Csikszentmihalyi, Abuhamdeh, and Nakamura (2014) argued that negative performance feedback may reduce the likelihood of flow because such feedback causes individuals to lose their confidence in task completion. ...
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Flow experience refers to a motivational state of complete concentration on a given task, which can immediately facilitate cognitive performance. Although research has identified feedback as strong antecedent of flow, differences between the effects of positive and negative feedback on flow and the moderating role of personality traits remain largely unexplored. In the present online-based experimental study, we investigated flow as mediator in the effects of normative feedback on performance in a logical reasoning task. Thereby, we also examined moderating effects of locus of control and conscientiousness on indirect effects of positive and negative feedback on task performance via flow. Our sample involved 240 participants, who were randomly assigned to three different feedback conditions (positive, negative, no feedback) after a logical reasoning task. Subsequently, the subjects solved a follow-up task for which they reported the degree of their flow experience. Moderated mediation path modelling showed that positive feedback exerted a positive indirect effect on task performance via flow for highly conscientious individuals. In contrast, negative feedback exerted negative indirect effects on task performance via flow for individuals, who exhibited an internal locus of control or low levels of conscientiousness. Finally, we derived theoretical implications for scholarly understanding of flow experience.
... Eventually, feedback may empower them and help them feel that they can control their performance with practical strategies to exploit their capabilities (Schunk & DiBendetto, 2020;Sheu et al., 2018;Sitzmann & Yeo, 2013). This is expected to promote and validate self-efficacy growth (Peifer et al., 2020;Shea & Howell, 1999;Tolli & Schmidt, 2008). ...
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Applicants’ self-cognitions toward selection procedures are decisive to produce favorable outcomes. Drawing upon the career self-management model, this study explored the impact of performance feedback after a simulated employment interview on interview self-efficacy (ISE) and outcome expectations. Participants (a sample of recent graduates; N = 240) were given timely feedback after the simulated interview with suggestions to improve their performance. The interviewer’s feedback was positively related to participants’ ISE measured after the feedback. A significant relationship between participants’ ISE and outcome expectations emerged. Feedback was related to outcome expectations only indirectly, via ISE. This study contributes to existing knowledge about ISE and provides practitioners with hints to help job seekers to master job search in troubled times. Practitioner notes • Interview self-efficacy is a major predictor of performance in the employment interview. • We show that performance feedback enhances interview self-efficacy and outcome expectations. • Job seekers in training should be given meaningful feedback to raise their interview self-efficacy.
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Embracing failure for the purpose of learning is a key trait in successful teams. Failure, however, is not the only source of learning. The majority of interventions in healthcare are successful, yet our prevailing efforts to extract learning intelligence tend to focus almost exclusively on failures, such as harm and errors. By considering the learning potential across the whole landscape of work from success to failure, we can widen the range of learning opportunities. The key steps to learn from excellence are first to recognise excellence, which can be highly subjective, and second to provide positive feedback. Positive feedback enhances learning through a number of routes, including increasing self-efficacy and intrinsic motivation. It may also help to improve relationships within teams and to offset negativity associated with blame cultures.
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Learning analytics (LA) has been widely adopted in research on education. However, most studies in the area have conducted LA after computer-supported collaborative learning (CSCL) activities rather than during CSCL. To address this problem, this study proposed a LA-based real-time feedback approach based on a deep neural network model to improve CSCL performance. In total, 72 university students participated in the study and were randomly assigned to an experimental or control group. The students in the experimental group learned with the LA-based real-time feedback approach, whereas the students in the control group learned with the conventional online collaborative learning approach. To analyse the data, both quantitative and qualitative methods were adopted. The results indicated that the LA-based real-time feedback approach significantly promoted knowledge convergence, knowledge elaboration, interactive relationships and group performance. The interview results also confirmed the effectiveness of the proposed approach. Practitioner notes What is already known regarding this topic Learning analytics (LA) has been widely used in education. Most studies in the area have presented LA results only after collaborative learning and have lacked real-time analysis and feedback. What this paper adds A LA-based real-time feedback approach was proposed and validated in the computer-supported collaborative learning (CSCL) context. The experimental results indicated that the LA-based real-time feedback approach significantly promoted knowledge elaboration, knowledge convergence, interactive relationships and group performance. Implications for practice and/or policy To shed light on progress in CSCL, real-time LA are recommended. Deep neural network models, such as bidirectional encoder representations from transformers, can be adopted to automatically analyse online discussion transcripts. Real-time feedback based on LA results can promote CSCL performance.
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Previous neuroimaging studies have suggested that the experience of flow aligns with a relative increase in activation of the dorsal raphe nucleus (DRN), and relative activation decreases of the medial prefrontal cortex (MPFC) and of the amygdala (AMY). In the present study, Dynamic Causal Modeling (DCM) was used to explore effective connectivity between those brain regions. To test our hypothesis that the DRN causally down-regulates activity of the MPFC and/or of the AMY, 23 healthy male students solved mental arithmetic tasks of varying difficulty during functional magnetic resonance imaging. A “flow” condition, with task demands automatically balanced with participants’ skill level, was compared with conditions of “boredom” and “overload”. DCM models were constructed modeling full reciprocal endogenous connections between the DRN, the MPFC, the AMY, and the calcarine. The calcarine was included to allow sensory input to enter the system. Experimental conditions were modeled as exerting modulatory effects on various possible connections between the DRN, the MPFC, and the AMY, but not on self-inhibitory connections, yielding a total of 64 alternative DCM models. Model space was partitioned into eight families based on commonalities in the arrangement of the modulatory effects. Random effects Bayesian Model Selection (BMS) was applied to identify a possible winning family (and model). Although BMS revealed a clear winning family, an outstanding winning model could not be identified. Therefore, Bayesian Model Averaging was performed over models within the winning family to obtain representative DCM parameters for subsequent analyses to test our hypothesis. In line with our expectations, Bayesian averaged parameters revealed stronger down-regulatory influence of the DRN on the MPFC when participants experienced flow relative to control conditions. In addition, these condition-dependent modulatory effects significantly predicted participants’ experienced degree of flow. The AMY was down-regulated irrespective of condition. The present results suggest a causal role for the DRN in modulating the MPFC, contributing to the experience of flow.
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This study explores normative feedback as a way to reduce rating errors and increase the reliability and validity of structured interview ratings. Based in control theory and social comparison theory, we propose a model of normative feedback interventions (NFIs) in the context of structured interviews and test our model using data from over 20,000 interviews conducted by more than 100 interviewers over a period of more than 4 years. Results indicate that lenient and severe interviewers reduced discrepancies between their ratings and the overall normative mean rating after receipt of normative feedback, though changes were greater for lenient interviewers. When various waves of feedback were presented in later NFIs, the combined normative mean rating over multiple time periods was more predictive of subsequent rating changes than the normative mean rating from the most recent time period. Mean within-interviewer rating variance, along with interrater agreement and interrater reliability, increased after the initial NFI, but results from later NFIs were more complex and revealed that feedback interventions may lose effectiveness over time. A second study using simulated data indicated that leniency and severity errors did not impact rating validity, but did affect which applicants were hired. We conclude that giving normative feedback to interviewers will aid in minimizing interviewer rating differences and enhance the reliability of structured interview ratings. We suggest that interviewer feedback might be considered as a potential new component of interview structure, though future research is needed before a definitive conclusion can be drawn. (PsycINFO Database Record
Chapter
The final chapter provides a short summary of all chapters of the book and points to similarities between the chapters and what these imply for future research. Future research topics that are discussed include the core components of flow, the differentiation of frequency and intensity of flow, and the duration of and dynamics within and between flow episodes. We further look at antecedents of flow beyond the demand skill balance and at the role of intrinsic and extrinsic reasons for the emergence of flow. For the development of autotelic personality, it is proposed that we can apply existing evidence regarding personality factors related to flow. Further, the chapter addresses a potential agreement regarding the measurement of flow using a combination of the Flow Questionnaire and the Componential approach to assess flow as a yes-or-no continuous phenomenon. We discuss the Experience Sampling Method (ESM) and provide ideas on how to make use of the full potential of ESM data. Finally, some speculation about consequences of flow, and the application of flow interventions, are addressed. The chapter ends with a personal view on the role of flow in development.
Article
Subjective time progression has been shown to serve as a heuristic cue for evaluating stimuli, tasks and events. The subjective feeling that “time flies” is a characteristic feature of flow experience. In four experiments, we investigated whether and how subjective time progression, as operationalized by announcing either shorter or longer time intervals than the actual time during task completion, affects recalled flow and subsequent performance. In Study 1, we were able to show that subjectively accelerated time progression increases recalled flow. Studies 2, 3, and 4 tested our central hypothesis, according to which the experience that time flies while working on a task should lead to better performance in a subsequent similar task. This effect was found in all studies. Studies 3 and 4 further revealed that, as expected, the effect was mediated by recalled flow, while controlling for potential alternative mediators. The findings from Study 4 further indicate a spillover effect such that participants who recalled higher levels of flow as a result of our manipulation also experienced higher levels of flow in a subsequent task. The present research contributes to an integration of naïve theories on subjective time progression, flow experience, and objective performance. The research provides preliminary evidence that recalled flow experience can be affected post hoc using time manipulation. These findings bear practical implications for applied pedagogical and organizational psychology.
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In this field study we examined both positive and negative developmental feedback given in managerial assessment centers in relation to employees' self-efficacy for their ability to improve their relevant skills assessed in the centers, the extent to which they sought subsequent feedback from others at work, and the career outcome of being promoted to a higher level position within the organization. We found that feedback was related to self-efficacy for improvement which was in turn positively related to feedback seeking, which was positively linked to the career outcome of promotion (e.g., feedback leads to self-efficacy for improvement leads to feedback seeking leads to promotion). In addition, we tested boundary variables for the effects of feedback in this model. Both social support for development and implicit theory of ability moderated the effects of negative feedback on self-efficacy. Having more support and believing that abilities can be improved buffered the detrimental impact of negative feedback on self-efficacy. We discuss implications for theory, future research and practical implications drawing upon literature on assessment centers, feedback and feedback seeking, employee development and career success. (PsycINFO Database Record
Article
Presents an integrative theoretical framework to explain and to predict psychological changes achieved by different modes of treatment. This theory states that psychological procedures, whatever their form, alter the level and strength of self-efficacy. It is hypothesized that expectations of personal efficacy determine whether coping behavior will be initiated, how much effort will be expended, and how long it will be sustained in the face of obstacles and aversive experiences. Persistence in activities that are subjectively threatening but in fact relatively safe produces, through experiences of mastery, further enhancement of self-efficacy and corresponding reductions in defensive behavior. In the proposed model, expectations of personal efficacy are derived from 4 principal sources of information: performance accomplishments, vicarious experience, verbal persuasion, and physiological states. Factors influencing the cognitive processing of efficacy information arise from enactive, vicarious, exhortative, and emotive sources. The differential power of diverse therapeutic procedures is analyzed in terms of the postulated cognitive mechanism of operation. Findings are reported from microanalyses of enactive, vicarious, and emotive modes of treatment that support the hypothesized relationship between perceived self-efficacy and behavioral changes. (21/2 p ref)
Article
Background: Lecturers give feedback on assessed work in the hope that students will take it on board and use it to help regulate their learning for the next assessment. However, little is known about how students' conceptions of feedback relate to students' self-regulated learning and self-efficacy beliefs and academic performance. Aims: This study explores student beliefs about the role and purpose of feedback and the relationship of those beliefs to self-reported self-regulation and self-efficacy, and achievement. Sample: A total of 278 university students in a general education course on learning theory and approaches in a research-intensive university. Methods: Self-reported survey responses for students' conceptions of feedback (SCoF), self-regulation (SRL), academic self-efficacy (ASE), and Grade Point Average (GPA) were evaluated first with confirmatory factor analysis and then interlinked in a structural equation model. Results and conclusions: Three SCoF factors predicted SRL and/or GPA. The SCoF factor 'I use feedback' had positive associations with SRL (β = .44), GPA (β = .45), and ASE (β = .15). The SCoF factors 'tutor/marker comments' and 'peers help' both had negative relations to GPA (β = -.41 and -.16, respectively). 'Peers help' had a positive connection to SRL (β = .21). ASE itself made a small contribution to overall GPA (β = .16), while SRL had no statistically significant relation to GPA. The model indicates the centrality of believing that feedback exists to guide next steps in learning and thus contributes to SRL, ASE, and increased GPA.