International Journal of Social Robotics (2022) 14:1013–1024
https://doi.org/10.1007/s12369-021-00848-9“I Have to Praise You Like I Should?” The Effects of Implicit
Self-Theories and Robot-Delivered Praise on Evaluations of a Social
Robot
D. D. Allan1· Andrew J. Vonasch1 · Christoph Bartneck1

Accepted: 19 November 2021 / Published online: 23 January 2022
© The Author(s), under exclusive licence to Springer Nature B.V. 2021

Abstract
Recent research suggests that implicit self-theories—a theory predicated on the idea that people’s underlying beliefs about
whether self-attributes, such as intelligence, are malleable (incremental theory) or unchangeable (entity theory), can influencepeople’s perceptions of emerging social robots developed for everyday use. Other avenues of research have identified a close
link between ability and effort-focused praise and the promotion of individual implicit self-theories. In line with these findings,
we posit that implicit self-theories and robot-delivered praise can interactively influence the way people evaluate a social
robot, after a challenging task. Specifically, we show empirically that those endorsing more of an entity theory, indicate morefavorable responses to a robot that delivers ability praise than to one that delivers effort praise. In addition, we show thatthose endorsing more of an incremental theory, remain largely unaffected by either praise type, and instead evaluate a robot
favorably regardless of the praise it delivers. Together, these findings expand the state-of-the-art, by providing evidence ofan interactive match between implicit self-theories and ability, and effort-focused praise in the context of a human-robot
interaction.
Keywords Implicit self-theories · Mindset · Human–robot interaction · Social robotics · Praise · Effort-ability

1 Introduction
Implicit self-theory, implicit theories, or, more colloquially‘mindset theory’, asserts that individual’s underlying beliefs
about whether self-attributes (e.g., personality and intelligence) are malleable (incremental theory) or are unchangeable (entity theory) causally affect motivation [14] andbehavior [27], with the largest effects occurring in situations
that involve challenges and setbacks [98].
Ample research suggests that feedback and praise, particularly, effort (i.e., hard work) and ability (i.e., intelligence)
praise, may foster implicit self-theories from an early age on(see [46], for review ).
Additionally, there is a longstanding and growing evidence base that suggests implicit self-theory exerts its effecton a wide array of self-attributes and downstream variables [26,81]. These range from intelligence [12,33,85],

B D. D. Allandwain.allan@pg.canterbury.ac.nz

1

University of Canterbury, Private Bag 4800, Christchurch
8140, New Zealandpersonality [17,35], morality [16,53], emotions [58,91], andrelationships [59,60,75], to evaluations of marketing messages [55,100], brands [67,79], technology [39,45,89] andfinancial decisions [71,82].
Consistent with these findings, we have recently reported
in this journal, the first empirical evidence of the influenceof implicit self-theories on people’s perception of emerging
social robots developed for everyday use [3].
In this article, we build on, and substantially extend,
our previous work by examining the effect of implicit selftheories on people’s responses to a robot that praises forability (i.e., intelligence), and for effort (i.e., hard work),
after completing a difficult reading and comprehension task.
We expect, and find evidence that entity theorists, whoseek flattering external validation regarding their capabilities,
and are liable to becoming defensive in the face of challenges[12,52,76], evaluate a social robot as more intelligent and likable after receiving ability praise (i.e., praise for intelligence)
than effort praise (i.e., praise for hard work). By contrast,
incremental theorists, who orient towards developing theirabilities, and exert greater effort when facing challenges[25,29], are unaffected by praise type. Hence, incremental

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theorists’ ratings of the robot did not differ between conditions.

1.1 Theoretical Framework and Hypotheses
1.1.1 Implicit Self-Theories and Praise
As noted, implicit self-theories are underlying beliefs people hold about the malleability or fixedness of self-attributes(e.g., intelligence, personality). A substantial body of researchsuggests there are two kinds of implicit self-theories held byindividuals: the incremental theory and the entity theory.1
Those who hold an incremental self-theory (incremental theorists) assume that self-attributes are mutable and amenable
to change through self-development efforts, practice, andeducation. Incremental theorists are driven to develop theirability (i.e., learning goals; [34]). Accordingly, they are prone
to seeking out learning opportunities [12], and tend to be
more learning-oriented [35]. Consequently, they are better
able to adapt to situational change, challenges, and setbacks,
relative to entity theorists [33].
In contrast, those who hold an entity theory (entity
theorists) assume that self-attributes are largely fixed andunsusceptible to change [30]. In consequence, they are driven
to validate their competence and/or avoid looking incompetent (i.e., performance goals; [34]). Thus, they are prone toseeking out situations and cues that provide favorable external evidence of their capabilities [77,78]. Furthermore, entity
theorists, compared to incremental theorists, are more riskaverse [82], sensitive to negative evaluations [52], and display
more helpless reactions in the face of failure [98].
Clearly, an exhaustive review of implicit self-theory is
not feasible here (but see[31], for review), however, five key
characteristics bear mentioning. Firstly, each theory occurs
with equal frequency within most populations [14,70]. Secondly, the differences in implicit self-theory are most salient
under conditions of challenge or when facing setbacks [98].
Third, although implicit self-theories are moderately stable[32,85] and can be measured as an individual difference
variable [64], they can also be temporarily altered,2 via experimental stimuli such as scientific articles [10,51] and video
media [68], in order to assess the causal role the theories
play [70]. Fourth, these contrasting self-theories appear toaffect a vast spectrum of human motivation, perception, andInternational Journal of Social Robotics (2022) 14:1013–1024

behavior [27,64,68,85]. Finally, many of the consequences of
one’s implicit self-theory appear to operate outside of one’s
awareness [99].
Crucially, a long tradition of research has consistently
found that implicit self-theories arise from, and are related
to, ability and effort-focused praise (e.g., [56,66,80]).
In a landmark study by Mueller and Dweck [72], fifthgrade children worked on a set of challenging tasks and were
subsequently praised, first with outcome praise (“Wow, you
did very well on these problems. You got [number of problems] right. That’s a really high score.”) The children were
informed that of the problems they had answered they had
solved at least 80%. This was followed by one of two typesof praise–ability (e.g., “You must be smart at these problems.”), and effort (e.g., “You must have worked hard at these
problems.”) A control condition received the initial outcome
praise only. Next, the children were given a set of problems
of increased difficulty, of which most failed. According to
Mueller and Dweck [72], the children who were praised fortheir ability viewed their intelligence as innate. Accordingly,
they rejected a hard task, in favor of an easier task that,
presumably, would pose no threat to their intelligence. Contrastingly, the children praised for their effort viewed theirintelligence as something that could be developed. Thus, thisgroup chose a hard task rather than an easier task in order tolearn from it.
Since this study,3 researchers have continued to find support for the interrelationship of implicit self-theories andability, and effort-focused praise (e.g., [18,47,66]).
For example, Gunderson et al. examined parents’ use ofability and effort praise in the presence of their children
aged 1–3 years. They demonstrated that parents’ use of effort
praise predicted children’s incremental theory of intelligence
5 years later [44]. Furthermore, in a study conducted by
Pomerantz and Kempner [80], parents of 8–10 year-olds wereinterviewed on a daily basis, in order to track their use of ability and effort-focused praise. Upon assessing the children’s
implicit self-theories 6 months later, they found that frequent
use of ability praise predicted children’s entity self-theory.
To be sure, this relationship is well demonstrated using
child samples. However, as other researchers have noted,
only a handful of studies have examined how ability praiseand effort praise interact with implicit self-theories in adultsamples, thus warranting further research (see [96]).

1 Although people tend to endorse one self-theory more than the other,

there is some evidence that people may hold implicit self-theories somewhere between the entity and the incremental theory [30,50,54].
2 It should be noted, that this is a well-used and well-cited practice,employed in the bulk of existing implicit self-theories research (see [30,52,64,100]). Furthermore, research has repeatedly demonstrated thatexperimentally induced self-theories’ bias preferences in accordancewith those found in studies measuring individuals’ chronic implicit selftheories as an individual difference factor [70].

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3 One might note that, like other seminal works, the Mueller andDweck [72] study, has been subjected to some criticism in recent years(e.g., [43]). Primarily, with respect to issues of replication and generalizability [65]. However, many of these criticisms have now beenaddressed (see [98]).

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1.1.2 HRI and Praise
Research on praise in HRI has been conducted from different perspectives. Some work has focused on: investigating
how humans attribute praise and punishment to robots [6,7],
robot-delivered praise for increasing user motivation [37,86],
self-efficacy [101], as well as personalized robot praise
toward children [88] and older adults [93]; other work has
evaluated the role of praise in game-based interactions [2]and nurturing praise in a therapy context [92], and stillother research has examined the relationship between robotdelivered praise, trust, and compliance [40].
Despite progress made, it seems particularly interesting that there is scant scholarly research on the role ofability-focused or effort-focused praise in the field. This is asurprising omission given that this form of praise is one of themost extensively researched praise types in the educational
and psychological literatures (see [13,19,61]), not to mention
it has received attention from researchers in adjacent fields
such as Human–Computer Interaction (e.g., [95]).
That said, to the best of our knowledge, there is one
published study to date that has (partially) explored theassociation between effort-focused praise and implicit selftheory. In particular, Davidson et al. [20] conducted an
unsupervised longitudinal study whereby children interactedwith either a Computer Aided Learning (CAL) system thatadministered effort-focused praise via headphones or one
that delivered the same praise via a social robot. This study
found that children who received effort-related praise fromthe social robot had a significant increase in their incremental self-theory score, compared to those who received praisefrom the CAL system.
However, even though this single study sheds at least somelight on the effect of effort-focused praise and implicit selftheory in HRI, it is hampered by several limitations. Firstly,
the researchers examined only a single praise type (effort)
on a single implicit self-theory dimension (incremental). In
addition, it did not investigate the role of implicit self-theories
in shaping attributions and behavior per se, therefore people’s evaluations of a robot post-task were not examined.
Moreover, this study used a relatively small sample (44 participants), composed of children (aged 6–10 years old), andit reported a few inclusive findings.
As such, it is unknown whether one’s implicit self-theory
orientation and robot delivered-praise can interactively determine one’s evaluation of a robot. Accordingly, the study
reported herein was designed to empirically investigate thisissue.

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seek validation [30,34], praise [73] and favorable judgments
about their performance [77]. According to Dweck [28]entity theorists’ sense of worth rests on demonstrating these
traits (see also, [12,27]). Relatedly, those endorsing an entity
theory are also more likely to describe their ideal romantic
partner as a person who would bolster their fixed qualities(i.e., praise them).
In contrast, incremental theorists prefer romantic partners
that will encourage their development (see [28]). Furthermore, incremental theorists are motivated by confronting achallenge, learning, and developing their ability [55,70,85].
Moreover, they are not generally content with gaining favorable competence feedback about themselves [30,68]. Hence,
given that incremental theorists are inclined to value effort,
and self-monitor their own progress [30,68], external praisethat provides flattering extrinsic validation of their capabilities is likely to carry less meaning for incremental theorists
than entity theorists.
Finally, it seems pertinent to note that we have recently
found that incremental theorists, evaluate robots favorably
regardless of how a robot is positioned (in this case, servant,or assistant; [3]).
Considering all the above, entity theorists would be
expected to be more partial to a robot that praises for ability than one that praises for effort. Additionally, we should
expect incremental theorists to be relatively unaffected byeither effort or ability-focused praise, and, in turn, rate therobot favorably in both conditions. Stated formally:
Hypothesis 1 (H1): Entity theorists will evaluate the robot
as more (versus less) likable after receiving ability (versus
effort) praise.
Hypothesis 2 (H2): Incremental theorists will evaluate therobot as higher (versus lower) in likability after receiving
both effort and ability praise.
Hypothesis 3 (H3): Entity theorists will evaluate the robot as
more (versus less) intelligent after receiving ability (versus
effort) praise.
Hypothesis 4 (H4): Incremental theorists will evaluate therobot as higher (versus lower) in intelligence after receiving
both effort and ability praise.

2 Method
The study hypotheses and analysis plan were pre-registeredand may be accessed via the Open Science Framework(OSF): https://osf.io/54nhk/. The study took place in May
2021. Data collection took place over the course of 5 days.4

1.1.3 Hypotheses
As indicated above, entity theorists’ basic assumption that
traits are fixed leads them to devalue effort and constantly

4 It

is important to make clear that in our pre-registration we
declared that we had started the data collection process at the timeof pre-registration. This, however, pertained only to the recruitment

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International Journal of Social Robotics (2022) 14:1013–1024

The experimental procedure was reviewed and approved by
the Human Research Ethics Committee of the University ofCanterbury (HEC 2020/130).

Both experimenters followed a detailed script. See https://
osf.io/j4mvg/ for the full script.

2.1 Recruitment

2.3 ParticipantsParticipants were recruited via social media postings, recruiting websites; and numerous posters, flyer drops, and wordof mouth around the University of Canterbury and the surrounding community. All advertisements indicated that the
study was designed to investigate the suitability of a robot
as a “test marker” (e.g., marking tests and exams in order tosupport educators) in the context of a reading and multiplechoice exercise. The robot test marker context allowed us toinconspicuously carry out the theory inductions, as well as
to give participants credible robot praise on a plausibly challenging task. Sample recruitment materials are available athttps://osf.io/z5x8s/ and https://osf.io/tqxcp/.
Inclusion criteria comprised (1) Participants 18 years orolder with fluent English and basic reading and comprehension skills, and (2) no experience with robotics and/or
any expertise in AI (e.g., data science and machine learning). Past research suggests that some forms of technicalexpertise and experience with robotics, both independently
and in combination, have positive and moderating effects on
people’s attitudes toward, and acceptance of, social robots(see [21,24,62,63]). Therefore, it is reasonable to presume
that the inclusion of such individuals may have potentially
produced distorted findings.

A total of 101 adults participated for a $10 gift card. One
participant was precluded from analyses a-priori on thebasis of failing to answer the manipulation check correctly.
Thus, the sample used for analyses comprised 100 participants (53 female, 44 male, 3 with no gender reported) fromChristchurch, New Zealand. They ranged in age from 18 to73 years old (M = 28.4, S D = 15.3). All participants had
a high-school education, and 31% reported having either anundergraduate degree or a postgraduate degree.

2.2 Design
We used a 2 (implicit self-theory: entity vs. incremental theory) ×2 (ability vs. effort praise) between-subjects factorial
design. The study was performed in two steps. In the firststep, participants’ implicit self-theories of intelligence weremanipulated impelling some toward an incremental theory
and others toward an entity theory. During the second step, we
administered a difficult reading and comprehension task onwhich participants were told they did well. Participants werethen presented with either effort or ability praise. We thenmeasured participants’ likability and perceived intelligence
scores regarding the robot. Participants were randomized into
four conditions: (1) entity theory/effort praise, (2) entity theory/ability praise, (3) incremental theory/effort praise, and(4) incremental theory/ability praise. The experiment was
conducted by one of two experimenters (both male) who
were dressed professionally and wore the same clothing.
Footnote 4 continued
component. In other words, we had not at that time conducted any of
the research described herein.

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2.4 Materials
2.4.1 Implicit Self-Theory Manipulation
Participants were randomly assigned to read one of two scientific Psychology Today articles adapted from Bergen [10],endorsing either an incremental (e.g., “up to eighty-eightpercent of a person’s intelligence is due to environmental
factors.”), or entity (e.g., “up to eighty-eight percent of aperson’s intelligence is due to genetic factors”) theory ofintelligence. This priming stimuli is commonly used andwell-validated in implicit self-theory research (see [10,52,
53,87]). After reading their respective articles, participants
were asked to (a) summarize the main point of the article inno more than three sentences, and (b) complete the implicit
theory of intelligence measure—which served as a manipulation check—described later in Sect. 2.6.

2.4.2 Task Materials
A challenging reading test was set based on [76], for which
praise was later given. The rationale for this was to provide
a challenging stimulus because the effects of implicit selftheory are most pronounced under conditions of challenge
or difficulty [83]. In particular, participants were given 3 min
to read an excerpt from Freud’s The Interpretation of Dreams.
Following Nussbaum and Dweck [76] the text was chosen
to be reasonably perplexing and the 3 min time limit was
purposely insufficient so as to allow for minimal comprehension. Furthermore, participants were instructed to answer fivemultiple-choice questions associated with the passage. To
ensure the participants felt uncertain of their performance andthus found the robot-delivered praise to be somewhat credible, the questions and answers were designed to be vague
and ambiguous.

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2.4.3 Manipulation of Praise
Drawing, in part, on the procedure outlined by Mueller andDweck [72], we manipulated the praise the robot administered. Specifically, the robot informed all participants thatthey had performed well (e.g., “Wow, you did very well”). It
is worthwhile emphasizing here, that all participants were
explicitly told they had been successful in terms of theirtask performance. (In other words, the praise was delivered
right after a supposedly successful performance on a difficult task). Following the outcome praise, participants weretold that they had 60% of the questions correct.5 They subsequently heard one of two types of praise from the robot: somewere praised for their effort (e.g., “You must have worked
hard at these questions” and “Interesting, the pattern indicates that you tend to put in a lot of effort when faced withchallenges, would that be right? Just answer yes or no”).
Whereas others were praised for their ability (e.g., “You mustbe smart at these questions” and “Interesting, the pattern indicates that you tend to rely on your intelligence when facedwith challenges, would that be right? Just answer yes or no”).
Additionally, when the experimenter re-entered the room andasked the robot how the test went, the robot replied, “Overall, the participant has done well at these questions, although
this participant is not one of the top performers, the patternindicates they are quite a hard worker”. Alternatively, therobot said, “Overall, the participant has done well at thesequestions, although this participant is not one of the best
performers, the pattern indicates they are quite smart”. It is
important to point out that the justification for having therobot repeat the praise is based on prior work suggesting thatrobots need to deliver feedback multiple times in order for it
to register as meaningful [37]. See https://osf.io/cs42y/ andhttps://osf.io/yu37e/ for video examples of both conditions.

Fig. 1 The robot used in this study: a NAO V5 by SoftBank robotics

2.5 Procedure
Participants were tested individually in a small room with around flat desk. Each participant was briefed about the natureof the study and inroduced to the social robot (A humanoid
NAO6 V5 from Softbank Robotics; see Fig. 1). The robot wasseated on a table in front of the participant for the durationof the experiment (see Fig. 2).
After providing consent, and completing a brief demographics questionnaire (age, gender, education), participants
were given a 1 page document that described the robot’s
supposed intelligent reasoning capabilities (a PDF version is
5 We used 60% instead of 80% adopted by Muller and Dweck [72],because there were only 5 questions used in the study described herein.
Therefore, we reasoned that 60% (3/5) was more tenable than 80% (4/5)
with respect to participants’ estimation of their own performance.

Fig. 2 Experimental setup with the following components: (1) Participant, (2) NAO robot, (3) iPad, and (4) Bell6 https://www.softbankrobotics.com/emea/en/nao.

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available at https://osf.io/zjvap/). This aspect of the coverstory was designed to ensure that participants perceived
the robot praise as well-reasoned, rather than simply a preprogrammed response (which, in fact, it was).
Participants were then instructed to complete a pre-study
task, in which their implicit self-theories were manipulated(described above in Sect. 2.4.1). Specifically, they were told
they had 6 min to read a short article on an iPad, and two anda half minutes to complete some questions associated withthat article. At this point, they were informed that the study
was about assessing the robot, not them. Further, they weretold that their answers would be automatically converted to a
special NAO code.7 They were then informed that after they
had answered the questions, they were to hold the iPad up in
front of the robot’s eyes and wait until the robot said, “OK
got it”.
When they had completed the pre-study task, participants
were told that they would advance to the primary study task.
They were subsequently advised that they would have 3 min
to read a section of text and 2 min to answer some questions
associated with that text (described in Sect. 2.4.2). Additionally, they were told not to worry if the text didn’t appear to
make sense at first, as it had been selected purely because
the content and the data produced, would be a suitable challenge for the robot to assess. Once again, participants wereadvised that the purpose of the study was to assess the robot,
not them. The participant was then told that their answers
would be converted to a NAO code, as in the pre-study task
and they would be required to hold it up to the robot’s eyes
until it says, “OK got it”. They were also advised that theexperimenter would leave the room until the participant hadfinished, so as not to distract them. As well, the participant
was instructed to ring the bell when the robot told them to doso. The experimenter then left the room.
After they had completed the study task, participants presented the NAO code to the robot. At this point, the robot
ostensibly scanned the test and delivered the praise manipulation, as described in Sect. 2.4.1. After delivering thispraise, the robot thanked the participant for their participationand told them to ring the bell. Afterward, the experimenter
entered the room and asked the robot how the test went, towhich the robot replied with either effort or ability-relatedpraise. The robot then asked if it should go into standby
mode, the experimenter replied, “Yes”, and then informed
the participant that the robot could no longer see or hear
them. This was done to encourage participants’ candid feedback. Next, participants completed the measures describedlater in Sect. 2.6. After completing these items, participants
were informed that the study was over, and debriefed. In

International Journal of Social Robotics (2022) 14:1013–1024

keeping with the debriefing procedure used by Nussbaum
and Dweck [76], special care was taken to expound the fictitious nature of the theory induction article. In particular, thefact that intelligence is understood to have both malleable andstable qualities [29]. Finally, participants were compensatedfor their participation, informally probed for suspicion,8 thendismissed.

2.6 Measures
2.6.1 Manipulation Check
We used the six-item Implicit Theories of Intelligence
Scale (ITIS; [27]) as a manipulation check for participants’
implicit self-theories. This is a well-established procedure forchecking the effectiveness of primed implicit self-theories(see [64,68,82]). This scale has three items that measure
incremental beliefs (e.g., “You have a certain amount ofintelligence, and you really can’t do much to change it”)
and three that measure entity theory beliefs (e.g., “You canalways greatly change how intelligent you are”). Participants
provided responses using a 6-point Likert scale (1 = strongly
disagree, 6 = strongly agree). The incremental items werethen reverse scored, and a mean score calculated for all six
items, with high scores representing greater endorsementof an incremental theory of intelligence. The internal consistency of this measure was excellent (α = 0.95, M =2.84, S D = 1.48).
2.6.2 Perceived Intelligence
Perceived intelligence was assessed using the five-item
Perceived Intelligence sub-scale of the Godspeed Questionnaire Series (GQS; [8]). Responses were made using
a 5-point scale (e.g., 1 = Unintelligent, 5 = Intelligent, and
1 = Ignorant, 5 = Knowledgeable). This scale has been usedin previous research to measure people’s impression of robots
post-interaction (e.g., [15,42,49]), particularly NAO robots(see [97]). This measure exhibited excellent internal consistency in our sample (α = 0.90, M = 3.97, S D = 0.95).
2.6.3 Likability
Likability was assessed with the five-item Likability subscale of the GQS [8]. Participants provided responses using a
5-point scale (e.g., 1 = Dislike, 5 = Like, and 1 = Unfriendly,
5 = Friendly). The internal consistency of this measure was
excellent (α = 0.93, M = 4.13, S D = 0.93).

7 A NAO code or “Naomark” is a circular symbol specific to the NAO

robot. The robot is able to detect, and respond to this symbol (see http://
doc.aldebaran.com/2-1/naoqi/vision/allandmarkdetection.html).

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8 None of the participants reported suspicion about any aspect of thestudy.

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3 Results

PraiseAbility

The dataset for this study can be found at https://osf.io/
j69nv/.

3.1 Main Analyses
3.1.1 Manipulation Check
An independent sample t-test on the ITIS indicated that participants who received the entity theory article scored higher
on entity theory (M = 1.66, S D = 1.13) than those who
received the incremental theory article (M = 3.93, S D =0.729), t(98) = −12, p < .001), thus, our implicit selftheory manipulation was successful.9

Perceived Intelligence

4.5

Effort

4.0

3.5

3.0

EntityIncremental
Implicit Self-Theory

Fig. 3 Perceived intelligence means and 95% confidence intervals forthe interaction between implicit self-theories and praise

3.1.2 Perceived Intelligence
Praise3.1.3 Likeability
A similar ANOVA test on participant’s ratings of likability
was also conducted. As expected, a significant interaction between implicit self-theory and praise on ratings oflikability was observed (F(1, 96) = 5.30, p = .024).
Planned comparison results revealed that for entity theorists,
ability-focused praise led to significantly higher likability ratings (M = 4.24, S D = 0.698) than effort-focusedpraise (M = 3.38, S D = 1.20), t(96) = 3.681, p =.002, d = 1.06(95%C I [0.47 − −1.65]). Furthermore, nosignificant difference between ability (M = 4.48, SD =0.552) and effort-focused praise on ratings of likability(M = 4.37, S D = 0.694), t(96) = −.510, p = .956, d =9 As described in Sect. 2.6, lower (vs. higher) scores on the ITIS indicate

more (vs. less) of an entity (vs. incremental) self-theory.

Ability
Effort4.5

Likability

We used a 2 (implicit self-theory) ×2 (praise) betweensubjects ANOVA on perceived intelligence. Results revealed
a significant interaction between implicit self-theory andpraise on perceived intelligence (F(1, 96) = 24.0, p <.001). Follow-up planned comparisons showed that participants who were primed with entity theory demonstrated
higher perceived intelligence scores for the robot whenpraised for ability (M = 4.26, S D = 0.606) versus effort(M = 2.96, S D = 1.26), t(96) = 5.855, p < .001, d =1.69(95%C I [1.06, 2.31]). However, results for participants
manipulated to believe in incremental self-theory showed nosignificant difference between ability (M = 4.20, S D =0.534) and effort-focused praise on ratings of perceived intelligence (M = 4.41, S D = 0.447), t(96) = −.974, p =.765, d = −0.27(95%C I [−0.82, 0.28]). Given these results,
hypotheses H3 and H4 were supported (see Fig. 3).

4.0

3.5

3.0

EntityIncremental
Implicit Self-Theory

Fig. 4 Likability means and 95% confidence intervals for the interaction between implicit self-theories and praise

0.14(95%C I [−0.40, 0.69]), was observed for incremental
theorists. These findings supported H1 and H2 (see Fig. 4).

4 Discussion
The findings presented herein provide evidence that implicit
self-theories (entity vs. incremental), and robot-delivered
praise (ability vs. effort) can interactively influence the way
people evaluate social robots after a challenging task. Moreprecisely, we demonstrate that entity theorists, who are prone
to conceal their shortcomings and seek out favorable judgments from others, rate a robot lower in both perceived
intelligence (H1) and likability (H2), when it praises themfor effort (vs. ability).
Conversely, incremental theorists, who are more selfdriven and learning-oriented, appear unaffected by praise

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type, such that they evaluate a robot high in both perceived
intelligence (H3) and likability (H4), regardless of the praise
it delivers.
We subsequently discuss the theoretical contributions ofthese findings, as well as limitations and directions for future
research below.

4.1 Theoretical Contributions
Building on our previous work (see [3]), which implicatedimplicit self-theories to be an important and unresearchedvariable in the field of HRI, we herein show that one’s
response to robot-delivered praise (ability, effort-focused) is
influenced by one’s implicit self-theory orientation. Morespecifically, in identifying the significant differential effectsof robot praise on two core dimensions of robot acceptance,
likability and perceived intelligence for entity theorists, inparticular, we enrich the HRI literature with respect to workon robot-delivered feedback (e.g., [1,37,40,48]) and robot
acceptance (e.g., [11,22,23,36,41,94]). By extension, thisstudy adds to the literature underscoring the importance of
examining under-explored psychological factors affecting
social robot acceptance [4,5,9,38,90,102].
Our work also contributes to the social psychologicalresearch on implicit self-theories. As discussed previously,prior work has long established the link between praise andbeliefs about intelligence, and has argued that the types ofpraise that children receive (e.g., ability vs. effort) plays animportant role in the development of children’s implicit selftheory (see [46], for review). Here, we extend this body ofwork by presenting what might be the first evidence of astrong and direct interactive match between implicit selftheory (entity vs. incremental) and praise (ability vs. effort),
outside of child samples and educational or learning-related
contexts.
More peripherally, our findings may be valuable to HRIinteraction designers interested in developing social robotic
products that are more humanly engaging. For example, ourfindings would suggest that a robot specially designed topraise for ability (e.g., “You’re great at this” or “You’re sosmart”), perhaps from time to time sporadically–could provide considerable enjoyment, satisfaction, and engagement
for entity theorists, and at the same time not diminish theuser experience for incremental theorists. In this manner,
future designers may draw on our findings and incorporate
implicit self-beliefs into the design of robotic social feedback
behaviors–to positively increase user satisfaction, or possibly even reduce entity theorists’ apparent robot anxiety andaversion (see [3]).

123International Journal of Social Robotics (2022) 14:1013–1024

4.2 Limitations and Future Directions
Though we found strong support for our hypotheses, our findings were nonetheless subject to certain limitations. First,
generalizations from our findings may be limited by ourdecision to use the NAO V5 robot. However, it might bementioned that some, such as Keizer et al. [57], have argued
that the NAO does not differ meaningfully from currentday humanoid robots in terms of features, capabilities, andintended use. Nonetheless, a replication employing alternative humanoid robots (e.g., Pepper, Zeno, or even Baxter) is
warranted.
Second, a control condition was not included in the study
design. Although, this decision was made on the basis of time,
space, and resource constraints (i.e., the inclusion of a control condition would have required 50% more participants),
future studies should include a control condition to comparewith the experimental group. Likewise, future research might
consider including a human control condition, in which ahuman delivers praise.
Another potential limitation might be that every participant who was recruited into the study was required, in part,to have no experience with robotics. We note, however, thata recent review by Naneva et al. [74] suggests, that many
people have yet to have contact with social robots (let alone
experience with robotics, more broadly). Thus, as the authors
seem to conclude, studies excluding these individuals may
be more generalizable than initially thought.
Notwithstanding the above limitations, the present study
indicates directions for potential future work. One intriguing area to be explored might be whether and how implicit
self-theories (incremental vs. entity), and robot-delivered
negative feedback, may interactively increase performance,
and the extent to which people assign more (vs. less) favorable robot evaluations and use-intentions, as a consequence.
To illustrate: There is some evidence that a robot’s negativeor impolite feedback can engender behavior change (see [48,69]) and increased task performance [84], although people
tend to prefer a robot’s positive feedback [40].
Given that incremental theorists, compared to entity theorists, exhibit positive effort beliefs in the face of adversity[52], and are more motivated by learning goals [30], whilefavoring goal progress cues [68], it could be that incremental(vs. entity) theorists respond somewhat positively to a robotthat displays negative or critical feedback to encourage better
performance.
In any case, future researchers could test this prediction
by implementing a human-robot interaction in the context ofa robot-assisted training task (e.g., [84]), and by measuring
implicit self-theory and different measures of robot acceptance (e.g., [3]).

International Journal of Social Robotics (2022) 14:1013–1024

5 Conclusions
We herein demonstrate that individuals endorsing an entity
self-theory rated a robot more favorably after it delivered
ability-focused praise following a difficult task. In contrast,
those endorsing an incremental theory did not differ in theirevaluations of a robot, regardless of its praise type. Thesefindings fall in line with our recent work indicating that
implicit self-theory is an important psychological variable
that affects how people view and evaluate social robots. Thesefindings, when considered together may provide an important
stimulus for further investigation into implicit self-theory andits consequences, in the domains of HRI and social robotics.
Acknowledgements This research was supported by the University ofCanterbury, New Zealand. The authors would like to acknowledge A.
David Nussbaum for providing the priming articles used in this experiment. The authors would also like to thank Jeremy Botting for his
research assistance during this project.
Data availibility Statement The dataset generated for this study is available at https://osf.io/j69nv/.

Declarations
Conflicts of interest The authors declare that they have no conflict ofinterest.
Ethical approval The authors declare that this article complies with the
ethical standards of this journal. Informed consent was obtained fromall participants involved in the study.

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D. D. Allan is currently a doctoral researcher in the Human Interface Technology Lab New Zealand (HIT Lab NZ) at the Universityof Canterbury. He has a background in design, marketing, innovation, and venture strategy, with a special interest in technology-based
entrepreneurship. His research interests are in the cross-sections ofsocial science, design, technology, and consumer behavior. His present
work in the field of social robotics examines the role of implicit selftheories in determining how individuals perceive, evaluate, and respondto, social robots.

Andrew Vonasch is a Senior Lecturer in social and personality psychology at the University of Canterbury, New Zealand. His research
examines how people make morally-relevant judgments and decisions.

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Christoph Bartneck is an associate professor and director of postgraduate studies at the HIT Lab NZ of the University of Canterbury. He
has a background in Industrial Design and Human-Computer Interaction, and his projects and studies have been published in leading
journals, newspapers, and conferences. His interests lie in the fields
of Human-Computer Interaction, Science and Technology Studies, andVisual Design. More specifically, he focuses on the effect of anthropomorphism on humanrobot interaction. As a secondary research interest
he works on bibliometric analyses, agent based social simulations, andthe critical review on scientific processes and policies. In the field of
Design Christoph investigates the history of product design, tessellations and photography.