Happy to help?
A systematic review and meta-analysis of the effects of performing acts of
kindness on the well-being of the actor☆
ABSTRACT
Do acts of kindness improve the well-being of the actor? Recent advances in the behavioural sciences
have provided a number of explanations of human social, cooperative and altruistic behaviour. These
theories predict that people will be ‘happy to help’ family, friends, community members, spouses, and
even strangers under some conditions. Here we conduct a systematic review and meta-analysis of the
experimental evidence that kindness interventions (for example, performing ‘random acts of kindness’)
boost subjective well-being. Our initial search of the literature identified 489 articles; of which 24 (27
studies) met the inclusion criteria (total N = 4045). These 27 studies, some of which included multiple
control conditions and dependent measures, yielded 52 effect sizes. Multi-level modeling revealed that
the overall effect of kindness on the well-being of the actor is small-tomedium (δ = 0.28). The effect was
not moderated by sex, age, type of participant, intervention, control condition or outcome measure.
There was no indication of publication bias. We discuss the limitations of the current literature, and
recommend that future research test more specific theories of kindness: taking kindness-specific
individual differences into account; distinguishing between the effects of kindness to specific categories
of people; and considering a wider range of proximal and distal outcomes. Such research will advance
our understanding of the causes and consequences of kindness, and help practitioners to maximise the
effectiveness of kindness interventions to improve well-being
1. Introduction
Do acts of kindness improve the well-being of the actor? Over the past few decades, advances in
the behavioural sciences have developed numerous theories of human social, cooperative and altruistic
behaviour. These theories — kin altruism, mutualism, reciprocal altruism, and competitive altruism —
make it possible to explain a variety of different types of kindness (for example, love, sympathy,
gratitude and heroism). And they predict that people will be ‘happy to help’ family, friends, community
members, spouses, and even strangers under some conditions.
More recently, there has been growing interest in using kindness as an intervention to boost
subjective well-being. The idea that, for example, ‘random acts of kindness’ can boost the well-being not
only of the recipient, but also the actor, and could thereby provide a simple, effective, inexpensive and
widely-available means of addressing social problems ranging from social isolation to more serious
mental and physical health conditions, has been taken up and promoted by a large number of research
groups, charities and government organisations (Aked, Marks, Cordon, & Thompson, 2008; Aked &
Thompson, 2011; see S1; Huppert, 2009).
Here we outline existing theories of altruism and their relation to kindness, and consider the
predictions these theories make about wellbeing. We then conduct a systematic review and meta-
analysis of previous experimental studies of the effects of kind acts on the wellbeing of the actor. And
we end with a discussion of the limitations of the existing literature, and make recommendations for
future research.
2. The causes of kindness
Kindness refers to actions intended to benefit others. Why and under what circumstances are
people kind to others? Why do people behave in prosocial, cooperative and altruistic ways? Recent
interdisciplinary research has provided a wealth of answers to these questions (Curry, 2016).
Humans evolved from a long line of social primates, who have been living in social groups for
over 50 million years (Shultz, Opie, & Atkinson, 2011). Group living affords numerous opportunities for
various different types of mutually beneficial cooperative interaction (Lehmann & Keller, 2006; Nunn &
Lewis, 2001; Sachs, Mueller, Wilcox, & Bull, 2004). Natural selection has favoured a range of evolved
psychological mechanisms for taking advantages of these opportunities, and realising the benefits of
cooperation. These mechanisms – kin altruism, mutualism, reciprocal altruism, and competitive altruism
– make it possible to identify and explain several different types of kindness.
2.1. Kin Altruism: people will be kind to their families
Natural selection favours kindness to genetic relatives, to family members (Hamilton, 1964).
Examples of such ‘kin altruism’ are widespread in nature (Gardner & West, 2014), most obviously in
cases of parental care (Royle, Smiseth, & Kölliker, 2012). Humans too possess adaptations for detecting
and delivering benefits to kin (Lieberman, Tooby, & Cosmides, 2007; Mateo, 2015), especially to
offspring (Geary & Flinn, 2001). Kin altruism can explain kindness in the form of love, care, sympathy and
compassion. And the theory predicts that these tendencies will be elicited by others who exhibit cues of
genetic relatedness, especially vulnerable children (Platek, Burch, Panyavin, Wasserman, & Gallup Jr,
2002).
2.2. Mutualism: people will be kind to members of their communities
Natural selection favours the tendency to coordinate, collaborate and be kind to others with
whom the actor shares a common interest – team mates, group members, coalition partners. Such
‘mutualisms’ – for the purpose of collective defence, or collaborative hunting – are widespread in nature
(Bissonnette et al., 2015; Boinski & Garber, 2000; Boos, Kolbe, Kappeler, & Ellwart, 2011; Harcourt &
Waal, 1992), and are an ancient and recurrent feature of human social life (Alvard, 2001; Wrangham,
1999). This process has led, in humans, to a psychology that forms and maintains groups (clubs, gangs,
clans, sects, nations, and so on), and acts to promote their interests (sometimes at the expense of rival
groups) (Balliet, Wu, & De Dreu, 2014). Mutualism can explain kindness in the form of loyalty, solidarity,
camaraderie, civic-mindedness, community spirit, and commitment to a cause ‘greater than oneself’.
The theory predicts that these tendencies will be elicited by other members of the groups with which
one identifies (including strangers) (Whitehouse & Lanman, 2014).
2.3. Reciprocal Altruism: people will be kind to those they might meet again
Natural selection favours kindness to those who might return the favour at a later date (Axelrod,
1984; Trivers, 1971).1 Surprisingly, few if any examples of such ‘reciprocal altruism’ have been found in
nonhuman species (Amici et al., 2014; Clutton-Brock, 2009). But in humans, reciprocal altruism is
implemented by psychological mechanisms that: detect those in need of help, initiate cooperation,
signal recognition of favours received, keep track of who has returned the favour and who has not,
make amends for favours not returned, and accept repentant cheats back into the fold (Cosmides &
Tooby, 2005; McCullough, Kurzban, & Tabak, 2013; Trivers, 1971). Thus, reciprocal altruism can explain
kindness in the form of sympathy (for those in need), trust (initiating cooperation), returning favours,
gratitude (for favours yet to be returned), forgiveness and friendship. Reciprocal altruism predicts that
these tendencies will most likely be elicited in repeated interactions where individuals expect to meet
again, where one's cooperative (or uncooperative) behaviour can be observed by others, and towards
others who have helped them in the past, or will be able to help them in the future (Kraft-Todd, Yoeli,
Bhanot, & Rand, 2015). This can includes kindness to strangers: a kind act may be a way of making a new
friend; after all, ‘a stranger is just a friend you haven't met yet’ (Delton, Krasnow, Cosmides, & Tooby,
2011; Krasnow, Delton, Tooby, & Cosmides, 2013).
2.4. Competitive Altruism: people will be kind to others when it enhances their status
Natural selection also favours kindness that impresses peers and attracts mates (Gintis, Smith, &
Bowles, 2001; Maynard Smith & Price, 1973). Many animals resolve status competition by engaging in
costly displays of prowess (Hardy & Briffa, 2013; Riechert, 1998). In humans, and perhaps some other
species (Zahavi & Zahavi, 1997), these displays includes altruistic acts that benefit the audience (Hardy &
Van Vugt, 2006; Hawkes, 1991; Hawkes, O'Connell, & Blurton Jones, 2001; Mazur, 2005; Miller, 2000;
Smith & Bleige Bird, 2000). This ‘competitive altruism’ can explain kindness in the form of generosity,
bravery, heroism, chivalry, magnanimity and public service. The theory predicts that these tendencies
will be elicited in the presence of rivals, or potential mates, where acting altruistically may enhance
one's status (Raihani & Smith, 2015). This includes acts of kindness to strangers: helping a stranger may
improve your status whether the recipient is in a position to return the favour or not (Barclay, 2011;
Raihani & Bshary, 2015).
Thus, multiple theories – kin altruism, mutualism, reciprocal altruism, competitive altruism –
explain multiple types of kindness. And the human capacity for culture—the ability to invent and share
new ways of living (Boyd, Richerson, & Henrich, 2011; Pinker, 2010)—has allowed us to build and
elaborate upon this benevolent biological foundation, with rules, norms and other social institutions
that further inculcate and amplify cooperation and altruism (Hammerstein, 2003). These theories
predict that people will be motivated to be kind to family, friends, colleagues, spouses, and even
strangers under some conditions.2 And the possession of such motivational systems leads us to expect
that helping others might make people happy.
3. The consequences of kindness
Subjective well-being – including happiness, life-satisfaction and positive affect – refers to a
range positively valenced psychological states (Dolan & Metcalfe, 2012; OECD, 2013). Why would
performing kind acts improve well-being? Why would helping make you happy? Broadly speaking,
happiness can be seen as an internal reward system for acting in ways that promote survival and
reproduction (Buss, 2000). Happiness is: “a psychological reward, an internal signaling device that tells
us that an adaptive problem has been, or is in the process of being, solved successfully” (Hill, DelPriore,
& Major, 2013). From this perspective, it is no problem to explain why ‘eating’ or ‘having sex’ makes
people happy; these behaviours meet important adaptive goals. And, for the reasons outlined above, it
is equally straightforward to explain why performing acts of kindness might make people happy: it is
because caring for family, maintaining coalitions, trading favours and increasing status are also
important adaptive goals (Schulkin, 2011). Indeed, we might even expect helping others to produce
more happiness than helping yourself: it is precisely because helping others can sometimes give a better
return on investment than helping yourself that evolution has favoured kindness in the first place.
Thus, the evolutionary behavioural science approach to altruism predicts that people will be
happy to help family, friends, community members, spouses, and even strangers under some conditions.
This prediction has received some support from the existing literature. A large body of research has
established an association between kindness and well-being (Anik, Aknin, Norton, & Dunn, 2009;
Konrath & Brown, 2013). However, much of this research has been correlational — showing, for
example, that people who spend more money on others are happier (Aknin, Barrington-Leigh, et al.,
2013), or people who volunteer to help others are healthier (Jenkinson et al., 2013).3 While such
correlational evidence is consistent with the prediction that people will be happy to help others, it is not
sufficient to establish a causal relationship between kindness and well-being. After all, it's possible that
helping makes you happy; but it could also be that happiness makes you helpful, or it could be that
some third variable – health, income, or personality – makes you both happy and helpful. The distinction
between correlation and cause is not a mere philosophical nicety; it is a genuine difference with
important practical implications. In the absence of a clear causal connection, kindness interventions may
not work. They may waste time and money, or displace other more effective interventions. Worse, they
may be counter-productive. If happiness causes helping (rather than the other way around), then forcing
unhappy people to help may make them less happy still.
In order to establish whether performing acts of kindness can cause happiness, it is necessary to
focus on the experimental literature. And so we undertook a systematic review and meta-analysis of
research that met the following inclusion criteria: (a) experiments that randomly allocated participants
to (b) interventions involving kind behaviour and controls and (c) subsequently measured and compared
participant well-being.
4. Methods
In order to identify suitable experimental studies of the effects of altruism on the altruist's well-
being, we conducted searches of the scientific databases Web of Science and PsychInfo for academic
articles. The most recent search was conducted on 16th November 2017. The process is summarised in
the flow diagram in Fig. 1. Searching topic, abstracts and keywords, we used the search string: (kindness
OR altruis* OR prosocial OR co-operat* OR cooperat*) AND (wellbeing OR well-being OR happiness OR
life satisfaction OR positive affect OR negative affect OR PANAS) AND (experiment* OR control OR
condition OR random* OR empirical OR trial) NOT (mindfulness OR meditation OR loving-kindness). This
search identified 712 articles. To this we added 36 articles identified by other means (following
references in books and journal articles, Google scholar searches, viewing academic researchers' web-
pages, reviewers' suggestions, and contacting authors to request unpublished data). After removing
duplicates, we were left with 489 articles
This initial set of 489 articles was screened. Two researchers (LAR and OSC) read the titles and
abstracts. Subsequently 432 articles were excluded for not meeting the inclusion criteria. These articles
were either: (a) not experimental (for example, were qualitative or correlational studies, or review
papers); (b) did not involve kind behaviour (for example, they involved hypothetical or recalled
kindness); (c) did not measure participant well-being (for example, they measured subsequent kindness,
or the happiness of the recipient); or were otherwise off topic (for example, kindness in animal
husbandry, climate change and planetary wellbeing). Cases in which the researchers disagreed were
given greater scrutiny and discussed, and where no consensus was reached, the articles were included in
the next stage of the analysis
The remaining 57 articles were then read in full, and assessed for appropriateness for the meta-
analysis (see S2 for the full list). This process excluded a further 33 records (and several studies from
included articles) for reasons summarised in Table S1.4 At the end of this process we were left with 24
articles, containing a total of relevant 27 studies that had experimentally tested the hypothesis that
kindness causes well-being. For each of these studies we coded the following characteristics:
• mean age of sample • sex of participants • location of study • type of participant (for example,
whether participants were ‘typically developed individuals’, as opposed to having been diagnosed with
some psychopathology) • type of intervention (for example, ‘random act of kindness’, prosocial
purchase, charitable donation) • type of recipient (for example, whether family, friend, stranger) • type
of control condition(s) (for example, no treatment, self-kindness, other activity) • dependent measure(s)
(for example, happiness, life-satisfaction) • size of the intervention group(s) • size of the control
group(s) • effect size(s) (Cohen's d)
Effect sizes were either taken directly from the paper, or computed from reported inferential or
descriptive statistics (Lenhard & Lenhard, 2016). For the handful of studies that reported outcomes at
multiple time-points, we coded the effect closest in time to the intervention.
5. Results
5.1. Study characteristics
The characteristics of the 27 studies are presented in Table 1. These 27 studies included a total
of 4045 participants (mean proportion male = 35%, mean age = 25.04, SD = 11.05).5
The majority of participants came from Canada, USA and Europe, although there were also
studies conducted in South Africa, Korea and Vanuatu. Most participants were university students,
although there were also two studies with children, one study of Vanuatu villagers, and one with elderly
participants. Most were ‘typically developed individuals’, although two studies involved participants
who scored highly on measures of social anxiety
The two most common interventions were ‘acts of kindness’ and ‘prosocial purchasing’. Typical
instructions for the ‘acts of kindness’ intervention were as follows: “During the coming week, please
perform at least five acts of kindness per day and report on them in the evening, including the responses
of others that you received. Examples of acts of kindness are: holding a door for someone at university,
greeting strangers in the hallway, helping other students in preparing for an exam, etcetera. It does not
matter whether you address your acts of kindness to people you know or not”. (Ouweneel, Le Blanc, &
Schaufeli, 2014)
Prosocial purchasing interventions involved giving participants a sum of money, and instructing
them to spend it on someone else. Most ‘acts of kindness’ involved a cost; but, the ‘prosocial spending’
studies that involved a windfall payment to the participant did not.
The recipients of kindness included colleagues and charities, but were for the most part left
unspecified, and could be ‘anyone’ – familiar or unfamiliar, family, friend, community member of
stranger
Control conditions also varied. Some studies compared acting kindly with doing nothing (thus
possibly confounding the effects of kindness with the effects of performing any novel fun activity),
whereas others compared acting kindly with some other non-social activity, or with helping oneself.
Most studies used a self-report measure of subjective well-being, happiness, life-satisfaction, or
positive and negative affect. These included the Subjective Happiness Scale (SHS; Lyubomirsky & Lepper,
1999), the Steen Happiness Index (SHI; Seligman, Steen, Park, & Peterson, 2005), the Satisfaction With
Life Scale (SWLS; Diener, Emmons, Larsen, & Griffin, 1985), the Positive Affect and Negative Affect Scale
(PANAS; Watson, Clark, & Tellegen, 1988) and Psychological Flourishing (Lamers, Westerhof, Bohlmeijer,
ten Klooster, & Keyes, 2011). Three studies used more objective measures: two used other-rated
smiling, and one used ‘other rated happiness’.
Some studies had multiple control conditions, and/or multiple outcome measures, and hence
provided more than one effect size; there were 52 in total.
5.2. Descriptive statistics
The effect size estimates ranged from −0.46 to 1.25 (M = 0.25, SD = 0.32). Sample sizes ranged
from 26 to 474 participants (M = 158.57, SD = 132.05). Several studies reported multiple effect sizes (1–
6, with most reporting one or two effect sizes).
5.3. Meta-analysis
Meta-analysis was conducted in R (R Core Team, 2017) and the Rpackages metafor
(Viechtbauer, 2010), and metaforest (Van Lissa, 2017), following the recommendations summarised in
(Field & Gillett, 2010). We used three-level meta-analysis to account for dependent effect sizes within
studies (Van den Noortgate, López-López, MarínMartínez, & Sánchez-Meca, 2015). Let yjk denote the j
observed effect sizes y, originating from k studies. The multi-level model is then given by the following
equations:
⎪ y β N σ ( ) β θ w w Nσ θ δb b Nσ ϵ whereϵ 0, where (0, ) where (0, ) jk jk jk jk jk k jk jk
The first equation indicates that observed effect sizes are equal to the underlying population
effect size, plus sampling error ϵjk. The second equation indicates that population effect sizes within
studies are a function of a study-specific true effect size, plus within-study residuals wjk. The third
equation indicates that the distribution of study-specific true effect sizes are distributed around an
overall mean effect, with between-study residuals bk. Results revealed that the overall effect size
estimate was δ = 0.28, 95% CI [0.16, 0.41], Z = 4.36, p < .001 (see Fig. 2). This is a small-to-medium
effect, approximately equivalent to an increase of 0.6 on a standard 0–10 happiness scale (Helliwell,
Layard, & Sachs, 2016). The within-studies variance component σw 2 was negligible, 0.00, 95% CI [ <
0.01, 0.02]. The between-studies variance σb 2 , on the other hand, differed significantly from zero, 0.08,
95% CI [0.04, 0.18]. The fact that the between-studies component was larger than the within-studies
component indicates that the variation in effect sizes was primarily accounted for by differences
between studies, whereas differences between effect sizes within the same studies were negligible.
Likelihood ratio tests also indicated that constraining the within-studies variance to zero would not
worsen model fit, whereas constraining either the between-studies variance or both variance
components to zero did lead to significant deteriorations in model fit (see Table 2). This again indicates
that there was substantial heterogeneity between average effect sizes across studies, but not between
effect sizes published within the same studies.
File drawer analysis (Rosenthal, 1979) revealed that 1919 unpublished, filed, or unretrieved
studies averaging null results would be required to bring the average unweighted effect size to
nonsignificance. Visual inspection of the Funnel plot (Fig. 3) did not clearly indicate asymmetry, which
could be a sign of publication bias. Begg's test of funnel asymmetry (based on random-effects meta-
analysis) similarly did not indicate significant bias, Z = 1.07, p = 0.28.
5.4. Moderation We coded several potential theoretical and methodological moderators:
proportion of male participants, average age of the sample, type of participant (typical, socially anxious),
type of intervention (acts of kindness, prosocial spending, other), type of control condition (nothing,
neutral activity, self-help, other), and outcome measure (happiness, life satisfaction, positive or negative
affect or emotion, other).
The small sample size limits our ability to include these moderators in mixed-effects meta-
analysis without risking overfitting (modeling random noise in the data, rather than true moderating
effects). We therefore used metaforest (Van Lissa, 2017) to screen for relevant moderators. This
technique uses the machine learning algorithm “random forests” to prevent overfitting, and to assess
the importance of several potential moderators. An added benefit is that metaforest can capture non-
linear relationships between moderators and effect size, and higher-order interactions. To this end,
many (in this case, 10,000) bootstrap samples are drawn from the original data, and a models is
estimated on each bootstrap sample. Then, each model's performance is evaluated on cases not part of
its bootstrap sample, yielding an estimate of explained variance in new data, Roob2 . We conducted
random-effects weighted metaforest, with clustered bootTable 1 strapping to account for the multilevel
structure of the data (ntree= 10000, mtry= 2). We replicated the analysis 100 times to ensure the
reliability of findings. The median estimated explained variance in out-of-bootstrap cases was negative
(Roob2=−0.11), with a large standard deviation across replications (SD = 0.19). When Roob2 is negative,
this means that the average effect size is a better predictor of out-of-bootstrap cases than the
modelimplied predictions. In other words, the model did not capture generalizable relationships
between the moderators and effect size, and we did not find evidence for associations between the
moderators and effect size.
6. Discussion
The results of this systematic review and meta-analysis of the experimental kindness literature
suggests that performing acts of kindness improves the well-being of the actor (δ = 0.28). The effect of
kindness is small-to-medium – comparable to other positive psychology interventions (such as
‘mindfulness’, ‘positive thinking’ and ‘counting your blessings’; d = 0.34, Bolier et al., 2013; d = 0.31, Sin
& Lyubomirsky, 2009; d = 0.44, Weiss, Westerhof, & Bohlmeijer, 2016).6 The effect was not moderated
by sex, age, type of participant, intervention, control condition or outcome measure. And there was no
evidence of publication bias. Together, these results suggest that policy-makers and practitioners are
correct to see kindness interventions as effective ways of improving wellbeing. And they support the
general claim that, as social animals, humans possess a range of psychological mechanisms that
motivate them to help others, and that they derive satisfaction from doing so.
However, in interpreting these results, a number of limitations should be kept in mind.
First, most of the reviewed studies were under-powered. The average sample size per condition
was N = 79; this gives power of only 1-β= 0.42 to detect a typical effect size of d = 0.28. In order to
detect such an effect with power 1-β = 0.80, future researchers should use a sample size of at least 202
per group (Faul, Erdfelder, Lang, & Buchner, 2007)
Second, most of the reviewed studies used non-clinical samples of students, from Western,
Educated, Industrial, Rich, Democratic societies (W.E.I.R.D.; Henrich, Heine, & Norenzayan, 2010). Thus
it remains unclear whether the current findings would generalise to clinical samples of participants
diagnosed with specific mental health problems, in non-WEIRD societies. Future research should employ
more representative community samples (perhaps focussing on social disorders; Qualter et al., 2015), in
a wider variety of cultures.
Third, earlier we defined kindness as ‘actions intended to benefit others’. The studies reviewed
here varied whether actions benefitting others were performed, they did not vary whether the benefits
were intended or not – in other words, they did not manipulate the motive behind the action. Previous
research has found an association between motive and outcome; one longitudinal study found that
volunteers motivated by a desire to help others lived longer than non-volunteers, but that volunteers
motivated by a desire to help themselves did not (Konrath, Fuhrel-Forbis, Lou, & Brown, 2012). If this
relationship is causal, then policy-makers should be aware that encouraging people to help others
because of the benefits to themselves may be counter-productive – it may, somewhat paradoxically,
mitigate or eliminate the effect. Further experimental research will be needed to investigate the role of
intention on the benefits of helping others.
Fourth, although the finding is consistent with the general evolutionary account of altruism
outlined above, existing research has not tested the more fine-grained predictions that arise from the
more specific theories of helping (kin altruism, mutualism, reciprocal altruism and competitive altruism).
For example, there has been little systematic investigation of whether different people benefit more
from performing acts of kindness under different conditions. And studies have not systematically varied
the type of recipient, for example family, colleague, friend, stranger. In fact, in most cases the recipient
was left unspecified – that is, they could be ‘anyone’. And so we do not know whether: people who have
lost touch with their families derive more pleasure from acts of kin altruism; or whether people are
happier giving to children as opposed to adults. We do not know whether, as mutualism predicts, people
are be happier giving to in-group as opposed to outgroups; or whether, as reciprocal altruism predicts,
people are happier giving to unlucky, as opposed to lazy, recipients (Petersen, 2012). Nor do we know
whether ambitious people (with more resources to spare) seeking status are happier engaging in acts of
competitive altruism, whether single people who are courting are happier helping help potential mates,
or whether there are any sex differences in the satisfaction derived from different kinds of helping
(Balliet, Li, Macfarlan, & Van Vugt, 2011). Thus future work should seek to fill these gaps in our
understanding. There is already a large literature on whether people behave more or less altruistically to
specific types of people; it would be fairly straightforward to add measures of subjective well-being to
replications and extensions of these designs.
Fifth and finally, existing research has tended to look at the immediate effects of kindness well-
being. Hence it is not clear what the longer-term effects of the intervention, on well-being or more distal
measures, may be. After all, previous research suggests that such effects are likely to be short-lived –
‘happiness’ provides an immediate reward for behaviour that has long-term benefits, and research on
the ‘hedonic treadmill’ suggests that people might have a ‘set point’ that they return to whatever
happens to them, good or bad (Ryan & Deci, 2001). If the function of altruistic behaviour is to help
families, make new friends, improve communities, increase status, or find a mate, then it would be
instructive for future experiments to measure these hypothesised longterm benefits. Do people
allocated to the kindness condition report better relations with their families? More identification with
their communities? More friends? More recognition and honours? More pride in one's achievements
(Sznycer et al., 2017)? More sexual partners (Arnocky, Piché, Albert, Ouellette, & Barclay, 2016)? More
committed relationships (Kogan et al., 2010)? More resilient marriages? If so, then future research might
be able to finally connect the two types of happiness – short-term hedonic pleasure, and long-term
eudaemonic components of the good life – that have hitherto remained apart.
7. Conclusion
Helping others makes you happy, but the effect is relatively modest. Further empirical work
testing the implications of more specific theories of social, cooperative and altruistic behaviour is
needed to determine whether the effect might be larger for some types of helpers, when helping some
types of recipients. This research will advance our understanding of the causes and consequences of
kindness, and help practitioners to maximise the effectiveness of kindness interventions.
