# A Model for Understanding and Reducing Developer Burnout

Bianca Trinkenreich1, Klaas-Jan Stol2, Igor Steinmacher3, Marco A. Gerosa3,
Anita Sarma4, Marcelo Lara1, Michael Feathers1, Nicholas Ross1, Kevin Bishop1

1Globant, United States<EMAIL_ADDRESS>2Lero and University
College Cork, Ireland<EMAIL_ADDRESS>3Northern Arizona Uniersity, United
States, {igor.steinmacher<EMAIL_ADDRESS>4Oregon State University,
United States<EMAIL_ADDRESS>
###### Abstract

Job burnout is a type of work-related stress associated with a state of
physical or emotional exhaustion that also involves a sense of reduced
accomplishment and loss of personal identity. Burnt out can affect one’s
physical and mental health and has become a leading industry concern and can
result in high workforce turnover. Through an empirical study at Globant, a
large multi-national company, we created a theoretical model to evaluate the
complex interplay among organizational culture, work satisfaction, and team
climate, and how they impact developer burnout. We conducted a survey of
developers in software delivery teams (n=3,281) to test our model and analyzed
the data using structural equation modeling, moderation, and multi-group
analysis. Our results show that Organizational Culture, Climate for Learning,
Sense of Belonging, and Inclusiveness are positively associated with Work
Satisfaction, which in turn is associated with Reduced Burnout. Our model
generated through a large-scale survey can guide organizations in how to
reduce workforce burnout by creating a climate for learning, inclusiveness in
teams, and a generative organizational culture where new ideas are welcome,
information is actively sought and bad news can be shared without fear.

###### Index Terms:

job burnout, work satisfaction, culture, belonging, inclusiveness

##  I Introduction

Developers’ well-being and work satisfaction have a strong influence on
workforce retention [1, 2]. When organizations invest in the health and safety
of its workforce, it is linked to organizational commitment among employees
[3] and results in returns that is 2x the amount invested [4, 5]. On the other
hand, employee attrition has significant costs, including disruption of
ongoing working in a team as well as costs involved in recruiting and training
a new team member. This is particularly important for the software industry
where ‘job-hopping’ is quite normal, with many developers changing jobs every
few years [6]. Developer retention is, therefore, a key to the long-term
success of software organizations.

Prior research in other fields suggests that burnout is an important factor in
employees’ intention to leave their job [7]. Burnout refers to an individual’s
experiences of exhaustion on physical, emotional, and cognitive levels [8].
Freudenberger was among the first to explore this concept, invoking a
dictionary definition as “to fail, wear out, or become exhausted by making
excessive demands on energy, strength, or resources” [9]. While there has been
considerable attention in the software engineering literature for themes such
as job satisfaction [10, 11, 12], there is a surprising paucity of research on
burnout. Job burnout has become increasingly relevant in today’s discussions
on retaining talent. The COVID-19 pandemic caused a major shift in working
patterns for knowledge workers starting in March 2020. Many developers felt
overwhelmed working from home while also needing to take care of family and
children. Others missed human contact with colleagues and support structures
available in the office. As the pandemic has started to wind down (at the time
of writing), scholars have coined the term “Great Resignation” to refer to
initial observations that many workers across a variety of domains are
voluntarily resigning from their job; one explanation is that the pandemic has
triggered people to rethink their goals and ambitions in life [13]. As in
other fields [14], burnout is also likely playing a role in IT staff’s
decisions to leave an organization.

It is important, therefore, to understand what causes burnout and factors that
can mitigate it. Following prior studies [15, 16], we look at how
organizational culture relates to burnout. In particular, we unpack this
relationship by investigating a number of salient themes that have attracted
interest in recent years, including employees’ sense of belonging and work
satisfaction.

Our goal is to identify the organizational and cultural antecedents that can
reduce burnout. To achieve our goal, we defined the following research
questions:
RQ1. How are organizational culture and burnout related?

RQ2. Does the relationship between organizational culture and burnout vary by
gender and leadership position?

We answer these questions within the context of software delivery teams in
Globant, a large company employing 25,000 people, and a global presence in 36
cities in 17 countries across five continents, which provides services in
digital transformation and assisting IT organizations in automation. Globant
invests in continuous training of its talent pool on technical and social
skills and has several initiatives in place to retain talent and avoid
attrition. Globant places the well-being of its employees at the forefront,
investing in research to identify and proactively implement strategies to
reduce employee burnout and attrition.

To answer our research questions, we developed a theoretical model of factors
associated with burnout grounded in prior literature and tested it using
structural equation modeling, moderation, and multi-group analysis. We tested
the model with data collected via an online questionnaire (n=3,281) for
current members of software delivery teams who work on different projects at
Globant. Fig. 1 summarizes the study design.

Figure 1: Methodological approach

Our results show that Organizational Culture, Climate for Learning, Sense of
Belonging, and Inclusiveness are positively associated with Work Satisfaction,
which in turn is associated with Reduced Burnout. A Climate for Learning
improves Work Satisfaction for employees who do not hold leadership positions
as compared to leadership. Team inclusiveness is positively associated with
work satisfaction and has a bigger impact on women. Women are 2x more
satisfied and less burnt out when their team is inclusive. National culture
also plays a role between work satisfaction and burnout. Living in a masculine
(and more competitive) culture further helps reduce burnout when men have work
satisfaction; national culture does not play a role for women. An
understanding of how these factors interplay can help organizations create a
welcoming environment that improves developers’ well-being and reduces
workforce attrition.

##  II Background and Hypothesis Development

We review prior work related to this study and develop a theoretical model
that reflects the interests of Globant managers.

###  II-A The Role of Organizational Culture in Sense of Belonging, Climate
for Learning, and Inclusiveness

An organization’s culture affects people’s daily work activities.
Organizational culture has been shown to influence software delivery
performance [17, 15], staff well-being, and retention [16], while also
enticing software developers to support the company’s business [18]. Westrum
developed a typology of organizational cultures based on human factors in
system safety, particularly in the context of accidents in technological
domains, such as aviation and healthcare [19]. The typology defines three
types of organizations in terms of information flow and psychological safety.
Pathological organizations exhibit low levels of cooperation across groups and
a culture of blame. Bureaucratic cultures emphasize rules and positions and
compartmentalize responsibilities by departments. Generative organizations are
performance-oriented, with good information flow, high levels of cooperation
and trust, and bridging between teams. The generative level can be achieved by
creating cross-functional teams to improve cooperation, holding blameless
postmortems, sharing risks and responsibilities, breaking down silos, and
encouraging bridging, experimentation, and novelty. An organizational culture
where members of the team cooperate with each other and share responsibilities
[19] creates feelings of membership or being part of a team [20]. This
organizational culture presents the organization as an extended family,
leading employees to develop a strong sense of belonging to the organization
[21]. Hence, we posit:

Hypothesis 1 (H1). A Generative Organizational Culture has a positive
association with a Sense of Belonging.

An organization that exhibits a climate for learning makes resources available
for continued education and offers continuous encouragement to teams to learn
by providing them space and time to acquire new knowledge and explore ideas
[17]. Organizational culture fosters the process of learning [22]. When
holding blameless retrospectives and having out-of-box thinking, a generative
organizational culture [23] creates a positive Climate for Learning [24] as
instead of punishing, the team is trained to learn from failures. Thus, our
second hypothesis is:

Hypothesis 2 (H2). A Generative Organizational Culture exhibits a Climate for
Learning.

When welcoming new ideas, a generative culture brings a positive tone to a
welcoming space and a spirit of friendliness that leads to feelings of
inclusiveness among the members of a team [20]. When engaging in
organizational culture, team members perceive an inclusive climate that leads
to increased work satisfaction [25]. This leads to our third hypothesis:

Hypothesis 3 (H3). A Generative Organizational Culture exhibits Inclusiveness.

###  II-B The Role of Sense of Belonging, Climate for Learning and
Inclusiveness in Work Satisfaction

The need to belong is a powerful, fundamental, and pervasive force that has
multiple strong effects on emotional patterns and cognitive processes, across
all cultures and different types of people [26]. Maslow [27] positioned
‘belonging’ as a basic human need, and Hagerty et al. [28] posited that a
Sense of Belonging represents a unique mental health concept. A sense of
belonging is key to work satisfaction [29], and productivity [26], and can
help to avoid attrition [30]. References to the importance of a sense of
belonging are found throughout the psychological, health care, and education
literature. On the other hand, a lack of a sense of belonging is linked to a
variety of ill effects on health, adjustment, and well-being [26]. Hence, we
propose our fourth hypothesis:

Hypothesis 4 (H4). Sense of belonging has a positive association with Work
Satisfaction.

Prior research on software delivery teams has shown that learning is
associated with Work Satisfaction for software delivery teams, as learning is
a valuable investment into the project’s future and also into the employee’s
own career [31]. Moreover, satisfying an employee’s need for growth requires
that the employee is satisfied with the opportunities to learn and advance at
work [21]. Thus we propose:

Hypothesis 5 (H5). Climate for Learning has a positive association with Work
Satisfaction.

When feeling included by the team, employees believe they are valued for their
unique personal characteristics and recognized as important members of the
organization [25]. A perception of being socially included improves an
individual’s well-being [32] and enhances their self-esteem and work
satisfaction [33]. So, our sixth hypothesis is:

Hypothesis 6 (H6). Inclusiveness has a positive association with Work
Satisfaction.

###  II-C The Role of Work Satisfaction in Reducing Burnout

A decline in work satisfaction could signal burnout [34]. Indeed, previous
research showed that Burnout has an inverse relationship with Work
Satisfaction [35, 36]. Thus, we propose:

Hypothesis 7 (H7). Work Satisfaction has a reverse association with Burnout.

###  II-D The Moderating Role of National Cultural Values on the association
between Work Satisfaction and Burnout

An individual’s response to stress is embedded within cultural beliefs.
Cultural values are being accredited with a prominent role in various work-
related predictor-outcome relationships, such as satisfaction, burnout [37],
and turnover [38]. Globant has geographically distributed teams and needs to
mitigate the social-derived challenges that are inherent in cultural
differences. There are various classifications attempting to quantify cultural
values such as the work by Hofstede [39], Schwartz [40], and the GLOBE study
[41]. In this study, we adopt Hofstede’s classification, which was previously
used to analyze the culture of software engineers [42] and to investigate
burnout [37]. Hofstede [39] defined the Hofstede’s 6-D framework with the
following six dimensions of culture per country that assume values from zero
to one hundred [43]:

Power Distance refers to authority and hierarchy and expresses the degree to
which less powerful members of a society accept and expect that power is
distributed unequally. High power distance means an acceptance of hierarchical
order in which people have a determined place. Low power distance means a
desire for an egalitarian distribution of power [43, 44]. In high power
distance cultures, social hierarchy is established and executed clearly and
without reason [45]. Hierarchy in an organization is seen as reflecting
inherent inequalities, centralization is popular, subordinates expect to be
told what to do, and the ideal boss is a benevolent autocrat [43, 44]. In
Hofstede’s classification [43], Mexico and India are examples of hierarchical
societies with high Power Distance.

Individualism represents the degree to which people in a society are
integrated into groups. High individualism indicates people who take care of
only themselves and their immediate families and should not rely (too much) on
authorities for support. In contrast, low individualism (collectivism)
reflects a closer integration into cohesive in-groups in which people protect
each other with unquestioning loyalty [43, 44]. Collectivists mostly pursue
group goals and improve group level engagement [46]. In Hofstede’s
classification [43], the United States is an example of a society with high
Individualism.

Masculinity is defined as a preference for achievement, heroism,
assertiveness, and material rewards for success. While high masculinity
societies are materialist and competitive, low masculinity culture
(femininity) is more cooperative, consensus-oriented, caring for the weak, and
prevailing the life quality [43, 44]. Japan is an example of high degree of
masculinity in Hofstede’s classification [43].

Uncertainty Avoidance expresses the degree to which people keep away from
ambiguity. Cultures high in uncertainty avoidance tend to focus on rules,
structured activities, employee security, and stability. Low levels of
uncertainty avoidance have a more relaxed attitude in which practice is more
important than rules [43, 44]. In Hofstede’s classification [43], Uruguay is
an example of a score in this dimension while China is the opposite.

Long Term Orientation measures the degree a culture will keep some links with
its own past while dealing with the challenges of the present and the future.
A high degree in this index indicates more pragmatic people who have
perseverance and patience to prepare for the future. On the contrary, a lower
value on this index (indicating short-term orientation) indicates that people
have a more narrow-minded focus and sensitivity to immediate outcomes of their
actions, tending to value steadfastness, and considering a societal change
with suspicion [43, 44]. People in such societies have a strong concern with
establishing the absolute truth; they are normative in their thinking, exhibit
great respect for traditions, a relatively small propensity to save for the
future, and a focus on achieving quick results. Argentina, for example, has a
high score in this dimension in Hofstede’s classification [43].

Indulgence is related to the degree of freedom the societal norms give
citizens to fulfill human desires. A high degree indicates a society that
relatively allows free gratification of basic and natural human desires
related to hedonism. Conversely, low levels of indulgence (restraint) indicate
a society that controls gratification of needs and regulates it using strict
social norms [43, 44].

Studies from two decades ago showed that Long Term Orientation [47] and Power
distance [48] could help foster organizational well-being. Subordinates who
are surrounded by a high power distance cultural value evaluate abusive
supervision as irrelevant to their well-being [48]. Individualism could reduce
well-being [49], as unpleasant life events are not met with sufficient social
support. In workplace contexts, managers face increasingly complex and subtle
differences among employees that reflect cultural influences from the
country’s culture. Thus, we argue that country culture moderates the link
between Work Satisfaction and Burnout. Conceptually, we model this as a single
moderator (see Fig. 2), but we propose that each of Hofstede’s six dimensions
has such a moderating role. Thus, we propose:

Hypothesis 8 (H8). (a) Power distance, (b) individualism, (c) masculinity, (d)
uncertainty avoidance, (e) long-term orientation, and (f) indulgence moderate
the effect of Work Satisfaction on Burnout.

Fig. 2 presents the theoretical model.

Figure 2: Theoretical model

##  III Research Design

Management at Globant was keen to understand the relationships that we
proposed in Fig. 2 and obtain an answer to RQ1, which develops an
understanding of how organizational culture and burnout in software delivery
teams are related. To evaluate the model, we conducted a survey among software
delivery team members at Global and analyzed the data using Partial Least
Squares (PLS) Structural Equation Modeling (SEM) [50]. SEM facilitates the
simultaneous analysis of relationships among constructs, each measured by one
or more indicator variables. Then, to answer RQ2, we used a Multi-Group
Analysis (MGA) to establish whether these relationships vary by gender and
leadership position.

A recent survey of the use of PLS in software engineering (which also provides
an introduction to PLS) revealed that PLS-SEM has been used to study a variety
of phenomena in software engineering [51]. For example, it has previously been
used to study job satisfaction and turnover intentions of software engineering
teams [11] and the success factors of a large-scale Agile Software
transformation process [51].

In this section, we discuss the measurement model (how each theoretical
construct was measured) and the data collection and analysis.

###  III-A Measurement model

The theoretical model comprising the hypotheses is based on a number of
theoretical concepts; some of the concepts cannot be directly observed (e.g.,
Climate for Learning, Organizational Culture, and Work Satisfaction)—these
concepts are represented as latent variables. A latent variable cannot be
directly measured or observed, but instead is measured through a set of
indicators or manifest variables. For the latent variables in this study, we
adapted existing measurement instruments when possible. We define the
constructs below and list the complete questionnaire in the replication
package [52].

Organization Culture was measured as a latent construct represented by six
five-point Likert questions. The questions were adapted from the Westrum
Culture [19], which has previously been used as an instrument to measure
organizational culture in software delivery teams [53, 15].

Belonging was measured using a five-point Likert question to assess aspects of
membership, as being part of the team.

Climate for Learning was measured as a latent construct represented by two
five-point Likert questions. The questions were inspired by DORA Research
Program [17] and designed to evaluate if members of the team perceive that the
team considers learning as an investment rather than a cost, and essential for
continued progress.

Inclusiveness was measured through one question to assess if the team has a
safe space for diversity in which everyone is welcomed and treated equally and
fairly.

Work Satisfaction was measured as a latent construct composed of two Employee
Net Promoter 10-scale Score (eNPS) questions [54, 55] and one five-point
Likert question about enjoyment. The eNPS questions were towards the team and
towards the company, which relies on asking the respondent the willingness to
recommend the team and the company to friends and colleagues.

Burnout was measured as a latent construct composed of two five-point Likert
items. While instruments exist to measure burnout, these are very long, which
would result in an overly long survey instrument. We took a pragmatic approach
and focused on two statements: (1) the extent to which a team has a manageable
workload with sustainable levels of stress, and burnout is not perceived as a
significant problem or risk; and (2) the extent to which tasks are assigned in
a way that allows enough time to achieve commitments, and team members are
able to focus on one process at a time. Both were measured as ‘reversed’
items, i.e., a strong disagreement indicated a higher level of burnout.

National Culture: Based on the respondents’ country of residence, we used
Hofstede’s classification of National Culture as moderators (Sec. II-D). This
classification’s six-dimensional approach to cultural variation includes power
distance, individualism/collectivism, masculinity/femininity, uncertainty
avoidance, long-term/short-term orientation, and indulgence [39].

###  III-B Data Collection and Analysis

We administered an online questionnaire using Globant
Glow,111https://os.starmeup.com/en.html which was answered by members of
software delivery teams at Globant.

The survey was sent to respondents by email using a corporate address. The
leader of each team encouraged team members to fill the questionnaire out
during regular meetings. We received 10,566 responses; however, our analysis
techniques require complete responses, and we removed 7,285 responses that
contained blanks. Our final sample size has 3,281 responses. Table I presents
a summary of the respondents’ characteristics.

TABLE I: Demographics of respondents (n=3,281) Attribute | N | Percentage
---|---|---
Gender
Men | $2,487$ |  $74.8$%
Women | $794$ |  $25.2$%
Country of Residence
Colombia | $789$ |  $24.0$%
Argentina | $721$ |  $22.0$%
India | $581$ |  $17.7$%
Mexico | $515$ |  $15.7$%
Uruguay | $211$ |  $6.4$%
Chile | $197$ |  $6.0$%
Peru | $128$ |  $3.9$%
USA | $55$ |  $1.7$%
Brazil | $53$ |  $1.6$%
Spain | $15$ |  $0.5$%
Belarus | $9$ |  $0.3$%
Others | $7$ |  $0.3$%
Roles
Leadership Positions
Project Manager | $248$ |  $7.6$%
Tech Manager | $34$ |  $1.0$%
Product Manager | $12$ |  $0.4$%
Other leadership roles | $5$ |  $0.2$%
Non-Leadership Positions
Developers | $1,723$ |  $52.5$%
Test/Quality Assurance | $656$ |  $20.0$%
Business Analyst/Intelligence | $158$ |  $4.8$%
ERP Tech/Functional | $98$ |  $3.0$%
DevOps Engineer | $71$ |  $2.2$%
Designer/Artist | $65$ |  $2.0$%
Data Architect/Scientist | $55$ |  $1.7$%
SysAdmin/Cloud Engineer | $45$ |  $1.3$%
Other non-leader roles | $111$ |  $3.3$%
Starting year at the company
Between 2021 and 2022 | $1,460$ |  $44.5$%
Between 2019 and 2020 | $1,081$ |  $32.9$%
Between 2018 and 2017 | $422$ |  $12.9$%
Between 2016 and 2015 | $181$ |  $5.5$%
Between 2014 and 2015 | $137$ |  $4.2$%

We used SmartPLS version 4 for the analyses; SmartPLS is a proprietary package
for analyzing PLS models. The analysis comprised three steps, with tests and
procedures in each step. The first step was to evaluate the measurement model
(Sec. IV), which empirically assesses the relationships between the constructs
and indicators. The second step was to evaluate the theoretical model that
represents the set of hypotheses (Sec. V-A). The third step was to evaluate
observed heterogeneity by multi-group analysis of gender and leadership
position (Sec. V-B).

PLS does not make assumptions about the distribution (such as a Normal
distribution) of the data; without knowing the distribution of the data,
parametric tests (which are based on a distribution with certain parameters)
cannot be used to establish the standard error and thus significance. Instead,
PLS packages employ a ‘bootstrapping’ procedure: it draws a large number (e.g.
5,000) of random ‘subsamples’ of the same size as the original sample (using
replacement). The model is estimated for each subsample, generating a sampling
distribution, which is used to determine a standard error [56], which can
subsequently be used to make statistical inferences. The mean path coefficient
determined by bootstrapping can differ slightly from the path coefficient
calculated directly from the sample.

The online appendix provides results of a variety of additional analyses and
validity checks [52].

##  IV Measurement Validity and Model Fit

###  IV-A Measurement Validity

As a first step, we conducted two recommended tests to ensure that a dataset
is suitable for factor analysis, i.e., that the variables in a dataset can be
reduced to a smaller number of factors [57, 58]. The first test is Bartlett’s
test of sphericity [57] on all constructs. We found a p-value < .01 (p-values
less than .05 indicate that factor analysis may be suitable). Second, we
calculated the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy. Our
result (.92) is well above the recommended threshold of .60 [58].

Afterward, we conducted several tests to validate the measurement of our
theoretical concepts, including convergent validity, internal consistency
reliability, discriminant validity, and collinearity, as discussed next.

####  IV-A1 Convergent Validity

First, we assess the convergent validity of the measurement instrument, i.e.,
we assess whether the questions (indicators) that represent each latent
variable are understood by the respondents in the same way as they were
intended by the designers of the questions [59]. This assessment relates to
the degree to which a measure correlates positively with alternative measures
of the same construct. Our model contains four latent variables (Climate for
Learning, Organizational Culture, Work Satisfaction, and Burnout). Changes in
the theoretical, latent construct should be ‘reflected’ in changes in the
indicator variables [56]; for example, if Work Satisfaction increases, a
concept we cannot measure or observe directly, we expect to see this change
reflected in the values of its indicators that we can measure or observe
directly.

We used two metrics to assess convergent validity: the Average Variance
Extracted (AVE) and the loading of an indicator onto its construct (the outer
loading). The AVE is the proportion of variance that is shared across
indicators. The AVE should be at least 50%, indicating that it explains most
part the variation in its indicators [56]. All AVE values for the three latent
constructs in our model are above this threshold of 50% (see appendix).

A latent variable is measured by two or more indicators; each indicator is
expected to have a loading of at least 50%, because the square of the loading
indicates the variance in the indicator that is explained, which should be at
least 50% (and 70%2 $\approx$ 50%) [56]. All loadings of the indicators of all
four latent constructs exceeded this as shown in Figure 3, which we considered
acceptable.

Figure 3: Path coefficients (p $<$ 0.05 indicated by a full line). Latent
constructs are represented as circles.

####  IV-A2 Internal Consistency Reliability

Second, we verified how well the different indicators are consistent with one
another and able to reliably and consistently measure the constructs. A high
degree of consistency means that indicators refer to the same construct. There
are several tests to measure internal consistency reliability. We performed
both the Cronbach’s and Composite Reliability tests; Cronbach’s frequently
shows lower values, whereas the Composite Reliability (CR) is a more liberal
test, which sometimes overestimates the values [56]. A desirable range of
values for both Cronbach’s and CR is between .7 and .9 [56]. Values below .6
suggest a lack of internal consistency reliability, whereas values over .95
suggest that indicators are too similar and thus are not desirable. All
Cronbach and CR values fell between .7 and .9 (see appendix).

####  IV-A3 Discriminant Validity

Third, we verified whether each construct represents characteristics that are
not measured by other constructs, i.e., we assessed the discriminant validity
of the constructs. A primary means to assess discriminant validity is to
investigate the Heterotrait-monotrait (HTMT) ratio of correlations [60]. The
discriminant validity could be considered problematic if the HTMT ratio
exceeds .9 [60]; some scholars recommend a more conservative cut-off of .85
[56]. The HTMT ratio between the four latent constructs ranged between .65 and
.71 (see appendix).

####  IV-A4 Assessing Collinearity

To ensure that the variables are independent, we calculate their collinearity
by means of the Variance Inflation Factor (VIF). In our model all VIF values
are below 1.7, well below the cut-off of 5 [56] (see appendix).

###  IV-B Model Fit

The overall model was measured through a standardized root mean square
residual (SRMR) composite factor model, which should be lower than .08 [61].
Thus, the values obtained for the complete model (.060), the men’s model
(.061), the women’s model (.061), the leaders’ model (.070), and the non-
leaders model (.60) have a good fit.

##  V Results

To answer RQ1, we evaluated the hypotheses in the structural model (Sec. V-A)
and, to answer RQ2, we performed a Multi-Group Analysis (Sec. V-B).

###  V-A RQ1. How are organizational culture and burnout in software delivery
teams related?

Table II shows the results for our hypotheses, including the mean of the
bootstrap distribution (B), the standard deviation (SD), the 95% confidence
interval, and the p-values.

Path coefficients (B) are interpreted as follows in this example for H1:
having B=.48 means that a unit-change of Organizational Culture’s standard
deviation triggers a direct change in Belonging of .48 $\times$ Belonging’s
standard deviation.

TABLE II: Standarized path coefficients, standard deviations and confidence intervals. coefficients with * are lower than .05 | B | SD |  95% CI
---|---|---|---
H1 Organizational Culture$\rightarrow$Belonging | $.48*$ | $.02$ |  (.44, .51)
H2 Organizational Culture$\rightarrow$Climate for Learning | $.54*$ | $.02$ |  (.51, .57)
H3 Organizational Culture$\rightarrow$Inclusiveness | $.45*$ | $.02$ |  (.41, .49)
H4 Belonging$\rightarrow$Work Satisfaction | $.24*$ | $.02$ |  (.20, .27)
H5 Climate for Learning$\rightarrow$Work Satisfaction | $.22*$ | $.02$ |  (.18, .25)
H6 Inclusiveness$\rightarrow$Work Satisfaction | $.12*$ | $.02$ |  (.08, .16)
H7 Work Satisfaction$\rightarrow$Burnout | $-.29*$ | $.02$ |  ($-.25$, $-.33$)
Moderators |  |  |
H8.a Power Distance × Work Satisfaction$\rightarrow$Burnout | $.08$ | $.05$ |  (.19, .00)
H8.b Individualism × Work Satisfaction$\rightarrow$Burnout | $.05$ | $.04$ |  (.13, .03)
H8.c Masculinity × Work Satisfaction$\rightarrow$Burnout | $-.02$ | $.05$ |  (.07, $-.12$)
H8.d Uncertainty Avoidance × Work Satisfaction$\rightarrow$Burnout | $-.01$ | $.05$ |  (.08, $-.01$)
H8.e Long Term Orientation × Work Satisfaction$\rightarrow$Burnout | $-.09$ | $.08$ |  (.05, $-.27$)
H8.f Indulgence × Work Satisfaction$\rightarrow$Burnout | $-.02$ | $.05$ |  (.08, $-.13$)

Results revealed that a Generative Organizational Culture has a positive
significant association with Sense of Belonging (H1, B$=$.48), Climate for
Learning (H2, B$=$.54), and Inclusiveness (H3, B$=$.45). We also found that a
Sense of Belonging to the team (H4, B$=$.24), Climate for Learning (H5,
B$=$.22), and Inclusiveness (H6, B$=$.12) have a positive and significant
association with Work Satisfaction, which includes feelings of joy and
enthusiasm to recommend the team and company as a place to work to friends
former colleagues. Finally, Work Satisfaction has a reverse (negative) and
significant association with Burnout (H7, B=$-0.29$). Hence, hypotheses H1 to
H7 were supported with p-values $<$ 0.001.

We also investigated whether the association between Work Satisfaction and
Burnout would change when considering respondents’ national culture, as
defined by Hofstede’s six dimensions (see Sec. II-D) (H8). This association is
not significantly affected (neither increased nor reduced) by any of these six
cultural dimensions. Power Distance (H8.a) and Individualism (H8.b) have a
positive, but insignificant moderation effect in the association between Work
Satisfaction and Burnout. Masculinity (H8.c), Uncertainty Avoidance (H8.d),
Long Term Orientation (H8.e), and Indulgence (H8.f) have a negative, but
insignificant moderation effect on the association between Work Satisfaction
and Burnout. Hence, H8.(a-f) are not supported for the complete dataset.

We assessed the relationship between constructs and the predictive
capabilities of the model. The R2 ranges from 0 to 1, with higher values
indicating a greater explanatory power. R2 values of .75, .50, and .25 are
considered substantial, moderate, and weak, respectively [56]. However, such
thresholds are rather arbitrary and generic, and do not consider the specific
context or research area; in some fields, an R2 value as low as .10 is
considered satisfactory [62]. The R2 values of the five endogenous variables
in our model (Belonging, Climate for Learning, Inclusiveness, Work
Satisfaction, and Burnout) are shown in Table III are acceptable ranging
between .20 and .39 (column ‘All’; further variation in range emerged after
the multi-group analysis, discussed later).

We also assessed the predictive relevance of the model, using the Stone-
Geisser Q2 measure. For this, we used the PLSPredict algorithm that is
available in the SmartPLS v. 4 package [63, 64]. Values larger than 0 indicate
the construct has predictive relevance, while negative values (smaller than
zero) indicate that the model does not perform better than the simple average
of the endogenous variable would do. The values were all positive, indicating
the construct has predictive relevance [56].

###  V-B RQ2: Does the relationship between organizational culture and burnout
vary by gender and leadership position?

RQ2 seeks to establish whether the theorized relationship between
organizational culture and burnout (as investigated for RQ1), varies when we
consider gender and leadership position. Are some of the hypothesized links
stronger for men than for women, or vice versa? Or are these associations
different for people in leadership positions vs. people not in leadership
positions? To answer this, we used multi-group analyses splitting by gender
and leadership position and exploring differences that can be traced back to
observable characteristics and may not be evident when examined as a whole.
The multi-group analysis involves running the PLS path model multiple times
for different groups, once for each group; groups are captured through
categorical variables (in this case, binary variables). Hair et al. [61]
proposed three steps to conduct such an analysis: (1) group creation; (2)
invariance test; and (3) result analysis.

####  V-B1 Step 1. Groups Creation

We grouped our participants to observe heterogeneity according to two
variables: gender (male = 0 and female = 1) and leadership (leadership role =
1 and non-leadership role = 0). We used pre-existing demographic data the
company maintains for its reporting requirements under government laws to
split the participants into different groups.

####  V-B2 Step 2. Evaluation of measurement invariance of composite models
(MICOM)

Measurement invariance is a mechanism to assess whether or not the loadings of
the items that represent the latent variables differ significantly across
different groups. In other words, we want to assess whether the differences
can be attributed to the theoretical constructs and not to how we measured
those constructs [61]. Comparing group-specific model relationships for
significant differences using a multi-group analysis requires establishing
configural and compositional invariance [65, 61]. Configural invariance does
not include a test and is a qualitative assessment of making sure that all of
the composites are equally defined for all of the groups such as equivalent
indicators per measurement model, equivalent treatment of the data, and
equivalent algorithm settings or optimization criteria. The configural
invariance is established in our model. Following that, compositional
invariance exists when the composite scores are the same across both groups,
and is statistically tested to assess whether the composite scores differ
significantly across the groups. For this purpose, the MICOM procedure
examines the correlation between the composite scores of both groups and
requires that the correlation equals 1. We ran the permutation test in
SmartPLS and verified that compositional invariance is established for all
latent variables in the PLS path model. We established partial measurement
invariance and thus multi-group analysis is suitable [66].

####  V-B3 Step 3. Groups Comparison and Analysis

Path coefficients generated from different samples are usually numerically
different, but the question is whether the differences are statistically
significant. We analyzed the differences between the coefficients’ paths for
the groups. If they are significant, they can be interpreted as having
moderating effects.

Gender: As Table III shows, Generative Organizational Culture has a strong and
significant relationship with Sense of Belonging and Climate for Learning for
both men and women. However, although Organizational Culture is also
associated with Inclusiveness for both men and women, the association is
stronger for women ( = .53) than for men ( = .41). Sense of Belonging and
Climate for Learning have a significant and similar relationship with Work
Satisfaction for both genders. Although both genders are satisfied by
Inclusiveness, women ( = .20) are two times more satisfied when the team is
Inclusive compared to men ( = .10). Lastly, the link between Work Satisfaction
and Burnout is the same for men and women. However, men (but not women) who
live in a competitive national culture, where people want to be the best
(i.e., high degree of Masculinity), have even less burnout.

TABLE III: Multi-Group Analysis: coefficients marked with $*$ are statistically significant; coefficients set in boldface indicate that the difference between groups (i.e. male vs. female, and non-leadership vs. leadership roles) is statistically significant | Multi-Group Analysis |  |
---|---|---|---
| Gender |  | Leadership |  |
| Male | Female |  | Non-leaders | Leaders |  | All
Sample size (N) | $2,487$ | $79$ |  | $2,982$ | $299$ |  | $3,281$
Belonging (R2) | $.23$ | $.24$ |  | $.22$ | $.25$ |  | $.23$
Climate for Learning (R2) | $.28$ | $.32$ |  | $.29$ | $.31$ |  | $.29$
Inclusiveness (R2) | $.17$ | $.28$ |  | $.20$ | $.25$ |  | $.20$
Work Satisfaction (R2) | $.38$ | $.41$ |  | $.39$ | $.35$ |  | $.39$
Burnout (R2) | $.31$ | $.37$ |  | $.34$ | $.36$ |  | $.33$
H1 Organizational Culture $\rightarrow$ Belonging | $.48*$ | $.49*$ |  | $.47*$ | $.50*$ |  | $.48*$
H2 Organizational Culture $\rightarrow$ Climate for Learning | $.53*$ | $.57*$ |  | $.54*$ | $.56*$ |  | $.54*$
H3 Organizational Culture $\rightarrow$ Inclusiveness | $.41*$ | $.53*$ |  | $.44*$ | $.46*$ |  | $.45*$
H4 Belonging $\rightarrow$ Work Satisfaction | $.25*$ | $.21*$ |  | $.24*$ | $.20*$ |  | $.24*$
H5 Climate for Learning $\rightarrow$ Work Satisfaction | $.22*$ | $.21*$ |  | $.22*$ | $.08$ |  | $.22*$
H6 Inclusiveness$\rightarrow$Work Satisfaction | $.10*$ | $.20*$ |  | $.13*$ | $.01*$ |  | $.12*$
H7 Work Satisfaction$\rightarrow$Burnout | $-.29*$ | $-.29*$ |  | $-.29*$ | $-.20*$ |  | $-.29*$
Moderators |  |  |  |  |  |  |
H8.a Power Distance × Work Satisfaction$\rightarrow$ Burnout | $.01$ | $.01$ |  | $.11$ | $.07$ |  | $.08$
H8.b Individualism × Work Satisfaction$\rightarrow$ Burnout | $.08$ | $-.01$ |  | $.16$ | $.03$ |  | $.05$
H8.c Masculinity × Work Satisfaction $\rightarrow$ Burnout | $-.10*$ | $.13$ |  | $-.17$ | $.01$ |  | $-.02$
H8.d Uncertainty Avoidance × Work Satisfaction $\rightarrow$ Burnout | $-.07$ | $.18$ |  | $-.01$ | $.01$ |  | $-.01$
H8.e Long Term Orientation× Work Satisfaction$\rightarrow$ Burnout | $-.13$ | $-.04$ |  | $-.10$ | $-.06$ |  | $-.09$
H8.f Indulgence × Work Satisfaction $\rightarrow$ Burnout | $.03$ | $-.18$ |  | $.15$ | $-.04$ |  | $-.02$

Leadership Position: As Table III shows, Climate for Learning has a strong and
significant relationship with Work Satisfaction for those who are not in
leadership positions. However, Climate for Learning is not associated with
Work Satisfaction for leaders. Lastly, leaders ( = .41) are close to two times
more satisfied by a Generative Organizational Culture when compared to those
who are not in leadership positions ( = .24).

##  VI Discussion

Human factors are receiving increasingly more attention in software
engineering research and industry. Themes such as work satisfaction have been
studied extensively and have been linked to employees’ intention to stay with
(or leave, when it is lacking) an organization. This has direct effects on
organizations’ capacity to deliver services and software products.

The past several years have seen dramatic changes in the way people work,
driven in large part by the Covid-19 pandemic and the resulting lockdowns,
forcing people to work from home. There are numerous studies on how this has
affected people in negative ways (e.g. [67]). One important theme in this
context is Burnout, which is the result of continuous exposure to unhealthy
levels of stress. However, there is a paucity of research in software
engineering on this topic. Globant, a major provider of software services that
has operations across five continents, is reliant on a healthy workforce to
conduct its business. Globant is interested in developing better insights into
the various factors that might play a role in employee burnout. Thus, in this
paper, we report on a large-scale survey at Globant with a primary focus has
been on Burnout and its antecedents.

In particular, we sought to understand the role of organizational culture in
relation to Burnout. Organizational culture has been shown to be an important
factor in the performance of employees and teams, including in software
delivery teams [53]. Our theoretical argument in this paper is that all
employees within an organization are exposed to the same organizational
culture; while they will experience this differently, we believe other factors
play a role in why people might experience burnout. In particular, we looked
at three factors: people’s Sense of Belonging, whether or not an organization
advocates a Climate for Learning, and people experiencing Inclusiveness.
Further, rather than having a direct link to Burnout, we believe that these
factors affect employees’ Work Satisfaction, and this is a major predictor for
people’s experienced Burnout.

We used questions based on an Organizational Culture typology that is focused
on how organizations process information and behave when things are not going
well, bringing together not only culture, but also management style. We
analyzed Organizational Culture as a latent variable that included attributes
about sharing bad news with no fear, considering failures as learning
opportunities, encouraging cross-functional collaboration, welcoming new
ideas, sharing responsibilities, and actively seeking information when needing
it.

We also considered the moderating role of national culture, considering the
six dimensions identified by Hofstede. Finally, we conducted our analysis for
the whole sample and conducted two different multi-group analyses,
distinguishing respondents by gender, and whether or not they are in a
leadership role.

Before we discuss the implications of our results, we discuss a number of
limitations of this study that should be kept in mind while interpreting the
findings.

###  VI-A Threats to Validity

####  VI-A1 Construct Validity

We adopted and tailored existing measurement instruments when possible, and
developed measurement instruments for some constructs based on prior
literature. Our analysis of the measurement model confirmed that our
constructs were internally consistent, and scored well on convergent and
discriminant validity tests. We defined new a construct called Work
Satisfaction that included hedonism and satisfaction towards the team and the
company. We acknowledge the fact that Burnout is a construct that can be
measured by more complex instruments [68]. However, many existing instruments
contain a large number of items (questions), which would be impractical in
organizational settings because this would negatively affect the response
rate. In this study we have used respondents’ country of residence as a proxy
for Power Distance as a dimension of culture as defined by Hofstede [43].
While also used in other studies [69], we acknowledge it is an approximation
and not a perfect measure. One potential issue is that we do not know how long
respondents have lived in their current country of residence. Another
potential issue is that contributors’ original culture that they grew up with
may differ from the culture they now live in. This is why we report the metric
as being surrounded by a specific culture instead of having a specific
culture. Measuring culture in a more precise way is an important avenue for
future work in general.

####  VI-A2 Internal Validity

We propose a series of hypotheses as associations between different constructs
rather than causal relationships, as the present study is a sample study,
rather than an experimental study [70]. Our overall argument is that employees
who perceive their organization to have what Westrum [19] labeled a Generative
Organizational Culture are satisfied and tend to experience levels of burnout;
this line of reasoning is easier to theoretically justify than the suggestion
that Burnout leads to a negative organizational culture (what Westrum referred
to as a Pathological organization) [19]. Further, it is likely that other
factors are at play. The coefficient of determination (R2) of the endogenous
variables ranged between .2 and .4 which in the software engineering context
can be considered reasonable. Thus, these results represent a useful starting
point for future studies.

Respondents are current employees and we did not collect data from past
employees. As the company does not offer the same questionnaire to people who
are leaving, we would not have the same data to compare the perspectives of
current and past employees. The relationship between burnout and intentions to
leave, and also the actual act of leaving are of interest for future work.

####  VI-A3 External Validity

This survey was conducted within Globant. The response rate numbers are
aligned with the overall distribution of the company and therefore can be
generalized across the company. Globant is a multi-national company with more
than 25,000 employees working in different national cultures. The responses
were sufficiently consistent to find full or partial empirical support for the
hypotheses. Additional studies that replicate our findings in other companies
can further bolster our results.

###  VI-B Implications of results

Our analysis highlights several key findings and implications. In the
following, we discuss the supported hypotheses.

H1. Generative Organizational Culture $\rightarrow$ Belonging: Our results
align with previous research that showed a sense of belonging emerges from a
people-centered culture [71] and that an openness to innovation and shared
responsibility helps to develop organizational belonging [72]. When working in
a team that welcomes new ideas, fosters collaboration, and shares
responsibilities [19], people understand how their work contributes toward a
common goal leading to affective commitment to the team. Our results showed
there was no significant difference between the groups for H1, which proposed
a positive association between a Generative Organizational Culture and a Sense
of Belonging, as shown in Table III.

H2. Generative Organizational Culture $\rightarrow$ Climate for Learning: In a
generative organizational culture, people do not fear failure because they are
trained to learn from mistakes [19]; learning is a key point, considered
essential and an investment. An inspiring culture that encourages and enables
employees to bring their best efforts and ideas to the team promotes a Climate
for Learning [71]. We found that a generative organizational culture is
positively associated with Climate for Learning and that there is no
significant difference between different groups (either in terms of gender or
whether or not respondents fulfilled a leadership role) for this association.

H3. Generative Organizational Culture $\rightarrow$ Inclusiveness: Although
the association between Organizational Culture and Inclusiveness is not
significantly different according to the leadership position, it is stronger
for women (B=.53) than for men (B=.41). This difference opens a path to
discuss what brings Inclusiveness for minority and majority groups. A lot has
been researched about providing safe place for diversity so that everyone
feels equally welcomed. However, results shed light that different factors
bring the feeling of being included for minority and majority groups.

Hypotheses 1-3 show the benefits of a generative organizational culture where
employees have the psychological safety to talk about failures and present new
ideas. Therefore, companies should reflect on their team and leadership
culture to promote the ideas of generative organizational culture.

Changing the way people behave and work changes culture. Teams can identify
helpful practices to create a generative culture that fosters information flow
and trust by examining the aspects of Westrum’s model of organizational
culture [19], focusing on those behaviors seen in the generative culture:

  * •

Cooperation and bridging. Break down silos and create cross-functional teams
that include representatives from each functional area of the software
delivery process, so everyone shares the responsibility for the software
delivery life-cycle. Encourage informal meetings between people who do not
understand (or are frustrated by) each other’s work. Ask them to understand
each other why they do what they do–and invite people to come up with new
ideas together.

  * •

Train the messengers and let failure lead to inquiry. People must be able to
take risks and feel safe to fail, and also to bring bad news without fear in
order to make improvements. Hold blameless postmortems, so teams surface
problems as early as possible, and solve them more effectively. Instead of
blaming, ask questions about the root-cause of failures, in order to improve
technical systems, processes, and the organizational culture.

  * •

Share risks and responsibilities. Quality, availability, reliability, and
security should be everyone’s job. One practical example can be ensuring that
developers share responsibility for maintaining their code in production.

  * •

Encourage novelty. Encouraging employees to explore new ideas can lead to
great outcomes. One example of this practice can be giving people time each
week for experimentation, hosting internal hackathons and conferences to share
ideas and collaborate. When releasing employees from habitual pathways and
repetitive tasks, they can be creative, bringing new ideas for processes and
products.

H4. Sense of Belonging $\rightarrow$ Work Satisfaction: A Sense of Belonging
is a human need [26, 73, 74]. Although it has several different antecedents
for people, employees from different groups showed higher levels of Work
Satisfaction when they feel they are part of a team. Our analysis based on
groups showed similar path coefficients for the association between Sense of
Belonging and Work Satisfaction, as we present in Table III. This indicates
that strategies that focus on making employees feel part of the team or the
company, pay off, because this positively influences satisfaction, regardless
of the group to which the developers belong. Therefore, companies could invest
in cohort building, creating opportunities where developers can socialize and
develop an emotional connection. Additionally, belonging can be fostered via a
team culture where individuals’ contributions are appreciated and they can see
how their work fits the team’s overall goals.

H5. Climate for Learning $\rightarrow$ Work Satisfaction: The association
between Climate for Learning and Work Satisfaction is significantly different
between leaders and non-leaders. While those not in leadership positions are
satisfied by having the Climate for Learning, the association was not
significant for leaders. There is also no difference when we group developers
by their gender. Based on these findings, in order to keep employees satisfied
in their work, we recommend that companies offer professional development
opportunities, where employees can learn new technology and management skills
needed to advance their careers.

H6. Inclusiveness $\rightarrow$ Work Satisfaction: The association between
Inclusiveness and Work Satisfaction holds positively across the whole sample.
Additionally, the association showed different strengths when we compared
genders. Women are twice more satisfied (B=.20) when having a welcoming and
safe space for diversity than men (B=.10). Women represent one of the gender
minorities and face prejudice and challenges in the tech workplace. A
welcoming and safe space allows them to thrive. Companies should evaluate the
gender diversity of their tech workforce, expending effort in diversity
recruitment and hiring, as well as in training programs that instill diversity
and inclusion principles in their teams.

H7. Work Satisfaction$\rightarrow$Burnout: Work Satisfaction has a negative
association with Burnout, with no significant difference across groups.Burnout
is the exhaustion caused by excessive and prolonged workplace stress, which
can happen in software delivery teams due to the pressure of deadlines and
high performance. However, we showed that Work Satisfaction represents an
alleviating factor in reducing Burnout, which is aligned with previous
research in software delivery teams [34]. So, although there were different
antecedents to Work Satisfaction when achieved, satisfaction reduces Burnout
across the groups of gender and leadership positions.

H8.c. Masculinity × Work Satisfaction $\rightarrow$ Burnout In a competitive
and materialist culture (high levels of Masculinity), there was a great impact
of work satisfaction in reducing burnout (This moderation had no effect for
women.) According to Hofstede’s framework of national cultures [43], in
Masculine cultures, men “should be” and women “may be” ambitious, work
prevails over family, there is an admiration for the strong, fathers deal with
facts and mothers with feelings, and girls cry and boys do not. In feminine
cultures, fulfilling multiple social roles without social judgment is
encouraged, so both men and women receive cultural support for prioritizing
family time over time spent on the job [43]. Finding differences between
groups is aligned with previous research that showed that people’s well-being
is achieved according to their current specific needs [21]. The result can be
interpreted as masculine cultures drive men to strive for achievement and
success, and when they perceive that they are successful (satisfied with their
work) it reduces the perception of burnout. In feminine societies, men might
feel uncomfortable and burn out more. Another possible interpretation is that
men can take other measures to avoid burnout in masculine cultures, because
the visible expressions of stress behavior may threaten the masculine value of
heroism [75]. Therefore, organizations should consider the national culture
where they operate and structure their incentives and career advancement
opportunities accordingly.

##  VII Conclusion

Attention to human factors is critical to software development employees’
ability to perform. Globant is a large software services organization whose
management sought to understand the concept of Burnout among their workforce.
In this paper, we report on a theoretical model that seeks to explain how
organizational culture and burnout in software delivery teams are associated.
A large-scale survey with over 3,000 respondents provided sufficient data to
test this model, and to distinguish between different subgroups (i.e.,
men/women and people on leadership/non-leadership roles). We argue that, given
the international nature of this study that also considers the role of
national culture (according to the Hofstede 6-D framework), albeit at one
company, these findings are of interest to other large multinational
organizations. Additionally, there are clear extension points of our study, as
well as opportunities to replicate this study, which we think can contribute
to a body of knowledge that considers critical human factors such as Burnout.

## Acknowledgments

We thank all the survey participants for their time and insights. This work is
partially supported by the National Science Foundation under Grant Numbers
1815486, 1815503, 1900903, and 1901031, and Science Foundation Ireland grants
no. 13/RC/2094-P2 to Lero, the SFI Research Centre for Software, and no.
15/SIRG/3293.

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