HumanitiesPoems Facing it” by Yusef komunyakaaMending wal

Humanities
Poems: “:Facing it” by Yusef komunyakaa
“Mending wall” by Robert Frost
Short stories: “ the thing around the neck'' by Chimamanda
Ngozi Adichie.
“The circuit” by Francisco Jimenez.
“My Sister's Marriage'' by Cynthia Rich.
Judy Baca, The Great wall (Los Angeles)
Architecture : Miriam Kamara, Yasema Esmaili, Jeanne Gang,
Zaha Hadid, Frank Lloyd Wright
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1
Demystifying AI:
What Digital transformation
leaDers Can teaCh You about

realistiC artifiCial intelligenCe
Jürgen Kai-Uwe Brock1 and Florian von Wangenheim2
SUMMARY
Recent years have seen a reemergence of interest in artificial
intelligence (AI) among
both managers and academics. Driven by technological
advances and public interest,
AI is considered by some as an unprecedented revolutionary
technology with the
potential to transform humanity. But, at this stage, managers are
left with little
empirical advice on how to prepare and use AI in their firm’s
operations. Based on
case studies and the results of two global surveys among senior
managers across
industries, this article shows that AI is typically implemented
and used with other
advanced digital technologies in firms’ digital transformation
projects. The digital
transformation projects in which AI is deployed are mostly in
support of firms’
existing businesses, thereby demystifying some of the
transformative claims made
about AI. This article then presents a framework for
successfully implementing AI in
the context of digital transformation, offering specific guidance
in the areas of data,
intelligence, being grounded, integrated, teaming, agility, and
leadership.
KeYwoRDS: artificial intelligence, polls and surveys,
managers, management,
management skills
I

n 2014, Dr. Julio Mayol wondered, “We have access to a vast
quantity of
data but it’s hard to extract meaningful information that helps
us improve
the quality of the care we provide.” Dr. Mayol, Medical
Director and Director
of Innovation at the Carlos Clinical Hospital in Madrid, Spain,
founded in
1787, found the answer after consulting with an external group
of technology advi-
sors: artificial intelligence (AI). About six months later, the
innovation unit of the
hospital, under his leadership, embarked on a project to apply
AI. Rather than opt-
ing for an off-the-shelf generic solution, the team worked
closely with a technology
1Fujitsu, Tokyo, Japan
2ETH Zurich, Zurich, Switzerland
865226CMRXXX10.1177/1536504219865226California
Management ReviewDemystifying AI: What Digital
Transformation Leaders Can Teach You
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CALIFORNIA MANAGEMENT REVIEW 00(0)2
provider of AI solutions and co-created an innovative new AI
application tailored

to their specific needs. After one year, the system was ready for
field testing. Six
months later, first results showed that the diagnostic and
patient’s risks assessment
solution can cut the time in half for preliminary assessment of
patient records,
with a 95% accuracy compared with eight domain experts who
were psychiatrists
with more than 20 years of experience. An unanticipated
positive side effect of this
immense increase in efficiency was that the medical staff had
much more time for
consultations and patient care, thereby increasing customer
satisfaction.1
This exemplary case of a successful AI2 application stands in
stark contrast to
mounting evidence of AI failures,3 gaps between firms’ AI
ambition and execu-
tion,4 and a general “post-AI-hype sobering.”5 Given the mixed
evidence and the
paucity of empirical insights related to the successes and
failures of AI implementa-
tion projects, we embarked on a global research project with the
aim of understand-
ing managers’ perceptions and evaluations of AI. This research
was informed by
insights derived from the opening case as well as publicly
available case study mate-
rial.6 All these cases were excluded from the survey
investigation. Between 2016
and 2018, more than 3,000 executives and managers were
surveyed globally from
across industries with a total of nearly 7,000 projects, including
the application of AI
and other advanced digital technologies. We do not necessarily

assume that manag-
ers know best, but they are important information sources about
current and future
AI potential for various reasons. First, given their involvement
and business inter-
est, they are the decision makers about future projects. Second,
their perceptions
and experiences will influence future implementation success.
Based on the above assessment (with high expectations and a
few success
stories on the one side, but frustrating experiences and stopped
projects on the
other side), we first establish the current prevalence of AI in
business (study 1) and
then explore key dimensions of successful AI implementations
(study 2). Specifically,
study 1 focused on the following research question (RQ1; see
Figure 1):
Research Question 1 (RQ1): To what extent has the application
of AI
diffused in business?
In study 2, we explored AI in business in more detail.
Specifically, we were
interested in the following three research questions (RQ2-RQ4;
see Figure 1):
Research Question 2 (RQ2): What are the anticipated perceived
business
impacts of AI? More specifically, to what extent are managers
expecting
impacts in the area of operations, offerings, and customer
interactions?7

Research Question 3 (RQ3): Assuming differences concerning
the per-
ceived business impact of AI, what explains those differences,
for example,
on a country, industry, firm level, executive (respondent), and
skill level?
Research Question 4 (RQ4): Given the opening case of AI
success,
can we identify leaders, firms that are experienced and
successful in
Demystifying AI: What Digital Transformation Leaders Can
Teach You 3
implementing AI-related projects?8 What makes them different
from lag-
gards, firms that have not yet moved beyond the planning phase,
in terms
of country-, industry-, firm-level factors, executive (respondent)
factors,
skills, and organizational traits? Furthermore, could these
leaders real-
ize more positive business impacts in areas such as operati onal
efficiency,
organizational agility, revenue growth, competitiveness, and
customer
experience? And how long does it usually take from the start of
an AI
project to achieving business impacts? In addition, we
investigate whether
leaders differ in their perception of AI implementation
challenges and key
success factors.

Research Structure and Guiding Frameworks
Figure 1 illustrates our research structure and the guiding
frameworks
we used. Phase 1 of our research, the exploration phase, was
informed by ini-
tial case study research (e.g., the opening case) and a first
digital transformation
Figure 1. Structure and guiding frameworks of the research.
Sources: Framework references (in alphabetical order): John
Hagel III and Marc Singer, “Unbundling the Corpo-
ration,” Harvard Business Review, 77/2 (March/April 1999):
133-141; E. T. Penrose, The Theory of the Growth of
the Firm (London: Wiley, 1959); Lynn W. Phillips, “Assessing
Measurement Error in Key Informant Reports: A
Methodological Note on Organizational Analysis in Marketing,”
Journal of Marketing Research, 18/4 (November
1981): 395-415; Everett M. Rogers, Diffusion of Innovations,
4th ed. (New York, NY: The Free Press, 1995); B.
Wernerfeld, “A Resource-based View of the Firm,” Strategic
Management Journal, 5/2 (April-June 1984): 171-
180; Robert K. Yin, Case Study Research: Design and Methods,
4th ed. (Los Angeles, CA: Sage, 2009).
and AI survey that aimed at understanding the extent of AI
diffusion across firms
worldwide. Given that AI is a recently adopted technology for
most firms, this
stage of our research was informed by diffusion of innovation

theory in general
and organizational innovation adoption implementation research
in particular.
From this body of past research, plus managerial interviews, we
developed our
stages of AI implementation model (see Figure 2). Phase 2 of
our research, the
descriptive phase, aimed at a broad yet deeper understanding of
AI in firms’ digi-
tal transformation worldwide. As the differential analysis
emerged in phase 2 of
our research, this phase was guided by three related
organizational frameworks:
the theory of the growth of the firm, the resource-based view of
the firm, and the
pragmatic firm conceptualization proposed by Hagel and
Singer.9 All three have
Figure 2. Status of AI implementation in firms (study 1).
Source: We derived these five stages from interviews with
managers and the organizational stages-of-
innovation-adoption-implementation literature (e.g., G. W.
Downs Jr., and L. B. Mohr, “Conceptual Issues in
the Study of Innovations,” Administrative Science Quarterly,
21/4 (December 1976): 700-714; M. A. Scheirer,
“Approaches to the Study of Implementation,” IEEE
Transactions on Engineering Management, 30/2 (1983): 76-
82; L. G. Tornatzky and B. H. Klein, “Innovation
Characteristics and Innovation Adoption-implementation: A
Meta-analysis of Findings,” IEEE Transactions on Engineering
Management, 29/1 (1982): 28-45; Everett M. Rogers,
Diffusion of Innovations, 4th ed. (New York, NY: The Free
Press, 1995).
Note: n = 1,614 firms, worldwide, 2016-2017: “Which best
describes the progress of your firm’s AI (Artificial

Intelligence) implementation?” AI = artificial intelligence; DX
= digital transformation.
Teach You 5
an internal resources focus in common and have guided our
assessments in terms
of firms’ experience, capabilities, challenges, and success
factors. As a multistage,
mixed-methods research design, research phase 1 informed
research phase 2.
Data Collection
Data for this study were obtained in two waves (see Figure 1).
Following a
set of digital transformation AI case studies, the first
exploratory survey was con-
ducted in 2016-2017, addressing the first research question. The
survey was con-
ducted globally, online, utilizing a database of executives and
senior managers
provided by an international market research firm. The second
survey was con-
ducted in 2017-2018, addressing RQs 2 to 4. The second survey
used a similar
methodology. It was conducted globally, online, and the same
market research
firm provided samples of executive respondents. Sampling was
guided by the
following:
• Global coverage: firms from key countries (in terms of

economy/GDP) in the
Triad.10
• A minimum country quota of n = 50 where possible (with the
exception of
New Zealand with n = 29 in study 2, this was achieved).
• North American Industry Classification System (NAICS)
industry sampling:
focus on manufacturing (NAICS code 23, 31, 32, 33),
information (NAICS
code 51), transportation (NAICS code 48, 49), retail (NAICS
code 41, 42, 44,
45), financial services (NAICS code 52), healthcare (NAICS
code 62).
• Minimum industry quota: n = 50.
• Focus on medium to large firms in terms of revenue and
employees.
• Senior respondents: focus on key informants of firms at C- or
VP-level or
above.
In order to assess response bias, we followed the logic of
Armstrong and
Overton11 and found no statistically significant differences at
the .05 level in
regard to any of the variables that we are reporting below. In
terms of common
method bias, we applied the marker variable approach and found
no significant
effects.12 Overall, we are confident that our findings can be
generalized to medium
to large Triad enterprises in the industries sampled and that our

results are not the
result of the instrument/sample used in our analysis. Sample
details are provided
in the endnote.13
AI in Business
To what extent are firms already using AI in their business? In
2016-2017,
the time of the first survey, AI applications had already diffused
quite broadly,
with only 15% of firms not yet having any AI plans and 20%
already having
delivered results (see Figure 2).
Interestingly, the application of AI was typically an integral
part of a firm’s
digital transformation project. With the exception of isolated
experimentation
with specific AI techniques such as deep learning, AI was not
used in isolation, but
as one technological element of several technologies aimed at
enhancing a firm’s
present and future business.14 It emerged that digital
transformation is often the
context for AI projects, such as call center transformation using
advanced analyt-
ics and AI or transforming operations using Internet of things
(IoT), advanced
analytics, and AI.
The Business Impact of AI

In the second survey, which was executed in 2017-2018, we
built on the
insights from the first survey and explored the role of AI in
firms more deeply.
What are the anticipated perceived business impacts of AI? AI
can impact the
internal operations of a firm, its offerings (in the form of smart
products and
services15), and how it interacts with its customers. The survey
data largely con-
firm this (Figure 3). The surveyed executives foresee AI to
impact their firms’
offerings. More specifically, they foresee AI to impact the
creation of smart ser-
vices, to automate operations and manufacturing, to support
decision-making
and knowledge management, and to automate customer
interfaces. Interestingly,
the strength of the anticipated impacts does not vary too much
(average range:
3.7-3.8 on a 5-point scale). This we interpret as the typical
fairly undifferentiated
perception by businesses of a new technology prior to wider and
deeper diffusion
and the emergence of standard business cases and applications.
Despite the largely undifferentiated perception of AI’s business
impact—
the perception of AI business impacts was also largely similar
across countries and
industries—some differences emerged. The impact on smart
services is more
Figure 3. Anticipated DX/AI impacts on business (study
2).Note: n = 1,218 firms,

worldwide, 2017-2018: “In terms of business impact of AI, to
what extent will AI make an
impact in each of following areas?”
Note: AI = artificial intelligence.
*Knowledge management refers to decision-making and
knowledge management support.
Teach You 7
pronounced in financial services and less in healthcare, and the
impact on manu-
facturing is more pronounced in the manufacturing sector.
However, these signifi-
cant differences only exhibited effects that are rather small.16
While, on average, AI’s anticipated business impact is seen as
moderate to high
across all the business impact categories investigated, 3% of the
ratings anticipate no
impact and 21% anticipate a high impact.17 What explains those
differences?
Of the 10+ factors at the country, industry, firm, and executive
(respon-
dent) level we examined,18 only digital skills have a strong
impact. Firms with
stronger digital skills anticipate stronger AI-induced business
impacts compared
with firms with weaker digital skills. This observation is stable
across industries
and regions (see Figure 4).

These digital skills comprised four, interrelated organizational
capabilities:
• Strategic capabilities: digital strategy and digital business
development skills.
• Technology capabilities: skills in new digital technologies
such as AI or IoT.
• Data capabilities: data science skills.
• Security capabilities: cybersecurity skills.
These results suggest that, just like with other technological
innovations in
the past, to realize the potential of the new digital technology,
AI requires specific
organizational capabilities as the firm and the new technology
align for best appli-
cation and impact.19 AI requires new information technology
(IT) skills that are
both AI-specific, such as machine learning skills, and generic,
such as understand-
ing of modern programming languages (e.g., Python),
application development
techniques (e.g., agile software development), and modern IT
architecture skills
(e.g., edge computing).20 In addition, data management and
analytical skills are
required. AI thrives on massive amounts of data requiring the
existence of digital
data, its management, and its analysis and synthesis. Given that
most data are
network generated (e.g., websites, sensor data from IoT
devices), security skills—
generic as well as AI-empowered—become vital to ensure

access rights, intrusion
detection, and data integrity.21 Last, these skills have to be
embedded in a coher-
ent and suitable strategic framework to ensure a guided
implementation and
wider organizational alignment and support.22
We find that firms that have already implemented and delivered
business
outcomes through three or more digital transformation projects
(stage 4; see
Figure 2) exhibit particularly strong digital capabilities.23
These firms we label
digital transformation leaders (DX leaders for short); this group
consists of about
8% of all firms surveyed.
Digital Transformation Leader Analysis
What makes these digital transformation (DX) leaders different?
In order
to find out, we compared them with those firms in our sample
that either had
no plans yet or were still in the planning phase (implementation
stage 0 and 1;
see Figure 2). We term these firms “laggards.” Following this
classification, we
compared 114 DX leaders with 424 laggards. We examined
organizational traits
Figure 4. Anticipated business impact of AI and firms’ digital

skills (a) across industry
(study 2)a and (b) across regions (study 2)a.
aAnticipated business impact and digital skills based on
separate summated scales, combining the individual
impact and skills items.
Teach You 9
in the area of strategy, leadership, data management, agility,
organizational pro-
cesses, and innovation, as well as country-, industry-, and firm-
level factors.
The DX leaders we identify came from across all countries
surveyed and are
as prevalent among traditional firms as among digital natives.24
They also did not
differ significantly with regard to the reported duration from
project start to busi-
ness impact.25 The weak industry differences we observe are a
reflection of the
different extent of digital transformation initiatives in the
industries surveyed,
with financial services leading and healthcare lagging.26 With
regard to traditional
organizational measures (such as size in revenue or the number
of employees),
DX leaders tend to be larger. They report higher revenues and
more employees,
but these differences are not very pronounced.27 Besides their
noted stronger digi-

tal capabilities, we identify organizational characteristics where
these leaders
excelled.
Organizational Characteristics
When compared with laggards, DX leaders differ significantly
and
strongly28 in seven organizational traits. In order of size of the
difference (effect
size) between the two groups, these were integrated data
management, CEO pri-
ority, security strategy, digital processes, digital strategy,
agility, and open innova-
tion ecosystem.
Integrated data management, the most pronounced difference
between lead-
ers and laggards, refers to the organizational capability of
managing customer and
organizational data in a holistic and integrated fashion, avoiding
data silos and
incompatible data formats. This aspect goes hand in hand with
AI’s dependence
on data. CEO priority refers to a firm’s leader prioritizing and
leading the firm’s
digital transformation efforts, which include the application of
advanced digital
technologies such as AI. An organization-wide security strategy
refers to the defini-
tion and execution of a cybersecurity strategy across the whole
organization.
Given the importance of data, a strategic approach to data
security—including the
management of access rights, intrusion detection, and disaster
recovery mecha-

nisms—is critical. Digital processes refer to the digitalization
of a firm’s core pro-
cesses such as sourcing, production, performance reviews, or
travel management
and expense claims. Digital processes are often the outcome of
digital transforma-
tion projects. Organization-wide digital strategy refers to the
development and exe-
cution of a strategic approach to digital transformation, an
approach that is
contrasted to unplanned or tactical approaches. Organizational
agility refers to a
firm’s ability to rapidly and flexibly respond to customers’
needs, adapt produc-
tion/service delivery to demand fluctuations, and implement
decisions in the face
of market changes. Agile organizations continuously search for
ways to reinvent
or redesign their organization and they can do so in a fast and
flexible manner as
they learn and adapt in the process. Innovation ecosystem refers
to the establishment
of an open ecosystem for innovation, beyond the boundaries of
the firm. Such
ecosystems tie into the resources, capabilities, and strength of a
firm’s network of
business relationships, such as ties with suppliers, alliance
partners, and custom-
ers (Figure 5).
Business Impact

The seven organizational aspects enable the leaders to achieve
much
greater business impacts compared with laggards. Leaders
report significantly
stronger actual business impacts in their projects compared with
laggards. We
find significantly higher levels of impact on transformations of
existing business
models, improvements in operational efficiency, increase in
revenue, strengthen-
ing of offerings’ competitiveness, and customer experience
enhancements. All of
the observed differences are strong (see Figure 6). This moves
beyond the mere
anticipation of business impact (Figure 3) to managers’
perception of real busi-
ness impacts actually achieved.
Challenges
Reflecting the importance of digital skills, the main challenge
for all firms
is lack of skilled staff and knowledge in digital technologies,
which was men-
tioned as an implementation challenge by more than half of the
firms combined.
Lack of organizational agility, internal resistance to change,
security risks, lack
of leadership and sufficient funding, as well as the challenge of
integrating new
digital technology with existing technology were stated as
challenges by about
a quarter of the firms each. Unavailability of suitable
technology partners and
Figure 5. Organizational characteristics of DX leaders (study 2).

Note: n = 538 firms (114 leaders, 424 laggards), worldwide,
2017-2018: “To what extent do you agree with
the following statements?” (from 1 = strongly agree to 5 =
strongly disagree; statements randomly rotated):
(1) Digital transformation is the top priority of our CEO; (2) We
are executing an organization-wide digital
transformation strategy; (3) We have achieved organizational
agility; (4) We have established an open ecosys-
tem for innovation; (5) We have digital business processes; (6)
We are managing customer and organizational
data in an integrated manner; (7) We are executing an
organization-wide cybersecurity strategy. DX = digital
transformation.
Teach You 11
unstable technology are mentioned as challenges by 19% and
13% of firms,
respectively (Figure 7).
Contrary to our expectations, we uncover few differences in
terms of chal-
lenges perceived by leaders versus laggards. Only
organizational agility, security
risks, and lack of leadership were challenges that the leaders did
perceive as less of
a challenge in their AI projects. However, the effect of these
differences was fairly
small.29 We interpret this rather surprising finding as follows.
Although the barri-
ers or challenges that firms perceive are similar, the DX leaders
have more experi-

ence and a stronger resource base to overcome them. This
becomes most obvious
when looking at the three challenges the DX leaders perceived
as significantly less
of a challenge and comparing those with challenges that are
perceived similar. For
example, lack of leadership: DX leaders stated lack of
leadership significantly less
often as an implementation barrier compared with laggards.
Given that DX lead-
ers had significantly more CEOs prioritizing digital
transformation, perceptions of
lack of leadership support should be lower. On the contrary,
both DX leaders and
laggards perceive lack of skilled staff as a key barrier. This is
despite the finding
that DX leaders have a stronger digital skills resource base.
Taken together, this
points to the view that perceived challenges are similar, but that
in some cases
(e.g., lack of leadership), the DX leaders have already
developed to a degree that
the challenges are less of an actual implementation success
barrier compared with
the laggards.
Figure 6. DX/AI business impact (study 2).
2017-2018: “To what extent have you delivered
outcomes specified in each of following statements?” (from 1 =
not at all to 5 = to a great extent; statements
randomly rotated): (1) Increase in revenue; (2) Improvement in
customer experience; (3) Strengthening of
competitiveness of products or services; (4) Efficiency
improvements; (5) Improvement of business agility; (6)

Transformation of business models. AI = artificial intelligence.
Success Factors
In contrast to the implementation challenges, which were
similar across
the firms surveyed, leaders differed compared with laggards in
terms of the
importance they attributed to factors contributing to digital
transformation out-
come success. The following eight success factors turn out to be
significantly dif-
ferent among the AI leaders: organizational agility, engagement
of skilled staff,
leadership, support from technology partners, investment,
culture, alignment of
new digital technologies with existing IT, and learning from
failed projects.
The biggest difference between the leaders and the laggards is
organiza-
tional agility. Leaders attribute much more importance to
organizational agility as
a factor of project success. Second, leaders have more engaged
staff with the
required digital skills and leadership support. Support from
technology partners,
sufficient funding, a supportive culture, alignment of new
digital technologies
with a firm’s existing technology, and learning from failure
were also rated much
higher as contributing factors of project success by the leaders

as compared with
the laggards (Figure 8).
Figure 7. DX/AI implementation challenges (study 2).
Note: n = 3,557 answers by 1,218 firms, worldwide, 2017-2018:
“Which of the following statements describes
your key challenges? Please select up to three” (categories
randomly rotated): (1) Lack of skilled staff**; (2)
Lack of knowledge of digital technology**; (3) Lack of
organizational agility; (4) Lack of leadership; (5) Fear of
change or internal resistance; (6) Unavailability of a right
technology partner; (7) Lack of funds; (8) Integrat-
ing digital technologies with existing IT; (9) Cybersecurity
risks; (10) Adoption of digital technolo gy too early,
before it is robust and stable; (11) Others. AI = artificial
intelligence; IT = information technology.
**Combined in the figure.
Teach You 13
DIGITAL: Guidelines for Successful AI Applications
The present research departed from the tension between AI
success sto-
ries on one hand and failures and frustrations on the other hand.
Based on our
research, we now identify seven areas for managerial action and
implementa-
tion. We use the acronym “D-I-G-I-T-A-L” to describe our
implications, where
“D” stands for data, “I” for intelligence, “G” for grounded, “I”
for integral, “T”

for teaming, “A” for agile, and “L” for leadership. We explore
these areas below.
The more DIGITAL a company is, the higher the likelihood that
their digital
transformation–embedded AI projects will succeed. For easier
comprehension,
Figure 9 illustrates the links between the empirical evidence
presented and the
elements of the DIGITAL implementation framework. Some of
the conclusions
that we draw from our analyses, obviously, refer to other
change projects within
the digital transformation of companies as well. With DIGITAL,
we also give
credit to this notion and remind that implementation of AI, in
its present con-
dition, is typically linked to digital transformation of
corporations in general.
For each of the elements of DIGITAL, we provide managers
with a few action-
inducing discovery questions in order to guide their AI
applications. Answering
any of those questions with a clear “No” should alert managers
to act accord-
ingly (see Figure 9).
Figure 8. DX/AI success factors (study 2).
2017-2018: “To what extent have the following
factors contributed to the overall outcomes you reported?”
(from 1 = not at all to 5 = to a great extent; state-
ments randomly rotated): (1) Engagement of skilled staff; (2)
Having the right organization and processes; (3)
Leadership by management; (4) Developme nt of an enabling
culture; (5) Support from technology partners; (6)

Investment by the business; (7) Alignment of new digital
technologies with existing IT; (8) Learning from failed
projects. AI = artificial intelligence; IT = information
technology.
Data
Just like Dr. Mayol alluded to in his opening quote, the
fundamental basis
for AI success is data. AI requires data, digital data, and in high
quality. The AI
machine learning technique of deep learning is particularly data
hungry. For
Figure 9. Proposed AI implementation success framework,
empirical evidence, and
action-inducing discovery questions.
Teach You 15
example, Google needed some 10 million images to teach its
Google brain sys-
tem to identify human faces, human bodies, and cats.30 In a
more recent appli-
cation, the number of images used increased to 300 million
images.31 Without
data available for training, AI cannot create value for firms, and

without skills to
acquire, manage, and analyze the data, valuable and actionable
insights cannot
be generated. Our research confirms this. Firms with strong data
capabilities are
expecting to derive more value from AI, and our DX leader
analysis showed that
integrated data management practices and digital processes,
which generate digi-
tal data, separate the leaders from the laggards significantly and
strongly.
Managers are, therefore, advised to start with a data inventory
check before
embarking on AI projects. The data inventory check should
address questions
such as “Do we own or have access to data that are relevant to
analytically solve
the business problem we are addressing?” “Are the data
available in the right digi-
tal format?” “Are the data sets sufficiently large to be efficient
and effective?” “Are
the data sufficiently complete, consistent, accurate, and
timely?”
Be Intelligent
Data are the necessary foundation for AI success, but data al one
are not
sufficient. We identified lack of skilled staff and knowledge in
digital technolo-
gies as the top AI implementation challenge and engaged skilled
staff as one of
the key AI implementation success factors. Therefore, managers
need to develop
digital intelligence in the form of suitable human skills within

their organization.
This intelligence extends beyond the necessary data-related data
science skills
to include the strategic-, technological-, and security-related
capabilities that
we discussed earlier. In fact, AI requires organizations to
develop human intel-
ligence. How shall managers develop this intelligence,
especially considering that
AI-specific technical talent is scarce?32
First, it is important to realize that AI success is not just a
function of tech-
nical skills such as data science capabilities and skills in new
digital technologies
and cybersecurity. Managerial skills in the form of strategic
capabilities are vital.
Our results showed that firms with stronger capabilities in the
area of digital strat-
egies and digital business development skills are expecting to
derive more value
from AI compared with firms with a weaker skills base. At the
heart of these
managerial skills is awareness and understanding. This implies
awareness of the
possibilities and requirements of AI and related new digital
technologies and an
understanding of how to best leverage this technology in the
idiosyncratic context
of the firm. Questions such as “How can AI help defend, grow,
or transform our
business?” or “How can AI improve operational efficiencies?”
are indicative.
Answering such questions does not require an in-depth how-to
technical under-
standing of AI, but does require managerial curiosity and

interest paired with
firm, customer, and industry knowledge.
Second, the required technical AI skills need to be attained. In
principle,
managers have two options. Develop technical AI skills
internally or acquire these
skills from outside the firm. Interestingly, we found no
difference in how leaders
approached skills scarcity compared with laggards.33 We
recommend a dual-
sourcing strategy. Managers should develop existing internal
skills and source
external talent at the same time in order to build the necessary
technical skill base
to ensure efficient and effective application of AI technologies.
Finally, digital intelligence has to do with patience. AI is not an
instant
panacea. Our research unearthed rather lengthy, multiyear
processes from
project start to impactful execution. Just like the successful case
that opened
this article, AI projects are usually not deterministic from start
to finish, but
emerge as the project participants learn, and the system
provides feedback. This
also distinguishes AI projects from many other IT projects,
where the end goal
is the successful implementation and use of a system. Especially
when embark-

ing on an AI project for the first time, managers should allow
the AI project
team to experiment and provide them with a generous timeline
to deliver
results and sufficient funding, one of the main barriers we
identified. This
includes allowing for a “failure culture” as the team learns. DX
leaders excelled
at learning from failure and it helped them to reap more benefits
from their AI
projects.
In summary, managers are advised to start with an internal
resources check
before embarking on AI projects. This check should address
questions such as “Do
we have a digital strategy in place?” “Do we have the
managerial and technical
skills required to support successful digital transformation with
AI? If not, how do
we develop or acquire these skills?” “Are we willing and able to
tolerate investing
in an emerging rather than deterministic AI digital
transformation journey, includ-
ing accepting failure?”
Be Grounded
Following the insights derived from more than 7,000 projects
world-
wide, we conclude that firms are mainly applying the new
digital technology
to improve their existing business(es) (see Figure 3). The
reported business
impacts of the DX leaders also suggest a grounded approach
with impacts such

as improving the existing offering, increasing revenue, or
enhancing operational
efficiency (see Figure 6). Managers embarking on AI projects
should take this
insight as suggestive of a rather grounded approach to AI, at
least initially. Rather
than pursuing high-flying “pie-in-the-sky” projects, firms
should “start small”
with AI and base the project in their existing core business(es).
Our opening case
illustrated this: a focused application area and a relatively small
project size. The
setup allowed for early results, and the project setup was not
made overly com-
plicated. Again, this also points to the need for using AI for
solving concrete busi-
ness problems, rather than viewing it as radical innovation and
business model
disruption from the start—this may happen eventually, but later.
AI, just as other
technologies, is ultimately not about technology but business
opportunities and
capabilities. Only when enough experience has been
accumulated should firms
proceed to more difficult and complex projects involving
innovation and new
business models. This grounded approach also signifies that
adopting AI is like
adopting other new technology successfully. Start small, test,
learn, and then
Teach You 17

apply more widely. As we noted earlier in this article, this
suggests that realizing
the potential of AI requires the firm and the new technology to
co-align for best
application and impact, and it rejects the notion of
technological or organiza-
tional imperatives.34
Before embarking on AI projects, managers should conduct a
reality check
in terms of scope and intent of the project(s). This check should
address questions
such as “Are we experienced enough and resourced properly for
the scope of the
project?” “Are we following an incremental, current business
focused approach
with our DX/AI project(s)?” “Do we have a DX/AI projects
roadmap?”
Be Integral
Successful firm-wide AI implementations require an integral,
holistic
approach. Being integral comes in six flavors: strategy,
processes, data manage-
ment, technology alignment, employee engagement, and culture.
As soon as AI leaves the experimental, feasibility-testing lab
environment
and is applied to a real business case, managers should first
make sure it is embed-
ded in and supportive of the firms’ digital strategy. The
existence of a digital strat-
egy separated the DX leaders from the laggards and signals the
importance of
viewing AI in a broader context. A firm’s digital strategy,

which, in essence, out-
lines and documents how a firm wants to achieve its strategic
objectives with the
help of digital technologies—including but not exclusive to
AI—channels its activ-
ities and provides for a guiding purpose.
Executing a digital strategy implies the “digitalization” of a
firm’s core
processes: from procurement processes to internal operations to
customer
engagement.35 AI cannot augment analog processes. Managers
should ask them-
selves how digital their firms’ core processes are. Our DX
leaders’ analysis showed
that digital processes significantly and strongly distinguished
them from laggards.
AI requires data. As firms digitize their operations, thereby
creating more
digital data, the need for an integrated data management
approach becomes
vital. It is, therefore, not surprising that integrated data
management was the
number one organizational characteristic differentiating DX
leaders from lag-
gards, because the mere existence of a lot of data is good but
not good enough.
Data, even if sufficiently large, complete, consistent, accurate,
and timely, are
limited if they “live” in isolation and are not connected with
other relevant data.
Subscribing to the view that firms are essentially consumers,
producers, manag-
ers, and distributors of information,36 all their data should be
connected and

integrated to allow for maximum value capture and knowledge
generation. To
address this challenging task, some innovative firms have
recently started to set
up so-called data lakes, a centralized repository that allows
them to store all their
structured and unstructured data and access in a unified way. As
firms employ AI
for more complex, broader tasks and processes (say, enhancing
customer experi-
ences), integrated data management becomes more important.
Enhancing cus-
tomer experiences, for example, requires tapping into data from
the firm’s ERP
(Enterprise Resource Planning), CRM (Customer Relationship
Management),
CMS (Content Management System), SMM (Social Media
Monitoring), and
other systems in order to ensure an integral approach to the
customer journey.
Integrated data management requires technology alignment.
Lack of it was
one of the key barriers to AI success and successful alignment
one of the key suc-
cess factors we identified. Technology alignment specifically
refers to the integra-
tion of new digital technologies, including AI, with a firm’s
existing technologies,
and it is all about the question of whether the new and the old
can “speak”

together and “understand” each other in terms of data. For
example, can the
firm’s legacy system provide the data required for an AI
application in a format
that it can compute? Managers should ensure that technology
alignment is looked
after and instruct experts to ensure seamless integration of the
old with the new.
Last, integral implies managers should ensure employee
engagement and a
supportive culture. Successful AI thrives with engaged skilled
staff and an enabling
culture, both of which help to overcome internal resistance and
lack of skills and
knowledge, two of the key barriers we identified. Engageme nt is
particularly
important for the employees that will be impacted by AI. As
firms seem to be par-
ticularly interested in applying AI in the creation of smart
services (see Figure 3),
for example, engaging and working with the service frontline
employees will be
instrumental to the success in smart services. Even though our
research supports
the augmentation view of AI, managers should be aware that
frontline employees
might fear displacement and should address this fear
proactively. Research on
how frontline employees’ roles, responsibilities, and actions are
likely to change
due to AI-automated customer interfaces is in its infancy, but it
is safe to assume
that managers who address such inherent concerns proactively
are more likely to
achieve employee engagement.

In conclusion, managers are advised to think about AI as a
broad means to
support its company-wide digital transformation efforts. To
ensure an integral
approach, managers should address questions such as “Have our
firm’s core busi-
ness processes been digitalized?” “Has our firm analyzed what
existing/new offer-
ings can benefit from DX/AI?” “Has our firm integrated all data
into one single
data repository?” “Is our firm’s existing IT compatible with the
DX/AI technology
we plan to adopt?”
Be Teaming
Our opening case illustrated that going for AI alone is unlikely
to lead
to success. Just as Dr. Mayol and his colleagues collaborated
with a technol-
ogy adviser and provider, DX leaders stated support by
technology partners as
a key success factor, and the unavailability of support by a
technology partner
was stated as an implementation challenge by nearly 20% of
firms. Teaming
with one or several technology partners—which include
technology generalists
such as IBM or Fujitsu, software firms such as Microsoft or
SAP, or consultancies
such as Accenture or Deloitte37—provides firms with two main
advantages. First,
it provides them with early access to new technology in a field
that is still ill-
defined and emergent. Second, it allows them to tap into the

economies of scope
Teach You 19
and project experiences that both technology firms and
technology consultancies
have accumulated over the past years as AI projects have
mushroomed. For most
firms, AI will, for the foreseeable future, not be an off-the-shelf
product. Hence,
teaming with technology partners to co-create a tailored AI
solution is going to
be the main approach for now.
The importance of teaming extends beyond the confines of
working with
technology partners and includes a firm’s business ecosystem,38
which includes
suppliers, competitors, customers, and alliance partners from
different industries.
Having established an open innovation ecosystem was one of
the organizational
characteristics that set the DX leaders apart from the laggards.
Open innovation
ecosystems are a means to develop innovative offerings
(products and services)
beyond the internal capacity and capabilities of the firms. This
approach recog-
nizes that great talent often resides outside of a firm. Given the
discussed AI tech-
nical skills scarcity, an open innovation ecosystems approach is
particularly fruitful
in the case of AI, and the DX leaders demonstrated this. Having

established an
Open Innovation Ecosystem was one of their defining
characteristics. Generally,
managers have two options: establishing their own ecosystem39
or joining an
existing one.40 Often, as was the case with Dr. Mayol and his
colleagues, the route
to take is shaped by the nature of the collaboration with the
technology partner(s).
Managers are advised to think about AI as an opportunity to
partner and
develop powerful ecosystems. To understand the teaming
options, managers
should address questions such as “Does our firm know with
whom to partner in
support of our DX/AI success?” “Does our firm know with
whom competitors
partner in their DX/AI projects?” “Did our firm develop or join
an ecosystem to
enhance its offerings?”
Be Agile
Organizational agility is both a key barrier and a key success
factor accord-
ing to our empirical analyses. Lack of it was the second most
important AI imple-
mentation challenge, and great levels of organizational agility
was the number
one AI success factor. We found agility to also be one of the
main business
impacts of AI. Agility is, therefore, both a central AI success
antecedent as well as
an outcome of successful AI implementations, thereby
reinforcing its importance

as an antecedent. How can managers foster organizational
agility? Organizational
agility research suggests that a firm’s ability to sense change
and to respond read-
ily to it by reconfiguring its resources, processes, and strategies
is at the core of
organizational agility.41 In the context of AI projects, this
relates to flexibility in
the way the project is approached and managed throughout its
life cycle, as AI
projects tend to be emergent rather than deterministic, as we
noted earlier (cf.
Be Intelligent).
Managers are advised to assess their company’s agility in a
realistic manner
and implement corrective actions if needed. The following
questions, guided by
the logic of Singh et al.,42 are instructive: “Compared to our
competition, how
quickly and frequently are we adapting our processes and
offerings to stay
competitive?” “Compared to our competition, how flexible are
we to accommo-
dating small, medium, and large changes to our processes and
offerings?”
Lead
Managers should lead and actively endorse the firm’s AI
project(s), just

like Dr. Mayol at the Carlos Clinical Hospital in Madrid, and
not relegate leader-
ship to the project management level. We identified executive
leadership as a
key success factor and lack of leadership support as a key
barrier. The DX leaders
we analyzed demonstrated this fact forcefully. One of their
defining organiza-
tional characteristics was a CEO who prioritized the firm’s
digitalization efforts,
including AI and other advanced digital technologies.
If data are the foundation for impactful AI, leadership provides
the trans-
formational energy for firms to be DIGITAL and, as a
consequence, successful with
AI. Notably, AI has several similarities with other technologies
to be implemented
in firms, and some of the aspects of our DIGITAL framework
would relate to other
change management projects as well. However, the broad nature
of AI (requiring
data, analyses, interdisciplinary teams) makes for some specific
requirements,
such as data, agility, and the teaming aspect.
Managers are advised to reflect honestly, “Is our executive team
and mid-
dle management comfortable and supportive of the changes that
DX/AI will bring
to our firm?” “Is our executive team and middle management
actively endorsing
and continuously communicating the status and progress of our
DX/AI activities
to all stakeholders?”

Conclusion and Outlook
AI certainly holds a lot of promise but it is not a panacea. In
order to reap
its benefits, we developed DIGITAL as a guideline for AI
success grounded in the
empirical insights of close to 7,000 DX projects that involved
new digital technol-
ogies such as AI. The basic approach of this article was to learn
from today’s DX
leader how to become the AI leader of tomorrow. At the same
time, in line with
the title of this article, we believe we can demystify a few
aspects of AI.
The results of this study imply that, in many ways, AI is similar
to other
technologies companies adopt and implement. It certainly is
typically deployed in
digital transformation projects, and, as such, shares many
similarities with other
digital projects. At the same time, the focus on self-learning
projects and long-run
scaling comes with several interesting findings, such as the
focus being integral,
teaming, and agile. In contrast to press reports and also some
academic papers,
our approach to AI is a contemporary and realistic one. Before
visions of “AI tak-
ing over everything” will become true, “realistic AI” will take
place for a long
time. It is and will be a competitive advantage to be quick and
effective in AI, and
our DIGITAL framework and the associated questions to
managers should help
overcome the barriers, but also some of the illusions, so that

“realistic AI” will
become real.
Teach You 21
A question that we also addressed in the survey was the future
role of
humans in AI projects, which we do not report here in detail due
to the contro-
versial and nonconclusive nature of the responses, and the
vastness of the topic.
But overall, it is interesting that firms are positive about AI as a
technology and
the role of humans. First, they expect that AI and humans will
work together in
the future, rather than working against each other or replacing
humans, for at
least quite some time. Furthermore, managers assume that we
will even see a
human premium emerging in the future, in the sense that people
will be paying
more to get personal, human-to-human services rather than AI
technology–
mediated services. Interestingly, leaders and laggards are united
in this view and
showed no significant differences.
Acknowledgments
The authors wish to thank Fujitsu, Corporate Executive Officer
and former Chief
Marketing Officer Yoshiteru Yamada, and Manager Noriaki
Tanaka for support-

ing the research project.
Author Biographies
Jürgen Kai-Uwe Brock is the former CMO of Fujitsu Americas,
currently work-
ing as a senior assignee at Fujitsu’s HQ in Tokyo, Japan (email:
[email protected]
fujitsu.com).
Florian von Wangenheim is Professor of Technology Marketing,
Department of
Management, Technology, and Economics at ETH Zurich
(email: [email protected]
ethz.ch).
Notes
1. For details regarding this case, see Instituto de Investigation
Sanitaria Case Study (IdISSC),
https://www.youtube.com/watch?v=NIDNmwYMjAE.
2. At present, to the best of our knowledge, no commonly
agreed definition of artificial intel-
ligence (AI) exists. Definitions range from “every aspect of
learning or any other feature of
intelligence . . . that a machine can be made to simulate” [John
McCarthy, M.L. Minski, N.
Rochester, and C.E. Shannon, “A Proposal for the Dartmouth
Summer Research Project on
Artificial Intelligence,” August 31, 1955, www-
formal.stanford.edu/jmc/history/dartmouth.
pdf] to “make computers do things at which, at the moment,
people are better” [Elaine Rich
and Kevin Knight, Artificial Intelligence, 3rd ed. (New York,
NY: McGraw-Hill, 2009)] to “AI

is whatever hasn’t been done yet” [Douglas Hofstadter, Gödel,
Escher, Bach: An Eternal Golden
Braid (New York, NY: Basic Books, 1980)] to, more recently,
“a system’s ability to correctly
interpret external data, to learn from such data, and to use those
learnings to achieve spe-
cific goals and tasks through flexible adaptation” [Andreas
Kaplan and Michael Haenlein
“Siri, Siri, in My Hand: Who’s the Fairest in the Land? On the
Interpretations, Illustrations,
and Implications of Artificial Intelligence,” Business Horizons
62/1 (January/February 2019):
15-25]. Given this, we purposely did not provide for an explicit
definition of AI in our sur-
vey research. However, we provided application examples in the
survey as illustrations of AI,
such as call center transformation using advanced analytics and
AI or transforming opera-
tions using Internet of things (IoT), advanced analytics, and AI.
Generally, our investigation
was guided by the broad and inclusive behavioral definition of
AI as originally advanced by
Brooks: “Artificial Intelligence . . . is intended to make
computers do things, that when done
by people, are described as having indicated intelligence”
(Rodney Allen Brooks, “Intelligence
mailto:[email protected]
https://www.youtube.com/watch?v=NIDNmwYMjAE
www-formal.stanford.edu/jmc/history/dartmouth.pdf
www-formal.stanford.edu/jmc/history/dartmouth.pdf

without Reason,” in Proceedings of the 12th International Joint
Conference on Artificial Intelligence,
ed. M. Ray and J. Reiter (Sydney, Australia: Morgan Kaufmann,
1991), pp. 569-595.
3. See, for example, Kartik Hosanagar and Apoorv Saxena,
“The First Wave of Corporate AI
Is Doomed to Fail,” Harvard Business Review Digital Arti cles,
April 18, 2017, https://hbr
.org/2017/04/the-first-wave-of-corporate-ai-is-doomed-to-fail;
Ben Taylor, “Why Most
AI Projects Fail,” Artificial Intelligence, March 1, 2018,
https://www.experfy.com/blog/why
-most-ai-projects-fail; Matthew Herper, “MD Anderson Benches
IBM Watson in Setback for
Artificial Intelligence in Medicine,” Forbes, February 19, 2017,
https://www.forbes.com/sites
/matthewherper/2017/02/19/md-anderson-benches-ibm-watson-
in-setback-for-artificial
-intelligence-in-medicine/#10c16c083774.
4. See, for example, Sam Ransbotham, David Kiron, Philipp
Gerbert, and Martin Reeves,
“Reshaping Business with Artificial Intelligence: Closing the
Gap between Ambition and
Action,” MIT Sloan Management Review, September 6, 2017,
https://sloanreview.mit.edu
/projects/reshaping-business-with-artificial-intelligence.
5. See, for example, Richard Waters, “Why We Are in Danger
of Overestimating AI,” Financial Times,
February 4, 2018, https://www.ft.com/content/4367e34e-db72-
11e7-9504-59efdb70e12f.

6. Some of these cases are publicly accessible, examples
include the following: https://
www.fujitsu.com/global/about/resources/case-studies/cs-
2017nov-siemens-gamesa
.html; https://www.youtube.com/watch?v=tpkQHlutSzo;
https://www.youtube.com/watch
?v=3Lqq6bjoip0.
7. This group of areas of AI business impact follows the logic
of John Hagel III and Marc Singer,
“Unbundling the Corporation,” Harvard Business Review, 77/2
(March/April 1999): 133-141
(see Figure 1). The authors argued that most companies are
essentially three kinds of busi-
nesses—a customer relationship business (customer
interactions), a product innovation busi-
ness (offerings), and an infrastructure business (operations).
8. This research question was guided by the thinking behind the
theory of the growth of the
firm (Penrose), which attributes a key role to experiential
knowledge. Edith T. Penrose, The
Theory of the Growth of the Firm (London: Wiley, 1959).
9. Hagel and Singer (1999), op. cit.
10. The term Triad refers to the three key economic regions in
the world (originally, the United
States, Europe, and Japan) as originally coined by Ohmae
[Kenichi Ohmae, Triad Power:
The Coming Shape of Global Competition (New York, NY: Free
Press, 1985)], though its modern
understanding has broadened [e.g., Alan M. Rugman and Alain
Verbeke, “A Perspective on
Regional and Global Strategies of Multinational Enterprises,”
Journal of International Business

Studies, 35/1 (2004): 3-18] to include North America, Asia, and
Oceania.
11. J. Scott Armstrong and Terry S. Overton, “Estimating Non-
response Bias in Mail Surveys,”
Journal of Marketing Research, 14/3 (1977): 396-402.
Armstrong and Overton suggest compar-
ing early and late respondents for differences along key control
variables, assuming that late
respondents are more similar to nonrespondents. If no
significant differences can be found,
one can assume no significant response bias exists. We used the
time stamps of the online
surveys to gauge possible differences but found none.
12. We derived the marker variable approach from Michael K.
Lindell and David J. Whitney,
“Accounting for Common Method Variance in Cross-selectional
Research Designs,” Journal of
Applied Psychology, 86/1 (2001): 114-121. We compared the
reported overall business impact
of AI (see Figure 4a and 4b) with a theoretically unrelated
measure added to the survey. The
measure used concerned firms’ United Nations Sustainable
Development Goals focus. The
two were unrelated (r = –.046; R2 = .003), suggesting common
method bias is not a major
concern in our study.
13. Sample details of the first survey, conducted in 2016-2017:
Total sample size: n = 1,614 (firms).
Regions: North Americas (Canada, United States) n = 314;
Europe (Finland, France,
Germany, Spain, Sweden, United Kingdom) n = 520; Asia
(China, Indonesia, Japan,

Singapore, South Korea, Thailand) n = 674; Oceania (Australia)
n = 106.
Industry split, North American Industry Classification System
(NAICS) code: 23, 31-33,
Manufacturing (n = 427); 51, Information (n = 195); 48-49,
Transportation (n = 56); 41/42,
44-45, Retail (n = 137); 52, Financial Services (n = 138); 62,
Healthcare (n = 100), other
(n = 661).
Firm size (employees): <100 full-time employees (FTEs; n =
0); 100-499 (n = 499); 500-
999 (n = 414); 1,000-4,999 (n = 440); 5,000+ (n = 261).
https://hbr.org/2017/04/the-first-wave-of-corporate-ai-is-
doomed-to-fail
https://hbr.org/2017/04/the-first-wave-of-corporate-ai-is-
doomed-to-fail
https://www.experfy.com/blog/why-most-ai-projects-fail
https://www.experfy.com/blog/why-most-ai-projects-fail
https://www.forbes.com/sites/matthewherper/2017/02/19/md-
anderson-benches-ibm-watson-in-setback-for-artificial-
intelligence-in-medicine/#10c16c083774
https://sloanreview.mit.edu/projects/reshaping-business-with-
artificial-intelligence
https://sloanreview.mit.edu/projects/reshaping-business-with-
artificial-intelligence
https://www.ft.com/content/4367e34e-db72-11e7-9504-
59efdb70e12f

https://www.fujitsu.com/global/about/resources/case-studies/cs-
2017nov-siemens-gamesa.html
https://www.youtube.com/watch?v=tpkQHlutSzo
https://www.youtube.com/watch?v=3Lqq6bjoip0
https://www.youtube.com/watch?v=3Lqq6bjoip0
Teach You 23
Firm size (revenue): <1M$ (n = 4); 1-10M$ (n = 182), 10-
100M$ (n = 581); 100-1Bn$
(n = 561); 1Bn$+ (n = 263).
Respondent characteristics:
Gender: Female (24%), Male (76%).
Age: 20s (n = 147), 30s (n = 515), 40s (n = 418), 50s (n = 303),
60s (n = 161).
Position: C-suite (n = 1,023), × VP-level (n = 591), Manager (n
= 0).
Sample details of the second survey, conducted in 2017-2018:
Total sample size: n = 1,535 (firms).
Regions: North Americas (Canada, United States) n = 137;
Europe (Finland, France,
Germany, Spain, Sweden, United Kingdom) n = 502; Asia
(China, Indonesia, Japan,
Singapore, South Korea, Thailand) n = 473; Oceania (Australia,
New Zealand) n = 106.
Industry split, NAICS code: 31-33, Manufacturing (n = 280);
51, Information (n = 93);

48-49, Transportation (n = 83); 41/42, 44-45, Retail (n = 472);
52, Financial Services
(n = 158); 62, Healthcare (n = 132), other (n = 0).
Firm size (employees): <100 FTEs (n = 9); 100-499 (n = 309);
500-999 (n = 348); 1,000-
4,999 (n = 357); 5,000+ (n = 195).
Firm size (revenue): <1M$ (n = 9); 1-10M$ (n = 157), 10-
100M$ (n = 425); 100-1Bn$
(n = 406); 1Bn$+ (n = 205).
Respondent characteristics:
Gender: Female (35%), Male (65%).
Age: 20s (n = 173), 30s (n = 474), 40s (n = 332), 50s (n = 171),
60s (n = 68).
Position: C-suite (n = 931) × VP-level (n = 262), Manager (n =
25).
14. Other technologies used were, for example, security
technologies, mobile technologies,
blockchain, cloud computing, IoT, and big data analytics. The
definition for digital transfor-
mation used was “Digital transformation refers to the
integration of advanced digital tech-
nologies (AI, IoT, and cloud-based services) into significant
areas of a business with the
aim of changing how the business operates and how value is
delivered to and created with
customers.”
15. For details on smart offerings, see, for example, Michael E.
Porter and James E. Heppelmann,
“How Smart, Connected Products Are Transforming
Companies,” Harvard Business Review,
93/10 (October 2015): 96-114.

16. The effect size for smart services was Cohen’s f = .121, and
for manufacturing automation
Cohen’s f = .142.
17. The numerical difference in the range of firms is a
reflection of missing values.
18. Factors examined include industry sector, country, firm
characteristics (revenue, number of
employees, digital/traditional), and respondent characteristics
(gender, age, title/position).
19. In the information technology (IT) literature, this co-
evolutionary view is referred to as the
emergent imperative. M. Lynne Markus and Daniel Robey,
“Information Technology and
Organizational Change: Causal Structure in Theory and
Research,” Management Science, 34/5
(May 1988): 583-598.
20. We derived this insight from digital transformation projects
involving AI of one of the
author’s employers, a global provider of IT services,
practitioner interviews, and the litera-
ture, such as Amir Sharif, “Harnessing Agile Concepts for the
Development of Intelligent
Systems,” New Generation Computing, 17/4 (December 1999):
369-380; Antonio Gulli,
TensorFlow 1.x Deep Learning Cookbook: Over 90 Unique
Recipes to Solve Artificial-intelligence Driven
Problems with Python (Birmingham, UK: Packt Publishing,
2017); Nicolas Seydoux, Khalil
Drira, Nathalie Hernandez, and Thierry Monteil, “Reasoning on
the Edge or in the Cloud?”
Internet Technology Letters 2/1 (January/February 2018): e51.

21. See, for example, Jian-hua Li, “Cyber Security Meets
Artificial Intelligence: A Survey,”
Frontiers of Information Technology & Electronic Engineering,
19/12 (December 2018): 1462-1474.
22. See, for example, Francisco J. Mata, William L. Fuerst, and
Jay B. Barney, “Information
Technology and Sustained Competitive Advantage: A Resource-
based Analysis,” MIS
Quarterly, 19/4 (December 1995): 487-505; Ferdinand Mahr,
Aligning Information Technology,
Organization, and Strategy: Effects on Firm Performance
(Wiesbaden: Gabler, 2010).
23. Obviously, given the cross-sectional nature of our survey,
we cannot claim any causal links
here. AI leaders might exhibit stronger digital skills as a resul t
of their digital transformation
efforts, and/or their stronger digital skills resulted in more
advanced digital transformation
efforts compared with the other firms.
24. We defined digital natives as firms that were less than 15
years old at the time of the survey
and marketing their offerings (products and services) purely
online. The survey included a
total of n = 648 digital natives.
25. For the majority of firms (61%), it took one or more years
before outcomes could be deliv-
ered. For 23% of firms, it took two or more years, and for 9%,

three or more years.
26. Small effect size for industry, Cramer’s V = .037.
27. Small effect size for revenue, Cramer’s V = .054, and for
employees, Cramer’s V = .071.
28. Throughout our DX leader analysis, we report both
statistical significance and effect sizes.
Effect sizes are estimates of the strength of an effect in a given
population. Especially when
sample sizes are large, like in our studies, inferential tests
become oversensitive and even
the smallest of effects turn out to be statistically significant. In
such cases, statistical sig-
nificance is actually insignificant and what matters more is the
found effect size. Cf. Jürgen
Kai-Uwe Brock, “The ‘Power’ of International Business
Research,” Journal of International
Business Studies, 34/1 (January 2003): 90-99. As we display in
Figures 5, 6, and 8, the effects
we found are medium to large according to Cohen’s effect size
classification. Jacob Cohen,
Statistical Power Analysis for the Behavioral Sciences, 2nd ed.
(Hillsdale, NJ: Lawrence Erlbaum,
1988). Compared with other studies in the field of international
business and organization
research (we use the effect size reviews of Ellis (2010) and
Paterson et al. (2015) as bench-
marks and visualized the average effect sizes they found in
Figures 5, 6, and 8), the effects
we found were stronger for all but one effect, indicating
substantial practical relevance for
managers. Paul D. Ellis, The Essential Guide to Effect Sizes:
Statistical Power, Meta-Analysis,
and the Interpretation of Research Results (Cambridge:
Cambridge University Press, 2010);

T.A. Paterson, P.D. Harms, P. Steel, and M. Credé, “An
Assessment of the Magnitude of Effect
Sizes: Evidence From 30 Years of Meta-Analysis in
Management,” Journal of Leadership &
Organizational Studies, 23/1 (February 2016): 66-81.
29. The found effect sizes were ϕ = .086, ϕ = .093, and ϕ =
.077, respectively.
30. Nicola Jones, “Computer Science: The Learning Machines,”
Nature, 505/7482 (January 8,
2014): 146-148.
31. Chen Sun, Abhinav Shrivastava, Saurabh Singh, and
Abhinav Gupta, “Revisiting
Unreasonable Effectiveness of Data in Deep Learning Era,”
arXiv:1707.02968, July 2017.
32. See, for example, David Cyranoski, “China Enters the Battle
for AI Talent,” Nature, 553/7688
(January 17, 2018): 260-261.
33. 51% of DX leaders developed skills internally, and 49%
sourced skills externally versus 52%
and 48% for laggards; χ2 value = .011, p value = .915 in a 2 × 2
table.
34. As discussed by Markus and Robey, the technological
imperative implies that technology
is an exogenous force, which deterministically shapes
organizations and people. M. Lynne
Markus and Daniel Robey, “Information Technology and
Organizational Change: Causal
Structure in Theory and Research,” Management Science, 34/5
(May 1988): 583-598. The
organizational imperative, in contrast, argues that organizations

and people have almost
unlimited choice over technological options and control over its
consequences. As illustrated
in our opening case, successful applications of AI in
organizations are neither following the
technological imperative nor the organizational imperative, but
the emergent imperative,
which holds that the uses and consequences of a technology in
an organization cannot ex
ante be fully determined but emerge from complex social
interactions within the firm and its
ecosystem.
35. See, for example, Gerrit Berghaus, René Kessler, Viktor
Dmitriyev, and Jorge Marx Gómez,
“Evaluation of the Digitization Potentials of Non-digital
Business Processes,” HMD Praxis der
Wirtschaftsinformatik, 55/2 (April 2018): 42c444.
36. See, for example, Kenneth J. Arrow, The Limits of
Organization (New York, NY: W.W. Norton,
1974); Arthur L. Stinchcombe, Information and Organizations
(Berkeley: University of
California Press, 1990).
37. The main technology partners our research identified were,
in alphabetical order: Accenture,
Amazon, Capgemini, Cisco, Dell, Deloitte, Fujitsu, Google,
Hewlett Packard Enterprise
(HPE), Hitachi, IBM, Microsoft, NEC, NTT Data, Oracle, PwC,
SAP, Salesforce, and TCS (Tata
Consultancy Services).
38. The view of a business ecosystem goes back to Moore, who
argued that “a company be
viewed not as a member of a single industry but as part of a

business ecosystem that crosses
a variety of industries. In a business ecosystem, companies
coevolve capabilities around a
new innovation: they work cooperatively and competitively to
support new products, satisfy
Teach You 25
customer needs, and eventually incorporate the next round of
innovations.” James F. Moore,
“Predators and Prey: A New Ecology of Competition,” Harvard
Business Review, 71/3 (May/
June 1993): 75-86.
39. See, for example, Henry Chesbrough, Sohyeong Kim, and
Alice Agogino, “Chez Panisse:
Building an Open Innovation Ecosystem,” California
Management Review, 56/4 (Summer
2014): 144-171; René Rohrbeck, Katharina Hölzle, and Hans
Georg Gemünden, “Opening
Up for Competitive Advantage—How Deutsche Telekom
Creates an Open Innovation
Ecosystem,” R&D Management, 39/4 (September 2009): 420-
430.
40. For the open innovation dimension of business ecosystems,
see Henry W. Chesbrough, Open
Innovation: The New Imperative for Creating and Profiting from
Technology (Boston, MA: Harvard
Business School Press, 2003).
41. See, for example, Carmen M. Felipe, José L. Roldán, and
Antonio L. Leal-Rodríguez, “An

Explanatory and Predictive Model for Organizational Agility,”
Journal of Business Research,
69/10 (October 2016): 4624-4631.
42. Jagdip Singh, Garima Sharma, James Hill, and Andrew
Schnackenberg, “Organizational
Agility: What It Is, What It Is Not, and Why It Matters,”
Academy of Management Proceedings,
2013/1 (2013): 11813-11813.
POSITIVE TEAM ATMOSPHERE MEDIATES THE IMPACT
OF
AUTHENTIC LEADERSHIP ON SUBORDINATE
CREATIVITY
HAO MENG, ZHI-CHAO CHENG, AND TIAN-CHAO GUO
Beihang University
We analyzed how authentic leadership (AL) predicts
subordinate creativity, taking into
account the possible mediator of positive team atmosphere.
Participants were 69 team
leaders and 335 team members working in enterprises in China.
Subordinates reported their
perception of there being an atmosphere of team trust and
psychological safety at work, in
addition to the AL of their supervisors, whereas supervisors
rated employee creativity. The
main findings were as follows: (a) AL positively predicted
employee creativity through the
mediators of both atmosphere of team trust and psychological
safety; and (b) atmosphere of
team trust and psychological safety positively affected teams’

knowledge sharing and, in turn,
creativity. Our results enrich understanding of the processes
through which AL improves
subordinates’ creativity. By promoting AL and a team
atmosphere of trust and psychological
safety, organizations may enhance employees’ creative
performance.
Keywords: authentic leadership, team atmosphere, trust,
psychological safety, knowledge
sharing, creativity.
Authentic leadership (AL), which was developed based on the
fields
of sociology, leadership science, positive psychology, ethics,
and positive
organizational behavior, is a leadership style that is associated
with positive
psychological abilities and a positive ethical atmosphere.
Luthans and Avolio
(2003) defined AL as a process that combines the positive
psychological ability
of leaders with a high-functioning organizational context. AL is
recognized as a
genuine, transparent, and morally positive leadership style that
can be used to
SOCIAL BEHAVIOR AND PERSONALITY, 2016, 44(3), 355–
368
© Society for Personality Research
http://dx.doi.org/10.2224/sbp.2016.44.3.355
355
Hao Meng, Zhi-Chao Cheng, and Tian-Chao Guo, School of
Economics and Management, Beihang

University.
Correspondence concerning this article should be addressed to:
Hao Meng, School of Economics
and Management, Beihang University, No. 37 Xueyuan Road,
Haidian District, Beijing City 100191,
People’s Republic of China. Email: [email protected]
AUTHENTIC LEADERSHIP AND SUBORDINATE
CREATIVITY356
promote effectively a positive attitude (e.g., job satisfaction)
and behaviors (e.g.,
creativity) in subordinates (George, 2003; Luthans & Avolio,
2003).
Employee creativity refers to the capability of the staff to
generate novel
and useful ideas (Amabile, 1988). In the era of the knowledge
economy,
employee creativity is a significant source of innovation in
enterprises. It can
not only enhance organizational effectiveness and survival but
can also improve
competitive organizational advantage (Amabile, 1988).
Therefore, in today’s
rapidly changing and highly competitive environment, an
increasing number of
researchers and managers are realizing the need to encourage
innovative behaviors
in their staff (Zhou & George, 2003). From an organizational
environment
perspective, studies of creativity, leadership, and leader
behavior are needed to
elucidate their influence on the creativity of subordinates; for

example, some
scholars have emphasized that leadership affects the
organizational environment
and has a decisive influence on the generation of employee
creativity (Shalley
& Gilson, 2004). Others have noted the importance of
recognizing the effects of
specific leadership behaviors and leadership characteristics
(e.g., AL; George,
2003) on actions—and modes of actions—that support, promote,
or restrict
creativity (Gardner, Cogliser, Davis, & Dickens, 2011; Rego,
Sousa, Marques, &
Pina e Cunha, 2014; Shalley & Gilson, 2004).
Although the positive effect of AL on subordinates’ behavior
and creativity
has gained considerable theoretical support, it remains
necessary to conduct
further empirical studies to understand how this mechanism
functions (Avolio
& Mhatre, 2012; Gardner et al., 2011). Psychological capital,
positive emotions,
and hope have been used as mediating variables in past studies
to reveal the
process through which AL affects creativity (Rego et al., 2012,
2014); however,
the possible mediator of a positive atmosphere at work has so
far been ignored.
A review of the relevant literature on employee creativity shows
that the creation
of an organizational atmosphere that allows employees to
attempt new methods
boldly without fear of punishment for failure is one of the
important roles of
leaders (Ekvall, 1996). Indeed, AL is recognized (George, 2003;

Luthans &
Avolio, 2003) as a genuine, transparent, and morally positive
leadership style that
is conducive to the creation of a good team atmosphere of tr ust
and psychological
safety, which, in turn, greatly facilitates staff willingness and
creativity. Through
the intervening mechanisms of trust and psychological security
in a work context,
AL and subordinate creativity are linked, and we hoped to
contribute to a fuller
explanation of this relationship. This study was a response to
Avolio and Mhatre’s
(2012) call for a wider range of intermediary mechanisms to be
investigated in
order to better understand how AL exerts a positive influence
on subordinates’
output. Therefore, our first purpose was to expand the linear
research network
that is focused on AL, and our second purpose was to establish
a model of
CREATIVITY 357
positive atmosphere as a mediating variable in the relationship
between AL and
employee creativity.
Literature Review and Hypotheses
AL can be used to address the overall decrease in the morality
of contemporary
leadership and the advancing crisis of trust inside organizations

(Avolio &
Mhatre, 2012). First, AL is focused on the promotion of what is
right rather
than on paying attention to short-term profits without moral
considerations;
simultaneously, the genuine, transparent, and moral nature of
authentic leaders
can help to rebuild the trust of subordinates and colleagues
(Avolio & Mhatre,
2012; George, 2003). Second, AL behavior can help to create a
positive team
atmosphere of trust and psychological safety, and subordinates’
perception of
working in such an environment promotes the generation of
creativity. Last,
working in an atmosphere based on trust and security can
promote knowledge
sharing among team members, which can facilitate creativity
(May, Gilson, &
Harter, 2004).
Therefore, we examined how a team atmosphere of trust and
psychological
safety acts as an intermediary variable in the influence
mechanism model of
AL on subordinate creativity. The theoretical model underlying
this study is
presented in Figure 1.
Figure 1. Model of the mechanism of the influence of authentic
leadership on subordinate
creativity.
Authentic Leadership
Initially proposed by Luthans and Avolio (2003), authentic
leadership contains

four dimensions: (a) self-consciousness, which refers to the
level of the leader’s
cognition regarding his/her own individual characteristics of
values, strengths
and weaknesses, motivations, and other traits; (b) relational
transparency, which
refers to the leader openly exchanging information with others
and expressing
his/her true inner thoughts and feelings while openly soliciting
different
Authentic leadership
Team atmosphere of trust
Team atmosphere of
psychological safety
Employees’
creativity
Knowledge
sharingH1
H4
H2
H3
H5
H6
H7

Note. * p < .05, ** p < .01.
CREATIVITY358
suggestions and ideas (Walumbwa, Avolio, Gardner, Wernsing,
& Peterson,
2008); (c) internalized morality, which refers to the leader
making decisions
based on high ethical standards rather than exhibiting appeasing
or self-serving
behavior when under external pressure (Avolio & Gardner,
2005; Gardner,
Avolio, Luthans, May, & Walumbwa, 2005; Walumbwa et al.,
2008); and (d)
balanced treatment, which refers to the leader fully soliciting
different views and
opinions before making a decision (Gardner et al., 2005;
Walumbwa et al., 2008).
In theoretical and empirical studies, researchers (Gardner et al.,
2005; Rego et
al., 2012) have demonstrated that the core elements of AL are
included in these
four dimensions.
Team Atmosphere of Trust
Team atmosphere of trust, which was proposed by Costigan,
Ilter, and
Berman (1998), refers to team members’ shared perception of
two aspects:
performance being in line with the characteristics of trust, and
how issues of

trust are addressed. Gardner et al. (2005) argued that AL
behavior is conducive
to creating a positive team atmosphere that is trusting and
inclusive and in which
ethics is valued. First, authentic leaders behave in a manner that
is consistent
with their own deeply held values and moral standards, and win
subordinates’
trust and recognition by both encouraging employees to voice
different opinions
and establishing collaborative relationship networks (Luthans &
Avolio, 2003),
thereby becoming role models for subordinates (Ilies,
Morgeson, & Nahrgang,
2005). Subordinates promote the formation of a consistent
manner of action
within the team and strengthen the sense of mutual trust among
team members
by learning the behavioral style of authentic leaders (Walumbwa
et al., 2008).
Second, through exposing their own true feelings, motivations,
and values,
authentic leaders promote both transparency in interpersonal
relationships within
the team and open communication to express team members’
true thoughts
and feelings, thus helping enhance the degree of trust within the
team (Avolio,
Luthans, & Walumbwa, 2004). Therefore, we proposed the
following hypothesis:
Hypothesis 1: Authentic leadership will be positively associated
with a team
atmosphere of trust.
From sociological and behavioral perspectives, knowledge
sharing is an

individual’s voluntary exchange of information and experience
with others
(Boone, 1997). Effective knowledge sharing, which is based on
mutual
understanding of, respect for, and trust among team members, is
facilitated
by an atmosphere of mutual trust that can enhance employees’
psychological
commitment to one another and reduce the fear of being taken
advantage of by
others (Chow & Chan, 2008). The stronger the atmosphere of
trust within a team,
as perceived by team members, the more active will be the
communication and
exchanges among members; therefore, the environment for the
generation of
CREATIVITY 359
knowledge-sharing behavior in individual interactions will be
more favorable
(van den Hooff & de Ridder, 2004). Therefore, we proposed the
following
hypothesis:
Hypothesis 2: A team atmosphere of trust will be positively
associated with
knowledge sharing.
Team atmosphere affects individuals’ creativity, with Amabile
(1988) having
noted that colleagues’ support and trust in a working
environment enables
team members to be more confident in their abilities, and

prompts employees
to attempt different novel methods in their work. Bock, Zmud,
Kim, and Lee
(2005) further observed that mutual trust among team members
provides support
for, and assistance with, the work of other colleagues, which is
conducive for
members to attempt tasks outside of their formal responsibilities
and to propose
new viewpoints regarding issues from different perspectives,
thereby helping
to generate more creative solutions. In addition, Zhou and
George (2003) noted
that innovative behavior can be generated in communication
among colleagues
and that trust can enhance the openness of interactions,
communication, and
cooperation among team members, thereby helping to generate
creativity.
Therefore, we proposed the following hypothesis:
Hypothesis 3: A team atmosphere of trust will be positively
associated with
individuals’ creativity.
Team Atmosphere of Psychological Safety
Psychological security in the workplace relates to employees’
perception of
a team atmosphere that prompts self-expression without fear of
the negative
impacts on their self-image, status, or career (Kahn, 1990). In
the context of
a team with psychological security, the members believe that
others will not
assign blame or seek revenge when mistakes are made, feedback
is provided, or

suggestions are presented (Edmondson, 1999).
AL can effectively promote subordinates’ trust in, respect for,
and identification
with leaders through establishing transparent relationships,
using inherently high
ethical standards, and objectively analyzing relevant data before
making decisions
(Gardner et al., 2005). Together, these lessen subordinates’
worry about risk and
increase their perception of psychological safety (Edmondson,
1999). Authentic
leaders tend to express their true thoughts, opinions, and behave
accordingly,
which can affect subordinates’ attitudes and behavior via the
role-model effect,
thereby causing subordinates to act their true selves in
interpersonal interactions
and helping to improve the team’s overall level of honesty and
integrity. This
decreases team members’ perception of interpersonal risk and
enhances their
sense of psychological security (Luthans & Avolio, 2003).
Therefore, we
proposed the following hypothesis:
CREATIVITY360
Hypothesis 4: Authentic leadership will be positively associated
with a team
atmosphere of psychological safety.
Tynan (2005) has argued that psychological security provides

the necessary
conditions for the occurrence of knowledge sharing. First, in a
very psycho-
logically safe atmosphere, employees do not need to fear that
knowledge-sharing
behavior will lead to plagiarism of their ideas and, thus, loss of
their competitive
edge or exposure of their weaknesses, and there is a willingness
and capacity
to learn and create knowledge by exchanging and sharing.
Second, a very psy-
chologically safe atmosphere means that team members have
fewer concerns
about interpersonal risks and take the initiative to support and
help others, which
facilitates the generation of knowledge-sharing behaviors
among team members
(Lin, 2007). Therefore, we proposed the following hypothesis:
Hypothesis 5: A team atmosphere of psychological safety will
be positively
associated with knowledge sharing.
Edmondson (1999) concluded that team psychological safety is
an important
factor that affects individual creativity. First, when employees
perceive themselves
to be in a safe working environment, they are less concerned
with interpersonal
risks and devote all of their energy to work tasks; moreover,
such an environment
may encourage employees to participate voluntarily in
innovation and exhibit
other positive behaviors (Baer & Frese, 2003). Second, in a very
psychologically
safe environment, employees do not worry about being
misunderstood, ridiculed,

or subjected to other interpersonal risks; therefore, they are
open and bold in
presenting constructive opinions and acting creatively (Kark &
Carmeli, 2009).
Last, team members who perceive their workplace’s internal
environment as
being safe can promote active exchanges and discussions within
the team, thus
helping to generate inspiration and creativity (Tynan, 2005).
Therefore, we
proposed the following hypothesis:
Hypothesis 6: A team atmosphere of psychological safety will
be positively
associated with individual creativity.
Knowledge Sharing and Individual Creativity
The positive impact of knowledge sharing on individual
creativity has been
confirmed in some studies. First, knowledge sharing among
team members can
supplement individuals’ existing knowledge, and promote
innovation abilities
and performance through the absorption of new knowledge (Lee
& Hong,
2014; van den Hooff & de Ridder, 2004; Yeh, Yeh, & Chen,
2012). Second, the
knowledge-sharing behavior of team members can enhance their
recognition
by colleagues and, thus, increase individual employees’ sense of
self-efficacy.
Notably, employees with high self-efficacy are more courageous
in attempting
new methods and techniques (Martinez, 2015). Therefore, we
proposed the
following hypothesis:

CREATIVITY 361
Hypothesis 7: Knowledge sharing will be positively associated
with individual
creativity.
Method
Participants
Leaders and employees of various departments of enterprises in
Beijing, Hebei,
and Shandong, China, were included in a field survey. We
considered each of the
departments of the enterprises to be a team. Leader–subordinate
pairwise data
collection was utilized to minimize homologous bias. Team
members completed
anonymous measures of AL, team atmosphere of trust, team
atmosphere of
psychological safety, and knowledge sharing, whereas leaders
completed
measures of team members’ creativity. First, we asked the
human resources
department of each enterprise to provide lists of leaders and
employees in each
department. Then, the respective coded survey forms were
issued to the leaders
and team members of the department, and collected on-site after
completion.
We issued 75 leadership survey forms and 387 subordinate

survey forms. After
excluding invalid forms that were not able to be paired, that is,
only leadership or
subordinate survey forms, 69 valid leadership survey forms
(valid rate of return =
92%) and 335 valid subordinate survey forms (valid rate of
return = 92%) were
included in the data analyses. In the leadership sample, males
the accounted for
84.8%, females 15.2%; 91.1% had a master’s degree or above,
remaining 8.9%
had a university degree, the average age was 39.9 years (SD =
6.67), the average
working years as the team leader was 5.81 years (SD = 1.93). In
the subordinate
sample, males accounted for 78.7%, females 21.3%; 81.8% had
a bachelor’s
degree or above, 18.2% had a college degree; the average age
was 28.6 years,
(SD = 4.67), and the average period of time spent as a member
of the team was
19.1 months (SD = 6.91).
Measures
We used the official Chinese versions of the scales described
below, for which
the validity and reliability have been established. Responses are
made on a
5-point Likert scale (1 = strongly disagree, 5 = strongly agree).
Authentic leadership. AL was measured using the 16-item scale
developed
by Walumbwa et al. (2008), which contains dimensions of
balanced treatment
(three items, e.g., “My immediate leader solicits viewpoints that

are different
from his/her own.”), internalized morality (four items, e.g., “My
immediate
leader makes decisions based on his/her core values.”),
relational transparency
(five items, e.g., “My immediate leader will admit to having
made a mistake.”),
and self-consciousness (five items, e.g., “My immediate leader
is able to improve
his/her interactions with others by soliciting feedback.”). The
Cronbach’s alpha
reliability coefficient in this study was .817.
CREATIVITY362
Team atmosphere of trust. We measured team atmosphere of
trust with three
items developed with reference to the trust scale developed by
Costigan et al.
(1998). An example item is “When faced with difficulties at
work, I believe I
can get help from my colleagues.” The Cronbach’s alpha
reliability coefficient
in this study was .900.
Team atmosphere of psychological safety. We measured team
atmosphere
of psychological safety with six items developed with reference
to the scale
developed by May et al. (2004). An example item is “In my
team, I can express
my true feelings about work.” The Cronbach’s alpha reliability
coefficient in this

study was .897.
Knowledge sharing. We measured knowledge sharing scale with
five items
developed with reference to the scale developed by van den
Hooff and de
Ridder (2004). An example item is “I will take the initiative to
share with other
colleagues the methods and experience that enhance work
efficiency.” The
Cronbach’s alpha reliability coefficient in this study was .841.
Creativity. To measure subordinate creativity, we used the four -
item scale
developed by Zhou and George (2003) in the Chinese context.
An example item
is “This employee proposed a new and practical idea to improve
performance.”
The Cronbach’s alpha reliability coefficient in this study was
.867.
Control variables. We controlled for age, gender, tenure in the
team, and level
of education because these variables may influence the
relationship between
leadership style and work-related outcome variables
(Walumbwa & Lawler,
2003).
Data Analysis
Structural equation modeling was used to test the theoretical
models and
hypotheses. AMOS version 21 was used to perform
confirmatory factor analysis
(CFA) on the measurement models to test their validity and

reliability, and then
to test the theoretical models and hypotheses (Anderson &
Gerbing, 1988).
Results
Measurement Model Evaluation
The validity and reliability of the measurement models were
tested via CFA.
The ratio of the chi-squared value to the number of degrees of
was 1.657, where values under 2 are considered to be
acceptable. The goodness-
of-fit index (GFI), adjusted goodness-of-fit index (AGFI),
comparative fit
indicator (comparative fit index, CFI), and nonstandard fitting
(Tucker–Lewis
index, TLI) indicators were all greater than the desired
minimum of .9. The root
mean square error of approximation (RMSEA) was within the
upper limit of
CREATIVITY 363
acceptability of .05. Table 1 shows that the average variance
extracted (AVE) of
each variable was much greater than .5, indicating that the
measurement model
had good convergent validity. Last, the discriminant validity of
the variables was
assessed by comparing the square root of the variable’s AVE
and the covariance

between the variables (Hatcher, 1994).
reliabilities (CR)
were greater than, or equal to, .7, indicating that the
measurement model had
very good reliability. The square roots of the AVE of each
variable, that is, the
numbers on the diagonal in Table 2, were much greater than the
covariance
among the study variables.
and Average Variance Extracted
for Each Study Variable
Authentic leadership 2.812 1.103 .817 .716 .610
Team atmosphere of trust 2.836 0.796 .900 .905 .872
Team atmosphere of psychological safety 2.771 0.847 .897 .898
.883
Knowledge sharing 3.127 1.055 .841 .838 .830
Creativity 2.864 0.872 .867 .836 .877
Note. CR = composite reliability; AVE = average variance
extracted.
Table 2. Covariance Among Latent Variables
Variable 1 2 3 4 5
1. Authentic leadership .781
2. Team atmosphere of trust .387 .872
3. Team atmosphere of psychological safety .748 .369 .883
4. Knowledge sharing .369 .561 .456 .830

5. Creativity .539 .558 .540 .514 .877
Note. Values on the diagonal are the square root of the average
variance extracted.
Structural Model Analysis
AMOS version 21.0 was used to test the structural models, and
the overall fit
indicators of the models are listed in Table 3. All of the fit
indicators met the
requirements. The fit indices were all within their respective
desirable levels:
The testing results for all of the hypotheses are shown in Figure
2. The path
coefficients in Figure 2 show support for our first three
hypotheses, indicating
that AL affected knowledge sharing and creativity through the
intermediary role
of team atmosphere of trust.
CREATIVITY364
Table 3. Fit Indicators of the Structural Models
Validity Value Acceptable at
–
df 80 –
GFI 0.945 > .90

AGFI 0.917 > .90
CFI 0.976 > .90
TLI 0.968 > .90
RMSEA 0.052 < .08
goodness-of-fit index, AGFI = adjusted
goodness-of-fit index, CFI = comparative fit index, TLI =
Tucker-Lewis index, RMSEA = root mean
square error of approximation.
Figure 2. Hypotheses testing results.
H4–H6 were also supported, indicating that AL affected
knowledge sharing
and creativity through the intermediary role of team atmosphere
of psychological
safety. Finally, H7 was supported, indicating that knowledge
sharing had a
positive effect on creativity. Therefore, all hypotheses proposed
in this study
were supported.
AL explained 47% of the variance in team atmosphere of trust
and 58% of
the variance in team atmosphere of psychological safety. In
addition, 46% of
the variance in knowledge sharing and 45% of the variance in
creativity were
explained by their respective antecedent variables.
Discussion
In this study, empirical data were used to validate the mode of
action of AL
on subordinate creativity via the two intermediary variables of

team atmosphere
of trust and team atmosphere of psychological safety. The seven
hypotheses
proposed in this study were supported, which enriches the
existing literature
Authentic leadership
Team atmosphere of trust
Team atmosphere of
psychological safety
Employees’
creativity
Knowledge
sharing.41***
.46***
.34**
.42**
.21**
.41**
.26**
Note. * p < .05, ** p < .01.

CREATIVITY 365
on AL and employee creativity. First, the results demonstrated
that AL can
effectively promote subordinate creativity, which is consistent
with previous
studies on the relationship between AL and creativity via other
mediating
variables (Costigan et al., 1998). In this study, we also
indirectly confirmed
that AL helps to create a positive within-team atmosphere (Bock
et al., 2005;
van den Hooff & de Ridder, 2004) and introduced team
atmosphere of trust
and team atmosphere of psychological safety into the
relationship between AL
and creativity, thereby highlighting the intermediary role of a
positive team
atmosphere. This intermediary has not been addressed in
previous studies; thus,
our finding constitutes an important developmental step for
related theories.
Second, we found that team atmosphere of trust mediates the
relationships
among AL, knowledge sharing, and creativity. The high ethical
standards
exhibited by authentic leaders and the transparent relationships
established
within the team all help to promote subordinates’ trust in their
leaders and in other
colleagues, thus creating a team atmosphere that is
characterized by a high degree
of trust. Perceptions of mutual trust and trustworthiness prompt
team members
to take the initiative to communicate and exchange and share

knowledge and
information, and this active knowledge-sharing behavior among
members can
enhance the team’s knowledge content, thus contributing to
improved creativity
(Boone, 1997).
Last, AL can influence knowledge sharing and creativity
through the
mediator of team atmosphere of psychological safety. Authentic
leaders actively
seek different perspectives and express their true thoughts and
ideas, which
encourages similar behavior in subordinates, thus creating an
open atmosphere
of communication. Likewise, team members’ trust in authentic
leaders can create
a strong sense of psychological safety, which helps to
strengthen cooperation
and knowledge-sharing behaviors among team members, thus
allowing internal
knowledge to be integrated within the team. This type of
environment is
conducive to inspiring members to use innovative methods to
solve problems and
overcome difficulties (Baer & Frese, 2003). Therefore, our
findings contribute
to a better understanding of the mechanism of AL in employee
creativity and
provide a theoretical reference for future studies.
Our empirical results in this study have strong practical
significance. First,
we found that AL is positively correlated with employee
creativity in a Chinese
context, which means that managers in Chinese enterprises

should intensively
cultivate AL behavior in management activities in order that
managers can
serve as role models for their employees and promote creative
contributions by
their employees. Second, managers should learn and understand
the behavioral
characteristics of authentic leaders and, when interacting with
subordinates, act
according to AL standards in order to enhance the degree of
subordinates’ trust
in both leaders and other team members. Working in an
environment that is
CREATIVITY366
based on trust prompts employees to exhibit positive attitudes
and behaviors,
which is conducive to generating knowledge-sharing behavior
and enhancing
employee creativity. Last, leaders should be aware of the
impacts of AL on team
atmosphere of psychological safety, and of team atmosphere of
psychological
safety on creativity. In their daily work, team leaders should
adopt the AL style
and strive to create an atmosphere of high psychological
security to ensure that
team members are unafraid of interpersonal risks, willing to
share knowledge
and information actively, and prepared to express their true
thoughts and views,
thereby enhancing their creativity.

HumanitiesPoems Facing it” by Yusef komunyakaaMending wal

HumanitiesPoems Facing it” by Yusef komunyakaaMending wal

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