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THE ISM JOURNAL OF
INTERNATIONAL BUSINESS
Vol. 3, issue 1, September 2019
INTRODUCTION
The intellectual contribution of business schools aims
at theory as well as practice. The four papers featured
in this issue advance theoretical knowledge while
impacting management practice. They meet the
four principles of what has been termed pragmatic
rigor: relevance, actionability, comprehensibility,
and ethical reasoning1. By connecting research
and real case problems, the papers are relevant to
managers, business leaders, and decision-makers. They
suggest precise actions that can be implemented by
organizations to address issues of competitiveness and
growth. They are comprehensible and easily understood
by a target audience and beyond. The papers include
ethical considerations as they ponder the impact of their
findings on diverse stakeholders, including society as
a whole. By complying with these four principles, the
papers provide a significant contribution to the debate
on engaged scholarship.
Sara Sadvandi and Daphne Halkias acknowledge the fact that
autonomous vehicles will become staples of future means of transport.
Their findings indicate the need to increase public awareness of the
benefits and limitations of self-driving cars. To gain users’ trust, the
authors recommend that vehicle manufacturers and governments work
closely together.
As companies are increasingly using the SaaS (Soware as a Service)
business model, Benjamin Cohen and Michael Neubert explore
the critical factors that underpin the valuation of SaaS companies and
call for a reassessment of traditional evaluation metrics.
Matthew Andrews and Stanley Smits analyze the synergy between
tacit knowledge exchanges and the effectiveness of teamwork
in organizations. They suggest practical actions to improve this
connection.
Fouad Kazim assesses the challenges faced by managers when
introducing digital transformation in their organizations. The findings
of this study suggest that, for digital transformation to be effective,
business leaders and operational managers need to adapt their
management styles and adopt agile and inclusive cross-functional
methodologies.
A most sincere thanks to all who contributed to this issue.
Enjoy the reading!
César Baena
Editor-in-chief
Dean and Director of Doctoral Research, ISM
1 Robey, D., Taylor, W., & Grabowski, L. (2018). Pragmatic rigor: Principles and criteria for
conducting and evaluating practitioner scholarship. Engaged Management Review, 2(3).
1 | ISM Journal Vol. 3, issue 1, September 2019 ISSN 2150-1076
CONTENTS
INTRODUCTION
César Baena .......................................................... 1
CHALLENGES OF HUMAN FACTORS ENGINEERING IN
THE COMING TRANSITION TO AUTONOMOUS VEHICLE
TECHNOLOGIES: A MULTIPLE CASE STUDY
Sara Sadvandi and Daphne Halkias ........................... 3
HOW DO CORPORATE VALUATION METHODS REFLECT THE
STOCK PRICE VALUE OF SAAS SOFTWARE FIRMS?
Benjamin Cohen and Michael Neubert ..................... 9
USING TACIT KNOWLEDGE EXCHANGES TO
IMPROVE TEAMWORK
Matthew Andrews and Stanley Smits ........................ 15
DIGITAL TRANSFORMATION & LEADERSHIP STYLE:
A MULTIPLE CASE STUDY
Fouad A. B. Kazim .................................................. 24
MEMBERS OF THE EDITORIAL BOARD ........................... 34
ISM Journal Vol. 3, issue 1, September 2019 ISSN 2150-1076 | 2
Abstract
The development of autonomous vehicles has significantly
accelerated in recent years, partially because it is becoming
technically more feasible but also because many significant
benefits of this technology could be offered to society. This
article is based on a qualitative multiple case study focusing
on the challenges brought forth by human factors engineering
in the coming transition to autonomous vehicle technologies,
such as public perceptions, market acceptance, and safety
issues. The units of analysis were subject matter experts’
insights on the topic of study. The results of this article focus
on improving the image that people have of self-driving cars
and on increasing awareness of the benefits and limitations
of autonomous vehicles. In conclusion, manufacturers are
encouraged to work with the government in order to increase
people’s trust regarding safety. To reach a high level of
acceptance, it can be assumed that further research is required
in order to learn more about concerns and to build solutions
that take into account the needs and worries of the end-user
customer.
Keywords: autonomous vehicles, self-driving cars, technology,
policy making, transportation safety
Introduction
Today, the automotive industry is facing significant changes which not
only could influence the design of vehicles but also revolutionize their
interaction with humans and reshape the design of roads and cities
(König & Neumayr, 2017; Silberg et al., 2012). The rise of self-driving
cars has the capacity to bring about significant improvements to fuel
efficiency, time, safety, and general mobility by removing the driver
from the equation (Douma & Palodichuk, 2012). Yet, a strong air of
uncertainty surrounds the introduction of this profoundly new and
different technology; all stakeholders may not be welcoming of such
change (Kyriakidis et al., 2017). Rather than concentrating on the
attitudes that users hold towards self-driving cars in terms of perceived
concerns and benefits, prior studies have instead commonly consulted
experts instead of the public or used a generally narrower focus (König
& Neumayr, 2017).
Background
Automated vehicles are defined as those motor vehicles capable of
automated driving and navigation without direct human assistance. The
origins of autonomous vehicles can be traced back to the late 1920s
when Achen Motor, a distributor of cars in Milwaukee, first demonstrated
phantom motor vehicles. Since then, other car manufacturers,
universities, and also electronics companies have experimented with
automated vehicles with limited success (Waugh, 2013). In fact, the
literal acceleration of self-driving car technology took place when the
US government sponsored the Defense Advanced Research Projects
Agency (DARPA) Grand Challenge in 2004. DARPA presented the first
long-distance competition for driverless cars, offering one million dollars
to the team that could create an autonomous vehicle capable of finishing
a 150-mile course. This challenge attracted more than 100 teams in its
first year (Thrun et al., 2006).
Statement of Problem
The problem is that challenges, such as public perceptions, market
acceptance, and safety issues, brought forth by human factors
engineering in the coming transition to autonomous vehicle
technologies have still not been fully addressed by the automotive
industry (Borenstein, Herkert, & Miller, 2017; Kyriakidis et al., 2017).
Human factors engineering refers to a discipline of applying human
characteristics, limitations, and capabilities to the design of systems,
Challenges of Human Factors Engineering in
the Coming Transition to Autonomous Vehicle
Technologies: A Multiple Case Study
AUTHORS: SARA SADVANDI AND
DAPHNE HALKIAS
3 | ISM Journal Vol. 3, issue 1, September 2019 ISSN 2150-1076
processes, and products. The primary goals are to reduce human error
and enhance the safety and productivity of the interaction between
humans and machines and their environment (Chapanis & Holstein,
2017).
Purpose of Study
The purpose of this qualitative multiple case study was to document
and describe the key insights of subject matter experts (SMEs) on
the challenges brought forth by human factors engineering in the
coming transition to autonomous vehicle technologies, such as public
perceptions, market acceptance, and safety issues. A multiple-case
design was used to satisfy the goal of this descriptive case study (Yin,
2017) on research on human factors and public perceptions regarding
autonomous vehicles as well as human factors engineering in the
coming transition to autonomous vehicle technologies.
Nature of Study
The nature of this study was qualitative. A multiple-case design by
Yin (2017) was used to understand the human factors and public
perceptions regarding autonomous vehicles as well as human
factors engineering in the coming transition to autonomous vehicle
technologies. The units of analysis were SMEs on the topic of the study.
Implementing multiple units of analysis provides the ability to analyze
compound current events in detail from their perspective and reduce
limitations of such analysis in comparison to the survey or trial designs
(Yin, 2017).
Literature Review
The scope of this literature review covers scientific research related to
the expectations and market perceptions of autonomous vehicles. The
literature reviewed was considered from an international perspective
while focusing mostly on French and US markets. It offers an overview
of autonomous vehicles as a concept in industries from the 1920s to
the most recent years. Within the scope of the literature review, there
is also a critical analysis of the challenges brought forth by human
factors engineering in the coming transition to autonomous vehicle
technologies, such as public perceptions, market acceptance, and
safety issues.
Theoretical Framework
Market acceptance and public perception are core elements for
successful integration of the autonomous vehicle in the international
market. General acceptance, described as “willingness for something”
(Fraedrich & Lenz, 2016, p. 22), refers to the process of agreement
by someone or something. In the context of autonomous vehicles,
Franken (2007) described market acceptance as “a positive attitude
on the part of a user or decision-maker towards accepting a thing or
situation” (p. 3) and mentioned that acceptance contains a positive
aspect. Franken (2007) divided acceptance into two components:
• Attitudinal acceptance: emotions and experience;
• Behavioral acceptance: an observable behavior or perception.
Adell (2009) offered a more specific definition for acceptance in the
context of driver assistance systems (DAS) as follows: “the degree to
which an individual intends to use a system and, when available, to
incorporate the system in his/her driving” (p. 31).
Theoretical foundations of acceptance: In the context of
automated vehicles, various theoretical models are applicable in
explaining their impact on market acceptance and public perception
(Venkatesh, Morris, Davis, & Davis, 2003). These theoretical models,
listed below, are derived from the theory of planned behavior, an
approach that describes human behavior based on public perceptions
of situational factors, social influence, and a human value system
(Ajzen, 1991):
• Theory of planned behavior (Ajzen, 1991)
• The technology acceptance model (Davis, 1989)
• The unified theory of acceptance and use of technology (Venkatesh
et al., 2003)
• Model of acceptance of driver assistance systems
• Model of acceptance of fully autonomous driving systems (Kelkel,
2015)
Human Factors and Market Perception
The interaction between humans and automated vehicles is not yet
resolved and requires more research. The challenges include the
impact of automated driving on human driver mental workload, its
situation awareness, as well as human acceptance, reliance, and
trust of automated systems (Brookhuis, van Driel, Hof, van Arem, &
Hoedemaeker, 2008; de Waard, van der Hulst, Hoedemaeker, &
Brookhuis, 1999; de Winter, Happee, Martens, & Stanton, 2014).
Further challenges are related to significant changes in human behavior
due to system automation (Gouy, Nibouche, Hoarau, & Costet, 2014).
Examples of this include the necessary skills that humans should have
to perform the driving task manually and the role of the human in an
emergency situation such as exceeding limits or automation failure
(Levitan & Bloomfield, 1998).
Public opinion and consumer behavior: Studies indicate that the
next generations will be early adopters of driverless vehicles; they will
be more interested in the new technology (Abraham et al., 2016).
According to Kelley Blue Book, the high cost of autonomous vehicles
is the primary reason that 57% of the next generation would not
welcome the self-driving cars (Duffer, 2016).
User resistance: Prior studies on automated vehicles were not
focused on users’ attitudes towards self-driving. Bekiaris, Petrica,
and Brookhuis (1997) were among the first to investigate user needs
and acceptance of technology. Later research continued to evaluate
psychological barriers and attitudes towards self-driving cars (Silberg
et al., 2012).
Research Methods
Population: The sample for this study was first recruited from two
population groups: 1) practitioners working in the automotive industry
and, in particular, in the area of autonomous vehicles, and 2) scholarly
researchers who have published papers on autonomous vehicles as
a disruptive technology and innovation in peer-reviewed scientific
journals.
Sampling criteria: An SME in this study is defined as an academic
scholar or operational expert who has conducted complex projects
or published in-depth research studies on the challenges of human
factors engineering in the integration of autonomous vehicle
technologies in society.
Material: Semi-structured interviews have a protocol of open-ended
questions based on the central focus of the study for data collection
of specific information on a participant’s expert knowledge (Patton,
2014).
Data collection: Semi-structured, open-ended interview questions
were developed as one method to fulfill the aim of the study, along
with appropriate data gathering methods designed to facilitate
accurate and efficient collection.
Validity and reliability: Construct validity is enhanced by using
more than one source of evidence during data collection and by
establishing a chain of evidence at the same time. Two main strategies
were proposed to ensure construct validity: 1) triangulation, which
means observing phenomena from different perspectives, and 2)
ISM Journal Vol. 3, issue 1, September 2019 ISSN 2150-1076 | 4
establishment of a clear chain of evidence that would allow the reader
to follow how the researcher proceeded from the initial propositions to
the final ones (Gibbert, Ruigrok, & Wicki, 2008).
Results of Study
The participants’ responses to the interview questions provided
insights into the study’s three questions and were analyzed thematically
for both consistent and divergent views. This thematic analysis was
synthesized with data from the extant literature, the investigator
triangulation process, and the researcher’s observational field notes to
provide answers to the research questions.
The findings of this study presented four themes that correspond
to Research Question 1 (RQ1), four themes corresponding to
Research Question 2 (RQ2), and four themes corresponding to
Research Question 3 (RQ3). Accordingly, the following discussion of
implications addresses each of these in turn.
RQ1: What are the insights of subject matter experts (SMEs)
regarding the challenges faced by the automotive industry in
terms of general public perceptions of autonomous vehicles?
The public’s fear of accidents. The first discovery for RQ1 is that the
public is worried about the abilities of this technology. They believe
that artificial intelligence can do many things better than humans, but
autonomous objects may potentially become very dangerous (Zhao,
Dimovitz, Staveland, & Medsker 2016). All participants agreed that
these vehicles can bring significant improvements to transportation
safety and have environmental benefits. The second discovery is that
current infrastructure has to be optimized for the use of autonomous
vehicles in order to reduce accidents (Venkatesh et al., 2003).
Fear of computer/machine domination over humans. All the
participants were concerned about the possibility of malfunction that
could cause the vehicle to act unpredictably. Furthermore, one of the
participants stated that the autonomous vehicle could be hacked the
same way any other computing device can. As all participants stated,
the least attractive characteristics of autonomous vehicles are related
to safety and privacy regarding how the necessary data are collected,
stored, maintained, and used.
The initial high cost. Based on the findings of this study, cost
is one of the main challenges when considering the marketing of
autonomous vehicles to the general public. The high costs of self-
driving cars are due, firstly, to all the autonomous services provided
via new sensors and technical features in the car (Dixit, Chand, & Nair,
2016) and, secondly, to the communication with infrastructure that
autonomous vehicles rely on like detailed maps of cities and their
surroundings for orientation.
The fear of killing someone. Worries regarding the fear of killing
someone can also be related to lack of trust (Abraham et al., 2016).
The findings of this study show that the general level of trust in machine
driving is limited. In this context, it was reported that, within the
reviewed studies, the majority of participants were concerned that
self-driving vehicles may drive as well as human drivers but that their
reactions in hazardous and unpredictable situations are not clear.
RQ2: What are the insights of SMEs regarding the challenges
faced by the automotive industry in terms of market
acceptance of autonomous vehicles?
Start by introducing the technology to new drivers. As most of
the participants stated, it is essential to motivate and educate future
users to reduce their fear of this new technology (Alaieri & Vellion,
2016). Fear is a defense mechanism; it will gradually decrease as the
public discovers that there is no threat. As mentioned by some of the
participants, assistive-driving technology and autonomous vehicle
features can easily be marketed to new drivers if introduced from
driving school.
Use social media to ease the acceptance process. All the
participants believed that user acceptance is a prerequisite for the
successful introduction of autonomous driving to the market. The
autonomous vehicle industry has to investigate the process of getting
users to agree with, approve of, or acknowledge the technology via
the media (Fraedrich & Lenz, 2016). Findings for RQ2 are that media
and communication technologies are considered to be of central
importance for reducing uncertainty, and thus the future success of
autonomous vehicles.
Initially offer free products and training. The main finding of this
section for RQ2 is that the autonomous vehicle industry has to further
investigate the possible behavioral changes that might come with the
implementation of autonomous vehicles because they will play an
important role in societal acceptance (Trappl, 2016).
Leverage people’s relationship with the act of driving to
support the acceptance process. The final finding for RQ2 is that
the higher the level of vehicle automation, the more the driving task is
shied from driver to system. Some participants were worried that, due
to the introduction of fully automated driving systems, drivers might
feel deprived of enjoyment and the feeling of being in control.
RQ3: What are the insights of SMEs regarding safety issues in
relation to human factors engineering in the coming transition
to autonomous vehicles?
Improve road safety with rebuilt transportation infrastructure.
As most of the participants noted, automated driving is a key
technology for future mobility and quality of life, and, consequently,
transport policy should be adopted for automated driving.
Autonomous vehicles deal with drivers and infrastructure, and they
have relative importance for road safety.
Start with assistive technology initially, and then move to
fully autonomous vehicles. As most of the participants stated,
autonomous services have to be integrated into society progressively.
Therefore, starting with assistive-driving features can lead to a
smoother adoption of fully automated vehicles.
Lawmakers’ cooperation with the automotive industry to
change motor vehicle laws. In the near future, driving rights will
be delegated to autonomous vehicles. However, it is necessary that
autonomous vehicles obey social and road rules. As mentioned by all
study participants, to develop and deploy highly or fully automated
cars into market-ready vehicles, there are many non-technical
challenges, including legal ones.
Future predictions of SMEs for driving in 2030. The final finding
for RQ3 is that automated driving has a fundamental economic and
social impact on society. Some of the participants said that self-driving
cars will not be mature enough to be in the market and used by the
public in 2030. They believed, however, that large numbers of freight
“
“Public perception and user acceptance
need to be taken carefully into account
in order for autonomous vehicles to be
successfully introduced to the market.
5 | ISM Journal Vol. 3, issue 1, September 2019 ISSN 2150-1076
vehicles will be equipped with self-driving functionalities and driver
assistance functions. One of the participants mentioned that some
public services such as buses or other forms of public transportation
will be automated, but automated highway driving will not be in place
by 2030.
Recommendations and Conclusion
This study found that autonomous vehicle technology is rapidly
moving forward with little attention to market acceptance. However,
it has significant impacts on reducing accidents, decreasing parking
demand, increasing fuel efficiency, improving road capacity, and
optimizing mobility for non-drivers. With the mass penetration of
autonomous vehicles, travel behavior might change in a way that will
impact the economics of transportation.
Recommendations for Practice
• The automotive industry should further assess the public
perception and attitudes towards highly/fully automated vehicles.
• The automotive industry has to gain a clearer image of what
the self-driving car entails by promoting and showing all of its
benefits.
• Manufacturers should start communicating early to get people
used to automated vehicles before they go to market.
• Manufacturers should be transparent by showing the number of
kilometers tested and by making accidents public.
• The media can be an effective way to demonstrate the
technology, and car manufacturers should provide real-life
demonstrations.
Recommendations for Future Research
• Additional insights can be gathered about the future
development of autonomous vehicles and their integration into
society with further analysis of additional case studies.
• More in-depth research should be pursued, particularly with
samples of end-user customers.
• Further in-depth studies will contribute to an understanding of
how self-driving cars can be evaluated through a human-centric
approach.
• A broader number of participants and SMEs should be
considered for future studies.
Conclusion
The focus of this study was to introduce self-driving vehicle
technologies and their barriers. Public perception and user acceptance
need to be seriously considered, so autonomous vehicles can be
successfully introduced to the market.
The results of this study focused on improving the image that people
have of self-driving cars and on increasing awareness of the benefits
that autonomous vehicles offer. Additionally, manufacturers were
encouraged to work with the government in order to increase people’s
trust regarding safety. Governments should focus on taking preparatory
measures to ensure that potential issues are resolved in a timely
manner, as was suggested by multiple interview participants. Problems
such as ethical dilemmas and regulatory changes can be sufficiently
addressed at this time, and potential infrastructural adjustments can
be implemented for the future. A lack of knowledge about this new
technology plays an important role in market acceptance and trust.
Future research could also target the impact of user acceptance on the
environment.
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About the authors
Sara Sadvandi is a portfolio manager at Dassault Systèmes in France. She has around 10
years experience representing complex industrial projects, notably in automotive and high
tech industries. Aer graduating from Polytechnique as a Master of Complex Industrial
System Engineering and Management, she started working for the PSA Group (Peugeot
Citroen) as a leader on specifications for autonomous vehicles. Sara has two patents and
has published several articles about the application of new engineering methods for
complex industrial projects.
Daphne Halkias is a core faculty member at the International School of Management.
She is also a fellow at the Institute of Coaching at Harvard Medical School and CEO of
Executive Coaching Consultants, an international consulting firm specializing in leadership
mentoring, coaching family firms, and developing academic research projects in the areas
of cross-cultural management and entrepreneurship. She is a distinguished academic who
has chaired and developed academic programs in the US and Europe, and a researcher
with books and paper publications spanning 25 years in organizational behavior,
corporate governance, entrepreneurship, cross-cultural management, family business,
negotiation skills, psychology, migration issues, and education. She has been an invited
keynote speaker in numerous business and social science forums, including the World
Entrepreneurship Summit and the Wendel Center for Family Enterprise at INSEAD, France.
Dr. Halkias is a member of several professional associations, but her strongest inspirations
come from membership in “Business Fights Poverty” and KIVA and the cause of fighting
poverty through leadership mentoring for sustainable entrepreneurship chronicled in her
book Entrepreneurship and Sustainability: Business Solutions for Poverty Alleviation from
around the World, co-authored with Paul Thurman from Columbia University.
7 | ISM Journal Vol. 3, issue 1, September 2019 ISSN 2150-1076
Abstract
This paper seeks to identify the critical factors determining the valuation of SaaS companies. This newly created business model
renders many evaluation metrics inapplicable. This creates a unique sub-industry of such companies for corporate valuation
purposes. Salesforce.com is used as the focal company of evaluation, allowing use of and reference to actual data and prices.
This paper aims to evaluate the accuracy and relevancy of specific valuation techniques and identify those best suited for a SaaS
company. This study uses a single case study research design, allowing for a deeper level of analysis. This paper has found that
standard valuation techniques were successfully able to evaluate a stock share price using quarterly financial data. The relative
valuation efforts were unable to derive a price range for the company. The peer analysis showed the importance of key factors
like growth, profitability, or lifecycle phase, which becomes evident in the calculated metrics. The calculations performed in
this paper shed light on the level of disconnect within the SaaS business model and standard valuation techniques. Companies
experiencing higher growth will not compare well with companies of greater profitability. This paper brings momentum to
defining an improved relative valuation metric that more robustly represents the value forecast of a SaaS company, provides
technical support for the valuation of SaaS companies, and furthers the discussion of creating new valuation metrics for fast
growth start-up firms.
Keywords: SaaS, soware as a service, corporate valuation, international finance, global marketing, international business, pay-
per-use, pricing strategy, soware industry, cloud-based soware
Introduction
The accurate valuation of companies is vital to ensure that stock markets
are reliably efficient and that merger, acquisition, and divestiture events are
handled fairly and appropriately. Without this fundamental expectation,
stock market volatility and liquidity would suffer. However, not all industry
segments can be evaluated using the same criteria, nor will analyst
companies fully adhere to the same set of valuation criteria. Due to
these factors, as well as overall global economic stability and individual
market crises, a multitude of valuation metrics and processes have been
developed and continue to evolve through the emergence of new
business industries and business models.
In particular, the emergence of companies employing the new soware
as a service (SaaS) business model has created a disconnect between the
valuation methods used within this sub-industry and elsewhere. Because
this collection of companies has flourished in recent years, thus becoming
permanent additions to the corporate landscape, the valuation techniques
warrant further understanding. By uncovering these processes, insights are
sought to lead to improved accuracy of valuing this particular sub-industry.
For the purpose of bringing specific discussion points and values to this
paper, Salesforce.com is chosen to be the focal company of evaluation.
In actuality, any young and growing company in the sub-industry would
suffice, and no particular bias towards this company shall be given. This
paper aims to be objective and insightful towards the accurate portrayal
and valuation of this and any other SaaS company.
For this purpose, this paper shall proceed by discussing the different
price-setting models, strategies, practices, and valuation methods of SaaS
How Do Corporate Valuation Methods Reflect the
Stock Price Value of SaaS Soware Firms?
AUTHORS: BENJAMIN COHEN AND
MICHAEL NEUBERT
9 | ISM Journal Vol. 3, issue 1, September 2019 ISSN 2150-1076
firms in the literature review. Following this shall be insights found through
the application of different valuation methods on the case study firm. This
paper concludes by providing the overall comparison of public valuation
methods found to be the most effective.
Literature Review
SaaS Pricing Model
This survey has selected Salesforce.com as a single case study firm
because it is one of the most famous international high-tech firms using a
SaaS price-setting model and it grants access to high quality financial data
due to its stock exchange listing.
SaaS is the abbreviation of soware as a service. Instead of investing
in Salesforce.com’s customer relationship management soware,
clients acquire a subscription-based license of a cloud-based soware
package. The license fee can be considered an operating rather than
capital expenditure, which is a standard model for a SaaS company and a
competitive advantage of this pricing model.
The first part of this literature review focuses on the theoretical framework
to describe the SaaS price-setting strategy, practice, and model of the
case study firm. For the purpose of this survey, the theoretical framework
of Cohen and Neubert (2017) and Neubert (2017a) (see Figure 1) will be
used to describe the SaaS of Salesforce.com as a pay-per-use price-setting
model.
Figure 1. Price-setting practice, strategy, and models. From “Lean
internationalization: How to globalize early and fast in a small economy,” by
M. Neubert, 2017a, Technology Innovation Management Review, 7(5), 16-22.
Retrieved from http://timreview.ca/article/1073
The paper of Neubert (2017a) is based on a report showing the
fundamentals of price-setting practices and how they apply to SaaS
companies (Accion, 2015). Accion (2015) defines the differences
between the price-setting practices of cost-informed, competition-
informed, and value-informed, and then identifies the value-informed
price-setting practice as the predominant strategy used by SaaS
companies, with the competition-informed price-setting practices
used in mainly mature markets. Salesforce.com operates as a pioneer
and market leader in the customer relationship management soware
market using predominately value-informed pricing practices within its
skimming-based price-setting strategy.
Huang (2014), who performed industry research covering the range
of price-setting models offered, states that the pricing mechanisms
are either usage-based, time-based, or a hybrid of the two. A pay-
per-use license contract like the one of Salesforce.com can use a
combination of fixed monthly licensing fees plus additional costs if the
usage exceeds the defined limit (e.g. number of users) or if the client
asks for product adaptations or individualizations. Time-based license
contracts consist of clients making extended time period reservations,
typically for one to three years. Cloud-based SaaS service providers
like AWS or Salesforce.com carefully balance their pay-per-use fees
depending on different factors like data storage, data retrieval, or data
upload volume (Deelman, Singh, Livny, Berriman, & Good, 2008;
Neubert, 2017b).
Valuation of SaaS Firms
The valuation of a corporation is influenced by many different variables,
and each valuation method considers each different variable to a
higher or lower extent (if at all). This is especially important for the
valuation of high-tech firms with innovative business models, like
Salesforce.com as an example of a young, fast-growing firm, which
uses a SaaS pricing model for its cloud-based soware products. Thus,
this second and final part of the literature review focuses on the impact
of a SaaS price-setting model on corporate valuation.
The selection of an appropriate valuation method depends on the
lifecycle stage of each firm (Trichkova & Kanaryan, 2015). In every
lifecycle stage, growth rates and profitability differ. The case study firm
Salesforce.com can be characterized as a relatively young firm in the
growth stages of its corporate lifecycle using suitable price-setting
strategies, practices, and models. In this development stage, the
revenue growth rate tends to be higher, and the profitability is lower
(Neubert & van der Krogt, 2017).
According to Newton and Schlecht (2016), revenue and especially free
cash flow growth are more than twice as important for the valuation of
SaaS companies as profitability (here: EBITDA margin). Gardner (2016)
confirms this finding in identifying the revenue growth rate as one of
the key factors that goes into assessing a firm’s revenue multiple for
corporate valuation. In the time period in which this survey analyses
the valuation of Salesforce.com, it is profitable but does not pay any
dividends. Feld Thoughts (2015) presents a combination of revenue
growth and profitability that states that a SaaS company’s combined
monthly recurring revenue (MRR) plus EBITDA profit margin should add
up to 40% or above.
In addition to Newton and Schlecht (2016), Smale (2016) discusses the
typified classification of SaaS businesses as having annual profit (seller
discretionary earnings [SDE]) multiple within the range of 2.5 to 4.0
times the annual profit. This range is a function of many variables, most
notably the age of the business, required time of owner involvement,
growth trend of business, and customer churn rate (Smale, 2016).
Tunguz (2016) provides historical data tracking the enterprise value
(EV) multiple of SaaS companies over time and identifies during this
time period sharp changes of the EV / forward revenue multiple void
of any notable economic crises or widespread instability. Other key
factors to assess the corporate valuation of SaaS firms are size of the
target market, customer retention rate, gross margin, and capital
efficiency (Gardner, 2016). Smale (2016) further elaborates that a
large amount of intrinsic corporate value lies within intangible or
qualitative measures of the firm. Examples of this include stability of
the earning power, owner-specific business relationships, business
traffic attributable to search engines and their algorithms, level of
competition within the business niche, and type of customers targeted
Price-Setting Practice
• Value-informed
• Competition-informed
• Cost-informed
Price-Setting Model
• Buy
• Rent/Lease
• Pay-per-use
Price-Setting Strategy
• Skimming
• Market pricing
• Penetration pricing
“
“The emergence of companies employing
the new soware as a service (SaaS)
business model has created a disconnect
between the valuation methods used
within this sub-industry and elsewhere.
ISM Journal Vol. 3, issue 1, September 2019 ISSN 2150-1076 | 10
by the company. Bancel and Mittoo (2014) present survey results of
356 European valuation experts with respect to the assumptions and
estimation methods for their valuation practices. The results find that
even when the textbook standard models for company valuation are
used, the textbooks do not fully define how to derive all input variables
and key factors (Festel, Wuermseher, & Cattaneo, 2013). In addition,
the company lifecycle is discussed as a means to justify the ease of
transition between ownership or to create a sense of urgency (also
compare to Trichkova & Kanaryan, 2015) because growth rates differ
depending on the lifecycle phase.
As the literature research has shown, significant amounts of research
have been conducted towards the three principle areas of this
study: SaaS business models, SaaS price-setting practices, and SaaS
corporate valuation. However, no prior research has been performed
that investigates the intersection of these three. Therefore, this survey
contributes to the impact of pricing decisions on corporate valuation
of SaaS firms using the following adapted theoretical framework (see
Figure 2).
Figure 2. The intersection of SaaS business models, price-setting practices, and
SaaS corporate valuation (source: the authors)
Research Methodology
This survey used a single case study research methodology. Individual
stock and market index data were collected from publicly available
websites (Scholz & Tietje, 2002; Yin, 2015). The main stock in focus
was then analyzed using widely accepted evaluation equations,
followed by advanced analysis techniques gathered from more
recent academic literature and reputable online sources, partially in
comparison to selected peers. The findings from these techniques
were then examined to compare valuation estimates as well as
applicability towards the SaaS profit model.
The purpose of the study has led to the following research question:
How do corporate valuation methods reflect the stock price value of
SaaS soware firms?
Corporate Valuation
Corporate valuation equations and metrics have been derived,
defined, and further developed continuously since the advent of
stock markets. Understanding a company’s fundamental metrics
and profitability has become the basis of comparison with its peer
companies. Most every modern economics textbook covers the
breadth of these calculations. Therefore, without providing derivations
or explanation for the following calculations, the standard valuation
metrics are provided below.
Discounted Cash Flow
The corporate valuation calculations can be separated into two main
groups: the discounted cash flow (DCF) method (including variants)
and relative valuation (RV) framework (Bancel & Mittoo, 2014). The
first method involves calculating the net present value (NPV) for the
stock’s dividend, current cash flow, and forecasted cash flow growth.
The summation of these three values is the resulting valuation of the
company. Specific to Salesforce.com, there have been no dividends
granted to date; thus, metrics related to dividends each calculate to
zero.
As seen in Figure 3, the calculations match considerably well with the
actual price history. Because of the year-over-year equations being
used, most of the short financial history is insufficient for calculating
corporate value. However, in the four quarters that are computed,
the calculation accuracy is within 5%, substantially within the margin
of error when taking into account non-financial sources such as news
releases, macro-economic forces, and price change momentum.
The DCF method was successfully used to derive a corporate valuation
for Salesforce.com of $78.91 per share using data through Q4 2017
fiscal quarter (31 January 2017). This estimation compares well to the
stock price of $79.10 per share on the same day. This corresponds to a
0.24% difference (compare to Figure 3)
Figure 3. DCF-based valuation of the case study firm
Although the cash flow being generated by the company has created
value, the bulk of the valuation comes from the free cash flow growth
rate being experienced and the expectation for continued growth
in future quarters, which is in line with the findings of Newton and
Schlecht (2016). This important finding shows the shareholders and
managers of SaaS soware firms how to increase their corporate
valuation. One growth driver is the use of a SaaS price-setting model.
As the example of our case study firm Salesforce.com shows, every
additional user or every additional activity a user performs (e.g.
increase of required storage or download volume) immediately results
in higher sales revenues.
The calculations of the DCF-based valuation use the CAPM model.
Figure 4 shows the parameters, results, and input factors and Figure 5
additional support for these calculations.
Relative Valuation
Relative valuation does not provide a method of calculation for
precisely valuing a company. Instead, it provides a range of value
metrics of a company’s peer group from which reasonable price
estimates can be bracketed. The selected peer group consists of SAP,
Adobe, Citrix, DXC, Blackbaud, Cognizant, and VM Ware (compare
to Figure 6). With these estimations, it is possible to gauge if a stock
is valued high, low, or on target relative to its peer/comparable
companies. Caution is to be used with this method to ensure validity
in company comparison, particularly taking into account the company
size, sub-industry, business model, growth focus, lifecycle phase, and
location.
The findings of the relative valuation suggest that traditional relative
valuation methods will not work at this point in the company’s lifetime
with this peer group set. In fact, the relative valuation methods do not
take into consideration the above-average expected free cash flow
Saas
business model
Saas
price-setting practices
Saas
corporate valuation
11 | ISM Journal Vol. 3, issue 1, September 2019 ISSN 2150-1076
Figure 4. WACC calculations within the standard CAPM model (source: the authors)
Figure 5. Derivation of beta and Jensen’s alpha using 36-month period (source: the authors)
ISM Journal Vol. 3, issue 1, September 2019 ISSN 2150-1076 | 12
Figure 6. Relative valuation comparison between CRM and seven comparable peer-group companies (source: Morningstar 2017)
13 | ISM Journal Vol. 3, issue 1, September 2019 ISSN 2150-1076
growth rate shown in the DCF valuation. Thus, the selection of an
appropriate corporate valuation method for a SaaS firm depends on
the stage of a firm’s development (Trichkova & Kanaryan, 2015).
As observed in Figure 7, all six of the relative valuation metrics
estimate a corporate valuation below the current stock price. These six
relative valuation metrics are: Price-Earning-Ratio (PER), Forward PER,
Price-Earning to Growth-Ratio (PEG), Price-Sales-Ratio, Price-Book-
Ration, and Price-Cash-Flow-Ratio. Thus, it can be suggested that the
traditional relative valuation methods do not reflect the full price paid at
a stock exchange of fast-growing high-tech firms.
Figure 7. Relative valuation of Salesforce.com using six metrics from seven
comparable companies (source: the authors)
Conclusions
The standard valuation calculations were successfully used to derive
a corporate valuation for Salesforce.com of $78.91 per share using
data through Q4 2017 fiscal quarter (31 January 2017). This estimation
compares well to the actual stock price of $79.10 per share on the
same day. This corresponds to a 0.24% difference.
The relative valuation process did not favorably estimate the valuation
of Salesforce.com through comparison with comparable peer
companies. The four closest comparisons estimate a valuation of
$67.42 per share, or a 17% difference with the price at the end of Q4
2017. This discrepancy is perceived to be due to the higher growth
phase of Salesforce.com as compared to similar companies and
thus suggests that company lifecycle is of greater importance when
selecting a peer set.
The literature search highlighted several additional concerns for
company valuation that cannot be quantified. Such important
characteristics of a company emphasize the human component
required in the valuation team, and no clear and conclusive conversion
to a mathematical formulation has been derived.
The guidelines on using rule-of-thumb estimates is that they are only
valid when the situation is analogous to when the rule was first derived.
The changing financial climate of the increasingly interconnected
and interdependent world is creating situations more complex than
ever before. All observations made here are strictly limited to SaaS
companies and within the short timeframe of the past 10 years. Their
applicability toward the future is only as valid as the assumption that
the future shall be stable and predictable. Single case studies have
a number of natural limitations and concerns like that of replicability,
generalizability, and reliability due to the small sample size. Thus, any
future study would benefit from the same level of intense examination
and investigation for other comparable SaaS companies and for longer
time periods.
References
Accion. (2015, March 23). Pricing your SaaS product. Retrieved from https://www. accion.
org/sites/default/files/Pricing%20Your%20SaaS%20Product%20vF.pdf
Bancel, F., & Mittoo, U. R. (2014). The gap between theory and practice of firm valuation:
Survey of European valuation experts. Journal of Applied Corporate Finance, 26(4), 106-
117. doi: 10.1111/jacf.12095
Cohen, B., & Neubert, M. (2017). Price-setting strategies for product innovations in the
medtech industry. In D. Vrontis, Y. Weber, & E. Tsoukatos (Eds.), 10th Annual Conference of
the EuroMed Academy of Business (pp. 459-473). Cyprus, GR: EuroMed Press.
Deelman, E., Singh, G., Livny, M., Berriman, B., & Good, J. (2008, November). The cost of
doing science on the cloud: The Montage example. In SC ’08: Proceedings of the 2008
ACM/IEEE Conference on Supercomputing (p. 50). Piscataway, NJ: IEEE.
Feld Thoughts. (2015, February 3). The rule of 40% for a healthy SaaS company. Retrieved
from http://www.feld.com/archives/2015/02/rule-40-healthy-saas-company
Festel, G., Wuermseher, M., & Cattaneo, G. (2013). Valuation of early stage high-tech start-
up companies. International Journal of Business, 18(3), 216-231.
Gardner, T. (2016, October 7). Determining the worth of your SaaS company. TechCrunch.
Retrieved from https://techcrunch.com/2016/10/07/determining-the-worth-of-your-
saas-company/
Huang, J. (2014). Pricing strategy for cloud computing services (Doctoral dissertation).
Singapore Management University, Singapore.
Neubert, M. (2017a). Lean internationalization: How to globalize early and fast in a small
economy. Technology Innovation Management Review, 7(5), 16-22. Retrieved from http://
timreview.ca/article/1073
Neubert, M. (2017b). International pricing strategies for born-global firms. Central
European Business Review, 6(3), 41-50.
Neubert, M., & Van Der Krogt, A. (2017). Lean internationalisation of high-tech firms.
International Journal of Teaching and Case Studies, 8(2/3), 133-150.
Newton, T., & Schlecht, I. (2016, June 14). How to value SaaS Companies. Catalyst
Investors. Retrieved from https://catalyst.com/2016/06/14/value-saas-companies/
Scholz, R. W., & Tietje, O. (2002). Embedded case study methods: Integrating quantitative
and qualitative knowledge. Thousand Oaks, CA: Sage.
Smale, T. (2016, July 6). SaaS valuations: How to value a SaaS business in 2017. FE
International. Retrieved from https://feinternational.com/blog/saas-metrics-how-to-value-
saas-business/
Trichkova, R., & Kanaryan, N. (2015, June). Startups valuation: Approaches and methods.
Paper presented at First Balkan Valuation Conference, Sofia, Bulgaria.
Tunguz, T. (2016, January 13). The downward pressure of public markets on startup
valuations. Retrieved from http://tomtunguz.com/pressure-publics-on-privates/
Yin, R. K. (2015). Qualitative research from start to finish. New York: Guilford Publications.
About the authors
Benjamin Cohen is a senior manager in the US petroleum industry. He holds an
International Executive MBA in finance from the International School of Management, a
Master of Science in petroleum engineering from the University of Southern California, and
a Bachelor in petroleum engineering from the Colorado School of Mines.
Michael Neubert is a PhD alumnus, core faculty member, and chair of the
Strategic Management Committee at ISM. He is an active researcher in international
entrepreneurship and finance, and publishes his research results in peer-reviewed
academic journals. His current research interests include FinTech, pricing decisions, and the
early and fast internationalization of high-tech start-up firms as well as the concepts of lean
and digital internationalization. In addition, Dr. Neubert is the CEO of C2NM LLC, a Swiss
consulting firm specializing in the field of international and intercultural management. It
offers services from the development of international strategies and global market research
to market entries, turnarounds, and market exits as well as intercultural trainings and
seminars. Dr. Neubert is also a partner of a private equity firm, which invests in high-tech
start-up firms. He supports the portfolio companies in the early and fast internationalization
of their products and business models, and integrates these experiences in the case studies
of his courses.
ISM Journal Vol. 3, issue 1, September 2019 ISSN 2150-1076 | 14
Abstract
This paper suggests ways to improve the synergy between
tacit knowledge exchanges and teamwork effectiveness. We
begin by assessing the relationship between tacit knowledge
exchanges and key building blocks which underscore how
organizational learning is analyzed. Following this, the
literature relevant to teamwork, organizational learning,
and knowledge management is reviewed. We propose
that as organizations evolve they should strive towards
a strategic knowledge management approach using
the learning organization as a practical model. Because
teams are a crucial vehicle for organizational learning and
knowledge management, we review the literature on context
fundamentals for effective teamwork. The findings suggest
that the context for organizational learning and effective
teamwork are similar, and that these are both similar to the
context for tacit knowledge in its early stages of development.
Knowledge exchanges and effective teamwork are highly
related and key for the generation of new knowledge. The
paper concludes by suggesting practical interventions and
guidelines to improve the synergistic exchanges between
tacit knowledge-driven organizational learning and effective
teamwork.
Keywords: tacit knowledge, teamwork, organizational
learning, knowledge management
Introduction
Much of what we do in the workplace is done cooperatively with
others. These ad hoc and more formal teamwork experiences provide
opportunities for shared learning, oen through an exchange of
experience-based tacit knowledge. By definition, such knowledge is
less structured than explicit knowledge, oen personal and context
specific (Andrews, 2017; Brown & Duguid, 1991; Nonaka, 1994;
Polanyi, 1966; Spender, 1993). When tacit knowledge exchanges
occur in the context of teamwork focused on resolving complex,
challenging, and dynamic issues, the learning opportunities become
more important both operationally and strategically (Barney, 1991;
Eisenhardt & Martin, 2000; Hitt, Keats, & DeMarie, 1998). Our
purpose in this paper is to suggest ways to improve the synergy
between tacit knowledge exchanges and teamwork effectiveness.
Organizations, especially those reliant on sophisticated knowledge
management, face an array of challenges due to dynamic complexity,
technological change, and international competition (Grant, 2010).
They respond by deploying multidisciplinary teams of specialists with
the formal knowledge needed to sustain organizational effectiveness
and competitive advantage (Alexander & van Knippenberg, 2014;
Yamklin & Igel, 2012). These specialists bring to the team the explicit
knowledge from their respective disciplines and their personal
experience to date in its application. An important determinant of
the team’s success is how well they can communicate both their
explicit and tacit knowledge to develop shared mental models
(Wilson, Goodman, & Cronin, 2007). The working hypothesis of many
knowledge management and teamwork experts is that improved
shared mental models result in greater team effectiveness; and, in
turn, improved team effectiveness generates more experience-driven
knowledge exchanges (Edmonson, 2002; Hass & Mortenson, 2016;
Jones & George, 1998; van der Vegt & Bunderson, 2005). Advances
in capturing and applying tacit knowledge (Ambrosini & Bowman,
2001; Andrews, 2017; McIver, Lengnick-Hall, Lengnick-Hall, &
Ramachandran, 2013) provide useful tools for the development of
new teams, orienting newcomers to existing teams and improving and
sustaining long-term team functioning.
The purpose of this paper is fivefold:
Using Tacit Knowledge Exchanges to
Improve Teamwork
AUTHORS: MATTHEW ANDREWS
AND STANLEY SMITS
15 | ISM Journal Vol. 3, issue 1, September 2019 ISSN 2150-1076
• To describe the important role of tacit knowledge exchanges in
support of the key building blocks that underscore organizational
learning;
• To explore how the literature on organizational learning,
the learning organization, and knowledge management are
interrelated;
• To summarize the literature describing key variables associated
with effective teamwork;
• To propose links between theories of organizational learning
and knowledge management, on the one hand, and theories of
organizational lifecycles on the other;
• To suggest practical interventions and guidelines to improve
the synergistic exchanges between tacit knowledge-driven
organizational learning and effective teamwork.
Organizational Learning and Teamwork Effectiveness
Here we review selected literat¬ure as background for our
recommendations to improve the synergy between tacit knowledge
exchanges and teamwork effectiveness. We start by introducing the
systemic challenges that have evolved over the last few decades
and then focus on learning and specialized teamwork as dynamic
capabilities to deal with dynamic complexity.
We start with the context described by Kimberly and Bouchikhi in
1995:
… Organizations, both large and small in a variety of industries
located around the globe are currently struggling with the basic
questions of how to enhance their capacity to innovate and
adapt rapidly to changing markets and technologies. (p. 9)
Eisenhardt and Martin’s (2000) solution was for firms to enhance
their dynamic capabilities, especially in high-velocity markets,
through learning; they define these capabilities as follows: “Dynamic
capabilities … in high-velocity markets … are simple, highly
experiential and fragile processes with unpredictable outcomes” (p.
1105). Eisenhardt and Martin (2000) state that “well-known learning
mechanisms guide the evolution of dynamic capabilities” (p.1105). A
common practice to enhance such capabilities through learning was to
organize diverse sets of experts into teams to deal with the challenges
of the time:
In recent years there has been a significant increase in the use
of ... multidisciplinary work teams…. The motivating premise
underlying the use of these teams is that when representatives
from all of the relevant areas of expertise are brought together,
team decisions and actions are more likely to encompass the full
range of perspectives and issues that might affect the success
of a collective venture. Multidisciplinary teams are therefore an
attractive organizing option when individuals possess different
information, knowledge, and expertise that bear on a complex
problem or issue. (van der Vegt & Bunderson, 2005, p. 532)
In summary, the performance and survival challenges faced by many
organizations require enhanced dynamic capabilities. Teams of experts
pooling their explicit discipline-based knowledge, while evolving and
perfecting their shared experience-based tacit knowledge, develop
the needed capabilities thereby improving and sustaining their
effectiveness.
The Learning Organization and Knowledge Management
Like individuals, organizations learn naturally from their experiences
(Smits & Bowden, 2015). However, that natural process, without
guidance, is a hit-and-miss phenomenon that many organizations
in dynamic situations can no longer afford. In Edmondson’s words,
learning is now a competitive imperative: “Today’s central managerial
challenge is to inspire and enable knowledge workers to solve, day in
and day out, problems that cannot be anticipated” (2008, p. 60). She
is not alone in her emphasis on the strategic importance of learning,
but one of many scholars and leaders sending similar messages. Some
examples include Argote and Miron-Spektor (2009), Gardner, Gino,
and Staats (2012), Rowden (2001), Wilson et al. (2007), and Yamklin
and Igel (2012). Here we briefly summarize and integrate three related
fields of theory and inquiry: organizational learning, the learning
organization, and knowledge management.
While organizational learning developed out of the fields of
organizational behavior and behavioral psychology in the late 1970s
(Andrews, 2018), recent scholarly attention explores the relationship
of this field to the concept of the learning organization and to the field
of knowledge management (Andrews, 2018; Andrews & Smits, 2018;
Prusak, 2001). Although many definitions of organizational learning
can be found in the scholarly literature, we prefer Argote and Miron-
Spektor’s (2009) definition which states that “organizational learning
is a change in the organization that occurs as the organization acquires
experience” (p. 4). Edmondson (2002) argues that organizational
learning happens primarily thanks to interactions among individuals
who form the small groups and teams within the larger organization.
Teams are therefore a crucial unit of and vehicle for organizational
learning. There is consensus within the scholarly literature that,
whether focusing on the entire entity (e.g. a company) or subsets
within the organization, the level of analysis must be collective rather
than an aggregate of individual learning (Klein, Dansereau, & Hall,
1994; Wilson et al., 2007). Collective routines and behavior may
thus be considered forms of organizational knowledge. Wilson et
al. (2007) further contend that any notion of collective (i.e. group or
organizational) learning must take into consideration processes of
sharing, storage, and retrieval of knowledge, and must be observable
as an outcome—for example, as a change in collective routines and
behavior over time.
The learning organization concept developed primarily in the 1990s
and is frequently associated with the work of Peter Senge who defines
learning organizations as places “where people continually expand
their capacity to create the results they truly desire, where new and
expansive patterns of thinking are nurtured, where collective aspiration
is set free, and where people are continually learning how to learn
together” (1990/2006, p. 8). Argyris and Schon (1996) assert that the
concept is a branch of the organizational learning literature developed
mainly by consultants and practitioners rather than scholars. In our
view, the concept is best conceived as an ideal type towards which
organizations may aspire by adopting best practices in organizational
learning designed to yield desired outcomes. This view is supported
by scholars such as Garvin, Edmondson, and Gino (2008), Heorhiadi,
La Venture, and Conbere (2014), Jagasia, Baul, and Mallik (2015),
King (2001), Kirwan (2013), Lazar and Robu (2015), Rowden (2001),
Shipton, Zhou, and Mooi (2013), and Yang, Watkins, and Marsick
(2004). In this paper, we refer to the Learning Organization Survey
developed by Garvin et al. (2008). This diagnostic tool designed to
foster learning consists of questions for members of the organization
to answer about the learning environment and practices in the given
organization. The survey is organized into three “building blocks”
which its authors argue are necessary conditions for organizational
learning to develop: “(1) a supportive learning environment, (2)
concrete learning processes and practices, (3) leadership behavior that
reinforces learning” (p. 110).
The field of knowledge management, which also developed primarily
in the 1990s, shares many of the same concerns as organizational
learning; these concerns include knowledge acquisition as well as
learning processes and outcomes. However, knowledge management
places more emphasis on managing what is learned (Argote,
2005). Citing Foss and Mahnke (2003), McIver et al. (2013) state
ISM Journal Vol. 3, issue 1, September 2019 ISSN 2150-1076 | 16
that “[knowledge management], a set of management activities…
has emerged as a particularly influential organizational competence
that shapes the work environment” (p. 597). The knowledge
management literature includes the richest discussion of different types
of knowledge, such as tacit versus explicit (Spender, 1993), different
epistemologies of knowledge (Cook & Brown, 1999), and modes of
knowledge creation (Nonaka, 1994).
We propose a synergistic relationship among the three fields both in
terms of research and practice. We concur with Argote (2005) when
she states that “if we had a deeper understanding of the processes
through which organizations learn, we could design better knowledge
management systems to capture and transfer knowledge acquired
through that learning” (p. 46). Moreover, our view is that links can be
made between the different phases in the lifecycles of organizations
and/or teams and the different fields discussed above. Elements of
organizational learning will transpire automatically when people work
together on common projects, but, as the group or organization
matures, it must adopt a knowledge management approach which
implies a more conscious and strategic perspective on the inventories
of knowledge already existing, on the processes in place to acquire
and generate new knowledge, and on the processes of knowledge
transfer and dissemination. The learning organization can function as
a springboard towards a knowledge management approach. It can
do this by providing tools to assess current organizational learning
according to the three “building blocks” proposed by Garvin et al.
(2008) with a view to leveraging strengths and managing weaknesses
to reach desired outcomes.
In summary, organizational learning is now a competitive imperative
to be nurtured by the learning organization model which can help
organizations achieve a strategic knowledge management approach.
Learning organizations provide a context for learning (Building Block
1 as per Garvin et al., 2008), similar to what research has shown to be
a condition for effective teamwork. We now review the literature on
teamwork to establish the synergistic relationship between experience-
based tacit learning in teams and effectiveness.
Teams and Teamwork
Ancona, Kochan, Scully, van Maanen, and Westney (1996) describe
the key features of a team as “members working interdependently
and being jointly accountable for performance goals” (p. 5). Citing
the work of Sundstrom and McIntyre (1991), they attribute team
success to a four-factor team effectiveness model that includes team
learning, defined as “how well team members acquire new skills,
perspectives, and behaviors as needed by changing circumstances”
(p. 9). Similarly, Nelson and Quick (2000) note that teams, as distinct
from groups, “emphasize shared leadership, mutual accountability,
and collective work products” (p. 138). The authors offer rationales for
the use of teams when work is “complicated, complex, interrelated”
(p. 144). Nelson and Quick (2000) state that “teams are appropriate
where knowledge, talent, skills, and abilities are dispersed among
organizational members and require integrated effort for work
accomplishment” (p. 144). So, in the descriptions of and rationales for
teamwork, we see connections among several variables: complexity,
change/adaptation, integration of knowledge, skills and abilities, and
interdependent functioning.
As work became more complex and as organizations experienced
more dynamic complexity, the homogeneous, supervisor-led teams
of the 1970s and 1980s morphed into self-managed work teams
(Garson & Stanwyck, 1997) and multidisciplinary teams (van der Vegt
& Bunderson, 2005) in the 1990s. Fast forward 20 years, and we have
Hass and Mortensen (2016) telling us that the teams in today’s business
world barely resemble those of the past. According to these authors,
today’s teams are “far more diverse, dispersed, digital, and dynamic
(with frequent changes in membership)” (p. 71). Nevertheless, Hass
and Mortensen (2016) argue that the success of today’s teams “still
hinges on a core set of fundamentals” (p. 71) which include context
fundamentals, process fundamentals, and learning fundamentals. Each
of these is discussed briefly below.
Context fundamentals.
Executive buy-in, direction, and support. Hass and Mortensen
(2016) argue that “the foundation of every team is direction that
energizes, orients, and engages its members” (p. 72). In knowledge-
based organizations, executives must cultivate and buy into an
“execution-as-learning” rather than an “execution-as-efficiency”
approach (Edmondson, 2008). In such organizations, “performance is
increasingly determined by factors that can’t be overseen: intelligent
experimentation, ingenuity, interpersonal skills, resilience in the face of
adversity” (Edmondson, 2008, p. 62). Executives can get behind this
approach by designing reward systems accordingly and by providing
appropriate material resources, information support systems, and
proper training and education (Haas & Mortensen, 2016). Executives
who cannot actively support this approach will, at best, not have any
impact on the teams they manage, or, at worst, negatively impact
them.
Psychological safety and high levels of trust. Edmondson’s (1999)
seminal study of psychological safety and team learning establishes its
centrality for team effectiveness. Reviewing her work, Cunha and Louro
(2000) observe that the concept is about more than members trusting
each other—it includes “an underlying shared belief in the value of
the team and a climate of mutual support” (p. 153). Later, Edmondson
describes psychological safety as “a sense of confidence that the team
will not embarrass, reject, or punish someone for speaking up. This
stems from mutual respect and trust among team members” (2002, p.
354). In a similar vein, Jones and George (1998) propose that, when
unconditional trust was present in relationships among team members,
a more synergistic and cooperative team dynamic was likely to develop
and that the intense interaction of these teams would likely generate
and actualize tacit knowledge.
Identification with the team, its mission, and processes.
Team functioning is influenced by the degree to which its members
identify with each other and the mission. This can be an issue for
multidisciplinary teams where its members belong to multiple teams—
for example, when members are also assigned as experts to (other)
discipline-based teams and project or functional teams (O’Leary,
Mortensen, & Woolley, 2011). van der Vegt and Bunderson (2005) find
that, “in teams with low collective identification, expertise diversity
was negatively related to team learning and performance; where team
identification was high, those relationships were positive” (p. 532).
Process fundamentals.
Cross-understanding and shared mental models. According
to Huber & Lewis (2010), teams that engage in tasks that require the
use of diverse knowledge and which work interdependently for task
accomplishment need to understand each other’s mental processes
in order to be successful. Their work describes the processes used
to achieve cross-understanding among multidisciplinary experts
which evolves to shared mental models defined as “a person’s
mental representation of a system and how it works” (p. 7). Haas
and Mortensen (2016) suggest that team leaders can help achieve
such a shared mindset by cultivating a common team identity and
understanding; they furthermore contend this team identity is a
necessary ingredient for success for today’s geographically dispersed
and diverse teams which rely on digital communication.
Implicit coordination. With the dynamic complexity underscoring
the need for diverse teams comes the challenge of quickly aligning
team responses to unexpected change. Rico, Sanchez-Manzanares,
Gil, and Gibson (2008) argue for teams to engage in implicit
coordination which transpires when “team members anticipate
the actions and needs of their colleagues and task demands and
dynamically adjust their own behavior accordingly without having
to communicate directly with each other or plan the activity” (p.
164). Rico et al. (2008) further posit the existence of an “underlying
mechanism” which allows for such coordination. They refer to this
mechanism as “team-level knowledge structures-team situation
models (TSMs),” defined as “dynamic, context-driven mental models
concerning key areas of the team’s work” (Rico et al., 2008, p. 164).
The authors assert that TSMs are both “shared and accurate” (p. 164).
Learning fundamentals.
Teamwork development: Relationship, knowledge, and skill
building. Teams do not suddenly emerge as accomplished entities;
rather, they go through lengthy periods of development. Tuckman
(1965) describes the typical team’s stages of development as forming,
storming, norming, and performing. Later teamwork development
scholars use other terms but continue to maintain that teams go
through stages of development. It takes time, effort, and sometimes
helpful guidance to develop trust, understand each other’s mental
models, and learn to anticipate needed adjustments to make the
team’s operations as smooth as possible. Tacit knowledge exchanges
play an important role here.
Continuous improvement through learning. Teams never stop
learning; they refine their practices through shared experience, adjust
to changes in mission, membership, resources, and technology
(Wilson et al., 2007). Much of the change that occurs in teams
through learning is an iterative process with day-to-day adjustments
accumulating over time to produce noticeable changes, some of
which may be institutionalized as best practices (Crossan, Lane, &
White, 1999; Raelin, 1997; Smits, Bowden, Falconer, & Strasser, 2014).
To conclude this section, we see the variables associated with
effective teamwork as consistent with Building Block 1 of the Learning
Organization Survey (Garvin et al., 2008). In our view, organizational
learning and effective “execution-as-learning” teamwork support and
stimulate each other.
Findings from the Literature Review
The findings from the literature review suggest that effective teamwork
is a dynamic capability of an organization and also that teams function
to a certain extent like microcosms of larger organizations. Parallels
can be drawn between the building blocks required for learning
organizations to develop and the context fundamentals required for
high performing teams to develop. Tacit knowledge exchanges are a
crucial component of team learning and new knowledge generation,
which are both necessary for organizations to differentiate themselves
from competitors. Moreover, we conclude there are parallels between
organizational lifecycles and approaches to organizational learning.
“
“Teams working in learning organization
environments are likely to be more
effective than those working in less
innovative settings and/or in settings
which fail to proactively assess and
leverage learning.
ISM Journal Vol. 3, issue 1, September 2019 ISSN 2150-1076 | 18
As an organization matures, it needs to move from a hit-and-miss
approach to a more conscious leveraging of learning and management
of knowledge. In the next section, we attempt to provide useful
guidelines showing how improved knowledge management practices
specific to capturing and applying tacit knowledge can help teams
form, mature, and function more efficiently and effectively
Synergistic Exchanges: Tacit Knowledge and Teamwork
Above, we referred to Tuckman’s (1965) forming, storming, norming,
and performing stages, and we stated that there was broad agreement
in the scholarly literature that teams pass through such phases in their
development. Later authors tend to replace Tuckman’s terms with
other similar descriptive terminology such as Nelson and Quick (2000)
who refer to mutual acceptance, decision making, motivation and
commitment, and control and sanctions. According to these authors,
it is in the final stage, control and sanctions, that a group has become
“a mature, effective, efficient, and productive unit” (Nelson & Quick,
2000, p. 287). We have chosen to structure our discussion of the
relationship between tacit knowledge and teamwork around three
periods in the lifecycle of teams: startup, maturation, and maturity.
We propose that variations of exercises based on Andrews’s (2018)
applications of Ambrosini and Bowman’s (2001) causal mapping
method and based on McIver et al.’s (2013) knowledge-in-practice
framework can accelerate the learning processes and outcomes at
each of the three phases in the team’s development cycle.
Team Startup – Major Task: The Development of Trust
Given the importance of psychological safety to organizational
learning (Garvin et al., 2008) and team functioning (Edmondson,
1999), the make-or-break issue early in the development of teamwork
effectiveness is the development of trust. Teams that rush the
interpersonal development among team members and fast-forward
to task issues oen experience Tuckman’s (1965) storming phase. As
Ancona and associates (1996) observe that, to evolve into trusting and
supportive units, team members need to experience empathy, equality,
and spontaneity. Developing that type of relationship requires shared
interpersonal experiences that include exchanges of tacit knowledge
(Jones & George, 1998). Facilitators who work with startup teams
to help them master the mutual acceptance stage have traditionally
used interpersonal exercises such as “Getting Acquainted Triads”
and “Johari Window: An Exercise in Self-Disclosure” (Pfeiffer & Jones,
1972). Here we suggest two exercises for getting acquainted that help
communicate team members’ work experiences and knowledge-in-
practice to help members understand each other’s mental models.
In the first exercise, new team members are asked to develop and
present a “causal map,” pinpointing three key factors they perceive
as determinants of the success they have had on teams to date and
describing the sub-factors and/or enabling conditions (causes) for
these three main factors. To illustrate, we present two very different
hypothetical team members. New Team Member A represents an
objective, explicit knowledge-oriented team member while New Team
Member B is more relationship-oriented.
The first factor New Team Member A identifies as a determinant of
her success on past teams is up-to-date knowledge of her discipline
(accounting), which she shares with her team members. She
describes the sub-factor (cause) which enables this as hard work on
her part to stay abreast of the latest technology and regulations. The
second factor she cites as a determinant of success is a willingness
to answer all questions from other members about her inputs. She
describes the enabling sub-factor as her expectation that others
will not have the same up-to-date knowledge; therefore, she feels a
sense of responsibility to inform them. The third factor she cites as a
determinant of success is the cooperative follow-up she provides even
when she does not think it is necessary. The enabling condition is her
willingness to work hard to keep her team happy. As per the causal
mapping method, New Team Member A then tells a few stories about
past episodes when she worked hard to please her team.
Meanwhile, the first factor New Team Member B cites as a determinant
of his success on past teams is his affinity for working with others to
solve problems and make progress. He describes the underlying
Factor A:
Up-to-date knowledge of
discipline shared with other
members
Cause:
Her hard work to stay abreast
of latest technology and
regulations
Cause:
Her expectation that other
team members will not
have the same up-to-date
knowledge
Cause:
Willingness to work hard and
keep the team happy
SUCCESS WORKING ON TEAMS TO DATE
Factor B:
Willingness to answer
questions from other team
members
Factor C:
Cooperative follow-up
Figure 1. Causal mapping of New Team Member A’s success on teams to date
NEW TEAM MEMBER A
19 | ISM Journal Vol. 3, issue 1, September 2019 ISSN 2150-1076
sub-factor (cause) as the fact that he likes other people, finds them
fascinating, and has studied non-verbal communication. The second
factor for his success on teams is his ability to listen carefully to others
to understand both the content and emotions behind what they
are saying. He describes the enabling sub-factor as his belief in the
importance of communication – he likes to “hear people out” and
“get everything on the table.” The third factor New Team Member B
identifies as a determinant of his success on teams is that he is a helper
and that he is willing to do whatever the team needs him to do. He
describes the enabling sub-factor as his affinity for working with people
from other areas of expertise. New Team Member B then relates several
stories about his cross-functional activities while participating on past
teams in his role as a strategic planner.
So, we have two very different new team members, both valuable
assets with much to contribute but who will contribute different
content using quite different participation styles. New Team Member
A will contribute to task accomplishment by helping the team with
its “information seeking” and “summarizing” functions while New
Team Member B will be a valuable resource for the team’s processing
functions by helping with its “harmonizing” and “encouraging”
functions (Ancona et al., 1996).
In the second exercise, new team members answer questions from
McIver et al.’s (2013) knowledge-in-practice framework from their
disciplinary perspective and team experience in order to communicate
the relationship of knowledge (explicit and tacit) to performance:
• What is high performance?
• How is high performance attained?
• What needs to be known?
• How does knowing take place?
• How is knowing applied?
• Can you describe specific examples based on work experience of
high performance?
This exercise is designed to generate knowledge-in-practice profiles
which can serve multiple purposes: orientation of members to each
other when new teams are formed, an inventory of team knowledge
assets, and a resource to orient new members to the team as members
are added for growth or replacement purposes.
Team Maturation – Major Task: Learning from Shared Team
Experiences
Our team is now fully functional, having moved to the next stage of its
development. Its task accomplishment and team process functions
are adequately developed, and it is engaged in productive activity.
This is an exciting period in a team’s history; it continues to get better
as it improves its methods, sharpens its skills, and innovates through
experimentation. It is positioned to implement Nonaka’s (1994) SECI
Model that generates and captures tacit knowledge, combines it with
existing explicit knowledge, and produces new forms of knowledge.
It is also in a position to begin developing a useful team culture that
helps members function more efficiently [internal integration] and deal
more effectively with external stakeholders and environmental factors
[external adaptation] (Smits, Bleicken, & Icenogle, 1994).
Team members, working interdependently, collectively experience
successes and failures, which are both valuable sources of learning.
Multiple forms of “aer-action” feedback help teams engage in action
learning (Argyris et al., 1985; Rowden, 2001) or what Edmondson
(2008) calls “execution as learning.” Here we suggest a relatively new
form of feedback: causal mapping of successes and failures. With
fully functioning teams that are still refining their activities and honing
their knowledge and skills, the aer-action analysis becomes a team
activity. Similar to what we suggest above in the team’s first phase of
development and to help bring new members on board, we suggest
the teams use Andrews and Smits’s (2018) integration of the Ambrosini
and Bowman (2001) causal mapping method and key questions
from McIver et al.’s (2013) knowledge-in-practice framework. Going
through this exercise is a learning experience in itself as well as a robust
methodology to uncover a team’s strengths, working knowledge, and
improvement challenges.
Team Maturity – Major Task: Coping with Change and Dynamic
Complexity
Teams with substantial shared experience develop a culture that helps
them deal effectively with internal integration and external adaptation.
As Schein (2010) notes:
The most useful way to think about culture is to view it as
the accumulated shared learning of a given group covering
behavioral, emotional, and cognitive elements of the group
members’ total psychological functioning. For shared learning to
occur, there must be a history of shared experience. (p. 10)
Mature teams have established routines embedded in team cultures
that improve efficiency but oen hinder or slow needed responses to
change (Schein, 2010; Smits & Bowden, 2015). Such stability can be
upended by changes in membership, something Haas and Mortensen
(2016) say is quite common with today’s teams. Here we discuss the
challenges faced by new members as they enter mature teams with
strong operating cultures and the challenges faced by mature teams in
terms of coping with dynamic complexity.
New members enter mature teams prepared to share their explicit
knowledge and with expectations shaped by their previous team
experiences. But each team is different, composed of unique
individuals and informed by the team members’ shared experiences to
date. The success or failure of the new member is largely determined
by how readily she/he adjusts to the new team environment and the
degree to which she/he identifies with its mission, members, and
modus operandi (Huber & Lewis, 2010; van der Vegt & Bunderson,
2005). Regarding modus operandi, Schein (2010) observes: “We
all know that one of the major activities of any new member … is to
decipher the norms and assumptions that are operating” (p. 13).
Nelson and Quick (2000) describe the behavioral norms in mature
groups as “well understood standards of behavior within a group …
benchmarks against which team members are evaluated and judged
by other team members” (p. 288). Such expected behaviors are oen
extremely subtle, tacit in nature, and even rooted in unique language
and non-verbal cues developed by the mature team over time. Here
we suggest two resources for new members entering a mature team.
Firstly, if the team has maintained the knowledge-in-practice profiles
developed by members when the group was formed as we suggest
above, such profiles, perhaps updated over time, could be useful to
introduce new members to existing members. Secondly, if the mature
team engages in ongoing “aer-action” causal modeling analyses,
recent ones will help newcomers better understand what the team
does to produce successful outcomes, an excellent clue for learning
behavioral norms.
Related to the subtleties of behavioral norms and also deeply rooted in
tacit knowledge are the implicit coordination mechanisms described
by Rico and associates (2008). It is no secret that mature groups lose
some of their spontaneity, react slower and with less innovation to
new complex situations, oen stemming from dynamic complexity. As
implicit coordination declines in effectiveness, the mature team’s ability
to deal effectively with change also declines (Trautlein, 2013). Mature
teams would be wise to use implicit coordination failures as learning
experiences to revisit how they cope by “causal mapping” recent
incidents with special attention to causal factors and needed knowledge.
In summary, teams never outgrow their need to learn (Smits et al.,
2014). The learning tasks change as teams form and mature, but
learning remains essential to continued success. At all stages of team
learning, experience-driven tacit knowledge has a key role to play,
and the exercises we suggest can help surface and disseminate this
knowledge in a useful way, thus rendering more efficient and effective
the team learning cycle.
Using Teamwork to Generate Useful Tacit Knowledge
Teams of experts challenged by complex problems in contexts
experiencing dynamic complexity are the perfect incubators of tacit
knowledge. As Nonaka (1991, 1994) discusses, socialization is the first
step in his SECI model of knowledge creation. During socialization,
tacit-to-tacit knowledge is exchanged during discussions and through
shared experiences. It is also the first step in the process of team
formation. When team members achieve high levels of trust, it shows
in their verbal and non-verbal exchanges; such teams exchange tacit
knowledge naturally (Jones & George, 1998). And, of course, the
learning organization’s (Garvin et al., 2008) first essential building
block posits many of the context variables associated with effective
teamwork, implying that effective teams are core elements in learning
organizations.
Why is it important to have effective teams generating tacit
information? Simply because generating such information is the
origin of innovation that provides organizational uniqueness – difficult
to imitate and essential to achieving and maintaining competitive
advantage (Andrews & Smits, 2018; Crossan et al., 1999; Lawrence,
Mauws, Dyck, & Kleysen, 2005; McIver et al., 2013). Without effective
teams generating tacit information for future organizational learning,
organizations struggle to distinguish themselves in a positive manner
from their competitors.
Concluding Statement
In the early stages of development of an organization, aspects of
collective learning will transpire more or less automatically as people
work together. However, as organizations mature, they should aspire
towards a learning organization model, making use of assessment
tools and best practices to achieve a more deliberate and strategic
knowledge management approach. Teams are a crucial vehicle for
this evolution, and today’s cross-functional, diverse, geographically
dispersed, technology-connected teams require an “execution-as-
learning” management approach appropriate for knowledge-based-
organizations. It is our working hypothesis based on theory, research,
and observations of teams in a variety of settings that effective
teams will produce useful tacit knowledge unique to the enterprise.
Moreover, teams working in learning organization environments are
likely to be more effective than those working in less innovative settings
and/or in settings which fail to proactively assess and leverage learning
(i.e. to adopt a strategic knowledge management approach). Further,
we contend that the synergistic relationship between knowledge
management practices that capture and apply tacit knowledge and
effective teamwork is enhanced by a better understanding of each.
Exercises developed from knowledge management literature can be
adapted and applied to team settings throughout the team’s lifecycle
to build trust and implicit coordination among the team members and
to help analyze the team’s past failures. Ultimately, this will accelerate
the team’s learning cycle which will impact the entire organization.
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About the authors
Matthew Andrews holds a PhD in international management from the International
School of Management and publishes research on organizational development and
learning, team dynamics, and knowledge management in technology companies and in
the healthcare sector. He has been working in higher education in France since 1997 as
a teacher, administrator, consultant, and accreditation peer reviewer. Before his current
position as Director of Academic Affairs at ISM, Matthew Andrews was the Dean of
Bachelor and Master of Business Administration programs at the Institut Supérieur de
Gestion in Paris, France.
Stanley J. Smits is Professor and Chair Emeritus of the Department of Managerial
Sciences at Robinson College of Business, Georgia State University. His research and
teaching interests focus on leadership development, organizational culture, teamwork, and
organizational change. He taught at the International School of Management from 2001 to
2014.
23 | ISM Journal Vol. 3, issue 1, September 2019 ISSN 2150-1076
Abstract
This research addresses the inability of executives and
managers to successfully meet the challenges associated
with the execution of digital transformations (DT) in their
organisations. We used a qualitative multiple case study to
identify the optimal leadership styles, characteristics, and
traits that could enable the successful implementation of DT
programmes in organisations headquartered in France. The
unit of analysis is individuals in organisations responsible for
planning and implementing DT initiatives. Eight individuals
were recruited for participation from medium and large
enterprises in the hospitality, healthcare, pharmaceutical, and
banking sectors in France. Data were collected from semi-
structured interviews using a protocol that was developed
for the purpose of this study. The results of the study indicate
that, although digital technology for transformation is
disruptive, operational performance leaders are prepared to
adapt their styles, characteristics, and traits to suit this new
digital era and to change their ways of working once given
a clear vision, commitment, and support from executives.
Leaders can positively influence, train, move, and fail through
experimentation while contributing to improved ways of
working at all levels by adopting co-creation and co-designing
cross-functional methodologies that are agile and inclusive.
Future research could explore the phenomenon of DT within
organisations of varying sizes in wider geographic regions and
industries.
Keywords: digital transformation, leadership, follower,
executives, styles, characteristics, traits
Introduction
The future of leadership styles in implementing digitalisation and
transforming operations remains an important area of investigation,
especially in establishing leadership practices that govern the
complexities of organisational digital transformation (DT) (Davenport
& Westerman, 2018; Kane, Palmer, Phillips, Kiron, & Buckley, 2018;
Weill & Woerner, 2017). The term digitalisation is defined as the
mass adoption of connected digital technologies as services by
consumers, enterprises, and governments (OECD, 2017a, 2017b;
World Economic Forum, 2016). Empirical work undertaken at the
industry level has determined the relationships between digitalisation,
productivity growth, the way work is performed, an evolution in
leadership style, reshaping and/or replacing business models,
increased collaborations, and increases in revenues for organisations
(Hesse, 2018; Libert, 2016; OECD, 2017a; World Economic Forum,
2015).
Research shows that DT will influence four dimensions of an
organisation: use of technology, change in value creation, structural
change, and financial aspects, plus new business models and the
creation of new market spaces (Matt, Hess, & Benlian, 2015). However,
success in DT occurs through leadership in light of the transformation’s
components, crosses, and organisational boundaries (Danoesastro,
Freeland, & Reichert, 2013). Westerman, Tannou, Bonnet, Ferraris,
and McAfee (2017) supported that leadership needs to align DT with
the leaders’ vision and continuous two-way communication. Once
organisations have had time to adjust, Bharadwaj, El Sawy, Pavlou,
and Venkatraman (2013) posited that they need to rethink the role of
digital technology within the broader strategic initiatives that involve
integrating information technology (IT) strategies with business
strategies, referring to this as DT. Researchers and practitioners have
thus acquired a better understanding of the concept of DT, exploring
the phenomenon that DT leadership is now considered a prime topic
for firms across the globe, which influences new research interests
and affects multiple business disciplines, especially those concerning
leadership (Bharadwaj et al., 2013; Ismail, Khater, & Zaki, 2017).
Digital Transformation and Leadership Style:
A Multiple Case Study
AUTHOR: FOUAD A. B. KAZIM*
* based on a dissertation supervised by Nicholas Harkiolakis
ISM Journal Vol. 3, issue 1, September 2019 ISSN 2150-1076 | 24
Leadership Problems Relating to Complex Transformation
The leadership problem is that executives and managers cannot
successfully address challenges associated with the execution of DT
in their organisations (Hesse, 2018; Parviainen, Tihinen, Kääriäinen,
& Teppola, 2017; Schneider, 2018; Singh & Hess, 2017). Some
studies indicate leadership is the issue in failing DT initiatives while
other studies blame issues with established management practices,
governance, culture, and executing complex transformation
programmes across organisations (Wokurka, Banschbach, Houlder, &
Jolly, 2017).
Researchers have indicated there are issues in leadership skills and
the acquisition of new competencies that align with DT practices and
support growth opportunities (El Sawy, Kraemmergaard, Amsinck,
& Vinther, 2016; Hesse, 2018). Other researchers make note that a
greater understanding is needed to determine who is best suited
to lead DT (Horlacher & Hess, 2016). Questions also remain about
leadership effectiveness and how to articulate the value of digital
technology (Kluz & Firlej, 2016). Besides, studies of successful firms
indicate that DT does not depend on technology adoption but on the
leadership mindset and its strategies (Bonch, 2016). The gap identified
exists between executives’ leadership intentions and the realisation of
successful DT initiatives (Ismail et al., 2017; von Leipzig et al., 2017).
Optimal Leadership Exploration and Identification
We explored and identified those optimal leadership styles,
characteristics, and traits that would enable the successful
implementation of DT programmes in organisations and determined
those that achieve a high level of effective leadership, aligning teams
on how to deal with digital transformational change. The study uses
the Harkiolakis (2017) leadership conceptual framework to explain the
appearance, evolution, and practice. It further explores the internal
features of the organisation in which groups operate in the broader
environment (stakeholders that influence the organisation). The
framework takes a modern approach at providing a comprehensive
understanding of the appearance and evolution of leadership as
fluid and dynamic, including conscious and subconscious thoughts,
feelings, and emotions (Gronn, 2000; Harkiolakis, 2017).
Literature Review
The Business Backdrop
Researchers do not have a unified view of the concepts of DT
practice (Morakanyane Grace, & O’Reilly, 2017). Practitioners view
the exploration of the process of digitalisation and its implications
in organisations as “digital transformation,” and consider it a global
megatrend with the ability to fundamentally change existing and future
industries and operations (Benzerga, Hauf, Pretz, & Bounfour, 2018).
There is universal adoption of the term DT by institutions (Kluz & Firlej,
2016; OECD, 2017a).
Hernandez, Faith, Prieto Martín, and Ramalingam (2016) explored
the factors that DT provides to the broader economy and broader
societal developments. Hesse (2018) argued that although broader
societal developments are bringing a collaborative social manner,
corporate culture is now being changed by digital technology through
disruption and revolution which requires re-invented leadership.
Companies using a digitally accomplished workforce are referred to as
“digital masters” and see digital not as a technology challenge but as a
transformation opportunity to use fast-moving technology to transform
leading business practice (Westerman & Bonnet, 2015).
In their study, Neumeier, Wolf, and Oesterle (2017) found that it is
not enough to merely adopt business models and digital business
strategies to change a strategic organisation position at any cost; it also
requires an improvement in capabilities that are flexible and adaptive
in turbulent market environments. Matt et al. (2015) indicated that such
elements as “technology,” “value creation,” and “structural change”
along with “financial aspects” formulate a DT strategy that serves as
a central concept to integrate the entire coordination, prioritisation,
and implementation of DT within a firm. Organisations need to
adopt digital characteristics — volatility, uncertainty, complexity, and
ambiguity of general conditions and situations of business — to best
understand the opportunities and risks today (Bongiorno, Rizzo, &
Vaia, 2018; Snow, Fjelstad, & Lander, 2017). Furthermore, Andersson,
Movin, Mähring, Teigland, and Wennberg (2018) argued that
enhancing the competitive digital positioning of firms does not solely
depend on technology nor its processes, but has a high component of
leadership deployment.
DT is fundamentally more about strategy and upgrading strategic
thinking (Rogers, 2016). Digital leaders tasked with focusing on
automation and process improvements are required to reimagine and
reinvent digital leadership across all business domains (Andersson et
al., 2018; Reis, Amorim, Melão, & Matos, 2018). A wide gap remains
between executives’ intentions and the realisation of DT initiatives,
especially relating to investments and strategy.
There is a lack of DT investigation concerning its primary challenges
and how top management leads such programmes (Andersson et al.,
2018; Ismail et al., 2017; Rogers, 2016). Davenport and Westerman
(2018) investigated the performance of leaders in this digital era,
providing evidence of high-profile failures due to the challenging
nature of understanding DT complexities. von Kutzschenbach (2017)
added that DT programmes do not have an enviable track record
of success even though their potential is limitless stating, “Digital
technologies have the potential to fundamentally transform the way
people in their organisation work” (p. 102). Further citing Schon
(1987), who highlighted a fundamental feature of organisations:
In the swampy lowland, messy, confusing problems defy
technical solution. The irony of this situation is that the problems
of the high ground tend to be relatively unimportant to
individuals or society at large, however great their technical
interest may be, while in the swamp lie the problems of greatest
human concern. (p.1)
The Main Players Leading Digital Transformation
Market complexities are causing conflicting demands on
institutions, with a direct impact on leadership structure. The
complexity and volatility in which organisations operate are
due to market pressures of technology advancement and
innovations that have changed the nature of the customer
relationship as well as the way in which the customer is handled
(Svejenova & Alvarez, 2016). As a result, organisations have
raised C-suite positions with new executive titles that specialise
in resolving institutional complexities (Svejenova & Alvarez,
2016).
The requirements to increase business performance through
effective use of IT and investments in technology typically fall
under the executive responsibility of the Chief Information
Officer (CIO). Yet, research continues to report that most of
these investments have not paid off as expected (Gerth &
Peppard, 2016). Berghaus and Back (2017) argued that the
critical challenges are at the initial phase — the “fuzzy front-
end” where managers struggle with initiating the process and
prioritising between activities. Weill and Woerner (2017) put
forward business cases that recommended the CIO, Chief
Digital Officer (CDO), and Chief Operating Officer as best
placed to determine the priorities of the organisation, and
further argued that the CEO plays an integral part in selecting
25 | ISM Journal Vol. 3, issue 1, September 2019 ISSN 2150-1076
the right executive to lead the transformation.
Bonch (2016) and Tumbas, Berente, and Brocke (2018)
further investigated the role of CDOs as the buffer between
the IT function and areas undergoing the DT, and concluded
that the CDO role is better suited to implementing digital
innovations, innovative leadership, and transforming mind-sets.
Hess, Matt, Benlian, and Wiesböck (2016) supported that,
for those companies whose digital focus is on the interface
with customers, the CDO should lead alongside the CIO,
and together they should actively communicate and closely
coordinate their strategies and initiatives.
Haffke, Kalgovas, and Benlian (2016) argued that DT has
brought about redundancy between the CIO and CDO
roles, therefore bringing the CIO role concerning DT to an
inflexion point. Additionally, the authors highlighted that the
responsibilities associated with these new roles were formerly
part of the CIO role. They further noted that there remains a
social alignment issue between CIOs and CEOs (Haffke et al.,
2016). There are organisations frustrated by the perceived
inabilities of their CIOs to drive the digital agenda, so some
are now either replacing them or hiring CDOs specifically to
drive the digital initiatives (Gerth & Peppard, 2016). Rickards,
Smaje, and Sohoni (2015) explored the CDO role as the
leader who aims to broker compromise and test new ways of
operating across departments by directing purpose to the DT
programme.
Additionally, CDOs are viewed as the institutional entrepreneurs
who develop and articulate the logic of action and identify
approaches that deal with existing organisational context
(Tumbas et al., 2018). The CDO is frequently created with a
direct reporting relationship to the CEO (Horlacher, 2016;
Horlacher & Hess, 2016). Tumbas et al. (2018) explained that
CDOs legitimise their role and mobilise resources through a
contracting logic of action with those of the CIO, with contrasts
being drawn across five dimensions: a focus of control,
value orientation, goal achievement, value chain location,
and reference industry. Conversely, these distinctions are
approached differently by institutionalised CIOs (Horlacher,
2016).
Additional research on DT leadership shows the CDOs have
been establishing themselves as the main transformers in the
C-suite (Horlacher & Hess, 2016; Singh & Hess, 2017). Solving
issues requires clear CDO roles that act across functions and
understand the expectations of business functions to build
relationships with customers and stakeholders (Chhachhi et al.,
2016; Tumbas et al., 2018).
Digital Transformation Leadership Challenges
Kreutzer, Neugebauer, and Pattloch (2018) argued that the
biggest challenge to DT is time. Its biggest enemy is the
organisational and individual indolence, especially found in
medium and large companies that block change processes.
Initial research determined that DT is looked at as a technology
change rather than a business change, thereby creating the
challenge for leaders (World Economic Forum, 2015).
Kreutzer and Land (2014) indicated that responding to
necessary changes in the environment is no longer about size,
speed, conformity, or strength, but the survival of the smartest.
They continued that it is an attack on existing business models,
sales concepts, and marketing communication, and that
executives are not considering these as an issue.
Neubauer, Tarling, and Wade (2017) argued that digital
innovations speed up the pace of change and make it harder
for leaders to create and sustain competitive advantage.
They claimed leaders need to change, challenging the
traditional views of a leader’s character as authoritative
and knowledgeable, and suggested the need for a
more collaborative and engaging approach focused on
empowerment of both individuals and teams. Goleman (2015)
and Sahyaja and Sekhara Rao (2018) supported that, due to
digitalisation, there are new variables — intellectual quotient
(Q), emotional quotient (EQ), digital quotient (DQ), personal
quality (PQ) — that affect leadership of digitalisation and
determine the leadership styles and characteristics to better
suit the digital era. The authors concluded that businesses are
not ready for the digital age. Research evidences that only
44% of managers and executives believe their companies are
adequately prepared for digital disruption (von Kutzschenbach,
2017). Worse, 50% of employees believe their company
leaders are lagging behind in digital innovation (Lynch, 2016).
Leadership needs to be aligned with digitalisation through
the action of employee empowerment and a shi in culture
(Ancarani & Di Mauro, 2018). Jakubik and Berazhny (2017)
supported that new business environments require a new
leadership paradigm that moves from egocentric towards
altrocentric leadership, solving the challenges of collaboration
and teamwork to create and enable high-performance teams.
Leaders’ challenges are associated with a lack of vision or an
incremental vision concerning DT (Fitzgerald, Kruschwitz,
Bonnet, & Welch, 2013; Kane, Palmer, Nguyen, Kiron, &
Buckley, 2015; Westerman et al., 2011). Furthermore, visions
from top management need to be radical and transformative
(Fitzgerald et al., 2013).
In addition, organisations pursuing digital opportunities face
challenges in engaging a digital-talented workforce (Colbert,
Yee, & George, 2016). Creusen, Gall, and Hackl (2017)
suggested that, during the pre-navigation phase, considering
the increased speed with which business ideas are realised
in the digital domain and the fast-advancing technology,
companies need to use agile working methods in conjunction
with digital expertise to act quickly and flexibly. Lenka, Parida,
“
“The digital era of disruptive
transformation is the catalyst that
has influenced leaders to better
clarify and communicate ideas to
achieve improved solutions.
ISM Journal Vol. 3, issue 1, September 2019 ISSN 2150-1076 | 26
and Wincent (2017) argued that co-creating value with
customers creates challenges within traditional industries,
and changes in capability requirements of the workforce can
support initiatives and innovations.
Within organisational decision areas, Ismail et al. (2017)
concluded that it is imperative that there be buy-in from leaders
and the board, especially as they articulate their vision and
prepare roadmaps of execution. The authors further cited
research undertaken by Kane et al. (2015) and Westerman et al.
(2017), who argued that the top-down approach is preferable.
Effects of Digitalisation on Leadership
The effect of digital technology challenges requires leaders
to proactively respond to the “new normal” in a world that is
volatile, uncertain, complex, and ambiguous (Vogel & Hultin,
2018). According to Bolden and O’Regan (2016), leadership
in the digital era requires the exercise of influence rather
than excessive force and power. Furthermore, the authors
underlined that leadership rules are being rewritten — no
longer are charismatic, omniscient, and omnipotent leaders
useful; a leader must know how and when to lead, support,
coach, facilitate, and influence others. Hamilton, Tee, and
Prince (2016) argued that the effects of DT on leadership calls
for a framework structured on a combination of motivational
tools and leadership styles that incorporate technopreneurial
leadership and a transformational, transactional, and authentic
leadership matrix. They further supported that through this
matrix trichotomous leadership styles with digital age solutions
can be found that provide the leader with insight into digital
structuration and that demonstrate the steps required to attain
digital leadership.
DT continually requires directive leadership (Hamilton et al.,
2016). Further indications are that digital leadership in context
has rules for competition to build cooperation between
generations, close the gap between strategy and operations,
attract the best talent, and solidify transformation in the
corporation. Schwarzmüller, Brosi, Duman, and Welpe (2018)
argued that new developments in the realm of DT are also
crucially changing the way in which leadership is exerted in
organisations. They highlight the importance of relationship-
oriented leadership in leading DT. Regarding digital leadership,
Wasono and Furinto (2018) stated that the concept is to
create an effect that combines leadership skills and digital
capability to optimise the benefit of digital technology in order
to increase business performance. Hesse (2018) put forward
that digitalisation is more than a technology trend that affects
the underlying foundations of leadership, directly or indirectly
challenging the boundaries especially for the new genre of
leadership theories such as transformational, authentic, or
servant leadership.
Wagner, Heil, Hellweg, and Schmedt (2019) argued that
leaders need to re-examine the notion of work within
organisations and create structures that incrementally enhance
business and deliver disruptive innovation as well as challenge
existing business models; this ambidexterity is regarded as the
holy grail leaders need to survive digital transformation.
Leadership Style, Traits, and Personality Characteristics
Associated with Digitalisation
Harkiolakis (2017) proposed that to operate in a competitive
and changing environment requires entrepreneurs who
function in unchartered territory and create something new from
environments with limited resources while being able to identify
and exploit opportunities along a successfully determined
network of paths. The author supported that entrepreneurial
leadership theory provides the opportunity for exploring DT
leadership.
Neubauer et al. (2017) proposed that the skills, competencies,
and behaviours that leaders require to succeed are found
in agile leaders who are humble, adaptable, visionary, and
engaged. They continued to describe their personality
characteristics as accepting feedback, acknowledging that
others know more, being willing to change their minds,
having a clear vision, and being open to communication
and interaction from all stakeholders. Neubauer et al. (2017)
argued for agile leadership traits and concluded that traditional
organisations can compete and win in this new digitally
disrupted world if their leaders are able to adapt.
Parr, Lanza, and Bernthal (2016) performed a personality
assessment to determine the character profile of 2,461
executive-level leaders. Their research characterised different
profiles of leaders based on their composite personality
structures. They concluded that “there is not a ‘one size fits
all’ personality model for leadership” (2016, p. 8) and that
power players have emotional stability, are agreeable and
conscientious, have socially creative communication, and are
open to new ideas. Kane et al. (2018) argued that the best
leaders possess common traits that developed their skills to
lead DT across their organisations effectively. Traits cited were
direction (providing vision and purpose), innovation (conditions
to experiment), execution (empowering people), collaboration
(across boundaries), inspirational leadership (getting people to
follow), business judgment (making decisions in uncertainty),
building talent (self-development), and influence (persuading
and influencing stakeholders).
Avolio’s (2007) accumulated research indicated that there are
some universal characteristics and traits that leaders possess,
offering that these are associated with effective leadership,
including persistence, tolerance for ambiguity, self-confidence,
drive, honesty, integrity, internal locus of control, achievement
motivation, and cognitive ability. Judge, Bono, Ilies, and
Gerhardt (2002) performed an extensive review of such
leadership character traits across the leadership literature,
noting that results of investigations relating personality traits to
leadership have been inconsistent and oen disappointing.
Most reviews of the literature have concluded that the trait
approach has fallen out of favour among leadership researchers.
Judge et al. (2002) argued that, although there is renewed
interest in dispositional explanations associated with attitudes
and behaviours, some researchers are still pessimistic in regards
to the personality variables in leadership. Other researchers
such as Conger and Kanungo (1998) explicitly mentioned the
trait approach seldom replicated in studies due to it being “too
simplistic” (p. 38). Judge et al. (2002) also referenced House
and Aditya (1997), who through a social scientific theory of
leadership came to the same consensus with the scholarly
leadership community, and stated: “It appeared that the search
for universal traits was futile” (Judge et al., 2002, p. 410). The
authors further noted that this was due to it being the early
stages in investigation of the phenomena. George, Sims,
Mclean, and Mayer (2011) interviewed and reviewed 1,000
studies on leadership and analysed 3,000 pages of transcripts
to determine the profile of a good leader. They stated that
their team was startled to see no identifiable or universal
characteristics, traits, skills, or style that lead to their success.
How Leadership Deals with the Complexities of Digital
Transformation
Harkiolakis (2017) categorised leadership by characteristics
that are considered to define the best leaders and argued that
such categorisations need to minimise any biases associated
with gender, social status, education, and other demographics
to achieve a more representative sampling of the leadership
population. OECD (2018) further argued for positive gender
selection of women leaders in the digital age.
Sow and Aborbie (2018) claimed that a leader’s style influences
an organisation’s direction regarding how it handles the
complexities associated with DT. They further noted that the
style of leadership is critical in employee-based involvement
and efforts to deal with change. Leadership styles are influential
in organisations and can implement norms, expectations, and
desirable outcomes during large-scale complex transformative
projects. El Sawy et al. (2016) argued leaders do the right
things for the strategic success of digitalisation, by thinking
differently across complexities pertaining to business strategies,
business models, enterprise platforms, mind-sets, and skill
sets along with the IT function and the workplace. The scale
and complexity of transformation requires an immense shi in
understanding leadership of ourselves, our team, and entire
organisations providing new opportunities but presents human
resources challenges (Lohrmann, 2017). Kohnke (2017) argued
that along with capabilities it is crucial for leaders to understand
the implication for employees, with attention focused on new
skills and competencies and new forms of leadership.
Methodology and Design
Sample
The study’s unit of analysis is the individuals leading the
planning and implementation of DT programmes and initiatives
in and across their organisations. The population included
all individual decision-makers, so including but not limited
to top executives and senior managers. The industries were
hotel hospitality, healthcare, pharmaceutical, and banking
within medium and large organisations based in France. The
participants were purposively selected managers, senior
management representatives, and executives with relevant
backgrounds and active in DT initiatives (Patton, 1990).
Procedure
The data collection method was in-depth semi-structured
interviews. The eight individuals were purposefully selected
to represent a range of leadership functional areas directly
associated with or affected by DTs. These participants were
recruited via professional network and LinkedIn professional
network connections (Aral, Dellarocas, & Godes, 2013).
The following research questions were adopted:
RQ1: What do practitioners of digital transformation
consider as key elements for the successful
ISM Journal Vol. 3, issue 1, September 2019 ISSN 2150-1076 | 28
implementation of digital transformation initiatives in
medium and large organisations in France?
RQ2: What leadership styles, characteristics, and
traits enable the successful implementation of digital
transformation programmes in business organisations?
Participants’ Demographics
Males made up the majority of the participant sample, with five
females represented in total with one at senior, three at mid-
level, and one at operational levels of leadership. This suggests
a gender bias of the results towards males (OECD, 2018). The
participants’ experience associated with DT ranged between
a minimum of 2 years and a maximum of 30 years in leading
DT initiatives across their organisation. This is an indication that
the results are not biased with respect to years of experience.
Regarding the distribution of participants’ educational levels,
the highest qualification was held by a female with a doctoral
degree; 12 held master’s degrees, 5 held bachelor’s degrees,
and 1 male held a high school and professional certification. The
participants were all educated and qualified for their positions,
which adds to the credibility of the study.
Participants’ job titles associated with DT ranged from
Chief Digital Officer, Chief Information Officer, to Director,
Vice President, Manager and IT Architect, and Head of
Customer Experience. Each job role was associated with a
team size of between 3 and 2,000 employees and directly
contributed towards DT planning and implementation of
digital initiatives. Participants associated themselves with the
following leadership styles: strategic, adaptive/servant, driver,
democratic/collaborative, directive, visionary, optimistic
along with participative/strategic, coaching, trusting, and co-
construction. Three participants believed that their leadership
styles were either adaptive or collaborative while two indicated
their styles were either strategic, democratic, or visionary.
Within the sample population of participants, the technology
experience in relation to DT ranged from a minimum of 2 years
to a maximum of 30 years. The sample selected participants
from different industries’ sectors and departments with a focus
on planning and implementing DT initiatives; as leaders, these
participants provided a reliable sample covering different
aspects of DT programmes.
Implications and Recommendations
The problem that this research investigated is that executives
and managers cannot successfully address challenges
associated with the execution of DT in their organisations.
In the literature, a gap was identified and explored between
executives’ leadership intentions, realisation, and the
explanatory effects of knowledge. The analysis of words
and phrases of these professional participants conveyed
a deep understanding of their organisations’ context at
undertaking planning and implementation of DT initiatives.
This qualitative multiple case study identified those optimal
leadership styles, characteristics, and traits that enable the
successful implementation of DT programmes in organisations
headquartered in France.
Implications
The implications of this research as suggested from its findings
are that organisations need a formal vision or similar statement
about DT to be communicated from senior executive positions
such as the CEO, CDO, or CIO that improves customer
engagement. For example, Participant 2a, a CIO, stated: Yes
clearly, it is to change the business model of the company. It is
a large part of the strategy to manage digital transformation
as an impact strategy. The statement comes from leadership
to be an enabler for all business lines of the company to
support their digital strategies. It is a very precise strategy on
documents. There is a clear operational strategy that was
communicated and documented. As such, this study’s findings
contribute knowledge that can be used to extend theory and
thus have implications for practice regarding organisational
communication approaches that need to be implemented for
DT transformation.
The findings of this research also have implications for theory
related to personal and professional development. DT
initiatives now require leaders at all levels to engage with their
development using instruments that detail areas of action,
helping them to make sense of DT requirements, and this
serves as a boundary object to communicate goals. Participant
2a explained her involvement in the DT projects as follows:
Given my role as a transformational leader specific in the area,
I usually get hired by the executive when they make the decision
that they need to do it, but they don’t know how, when, what...
and they are looking for a leader to come in and really detail
out a strategy and then put a roadmap together to get the
organisation there.
With respect to leadership styles, participants indicated that
leaders need to operate cross-functionally, be people-oriented,
and possess and communicate a clear vision that is prepared
for significant disruption from legacy systems and migrating
systems towards data-driven digital platforms. Participant 6b
explained his detailed involvement in DT projects: Yes, I had
the chance to work on the IT part of the digital transformation
systems in the beginning mainly by designing the change from
legacy systems to customer-oriented mobile applications as well
as providing technical facilities for customer services so that they
can be more reactive through social media in real time as well as
keeping IT along with improving the image of the organisation
from outside the company via social media interfaces and
improving technologies with a specialist focus on data science.
The study results have implications for practice regarding
reducing the length of time taken to complete initiatives.
Participants indicated that the average time spent ranged from
2 to 3 years (40%). The implication for achieving digital maturity
from 2 years to 3 years has to do with leadership commitments
to digital development and leadership support.
With respect to senior initiators of DT projects, there is a
need for candidates with capabilities, competencies, and
core leadership skills for planning initiatives. Executives and
managers need to create agile teams with capabilities and the
mind-set to create value. Goal achievements depend on several
factors, including the stage of maturity, the types of initiatives
being undertaken, the value of initiatives, and the structure of
teams focused on customer experience. Participant 2c noted:
To change the digital transformation it’s a business model,
management model and a technology model transformation
29 | ISM Journal Vol. 3, issue 1, September 2019 ISSN 2150-1076
that requires a clear vision and a digital mind-set that is led to
from the top, and implemented through autonomous teams that
innovate to give us additional business opportunities.
The meaning of DT, fully understanding its impact on job
roles, the connection with technology and direct use of the
cloud, and its significance for social media and the use of
digital tools have direct bearing on how teams work together,
cross-functionally or across different geographic boundaries.
Participant 1a stated: In summary, DT is used [for] leveraging
all forms of data, information that is able to be craed into an
efficient and effective organisational and operational structure
whose effort achieves a superior customer-centric outcome.
The aim, according to participants, is to engage more with
their people and improve plans associated with customer
experience through increased team collaboration to achieve
results. Participant 2a explained by indicating how they quantify
achievements of their initiatives, stating: In my team, I get the
quantifiable goals and we really track against it and set those
as the targets. It’s setting the right impact goals that are more
meaningful to the business and drive more the adoption, but
you can see other digital transformation goals tend to be about
activity-based data, measures without quantification, without
true impact.
Essentially, for successful implementation, a clearly defined
vision, strategy, and implementation plan is needed as well as
a roadmap detailing transformation through a clear language
that is common to everyone involved. Participant 5a expressed
their meaning of success, stating it is to define clearly and have
alignment across the whole organisation on what it means to
be digital…to have faster, leaner, more efficient communication
and engagement between us…invest in training for people
to understand…so we need to train people, to build capacity
because you cannot implement anything without capacity,
without investing in knowledge and changing mind-sets.
This research underlines the opportunities and benefits that
DT allows to improve work processes, speed, and the ability
to experiment and fail fast. Additionally, the findings show it
enables achievement of the cultural and structural changes
associated with creating combinations of millennial and mature
team members. Participant 1a’s statement demonstrates the
related implications: Having as much efficiency and effectiveness
across our organisation which means being aligned in terms
of our operations and our efforts [to] be able to have an
organisation-wide effort. Participant 1c further stated: Cultural
change, people see the cultural impact of a DT. Participant 2a
said: You understand your customers better and you can respond
to them in a more precise manner.
The findings of this research further have implications
associated with innovation and optimisation of legacy
systems as well as increased investment to build digital assets.
Participant 2c noted: To optimise our productivity opportunities
is through innovation, to increase or to enlarge our assets close
to hospitality…for the team to take decisions to fail fast and to try
again. This is a clear indication of the implication of not being
successful in implementing digital initiatives.
Further, study results highlight the implication of challenges in
implementing DT, which participants regarded to be changing
mind-sets, breaking team silos, and shiing the culture of the
organisation. The findings also have implications for employee
knowledge sharing, opportunities to be constantly connected
to the workplace through cloud services and mobile devices,
and the ability to communicate with others via instant message,
with customers through social media, and with regional teams
through teleconferencing. Additionally, there is a need to get
everyone around the table. Participant 1a stated: Some of the
key challenges faced are leadership commitment and buy-in.
That has been the primary struggle. The second is change
management as a whole, along with legal and compliance
concerns that arise through digital transformation due to its
poorly understood nature or those possibilities that are not
considered as a new business model.
Recommendations for Practice
The results and findings of this research suggest an
organisation’s vision statement on DT derived from executives
needs a strategic planning and implementation process that
ensures each level of the organisation understands what DT
means to them. Company executives need to provide answers
to leaders on the new structural changes required for DT. In light
of what was found in this study, this can be achieved through
increased training where leaders address the expectations of
stakeholders and partners by clearly defining a roadmap, clarity
of intention, and a reiteration of top-level commitment.
Study results further indicate that leaders need to continuously
experiment with digital innovations to include fail-fast and
accelerated scenarios for digital innovations as a pre-defined
part of a vision with clearly defined roles and responsibilities
for all teams and stakeholders who assume the role of digital
catalysts. From an internal operational perspective, the findings
demonstrate why leaders must have greater influence on the
employment policies of their teams’ construction to align with
modern organisational norms, thereby improving gender
and generational gaps in teams as well as aligning with IT
acquisitions and building specialised know-how and new
digital competencies across business units. Leading DT requires
a centralised core that maintains influence and power across
decentralised business units. Such units need autonomy in
decision-making on digital strategies; digital leaders need to
have a high level of entrepreneurial leadership accomplishment
to overcome the challenges and exploit opportunities.
Based on these research findings, it is clear that unit managers
need to assess the role of their IT departments and how
proactive they are in their approach to new technologies. Such
units need to act as a service provider rather than in their current
static nature; this way, unit managers have increased strategic
options from which choices can be made. From the employee
perspective, the study showed that individuals associated with
the deployment and planning of DT initiatives are committed in
that they use the tools for collaboration with other employees
across regions, engage with external partners, and promote
their digital solutions to external stakeholders to demonstrate
best practice on customer engagement and improved
customer journeys.
It is clear that leaders need to find ways to understand the digital
process and get better acquainted with digital tools associated
ISM Journal Vol. 3, issue 1, September 2019 ISSN 2150-1076 | 30
with key areas of performance. Leaders have the opportunities
to leverage the affinity and openness of the workforce to adapt
their leadership styles, characteristics, and traits and to increase
their involvement in the DT process.
Recommendations for Future Research
Future research could explore the phenomenon of DT within
organisations of varying sizes and covering a wider geographic
region and industries. This can be done using alternative
methodologies that include quantitative and mixed methods
approaches. An additional focus on gender could also be
explored to determine if there are differences between men
and women related to leadership styles and approaches to
DT as well as their experiences in the implementation of DT
initiatives and the barriers or challenges they may be confronted
with in relation to their gender.
Future research could extend the ideas of the International
Monetary Fund’s research on gender, technology, and the
future of work (Brussevich et al., 2018). This could generate
greater understanding of how today’s employees envision
future workplaces in the context of shiing demographics,
emerging work practices (e.g., remote work, online
collaboration), increasingly international and multicultural
companies, and emerging economies and new financial
realities.
Lastly, research could explore this area from an internal
perspective by investigating the cultural change dimension
that leadership and DT bring to an organisation. Such an
investigation could delve into the culture of learning and what
it takes to develop learning cultures associated with DT and
leadership.
Conclusion
This study has addressed the problems associated with the
critical failures of many practitioners involved in planning and
implementing DT initiatives by providing the explanatory detail
needed to create an environment that is cross-functional,
co-creates value between business and technology, and
builds team cohesion from top-down and bottom-up through
teams’ communication of their experiences of how best
to plan and implement initiatives. It can be concluded that
within organisational practice individuals need to lead DT
from the formation of teams that include individuals with skills
and a willingness to learn digital technologies. It has now
become imperative for leaders leading in this new digital era
to improve the vision, communicate effectively, and include
ideas from both team members and all stakeholders across the
organisation. This will see increased support and cooperation
for initiatives and provide a greater understanding of DT’s value.
One could argue that the digital era of disruptive transformation
is the catalyst that has influenced leaders to better clarify
and communicate ideas to achieve improved solutions that
stem from increased cooperation and co-creating value, and
built through increased cross-functional relationships using
a leadership style that is open and authentic. The results of
this study have indicated that, even though leading DT may
be considered messy and a confusing problem, it is not an
impossible feat to address with the right adjustments from
leaders on leadership style and implementation approaches
(von Kutzschenbach, 2017). The results of this research
contribute to the literature in this area and issues associated with
technologies considered to be disruptive.
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About the author
Fouad Kazim is a Chartered Manager of CMI and entrepreneur. His earlier career extends
from the world of finance and financial services, in firms such as City Fund Management,
Prebon Yamanie, Bloomberg LP, and Kalahari Soware Solutions. In 2009, he le London
and moved to Paris to launch and co-found with Béatrice Querette merchanfeeling.com
and Trendfeeling.com. Fouad serves as co-CEO and CDO at both companies.
33 | ISM Journal Vol. 3, issue 1, September 2019 ISSN 2150-1076
MEMBERS OF THE EDITORIAL BOARD
EDITOR-IN-CHIEF
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Dean & Director of Doctoral Research
International School of Management
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International School of Management
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Matthew Andrews
Professor &
Director of Academic Affairs
International School of Management
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Professor
University of Liverpool
Ivonne Chirino-Klevans
Professor
International School of Management
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Professor & Associate Dean
International School of Management
Tobias de Coning
Professor
International School of Management
Daphne Halkias
Professor
International School of Management
John Hampton
Professor
International School of Management
Joseph Onochie
Professor
Baruch College
Ivo Pezzuto
Professor
International School of Management
Michael Wynne
Professor
New York University
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