The information of daily workers is stored used for calculation of KPIs is generated by the leaders who work remotely by installing the methodologies of calculation. The report is used to understand the methodologies of calculation used for the calculation of KPIs and how the virtual leaders perform them remotely. The virtual team that is taken for study to one organization for the convenience. The study/research/ report is generated to initiated by the question, 1. How Machine Learning measures the KPIs of workers in an organization in the virtual teams by the virtual leaders? 2. How far the results of the calculation of Machine Learning that takes data on KPIs be trusted?

1. Simi Paxleal Jaiwant Simon (2003548 and 16)
Machine Learning Influence on KPIs of
Virtual Leadership
Implicitly in a virtual team of an Organization
Digitalisation in management
(Master School)
2020

2. CONTENTS
1 ABSTRACT ..................................................................................................................3
2 INTRODUCTION ..........................................................................................................3
3 OVERALL VIEW...........................................................................................................4
3.1 Theoretical Background..........................................................................................4
3.2 Literature Review....................................................................................................5
3.2.1 Machine Learning.............................................................................................5
3.2.2 Leadership .......................................................................................................6
4 DATA ANALYSIS AND METHODOLOGIES ..............................................................10
4.1 The Virtual Team Maturity Model VTMM ..............................................................10
4.1.1 The Virtual Team Maturity Model Levels........................................................11
4.1.2 The VTMM Key Performance Indicators ........................................................12
4.1.3 KPIs formulation by Virtual Leaders ...............................................................12
4.2 Data Collection using AI / Machine Learning Algorithm by Virtual Leaders ..........14
4.2.1 Key Performance Indicator and Machine Learning Process ..........................14
4.2.2 Data Gathering...............................................................................................15
5 SWOT ANALYSIS ......................................................................................................15
REFERENCES ..................................................................................................................16
LIST OF TABLES ..............................................................................................................19
LIST OF FIGURES ............................................................................................................19
Appendix. Lists of figures and tables

3. 3
1 ABSTRACT
Next generation virtual leaders, global leaders and advances in technology
are leading to the explosion of virtual teams in order to execute business strat-
egies. Adoption of permanent structures or methods to determine and calcu-
late the performance of the employees and to access to best talent with rich
cultural diversity as a form of competitive advantage.
The Machine Learning influencing into the virtual leadership styles that helps
in determining the KPIs who work remotely is soon to become the future of
many companies and some are already implementing the structure to make
the Artificial Intelligence to handle the next generation methodologies to for-
mulate Key Performance Indicators (KPIs). The study is about the problems
and few possible solution to overcome while implementing the said methodol-
ogies and technology that can make the future of organisation productive with-
out any restrain.
2 INTRODUCTION
Machine Learning has been increasingly adopted by recent technology-based
organizations aiming at achieving the competitive advantage among its com-
petitors and move towards their future aspects of the digital needs that may
come in future. As machines are replacing the human jobs and place of work
is becoming more remote, the growing organizations must also revolutionize
its traditional methods of leadership and its KPIs (Key Performance Indicators)
that are greatly influenced by Machine Learning. As Machine Learning helps
the leaders to understand and predict the performance rates of the workers
with pre-loaded data of the performance to know the future brings a greater
potential to the virtual leadership itself.
The information of daily workers is stored used for calculation of KPIs is gen-
erated by the leaders who work remotely by installing the methodologies of
calculation. The report is used to understand the methodologies of calculation
used for the calculation of KPIs and how the virtual leaders perform them re-
motely. The virtual team that are taken for study to one organization for the

4. 4
convenience. The study / research/ report is generated to initiated by the
question,
1. How Machine Learning measures the KPIs of workers in organiza-
tion in the virtual teams by the virtual leaders?
2. How far the results of the calculation of Machine Learning that
takes data on KPIs be trusted?
This study predicts the theoretical issues of the measurement on KPIs and
how effective they are under the virtual leadership perspective, and how it is
necessary for improving team effectiveness in support of innovation. It gives
the overall view of the practices used by the virtual leaders that will become
more pronounced in the current and future growing companies in the digital
world.
3 OVERALL VIEW
This chapter focus on the ways to understand the source of creation of Ma-
chine Learning that come to help the virtual leadership from the history or the
literature view, formulation and methods developed to perform the function of
Virtual leader then their strengths, weakness, opportunities and possible prob-
lems or threats will be in the discussion
3.1 Theoretical Background
The Information technology (IT) sector is a fast-paced growing industry driven
by exciting changes and new opportunities most especially to start-up compa-
nies. As many start-up companies are founded, the support team in this high
technology organizations are under exceptional pressure (DBKay and Associ-
ates, 2003). The promise of making customer support in the organization is in-
creasingly compelling as a new generation of technology today focused on re-
solving customer problems and aims to satisfy customer needs.
A significant challenge and problems in most start-up virtual companies are
that performance metrics and indicators are not fully defined, developed and
implemented or using every possible test case as the framework for perfor-

5. 5
mance management is not strategically in-place. There is an apparent diffi-
culty in providing the visibility of goals and objectives set across the organiza-
tion. The research makes it more exciting and essential to study these chal-
lenges in this master thesis.
3.2 Literature Review
The collection of data for study comes from the after coming statements from
various research papers that act as source and background for the research.
The dispersed team are becoming a norm of doing the business across globe,
according to IT Reseller Magazine (2009). It is estimated that 41 million corpo-
rate employees globally will spend at least one day a week as a virtual worker
and 100 million will work from home at least one day a month (Jury, 2008).
3.2.1 Machine Learning
Machine learning (ML) is programming a computer to be able to do tasks
without explicit instructions, similar to the learning process of a human or
animal, by examples. In machine learning the two terms Features and Labels
are frequently used. In this report, a feature will represent a feature vector,
which contains the known attributes of an instance. For example, using ML to
estimate effort to develop software, the feature vector can be the size and
complexity of the project. A label is the desired output for a feature vector in a
ML algorithm.
Using the same example, the actual effort of the project is the label. A
simplified way of describing this relationship is f(x) = y where x is a vector
containing features, y is a label and f is a machine learning algorithm. As ma-
chine learning helps the leaders who can potentially work in various parts of
the world, as the machine or the Artificial Intelligence(AI) is loaded within the
production systems i.e., the computer networked systems to record the data
on daily basis helps to perform the calculation of their performance is the gen-
eral concept.

6. 6
3.2.2 Leadership
The 21st century in today’s industrial trends changed the pavement on how
the industry evolves and operates within the organization. It has rapidly in-
creased and changed in many different aspects as were conceptualized by
(Poole and Van de Ven, 2004) in his basic ideas as previously illustrated.
In an organization, a fundamental aspect of successful management is driven
by respect among team members, shared leadership, business strategies,
economic implications and team performance. In his book, (Frankel, 2008) be-
lieves that management organization is designed to enhance facilitation and
administer productive activities.
The information derived from data collection focuses on the Huddly Support
team. The teams' role of shared leadership and cognition to establish a pro-
found understanding of team dynamics and team effectiveness. While (Pearce
and Sims Jr, 2002) also added that shared leadership is found to be a more
significant predictor of team dynamics and likely effectiveness.
3.2.2.1 Virtual Leadership
Now most future trending companies tend to have virtual leaders than the
traditional to cut the travel expenses and offsite expenses and other
insurance, compensation for the foreign leaders. Table 1 indicates the
outcome of a global study conducted by the CIO Executive Council (2007) in
which Chief Intelligence Officers (CIOs) identified their most common
challenge as managing global virtual teams.
Due to certain changes to formulate the virtual leadership styles like the trend
toward physically dispersed work groups has necessitated a fresh inquiry into
the role and nature of team leadership in virtual settings (Kayworth, et al.,
2002). Connaughton and Daly (2004) highlights that 90% of the 500 virtual
managers studied perceived managing from afar to be more challenging than
managing people on site.

7. 7
Table 1: CIOs rate of globalisation challenges in terms of their relative
importance and scope
Challenge
Very
Important
Worldwide
Scope
1.Managing virtual Teams 70% 70%
2.Consolidation 61% 70%
3.Centralized/decentralized system decisions 61% 65%
4.Organisational structure 43% 70%
5.Leadership/ownership/governance 48% 61%
6. Global vendor partner selection 35% 65%
10. Cultural issues and appropriate behaviour 30% 43%
Source: CIO Executive Council poll, May 2007
3.2.2.2 Organizational Structure with Virtual Teams
Huddly is the people company that uses technology as instrument bring
together as a team that has expanded structure with different levels,
combination of physical and virtual teams. Due to increasing global
competition, every team member in each department are experts, well-
experienced and highly skilled trained to adapts change and responses.
(Kozlowski and Bell, 2001), argues that team events reflect the number and
the type of people on the team.
At Huddly, the team processes and results are strongly influenced by the
combination of the member attributes, in which such effect can build more
effective teams. For instance, a person who likes selling products and
services has proper public relations management and is most likely be part of
the commercial team such as sales or marketing.
Same with a technical person who likes attaining technical concerns and
customer success is a perfect fit for the support department and so on.
According to the research by (Moreland and Levine, 1992), there are three
dimensions that makeup of team composition. First, demography, size,
personality, abilities, and skills are the different characteristics of a team that

8. 8
each team member can be studied from. Second, is the assessment within the
group of the distributed characteristics using Machine learning.
Sometimes, unique configurations are measured as well in addition to central
tendency and variability measures. Lastly, is the different analytical view is
considered. The team composition at Huddly is unique on its own with major
number of workers are involved in support and sales team. So the study focus
on that department
3.2.2.3 Optimal Virtual Leaders to Support Virtual Team
The total headcount of Huddly is currently 51 employees composed of 78%
men and 22% women. The department is composed of vertical teams. Each
has a team manager and a team leader responsible for giving directives,
setting priorities and goals for the team. As explained by (Samuel, 2010), that
this type of team creates a vision and set success factors according to its pri-
mary goal.
As a team, information sharing, coordination towards activities and supporting
each team members and their functions is currently being practiced. In the pa-
per by (Kozlowski and Bell, 2001), they pointed out that researchers (Katzen-
bach and Smith, 1993) implied, teams should have at least a dozen member
while (Scharf,1989) recommended that 7 members in a team are the best
size. (Rogers et al., 2010), supported the recommendation that the perfect
team size is seven.
Their study emphasized that fewer team members decrease the number of
decision nodes in a network which increases efficiency. It also means that
smaller teams show to be more flexible in an informal structure which is a con-
siderable advantageous concerning decision-making. The idea is also sup-
ported by the Ringelmann Effect commonly known as social loafing

9. 9
Figure 1. The Ringelmann Effect (Simplified by Hesketh 2018) – Huddly Content Strategist
(Hackman and Vidmar, 1970), conducted team size assessment in a more
subjective approach by examining each team members own idea of the group
size impact according to process and performance. In the study, the research-
ers assessed the effect of group size 2-7 with a series of
various task. Each team members were asked a.) is the group too small or b.)
too big for the task. Figure 12 indicated the common answers based on the
two questions. It showed that the percentage of those who believe their team
is too large and few in the 7- person group thought the opposite. Through this
team assessment, it was discovered that the optimum team size is
4.6 members.

10. 10
Figure 2: Optimal Team Size (Hackman and Vidmar, 1970)
4 DATA ANALYSIS AND METHODOLOGIES
This chapter covers the methods and formulation used for performing the
questionable function of virtual leaders that provides the assumptions and in-
formation for how and why the problem statement arose and how to tackle in
the theoretical way.
4.1 The Virtual Team Maturity Model VTMM
The VTMM serves as a reference model against which virtual teams can be
assessed and whereby gaps in the performance can be identified and closed.
The model was validated by an expert panel of over 80 members, which was
convened by following rigorous selection criteria where job title and qualifica-
tion was considered. Statistical analysis of the feedback from the panel vali-
dated the assumptions of the model and showed that the VTMM adds true
value to virtual project teams.
The model is composed of 11 virtual team processes and four maturity levels.
Each process is described by inputs, methods and outputs. These are meas-
ured by key performance indicators (KPIs), which gauge how well a process is

11. 11
present in a virtual team. VTMM development was also influenced by the work
of Jehle and Zofi.
4.1.1 The Virtual Team Maturity Model Levels
Four levels is a good compromise between practical application and
differentiation of maturity:
• At the undefined level, there are many gaps compared to the reference
model. The success of the team cannot be traced back to the conformance to
virtual team processes. Many processes, tools or cultural elements are miss-
ing and most likely also unknown to the virtual team members, leaders and
sponsors. Relies on individual strengths and charisma of the leader and/or its
team members and does not know what to do to improve the performance of
the virtual team.
• At the basic level, the team is aware of the requirements to increase the per-
formance of the virtual team. All the quick wins have been implemented. The
team performance and productivity increased, the level of conflict decreased
and the team members have developed trust and deepened their relation-
ships.
• At the advanced level, all elements of the VTMM are present: the virtual
team has a positive culture, the different tools are used appropriately for differ-
ent tasks and either the virtual team processes are fully implemented or the
decisions not to implement them have been well documented. The level of
conflict is low and the performance is high. The team invests time into relation-
ships with other team members. Complex tasks are managed successfully
and issues are tabled early and dealt with efficiently.
• At the mastery level, all elements are present. The team is in a state of ”
flow“, and the performance is very high. The team has implemented
knowledge management processes and works on the optimization of pro-
cesses, tools and culture. The team performance will maintain this level of
performance even if there are changes in the team (members leaving or enter-
ing the team).

12. 12
4.1.2 The VTMM Key Performance Indicators
Each VTMM process is defined through KPIs, which have a different quality
for each of the levels. The “Organize Get-to-know-each other”-
process is shown as an example in Table 3:
Table 2: The VTMM KPI´s of the “Organize Get-to-know-each-other”-process
4.1.3 KPIs formulation by Virtual Leaders
Key performance indicators (KPIs) are used today in organizations to measure
a process or characteristics of products and how they change. Dissecting the
phrase Key performance indicator gives an idea of what it represents. Key,
relates to the indicator being key to the business. Performance, is defined to
be measured over the future.
4.1.3.1 Calculation of the maturity level
The KPI’s of each level of the process have a point value according to the ma-
turity level:
• Undefined: 0 Point
• Basic: 1 Point
• Advance: 2 Points
• Mastery: 3 Points

13. 13
Then each team member rates the presence of a process according to the
KPI. The level is calculated by the sum of the assessment for each process di-
vided by the number of team members. A full number needs to be achieved
for the level, e.g. 1.8 is still level 1 and not level 2.
During this assessment, differences in perception become visible, too. If one
sub-team gives high scores and the other sub-team for the same process low
scores, then there is a difference in perception, which needs addressing by
the team leader. The VTMM assessment for a client-supplier assessment is
implemented.
4.1.3.2 The Balanced Scorecard
The Balanced Scorecard in strategy-focused performance management that
uses three distinct processes to coordinate employees to the strategy: com-
munication and knowledge-based, developing personal and team objectives,
and incentive and reward systems for encouragement according to (Kaplan
and Norton, 2001b).
Figure 3: The Four Perspectives of the Balanced Scorecard (Kaplan and Norton, 2001a)
To be entirely strategic and useful to communicate the Balanced Scorecard, it
is imperative that all employees understand the strategy itself and administer

14. 14
contribution to the organization’s objectives (Murby and Gould, 2005). In per-
formance management context according to (Poister, 2015) it highlights man-
aging, motivating and rewarding people, business units and agendas with an
eye toward achieving desired outcomes. This performance management ap-
proach is more likely to highlight the development and maintaining the perfor-
mance-oriented organizational cultures where suitable, develop performance
orientations and approaches in producing the desired results.
4.2 Data Collection using AI / Machine Learning Algorithm by Virtual
Leaders
The framework of performance management exerts to collect data regarding
performance level achieved using the performance measures and indicators
set to reflect the effort of the performance management purposes. In other
words, as stated by (Poister, 2015), that performance management is a meas-
ure that tracks inputs, activity level, outputs or results which might use the
measure of employee’s efficiency, effectiveness, balance, cost effectiveness,
and customer satisfaction. (Murby and Gould, 2005), noted that the practice of
the Balanced Scorecard could be a useful framework to reward employees in
attaining the agreed performance target.
4.2.1 Key Performance Indicator and Machine Learning Process
A lot of this process can be automated, either by using functions that are
established by the framework, Scikit-learn, or by writing custom methods. This
leads to a lot of information about results that are not required to be disclosed
and will therefore not be presented in the results section. An example of this is
the GridSearchCV method in the Scikit-learn library that establishes a grid of
combinations of parameters to apply to cross validation and select the best
model thereafter.
The process of finding a suitable machine learning algorithm is a straight
forward process which includes the following steps: 1. Gather raw data. 2.
Split the data into testing and training sets. 3. Clean the data and transform it
in to a correct representation that the Machine Learning algorithm can use. 4.
Find a suitable algorithm e.g. by using Figure 2.3. 5. Run the algorithm with

15. 15
hyper parameters. 6. Evaluate the performance of the algorithm. 7. Optimize
the hyper parameters or change algorithm.
4.2.2 Data Gathering
The raw data available was the underlying data used to form their current
KPIs. This data was even more detailed then the KPI, as the KPI aggregated
the data. Data aggregated to some level, was extracted from a database. The
data contained information about the supplier for each component, their
placement, the module name, the related car project, and how many
parameters each status bin contained. This was arranged by weeks and there
were approximately 20,000 rows of data. An example of a data row is seen in
table 3,
Table 3. Part of example data from Virtual Data Collection
where the headings 10...120 refers to the status bins of all 11 status levels. In
the status bin, the number of parameters labeled with this status is displayed.
An average value was then calculated for each week using the bins and
number of parameters.
5 SWOT ANALYSIS
Virtual Leaders are benefited by the Machine Learning through the use of
Methodologies like Scikit-learn function MultiOutputRegressor, X regressors,
where X is the width of the output data. Helps in identifying the KPIs of the
performance of the employees, on the contraints like the project duration,
efficiency, iteration, production and service cost and deadline being met are
considered in collection for data.
An inducer algorithm, such as Linear Regression or a SVR with a linear
kernel, models the data in a completely different way than an instance based
learner such as KNN. Selecting one over the other can yield different results.

16. 16
Since there is a difference in implementation between a Linear SVR and a
Linear Regression algorithm, both of these are investigated, together with
KNN. As some sources argue that the RBF kernel of the SVR is also seen as
an instanced based learner, this is also included.
Scikit-learn implements a function named GridSearchCV, which is short for
grid search cross validation, this can be used to determine the optimal
algorithm together with a number of specified hyperparameters.
GridSearchCV establishes a grid of each algorithm with all possible
combination of the parameters given and their respective values and by
utilizing cross validation and the R2 score finds the optimal solution.
The problems concerned with the hypothesis of the problem is the formulation
itself as it can become a concerning problem in future as the formulation has
to be changed on the case of algorithm failure due to malfunction of the virtual
team in case the KPIs of virtual leader does not match the virtual team or the
natural calamity struck at the place of the individuals but the machine is not
aware and consider the absence of the data from virtual team itself. And
factors of KPIs calculation cannot determine the proficiency and uncertanity of
hardwork of individuals is a drawback but still helps leaders to determine the
success rate of the teams to certain extend
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Appendix
1/1
LIST OF TABLES
Table 1: CIOs rate of globalisation challenges in terms of their relative
importance and scope....................................................................................6
Table 2: The VTMM KPI´s of the “Organize Get-to-know-each-other”-pro-
cess......................................................................................................... 12
Table 3. Part of example data from
VCC.............................................................................................................. 15
List of Figures
Figure 1. The Ringelmann Effect (Simplified by Hesketh 2018) – Huddly Con-
tent Strat-
gist..................................................................................................................9
Figure 2. Optimal Team Size (Hackman and Vidmar, 1970) ………….…….10
Figure 3. The Four Perspectives of the Balanced Scorecard (Kaplan and Nor-
ton, 2001a) …………………………………………………………..……….13