St. Petersburg State University
Graduate School of Management
Master in International Management Program
COOPETITION AS A LEAD GENERATING MECHANISM: DESIGN, MODELING
AND SIMULATION.
Master's Thesis
Concentration: Master in International Management
Maksim Shlegel
Research advisor:
Nikolay A. Zenkevich, Associate Professor
St. Petersburg
2016
ЗАЯВЛЕНИЕ О САМОСТОЯТЕЛЬНОМ ХАРАКТЕРЕ ВЫПОЛНЕНИЯ
ВЫПУСКНОЙ КВАЛИФИКАЦИОННОЙ РАБОТЫ
Я, Шлегель Максим Александрович, студент второго курса магистратуры
направления «Менеджмент», заявляю, что в моей магистерской диссертации на тему
«Коопетшн, как лидогенерирующий механизм: разработка, моделирование и симуляция»,
представленной в службу обеспечения программ магистратуры для последующей
передачи в государственную аттестационную комиссию для публичной защиты, не
содержится элементов плагиата.
Все прямые заимствования из печатных и электронных источников, а также из
защищенных ранее выпускных квалификационных работ, кандидатских и докторских
диссертаций имеют соответствующие ссылки.
Мне известно содержание п. 9.7.1 Правил обучения по основным образовательным
программам высшего и среднего профессионального образования в СПбГУ о том, что
«ВКР выполняется индивидуально каждым студентом под руководством назначенного
ему научного руководителя», и п. 51 Устава федерального государственного бюджетного
образовательного
учреждения
высшего
профессионального
образования
«Санкт-
Петербургский государственный университет» о том, что «студент подлежит отчислению
из Санкт-Петербургского университета за представление курсовой или выпускной
квалификационной работы, выполненной другим лицом (лицами)».
___________________________________________ (Подпись студента)
________26.05.2016____________________________ (Дата)
STATEMENT ABOUT THE INDEPENDENT CHARACTER
OF THE MASTER THESIS
I, Maksim Shlegel, second year master student, program «Management», state that my
master thesis on the topic « Coopetition as a lead generating mechanism: design, modeling and
simulation», which is presented to the Master Office to be submitted to the Official Defense
Committee for the public defense, does not contain any elements of plagiarism.
All direct borrowings from printed and electronic sources, as well as from master theses,
PhD and doctorate theses which were defended earlier, have appropriate references.
I am aware that according to paragraph 9.7.1. of Guidelines for instruction in major
curriculum programs of higher and secondary professional education at St.Petersburg University
«А master thesis must be completed by each of the degree candidates individually under the
supervision of his or her advisor», and according to paragraph 51 of Charter of the Federal State
2
Institution of Higher Professional Education Saint-Petersburg State University «a student can be
expelled from St. Petersburg University for submitting of the course or graduation qualification
work developed by other person (persons)».
___________________________________________ (Student's signature)
________26.05.2016___________________________ (Date)
3
АННОТАЦИЯ
Автор
Шлегель Максима Александрович
Название магистерской Коопетшн, как лидогенерирующий механизм: разработка,
диссертации
моделирование и симуляция
Факультет
Высшая школа менеджмента
Направление
Менеджмент
подготовки
Год
2016
Научный руководитель Зенкевич Николай Анатольевич
Описание цели, задач и Целью данной магистерской работы является определение
основных результатов
потенциального воздействия на отрасль, лидогенерирующей
коопетициии на базе интернет-платформы среди компаний,
работающих в данной отрасли.
Результатом данной магистерской работы является подробное
описание концепта механизма лидогенерирующей коопетиции,
на базе интернет платформы. С помощью средств агентного
моделирования
и
симуляции,
были
получены
данные,
позволяющие предполагать, что разработанный инструмент
потенциально способен оказывать положительный эффект на
некоторые отрасли, и выгоден для большинства компаний
участников данных отраслей. Так же на базе полученных
результатов можно предполагать, что данный инструмент
способен повышать степень прозрачности рынка, к которому он
будет применен.
Ключевые слова
Коопетиция, коопетишн, кооперация, конкуренция, теория игр,
агентное моделирование, симуляция, коалиционное разбиение,
прозрачность рынка, распределение выигрыша
4
ABSTRACT
Master Student's Name
Maksim Shlegel
Master Thesis Title
Coopetition as a lead generating mechanism: design, modelling and
simulation
Faculty
Graduate School of Management
Main field of study
Management
Year
2016
Academic Advisor's
Nikolay A. Zenkevich
Name
Description of the goal,
The goal of this master thesis is to define potential impact that can be
tasks and main results
caused by a lead generating internet platform-based coopetition
among companies, which operate in one industry, on this industry.
The main result of current master thesis is a detailed description of
the concept of the lead generating internet platform-based
coopetition. With the tools of agent-based modeling and simulation,
there were obtained results that could be used as a base for suggestion
that the developed concept can potentially cause a positive effect on
some industries and can bring some extra profitability for most
companies that operate on this particular industry. Also on the basis
of the results it can be assumed that the developed instrument is also
able to increase the degree of transparency of the market to which it
is applied.
Keywords
Coopetition, cooperation, competition, game theory, agent-based
model, simulation, coalitional partition, market transparency, pay-off
distribution
5
TABLE OF CONTENT
INTRODUCTION ......................................................................................................................... 8
1. STATE-OF-THE-ART of COOPETITION, COOPERATIONAL GAME THEORY
AND PLATFORM BASED MARKETS ................................................................................... 10
1.1 Background .............................................................................................................................. 10
1.2 Concept of coopetition ............................................................................................................ 10
1.3 Cooperative game theory ......................................................................................................... 17
1.4 Platforms and platform-based markets .................................................................................... 24
1.5 Research problem, objectives and delimitation ....................................................................... 27
1.6 Research methodology and organisation of the study ............................................................. 29
1.7 Summary of Chapter 1............................................................................................................. 29
2. RESEARCH DESIGN and METHODOLOGY of LEAD GENERATING INTERNET
PLATFORM-BASED COOPETITION STUDY ..................................................................... 31
2.1 Starting point of approaching lead generating internet platform-based coopetition study...... 31
2.2 Design of a concept ................................................................................................................. 32
2.3 Agent-based model simulation ................................................................................................ 32
2.4 Limitations of the model.......................................................................................................... 34
2.5 Data collection ......................................................................................................................... 34
2.6 Validation of the model ........................................................................................................... 35
2.7 Experimental design ................................................................................................................ 36
2.8 Simulation software ................................................................................................................. 36
2.9 Summary of Chapter 2............................................................................................................. 36
3. DESIGN OF A LEAD GENERATING INTERNET PLATFORM-BASED
COOPETITION .......................................................................................................................... 38
6
3.1 Description of lead generating internet platform-based coopetition ....................................... 38
3.2 Coalitional partition stage ........................................................................................................ 40
3.3 Possible strategies of companies ............................................................................................. 42
3.4 Profit and ROAS – individual and coalitional ......................................................................... 43
3.5 Summary of Chapter 3............................................................................................................. 45
4. MODELING AND SIMULATION OF LGIPBC ................................................................. 46
4.1 Model mechanics description .................................................................................................. 46
4.2 Parameters for the simulation .................................................................................................. 50
4.3 The simulation results and analysis ......................................................................................... 55
4.4 Summary of Chapter 4............................................................................................................. 60
5. CONCLUSIONS ...................................................................................................................... 61
5.1. Discussion of the findings ...................................................................................................... 61
5.2 Practical implications .............................................................................................................. 63
5.3 Limitations ............................................................................................................................... 63
5.4 Theoretical implications and further research ......................................................................... 64
REFERENCES ............................................................................................................................ 67
APPENDIX 1. BASE PARAMETERS FOR ALL SIMULATION ROUNDS ...................... 73
APPENDIX 2. ROAS AND PROFIT TESTS (OBSERVED COMPANY TESTS) .............. 74
APPENDIX 3. IDENTIFICATION OF A LINK BETWEEN ROAS OF A COALITION
AND NUMBER OF MEMBERS OF THIS COALITION ...................................................... 80
APPENDIX 4. UTILITY TESTS ............................................................................................... 81
APPENDIX 5. WEB-DESIGN STUDIO QUESTIONNAIRE ............................................... 83
APPENDIX 6. FUNCTION THAT DEFINES A CHOICE OF A CLIENT.......................... 84
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INTRODUCTION
Nowadays there can be detected a growing interest to the topic of coopetition as a
strategy of inter-firm relationships (Bouncken et al., 2015). Academic literature defines
coopetition as a kind of interaction among organizations, which simultaneously cooperate and
compete to each other (operating in one industry) to improve their financial results
(Brandenburger and Nalebuff, 1996). Another significant trend of modern business environment
are internet-based platforms, which also occasionally characterized as a multi-sided markets.
These platforms simultaneously try to satisfy needs of more than one group of users (Armstrong,
2006). Examples of such platforms are: Youtube, Uber, Amazon Marketplace. Topic of current
research is located on intersection of these two spheres of academic knowledge. However, to
make them work together author also uses some concepts and principles of cooperative game
theory, due to the fact that coopetition involves cooperation as one of its components, and
cooperation is discussed widely in terms of cooperative game theory (Chakravarty, Mitra and
Sarkar, 2015).
The goal of this master thesis is to define potential impact that can be caused by a lead
generating internet platform-based coopetition among companies, which operate in one industry,
on this industry. To reach this goal there was defined a list of objectives. Creation and
description of a design of a lead generating internet platform-based coopetition. Detection of a
potential impacts of the suggested lead generating internet platform-based coopetition on
individual participants of market with different price/quality strategies. Identification of a
possible impact of number of the lead generating internet platform-based coopetition members
on the effectiveness of the lead generating internet platform-based coopetition. Definition of
effects that number of the lead generating internet platform-based coopetition participants can
cause on an average utility of clients of industry, which applies lead generating internet platformbased coopetition.
The structure of the thesis sticks to the order described above. It contains 5 chapters. The
first chapter of current research discusses topics of academic knowledge, which are used by
author to create a design of a lead generating coopetition mechanism. Main theoretic concepts
and fields of knowledge, used in current research: coopetition, game theory, multisided internet
platforms. In the second chapter author describes a methodology, which he uses to make current
research. Then in the third chapter author moves to the coopetition lead generating mechanism
design description. In the fourth chapter there is a description of an agent-based model, used to
run a simulation of a market with one product and one advertising tool. Parameters and border
values for this model are taken from Russian web-design industry researches described in the
8
second section of the fourth chapter. The third section of fourth chapter provides the analysis of
results that were collected from the simulation. In terms of this analysis author tries to answer the
reach second, third and fourth objectives of current research. Finally in the last chapter there is a
discussion of findings and contributions of this work from the perspectives of theory and
practice, limitations and further directions of possible studies.
The main theoretical developments of current research is a concept of lead generating
internet platform-based coopetition and evaluation of its potential impact on a particular
industry.
9
1. STATE-OF-THE-ART of COOPETITION, COOPERATIONAL GAME THEORY
AND PLATFORM BASED MARKETS
Object of the study is a coopetition as a lead generating mechanism. It is designed standing on
the principles of multi-sided internet platforms and coopetition. Current chapter is designed to
describe principles, rules, and concepts, which are used as a basis for a concept of lead
generating coopetition, described in the third chapter of the research.
1.1 Background
For recent years there is a trend that demonstrates a dramatic increase of popularity of
coopetition (simultaneous cooperation and competition) as a strategy for development of
companies (Brandenburger and Nalebuff, 1996; Bengtsson and Kock, 2000). Especially this
trend could be detected in academic literature and researches (Bouncken et al., 2015). Nowadays
coopetition starts to be discussed from the perspective of the Game theory (Kalai and Kalai,
2012). And used as a strategy for internet platforms, such as Amazon Marketplace (Ritala,
Golnam and Wegmann, 2014). These trends became a starting point of current research, and
pushed author to the idea of design of a concept of lead generating coopetition that could work
on a base of multi-sided internet platform. However, from the standpoint of author, design of
such concept requires investigation of several theoretical fields, such as: coopetition, cooperative
game theory and internet-based platforms.
1.2 Concept of coopetition
There are several ways of possible interaction among organizations. One of the
classifications gives us four following types: competition, collaboration, coexistence and
coopetition (Bengtsson and Kock, 1999). Coopetition is a kind of interaction, when firms
cooperate and compete to each other (operating in one industry) to improve their financial results
(Brandenburger and Nalebuff, 1996). In other words entering a coopetition firms try to increase
the values of the whole market to share it in competition later: “to create a bigger business pie,
while competing to divide it up” (Walley, 2007). One of the best explanations of the phenomena
coopetition refers to Kirk S. Pickett who in 1913 described the relationship among oyster
dealers, saying that all of them are not just in competition with each other, but in cooperation
developing more business for each participant of the market, which means that these oyster
dealers in co-opetition now, not in competition (Cherrington, 1976). Basing on all
abovementioned information we can derive that coopetition is a kind of competition in terms of
10
cooperation, when all players try to make market on which they play “bigger”, to share this
“bigger” market among them by competition activities.
In other words coopetition is an inter-firm strategy, when companies at first focus of the
increase of the profit that their industry can give to them. At that stage they try to make bigger
the market or sphere of business that they operate on. To make that, companies start some kind
of collaborative relationships among them. As the additional value was created, companies start
to be rivals to capture the biggest part of this additionally created value on their own. As a result
there is an increasing chance to create a common win-win situation for the whole industry for all
its participants through a larger market creation (Liu, 2013).
The origin of a coopetition as a concept of interfirm business model is not clear. From
one stand point it could be derived from the game theory and stands on the idea of real-world
games with mixed motives of players (Mariani, 2007) and potentially the principles of
coopetition were described far before the term was introduced and accepted by academics. From
another position, which tends to be more popular among academics, coopetition first was used
and described at some extent by Raymond John Noorda who talked about contemporaneous
cooperation and competition among organization (Zhang and Frazier, 2011). However even
though the term was introduced to society in 1980/90s, coopetition as a field of actual academic
research was first described and analyzed by Brandenburger and Nalebuff as a new set of
principles for interaction among organizations in terms of alliances. It is considered that book
Co-opetition (Brandenburger and Nalebuff, 1996) became the initial starting point, after which
scholars and business world started to pay attention to the coopetition as a potential strategy of
interaction among companies.
One of the argumentations “For” coopetition as a choice of inter-firm relationships that
have a potential to capture additional value is the resource-based argumentation (Lavie, 2006).
One of the general strategies used in terms of alliances is to use supplementary and
complementary resources in an integrated way. Such approach has a potential to create more
value comparing to the cases, when above-mentioned resources are used separately. This
additional value could be expressed in innovations, differentiation of organizations, cost
reduction, expansion of the market, cooperative manufacturing and distribution of products.
Another potential field of coopetition-based type of interaction between companies that stands
on the idea of resources is their utilization. Through cooperation organizations manage to create
an additional value through cooperative utilization of their resources. At the same time they
manage to capture some individual portion of Joint-created values through the utilization of their
11
specific resources (Ritala and Hurmelinna-Laukkanen, 2009). Nowadays coopetition velocity
increases dramatically, which can be proved by recent researches in ICT sector (Basole, Park and
Barnett, 2015).
Later there appears classification of business activities, dividing them by the “aim” in
terms of coopetition, dividing them to downstream (or output) activities and upstream (or input
activities). Upstream activities are those which are dedicated to “create a bigger business pie”. In
other words they can be called cooperative. These are common research investments, collective
buying of raw materials or services (with discounts) and other activities that make all industry to
grow. Downstream activities are based on the competition part of coopetition. This is marketing,
branding, pricing and other activities that make one company to get a bigger part from the
common “pie”. As a result there is an attempt to classify coopetition cases by the criteria of
competition and cooperation degree in their coopetition relationship, which led to the following
typology (Bengtsson and Kock, 2000):
1)
Upstream-dominated relationship: In such type of coopetition organizations put
into the top corner “cooperation” as a main driver of interactions.
2)
Downstream-dominated relationship: The main driver of interaction among
organizations in such type of coopetition is a competition among participants of the process.
3)
Equal relationship: Competition and cooperation components stay in some kind of
balance and considered as equally important by participants of coopetition
At the same time coopetition has some potential problems for companies. There are some
risks for opportunistic behavior (Brandenburger and Nalebuff, 1996), when participants can act
selfishly when particular circumstances provide them a chance for this. This can be connected
with knowledge expropriation, breach of trust and etc.
There are some proves to the issue, that coopetition can potentially provide small and
medium enterprises (SMEs) with added value, cost reduction and other factors, which could be a
good growth and development opportunity for the company (Thomasona, Simendingera, and
Kiernanb, 2013). That comes from the statement, that because of the size of these companies,
they have a number of issues, which can be a serious barrier for their development. These
limitations could lie in the field of resources, market presence, current workforce capabilities.
One of the possible solutions of problems that come from these limitations is a coopetitive form
of interaction between firms. Starting coopetitive relationships companies get a chance to boost
their competitive position, benefit from the improvement of resources available to them, and start
12
some international projects. At the same time coopetition starts to be used by SMEs from the
perspective of management of their potential risks (Morris, Koçak and Özer, 2007)
If we analyse motivation of companies to enter coopetitional relationships with other
organisations, there is one of the main reason, why companies do this – improvement of their
competitive positions. This could be reached through inter-organisational learning practices and
reception of valuable and strategically important resources from such inter-actions (Luo 2004).
However these are not the only way of competitive position improvement. There are many
examples such as (Garrette, Castaner and Dussauge, 2009; Tong and Reuer, 2010; Rothaermel
2001; Koh and Venkatraman, 1991):
Adaptation of partners experience and knowledge: When organisations enter close
relationships (as coopetition or cooperation) they enter a common “knowledge pool”.
Participation in such pool gives them a chance to obtain some knowledge and experiences from
their competitors;
Common establishment of new knowledge: Through coopetition organisations are
able to combine their creative skills to generate some new knowledge, which can be used by a
particular coopetition group. Such knowledge provides all members of this group with additional
competitive advantage;
Joint research and development: Entering joint R&D projects companies get a
chance to manage risks and increase budgets of research activities;
Defence from innovations (radical ones) that potentially can damage a company:
Getting in touch through coopetition with key competitors organisations can get an opportunity
to protect their business from sudden appearance of radical innovations on the market. That
could be reached through creation of common informational field, knowledge sharing and
common R&D projects;
Creation of entry barriers for newcomers and foreign competitors: Coopetitional
inter-actions of organisations provide them with a potential to defend their territory with help of
price, technology or market instruments;
Getting cost reduction through the increase of scale of some operations that can be
done in coopetition (upstream ones): For example, if five organisations make one order from a
supplier of goods, they can get a sufficient discount and reduce their costs significantly.
International organisations can get into coopetition with its competitors as on local
territories, where they try to expand their share, as on the global scale, running coopetitional
inter-actions with global rivals. From the perspective of global growth and development
13
coopetition can help multinational companies to decrease risk level and reduce costs, that arise
when company tries to expand on new markets. Entering coopetition organisations can even
overcome some governmental barriers (Luo, 2007).
Cooperation with competitors in contrast with a cooperation with organisations that
provide products and services, which differ from those, which are produced by a company has a
potential, which rarely can be achieved through cooperation with the second ones (Garrette,
Castaner and Dussauge, 2009). That is because of the different outcomes that each type of
cooperation brings to organisations. In case of coopetition organisations get extra opportunities
through resource addition effects, when organisations combine their resources to reach some
bigger goals. That could be especially profitable when coopetitional group decides to enter
foreign markets. Individually organisations can have some problems with manufacturing
resources or lack some marketing force to enter a new geographical market. However, entering a
coopetitional relationships with competitors, who have the same interests and face similar
problems, organisations get an opportunity to start developing together on these new markets,
simultaneously competing for a share from the new concurred territories (Luo, 2007). That
stands on the idea that company can get its strategic market advantage not only using its own
resources, but also getting accesses to power that other organisations have (through coopetitive
relationships to them).
One of the ways how academic society tries to prove strategic potential of coopetition is a
case analysis, of big international companies, which have already applied coopetition in their
practice. In 2014 there was published an analysis of Amazon.com coopetition business model
(Ritala, Golnam and Wegmann, 2014). One of the questions discussed in the paper is Amazon’s
Marketplace which became a platform where Amazon let its competitors, with the same
products, so that clients could compare and make the best choice (which is not always Amazon).
Also Amazon have started a program that helped its offline competitors to go online with their
books. Logically these competitors also have joined Marketplace.
Even though in 2006 28% of products were sold by a third party, Amazon has
demonstrated a three-fold growth of revenues comparing to the year 2000, when only 6% of
products were sold by a Third-party through the marketplace. Creation of the coopetition
platform helped Amazon to get out from the possible bankruptcy that looked pretty close in
2000, increasing profits of the company and attracting new customers. And commissions and
subscription fees for competitors provided Amazon with guaranteed money, even if customers
bought products from their competitors (Ritala, Golnamb and Wegmann, 2014).
14
Understanding coopetition and its potential from the perspective of value addition and
profitability it is important to analyse and examine potential conditions that might cause effect
on the process of formation of coopetition among companies. There are at least five issues that
cause influence on this process:
Environment: Coopetitive strategy of organisations can be influenced by context in which
these companies operate. This context can be described by the governmental policy, resources
peculiarities, competition level, quality of services and others (Lado, Boyd and Hanlon, 1997).
For instance in environment where companies have a high probability of intervention from
abroad, organisations will have a motivation to cooperate to protect their market and at the same
moment of time to compete for the market that they defend. In such case organisations have
more motivation to cooperate, so coopetition starts to be up-stream dominated. As an opposite, if
organisations face the situation when there is a little possibility of intervention, there is a chance
that companies start to compete more than cooperate.
Nowadays many industries face a dramatic growth of competition due to such factors as
internationalisation, innovation growth, internet development and etc. As a result organisations
have to find solutions, how to fight uncertainties that arise from such situation. That brings
competing sides to the idea of cooperation with each other (Burgers, Hill and Kim, 1993).
As an example, when companies face a problem of innovations that have a potential to
change the whole market and cause effect on the choice and reactions of customers, cooperation
among rivals can move its focus to the question of adaptation of organizations to the quickly
changing environment. Doing this together companies increase their chances to succeed and stay
on the market (Burgers, Hill and Kim, 1993).
Coopetitional costs: Entering a coopetition with other organisations, company has to pay
attention to the fact, that occasionally such relationships cause some additional costs to arise
(coopetitional costs). Such costs appear due to increasing complexity of relations that come from
growth of participants (Lado, Boyd and Hanlon, 1997). As coopetition involves a cooperative
component, it is possible to assume that some concepts of cooperation theory are applicable to
coopetition concept. Cooperative theory describes costs that arise when companies try to
maintain the cooperative relationships and potential losses connected with an opportunistic
behaviour (Das and Teng, 2000). All these issues definitely can cause some effects on the form
of coopetition among organisations. It is vital for organisations, to get overwhelm these costs
with incomes and value that coopetition that they enter can bring to them. Due to this, companies
probably have to think, which benefits such coopetition should bring to them.
15
Size of companies: Small and large organisations statistically are less interconnected with
their partners comparing to the medium-sized organisations. Due to the tendency that small
companies usually niche ones, they do not have enough power and competitive potential to cause
any influence on their industry or alliance that they enter. Situation around large organisation is
affected by the antirust policy of modern governments, which put relations among big companies
under a strict monitoring and try to coordinate them. Also it is important to admit, that big
international organisations have access to much more resources in comparison with SMEs, as a
result motivation to cooperate among these organisations decreases. Medium companies at the
same time already have some possibilities to cause some influence on their industries, but still
are not big enough to face all difficulties connected with market turbulence alone. That makes
intermediate companies an ideal subject for cooperative relationships (Burgers, Hill, and Kim,
1993), and potentially make coopetitive inter-actions at lease potentially interesting for them.
How coopetition effects on the competition on a particular market? That question is
examined mostly from the perspective of how cooperation influences on the market. However
there are also some researches made in coopetition context (e.g. Oxley et al., 2009).
Different researches provide quiet opposite data. While one group of researches provide
us with the information and evidence, that cooperation among organizations reduces the degree
of competition on the market (Tong and Reuer, 2010). Another group of scientists state that
cooperation and coopetition cause an increase of competition on the market (Gnyawali, 2006).
Common research and development programs (widely announced on a particular market) also
cause some positive affect on the particular market value, not only on members of coalition, but
also on other companies, that do not enter this coalition. Basing on this research authors state
that there could be observed an increase of prices of shares of companies that do not enter an
alliance could be a result of expected decrease of competition on the market (Oxley et al., 2009).
Basing on the assumption, that coopetition can be risky, companies that enter it, can have
some problems with the trust-building issues. Some sources and researches suggest that the most
significant role in the trust building process goes to a calculative process (Faulkner, 2000;
Lewicki and Bunker, 1996). Dyadic coopetition depends mostly on the cost-benefit analysis.
Absence of benefits that individual can calculate makes other trust-building mechanisms not
sufficient for starting some kind of coopetition. Emotional base plays some kind of moderating
role. Reputation based trust decreases opportunistic risks, but tends to be not sufficient enough
for the coopetition decision procedure. Analysis of potential partner capabilities tends to be a
part of the cost-benefit analysis (Czernek and Czakon, 2016). However the problem of trust
16
could be potentially avoided if there would be no potential interactions between participants of a
coopetition. Instead of this organizations could interact with a third party, whose main interest
would be a coopetition as it is. That party could have its interest from the additional value that
was gained through a coopetition. That makes this third party potentially more credible than
other participants of alliance, who can try to get their profit with cheating.
One of the potential sources of coopetition concept is a game theory, there is a number of
researches and theories that observe coopetition from this (game theory) perspective. One of
these demonstrates how coopetition among competitors brings both a chance to get high profit
calling it “coco value” (cooperative/competitive value) through game theory and minimax
strategy (Kalai and Kalai, 2012). According to this research coco activities bring the most
profitable result for both sides, even in cases of Bayesian games, when organizations have
incomplete information concerning characteristics of other players of the game. Nowadays topic
of coopetition in game theory tends to be an emerging one and seems to have a big amount of
research gaps.
1.3 Cooperative game theory
In terms of current research author does not try to develop or tests any concept of the
game theory. However many principles and concepts (such as game partition or coopetitional
games) help author to build mechanisms and ideas, described in the following chapters.
The game theory was described and introduced to society as a mathematical tool for a
strategic planning in 1928. That was done by John von Neumann in his article “On Game
Theory”. In this article Neumann describes basic principles of matrix games. Later in 1944
Neumann being co-authored by Oskar Morgenstern publishes their book “Game Theory and
Economic Behavior” (Von Neumann and Morgenstern, 1944).
The game theory tries to combine principles of and concepts of philological field of
knowledge with mathematical methods of analysis and modelling of strategic decisions. That
makes it interesting not only from the perspective of science and pure theory, but also to be a
powerful tool for leasers of governments, politicians, business owners and ordinary people.
However the game theory works with an assumption that each participant of the game makes the
most rational choice (basing on some grounds principles) (Von Neumann and Morgenstern,
1944). Otherwise games become unpredictable and these cases refer to other fields of
knowledge.
17
One of the most widely discussed topics of the game theory is the “Prisoner’s dilemma”
(Gilbert, 1996). The main idea of the game it that two bandits are arrested, separated, and
suggested to provide some evidence against each other. As a result bandits have a choice:
-
If both provide evidence against each other, both get average prison term,
-
If both do not provide any evidence, that both get minimum prison term,
-
If one provides evidence against the second one, and the second one does not
provide evidence against the first one, the first one gets freedom, and the second one gets the
maximum prison term
It is accepted, that such type of game is a non-zero-sum game. That means that this is a
game, when decision of one player does not mean that second player losses or wins necessarily.
There is always a chance for a win-win situation, when a group gets maximum pay-off
(Binmore, 2007).
The best (dominant) strategy for both bandits is to betray each other and get medium
term. Even though cooperation has a greater potential for both (if we evaluate an pay-off of the
group), it is the most risky option, while non-cooperation means that each bandit gets his result
out of two options, where each option is not the worst one (Gilbert, 1996).
There are several ways and approaches how to solve these games, however one of the
most often used ones is Nash equilibrium. Actually this is a generalisation of minimax strategy
suggested by Neumann in 1944 in his book (Kelly, 2003).
The main concept of this approach is that each competitive game with a final has at least
one equilibrium solution. Nash equilibrium is the situation, when each participant of the game
chooses the solution, which maximises potential pay-off of this participant (when all participants
know all possible decisions of other players). However such situation is possible only if all
participants of the game take their decisions rationally, applying all knowledge and data that they
have. The main goal of such players is to maximise their own profit (Nash, 1950).
The common concept of Nash equilibrium is widely used in the game theory to resolve
different games. For example equilibrium of Prisoner’s dilemma by Nash is a non-cooperative
strategy for both bandits. Standing on the idea of maximisation of a pay-off that a particular
individual gets. Nash equilibrium is not widely used in games that try to describe cooperation.
However, for these games there are analogy, such as “the core” concept (Parrachino, Zara and
Patrone, 2006).
18
All games described in the game theory can be divided into two main categories (see Fig.
1.1).
Cooperative
games
Cooperative games
with transferable
utility
Cooperative games
with nontransferable utility
Static games of
incomplete
information
All Games
Non-cooperative
games
Static games of
complete
information
Dynamic games of
complete
information
Dynamic games of
incomplete
information
Figure 1.1 - Classification of games (Gibbons, 1992).
The first category is a non-cooperative games (such as Prisoner’s dilemma). The second
category of games is a Cooperative games, which tries to describe inter-relations between
companies that try to organise some kind of coalitions or alliances (Gibbons, 1992).
As it was mentioned befor games that describe cooperation deal with coalitions or
alliances, that players organise in the game process. As a result main decision makes in such type
of games is a coalition. Players in terms of such games are allowed to make agreements that
regulate the procedure of pay-off distribution among all players of the game (partitipants of the
coalition).
Cooperative games are also called coalitional games due to the fact that in cooperative
games a coalition makes the decisions about the strategies to be chosen instead of individual
players as in non-cooperative games. In cooperative games the players can also form binding
agreements about the division of pay-offs (Harsanyi and Selten, 1988).
There is also a subdivision among coopartive games to two types of coopeartional games:
-
Games with transferable utility: This type of games describes situation , when one
player can transfer its utility to another player not facing any kind of loss. In this case researches
do not estimate the income of each particular person, but work with the utility of the whole
19
coalition. In another words transferable ulility means that it does not matter, who exaclty gets
utility in the coaltion, and how many transerts of this utility were made. In all situations total
utility of the alliance remains the same.
-
Games with non-transferable utility: Such typpe of games suggests that players
cannot transfer utility that they get between other players of the game (paritipants of the coaliton)
(Harsanyi and Selten, 1988).
In cases of Non-cooperative games there are four sub-catergorites, which can be
derrieved basing on two main criteria: static/dynamic games, how much iformation each palyers
has about other players (Gibbons, 1992):
-
Games with a complete information: In this category of games all players have al
information concearning each player of the game. This knowledge also includes a pay-off
fuction infromation (for each partiticiapnt of the game).
-
Games with an incomplete information: This is the type of games, when players
can not be sure that they have all information about other players. That also means that they
cannot be sure about a payoff fuction of other participants of the game.
-
Static games: This is a category of games, when all paritcipants make their
deciisons (choose their strategies) at one (the same) particular moment of time. In other words,
that make their choice simmutaniously. That means that there is not information concearning any
actions, that were done before (in terms of this particular game), because there were not actions
in the past.
-
Dynamic games: In terms of such games players have some information about
some moves and actions that were done before the moment, when they haave to do their choice.
The other name for such type of games is a “sequental games”.
However, the abovementioned typologi is not the only one, that is applied in a scientific
field. There are many claissifications that help to understand, which particular game we analise.
There are only some of the examples of such classifications:
-
Number of persons claissification: Following this classification we devide games
to two-person and n-person games, where n-person games are those, where number of players is
more then two (Davis, 1997).
-
Number of repetitions classification: There are two main categories that come
from such classification: games with infitine number of repetitions or finite number of repetitions
(Osborne, 1994).
20
-
Sum-based classification: When researcher uses this classification he chooses
between zero-sum games and non-zero-sum games. Key determinant of this clasification is the
question whether pay-offs of particiapnts are balanced, so that if one wins something, then secon
looses (zero-sum), or there is a chance for win-win option (non-zero-sum) (Binmore, 2007).
Current research deals with the coopetition which deals with cooperation as one part the
coopetition concept. That makes cooperative games to be a potential source of information,
rules, principles and instruments, which can help us to analyse coopetition from the perspective
of the game theory.
As it was mentioned before non-cooperative games describe the situations, when players
choose their strategy from the perspective of individual profit and pay-off maximization.
Cooperative games in contrast with the first ones operate with a pay-off of the coalition, its
strategies and rules an principles how players dive pay-offs from the particular game (Harsanyi
and Selten, 1988).
In non-cooperative games equilibrium, point is often defined with the help of Nash
equilibrium instrument (when players try to maximise their profits), while equilibrium of a
cooperation games lies in the field of definition of a stable pay-off distribution principles. These
principles should be accepted by all members of the coalition. This is how equilibrium in
cooperation games could be reached (Peleg and Sudhölter, 2003).
Cooperation games stand of the obligations that parties (players) take when they enter an
alliance. That is very similar to the real-world agreements that have also some punishment for
those who break them. These obligations should be accepted by participants of the game,
otherwise no coalition will be formed, and as a result there will be no game at all. That means,
that even though cooperation games deal with strategies of the coalition, they also pay attention
to the preferences of each player (on the game creation stage), so that players would be interested
in participation in this game (Peleg and Sudhölter, 2003).
As a result there is a big focus on the principles how pay-off generated in terms of the
game is distributed between participants. That brings following questions to the top importance
positions in the cooperative game theory:
-
What coalition can be formed?
-
How profits generated by a coalition can be divided?
21
The first question is also discussed from the perspective of, “What principles should be
applied for the coalition partition?” (Parrachino, Zara and Patrone, 2006).
As it was described previously there is a division of cooperative games to transferable
and non-transferable utility games. In transferable case participants can exchange with their payoffs without any loss from their side, or from the perspective of a coalition. Usually these payoffs are represented by money, which occasionally are evaluated equally by all players. However
these transferable utilities can also be represented with other instruments (for example, there
could be used some derivatives of money) (Peleg and Sudhölter, 2003). Non-transferable utility
games are not going to be used in terms of current research, and as a result will not be analysed
deeply in current theoretical background description.
Now let us describe common principles of coalitional games using mathematical
instruments. Occasionally in the game theory literature set of players that take part in the game is
shown as 𝑁, where 𝑁 = {1,2, … , 𝑖, … , 𝑛}, where 𝑖 is a current player and 𝑛 is a number of
players. 𝑁 is also called as a grand coalition. The characteristic form of an n-person cooperative
game is a pair (𝑁, 𝑣) where 𝑣 is a function that associates a real number 𝑣(𝑆), where 𝑆 is a
coalition that was organised on the base of 𝑁, and can be described as its subset 𝑆 ∈ 𝑁. If there is
no coalition, then 𝑣(∅) = 0.
Coalition has an opportunity to distribute its total pay-off 𝑣(𝑆) in all feasible ways
between the players that entered a coalition, that can be described as all payoff vectors 𝑥 ∈ ℝ𝑠 ,
which satisfy:
∑𝑖∈𝑆 𝑥𝑖 ≤ 𝑣(𝑆)
Each player of a coalition S has its marginal contribution, which can be described in the
following way: 𝑀𝐶𝑖 = 𝑣(𝑁) − 𝑣 (𝑁\{𝑖}), , where 𝑀𝐶𝑖 is a marginal contribution of a particular
player. This is a value which each particular player adds to a coalition that he enters
(Chakravarty, Mitra and Sarkar, 2015).
There are characteristics that occasionally are used to describe and classify coopetitional
games (Gambarelli and Owen, 2004):
Superadditivity: If two coalitions join into one coalition, their value is not less than value,
which they could generate if they acted on their own. 𝑣(𝑆 ∪ 𝑇) ≥ 𝑣(𝑠) + 𝑣(𝑇), where 𝑆, 𝑇 ∈ 𝑁,
and 𝑆 ∩ 𝑇 = ∅;
Monotonicity: If there is two coalitions, coalition with a more participants gets bigger
value. 𝑣(𝑆) ≤ 𝑣(𝑇), where 𝑆 ∈ 𝑇.
22
To define the most appropriate way of pay-off distribution there must be accepted some
rules or agreements. They should be accepted by all participants of the game (otherwise there
will be no game at all. Rules accepted by all participants of the game are usually called as
“solution”, or a “solution concept”. These accepted solutions have some common principles,
which are widely described in the academic literature (e.g. Parrachino, Zara and Patrone, 2006).
Some of these principles are described below:
Let 𝐺 be a set of games, 𝑖 is a current player. A solution on 𝐺 is demonstrated with a
function 𝑓 which associates with each game, (𝑁, 𝑣) ∈ 𝐺 a subset 𝑓(𝑁, 𝑣) of 𝑋 ∗ (𝑁, 𝑣), where
𝑋 ∗ (𝑁, 𝑣) is the set of feasible payoff vectors for the game (𝑁, 𝑣), and
𝑋 ∗ (𝑁, 𝑣) = {𝑥 ∈ ℝ𝑁 | 𝑥(𝑁) ≤ 𝑣(𝑁)}
1)
A solution 𝑓 on 𝐺 is rational from the perspective of individual player if (𝑁, 𝑣) ∈
𝐺 and 𝑥𝑖 ∈ 𝑓(𝑁, 𝑣), then 𝑥𝑖 ≥ 𝑣({𝑖}) for all 𝑖 ∈ 𝑁. That means that each player i entering a
coalition can earn at least a pay-off that this player could get, if he acted solo out of the coalition.
Otherwise the solution is not rational from the perspective of individual player, which means that
this player has no interest to join a coalition 𝑁.
2)
A solution 𝑓 on 𝐺 is efficient if (𝑁, 𝑣) ∈ 𝐺 and 𝑥 ∈ 𝑓(𝑁, 𝑣), then 𝑥(𝑁) =
𝑣(𝑁).
Efficient solutions of the game satisfy the condition that pay-off of the coalition is totally
distributed between all players. At the same time all individual vectors are efficient and players
get at least 𝑣({𝑖}),
To proceed there must be introduced additional parameters: 𝑀𝐶𝑖𝑚𝑎𝑥 (𝑁, 𝑣) and
𝑀𝐶𝑖𝑚𝑖𝑛 (𝑁, 𝑣) the maximum and minimum marginal contribution of current player 𝑖 to a coalition
in a game (𝑁, 𝑣).
3)
Function 𝑓 on 𝐺 is efficient if following conditions for 𝑖 ∈ 𝑁 are satisfied:
((𝑁, 𝑣) ∈ 𝐺 𝑎𝑛𝑑 𝑥 ∈ (𝑁, 𝑣) → 𝑥𝑖 ≤ 𝑀𝐶𝑖𝑚𝑎𝑥 (𝑁, 𝑣), 𝑎𝑛𝑑
((𝑁, 𝑣) ∈ 𝐺 𝑎𝑛𝑑 𝑥 ∈ (𝑁, 𝑣) → 𝑥𝑖 ≥ 𝑀𝐶𝑖𝑚𝑖𝑛 (𝑁, 𝑣)
That means that player can at least ask coalition to provide him/her with 𝑀𝐶𝑖𝑚𝑖𝑛 (𝑁, 𝑣),
however there should be no chance to ask for a pay-off which exceeds 𝑀𝐶𝑖𝑚𝑎𝑥 (𝑁, 𝑣)
If coalition sticks to these principles, there is a chance that game will be efficient.
However that does not mean, that solution concept provides coalition with some one particular
23
strategy, how pat-off should be distributed. Occasionally it depends on the allocation principle
chosen by coalition (Parrachino, Zara and Patrone, 2006).
Question pay-off of allocation remains to be opened, as there are many concepts, which
try to organise allocation in some way. Some of these are: stable sets, core, bargaining sets,
Shapley value. Each concept stands on its assumptions and principles of fairness, however, these
principles are not universal, as a result, each concept is stable, only if we accept this or that
principle of fairness (Chakravarty, Mitra and Sarkar, 2015). However, in terms of current
research there is no need to go deep into each of these concepts.
1.4 Platforms and platform-based markets
Current study is concentrated mainly on design of a tool that could be used by internet
platforms (e-platforms). E-platforms nowadays tend to focus on running business through the
internet, and also could be called as pure-players. Pure players are the organizations that operate
only in the Internet and do not have any physical stores or spaces (Sharma and Sheth, 2004).
Nowadays we face a significant growth of popularity of platforms that launch and
maintain interactions between two or more parties (sides) (Caillaud and Jullien, 2003; Rochet
and Tirole, 2003; Armstrong, 2006) – such as Airbnb, Amazon, and Uber.
In terms of current research internet platforms theory and concept of multi-sided market
is used mainly to describe a tool (two-sided platform) that could be used as a base for the lead
generating coopetition. That is why there is no description of mathematical models that try to
describe business model of different internet platforms. The main idea of current paragraph is to
provide a brief description of internet-platform business model and provide some examples and
peculiarities of it.
These platforms manage to create value gain incomes from intermediation between
different parties of users, satisfying their needs (Osterwalder, Pigneur and Smith, 2010).
Occasionally sides that get into the focus of multi-sided platforms are business audience that
provides market with some kind of services or goods, and customers that could be described as
end-up users. The first group of users also could be called as advertisers (Rochet and Tirole,
2003).
The most part of researches admit that focus on more than one side if a relevant
characteristic that describes modern industries in different extent (depends on the industry).
24
“Multisideness” became a new strategic tool, which is widely used by many organizations that
manage to demonstrate significant results.
Two-sided markets work with the intragroup and intergroup network effects which are
also called cross-group effect one of the definitions of which is: cross-group network effects
occur. The benefit enjoyed by a user on one side of the platform depends upon how well the
platform does at attracting users on the other side (Amstrong, 2006).
Basing on this we can see that YouTube could be called a two-sided internet platform
which operated with the above-mentioned phenomena of cross-group effect, when its revenues
from advertising depend on how regular video subscribers are satisfied.
Another significant example of a multi-sided platform, that is widely described in a
literature is Amazon company, that moved from a simple retailer to the two-sided model, adding
another retailers to its business process, and suggesting them to sell their products on the internet
based platform, called Marketplace (Ritala, Golnam and Wegmann, 2014) and as it was
mentioned before, Even though many of analytics tried to persuade Amazon, that such approach
is too risky, today we can see, that that move became a significant step that gave the company
(Amazon) a chance to survive and continue its growth.
Concentration on clients and on the market development (not on competitors), gave
Amazon a boost for the further development, which gives it a chance and fuel to develop not
only their own company, but the whole on-line industry, giving us a chance to propose that
platforms, designed following the principles and goals of coopetition have a great potential to
everybody.
Zhu and Iansity analyze entry barriers and success models on the platform based market
on the example of X-Box experience. Basing on the regression analysis authors highlight indirect
network effect as one of the key factors that gives a new platform a chance to stay on the market
and increase the number of subscribers in a short term. Also authors purpose that discount factor
can play a significant role in the platform market entrance. However its significance is twice
lower than first factors influence.
Indirect network effect is the situation when the increase of use of one product or network
spawns the value of the complementary product or network (Sundararajan, 2013). This term is
also connected with the cross-side networks and two-side markets, that will be discussed later.
25
However it is necessary to highlight that authors suggest that even nowadays we face the
decrease of the indirect network effect impact (especially in some particular spheres like webbrowsers). Also it is necessary to pay attention to the fact that the research made in terms of
limitations that make it hard to apply for many real case situations (Zhu and Iansity, 2012).
Basing on the research of platform market leader driven by Gezinus, Hidding,, Williams
and Sviokla we can come to the conclusion that on the market of internet platforms first movers
seem to be not in the best position, because usually followers take first places on this field. Also
in their analysis of platforms authors highlight for main drivers of current platform popularity
(Hidding, Williams and Sviokla, 2011):
-
Modularity;
-
Increased interconnectivity;
-
Self-organization;
-
Low marginal cost of production, which makes the advent of two-sided markets
more prevalent.
That drives us to the idea that any new platform that wants to succeed on the market
needs to have all these four characteristics, and also ideally should not be a first mover on its
field, so that customers would already be aware with some core functions and services that this
platform provides them.
One of the key questions of internet based markets that focus on more than one side is to
determine, which of the sides provides a more significant contributions to demand of its
complement (the other side). In other words there is a question, why parties might join the
internet platform. As a result we can meet the idea that consumer side sees as a motive any
benefits and additional values that are offered by Internet platform.
At the same time, producer side has motives that are mainly linked to the number of
potential customers that are classified by this business as a target audience. Second possible
reason for service providers to start being a user of some platform is a possible usefulness of
information and data that could be collected from its audience. As an example of the second
reasoning there are some proofs that B2B companies that tend to be involved in two-sided
markets usually get benefits from the private data, that their consumers leave on platforms they
use (Fish 2009). One of the possible outcomes from such information could be a wellconcentrated advertising, those bases on the personal information (age, gender etc.) of users of
such social networks as Facebook.com or vk.com. This information could be used to define
whether some person could be a potential user of some services or not.
26
One more significant peculiarity of multi-sided platforms as a form of business model is
that usually on of the sides is not charged for the value, that it gets from the platform.
Occasionally end-up users category (customers) is not charged for platform usage (that get some
services of the platform for free), while business participants that intend to sell their product or to
get some valuable data act as subsidizers paying to reach their target audience. That means that
platforms need to find and demonstrate a good reason for end-up consumers to join the platform
for free, so that there could be created a significant value for services and goods suppliers
(Mahadevan, 2000).
Abovementioned peculiarities connected with the value creation issue for two different
groups of users, pushes the most pert of internet platforms to the business model that consists
from a set of steps. Movement from one step to another demonstrates the evolution of a business
model that seems to by typical for many successful internet ventures (Muzellec, Ronteau and
Lambkin, 2015). On early stages internet platforms concentrate on the values proposition
towards end-consumers, persuading them to join a platform. At this stage platforms usually
ignore any other sides. That continues until the number of users of a platform reaches some kind
of critical mass that could become interesting for B2B clients of the platform.
At the second stage of development platform moves its focus on business that is
interested in end-up customers, which were already attracted to the platform. At this stage
platform starts to get its first revenues. After venture reaches its first financial goals it moves to
the third stage, which could be characterized as a reconsideration of all its services it order to
increase the value for both sides of their users. Authors call this business model as B2BandC
oriented model (Muzellec, Ronteau and Lambkin, 2015).
1.5 Research problem, objectives and delimitation
Research gap. Nowadays coopetition is discussed mainly from the disruptive perspective,
of how some organisations manage to run it. However, it seems to be difficult to find any
research materials, which would attempt to create some kind of a practical coopetitional
framework that could be applied. Also coopetition still remains to be poorly discussed from the
perspective of impacts, which it potentially can cause on the scale of an industry. Today it is
mainly analysed from the perspective that evaluates effects of coopetition on a scale of one
particular company.
Delimitations. It was decided to concentrate on one group of marketing activities that
seems to be common for nearly all commercial organisations. This is a lead generating group of
27
activities, which is connected with the procedure of getting potential orders or requests on
services of a company (leads).
Also it was decided to reduce the scale of the research and its problems, from the whole
market to one particular industry. The choice of industry based on the personal professional
experience of the author and availability of the information that describes this industry.
The main research question. What impact can be caused by a lead generating internet
platform-based coopetition among companies, which operate in one industry, on this industry?
There is a set of sub-questions that need to be answered:
2.
What is a potential design of a lead generating coopetition process among
companies, which operate in one industry?
3.
What is the possible impact of a lead generating coopetition on companies with
different price and quality strategies?
4.
How the number of the coopetition process participants influences on
effectiveness of lead generating coopetition?
5.
How the number of the coopetition process participants influences on average
utility that clients get?
The goal of the research. To define potential impact that can be caused by a lead
generating internet platform-based coopetition among companies, which operate in one industry,
on this industry. To reach this goal there was defined a list of objectives:
. Creation and description of a design of a lead generating internet platform-based
coopetition;
Detection of a potential impacts of the suggested lead generating internet
platform-based coopetition on individual participants of market with different price/quality
strategies;
Identification of a possible impact of number of the lead generating internet
platform-based coopetition members on the effectiveness of the lead generating internet
platform-based coopetition;
Definition of effects that number of the lead generating internet platform-based
coopetition participants can cause on an average utility of clients of industry, which applies lead
generating internet platform-based coopetition.
28
In terms of the current research effectiveness of a lead generating coopetition is evaluated
through revenue on advertising spent (ROAS) due to the assumption that many companies that
try to generate leads spend some advertising budgets on such activities.
1.6 Research methodology and organisation of the study
The research starts with a description of different theories, concepts and methods that try
to explain various fields of business connected with a coopetition and platforms. Basing on this
theories and materials, described mainly in the first chapter (Chapter 1), author tries to generate a
new mechanism.
The second chapter (Chapter 2) describes the methodology used in current master thesis.
States the research gap, describes the method of, lead generating coopetition concept design, and
finally discusses a simulation of an agent-based model used to define potential impact of lead
generating internet platform-based coopetition on one industry with one product.
Design of this lead generating coopetitional mechanism is described in the third chapter
of the study. The whole third chapter (Chapter 3) is used to reach the first objective of current
research. Author designs a concept of lead generating coopetition using some instruments from
cooperative game theory and self-designed mathematical formulas. However it is important to
admit that this mechanism is only a concept that needs to be checked and tested.
The fourth chapter (Chapter 4) describes the agent-based model and analysis of its
simulation results. Parameters for the simulation are taken from the research of the real web
design market of Russia. Parameters from the real world are used to make simulation more
realistic. Terms, parameters and analysis of results of the simulation are used to accomplish
second, third and fourth objectives.
Finally there is a discussion of results of current research, provided in the last fifth
chapter (Chapter 5). Also the final chapter contains limitations, discussion of further research
questions that arise basing on current research, managerial and theoretical implications of this
master thesis.
1.7 Summary of Chapter 1
1) The first research gap lies in the field of design of practical coopetitional concepts and
strategies. The second research gap is an issue of potential industrial impact of coopetition.
29
2) The goal of the research is to design and define potential impact of a lead generating
coopetition among companies, which operate in one industry, on the base of internet based
platform. Author tries to reach answering following questions: What is a potential design of a
lead generating coopetition process among companies, which operate in one industry? What is
the possible impact of a lead generating coopetition on companies with different price and
quality strategies? How the number of the coopetition process participants influences on
effectiveness of lead generating coopetition? How the number of the coopetition process
participants influences on average utility that clients get?
3) The research is organised in the following way. First author describes a concept of lead
generating internet platform-based coopetition, using mathematical instruments and instruments
used in cooperative game theory. Then author describes an agent-based model and runs its
simulation, to evaluate potential impact of designed concept on industrial level.
30
2. RESEARCH DESIGN and METHODOLOGY of LEAD GENERATING INTERNET
PLATFORM-BASED COOPETITION STUDY
2.1 Starting point of approaching lead generating internet platform-based coopetition
study
There is a growing number of researches that describe coopetition strategies of different
organisations operating all around the world. Such papers provide a deep analysis of actual
activities made by these companies and provide some financial and statistical data as a proof of a
potential benefits underlying coopetition phenomenon (Lacoste, 2012; Eisenhardt, 1989).
The phenomenon of coopetition arises various questions such as trust building among
organisations or security of companies that choose a coopetition as a strategy (Czernek and
Czakon, 2016; Pellegrin-Boucher et al., 2013). Also academic literature demonstrates various
attempts to classify different coopetition strategies, types and activities through analysis of actual
experience of organisations (Rusko, 2011).
As a result, nowadays experts try to design, create and describe various coopetition tools
and instruments. To confirm the potential effectiveness of such instruments academics use
experience from other fields of knowledge, such as game theory (Kalai and Kalai, 2012).
To define the first research gap, it is important to admit that the number of researches that
provide companies with tools “How to run coopetition” is much less than papers that try to
describe this phenomenon or classify it.
Also researchers focus mainly on coopetition effects in the scale of one company. As a
result, nowadays there is a deep understanding of “What individual companies can achieve from
a coopetition” (e.g. e.g. Song and Lee, 2012; Shih et al., 2006; Salvetat and Ge´raudel, 2012).
However, due to the fact that even though coopetition starts to emerge as a strategy, it still
remains not so common practice. As a result there are few possibilities to explore effects, which
coopetition is able to bring to the whole particular market or industry.
One of instruments, that could be used as a base for a coopetition as a strategic tool for
the whole particular industry is an internet based platform. The phenomenon of internet platform
(e-platform) is a modern one (Armstrong, 2006). Its current popularity became possible with a
rapid development of internet all around the world. The most frequent type of internet platforms
is a multisided platform, which provides services for different (usually interconnected) groups of
users.
31
Due to its mechanics, internet based platforms already started to provide services for
competing companies. There are many types and forms of services, which are provided at this
moment of time. There are even come examples of platforms that operate on the principles of
coopetition (Ritala, Golnam and Wegmann, 2014).
At present moment of time, question of a coopetition strategies, that could be ran through
platforms is examined from the descriptive point of view with the means of case analysis tools.
However questions of possible influence on some particular industry of one of coopetition
strategies organised on base of an internet platform is not examined as it could be and could be
also classified as a research gap. Filling this gap could be valuable as from the perspective of
academic knowledge, as from the practical usage of coopetition strategies in modern economy.
2.2 Design of a concept
To design of a concept of internet platform-based coopetition among organisations with a
base upstream activity aimed at the generation of leads, author uses induction. Author uses
theoretical description of three phenomena of modern economy, business and strategy
environment: coopetition, cooperational game theory and internet two-sided platforms.
Combining principles of these three fields of knowledge author comes up with a concept
of internet-based platform, which could be able to organise lead generating coopetition among
organisations, which operate in one industry. After the design is done, author has to answer
following questions:
What is the possible impact of a lead generating coopetition on companies with
different price and quality strategies?
How the number of the coopetition process participants influences on the
effectiveness of lead generating coopetition?
How the number of the coopetition process participants influences on average
utility that clients get?
2.3 Agent-based model simulation
To answer the abovementioned questions it is needed to evaluate possible outcomes of a
complicated system functioning. Such outcomes tend to be hardly evaluated and predicted with
simple mathematical calculations. Also it is important to pay attention to the fact that possible
outcomes of such system functioning depend on various decisions of different participants of a
32
market (competitors, clients). Abovementioned conditions tend to be reasonable grounds to take
a simulation of agent-based model as a way to test effectiveness of a suggested concept of
competition interaction.
Simulation is used mainly in researches, when complexity of examined systems becomes
so high that basic simple calculations are not enough to get some significant results. In academic
researches simulation is described as a problem-solving method (Banks, 2000). The main idea of
simulation is to build a model, which could be able to describe real processes at some extent
(Law and Kelton, 2000). One of possible applications of a simulation is a prediction of possible
results of processes with different values of variables.
To run the simulation a model is required. In terms of the current research author uses
agent-based modelling (ABM). The main component of ABM is the “agent”. The whole
simulation in case of AB modelling bases on functions and parameters of agents, that define
what they are, what they do and how they behave (Wooldridge and Jennings, 1995). In ABM
agents get some set of rules that define their:
Boundaries - their limitations, interconnections with other agents and etc;
Behaviour and decision-making capabilities – describe how agents make their
choice under various circumstances.
AB models describe the interactions of various agents that are situated in different
situations and receive some programmed inputs concerning the state of environment and
different agents. When agents get these inputs, they respond basing on some logic. Actions of
agents of ABM can be reactive and proactive, basing on their objectives, environment and rules
of a model (Wooldridge and Jennings, 1995).
In other words AB modelling operates with the modelling of the behaviour and
interactions of various agents with different objectives and parameters, in an environment
defined by some set of rules and principles, over time. It is important to pay attention to the fact
that agents can act on their own basing on their personal goals, or share some common goals,
acting in an organisational context (Jennings, 2001).
There is a string view that AB modelling suits the best, situations that run without or with
a small influence of central coordination on the behaviour of agents. In other words agent base
models are used to simulate bottom-up problems and cases, when behaviour and decisions of
individual agents can cause some global effects and trends (Macy and Willer, 2002).
33
2.4 Limitations of the model
In terms of the current research there is a number of terms and limitations that make it
possible to build a simulation that could be used as a base for some conclusions and further
analysis.
1)
AB model built in terms of current research assumes that there is only one product
on one market, with no other goods, which could cause any effect on choice of customers;
2)
There is only one advertising tool, used on the market – Pay Per click advertising.
Other advertising and marketing instruments cause no effect on number of leads, that
organisation gets;
3)
Each client makes his choice basing on the principles of Utility maximisation;
4)
Each client makes his purchase only once in terms of one simulation.
2.5 Data collection
When the model is described and built, it is important to set its parameters. It was decided
to use parameters from the real world (from some industry that potentially could apply lead
generating internet platform-based coopetition). it was decided to use Russian web-design
market, due to the ready availability of data that describes this industry.
Basing on web-design market research conducted by the Russian analytical portal CMS
magazine there was taken the following data:
-
Number of companies that currently operate on Russian web-design market;
-
Average turnover of web-design studios in different regions of Russia;
-
Segmentation of companies basing on the price criteria;
-
Identification of instruments that web-design studios use a lead generating tool.
There were two prior methods of data collection (CMS magazine, 2012):
-
Questionnaire that was answered by 450 executives of Russian web-design
studios (see Appendix 5);
registered
Data collected from 1234 organisations, basing on the profiles of companies
on
web-portal
“Runet
Rating”
(in
Russian
Рейтинг
рунета
-
http://www.ratingruneta.ru).
34
Basing on the information provided by Yandex Direct budget planning tool there was
received information concerning Pay-per click advertising tool parameters and some information
about the market potential (Yandex, April 2016):
-
Cost per-click rates;
-
CTR rates;
-
Number of potential clients.
Yandex is a Russian search engine, which provides services of PPC advertising for
organisations that try to find clients on the Russian market.
Statistics of conversion rates (CVRs) of web-sites of organisations from different spheres
of business was taken from the survey made by online advertising company “WordStream”
among 1,000 landing pages. There was analysed the statistical probability and its distribution
(basing on the statistics of these landing pages) that people will leave their request on services,
provided on particular web-page. Later this statistics was separated to different industries (Kim,
2014).
To define, which percent of total revenue organisations invest into advertising there was
used a statistics provided by The CMO Survey in terms of the annual research of marketing
trends. Information was taken from 3120 organisations that operate in different spheres of
business. There was made an e-mail contact survey with follow-up reminders. As a result there
was a 9.3% respond rate (289 respondents). Research was held from January to February 2016
(The CMO Survey, 2016).
Data, taken from the abovementioned sources was used to define the borders of key
parameters that describe the environment and agents behaviour and characteristics in terms of
current research.
2.6 Validation of the model
Before any data got from the simulation could be used as a base for some conclusions and
analysis, it is important to validate the model. Validation of a model proves that a model is
calibrated properly and is able to provide the data that at some extent could be close to the data
from the real systems. One of the ways, how validation of the model could be made is to show it
to the experts, who can examine it and say, that a particular model is valid. Such method of
validation is called faced validation (Leemis and Park, 2012).
35
In terms of the current research the model was demonstrated to scientific supervisors who
are considered as experts. The experts ensured that the behaviour of the model reflects to the
reality to the level so that its results could be called sufficient and creditable.
2.7 Experimental design
Current research is based on the experimental design which tests the model with different
parameters. Tests with various parameters provide author with the outputs, which are used by to
detect trends, impacts and phenomena that could be used as a base for hypothesis testing.
The simulation of a lead generating platform-based coopetition evaluates the following
outputs:
ROAS: Revenue on assets spent by company (or coalition) on advertising;
Profit: Difference between total income gained in terms of one simulation and
money spent on advertising.
2.8 Simulation software
The simulation of a AB model in terms of current research is made on the base of a
AnyLogic 7.3.1 Personal Learning Edition. It is a program based on Java program language that
works with agent-based, discrete event, and system dynamics modelling approaches. The main
reason for using AnyLogic is its availability. The version used by author is free of charge. Also
AnyLogic provides its users with a graphic interface, which simplifies the process of modelling
and simulation. Due to the peculiarities of this version of the software there are only two ways of
distribution used to describe the parameters: union and triangular distributions.
2.9 Summary of Chapter 2
Current research goes through the following stages (see Fig. 2.1):
1.
Author develops and describes the design of a concept of a lead generating
internet platform-based coopetition (LGIPBC). It is done basing on three main theoretic fields of
knowledge (coopetition, cooperation game theory and two-sided internet platforms);
2.
Author designs an agent-based model for a simulation that helps to answer the
second, third and fourth sub-questions of current research;
3.
Author takes parameters for the designed model. It was decided to use data that at
some extent describes Russian Web-design market of year 2012;
36
Figure 2.1 - The research structure
4.
Simulations of the agent-based model (built on the following software: AnyLogic
7.3.1 Personal Learning Edition) with a parameters taken from the real market provides author
with data, that could be used to reach second, third and fourth objectives;
5.
Author analyses the data, that he gets from the simulations, and uses the results of
the analysis as a ground for answer on the above-mentioned questions;
6.
Finally there is a discussion of findings, potential implications, and limitations of
current research.
37
3. DESIGN OF A LEAD GENERATING INTERNET PLATFORM-BASED
COOPETITION
3.1 Description of lead generating internet platform-based coopetition
To answer the first sub question of the current research and meet its first sub-aim, author
attempts to create a design of a lead generating internet platform-based coopetition (LGIPBC).
This concept bases on the idea of co-invested advertising campaigns of the product. Companies,
which distribute the same product, gather into coalition on the base of the internet platform
(Operator). Operator provides coalition that gathers on its base a web-page and runs an
advertising campaign on the advertising budget of the coalition. Advertising campaign generates
traffic of potential clients on the web-page of the coalition. Generated traffic convers into
requests for product distributed by members of the coalition (leads). Each lead, generated by a
co-invested advertising campaign of the coalition, spreads among all members of this coalition,
and after members of the coalition get lead, they start competing for it, with their sales strategies.
Described concept includes competition and cooperation at different stages of their interaction
process. That means that it can be classified as a concept of a coopetition among companies
(Brandenburger and Nalebuff, 1996).
Operator charges members of a gathered coalition for its organization, coordination
services and organization of the advertising campaign on the budget of the formed coalition.
Operator offers companies that produce the same product to join one of coalitions. Coalitions
base on groups of companies allocated by the Operator on the market of one particular product.
Allocation of groups bases on characteristics of product distributed by companies on the market.
Following characteristics could be used as a base for a group allocation process:
-
number of functions;
-
quality of design;
-
price.
Operator also provides participants with a forecast of possible average price of one lead,
that participants can get. Possible average price of one lead is inversely related to the number of
companies that enter a coalition.
Each organization decides, whether it is ready to join one of announced coalitions or it
rejects the offer made by the Operator. If organization accepts the offer than it needs to decide,
coalition on base of which exact group it joins (basing on its own perception of its product and
its strategy).
38
The main benefit that members of each particular coalition get is a decrease of average
price for one lead. This is archived by the following mechanism:
1)
Each company that wants to join a coalition pays an entrance fee of this coalition.
Entrance fee is set by the Operator;
2)
Total sum of the entrance fees, paid by members of the coalition is used by the
Operator as an advertising budget;
3)
Operator distributes advertising budget of a particular coalition on the advertising
instruments that attract traffic of potential clients on the web-page of the coalition;
4)
That traffic of potential clients converts to leads;
5)
Operator provides all members of the colocation with a full access to all leads,
generated by the web-page of this coalition.
As a result each member of the coalition gets leads that were generated on advertising
budget of the coalition. Web-page of the coalition generates more leads with a cheaper price of
one lead for one member of the coalition, if we compare it to the price of one lead generated by a
solo advertising campaign led by one company for its own brand.
When participants of the coalition start getting leads, competition part of the LGIPBC
begins. At this point everything depends on the specific features of participant’s individual
marketing policy, their sales systems, quality of the product and etc. After all leads are given to
all members of the coalition, Operator stops the LGIPBC session and suggests members to join
the next one.
There are three main stages of LGIPBC:
-
Coalition partition stage;
-
Co-invested lead generation (cooperating activities);
-
Competition for customers.
As it was mentioned before Operator is an internet platform. The first group of users of
this internet platform consists of companies, which distribute some product. The second group of
users (second side) is represented by individuals and organisations, which could be potential
customers of the first group of users of the internet platform. That means that this platform could
be classified as a two-sided internet platform (Amstrong, 2006).
39
Basing on the conclusion that Operator is a two-sided internet platform, there are grounds
for discussion of functions and services that could be provided to the second group of users
(potential clients of the first group). However, in terms of the current master thesis, this issue is
not discussed due to the fact that, from the standpoint of author, it does not refer to the
coopetition in a straight way.
3.2 Coalitional partition stage
Coalitional partition is held among all companies that produce the same product
(Companies) with different levels of characteristics that describe it. 𝑁 = {1, … , 𝑖, … , 𝑛} – set of
Companies, n > 0, number of Companies, 𝑖 ∈ 𝑁 – current Company.
Each Company 𝑖 produces a product that can be descried in some way. Operator
announces characteristics of this product (Characteristics). R = {R1 , . . , R 𝑘 , . . . , R 𝑟 } – set of
Characteristics, r – number of characteristics. 𝑅𝑘 ∈ 𝑅 – particular characteristic.
After a set of Characteristics was announced, Operator defines maximum and minimum
levels of each Characteristic on the market of a product produced by the Companies (Market).
Operator defines maximum and minimum levels of each Characteristic on the Market basing on
the research of this Market: 𝑀 = {𝐿𝑅1 : 𝐿𝑅1 , … , 𝐿𝑅𝑘 : 𝐿𝑅𝑘 , . . . , 𝐿𝑅𝑟 : 𝐿𝑅𝑟 } – Market. 𝐿𝑅𝑘 – level of
a particular characteristic, 𝐿𝑅𝑘 – minimum level of a particular Characteristic on the Market,
𝐿𝑅𝑘 – maximum level of a particular Characteristic on the Market
After the Market is described, Operator starts to distinguish particular groups of
Companies on the Market. That process is made in the following way:
1)
Operator divides the market with the help of cauterization. As a result he
distinguishes a set of groups: G = {G1 , … , G𝑗 , . . , G𝑔 } – set of Groups, g – number of Groups, G𝑗 –
a particular Group;
2)
Operator defines border Levels of each Characteristic 𝑘 for each particular group:
𝑗
𝑗
; LR 𝑘 – minimum level of a particular Characteristic 𝑘 in a particular group, 𝐿𝑅𝑘 – maximum
level of a particular Characteristic 𝑘 in a particular group;
3)
As a result each particular group 𝑗 out of a set of Groups can be described in the
𝑗
𝑗
𝑗
𝑗
𝑗
𝑗
following way: 𝐺𝑗 = {𝐿𝑅1 : 𝐿𝑅1 , … , 𝐿𝑅𝑘 : 𝐿𝑅𝑘 , . . . , 𝐿𝑅𝑟 : 𝐿𝑅𝑟 }.
40
Each Company 𝑖 on the Market can refer itself to one of the groups. It makes its choice
basing on its own perception of Levels of Characteristics of its own product. LR 𝑘 (𝑖) –
perceptional level of a particular Characteristic 𝑘 by the current Company 𝑖. As a result each
Company
can
make
its
own
Characteristic
profile
of
its
product
(Profile).
𝐶𝑃𝑖 = {𝐿𝑅1 (𝑖), 𝐿𝑅𝑘 (𝑖), … , 𝐿𝑅𝑟 (𝑖)} – profile made by a current Company 𝑖.
Operator announces that on the base of each group 𝑗 there can be formed only one
coalition 𝑆𝑗 . To enter a particular coalition 𝑗 Company has to pay an entrance fee. Operator
defines amount of entrance fee for each particular group 𝑗, ASj > 0, basing on the analysis of the
Market.
After groups are defined, operator offers each participant to decide, to which group he
refers himself. Each Company 𝑖 makes its choice basing on its own perception of characteristics
of their product.
Finally Operator announces the expected level of average lead price reduction 𝑃𝑅 from
the perspective of individual investments ASj of one particular member of coalition 𝑆𝑗 for each
coalition formed on base of a particular group 𝑗 at different levels of coalition advertising budget.
𝑃𝑅𝑗 (𝑋𝑆𝑗 ) =
𝑋𝑆𝑗 −𝐴𝑆𝑗
𝑀(𝑋𝑆𝑗 )
,
(4.1)
where 𝑋𝑆𝑗 > 0 – advertising budget of a particular coalition 𝑆𝑗 ,
𝑋𝑆𝑗 = ASj ∗ dj ,
(4.2)
dj > 0 – number of members of a particular coalition 𝑆𝑗 .
Function 𝑀(𝑋𝑆𝑗 ) > 0, describes a relationship between the amount of investments in
advertising company and the number of leads that come from this advertising company. This
function can be derived by many ways, one of which (but not unique) is a regression analysis. It
depends on:
-
Target audience of a coalition;
-
Advertising instruments, used by coalition;
-
Season, when advertising campaign is held.
41
Each additional participant that joins coalition 𝑗 decreases 𝑃𝑅𝑗 . That means, that if there
would be no competition increase, connected with the growth of the member of coalition
members, it would be a wise strategy for Companies, to form maximum coalition, that could
maximise the reduction of price of one lead for its members.
Operator uses 𝑃𝑅𝑗 as an additional motivation for Companies to enter one of coalitions.
Basing on the researches of trust building among companies, there are some grounds to suggest
that organisations make their choice whether they trust or no, mainly basing on estimations made
with the help of calculations (Faulkner, 2000; Lewicki and Bunker, 1996). Level of average lead
price reduction from the perspective of individual investments of one particular member of
coalition 𝑃𝑅𝑗 is the instrument aimed to satisfy trust-building calculations criteria.
After all important information was announced, Companies decide, whether they want to
join one of coalitions formed on the base of groups. If there are no Companies that join some
particular coalition, than this coalition is not formed.
3.3 Possible strategies of companies
It is important to understand that each Company 𝑖 has a right to join a coalition that bases
on a group with , which does not meet characteristics of this participant. However, such strategy
can reduce the number of leads converted to orders by this particular Company, because Levels
of Characteristics of its services may not meet expectations of potential customers that can be
gathered by a coalition, that Company joined.
From the perspective of the whole industry LGIPBC implies a set of possible strategies
that could be chosen by Companies. At first each Company should decide if it wants to join a
coalition or no. That means that company has to options:
-
To join a coalition (Join);
-
Not to join a coalition (Avoid)..
If Company 𝑖 chooses to join one of coalitions, then it has to decide, whether it joins a
group with a product, which characteristics levels are similar to characteristics of a product of
this company (basing on its own perception), or to join another group. As a result we get the
following options:
-
To join a group of equals (peer group);
-
To join a group with a higher characteristics levels (higher group);
-
To join a group with a lower characteristics levels (lower group).
42
Finally, when Company decides to join a coalition and chooses which exact coalition it
chooses, it should make a choice whether it invests its advertising money only into promotion of
the web-page of his coalition, or part of its budget goes to advertising of its own web-site. This
choice could be described in two options:
-
To invest only into promotion of a coalitional web-page (all in coalition move)
-
To distribute advertising budget among its own web-site and coalitional web-page
(distribution move)
As a result we get the following tree of seven possible strategies (see Fig. 3.1).
Figure 3.1 - Possible LGIPBC strategies for Companies
Depending, on LGIPBC strategy that Company makes it can potentially get different
results. All these strategies are examined in mathematical simulation, described in fourth chapter.
3.4 Profit and ROAS – individual and coalitional
After coalition is formed, Operator starts an advertising campaign with a budget 𝑋𝑆𝑗 ,
gathered from all entrance fees, paid by members of a coalition 𝑆𝑗 . Each coalition gets its webpage that is located on the platform. This page gives a potential customer, to get an
understanding, which companies entered each particular coalition, to decide, weather they are
ready to send a request for services on the platform (for this coalition) or no.
When potential client leaves a request for services, each member of the coalition gets this
request. At this moment of time, members of a coalition start competing for this particular lead,
to convert this lead into a contract. This is the moment, when the LGIPBC starts to be
competitive.
43
When advertising budget of a particular coalition ends up, and a flow of leads stops, there
starts a process of evaluation of effectiveness of a LGIPBC session for each coalition and its
participants.
In terms of current research effectiveness of each LGIPBC session is evaluated through
two values: Profit and ROAS.
Evaluating profit 𝑉(𝑆𝑗 ), of a coalition 𝑆𝑗 we take into account a total sum of investments
that were spent on advertising campaign, and total income, from all sales, made by all members
of a coalition, while an advertising campaign of this coalition was active.
𝑉(𝑆𝑗 ) = 𝐼𝑆𝑗 − 𝑋𝑆𝑗
(4.3)
𝑉(𝑆𝑗 ) – profit of a particular coalition 𝑆𝑗 ,
𝑋𝑆𝑗 > 0 – advertising budget of a particular coalition 𝑆𝑗 ,
𝐼𝑆𝑗 ≥ 0 – total income, that one coalition 𝑆𝑗 managed to get at the end LGIPBC session
𝑗
𝐼𝑆𝑗 = ∑ 𝐼𝑖 ,
(4.4)
𝑗
where 𝐼𝑖 ≥ 0 – individual income, that one member of one particular coalition 𝑆𝑗 managed to
get at the end of a LGIPBC session.
It can be concluded, that each member 𝑖 of a coalition 𝑆𝑗 can evaluate only their own
personal profits 𝑉𝑖 (𝑗):
𝑗
𝑉𝑖 (𝑗) = 𝐼𝑖 − 𝐴𝑆𝑗
(4.5)
On the base of personal profit there is a possibility to calculate the return on advertising
spends (ROAS) of each member of a coalition 𝑆𝑗 :
𝑗
𝑅𝑂𝐴𝑆𝑖 (𝑗) = 𝐼𝑖 /𝐴𝑆𝑗
(4.6)
where 𝑅𝑂𝐴𝑆𝑖 (𝑗) – means the return on advertising spends of a current member of a particular
coalition 𝑆𝑗 ;
Finally to evaluate the effectiveness of money spend on advertising campaign of a
particular coalition 𝑆𝑗 ROAS of each particular coalition should be calculated:
44
𝑅𝑂𝐴𝑆𝑆𝑗 = 𝐼𝑆𝑗 /𝑋𝑆𝑗
(4.7)
Profit of each member cannot be announced or predicted before a LGIPBC session is not
finished. These values depend on a number of factors including:
-
Quality perception of clients;
-
Current market trends;
-
Economic situation in a country.
In terms of this research, there is an attempt to simulate client’s behaviour to try to
predict possible profits and evaluable potential successful strategies, that could maximise profits
of coalition and each its participant.
3.5 Summary of Chapter 3
1) Lead generating internet platform-based coopetition (LGIPBC) includes three main
steps: coalitional partition, when coalitions are organised; co-invested advertising lead
generating campaign; and competition for contracts (companies try to convert leads into
contracts). The first two stages can be classified as up-stream coopetition activities, while the last
stage is a down-stream activity. All three stages are coordinated by an internet-based multi-sided
platform (Operator);
2) Coalitional partition is organised on the base of market or industry grouping. This
grouping is made on the basis of characteristics of a product of this market. Each group can have
only one coalition. If company wants to join some particular coalition, it has to pay an entrance
fee that depends on a group that used as a base for this coalition;
3) Organisations can choose between 7 main strategies, suggested by LGIPBC. These
strategies stand on three main decisions: to join or to avoid a coalition, organised on the base of
LGIPBC; to join a coalition organised on the base of group with quality higher, lower, or equal
to quality of a company; to use LGIPBC as the only source of leads, or also invest into
advertising of an own web-site;
4) Results of and effectiveness of LGIPBC are calculated trough profit (4.3), (4.5) and
ROAS (4.6), (4.7) estimation.
45
4. MODELING AND SIMULATION OF LGIPBC
4.1 Model mechanics description
To estimate potential effectiveness of LGIPBC, there was used a simulation of an
agent-based model. In current part there is a description of the model, used to run the
simulation, its environment, behavior and parameters of its agents;
1)
The model simulates market of companies that distribute only one product
(Companies) with one possible coalition on this market 𝑔 = 1 (𝑆1 = Coalition);
2)
There is one company (i = 1) all parameters of which are manually settable
values (the Observed Company);
3)
Number of Companies, which operate on the market n ≥ 0 is a manually
settable value, 𝑁 = {1, … , 𝑖, … , 𝑛} – set of Companies, 𝑖 ∈ 𝑁 – current Company;
4)
Number of clients on the market nl ≥ 0, is a manually settable value, nl ∈ 𝑁𝐿,
𝑁𝐿 = {1, … , 𝑙, … , 𝑛𝑙}; NL – set of clients, 𝑙 ∈ 𝑁𝐿 – current client;
5)
Number of companies that gather into Coalition 𝑑1 > 0 is a manually settable
6)
The value of coalition entrance fee 𝐴𝑆1 > 0 is a manually settable value;
7)
The coalition gets its total advertising budget 𝑋𝑆1 is calculated according to
8)
Each Company (Coalition) chooses its own advertising budget 𝐴𝐵𝑖 ≥ 0 for
value;
(4.2);
each period of time. In terms of the simulation, this budget is assigned on the basis of uniform
distribution and falls into the range with settable borders, where 𝐴𝐵 is a maximum advertising
budget and 𝐴𝐵 is a minimum one for the Market;
9)
Each member of the Coalition has an advertising budget 𝐴𝐵𝑖 ≥ 𝐴𝑆1. If
𝐴𝐵𝑖 = 𝐴𝑆1 , than it means that a particular member of the Coalition invests only into the coinvested advertising campaign, and does not invest into advertising campaign of his own webpage. If 𝐴𝐵𝑖 > 𝐴𝑆1 , than it means that a particular member of the Coalition invests money
into advertising campaign of the web-page of the Coalition and also he invests into
advertising campaign of his own web-page;
46
10)
Each Company 𝑖 gets its quality level 𝑞𝑖 – an integer value that is randomly
assigned on the basis of uniform distribution out of 𝑄 = {𝑞: 𝑞} – set of quality levels, 𝑞𝑖 ∈ 𝑄.
11)
Each quality level 𝑞 gets its middle price of a quality level (𝑀𝑃𝑄𝐿(𝑞));
12)
When company 𝑖 gets a particular level of quality, it also gets its price 𝑝𝑖 ,
which is randomly assigned on the basis of uniform distribution and falls into the range:
𝑝𝑖 ∈ [𝑀𝑃𝑄𝐿(𝑞) − 𝜀 ∗ 𝑀𝑃𝑄𝐿(𝑞); 𝑀𝑃𝑄𝐿(𝑞) + 𝜔 ∗ 𝑀𝑃𝑄𝐿(𝑞)]
(5.1)
where ε and ω fall into a range from 0 to γ ≥ 0 is a manually settable value.
𝜀 ∈ [0; γ], and 𝜔 ∈ [0; γ] are randomly assigned on the basis of uniform distribution.
According to (4.9) there can be calculated maximum and minimum possible prices on
the Market. Minimum possible price on the Market: 𝑝 = 𝑀𝑃𝑄𝐿(𝑞) − γ ∗ 𝑀𝑃𝑄𝐿(𝑞), while
maximum possible price on the Market can be calculated in the following way: 𝑝 =
𝑀𝑃𝑄𝐿(𝑞) + γ ∗ 𝑀𝑃𝑄𝐿(𝑞);
13)
Each Company has its own web-page;
14)
The Coalition has its own web-page;
15)
Each Company (Coalition) uses pay-per click (PPC) advertising as an
advertising instrument, when advertisers pay a pay-per click cost (𝑃𝑃𝐶𝐶 ≥ 0), each time,
when their advertisements are clicked;
16)
PPC advertising is the only way of promotion on the market;
17)
When potential client gets on the web-page that belongs to a particular
Company (Coalition), that means that this potential client has clicked on the advertisement of
this Company (Coalition), advertising budget of this Company (Coalition) reduces on 𝑃𝑃𝐶𝐶,
of this Company (Coalition);
18)
There are four 𝑃𝑃𝐶𝐶 rates, which are manually settable values;
19)
In terms of simulation 𝑃𝑃𝐶𝐶 is assigned to each Company on the basis of
uniform distribution between the set of possible options. That simulates the choice, which
each Company makes concerning, 𝑃𝑃𝐶𝐶 rate that it uses;
20)
𝑃𝑃𝐶𝐶 of the Coalition is a manually settable value;
47
21)
Particular 𝑃𝑃𝐶𝐶 defines the probability, that potential client will click on the
advertisement of a Company that was assigned with a particular 𝑃𝑃𝐶𝐶. That probability is
called a click-through rate (𝐶𝑇𝑅 > 0);
22)
Each Company starts its advertising campaign at a random period of time in
terms of manually settable borders;
23)
Coalition and Observed Company start their advertising campaigns from the
beginning of the simulation;
24)
Conversion rate (𝐶𝑉𝑅 ≥ 0) defines a probability that a particular client, who
has entered a web-page of a particular Company (Coalition), makes a request on its services.
Each Company gets its 𝐶𝑉𝑅𝑖 out of the 𝐶𝑉𝑅 range according to the triangular distribution,
where 𝐶𝑉𝑅 – minimum possible 𝐶𝑉𝑅 (manually settable value), 𝐶𝑉𝑅 – maximum possible
𝐶𝑉𝑅 (manually settable value), and 𝐶𝑉𝑅 𝑚 – the most possible (manually settable value);
25)
𝐶𝑉𝑅𝑆1 of the web-page of the coalition is a manually settable value;
26)
When a particular client leaves a request on a web-page of a particular
company, this company gets a status of “Potential contractor” of this client;
27)
If a particular client leaves a request on a web-page of the Coalition, all
members of the Coalition gets a status of “Potential contractor” of this client;
28)
Each client 𝑙 has his desired number of requests 𝑁𝑂𝑙 > 0, which he leaves on
web-pages. 𝑁𝑂𝑙 is randomly assigned on the basis of uniform distribution to each client and
falls into the range with a manually settable borders;
29)
If client leaves a request on a web-page of a Company (Coalition) but he did
not get his desired number of requests, he continues to visit web-sites of other Companies (but
never gets back on the web-page, on which he left his request);
30)
If client leaves a request on a web-page of a Company (Coalition) and gets his
desired number of requests, he stopes to visit other web-pages;
31)
After client stops to visit web-pages, he has to make a choice and pick one
Contractor out his set of Potential Contractors;
32)
Potential client behaviour description:
48
a.
Each potential client gets his own subjective level of quality of each Potential
Contractor 𝑞𝑙 (𝑖) ≥ 0,
[𝑞 − 𝑞𝑖 ∗ 𝛼; 𝑞𝑖 + 𝑞𝑖 ∗ 𝛽], (𝑞𝑖 − 𝑞𝑖 ∗ 𝛼) > 0 ,
𝑞𝑙 (𝑖) ∈ { 𝑖
[0; 𝑞𝑖 + 𝑞𝑖 ∗ 𝛽], (𝑞𝑖 − 𝑞𝑖 ∗ 𝛼) ≤ 0 ,
(5.2)
where α and β fall into a range from 0 to τ, where τ is a manually settable value. Here
𝛼 ∈ [0; 𝜏], and 𝛽 ∈ [0; 𝜏], where 𝛼 and 𝛽 are randomly assigned on the basis of uniform
distribution
b.
Every client 𝑙 has his quality perception level 𝜃𝑙 , which falls into the quality
perception level range of the Market: 𝜃𝑙 = [𝜃; 𝜃̅], where 𝜃 = 𝑝/𝑞, and 𝜃 = 𝑝̅/𝑠̅ ;
c.
Every client tries to maximise his subjective utility that a potential client gets
from a particular company for its price 𝑈𝑙
𝑈𝑙 (𝑝𝑖 , 𝜃𝑙 , 𝑞𝑙 (𝑖)) = {
𝜃𝑙 ∗ 𝑞𝑙 (𝑖) − 𝑝𝑛 , 𝜃𝑙 ∗ 𝑞𝑙 (𝑖) > 𝑝𝑖 ,
0, 𝜃𝑙 ∗ 𝑞𝑙 (𝑖) ≤ 𝑝𝑖 .
(5.3)
As a result, if a potential client chooses between 5 organisations (potential
contractors), he always gives his choice to the company that provides him with the maximum
subjective utility;
33)
To simulate different market environments and various individual strategies
current model includes a set of manually settable scenarios:
a.
There is a coalition on the market. Advertising budget of each organisation that
entered a coalition can be higher than a coalitional entrance fee (companies invest into
coalitional web-page and into their own web-sites),
𝐴𝐵𝑖 ≥ 𝐴𝑆1 .
b.
The observed company enters the coalition; however its advertising budget is
equal to the entrance fee of the coalition.
𝐴𝐵1 = 𝐴𝑆1 ;
34)
The quality level: of the observed company, which defines its personal quality
move, is manually settable:
a.
If the Observed Company gets manually set 𝑞1 = 2, than the Observed
Company has chosen “higher group move”;
49
b.
If the Observed Company gets manually set 𝑞1 = 3, than the Observed
Company has chosen “peer group move”;
c.
If the Observed Company gets manually set 𝑞1 = 4, than the Observed
Company has chosen “lower group move”;
35)
To evaluate the effectiveness of different strategies there is a need for
calculation of profit and ROAS of Company (Coalition);
a.
ROAS
of
Company
1
is
calculated
in
the
following
way:
𝑅𝑂𝐴𝑆1 = 𝐼1 /𝐴𝐵1 where 𝑅𝑂𝐴𝑆1 – return on advertising spends of Company 1, 𝐼1 ≥ 0 –
income of Company 1;
b.
ROAS of the Coalition 𝑆1
is calculated in the following way:
𝑅𝑂𝐴𝑆𝑠1 = 𝐼𝑠1 /𝑋𝑠1 where 𝑅𝑂𝐴𝑆𝑠1 - return on advertising spends of the Coalition, 𝐼𝑠1 ≥ 0 –
income of the Coalition;
c.
Profit of a Company 1 is calculated in the following way: 𝑉1 = 𝐼1 − 𝐴𝐵1 ;
d.
Profit of the Coalition 𝑆1 is calculated in the following way: 𝑉𝑠1 = 𝐼𝑠1 − 𝐴𝑆1 ;
4.2 Parameters for the simulation
To run the simulation of the LGIPBC model, it was decided to use data from some
particular market. Through this, results of the simulation could be closer to reality. Also that
could ease the process of interpretation and analysis of results.
It was decided to use web-design market as a base for LGIPBC model basing on the
following criteria:
1)
Design of new web sites has an approximate 85% share in the structure of the
income of an average Russian web-design studio. That could be a base for a statement that
there is a market for the product (design of a new web-site), and web-design studios
potentially have enough motivation to attract clients through advertising activities.
2)
Respond to the question “From which sources you company gets new clients”,
which provided respondents (CEOs of the companies) with multiple choice demonstrated the
following tendencies:
From 80 to 90% of all Russian web design studios get their clients through a
personal recommendations
More than 60% of new clients came with the web design studio link, disposed
on its previous projects
50
At least 30% of all new clients found these companies with a search engines
(Google, yahoo and etc.)
From 16% to 21% of new clients came from the PPC advertising (Yandex
direct and Google Adwords)
From 17% to 27% of new clients came from thematic portals and different
platforms, that help companies to get clients (such as Avito.ru)
At the same time approximately 45% of all web design studios planned to spend the
most part of their advertising budget on PPC advertising. Basing on this data there could be
made a conclusion that PPC advertising (the only advertising activity used in model) is used
by web-design market and characteristics this market could be used as a parameters for the
simulation model.
To define the range of possible advertising budgets it was decided to apply one of
approaches of advertising budget identification through a turnover of a company. According
to one of these approaches, company should use some percentage from its turnover for some
period of time, as an advertising budget for the next period of time. That means that to define
potential borders of advertising range, it is needed to know average turnover of web-design
studios and which average share of this turnover could be used by them as an advertising
budget.
In 2011 Russian web design market faced a significant growth, with approximately
53% growth, comparing to the previous year and reached 14.9 billion rubbles volume. With
the growth of the market, web design studios faced a significant increase in their turnover
levels demonstrating 11.9 million rubbles average annual turnover in 2011 - 34% growth
comparing with 2011 (see Fig. 4.1).
Distribution of total annual turnover among companies operating in different regions
of Russian Federation looks in the following way:
Central Federal District - 17 881 077 rubbles
Northwestern Federal District - 12 645 474 rubbles
Ural Federal District - 11 965 143 rubbles
Siberian Federal District- 5 287 525 rubbles
Volga Federal District - 4 540 238 rubbles
51
Southern Federal District
Far Eastern Federal District - 1 240 000 rubbles
- 1 390 925 rubbles
Figure 4.1 - Average annual turnover of Russian web design studio (million rubbles) (CMS
magazine, 2012)
According to Chief Marketing Officer survey 2016, Average advertising budgets of
companies that offer services in B2B sphere falls around 8,6% from the total revenue of a
company. That brings us to the conclusion that average advertising budget of a web design
studio is approximately 85,000 rubbles per month. It is decided to use this amount as an
advertising budget of the observed company as the most expected one (𝐴𝐵1 = 85,000). The
top border of advertising budget range (𝐴𝐵) is set on level of average monthly advertising
budget of the Central Federal District – 128,000 rubbles.
Number of Companies (n) on the Market, there was made basing on the web-design
market segmentation by the price criteria. In 2012 there was approximately 2,600 web design
studios operation on the Russian market. Price diversification among Russian web design
studios is pretty wide. Prices of organisations that operate in low-cost segment start with
5,000 rubbles and end up with companies that produce web-sites for prices that start from 2
million Rubbles. In the research that describes the web-design market, the most part of web
design companies that operate on Russian market were distributed to 7 main price categories
(price of an average web-site for an organisation):
1.
Less than 50,000 rubbles (35.9%)
2.
From 50,000 to 100,000 rubbles (31.5%)
3.
From 100,000 to 200,000 rubbles (18%)
52
4.
From 200,000 to 300,000 rubbles (8.8%)
5.
From 300,000 to 500,000 rubbles (2.8%)
6.
From 500,000 to 700,000 rubbles (1.6%)
7.
Above 700,000 rubbles (1.6%)
Basing on the analysis it was decided to form groups basing of their pricing policy of
organisations. It was decided to reduce the number of groups from 7 to 3 (see Table 4.1).
Table 4.1. Grouping of companies on a price basis
Price
category
Price range
Percentage of
Estimated number of
participants
participants
1
Less than 50,000 rubbles
35.9%
933.4
2
From 50,000 to 200,000 rubbles
49.5%
1287
3
Above 200,000 rubbles
11.5%
379.6
One of the main motivations to unite all companies with prices above 200,000 in one
group, was the assumption that clients, which can afford themselves a web-site for 500,000
rubbles, do not use PPC instruments to look for a contractor as often, as those, who look for a
cheap or middle-priced products. That means that leaving categories with high prices as
separate ones could make them unpopular among companies.
The second and third price categories were united in one common group, to make
representatives of this group to be the most numerous group of companies, which could
represent approximately half of the market.
In terms of current simulation it was decided to use second group as a total market (n
= 1287), because it has a clear price borders that could be used as a price borders of the
model: 𝑝̅ = 50,000, 𝑝 = 200,000
One of the forms of PPC advertising is a PPC advertising based on the platform of
search engines. When people search some word or phrase using one of search engines, they
get PPC advertisements in special fields of a page with a search results. According to the data
collected by Yandex company (Russian search engine), which provides Russian business with
53
the PPC advertising services, in April 2016 PPC campaign built on one search phrase
«Заказать сайт» (To order a web-site) would have the following terms and characteristics (on
30 days scale):
Average number of ad showings – 66,630
Click-through rate (CTR) – varies from 0,64% to 6,31% depending on the rate
(average price of one click), that organisation chooses for its promotion (see Fig. 4.2).
Figure 4.2 - CTR (%) dependence on the average price of one click (Yandex, April 2016)
Basing on this data, the maximum number of potential clients that visit a web-site of
one particular studio can reach the number - 4205 visitors, that number is used to define the
number of clients on the simulated Market (l = 4205). Estimated budget, needed to get such
number of visitor is above 1 242 000 rubbles.
In terms of the current simulation average price per one click rates are used as PPCC
rates (see Table 4.2):
Table 4.1 - PPC advertising instrument costs and CTR (Yandex ,April 2016)
PPC advertising instrument
Price per one click (PPCC)
144
253
280
376
CTR
0.64%
1.05%
5.46%
6.31%
Finally it is important to estimate, how many visitors of web design studios web-sites
convert to actual leads leaving their request for web-site development services. According to
54
“WordStream” company data (see Table 4.3) median conversion rate of the Internet resources
is around 2.23% (B2B service), which means that approximately only 2 out of 100 visitors of
a web-site of a web-studio convert into leads (Kim 2014). That means that even if company
pays minimum price per one click on its ad in PPC campaign (144 rubbles), one lead costs it
approximately 7,200 rubbles.
Table 4.3 - Conversion rates of web-sites in different industries (Kim, 2014)
4.3 The simulation results and analysis
In terms of current research there were made more than 300 simulation rounds. Basing
on the data, received from these simulation round there can be made some conclusions and
suggestions. The values of all parameters of the simulation were taken from the analysis of
the processes and trends that take place in the web-design industry (see Appendix 1).
To answer the second sub question of the current research (What is the possible impact
of a lead generating coopetition on companies with different price and quality strategies?)
author runs a series of tests with the observed company (see Appendix 2). The aim of these
tests it to detect the best scenario (from the perspective of profit and effectiveness) for
different combinations of price and quality of the services provided by the observed company.
Criteria of effectiveness is evaluated through ROAS.
As a result, there were created profiles that demonstrate different levels of profit and
ROAS at different scenarios (see Table 4.4). The main aim of these profiles is to help to
define the best scenarios from perspectives of ROAS and profit.
55
Table 4.2 - RAOS and profit profile of observed company with high quality and low price
Price on
services
of the
Scenario
1
2
3
4
5
6
ROAS
1.412429
61.904
0.58851
9.18338
10.0047
28.5714
Profit
35040
127900
-34960
695064
744040
579000
observed
company:
50,000
The strategy(s) with the highest profit
2
The strategy(s) with the highest ROAS
2
The strategy(s) with the lowest profit
3
The strategy(s) with the lowest ROAS
3
When profit of the observed company is used as an effectiveness criteria, outcomes of
simulations demonstrate that in most cases companies benefit from Scenario 4 and Scenario 2
(see Fig. 4.3).
Figure 4.3 – Best individual scenarios from the perspective of profit,
The only category of companies that did not benefit from a coalition presence on the
market is companies with low quality and high or upper-average prices. Basing on this data
there could be made an assumption that presence of a LGIPBC has an impact on profits of
56
companies of a particular industry. In addition to that there is a base to suppose that this
impact could be classified as positive.
In cases when ROAS is taken as main effectiveness criteria, simulation demonstrates
pretty close results (see Fig. 4.4). The only significant difference is that there also appears
Scenario 6 as a potential effective scenario for organisations that have low costs and high or
low quality of services. ROAS perspective also demonstrates that companies with high or
upper-average prices and low quality benefit from situations, when there is no LGIPBC on the
market. All other participants get an increase of ROAS when LGIPBC is working and they
take part in coopetition.
Although, in both effectiveness tests Scenario 2 seems to be not a realistic one,
because it seems to be impossible, that all members of the Coalition refuse to invest their
money into their own web-site. However simulation results demonstrate that organisations
with high quality/high and upper-average price combination and Companies with medium
quality/low and lower-average price get the best results from such scenario. That also could
be used as a base for the assumption that LGIPBC increases the transparency on the market,
making its clients to find Contractors, which suit their needs the most.
Figure 4.4 – Best individual scenarios from the perspective of ROAS
The third important assumption that can be made basing on the ROAS tests is the idea,
that Scenario 6 of LGIPBC could be effective for companies with a low price policy. It means
that companies with a low-price policy can afford themselves not to invest into their own
57
advertising campaigns, but use only the coalition, as the only source of leads, that they get.
Basing on this assumption there could be also made an additional assumption, that there is a
probability, that LGIPBC has a potential to decrease average prices in one particular industry.
According to the abovementioned tests results there is a sufficient basis to state that
LGIPBC has a positive impact on industry, and can increase profits and effectiveness of
advertising campaigns of its participants (except those who have high or upper-average prices
and low quality).
The next set of simulation tests was made to answer the third sub-question (How
number of the coopetition process participants influences on effectiveness of lead generating
coopetition?). Using ROAS as criteria of effectiveness author gets outputs (see Appendix 3),
which could be used a base for the conclusion that answers the third sub-question of current
research: Number of members of the coalition has an impact on the ROAS of the coalition
(see Fig. 4.5).
2.50
2.00
ROAS
1.50
1.00
0.50
0.00
5
10
20
40
80
120
200
350
700
NUMBER OF MEMBERS OF A COALITION
Figure 4.5 – Dependence of ROAS of the coalition on the number of members of the
coalition.
There could be observed a clear increase of ROAS until the number of members of a
coalition reaches some particular level. After this level there is another clear trend that
demonstrates the decrease of ROAS of the coalition.
58
One of the possible reasons for such trend could be that average income of coalition
starts to decrease, when the number of participants grows. Growth of the number of
participants could cause the transparency increase and decrease of the prices as a result. In
other words client see, who has the same quality but lower price, and buy from them.
The second test submits the assumption, that LGIPBC has a potential for the increase a
transparency of a particular market, however, from the standpoint of author, this assumption
should be checked in a more precise way.
Finally there were made tests that aimed to define if c appearance on the market and
growth of number of its members can potentially increase average utility of one client on the
market (see Appendix 4). As a result there was detected a following tendency (see Fig. 4.6):
Increase of Utility (comparing to
Scenario “No coalition”)
30%
25%
20%
15%
10%
5%
0%
0%
1%
8%
31%
62%
100%
Number of members (% out of maximum)
Figure 4.6 - Dependence of average utility of a client from number of members
Basing on the results of utility tests we can assume that increase of the number of
members of a coalition that bases on the LGIPBC (and its existence) have a potential to
increase average utility on the market. As a result, level of satisfaction of an average client
can increase significantly.
That phenomenon detected in terms of simulation can be explained with an
assumption that increase of number of member of a coalition gives a client a chance to
compare more offers at once and define the best one (from subjective position of a client)
This potential benefit that market can get from LGIPBC applying also could be used
as a ground for the assumption that LGIPBC can become a source of market transparency
59
significant growth, which means an increase of competition among companies and all
outcomes that derive from that.
4.4 Summary of Chapter 4
1) Author described an agent-based model using concept of LGIPBC as a basis. To
model competition, author describes the rules, how clients make their choice, basing on their
subjective perception of quality.
2) Main parameters and border values of the agent-based model described in the first
section of current paragraph are taken from the set of marketing researches, which try to
describe Russian web-design industry.
3) The main conclusions made on the base of simulation results. All companies of
industry (except those, which have high price and low quality) get additional profits and
ROAS from LGIPBC applied to their industry. There is a trend that demonstrates that ROAS
of a coalition organised on the base of LGIPBC grows until some particular number of
members of the coalition is reached. Then ROAS of this coalition starts to decrease (when
number of members gets bigger). That means that potentially for each coalition there could be
defined an optimal number of members. Finally average level of utility, that clients get from
their choice of contractor grows (in terms of an industry), when LGIPBC is implemented on
this industry.
4) Basing on the main results of current research, there also can be made an
assumption, that LGIPBC potentially can increase transparency of some particular industry.
However, this hypothesis should be checked more precisely.
60
5. CONCLUSIONS
5.1. Discussion of the findings
The main goal of current research: To define potential impact that can be caused by a
lead generating internet platform-based coopetition among companies, which operate in one
industry, on this industry.
To do that author had to reach following objectives:
Objective 1: Creation and description of a design of a lead generating internet
platform-based coopetition.
In terms of current research there was an attempt to create a design of a LGIPBC
among companies that distribute the same product or service on the same market. The concept
of LGIPBC design and mechanics is described in Chapter 3 of current master thesis. It
describes a co-invested way of lead generating among companies that voluntarily join a
coalition of companies, which produce the same product close in its characteristics. Coalition
forming and lead generating processes are coordinated by a two-sided internet based platform.
First group of users of this two-sided internet based platform is represented by distributors of
a particular product and the second group is represented by their potential clients. Each lead
generated by coalition with its co-invested lead generating campaign is available to each
member this coalition. As members of the coalition get lead, they start competing for the
chance to convert this lead to an actual client. LGIPBC can be classified as a coopetition
because it compliance with two its basic signs (Walley, 2007):
1)
Companies cooperate to make the pie bigger
2)
After companies managed to make the pie bigger, they start competing for it.
That means that the first objective of the current research can be considered as
achieved.
Second, third, and fourth objectives of current research were achieved through the
simulation of the agent-base model. This model tries to describe the market of one product
with a chance of a coopetitional coalition formation. It is described in the first section of
Chapter 4. To define parameters, which could describe the environment and agents of this
model, there was used a data from the Russian web-design market, as it was considered as
suitable for LGIPBC. Description of parameters is provided in the second section of Chapter
4. Main results of simulation tests are provided in Appendixes 2-4.
61
Objective 2: Detection of a potential impacts of the suggested lead generating internet
platform-based coopetition on individual participants of market with different price/quality
strategies.
The agent-based simulation of an industry of one product with inputs taken from the
Russian web-design industry demonstrates that the nearly all participants of industry can gain
additional profits and increase their ROAS with the help LGIPBC. The only category of
companies that does not win from LGIPBC appearance on the market are companies with
inflated prices and low quality. This set of conclusions could be considered as a base for the
presumption that the second sub-aim of current research was achieved by the author (Chapter
4, section 3).
Objective 3: Identification of a possible impact of number of the lead generating
internet platform-based coopetition members on the effectiveness of the lead generating
internet platform-based coopetition.
There was detected a tendency, that ROAS of organisations that participate in
LGIPBC depends on the number of participants. Marginal ROAS stays positive until the
number of members of a coalition reaches some critical point, after which there is a clear
decrease of ROAS could be observed. Potential reason for such tendency could be a decrease
of average income of each member in the coalition, with an increase of the number of its
members. That gives the author a right to suggest that the last sub-aim was also achieved.
There could be made a conclusion, that number of members of coalition gathered on the base
of LGIPBC causes some influence on effectiveness of money spent on advertising by its
members. Also that could be a sign of potential increase of market transparency in cases when
market starts to apply LGIPBC. Each new member increases the transparency on the industry.
So as a result clients manage to find the same quality for lower price (Chapter 4, section 3).
Objective 4: Definition of effects that number of the lead generating internet platformbased coopetition participants can cause on an average utility of clients of industry, which
applies lead generating internet platform-based coopetition.
Finally there were made tests that aimed to define, if industry that applies LGIPBC
increases average (and total as a result) utility of clients of this industry. Results collected
from the simulation of the model can be used as a ground for an assumption, that: The more
companies of a particular industry enter coalitional relationships on the base of LGIPBC, the
higher average utility clients of this industry get. That could be explained by the assumption
62
that LGIPBC provides clients with a chance to compare different offers from many potential
contractors. As a result clients have access to more options and they can choose a better offer.
These results also can be used as a base for the assumption that LGIPBC can increase
transparency of some industries (Chapter 4, section 3).
5.2 Practical implications
In the field of managerial and practical use of the current research there is a clear
possibility and interest to imply the LGIPBC on the base of some real multisided lead
generating platform to test potential of the designed concept in the real life conditions.
However, it is important to understand that in terms of master thesis work this instrument can
be described as a static one (everybody make their choice at the same moment). Also it is
important to understand, current research does not deal with LGIPBC from the perspective of
one single repetition (potential effects of reputation or strategy modification through time are
not examined in terms of this research).
LGIPBC can be used as an instrument that helps market to displace companies with
high prices and low quality of distributed product out of the market. That makes it to be a
good chance for industries to increase the common level of satisfaction of clients and make
market conditions to be more transparent.
Also LGIPBC could be applied as a chance for companies which have low prices and
low quality (start-ups) to get their first clients with a reduced sum of money invested into their
advertising campaigns.
Finally LGIPBC has a potential to provide organisations with additional money
(released from the advertising budgets), which could be used on the improvement of quality
of the service or good that they distribute, or to invest these money into R&D. As a result that
makes LGIPBC to be a possible way of growth and improvement of industry that manages to
apply it.
5.3 Limitations
The first limitation of current master thesis is connected with peculiarities of LGIPBC.
It still needs to be modified, to become more realistic. For example now LGIPBC suggests
that all companies that want to join a coalition make their decision at once. Even though it is
possible, from the standpoint of author, that ability to join a coalition at any moment of time
could change the whole mechanism dramatically.
63
All other limitations of results achieved in terms of current study derive from the
limitations of the model that was used to examine potential of LGIPBC. It is not clear how
Operator could predict results of advertising campaigns, if they stand on the base of more than
one advertising instrument, and what potential result could be got, if market uses all
instruments and Operator stays only with the PPC instrument.
Also current research does not pay any attention to the potential reputational effects,
which could also cause some effect on average price of one lead for one particular member of
the coalition, ROAS and profits which LGIPBC can generate That is because now each new
simulation session suggests, that there was no Coalition before, and there will be no coalitions
in the future.
Due to the fact that in terms of model author uses only one grouping characteristic
(price) it is not clear, how situation could change, if there would be used a set of
characteristics, as a base for group formation.
Finally, current version of the model simulates only market with only one coalition on
it. If model could be able to simulate the process of coalitional partition among two or three
coalitions at once and then there would be a simulation of more than one coalition operating
on the market, potentially results could differ from current ones significantly.
5.4 Theoretical implications and further research
From the perspective of theoretical contributions, current master thesis explore
coopetition not from the descriptive point of view, as the most part of modern researches (e.g.
Luo 2004; Basole, Park and Barnett, 2015), but from the position of potential practical
implementation of coopetition as a tool.
Current research tries to create an applicable
framework or a tool, that could be applied to industry through a two-sided internet based
platforms. If academic society admits that LGIPBC could be considered as a coopetitional
strategy, than this concept could become a base for the new branch of theoretic researches and
tests (simulation and real ones).
At the same time current research provides some additional data to the question of
how coopetition influences on competition, which only starts to be discussed in current
academic literature (e.g. Oxley et al., 2009). It demonstrates a potential to help markets, to
increase their transparency and push organisations with low quality and high prices out of
market. Also there are results that show, how average price of a product decrease, when
64
coopetition involves more participants. That could be a sigh of potential increase of
competition on the market if it applies LGIPBC.
Also current research suggests that competition could be considered as a potential
solution of pay-off distribution in cooperation games (or at as one more distribution concept).
Today there are many concepts of fair distribution of a coalitional pay-off, however each of
these concepts stands on the assumption that some particular principle, that lies in its
basement is fair (Chakravarty, Mitra and Sarkar, 2015). LGIPBC using a coopetitional
principles demonstrates how coalition can exist without any pay-off distribution problems,
because each participant of the coalition gets all leads, and then all members of the coalition
compete for these leads. The only question that remains to be opened is: How LGIPBC could
work with other coalitional partition principles (if it could).
However, one of the main theoretical contributions of the current research is a list of
questions and further theoretical researches that should be examined in future. One of these is
the data that shows how companies with low quality and high prices benefit only from
scenarios, when there is no coopetition in the industry. That could be a base for the
hypothesis, that coopetition could be used as a tool that could increase a transparency of a
particular industry or the whole economy in common.
LGIPBC could potentially be used as a base for creation of a Coopetitional Game
(Game theory). As a game It has several steps: coalitional partition, and then competition for
clients. That means that this game could be a static one, with an incomplete information. Payoff of such game would be non-transferable (Gibbons, 1992). There also could be made
experiments to define if such coopetitive game could be checked on superadditivity and
monotonicity characteristics
Also there is still actual a question of limitations for coopetition. Can all companies of
industry enter a coopetition without decrease of average profit? Can coopetition be a tool that
could define the optimal number of participants on the market?
There is also should be answered a question: Which industries can apply LGIPBC and
which cannot? That is because it is not clear, what characteristics particular industry should
have, so that LGIPBC could be affective for its participants.
Current research deals with the principles of choice (how clients make their choice
between potential contractors). Using the same simulating model, with some modifications
there is an opportunity to evaluate how total and average Utility of clients change if there is a
65
lead generating coopetition inter-firm relationship on the market. There is a possibility that
total utility grows, when companies get into coopetition relationships.
In terms of the lead generating platform-based coopetition concept there should be
made more empirical tests (probably on the base of the real platform). These tests could have
a significant impact on development of industries and possibly change the principles of interfirm relationships in future. Fit is not clear which particular industries can apply LGPC as a
tool. Because of the peculiarities and special conditions, this could be considered as serious
barriers LGPC use.
Finally there still remains unanswered a question: “Which instruments and services
internet platform could be provide to its second group of users (clients of product distributes),
so that the first group would be able to increase its profits and effectiveness of advertising
budget?”
66
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72
APPENDIX 1. BASE PARAMETERS FOR ALL SIMULATION ROUNDS
Number of companies that
operate on particular market
(N)
Number of potential clients
(NL)
1287
Prices range
4205
Quality level (QL)
Middle price of a quality level
(MPQL)
Left price limit (LPL) %
Right price limit (LPL) %
Conversion of a web-site
(from visitor to lead) CVR
2
3
4
100,000
116,500
133,000
50%
50%
Minimum
Average
Maximum
0
2.23
5
PPC advertising instrument
Price per one click (PPCC)
144
253
CTR
0.64%
1.05%
280
5.46%
376
6.31%
Minimum Maximum
50,000
200,000
CVR of the observed
company
CVR of the coalition
Cost of one click chosen by
the coaliiton
Cost of one click chosen by
the observed company
Coalitional entrance fee
Advertising budget of the
observed company
Number of requests that a
client makes (NO)
2.23
2.23
144
144
21000
85000
Minimum Maximum
1
15
73
APPENDIX 2. ROAS AND PROFIT TESTS (OBSERVED COMPANY TESTS)
Quality level of an observed company - HIGH
Price on services of the
observed company:
50,000
60,000
75,000
100,000
Scenario
1
2
3
4
5
6
ROAS
Profit
The strategy(s) with the highest profit
The strategy(s) with the highest ROAS
The strategy(s) with the lowest profit
The strategy(s) with the lowest ROAS
ROAS
Profit
The strategy(s) with the highest profit
The strategy(s) with the highest ROAS
The strategy(s) with the lowest profit
The strategy(s) with the lowest ROAS
ROAS
Profit
The strategy(s) with the highest profit
The strategy(s) with the highest ROAS
The strategy(s) with the lowest profit
The strategy(s) with the lowest ROAS
ROAS
Profit
The strategy(s) with the highest profit
The strategy(s) with the highest ROAS
The strategy(s) with the lowest profit
The strategy(s) with the lowest ROAS
1.412429
35040
61.90476
1279000
0.588512
-34960
9.183385
695064
10.00471
744040
28.57143
579000
9.183385
695064
2.824859
155040
34.28571
699000
5.298107
365040
2.648305
140040
42.85714
879000
10.59621
815040
2.354049
115040
19.04762
379000
2
2
3
3
2.118644
95040
40
819000
2.824859
155040
2
2
1
1
1.765537
65040
75
1554000
0.882768
-9760
4
2
3
3
3.531073
215040
28.57143
579000
1.177024
15040
4
2
3
3
74
Pice on servicies of the
observed company:
125,000
140,000
150,000
Scenario
1
2
3
4
5
6
ROAS
Profit
The strategy(s) with the highest profit
The strategy(s) with the highest ROAS
The strategy(s) with the lowest profit
The strategy(s) with the lowest ROAS
ROAS
Profit
The strategy(s) with the highest profit
The strategy(s) with the highest ROAS
The strategy(s) with the lowest profit
The strategy(s) with the lowest ROAS
ROAS
Profit
The strategy(s) with the highest profit
The strategy(s) with the highest ROAS
The strategy(s) with the lowest profit
The strategy(s) with the lowest ROAS
2.942561
165040
5.952381
104000
1.471281
40040
4.415089
290040
1.471281
40040
5.952381
104000
6.591337
475040
0
-21000
7.062147
515040
0
-21000
1.647834
65040
0
0
1.765537
65040
0
-84960
4
2 and 6
3 and 5
3 and 5
0
8.241499
0
615040
4
4
2 and 3
2 and 3
0
35.71429
-84960
729000
4
4
2 and 3
2, 3 and 6
75
Quality level of an observed company – MEDIUM
Price on services of the
observed company:
50,000
60,000
75,000
100,000
Scenario
ROAS
Profit
The strategy(s) with the highest profit
The strategy(s) with the highest ROAS
The strategy(s) with the lowest profit
The strategy(s) with the lowest ROAS
ROAS
Profit
The strategy(s) with the highest profit
The strategy(s) with the highest ROAS
The strategy(s) with the lowest profit
The strategy(s) with the lowest ROAS
ROAS
Profit
The strategy(s) with the highest profit
The strategy(s) with the highest ROAS
The strategy(s) with the lowest profit
The strategy(s) with the lowest ROAS
ROAS
Profit
The strategy(s) with the highest profit
The strategy(s) with the highest ROAS
The strategy(s) with the lowest profit
The strategy(s) with the lowest ROAS
1
2
3
4
5
6
1.176471
15040
30.95238
629000
1.176471
15040
3.529412
215000
1.764706
65000
19.04762
379000
2.118644
95040
28.57143
579000
0
-84960
21.42857
429000
4.708098
315040
4.761905
79000
2
2
1 and 3
1 and 3
1.412429
35040
31.42857
639000
1.412429
35040
5.651314
395064
2
2
1 and 3
1 and 3
1.765537
65040
32.14286
654000
0
-84960
5.298107
365064
2
2
3 and 5
2 and 3
3.531073
215040
4.761905
79000
4.708098
315040
23.80952
479000
4
4
2 and 6
1
76
Price on services of the
observed company:
125,000
Scenario
ROAS
Profit
The strategy(s) with the highest profit
The strategy(s) with the highest ROAS
The strategy(s) with the lowest profit
1
0
-84960
2
3
4
5
6
0
-21000
2.942561
165040
5.886785
415064
4.413842
290040
0
-21000
1.647834
55040
0
-21000
1.765537
65040
0
-21000
4
4
2 and 6
1, 2 and 6
The strategy(s) with the lowest ROAS
140,000
150,000
ROAS
Profit
The strategy(s) with the highest profit
The strategy(s) with the highest ROAS
The strategy(s) with the lowest profit
The strategy(s) with the lowest ROAS
ROAS
Profit
The strategy(s) with the highest profit
The strategy(s) with the highest ROAS
The strategy(s) with the lowest profit
The strategy(s) with the lowest ROAS
1.647834
55040
0
-21000
0
-84960
3.2966
195064
4
4
3
2, 3 and 6
0
-84960
0
-21000
0
-84960
3.532071
215064
4
4
1 and 3
1, 2, 3 and 6
77
Quality level of an observed company – LOW
Price on services of the
observed company:
50,000
60,000
75,000
100,000
Scenario
ROAS
Profit
The strategy(s) with the highest profit
The strategy(s) with the highest ROAS
The strategy(s) with the lowest profit
The strategy(s) with the lowest ROAS
ROAS
Profit
The strategy(s) with the highest profit
The strategy(s) with the highest ROAS
The strategy(s) with the lowest profit
The strategy(s) with the lowest ROAS
ROAS
Profit
The strategy(s) with the highest profit
The strategy(s) with the highest ROAS
The strategy(s) with the lowest profit
The strategy(s) with the lowest ROAS
ROAS
Profit
The strategy(s) with the highest profit
The strategy(s) with the highest ROAS
The strategy(s) with the lowest profit
The strategy(s) with the lowest ROAS
1
2
3
0.588235
-35000
2.380952
29000
0.588235
-35000
0.706215
-24960
2.857143
39000
0.882768
-9960
0
-21000
1.177024
15040
0
-21000
4
3.529412
215000
4
6
1 and 3
1 and 3
0.706215 4.238485
-24960
275064
4
6
1 and 3
1 and 3
0 3.532071
-84960
215064
4
4
3
2, 3, and 6
0 2.354714
-84960
115064
4
4
3
2, 3, and 6
5
6
2.352941
115000
11.90476
229000
2.824859
155040
5.714286
99000
2.648305
140040
0
-21000
1.177024
15040
0
-21000
78
Price on services of
the observed
company:
Scenario
ROAS
Profit
125,000
140,000
150,000
1
2
3
4
5
6
1.471281
40040
0
-21000
0
-84960
0
-84936
0
-84960
0
-21000
0
-21000
1
1
3, 4 and 5
2, 3, 4, 5 and 6
0
0
-84960
-84936
0
-84960
0
-21000
0
-21000
1
1
3, 4 and 5
2, 3, 4, 5 and 6
0
0
-84960
-84936
0
-84960
0
-21000
The strategy(s) with the highest profit
The strategy(s) with the highest ROAS
The strategy(s) with the lowest profit
The strategy(s) with the lowest ROAS
ROAS
Profit
The strategy(s) with the highest profit
The strategy(s) with the highest ROAS
The strategy(s) with the lowest profit
The strategy(s) with the lowest ROAS
ROAS
Profit
The strategy(s) with the highest profit
The strategy(s) with the highest ROAS
The strategy(s) with the lowest profit
The strategy(s) with the lowest ROAS
3.295669
195040
3.531073
215040
1
1
3, 4 and 5
2, 3, 4, 5 and 6
79
APPENDIX 3. IDENTIFICATION OF A LINK BETWEEN ROAS OF A COALITION AND NUMBER OF MEMBERS OF THIS COALITION
Entrance fee
21,000 rubbles
42,000 rubbles
Average
ROAS of a coalition at particular number of members of a coalition
5
10
20
40
80
120
200
700
1287
members members members members members members members members members
1.43
1.71
1.38
2.43
2.34
2.21
1.89
1.89
1.66
1.61
1.68
1.97
1.66
1.44
1.77
1.55
1.44
1.01
1.52
1.70
1.68
2.05
1.89
1.99
1.72
1.67
1.33
80
APPENDIX 4. UTILITY TESTS
Utility distribution
Number of
members of
coalition
Average
Utility of a
client
Number of
members (%
out of
maximum)
Increase of Utility
(comparing to
Scenario “No
coalition”)
0
30,208
0%
-
10
30,637
1%
100
31,115
8%
1%
3%
81
Utility distribution
Number of
members of
coalition
Average
Utility of a
client
Number of
members (%
out of
maximum)
Increase of Utility
(comparing to
Scenario “No
coalition”)
400
31,715
31%
800
34,546
62%
14%
1287
(maximum)
37,746
100%
25%
5%
82
APPENDIX 5. WEB-DESIGN STUDIO QUESTIONNAIRE
1. What is your forecast of changes in the average cost of developing websites?
2. In which sectors you expect the greatest rise in demand for web services?
3. Which channels of promotion you want to send the bulk of the company's marketing budget (2012)?
4. From what sources most often clients learn about your company's?
5. What services does your company have brought the greatest profit in the past year?
6. For which services you expect the greatest growth in demand in 2012?
7. What level of salary your Sales Manager gets?
8. What level of salary your Project Manager gets?
9. What level of salary your Director of Marketing and PR gets?
10. What level of salary your Manager Marketing and PR gets?
11. What level of salary your Technical Director gets?
12. What level of salary your Programmer gets?
13. What level of salary your Art Director gets?
14. What level of salary your Designer gets?
15. What level of salary your Technical Designer gets?
16. What level of salary your HTML-coder gets?
17. What level of salary your SEO-specialist gets?
18. What level of salary your Content Manager gets?
19. Due to some experts you plan to expand the state in 2012?
20. What is the turnover of your company in 2011?
83
APPENDIX 6. FUNCTION THAT DEFINES A CHOICE OF A CLIENT
int i=0;
Company result = requested_companies.get(i);
double best_cust_opinion = get_Main().get_opinion_quality(result.quality);
double poleznost_best = quality*best_cust_opinion-result.price;
double poleznost=0;
double poleznost_output=0;
double cust_opinion=0;
for(Company cur:requested_companies){
cust_opinion=get_Main().get_opinion_quality(cur.quality);
poleznost=quality*cust_opinion-cur.price;
if(cur.price==0){
System.out.println("Current price: "+cur.price+" index: "+cur.comp_index);
getEngine().pause();
}
if(poleznost>poleznost_best){
//новая компания
84
poleznost_best=poleznost;
}else if(poleznost==poleznost_best andand uniform()>0.5){
//новая компания
poleznost_best=poleznost;
result=cur;
best_cust_opinion=cust_opinion;
}
}
selected_comapny=result;
//company id
if(get_Main().coop_companies.contains(selected_comapny)){
get_Main().excelFile.setCellValue(selected_comapny.comp_index, 1, get_Main().row, 1);
get_Main().excelFile.setCellValue(selected_comapny.quality, 1, get_Main().row, 2);
get_Main().excelFile.setCellValue(best_cust_opinion, 1, get_Main().row, 3);
get_Main().excelFile.setCellValue(selected_comapny.price, 1, get_Main().row, 4);
get_Main().excelFile.setCellValue(selected_comapny.profit, 1, get_Main().row, 5);
get_Main().excelFile.setCellValue(quality, 1, get_Main().row, 6);
85
poleznost_output=max(0,poleznost_best);
get_Main().excelFile.setCellValue(poleznost_output, 1, get_Main().row, 7);
get_Main().client_poleznost.add(poleznost_output);
get_Main().row++;
}
selected_comapny.n_orders++;
result=cur;
best_cust_opinion=cust_opinion
86
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