politecnico di milano master of management engineering · 2017. 10. 12. · study. 2.1 mobile...
TRANSCRIPT
Politecnico di Milano Master of Management Engineering
THE MOBILE PAYMENTS AND ANTICIPATED TRENDS IN INTERNATIONAL STARTUP: OVERVIEW ON THE SERVICES
AND THE EVOLUTION OF THE APPLICATION SCENARIO
Supervisor: Prof. Alessandro Perego
Assistant Supervisor: Eng.Valeria Portale
Report of:
Omar Zayed - 842569
Academic Year 2016 - 2017
2
3
Acknowledgement
Firstly, I would like to thank the Osservatorio of Mobile Payments &
Commerce for providing me with the opportunity to expand my knowledge base by
allowing me to work on this report and learn and explore a new field that is different
from my background. Their constant help, support and guidance throughout the
study puts me in a position of appreciation.
Last but not least, I would like to express my gratitude, love and thanks to my
family that supported me every step of my way until this day today. I hope one day
I would be able to repay a fraction of everything they went through and did for my
sake and future.
4
Table of Contents
Executive Summary ...................................................................................................................... 8
1 Introduction ........................................................................................................................... 9
2 Literature Review ............................................................................................................... 11
2.1 Mobile Payments Overview ..................................................................................................... 11
2.1.1 M-payments Growth .............................................................................................................. 11
2.1.2 Technologies and Classification of M-payments ................................................................... 13
2.1.3 M-payments Business Models ............................................................................................... 15
2.1.4 Revenue Streams .................................................................................................................... 17
2.1.5 M-payments Ecosystem ......................................................................................................... 18
2.1.6 M-payments Adoption ........................................................................................................... 22
2.1.7 Mobile Payments in Italy ....................................................................................................... 26
2.2 Startup Ecosystems .................................................................................................................. 27
2.2.1 Policy ..................................................................................................................................... 28
2.2.2 Finance ................................................................................................................................... 32
2.2.3 Culture.................................................................................................................................... 34
2.2.4 Supports ................................................................................................................................. 40
2.2.5 Human Capital ....................................................................................................................... 41
2.2.6 Markets .................................................................................................................................. 43
3 Methodology ........................................................................................................................ 45
4 International Startup Trends............................................................................................. 46
4.1 Startup Category Trend .......................................................................................................... 46
4.2 Target Trends ........................................................................................................................... 51
4.3 Mobile Payment Startups’ Geographic Distribution ............................................................ 55
5
4.3.1 Mobile Payments by Region .................................................................................................. 55
4.3.2 Country Analysis ................................................................................................................... 61
5 Discussion............................................................................................................................. 74
5.1 Future Trends ........................................................................................................................... 74
5.1.1 Mobile Wallets ....................................................................................................................... 74
5.1.2 Bitcoin .................................................................................................................................... 74
5.2 Limitations and Further Research ......................................................................................... 78
5.2.1 Limitations ............................................................................................................................. 78
5.2.2 Further Research .................................................................................................................... 78
6 Conclusion ........................................................................................................................... 79
7 Bibliography ........................................................................................................................ 80
6
List of Figures
Figure 1 - Growth in mobile payments (Sherman, 2014) ............................................................. 12
Figure 2 - Mobile payments technologies and services (Mathew, et al. 2010) ............................ 13
Figure 3 - Mobile payment categories (Vizzarri & Vatalaro, 2014)............................................. 14
Figure 4- Third-party payment process (Lao & Liu, 2011) .......................................................... 16
Figure 5- Third-party support of micro- and macro-payments (Li & Luo, 2008) ........................ 17
Figure 6 - Startup Growth (Crunchbase, 2016) ............................................................................ 27
Figure 7 – Startup entry by year ................................................................................................... 46
Figure 8 - M-payments startup categories 2015 (Osservatorio, 2016) ......................................... 47
Figure 9 - M-payments startup categories 2016 ........................................................................... 48
Figure 10 – Total & Average Funding: 2015 vs 2016 (Osservatorio, 2016) ................................ 49
Figure 11 – Target composition of mobile payment startups ....................................................... 51
Figure 12 - Targets of categories .................................................................................................. 52
Figure 13 - Funding per target ...................................................................................................... 53
Figure 14 - Target composition of founding startups between 2011-2016 ................................... 54
Figure 15 – Mobile payments startups’ allocation ........................................................................ 55
Figure 16 - U.S VC and angel investment amounts (Crunchbase, 2016) ..................................... 56
Figure 17 - Mobile payments usage in 2014 (McDermott, 2015) ................................................ 57
Figure 18 - Startup funding per continent ..................................................................................... 58
Figure 19 - Continent total funding per category.......................................................................... 59
Figure 20 - Continent average funding per startup per category .................................................. 60
Figure 21 - Mobile Payments in North America .......................................................................... 62
Figure 22 - Number of mobile payment startups in European countries ...................................... 63
7
Figure 23 - Mobile Payments users (Visa, 2016) ......................................................................... 65
Figure 24 – Mobile Payments in Asia........................................................................................... 66
Figure 25 - Mobile Payments in South America .......................................................................... 70
Figure 26 - Mobile Payments in Africa ........................................................................................ 71
Figure 27 - Mobile Payments in Australia .................................................................................... 73
Figure 28 - Diffusion of Technologies (TSYS, 2016) .................................................................. 77
8
Executive Summary
This paper aimed to study the international trends of mobile payments startups. Mobile
payments is the payment of any good or service with a smartphone. Literature has been focused
on technology, the ecosystem and consumer adoption and there is hardly any literature on the
startups in the mobile payments industry that according to Schumpeter may be the cause of
disruption of mobile payments industry. There are a lot of startups that incorporate mobile
payments in different forms. The crunchbase database of startups was used as a base to start from.
All startups that incorporate mobile payments were scouted and then went through a further
selection to find the startups that focus on mobile payments. The database was then kept updated
from the crunchbase database as well as other sources. Firstly, the startup ecosystem was identified
to find all the factors that affect startups and entrepreneurial activity in general based on Isenberg’s
model. Secondly, data was analyzed and represented in graphs and different conclusions were
discovered. The analysis showed that even though mobile wallets is the heart of mobile payments
startups it will reach a point of saturation and only a few will remain. Bitcoin and P2P are, however,
the categories of mobile payments that are expected to be present more in the future. The study
also determined that the investments started to cross the borders, specifically move from the U.S
to markets that show potential in Asia. Finally, taking examples from Africa and Asia,
governmental policies to encourage digital payments have the most significant effect on the mobile
payments industry growth and use of the technology.
9
1 Introduction
The technology that has recently been introduced to the most used technical device, mobile
phones, has developed to provide mobile phones with functionalities that surpass its basic use of
telephony. The functionalities have been introduced as an opportunity to use mobile phones as a
medium to offer value added services to the users due to its massive diffusion. One of the most
prominent opportunities for mobile devices is mobile commerce, since it can market, sell and
deliver products and services to a wider range of customers than any other technical device
(Dahlberg, Mallat, Ondrus, & Zmijewska, 2008). Mobile commerce incorporates the phenomenon
of mobile payments or m-payments.
Mobile payments is defined as any payment of goods or services by mobile terminals such
as mobile POS, personal digital assistant, or a smartphone via a wireless network or other
communication technologies (Tong, Zhou, & Liu, 2005). Mobile payments can be used to purchase
tickets, parking fees, bills, digital content, such as music, games and e-books, and transportation.
Using mobile payments for physical goods is also made possible through ticketing and vending
machines. Instruments that allow for mobile payments to take place are defined to be mobile credit
cards and mobile wallets (Dahlberg et al., 2008).
The aim of this study is to predict the mobile payment technologies that will be available in
the market within the next few years. Part of the study will also include testing the effects of
startups on the mobile payments market and the innovation they offered in that industry. This will
be achieved by studying the mobile payment startups that have emerged in more startup-friendly
countries. The international trend in startups concerning m-payments will help clarify the position
of the markets where the investments are being made and the magnitude of those investments.
Analyzing the number of investments and their magnitude, along with startup analysis, will give a
10
very clear picture on what barriers and obstacles that the market is trying to overcome. Once the
startup data is analyzed, a market condition comparisons will be conducted to assess the markets
that will host certain technologies.
11
2 Literature Review
Due to the significant growth of mobile payments over the past decade there has been a
significant number of papers published to match the growth of the phenomenon. Three main
dominant categories have been the focus of research made on mobile payments. The categories are
strategies and ecosystems of mobile payments, mobile payments security and technology, and
mobile payments adoption (Dahlberg, Guo, & Ondrus, 2015). However, to be able to study and
analyze the mobile payments startup space two parts must be comprehended. Firstly, a deep
understanding of mobile payments market and the different variations and business models
available. Secondly, the understanding of startups and entrepreneurial activity and the factors that
influence them. These, two understandings should provide a solid knowledge base to initiate the
study.
2.1 Mobile Payments Overview
2.1.1 M-payments Growth
The growth of mobile payment is extraordinary as it is expected to reach global transactions
of $1.3 trillion in 2017 (Teo, Tan, Ooi, Hew, & Yew, 2015) . Figure 1 shows the growth in mobile
payments and its different applications from 2012 until 2015. The rapid, and immense growth of
mobile payments since its introduction caused it to become one of the major economic drivers as
it has reached many isolated people living in rural areas where they do not have easy access to
banks and financial services and made them engage in commercial activity. The growth has
focused more on satisfying the needs of mobile users and less on the technology itself (Mao &
Chen, 2016). The users’ needs developed from having games, ringtones and screensavers on their
phone bill to having most goods and services purchased to be paid by the phone bill, resulting in
12
phone bills’ purchases accounting for most mobile payments transactions in 2015 by $726 billion.
This development of the phone bill has triggered the role of telecommunication companies as
payment vehicles for mobile payments. The second largest mobile payments application, peer-to-
peer transactions, with $217 billion in 2015 has contested older money transfer methods such as
Western Union as mobile purchases, the third largest application with $177 billion in 2015, has
contested the use of traditional payment methods such as cash, cheques, credit and debit cards
(Sherman, 2014).
Figure 1 - Growth in mobile payments (Sherman, 2014)
13
Figure 2 - Mobile payments technologies and services (Mathew, Balakrishnan, & Pratheeba, 2010)
2.1.2 Technologies and Classification of M-payments
Mobile payments offer many services for the end-user, however, facilitating the services are
different technologies, that secure successful cashless payments’ delivery between sender and
receiver, of which a technology may enable one or all services. These technologies include SMS,
near-field communication (NFC), RFID, WAP protocols, Wi-Fi, Internet, Unstructured
Supplementary Service Data (USSD) (Mathew et al., 2010). Figure 2 shows a schematic of the
technologies and the services they enable.
As shown in Figure 3 Mobile payments can be classified into three categories; online
payment, in-store payment and money transfer. Online payment is when the user uses the internet
on their mobile, m-commerce, or on any other device, e-commerce, to purchase goods or services
whereas in-store payment is when a user uses their mobile to pay for a good or service via mobile
terminal while in the store. Finally, the third category is money transfer, where a user can purchase
14
a good or service by the transfer of e-money. Mobile payments can be classified by two main
aspects; 1) online or offline payments, 2) remote or proximity (Vizzarri & Vatalaro, 2014).
Remote mobile payments are online payments that require customers to surf the internet on their
mobile in search of the product or service , and use an installed app to pay, while the funds for the
purchase may be stored in a prepaid account or withdrawn directly from the bank account (Taylor,
2016). Mobile proximity payment is when a mobile phone is used for in-store payment of a good
or a service at a physical payment terminal. Proximity payments can be made both online and
offline (Gannamaneni, Ondrus, & Lyytinen, 2015). Also called contactless payments, make use of
the NFC technology and require the use of a specific app either a wallet or the buyer’s financial
institution app (Vizzarri & Vatalaro, 2014). A mobile wallet is an application hosted by the device,
required in the offline mode of the mobile proximity payment, that has access to the secure element
(SE) which holds the credit/debit card information or prepaid value to execute the payment
(Taylor, 2016). However, in the online mode of mobile proximity payment, the buyers’ financial
institution app connects online to retrieve card details, therefore the mobile phone serves as a credit
or debit card.
Figure 3 - Mobile payment categories (Vizzarri & Vatalaro, 2014)
15
Ruijun, Juan, and Jiacai, (2010) have studied the Chinese m-payments market. They studied
the different technologies and business models available. The focused their study on RFID
technology and different contactless payments based on the technology. They compared the
different technologies and business models. They also identified the problems with the technology
and operation pattern to be; 1) Payment security, 2) Different technical standards, 3) Not attractive
to users, 4) the industry chain and profit distribution should be designed and built.
2.1.3 M-payments Business Models
The stakeholders involved in the mobile payments chain include customers, merchants,
financial institutions, mobile network operators (MNOs), third party service providers, hardware
and software providers and supervisors that include governments and international regulatory
agencies. There are three main mobile payment business models; based on MNOs, banks and third
party providers (Lao & Liu, 2011).
The carrier-based, or MNO-based, model is one where the customer has either a billing
system with their MNO, where the amount paid appears on the customer’s bill, or has a prepaid
balance that is dedicated for purchasing products. This business model does not allow macro-
payments, only micro-payments, since the risk of default would be too high since customers do
not pass any financial background check. However, this model have trust as an important
advantage because the MNO is the only actor and act as a one-stop-shop (Van Bossuyt & Van
Hove, 2007).
The bank-based model, is one where mobile network operators are not included in the
payment process. Banks are responsible for the management of the payments through their mobile
application. MNOs only benefit when banks use SIM-based technologies, and therefore, pay a fee
16
for the mobile network operators. Since payments in this model is done through the bank account,
both micro and macro-payments are supported by the model (Asghari, Amidian, Muhammadi, &
Rabiee, 2010).
Finally, the third-party business model involves a third party independent from banks and
from network operators, but uses their infrastructure to manage the payments as shown in Figure
4. Infrastructures include the mobile MNO’s network for internet connection and the bank’s
account for payment. This business model solves two issues with the models mentioned above.
The first issue is that, as shown in Figure 5, it supports both micro and macro-payments. The
second issue is that it allows a customer to manage more than one bank account at the same time
(Y. L. Y. Li & Luo, 2008).
Figure 4- Third-party payment process (Lao & Liu, 2011)
17
Figure 5- Third-party support of micro- and macro-payments (Li & Luo, 2008)
Vizzarri and Vatalaro (2014) state that Over-the-top (OTT) internet companies and
merchants can act as trusted third-parties, therefore the third-party payments model is further
divided into two, OTT-based and Merchant-based. OTTs, such as Apple and Google, can act as
trusted third parties due to their experience in e-commerce organization, while merchants, such as
Starbucks, have direct initiatives with their customers usually through loyalty programs.
2.1.4 Revenue Streams
Revenue streams have been the major cause of m-payments’ failure. This is because it’s the
main reason that stakeholders do not collaborate, and instead strive to come up with a technology
in hope of it prevailing. This in turn, caused non-standardization of the models, and deters users
and merchants from investing, and taking part in a technology, in fear of its discontinuity in the
future as the phenomenon matures. Collaboration between stakeholders is specifically difficult
18
because other than the fact that there are many stakeholders, each player expects to make profit
per transaction regardless of the importance of the role they play, in terms of added value, in the
model (Au & Kauffman, 2008). Revenues act as barriers to collaboration between banks and
MNOs in terms of strategy. The first has a long-term oriented revenue strategy by eliminating cash
handling costs while the latter has a contrasting short-term revenue strategy by generating
additional revenue from monetizing their SIM card SE (De Reuver, Verschuur, Nikayin, Cerpa, &
Bouwman, 2015). However, revenue streams do not have to be based per transaction, they can
emerge from different elements and take different forms. Customer loyalty programs, and
personalized shopping may create revenue streams by cross-selling or up-selling. Revenue may be
also created by post- mobile payment marketing offers of complementing products from where the
customers completed their purchase. Integrating customers, through mobile wallets allowing them
to manage coupons or shopping lists with in-store navigation of the chosen products, is another
revenue stream. Targeted marketing campaigns is a very important tool that generates revenue,
through the analysis of the time and place and frequency of goods bought, permitting merchants
to send offers and purchase reminders at the right time to the right customer (Pousttchi &
Hufenbach, 2012).
2.1.5 M-payments Ecosystem
Schamberger, Madlmavr, and Grcchenia (2013) proposed an ecosystem that will help NFC
contactless payments integrate in the market. The problem that they were solving was that there is
no evident reason why NFC from being adopted except of the fact that stakeholders were not
participating in a collaborative system. They opted to plan a feasible system with minimal impact
on the existing infrastructure to attract the stakeholders into participation. They supported the idea
19
of mobile issuing, virtualization of payment cards, with a new component involved in the
ecosystem, which is the Business Service Manager (BSM). The BSM’s main functions are
integrating with the MNOs, integrating with service providers, and acts as an application signing
authority on bank and wallet apps, but does not hold any information.
Henningsson and Hedman (2014) developed the Digital Ecosystem Technology
Transformation (DETT) framework. DETT framework is a blend between the business and
technology ecosystems. The relationship between business and technology in digital ecosystems
is very intriguing, however most literature had taken a stagnant approach in identifying and
studying this very dynamic relationship. The aim of the paper was to clarify the fusion-relationship
between business and technology in the digital ecosystems, by tackling the distributive and
emergent characteristics of some transformations. The developed framework was divided into
micro-, meso- and macro- levels based on technology positioning. Collaborations and competition
existed on each level vertically, and horizontally, respectively.
Hedman and Henningsson (2015) resume their research on mobile payments ecosystem by
studying the market cooperation in the m-payments ecosystem. This study adds to their previous
study, merging business and technology ecosystems, by integrating market cooperation theories.
They developed the mobile payment market cooperation (MPMC) framework in which they
analyze for how technology is used on all levels of their framework. Their study states, in this
innovation war, technology is a very important weapon. Therefore, the market cooperation is being
found to be a balance between defensive and offensive technology-based strategies. Market
incumbents use a defensive strategy of build-and-defend hoping to create advantages in the
organization and efficiency due to their position and power over the suppliers. On the other hand,
20
new entrants use an offensive technology-based battering-and-ram strategy gaining advantages
due to resisting price competition.
Liu, Kauffman, and Ma (2015) analyzed the effects of market competition and collaboration,
and governmental regulations on the evolution and progression of the technological changes. This
article contributes to determining the development, initiation and effect of technological
innovation on the market. The effects of all issues related to the market cooperation and
competition, and governmental regulations were analyzed and found whether they stall or initiate
innovation. The patterns of the m-payments technology-based innovation were identified in
relation to the growth of m-payments.
Staykova and Damsgaard (2015) acknowledged the fact that the new digital payments
solutions have been disruptive to a very stable and profitable market. However, the numerous
solutions and technologies have moved the market into a flux state and now the market is fighting
to reach stability once again. They decided to investigate the factors that help digital solutions
succeed. Focusing their study on the Danish market, their framework was constructed mainly on
two factors, time of entry and time of expansion. They found that in the digital payments market,
there is a significant first-mover advantage, however early followers have the chance in surviving
in the market if the successfully imitate the first mover in both entry and expansion stages. They
highlight the importance of the expansion timing, as the first-mover may lose the advantage if the
expansion strategy was delayed, since evolvability is a competitive advantage in the digital market.
Guo and Bouwman (2016) identified the critical role merchants play in the adoption of m-
payments. Based on that, they noticed the gap in research that focuses on the reasons and factors
that affect merchants’ adoption of the technology. Their research, based on the Chinese market,
proposed a framework for analyzing the m-payments ecosystem. Based on merchants’ perspective
21
the m-payments’ ecosystem is divided into three tiers (groups of actors) that collaborate and
compete. Tier 1, ‘core business’, included merchants, end-users and m-payment platforms. While
Tier 2, ‘extended network’, included ‘core business’, m-payments platform providers, and
suppliers of merchants. Finally, Tier 3, ‘business ecosystem’, included ‘extended network’, labor
unions, competing organizations and governmental regulation bodies. Their findings were 13
factors that affect merchant adoption of m-payments. These factors can be divided into five
categories; 1) Organizational factors, 2) Technology factors, 3) Demand factors, 4) Inter-
organizational factors and 5) Environmental factors. Extending their findings, adding that it’s the
clustered configurations of these interdependent elements that form the decision. Advising m-
payment providers to consider the complexity on multi-level issues that merchants must deal with.
Guo and Bouwman, (2016b) aimed to understand what characterizes a successful m-payment
ecosystem and the role of the market coopetition in sustaining and supporting such system. The
Chinese market was the market of study in this article, with possibly slightly different factors than
other markets. Alipay wallet was their case study, as it is one of the leading players in the Chinese
m-payments market. They perceived the actors in the ecosystem to be shaped based on their
resources and capabilities. Cooperation between actors in the ecosystem is to complement one
another’s resources. Since the market is very dynamic, the complementing resource configurations
required to maintain a strong position in the ecosystem change. These changes determine the
dependency relationships among different actors, causing the market to have an unstable power
balance that depends entirely on the actions taken by the actors. All actors hope to decrease their
dependency on other players and elevate their role and dependency of others.
22
2.1.6 M-payments Adoption
Mallat (2007) contested adoption theories, through a qualitative study, that the adoption of
mobile payments was not dependent on the same factors as the theories claimed. However, the
author argued that the dynamic characteristic of the m-payment adoption unveiled other factors
that were situational such as urgency or unavailability of other payment methods. Users were found
to be skeptical of the technology based on fear that they may end up paying more than once for
the same product due to glitches or lack of responsiveness of the system, or their own human error.
Users also presented concern over lack of transaction control, where the absence of receipts can
cause a problem in asking for a refund in case of any mishap. Therefore, the author concluded that
providers should provide sufficient documentation and clear procedures in case of transaction
errors. The study also suggests that business models should not be designed to charge customers
because of premium pricing, and should work to develop common standards to trigger the network
externalities effect.
Rouibah (2009) conducted a study in Kuwait trying to answer the question “Why do mobile
payment services fail? “. The key finding in this paper revolves around live demonstrations of
mobile payment. Considering gender, males have been found to initially view mobile payments as
an alternative for Smartcards, and therefore do not need to adopt such technology. However, this
has proved invalid after the live demonstration took place. Females, on the other hand, have
favorable attitude to the idea of mobile payments as they tend to not own Smartcards. Therefore,
the study suggests that to convey the benefits of mobile payment, advertisers should pursue live
demonstrations and clarify the complementarity of the technology to electronic payment.
Yang, Lu, Gupta, Cao, and Zhang (2012) aimed to explore the factors that affect the pre-
adoption of the mobile payment services. For further understanding, they also explored the
23
progression of these factors in pre- and post-adoption stages from a holistic perspective. The
holistic perspective includes behavior, social influences, and personal traits. The study found that
the effect of social influences, personal traits’ and behavioral influences on adoption intention vary
significantly from the two studied stages. The authors mentioned some methods in to help service
providers to increase the adoption. They suggested service providers to design some assurance
procedure to reduce perceived risk and uncertainties. Also, it was noted that the adoption of the
technology can be a way to enrich one’s social status or group affiliation. Service providers should
also be targeting the users based on their personal traits to facilitate deployment and diffusion.
Liébana-Cabanillas, Sánchez-Fernández, and Muñoz-Leiva (2014) tested the impact of age
on the different behavioral factors affecting the adoption of the mobile payment technologies by
consumers. External influence, based on social image, perceived usefulness, and perceived risk, in
that order, has been found to encompass the highest significance on the adoption of m-payments.
Older customers were found to be more inclined to easy-to-use and simple tools due to their lower
technological abilities. Moreover, the younger segment of customers should be targeted through
marketing trust, clarity, and simplicity. Finally, the greater intention to use new technologies was
found to exist more in younger customers.
Gannamaneni et al. (2015) aimed in their paper to identify the factors that affect mobile
payment platforms and cause them to fail. The variety of the factors investigated are expanded to
the multi-level framework they used for investigating four cases form different countries rolled
out in different times. Their multi-level framework was divided into three levels, the user level,
platform level, and sponsor level. Factors that affect the failure or success of a mobile payment
platform were identified in each level. In the sponsor level, which included the MNOs, financial
institutions, credit card companies and mobile device manufacturers etc., the absence of a win-win
24
business model along with the lack of collaboration were found to be prominent in all failure cases.
Non-standardized technology was found to be the sole factor that contributes to platform’s failure
at the platform level. As not all users have the adequate mobile device and not all merchants have
the required technology, this difference arise from the reluctance of users to invest in a specific
technology that may not be interoperable with ones to emerge. Finally, at the user level, mobile
payments do not offer added more value than the card payments. Therefore, necessary to success
a platform must offer added value feature to revert consumer behavior towards the technology.
Pal, Vanijja, and Papasratorn (2015) have conducted a study to understand the behavior of
users towards adopting the NFC technology in mobile payments. They considered two user-
oriented and four market-oriented factors in their model. For a better understanding of user
behaviors, they classified the users into two categories early, and late adopters. Early-adopters
have found to not view the potential usefulness of this technology at the time being due to its
underdevelopment. However, user mobility, reachability, and background knowledge about the
technology all affect the perceived ease of use. On the other hand, late adopters’ most important
factor was found to be the perceived usefulness, and will only adopt the technology if mass
adoption occurs.
Cocosila and Trabelsi (2016) surveyed around 300 participants, in Canada, in this article to
empirically study the consumer adoption of the contactless m-payments using the NFC technology.
Their model tested the combined effect of perceived value and risk on consumer adoption. Their
study has found that the sacrifice factors in the model do not have a negligible role, however they
have also found that consumers see more benefits than risks in the NFC contactless m-payments.
They have therefore advised that issuers of payment cards should emphasize the value, or utility,
in using the technology.
25
De Kerviler, Demoulin, and Zidda (2016) have researched the factors that affects the
consumer behaviors in adopting the mobile info-search and the proximity mobile payment. They
claimed that the use of mobiles for in-store info-search require less encouragement due to its
familiarity, and therefore it’s less affected by perceived benefits and risks. They determined that
enjoyment is a key factor of proximity mobile payment adoption since the users need hedonic
benefits to compensate for the pain of paying. In conclusion, they suggest that retailers should not
only incentivize users by functional benefits, as it would be insufficient, but instead focus on the
hedonic benefits and changing the shopping experience for the customers through visual or vocal
interfaces.
Francisco Liébana-Cabanillas, Muñoz-Leiva, and Sánchez-Fernández (2017) have produced
a new interesting study that investigates method and ways to promote mobile payments using, and
considering the effect of, social media. They also included the effect of age, gender, and
experience. Their study is based on the claim that technological developments cause changes in
the consumer behavior. The emergence of social media and mobile technologies created new
streams of marketing, sales, and advertising. The study suggests that if new companies entering
the market of m-payments, they should consider target marketing. If the solution addresses
inexperience men, then the marketing campaign should focus on utility and simplicity, however if
its targeting women the campaign should be focused on privacy and trust. Targeting the young
generation, however, differs as both campaigns strategies could be used. On the other hand,
targeting older generations should exploit the advantages of their vulnerability towards third-party
influences, using tools such as rumor management on the internet and external internet influencers.
26
2.1.7 Mobile Payments in Italy
The Italian mobile payments market, contactless payments in specific, is on the verge of
take-off as global players’ entry is scheduled for early 2017. The entry could be viewed as a threat
to the Italian companies, and therefore they must collaborate to come up with solutions that gives
them the first-mover advantage. The Italian contactless infrastructure is somewhat
underdeveloped, in comparison with other EU members, with 20% of payment cards and 25% of
POS have the contactless feature as of 2015. However, there are several indicators in the
contactless infrastructure growth that there is a potential of contactless mobile payments in the
Italian market. Indicators such as the growth in the number of transactions, transaction amount,
and number of contactless POS of 275%, 250%, and 100%, respectively, between 2014 and 2015.
These numbers, when compared to the 67% increase in contactless cards’ diffusion enforced by
issuers over the same period, indicate another important factor, which is the consumer adoption of
new technologies (Osservatorio Mobile Payment & Commerce, 2016)
The aforementioned literature has been based on how big market players and stakeholders
act in in the ecosystem affecting consumer adoption, and success or failure of technologies.
However, according to Schumpeter Mark I, also known as creative destruction, innovation can
emerge from entrepreneurs disrupting and changing the market dynamic. There has also been a
startup boom in the last few years, in terms of invested capital as shown in Figure 6 with many
startups, in different industries, continuing to succeed to this day filing for IPOs, and performing
very well in the stock market. Recently, the economy has been also witnessing a record number of
unicorn startups, ones valued over one billion dollars. This draws the attention to the startup market
being a very important factor in any industry.
27
2.2 Startup Ecosystems
The role that startups and entrepreneurship take in the mobile payments market are trusted
third-party platform providers. They use banks’ and MNO’s infrastructure to provide the
consumers with different and various solutions with different competitive advantages, that can be
split into the categories mentioned in the methodology. However, to be able to analyze trends and
make predictions of the future of mobile payments, the factors that affect startups and
entrepreneurship in general should be acknowledged. This acknowledgement leads the way to the
identification of the factors that have the most significant effect on mobile payments specifically.
Reflecting on Isenberg's (2011) entrepreneurship ecosystem, six main domains were identified to
influence entrepreneurship. The relationships between the domains were not identified, however,
they provide a view on the entrepreneurs’ outlook on the environment surrounding them that
affects both their decision making and success. The six domains, with no specific order of
importance, are 1) policy, 2) finance, 3) culture, 4) supports, 5) human capital and 6) markets.
Figure 6 - Startup Growth (Crunchbase, 2016)
28
Each of these domains have subsets and more detailed factors that will be explained below. In this
paper, these domains will support the analysis of the mobile payments startups in each country. It
is also used as a technique of identifying the strengths and weaknesses in each country and if there
is a relationship between environment deficiencies or potencies and the emergence or rejection of
certain categories.
2.2.1 Policy
2.2.1.1 International Agreements
One very significant subset of the policy factor are the governmental policies applied in a country.
Openness and globalization were found to have a positive effect on entrepreneurial activities. As
an economy becomes more open, barriers to entry decrease and international players become
relevant to the country and therefore corporate incumbents of the country are less urged to pay for
protection against domestic entrepreneurs. The participation in international agreements of trade
and investments are means of approaching an open economy and said to be very beneficial in
corrupt countries in terms of entrepreneurship and consumer gains (Norbäck, Persson, & Douhan,
2014).
2.2.1.2 Tax Policies
Taxation is one of the governmental policies that has a very significant effect on
entrepreneurial activity. Taxation may cause harm for the entrepreneurial activity and environment
or it may stimulate it. Taxation affects entrepreneurial activity through two different mediums, it
either affects the birth of startups or the venture capital investment in startups. The higher the
taxation index of a country, the lower the venture capital mean in terms of deals and the lower the
venture capital dollars that are invested (Cumming & Li, 2013). A deeper insight in venture
29
capital’s role shows it contributes with two ways of facilitating entrepreneurship, which are
through advice and mentorship or through funding. One tax that induces entrepreneurial activity
is the wage tax, since it makes the entrepreneur’s options less attractive, and therefore the market
experiences more startups. This, however, may cause the supply of entrepreneurship to increase
and diminishes the quality percentage of the startups, increasing the risks of financing and in turn
leaves VCs skeptical about providing funds, and therefore more businesses fail. On the other hand,
capital gains taxation has the opposite effect on advice and number of firms. This happens due to
VCs exiting the market due to lower profits, so a few startups get financed to engage in the market,
and to fill the demand they need more advice from their VCs to be more productive(Keuschnigg
& Nielsen, 2002). Moreover, it was found that the lower the capital gains tax the higher the
incentive is for investments to target early stage high-tech startups relative to later stage low-tech
startups (Da Rin, Nicodano, & Sembenelli, 2006). A uniform income tax had no effect on number
of startups nor the advice and a progressive tax, which simply means the more money one makes
the more taxes they pay and vise versa, had a negative effect on both (Keuschnigg & Nielsen,
2002).
2.2.1.3 Labor Laws
The government may also impact entrepreneurship through labor laws. Out of two
legislations that may affect the entrepreneurial activity, minimum wage, and hiring and firing
restrictions, the first has a more significant effect. Minimum wage is found to negatively affect
entrepreneurship, since startups may find it difficult to afford low-skilled labor at the minimum
wage price (Cumming & Li, 2013). On the other hand, the decrease in hiring and firing restrictions
causes an increase in the expected return on high-tech investments (Da Rin et al., 2006). Other
30
labor policies affect entrepreneurship. These include the presence or absence of labor unions and
government employment. The stronger the labor unions are in a country the less flexible operating
business activities are in a country and therefore increasing costs and discouraging the creation of
new firms. Government employment on the other hand may stimulate the creation of new firms by
creating a demand and opportunities. It may also have the opposite effect by crowding-out goods
and services opportunities that could have been offered by startups. Additionally, creating a
competitive labor market affecting startups’ search and attainment of labor, slowing down the
entrepreneurial process (Cumming & Li, 2013).
2.2.1.4 Government Spending
Increased total government spending has always been associated with a decrease in
entrepreneurial activity. However, government spending allocation results in increased
entrepreneurial activity when its allocated to social and public goods rather than private goods if
the total spending is held fixed. These remarks do not take the methods of increasing the public
spending, such as through tax policies, into account, and therefore may not hold true under
interaction with other factors that affect entrepreneurship. Specially that cutting private spending
may not be politically promising to some governments (Islam, 2015).
2.2.1.5 Bankruptcy Laws
Part of entrepreneurship requires entrepreneurs to challenge the significant risk of failure that
affects the majority of startups. With a high probability of failure bankruptcy laws become more
relevant and influence the entrepreneurship activity. Entrepreneurship-friendly bankruptcy laws
decrease entry and exit barriers encouraging more entrepreneurs to take risks amplifying
31
entrepreneurial activity. Bankruptcy laws can be entrepreneurship-friendly by diminishing both
the time and the costs of the bankruptcy process. These two aspects of the bankruptcy process are
however linked to the increased entrepreneurial activity of the following year. Discharging
entrepreneurs from bankruptcy debt is also considered to be entrepreneurship-friendly since the
income of the entrepreneur is not committed to pay debt associated with the bankruptcy and can
therefore be invested in the creation of new firms (Lee, Yamakawa, Peng, & Barney, 2011).
2.2.1.6 Financing Policies
Studies show that governmental financing policies can create and enhance and develop the
VC market and create financing capital. Learning form the cases of Singapore and Taiwan,
governments may get involved in kick-starting the VC market by co-funding and managing VC
funds by governmental organizations or by companies linked to the government. Both countries
developed relations to the U.S. Silicon Valley, and by adopting the U.S. VC model and modifying
it to fit its environment, Singapore created financing capital and the encouraged investments as
well as fostering innovation. Slight deregulation of the financial market also helped Singapore in
reaching their goal since it allowed capital mobility and the diffusion of VC. Both Countries have
formal business angel networks and formal VC networks. Both countries also have stock markets
for technology based firms to provide exit strategies for their technology-based startups through
IPOs (Wonglimpiyarat, 2013).
32
2.2.2 Finance
2.2.2.1 Financial Capital
What is meant about the finance factor mentioned by Isenberg is the availability of financing
to entrepreneurs. Financing entrepreneurship comes in many forms such as angel investors,
venture capital, direct family and friends, and banks. Some startups get all the mentioned forms of
financing but at different stages of their business. The two forms of financing that offer the largest
sums of investments to entrepreneurs at late stages of the business, to scale the business, are banks
and venture capital. Each with their own advantages, bank loans allow the entrepreneurs to retain
the full control and shares of their startups, which may be an incentive for entrepreneurs to employ
more time effort in the success of the business. On the other hand, venture capital may provide one
aspect that entrepreneurs will not find with banks, managerial advice, but everything has a price
and the price is equity. VC’s investments are concentrated in industries where they have significant
amount of knowledge and can offer and be part of the management process. Entrepreneurs also
benefit from VC investment if the VCs have high incentives to contribute to the startup. Therefore,
in the case where the VC competitive advantage doesn’t apply and they cannot provide managerial
advice, bank financing is preferred. Bank financing was also found to be preferred by startups that
have low probability of success (Bettignies & Brander, 2007).
The financial climate is therefore one of the most important factors affecting
entrepreneurship. Taking the U.S. as an example, interstate banking deregulation was found to
have a positive sustainable effect on entrepreneurship. it also influenced the number of business
closures, which also increased, however, most of the closures were some of the new startups, since,
as mentioned before, failure is a major part of entrepreneurship, and few successes are met with
more failures. The long-term entrants, however, were able to sustain larger sizes and more success
33
(Kerr & Nanda, 2009). Interstate deregulation means that more banks are able to open more
branches increasing the competition between banks. This results in a rise in entrepreneurship
activity and the birth of firms. However, deregulation also cause bigger banks to expand and
diminishes the power of lower banks, which was found to counterintuitively help entrepreneurs
rather than harm them (Black & Strahan, 2002).
Angel investors are commonly mistaken as VCs. The differences between angel investors
and VCs are in the stage of the business in which they invest, their competitive advantages, the
sources of their funds, and in terms of the return they seek. Business angels invest in the early seed
stage of startups while VCs invest in the growth and marketing stage. Angel investments are tied
to their own connections and networking, and therefore is it more locally focused. While, VCs
investments are based on due diligence and they provide managerial advice to help scale the
business, and therefore they have more global investments. Since they have different sources of
funds they seek different amounts of returns. Angel investors seek lower returns than VCs since
they use their own savings, while VCs use institutional investors’ money (Avdeitchikova,
Landström, & Månsson, 2008). Angel investors are very important in entrepreneurial activity since
they provide funding at the early stage when bank financing is deemed costly for entrepreneurs,
and the due diligence doesn’t match VC requirements. Angel investors were found to invest higher
amounts in startups as more entrepreneurial-friendly governmental policies were in effect. The
effect is more significant areas with higher regional economic growth than areas with low
economic growth, so economic growth is also a factor considered by business angels when
deciding how much to invest. When in effect, the governmental policies guide angels to make
better investments with higher returns (C. Li, Shi, Wu, Wu, & Zheng, 2016).
34
2.2.3 Culture
Culture plays a very big role in entrepreneurship, by playing a big role in the life of an entrepreneur.
Culture exists in all forms of relationships between an entrepreneur and their family and friends
and their surrounding environment in general, throughout their lives. Over the years, personalities
are highly affected by the culture that they grow up in. And as mentioned before, entrepreneurs
are unique in their set of skills and have specific set of skills that are not always taught at school,
but acquired through experience.
2.2.3.1 Culture Values
One of the most prominent works on culture research is Hofstede’s five cultural dimensions.
It is considered a cornerstone in that field and have been the basis of research in various other
fields. It must be noted that Hofstede’s indices reflected the average of a culture, and this
information must not be applied in a general sense. A few studies were conducted aiming to find
a relationship between Hofstede’s culture dimensions and entrepreneurship and innovation. The
studies have spawned some conflicting findings. In a broad sense, some evidence shows that
culture characteristics have some effect on national levels of entrepreneurship, but not necessarily
consistent over time (Hayton, George, & Zahra, 2002).
2.2.3.1.1 Power Distance
This dimension deals with hierarchal power, and the view of a culture on acceptance of
hierarchal power and power distribution. What is referred to as power in this dimension is actually
the perception of power. Hierarchy exists everywhere around the world there will be always
someone that has more power than the other, however, what this value measures is the perception
35
and respect of the under-powered towards the one in power in societies and organizations.
Distance, is the reaction to that power gap perception, which can either be met with respect and
understanding, large power distance, or can be often questioned and challenged, small power
distance (Nguyen-Phuong-Mai, Terlouw, & Pilot, 2014). Large power distance cultures are
characterized by expectation of inequality, autocratic leadership, and paternalistic management
style. While small power distance cultures experience a more decentralized authority, rights
consciousness, and a collaborative management style. Power distance has been found to be
associated with national levels of innovation and entrepreneurship. A positive correlation between
power distance and national levels of entrepreneurship was found, meaning the higher the power
distance the higher entrepreneurship is experienced on a national level. Though, more studies are
needed in that domain to establish more evidence, since the effect was found to be the opposite in
another study and therefore not consistent over time (Hayton et al., 2002).
2.2.3.1.2 Uncertainty Avoidance
Uncertainty, is not knowing what lies ahead in the future. Fear comes in two forms, one is
the fear of something or a known object like the fear of spiders, or it may be the fear of the unknown
or what may lie ahead and this is usually inherited over a person’s lifetime, and affected by the
culture. Moreover, avoidance is how a person or a culture reacts to the fear of the known that lies
ahead. This is also implanted in a person’s personality and behavior since they were a child,
learning from the generation before them. Uncertainty avoidance is how a culture reacts in terms
of stress levels to risks that lie in the unknown future, either by challenging the risks head on or
by avoiding the creation and promotion of risk-bearing situations. There are two ways,
incorporated by societies, of dealing with uncertainty. First, what is institutional rules, which are
36
the official regulations, laws and specific guidelines that one must follow to lower the effect of
uncertainty. Second, social rules, which are a set of virtues, or a code of conduct on how to deal
with society and how to react to certain circumstances. The indices, are, however, more relevant
to the institutional rules (Nguyen-Phuong-Mai et al., 2014). Cultures with weak uncertainty
avoidance are characterized with flexibility, risk-taking, tolerance of opinions, and organizational
promotion based on merit. While strong uncertainty avoidance cultures are characterized by
avoidance of risk, promotion based on seniority, lack of tolerance for opinions and authority
respect. Uncertainty avoidance was one of the significant dimensions that had evidence proving
its negative correlation with entrepreneurship on national levels. This finding is intuitive because
entrepreneurship involves risk taking and so a culture with low uncertainty avoidance will
experience higher levels entrepreneurship than its cultures that avoid risk taking (Hayton et al.,
2002).
2.2.3.1.3 Individualism vs. Collectivism
All humans belong in a group one way or another, since one cannot really survive on their
own since we are social species. However, societies give different importance to the relationship
that one keeps with their in-group. Societies that give higher importance to their in-group and
maintaining a strong bond within the group are assumed, collectivistic, where a person’s self-mage
is described in terms of “we”. While, on the other hand, societies that that give less importance to
these bonds, and reveal a certain tendency of independence are considered individualistic, where
self-image is described in terms of “I”. In other words, individualistic communities are considered
to have a loosely-knit social framework, while collectivism is considered a tightly-knit social
framework. Collectivism and individualism are about one individual’s importance as to belonging
37
to their in-group, hence cannot be used between groups but within groups (Nguyen-Phuong-Mai
et al., 2014). Individualistic cultures are characterized by people focusing on self-interest and
goals, valuing self-sufficiency, adopt contractual relationships, and assume their beliefs are unique.
Collectivistic cultures, on the other hand, are characterized by sharing resources and giving up self
interest in the sake of the group’s interest, celebrate hierarchy within a group, and behave
according to social norms. Evidence support the positive relationship between individualism and
entrepreneurship. It is one of the dimensions, together with uncertainty avoidance, that showed
consistency over time (Hayton et al., 2002).
2.2.3.1.4 Masculinity vs. Femininity
Masculinity and femininity in describing a culture is not to be taken literally, however it is
what we stereotypically assign as masculine and feminine traits. A culture consists of both genders,
nevertheless, there is not one individual that is completely masculine or feminine, in terms of traits.
Cultures are judged on all aspects, such policy or industry based on masculine or feminine traits
that are attributed in their behavior and motives. Masculinity and femininity in this dimension are,
in other words, career success and quality of life, respectively. A culture needs both masculinity
and femininity to survive (Nguyen-Phuong-Mai et al., 2014). Masculine cultures are characterized
by having gender roles clearly defined, do not consider compassion important, importance is given
to mastery in different roles, success of males is highly materialistic, and health and wealth are
qualities of a husband different of those of a boyfriend. Feminine cultures, in contrast, have
overlapping gender roles, same desired qualities in husbands or boyfriends, success has a non-
materialistic aspect, and quality of life is the concern of both genders. The higher a culture score
on the masculinity vs. femininity index the more it leans towards masculinity. The very few studies
38
that examined this dimension’s effect on entrepreneurship, was inconclusive of any significance
(Hayton et al., 2002).
2.2.3.1.5 Long-term vs. Short-term Orientation
Inspired by the principles of Confucianism and its virtues, Hofstede developed this
dimension that describes a culture behavior towards the concept of time. Confucianism is a
philosophy based on being prepared for the future, by obtaining education and experience, working
hard, spending adequately, through perseverance and patience. This dimension is aimed to reflect
a culture’s planning process for the future regardless of how well or organized the planning is.
This incorporates the significance a culture assigns to the past, present and future, and therefore
understands a culture’s present actions and steps. The dimension is split into, short-term oriented
and long-term oriented cultures. Short-term oriented cultures focus more on the past and the
present. In contrast, long-term oriented cultures focus more on the future. Short-term oriented
cultures focus more on immediate rewards while long-term oriented cultures may suffer loss and
hardship in the present in hope of a better future. Moreover, short-term oriented cultures may be
rigid in terms of traditions, whereas the long-term oriented gives chance for adaptation and respect
traditions simultaneously (Nguyen-Phuong-Mai et al., 2014). Characteristics of short-term
oriented cultures consists of, other than what is mentioned, emphasis on stability, personal
steadiness and exchanges of gifts and favors. Dynamic, perseverant, economical are adjectives that
describe long-term oriented cultures, however the most important aspect is that is has been linked
by Hofstede to economic growth.
39
2.2.3.2 Culturally Endorsed Implicit Leadership Theories (CLTs)
Culture plays an important role of how people view leadership. There are many leadership
types and this can be seen in leaders around the world. Leaders around the world, to different
cultures, have different methods, qualities and skills, and yet they are still all leaders. This is
evidence that there is not one right way to lead or a set of worldwide established qualities. The
reason behind this phenomenon is that culture subconsciously dictates what are the qualities of a
good leader. These implicit stereotypes, views, opinions and beliefs of what good leadership
entails are the CLTs. In fact, CLTs serve as a guide to what qualities are needed for a leader to
emerge and hence leaders that emerge are ones who hold these attributes. In other words, leaders
emerge and succeed if their leadership style matches the CLTs of their followers. CLTs may
encourage individuals to become leaders, if they believe that they have the required skills and
qualities to become one, and therefore they actively seek leadership (Lord & Maher, 1991). Based
on that the research on culture dimensions was conflicting, and assumed to have an indirect effect
on entrepreneurship, a study that examined two CLTs, which are self-protective and charismatic,
as mediation between the effects of the two Hofstede dimensions with conclusive effects on
entrepreneurship, uncertainty avoidance and individualism. The study found that more
entrepreneurship existed in cultures that view self-protective and charismatic CLTs desirable. The
study also found that both CLTs examined in the paper partially mediated the effect of cultural
values of uncertainty avoidance and individualism. The highlighted mediated effects of cultural
values help examine cultural values in a different way that may prove fruitful. While charismatic-
CLT was proven to be a wanted value in a leader in all countries the study examined, self-
protective-CLT did not measure in support (Stephan & Pathak, 2016).
40
2.2.4 Supports
2.2.4.1 Non-governmental organizations
Like governmental institutions, NGOs also have an effect on entrepreneurship. As NGOs
grow to become international and available worldwide, their influence becomes stronger. The
effects of NGOs on entrepreneurship does not come in a direct from, however, it is transferred
through governmental regulations, allocation of funding, and through R&D. NGOs have been the
main cause of restrictive regulations in many countries and regions. For example, the restriction
and ban of genetically modified organisms (GMOs) and GM food in the European Union. They
have also been the main cause of the initiation of many environmental ministries and departments.
NGOs do not influence the government through power or funds, but through the awareness of the
public, and hence the public opinion. Two factors that entrepreneurs take into account are
regulation, and institutional funding in order to find the product that will appeal to consumers.
These are the same exact factors influenced by NGOs, not to mention public opinion.
Entrepreneurs will, in turn, react according to these shifts to ensure their presence in an
environment that promotes their success. The shifts in funding, regulation and research incurred
by NGOs, while mostly beneficial for the general well-being and the environment, comes with a
cost of wealth destruction (Auplat, 2006).
2.2.4.2 Infrastructure
Infrastructure does not have to be roads and distribution channels, however, support
infrastructures such as incubators, technology centers, and universities may affect and contribute
to the entrepreneurial activity. These infrastructures, supported by the government, aim to support
entrepreneurship and innovation. Incubators offer a variety of services to entrepreneurs ranging
41
from advice, to innovation support, providing infrastructure, and support the business development
in their local market. Technological centers support the innovation and helps integrate new
technological developments, and provide testing labs to ensure the quality of products and raw
materials to improve the competitiveness of the businesses. Universities are considered to bring
one of the major contributions to the entrepreneurial activity, talent. Some also provide valuable
education and technology to enhance entrepreneurial activities. Not a single infrastructure, of the
ones mentioned, is directly liked to growth, however combining the infrastructure proved to have
a greater impact on growth of young innovative firms since they all have different entrepreneurial
objectives. Moreover, enhancing innovation is directly linked to the use of incubators (Roig-
Tierno, Alcázar, & Ribeiro-Navarrete, 2015).
2.2.5 Human Capital
2.2.5.1 Education
As the weight of entrepreneurship is increasing in providing employment opportunities and
contributing to the solution of unemployment, where the private and public sector have been
stagnant for some years, educational institutions’ role in entrepreneurship education is also
increasing. Educational institutions have proved to influence entrepreneurship, through training,
teaching and encouraging students and recent graduates about entrepreneurship. Even with low
R&D spending universities can stimulate entrepreneurship through their program design and
revisiting their mission and vision to match the current trend (Başçı & Alkan, 2015). This influence
has reached the policy makers and some countries have created laws to stimulate this activity in
all universities to have a mass effect and enhance entrepreneurship. According to the Bayh-Dole
policy in the U.S. intellectual ownership of research has been transferred to universities instead of
42
research sponsors. Recent graduates were found to be twice as likely to start a business within
three years of their graduation than their professors, or faculty-spinoffs. The finding was not
limited to certain categories of schools, and the businesses were not found to be of low quality
considering their level of education. Recent graduates that create startups in the field of which
they have their degree have better earnings and survival potential than their employed peers.
Graduating more science, technology and engineering students positively affect the creation of
startups (Åstebro, Bazzazian, & Braguinsky, 2012). The type of formal education also has an
effect on entrepreneurship, other than the fact that it provides the general entrepreneurial skills,
where higher education has little positive effect beyond secondary education (Estrin, Mickiewicz,
& Stephan, 2016).
2.2.5.2 Entrepreneurial skills
Specific entrepreneurial skills differentiate between a good idea and a successful startup.
They are necessary to carry out the required preparations needed in the venturing process. These
preparations include problem solving, pitching the business, recognizing business opportunities,
defining a long-term strategy and goal, risk assessments, and identifying both the mission and the
vision of the company along with many other personal traits. However, these skills are not acquired
from education like general skills, they are realized through the entrepreneur’s experience. People
that embrace these skills often seek entrepreneurial opportunities since they are not always very
tempted by wage-employment. Because, progression in the corporate hierarchy is based on
organizational compliance and industry specific skills, and do not value some of the specific
entrepreneurial skills (Estrin et al., 2016).
43
2.2.6 Markets
2.2.6.1 Social Capital
Entrepreneurs’ networks play a very important role in the entrepreneurial activity. Networks
provide entrepreneurs help in many areas, such as funding, mentorship and opportunities. networks
are characterized by different aspects, such as in terms of size, diversity and strategy. As important
networks are for entrepreneurship, it is also important to identify what are the optimum features
and structures of the networks that help the entrepreneur’s business or startup. Entrepreneurs can
be engaged in two different types of entrepreneurial opportunities defined as discovery or creation.
Discovery opportunities, are ones caused by market imperfections, such as unmet demand or
unmet supply. The market imperfections are caused by other unrelated factors such as technology
or change in consumer taste. The entrepreneur’s role is limited in this context, since they only
search for these opportunities to exploit, while these opportunities will exist regardless of the
entrepreneur. On the other hand, creation opportunities are a result of entrepreneurs that play a
more active role of creating a new product or service and generating the demand for their creation.
The creation process requires a different set of skills that include novelty, perseverance and
unorthodox thinking. Entrepreneurs that engage in the discovery context of opportunities tend to
have individuals with similar backgrounds in the strategic networks, therefore a homogeneous
strategic network. In contrast, entrepreneurs that engage in the creation context of opportunities
have a heterogeneous strategic network, including individuals with different backgrounds (Upson,
Damaraju, Anderson, & Barney, 2017). The entrepreneur’s network is not only affected by the
context of opportunity they engage in, but also the culture in which they hold their networks.
Culture’s effect on networks ten to be stronger than the entrepreneur’s individual characteristics’
effect. Both cultures, ones that are characterized by trust and others characterized by rationality,
44
promote significance in work-related, market and professional networks, which is the main
contributor to the entrepreneurs’ innovation and growth. However, the importance of the network
size is promoted only by trusting cultures, while the importance of the diversity of the network is
encouraged by rational cultures (Schott & Cheraghi, 2012).
A firm’s network is split into three types of networks, buyer-supplier network, peer network
and equity networks that are. As mentioned earlier, networking is important for innovation in a
firm. Buyer-supplier, and peer ties directly affect innovation by providing more knowledge and
heterogeneity, and equity ties have an indirect effect on innovation. Increasing a firm’s network is
beneficial for their innovation, however, a crowding out effect may exist. Thus, firms or startups
must strategically choose the networking the engage in to avoid the crowing effect (Hao & Feng,
2016). A startup’s network is implanted in the entrepreneur’s network. Therefore, the type of
networking the entrepreneur engages in affects the network of their startup and hence the
performance of the startup. Entrepreneurs can engage in two types of networking, public and
private networking. Public networking includes the work-related, market and professional
networking. Private networking, however, includes family, and friends. The more the entrepreneur
engages in public networking over private networking, an increase of their startup’s network is in
effect, therefore, increasing the startup’s innovation, and vice versa (Jensen & Schott, 2014).
45
3 Methodology
To be able to draw conclusions of international trends, the understanding of the factors that
affect startups in general must be studied. This understanding gives a clearer picture of the
environment in any country being analyzed and what features of the environment may have
sparked, stimulated, or delayed the implementation of mobile payments technology. A data set of
all the existing startups was obtained from the Crunchbase database. The analysis of mobile
payments startups requires the modification of the data set to obtain results that are specific to
mobile payments. To compose the data set, from the Crunchbase database, of mobile payment
startups that the paper uses for the analysis the following steps were conducted:
1. All startups involved in mobile payments and m-commerce were extracted and inserted
into another excel file.
2. Startups are no more that 5 years old, by definition, therefore the startups were filtered to
be founded not before 2011.
3. Startups were filtered further, by reviewing the offered services of the startups, and
selected the ones that were entirely focused on mobile payment services as their core
business not just support mobile payments.
4. One of the following primary tags was given to each startup to indicate its service:
• Payments Acceptance
• Bitcoin
• Wallet
• P2P
• Technological solutions
46
• Other
5. The excel file was thoroughly reviewed and updated to account for the most recent funding
and/or acquisitions.
6. Finally, the excel file was updated with other startups found that are not present in the
Crunchbase database.
4 International Startup Trends
4.1 Startup Category Trend
Figure 7 – Startup entry by year
Starting off by looking back on the five years, where companies starting in those years count
as startups, an obvious observation is that the year 2013 can be named the peak year of mobile
payments startups. In that year, most categories of mobile payment startups experienced the
43
15
18
4
2
45
16
12
3
20
25
22
20
9
11
65
21
3
6
10
13
5
1
17
22
12
8
1
14
18
31
19
0
5
10
15
20
25
30
35
2011 2012 2013 2014 2015 2016
Bitcoin Mobile Commerce Mobile Wallet Other p2p Payment Acceptance Technological Solutions
47
highest number of founding startups over the five years. However, after that year founding startups
in mobile payments have been decreasing up to this date. Looking at the most recent year in the
study, it can also be noted that half the startups in 2016 were in the bitcoin category. This remark
is unexpected since bitcoin has only experienced a comparable number of entering startups in
2013, whereas before that year, it was not significant. In support of this evidence, according to
Blockchain1 statistics, the trading volume of bitcoin reached about $240,000 from when it started
trading in 2010 and through 2012. Since 2013, the increase in bitcoin trading volume became
substantial reaching about $227 million in March 2017.
Figure 8 - M-payments startup categories 2015 (Osservatorio Mobile Payment & Commerce, 2016)
1 https://blockchain.info/charts/trade-volume?timespan=all
Payment Acceptance
20%
Other 3%
Bitcoin3%
Technological Solutions
18%p2p10%
Mobile Wallet29%
Mobile Commerce
17%
48
Figure 9 - M-payments startup categories 2016
Contrasting Fig.8 with Fig.9, several changes in the type of startups in the market can be
observed. One of the most significant changes is the increase of Bitcoin startups from 3% to 12%
of the market. This coincides with the current growth of the bitcoin and cryptocurrency market, as
it has recently reached record highs. The increase in technological solutions, from 18% to 22%,
corresponds with the decrease in the accepting payments category. As shown in Fig.7 accepting
payments suffered very low number of startups in the years where the market was peaking along
with almost all other categories. This may be due to the reason that startups have moved from
merely offering a platform and/or devices for accepting payments to offering businesses more
complete solutions to help businesses with productivity, performance and customer relationship.
One category that maintained its presence in startups every year is the mobile wallet, and it has
also preserved its biggest share of the startup market. Its weight may come as a part of a gap
between current adoption its need of reaching critical mass in order to succeed creating room for
more players in the market. Mobile wallets are expected to engulf transactions worth a whopping
Payment Acceptance
16%
Other4%
Bitcoin12%
Technological Solutions
22%
P2P10%
Mobile Wallet26%
Mobile Commerce
10%
49
$1,656 billion in 2017, with a staggering 41% of the U.S. payments’ market according to
(Christensen, 2016). This proves that mobile wallet is still the most relevant category in the market.
Figure 10 – Total & Average Funding: 2015 vs 2016 (Osservatorio Mobile Payment & Commerce, 2016)
With a grand total of $3.5 billion worth of investments, the mobile payment industry is
experiencing massive support and investments. The investments are all in favor of finding the
prevailing standard and gaining first mover advantage. Evident observations drawn from Fig. 10
may seem dull, only until compared to Fig. 9 will the observations become noteworthy. It can be
noted from the above figure that mobile commerce, even though it only constitutes 10% of the
available mobile payment startups, has received the second biggest amount of funding, second to
payment acceptance. This may be due to the fact that mobile payments have stretched the reach of
477
701
516
352
353
786
368
167
863
525
86
397
924
304
0 200 400 600 800 1000
Bitcoin
Mobile Commerce
Mobile Wallet
Other
p2p
Payment Acceptance
Technological Solutions
2015 Total Funding ( million $ )
2016 Total Funding ( million $ )
12.2
18.9
6.4
27.1
10.1
14.3
5.1
20.8
16.2
6.2
10.7
12.8
15.9
5.8
0 5 10 15 20 25 30
Bitcoin
Mobile Commerce
Mobile Wallet
Other
p2p
Payment Acceptance
Technological Solutions
2015 Average Funding per Startup ( million $ )
2016 Average Funding per Startup ( million $ )
50
the e-commerce industry like no other technology by embedding the tool of participating in e-
commerce in the most mainstream and essential technological device existing today. Moreover,
according to comScore the US retail e-commerce has increased 18% in Q4 2016 vs. Q4 2015. The
mobile portion has, however, increased 45% YoY (year-over-year), which is significantly higher
than the desktop increase, to occupy 21% of the $109 billion of online sales of Q4 2016 (comScore,
2017) .Overlooking the other category, and only basing remarks on the main categories, Mobile
wallet startups do not receive the biggest share of funding even though it maintained its biggest
market share of mobile payments startups. Along with startups that offer technological solutions,
they both receive the lowest average funding, which could be explained by their highest shares of
existing mobile payment startups. The only categories of startups that have received more funding
than the year before, are bitcoin and technological solutions. However, technological solutions’
increase is incomparable with the one of bitcoin.
Judging by the presence, funding, and the trend of entry over the past five years it is safe to
say that even though other categories, such as mobile wallet and technological solutions, remain
the most evident categories in mobile payment industry, bitcoin is indeed the category on the rise
and the focus of investors and consumers is flowing towards the use of bitcoin. Even though it is
an unpredictable, fluctuating, and de-regulated currency it has been quoted by many experts to be
the future and the currency of 2017.
51
4.2 Target Trends
Mobile payments, when implemented, has direct benefits on both consumers and businesses.
Its success is unreachable relying on one party adopting the technology, however, the adoption of
the technology by both parties complement each other. Nevertheless, targeting businesses or
customers does not necessarily occur simultaneously.
Figure 11 – Target composition of mobile payment startups
The figure above shows that there is a balance between targeting businesses and targeting
consumers. Even though slightly more startups target consumers than businesses, it is
understandable because there is a larger customer market than business.
B2B38%
B2B2C9%
B2C53%
B2B B2B2C B2C
52
Figure 12 - Targets of categories
Figure 12 shows the targeting composition of each category. Most of the B2B targeting
happens in the categories of technological solutions and payment acceptance. Technological
solutions target businesses as they provide either technologies that offer innovative alternatives to
existing troubles or new solutions where they provide platforms that help businesses productivity
and customer relations. Payment acceptance on the hand target mostly businesses to help them
accept different kinds of payments depending on the type of the business via supporting platforms
or even hardware. However, it has a significant share of the B2B2C startups that exist in the market
today, since they may also provide payment accepting platforms to independent contractors. These
two categories have in fact the lowest number of startups targeting customers. Mobile commerce
is the most balanced category in terms of targeting, while offering the highest number of B2B2C
between the mobile payments startups. As mentioned before, mobile payments completely
changed the game of e-commerce and allowed for many marketing and revenue streams to emerge.
B2B startups may be the lowest number in mobile commerce due to the reason that almost all big
50
2
9
74
3 3 38
3 28
11
2
13
34
6
35
86
26
PaymentAcceptance
Other Bitcoin TechnologicalSolutions
p2p Mobile Wallet MobileCommerce
B2B B2B2C B2C
53
chains have their own e-commerce outlet with established CRM. However, B2B2C is mostly
important in mobile commerce because it allows for targeted marketing and CRM for SME’s or
untapped industries. One obvious take from Figure 11 is that the only category that targets only
businesses or consumer and not both is P2P. Even though P2P startups are not expected to provide
platforms that allow for business-to-consumer transactions, and therefore target both businesses
and consumers, but it is also unexpected to detect no startups that target both business and
consumer in terms of providing a platform where business-to-business transactions are allowed in
parallel with consumer-to-consumer transactions. This may be due to them view providing a
platform with both kinds of transactions may affect revenue streams, for example two businesses
may transact as consumers avoiding charges that incur on businesses and not on consumers.
Figure 13 - Funding per target
As shown in Figure 13 the B2C startups get the most funding as expected since they
constitute more than half of the market according to Figure 11. However, this doesn’t mean that
1,106.3
737.9
1,708.4
8.6 27.3 9.70.0
200.0
400.0
600.0
800.0
1,000.0
1,200.0
1,400.0
1,600.0
1,800.0
B2B B2B2C B2C
Sum of Total Funding [million $] Average Funding per Startup [million $]
54
they experience the lowest average funding per startup. One the other hand, it is B2B2C
unexpectedly owns about 20% of the funding while only securing 9% of the startups in the market.
Due to this funding information, the startups targeting both businesses and customers have almost
three times as much average funding per startup than serving one type of end-user. The data in
Figure 13 and Figure 11 show that the B2B2C startups’ share difference between funding and
presence in the market takes more from the B2B startups than the B2C. This may be an indicator
that the funding favors targeting customers more than businesses.
Figure 14 - Target composition of founding startups between 2011-2016
Analyzing the composition of the market in terms of marketing targets over time we can see
in Figure 14 that older startups founded in the first two years of the study were mostly B2B. The
year 2013 marked the year where the composition toppled in favor of the B2C. Ever since that
year, which has been marked before as the boom of mobile payment startups, the gap in the
composition of the new startups started significantly increasing. This information matches the
information provided Figure 7, where the categories of startups that have been on a trending,
51% 49%
39%
28%
4%0%
6% 7%11% 10%
4%0%
43% 44%50%
62%
91%100%
2011 2012 2013 2014 2015 2016
B2B B2B2C B2C
55
present and founded after 2013 such as bitcoin, P2P, mobile wallet and mobile commerce, which
mainly focus on targeting customers causing B2C startups becoming the trend expected for the
next few years. B2B2C startups has maintained somewhat of a consistent presence in the market
and does not show signs of positive trends especially that the trending categories of mobile startups
focus more on customers than both.
4.3 Mobile Payment Startups’ Geographic Distribution
4.3.1 Mobile Payments by Region
Figure 15 – Mobile payments startups’ allocation
As expected, North America has the biggest share of mobile payments startups. North
America’s share defines it as the hub of mobile payments, this may be due to the fact that it is the
continent of the most prominent tech spots in the world. A lot of tech innovations start there and
Africa1% Asia
17%
Europe26%
North America51%
Oceania1%
South America4%
56
they have the biggest presence of Venture Capital funding and investors. Even Apple Pay has first
launched in the U.S. before moving to other continents. However, the US VC investments have
decreased from 2015 to 2016, after a constant increase since 2012, as shown in Figure 16. Europe
on the other hand occupies the second biggest share with 26%. Followed by Asia with a share of
the mobile payment startups that is 17%. A very important reminder is that the location of which
startups are found is not an indicator to consumer adoption willingness or readiness of mobile
payments. It is, however, an indicator on where funding is available and other market welcoming
factors.
Figure 16 - U.S VC and angel investment amounts (Crunchbase, 2016)
The reason why the U.S VC investments have experienced a decrease is because a couple of
the biggest Venture Capital firms, Sequoia Capital and Accel Partners, have started to
internationally invest. Both firms dedicated portions of their investments for investing in Asia,
particularly China and India. Also, the tech giant, Apple, invested a sizeable amount of $1 billion
57
in the ride-hailing company in China (Crunchbase, 2016). This whole shift in investments, and
the positive outlook on international investments maybe due to the realization that other markets
may have high potential specially that, in terms of usage, North America is not the leader in mobile
payments. According to Figure 17 Asia was the leading market in terms of consumer usage in the
year 2014, but in support of that data Deliotte had predicted the highest growth in usage to belong
to Europe and North America, while the major share still belonging to Asia.
Figure 17 - Mobile payments usage in 2014 (McDermott, 2015)
North America 19%
Europe 14%
Asia 38%
Africa 25%
South America 4%
58
Figure 18 - Startup funding per continent
Figure 18 goes hand in hand in complementing Figure 15, and as it has been mentioned
before that startups tend to start in regions where funding opportunities are higher. The funding
graph follows the same pattern as the market share pie chart (Figure 15) starting with the largest
shares belonging to North America, Europe, and Asia, respectively. Also, continuing the order for
the lowest three. However, it is natural that the continent with the most startups has the biggest
share of funding. Therefore, the observations should be based not on the sum of total funding but
the average funding per startup. Now based on this factor, a different pattern can be analyzed.
Based on the average funding per startup, surprisingly South America comes in first place over
North America, which has about half the mobile payment startups in the market. In order to help
understand this surprising data, looking in the dataset was important. Three startups, in particular,
have received funding amounts that drove up this average to this point. One of these startups had
received $ 25 million in total funding, in two years and the other two received $ 77 million and $
3.54
354.47
734.96
2242.92
12.57
204.11
0.89 6.01 9.19 13.19 2.52 15.70
500
1000
1500
2000
2500
Africa Asia Europe North America Oceania South America
Sum of Total Funding [million $] Average Funding per Startup [million $]
59
98 million in 3 years and 5 years, respectively. Due to the fact that these three startups share more
than one thing in common, but one of the things in common is that they are all in the mobile
commerce category. Consequently, it is important to understand in which category does each
continent focus on to be able to draw accurate conclusions about the geographic regions of mobile
payments. Figure 19 and 20 aim to underline this issue.
Figure 19 - Continent total funding per category
The funding in Figure 19 provides a good overview to understand what startup categories
are offered in each continent. This allows conclusions, such as understanding the consumer
behavior in the different markets, to be drawn when contrasted against Figure 20. Without a doubt,
North America provides the most funding in each category since it constitutes more than half of
0.0 100.0 200.0 300.0 400.0 500.0 600.0 700.0
Bitcoin
Mobile Commerce
Mobile Wallet
Other
p2p
Payment Acceptance
Technological Solutions
Sum
of
Tota
l Fu
nd
ing
[mill
ion
$]
Sum of Total Funding [million $]
BitcoinMobile
CommerceMobile Wallet Other p2p
PaymentAcceptance
TechnologicalSolutions
South America 0.0 194.2 0.0 0.0 0.2 8.0 1.7
Oceania 0.5 0.0 6.6 0.0 0.0 2.4 3.1
North America 289.2 338.0 275.5 350.3 225.3 572.0 192.6
Europe 147.0 163.2 124.6 0.2 94.7 104.4 100.9
Asia 38.5 5.3 108.8 1.6 32.8 97.6 69.8
Africa 1.7 0.0 0.2 0.0 0.0 1.7 0.0
60
the funding worldwide. We can see that mobile commerce takes up most of the funding in Europe
and South America, with a more significant magnitude in South America. Mobile wallet on the
other hand is the focus of both Asia and Oceania. Africa’s marginal funding has been split between
Bitcoin and Payment Acceptance startups. The non-existent funding of some categories in some
continents show markets and potential that is untapped. Conversely, many reasons may be behind
such phenomenon as financial market characteristics or underdevelopment of countries or
consumer traits that are in the region which will be investigated later in the report.
Figure 20 - Continent average funding per startup per category
Regarding Figure 20, the funding received per startup category indicates market projected
demand, success and positive consumer behavior. Since, the more vigorous the funding is the more
potential a startup has. The effect of the three South American startups mentioned before is
0.0 10.0 20.0 30.0 40.0 50.0 60.0
Bitcoin
Mobile Commerce
Mobile Wallet
Other
p2p
Payment Acceptance
Technological Solutions
Ave
rage
of
Tota
l Fu
nd
ing
[mill
ion
$]
Average of Total Funding [million $]
BitcoinMobile
CommerceMobile Wallet Other p2p
PaymentAcceptance
TechnologicalSolutions
South America 0.0 48.6 0.0 0.0 0.1 8.0 0.3
Oceania 0.5 0.0 3.3 0.0 0.0 2.4 3.1
North America 15.2 15.4 6.9 31.8 16.1 20.4 5.4
Europe 24.5 20.4 5.2 0.2 8.6 8.7 5.6
Asia 3.5 1.8 8.4 1.6 4.1 8.1 6.3
Africa 0.9 0.0 0.2 0.0 0.0 1.7 0.0
61
noticeable with the highest average funding per startup in any continent and in any category.
Matching of both Figure 20 and Figure 19, if anything, shows a very successful, and profitable
market of mobile commerce in South America. Asia, on the other hand, has more funding per
startup dedicated to mobile wallet, which probably means mobile wallet consumer behavior and
willingness is supreme. P2P, payment acceptance, and the other category, which includes mostly
fundraising startups, are categories that are mostly appreciated in the region of North America
even though P2P startups is one of the least funded categories in North America. The remaining
two categories of technological solutions and bitcoin are supremely funded in Europe, however,
the substantial difference of $20 million between the two funding averages per startup in the favor
of the latter indicates a view on the increasing importance of bitcoin in Europe. If the other mobile
payment category was excluded from the data, due to its miscellaneous composition, analyzing the
table in a wider perspective, bitcoin startups in Europe has the highest number of funding per
startup second only to South America’s mobile commerce category which includes anomalies that
have been mentioned.
4.3.2 Country Analysis
In this section, an in-depth analysis will be commenced in order to identify the biggest
markets in terms of countries in each continent. This analysis will be in contrast with chapter 5,
which analyzed the startup ecosystems and factors that affect entrepreneurship, helping to draw
conclusion as to why certain countries invest more, less or in specific categories according to their
characteristics and their environment.
62
4.3.2.1 North America
Figure 21 - Mobile Payments in North America
In 2015, North America has seen a 53% consumer awareness of mobile payments, however,
only 18% were found to use the technology (Accenture Consulting, 2015). The U.S has, by far,
the highest number of operating startups in Mobile Payments in North America, and hence the
world. Moreover, Canada comes in second place, and Mexico in third place with a very marginal
number of two startups. While, the average funding per startup of Mexico is also marginal, Canada
has an average funding per startup that is relatively close to the one experienced in the U.S,
according to Figure 21. However, this data is more of an indicator of an entrepreneurial-friendly
environment rather than consumer readiness and usage. After all, the most prominent technology
cluster in the world, Silicon Valley, is in the U.S.
Canada is considered to have infrastructure, that support mobile payments more than the U.S,
such as a higher smartphone penetration of 75% against the 70% of the U.S. Moreover, Canada
18
5
174
8.332.11
13.84
0
20
40
60
80
100
120
140
160
180
200
Canada Mexico USA
Number of Startups Average of Total Funding [million USD]
63
was one of early adopting countries of Mobile Payments technologies, such as the contactless
technologies (McDermott, 2015). Hence, the number of NFC-ready terminals are high, 75% of
major retailers. Moreover, it’s becoming more mainstream to use contactless cards, because
according to Visa and MasterCard 25% and 27% of their card transactions in 2015 were
contactless, respectively (Smart Payment Association, 2016). Since there is high adoption of
digital payments, and supporting infrastructure is widely present therefore, mobile payments
technologies, such as mobile proximity payments, roll-out to consumers suffer minimal
fragmentation (McDermott, 2015).
4.3.2.2 Europe
Figure 22 - Number of mobile payment startups in European countries
1
5
3
1
3
18
1 1 1
13
1 1
4
1 1
4
1
4
28
4
0
5
10
15
20
25
30
64
The European growth in mobile payment is truly remarkable, coinciding with Deloitte’s
prediction, a three-fold growth from 18% to 54% between the years 2015 and 2016, respectively.
Moreover, the percentage of people stating they never used mobile payments and did not intend to
use it fell from 38% in 2015, to 12% in 2016. 35% of regular users of Mobile Payments in Europe
prefer using their mobile phones, 32% prefer wearables and 28% prefer using their tablets (Visa,
2016). Since many countries in Europe has one mobile payments startup, the average funding per
startup indicator doesn’t deem validity in the analysis. However, Figure 22 shows the biggest
contributors to the mobile payments space. The UK is the leading country in Europe, and only
second to the U.S worldwide. The difference in the number of startups of those two countries
corresponds to the differences in the markets, such as size, scalability, and number of players. This
is due to many factors, one of which it is the financial capital of the world, and the strongest m-
commerce and e-commerce in Europe. Germany in second place is also not a surprise, because it
is one of the economic powerhouses of Europe. Collectively, UK, Germany and France count for
70% of Europe’s e-commerce. However, in the figure above Italy is in third place in mobile
payments startups, with a broad difference with its successor, and this could be because Italy is
second only to the UK in mobile payments with 8 million people in 2014 purchasing goods on
their phones at least once a month (McDermott, 2015).
With one of the strongest economies in Europe, a limited number of players and a scalable
market size, the UK is considered the roll-out country in Europe for new mobile payments
technologies. The fin-tech industry has been quoted by the government to experience a yearly
growth of 74% from 2008 to 2015 against a global growth of around 27%. Any innovation needs
about 500,000 people to reach scale, and since the market size is not as big as the U.S, all
innovations compete for the same customers allowing for an easier achievement of one technology
65
or innovation (Consult Hyperion, 2015). According to the Digital Payments report published by
Visa in 2016, contactless cards usage has a positive impact on the mobile payments intentions,
52% of contactless card users expressed interest in paying through their phone against 32% of no-
contactless card users. Currently 74% of the UK are mobile payments users, however with
contactless payments being the norm in the UK now across all age groups we expect to see a
significant increase of this number in the short-term.
Figure 23 - Mobile Payments users (Visa, 2016)
The figure above shows a very intriguing pattern, the top mobile payment usage is seen in the very
developed, Nordics, or in developing countries like Poland and Romania. Poland has been Visa’s number
one European market for contactless payments in 2014 with the transactions amounting to 40% of all Visa
payments in Poland. The Nordics earned their position in mobile payments, with very high penetration
rates, a very highly developed telecommunications network and with their renowned pioneering in
manufacturing handsets (McDermott, 2015). The European average of mobile payments usage was said to
89% 87% 86%
79% 79% 78% 77% 75% 74% 74% 73% 73% 73% 72%69%
64%59%
66
be 77% in 2016 by Visa in their latest Digital Payments study. Mobile payments usage are ones who have
used their mobile phones to make a payment. The European average of the regular users of mobile payments
is 54%. Germany is the second country in Europe with mobile payments startups, however it scores below
average in mobile payments usage. Comparing Germany to Italy, which relatively has a much better
position. Italy is in the top three countries with mobile payments startups and a mobile payments usage
closer to the European average. While 49% of respondents in Germany expressed interest in using their
mobiles in the future to make payments, it is contrasted by 76% in Italy (Visa, 2016). There is no doubt
there is a stronger mobile payments market in Italy than Germany. The reason behind Germany not having
a huge mobile payments usage could be because the age group that uses mobile payments the most is
between 18 – 24 year olds, and Germany has relatively less of this age group.
4.3.2.3 Asia
Figure 24 – Mobile Payments in Asia
9
14
1
8
6
45
4
6
1 12
1 1
Number of Startups
67
Asia is a major market for mobile payments, with a very high on-line population, mobile
payments usage rates, and specially with top-ranked international investors recently investing
there. Figure 24 shows the countries with most number of startups. Asia is a very diverse continent
with countries participating in the whole spectrum of commerce with countries that have a GDP
of a few hundred per capita and some of the richest countries in the world. It also has the highest
percentage of the world’s population.
With a very dynamic market, a young population eager to try new technologies along with a
supporting regulatory environment, India’s mobile payments market is expected to experience
huge growth. The Indian government is very supportive in promoting digital payments and has set
short-term and medium-term actions to fast-track the adoption. As shown in the figure above, India
leads the Asian market in terms of startups and is supported by their number of users. The number
of active wallet users in India is between 80 – 85 million users reaching 747 million transactions
in 2016. However, users prefer micro transactions in m-wallets. 65% of customers that become
aware of digital payments move on to trying and 81% of customers of digital payments prefer it to
other no-cash payments such as credit or debit cards. A lot of credit for this growth and adoption
goes to the creators of the wallets by giving users massive discounts and offers that appeal for the
consumer, specially the non-metro consumers. More metro consumers on the other hand, value
one-click payments than the offers. The least used function in India is the in-store payments,
coming last to bill payment, mobile recharge and e-commerce. This is due to the limited reach and
limited merchant outlets that accepts such functions, however, it is expected to gain traction and
gain significance in the future (BCG, 2016). A recent research shows that 70% of the consumers
68
are willing to use mobile payments more often, however, the constant network dropping and the
poor experience is what stopping this adoption to take place (Lupu, Mual, & Van Stiphout, 2016).
China, second only to India in the number of mobile payments startups, is a very particular
case. The leading ride-hailing company, Didi Chuxing, has led the phenomenal year in terms of
investments in Beijing, China. Didi Chuxing has raised $4.5 billion in 2016 to bring its total
funding to around $7 billion. Since, these numbers are too high and considered unorthodox, this
company was not included in the funding analysis. Nonetheless, China has been experiencing
significant growth in VC investments across all its sectors and industries (Crunchbase, 2016).
China’s mobile commerce is expected to reach 24 % of ecommerce sales in 2017. China has about
500 million people use a smartphone is the primary vehicle for internet usage. Moreover, the use
of mobile payment apps grew by 73% in 2014 (McDermott, 2015). In China, some significant non-
bank entrants have caused disruptions in the payments market. Entrants such as Alibaba, the
number one ecommerce website in China, developed Alipay which had cool features and grew to
encompass more than just paying on the website to become the leading wallet for online and offline
transactions with 50% of all online transactions in China in 2015 worth about $500 billion. Another
entrant that disrupted the market is WeChat Pay, which provides peer-to-peer payments, by gaining
700 million active users in three years. Those two players are now expanding their financial
portfolio to include loans, insurance and other financial services. (BCG, 2016).
Israel does not have a huge population like China or the same attraction of VC investments,
however, in the mobile payments industry they have a very good usage rate of 87% which is similar
to the rates reported in the Nordics (Visa, 2016). Israel, is also very well known for its
entrepreneurial-friendly government policies and a supportive environment for its startups in
general.
69
Japan, is one of the most connected countries in the world. The use of the NFC technology
of a smartphone has not only been utilized for in-store payments but also may be used in public
transportation. Probably the only place where the NFC technology in mobile payments doesn’t
suffer low adoption rates. Japan has a very dense urban population with smartphones owned by
more than 55% of the population. Japan had the highest paid app usage in the world and the third
highest app usage globally, with an average of 40 apps per phone in 2014 (McDermott, 2015).
Singapore, one of the richest countries in the world, has a very high smartphone penetration
of 87% has a valuable m-commerce. During the first three months of 2014, 35% of smartphone
users made purchases on their smartphones reaching a value of $1.2 billion (McDermott, 2015).
Russia, on the other hand, has not been taking up mobile payments in way that is compared
to any of the mentioned countries It has been always limited by its internet penetration and its
unstable regulation. Mobile payments accounts for 6% of online transactions but only 0.5% of its
value. Russia’s online payments are starting to increase sue to cross-border commerce such as the
use of PayPal for U.S and European websites while using cards for Chinese websites (Lupu et al.,
2016).
Both South Korea and Turkey show a lot of potential in the mobile payments market, even
if the countries don’t match others in their region with the number of startups, but South Korea’s
m-commerce has been growing at a CAGR of 150% since 2011 and Turkey has the highest mobile
payments usage of 91%, which is higher than any European country, according to Visa.
70
4.3.2.4 South America
Figure 25 - Mobile Payments in South America
Latin America has experienced great growth in the usage of mobile payments however it still
stands at one-fifth the number of users in North America in 2015 according to Statista. At a glance,
there is a leading country in South America when it comes to mobile payments as shown in Figure
25, Brazil. The rest of the countries that contribute to the mobile payments market have little
differences in terms of number of startups or even funding. Brazil, however, as an emerging
economy it is exploiting its boom in commerce and have three mobile payments applications that
have massive funding. 50% of startups in Brazil die within the first four years, and while there
were about 10,000 startups in the 2012, they only moved 0.4% of Brazil’s GDP, and therefore
don’t yet have significant economic impact, however they have significant social impact (Moroni,
Arruda, & Araujo, 2015). In 2014, growing 83% from the previous year mobile commerce
accounted for about 5% of total ecommerce transactions and one in three smartphone users made
purchase with their phone at least once a week (McDermott, 2015). Brazil remains the region of
2
7
210.12
32.54
0.195 0.0710
5
10
15
20
25
30
35
Argentina Brazil Chile Peru
Nuber of Startups Average of Total Funding [million USD]
71
highest venture capital activity in South America with some activity rising in Argentina
(Crunchbase, 2016). Government regulation in Brazil plays a big role in this pattern, as well as
education. Financial literacy is part of the schooling curriculum in Brazil reaching 5000 high
schools and design to appeal to young people (VISA, 2016).
4.3.2.5 Africa
Figure 26 - Mobile Payments in Africa
Africa, is the second largest continent in terms of population after Asia, with 1.2 billion
people, however, Africa is very different from Asia in terms of development. Africa also lacks
venture infrastructure to match the available potential. Africa’s internet penetration does not
compare to any other continent. However, Africa is showing some interest in mobile payments
even though a lot of countries are poor and not everyone owns a smartphone. From market
imperfections emerges opportunity.
1 1 1 1 1
1.7
0
0.16
1.65
0.03
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Kenya Nigeria Rowanda South Africa Zimbawe
Numberof Startups Average of Total Funding [million USD]
72
In Kenya, a mobile payments application takes advantage of the number of unbanked
people, 20 – 25 million people constituting 60%-70% of the adult population, and caters to them.
M-PESA, the Kenyan application is promoted as a safer mean to carry cash and was designed to
work on basic features mobile phones. With tremendous support from governmental regulations
this app is currently with 80% market share and with 25% of the GDP of Kenya being wired
through it, M-PESA became a very strong player in Kenya (BCG, 2016). Kenya, like all the other
African countries has one mobile payments startup that received more funding than all the others
in its continent, this startup is in the bitcoin category.
According to Figure 25, the only country that matches the funding that exists in Kenya is
South Africa. This may be because of its strong economic position in Africa and a relatively high
internet penetration rate. On the other hand, Nigeria’s zero in funding is just an indication that the
invested amount was undisclosed. Nigeria, with the strongest economy in Africa has a couple of
incubators, that provide seed-round funding for companies and encourage entrepreneurial activity.
South Africa also has an incubator that helps in funding startups (Crunchbase, 2016).
73
4.3.2.6 Oceania
Figure 27 - Mobile Payments in Australia
In this large geographic part of the world, Australia is the only contributor to the mobile
payments industry and acts as the hub of the region. However, Australia has recently reached
record highs in venture investments of $ 568 million in 2016. Since this is a growing number, it
looks like investors are willing to spend more on startups in Australia and this should encourage
entrepreneurial activity in the country. Most investment are in the IT companies (Crunchbase,
2016). Australia is considered to have 75% of consumers with smartphones and half of them
downloaded a banking or finance app in 2014. Due to this uptake, even though slow, encouraged
retailers and financial institutions to roll-out mPOS for simplified checkouts and other mobile
payment solutions as means of becoming ready for when mobile payments reach tipping point
(McDermott, 2015).
5
2.51
0
1
2
3
4
5
6
Nuber of Startups Average of Total Funding [million USD]
Australia
74
5 Discussion
5.1 Future Trends
5.1.1 Mobile Wallets
Mobile wallets is the most prominent category of mobile payments and the number of
applications are on the rise. The market has seen wallets of all kinds, ones of smartphone
manufacturers, banks, merchants, tech giants and other third party applications. The number of
wallets will continue to rise significantly with the growth of digital payments until the point where
only a few wallets will remain and capture the whole market share. This point will be reached
when a wallet is able to seize the consumer needs and offer the features that increases adoption,
which are simplicity and loyalty programs (MEC, 2016). These features indicate that the consumer
mindset is shifting from the transactions to the services provided. However, who will be able to
offer such a wallet is unknown.
5.1.2 Bitcoin
From the analysis of the mobile payments startups, Bitcoin has emerged as the category that
is attracting the most interest. The adoption of bitcoin is growing and consumers are starting to be
more aware of such currency. Bitcoin is considered to be a form of global currency that allows the
exchange of money independent from any regulations and governments. Bitcoins are stored in
digital wallets and requires authorization for exchange. The exchanges cannot be altered once they
go through after the authorization and all transactions are stored online in a central public ledger
(MEC, 2016). Bitcoin is a digital currency under a wider technology called Blockchain. The
Blockchain technology is decentralized, which helps bitcoin suffer lower levels of transaction
75
execution failures making it more reliable (Capgemini, 2016). In other words, the exchange of
Bitcoin is the digital equivalent of hand-to-hand cash transactions with no third-party
intermediaries. Trading and transacting in Bitcoin has been facilitated by the development of
supporting e-wallets, trading platforms, and more service providers (Deloitte, 2015). Many
platforms already support Bitcoin such as Expedia, Dell and Overstock.com.
The exchange of Bitcoin happens in an anonymous environment because the wallets are
associated with addresses that are composed of characters and numbers. This feature of Bitcoin
drew attention from hackers and other law breakers. From this feature of Bitcoin rises one
important advantage of Bitcoin, which is low risk of personal data theft. There are no names or
credit card numbers that are at risk online, only the Bitcoin total of any account. However, this
feature also acts as a disadvantage since it encourages money laundering and transactions
involving illegal activities (Deloitte, 2015).
The potential of Bitcoin for cross-border payments and the reason why many banks are
expressing interest in supporting and testing the technology is because of the speed feature of this
technology (Capgemini, 2016). While international wire transfers may take up to a few days, the
decentralized nature of this technology allows international transfers to be completed within the
hour, since, no third-party authorization is needed, with very low or no transaction fees, unlike
charged by banks and credit cards (MEC, 2016; Deloitte, 2015).
With Bitcoin, it is impossible to spend the same Bitcoins twice eliminating the possibility of
double payment. As with cash payments, once a transaction is finalized chargebacks do not exist
with Bitcoin, which may be viewed as both an advantage and a disadvantage. The fact that there
are no chargebacks is advantageous for the merchants since the risk of chargebacks they bear with
credit cards is eliminated. However, the consumer may bear some risk with this feature, which is
76
the risk of fraudulent activity. With no reversals, the consumers bear all the risk with fraudulent
activity that might take place in a transaction (Deloitte, 2015).
Other advantages of Bitcoin include the ease of use which is something consumer look for.
Anyone can transact with Bitcoin without the need for credit cards or pin numbers as long as they
have internet connection and known e-mail addresses. However, there is a disadvantage that comes
in contrast with ease of use, which is the permanent loss of account access in case of loss or stolen
keys to the wallet. (Deloitte, 2015).
With the potential Bitcoin brings to the world of payments, a lot of its features result in
disadvantages just like any technology. One of its main features, decentralization, acts as a
disadvantage. While, it is faster than any wire transfer, bandwidth limits and decentralization only
allows seven transaction to be made per second, which is lower than credit cards. Due to
decentralization, if the keys to an account was stolen, the fraudulent third-party can fully control
and imitate the original account holder and as soon as any bitcoins are transferred they will never
be recovered. Decentralization also causes any incorrect transfers of Bitcoin irreversible, and the
only way for one to retrieve an incorrect transaction is for the receiving party to send it back
(Deloitte, 2015).
Finally, Bitcoin has great potential to disrupt the payments market and change the game,
however, it lacks regulation structure and comes with a lot of legal ambiguity. Some countries ban
the use of bitcoins, while some countries are calling for the regulation of Bitcoin. The regulation
of Bitcoin is important for consumer protection and financial integrity to attract more consumers
and acquire the benefits of this technology (Capgemini, 2016). The exact potential of Bitcoin is
yet to be known and the implications are being discovered as adoption rates increase. Stakeholders
in the financial industry are interested in Bitcoin to really understand what and how it can affect
77
the market. Bitcoin is by far the latest technology in the mobile and digital payments industry.
Awareness is slowly growing and adoption is still minimum. However, it is one of the categories
on the rise and receiving a considerable amount of funding in Europe. Considering the UK as the
leading country in mobile payments in Europe despite the Nordics usage, but for its well
established payments infrastructure, size and venture capital structure. It can be assumed that
technologies in Europe follow the UK trends. One evidence that Europe follows the trends in the
UK is that the tech giants, such as Apple Pay launched their wallet with NFC technology in the
UK before other European Countries.
Figure 28 - Diffusion of Technologies (TSYS, 2016)
According to the UK Consumer Payment Study, the figure above shows the position and
maturity of the different digital payments technologies in the UK. The graph shows Bitcoin as the
technology of the innovators with a very low usage level. Even though P2P and mobile wallets are
at a more mature position they did not reach the tipping point, with a chance of failure as shown
in the figure. As mobile wallets reach maturity and mainstream usage as expected by experts
78
Bitcoin will also have gained momentum and probably at that time it would have reached some
international guidelines and regulation that would probably increase adoption by allowing better
marketing and consumer awareness.
5.2 Limitations and Further Research
5.2.1 Limitations
There is one main umbrella of which most of the limitations of this paper fall underneath,
which is the lack of time-series data. Even though some time-series data was available and was
used in the best way possible, the existence of the rest would have definitely made a difference.
One limitation to this research is that it had a static view of the funding of the categories in the
different regions. This did not allow the reflection of the shifts in funding due to the progression
of the consumer mindset. Another limitation of this research is that it was missing the funding per
category in every year of the analysis. If this data was available it would have provided enough
and sufficient data to have more reliable conclusions about the future trends.
This paper also assessed the existing mobile payments startups in the market, where the
analysis of the startups that have experienced successful exits and their performance in the market
place could have also helped in the investigation of consumer behavior and needs.
5.2.2 Further Research
A continuation of the studies of this paper would be to address the limitations of this paper
by having a more dynamic view of the funding. In addition to assessing mobile payments from a
wider perspective and consider the exits and acquisitions of some startups. The studies of the
regulations that are expected to be applied to Bitcoin, if any, and what is the extent to which
79
regulation should be applied, since deregulation gives bitcoin features that are considered
advantages.
6 Conclusion
This paper provided a wide overview on the current mobile payments startup market, and
insight on what are the rising technologies offered by the startups. The analysis has yielded that
mobile wallets remain the most prominent category of mobile payments with the most number of
startups. However, the analysis also showed two categories that are on the rise, which are P2P and
Bitcoin. The paper states that P2P payments preceded Bitcoin in terms of adoption and usage.
Bitcoin, has attracted the interest of a lot of stakeholders and has received significant funding from
Europe. Moreover, the price of Bitcoin has recently reached record highs indicating that more
development is happening to the technology.
Payment platform providers have also been found to provide more B2C platforms recently
regardless of the category. This trend is because the payment acceptance and technological
solutions startups, which are the main categories that support B2B, are decreasing compared to
other categories of mobile payments that support B2C like Bitcoin and P2P.
Throughout the analysis, the importance of supportive governmental regulation proved to be
very weighty for the mobile payments industry, and the presence of incubators comes in second,
supported by examples in developed regions such as the UK or in developing regions such as
Kenya. Kenya, Nigeria and South Africa are the leading countries in Africa, because of
governmental support in Kenya and the relatively strong presence of incubators in Nigeria and
South Africa. Many governments have recognized their costs of cash and decided to take action
steps in diminishing this cost by implementing friendly regulation such as India.
80
The effect of government initiatives to encourage digital and mobile payments is areas where
there is huge potential in terms of smartphone penetration and e-commerce, is reflected on the fact
that many US VC investors are engaging more in cross-border investments. India, China and Brazil
serve as an example for this case. All three countries have high smartphone penetration, high
commerce potential due to the large populations and government initiatives and they have seen
cross-border investments from US VC investors. Therefore, borders are slowly losing the function
of surrounding VC investments.
7 Bibliography
Accenture Consulting. (2015). 2015 North America Consumer Digital Payments Survey.
Retrieved from http://cdn.pymnts.com/wp-content/uploads/2015/11/Accenture-Digital-
Payments-Survey-North-America-Accenture-Executive-Summary.pdf
Asghari, F., Amidian, A. A., Muhammadi, J., & Rabiee, H. R. (2010). A fuzzy ELECTRE
approach for evaluating mobile payment business models. Proceedings - 2010 International
Conference on Management of E-Commerce and E-Government, ICMeCG 2010, 351–355.
https://doi.org/10.1109/ICMeCG.2010.78
Åstebro, T., Bazzazian, N., & Braguinsky, S. (2012). Startups by recent university graduates and
their faculty: Implications for university entrepreneurship policy. Research Policy, 41(4),
663–677. https://doi.org/10.1016/j.respol.2012.01.004
Au, Y. A., & Kauffman, R. J. (2008). The economics of mobile payments: Understanding
stakeholder issues for an emerging financial technology application. Electronic Commerce
Research and Applications, 7(2), 141–164. https://doi.org/10.1016/j.elerap.2006.12.004
Auplat, C. (2006). Do NGOs influence entrepreneurship?: Insights from the developments of
81
biotechnology and nanotechnologies. Society and Business Review, 1(3), 266–279.
https://doi.org/http://dx.doi.org/10.1108/MRR-09-2015-0216
Avdeitchikova, S., Landström, H., & Månsson, N. (2008). What do we mean when we talk about
business angels? Some reflections on definitions and sampling. Venture Capital, 10(4),
371–394. https://doi.org/10.1080/13691060802351214
Başçı, E. S., & Alkan, R. M. (2015). Entrepreneurship Education at Universities: Suggestion for
a Model Using Financial Support. Procedia - Social and Behavioral Sciences, 195, 856–
861. https://doi.org/10.1016/j.sbspro.2015.06.364
BCG. (2016). Digital Payments 2020.
Bettignies, J. De, & Brander, J. A. (2007). Financing entrepreneurship : Bank finance versus
venture capital. Journal of Business Venturing, 22, 808–832.
https://doi.org/10.1016/j.jbusvent.2006.07.005
Black, S. E., & Strahan, P. E. (2002). Entrepreneurship and Bank Credit Availability. Journal of
Finance, LVII(6), 2807–2833.
Capgemini. (2016). Top 10 Trends in Payments in 2016.
Christensen, D. (2016). B2C Payments – A New Way to Pay. Bank of America Merrill Lynch.
Retrieved from
http://c.ymcdn.com/sites/www.mnafp.org/resource/resmgr/2016_Conference_Handouts/201
6__4G_B2C_Payments_-_A_Ne.pdf
Cocosila, M., & Trabelsi, H. (2016). An Integrated Value-Risk Investigation of Contactless
Mobile Payments Adoption. Electronic Commerce Research and Applications, 20, 159–
170. https://doi.org/10.1016/j.elerap.2016.10.006
comScore. (2017). Mobile Pushes 2016 Online Holiday Spending Above $80 Billion. Retrieved
82
March 10, 2017, from http://www.comscore.com/Insights/Blog/Mobile-Pushes-2016-
Online-Holiday-Spending-Above-80-Billion
Consult Hyperion. (2015). The Future of Payments: How payments in the UK will evolve in the
coming years, (August), 20.
Crunchbase. (2016). Global Innovation Investment Report 2016.
Cumming, D., & Li, D. (2013). Public policy, entrepreneurship, and venture capital in the United
States. Journal of Corporate Finance, 23, 345–367.
https://doi.org/10.1016/j.jcorpfin.2013.09.005
Da Rin, M., Nicodano, G., & Sembenelli, A. (2006). Public policy and the creation of active
venture capital markets. Journal of Public Economics, 90(8–9), 1699–1723.
https://doi.org/10.1016/j.jpubeco.2005.09.013
Dahlberg, T., Guo, J., & Ondrus, J. (2015). A Critical Review of Mobile Payment. Electronic
Commerce Research and Applications, 14(5), 265–284.
Dahlberg, T., Mallat, N., Ondrus, J., & Zmijewska, A. (2008). Past, present and future of mobile
payments research: A literature review. Electronic Commerce Research and Applications,
7(2), 165–181. https://doi.org/10.1016/j.elerap.2007.02.001
de Kerviler, G., Demoulin, N. T. M., & Zidda, P. (2016). Adoption of in-store mobile payment:
Are perceived risk and convenience the only drivers? Journal of Retailing and Consumer
Services, 31, 334–344. https://doi.org/10.1016/j.jretconser.2016.04.011
De Reuver, M., Verschuur, E., Nikayin, F., Cerpa, N., & Bouwman, H. (2015). Collective action
for mobile payment platforms: A case study on collaboration issues between banks and
telecom operators. Electronic Commerce Research and Applications, 14(5), 331–344.
https://doi.org/10.1016/j.elerap.2014.08.004
83
Deloitte. (2015). Bitcoin and Analytics Assessing the opportunities and vulnerabilities of the
cryptocurrency marketplace Contents.
Estrin, S., Mickiewicz, T., & Stephan, U. (2016). Human capital in social and commercial
entrepreneurship. Journal of Business Venturing, 31(4), 449–467.
https://doi.org/10.1016/j.jbusvent.2016.05.003
Gannamaneni, A., Ondrus, J., & Lyytinen, K. (2015). A post-failure analysis of mobile payment
platforms. Proceedings of the Annual Hawaii International Conference on System Sciences,
2015–March, 1159–1168. https://doi.org/10.1109/HICSS.2015.141
Guo, J., & Bouwman, H. (2016a). An analytical framework for an m-payment ecosystem: A
merchants’ perspective. Telecommunications Policy, 40(2–3), 147–167.
https://doi.org/10.1016/j.telpol.2015.09.008
Guo, J., & Bouwman, H. (2016b). An ecosystem view on third party mobile payment providers:
a case study of Alipay wallet, 18(5), 56–78. https://doi.org/10.1108/info-01-2016-0003
Hao, B., & Feng, Y. (2016). How networks influence radical innovation: The effects of
heterogeneity of network ties and crowding out. Journal of Business & Industrial
Marketing, 31(6), 758–770. https://doi.org/10.1108/JBIM-09-2012-0165
Hayton, J. C., George, G., & Zahra, S. A. (2002). National Culture and Entrepreneurship: A
Review of Behaviroal Research. Entrepreneurship Theory & Practice, 26(4), 33–52.
https://doi.org/Article
Hedman, J., & Henningsson, S. (2015). The new normal: Market cooperation in the mobile
payments ecosystem. Electronic Commerce Research and Applications, 14(5), 305–318.
https://doi.org/10.1016/j.elerap.2015.03.005
Henningsson, S., & Hedman, J. (2014). Transformation of digital ecosystems: The case of digital
84
payments. Lecture Notes in Computer Science (Including Subseries Lecture Notes in
Artificial Intelligence and Lecture Notes in Bioinformatics), 8407 LNCS, 46–55.
https://doi.org/10.1007/978-3-642-55032-4_5
Isenberg, D. (2011). The Entrepreneurship Ecosystem Strategy as a New Paradigm for Economic
Policy: Principles for Cultivating Entrepreneurship. The Babson Entrepreneurship
Ecosystem Project . Retrieved from
http://www.innovationamerica.us/images/stories/2011/The-entrepreneurship-ecosystem-
strategy-for-economic-growth-policy-20110620183915.pdf
Islam, A. (2015). Entrepreneurship and the Allocation of Government Spending Under Imperfect
Markets. World Development, 70, 108–121. https://doi.org/10.1016/j.worlddev.2015.01.002
Jensen, K. W., & Schott, T. (2014). Start-up firms’ networks for innovation: Embedded in
entrepreneurs’ networks in private and public spheres. ASONAM 2014 - Proceedings of the
2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and
Mining, (Asonam), 56–61. https://doi.org/10.1109/ASONAM.2014.6921560
Kerr, W. R., & Nanda, R. (2009). Democratizing entry : Banking deregulations , financing
constraints ,. Journal of Financial Economics, 94, 124–149.
https://doi.org/10.1016/j.jfineco.2008.12.003
Keuschnigg, C., & Nielsen, S. B. (2002). Tax policy, venture capital, and entrepreneurship.
Journal of Public Economics, 87, 175–203. Retrieved from www.elsevier.com
Lao, G., & Liu, H. (2011). Study of Mobile Payment Business Model Based on Third-Party
Mobile Payment Service Provider. 2011 International Conference on Management and
Service Science, 1–4. https://doi.org/10.1109/ICMSS.2011.5999096
Lee, S.-H., Yamakawa, Y., Peng, M. W., & Barney, J. B. (2011). How do bankruptcy laws affect
85
entrepreneurship development around the world? Journal of Business Venturing, 26, 505–
520. https://doi.org/10.1016/j.jbusvent.2010.05.001
Li, C., Shi, Y., Wu, C., Wu, Z., & Zheng, L. (2016). Policies of promoting entrepreneurship and
Angel Investment : Evidence from China. Emerging Markets Review, 29(950), 154–167.
Li, Y. L. Y., & Luo, S. L. S. (2008). Research on Mobile Payment in the E-Commerce.
International Conference on Management of E-Commerce and E-Government, (3), 100–
103. https://doi.org/10.1109/ICMECG.2008.83
Liébana-Cabanillas, F., Muñoz-Leiva, F., & Sánchez-Fernández, J. (2017). A global approach to
the analysis of user behavior in mobile payment systems in the new electronic environment.
Service Business. https://doi.org/10.1007/s11628-017-0336-7
Liébana-Cabanillas, F., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2014). Antecedents of the
adoption of the new mobile payment systems: The moderating effect of age. Computers in
Human Behavior, 35, 464–478. Retrieved from
http://www.scopus.com/inward/record.url?eid=2-s2.0-
84897990989&partnerID=40&md5=2443744ee6cae76bd441f0b2505f6fe0
Liu, J., Kauffman, R. J., & Ma, D. (2015). Competition, cooperation, and regulation:
Understanding the evolution of the mobile payments technology ecosystem. Electronic
Commerce Research and Applications, 14(5), 372–391.
https://doi.org/10.1016/j.elerap.2015.03.003
Lord, R. G., & Maher, K. J. (1991). Leadership and Information Processing: Linking
Perceptions and Performance. Routledge. Retrieved from
https://books.google.it/books?id=muUOAAAAQAAJ
Lupu, S., Mual, M., & Van Stiphout, M. (2016). Ecommerce Payment Methods Report 2016.
86
Mallat, N. (2007). Exploring consumer adoption of mobile payments - A qualitative study.
Journal of Strategic Information Systems, 16(4), 413–432.
https://doi.org/10.1016/j.jsis.2007.08.001
Mao, L., & Chen, S. (2016). The growth of mobile payment and effect on consumption via cash
and bankcard. Proceedings - 2015 8th International Conference on BioMedical Engineering
and Informatics, BMEI 2015, (Bmei), 872–877.
https://doi.org/10.1109/BMEI.2015.7401625
Mathew, M., Balakrishnan, N., & Pratheeba, S. (2010). A study on the success potential of
multiple mobile payment technologies. Technology Management for Global Economic
Growth PICMET 2010 Proceedings of PICMET 10, (March), 1–11. Retrieved from
http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5603421
McDermott, K. (2015). The mobile payments revolution.
MEC. (2016). Spotlight on Creating A Digital Payments Roadmap. Retrieved from
http://www.mecglobal.com/assets/publications/2016-12/Spotlight-On-Creating-A-Digital-
Payments-Roadmap-December-2016.pdf
Moroni, I., Arruda, A., & Araujo, K. (2015). The Design and Technological Innovation: How to
Understand the Growth of Startups Companies in Competitive Business Environment.
Procedia Manufacturing, 3(Ahfe), 2199–2204.
https://doi.org/10.1016/j.promfg.2015.07.361
Nguyen-Phuong-Mai, M., Terlouw, C., & Pilot, A. (2014). Revisiting facework with a new
analysis instrument: Face Strategies and Face Negotiation in Intercultural Communication.
Journal of Intercultural Communication, 36.
Norbäck, P.-J., Persson, L., & Douhan, R. (2014). Entrepreneurship policy and globalization.
87
Journal of Development Economics, 110, 22–38.
https://doi.org/10.1016/j.jdeveco.2014.04.006
Osservatorio Mobile Payment & Commerce. (2016). Mobile Payment & Commerce: engage
your customers.
Pal, D., Vanijja, V., & Papasratorn, B. (2015). An Empirical Analysis towards the Adoption of
NFC Mobile Payment System by the End User. Procedia Computer Science, 69, 13–25.
https://doi.org/10.1016/j.procs.2015.10.002
Pousttchi, K., & Hufenbach, Y. (2012). Mobile payment in the smartphone age - Extending the
mobile payment reference model with non-traditional revenue streams. ACM International
Conference Proceeding Series, 31–38. https://doi.org/10.1145/2428955.2428970
Roig-Tierno, N., Alcázar, J., & Ribeiro-Navarrete, S. (2015). Use of infrastructures to support
innovative entrepreneurship and business growth. Journal of Business Research, 68(11),
2290–2294. https://doi.org/10.1016/j.jbusres.2015.06.013
Rouibah, K. (2009). The Failure of Mobile Payment : Evidence From Quasi- Experimentations.
EATIS, 153–159.
Ruijun, G., Juan, Y., & Jiacai, W. (2010). Research on mobile payment technology and business
models in China under e-commerce environment. Lecture Notes in Computer Science
(Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in
Bioinformatics), 6485 LNCS, 334–343. https://doi.org/10.1007/978-3-642-17569-5_33
Schamberger, R., Madlmavr, G., & Grcchenia, T. (2013). Components for an interoperable NFC
mobile payment ecosystem. 2013 5th International Workshop on Near Field
Communication, NFC 2013. https://doi.org/10.1109/NFC.2013.6482440
Schott, T., & Cheraghi, M. (2012). Entrepreneurs’ networks: Size, diversity and composition
88
shaped by cultures of rationality and trust. Proceedings of the 2012 IEEE/ACM
International Conference on Advances in Social Networks Analysis and Mining, ASONAM
2012, 220–226. https://doi.org/10.1109/ASONAM.2012.46
Sherman, M. (2014). An introduction to mobile payments: market drivers, applications, and
inhibitors. Proceedings of the 1st International Conference on Mobile Software Engineering
and Systems - MOBILESoft 2014, (JUNE 2014), 71–74.
https://doi.org/10.1145/2593902.2593921
Smart Payment Association. (2016). An Overview of Contactless Payment Benefits and
Worldwide Deployments.
Staykova, K. S., & Damsgaard, J. (2015). The race to dominate the mobile payments platform:
Entry and expansion strategies. Electronic Commerce Research and Applications, 14(5),
319–330. https://doi.org/10.1016/j.elerap.2015.03.004
Stephan, U., & Pathak, S. (2016). Beyond cultural values? Cultural leadership ideals and
entrepreneurship. Journal of Business Venturing, 31(5), 505–523.
https://doi.org/10.1016/j.jbusvent.2016.07.003
Taylor, E. (2016). Mobile payment technologies in retail: a review of potential benefits and risks.
International Journal of Retail & Distribution Management, 44(2), 159–177.
https://doi.org/http://dx.doi.org/10.1108/MRR-09-2015-0216
Teo, A.-C., Tan, G. W.-H., Ooi, K.-B., Hew, T.-S., & Yew, K.-T. (2015). The effects of
convenience and speed in m-payment. Industrial Management & Data Systems, 115(2),
311–331. https://doi.org/10.1108/02635570710734262
Tong, F., Zhou, X., & Liu, S. (2005). The value chain of mobile e-payment. Proceedings of the
7th International Conference on Electronic Commerce - ICEC ’05, 880.
89
https://doi.org/10.1145/1089551.1089724
TSYS. (2016). U.K. Consumer Payment Study.
Upson, J. W., Damaraju, N. L., Anderson, J. R., & Barney, J. B. (2017). Strategic networks of
discovery and creation entrepreneurs. European Management Journal.
https://doi.org/10.1016/j.emj.2017.01.001
Van Bossuyt, M., & Van Hove, L. (2007). Mobile payment models and their implications for
NextGen MSPs, 9(5), 31–43. https://doi.org/10.1108/14636690710816435
Visa. (2016). Digital Payments Study 2016.
VISA. (2016). Accelerating The Growth of Digital Payments in India: A Five-Year Outlook.
Retrieved from
http://www.visa.co.in/aboutvisa/research/include/Digital_Payments_India.pdf
Vizzarri, A., & Vatalaro, F. (2014). M-Payment systems: Technologies and business models.
2014 Euro Med Telco Conference - From Network Infrastructures to Network Fabric:
Revolution at the Edges, EMTC 2014. https://doi.org/10.1109/EMTC.2014.6996626
Wonglimpiyarat, J. (2013). Innovation financing policies for entrepreneurial development —
Cases of Singapore and Taiwan as newly industrializing economies in Asia. Journal of High
Technology Management Research, 24, 109–117.
Yang, S., Lu, Y., Gupta, S., Cao, Y., & Zhang, R. (2012). Mobile payment services adoption
across time: An empirical study of the effects of behavioral beliefs, social influences, and
personal traits. Computers in Human Behavior, 28(1), 129–142.
https://doi.org/10.1016/j.chb.2011.08.019