Mobile internet business models in emerging markets

Int. J. Business Environment, Vol. 3, No. 4, 2010

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Mobile internet business models in emerging markets Katri Hanninen DataInfo Oy, P.O. Box 725, 00051 Sonera, Finland Fax: +358-20-7766340 E-mail: [email protected]

Jukka Hallikas School of Business, Lappeenranta University of Technology, P.O. Box 20, FIN-53851, Lappeenranta, Finland, E-mail: [email protected]

Mikko Pynnönen* Technology Business Research Center, Lappeenranta University of Technology, P.O. Box 20, FIN-53851, Lappeenranta, Finland E-mail: [email protected] *Corresponding author Abstract: The spread of the mobile internet into emerging markets has created interest in developing strategies and new business models suitable for doing business in the emerging markets. This requires the identification of alternative business model structures and fitting business models in different business environments. This study was conducted using both quantitative and qualitative research methods. Internally homogeneous country clusters of European countries were formed with cluster analysis. The selected four country clusters allowed for the designing of suitable business models for different kinds of markets. Analytical methods were used for assessing the priorities of market attributes in each cluster and evaluating the business model fit in each cluster. The main contribution of this paper is the systematic methodology and process to segment the countries and to analyse the fit of business models to these segments. The paper also reveals the critical business model elements and the business models profiles for each market. Keywords: country clusters; diffusion; innovation; business model; mobile internet; emerging markets; analytic hierarchy process; quality function deployment; QFD. Reference to this paper should be made as follows: Hanninen, K., Hallikas, J. and Pynnönen, M. (2010) ‘Mobile internet business models in emerging markets’, Int. J. Business Environment, Vol. 3, No. 4, pp.427–444. Biographical notes: Katri Hanninen works at DataInfo Oy with a development project related to e-procurement. She holds an MSc (Econ.) from Lappeenranta University of Technology.

Copyright © 2010 Inderscience Enterprises Ltd.

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K. Hanninen et al. Jukka Hallikas is a Professor of Supply Management at the School of Business at Lappeenranta University of Technology, Finland. His research interests focus on the management of value networks, risk management in supply networks and learning in interfirm relationships. He has published several scientific articles, books and book chapters on interfirm relationships and supply management. Mikko Pynnönen is an Associate Professor at the Faculty of Technology Management and Project Manager at the Technology Business Research Center at Lappeenranta University of Technology, Finland. He holds a DSc (Econ.) degree from Lappeenranta University of Technology. His main research interests include business models and value networks in the ICT industry. He has published several scientific articles on networked business models and customer value in ICT.

1

Introduction

The concept of mobile internet refers to these internet-based services used via a mobile phone (Paavilainen, 2001). As can be seen, enormous steps have been taken since the launch of the first mobile phones, and the internet has redefined the function of mobile phones significantly. Mobile internet services are becoming more and more commonly used in developed countries, and when these markets become saturated, growth has to be found elsewhere (Pagani and Fine, 2008). In emerging countries, mobile phones are still mainly used for calling, but growth potential for mobile internet services is apparent. In many emerging countries fixed line infrastructure, if it exists, is deficient and the mobile internet could fill this gap. Nevertheless, the emerging markets differ from the developed ones in many ways and there are several factors that have an influence on the diffusion and adoption of new products and services. Diffusion is a process by which innovations are communicated over time among the members of a social system using certain channels (Rogers, 1995). Many services in the ICT sector are nowadays homogeneous. A good example of this is the mobile phone and internet connections in which the only differences are in connection speeds, additional services and prices. These days, internet connections are mainly broadband connections in which an average user gets an internet connection with high enough speed and a fixed price. Hence, the only things that the service providers can compete with are the prices and additional services, such as the size of a mailbox or software packages. Mobile phone connections are also competing mainly with prices. According to Gruber (2000), factors related to competition, such as the intensity of competition and numbers of firms supplying telecommunication services are considerably important in affecting the speed of diffusion. Telecommunication markets are extremely uncertain in relation to both technologies and markets. Many high technology products fail and 90–95% of product ideas never end up in the market (Tidd et al., 2001). There is uncertainty related to the size and features of the upcoming markets. In addition, the developer and the marketer of a new innovation should be aware of the changing need that the new innovation or technology is able to satisfy (Miller, 2001). To control these risks the firm’s business model has to be designed so that it takes

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these factors into account. A business model’s strength as a planning tool is that it concentrates on fitting all the elements of the system together (Magretta, 2002). The main objective of this study is to find out what kinds of business models are suitable for doing mobile internet business in the emerging markets and how they enhance the diffusion of the mobile internet. The aim is also to find out different factors that affect the diffusion of the mobile internet, by either enhancing or delaying it. In the study, we use a process where we create country clusters and define suitable business models for each cluster. The paper is structured so that first we discuss the business models and diffusion of innovation. Then, we introduce the mobile internet technologies considered in this study. The empirical research consists of analytical steps for identifying the characteristics of mobile internet markets and assessing the fit between the markets and business models. Finally, we discuss the findings of the study and propose new research directions based on our findings.

2

Business models and diffusion of innovation

It is often very difficult to get a new idea adopted, even when it has obvious advantages. Many innovations often require a period of many years from the time they become available to the time they are widely adopted (Wareham et al., 2004). For that reason, a common problem for many organisations is how to speed up the rate of diffusion of an innovation. According to Rogers (1995), diffusion is a process by which an innovation is communicated through certain channels over time among the members of a social system. The term diffusion, however, is applied to processes that involve some mechanism of information transfer or contagion, e.g., the spread of disease, sales of a new product or the adoption of new technologies (Carrillo and González, 2002). For this reason, diffusion is a key concept in technological and marketing studies. All the innovations are not identical when it comes to their rate of adoption, and their characteristics help to understand their different rates of adoption. The five main characteristics of innovations are relative advantage, compatibility, complexity, trialability and observability (Rogers, 1995). These characteristics have an effect on potential customers’ decisions to buy the innovation. Part of the characteristics may enhance and part of them may prevent the adoption of a new product (Sundqvist et al., 2002). Relative advantage, compatibility trialability and observability have been found to be positively related to the rate of diffusion, and complexity is highly negatively correlated to the diffusion rate (Herbig and Day, 1992). When dealing with high technology products there are diffusion-related aspects such as constant development of technology (improvements in technology) and the possibility to replace old products with new higher technology products. It may also be argued that the high technology innovations can often be more uncertain than so-called conventional innovations because of the fact that their technology is more complicated and therefore more difficult for consumers to understand. Continuous developments/improvements may postpone the consumers’ adoption decisions so that they may skip over the existing generations of technology and wait for significant improvements (Sundqvist et al., 2002). According to Robertson and Gatignon (1986), unfamiliarity caused by complex, high-technology products make it difficult to evaluate and make judgments about the

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products. In addition, high-technology innovations are often costly and they have high switching costs. When it comes to the diffusion of the internet, an important driver is application variety. However, application variety also poses a challenge for interoperability. Although standards improve interoperability, they represent a moving target because application variety is constantly increasing (Dutta and Roy, 2003). To manage the uncertainty of the diffusion of the mobile internet in the emerging markets the firm has to design the business model to fit the target market (Morris et al., 2006). According to Timmers (2000), a business model can be defined as: 1

an architecture for product, service and information flows, including a description of the various business actors and their roles

2

a description of the potential benefits for the various business actors

3

a description of the sources of revenue.

Mansfield and Fourie (2004) define the business model as a contingency model that discovers an optimal mode of operation for a specific situation in a specific market. From the innovation perspective, Chesbrough and Rosenbloom (2002) state that the business model offers a consistent framework that takes technological characteristics and potentials as inputs and converts them through customers and markets into economic outputs. The business model is therefore considered a mechanism that mediates between technology development and economic value creation. Chesbrough and Rosenbloom (2002) and Chesbrough (2003) offer an operational definition for the functions of a business model. The functions of a business model are: 1

to articulate the value proposition

2

to identify a market segment

3

to define the structure of the value chain

4

to estimate the cost structure and profit potential of producing the offering

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to describe the position of the firm within the value network

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to formulate the competitive strategy.

It is also important to distinguish between strategy and a business model. According to Mansfield and Fourie (2004), strategy is the behaviour of the management that is concerned with the firm’s creation of sustainable competitive advantage. Strategy is a combination of deliberate actions, tactical responses and organisational learning. It is the pattern of decisions that describes the firm’s products and markets, objectives, plans and range of business. In brief, strategy is the way the firm attains its desired future. The focus of strategy is on the future, whereas, business models emphasise customer centricity as the source of value creation. Osterwalder and Pigneur (2002) perceive a business model as the conceptual and architectural implementation of a business strategy and as the foundation for the implementation of business processes. When considering the challenge and characteristics of business model development in the emerging markets, it is often observed that potential customers can certainly be found in the emerging countries but doing business with low income customers has some


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special characteristics. According to Anderson and Markides (2007), important issues to take into account to be successful in the emerging countries are making sure that the products and services are affordable to the customers, adapting existing products and services to customers who have fewer resources or a different cultural background and ensuring the availability of products and services and marketing. Chesbrough et al. (2006) also believe that, when serving customers in the emerging markets, innovative product design is essential, but not sufficient in itself. What is also needed is a strong focus on the development of a comprehensive business model. Finding customers is not usually the biggest problem in the emerging markets. The 20 biggest emerging economies have more than 700 million households that can be considered as potential customers. Probably, the biggest problem concerning products and services is ensuring the affordability for these low income customers. Products and services have to match the cash flows of customers who are more often paid on a daily than weekly or monthly basis (Anderson and Markides, 2007). The means of financing and different pricing models that enable customers to purchase new products are vital elements in a business model in the emerging markets (Chesbrough et al., 2006). When an innovative product has been developed, it is important to establish a connection between the product and its value to customers (Chesbrough et al., 2006). Weak value proposition can be a remarkable weakness in a business model. The value for a customer is usually a relative advantage of a new product compared with the existing, corresponding products. It is important that the value is somehow proven to the customers. This can be done, e.g., by enabling the trial of new products and thus, making the customers discover the advantages and value of the products themselves. Challenges related to the availability of products or services and marketing is one considerable issue in the emerging countries.

3

Method and process

In this study, cluster analysis is used for grouping countries in different types of clusters so that the countries in the same cluster are more similar to one another than they are to countries in other clusters. After this, we define suitable business models for each cluster. The analytical hierarchy process (AHP) model (Saaty, 1999) and quality function deployment (QFD) matrix (Clegg and Tan, 2007) matrix are used for this purpose. The AHP method is used for assessing the business environment in the country clusters, and QFD for assessing the business model fit in the country clusters. The process consists of four phases: 1

cluster analysis of the countries

2

mapping the basic types of current mobile internet business models

3

assessment of the business environment in the country clusters

4

assessing the fit of a business model in the country clusters.

The basis of the analysis is the cluster analysis of the countries. The cluster analysis is used to group 40 European countries into internally homogeneous country clusters.

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Cluster analysis groups objects into clusters in such a way that objects in the same cluster are more similar to each other than they are to objects in other clusters (Hair et al., 1998). All the European countries, except the smallest ones like Monaco and Luxembourg, are included in this study. The cluster analysis is conducted based on two different variables: the digital opportunity index (DOI) and the Gini index (G) (Gini, 1921). The values of the DOI in different countries are from the World Information Society Report (ITU, 2007) and the values of G are from The World Factbook (2007). The DOI is an index based on information and communication technology (ICT) indicators that are internationally agreed upon. It is a standard tool that, e.g., governments, operators, development agencies and researchers can use in measuring the digital divide and compare ICT performance across countries. It measures countries’ ICT capabilities in infrastructure, access path and device, affordability and coverage and quality. The DOI is based on 11 ICT indicators, grouped in three different categories which are opportunity, infrastructure and utilisation (ITU, 2007b). The G measures the inequality of income distribution. In other words, it indicates the extent to which the distribution of income among individuals within an economy deviates from a perfectly equal income distribution. A G of zero means perfect equality and an index of 100 means perfect inequality (OECD, 2007). In the beginning of this study, various statistical data – e.g., GDP, population density, share of urban population, literacy, unemployment rate and age distribution – were collected from the selected countries and relationships between these different variables were tested. All the other variables except the DOI and the G correlated strongly with each other, and that is one reason why they are used as the basis of cluster analysis. The other reason for using the DOI and G is that they are appropriate for examining the diffusion of the mobile internet under different circumstances in different countries: the G indicating the heterogeneity of purchasing power and the DOI indicating the heterogeneity of infrastructure and possibilities to use ICTs. It is important to standardise the variables although it is not mathematically compulsory. If the variables are not measured on the same scale and if they are not standardised, the results are dominated by those variables that can have high values. While conducting the cluster analysis in this study, the values of the DOI and G were standardised because they are measured on different scales. In this study, the cluster analysis is conducted with SPSS software which is designed for the analysis of quantitative research material. The algorithm that is used is two-step cluster analysis, which uses both hierarchical and non-hierarchical techniques, and the distance measure that is used is log-likelihood. The framework of Chesbrough and Rosenbloom (2002) and Chesbrough (2003) is applied for mapping the existing business model types of mobile internet business models. The process of this phase is based on a value network based business mapping framework (Pynnönen et al., 2008). In the assessment of the business environment in the country clusters, the AHP model is used. It is a mathematically based technique for analysing complex situations, and it has become successful in helping decision-makers to structure and analyse a wide range of problems (Golden et al., 1989). When assessing the fit of a business model in the country clusters, the QFD process is used. It is a process or a methodology for planning products and services, and it can help an organisation gain customer focus and determine those areas of customer concern where team involvement and use of specialised tools can be most useful (Day, 1993).


4

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The aim of this chapter is to form business models for different country segments, focusing on the emerging markets, with suitable combinations of business model elements. First, the completion of cluster analysis and its results are described. Then, the results of the cluster analysis, four different country clusters, are defined and compared with each other. After describing the clusters, four examples of different business models and their elements are presented in order to explain what kinds of business models exist in the ICT sector at present. Then, the business model elements are combined with different country clusters using the AHP and QFD. Finally, suggestions for mobile internet business model profiles are given for each country cluster.

4.1 Results of cluster analysis and description of country clusters Results of the cluster analysis can be seen in Figure 1 and the cluster definitions in Table 1. One country (Iceland) was excluded from the cluster analysis because the G of Iceland was not available from the information source that was used. According to the World Information Society Report (ITU, 2007), the DOI is on a low level when it is 0.30 or less. It is on a medium level when it has values between 0.30 and 0.49 and on a high level when the values are more than 0.49. A G of 0 represents perfect equality in income distribution and an index of 100 represents perfect inequality. In this study, the values of the DOI and G are discussed as relative, not absolute, values meaning that they are always compared with the DOI and G values in other clusters. Figure 1

Country clusters (see online version for colours)

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In order to differentiate the clusters from each other, it is useful to describe and name them. Attributes that are used in the descriptions are both the ones that are related to the variables used as the basis of cluster analysis and other attributes describing the characteristics of each cluster. The descriptions in Table 1 aim at describing both the characteristics of clusters and differences between them. Table 1

Definition of clusters

Clusters Cluster 1 ‘polarised developing’

Description High G, low DOI Both poor and rich people Heterogeneous segment Insufficient infrastructure

Countries Armenia, Azerbaijan, Georgia, Greece, Moldova, Poland, Portugal, Russia, Serbia and Montenegro and Turkey

Lower readiness for adoption Cluster 2 ‘homogeneous developing’

Low G, low DOI More poor people Homogeneous segment Insufficient infrastructure

Albania, Belarus, Bosnia Herzegovina, Bulgaria, Croatia, Kazakhstan, Macedonia, Romania and Ukraine

Lower readiness for adoption Cluster 3 ‘homogeneous developed’

Low G, high DOI Balanced income distribution Homogeneous segment

Belgium, Cyprus, Czech Republic, Denmark, Finland, France, Germany, Hungary, Malta, Slovakia, Slovenia and Sweden

Better infrastructure Higher readiness for adoption Cluster 4 ‘polarised developed’

High G, high DOI Both poor and rich people

Austria, Estonia, Ireland, Italy, Netherlands, Spain, Switzerland and UK

Heterogeneous segment Better infrastructure Higher readiness for adoption

In cluster 1, the DOI ranges from 0.33 to 0.61. It is on a relatively low level in all the other countries in this cluster except in Portugal (0.61). The low DOI indicates that the infrastructure is insufficient, people have limited possibilities to use ICTs, and the readiness to adopt new innovations and technologies is relatively low. The G varies between 33.2 and 42. This means that income distribution is unequal, both poor and rich people exist in the countries of this cluster. Because of this fluctuation in income, cluster 1 can be characterised as polarised. Similarly, the DOI is on a relatively low level in cluster 2 as well. It ranges from 0.37 to 0.54. Also in this cluster, the infrastructure is insufficient and the readiness to adopt new technologies is relatively low. The G ranges from 26.2 to 31.6. It indicates equal distribution of income but people in this cluster still have relatively low income. This cluster can be described homogeneous by nature. In cluster 3, the DOI is on a relatively high level and it varies between 0.55 and 0.76. The infrastructure and possibilities to use ICTs are relatively good and hence, also the readiness to adopt new innovations is better than in clusters 1 and 2. The G is between


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23.2 and 28.4 and it means that income distribution is in balance. Cluster 3 can be characterised as homogeneous. Cluster 4 is considerably similar to cluster 3, except for the difference in income distribution. The DOI is relatively high, ranging from 0.61 to 0.71, but income distribution is unequal. The G varies between 30.9 and 36, which means that both poor and rich people exist in this cluster and it can be described polarised. People in cluster 4 have, however, relatively high income. It can be seen from the definitions of the four different clusters above that clusters 1 and 2 are emerging when observing them from a point of view of the DOI and the G. Both in cluster 1 and 2 income levels are on a fairly low level and also, the DOI being relatively low in both clusters, the possibilities to use and adopt the internet and other ICTs are relatively low.

4.2 Mobile internet business models for emerging markets After conducting the cluster analysis and describing the country segments, we explore what kinds of existing mobile internet business models are appropriate for the emerging countries. The examples of the business models’ business environment in different clusters is described with market attributes. Then, the fit of different business model elements in each cluster is assessed. Like the business model examples, market attributes and their realisations and the fit of business model elements in different clusters are assessed in an expert workshop. Table 2

Examples of business models Wireless broadband

Mobile broadband

Ad funded

Inhabitants of sparsely populated areas

Urban users

Young people

People with low incomes, communities

WiMAX, @450

3G

3G

WiMAX, @450

Terminal, installation, opening and monthly fee

Opening and monthly fee

‘Paid’ by receiving and responding to ads

Terminal installation, Prepaid, Based on use

Value proposition

Bandwidth, also in sparsely populated areas

Bandwidth and advanced services

Free of charge, Peer group inclusivity

Low cost payments only when used

Value chain

Service provider

Mobile operator

Mobile operator, Advertisers

Mobile operator, local entrepreneurs

Terminal installation

Service development

Advertiser acquisition

Terminal installation

Customer Segment Technology (current) Pricing

Cost structure

Rural access points

Four generic mobile internet business models presented in Table 2 were selected as a case study. All the examples are not purely mobile internet business models but they can also be applied for that purpose. These business models have different target customers, they have different kinds of value propositions and pricing models and also the value networks and cost structures vary between different business models. The technology that can be

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used to implement these business models has three alternatives. It is important to notice that these business models are generated from a mobile operator’s point of view. Another limitation is that information concerning the cost structure was not available, and therefore, cost structure is not thoroughly described here. The Wireless Broadband business model enables using a broadband internet connection wirelessly also in sparsely populated areas. Broadband can be implemented either with WiMAX or @450 technologies, which are suitable for building a broadband connection in remote and sparsely inhabited areas. The costs of this broadband connection for a customer consist of the price of a terminal, installation costs, opening the connection and a monthly fee, which depends on the speed of the connection. Besides the operator, the value network consists of a service provider and a network manufacturer. The mobile broadband business model makes it possible to use mobile and internet services regardless of time and place. The 3G technology is able to offer high bandwidth and advanced services. Urban users are the target customers of this business model. The customer has to pay for the opening of the broadband connection and a monthly fee, which depends on the speed of the connection. An essential actor in the value network in this business model is the service provider. The ad funded business model is addressed to young people who are willing to make the effort to get free voice minutes. The idea of this model is that customers pay their connections by receiving and responding to advertisements. Subscribers fill out a questionnaire on the internet which includes personal details and interests and then receive a telephone SIM card offering with a number of voice minutes and text messages per month. Advertisements sent to the phone are based on the answers. A significant part of a value network in this model, advertisers get an opportunity for direct engagement with a young audience with real-time feedback. Besides free voice minutes this business model offers peer group inclusivity as a value proposition to customers. In the rural access point’s business model, the idea is to offer low income customers a possibility to use the internet and mobile internet services. The pricing model is designed so that the customer has to pay for the connection only when it has been used. Alternatives in pricing are prepaid and based on use type of pricing. The technologies with which this business model can be implemented are @450, WiMAX and 3G. @450 and WiMAX enable internet connections in rural and sparsely inhabited areas. 3G, on the other hand, enables mobile internet connections via a mobile phone that can be, e.g., in common use in a rural village or community. In this business model, the value network consists of local entrepreneurs and government that can, e.g., subsidise building the infrastructure.

4.3 Business environment in country clusters In order to combine country clusters and business model elements, the country clusters are first described with market attributes derived from the DOI and the G. Market attributes derived from the DOI are: 1

a lack of mobile internet services

2

limited internet access functionality in handsets

3

insufficient backhaul networks.

Market attributes derived from the G are:

Mobile internet business models in emerging markets 1

large low income segment

2

large middle income segment

3

large high income segment.

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After this the market attributes and country clusters are formed as an AHP model (Table 3). In this study, the AHP is implemented with expert choice software. Table 3

AHP model

Goal: business growth potential

Lack of mobile internet services Limited internet access functionality in handsets Insufficient backhaul networks Large low income segment Large middle income segment Large high income segment

Cluster 1 Cluster 2 Cluster 3 Cluster 4 l: .341

l: .543

l: .074

l: .042

g: .341

g: .543

g: .074

g: .042

l: .156

l: .243

l: .236

l: .191

g: .053

g: .132

g: .017

g: .008

l: .238

l: .243

l: .051

l: .078

g: .081

g: .132

g: .004

g: .003

l: .165

l: .243

l: .166

l: .079

g: .056

g: .132

g: .012

g: .003

l: .210

l: .195

l: .041

l: .179

g: .071

g: .106

g: .003

g: .008

l: .022

l: .056

l: .432

l: .046

g: .008

g: .030

g: .032

g: .002

l: .210

l: .020

l: .074

l: .427

g: .071

g: .011

g: .005

g: .018

As can be seen in Table 3, the AHP model in this study is comprised of four different country clusters and market attributes describing them. The realisation of market attributes in each cluster is assessed using a pair-wise comparison technique. This means that each of the attributes is compared with all the other attributes to assess its relative realisation in every cluster. The clusters are also compared with each other using the pair-wise comparison technique in order to prioritise them against business growth potential. The number after each cluster indicates this relative growth potential, whereas, the number after each market attribute indicates its relative realisation inside one cluster. In the context of market attributes l means local and it is the realisation without taking business growth potential into consideration, and g means global and it is the realisation when taking growth potential into consideration. Clusters 1 and 2 are assessed to have the most potential when considering the growth of business. This is because in both of these clusters very few people are using the internet and especially the mobile internet. Growth for mobile internet business could therefore be found in these clusters. Figure 2 demonstrates the relative realisations of market attributes and the columns above clusters demonstrate the growth potential of each cluster. Both clusters 1 and 2 are dominated by limited internet access functionality in handsets; cluster 3 is dominated by a large middle income segment and cluster 4 by a large high income segment. When considering the overall realisation of market attributes, limited internet access functionality in handsets seems to be the most sensitive one.

438 Figure 2

K. Hanninen et al. Realisation of market attributes

4.4 Assessment of business model fit in country clusters The next step in the study was to combine business model elements with country clusters. This is performed with the QFD matrix which is employed to complement and support the AHP model. Table 4 shows an example analysis matrix of cluster 2. We chose to use the local values of the AHP analysis that do not take the growth potential into account to keep the results comparable with each other. The model is applied to assess the relationship between the weighted market attributes and the elements of the business model. Market attributes and business model elements are the inputs of a QFD matrix in this case. The objective of the relationship analysis is to assess the possibility of influencing the market attributes with the elements of a business model. The relationship strengths are assessed as follows: empty = no relationship, 1 = weak relationship, 3 = medium relationship and 9 = strong relationship. As outputs, the QFD matrix emphasises those business model elements that determine the success of a business model in different clusters. In this case, the market attributes are on the vertical axis and the elements of a business model are on the horizontal axis of the QFD matrix. The business model elements that are included are technology, pricing, value proposition and value network. Under realisation in Table 4, the local realisation weights of the market attributes are displayed as they were assessed in the AHP model. The total shows the importance of each alternative of the business model element in the cluster that is in question and it is the vertical sum of relationship numbers weighted with the realisations of market attributes. The relative weight indicates the relative importance of each alternative of the business model element. Sensitivity indicates the significance of each market attribute in contrast to the business model, and it is the horizontal sum of relationship numbers weighted with market attribute realisations. The realisation weights of market attributes are different in each country cluster and therefore the matrix gives different importance profiles for business model elements in different clusters.

Mobile internet business models in emerging markets Table 4

Fit between country cluster attributes and business model elements

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440 Figure 3

K. Hanninen et al. Business model profiles in different clusters

Figure 3 displays the mobile internet business model profiles of all the clusters based on a relationship analysis conducted in the QFD matrices. It can be seen from all the profiles that, in general, pricing and value proposition get the highest values and hence, they are the most critical business model elements when trying to influence the market attributes in this case. This does not mean that the other elements are insignificant; they just do not have so much influence as far as these certain market attributes with certain realisation weights are concerned. The technology does not seem to be important in these business models. It is partly due to the fact that implementation costs are not taken into account in this study and, on the other hand, for a customer it makes no difference which technology is used to implement the business model as long as the technology is affordable and working properly. We will next describe briefly the business model profile characteristics of clusters 1 and 2. The business model profile in cluster 1 ‘polarised developing’ is as follows. The usage of the internet is on a relatively low level and income distribution is unequal. The most sensitive market attributes describing this cluster are lack of mobile internet services, large low income segment and large high income segment. As already discovered earlier, pricing and the value proposition are the most critical business model elements in general and this is also true in cluster 1. The most appropriate pricing models in cluster 1 are a monthly fee and pricing that is based on use. Different types of pricing models are needed because both poor and rich people exist in this cluster. A monthly fee is targeted to people with higher income and based on use type of pricing for people with lower income. Value propositions in this cluster are high bandwidth and advanced services, for which the well-off are, willing to pay and ad-funded internet access and services that people with low income value. The value network in cluster 1 comprises a network manufacturer and mobile device manufacturer, which are important because the infrastructure is insufficient and a lack of mobile phones with internet access is also dominant in this cluster. The technology needed to implement the mobile internet business model in cluster 1 can be any of the given alternatives – 3G, @450 or WiMAX – because essential from customers’ point of view is that the technology is working and they are able to afford it.


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In cluster 2, ‘homogeneous developing’, the usage of the internet and mobile internet is on a relatively low level and income distribution is equal. The most sensitive market attributes in cluster 2 are a lack of mobile internet services and the large low income segment, although income is relatively equally distributed. The most critical business model elements in cluster 2 are value proposition and value network. Because of the large low income segment and lack of mobile internet services, the most important value propositions in this cluster are ad funded internet access and services and affordable low bandwidth and services. Like in cluster 1 and for the same reasons, also in cluster 2, the most important actors in the value network are the network manufacturer and mobile device manufacturer. Government and local companies and entrepreneurs are important actors as well because the government is able to subsidise the possibilities to use ICTs, and local companies and entrepreneurs, cooperating with the operator, are relevant in enhancing the diffusion of new technologies and services. The large low income segment is significant in determining which pricing models are critical in this cluster. The most important pricing models are prepaid, based on use and free of charge (ad funded). Technology can be either 3G, @450 or WiMAX because it is not relevant from a customer’s point of view. Actors in value networks are somewhat different in the developed than in the emerging clusters. In both emerging clusters, the most important actors in the value network are network manufacturers and mobile device manufacturers, because the infrastructure is deficient and there is a lack of internet capable mobile phones. Network manufacturers are assessed to be important only in cluster 3, in which the infrastructure is not insufficient but it is not as good as in cluster 4. Government and local companies and entrepreneurs are also included in the value networks of developed clusters but they are not as significant actors as in the emerging clusters, in which more subsidies from the government are needed, e.g., to improve the infrastructure. In addition, it is more important to cooperate with local entrepreneurs in those countries than in the developed countries to get acceptance for the products or services that are offered.

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Discussion and conclusions

The aim of this study was to contemplate the factors that affect the diffusion and adoption of the mobile internet in the emerging markets and to offer guidelines for designing business models for different markets. The main objective was to find out what kinds of business models are suitable for doing mobile internet business in the emerging markets. Diffusion and business models were discussed both in the theoretical and empirical part of the study. This paper contributes on innovation diffusion and adaptation literature (e.g., Wareham et al., 2004; Rogers, 1995). The main contribution of this paper is the systematic methodology and process to first analyse the country segments (clusters) and their business environment and second the fit of business models to the segments. In the empirical part of the study, 40 European countries were first clustered into four internally homogeneous country segments. After that four examples of existing business models and their elements in the ICT sector were presented. The AHP and QFD techniques were applied to fit business model elements with different country clusters.

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Finally, suggestions for business model profiles were given, and the differences of business models between the developed and emerging countries were discussed. The AHP model demonstrated that the most critical market attribute in all of the clusters is the limited internet access functionality in handsets. This can be interpreted as a lack of handsets in general, especially in the emerging countries. Based on the QFD analysis, the most important mobile internet business model elements in the emerging country clusters are pricing, value proposition and value network. These are the elements that have the best influence on the dominating market attributes in the emerging clusters. It was also found that the technology is not a central business model element. When the differences in mobile internet business models between the developed and emerging markets were compared, it was noticed that there are differences in pricing models, value propositions and value networks. The differences in pricing models are dependent on the fact that people in the emerging countries have lower income than people in the developed countries. In the developed markets, a monthly fee is a suitable pricing model, whereas in the emerging countries more suitable pricing models are prepaid or based on use type of pricing. Also, different value propositions are needed in different types of markets, because people with different types of needs value different things. In the developed markets high bandwidth and advanced services are valued, whereas, the affordability of services is more important in the emerging markets. The significance of the actors in the value networks varies between the emerging and developed markets. There is more need for network manufacturers in the emerging countries because of the insufficient infrastructure. Government and local companies and entrepreneurs are also more important actors in the emerging than in the developed countries. Subsidies from the government are needed, e.g., to improve the infrastructure, and local companies and entrepreneurs can facilitate the acceptance of new products. When examining the results of this study, certain limitations need to be taken into account. First, the cluster analysis is a descriptive and theoretical research method and it is used primarily as an exploratory technique. The results of the cluster analysis are entirely dependent on the variables used. Cluster analysis in this study is conducted based on the DOI and G, and the results are entirely dependent upon them. If the variables of this study were different, the results of cluster analysis would also probably be different. The second limitation is that the market attributes used when assessing the business model fit in the country clusters are derived from the DOI and the G and consequently, they have an influence on the results. There is definitely variation in other market attributes between the countries inside the same clusters and therefore, it is important to notice that this study is conducted on a relatively general level. Furthermore, it has to be noticed that the assessments concerning the realisation of market attributes in different clusters and the relationship analysis concerning business model elements and market attributes are based on subjective judgments. The final limitation of this study concerns the elements of the business model when suggesting appropriate business model profiles for different country clusters. The finding that the technology is not a significant element of a business model results partly from the fact that implementation costs were not taken into account. On the other hand, it was also pointed out that from a customer’s point of view technology is not relevant, as long as it works and the customer can afford it. Future research directions are related to the above-mentioned generality of this study. The specificity of assessing the market attributes in different clusters, and accordingly the business model fit in different countries, can be improved by concentrating exclusively on few countries. Then, it would be possible to examine all the countries and their special


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characteristics separately, and the assessment of the business model fit could therefore be more specific.

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