Dissataion - Ranga Perera (CB002688)

MSc. in TECHNOLOGY MANAGEMENT
DISSERTATION
ON
DEVELOPMENT OF A CONSUMER TECHNOLOGY ADOPTION MODEL
FOR MOBILE DATA SERVICES WITH UTILITARIAN AND HEDONIC
VALUE PROPOSITIONS
BY
Ranga Perera
CB002688
7TH SEPTEMBER 2009
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DISSATATION
Student Name: Ranga Naresh Perera
Student Number: CB002688
Email Address: perera_ranga@yahoo.com
Award Name: MSc in Technology Management
Site Name: APIIT Sri Lanka
Title of Project: DEVELOPMENT OF A CONSUMER TECHNOLOGY
ADOPTION MODEL FOR MOBILE DATA SERVICES WITH UTILITARIAN
AND HEDONIC VALUE PROPOSITIONS
Supervisor: Professor Kennedy Gunawardena
External supervisor: Associate Professor Ms. Geetha Kanaparan
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Abstract
This research investigates the adoption of two mobile data services with utilitarian and
hedonic value propositions in Sri Lanka. The proposed Technology adoption model has
been built based on empirical academic research into consumer motives of cognition,
hedonics, social influences and studies into consumer behavior attitude and intension.
The model attempts to explain 40%-54% of the Sri Lankan consumer behavior in the
context of selected mobile data services. There are a number of important findings from
this research, including identification of key determinants of technology adoption in
mobile data service, the importance of variables such as perceived usefulness, perceived
ease of use and comparative advantage in the adoption decisions. The research further
explores the relationship of hedonic motives and their influence of attitude towards
adoption and adoption intension.
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Acknowledgement
The writing of this thesis has been one of the most significant academic challenges I have had to
face. Without the patience, support and guidance of the following people I would have not been
able to complete this journey.
Professor Kennedy Gunawardena who undertook to supervise my Dissertation with short notice
in June. Had it not been for the wisdom, knowledge and commitment of Professor Kennedy, I
doubt that I would have been able to present this dissertation. In June when Professor took the
supervision of my research, I was lost and confused. His knowledge and experience guided me,
inspired me and motivated me. I hope this research justifies the support and confidence you
extended to me.
Professor Gordon C. Bruner II from the Southern Illinois University, USA who was kind enough
to provide me research papers and advise on the Consumer Acceptance of Technology model,
which I used as the foundation of this dissertation. Professor Herbjørn Nysveen from Norwegian
School of Economics and Administration for research papers on Mobile Data Services.
The volunteers and provincial coordinators who helped in mammoth task of distributing and
collecting the questionnaires nationally. Thanks to your friendship and interest I was able to
undertake one of the first national surveys on mobile data services adoption and usage in Sri
Lanka.
To Eranga, Sepali, Nayomi, Bashini, Harshini, Chethani, Priyanwada, Janaki, Sadani, Harsha and
Eureka who entered the 450+ questionnaires painstakingly by working day and night, I am
humbled at your friendship and dedication. All the analysis in this research was possible due to
this effort.
To my friends Eranjan and Leshani who extended their valuable support and time to ensure that
this dissertation was a success.
And Finally to my Mom and brother
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Table of Contents
1. Introduction............................................................................................................... 11
1.2 Problem overview............................................................................................. 13
1.2.1 Problem statement..................................................................................... 13
1.2.2 Aims.......................................................................................................... 13
1.2.3 Objectives ................................................................................................. 13
1.3 Justification for selection of Mobile Data Services for research .................. 14
1.4 Significance of the study................................................................................... 17
1.4.1 Theoretical significance............................................................................ 17
1.4.2 Significance to other stakeholders ............................................................ 19
1.5 Scope and Limitations....................................................................................... 20
2. Background............................................................................................................... 21
2.1 Mobile telecommunication industry overview ................................................. 21
2.2 Mobile Technology Evolution.......................................................................... 24
2.3 Mobile Data Services........................................................................................ 27
3. Literature review....................................................................................................... 32
3.1 Overview of the selected research area............................................................. 32
3.2 Review of literature on research subject........................................................... 33
3.2.1 Motives – Utility vs Hedonics .......................................................................... 33
3.2.2 Technology adoption models and Mobile Data Services adoption .................. 35
3.3 Literature review on selected independent variables............................................ 41
3.3.1 Independent variable 1 - Perceived usefulness ......................................... 41
3.3.2 Independent variable 2 - Perceived ease of use ........................................ 42
3.3.3 Independent variable 3 - Relative advantage............................................ 43
3.3.4 Independent variable 4 - Pleasure............................................................. 44
3.3.5 Independent variable 5 - Arousal.............................................................. 44
3.3.6 Independent variable 6 - Dominance........................................................ 45
3.3.7 Independent variable 7 - Social Influences............................................... 46
3.3.8 Attitude and Intention ............................................................................... 47
3.3.9 Short Message Service – Mobile Data Service used to test the cognitive
utilitarian value proposition...................................................................................... 48
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3.3.10 Mobile Ringtone – Mobile Data Service used to test the hedonic value
proposition ................................................................................................................ 48
3.3.11 Utilitarian Motives.................................................................................... 50
3.3.12 Hedonic Motives....................................................................................... 51
4. Solution..................................................................................................................... 52
4.1 Solution overview............................................................................................. 52
Proposed model for mobile services adoption in Sri Lanka (Sri Lanka Consumer
Acceptance of Technology Model – SLCAT).............................................................. 53
4.2 List of developed hypothesis ............................................................................ 54
5. RESEARCH METHODOLOGY.............................................................................. 60
5.1 Research Philosophy......................................................................................... 61
5.2 Research Approach....................................................................................... 61
5.3 Research Strategy.............................................................................................. 62
5.4 Pilot study ......................................................................................................... 63
5.5 Time Horizon.................................................................................................... 63
5.6 Determining the Sample and Sample Size........................................................ 64
5.7 Questionnaire design – Likert scales used........................................................ 66
5.8 Treatment of data.............................................................................................. 67
6. Deliverable................................................................................................................ 68
6.1 Descriptive Analysis ......................................................................................... 68
6.1.2 Respondents by Gender ............................................................................ 68
6.1.3 Respondents by Age ................................................................................. 69
6.1.4 Respondents by Province of residence ..................................................... 70
6.1.5 Respondents by Education level............................................................... 72
6.1.6 Respondents by Employment status ......................................................... 73
6.1.7 Respondents by monthly income level ..................................................... 74
6.1.8 Mobile Data Services Awareness ............................................................. 76
6.2 Statistical analysis of data................................................................................. 77
6.2.1 Utilitarian model testing using SMS......................................................... 78
6.2.2 Hedonic model testing using Mobile Ring tone ....................................... 78
6.3 Hypothesis Testing............................................................................................ 79
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6.4 Simple liner model building.............................................................................. 89
6.5 Model building.................................................................................................. 95
6.5.1 Utilitarian Product of SMS ....................................................................... 95
6.5.2 Attitude towards adoption......................................................................... 95
6.5.3 Intension to adopt...................................................................................... 98
6.5.4 Hedonic Product of Mobile Ringtone..................................................... 101
6.6 Data Analysis Summary ................................................................................. 104
6.6.1 Utilitarian product – SMS adoption model testing ................................. 104
6.6.2 Hedonic product – Mobile Ringtone adoption model testing................. 114
7. Discussion............................................................................................................... 122
8. Recommendations................................................................................................... 134
10. Future researchReferences.................................................................................. 144
10. References........................................................................................................... 145
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List of Tables
Table 1: Mobile Telephony systems................................................................................. 26
Table 2: Mobile Data Services classification.................................................................... 29
Table 3: Summary of Litreture review - Utilitarian motives............................................ 50
Table 4: Summary of literature review - Hedonic motives............................................... 51
Table 5: : Literature review summary - Attitude and intension........................................ 51
Table 6: Literature review summary - MDS with utilitarian and hedonic propositions... 51
Table 7: Hypothesis for utilitarian motives in SMS ......................................................... 54
Table 8: Hypothesis for hedonic motives in SMS ............................................................ 55
Table 9: Hypothesis of social influences in SMS............................................................. 56
Table 10: Hypothesis attitude and intension in SMS........................................................ 56
Table 11: Hypothesis for utilitarian motives - M-Ringtone.............................................. 57
Table 12: Hypothesis for hedonic motives in M-Ringtones............................................. 58
Table 13: Hypothesis for Social influences - M-Ringtones.............................................. 58
Table 14: Hypothesis Attitude and intesion - M-Ringtone............................................... 59
Table 15: Questionnaire distribution ................................................................................ 64
Table 16: Respondents by Gender.................................................................................... 68
Table 17 : Respondents by Age ........................................................................................ 69
Table 18: Respondents by Province of residence ............................................................. 71
Table 19: Respondents by Education level....................................................................... 72
Table 20: Respondents by Employment status................................................................. 73
Table 21: Respondents by monthly income level............................................................. 74
Table 22: Colour Display vs Black/White display ........................................................... 75
Table 23: Mobile Data Services Awareness ..................................................................... 76
Table 24: Test values for internal consistency – SMS...................................................... 78
Table 25: Test values for internal consistency - M-Ringtones ......................................... 78
Table 26: Correlation Matrix for SMS.............................................................................. 79
Table 27: Utilitarian model testing using SMS................................................................. 82
Table 28: List of accepted hypothesis (alternative) – Utilitarian product ........................ 83
Table 29: List of Accepted Null Hypothesis..................................................................... 83
Table 30: Correlation Matrix for hedonic motives ........................................................... 84
Table 31: Hypothesis testing for Hedonic model ............................................................. 87
Table 32: List of accepted hypothesis – Hedonic Product................................................ 88
Table 33: Simple liner model building – SMS ................................................................. 91
Table 34: Simple liner model building - Mobile Ringtones ............................................. 94
Table 35: Variable ranking based on correlation to Attitude towards adoption............... 95
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List of Figures
Figure 1: World mobile subscribers.................................................................................. 22
Figure 2: Cellular subscriber growth rate in Sri Lanka..................................................... 23
Figure 3: Evolution of GSM Technologies....................................................................... 25
Figure 4............................................................................................................................. 28
Figure 5: Proposed classification of MDS........................................................................ 29
Figure 6: Techno-centric MDS classification................................................................... 30
Figure 7: Four tiered MDS classification.......................................................................... 31
Figure 8: Classification of consumer value ...................................................................... 34
Figure 9: Proposed model for mobile services adoption in Sri Lanka.............................. 53
Figure 10: Research Onion (Saunders et al, 2007a) ......................................................... 60
Figure 11: Respondents by Gender................................................................................... 69
Figure 12: Respondents by Age........................................................................................ 70
Figure 13: Respondents by Province of residence............................................................ 71
Figure 14: Respondents by Education level...................................................................... 72
Figure 15: Respondents by Employment status................................................................ 73
Figure 16: Respondents by monthly income level............................................................ 74
Figure 17: Colour Display vs Black/White display 75
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1. Introduction
Mobile technology has taken rapid strides in its diffusion across the global. These
quantum leaps in penetration are not only global phenomenon but one also experienced in
the local context of Sri Lanka. In 1992 Sri Lanka had 2,644 mobile phone subscribers.
Today 17 years later the number stands at 11 million (TRC-SL 2008). While mobile
penetration rates are impressive, with 50%-60% average annual growth rates experienced
in Sri Lanka, the strategic prospects of the mobile telecommunication industry are up for
discussion. What comes after you have sold every one a mobile phone?. Signs are
ominous. Across the globe the average revenue per unit (ARPU) are significantly
depreciating (ABI Research 2009; Mälarstig et al. 2007). These issues are compounded
with increase competitive structures and global market competition. The industry seized
on an emerged opportunity in the early 1990 with a new application called Short
Messaging Service. The mobile phone and its use were viewed in a different light than a
simple communication device, rather the gateway to a plethora mobile data services. The
industry spent the next decade investing in high bandwidth, high capacity and new
mobile data services product lines, awaiting the next killer application (C. Carlsson et al.
2005b). However, today after spending billions of dollars into 3G licenses and
sophisticated new services such as MMS, Mobile Internet, Mobile Banking, the “next
killer application” is yet to emerge. SMS still remains the most popular mobile data
service in all markets including the USA(Nielsen Research 2008) and European markets
such as Finland (C. Carlsson et al. 2005b) and Norway(Nysveen et al. 2005b). While
academics and industry in developed countries have focused on studying mobile data
services with new vigor, in developing Countries like Sri Lanka, industry and regulators
seem to be unaware of these global trends and threats.
The aim of this research is analyze the key variables involved in understanding and
predicting consumer behavior of technology adoption. Through this analsys, it is
expected that a behavioral model can be produced which can be identify scientifically the
relationships between the drivers of consumer attitude to adopt and intension to adopt
mobile data services. While there are models researched and developed in countries like
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Finland (C. Carlsson et al. 2006), Norway (Pedersen et al. 2002), Korea (B. Kim et al.
2009) and USA, development of an indigenous technology adoption model is essential in
the context of Sri Lanka because of the different socio-economic cultural paradigms.
Further due to the regional similarities in South East Asia, the inter-portability of this
model may help diffusion of mobile data services in similar regional countries.
To undertake this study we recommend identifying key research into information
technology adoption including empirically tested models such as the Technology
Adoption Model (Davis 1989; Davis et al. 1989) and diffusion of innovation models
(Rogers 2005). Further as this model involves operations within the consumer context, it
is proposed that research into better understanding the variables that influence the attitude
towards adoption and intension to adopt be researched. Further, the recent research done
on developing a unified theory for technology adoption (Kulviwat et al. 2007; Kulviwat
et al. 2008; Nasco et al. 2008) provides an important starting point. Therefore it was
decided that the study would focus on the logical motives and hedonic motives of ‘Fun
and entertainment’. While motives guide the decision, what nature of value propositions
influence these motives. The second focus of the research would be on value propositions
and their interrelation to technology adoption.
Based on this analysis it was decided that the research would study two mobile data
services products. One which has primarily a cognitive utilitarian value proposition and
another that has primarily a hedonic value proposition. This research would then enable a
better understanding of the behavior of the model in these different context. The balance
of this document will relate to the building of the proposed model based on empirical
research and testing of the model in the context the Sri Lankan consumer through a
market survey. It is expected that this research path would enable the achievement of this
ultimate objective.
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Problem overview
Problem statement
The mobile data services adoption in Sri Lanka remains at a very low rate in comparison
to the penetration of mobile phone technology which is estimated to be at 55% (TRC-SL
2008). Research indicates that the future revenues of mobile telecommunication industry
will depend on the provision of mobile data services rather than on voice calls (Kunin et
al. 2005; C. Carlsson et al. 2005b). The dramatic drops in average revenue per user on
voice calls across the globe are an indication of future trends (ABI Research 2009).
Further in most matured telecommunication markets, where mobile penetration has
exceeded 80% reach of the general population, the industry was compelled to look for
more viable sources of revenue other than voice and new subscriber connection fees
(Mälarstig et al. 2007). While the strategic response of the mobile industry was to invest
in expensive 3G technology, the global adoption rates of mobile services that use this
platform remains very low.
Aims
To proposition an analytical model that identifies the key attitudinal influences involved
in the adoption of selected Mobile Data Services in the Sri Lankan market context. This
model could be used by the Telecommunication industry and Mobile Data Services
application vendors to identify key consumer relationship variables that influence the
adoption and diffusion of their products and services.
Objectives
– To analyze the nature and behavior of existing relationships between cognitive
utilitarian motives, hedonic motives, social influences and their impact on the
consumers attitude and intension to adopt key Mobile Data Services in Sri Lanka
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– To develop a statistical model that analyses the influence of cognitive utilitarian
motives, hedonic motives and social influences to predict the adoption attitude
and intension to adopt the selected Mobile Data Service of Short Message Service
(SMS) which has a dominant utilitarian value proposition in Sri Lanka
– To develop a statistical model that analyses the influence of cognitive utilitarian
motives, hedonic motives and social influences to predict the adoption attitude
and intension to adopt the selected Mobile Data Service of Mobile Ringtone
which has a dominant hedonic value proposition in Sri Lanka
– To analyze the determinant factors that influence the adoption of Mobile Data
Services based on the developed statistical analysis models for utilitarian and
hedonic products
Justification for selection of Mobile Data Services for research
The mobile telecommunication industry has invested billions of dollars in improve the
network bandwidth capacity, mobile phone capacity and overall infrastructure to support
the expanded usage of mobile devices beyond being simple communication devices (.
The anticipation and excitement was that the introduction of 3G would provide mobile
telephone subscribers access to a vast array of mobile data services. However actual
adoption of Mobile Data Services across global markets remain consistently low. On
commenting on this low rate of adoption of Mobile Data Services, states “Our results are
consistent with previous research. Mobile services still have much less users than
envisioned and their usefulness is being questioned by consumers”. While the low
adoption rates of mobile data services are symptoms of consumer perceptions, (Gilbert &
Kendall 2003) outline the need to change the behavioral patterns of consumers to ensure
viable adoption and usage. They state that “MDS are a current example of technology
enabled discontinuous innovation, similar from the economic and behavioral perspectives
to the Internet. Such innovations will succeed only if adopted by a critical mass”. The
researchers highlight the critical need for creating new value and new behavior patterns
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to ensure sustainable usage of these innovative products. Elaborating on the behavior,
they state that “..such behaviors include acquiring the enabling technology, learning to
use it, applying it to solve problems or adding value in everyday life, and communicating
what one has learned about it to others”. However industry and academia have been only
starting to recognize the need for identify and build viable and empirically tested
consumer adoption models to enhance the overall adoption of the technology.
The lack of research into mobile data services has been a key issue identified by many
researchers. Umino (2004) in a report on OECD countries notes that there is a general
lack of research into the area of Mobile Data Services both by Government and Industry.
The researcher states “Often mobile data is not yet presented separately from aggregate
data. Industry or government sponsored studies focus only on certain markets or
technologies and definitional constraints make it difficult to compare data across studies.
Further research in this area is worth undertaking”. This lack of research and focus may
be stemming from the industries original concern about new connections. Carlsson et al
(2005) commenting on the lack of industry focus on mobile data services comments
“Gartner Inc. in a recent report still focus on the handset market…. It is strange that not
much is reported on the development of mobile services..”. Thus the evolving nature of
the mobile telecommunication industry, which at its inception presented a value
proposition of a simple communication solution, to todays’ mobile data services, which
are value added services may be key reasons for these gaps in research. While the reasons
behind the lack of research may be varied, this lack of understanding of the mobile data
services phenomena has presented the industry with a major challenge. This challenge
has presented itself as an additional risk into the investments towards mobile data
services in general. Carlsson et al. (2006) observers on this increased risk that has
inherited into the Mobile Data Services market as “Year after year the mobile service
market(s) produce(s) new services and applications that due to complexity or lack of
relevance fail to meet the consumers’ expectations”. Therefore the need for research into
understanding the consumer and technology application has emerged as an important of
the overall mix in the product development cycle of mobile data services.
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Therefore there is a clear need to understand the consumers adoption and usage
preferences towards mobile data service. A number of researchers have explored the
applicability of different psychological models within the context of mobile data services.
These notable researchers include, but are not limited to, Carlsson et al. (2006) in the
Finnish mobile services market, Pedersen et al. (2008) in the Norwegian market, Bina et
al. (2007) in the Greek market. However, when exploring the existing research, it is clear
that research are presented within predominantly from Western and European countries
which have mobile penetration rates exceeding 80%.
There are only few research that has been done to analyze the consumers of South East
Asia including consumers within Sri Lanka, India, Bangaladesh and other East Asian
emerging economies. Commenting on the lack of research into developing countries Gao
& Rafiq (2009) state that “We lack knowledge about the characteristics of mobile
telecommunications transformation in developing countries, and the social and
technological factors that impact this process”. In their litreture review covering a period
of 5 years between 2003 and 2008 they have found eight published articles on mobile
telecommunication industry in developing countries. However the critical features of this
analysis are that these literatures have been prepared based on secondary research and not
primary research.
The huge value of investments made into building 3G+ networks and the ubiquitous (ref)
nature of mobile phone technology presents both a threat and opportunity for a country
like Sri Lanka. Emerging from a three decade old conflict situation, the mobile
technology has a huge potential of enriching and thrusting the rural agrarian economy of
the island rapidly into the 21st
century. Mobile Internet, Video calls and other ranges of
mobile data services through 3G + networks would provide the stimulus and hope to our
country. However, understanding the intentions and barriers of the Sri Lankan consumer
in the adoption of this innovative technology is crucial for the penetration of mobile data
services products and services in Sri Lanka. It is based on these reasons that this research
area was selected.
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Significance of the study
Theoretical significance
The research conducted under this project seeks to contribute towards a number academic
interest areas.
Significance of the market data for academic study
The research into adoption of mobile data services is considered by many researchers as a
key gap area in the existing knowledge. Further majority of the available research has
been undertaken in developed markets such as in the USA, Europe and developed
economies in Asia such as Japan and Korea. Therefore this research into the use,
adoption and adoption intension of mobile data services in a country such as Sri Lanka
will be beneficial to understanding the attitudes of a population which has unique
demographic and psychographic characteristics. These characteristics include the high
literacy rate of 96% (G15 2008), the rising Gross Domestic Income of over US$ 1200
which has risen by US$ 150 within the last 3 years, the low computer penetration rate of
and internet penetration of 2% (G15 2008). These characteristics combined with the
estimated mobile phone penetration rate of 54%(TRC-SL 2008) makes this research in to
the study of mobile data services an important and long-term significant study.
The study cover 6 of the 9 provinces and can be used for province wise analysis.
There is currently no available data for academic analysis of the handset types and
capabilities used by the Sri Lankan consumer. This information is particularly important
because the capacity of the mobile phones carried by the consumers in Sri Lanka should
influence decisions on the types of Mobile Data Services that can be promoted in the
island. Further this information should provide a valuable decision and strategic options
consideration tool for mobile telecommunications companies, on whether their current
strategy of not getting involved in the handset market is compatible with their network
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investment strategy. In order to successfully launch mobile data services that are accepted
by the consumers, the handsets they use have a major influence on the decision making
process. Therefore it is envisioned that this research would initiate a dialog on this issue.
The research focuses on understanding the existing market share of the five
telecommunication services providers of Sri Lanka.
It studies the switching habits of consumers in terms of change of mobile
telecommunications providers, reasons and switching time frame. The cross referencing
of this information with demographic information of users should provide information
vulnerable market segments that are likely to shift to other telecommunications providers.
The research investigates the current usage of native language features in mobile phones
users. This research information would provide information on the popularity and actual
usage of native language features.
The research focuses on consumer awareness of selected mobile data serives, one time
usage and regular usage and the consumers attitude towards the future adoption of the
services.
Significance of the research proposition and hypothesis testing
The research proposition was built on key Information Systems theories of Technology
Adoption Model, Diffusion of Innovation model and PAD model. The significance of
each of these theories towards the adoption intention in the context of the population of
Sri Lanka will be tested through this research.
The applicability of the Consumer Acceptance of Technology model has not been tested
in a wide national study prior to this research. This would be the first occasion the
propositions applicability is tested within a unique market such as Sri Lanka.
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Significance to other stakeholders
 Provide the government and regulators insights into the importance of promoting
mobile data services through policy frameworks based on the key influences
identified through this research
 Provide the mobile telecommunication industry a better understanding of the
influences of motives and attitude towards adoption of utilitarian and hedonic
value propositioned mobile data services.
 Provide software and related technology developers of mobile data services
applications a model to test their product prototypes prior to expensive releases to
market.
 Help in influencing the technology infrastructure investments done by the
government as a part of developing the information communication technology
infrastructure of urban and rural Sri Lanka.
 Provide greater insights to brand and marketing managers in investing their
marketing budgets and understanding of societal influences on adoption.
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Scope and Limitations
 The research is undertaken within the geographical boundries of Sri Lanka and
may be unique in its findings
 The consumers surveyed were primarily from urban and rural areas of Western,
North Western, Southern and Central Provinces
 Consumers from other provinces including Northern, Eastern, Uva and North
Central province have not contributed
 Due to the small sample size cannot provide analysis at provincial level
implications of the model
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2. Background
2.1 Mobile telecommunication industry overview
The global penetration of mobile phones reached a new height by the end of 2008 when
the International Telecommunication Union (ITU) declared that it estimates the global
telecommunication subscriber base to be 4 billion ITU (2008). This estimated figure is an
increase of over 1 billion mobile subscribers within a period of one year (ITU, 2007). In
late 2007 the global mobile subscriber base was estimated to be around 3 billion
subscribers and was equivalent to 50% of the global population. The year-on-year
average growth of the global mobile telecommunication subscription between the years
2000 to 2008 has been at an average of 24%. While these figures would indicate that the
global penetration of mobile phones are at 61% and that on average every other person
should have a mobile phone, the information needs to be qualified. Its is noted that the
figures represent subscriptions and not actual persons, an individual may have multiple
subscriptions and mobile phone operators methods of counting the prepaid and post paid
consumer may create duplication. Noting this point, it is estimated that over 30 countries,
predominantly in Europe have mobile penetration rates exceeding their country
populations, the highest being Italy at the rate of 151 subscribers for a population of 100
in 2009(ITU, 2009). While these qualifications are valid, Ratan et al. (2007) in their
research of the Bangladesh mobile market note that through ‘Village Phone Program’
each village is provided with a single mobile phone which is shared between a number of
persons.
These impressive mobile phone penetration figures combined with the analysis by ITU
(2009) that Mobile subscriptions accounted for 61% of the total communication
subscriptions, while standard phone line subscriptions were at a low 26% solidifies the
future importance of mobile technology. Further compounding this trend is the increase
in the average usage minutes of mobile phones. The ITU (2009) analysis of average
minute usage suggests that the number of minutes spent by subscribers on mobile phones
are rising while the usage minutes of fixed phones are reducing. Another important
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observation in this analysis is that users of fixed phone lines are spending an increased
number of minutes communicating with mobile phone subscribers. Other important
global trends are in the dramatic reduction in prices of mobile calls. The estimates
indicate that there is an average reduction of over 20% in call charges associated with
mobile phones.
Figure 1: World mobile subscribers
Extracted from ITU (2008)
While the global penetration rates of mobile phones are impressive, these figures are
sustained primarily through the four BRIC countries of Brazil, Russia, India and China.
Based on estimates the total subscription rates of these economies have an estimated 1.3
billion subscription. ie. One third of the world mobile phone subscribers. While China
has over 600 million subscribers, the Indian subscriber rate is estimated to be 296 million.
This represents a very low penetration rate of 20% in comparison with BRIC countries
and regional countries such as Sri Lanka which has an estimated penetration of 55% by
2008. However, these figures indicate that the mobile penetration and growth will remain
healthy over the next few years in the region.
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Figure 2: Cellular subscriber growth rate in Sri Lanka
Extracted from TRC-SL (2008)
In Sri Lanka the mobile phone penetration rate has been at a dramatic pace and has
mimicked the global trends closely. In terms of the supply side there are five mobile
telecommunication companies with one new entrant Airtel coming to the market in early
2009 (TRC-SL, 2008). Between the year 2007, where the mobile phone subscribers were
estimated at 7.9 million and 2008 where the figure rose to 11 million, the annual
increment year-on-year has been has been 39%. With the country emerging from a three
decade old conflict situation, Sri Lanka would most likely see the mobile penetration
rates reaching over 80% from the existing rate of 55% of the population within the next 3
years. In comparison to these mobile phone penetration figures the fixed access phone
connection has grown by 20% in 2008 to a figure of 3.4 million phones. It is in the year
2001/2002 that the mobile phone connectivity rate surpassed that of mobile phones in Sri
Lanka. An interesting statistic published is the number of pager connections in the island
which stood at over 10,000 in the year 1996 has seen a complete decline by 2005. While
published research is not available, this may be due to popularity of SMS services.
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While Sri Lanka macro economic indicators such as Gross Domestic production (average
rate of 5% to 6% REF) and Gross Income (US$) have see improvements over the last few
years, its internet penetration rate remains at very low level of 2%. Further there is no
available information on Mobile Data Services usage and related trends.
2.2 Mobile Technology Evolution
In order to thoroughly appreciate the current mobile industry issues, risks and the
implications of Mobile Data Services, it is crucial to understand the underlying
technology, reasons for technological evolution, the technology evolutionary path, factors
that pushed and pulled the evolution, the current point and future evolutionary path.
The second generation of mobile phones also known as 2G started appearing in the early
1990s. Kunin et al. (2005) states that “Most 2G standards are based on circuit-switched
technology, and they have provided the mobile telecommunications industry with an
exponential growth in terms of the numbers of subscribers as well as new types of
services”. Among the most successful technology variants of the 2G included technology
standards such as CDMA (Code Division Multiple Access), TDMA (Time Division
Multiple Access), Global System for Mobile (GSM). The CDMA technology is a digital
wireless technology that has the capability to provide simultaneous access for subscribers
to share radio frequency. The researcher describing some of the distinguishing features of
CDMA “a voice or data call is assigned a unique code that distinguishes it from others
and all of the signals hop and spread over a shared frequency band”. Kunin et al. (2005)
states that as of 2004 CDMA based mobile telecommunication systems were operational
across 63 countries and services an estimated 200 million users. Originally known as the
IS-54 standard, TDMA technical platform has the capability of delivering as much as six
times more information using the same bandwidth than the first generation analog
technology. It is estimated that the TDMA technology which was simultaneously
developed and implemented with CDMA technology serves approximately 113 million
subscribers. The GSM technology is considered the most widely adopted platform in the
2G family. It uses a combination of Frequency Division Multiple Access (FDMA) and
24

Time Division Multiple Access (TDMA). This technology has the capacity to deliver
over eight calls over a single channel.
These underlying technologies supported the deployment of a range of value added
services other than voice. Carlsson et al. (2005) in their analysis of the evolution of
mobile applications identifies that SMS which was available with GSM platform since
the early 1990s started to become unexpectedly popular by 1995. Mobile based internet
browsing services was enabled by 1999 through the deployment of Wireless Application
Protocol (WAP) over the GSM networks. While WAP was introduced aimed at linking
the internet with mobile devices its performance and willingness by subscribers to adopt
the technology was poor (Teo & Pok, 2003).
Figure 3: Evolution of GSM Technologies
Extracted from Carlsson et al. (2005)
Continuous technology upgrades to the 2G platform continued since its introduction.
These technology upgrades, which positioned between the 2G (GSM) standard and 3G
(UMTS), included enhancements to GSM in the form of General Packet Radio Service
(GPRS) are noted as 2.5G (Carlsson et al. (2005). GPRS is considered a pivitol
technology enhancement as it introduced the concept of “always-on” capability (Kunin et
al. (2005), which mean that users only had to pay for actual downloads instead of
connectivity. Further the use of packet based data transfer meant that the cost of
operating the service was much cheaper than circuit switched networks (Carlsson et al,
2005). OECD commenting on 2.5G technology platform states that “Many operators are
deploying services with these technologies instead of waiting for 3G since they are
capable of delivering many of the 3G services with higher speeds than 2G.”
25

Table 1: Mobile Telephony systems
Extracted from Kunin et al. (2005)
Kunin et al. (2005) commenting on 3G mobile technology states that “is a generic term
for a set of mobile telephony technologies using a set of high-tech infrastructure
networks, handsets, base stations, switches and other equipment to allow high-speed
Internet access, broadband audio-visual services, and voice and data communications”.
While the 3G technology has a wide bandwidth between 128Kbps to 2 Mbps, the
technology has demonstrated much faster speeds. Among the key distinguishing features
of 3G technology is the wider bandwidth that enables the usages of rich mobile data
services applications such as video calls, mobile internet, high quality audio and visual
services delivery to consumers.
Beyond the 3G technology lies the 4G IP based technology, with an estimated speed 10
time more than that of 3G, with the capability of handling “volatile traffic patterns such
as multiple transmissions of multimedia messages from camera phones” (OECD 2005).
26

2.3 Mobile Data Services
The global Revenue derived from Mobile Data Services have exceeded US$ 200 billion
in the year 2008. This rise in income is an increase of over US$ 43 billion from the
previous year, an estimated increase of over 22% (Cellular-news, 2008). These revenue
figures represent approximately 20% of the total revenue earned by telecommunications
providers. The Filipino telecom provider Smart Communications recorded 50% of their
total earnings from Mobile Data Services. These revenue trends indicate the important
role that Mobile Data Services will play in the future telecom market. While Short
Messaging Services were the initial driver of growth, the industry has been searching for
new “killer applications” which leverage the network capacities setup through the
institution of 3G technology (C. Carlsson et al. 2005). It is therefore anticipated that the
Mobile Data Services would be the driver of growth in the telecommunication industries
where mobile penetration has achieved saturation level.
Bina et al. (2007) in defining Mobile Data Services states that “encompass all non-voice
value-adding services accessible through mobile networks that are designated to augment
end-user experience with mobility and enrich mobile business models for operators,
service providers and other industry constituents”. While this is a general all
encompassing definition, researchers have sought to better define and understand Mobile
Data Services through consistent study. Kunin et al. (2005) in their early study of Mobile
Data Services sought to categorize them into communications, transactional and content
based services. While this classification attempts to identify the Mobile Data Services
from a technology perspective, it lacks the detailed classifications and categorizations
necessary for detailed study of the products and services. Further the classification by the
researcher is based on technology criteria and not a consumer centric perspective. This
classification lacks the depth of analysis and attempts to basket all mobile data services
into one group. However, for the development and positioning of Mobile Data Services it
is crucial that better understanding and analysis of the portfolio be undertaken.
27

Figure 4
Extracted from Kunin et al. (2005)
Carlsson et al. (2005) in their analysis of Mobile Data Services in the Finnish market
have rarely attempted to define Mobile Data Services. Rather their focus has been on the
adoption of the technology and therefore the Mobile Services they have used have been
categorized in a more practical classification. Namely, the MDS have been classified into
Communication, Entertainment, Reservation and purchases, and Information. Into these
four major classifications of the services, they have incorporated a total of six-teen (16)
services. However, the problems associated with the definition of mobile data services
could be highlighted through such classification. Under Communication product ranges
the researcher has included SMS services which are primarily interpersonal in nature.
However, Bina et al. (2007) in their definition of MDS specifically state that “all services
afforded through a mobile network except for voice communication and interpersonal
SMS exchanges”. While the researchers have not commented further on this exception, it
is clear from the analysis that they view MDS in the context of business value creation.
However, not withstanding this interpretation by the researcher, SMS is considered to be
the most popular MDS and the foundation of todays’ recognition and pursuit of killer
applicationsCarlsson et al. (2005a). Further commenting on the popularity of the MDS
products in the United States, Nielsen Research (2008) identified that 53% of the US
consumers were using SMS services as oppose to the next most popular MDS which was
MMS has a subscriber base of 26%. In research done in the Finnish mobile market where
penetration rates have exceeded 80%, over 92% of mobile users regularly use SMS
28

Carlsson et al. (2006) Therefore the exclusion done by Bina et al. (2007) points to the
need to study the context and spatiality of MDS.
Table 2: Mobile Data Services classification
Extracted from Gilbert & Han (2004)
A more comprehensive analysis matrix of MDS was presented by Pedersen et al. (2002)
in their analysis of the Norwegian MDS consumer. The matrix attempted to classify MDS
based on the perceived motives and technology characteristics of MDS. The technology
characteristics used by the researchers are communication and transaction. These
dimensions of MDS are cross matched with purpose of usage, where the researchers
introduce the motives of entertainment and utility. This classification is considered by
many researchers as one of the most important cross combinations used in the analysis of
MDS (Nysveen et al., 2005).
Figure 5: Proposed classification of MDS
Extracted from Pedersen et al. (2002)
29

In contrast to the Communication Vs Transaction and Utilitarian Vs Entertainment
classifications of MDS of Pedersen et al. (2002), Verkasalo (2006) seeks to classify
services based on a technology based classification. He uses the continuums of
Communication Vs Content and Interactive vs Background traffic dimensionality.
However, this classification is also primarily a technology centric analysis of these
portfolios of MDS. Beyond this classification of MDS Verkasalo (2006) presents a more
detailed classification of MDS operating on symbion operating systems. Here the main
categories for the classification of MDS include Browsing, Config, Games, Infotainment,
Messaging, Multimedia, Personal Information Management, Productivity, Unknown and
Utility. While these classification relate to applications available in mobile phones, the
products included as part of the analysis relate to MDS. The researchers definition of
MDS as “mobile services which are based on the IP architecture” confirms the
concentration on technology in stead of consumer perspective or service delivery
perspectives.
Figure 6: Techno-centric MDS classification
Extracted from Verkasalo (2006)
30

However one of the most comprehensive analysis and classifications of MDS was
presented by Heinonen and Pura (2006). Their complex analysis of MDS attempts to
classify MDS based on type of “consumption, the context, the social setting and
relationship”. Unlike the classifications of MDS by Verkasalo (2006) which was
primarily technology centric, the researchers attempt view MDS from a consumer service
context. In their criticism of the existing literature on MDS classification, they point-out
that no significant effort has been undertaken to study the classifications of MDS, rather
the existing literature have been produced as a part of a specific aspect of study of the
MDS in terms of intension to use, segmentations, sociability etc.
Figure 7: Four tiered MDS classification
Extracted from Heinonen and Pura (2006)
31

3. Literature review
3.1 Overview of the selected research area
Significant research and wide body of knowledge has developed over the past years on
the cogitation and ruminative research of Mobile Data Services adoption across the
globe, in relation to identified markets and on specific mobile data services context.
While the nature of research have been multifarious including industry researchers,
behavioral and social scientists contributing their perspectives, the key thrust area of the
research has remained focused on understanding the adoption of these range of
innovative mobile product and services by the consumer of mobile telephony. Primarily
two significant schools of thought have emerged as the benchmarks for these studies,
namely diffusion research (Rogers 2005) and adoption research (Davis 1989). However
when commenting on research paradigms, it should be noted that compelling research
have been also been undertaken on other promising research directions including (Bina et
al. 2007) on the Triandis (1980) model, the application of Uses and gratifications
research and domestication research by (Pedersen et al. 2002), fit-viability model
proposed by Tjan (2001) which combine the theory of technology and task fit within an
organization, Self-efficacy Theory (Bandura, 2001) were considered during the initial
phased of the LT review.
Excogitating the propositions of the above research, the Consumer Acceptance of
Technology (Kulviwat et al. 2007) distinguishes itself by attempting to build the model
by balancing the logical utilitarian elements of the adoption research (Davis 1989; Rogers
2005) with theories of emotion and affect (Mehrabian & Russell, 1974), to present a
unified theory on technology adoption. The application of this unified theory presents a
potentially powerful prediction and consumer explanation model. This chapter of the
literature review focuses on exploring the critical aspects of the conceptual model
propositioned by this dissertation through a through analysis of the key constructs. It is
hoped that this process would further validate the suitability of exploring the adoption of
32

Mobile Data Services based on the Consumer Acceptance of Technology theory and
indigenous industry specific variables.
3.2 Review of literature on research subject
3.2.1 Motives – Utility vs Hedonics
The motives of utility and hedonics formulate a significant composition of the
proposition hypnotized by this research into MDS. This section of the Literature Review
attempts to provide an analysis and definition to these terms.
Understanding the ‘perceived value’ or the benefits customers intend to derive by
acquiring a product or service has been one of the most researched areas in marketing
theory. The decision by Marketing Sciences Institute (2006) to earmark the definition of
‘value’ as a priority research area highlights the continuing and evolving importance of
the subject. Fernández & Bonillo (2007) in their review of research on the subject
observe that, ‘perceived value’ is a result of “interaction” between the customer and the
selected the product or service. Therefore understanding the motives that drive and
influence this interaction is essential in the context of any exchange between a customer
and the provisioning of products or services. On motives and the nature of ‘perceived
value’, the researchers indicate that it may be “...preferential, perceptual, and cognitive-
affective”. It should therefore be appreciated that utilitarian and hedonic motives are only
two key motives that are part of a large portfolio of possible motives that underline the
consumers buying decision. Fernández & Bonillo (2007) identify a large body of research
into ‘perceived value’ while categorizing them into uni-dimensional and multi-
dimensional approaches. While they differentiate between the uni-dimensional and multi-
dimensional approaches because the former propositions a single overall measure to
‘perceived value’, while the latter accepts that multiple components may be used to
define value. However a more pertinent observation between these two classifications is
the evolution of importance placed on utilitarian motives in the more classical uni-
dimensional research and the emerging emphasis of hedonics in multi-dimensional
33

research. It is indeed surprising to note this same evolution of emphasis on utilitarian
motives to hedonics in information systems theory. The once dominant theories such as
Technology Acceptance Model (Davis 1989; Davis et al. 1989) which proposition the
importance of utilitarian motives have now started to incorporate and accept hedonics as
boundary conditions (Heijden 2004; Venkatesh 2000) . Indeed as Ayyagari (2006) notes
on the problems raised due to key Information Systems research such as TAM not
incorporating hedonics, “this might undermine the cumulative results of TAM studies
over the past decade”. Therefore Information Systems researchers such as Kulviwat et al.
(2007) and MDS researchers (C. Carlsson et al. 2005a; C. Carlsson et al. 2005b; C.
Carlsson et al. 2006; Bina et al. 2007; Heinonen & Pura 2006; Nysveen et al. 2005b)
have continued to incorporate hedonics to improve the prediction capabilities of their
research constructs.
Figure 8: Classification of consumer value
Extracted from Fernández & Bonillo (2007)
When considering the different motives that influence consumer decisions, the research
undertaken by Sheth et al. (1991) on consumer value, which has been classified by
34

Fernández & Bonillo (2007) as multi-dimensional, identifies five key ‘values’ that
influence the choice of consumers. Namely, functional, conditional, social, emotional and
epistemic values. It is however important to note that the researcher defines functional
value by stating “ ..functional, utilitarian and physical performance”, this statement
underpins the utilitarian motive selected as a part of the MDS research. Further in
defining emotional value, the researcher states “..arouse feeling or affective state”. It
should be noted that hedonic motives are also known as affect and are part of this
research into MDS. It is therefore necessary to appreciate that the two motives of utility
and hedonics considered as part of this research have significant and empirical theoretical
bases.
Fernández & Bonillo (2007) in defining utilitarian value based on Babin et al. (1994)
research as “instrumental, task-related, rational, functional, cognitive, and a means to an
end” and hedonic value as “reflecting the entertainment and emotional worth of shopping
non-instrumental, experiential, and affective”. hedonic value derived from the usage of a
product or service could be identified with fun or entertainment motive. Bina et al. (2007)
in defining affect “the feelings of joy, elation, or pleasure, or depression, disgust,
displeasure, or hate associated by an individual with a particular act”.
3.2.2 Technology adoption models and Mobile Data Services adoption
The Technology Adoption Model (Davis 1989) is one of the most widely used models in
explaining user adoption behavior in relation to innovative technologies especially within
the context of mandatory settings (Pedersen et al. 2008). Technology Acceptance Model
(TAM) proposed by Davis (1989) conjectured that an individuals cognitive behavioral
intent to adopt a given technology is influenced by two main constructs of perceptions,
namely, perceived usefulness and perceived ease of use. TAM further postulates the
significance of behavioral intension on the attitude of the individual towards adoption.
Davis (1989) defines perceived usefulness as “the degree to which a person believes that
using a particular system would enhance his or her job performance” and perceived ease
35

of use as “the degree to which a person believes that using a particular system would be
free of effort”. The significance of these definitions buttress on the individuals
perceptions and not if the system in consideration is actually useful or easy to use.
Although the original postulation of TAM has been used to research and explain users
intention to use in organizational or mandatory context, Davis et al. (1989) describe the
universal adoptability of the TAM variable in computer and information systems by
users. However, the emphasis of cognitive process and its application within mandatory
settings has meant that researchers (Pedersen et al. 2002) have concluded that TAM is a
utilitarian theory on adoption of technology.
The original construct of TAM is primarily based on Theory of Reasoned Action (TRA)
proposed by Fishbein and Ajzen (1975). The application of TRA is general in comparison
to TAM, and focuses on explaining conscious behavior (Davis et al., 1989). Out of the
four variables identified in the TRA model, namely, Attitude towards behavior
(influenced by Beliefs and Evaluations), Subjective Norms (influenced by Normative
Beliefs and Motivations to comply), Behavioral Intension and actual behavior, TAM
focuses on the variables of Attitude toward use and Behavioral Intension. Taylor & Todd
(1995) in their evaluation of this proposition of Davis et al. (1989) suggest that TAM is a
special case of TRA in its application within technology adoption context. While Davis et
al. (1989) invited research into the investigation of influences of social influences, the
exclusion of this variable in the TAM due to lack of evidence of influence, remained a
point of vigorous discussion by researchers. Researches such as Mathieson (1991)
findings supported the assertions of Davis et al. (1989) on the exclusion of the subjective
norm variable within mandatory setting. However, recent studies by the original authors
and other researchers have significantly changed this proposition. Venkatesh & Davis
(2000) in their extension of the Technology Acceptance Model included the variable of
subjective norm. Further Researches such as Venkatesh & Morris (2000), Lucas & Spitler
(1999) have supported this inclusion of the social norms variable as their individual
research has identified strong influences between this variable and attitude towards
adoption within mandatory settings. It should also be noted that Theory of Planned
Behavior (TPB) is an extension to TRA by Ajzen (1991) and proposes the variable
36

“behavioral control” to explain instances where the individuals behavior is influenced by
internal and external constraints. This inclusion of behavioral control variable has
significantly improved the predictive power of TBP considering the fact that Behavioral
intension is explain as a weighted factor of intension to use and behavioral control
(Taylor & Todd 1995).
The original TAM theory has been extensively changed and modified to improve the
validity of its predicting capability. Venkatesh & Davis (2000) included subjective norms
as “peer pressure”, that influence the persons beliefs in using the IS. Venkatesh et al.
(2003) proposed Unified Theory of Acceptance and Use of Technology (UTAUT) claims
to explain over seventy percent of variance in intention of usage behavior in both
voluntary and non-voluntary settings. However, the application of the TAM theory within
mandatory and organizational setting has meant that TAM has been categorized as a
rational, cognitive theory (Pedersen et al. 2002). (Kulviwat et al. 2007) in their construct
of Consumer Acceptance of Technology model, have pointed out that in two research
undertaken by Davis et al. (1992) and (Riemenschneider et al. 2002), the construct of
affect has been deliberately excluded, as the researchers believed that the inclusion of
hedonic variable was inappropriate within organization settings. The consistent
exclusions of affect from the primary proposition of TAM and its various subsequent
flavors have meant that researchers seeking to understand consumer behavior, which is in
many regards voluntary, have supplemented the main TAM construct. Pedersen et al.
(2002) in their analysis of E-commerce and Mobile data services adoption have used
domestication research (Haddon, 2001)(as cited by Pedersen et al. (2002) and uses and
gratification research (Leung & Wei 2000), whereas, the Consumer Acceptance of
Technology theory has used TAM with the Pleasure, Arousal and Dominance theory of
Mehrabian & Russell (1974).
Kulviwat et al. (2007) et al contend in their analysis that theories such as diffusion of
innovation (Rogers, 1995) and TAM ((Davis et al.,1989)) in their application within
consumer adoption of innovations have not considered the impact of affect, rather depend
on cognition to fully explain behavior. Heijden (2004) and Venkatesh (2000) have
37

attempted to incorporate non-utilitarian aspects into TAM, their main problem has been
that they have been built on the cognitive model. Bina et al. (2007) criticize these
developments by pointing out that “they do not differentiate the affective from the cognitive
dimension and further assume that a person is located on an affective and cognitive bipolar
evaluative dimension”. Kulviwat et al. (2008) et al highlight the implications in identifying
the moderating influence of the nature of task the individual engaged in, whether it be
hedonic or utilitarian on the acceptance of technology. An individuals cognitive process will
be influenced by either utilitarian motives or hedonic based on the intension and experience
they may have derived prior to adopting the technology. Thus, the intension of individuals
may be equally influenced by hedonic and utilitarian motives. Therefore, in voluntary
settings the exclusion of either motive may not provide a strong construct of evaluating
consumer acceptance of technology. Kulviwat et al. (2008) in defining the utilitarian task
identifies that the task orientation primarily problem solving. This cognitive process
therefore influenced by logical, reason based approach. The need for including affect in
predicting the behavior of consumers was proposed by a number of theories such as the
Triandis (1980) and propositioned by Bina et al. (2007) in relation to Mobile Data
Services. In defining affect “the feelings of joy, elation, or pleasure, or depression,
disgust, displeasure, or hate associated by an individual with a particular act”. Bina et
al. (2007) uses the triandis theory to propose an alternative approach to analyzing the
adoption of mobile data services. Further leading researchers on mobile data services
such as Carlsson et al. (2005) use hedonic factors such as enjoyment to identify consumer
motives, while using TAM as the main construct of the research. Pedersen et al. (2002)
look to the hedonic variables of uses and gratification research to partially explain the
adoption of MDS.
Researchers on the adoption of mobile data services have been using a variety if variables
to construct the influence of hedonic variables on MDS. These variety of constructs to
monitor hedonics have not been limited to MDS but researchers in variety of fields such
as Electronic commerce, telecommunications etc. have been focusing on this regard.
Carlsson et al. (2005) uses two hedonic variables of “Enjoyment” and “new possibilities”
as the basis of evaluating potential user preferences for adoption of mobile data services.
38

Bina et al. (2007) in incorporating the hedonic variable assessment criteria identify “fun”,
“enjoyment”, “killing time” as the potential candidate emotions towards adoption. In
contrast to these simple approaches, Pedersen et al. (2002) incorporate the uses and
gratification research to correlate the hedonic variable with adoption. While gratification
research is capable of identifying a wide range of gratifications such that was identified
by Leung & Wei (2000) including “fun-seeking”, “entertainment”, fashion and status”,
both these research point to the fact that the emotion continuum of humans are wide and
need to be captured within model that can present it within a parsimonious and
manageable content. Kulviwat et al. (2007) propositions the Consumer Acceptance of
Technology by combining the three dimensions of the Pleasure, Arousal and Domination
model (PAD) by Mehrabian & Russell (1974) to fill the vacuum in the monitoring
construct for affect. The methodology proposed by Kulviwat et al. (2007) to analyze the
affect is through the environmental psychology theory of pleasure, arousal, and
dominance (PAD) by Mehrabian and Russell’s (1974). These researchers contend that the
emotional response signaled by an individual the physical environment and social
environment can be measured within the dimensions of pleasure, arousal, and dominance.
The emotional response of the individual is mapped as a point within the three
dimensions of the PAD variables. The main basis of the Consumer Acceptance of
Technology (CAT) theory is the premise that “Consumers may adopt high-technology
products not only to obtain useful benefits but also to enjoy the experience of using
them”(Kulviwat et al. 2007). Thus unlike the TAM and its related TRB and TPB, CAT
the prediction of consumer adoption of an innovation, especially in the context of
consumer items, the role of affect should be taken into consideration.
The incorporation of relative advantage as a variable that influences the cognitive
utilitarian decision is another distinctive features of the CAT model. The theory focuses
on improving the cognitive conceptualization of belief by introducing the variable –
relative advantage. Kulviwat et al. (2007) in describing relative advantage as “relative
advantage means that the innovation is believed by the adopter to be superior in some
way to what it is intended to supersede”. This is an interesting integration of the diffusion
research with that of the TAM.
39

Researchers on innovation and hi-technology adoption have acknowledge the causal
relationship that exists between the recognition of society and impact on attitude to adopt.
Rogers (1995) identified social systems variable including, social system norms,
tolerance of deviancy, communication integration, as one of the key groups of variables
that influence the knowledge variable/dimension of consumer. Venkatesh & Davis (2000)
identified the variable of subjective norms in their extension to the TAM model.
Kulviwat et al. (2008) in their theory recognizes the role of social influences on adoption
behavior. In their research on “private” and “public” consumption and the influence on
attitude, they observe “It seems, therefore, that adoption decisions regarding
technological innovations are more susceptible to social influence when consumption of
the product is visible to others”. This observation has major implications on the
communication strategy of firms towards their products and services.
40

3.3 Literature review on selected independent variables
3.3.1 Independent variable 1 - Perceived usefulness
Davis (1989) in defining perceived usefulness states that it is the degree to which using
an information system is thought to improve the activities they are performing. In the
context of TAM, perceived usefulness is considered to be the most powerful predictor of
behavioral intent (Taylor & Todd 1995). In its original application within organizational
context, this variable represented the individual belief that its adoption and usage would
result in an increased performance of the job (Davis et al. 1989). In fact Davis (1989)
suggests that the variable of perceived usefulness is more important than that of
perceived ease of use, this contention was supported by Hu et al. (1999). However, as
MDS represents the consumer context, the validity of the variable may be debatable.
Bruner II & Kumar (2005) in their research on the applicability of TAM in consumer
context found that usefulness could be considered an important variable even in
consumer context. However, Pedersen et al. (2002) have identified that the prediction
capability of the usefulness variable is more strong based on the task context.
Specifically, that the usefulness is more important in relation to utilitarian MDS such as
text messaging and payment than entertainment services which are more hedonic
dependant. These research findings of stronger prediction capability of utilitarian motives
in relation to perceived usefulness rather than hedonics were confirmed by Nysveen et al.
(2005b) and Nysveen et al. (2005a). Kulviwat et al. (2007) supports this finding on the
nature of influence of perceived usefulness in the context of products used for utilitarian
motives rather than for hedonic purposes.
Within regional settings research done by Kim et al. (2007) into the Singapore Mobile
Internet usages market, Kim et al. (2009) in to the Korean wireless pay-per-view market
and Hong et al. (2006) into the Mobile internet market of Hong Kong have empirically
accepted the role played by perceived usefulness.
41

3.3.2 Independent variable 2 - Perceived ease of use
Jenson (2006) in his critique of the MDS industry points to “default thinking” in
designing and implementing products and services. In his example of the MMS, Jenson
points to the failure of MDS industry to comprehend the value proposition and
complexity of using MMS, and instead proposing it as a natural extension to SMS. This
suggestion of industry pushing forth technology innovations and line extensions without
considering there practical usability and specifically ease of use is propositioned by him
for the failure of many MDS. Carlsson et al. (2006) supports this proposition in the
Finnish MDS market by point to mismatches of expectations between industry experts
and consumers. The survey identifies that while the industry has been introducing new
and more complex applications for MDS, consumers in general have been slow in their
adoption and continued usage of MDS. Davis (1989) defines perceived ease of use as
“the degree to which a person believes that using a particular system would be free of
effort”. This variable points to expectation of effort involved in using product or service.
Kulviwat et al. (2007) while accepting the importance of perceived ease of use as a
determinant in influencing attitude, considers the influence as indirect. They note that
rather than directly influencing the attitude of the user, it has a direct impact on the
perceived usefulness rather than intension directly. This conclusion is empirically tested
by Bruner II & Kumar (2005) who note that perceived ease of use indirectly influences
both the usefulness and fun variables. However the direct influence capability and
indirect influence capability of ease of use in the context of four mobile data services is
recognized by (Nysveen et al. 2005b) and mobile chat services (Nysveen et al. 2005a).
Here too the ease of use was noted to directly influence both attitude toward use and
usefulness.
Significantly Kim et al. (2009) rejects the influence of ease of use in the context of
inexperienced Mobile internet users and experienced users in Korea. However, the
influence of this variable is identified as important in the context of continuing usage
42

intension. This finding of strong influence of the ease of use variable in post adoption
was confirmed by empirical research done by Hong et al. (2006) in Hong Kong.
3.3.3 Independent variable 3 - Relative advantage
Rogers (2005) included relative advantage as a part of the product variables that
influence the diffusion of innovation. In analyzing the characteristics of innovation to
include relative advantage, compatibility, complexity, triability and observability, Rogers
(2005) note that an innovation being “better” than its existing alternatives is essential.
The decision by Kulviwat et al. (2007) to incorporate relative advantage as a variable in
their research model is interesting because of very few new research literature on the
empirical testing of this variable. Plouffe et al. (2001) in testing the Perceived
Components of Innovation model which Moore & Benbasat (1991) proposed, states that
relative advantage is the most important predictor of adoption intension. The focus of the
formers’ research is comparing the prediction capability of Technology Adoption Model
with Perceived Components of Innovation model. They note that there is similarity
between the variable of perceived usefulness and relative advantage variables. It is stated
in their analysis that dependence on perceived usefulness alone may be misleading as this
variable has a number of sub-classifications – including relative advantage. Kulviwat et
al. (2007) acknowledges the lack of empirical research into the influence of relative
advantage to adoption intension in the context of information systems research, especially
in mandatory settings where uses lack the options of comparing information systems.
However, this decision by the researchers to incorporate the relative advantage variable
was important, as this variable emerged as the most important influencer of intension,
less influential than perceived usefulness and more influential than ease of use.
43

3.3.4 Independent variable 4 - Pleasure
While there is a wide body of research that acknowledges hedonic motives (Bina et al.
2007; C. Carlsson et al. 2005; Childers et al. 2001; Heijden 2004; Heinonen & Pura 2006;
Hong et al. 2006; Kim et al. 2009) they do not attempt to proceed beyond motives of fun,
entertainment. The research proposition of Kulviwat et al. (2007) is unique in that they
attempt to develop a more deeper analytical model towards hedonic motives by
incorporating Mehrabian & Russell (1974) empirically tested Pleasure-Arousal-
Dominance scales. Lee et al. (2003) describes the pleasure emotion as “the extent to
which a person feels good”. They note of a number of research which indicate that the
emotion of pleasure, in combination with arousal and dominance, have been identified as
a stimulus in increasing purchasing behavior of customers. The research conducted by
Lee et al. (2003) confirmed the validity of pleasure in the context of online shopping. Wu
et al. (2008) in their research into the influence of pleasure and arousal in the context of
online shopping note the validity of these measures in predicting consumer buying
behavior. Wulf et al. (2006) have developed a comprehensive website evaluation model
using pleasure as the key boundary condition between the evaluation of websites and
their success.
3.3.5 Independent variable 5 - Arousal
Arousal formulates the second bipolar variable in assessing hedonic motives as proposed
by Mehrabian & Russell (1974). This bipolar nature is represented within the continuum
of feeling of being aroused to that of un-aroused. Kulviwat et al. (2007) notes that the
state of arousal is a result of a reaction of an individual to presented stimuli, influenced
primarily by the emotion of excitement. Wu et al. (2008) have identified and incorporated
arousal as an essential element in combination with pleasure to influence use buying
behavior in the online shopping and website designing context. These findings were
confirmed in an earlier research into stimulating consumer buying behavior in internet
shopping malls undertaken by Lee et al. (2003). While these research identify the
44

variable of arousal and its influence and interplay in the consumers buying decision, a
more unique approach to appreciate arousal was proposed by Wirtz et al. (2000). They
introduce the concept of target level arousal as a moderating variable in the satisfaction of
consumers. They proposition that the satisfaction felt by the consumer is based on their
expectation of a given situation or environment. For example, the expectation of the
consumer is selecting a restaurant is for a low arousal experience vs. deciding to go to a
disco is has an embedded high arousal experience. Therefore the level of satisfaction felt
by the consumer is based on the expectation vs actual experience. They empirically
validate this proposition using dinning experience in the Singapore market.
3.3.6 Independent variable 6 - Dominance
The variable of dominance was posited by Russell & Mehrabian (1974) as the third axis
of the PAD dimensional analysis of affect. This bipolar continuum extends from
emotional state of Dominance in which the individual feels greater control over the
innovation to Submissiveness. During the emotional state of Submissiveness the range of
emotion experienced by the individual include those of anger, fear, frustration, confusion
(Russell & Mehrabian, 1977). Kulviwat et al. (2007) when incorporating dominance as
part of the Consumer Acceptance of Technology model noted that there has been
significant debate among researchers on the validity of this variable. This issue of validity
was once again raised when dominance was rejected based on it weak influence on
attitude towards adoption. However, the researchers who propositioned the Consumer
Acceptance of Technology model further investigated the dominance dimension (Nasco
et al. (2008). The researchers note the empirical findings of Yani-de-Soriano & Foxall
(2006) on the role of dominance in the context of consumer setting and their forceful
argument on the validity of this variable. Based on their research into the role of
dominance, they note that in many instances the direct effect of the variable is masked.
45

3.3.7 Independent variable 7 - Social Influences
Glotz et al. (2005) in their international review and research into Mobile phones and their
social and cultural usage note that “enabler of social interactions, hierarchies and
communication”. Bina et al. (2007) incorporate the social influences as part of the
research into the Greek market. In defining the social factor “social factors try to capture
the congruency between social norms and individual beliefs and how the human part of
an individual’s environment affects one in performing a specific behavior”. Venkatesh &
Davis (2000) have incorporated ‘subjective norms’ as an extension to the Technology
Adoption Model, in recognition of influence from the cultural and norms. The lead
researchers of Technology Adoption Model also made further research on the moderating
effects of public and private consumption and adoption of technology. The importance of
social influences have also been highlighted in Rogers (2005) Diffusion of innovation
theory. Here social systems variable is a key variable that influences the inception stage,
identified as Knowledge stage, of the adoption process. Among the sub-variables that
have been identified by the researcher are social systems norms, tolerance of deviancy,
communication integration. López-Nicolás et al. (2008) have also validated the social
influences variable in the Dutch consumer market setting. However, they make an
important observation that communication media has a positive influence effect on social
norms. The identification of this relationship is considered important because MDS
vendors can influence the attitude of society enabling greater adoption. Wei (2008) notes
that the general perception in the USA market for mobile phones is primarily as a
communication device. This general perception has created a social barrier towards the
adoption of MDS. Therefore the researcher suggests the usage of media to change this
perception of society.
46

3.3.8 Attitude and Intention
The relationship between attitude and intension information systems research was
established as a result of the work done by Davis (1989); Davis et al. (1989) in the
technology adoption model based primarily on the findings of theory of reasoned action
Fishbein & Ajzen (1975). Kim et al. (2009) defines attitude in terms of Information
Systems as “a psychological tendency expressed by evaluating a particular entity in terms
of the degree of positiveness about IS”. Kulviwat et al. (2007) in analyzing the attitude
toward the act in the context of Technology Adoption Model, identifies the cognitive
dimension of the variable by stating that this “refers to the evaluative judgment of
adopting a piece of technology”. Therefore attitude in the context of its role in
influencing the intension of consumers could be viewed as decision or ‘judgment’.
However, Cohen & Areni (1991)(as cited by Kulviwat et al. (2007)) also point to the fact
that like all human emotions, instead of being completely cognitive, hedonics also play a
role in this judgment. This interplay between cognition and hedonics has also been the
basis of the subsequent proposition of Consumer Acceptance of Technology model by
Kulviwat et al. (2007). However, there has been significant debate among researchers of
information systems research on the validity of the influence of attitude to behavioral
intension. Due to the weak empirical evidence to support the influence of attitude on
intension Venkatesh & Davis (2000) decided to remove this motive from their extended
version of Technology Adoption Model. Researchers such as Adams et al. (1992) proved
that the Technology Adoption Model was robust even without the inclusion of attitude.
While it would have been an easier proposition to drop attitude towards adoption from
the research model, it was noted that majority of the research which decided to remove
attitude from the model mix were done in Information System mandatory settings. This
research focuses on consumer behavior and removing an important predictor could
potentially reduce the explanatory power of the model. It was further noted that through
the work of Bruner II & Kumar (2005) pointed to the mediating role between attitude and
intension. Further researchers such as Ayyagari (2006) have started to identify the
47

presence of hedonics even within the Technology Adoption Model. Therefore both
variable were included as part of this research study.
3.3.9 Short Message Service – Mobile Data Service used to test the cognitive
utilitarian value proposition
This research intend to study the behavior and influence of utilitarian and hedonic
motives, social influences on selected mobile data services with utilitarian and hedonic
value propositions. The mobile data service selected to represent utilitarian value
proposition is the popular Short Message Service. This service is known as the “killer
application” which was responsible for the identification of the mobile data services
industry and subsequent investments into 3G technology (C. Carlsson et al. 2005b; Kunin
et al. 2005).Pedersen et al. (2002) in their research into mobile data services classified
SMS as having predominantly utilitarian value propositions. This was primarily due to
the recognition that SMS was used to achieve a specific purpose and the intended value
derived from using the product is task oriented. However, Pedersen et al. (2002) notes
that there is “..potential for entertainment in addition to utility. This classification of SMS
within the value propositions of utility has been confirmed by researchers such as (C.
Carlsson et al. 2005a; C. Carlsson et al. 2005b; Nysveen et al. 2005b). Heinonen & Pura
(2006) in their complex analysis of mobile data services based on consumption, context,
social setting and relationship agree on the classification done by Pedersen et al. (2002)
and Nysveen et al. (2005). It was therefore decided to use SMS as the basis for testing the
behavioural model on utilitarian mobile data services.
3.3.10 Mobile Ringtone – Mobile Data Service used to test the hedonic value
proposition
The original selection for hedonic value proposition mobile data services was Mobile
Gaming. However, due to the very low adoption rates among the pilot survey respondent,
it was decided to find a mobile data service which had similar characteristics of value.
Based on the responses from the pilot study it was noted that mobile ringtones were wide
used by the respondents (45%) and therefore used as the basis of testing the research
48

model. Carlsson et al. (2005) in their classification of mobile data services identified that
mobile ringtones are of the entertainment category. Further using the classifications
matrix used by Pedersen et al. (2002) to categorized data services, mobile ringtones can
be considered to fall within the category of “Entertainment/ Transaction” the transaction
classification is relevant to mobile ringtone as they are used based on fee. Further the
hedonic motives of mobile ringtones were accepted by Heinonen & Pura (2006).
However in Verkasalo (2006)’s techno-centric mobile data service classification mobile
ringtone were not recognized. However, it was decided to use this product as the basis of
assessing hedonic value proposition because users adopt this product primarily due to its
entertainment value.
49

Summary of Literature Review
3.3.11Utilitarian Motives
Variable Literature Finding
Perceived Usefulness Kulviwat et al. (2007)
Taylor & Todd (1995)
Davis et al. (1989)
Davis (1989)
Bruner II & Kumar (2005)
Pedersen et al. (2002)
Nysveen et al. (2005b)
Nysveen et al. (2005a)
Kim et al. (2007)
Kim et al. (2009)
Hong et al. (2006)
Positive Relationship
Perceived Ease of use Davis (1989)
Carlsson et al. (2006)
Kulviwat et al. (2007)
Nysveen et al. (2005b)
Nysveen et al. (2005a)
Kim et al. (2009)
Hong et al. (2006)
Relative Advantage Rogers (2005)
Kulviwat et al. (2007)
Plouffe et al. (2001)
Moore & Benbasat (1991)
Table 3: Summary of Litreture review - Utilitarian motives
50

3.3.12Hedonic Motives
Pleasure Kulviwat et al. (2007)
Lee et al. (2003)
Mehrabian & Russell (1974)
Wu et al. (2008)
Wulf et al. (2006)
Arousal Kulviwat et al. (2007)
Wu et al. (2008)
Lee et al. (2003)
Wirtz et al. (2000)
Dominance Nasco et al. (2008)
Yani-de-Soriano & Foxall (2006)
Table 4: Summary of literature review - Hedonic motives
Attitude and Intension
Attitude and Intension Kulviwat et al. (2007)
Positive relationship
Table 5: : Literature review summary - Attitude and intension
Mobile data services with Utilitarian and Hedonic value propositions
Mobile Data Service Literature Finding
Short Message Service Cognitive utilitarian
value proposition
Mobile Ringtone Hedonic value
proposition
Table 6: Literature review summary - MDS with utilitarian and hedonic propositions
51

4. Solution
4.1 Solution overview
The research focuses on two significant issues associated with mobile data service
technology. First is the analysis of existing behavioral relationships between cognitive
utilitarian motives, hedonic motives and social normative influences and attitude towards
adoption and adoption intension. The key variables identified to represent utilitarian
cognitive motives were perceived usefulness, perceived ease of use and comparative
advantage. These motivational influences were selected primarily based on the core
propositions of the Consumer Acceptance of Technology (Kulviwat et al. 2007) which
was based on the Technology Adoption Model (Davis 1989; Davis et al. 1989) and
Diffusion of innovation (Rogers 2005). The variables of Pleasure, Arousal and
Dominance were selected based on the Consumer Acceptance of Technology model. The
second element of the research is the study of the behavior of the selected variables in the
context of Short Message Services (SMS) which has an established utilitarian value
proposition(Pedersen et al. 2002; Nysveen et al. 2005b; C. Carlsson et al. 2006; Heinonen
& Pura 2006) and Mobile Ringtones (C. Carlsson et al. 2005b; C. Carlsson et al. 2005a;
Pedersen et al. 2002; Nysveen et al. 2005a) which has an established hedonic utilitarian
value proposition.
Based on these variables the following conceptual module will be used as the basis of the
two studies.
52

Proposed model for mobile services adoption in Sri Lanka (Sri Lanka Consumer
Acceptance of Technology Model – SLCAT)
Utilitarian motives
Social Influences
Perceived Usefulness
Ease of Use
Attitude towards
adoption
Adoption
IntensionComparative
advantage
Hedonic motives
Pleasure
Arousal
Dominance
Figure 9: Proposed model for mobile services adoption in Sri Lanka
53

4.2 List of developed hypothesis
Model for analyzing Utilitarian value proposition – Using SMS
4.2.1 Utilitarian motives in the adoption of Utilitarian Services (SMS)
No Variable tested Hypothesis
1. Perceived usefulness in
the context of
Utilitarian Services
adoption and attitude
towards adoption
Hypothesis 1
H01: There is weak influence of perceived usefulness in the context of attitude towards
adoption of utilitarian Mobile Data Services in Sri Lanka
Ha1: There is strong influence of perceived usefulness in the context of attitude towards
2. Perceived Ease of use
in the context of
towards adoption
Hypothesis 2
H02: There is weak influence of perceived ease of use in the context of attitude towards
Ha2: There is strong influence of perceived ease of use in the context of attitude
towards adoption of utilitarian Mobile Data Services in Sri Lanka
3. Relative advantage in
the context of
towards adoption
Hypothesis 3
H03: There is weak influence of relative advantage in the context of attitude towards
Ha3: There is strong influence of relative advantage in the context of attitude towards
Table 7: Hypothesis for utilitarian motives in SMS
54

4.2.2 Hedonic motives in the adoption of Utilitarian Services (SMS)
4. Pleasure in the context
of Utilitarian Services
towards adoption
Hypothesis 4
H04: There is weak influence of pleasure in the context of attitude towards adoption of
utilitarian Mobile Data Services in Sri Lanka
Ha4: There is strong influence of pleasure in the context of attitude towards adoption of
5. Arousal in the context
of Utilitarian Services
towards adoption
Hypothesis 5
H05: There is weak influence of arousal in the context of attitude towards adoption of
Ha5: There is strong influence of arousal in the context of attitude towards adoption of
6. Dominance in the
context of Utilitarian
Services adoption and
attitude towards
adoption
Hypothesis 6
H06: There is weak dominance of arousal in the context of attitude towards adoption of
adopt utilitarian Mobile Data Services in Sri Lanka
Table 8: Hypothesis for hedonic motives in SMS
55

4.2.3 Role of Social Influences in the context of Utilitarian value proposition
7. Social influence in the
context of Utilitarian
attitude towards
adoption
Hypothesis 7
H07: There is weak influence of social influences in the context of attitude towards
Ha7: There is strong influence of social influences in the context of attitude towards
Table 9: Hypothesis of social influences in SMS
4.2.4 Relationship between Attitude towards adoption and adoption intension in the
context of Utilitarian value proposition
8. Relationship between
attitude towards
adoption of Utilitarian
service and Adoption
Intension
Hypothesis 8
H08: There is weak influence of attitude towards adoption and adoption intension in
the context utilitarian Mobile Data Services in Sri Lanka
Ha8: There is strong influence of attitude towards adoption and adoption intension in
the context utilitarian Mobile Data Services in Sri Lanka
Table 10: Hypothesis attitude and intension in SMS
56

4.2.5 Model for analyzing Hedonic value proposition – Using Mobile Ringtone
9. Perceived usefulness in
the context of Hedonic
attitude towards
adoption
Hypothesis 9
H09: There is weak influence of perceived usefulness in the context of attitude towards
adoption of hedonic Mobile Data Services in Sri Lanka
Ha9: There is strong influence of perceived usefulness in the context of attitude towards
10. Perceived Ease of use
in the context of
Hedonic Services
towards adoption
Hypothesis 10
H010: There is weak influence of perceived ease of use in the context of attitude
towards adoption of hedonic Mobile Data Services in Sri Lanka
Ha10: There is strong influence of perceived ease of use in the context of attitude
towards adoption of hedonic Mobile Data Services in Sri Lanka
11. Relative advantage in
the context of Hedonic
attitude towards
adoption
Hypothesis 11
H011: There is weak influence of relative advantage in the context of attitude towards
Ha11: There is strong influence of relative advantage in the context of attitude towards
Table 11: Hypothesis for utilitarian motives - M-Ringtone
4.2.6 Hedonic motives in the adoption of Hedonic Services (Mobile Ringtone)
12. Pleasure in the context
of Hedonic Services
towards adoption
Hypothesis 12
H012: There is weak influence of pleasure in the context of attitude towards adoption of
adopt hedonic Mobile Data Services in Sri Lanka
57

Ha12: There is strong influence of pleasure in the context of attitude towards adoption
of hedonic Mobile Data Services in Sri Lanka
13. Arousal in the context
of Hedonic Services
towards adoption
Hypothesis 13
H013: There is weak influence of arousal in the context of attitude towards adoption of
hedonic Mobile Data Services in Sri Lanka
14. Dominance in the
context of Hedonic
attitude towards
adoption
Hypothesis 14
H014: There is weak influence of dominance in the context of attitude towards adoption
of adopt hedonic Mobile Data Services in Sri Lanka
Table 12: Hypothesis for hedonic motives in M-Ringtones
4.2.7 Role of Social Influences in the context of Hedonic value proposition
15. Social influence in the
context of Hedonic
attitude towards
adoption
Hypothesis 15
H015: There is weak influence of social influence in the context of attitude towards
Ha15: There is strong influence of social influence in the context of attitude towards
Table 13: Hypothesis for Social influences - M-Ringtones
58

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