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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mailto:perera_ranga@yahoo.com

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 Herbjrn 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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http://www.nhh.no/en/research---faculty/department-of-strategy-and-management/sol/cv/nysveen,-herbj%C3%B8rn.aspx

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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Abbreviations

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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; Mlarstig 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

(Mlarstig 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.

23

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. Fernndez & 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. Fernndez & 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 Fernndez & 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

Fernndez & 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.

Fernndez & 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 Russells (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

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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.

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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 individuals 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