Supply-side factors’ effect on the diffusion of an innovation across ASEAN Countries
David Corkindale The University of South Australia
Beng Chea Institute for Infocomm Research (I2R), Singapore
Abstract
We seek to add to the mainstream of cross-country research on the diffusion of innovations
(DOI) by examining the influence of supply-side readiness variables and specifically the
evidence for their being associated with the adoption of the mobile phone across ASEAN
countries. We derive some hypotheses and examine these against 21 years of annual data.
From this we find that there is evidence for supply-side factors’ influence on the DOI.
Introduction
The relationship of product adoption and international marketing strategies has gained
importance with increasing globalization. Supply-side capability and infrastructure readiness
factors have received very limited attention in marketing studies of the DOI. Two major,
multi-country studies on the adoption of innovations have been undertaken recently
(Talukdar et al, 2002; Van den Bulte & Stremersch, 2004) but neither examined the possible
influence of supply-side factors. This study investigates this using data related to the
adoption of mobiles phones across ASEAN countries.
Literature Review
Many scholars (Gatignon et al., 1989; Helsen, 1993; Putsis et al., 1997; Takada and Jain,
1991; Van den Bulte, 2004; Talukdar et al, 2002; Tellis et al., 2003) have studied the
diffusion of innovations across different countries in mainly the Americas and Europe using
the Bass Diffusion Model (BDM) to describe and paramatise the process. The choice of
variables examined that might affect the parameters of the BDM has focussed upon mainly
socio-economic variables and been rather limited. Talukdar et al (2002) argued for the need
to examine a larger set of potential covariates and examined the diffusion of six products
across 31 mainly developed countries from Europe, Asia and North and South America and
introduced nine new potential covariates. None were infrastructure related. Van den Bulte &
Stremersch (op cit) conducted a meta-analysis based upon 293 observations on 52 consumer
durables in 28 countries reported in 46 publications. Again, none of the covariates tested for
were on infrastructure or supply-side readiness.
It has been argued that different rates of technology adoption across different countries is an
efficient response to resource differences (Comin & Hobijn, 2003) and that, in effect, supplyside readiness factors may be the chief explanatory variables.
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Research Questions, Hypotheses and Research Model development
Upon the brief review of the literature we formulate the following Research Question:
•
Is there evidence that supply-side readiness can affect the rates of diffusion across a
range of ASEAN countries?
Supply-side readiness refers to the standardization policy, the intensity of competition and the
readiness of the underlying infrastructure that support the adoption and proliferation of the
mobile phone. How these factors are expected to affect the rate of adoption and diffusion of
mobile phones are elaborated below.
Standardisation Policy
Government regulatory bodies can intervene in the marketplace and influence the diffusion of
mobile phones. For example, government policies towards the standardization process and
market competition, among other things, shape the diffusion process. Kauffman and
Techatassanasoontorn (2005) identified two practices of standardization that involve the
formulation of government policies that relate to new technology diffusion: market-mediated
policy and a regulated regime. A country that employs a market-mediated policy does not
impose specific wireless standards to which operators must conform. Instead, dominant
standards emerge through market forces. In contrast, a country that uses a regulated regime
has a regulatory body impose a certain wireless standard for all operators to comply. A wellknown example is the agreement among European countries to use the GSM standard. We
expect countries that use a regulated regime to exhibit faster diffusion rates. Unfortunately,
all the countries being studied employ the market mediated policy thus it was not possible to
include standardization policy as a differentiating factor.
Intensity of competition
The intensity of competition is expected to influence the diffusion rates. This was noted in
previous studies of mobile phone diffusion (e.g., Gruber and Verboven, 2001a; Gruber and
Verboven, 2001b; Kauffman and Techatassanasoontorn, 2005). Competition usually leads to
lower prices, lowering the adoption barrier for price-sensitive consumers. Therefore, if a
country has a higher intensity of competition, we expect the costs of mobile phone ownership
to be lower and have a more positive effect on diffusion rates. Kauffman et al. (2005) find
that higher competition in the digital mobile phone industry, measured in terms of high
number of operators and low service prices tend to accelerate the diffusion process during the
partial diffusion state. For this study, we will use the number of competing mobile phone
operators to measure competition. The hypothesis is as shown below:
H1: The intensity of competition in a country is positively related to the rate of diffusion
Infrastructure Readiness
Mobile phone operators need to make huge investments in network infrastructure and
licenses for the appropriate radio frequencies in order to provide mobile telecommunications
services. Advanced technology diffusion rates have been known to be positively related to a
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country’s wealth and its infrastructure development. Gruber and Verboven (2001b) find that
countries with a high income per capita tend to be more advanced in adopting mobile phones
but the effect diminishes over time, and countries with a large fixed network tend to be more
advanced in adopting mobile phones (Gruber and Verboven, 2001b). However, data on the
investments made by individual operators are not as readily available as some operators are
less willing to reveal such information in detail. Thus, for the purpose of this study, a
reasonable proxy to use would be the GDP per capita of a country. We expect a wealthy
country to have the ability to invest more and/or faster in infrastructure development.
Another possible proxy is the information and communications technology (ICT) expenditure
per capita or as a percentage of GDP but the data for the ASEAN countries of interest is
incomplete. Yet another proxy for infrastructure investment is the number of TV sets per
capita, the argument being that in these countries TV broadcast towers and infrastructure are
used to provide mobile phone coverage. Finally, it can be suggested that literacy levels in a
country are a facilitator of mobile phone provision capability a one result of investment in
community infrastructure like schools. Hence, in relation to the diffusion of mobile phones in
each country:
H2: The GDP per capita in a country is positively related to diffusion rate.
H3: TV sets per capita in a country is positively related to diffusion rate.
H4: The literacy rate in a country is positively related to diffusion rate.
Methodology
The mobile phone meets all of Rogers’ five attributes for favouring the adoption of an
innovation (Rogers, 2003): relative advantage over fixed line phones; compatibility with
methods of use; low complexity to understand; ease of trial; well observable benefits. Hence
data on the adoption of mobile phone was collected for six of the ten ASEAN countries for
the 21 years prior to 2005, there being insufficient data available for the others. The six
countries collectively had 87% of the ASEAN population in 2005 and represent a variety of
socio-economic states. The year of initial mobile phone adoption ranged from 1985 to 1992.
The mobile phone penetration rate was defined as the number of mobile phone subscribers
divided by the population (citizens and residents over 10 years old) of each country. This
definition is consistent with the data provided by the regulators of these countries and
increasingly more adults are subscribing to more than one mobile phone. The data for this
study was collected from reliable secondary sources such as the ITU, World Bank,
UNISTAT, OECD, World Development Indicators and other national sources: standards
organisations, IT media, market research companies.
To summarise: we had data on the annual number and cumulative number of mobile phones
in use in a country over 21 years together with annual data on the four hypothesised
covariates: the GDP per capita; the literacy rate ( % of the population literate); an index of the
competition intensity; the number of TV sets owned per capita. Regressions were run on this
data to examine the hypotheses.
Findings
The independent variables GDP per capital and TV sets per capita were highly and
significantly correlated (0.63); no other significant correlations were present among the
independent variables. The three remaining independent variables were regressed against the
dependent variable, the cumulative number of mobile phones per capita of those persons over
3
10 years old. Tables 1 and 2, below, show the outcome of this where all variables were
entered at simultaneously.
The variable identifications are:
Cumulative number of mobile phones per capita (persons over 10 years old) = V37
The GDP per capita
= V12a
The Index of competitive intensity
= V11
The literacy level
= V7
Model
1
R
R Square
.713(a)
Adjusted R
Square
.508
Std. Error of
the Estimate
.496
.1932591
a Predictors: (Constant), V11, V12a, V7
Table 1. The overall regression model outcome.
Unstandardized
Coefficients
Model
1
B
(Constant)
Std. Error
-.831
.308
V7
.008
.003
V12a
.023
.003
V11
.048
.008
Standardized
Coefficients
t
Sig.
Beta
B
Std. Error
-2.700
.008
.158
2.414
.017
.582
8.982
.000
.374
5.729
.000
a Dependent Variable: V37
Table 2. The values of the Coefficients in the regression model.
Discussion and Limitations.
From the findings above it can be seen that:
Hypotheses 1, 2 and 4 are supported. It was found that technically H3 was supported in that
there was a strong and significant correlation between mobile phones adopted and the number
of TV sets existing in a country. However, the partial correlation was not significant if GDP
per capita was controlled for. The model derived from the three independent measures of
aspects of supply-side factors in a country explains almost 50% of the variation in mobile
phone numbers across these ASEAN countries across the 21 year period.
From these results it can be seen that the answer to the Research Question is that there is
some evidence that supply-side factors in a country may explain the differing rates of mobile
phone adoption across ASEAN countries.
The major factor explaining the rate of adoption in mobile phone adoptions in the analysis
done in this study was GDP per capita. This has been found in previous studies and could be
argued to reflect the general level of wealth of the population and individuals’ ability to buy
one and not an infrastructure influence. However, the sizes of the standardized beta values of
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the other two proxies for infrastructure are also suggesting them to be major contributors to
explaining the variation in mobile phone adoptions. This supports the argument for the
inclusion of infrastructure readiness factors to be included in future studies of cross-country
innovation adoption research.
Limitations
A main limitation in providing evidence to address the research question is that no direct
measures of supply-side, infrastructure factors were used in the analysis. Assumptions have
been made that certain proxies do reflect these.
The other limitations of the study include a lack of consideration of:
• The Network effect that could influence the adoption of mobile phones
• The wider Economic differences between countries, other than GDP, were not
controlled for
• A Cross-border influence was assumed not to be present
• The different times at which mobile phones entered the market in different countries
was not considered in the analysis. It could be that in countries entering late might
catch up more quickly due to outside pressure to conform.
• A linear relationship between the dependent and independent variables was assumed
in the analysis but no non-linear form gave higher r ² value.
• It was assumed that there was no interaction effects among the independent variables
in the final model; correlations among them were low, however, and not significant.
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