phone use are associated with higher odds of mobile adoption, especially with respect to the perceived
emotional benefits.
Table 1: Binary logistic model for mobile adoption in Myanmar in 2015
Coeffici
ent (β)
Odds
Ratio
Change in
Odds Ratio
due to 1
unit
increase in
variable
p-value
Gender (0=male; 1=female)
-0.536
0.585
-42
0.00
Secondary education being the highest obtained (0=no,
1=yes)
0.438
1.55
55
0.00
Tertiary education being the highest obtained (0=no, 1=yes)
1.564
4.777
378
0.00
Having television at home (0=no, 1=yes)
0.61
1.84
84
0.00
Having electricity at home (0=no, 1=yes)
0.342
1.407
41
0.00
Employment status (0=not employed; 1=employed)
0.608
1.836
84
0.00
Perceived economic impact of mobile (scalar variable: 1-5)
0.067
1.07
7
0.25
Perceived knowledge impact of mobile (scalar variable: 1-5)
0.122
1.13
13
0.04
Perceived emotional impact of mobile (scalar variable: 1-5)
0.235
1.265
27
0.00
Proportion of family members having mobile (scalar variable:
1-10)
0.304
1.355
36
0.00
Proportion of friends having mobile (scalar variable: 1-10)
0.047
1.048
5
0.00
Monthly household expenditure (MMK)
0.115
1.122
12
0.01
Age of respondent (no. of years)
-0.005
0.995
-0.5
0.09
Constant
-3.439
0.032
-97
0.00
Source: LIRNEasia
Smartphone and Internet use
The data showed that 66% of mobile subscribers owned a smartphone and 37% owned a feature (or ‘keypad’)
phone (3% owned both). As such, more than two thirds had Internet-ready features such as a browser,
applications and Wi-Fi features. This is a penetration rate higher than Thailand, Myanmar’s much-richer neighbor,