This paper builds upon previous research on innovation adoption. It aims to test the differences and effects of age, size, prior technology, and proximity on institutional innovation adoption. The theoretical framework initiates by providing literature on the area of innovation adoption. The paper distinguishes between organizational innovation adoption and the individual adopter within an organization. It seeks to analyze what controls could affect decisions to adopt innovations. A non-experimental cross-sectional study design in adopting mobile applications gets a sample of 208 universities worldwide. The hypotheses are that higher age, bigger size, more prior technological changes, and higher proximity support a faster innovation adoption by universities. Results partially support the hypotheses and lead to understanding the innovation adoption in a particular institutional context of innovations influenced by adopting the organization's users.

1. MOBILE APPLICATIONS AS AN INNOVATION
ADOPTION IN UNIVERSITIES: IMPACT OF
AGE, SIZE, PREVIOUS TECHNOLOGY
TRANSFORMATIONS AND PROXIMITY
ERIC TALAVERA - EQUAA
NESTOR U. SALCEDO - ESAN

2. 15 minute track
 What´s it about?
 Theoretical support
 Sample and methodology
 Results
 Discussion and Conclusions

3. What´s it about?
 Innovation adoption by
universities.
 How do certain variables affect
the adoption of mobile
applications by universities?

4. Theoretical support
Age
• (Li, Wu, Luo, &
Zhang, 2013)
• Gambardella
and Torrisi
(2001)
• Giunta &
Trivieri, 2007
Size
• Pan & Jang,
2008; Cho,
2006; Yao, Xu,
Liu & Lu, 2003
• Zhu & Kraemer,
2005, 2003
Previous
Tech.
Adoption
• Rogers (1995)
• Li, Wu, Luo, &
Zhang, 2013
Proximity
• Howells 2002
• Torre and Gilly
2000
• Boschma, 2005

5. Model
Mobile App Innovation
Adoption
Organization Age
Organization Size
Previous Tech. Transformation
Proximity

6. Sample and Methodology

7. Timeline
December 12, 1991
January 9, 2007
October 22, 2008
December 12, 2013

8. Timeline
December 12, 1991
January 9, 2007
October 22, 2008
December 12, 2013
A
B
A
B

9. Variables
VARIABLE’S CODE VARIABLE’S NAME
University Name of the university
Foundation Year of university foundation
Age of university (Age) Number of years since university was founded
Students (Size) Number of students
Country (Proximity) Country of the university
App First Registry Date in which the app was uploaded to google playstore
App Age since Release (I.A.) Age of App First Registry since Release of First Mobile App (January 9, 2007)
Webpage Launch Date in which the web page was first uploaded
Web Age since Release (P.T.T.) Webpage Launch since Release of First Website by a University (December 12, 1991)
Web Changes Number of changes the web page has undergone
Average Changes per Year (P.T.T.) Number of Web Changes has undergone divided by the number of years since the Webpage Launch

11. Group Statistics
New Old N Mean
Std.
Deviation
Std. Error
Mean
app age since
release
Old 89 2054.93 342.194 36.272
New 89 2203.58 365.293 38.721
Independent Samples Test
Levene's Test for Equality
of Variances t-test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence Interval of
the Difference
Lower Upper
app age since
release
Equal variances
assumed
.054 .816 -2.802 176 .006 -148.652 53.057 -253.361 -43.943
Equal variances
not assumed
-2.802 175.254 .006 -148.652 53.057 -253.364 -43.940
Group Statistics
Small Big Univ N Mean
Std.
Deviation
Std. Error
Mean
app age since
release
Small Univ 83 2167.83 364.067 39.962
Big Univ 77 2052.91 344.887 39.304
Independent Samples Test
Levene's Test for Equality
of Variances t-test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence Interval of
the Difference
Lower Upper
app age since
release
Equal variances
assumed
.088 .767 2.046 158 .042 114.922 56.165 3.991 225.854
Equal variances
not assumed
2.050 157.928 .042 114.922 56.051 4.216 225.628
H1:
AGE
=
SUPPORTED
H2:
SIZE
=
SUPPORTED

12. Group Statistics
Fast Slow Web Adopt N Mean Std. Deviation
Std. Error
Mean
app age since
release
Fast Web Adopt 92 2138.77 332.411 34.656
Slow Web Adopt 86 2119.08 390.444 42.103
Independent Samples Test
Levene's Test for Equality
of Variances t-test for Equality of Means
F Sig. t df Sig. (2-tailed)
Mean
Difference
Std. Error
Difference
95% Confidence Interval of
the Difference
Lower Upper
app age since
release
Equal variances
assumed
2.870 .092 .363 176 .717 19.690 54.237 -87.349 126.730
Equal variances not
assumed
.361 167.416 .718 19.690 54.532 -87.968 127.349
Group Statistics
Less More Web Ch Yrs N Mean
Std.
Deviation
Std. Error
Mean
app age since
release
Less Changes 89 2197.53 371.995 39.431
More Changes 89 2060.99 337.494 35.774
Independent Samples Test
Levene's Test for
Equality of Variances t-test for Equality of Means
F Sig. t df Sig. (2-tailed)
Mean
Difference
Std. Error
Difference
95% Confidence Interval
of the Difference
Lower Upper
app age since
release
Equal variances
assumed
.258 .612 2.565 176 .011 136.539 53.241 31.466 241.613
Equal variances
not assumed
2.565 174.359 .011 136.539 53.241 31.459 241.620
H3a
=
NOT
SUPPORTED
H3b
=
SUPPORTED

13. Group Statistics
USA vs World N Mean
Std.
Deviation
Std. Error
Mean
app age since
release
USA 93 2056.33 333.305 34.562
World 85 2209.05 374.393 40.609
Independent Samples Test
Levene's Test for
Equality of Variances t-test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence Interval of
the Difference
Lower Upper
app age since
release
Equal variances
assumed
.184 .668 -2.879 176 .004 -152.714 53.047 -257.404 -48.023
Equal variances
not assumed
-2.864 168.868 .005 -152.714 53.325 -257.984 -47.443
H3a
=
NOT
SUPPORTED

14. Discussion and Conclusions
 The relevance of the study shouldn't apply to university decision makers only, but to all
organization managers. The choice to whether adopt an innovation or not will be
affected by the age, size and the proximity to other competitors who adopt an
innovation.
 Previous technology doesn’t have a direct effect on the behavior to innovate. Previous
technology adoption doesn’t have a relation with new technology adoption, but previous
technology changes have a relation with mobile apps adoption.
 Innovation adoption in universities is potentially affected by the number of users of the
organization (students), which does not happen necessarily in business scopes.
 Innovation adoption in universities isn’t necessarily related to improving the
organization´s performance as happens in business fields.
 One of the limitations was the number of comparisons made between universities of the
same country and universities of different countries.
 Another future line of research would include a time series analysis of mobile app
adoption, given it is still a relatively new market, specially for the universities.