Proceedings of World Business, Finance and Management Conference
14 - 15 December 2015, Rendezvous Grand Hotel, Auckland, New Zealand
ISBN: 978-1-922069-91-7
Institutional Technology Users and Non-Users: Is Their
Anything in Between?
Jason MacVaugh
This paper presents the theoretical and methodological underpinnings of an international
evaluation of individual adoption of institutional technology. A two-part study in the summer of
2011 evaluated the perceptions of institutional technology held by lapsed and/or non-users in
a research field where user perception is the dominant paradigm. The paper provides the
information needed for peers to assess the study’s hypotheses against other methods for the
analysis of technology acceptance.
Keywords: Diffusion of Innovation, technology acceptance, limits of technology
I. Introduction
Non-adoption of institutionally provided technology is one of the central concerns of
organisations investing in technological innovation. In some cases individuals
unconsciously disregard the functionality of new technology, while others may actively seek
to oppose it [1]. Often, legacy technology users do not chose to use functionally similar
newer products when they become available. In the context of organisations, some
employees may prefer a competing technological solution, thereby bypassing operational
planning and control. Further, some who have tried a new technology may later become
non-users, given their dissatisfaction with the experience [2].
The present research project attempts to close the disconnect in understanding between
the standard innovation/marketing texts of Rodgers [3] and Davis [4], and the
social/historical perspectives of Mokyr [5] and Bauer [6] by begin from the position that
diffusion of innovation is not best understood by identifying who will adopt early and who will
be late, but rather by understanding who might not adopt and why.
The sections that follow report on a study of institutional technology users; analysing the
extent to which the technological perspectives that help predict technology adoption are still
valid when the dependant scale includes a more sociological perspective on technology
use.
Ii. Theoretical Background
The theory of adoption and diffusion of innovations [3] is the traditional starting point for
analysis of new technology adoption. Accepting this framework, non-adoption can only be
explained as the final outcome of adoption processes that failed. But while Rogers [3]
argues that a great number of conditions may inhibit the success of the adoption process, in
his text he only refers to the possibility of technology non-adoption in the context of religious
opposition (using the Amish as exemplar), showing the model of technology adoption as an
evaluation of diffusion stages from 1 to 100% of Adopters.
The trend of focusing on adopter characteristics (and often in „mandatory‟ adoption
contexts), rather than those who do not adopt, is an implicit theme in more recent empirical
studies of technology acceptance. Probably the most significant empirical work in this field
in the last 15 years is [7] UTUAT. In this study, Venkatesh, Speier and Morris combine each
____________________________________________________
Dr. Jason MacVaugh, Kwansei Gakuin University, Nishinomiya, Japan, Email: macvaugh@kwansei.ac.jp
Proceedings of World Business, Finance and Management Conference
14 - 15 December 2015, Rendezvous Grand Hotel, Auckland, New Zealand
ISBN: 978-1-922069-91-7
of the other major sets of variables explaining technology acceptance, and apply the each
construct to a largescale survey of four companies. With this data they were able to first
combine variables (factor analysis, regression) and then test the synthesised model in such
a way that predicts about 70% of behaviour intention to use institutionally provided
technology.
It is usually only in sociological research that data on non-user behaviour is seen. Selwyn
[8] synthesise research from both Europe and the United States that indicates low or nonusers of technology have many and varied attributes and perspectives behind their nonadoption: and should not be simply labelled as technophobic or materially poor. Selwyn [8]
re-categorises the traditional user/non-user scale, recognising at least five potential groups:
frequent users; fairly frequent users, infrequent users, lapsed users and non-users. If those
in the organisation who are simply not aware of the institutional technology are taken into
account, there are then three kinds (lapsed, non and unaware) of potential users whose
perspective on an institutional technology may not predict their actual use.
Both research on adopter perspectives and an understanding of the alternative
perspectives of lapsed and non-users would be valuable to organisations introducing new
technology in the workplace, and so the research that follows applies the six independent
variables and four moderating variable from Venkatesh et al. [7] and the five dependent
variables plus two moderating variables on use from Selwyn [8]. This synthesis allows for
tests to see if variables for predicting use are useful in determining lapsed or non-use.
III. Hypotheses
In Venkatesh et al. [7] Performance Expectancy, Effort Expectancy, and Attitude to
predict Intention to Use, which in turn predicts Use; as does the variable Facilitating
Conditions. In addition, they include Self-Efficacy and Anxiety in their model due to their
significance in other empirical studies used to create the UTUAT.
Performance Expectancy is a measure of the perceived utility or perceived relative utility of
a technology. It is also the variable with the strongest predictive power in the UTUAT. Given
this relationship:
H1: Performance Expectancy will have low scores for Frequent and Fairly frequent users;
average scores for infrequent users, and low scores for lapsed and non-users.
Effort Expectancy is a measure of perceived ease of use of a new technology. In some
studies it is seen as an antecedent of Performance Expectance, but more often held to be
the second best predictor of intention to use a new technology. This being said, without
direct experience of use, non-users may have little understanding of the effort needed to
use a new technology, so there is less likelihood of a uniform pattern to their opinion. Given
this relationship:
H2: Effort Expectancy will have low scores for Frequent and Fairly frequent users; average
scores for infrequent users, high scores for lapsed users, but no pattern to the scores of
non-users.
Attitude is a measure of personal opinion, which is in theory supposed to be independent of
Performance and Effort Expectancy. In sociological studies the measurement of attitude is
considered an important feature of understanding human-technology interaction, but in
measures of the UTUAT [7] it has the lowest predictive power of intention to use
technology.
Proceedings of World Business, Finance and Management Conference
14 - 15 December 2015, Rendezvous Grand Hotel, Auckland, New Zealand
ISBN: 978-1-922069-91-7
H3: Attitude will have high scores for Frequent and Fairly frequent users, low scores for
infrequent users, but no pattern to the scores of for lapsed users and non-users.
Facilitating Conditions are a measure of the organization‟s support for users of technology.
Once the behavioral choice to use a technology is made, there are many potential factors,
such as personal Vs shared access or adequate training, that could limit an individual‟s use.
Facilitating Conditions are said [7] to be the only direct influence on use in the UTUAT
model.
H4: Facilitating Conditions will have high scores for Frequent and Fairly frequent users low
scores for infrequent users, but no pattern to the scores of for lapsed users and non-users.
Self-Efficacy is a measure of an individual‟s perception of their ability to learn and use a
new technology. Depending on the study and question bank used, this may or may not
factor with Effort Expectancy. In the original UTUAT [7] test of four companies, Self-Efficacy
was not a significant factor, but in later international tests of the model, it was shown to be
more significant.
H5: Self-Efficacy will have high scores for Frequent and Fairly frequent users, low scores
for infrequent users, but no pattern to the scores of for lapsed users and non-users
Anxiety is a measure of an individual‟s fear of using or misusing a technology. A popular
theme in technology research since the 1980s (as per „technophobia‟) the Anxiety variable
is heavily influenced by moderating variables such as age and experience.
H6: Anxiety will have low scores for Frequent and Fairly frequent users, average scores for
infrequent users, but no pattern to the scores of for lapsed users and non-users
Due to their role, their time in post, their location, or their social and formal interactions,
unaware potential users exist in every organization. Implicit in the UTUAT and other studies
of technology adoption is awareness of the technology, thus noting the number and
characteristics of unaware potential users will also help to explain non-use of institutional
technology.
IV. Method
A. Approach
In the summer of 2011 a survey of individual technology users will be conducted in
South Africa, the UK, Italy, Finland, China, Japan, India and Vietnam. First the terminology
will be piloted to check the local translation provides consistent understanding across each
country context. Then the revised survey will be taken in two organisations, one public and
one private (N~50 in each case).
B. Variables
The following characters are used to indicate the purpose of each question:
P= preliminary question
IV= independent variable
MV= moderating variable
DV= dependant variable
Proceedings of World Business, Finance and Management Conference
14 - 15 December 2015, Rendezvous Grand Hotel, Auckland, New Zealand
ISBN: 978-1-922069-91-7
X= institutionally recommended technology
The questions in part one indicate technology awareness, intention to use, and present
use of system X.
P: Are you aware that system X is available in this organisation?
P: When did you first hear about system X?
DV: How often do you use system X (Selwyn scale)?
The questions in part two use a 7-point scale to evaluate perceptions of the technology.
IV: Using system X enables me to accomplish tasks more quickly
IV: Using system X increases my productivity
IV: I find using system X easy
IV: Learning to use system X was easy for me
IV: Using system X is a good idea
IV: I like using system X
IV: People who are important to me think that I should use system X
IV: People who influence my behaviour think that I should use system X
IV: I feel apprehensive about using system X
IV: System X is somewhat intimidating to me
IV: I can complete a task using system X If there was no one around to help
IV: I can complete a task using system X If I have enough time to complete the job
IV: I have the resources necessary to use system X
IV: I have the knowledge necessary to use the system
IV: System X is not compatible with other systems I use
The questions in part three control for moderating factors, including all of those used in the
Venkatesh et al. [7] study and two of the most significant factors in the Selwyn study not
used by Venkatesh et al. [7]
MV: Using System X is mandatory
MV: I am experience in using system X
MV: Gender
MV: Age
MV: Location
MV: Role
V. Conclusion
This paper provides a brief overview of the indicative literature regarding the limits to
the diffusion of technologically innovative new products. It is based on a perspective of
innovation diffusion moving beyond the adopter‟s bias and towards a conditional
perspective of institutional technology acceptance.
We expect the data analysis to:
Proceedings of World Business, Finance and Management Conference
14 - 15 December 2015, Rendezvous Grand Hotel, Auckland, New Zealand
ISBN: 978-1-922069-91-7
Show a similar predictive power for users as the original UTUAT model
Show that non-users and lapsed users have independent variable means that are not
a direct correlation to their amount of usage
Show a significant percentage of any sample group is made up of those who are
unaware of the institutional technology being surveyed
In future analysis of this data set the author plans to test the effects of group
membership on technology acceptance. The author also plans a paper exploring the
behaviours of older generation technology users and those who bring current generation
but non-institutionally supported technology into the workplace.
Acknowledgment
This was supported by a four-year research project, made possible by the Japanese
Society for the Promotion of Science (JSPS) “Kakenhi” funding, while working at the Japan
Advanced Institute of Science and Technology (JAIST).
References
[1] A. Randal, “Reinterpreting „Luddism‟: resistance to new technology in the British
Industrial Revolution,” Resistance to New Technology, M. Bauer, Ed. Cambridge:
Cambridge University Press, 1995, pp. 57-80.
[2] P. Kingsley and T. Anderson, “Facing life without the internet,” Internet Research:
Electronic Networking Applications and Policy, 8(4), 1998, pp. 303-312.
[3] E. Rogers, Diffusion of innovations, 5th Ed, The Free Press: New York, 2003.
[4] F. Davis, “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of
Information Technology,” MIS Quarterly, (September), 1989, pp. 319-340.
[5] J. Mokyr, The Lever of Riches, Oxford University Press: New York, 1990.
[6] M. Bauer, Resistance to New Technology, Cambridge: Cambridge University Press,
1995.
[7] Venkatesh, V., Speier, C. and Morris, M. “User acceptance of information technology:
toward a unified view,” MIS Quarterly, 2003, 27(3), pp. 425-477.
[8] Selwyn, N. “Digital division or digital decision? A study of non-users and low users of
computers,” Poetics, 2003, 34, pp. 273-292.