Description
This survey sought to establish how performance expectancy (PE), effort expectancy (EE),
social influence (SI) and facilitating conditions (FC) related to the use of ICT by
undergraduate students in the School of Education at Makerere University. Descriptive
analysis was done using percentages and means, while hypotheses were tested using Multiple
Regression. The results suggested that EE and FC were significant positive correlates of the
use of ICT, while PE and SI were not. These findings imply that to enhance the use of ICT
by the undergraduates in the School, the ICT change agents have to enhance and maintain the
EE or perceived user friendliness of ICT by acquiring ICTs that are user friendly ICT, that is,
whose user interfaces (e.g. screens) give clues to users on what to enter and how to enter, in
addition to being accompanied by user manuals. They also have to raise and maintain FC that
is the levels of perceived compatibility of ICT among the students by soliciting their views
on what hardware and software should be acquired.
Key words; Unified theory, Information Communication Technology, Undergraduates
Rogers (2003) attributes the start of the study of innovation, diffusion, and adoption
to a French lawyer and judge by the names of Gabriel Tarde around 1900. Tarde reportedly
“kept an analytical eye on trends in his society as represented by the legal cases that came
before his court” (Rogers, 2003, p. 41). The purpose of his scholarly observations was to
learn why given one hundred different innovations conceived at the same time, ten will
spread while ninety will be forgotten. Tarde observed certain generalizations about the
diffusion and adoption of innovations that he called, “laws of imitation” (Tarde, 1903 cited in
Rogers, 2003). Tarde’s keyword “imitation” which today is termed as “adoption” of an
innovation, implied and still implies that an individual learns about an innovation by copying
someone else’s adoption of the innovation. Rogers (2003) observes however, that Tarde’s
creative insights were not immediately followed by empirical studies until the 1940s, because
social scientists of Tarde’s day (about 1900) lacked methodological tools to conduct the
studies. When empirical studies of innovation diffusion and adoption began, “research traditions” emerged, which according to Rogers (2003) included anthropology, early
sociology, rural sociology, education, public health and medical sociology, communication,
marketing and management, geography and general sociology research traditions. Rogers
(2003) defines a “research tradition” as consisting of an academic discipline (e.g.
anthropology) or a sub-discipline (e.g. early sociology), adding that each research tradition
concentrated on investigating the diffusion and adoption of one major type of innovation. For
example the “education research tradition” specialized in educational innovations. In
particular, while attributing early educational diffusion and adoption studies to one
institution, Columbia University’s Teachers’ College, Rogers critiqued the educational
research tradition for minimal contribution to innovation diffusion and adoption research.
Specifically, he said that “although it is an important research tradition…, education is less
important in terms of its contribution to the theoretical understanding of the diffusion [and
adoption] of innovations” (p.61), which observation although a bit dated, still calls for more
diffusion and adoption researches in educational institutions. Several published past studies
(e.g. Huang, Hood & Yo, 2013; Kelleher & Sweetser, 2012; Lule, Omwansa & Waema,
2012; Magro, 2012; Sato & Zouain, 2012; Tyagi, 2012; Veletsianos & Kimmons, 2013;
Zafiropoulos, Karavalis & Vrana, 2012) on innovation diffusion and adoption can be cited.
However as recent reviews on such studies (e.g. Awa, Ukoha & Emecheta, 2012; Buabeng-
Andoh, 2012) reveal, few of these studies have been done outside the developed world.
Further, most of these studies have been done on other innovations (e.g. electronic
commerce, banking and tax collection) than educational ones (e.g. electronic learning and
digital libraries). Such contextual gaps call for more researches in the developing countries
such as Uganda, especially in educational institutions such as Makerere University. Granted,
some studies (e.g. Bakkabulindi, 2011a, b; 2012a, b; Bakkabulindi & Oyebade, 2011;
Bakkabulindi & Sekabembe, 2010; Bakkabulindi & Ssempebwa, 2011) have been on the
adoption of ICT innovations in Makerere University, particularly in the College of Education
and External Studies (CEES). However most of these studies have targeted lecturers,
administrators and postgraduate students and not undergraduate students. This study was
intended to narrow this “sampling gap”, by focusing on undergraduate students this time
round. The study took place in a school in Makerere University, which was among the
pioneer organizations in Uganda to acquire information and communication technology
(ICT) in the form of computers. To be exact, in 1971 Makerere commissioned the third
computer in Uganda after the Treasury Department of the Ministry of Finance and Uganda
Electricity Board had both unveiled theirs in 1967. However, during the upheavals in Uganda
in the 1970s and 1980s Uganda slid into political and economic disintegration, leading to
social sector spending being cut, hitting education the hardest (Musisi & Muwanga, 2003).
This left Makerere impoverished and almost bankrupt to the extent that there was virtually no
application of ICT to either teaching and learning, or institutional management during the
1971-1985 period. Fresh awareness of ICT came to the University in the 1990s, and with the
assistance of the International Development & Research Council (IDRC). In 1991 Makerere
became one of the first universities in Sub-Saharan Africa to use electronic mail.
Furthermore, from 1999, the university adopted ICT as an integral part of its
rebuilding. For example, by late 2000, the University had developed an ICT Policy and
Master Plan, 2001 – 2004 (Makerere University, 2001; Musisi & Muwanga, 2003).
According to the plan, Makerere University committed itself to a number of things such as;
to assure availability of all anticipated ICT services at any workplace in the University. To
assure availability of user-level data communication services such as e-mail, access-to-
internet and internet/ intranet services. Thirdly, to promote office computing, fourth, to
improve the efficiency and effectiveness of library operations, among others. With those
objectives in mind, Makerere University and its donors have sunk large amounts of money
on ICT. For example the Makerere Directorate of ICT Support, DICTS (Tusubira, Mulira,
Kahiigi & Kivunke, 2008, pp.54–59) reports that the university planned to disburse as much
as $13.3 million on ICT projects for the period, 2005 to 2009. This is a staggering sum by the
standards of an institution in a young country.
The background has underscored the use of ICT as having the potential to benefit the
individual user and his/ her organization, and hence the large sums of money committed by
Makerere University towards ICT. Unfortunately however, the use of ICT by students in
Makerere University has consistently been reported to be low (e.g. “Mamdani calls for
reforms at Makerere”, in the Daily Monitor newspaper of July 2, 2012), which quoted the
Director of the Makerere Institute of Social Research (MISR) as noting that the more the
university avails students of online resources, the more the reading culture of students
reduces, implying non-use of the ICT resources. A major question for the study was, what the
common characteristics of those individual students that are apparently ahead of others in use
of ICT, were. Unearthing those correlates is important to have an idea of the “critical success
factors”, CSF (Andersen & Gottschalk, 2001; Hung, Hung, Tsai & Jiang, 2010; Selim, 2007;
Tusubira, et al., 2008) that can be manipulated to enhance the use of ICT in the University.
Theoretical Framework
In this study, the dependent variable (DV) that is, the “use of ICT” was considered as
a “use of an innovation”, and hence led to the invocation of a use of innovations theory,
namely Venkatesh and colleagues’ (2003) Unified Theory of Acceptance and Use of
Technology (UTAUT). The UTAUT relates the use of innovations such as ICT to four core
constructs, namely the “performance expectancy” (PE), the “effort expectancy” (EE), the
“social influence” (SI) and the “facilitating conditions” (FC). Studies basing on the UTAUT
as their theoretical basis can be found (e.g. El-Gayar, Moran & Hawkes, 2011; Gupta,
Dasgupta & Gupta, 2008). However from their analysis of 450 articles, Williams, Rana,
Dwivedi and Lal (2011), reported that, “although a large number of studies have cited the
[UTAUT] originating article [Venkatesh et al., 2003] since its appearance, only 43 actually
utilized the theory or its constructs in their empirical research” (p. 231), implying that so far
most researchers just cite UTAUT instead of actually using it. This study was intended to
narrow this theoretical gap where UTAUT is generally ignored. On the basis of this theory, it
is proposed that the levels of use of ICT will vary with a student’s level of perception of ICT
in terms of its “performance expectancy” (PE), “effort expectancy” (EE), “social influence”
(SI), and “facilitating conditions” (FC).
Hypotheses
Performance Expectancy as a Correlate of Adoption of Innovations
Venkatesh and colleagues (2003) in their Unified Theory of Acceptance and Use of
Technology (UTAUT) define “performance expectancy” (PE) as the degree to which a
potential adopter believes that using an innovation will help him or her improve performance on the job. PE is thus what Nov and Ye (2009) refer to as “Job Relevance” (JR) that is the fit
between an innovation and the job goals a user needs to accomplish. PE and JR are similar to
what Rogers (2003) terms as the Perceived Relative Advantage (PRA) of an innovation,
which he defines as the degree to which the innovation is perceived as being better than the
idea it supersedes, and is often expressed as economic profitability, and as conveying social
prestige. PRA can also be measured in terms of the convenience and satisfaction (Kelleher &
Sweetser, 2012) that the innovation brings to the adopter or user. In his Technology Adoption
Model (TAM), Davis (1989) refers to PE as the “perceived usefulness” (PU) of an
innovation, which he (Davis) defines as the “prospective user’s subjective probability that
using a specific application system will increase his or her job performance” (cited in Gupta,
et al., 2008, p. 144). Thus clearly, apart from using different phrases, the terms performance
expectancy (Venkatesh, et al., 2003), job relevance (Nov & Ye, 2009), perceived relative
advantage (Rogers, 2003), and perceived usefulness (Davis, 1989) are synonyms. UTAUT
(Venkatesh, et al., 2003) assert that the greater the performance expectancy of an innovation,
the more rapid its adoption. On the basis of this hypothesis, it is reasonable to expect the PE
of ICT to be associated with higher levels of ICT use. Recent past studies (e.g. Bakkabulindi,
2012a, 2011b; Drent & Meelissen, 2008; El-Gayar et al., 2011; Gupta, et al., 2008; Lee,
Yoon & Lee, 2009; Lule, et al., 2012; Putzer & Park, 2010; Travica, 2008; Zafiropoulos,
Karavasilis & Vrana, 2012) positively relate PE and the use of innovations. But studies
giving a different result (e.g. Bakkabulindi, Osunsan, Kazibwe, Samanya & Mabonga, 2010)
can also be got. Such contentious empirical results make it fitting to test the hypothesis to the
effect that; performance expectancy (PE) is positively correlated with the use of ICT.
Effort Expectancy as a Correlate of Adoption of Innovations
In their UTAUT, Venkatesh and colleagues (2003) coined a new term “effort
expectancy” (EE) of an innovation. This is the degree of ease associated with the use of the
innovation, that is, the degree to which a potential adopter considers the use of the innovation
to be free of effort. In his Innovation Diffusion Theory (IDT), Rogers (2003) uses the term
“perceived user friendliness” (PUF), which is the “degree to which an innovation is
perceived as relatively difficult to understand and use” (p. 257) as the equivalent of EE. In
his Technology Acceptance and Adoption Model (TAM), Davis (1989) refers to Venkatesh,
et al., (2003)’s EE or Rogers’ PUF as the “perceived ease of use” (PEU) of an innovation,
which he (Davis) defines as the “degree to which a prospective user expects the target system
to be free of effort” (cited in Gupta etal, 2008, p. 144). Thus apart from using different
phraseology, the terms effort expectancy (Venkatesh et al., 2003), perceived user friendliness
(Rogers, 2003) and perceived ease of use (Davis, 1989) are the same and are used
interchangeably in this paper. Al-Hajri and Tatnall (2008) contend that understanding EE is
important because it has implications for the design of training intervention to manipulate the
perception of EE. Recent past studies that have positively related EE and the use of
innovations are many (e.g. Bakkabulindi, 2012a, 2011b; El-Gayar, et al., 2011; Garcia-
Valcarel & Tejedor, 2009; Gupta, et al., 2008; Lee, et al., 2009; Lule, et al., 2012; Travica,
2008). However, other studies (e.g. Bakkabulindi, Osunsan, et al., 2010; Hung, et al., 2010;
Zafiropoulos, et al., 2012) have found EE not to correlate with innovation diffusion and/ or
adoption. Hence in this paper it was imperative to test the hypothesis that effort expectancy
(EE) positively correlated with the use of ICT.
Social influence (SI) refers to the extent to which an individual is attracted to an
innovation on account of how that individual sees others in the society benefiting from the
use of the innovation. This term is similar to Rogers’ (2003) “perceived observability” (PO),
which is the degree to which the results of an innovation are visible to others. Thus SI or PO
can also be referred to as the perceived communicability of an innovation. Rogers (2003)
observes that whereas some ideas are easily observed, communicated or described to other
people, other innovations are difficult to observe, communicate or describe to others. For
example, an innovation such as ICT, has two components; (i) hardware which are the
physical parts of ICT, and (ii) software that consists of the instruction base for the
technology. Thus the software component of a technological innovation (e.g. ICT) is not so
apparent to observation. So innovations in which the software aspect is dominant possess less
observability, and usually have a relatively slower rate of use (Rogers, 2003). Different
phraseology notwithstanding therefore, the terms social influence (Venkatesh, et al., 2003),
perceived observability, communicability (Rogers, 2003), are the same and are used
interchangeably in this paper. Several recent studies (e.g. Bakkabulindi, 2012a, 2011b) have
positively related social influence (SI) or the perceived observability (PO) and the use of
innovations. Thus the study being reported on, had enough ground to propose that; social
influence (SI) positively correlates with the use of ICT.
Facilitating Conditions as a Correlate of Adoption of Innovations
UTAUT (Venkatesh, et al., 2003) uses the term “facilitating conditions” (FC) for an
innovation to refer to the “degree to which an individual believes that an organizational and
technical infrastructure exists to support use of the system” (in Gupta et al., 2008, p. 146).
Thus the term FC is synonymous with what Rogers (2003) in his IDT calls the perceived
compatibility (PC) of an innovation that is the degree to which the innovation is perceived as
consistent with the existing values, past experiences, and needs of potential adopters. Rogers
(2003) contends that an innovation can be compatible or incompatible with (i) the socio
cultural values and beliefs (ii) the previously introduced ideas and/ or (iii) the client needs for
the innovation. Perceived compatibility is positively related to an innovation’s rate of use
(Rogers, 2003) in that an innovation or new idea that is more compatible is less uncertain to
the potential user and fits more closely with the individual’s situation. Such compatibility
helps the individual to give meaning to the new idea so that it is regarded as more familiar.
Recent studies (e.g. Travica, 2008; Zafiropoulos et al., 2012), positively relating facilitating
conditions, FC (Venkatesh, 2003) or perceived compatibility, PC (Rogers, 2003) and the use
of innovations can be found. Ironically, despite such massive empirical support for the
hypothesis, contrary findings (e.g. Bakkabulindi, 2012a, 2011b; Bakkabulindi, Osunsan et
al., 2010) can be cited. Hence empirically, support for and against the hypothesis is almost at
par. This suggested the need for further studies to test the postulation that facilitating
conditions (FC) positively correlate with the use of ICT.
