Effective Implementation of Educational Technology
Innovation in Higher Education Institutions: A Survey of
Selected Projects in African Universities
Abstract
The purpose of this paper was to provide a deeper understanding of
what would spur effective implementation of technology in higher
education institutions. This was by looking at both descriptive
issues and also delving deep into underlying issues. This
multinational study was based on 26 technology implementation
projects drawn from seven (7) universities spread in six (6)
countries in sub-Saharan Africa. The exploratory study adopted a
critical realism method based on mixed-method approach. A total of
105 usable survey responses were received with 53 interviews
conducted. Logistic regression model was used as the inferential
for analysing quantitative data where SPSS version 17 and R
statistical packages were used. Interview data was analysed using
theoretical thematic analysis model. The study results demonstrated
1
that monitoring and evaluation, top management, organizational
culture, team leadership, financial motivation, organizational
climate and innovation efficacy were significant within the model.
However, it was noted that Technology framing, innovation
environment and innovation attributes equally affected technology
implementation. The study was limited to a specific funded
project, there is need to test the results on another similar
technology project. HEIs should invest in capacity development in
technology to enhance absorptive capacity. Further, there is need
to enhance technology transfer for any externally funded project so
as to build institutional absorptive capacity for technological
innovations. The paper went beyond the normal descriptive nature on
but brought out the underlying issue in technology implementation
especially in a challenged environment.
Keywords : Technology innovations, Organizational Theory,
implementation effectiveness, Fidelity in Implementation,
Innovation Framing, Absorptive capacity, technology Transfer,
Logistic regression, Higher education Institutions
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Introduction
The question on how to scale up success in implementation of
instructional technologies in HEIs has been a concern to
researchers. This is because generally the success rate in adoption
and implementation of technological innovations has been less than
30% (Peansupap and Walker, 2005, Gichoya, 2005, Kumar, 2007,
Michaelis, Stegmaier, & Sonntag, 2010 ) . There is a lot of
research done on ICT adoption amidst this high failure. This study
undertook to investigate success and failure of education
technology innovation projects, with emphasis of the PHEA-ETI
funded projects between 2008 – 2012. African universities have
invested heavily in technology for all sectors of education, but
with below par success in implementation. The heavily funded
projects has resulted to short-lived success or outright failure
(Kirschner, Hendricks, Paas, Wopereis, & Cordewener, 2004).
Traditionally, technical issues were viewed as being major cause of
implementation failure but contemporary research has shown social
failure impacts more (Peansupap and Walker, 2005). This study
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undertook a survey of the said projects to determine why some
projects were effectively implemented while others were a failure.
This was aimed at ensuring future projects realize more effective
implementation. While most adoption theories are silent on
organizational factors, its is clear that decisions to adopt
technology are organizational in nature (King and Gribbins, 2002).
The traditional model, such as Technology Adoption Model (TAM),
Diffusion of Innovation Model and MIS success model (DeLone and
McLean, 2003) have focused more on individual behavior or decision.
Technology adoption is complex and involves myriad levels of
stakeholders in adoption and implementation. The challenge is in
knowing what organisaztional designs will lead to effective
implementation of ICT projects. The study adopted an
organizational theory model developed by Weiner, Lewis and Linnan
(2009) whose work was again based on study by Klein, Conn and Sorra
(2001). This paper provides (1) an overview of organizational
models and structures that can facilitate technology implementation
in HEI, and (2) delves deeper into implementation to understand
underlying issues.
4
Implementation in the context of this study looked at the stage
after organization (top management) has assessed a technological
innovation and agreed to adopt and handed it to team that would
oversee its use. It is the stage after primary decision to adopt an
innovation has been made. While top management makes decision to
adopt, the middle level, or affected department implements the
innovation. Implementation effectiveness results when an innovation
has been put to use by users.
Adoption of technologies in universities can be regarded as
innovation because HEIs use them as a new approach in teaching and
learning. Dede (2000) observed that these innovative technology-
based models when adopted in teaching and learning had the
potential dramatically improve educational outcomes. In the PHEA-
ETI case, integration of ICTs in education was the most popular of
the innovations. ICT denote technologies computers, internet,
mobile, TV and radio. Due to technological convergence, we mainly
see ICT as synonymous with computers thus ICTs But a better wording
would be computing devices because in modern society especially in
Africa, mobile phones have become more common than computers while
5
the convergence of these technologies have realized
interoperability.
Related study
Implementing Computerized Technology
Several studies have tried to explain what might lead to successful
adoption of innovations. However, as noted in the literature, most
of these studies address only the initial stage of innovation
adoption, what could be referred to as decision to adopt stage. But
one study which came up with a model that looked at the whole
process of implementation, from initial decision making to
innovation effectiveness was by Klein et al (2001). This work has
been greatly cited, reviewed, critiqued and modified. Klein et al
(2001) studied implementation of computerized technology and
proposed a model. Specifically, the study looked at manufacturing
resource planning (MRP) that was software integrated by
manufacturing firms to assist in their processes. The software
assisted firms in tracking production schedules, inventory control,
supply of parts management and management of sales. Specifically,
the studied the plans that had gone live, that is they had begun to
6
use, with the MRP system utmost 24 months before commencement of
the study. 39 plants based in United States were considered for the
study. Respondents for the survey tool used were plant managers,
other managers and supervisors that were involved with the system,
team involved in the implementation of the system and users of the
system. In total there were 1219 respondents. The survey tool
mainly used a 5-point Likert scale measures with the plant being
the unit of analysis.
In their regression analysis findings, the following variables were
found to be significant and thus could be used to measure
computerized technology implementation effectiveness: financial
resource availability (r=0.42, p<0.01); management support (r=0.31,
p<0.05); implementation climate (r=0.40, p<0.01); implementation
policies and practice (r=0.37, p<0.001). The study used structural
equation modeling to test overall fit of model. The study noted
that although it was a preliminary study, it was the significantly
contribution to implementation research that had gained little
attention. Further they noted that success in implementing
innovations had great influence on organization’s survival. The
7
study therefore noted that that for first time it elicited the
information that management support, financial resource
availability , policies and practices and implementation climate
would see different organisations either make the implementation
process effective or not. Maditino, Hatzoudes and Tsairidis (2011)
studied the factors that affect the effective implementation of ERP
system. Maditino et al (2011) study adopted Klein et al (2001)
model. They came up with a questionnaire that was distributed to
361 companies in Greece. The September to December 2008 data
collection saw 108 usable questionnaires returned. Structural
Equation Modelling (SEM) was used to analyze data. Just like Klein
et al. (2001), they noted that top management support greatly
determined effective implementation. Other factors included: user
support, consultant support, communication effectiveness, conflict
resolution and knowledge transfer.
As noted, this Klein et al (2001) study only relied on survey
method and thus might have ignored the details behind the figures.
This is an area that further research would have looked into. The
8
study also looked at only one innovation that meant that there was
limitation in generalization.
Figure 2.4: Klein, Conn and Sorra’s (2001) model’s Innovation
Implementation Effectiveness Model
Organizational Innovation Implementation Effectiveness
Sawang and Unsworth (2011) study aimed at validating an earlier
model by Sawang, Unsworth and Sorbello 2006) that proposed on
implementation effectiveness. The Sawang, Unworthy and Sorbello
(2006) model was a test of Klein et al (2001) model and was more of
a comparison between Thai and Australian firms. In the 2011 study,
9
Sawang and Unsworth (2011) surveyed 135 organisations. In this
study, the human resources were noted as a significant contributor
to implementation effectiveness. Specifically, the study found out
that the skilled employees’ availability was positively related to
implementation effectiveness of innovations in firms. The initial
model that this study adopted had the variables; Financial
resources availability; Top management support; Implementation
policies and practices; Implementation climate. Some 750 firms were
selected from Australian Business Register where the unit of
analysis was firm itself. From the sampled firms, only 135 firms,
which the study computed as 18% response rate responded. From the
study, the following variables were found to be significant:
Financial resources availability with = 0.76 and two items
within; Top management support with three items and = 0.77;
Implementation policies and practices with six items and = 0:81;
Implementation climate with three items and = 0:92;
Implementation effectiveness where the employees in the firms were
asked to describe their experience with innovation. Again the
variable was tested for internal reliability and had an = 0.74.
Human resources availability had two items that were adapted from
10
Nystrom et al.’s (2002) study. The internal reliability estimate
was 0.73. The enhanced model had adequate fit to the sample in the
study.
Like Klein et al (2001) study, Sawang and Unsworth (2011) study
used only survey method. This means that the study did not consider
underlying issues.
Methodology
The study was based on the Partnership for Higher Education,
Education Technology Initiative1 (PHEA-ETI) funded multinational
projects run in six (6) countries in sub-saharan Africa. The
projects were run between 2008 and June 2012. The PHEA initiative
was started with the aim of supporting HEIs in sub-Saharan Africa
(Parker, 2010; Lindow, 2011). Specifically, by the year 2011, nine
different African countries had received support from PHEA.
According to Lindow (2011), these countries were: Egypt, Ghana,1 The Partnership for Higher Education in Africa (PHEA) was started as a joint venture of four US foundations in 2000 and subsequently grew to seven foundations. The foundations were the Carnegie Corporation of New York, the Ford Foundation, the John D. and Catherine T. MacArthur Foundation, the Rockefeller Foundation, the William and Flora Hewlett Foundation, the Andrew W. Mellon Foundation, and the Kresge Foundation
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Kenya, Madagascar, Mozambique, Nigeria, South Africa, Tanzania, and
Uganda. The Education Technology Initiative (ETI) was aimed at
accelerating the use of technology in teaching and learning. The
projects, which ran between 2008 and 2012, endeavoured to achieve
accelerated creativity and use of ICT in the education sector. By
financing the technology-based projects, the objective was to
stimulate uptake of new technologies, mainly ICT, in selected
universities in Africa.
This study used multi-method approach where both survey
questionnaire and interviews were used to collect data. The survey
questionnaire was distributed through email. Truell (2003) believed
that the appropriateness of Internet tools for survey data
collection was pegged on research settings and goals. In this
study, web based survey was appropriate because all the study was
on technology adoption and all projects under implementation were
using internet and ICT. For this study, respondents were working
on technology projects, were based in HEI and were predominantly
using computers to implement the projects. This study was
technology based where all respondents had access to internet and
email, further; the respondents were drawn from different12
countries. The wide geographical spread of respondents made it not
economically viable for face to face administration and equally
costly for posting the questionnaire. Schmidt et al (2012) argued
that 20% was a too low while 80% was a de facto standard response
rate. In this study, out of the expected 163 respondents, there
were 105 completed survey responses giving a response rate of
64.4%. This response rate was also far much higher than what was
recorded in similar studies in the implementation science domain
(Sawang, Unsworth & Sorbello 2007, Sawang & Unsworth 2011). The
64.4% response rate in this study could therefore be regarded as
acceptable from cited literature. The questionnaire was distributed
through email where a hot-link was provided for access to web-based
survey.
Interview responses were transcribed verbatim and then theoretical
thematic used to perform the analysis. The transcribed write-up was
read and re-read and the analysts extracted the overarching themes,
referred to as thematic analysis (Schmidt et al ,2012). Schmidt et
al (2012) contended that thematic analysis was one of the common
methods of analysis in qualitative research. The theoretical
13
thematic analysis framework ensured that units with meanings that
were related to the study objectives were identified. The meanings
were coded against subcategories as per the framework adopted
Measures
The construct used were adapted from Klein, Conn and Corra (2001)
tool. Others were adapted from Zint and Montgomery (2010).
Implementation effectiveness - This is the dependent variable and was
measured as 1 if the technological innovation was in use by the
time study data was being collected (effectiveness in
implementation), or 0 otherwise. Only one item was included in the
questionnaire where the respondent indicated whether the technology
was in use or not.
Monitoring and evaluation - This independent variable measured the role
the external stakeholders played in implementation of the
technological innovations. This was measured in terms of
involvement in vouching for projects with top management, follow-up
on the implementation process, and evaluators. A five point scale
ranging from 1 “strongly agree” to 5 “strongly disagree” was used.
14
The project implementer indicated how well each statement fitted
with the specific project involved.
Financial motivation - The role of financial rewards in project
implementation was adapted from the study by Sawang and Unsworth
(2011). This attribute was measured in terms of financial
compensation. A five point scale ranging from 1 “strongly agree” to
5 “strongly disagree” was used. The project implementer indicated
how well each statement fitted with the specific project involved.
Organizational culture - This variable looked at the organization in terms
of beliefs. This was measured in terms of the following: existing
ICT infrastructure; assistance in using the innovation; awareness
of the new system. A five point scale ranging from 1 “strongly
agree” to 5 “strongly disagree” was used. The project implementer
indicated how well each statement fitted with the specific project
involved.
Organizational climate - In the current study, this variable looked at the
social environment that defined the implementation of a specific
project. While culture was understood to cut across the
organization, climate was understood as being restricted to a
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project and/or task. This variable looked at skills (training);
facilitation; and lack of obstacles. The attributes were adopted
from Patterson et al. (2005), Dong, Neufeld, and Higgins (2008) and
Sawang and Unsworth (2009). A five point scale ranging from 1
“strongly agree” to 5 “strongly disagree” was used. The project
implementer indicated how well each statement fitted with the
specific project involved.
Project leadership - Leadership attributes looked at leader
voluntariness; dedication to the project; previous projects
managed; and ICT knowledge. A five point scale ranging from 1
“strongly agree” to 5 “strongly disagree” was used. The project
implementer indicated how well each statement fitted with the
specific project involved.
Top management - This variable was measured in terms of the role of
top management in the following: appointing team leaders; and
willingness to provide resources. A five point scale ranging from 1
“strongly agree” to 5 “strongly disagree” was used. The project
implementer indicated how well each statement fitted with the
specific project involved.
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Innovation efficacy - This variable was measured on how well the users
believed the innovation would foster of their values and,
specifically, whether the innovation would add value to their work.
A five point scale ranging from 1 “strongly agree” to 5 “strongly
disagree” was used. The project implementer indicated how well each
statement fitted with the specific project involved.
Study Results
Citing Straub et al. (2004), Dwivedi et al. (2006) argued for the
need of reliability test so as to confirm the internal consistency.
In this study, individual item reliability was measured using
Cronbach's alpha. Krippendorff and Bock (2007) noted that it would
statistically not be plausible to expect either a 1 or a 0 but can
be any point between the two thus if the estimated Cronbach’s alpha
is above 0.7, then the instrument would be deemed to have a high
internal consistency, thus high reliability (Dwivedi et al. 2006,
Sanchez-Franco & Rondan-Cataluña 2010) . Krippendorff and Bock
(2007) put .667 as a minimum acceptable level of reliability.
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When the items were tested for reliability using SPSS version 16,
the determinant financial availability had an index of 0.350 but
rose to 0.717 after four (4) iterations. This means four items
describing financial availability were dispensed with. Project team
had a test result had an index of 0.645 but rose to 0.722 after two
iterations meaning two sub-variables were removed. Top management
had a reliability of 0.738 with no iteration. User training had
0.794 after two iterations. .system efficacy had a reliability
result of 0.774. Organizational culture gave an output of 0.774
after two iterations. When a test of reliability was done on the
all contributing factors, it was 0.839 with no iteration. This
therefore meant that there was a high consistency in the responses
provided and thus all items making up the determinants were
regarded for this study.
Internal consistency was done using the split half reliability
test. Under this test, if all items are drawn from the same domain,
then the two halves should correlate highly with each other. When
this test was run on the data, financial availability’s reliability
was 0.70 after four(4) iterations. Golafshani (2003) argued that
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iterations helped to stabilise the results. During those
iterations, the four items that were noted to have low correlations
were removed. The variable project team had an internal consistency
of 0.734 after two (2) iterations. Financial motivation had a value
of 0.7 after 2 iteration. Top management had an internal
consistency of 0.842 while user training had a value of 0.712 after
one iteration. System efficacy had a value of 0.831 with no
iteration. Organisational culture internal consistency result was
0.735 after three iterations. Team leadership’s internal
consistency result was 0.661 after three iterations. Finally when a
test of internal consistency was done on all the variables included
in the model, the result was 0.848 with no iterations. This meant
that all the variables had a high internal consistency and thus
could all be considered as determinants to innovation
implementation effectiveness.
Innovation Implementation Effectiveness
The overall objective of the study was to determine if projects
were effectively implemented or not. Shea, Pickett and Li (2005)
posited that to measure technology effectiveness in a firm, one
19
needed to test if it was in use. Pickett and Li (2005) contention
was supported by (Weiner et al., 2009) who believed early use was
an important measure of implementation effectiveness because it
illustrated the organizations’ intention to make use of the
technology. This informed the definition of the dependent
variable, innovation implementation effectiveness. The dependent
variable, which measures implementation effectiveness, was
categorically represented by “Yes” and “No”. Yes was equal to 1 if
implementation was effective, while No was equal to 0 (that is,
where there was no effective implementation).
From the survey, the responses to the question “Are you using the
technology for teaching and learning?” 38 out of 105 respondents
answered affirmatively. This was 36.1% responses with yes, 59
responses were negative while nine (9) respondents did not enter
either yes or no. During the interview process, the interviewer
asked the respondents: “Are you using the technology you were
implementing for teaching and learning or disseminated the
findings?”. The interview responses clearly showed few initiatives
were in use. Below is a partial use of innovation.
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We are using the platform to teach and you can see that we are struggling in getting
to only create five courses or six courses and you have had 12 of them, that means
we are going to have if not all because out of eight, six are functional, the other two
are still grappling to getting functional but six are functioning well and they are
actually using it to teach and to assess and evaluate their students so which is good
for us.
From the responses, it was clear none of the technology projects
had met all the set objectives. This meant that the effective
projects could be classified as partial success. Kumar (2007) made
similar findings when studying technology projects in India. This
meant that the PHEA-ETI funded project supported Kumar’s findings
that Kumar most technology projects in developing countries rarely
meet all objectives set out thus those that could be classified as
successful fell under the category of partial- success.
Though the quantitative and qualitative data were analysed
separately, there was convergence of the findings on the number of
projects that were effectively implemented. For example, the
qualitative results supported the quantitative findings that only
about 30% of the projects were effectively implemented. This low
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rate of success in technology implementation in higher education
supports earlier findings in the area (Johnson, 2000; Gichoya,
2005; Kumar (2007), Sawang & Unsworth, 2011; and Then & Amaria,
2013).
Individual Determinants
M &E Financia
l
Organizat
ion
Organizat
ion
Leadershi
p Style
Top
Management
Innovati
onM &E 1.000Financial
Resource
0.021 1.000
Organization
Culture
-0.119 -0.101 1.000
Organization
Climate
-0.096 0.044 -0.083 1.000
Project
Leadership
0.120 0.070 -0.015 0.112 1.000
Top Management 0.086 0.091 -0.136 0.088 0.113 1.000
Innovation
Efficacy
0.079 0.053 -0.009 0.073 0.025 0.062 1.000
Source : Data collection March 2012 – May 2013
From the correlation matrix (table 1), correlation of 1.0 means the
two variables are perfectly correlated while a correlation of 0.0
means no correlation. From the matrix above, the cells with a
correlation of 1.0 show the correlation of the explanatory variable
itself. What were important in the test are the figures to the left
of 1.0 correlation. As one moves down columns row by row, it is
22
clear that the values were closer to 0.0 than 1.0. From the
regression results (Table 3), it is evident that none of the
independent variables in the current analysis had a standard error larger
than 2.0, which confirmed that there was no evidence of
multicollinearity.
LR chi2 15.08Prob > chi2 0.0350Log likelihood -62.234582Pseudo R2 0.1081Cox & Snell R2 0.54Nagelkerke R2 0.63Source : Data collection March 2012 – May 2013In terms of the inferential statistics: The LR chi-square of 15.08
with a p-value of 0.035 and the log likelihood of -62.23 showed
that the model was better than a null one. The test intercept, the
constant, which had p-value<0.05, suggested that the intercept was
of importance in the model and thus must be included. Another
descriptive measure of goodness of fit used was R2. The results of
the model showed that the Cox & Snell R2 was 0.54 while the
Nagelkerke R2 was 0.63. This meant the predictor variables
explained between 54% and 63% of innovation implementation
effectiveness.
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The inferential goodness-of-fit test is the Hosmer-Leme (H-L) test,
which yielded X2 of 6.975 and a p-value of 0.432. The null
hypothesis could have stated that the data fits the logistic
regression model, with the alternative saying that the data does
not fit the logistic regression model. A p-value of 0.432 in the
current study means that the null hypothesis was not rejected.
This meant that there was evidence for goodness of fit of the data;
that is, the logistic regression fitted the data perfectly. The
findings were similar to those of Peng et al. (2002) and Dwivedi et
al. (2010).
Table 3 - Regression of individual predictorsVariables β SE df Sig.
Monitoring and evaluation 0.217 0.393 1 0.581Financial resource
motivation
0.511 0.275 1 0.063*
Organizational culture 0.822 0.338 1 0.015**
Organizational climate 0.318 0.434 1 0.464
Project leadership 0.262 0.261 1 0.316
Top management 0.681 0.161 1 0.049**
Innovation efficacy 0.651 0.350 1 0.063*
Constant 0.585 0.223 1 0.009**
Source : Data collection March 2012 – May 2013
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The regression results (table3) present the test on individual
predictors. The standard deviations and significant results of each
variable are presented. The log odds of a technology innovation
being implemented effectively were found to be positively related
to monitoring and evaluation. The coefficient of monitoring and
evaluation not significant from survey data (α = 0.581) meaning the
variable had no effect on implementation effectiveness from the
survey results. However, the qualitative data showed that the
SAIDE team played a key role in the projects implementation
process. . In all the 26 projects, the role of sponsor in
facilitating training was noted. The interview excerpt below
demonstrated training role:
Yes SAIDE staff used to train. They came periodically to have a look at what we have
done, criticize it, and tell us what we have not done well and what still needs to be
done. For example, on my GES 104 science one of my evaluation comments has been
that there should be pictures and things that are attached to do with that.
These results resonated with the findings of Saak (2007), that
sponsor involvement in the project is positively related to project
success
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Financial resource motivation was found to be positively related to
innovation implementation effectiveness (β = 0.511 and p = .063).
Albeit a weak statistical relationship between the predictor and
predicted variable (at 10% confidence), it means that technology
innovation implementation effectiveness increased with an increase
in financial motivation to implementers. The qualitative findings
corroborated the survey findings that financial resource motivation
was a big motivator for the implementers to be involved. The
interview responses had 100% of all respondents indicating that
money boosted implementers’ involvement in the project. Equally,
Organisational culture was found to have a positive coefficient
and was statistically significant in determining innovation
implementation effectiveness (β = 0.822 and p = .015). Other
variables (Organizational climate, Project leadership, Top
management, Innovation efficacy) also were positively related with
β = 0.318, 0.262, 0.681 and 0.651 respectively. However , on
significance test, Organizational climate and Project leadership
had p-values greater than 0.05 indicating they did not determine
innovation implementation effectiveness. The qualitatrive findings
however showed they were significant. Where there was lack of
26
congruence between quantitative and qualitative findings the study
relied on qualitative results as the correct position. Wagner et
al. (2013) argued that incongruous results between quantitative and
qualitative findings could yield a more nuanced and comprehensive
understanding of the phenomenon, giving various viewpoints. Glaser
(2008: 1) argued that qualitative data provides the “meaning and
factual interpretation” .Anderson (2006: 3) who argued that the
“qualitative data generated rich, detailed and valid (process)
data”, thus enabling the researcher to gain in-depth understanding
of the phenomenon.
The overall statistics shows the result of including all of the
predictors into the model. According to the results, the overall
model which includes all the independent variables in the model
will be significance (score=7.198, p=0.303). This is also supported
by the omnibus tests of model coefficients where the overall model
was established to insignificant (χ (6) = 8.094, p=0.231). This
thus confirms the earlier position taken in the study that all
variables in the model were found to be significant in determining
innovation implementation effectiveness in HEIs.
27
Emerging Patterns
Overall emerging issues from the interviews could be summaried as:
poor innovation framing; lack of fidelity in project implementation
and existence of silo mentality. All these could be attributed to :
lack of institutional absorptive capacity and lack of technology
transfer.
Innovation framing, which is a management-level issue, looked at
how the innovation is viewed in terms of the strategic plan and
also political considerations. Top management makes the decision to
adopt an innovation, but the decision must first be informed by how
well the technological innovation fits within the university’s
strategic plan. In terms of the issue of having a committee, Allard
(2003) argued that bringing relevant people into the planning
committee assisted in proper framing.
Lack of proper framing was evident in the study as per interview
excerpts.
you have this multiple sources of knowledge coming in and when you are using the
knowledge sometimes you can’t attribute it to one source that this came from PHEA
because I for instance I have a lot of things I got from UCT [University of Cape Town]
28
which I use here, there are other things which are used from PHEA so it becomes
difficult to actually pinpoint.
In some instances, it was noted that the way the projects were
introduced to university meant they were bound to fail. This is
where all stakeholders were not involved in the adoption process.
Johnson(2000) called this framing of the innovation. The projects
were therefore more or less for show. Proper framing would also
have facilitated technology transfer while first developing
adoptive capacities by the affected institutions. McGregor (2002)
used qualitative method to determine success of implementing an
innovation in school library. McGregor found out that principles
played a key role in the innovation success, but noted when the
initial idea was floated; it was unclear to them until it was
explained by the library section. This clearly shows that role of
framing as consultative
Lack of institutional absorptive capacity and poor technology
transfer was also evident from the replicated projects in the
institutions. What was apparent from the interviews was that the
sources of project ideas were influenced by mostly external
29
stakeholders who had an impact on how the projects were
conceptualized in terms of objectives and expectations. It was also
noted that there was the changing nature of projects, their names
and sponsors, constantly required the enrolment of stakeholders.
The excerpt below demonstrated the noted change:
Yes we are using the system and training was there. But my first learning
management system was KEWL and then from KEWL to Blackboard.
This may create a situation in which an innovation becomes
"paralyzed by analysis", since with every change, the project needs
to be (re)initiated. The process of stakeholder enrolment is
critical for creating an innovation environment However; this
process was fraught with challenges in all of the organizations
that formed part of the current study.
While in some of the instances, there was clear conflict among the
implementation sections of the university. This could be attributed
to existence of “Silos mentality” where the affected sections were
not ready to collaborate share knowledge. The excerpt below
illustrated the existence of silo mentality. “They don’t share;
30
they would rather use people’s stuff but they don’t want to share”
and in another instance,
I was meant to come up with content, Yes, someone else was doing the animations,
we were supposed to provide them with our course materials....didn't see anywhere
where we worked together...and if we were supposed to then nobody asked me to
work with them 2. Because it will take me way too long to figure out what/which
modules to animate, how to animate and would eventually need an education
person/expert to assess if the material/animations will be cognitively
perceived/understood by students.
Mirriahi, Dawson and Hoven (2012) discovered that collaboration
played a key role in technology acceptance and use in HEIs. The
findings above corroborate findings by Indeje and Zheng (2009) who,
while interviewing users of IFMIS, noted that the centrality of the
system caused “disquietedness” in its use by other departments.
Fidelity of implementation is the extent to which the delivered
technology intervention adheres to the original design (Shapley,
Sheehan, Maloney & Caranikas-walker, 2010; Javeri & Persichitte,
2007). From the interviews conducted, although some of the
technology implementation projects were seen to be on course, some
31
had digressed from the main goal. Four projects , comprising 11% of
all projects lacked fidelity in implementation. The excerpt below
illustrates this theme.
Yes, and apart from that it had its limitations: number one, unless it is archived
students cannot go back to that same session. Once the sessions [is] over, one hour,
two hours, that’s it? […] so I sat them down as the director, I sold this idea to them;
something came up so suddenly then I carried them along under this new proposal,
the revised proposal is that we will simulate a class instead of having radio and TV
session as initially proposed.
OK, the Internet is there but the bandwidth was not adequate – very erratic – and
university had not increased it even though now it has been increased and probably
could accommodate it but at then it was not possible. So even without going
through all that kind of things they still had problems. So we changed to offline
mode via CDs
Another case of lack of fidelity is illustrated by the response in
the excerpt below:
No, that was the first phase; the next phase [...] had materials so they were to add
graphics and animations. But let me be clear, they did not add animation to all 12. I
think they picked a few [laughs] and added graphics..
32
Lack of implementation fidelity could be explained to have resulted
from poor framing of the project at conception, resulting in
project complexity. Examples of some of the reasons given to
justify the changes made to the projects during the course of
implementation were obtained from the interviews
Further study
This study mainly focused on funded technology implementation
initiatives, future research could focus on internal initiatives by
HEI. This could provide insights into how other non-monetary
incentive could affect implementation.
Another area of future research could be to apply the
organizational theory in private HEIs. Except UCM, all other
institutions under study were public HEIs. Private institutions
could have a different implementation environment thus the findings
would vary. Carrying a study using the same model on private HEIs
would thus help to provide some comparison from a different
environmental perspective.
Previous studies on HEIs e-readiness have focused on technology
aspects but ignored the human capacity. A study on technology33
readiness that would address the organizational issues would
complement the technology readiness. This study would there provide
an indication on the technological absorptive capacity amongst HEIs
in Africa. The study would be termed as exhaustive and more
informative to HEIs.
From the study also elucidated that there were several previous
technological initiatives in the HEIs under study. The PHEA-ETI was
thus not new but was more of a redundant initiative. A study on the
effectiveness of sponsors in capacity building in challenged
technology environment would provide much needed insights.
Literature in this study is replete with information on knowledge
transfer from HEI to industry. Further, there is the
acknowledgement of HEI as the antecedent factor to building
absorptive capacity of technology in firms. But HEI need also be
consumers of their own inventions and innovations. Future research
on how well HEI play these roles internally would provide much
needed insights.
Conclusions
34
The paper describes the determinants of technology innovation
implementation in HEIs. This is in a bid to mitigate the low rate
in application of technology in technology in teaching and
learning. The study adopted previous empirical studies on
implementation effectiveness. These studies were further
improvement of Klein et al (2001) technology implementation model.
A pilot test was done on the instrument developed. Then the
reliability test of the data received was done. The variables were
found to be reliable. Of the two new constructs extended on
previous studies, project team and system importance were found to
be significant contributors to innovation implementation
effectiveness. The model can therefore extend previous work in the
domain and can thus be used by technology implementers and top
management as a guide. In the discursive elements, it became clear
that most institutions there were lack of proper framing of the
innovations. Further, universities lacked absorptive capacity for
technology innovations, were poor in facilitating technology
transfer and that the universities were more inclined to technology
adoption than generation.
Acknowledgements35
This paper is part of “;;;;;;;;;;;;;” ongoing Phd study that was
supported by South African Institute of Distance Education (SAIDE).
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