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

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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.

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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

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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

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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

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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

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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,

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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

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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

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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.

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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

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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

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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

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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.

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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

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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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