ict in south africa universities

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Article Title Page

Determinant Factors of Information Communication Technology (ICT) Adoption byGovernment-owned Universities in Nigeria: A Qualitative Approach

Sunday C. EzeBusiness and Management Research InstituteUniversity of BedfordshireLutonUK

Hart O. AwaDepartment of MarketingUniversity of Port Harcourt

Port HarcourtNigeria

Joseph C. OkoyeDepartment of Public AdministrationNnamdi Azikiwe University

Awka, AnambraNigeria

Bartholomew C. EmechetaDepartment of ManagementUniversity of Port HarcourtPort HarcourtNigeria

Rosemary O. AnazodoDepartment of Public AdministrationNnamdi Azikiwe University

Awka, AnambraNigeria

NOTE: affiliations should appear as the following: Department (if applicable); Institution; City; State (US only); Country.No further information or detail should be included

Corresponding author: Sunday C. EzeCorresponding Authors Email: [email protected]

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Acknowledgments (if applicable):

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Structured Abstract:

Purpose - Recently, Nigerias university/college system has witnessed unprecedented competition following theinflux of private universities, most of whom have better ICT base. In spite of this and the potential benefits of ICTin building competitiveness, adoption by government-owned institutions has remained very low. Of course, thereare several socio-economic, technology, idiosyncratic and organization factors that hinder or drive ICT adoption.The primary objective of this paper is to specifically investigate and prioritize the effects of 13 factors indetermining ICT adoption in Nigerian universities.

Design/methodology/approach - The constructs of theoretical framework of technology-organization-environment(T-O-E) underpins the survey. The survey adopted in-depth unstructured and semi-structured interviews with 30


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senior executives drawn purposefully from at least one university in each of the five state capitals in the South-eastern Nigeria. Theunstructured interviews explained the current states of ICT adoption by the schools beyond what extant theories can offer; and semi-structured interviews validated issues resulting from unstructured interviews. Thematic Analysis Techniques (TAT) was used and

more specifically, latent level inductive coding was used to categorize the factors whereas Nvivo software facilitated the coding ofdata into the appropriate categories.

Findings - Evidence from the study shows that irrespective of the perceived competitive pressures and perceived benefits of ICTsolutions, government-owned universities are yet to exploit its full potentials in their operations. This behaviour is informed byincessant corrupt practices; irregular energy supply and internet connectivity/accessibility; lack of financial capacity, expert skills,managerial and technical flexibility/support; and poor regulatory policies and government supports.

Research Limitations/Implications - Taking a sample of universities in the south-eastern Nigeria limits the surveys power ofgeneralization. Therefore, extended data and measures are required by replicating this study in other geographic locations in orderto improve validity and reliability, and possibly build theories. While the factors investigated accord differential weight(s) of influence;the paper advised on creating policy framework spanning supportive and regulatory machineries.

Originality/Value - The postulate of most ICT theories that advanced technologies target large firms because of their financial andtechnical capabilities rarely applies to Nigerian universities. Therefore, this paper is one of the early inquiries that offer interesting

insights into adoption of ICT solutions from Nigerian universities, and attempt to validate such ICT theories. The paper raised somechallenges that will serve as points of departure to future researchers and provides university management, government, policymakers, and other stakeholders the bases for encouraging ICT adoption.

Keywords: ICT, adoption, universities, government

Article Classification: Research paper

For internal production use only

Running Heads:


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Determinant Factors of Information Communication Technology (ICT)

Adoption by Government-owned Universities in Nigeria:

A Qualitative Approach

Introduction

ICT adoption research has received enormous global attention (Benbasat and Barki, 2007;

Kannabiran and Dharmalingam, 2012) with interest spanning different systems and

applications in different contexts/settings (Venkatesh et al., 2007). Despite this, home-based

scholarly inquiries seem almost silent on the critical factors that constantly inhibit and/or

drive ICT adoption in government-owned universities (Oyeyinka and Adeya, 2004). Studies

in this area are critical because education significantly drives socio-economic growth, andICT is a strategic tool (Ongori, 2009; Orlikowski and Lacono, 2001) that creates integrative

and collaborative community (Alberto and Fernando, 2007; Alba et al., 2005) culminating to

transparency, value-added knowledge sharing, network externalities, operational efficiency

and flexibility, and improved competitiveness (Ongori and Migiro, 2010; Raymond and

Bergeron, 2008). ICT is a capital project; it involves, and even absorbs more, risks (Pan and

Jang, 2008) and often changes how colleges work and/or learn. With ICT, universities access

and disseminate information real-time; and up-date curricula, teaching, and research in order

to achieve ideal academic goals (Babalobi, 2010).

In the developed world, ICT has helped colleges to compete academically within and beyond;

in emerging economies such as china and India, it is gaining much attention; but in

developing nations like Nigeria, only very little attention goes to it. The general assumption

of most ICT theories is that advanced technologies target large firms because large firms are

financially strong and have technical capabilities to identify alternative technologies that

would suit their operational requirements (Ongori, 2009; Scupola, 2009; Yesbank, 2009;

Kannabiran and Dharmalingam, 2012). But extant literature (Oyeyinka and Adeya, 2004;

Babalobi, 2010) shows that though government-owned universities are treated as large

organizations in Nigerian context, they seem to lag behind in exploiting ICT solutions to the

fullest. Perhaps, the sluggish diffusion of internet from 0.1 % in 2000 to 7.4% in 2009

especially amongst those living in urban cities (Babalobi, 2010) explains that Nigerian

colleges seem to stand out from the general theories of adoption. Further, in categorizing the

stages of ICT adoption in Nigerias education sector into four- emerging, applying, infusion

and transformation, Babalobi (2010) emphasized that only few sectors in Nigeria have

implemented ICT beyond the emerging stage. For instance, the current statistics shows that


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90% of colleges are still at the emerging stage, 7% in applying stage and 3% at infusing and

transformation stages (Babalobi, 2010).

The government of Nigeria has launched laudable programmes to encourage ICT adoption

especially in universities. Amongst such programmes are Education Trust Fund (ETF),

National Telecommunications Policy (NTP), National Information Technology Development

Agency (NITDA), Nigerian Satellite Systems Programme (NSSP), and Science and

Technology Policy (STP). Yet the consensus is that the acute government interference and

bureaucratic bottlenecks of these agencies limit the ideal diffusion of ICT solutions

(Oyeyinka and Adeya, 2004; Eze et al., 2011). If well supported, these agencies are expected

to drive Nigerias education standard beyond classroom teaching and learning by

implementing ICT platforms that enable teachers and students share knowledge for teaching

and research. However, the populous approach to understanding ICT adoption has been

proposing models merely from existing theories and using quantitative paradigm perhaps

without sound and rigorous empirical validation to unravel their applicability.

Studies (Chen and Hirschheim, 2004; Williams et al., 2009; Lai, 2007; Oyeyinka and Adeya,

2004) show that positivism (64.8%) is employed more than interpretive paradigm (22.6%). A

review of Nigerians articles published in 12 Journals of Western economies between 2000

and 2011, on ICT adoption reveals very few (e.g., Apulu et al., 2011; Apulu and Ige, 2010)

qualitative surveys. While quantitative research is applauded for its predictive powers,

Benbasat and Barki (2007) and (Silver 2007) argued on the need for more rigorous

techniques (qualitative research) that explain phenomena in a deeper sense. Therefore, the

purpose of this survey is to use qualitative approach to explore and prioritize the key drivers

and barriers of ICT adoption in Nigerian universities and to explain why ICT adoption is very

slow even when popular theories (see Ongori, 2009; Scupola, 2009; Yesbank, 2009;

Kannabiran and Dharmalingam, 2012; Awa et al., 2011) suggest that large organizations are

supposed to adopt ICT solutions faster. The paper stands out since previous local inquiries

(e.g., Lai, 2007; Oyeyinka and Adeya, 2004) on ICT adoption in government-owned

institutions focused on positivist approach and of those inquiries (e.g., Apulu et al., 2011)

that used qualitative approach, none was underpinned by T-O-E framework. This paper is

structured as follows. First, it reviews previous studies on ICT adoption and research

propositions bordering on T-O-E framework. Next, it presents the research methodology,

findings and discussions, followed with implications, future research directions and finally

conclusion.


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Theoretical Underpinning and Framework

ICT refers to array of primary digital technologies and devices deployed to create, process,

analyze, store, retrieve, and disseminate information within a community (Ongori and

Migiro, 2010; Ritchie and Brindley, 2005). ICT defines an organized communication

networks including software, hardware, telecommunications, and information management

technologies (Apulu et al., 2011) that allow for flattened organizational hierarchy, and social

networking amongst value-chain members. These definitions portray ICT applications as

value-added and automated architectures that encourage inter-firm alignment and

operationally effective interactions between people, businesses, and governments

(Ssewanyana, 2009). In colleges, ICT applications permit knowledge sharing

amongst/between teachers and students; and encourage collaboration and timely reaction to

issues. The integrative and collaborative platforms of ICT allow students to access school

website, results, and library facilities; to pay fees and submit assignments; to share news and

information services; and perhaps to receive lectures online. By these, institutions overcome

dishonest behaviours and enjoy transparency, value-added information, network externalities

and knowledge sharing,operational agility and efficiency, and improved competitiveness.

However, several theories provide explanatory lenses to understand adoption behaviour.

Prominent amongst them are Innovation Diffusion Theory (IDT) (Rogers, 1983), Technology

Acceptance Model (TAM) (Davis, 1989), Technology-Organization-Environment (T-O-E)

(Tornatzky and Fleicher, 1990), Decision Maker-Technology-Organization-Environment (D-

T-O-E) (Thong, 1999), Actor Network Theory (ANT). While some of these theories evolve

from the theory of reasoned action and have their principal constructs cross-cutting, each

contributes to the underpinning literature of adoption. Everette Rogers five characteristic

constructs (relative advantage, complexity, compatibility, trialability, and observability),

which potentially determine adoption rate were shaped by three adoption predictors- leader

characteristics (attitude toward change), internal (centralization, formalization, complexity,

interconnectedness, etc) and external (systems openness) characteristics (Merono-Cerdan,

2008). Rogers (1995) technology characteristics support TAMs perceived usefulness (PU)

and perceived ease of use (PEOU). However, because decision-makers are specific internal

organizations properties, T-O-E framework is similar to Rogers (Zhu et al., 2003).

Tornatzky and Fleichers (1990) T-O-E seems more consistent to the study of ICT adoption

in Nigerian universities because it consists of seemingly wider generic explanatory

constructs. Specifically, T-O-E model is chosen for this work because, aside Thongs (1999)


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model, it is about the only model that emphasizes more on individual difference factors

(IDFs) to underpin the idiosyncratic nature of decision-makers while recognizing theinfluence of technology development and organizations conditions involving necessary

business and organizational reconfiguration shaped by industry environment. The framework

enjoys wide applicability and underpins many empirical studies, including the present paper.

For instance, T-O-E factors were found fundamental for Electronic Data Interchange (EDI)

adoption (Lacovou et al., 1995; Kuan and Chau, 2001), for supportive evidence in IT field

(Zhu et al., 2003), and for analyzing barriers to ICT adoption amongst SMEs (Thong, 1999).

The propositions drawn from the individual constructs of T-O-E show relationship with the

dependent variable (see figure 1).

Figure 1: Conceptual framework

TechnologyTechnology is a force for creative destruction (Kotler and Keller, 2009); it describes task

interdependence, the degree of equipment automation, uniformity or complexity of

production processes and materials, the degree of routines of the task and supportive systems

(Szilagyi and Wallace, 1980). Apparently, technology describes ICT infrastructures, internet

skills, and know-how existing in the firm and those in the market. ICT infrastructures provide

platforms upon which on-line communities interact real-time; internet skills offer the

technical know-how to develop and operate applications; and ICT know-how provides

business and managerial skills to effectively apply the facilities (Zhu et al., 2003). Therefore,

technology competence transcends physical assets and includes intangible resources, which

P1

P2

P3

P4

P5

ICT Readiness

Government Support

Relative Advantage

Managerial Willingness

Regulatory Policies

ICT Adoption


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perhaps generate competitive advantage since skills and know-how complement physical

assets and are more difficult-to-copy by rivals. Kwon and Zmud (1987) reported thatsuccessful ICT adoption depends largely on the relevance of the internal technology

resources- infrastructures, technical skills, developers, and user time. Institutions with higher

competence and technology readiness are more disposed to adopt ICT (Zhu et al., 2003).

Relevance defines IDTs relative advantage and TAMs perceived usefulness (PU), which,

measures the degree to which an innovation is perceived superior to existing substitutes.

Thus, prospective user(s) make subjective probability that using a particular application

improves outcomes more than alternatives (Awa et al., 2010). Respectively IDT and TAM

discussed the overlapping constructs of complexity and PEOU to measure prospective users

mental efforts required of the use of a planned application. The other three constructs (termed

experience-related) of IDT are often treated as moderating variables that directly interact with

PU and PEOU. For instance, compatibility defines consistency between ICT applications and

existing belief/value, knowledge, and experience infrastructures; observability measures

access to visibility and imagination of results; and trialability defines experimentation on a

limited scale (Awa et al., 2011). Further, the perceived behavioural control (PBC) added by

Ajzens (1991) theory of planned behaviour recognizes users perception of resource

constraints for operating the planned applications. Therefore, PBC is a strong determinant

though prior research shows that it might be influenced by PEOU (Venkatesh et al., 2007). IT

readiness and IT infrastructure were used to capture the technology context, leading us to the

first and second propositions.

P1:ICT readiness positively influences its adoption in Nigerian universities.

P2:ICTs relative advantage positivelyinfluences its adoption in Nigerian universities.

Organization

Some ICT studies described organization context using firms scope of business operations,

size, and size related issues such as slack resources and specialization (Damanpour, 1992;

Pan and Jang, 2008). Other studies included cultural and structural configurations (Sheriden,

1994; Ongori, 2009), quality of human resource and complexity of managerial structure

measured in terms of centralization, formalization, and vertical differentiation (Glover and

Goslar, 1993; Scupola, 2009). Further, information sources and communication channels

influence ICT adoption (Rai, 1995; Yesbank, 2009), especially amongst colleges, where

network externalities heavily influence innovation (Julien et al., 1996; Kannabiran and

Dharmalingam, 2012). Much scholarship reports on organizations size as a resilience to


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environmental shocks (Awa et al., 2010); adoption may be slower in smaller institutions

perhaps because they rarely possess economy of scale advantage and facilitating slacks aswell as the strengths to bear the associated risks and to encourage trading partners to adopt

technology with network externalities (Zhu et al., 2003).

National Statistical Resources from some OECD countries report that diffusion of ICT

amongst large firms in 1999 was 80 - 86 percent; for firms with 20 employees or more, 61-95

percent; and for very small firms, 19-57 percent (OECD, 1999). A seemingly contrary

argument by some scholars (e.g., Thong, 1999; Zhu et al., 2003; Pan and Jung, 2008)

suggests that often top managements idiosyncrasy and knowledge about ICT may be thornier

barriers than size since organizations growth is shaped by decision-makers functional and

emotional peculiarities about future, alternatives, and consequences. ICT adoption is further

measured by group heterogeneity and cohesiveness as well as group members functional

tracks, education, age, gender, and experience (Awa et al., 2011). Experience is long rated a

significant individual difference factor in innovation acceptance research; favourable

experience measured by PU, PEOU, and other constructs may influence adoption of similar

applications on accounts of stimulus generalization and technology cluster (Awa et al., 2010).

Studies (Becker, 1970) show that education influences personal innovativeness, belief/value

systems, cognitive preferences, and receptivity of an innovation. Weak education attracts risk

aversion, threats to change and imitating the innovators, who may be more educated, more

cosmopolitan in their social relationship, more exposed to mass media, and more active

outside their community (Bass, 1969). Age and gender of the decision-maker(s) influence the

propensity to seek and try out novelties. In most technology-led markets, early adopters are

commonly young and perhaps males; the German market for mobile phone is 60 percent male

and 40 percent female (Lu et al., 2003). Age directly impacts on usefulness perception and on

workers performance of computer-based tasks; younger executives appear much more

associated with corporate growth (Hedges, 2010; Den Hoogen, 2010; Child, 1974) since they

take much risk. The conservative stance of the older executives is explained by their

premiums on career and financial security; lack of mental and physical stamina to grasp

novelties; greater psychological commitment to corporate status-quo; and lack of social

enabling environment for novelties (Hambricks and Mason, 1984). Based on these, we make

the third and fourth propositions.

P3: Managerial willingness to take risks positively influences ICT adoption in

Nigerian universities.


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Environment

The concepts of environmental determinism and strategic choice necessitate changemanagement and minimization of operational surprises. Managing change involves

anticipation of, and responding to, environmental trends (Abell, 1978) and the deployment of

actions that result in the design and activation of strategies to simultaneously achieve

corporate, business, and functional objectives of an organization (Pearce and Robinson,

2000). Thus, environmental changes must be anticipated, monitored, assessed, and

incorporated into decision-making process because they suggest radical changes in resource

requirements though sometimes firms resources and key competencies are rarely easy to

adjust (Awa and Kalu, 2010).Change is crucial for businesses to grow and benefit from ICT

especially where there is transparency, openness and competitive framework, clear

legislation, easy set up and stable legal treatments within and across economies (OECD,

2004). The business environment is volatile, forcing strategists to uphold proactive and

flexible structures instead of simply reacting to environmental changes.

Institutions ability to improve their academic standards is influenced by opportunities and

threats as well as strengths and weaknesses imposed by its environment (Raymond, 2001).

There is a correlation between decision to adopt ICT and such factors as peer influences, rate

of technical change, market volatility and coercive influences perhaps from customers

(Raymond and Blili, 1997). Also, there is a strong relationship between an institutions

decision to use ICT and coercive influences from government authorises and other regulatory

agencies. ICT adoption may be influenced by the governments if they (governments) outline

the requirements for adoption, the legal protection for the ICT and perhaps, the incentives.

Tornatzky and Fleishers (1990) theory measures environment in terms of the influence of

industry practice, consumers and trading partners readiness, competition, and government.

Therefore, we suggest proposition three.

P4: Regulatory policies positively influence ICT adoption in Nigerian universities.

P5: Government supportprogrammespositively influence ICT adoption in Nigerian

universities

Methodology

IT adoption studies have been criticized for focusing on confirmatory statistical techniques

(Silver, 2007; Schwarz, 2007). It is argued that researchers that always use quantitative

method often remain unquestionable and when there are irregularities especially in thetheory used, it is attributed to other factors such as the instruments, sample, and sample size


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(Silver, 2007). IT adoption research requires not just explanatory theories rather methods

that can help explain phenomena in broader ways. Therefore, qualitative approach isadopted since it serves as a useful alternative and provides richer insights and results (see

Lee, 2003).

Sampling Procedure

Since qualitative research emphasizes on discovery and explanation of peoples experiences

(Schulter and Avital, 2010) and not statistical generalization, purposeful sample of thirty

(30) subjects was drawn from at least one university in the five state capitals of South-eastern

geo-political zone of Nigeria. While the essence of sampling all states was to ensure full

representation; the choice of institutions in the state capitals was informed by the sureexistence of internet services. The opinions of six (6) respondents in each institution were

sampled through unstructured and semi-structured interviews. They included senior registry

staff, senior staff of the Vice Chancellors and Deputy Vice Chancellors offices, Deans of

faculties, Heads of Departments, and professors/other lecturers. See participants profile

below.

Table 1: Profile of Participants

Participant Staff Category School

AI,2,3 Academic Staff Enugu State University of

Technology (ESUT)

A4, 5, 6 Non-academic Staff ESUT

B1,2,3 Academic Staff Michael Okpara University (MOU)

B4,5,6 Non-academic Staff MOU

C1,2,3 Academic Staff Ebonyi State University (EBSU)

C4,5,6 Non-academic Staff EBSU

D1,2,3 Academic Staff Imo State University (IMSU)

D4,5,6 Non-academic Staff IMSU

E1,2,3 Academic Staff Nnamdi Azikiwe University(NAU)

E4,5,6 Non-academic Staff NAU

The letters with sub I, 2, 3 stands for the opinions of academic staff and those with sub 4, 5, 6

stand for the opinions of non-academic staff, and the frequency of the supporting cases

appeared in percentages (see table 2).

Unstructured and semi-structured interviews

The purpose of unstructured interviews was in two-fold; first, to help understandthe full

richness of respondents opinions/narratives on the current state of ICT adoption by the


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universities and second, to help develop semi-structured interview questions for the second

round of data collection. The interviews bordered on thirteen (13) ICT adoption determinantswithin the contexts of T-O-E; and specifically on: (1) the extent(s) to which universities adopt

ICT applications? (2) what factors drive or hinder adoption behaviour? (3) if ICT platforms

improve operational outcomes.A formal letter was sent ahead of time on the purpose of the

research and confidentiality of information. The interviews scheduled for 40 minutes lasted

more in some cases and with the respondents permission; audio tape recorder was used to

minimize bias and errors resulting from relying on memories. Supportive instruments used to

develop deeper understanding of the points raised were electronic reports and power point

presentation materials. In order to enhance, validate, and confirm the outcomes of findings,

semi-structured interviews were conducted with some key respondents identified in the first

round of interviews.

However, because codes generated emerged inductively from interview transcripts (see Miles

and Huberman, 1994), the study adopted data-driven thematic analysis technique (TAT) at

latent level with the aids of Nvivo software and tried to identify and examine the underlying

ideas, assumptions, and conceptualizations, instead of reading only the surface meaning of

the data. Boyatzis (1998) data-driven stage approach was adopted. First, the researchers

reduced the data by identifying the portion(s) of the text(s) that potentially reveals emerged

themes. Second, themes were identified within cases and third, themes were compared

across cases. The identified themes were then clustered in stage four and helped the

development of codes..

Validity and Inter-coder Reliability

Verification at stage five means reliability checks .The data were lifted from the original

textual contexts and placed in charts consisting of T-O-E factors (see figure 2) and validated

through cross case comparisons of supporting evidence or triangulation of multiple copies of

one source (see Lincoln and Guba, 1985). Reliability analysis technique used was percentage

agreement because the data coded were nominal and required a judgement by the judges

(see Boyatzis, 1998). Inter-coder reliability was performed to measure the extent to which

independent coders evaluate and assign the same rating to each of the objects (see Bryman,

2008). Holstis technique was deployed; it counts the number of judgements that were the

same and divide the sum by the number of judgements. The values range from 0 to 1

reflecting an absence of reliability to perfect reliability respectively. The texts and categories

were rated by four judges who related the extracted quotes to the factors. The first two found


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82 percent consistency with the coded texts and the last two found 92 percent; all surpassing

Miles and Hubermans (1994) 70 percent benchmark.

Table 2: Reliability test

Area Number of judges ReliabilityFirst two judges Second two

judgesFactors 4 0.815(82%) 0.921(92%)

Analysis and Findings

Table 3 shows the factors drawn from the T-O-E framework guided the interviews. Each

factor has cross-case supports and a percentage to reflect the weight of its frequency. Further,

each factor factored into the analysis met the 4 cases benchmark of Macredie and Mijinyawa

(2011).

Table 3: Factors and frequency of supporting cases

Context Factors Related Cases Total

Technology Electricity supply A1,2,3,4,5,6; B2,3,5,6; C1,2,3,5,6;

D2,3,4,5,6; E2,3,4,5,6.

25 (83%)

Expert skills A1,2,4,5,6; B1,2,3,4,6;

c2,3,4,5,6; D1,2,4,5,6; E3,4,5,6.

24 (80%)

Internet connectivity and

accessibility

A1,2,3,4,5,6; B1,2,3,4,5;C1,2,3,4,5;

D2,3,4,5,6; E1,2,3,4,5,6

27 (90%)

Obsolete technologies A1,2,3,4,5; B2,3,4,5,6; C2,3,4

D2,3,5,6; E1,2,4,5,6

22 (73%)

Technology support A1,2,3,4,5,6; B1,3,5,6; C1,2,3,4,5,6;

D2,3,4,5,6; E2,3,4,5,6.

26 (86%)

Institution Embezzlement A1,2,3,4,5; B2,3,4,5,6; C2,3,4,5,6

D1,2,3,5,6; E1,2,4,5,6

25 (83%)

Institutional Support and

willingness to adopt

A2,3,4,5; B1,2,3,4,6; C1,2,3,4,5,6

D2,3,5,6; E1,2,3,4,5.

24 (80%)

Size of Institutions A1,2,3,4,5; B2,3,4,5,6; C2,3,4,5

D2,3,5,6; E1,2,4,5,6

23 (77%)

Incentives A1,2,4,5,6; B2,3,5,6; C1,2,3,5,6;

D2,3,5,6; E2,3,4,6.

22 (73%)

Environment Funding A1,2,3,4,5,6; B1,2,3,5,6; C1,2,3,5,6;

D1,2,3,4,5,6; E2,3,4,5,6.

27 (90%)

Requirements for adoption A1,2,3,6; B1,3,5,6; C1,2,3,4,6;

D2,4,5,6; E2,3,4,5,6.

22 (73%)

Legal protection A2,3,4,6; B1,3,5,6; C1,3,4,5;

D3,4,5,6; E2,4,5,6.

20 (67%)

Tax laws A1,2,3,4; B1,3,5,6; C1,2,3,6; D2,4;

E2,3,4,5.

18 (60%)


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Figure 2 presents all the T-O-E factors across cases and measures the differential strengths of

each in ICT adoption behaviour. Cases/interviews were kept in the same order in each chart

to identify their source. Thus, analysis involved identifying and categorizing factors from

each case and arranging them in order of similarities. This analysis is unique because it often

leads to testable theories

Figure 2: Strength of factors across cases.

From table 4 below, the pace of adoption differs with institutional willingness; while some

universities are extremely sluggish in their adoption behaviour, others are not. In terms of

size, a somewhat mixed reaction was observed; whereas some participants (A1,2,3;D5; E2,6)admitted size as a factors; others (A4,5;B2,3,6;C4,5D2,3,6; E4) did not.

Table 4: Findings

Context Factors Quotes Cross-case supports

Technology Electricity supply Steady power supply in Nigeria is almost

non-existence; the high cost of

computers and generating set in Nigeria

discourages ICT(A1).

A1,2,3,4; B2,3,5,6;

C1,2,3,5,6; D2,3,4,5,6;

E2,3,5,6.

0

10

20

30

40

50

60

70

80

90

100


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Expert skills Few people are proficiently trained to

teach and to encourage others to learnand apply ICT solutions (A1).

Employments are rarely based on

merits; therefore, most institutions end

up hiring mediocre based on

favouritism. If under-minded, the future

of Nigerian education will be in

jeopardy (A6).

A2,3,5; B2,3,4;

C2,4,6; D2,5,6;E3,4,5.

Inter connectivityand accessibility

ICT is novel and the cost of connectivity

is exorbitant for universities since

government provide little support to

schools (B1). Cost is expressed in terms

of facilities, unreliable access to internet

services, power and infrastructure,

which is rarely attainable by an average

Nigerian (E2)

A1,2,3,4,5, B2,4,5;

C1,2,3,4,5 D2,5,6;

E1,4,5

Obsolete

technologiesThe use of obsolete technologies has

influenced ICT adoption in Nigerian

Universities; people are impatient to

learn new technologies. Often people

have too much to do, find manuals about

new technology difficult to understand,

and end up with poor results (E6).

A1,4,5; B2,3,5,6;

C2,3,4 D2,3,5,6;

E1,2,5.

Technology support Hiring mediocre to maintain existing

ICT applications end up destroying the

available few (C3). I used to have a

computer connected to the internet but

since people employed to maintain it

damaged it because of inadequate

expertise, the school is yet to replace it

(A6).

A4,5; B1,3,5; C1,2,4;

D2,3,4,6; E2,3,4, 5.


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The increasing rates of kidnapping and

suicide bombing hinder foreigninvestment in Nigeria and ultimately

investment in ICT maintenance (D3).

A4,5; B1,5; C1,2,4;

D4,6; E2,3,5.

Institution Embezzlement Obviously, embezzlement is one factor

associated with developing societies and

Nigerian universities are not an

exception; the misappropriation of

public funds causes barriers to ICT

adoption, thereby rendering its

processes ineffective (A3).

A,4,5;B2,4,5; C2,5,6;

D1,2,3; E1,2,4,5

Institutional support

and willingness to

adopt

Most top executives lack adequate

exposure to ICT and its inherent benefits

largely because of insufficient

government support (A5). However

because top executives are regarded as

the decision-makers and perhaps

models, whatever they agree upon will

be passed down (A2). There is little or

no management support on the grounds

that (D5) observes absence of adequate

adoption polices and ICT curriculum for

fear of defacing corporate status-quo.

(A4) said that because of threats of

novelty owing to top executives limited

IT friendliness, the willingness to adopt

is relatively low.

A3,5; B1,2,3,4,6;

C1,2,3,4,5,6; D2,3,6;

E1,2,3,4,5.

Size of institution No matter the length and breadth of an

institution it will carry the size of its own

internet accessibility (E5). Looking at

the size of institutions in Nigeria, it

A1,2,3,4,5; B2,3,6;

C4,5D2,3,5,6; E2,4,6


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becomes a big challenge for the

government to build ICT centres in allthe universities; small institutions rarely

have the cognate resources to engage in

ICT adoption(E1).

Incentives All things being equal, low incentives

from government have caused serious

setbackin the development of ICT skills

(A4). I am a bit old and you do not

expect me to start going for ICT

training; such training should target

younger generations. Even if incentives

are provided, employee resistance to

change may set a formidable barrier;

thereby raising great concerns over

what the future holds for education

systems in Nigeria (A6).

A1,5,6; B2,5,6;

C1,2,5,6; D2,3,5,6;

E2,3,4,6.

Environment Funding Poor funding and corruption greatly set

a barrier on embarking on capital

intensive projects like ICT (E2).

Project(s) with poor funding structure

are often abandoned (A3). Funds meant

for ICT are often misappropriated and

diverted either by top government

officials in the sector or by the

management of these institutions (C6).

A1,2; B1,3,6; C1,2,5;

D4,5,6; E3,4.

Legal protection We have some legal requirements

approved by the university commission;

but there is need for other legal

protection needed for ICT adoption (B1).

Legal requirements were extended to

A2,4,6; B,6; C3,5;

D3,4,6; E2,4,5


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cybercrimes, which a participant notes

requires an urgent attention (A3). Suchrequirements should not be too stringent

otherwise it may breed reluctance on the

part of local and foreign investors as

well as other stakeholders (A5).

Tax laws

Requirements for

adoption

Outrageous tax laws and duties hinder

and discourage ICT adoption in Nigeria.

ICT bodies may not just be ready

because of the harsh tax laws on ICT

gadgets. Therefore, we suggest that such

taxes should be reduced to encourage

Nigerian universities and other investors

(E3).

The requirements for adoption of ICT in

Nigerian universities should include

sufficient funds to acquire ICT

infrastructures since subventions from

governmentsare inadequate (A1). If the

requirements for the adoption of ICT are

made simple and enforceable, then

adoption in Nigerian universities will be

easierand faster (A2).

A1,3,4; B1,5,6;

C2,3,6; D2,4; E2

A3,6; B1,5,6; C4,6;

D2,4,6; E2,3,4,5.

Discussion

ICT infrastructure is a key component of ICT development; it assists socio-economic

development and promotes operational efficiency. Our investigation shows that electricity,

internet connectivity, technology support, obsolete technology, and know-how are the most

significant determinants of ICT adoption in Nigeria. The under-developed natures of these

factors impede adoption. Internet access is quite exorbitant owing to high cost of bandwidth,

and requires alternative and/or supportive funding arrangements that span even maintenance


18/24

of other equipment. Previous studies (e.g., Oshikoya and Hussain, 2007; Folorunsho et al.,

2006; Kapuruandara, 2006; Akpan-obong, 2007; Apulu and Ige, 2010; Arikpo et al., 2009)support the findings when they emphasized Nigerias poor environmental and infrastructural

standards. Inadequate telecommunications infrastructures, poor internet connectivity, and

high cost of ICT implementation force many Nigerian institutions to ignore effective use of

ICT solutions and rather use resources for other purposes that promise faster returns

(Folorunsho et al., 2006; Akpan-obong, 2007; Arikpo et al., 2009). Electricity supply in

Nigeria accounts for about 80 percent below expectations; thus, only about 20 percent

Nigerians has stable power supply (Akpan-obong, 2007; Baker, 2008).

Institutional readiness is a major determinant of ICT adoption; readiness reflects how the

institutions understand ICT facilities and their benefits. Adoption is influenced by technology

networks that enhance learning capability, willingness, trust, top management support, and

motivation. However, the study revealed that most institutions are not ready to adopt ICT

because they lack the necessary skills. In the area of organization/institution, the study

revealed that embezzlement is the most significant ICT adoption barrier, followed by

institutional support, firms size and willingness, and adoption incentives. Corruption breeds

embezzlement, misappropriation, and other social vices that impede socio-economic growth.

Previous studies (Dike, 2005; Ojukwu, 2006) and recent ones (Apulu et al., 2011)

confirm this finding as they suggest that corruption is almost the way of life of Nigerians.

Often money meant for improving teaching standards (investment in ICT) is siphoned.

Literature (e.g., Raymond and Blili, 1997; Tornatzky and Fleisher, 1990) supports the strong

correlations between ICT adoption and institutional size but the result of this study seems

mixed. While some participants support the relationship, others agree with such scholars (see

Zhu et al., 2003;Pan and Jang, 2008) that size does not matter. The cost of acquiring these

technologies is high and discourages investment decisions by policy planners and

management.

Under environment, funding accounted for the most significant ICT adoption barrier,

followed by such regulatory policies as adoption requirements, legal protection, and tax laws.

Nigerian government seems to be working hard to enhance technology in some sectors such

as banking, education, and communication but the findings show that the key requirements

(e.g., adequate funding, access to ICT development grant, top management support and

proper training) are still lacking.


19/24

Practical Implications

These conclusions imply that appropriate policy framework should be put in place byuniversities and governments to create enabling environment within which ICT solutions

diffuse in Nigerian colleges. Specifically, governments support should be real and loans for

ICT adoption should be made less stringent for institutions without losing sight of retraining

staff to meet the impending challenges. Improvements in adoption involve putting the right

infrastructures including reliable internet connectivity, fixed and workable telephones, and

qualified experts (Apulu and Ige, 2010). The inclusion of ICT in institutions curriculum is

necessary to develop the skills needed to improve its efficiency. Further, legal framework is

urgently needed to encourage ICT investors and support ICT infrastructures, to enhance ICT

adoption, and to punish those who embezzle ICT funds. Infringement acts require the co-

operation of legislative, executive, and judicial arms as well as the end-users to make it work.

Finally, the benefits of ICT solutions should made known to policy makers while effort

should be made to re-engineer Power Holding Company of Nigeria (PHCN) to live up to

expectations.

Limitations and further research directions

This study has some limitations that provide bases for future research. First, the sample size

may limit the generalization and implications of our findings. That we took sample from

senior executives of government-owned universities in the South-eastern Nigeria limits the

power of exact generalization against those institutions not investigated on the recognition

that every state and college is idiosyncratic. However, a sure way of building external validity

and possibly theories is by replicating this study in other states, sectors, and perhaps

economies for cross learning. Second, all the measures used seem subjective and prone to

common method bias, though concrete steps were taken to minimize their effect on results.

Third, the opinions of junior executives were not captured in the survey; therefore, future

scholars are challenged to take up a comparative survey in this direction.

Conclusions

In the knowledge economy, institutions rarely go solo; they need real-time value-chain and

network externalities to enhance operations. This study demonstrated how qualitative method

generates rich insights via assessing the influence of 13 factors on adoption of ICT in

Nigerian institutions. Evidence from the study led to four conclusions; first, government-

owned universities are yet to exploit the full potentials of ICT solutions in their operations;


20/24

thereby, discouraging investments and ultimately employments. Most frontline executives of

Nigerian institutions are eluded by decisions involving radical operational changes. Secondthough the pace of adoption differs amongst the schools, ICT readiness and relative

advantage are influenced by availability of energy; expert skills, training, and technical

support; managerial flexibility; and the need to build an on-line community. Third,

managerial willingness to adopt ICT solutions is shaped by institutional supports, managerial

agility, corruptions and other social vices, incentives, and size. And fourth, regulatory

policies and government supports in the forms of legal protections, tax laws, requirements for

adoption, and outright funding are necessary for ICT diffusion amongst colleges.


21/24

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