versión On-line ISSN 2076-3433
versión impresa ISSN 0256-0100
S. Afr. j. educ. vol.35 no.4 Pretoria nov. 2015
Dauda Dansarki IsiyakuI; Ahmad Fauzi Mohd AyubII; Suhaida AbdulkadirII
IFaculty of Educational Studies, Universiti Putra Malaysia, Malaysia and School Of Business Education, Federal College of Education (Technical) Bichi, Kano State, Nigeria firstname.lastname@example.org
IIFaculty of Educational Studies, Universiti Putra Malaysia, Malaysia
This study has empirically tested the fitness of a structural model in explaining the influence of two exogenous variables (perceived enjoyment and attitude towards ICTs) on two endogenous variables (behavioural intention and teachers' Information Communication Technology (ICT) usage behavior), based on the proposition of Technology Acceptance Model (Davis, 1989a). The sample was 212 teachers from Business Education faculties of 13 tertiary colleges in the northwestern region of Nigeria. As one of the major developing countries in Africa, Nigeria has invested a lot of resources in ICTs for the past several years to ensure the appropriate uptake and integration of technology across the important sectors of the country's economy, especially the education sector. Unfortunately, the country's standard of ICT adoption has remained low for many years. Congruently, its educational sector has remained incapacitated by lack of adequate ICT facilities and lack of skilled ICT-manpower, with school teachers using obsolete tools in the classroom, and some of them buying and using ICTs out of their own volition. Teachers' use of ICTs in tertiary schools' has remained poor in Nigeria, and research initiatives on ICT usage behaviour are rare and predominantly descriptive in nature. Past studies have dwelt on investigating the influence of physical infrastructural facilities on teachers' use of technology in the classroom. The current study has investigated the influence of teachers' perceptive beliefs, attitudes and intentions on their technology usage behaviour, using Structural Equation Modeling (SEM). Findings have shown that teachers' perceived enjoyment of ICTs influences their ICT usage behaviour in the classroom (β = .281, p < .05); teachers' perceived enjoyment of ICTs influences their intention to use ICTs (β = .740, p < .001); teachers' ICT attitude influences their intention to use ICTs (β = .122, p < .05); teachers' ICT attitude influences their ICT usage behaviour (β = .512, p < .001) and teachers' behavioural intention influences their ICT usage behaviour ICTs (β = .-368, p < .05). Teachers' behavioural intention to use ICTs has, however, predicted a decrease in their self-reported ICT usage behaviour. This study will benefit school leaders, curriculum planners and researchers in technology acceptance behaviour in Africa, by giving them guidance in taking decisions concerning teachers' perceptions and intentions of using ICTs in the classroom. The study will play a vital role in filling up the research gap that exist in technology acceptance behaviour among business education faculties across tertiary institutions in Nigeria and the rest of Africa. Future research on the subject matter may attempt to investigate the moderating roles of voluntariness and compulsory standards in influencing teachers' ICT usage behaviour.
Keywords: attitude towards technology; behavioural intention; business education; developing country; ICT usage behaviour; Nigeria; perceived enjoyment; South Africa; teachers
Successful adoption and usage of ICTs in education is fundamental to the paradigmatic shift in both content and pedagogy that is at the heart of education reform in the 21st century (Onyia & Offorma, 2011). Use of ICTs has become necessary in the education process, because it has the capacity of promoting quality of education (Johnson, 2007). Educational institutions (mostly those in the advanced regions of the world) are vigorously implementing highly integrated ICT schemes with competent personnel, using ICTs in virtual classrooms (López-Pérez, Pérez-López, Rodriguez-Ariza & Argente-Linares, 2013). Unfortunately, most regions of Africa are yet to embrace ICTs appropriately (Anderson, 2010). In Europe, America, Australia and most of Asia, teachers have adapted to using ICTs in the classroom (López-Pérez et al., 2013), but in Nigeria and most of the countries of Africa, teachers are still struggling with obsolete tools (Mbaba & Shema, 2012; Ubulom, Enyekit & Onuekwa, 2011; Umoru, 2012).
Although the contributions of ICTs to national growth are highly valued in Nigeria, South Africa and the rest of the developing countries of Africa (Awosejo, Ajala & Agunbiade, 2014), studies have revealed that ICTs have not permeated to a great extent in many higher learning institutions of the African region, due to extant socioeconomic and technological challenges (Sife, Lwoga & Sanga, 2007). While information about technology acceptance from developed countries and other emerging societies are well represented (Kwak, Park, Chung & Ghosh, 2012; Zhang, Gao & Ge, 2013), perspectives from Africa (Nigeria and South Africa inclusive) are scarce (Arekete, Ifinedo & De Akinnuwesi, 2014; Ojiako, Chipulu, Maguire, Akinyemi & Johnson, 2012).
Tackling ICT usage problems in emerging economies like Nigeria and South Africa requires in-depth research. But, while ICT studies across the globe are increasing daily, they are still rare in Africa (Jegede, Dibu-Ojerinde & Ilori, 2007). Unfortunately, ICT studies from countries of developed regions may not always apply to countries of developing or underdeveloped regions, owing to cultural peculiarities, population, sampling, and/or designs limitations (Rastogi & Malhotra, 2013). Nonetheless, Nigeria, South Africa and the rest of Africa need to tackle their ICT usage issues, especially in education. Thus, undertaking indigenous research peculiar to their technological dispositions is fundamental. So far, prevalent studies have focused on teachers' and students' competencies, (Jegede et al., 2007; Onyia & Offorma, 2011), enabling environment (Oghogho & Ezomo, 2013), government policies (Akinsola, Herselman & Jacobs, 2005), and other similar descriptive constructs. For instance, Iloanusi, NO and Osuagwu (2009) have observed that the Nigerian government has placed greater emphasis on ICT administrative and financial transactions, while relegating the education sector to the background. It was also observed that Nigeria's educational system has been languishing over the past several years, owing to infrastructural deficiencies and the weak economic framework of the country (Aduwa-Ogiegbaen & Iyamu, 2005; Asogwa, 2013a; Ololube, Egbezor & Kpolovie, 2008; Oye, Noorminshah & Rahim, 2012). But there are other important issues that have negative effects on Nigeria's education sector, which are not unconnected with the behavioral disposition of teachers towards the use of technology in education. Such are the types of issues that were investigated in this study. Overall, teachers' willingness to shift their teaching methodologies from traditional approaches to new approaches have implications on the success of Nigeria's education system.
Lack of ICT integration in educational institutions of developing countries is a key factor in the existing gap between such countries and developed ones. The failure of technology adoption in education has become an issue of great concern in African countries that want to develop (including Nigeria and South Africa) (Arekete et al., 2014; McGrath & Akoojee, 2009; Oye et al., 2012).
In a report by International Telecommunication Union (ITU, 2013), empirical evidence has shown that in developing countries (such as Nigeria and South Africa), fewer people are able to benefit from the potentials of ICTs. International Telecommunication Union (ITU) had used an ICT Development Index (IDI), referred to as IDI Use sub-index, which was composed of three indicators: internet users per 100 inhabitants fixed (wired), broadband subscriptions per 100 inhabitants, and wireless-broadband subscriptions per 100 inhabitants, to measure the uptake of ICTs and the intensity of usage (an indispensable factor for countries that aspiring to become information economies and societies) across the world.
In line with the parameters set by ITU, for measuring ICT usage levels, Nigeria was ranked 98th, 101st, and 93rd for the years of 2010, 2011 and 2012, respectively - out of an approximated number of 155 countries. The country's IDI Use sub-indices for the three years were: 0.82, 1.05 and 1.72, respectively (out of a maximum score of 10). South Africa was ranked 87th, 89th, and 75th, for the same years, ahead of Nigeria each year. Her IDI Use sub-indices for the three years were: 1.26, 1.46 and 2.35 (out of the same maximum score of 10). Although South Africa was ahead of Nigeria in IDI Use sub-indices rankings, according to ITU (2013), both countries have fallen below the average rankings in terms of ICT usage, and, whereas the countries with the highest levels of ICT use have reached IDI values approaching 9 (out of a maximum of 10), the countries with the weakest ICT use levels (mostly from Africa) have IDI values of only one or less (ITU, 2013).
In a case study conducted by Yusuf and Balogun (2011) in a Nigerian university, findings have revealed teachers' lack of competence for the integration of ICTs in the curriculum of the Nigerian university. Although teachers' attitudes were evaluated in the study, findings did not show significant correlation between teachers' attitudes toward ICT usage and their lack of ICT competence - probably because the study was purely descriptive, rather than inferential. This implies the need for replicating such a study with an inferential approach, providing part of the rationale for the current study. In a pilot study conducted at the University of Jos in Nigeria, among 100 teachers have also shown that most teachers in Higher Education Institutions (HEIs) of Nigeria were not confident of their intentions to use ICTs in the classroom (Oye, Iahad & Rabin, 2011). Findings in the study have also revealed significant correlations between teachers' attitudes and intentions to use ICTs in the classroom. However, being a pilot study, the generalisability of the work was limited. Hence, this study was conducted among a larger sample of 212 teachers' in 13 HEIs located in seven Nigerian states. One of the prevalent descriptive studies around Southwestern Nigeria was that conducted by Ajayi (2008) among six Colleges of Education, to examine the use of ICTs for teaching in the colleges. Findings in the study have shown that ICTs were not adequately used for teaching in these colleges, due to intermittent supply of electricity, inadequate ICT facilities and lecturers' incompetence in the use of ICTs. Overall, Ajayi (2008) and the rest of the past studies discussed above, have not attempted to employ the use of SEM in conducting their studies. This is a methodological gap, and part of the aim of this study is to fill up such gap. Structural Equation Modeling (SEM) is a multivariate statistical programme that is increasingly being used among contemporary researchers in education and in social sciences. The programme provides the chance for simultaneously accessing pictorial explanations to multiplefactorial problems (Hair, Black, Babin & Anderson, 2010). It is very effective at minimising residual errors, because it attaches error terms not only to the endogenous variables being investigated, but to the indicators as well. This study will provoke teachers in Nigeria, South Africa and the rest of Africa to rethink their positions on ICT, and to step up to face the current technological challenges and upheavals taking place in global education.
The meaning of ICTs
Whereas in the past, Information Technology (IT), was used as a term to describe the integration of computers and computer peripherals like printers, floppy disks drives, scanners and the early digital cameras; today, ICTs are used as a term to describe the technologies of the internet, along with computer networks, world wide web, email and search engines used in the production and sharing of information (Anderson, 2010). ICTs are technologies that enable us receive information and communicate or exchange such information with others.
ICT adoption in Nigerian education
In a model conceived by (Anderson, 2010), two dimensions of ICT integration in education are depicted, namely: technology integration and pedagogy integration. The technology dimension represents the systematic acquisition of all the tools of which ICT comprise, and the pedagogical dimension represents a continuum of changing teaching practices, owing to the adoption of varieties of ICT tools. In addition to this, there is a general consensus that ICT integration in education proceeds progressively in a series of broad stages known as emerging stage, applying stage, infusing stage and transforming stage (Anderson, 2010).
With reference to United Nations Educational, Scientific and Cultural Organisation (UNESCO) 's model of ICT adoption and use in education, Iloanusi, ON and Osuagwu (2011) postulated that 90% of Nigerian educational institutions are in the emerging phase of ICT adoption and use, while only 7% are in the applying phase, and only 3% are in the infusing and transforming phases. Implicit in this proposition is that Nigeria's educational sector is generally in a stage of infancy when it comes to ICT adoption and use. To support this, Asogwa (2013b), Delaviz, Andrade, Pouwelse and Epema, (2012) and Ololube et al. (2008) have posited that much of the difficulty faced in Nigerian education lies in the use of ICTs, owing to shortage of skilled manpower, poor electricity and serious neglect of the education sector.
Use of ICTs in business education across Nigeria
Business Education is concerned with the economic development of individuals and the provision of knowledge and skills to them in business and technology in a way that enables them to share what they know unto others.
Over the decades, business education has evolved into an indispensable aspect of human activity that serves productive purposes and meets with the needs of mankind in the current technology-driven world, be it socially, educationally, and otherwise (Beaumont, Austen, Atkins, Burdon, Degraer, Dentinho, Derous, Holm, Horton, Van Ierland, Marboe, Starkey, Townsend & Zarzycki, 2007; Renshaw, Trott & Friedenberg, 1988).
Although in Nigeria, business education has been purported to serve as a platform for producing skilled business teachers, office administrators and businessmen and omen that can effectively compete in the world of work and in enterprise, as employees, entrepreneurs and as employers; it was observed in Ekpenyong and Nwabuisi (2003) that business education was not given priority in Nigeria at its inception stage, where instead, it was only sustained through private and individual institutions, rather than through government. Until recently, most Nigerian universities did not see the need for including business education courses and other technical and vocational teacher education courses in their academic programmes. This might probably account for the slow pace of the development of business education in the Nigerian context.
From empirical evidence, to achieve a sustainable development in business education, the application of ICTs is fundamental (British Educational Communications and Technology Agency or BECTA, 2000; Mann, Shakeshaft, Becker & Kottkamp, 1999). It was revealed in Dellit (2001) that since ICTs can be applied in preparing and delivering business models, as well as in the management of various aspects of the learning process; business educators are faced with the task of either pushing the boundaries of ICTs in education and exploiting its capacities to improve their outputs beyond their current familiar paradigms and limits; or to just remain confined to their familiar boundaries. However, as supposed by Dellit (2001), if business educators and the general mainstream of the teaching profession embrace ICTs, then newer, better business models of ICT enabled education would fill the educational atmosphere and more avenues for accelerated academic breakthroughs would be created. This implies that teachers in Nigeria, South Africa and the rest of Africa ought to ensure that they effectively use ICTs in executing their classroom functions.
Concerned with the inadequacies of ICT adoption among business education faculties in Nigeria, Isiyaku (2007) and Jonathan (2012) advocated that business education teachers must modernise their methods and delve into research efforts that focus on the use of technology in the classroom. In other words, to ensure that the competences required for teachers in business education continue to reflect on the changing technological trends confronting them and their students, it is crucial for teachers to be abreast with relevant technology. Invariably, investigating teachers' behavioural dispositions towards use of ICTs in the classroom is an important strategy for ensuring the success of ICT integration policies in educational institutions within Africa (Tilbury & Ryan, 2011). In most of the learning institutions of the developed countries of the world, sophisticated computer hardware and software, as well as interactive learning tools like the 'collaborative black-boards', have become the order of the day (Larkin & Belson, 2005). This implies that the learning institutions of African countries (especially at the business education faculties) ought to abandon manual tools and the traditional 'classroom black-boards' and embrace new technologies in the classroom (Isiyaku, 2009; Ugwuogo, 2013). In our study, the fitness of a structural model in explaining the influence of perceived enjoyment, attitude towards technology, and behavioural intentions on ICT usage behaviour, will be tested by the use data from business education teachers in 13 tertiary colleges of Northwestern Nigeria. Considering the nature of this research, it is expected that the findings of the study will be applicable not only to Nigeria as a developing country, but to the rest of the countries of Africa who somehow share the same ICT culture and peculiarities.
Theoretical/Conceptual Framework This study borrows insight from the original work of Davis (1989a) in Technology Acceptance Model as well as in the recent works of Venkatesh and Bala (2008) for Technology Acceptance Model III (TAM III), in which it was postulated that one's behavioral intention to use a system could be determined by system characteristics, such as perceived enjoyment, as well as by one's attitudes towards the system; and that one's actual use of the system could be well predicted by one's behavioral intention to use the system. Consistent with TAM, the current study assumes that teachers' ICT usage behaviour is determined by their intentions to use ICTs in the classroom, while their perceived enjoyment of using ICTs and their attitudes towards using ICTs are antecedents to their intentions to the usage of such ICTs in the classroom.
Empirical evidence has shown that teachers' perceived enjoyment of ICTs could influence their overall intention of using ICTs in the classroom. Perceived enjoyment was defined as the extent to which the use of a specific system is perceived to be enjoyable in its own right, aside from any performance consequences resulting from the system use (Venkatesh, 2000). Perceived enjoyment was also defined as the perception of inherent enjoyment in using computers; apart from the anticipated improvement in performance they will bring (Davis, Bagozzi & Warshaw, 1992). In the context of our study, perceived enjoyment refers to the extent to which teachers perceive that using ICTs in the classroom is enjoyable. This is indicated by the teachers' opinions on whether they perceive ICTs to be enjoyable or not, and on whether they perceive the actual process of using ICTs to be enjoyable or not.
The theoretical underpinning for perceived enjoyment was based on the important role of the construct in helping individuals to adjust to new technologies illustrated in TAM III (Venkatesh & Bala, 2008). In an online survey conducted among news portals and forums, sport clubs and personal contacts in Germany by Kroenung, Jaeger and Kupetz (2015), findings have revealed a strong influence of perceived enjoyment on the usage of online shopping for people without mobility impairments. Also, in a study conducted on a sample of 1,280 resident doctors from 13 tertiary healthcare institutions in South-Western Nigerian on their utilisation of internet health information resources in tertiary healthcare institutions in the country, findings have revealed that perceived enjoyment has significant relationship with the utilisation of such resources (Ajuwon & Popoola, 2015).
Therefore, our study hypothesizes H1: perceived enjoyment has significant direct effect on ICT usage behaviour.
Furthermore, the findings of Davis et al. (1992), have revealed the significant impact of perceived enjoyment on behavioural intention. Atkinson and Kydd (1997) and Van der Heijden (2004) have also revealed the strong impact of perceived enjoyment on behavioural intention for hedonic systems. Perceived enjoyment has also explained the intention to use Smartphone applications for users and non-users in (Verkasalo, López-Nicolas, Molina-Castillo & Bouwman, 2010).
Therefore, our second hypothesis is H2: perceived enjoyment has significant direct effect on behavioural intention.
Attitude towards ICTs
Attitude towards ICTs was defined as a person's general evaluation or feeling towards ICTs and specific computer and internet-related activities (Smith,
Caputi & Rawstorne, 2000). Attitude towards ICTs or ICT attitude refers to the way in which people think and feel towards ICTs, their demeanour, and how they react to ICTs, as well as how they react to change initiatives that have to do with ICTs (Wilkinson & Schilt, 2008).
Attitude towards ICTs can also be referred to as the extent to which a teacher exhibits favourable or unfavourable dispositions towards using ICTs in facilitating classroom instructions. Attitude towards ICTs is one of those important factors that influence teachers' intention for using ICTs in the classroom (Rana, 2012). It relates to the extent to which ICTs are considered to be pleasant, Venkatesh, Morris, Davis, GB and Davis, FD (2003), the extent to which ICTs are considered as good idea (Venkatesh et al., 2003), the extent to which ICTs are considered appealing (Teo, 2010) and the extent to which ICTs are considered interesting (Teo, 2011). Fishbein (1979) postulated that attitude determines behavioural intention towards behavioural performance. Dishaw and Strong (1999) confirmed this assertion when they found significant positive relationships between attitude and behavioural intention in their attempt to extend the technology acceptance model with task-technology fit constructs.
TAM 1 has given attitude towards ICTs a theoretical underpinning in our study, where it theorised that attitude towards technology was a significant determinant of intention to use technology. In a study conducted among 59 faculty members from Shaqra University, Saudi Arabia, to understand academics behavioural intention towards using learning management systems by Alharbi and Drew (2014), findings revealed that attitude positively affects behavioural intention. Also, using a mixed method approach to understand the acceptance and usage of ICTs in a Nigerian university, Oye et al. (2012) found attitude to be a strong determinant of behavioural intention towards ICT usage.
Therefore, our study hypothesises H3: attitude towards ICTs has significant direct effect on behavioural intention.
In Rana (2012) it was posited that teachers' attitudes are among the fundamental factors influencing successful ICT adoption in the classroom. This was earlier observed in Oye et al. (2012) where attitude was found to be a strong determinant of ICT use. Congruently, in a recent study conducted in Nigeria on the antecedent factors to end-users symbolic acceptance of technology by Arekete et al. (2014) findings have shown that attitude has strong impact on usage of technology.
Based on the foregoing, our study hypothesises H4: attitude towards ICTs has significant direct effect on ICT usage behaviour.
Behavioural intention towards using ICTs
The underlying assumption of TAM is that people's computer use can be predicted reasonably well from their intentions (Davis, Bagozzi & Warshaw, 1989b). This is the theoretical underpinning for investigating the role of behavioural intention in determining teachers ICT usage behaviour in this study. TAM considers behavioural intention to be the core measure for technology acceptance (Pynoo & Van Braak, 2014). As defined in Teo (2011), behavioural intention refers to the degree of a teacher's willingness to use technology. In this study, behavioural intention is defined as the degree to which teachers are determined and intending to use ICTs in the classroom. This was indicated by the opinions of teachers regarding whether they would use ICTs in subsequent school semesters or not, as well as regarding whether they would use the ICTs regularly during the coming semesters or not.
Studying teachers' behavioural intention is fundamental to understanding their commitment to the use of technology in the classroom; and the extent to which teachers are willing to use ICTs in the classroom will determine whether or not they eventually use them. As posited in Fishbein and Ajzen (2011) and in Venkatesh (2000) and Venkatesh and Bala (2008), behavioural intention is determined by one's perception of personal factors such as attitude towards the behaviour and system characteristics, such as perceived enjoyment. Hence, Venkatesh (2000) and Venkatesh and Davis (1996), have found strong, significant, positive relationships between behavioural intention and actual usage behaviour. In the same vein, Kim (2008) has found there to be a significant relationship between behavioural intention and actual usage of a smartphone.
Therefore, our study hypothesises H5: behavioural intention as having a significant direct effect on ICT usage behaviour.
ICT usage behaviour
ICT usage behaviour can be defined as one's frequency of use of ICTs (how often one uses ICTs) and one's volume of work done with ICTs (how much work one does with ICTs) over a specified period of time (Kim, 2008). ICT usage behaviour can also be defined as the extent to which ICTs are used daily, and to refer to the frequency of their usage in proportion to the amount of task performed
with the ICTs (Igbaria, Iivari & Maragahh, 1995). In our study, ICT usage behaviour is referred to as the frequency with which teachers use ICTs in the classroom in relation to the types of jobs they perform and the volume of the jobs they perform with the ICTs. Teachers' opinions on the frequency with which they use ICTs in preparing continuous assessments, and their opinions on the volume of classroom functions they perform each day using ICTs, are indicative of their ICT usage behaviour.
The theoretical underpinning for investigating teachers' ICT usage behaviour in our study was derived from TAM, where strong linkages and affinities were theorised between usage behaviour on one hand, and perceptions, attitudes and intentions on the other hand (Ajzen, 1985; Davis, 1989; Fishbein & Ajzen, 1975; Venkatesh & Bala,
2008; Venkatesh & Davis, 2000). Extant research has consistently shown that there are strong correlations between perceptual beliefs, attitudes, intentions and usage behaviour (Ajzen, 1991; Fishbein & Ajzen, 2011). Although in studies conducted by Duyck, Pynoo, Devolder, Voet, Adang and Vercruysse (2008) and Pynoo and Van Braak (2014), behavioural intention was not found to reasonably predict self-reported use, findings in Venkatesh (2000) and Venkatesh and Davis (1996) have revealed strong and significant positive relationships between behavioural intention and actual usage behaviour.
Overall, the conceptual framework of this study is consistent with TAM 1 of Davis (1989a) and TAM III of Venkatesh and Bala (2008). The variables that constitute the framework of the study, namely: perceived enjoyment (PE), ICT attitudes or attitudes toward technology (ATT), intention toward technology use, or behavioural intention (BI) and technology or ICT usage behaviour (USE) are represented in Figure 1, with the proposed hypothetical paths that portray the five hypotheses of the study.
This study has employed a survey research design and has used SEM to analyse and interpret the associations of PE and ATT (being exogenous variables) with behavioural intention and ICT usage behaviour (being endogenous variables). The study data was analysed using AMOS v21 in three important SEM stages, namely: assessing the confirmatory factor analysis (CFA); assessing the measurement model; and assessing the structural model (Hair et al., 2010). Data was screened for missing values and outliers and convergent validity and discriminant validity were established for all the constructs investigated in the study.
Participants of our study were 212 business education teachers from 13 tertiary colleges in Northwestern Nigeria. A majority of the respondents were males, at 134 (63.2%), while females were the minority, at 78 (36.8%). The average age of the respondents was 43 years of age (26 years being the minimum and 63 years being the maximum). Most of the respondents were degree/higher national diploma (HND) holders, at 107 (50.5%). Ninety-nine of them (46.7%) were first degree holders, while only six of them (2.8%) were doctoral degree holders. A majority of the respondents have worked for a period of two to eight years; while 44 of them (20.8%) have worked for a period of nine to 14 years. Forty-two of them (19.8%) have worked for 15 to 20 years, while 20 of them (9.4%) have worked for 21 to 26 years. Only seven of them (3.3%) have worked for a period of 27 to 33 years as business education teachers. Most of the teachers were at the status of lecturers/instructors, at 72 (34%). Fifty-six of them (26.4%) were senior lecturers/senior instructors, while 42 of them (19.8%) were assistant lecturers/assistant instructors. Only 16 of them (7.5%) were principal lecturers/principal instructors.
A structured survey instrument was used with items adapted from previously validated works by Venkatesh and Bala (2008) for perceived enjoyment; by Teo (2010) and Venkatesh et al. (2003) for attitude towards ICTs; by Cheung, Lee and Chen (2002) and Davis (1989a) for behavioural intention, and by Kim (2008) and Venkatesh and Bala (2008) for ICT usage behaviour. Participants were asked to provide demographic information and to respond to 33 items on the four constructs in the study, namely: PE (six items), ATT (six items), BI (seven items) and USE (14 items). Each statement was measured on a five-point Likert-type scale. For PE, ATT and BI, the measurement ranged from 1 = 'strongly disagree' to 5 = 'strongly agree'. For USE the measurements ranged from 1 = 'never' to 5 = 'very frequently' and from 1 = 'none' to 5 = 'very much'. All reversed items in the instrument were reversed-scored (DeVellis, 2003). Confirmatory Factor Analysis (CFA) was conducted on the 33 items in the survey instrument, and on the final analysis, only 13 items were retained (see Appendix A).
To assess the normality of a set of data using AMOS, researchers usually report the skewness and kurtosis of such data. According to Byrne and Van de Vijver (2010), data may be assumed to be normal if its skewness is within a value range of ±2 and its kurtosis is within ±7. However, Kline (2011) opined that for a normal distribution of data, skewness should be within a value range of ±3, while kurtosis should be within a value range of ±10. Table 1, presents the results of skewness and kurtosis analysis on each of the items that measure the constructs of our study.
From Table 1, the means and standard deviations of teachers' responses on the indicators that were used for investigating the variables in our study could be seen. Among the constructs, indicators for ICT usage behaviour seem to have lower mean ratings, indicating low level of ICT usage among the respondents. The highest mean ratings are among the indicators for behavioural intention, indicating that teachers have high intentions for using ICTs in the classroom.
The Measurement Model
The measurement model of this study has sufficient number of valid indicators for each construct. In assessing CFA, the minimum number of indicators for each construct should be three, and each indicator should load above .50 (Hair et al., 2010). As can be seen on Table 2, there are at least three indicators for each construct investigated and the standardised regression weights of the measurement model for the indicators have ranged from 0.649 to 0.979, which indicates valid factor loadings for all the items in the model. The critical ratios of all the items were significant at 0.001 levels, and their multiple squared correlations (R2) have ranged from 0.421 to 0.984, indicating that the items were explained by their predictions at a range of 42% to 97 percent.
Test of the measurement model Average variance extracted (AVE) and construct reliability measures were used to test the convergent validity of the constructs in the measurement model of the study. When AVE of a construct is valid, it means the variance attributable to the construct in relation to the variance attributable to measurement errors are adequate (Fornell & Bookstein, 1982). For valid construct AVEs, values must be > 0.50, which is indicative of adequate convergent validity. Table 2 shows that all the AVE values in the measurement model of this study are > .50, which means convergent validity is achieved. A construct reliability (CR) test was also conducted on all items to assess their reliability with regard to how they measure their respective constructs, where as a rule of thumb, all values must be > .70 (Fraenkel, Wallen & Hyun, 2012; Pallant, 2010). Table 2 shows that each and every construct has valid construct reliability, with values ranging from 0.782 to 0.958. This additionally satisfies the condition for convergent validity for all the constructs (Hair et al., 2010).
The discriminant validity of the constructs in the measurement model was also tested by comparing the AVE of every given construct with the squared correlations between that construct and other constructs. If the AVEs are greater than the off-diagonal elements in the corresponding rows and columns, and are also greater than the squared correlations between a given construct and other constructs in the model, discriminant validity is considered to be adequate; otherwise it is considered inadequate. When the variance shared between a construct and any other construct in a model is less than the variance shared by the construct with its indicators, discriminant validity is said to be achieved (Fornell & Larcker, 1981).
From the correlation matrix on Table 3, the AVE values in all the diagonal elements are greater than the values of the squared correlations between one construct and the other constructs in the columns of the off-diagonal elements. This is an indication that all the conditions of discriminant validity have been achieved (Fornell & Larcker, 1981).
The Structural Model
Figure 2 depicts the structural model of ICT usage behaviour of business education teachers in Nigerian tertiary colleges. The model is made up of two exogenous variables, namely: PE and ATT, as well as two endogenous variables, which are: BI and USE. With only 13 indicators, our model has validly explained the trend of ICT usage behaviour among the target population of the study, satisfying all the model fit criteria (absolute, parsimonious and incremental fit indices) (Hair et al., 2010). This indicates the model's parsimony or simplicity in explaining ICT usage behaviour, with a lesser number of valid estimated parameters. The absolute fit measures indicates the extent to which the model fits with the observed covariance matrix, and the indices are: chi-square statistics, goodness of fit index (GFI) and the root mean square error of approximation (RMSEA); the incremental fit measures compare the proposed model with the independence or null model and the indices are: Tucker-Lewis index (TLI), normal fit index (NFI), comparative fit index (CFI), relative fit index (RFI), and incremental fit index (IFI) (Hair et al., 2010). The model has met with all the criteria for its goodness of fit: chi-square = 68.821, df = 59, relative chi-square = 1.166, GFI = .950, Adjusted Goodness of Fit Index (AGFI) = .923, CFI = .995, IFI = .995, TLI = .993, root mean square residual (RMR) = .020 and RMSEA = .028; and all its indices and factor loadings are satisfactory, indicating its stability in all theoretical parameters.
Test of the structural model
The test result of the structural model has shown that all the five hypotheses of our study (H1, H2, H3, H4 and H5) were supported by the study data (see Table 4). Perceived enjoyment (PE) has significant influence on ICT usage behaviour, as well as on behavioural intention; attitude towards ICTs has significant influence on behavioural intention, as well as on ICT usage behaviour; and behavioural intention has significant influence on ICT usage behaviour. However, behavioural intention has predicted a decrease over teachers' ICT usage behaviour, by its negative beta (regression weight) on ICT usage behaviour.
The test of the two endogenous variables in the study model (behavioural intention and ICT usage behaviour) has revealed that perceived enjoyment and attitude toward ICTs have explained about 64% of the variance in teachers' behavioural intention for using ICTs, with an R2 of 0.638. Congruently, the combined influence of perceived enjoyment, attitude toward ICTs and behavioural intention have explained 27% of the variance in teachers' ICT usage behaviour, with an R2of 0.270.
This study has explained the roles of two exogenous variables, that of perceived enjoyment and attitudes towards ICTs in determining teachers' use of ICTs in the classroom, or their intention thereof. The study has shown that teachers will use or will intend to use ICTs when they perceive that the ICTs are enjoyable and when they feel favourably disposed towards them. These findings are consistent with the theoretical assumptions of TAM and the positions of studies such as Ajuwon and Popoola ((2015), and Kroenung et al. (2015), where perceived enjoyment influenced use of technology; Van der Heijden (2004) and Verkasalo et al. (2010) where perceived enjoyment impacted on behavioural intention; Alharbi and Drew (2014) and Dishaw and Strong (1999), where attitude influenced behavioural intention; and Arekete et al. (2014), where attitude influenced technology usage behaviour.
Interestingly, our model has revealed that perceived enjoyment had the strongest influence on teachers' intention to use technology, and attitude toward ICTs had the strongest influence on teachers' self-reported use of technology in the classroom. Invariably, teachers' behavioural intention of using ICTs was more affected by their perceived enjoyment of ICTs, and their use of ICTs was more affected by their ICT attitudes. Unfortunately, teachers that participated in this study were not able to use ICTs in the classroom as much as they intended. Only 27% of the variance in their ICT usage behaviour was explained by perceived enjoyment, attitudes towards ICTs and behavioural intention. This implies that there are other important explanations associated with about 73% of the variance in teachers' ICT usage behaviour in the North-Western region of Nigeria. Being that authorities in tertiary schools of Nigeria and the rest of Africa would expect teachers not to stop at their intentions of using technology but to also use it, there is a need to compensate teachers' good intentions and attitudes towards using technology, with adequate ICTs and regular training programmes and incentives/policies to support their usage of ICTs in the classroom. Additionally, school authorities can combine the priority of the usefulness of ICTs with the pleasure derived from them by teachers to ensure that they make them adequately available for use. Although these findings have important implications for ensuring appropriate ICT uptake in Nigeria and the rest of Africa, further research may be needed to investigate how the perceptions, beliefs and attitudes of school leaders towards ICTs affect the appropriate integration and implementation of ICTs in schools across Africa. In the same vein, using self-report scales to measure the variables in this study suggests the possibility of common method errors and other unexplainable and unforeseen circumstances that might have affected the results of the study. Hence, further studies may employ experimental or qualitative designs to observe and investigate the phenomena of interest more closely.
Aduwa-Ogiegbaen SE & Iyamu EOS 2005. Using Information and Communication Technology in secondary schools in Nigeria: Problems and prospects. Educational Technology & Society, 8(1):104-112. [ Links ]
Ajayi I 2008. Towards effective use of Information and Communication Technology (ICT) for teaching in Nigerian Colleges of Education. Asian Journal of Information Technology, 7(5):210-214. [ Links ]
Ajuwon GA & Popoola SO 2015. Influence of motivational factors on utilisation of Internet health information resources by resident doctors in Nigeria. The Electronic Library, 33(1):103-119. doi: http://dx.doi.org/10.1108/EL-12-2012-0159 [ Links ]
Ajzen I 1985. From intentions to actions: A theory of planned behavior. In J Kuhl & J Beckmann (eds). Action control. Berlin, Heidelberg: Springer. [ Links ]
Ajzen I 1991. The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50:179-211. [ Links ]
Akinsola OS, Herselman ME & Jacobs SJ 2005. ICT provision to disadvantaged urban communities: A study in South Africa and Nigeria. International Journal of Education and Development using ICT, 1(3):19-41. [ Links ]
Alharbi S & Drew S 2014. Using the technology acceptance model in understanding academics' behavioural intention to use learning management systems. International Journal of Advanced Computer Science and Applications (IJACSA), 5(1):143-155. Available at http://www98.griffith.edu.au/dspace/bitstream/handle/10072/62162/94819_1.pdfj sessionid=BFFBB0C5F7BF38AECA7E4CBA8411AC53?sequence=1. Accessed 26 October 2015. [ Links ]
Anderson J 2010. ICT transforming education: A regional guide. Bangkok, UNESCO/Thailand: Asia and Pacific Regional Bureau for Education. Available at http://unesdoc.unesco.org/images/0018/001892/189216e.pdf. Accessed 1 November 2015. [ Links ]
Arekete S, Ifinedo P & De Akinnuwesi BA 2014. Antecedent factors to end-users' symbolic acceptance of enterprise systems: An analysis in Nigerian organizations. In Adaptive Science & Technology (ICAST), 2014 IEEE 6th International Conference on. IEEE. doi: 10.1109/ICASTECH.2014.7068108 [ Links ]
Asogwa BE 2013a. Electronic government as a paradigm shift for efficient public services: Opportunities and challenges for Nigerian government. Library Hi Tech, 31(1):141-159. doi: 10.1108/07378831311303985 [ Links ]
Asogwa BE 2013b. The readiness of universities in managing electronic records: A study of three federal universities in Nigeria. The Electronic Library, 31(6):792-807. doi: 10.1108/EL-04-2012-003704-2012-0037 [ Links ]
Atkinson MA & Kydd C 1997. Individual characteristics associated with World Wide Web use: an empirical study of playfulness and motivation. ACM SIGMIS Database, 28(2):53-62. doi:10.1145/264701.264705 [ Links ]
Awosejo PP, Ajala EB &Agunbiade OY 2014. Adoption of Accounting Information Systems in an organization in South Africa. African Journal of Computing & ICT, 7(1):127-136. Available at http://www.ajocict.net/uploads/V7N1P15-2014_AJOCICT_-_Paper_15.pdf. Accessed 26 October 2015. [ Links ]
Beaumont N, Austen MC, Atkins JP, Burdon D, Degraer S, Dentinho TP, Derous S, Holm P, Horton T, Van Ierland E, Marboe AH, Starkey DJ, Townsend M & Zarzycki T 2007. Identification, definition and quantification of goods and services provided by marine biodiversity: Implications for the ecosystem approach. Marine Pollution Bulletin, 54(3):253-265. doi: 10.1016/j.marpolbul.2006.12.003 [ Links ]
British Educational Communications and Technology Agency (BECTA) 2000. Connecting schools, networking people: ICT planning, purchasing and good practice for the National Grid for Learning. Coventry: BECTA. [ Links ]
Byrne BM & Van de Vijver FJR 2010. Testing for measurement and structural equivalence in large-scale cross-cultural studies: Addressing the issue of nonequivalence. International Journal of Testing, 10(2):107-132. doi: 10.1080/15305051003637306 [ Links ]
Cheung CM, Lee MK & Chen Z 2002. Using the Internet as a learning medium: an exploration of gender difference in the adoption of FaBWeb. In System Sciences, 2002. HICSS. Proceedings of the 35th Annual Hawaii International Conference on. IEEE. [ Links ]
Davis FD 1989a. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3):319-340. Available at http://iris.nyit.edu/~kkhoo/Spring2008/Topics/TAM/PercieveUsefulness_MIS.pdf. Accessed 5 November 2015. [ Links ]
Davis FD, Bagozzi RP & Warshaw PR 1989b. User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35(8):982-1003. doi: 10.1287/mnsc.35.8.982 [ Links ]
Davis FD, Bagozzi RP & Warshaw PR 1992. Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14):1111-1132. doi: 10.1111/j.1559-1816.1992.tb00945.x [ Links ]
Delaviz R, Andrade N, Pouwelse JA & Epema DHJ 2012. SybilRes: A Sybil-resilient flow-based decentralized reputation mechanism. In IEEE 32nd International Conference on Distributed Computing Systems (ICDCS). IEEE. [ Links ]
Dellit J 2001. Using ICT for quality in teaching-learning evaluation processes. Learning Federation Secretariat Australian Education Systems Officials Committee [EN]. Available at http://www.ictliteracy.info/rf.pdf/UsingICTQuality.pdf. Accessed 26 October 2015. [ Links ]
DeVellis RF 2003. Scale development: Theory and applications. Newbury Park, CA: Sage Publications. [ Links ]
Dishaw MT & Strong DM 1999. Extending the technology acceptance model with task-technology fit constructs. Information & Management, 36(1):9-21. doi: 10.1016/S0378-7206(98)00101-3 [ Links ]
Duyck P, Pynoo B, Devolder P, Voet T, Adang L & Vercruysse J 2008. User acceptance of a picture archiving and communication system. Applying the unified theory of acceptance and use of technology in a radiological setting. Methods of Information in Medicine, 47(2):149-156. doi: http://dx.doi.org/10.3414/ME0477 [ Links ]
Ekpenyong LE &Nwabuisi J 2003. Business teacher education in Nigeria: projecting a new direction. Journal of Vocational Education & Training, 55(1):33-46. doi: 10.1080/13636820300200217 [ Links ]
Fishbein M 1979. A theory of reasoned action: some applications and implications. In H Howe & M Page (eds). Nebraska Symposium on Motivation. Lincoln: University of Nebraska Press. [ Links ]
Fishbein M & Ajzen I 1975. Belief, attitude, intention and behavior: An introduction to theory and research. Redaing, MA: Addison-Wesley. [ Links ]
Fishbein M & Ajzen I 2011. Predicting and changing behavior: The reasoned action approach. New York: Taylor & Francis. [ Links ]
Fornell CG & Bookstein FL 1982. Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19(4):440-452. [ Links ]
Fornell CG & Larcker DF 1981. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1):39-50. doi: 10.2307/3151312 [ Links ]
Fraenkel JR, Wallen NE & Hyun HH 2012. How to design and evaluate research in education (8th ed). New York, NY: McGraw-Hill. [ Links ]
Hair Jr JF, Black WC, Babin BJ & Anderson RE 2010. Multivariate data analysis: a global perspective (7th ed). Upper Saddle River, NJ: Pearson Education. [ Links ]
Igbaria M, Iivari J & Maragahh H 1995. Why do individuals use computer technology? A Finnish case study. Information & Management, 29(5):227-238. doi: 10.1016/0378-7206(95)00031-0 [ Links ]
Iloanusi NO & Osuagwu CC 2009. ICT in education: Achievements so far in Nigeria. In A Méndez-Vilas, A Solano Martin, JA Mesa González & JMesa González (eds). Research, reflections and innovations in integrating ICT in education. Badajoz: FORMATEX. [ Links ]
Iloanusi ON & Osuagwu CC 2011. Clustering: Applied to data structuring and retrieval. International Journal of Advanced Computer Science and Applications (IJACSA), 2(11):100-105. Available at https://thesai.org/Downloads/Volume2No11/Paper%2016%20Clustering%20Applied%20t o%20Data %20Structuring%20and%20Retrieval.pdf. Accessed 28 October 2015. [ Links ]
Isiyaku DD 2007. Preparing the business education teacher for national reform in business education. Bichi Journal of Education (BIJE), 1:21-30. [ Links ]
Isiyaku DD 2009. Applying information technology in business education. Bichi Journal of Business Education (BIJOBE), 2(1):58-64. [ Links ]
International Telecommunication Union (ITU) 2010. Measuring the information society. Geneva, Switzerland: ITU. Available at https://www.itu.int/newsroom/press_releases/2010/pdf/PR08_ExecSum.pdf. Accessed 28 October2015. [ Links ]
ITU 2011. Measuring the information society. Geneva, Switzerland: ITU. Available at http://www.itu.int/net/pressoffice/backgrounders/general/pdf/5.pdf. Accessed 28 October 2015. [ Links ]
ITU 2012. Measuring the information society. Geneva, Switzerland: ITU. Available athttps://www.itu.int/en/ITUD/Statistics/Documents/publications/mis2012/MIS2012_ without_Annex_4.pdf. Accessed 28 October 2015. [ Links ]
ITU 2013. Measuring the information society. Geneva, Switzerland: ITU. Available at https://www.itu.int/en/ITU-D/Statistics/Documents/publications/mis2013/MIS2013_without_Annex_4.pdf. Accessed 28 October 2015. [ Links ]
Jegede PO, Dibu-Ojerinde OO & Ilori MO 2007. Relationships between ICT competence and attitude among some Nigerian tertiary institution lecturers. Educational Research and Review, 2(7): 172-175. Available at http://www.academicjournals.org/journal/ERR/article-full-text-pdf/AEECCAF3576. Accessed 29 October 2015. [ Links ]
Johnson OA 2007. Enhancing quality in higher education through information and communication technology in Nigeria. Access, equity and quality in higher education. NAEAP Publication. [ Links ]
Jonathan AO 2012. Influence of information and communication technology (ICT) on business education programmes in tertiary Institutions. Knowledge Review, 26(4):41-48. Available at http://www.globalacademicgroup.com/journals/knowledge%20review/INFLUENCE%20OF% 20INFORMATION%20AND%20COMMUNICATION.pdf . Accessed 29 October 2015. [ Links ]
Kim SH 2008. Moderating effects of job relevance and experience on mobile wireless technology acceptance: Adoption of a smartphone by individuals. Information & Management, 45(6):387-393. doi: 10.1016/j.im.2008.05.002 [ Links ]
Kline RB 2011. Principles and practice of structural equation modeling (3rd ed). New York, NY: Guilford Press. [ Links ]
Kroenung J, Jaeger L & Kupetz A 2015. System characteristic or user purpose? A multi-group analysis on the adoption of online shopping by mobility impaired and unimpaired users. ECIS 2015 Completed Research Papers, Paper 112:1-17. doi:10.18151/7217400 [ Links ]
Kwak YH, Park J, Chung BY & Ghosh S 2012. Understanding end-users' acceptance of enterprise resource planning (ERP) system in project-based sectors. Engineering Management, IEEE Transactions on, 59(2):266-277. doi:10.1109/TEM.2011.2111456 [ Links ]
Larkin TL & Belson SI 2005. Blackboard technologies: A vehicle to promote student motivation and learning in physics. Journal of STEM Education, 6(1):14-27. [ Links ]
López-Pérez MV, Pérez-López MC, Rodriguez-Ariza L & Argente-Linares E 2013. The influence of the use of technology on student outcomes in a blended learning context. Educational Technology Research and Development, 61(4):625-638. doi:10.1007/s11423-013-9303-8 [ Links ]
Mann D, Shakeshaft C, Becker J & Kottkamp R 1999. West Virginia story: Achievement gains from a statewide comprehensive instructional technology program. Beverly Hills, CA: Milken Family Foundation/Charleston: State Department of Education. Available at http://files.eric.ed.gov/fulltext/ED429575.pdf. Accessed 30 October 2015. [ Links ]
Mbaba AE & Shema IM 2012. Analysis of the frequency of academic staff and students' use of information and communication technology (ICT) in Katsina State College of Education. International Journal of Research in Engineering, IT and Social Sciences (IJREISS), 2(10):157-167. Available at http://www.indusedu.org/IJREISS/October2012%28pdf%29/14.pdf. Accessed 30 October 2015. [ Links ]
McGrath S & Akoojee S 2009. Vocational education and training for sustainability in South Africa: The role of public and private provision. International Journal of Educational Development, 29(2): 149-156. doi: 10.1016/j.ijedudev.2008.09.008 [ Links ]
Oghogho I & Ezomo PI 2013. ICT for national development in Nigeria: Creating an enabling environment. International Journal of Engineering and Applied Sciences, 3(2):59-66. Available at http://eprints.lmu.edu.ng/152/1/Final%20ICT%20for%20National%20development%20paper.pdf. Accessed 30 October 2015. [ Links ]
Ojiako U, Chipulu M, Maguire S, Akinyemi B & Johnson J 2012. User adoption of mandatory enterprise technology. Journal of Enterprise Information Management, 25(4):373-391. doi: http://dx.doi.org/10.1108/17410391211245847 [ Links ]
Ololube N, Egbezor D & Kpolovie P 2008. Education policies and teacher education programs: Meeting the millennium development goals. Journal of Teacher Education for Sustainability, 9(1):21-34. doi: 10.2478/v10099-009-0016-3 [ Links ]
Onyia C & Offorma GC 2011. Learning style preferences of Nigerian University undergraduates: Implications for new faculty development model. Journal of Learning in Higher Education, 7(2):145-165. Available at http://jwpress.com/JLHE/Issues/JLHE-2011-Fall.pdf#page=91. Accessed 30 October 2015. [ Links ]
Oye ND, Iahad NA & Rabin ZA 2011. A model of ICT acceptance and use for teachers in higher education institutions. International Journal of Computer Science & Communication Networks, 1(1):22-40. Available at http://www.ijcscn.com/Documents/Volumes/vol1issue1/ijcscn2011010105.pdf. Accessed 30 October 2015. [ Links ]
Oye ND, Noorminshah AI & Rahim NZA 2012. Using mixed method approach to understand acceptance and usage of ICT in Nigerian Public University. International Journal of Computers & Technology, 2(3):47-63. Available at http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.300.8018&rep=rep1&type=pdf. Accessed 26 October 2015. [ Links ]
Pallant J 2010. SPSS survival manual: A step by step guide to data analysis using SPSS. New York: McGraw-Hill International. [ Links ]
Pynoo B & Van Braak J 2014. Predicting teachers' generative and receptive use of an educational portal by intention, attitude and self-reported use. Computers in Human Behavior, 34:315-322. doi:10.1016/j.chb.2013.12.024 [ Links ]
Rana N 2012. A study to assess teacher educators' attitudes towards technology integration in classrooms. MIER Journal of Educational Studies, Trends and Practices, 2(2):190-205. Available at http://www.mierjs.in/ojs/index.php/mjestp/article/view/74/50. Accessed 30 October 2015. [ Links ]
Rastogi A & Malhotra S 2013. ICT skills and attitude as determinants of ICT pedagogy integration. European Academic Research, I(3):301-318. Available at http://euacademic.org/UploadArticle/22.pdf. Accessed 30 October 2015. [ Links ]
Renshaw V, Trott Jr EA & Friedenberg HL 1988. Gross state product by industry, 1963-86. Survey of Current Business, 68(5):30-45. Available at https://fraser.stlouisfed.org/docs/publications/SCB/pages/1985-1989/10547_1985-1989.pdf. Accessed 30 October 2015. [ Links ]
Sife AS, Lwoga ET & Sanga C 2007. New technologies for teaching and learning: Challenges for higher learning institutions in developing countries. International Journal of Education and Development using ICT, 3(2):57-67. Available at http://ijedict.dec.uwi.edu/viewarticle.php?id=246. Accessed 30 October 2015. [ Links ]
Smith B, Caputi P & Rawstorne P 2000. Differentiating computer experience and attitudes toward computers: an empirical investigation. Computers in Human Behavior, 16(1):59-81. doi:10.1016/S0747-5632(99)00052-7 [ Links ]
Teo T 2010. Validation of the technology acceptance measure for pre-service teachers (TAMPST) on a Malaysian sample: A cross-cultural study. Multicultural Education & Technology Journal, 4(3):163-172. doi: 10.1108/17504971011075165 [ Links ]
Teo T 2011. Factors influencing teachers' intention to use technology: Model development and test. Computers & Education, 57(4):2432-2440. doi:10.1016/j.compedu.2011.06.008 [ Links ]
Tilbury D & Ryan A 2011. Today becomes tomorrow: Re-thinking business practice, education and learning in the context of sustainability. Journal of Global Responsibility, 2(2):137-150. doi: http://dx.doi.org/10.1108/20412561111166012 [ Links ]
Ubulom WJ, Enyekit EO & Onuekwa FA 2011. Analysis of ICT accessibility and utilization in teaching of Business Studies in secondary schools in Adoni Local Government Area of Rivers State. Academic Research International, 1(3):349-354. Available at http://www.savap.org.pk/journals/ARInt./Vol.1%283%29/2011%281.3-35%29.pdf. Accessed 31 October 2015. [ Links ]
Ugwuogo CC 2013. Business education and national development: Issues and challenges. Journal of Educational and Social Research, 3(4): 129-134. [ Links ]
Umoru TA 2012. Barriers to the use of information and comm unication technologies in teaching and learning business education. American Journal of Business Education (AJBE), 5(5):575-580. Available at http://www.cluteinstitute.com/ojs/index.php/AJBE/article/view/7214/7284. Accessed 1 November 2015. [ Links ]
Van der Heijden H 2004. User acceptance of hedonic information systems. MIS Quarterly, 28(4):695-704. [ Links ]
Venkatesh V 2000. Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4):342-365. http://dx.doi.org/10.1287/isre.11.4.342.11872 [ Links ]
Venkatesh V & Bala H 2008. Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2):273-315. Available at http://onlinelibrary.wiley.com/doi/10540-5915.2008.00192.x/pdf. Accessed 5 November 2015. [ Links ]
Venkatesh V & Davis FD 1996. A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3):451-481. doi:10.11540-5915.1996.tb00860.x [ Links ]
Venkatesh V & Davis FD 2000. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2):186-204. [ Links ]
Venkatesh V, Morris MG, Davis GB & Davis FD 2003. User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3):425-478. [ Links ]
Verkasalo H, López-Nicolas C, Molina-Castillo FJ & Bouwman H 2010. Analysis of users and non-users of smartphone applications. Telematics and Informatics, 27(3):242-255. doi:10.1016/j.tele.2009.11.001 [ Links ]
Wilkinson P & Schilt J 2008. ABC of ICT: An introduction to the attitude, behavior & culture of ICT. Norwich, UK: Van Haren Publishing. [ Links ]
Yusuf MO & Balogun MR 2011. Student-teachers' competence and attitude towards information and communication technology: A case study in a Nigerian university. Contemporary Educational Technology, 2(1):18-36. Available at https://unilorin.edu.ng/publications/yusufmo/Publication%2036.pdf. Accessed 30 October 2015. [ Links ]