Use of Integrated Tertiary Software by line managers

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The objective of this paper is to establish and provide understanding of the social, cultural and organizational factors that explain the behaviours of line managers towards adoption and use of Integrated Tertiary Software (ITS). The validated the Unified Theory of Acceptance and Use of Technology (UTAUT) in which two new constructs attitude and awareness were introduced. Data was collected using structured interviews and close-ended questionnaires. Structural equation modelling (SEM) was applied for confirmatory factor analysis and a structure model was developed. Results show that awareness directly predict actual usage of ITS similarly, attitude, social influence, performance expectancy and effort expectance significantly influence line managers behavioural intention to adopt and use ITS. Whereas the direct influence of facilitating condition to actual usage of ITS was found insignificant. This study contributes to organization's understanding that organizational structures, the people and processes that depend on information technology play a significant role in the adoption and usage of the IT systems. Furthermore, these findings advance theory and contribute to future research on users' adoption and use of technology.

Keywords: UTAUT, Technology Acceptance and Usage, ITS, ERP


Integrated Tertiary Software (ITS) is an Enterprise Resource Planning (ERP) system that is composed of varied transactional functionalities. This information system is employed mostly in institutions of higher learning, hence the word "tertiary" in its name. ITS, like any other ERP, helps users in processing daily business transactions, control management activities, and facilitates planning and communication of business targets and goals. It helps to optimize the flow of information and resources throughout an organisation's entire supply chain. The adoption and effective use of ITS could improve an organization's efficiency in its day to day operations. However, organizations should ensure that the introduction of information systems, like ITS do not only intend to automate manual tasks, but also to "informate" management tasks. Hence, if organizations are to benefit from ITS functionalities, for example; reducing costs by improving efficiencies through computerization; enhanced decision-making by providing accurate and timely enterprise-wide information, then ITS should be adopted and fully utilized.

As its name suggests, many of the organisations that use ITS are educational tertiary institutions and the use of ITS system is mandatory for operational users. However, in the research Boudreau (2003) it was established that the extent to which ITS is used by line managers for decision making is largely voluntary. These managers undermine the complexity of the system which puts them to a disadvantage when they try to learn using it in their daily reporting requirements. Much as this is so, the demand to use ERP in general within organisations is tremendously high. Researchers (Wright & Wright, 2002; Supramaniam & Kuppusamy, 2010) attribute the reasons for this exponential increase to; competitive pressures to reduce the cost of production, increased need for revenue growth, competitive advantage both locally and globally, resource maximization, and adequate response to current market challenges. Enormous investments have been made by organisations in this regard, however, ERP's effect on organizational performance and effectiveness is still lacking.

Researchers (Kendall et al., 2001; Macharia, 2009) studied the cause for this slow pace of adoption and ineffective usage but only limited their findings macro and technological factors. However, other researchers (Venkatesh et al., 2003, Jong & Wang, 2009; and Marchewka et al., 2007) argue that there exist many other factors that influence individual's adoption acceptance and use of technology. They categorized these factors as, social, cultural, organisational and individual factors. On the same note, Zhang et al (2002) suggested that people characteristics, technical problems and vendor support also play a major role. They also agreed that organizational culture may hinder effective usage of an information system more especially if the system is developed by foreign vendors. They attributed this to the fact that people tend to use products that support the work practices determined by their own culture. Molla and Loukis (2005) noted that, ERP success should be looked into two cultural perspectives; the 'system culture' that is the culture of the developing firm and the 'host cultural' the one for the implementing organisation's project team, managers and end users. Sumner (2000) adds that, investigating cultural influences on the adoption and effective usage of ERP should embrace the influence of organizational factors as ERP implementation causes significant cultural transformation to the organisation and tends to reset organisational values in terms of discipline, change and processes. It is paramount that, organisational, social and cultural factors are investigated as they are core antecedents for successful adoption and use of technology.

Subsequently, ITS software solutions the developers, ITS evula and ITS business solutions, the deliverers and the service divisions of ITS Holding need more understanding of how line managers adopt and use ITS systems and respond to ITS functionalities in a manner that is most effectively apply on management processes and smooth operation of business. Similarly, knowing line managers' intentions and understanding, the factors that influence their beliefs about adopting and using ITS, can help administrators to understand the future version of ITS to be invested in for smooth operations. Therefore, it is necessary to conduct research that deals more intensively with line managers' awareness of, attitude towards, and intention for effective use of ITS. However, this study found little research done towards empirical testing of line managers' adoption and use of ITS by examining the organisational, social and cultural factors that may influence their adoption and use of ITS.

In their research on information systems' full utilisation as a key dependent variable, Trice and Treacy (1988) suggested that behaviours with unclear determinants can best be explained by referring to an suitable reference theory. Therefore this study proposed a theoretical framework for line managers', adoption and use of ITS by validating the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003). The objectives of this study are to validate the relationship of line managers' adoption and use of ITS with UTAUT. Selected constructs awareness, and attitude are incorporated in UTAUT to develop a general linear structural model of ITS adoption and use by line managers that would provide organisations with better implementation strategy. The study also determines some descriptive characteristics of ITS and the introduced constructs. With this model and the theoretical foundation, this study is expected to contribute on the understanding of line managers adoption and use of ITS. Basing on these and future findings ITS software developers will have information processing requirements that will help them improve on the ITS functionalities in the later versions. Also this study will contribute theoretically in the information system research of explaining individual behaviour towards adoption and use of technology.

2. Related Work and Theoretical justifications

Generally, users will adopt and use an information system that meets their requirements; this implies that such a system must be developed in the context of their social interest. In order for an organization to achieve users' adoption and effective usage it needs a clearer understanding of the relationships between the organizational factors, cultural and social factors that influence the process within which an organization operates. In their study (Hussein et al., 2005), identified six organisational factors; top management support, management style, decision-making structure, goal alignment, managerial IT knowledge, and resources allocation. Most of these organisational factors are highly controlled by line managers (Willcocks & Sykes, 2000), who are supposed to be key users of the system even though many of them are not comfortable with the IT related activities. The role of the Chief Executive Officer (CEO) and line managers in IT project innovations is explained in the research of Caldwell (1997, p. 100) who indicated that CEOs and line managers who are comfortable with IT will initiate one third of these projects within their organisations while only less than 15% may be attributed to those not comfortable with IT. Researchers (Wager et al, 2005) argue that, apart from innovations, it is the responsibility of line managers to; ensure that the organization has an IT strategy, balance the perspectives of users and IT, establish processes for budgeting, acquire and implement applications and infrastructure, ensuring that IT purchases conform to policies and procedures, develop and modify the responsibilities of personnel IT and other users, ensure that IT applications and activities conform to relevant regulations and internal controls and encourage IT experimentation. Therefore, their role in ITS adoption and usage needs a significant attention.

As Friedman and Hoffman (2001) noted, benefiting from information technology (IT) will require the organisation not only to focus on the technology and the roles it play in an organisation, but more significantly on the organizational structures, processes and people that depend on it. Unfortunately, research (Delone & Mclean, 2002; Poon & Wagner, 200; Friedman &Hoffman , 2001) has indicated that organizations pay little attention to the people who administer and use the system and the factors like organizational culture; social structure; processes and practices; individual behaviours and perception; information politics; and models of information sharing that influence their behaviour for adoption and use.

In Lucas's model (1975), it is predicated that usage of the system influences users' performance which in turn affects system usage and that both usage and performance are influenced by individual and situational factors. Situational factors on other hand may arise from different sources among them are organizational and social surroundings whereas individual perception may highly be influenced by cultural factors. Both user's attitude and analysis of actions influence decision style which in turn affects system usage. Other researchers (Mock, 1973; Ives et al., 1980; Hussein et al., 2005) suggest that, much emphasis is needed to establish the behavioural characteristics of users, mostly the decision makers who operate in an entirely fixed social, personal, and structural environment since these behavioural constraints greatly impact on their performance. In this perspective such factors like; individual/psychological factors; organizational and interpersonal factors; sociological and environmental factors; information structure factors and; decision maker performance factors need to be investigated together with their impact on the system usage. From this perspective this study drew its first hypothesis.

H1: Organisational contextual factors such as cultural, social and organisational influences line mangers adoption and use of ITS.

Researchers (Griffith et al., 1999; Aladwani, 2001; Aiken, 2002) on ERP, concur that a big number of ERP projects ends up catastrophically there is a great need to ensure that top management and line managers understand, adopt, accept and support the use of ERP systems. In their research (Williams, 1982; Hussein et al., 2005) argue that, line managers play an important role in the adoption and use of ERP as they highly influence sensitization and training of other users in total quality management and change management strategies and also are influential in the decision making styles. Dieringer (2004) adds that, failure to get management buy-in results is an automatic project failure.  That, it is important for organisations to ensure that managers at all levels get involved in ERP introduction and at all stages of implementation until it achieves successful usage and full utilization.

Recent developments have seen many organizations using ERP with several institutions of higher learning using ITS. However, this increment in the use of ERPs calls for a systematic address of the factors that cause failures of ERPs adoption and successful usage to be understood and solutions leading to success need to be found (Calisir & Calisir, 2004). In this regard, researchers (Stratman and Roth, 1999; Aladwani, 2001; Al-Mashari, 2002; Calisir & Calisir, 2004) went ahead to establish these factors. All of them noted that top management support is very crucial and where managers fail to embrace the system, its chances of failing becomes exponentially high. As also noted by (Griffith et al., 1999; Bingi et al, 1999), ERP need full adoption by line managers especially in their early stages of implementation. This is because ERP integrates various business functions in the different departments of the organization hence a high level of teamwork is needed. Also with other success factors noted; business case, change management, project management, training, and communication (Al-Mashari, 2002) it is evident that if line managers fail to play their significant roles failure of the system becomes obvious.

The Unified Theory of Acceptance and Use of Technology (UTAUT)

Several scholars in individual behavioural research have investigated and developed theoretical frameworks of information technology acceptance, adoption and usage. Among these theories are; Theory of Reasoned Action (TRA) (Ajzen and Fishbein, 1977); Theory of Planned Behavior (TBP) (Ajzen, 1991); Technology Acceptance Model (TAM) (Davis, 1989); and Diffusion of Innovation Theory (IDT) (Rogers, 1962). With continued replication and modification of these theories, Venkatesh et al. (2003) unified eight of them and developed UTAUT. Those unified theories included; TRA, TAM,TPB, IDT, Motivational Model (MM) (Davis et al., 1992), Model Combining the Technology Acceptance Model and Theory of Planned Behavior (C-TAM-TPB) (Taylor and Todd , 1995), Model of PC Utilization (MPCU) (Triandis, 1980; Thompson et al., 1991), and Social Cognitive Theory (SCT) (Bandura, 1986; Compeau and Higgins, 1995).

UTAUT explains user intentions to use technology and subsequent usage behaviour. In their study (Venkatesh et. al., 2003) found the four key constructs of UTAUT; performance expectancy, effort expectancy, social influence, and facilitating conditions directly influencing intention to use and behaviour. These four constructs are moderated by gender, age, experience, and voluntariness of use as shown in figure 1. When Venkatesh et. al. (2003) validated it in a longitudinal study; they found it to account for 70% of the variance in usage intention. Later research (Pu-Li, and Kishore, 2005; Cody-Allen and Kishore, 2006; Tibenderana and Ogao, 2008) confirmed that UTAUT's constructs are very predictive. However, their findings recommended that UTAUT's usefulness can best be optimized if its constructs are modified to fit the technology at study and the situation at hand.

Figure1. The UTAUT Model (Source: Venkatesh et al., 2003)

This study presumed that when awareness and attitude are incorporated together with the four constructs of UTAUT; performance expectancy, effort expectancy, social influence, and facilitating conditions would explicitly examine the organisational contextual factors such as cultural, social and organisational variables that can explain line managers' behaviour towards ITS adoption and usage.

Performance expectancy: this relates to the degree to which an individual believes that using ITS will help him or her to obtain intensity more easy to display. The belief of the individual that when ITS is used better job performance will be attained (Venkatesh et. al., 2003). From this perception this study derived its second hypothesis

H2: Performance expectancy will positively influence line managers' behavioural intention to adopt and use of ITS

Effort Expectancy: this relates to the degree of an individual perceives ITS easy to use. It is looked at the ease of use associated with the ITS software (Venkatesh et. al., 2003). This study assumed that since line managers have limited time to learn new IT innovation, a system assumed to be less complicated would attract many to use it hence, enlisting a positive behaviour intention. This study derived its third hypothesis from this concept.

H3: Effort expectancy will positively influence line managers' behavioural intention to adopt and use of ITS

Both performance expectancy and effort expectancy are perception of an individual towards the technology of the system (Schaper & Pervan , 2007). They are therefore looked at in this study in the technological context though may highly be influenced by individual context.

Social Influence: this is the degree, to which an individual believes that important other people he or she associates with, are expecting him/her to use the technology (ITS). This implies that what the individual experiences receives others' influence degree (Venkatesh et. al., 2003). This construct assumes or tests the level of compliance of an individual to his/her social surrounds and it is perceived in this study in terms of social context. According to Triandis (1979), one's personality reflects his/her cultural way of perceiving the social environment. He called this subjective culture of the group that is consisted of norms that is to say; one's way of thinking that what he/she is doing is critically judged by other members of the cultural group. He further asserts that internalisation of cultural forms the social factors that influence the intention to behave. Hence, another hypothesis is derived from this perspective

H4: Social influence will positively influence line managers' behavioural intention to adopt and use of ITS

Researchers (Triandis,1979; Ditsa, 2003) identified subjective norms, values, roles and social situation to be some of the few variables that define social factors. They further assert that social factors are further influenced by cultural setting of a given group of users may greatly influence the intention to use.

Facilitating Conditions: this relates to organizational factors that define the degree to which an individual believes that an organisation and the technical infrastructure that do exist plays a role in facilitating the use of the technology (ITS) (Venkatesh et. al., 2003). Such organizational factors like the; top management support, committed executive sponsorship, management of user resistance and expectations, availability of user support group on ITS, training and the provision of computer help desk are included in here (Ditsa, 2003). This study derived its fifth hypothesis from this understanding.

H5: Facilitating conditions will positively influence line managers' behavioural actual use of ITS

Awareness and Attitude: On top of these four constructs this study assumed that an introduction of two more constructs awareness and attitude will enhance the model to better predict line mangers adoption and use of ITS. Both awareness and attitude are individual factors that are highly influenced by their cultural and social settings (Schaper & Pervan , 2007). In this study it is assumed that line managers' attitude towards ITS is highly influenced by their past experience with web-based tools and computer systems in general. This in turn is influenced by their cultural and social settings.

In their study (Venkatesh et al., 2003) decided to exclude attitude basing on the fact that since their research was conducted in an organization. In many organizations once a system has been introduced, mandatory usage is enforced because of performance benefit. However this has two implications. First, users may see this as a threat to their normal ways of doing work hence causing resistance which cause a negative attitude towards using such a system (Calisir & Calisir, 2004). Secondary, users may have been using another system to which they have got used to, if such a system has been performing to their expectation, this may cause a negative attitude towards the new system hence affecting its usage. Therefore, attitude moderated by the first experience with the system may have an influence with the intention to use a system. Schaper and Pervan (2007) also argued that there is direct relation between attitude and intention. This led to H6 and H7.

H6: Awareness will positively influence line managers' actual usage of ITS

H7: Attitude towards ITS will positively influence line managers' behavioural intention to adopt and use of it

In the UTAUT model, Venkatesh et. al. (2003) suggested four moderating factors that were found to influence intention and/or use behaviour these are; gender, age, experience with technology and voluntariness of use. As shown in Figure 1, age and experience directly influence intention to use while voluntariness moderate social influence to enlist behaviour intention. Since the use of ITS is mandatory in these organisations, this study didn't consider voluntariness of use as significant moderating factors. Therefore, two moderating factors were introduced; level of interaction with the system and whether or not the respondent found the ITS system already operational at the place of work. Experience with technology was also modified to experience with web-based tools. These new moderating factors together with those suggested by UTAUT are tested in this study to establish their influence on behaviour intention. This assumption led to hypothesises 8, 9 and 10.

H8: There would be a significant positive relationship between behaviour intention and actual ITS usage.

H9: A line manager who finds ITS at the place of work will adopt and use it more quickly

H10: Managers who have experience with web-based tools will adopt and use ITS more quickly

Figure 2: Research model


The study targeted participants at the ITS user group conference held at the International Convention Centre, East London, South Africa 08th - 10th March 2010. This is an annual conference intended for senior administrators mainly from tertiary institutions and other ITS users from organisations using ITS who range from senior managers to on-line users. This conference basically discusses enhancements to the ITS Systems and administrative procedures, business processes and common problems which are encountered within the support structures of the organisation. Data was collected using structured interviews and close-ended questionnaires based on the instrument developed by Venkatesh, et. al. (2003). A seven-point Likert scale with strongly disagree (1) and strongly agree (7). Out of the 150 questionnaires that were distributed 70 were returned and out of these, only 64 were usable. The participants who returned the questionnaires were from; Makerere University, Tshwane University of Technology, University of Limpopo- MEDUNSA, University of Swaziland, Northlink College and University of Ghana.

Data Analysis

The structured interviews were meant to get the inner feelings, perception, experience and attitude of the users towards ITS adoption and usage. In all 15 participants were interviewed, out of these 3 participants from ITS software solution and 2 from ITS abacus preferred to answer the questions as a group. These interviews helped in the write up of this paper and in the formulation of the attributes for each construct. Data recording and analysis was done using SPSS 17.0 for windows. Structural equation modeling was done using AMOS 17.0. Table 1 shows the demographics of the participants.

The majority of the respondents (92.2%) were at the managerial level, with only 7.8% ordinary users. This shows that the study achieved its target of managers putting into consideration that 93.7% of the respondents interact with ITS at managerial levels. It is also interesting to note that over 90% of the users have had experience with other web-based tools putting them at good chance of using ITS.

Table 1: Demographic Data of the Respondents (N=64)

Investigated Factors



Percentage %

Job Position

Manager Finance



Manager IT



Manager School



Senior Manager



Other User



Level of interaction with the system

group leader



line manager



senior manager













Web-based tools used before

Google chat



Face book



Internet Banking



Online payment systems






Yahoo messenger



Never used any



ITS found me at work







Similarly a big number of the respondents (79.2%) found ITS already being used at their place of work. Users who find a system being already used at their place of work try their best to adjust to the job requirements of using such a system (Calisir & Calisir, 2004) unlike those whom the system found already working at the place. Such users will be aiming to cope up with work standards and their intention to use the system will be high resulting to actual usage (Venkatesh et. al., 2003; Davis, 1989).

Internal consistency reliabilities of the constructs of the proposed model were tested with Cronbach's Alpha coefficient, which is recommended to be at least 0.7 being acceptable (Pallant, 2005). The model reported a reliability of 0.815 and the reliability of each of construct of the model is as shown in Table 2. Initially two constructs attitude and facilitating conditions were the only two constructs with alpha coefficient less than 0.700 with values 0.295 and 0.688 of standardized items respectively. Attitude had five attributes ATT1: Using ITS is a good idea, ATT2: Using ITS is a bad idea, ATT3: ITS systems make our work more interesting, ATT4: I like using ITS and, ATT5: I only use ITS because I am busy in office otherwise I wouldn't be using it. Attribute two (ATT2) and five (ATT5) were reporting negatively hence their coding were changed and attitude gave a standardized Cronbach alpha of 0.709. Similarly facilitating condition had one of its attribute FC3: ITS is not compatible with other applications also reading negatively, much as the coding for this attribute was not changed it was found to be causing the lower reliability scale. Also 0.688 being close to 0.700, this made facilitating conditions to be considered acceptable for use in this study.

Table 2: Cronbach's Alpha reliability


Cronbach's Alpha

Cronbach's Alpha Based on Standardized Indicators

N of Indicators

Awareness (AW)




Attitude (ATT)




Effort Expectance (EE)




Performance Expectance (PE)




Social Influence (SI)




Facilitating Conditions (FC)




Behavioral Intention (BI)




More analysis was made based on Structural Equation Modeling (SEM) with maximum likelihood estimation. SEM is a statistical technique for testing and estimating causal relations using a combination of statistical data and qualitative causal assumptions. It is an extension of the general linear model (GLM) that enables a researcher to test a set of regression equations simultaneously, test traditional models, as well as complex relationships and models, such as confirmatory factor analysis and time series analyses(Pearl, 2000). Since SEM allows confirmatory modeling, this research found it appropriate in representation of the set hypotheses in the causal model. SEM has been proven to work well with already validated models or models with strong theoretical frameworks and has good mathematical and statistical backgrounds (MacCallum & Austin, 2000). Measurement model was tested using the confirmatory factor analysis (CFA) and a two step analytical procedure was used to conceptualize the structural model.

Results and Discussions

A close examination of the factors being investigated in this study, organizational, social and cultural factors show that all of them are composite or multidimensional as they contain hidden or latent factors. As Kamata et al., (2003) noted, the use of Cronbach's α, alone to test the reliability of such factors is limited. Hair et al. (2006) suggest that, in assessing the reliability of such factors composite reliability, average variance extracted and unidimensionality should be tested so as to get the internal consistence of the measurement model. The overall reliability of these heterogeneous but similar factors should be measured together with the correlation of each of the composite factors or the factor loading.

Table 3: Factor Loading, Composite Reliability and Average Variance Extracted



Factor Loading

Composite Reliability

Average Variance Extracted (AVE)

Awareness (AW)















Attitude (ATT)














Expectancy (EE)











Performance Expectancy (PE)











Social Influence (SI)











Facilitating Conditions (FC)













Behavioral Intention (BI)











According to Hair et al. (2006), composite reliability estimates the extent to which a set of latent (un observed) construct indicators share in their measurement of a construct whereas the average variance extracted is the amount of common variance among latent construct indicators. Basing on (Heir et al., 2006; Kamata et al., 2003; and Jöreskog & Sörbom, 1993) the following equations are derived to estimate the composite reliability and average variance extracted. Assuming parameter S is the standardized loadings for the indicators of a given latent variable and, E is the corresponding error term then;

E = 1- S2 ………………………………………………………………………………...……. (1)

Composite reliability = CR= [∑1….n (S)]2 / ([∑1….n (S)]2 + ∑1….n (E))……………….. (2)

Average variance extracted=AVE = [∑1….n (S)2 ]/ ([∑1….n (S2)] + ∑1….n (E))……….. (3)

Where n is the number of indicators for each construct.

Values of composite reliability and average variance extracted should be equal or above the threshold value of 0.7 and 0.5 respectively. Therefore the values in Table 3 show good validity of both the construct and the individual indicators.

After the reliability has been measured, it is paramount to assess the model fitness to establish whether the relationships are consistent with the theoretical expectations. As researchers (Heir et al., 2006; Jöreskog & Sörbom, 1993) also noted, relevant model fit indices should be compared with their corresponding recommended values in order to establish and recommend a good model fit. Fit measures exist in four basic categories namely; the Chi-Squared test (χ2), absolute; incremental; and parsimony fit measures. The absolute fit indices are direct measures of how well the proposed model reproduces the observed data or fits the sample data. Other than the χ2, other fit measures may include though not limited to; root mean square error of approximation (RMSEA), goodness-of-fit statistic (GFI) and the adjusted goodness-of-fit statistic (AGFI), root mean square residual (RMR) and standardised root mean square residual (SRMR). However, for small sample sizes that might have slightly departed from normality, instead of using the chi-squares, the chi-square per degree of freedom (χ2 / d.f.) should be used this helps to makes the model to be less dependent on the sample size (Heir et al., 2006). Jöreskog and Sörbom (1993) adds that, a violation of distributional assumptions makes the χ2 to lose its validity, hence leading to the rejection of good models or the retention of bad ones.

In the category of absolute, incremental and, parsimony fit measures several other goodness-of-fit measures exist depending on the software and version used. AMOS derives up to 25 different goodness-of-fit measures from which a researcher can make a choice depending on the methodology he intends to apply and the complexity of the model. However, Hair at al. (2006) suggested that, a researcher may use χ2 statistics, with at least one absolute index such as RMSEA and an incremental index like the comparative fit index (CFI). He further recommend that, for model that include a comparison of varying complexity, a researcher should add one other fit index from a choice of parsimony normed fit index (PNFI) which is derived from the normal fit index (NFI), GFI and SRMR. Table 4 shows a comparison of fit index measures with their corresponding threshold to check the fitness of the model Figure 3 with the collected data.

Table 4: Structural Model Measurement Basing on Fit Indices

Fit Indices

Measurement Model


Recommendation of the StructuModel



≤ 3.000

Less than the threshold, shows model is good



≤ 0.060

Less than the threshold, shows model is good



≥ 0.950

Greater than the threshold, shows model is good



≥ 0.90

Greater than the threshold, shows model is good

From Table 4 the fit indices indicate that the structural model is relatively fitting the examined data.








Figure 3: Path Diagram of the Structure Model for Adoption and Use of ITS

As Francis (2003) noted, on using AMOS to determine the hierarchical regression, the independent constructs (predictor variables) are entered in sets of variables according to a pre-determined order that may deduce some fundamental intermediating relationships between the independent and the dependent variable. In this study, behavioral intention construct the mediator is influenced by performance expectance, effort expectance, social influence and attitude to predict actual ITS usage. It was also hypothesized that facilitating condition and awareness directly influence actual ITS usage. Analysis of results shows that performance expectance, effort expectance, social influence and attitude count for 54% of the variance in behavioral intention to influence actual ITS usage. Attitude (β = 0.31) highly contributed to behavioral intention than social influence (β = 0.24), effort expectancy (β = € 0.21), and performance expectance (β = 0.12). Similarly, the direct influence of awareness on actual ITS usage was significantly high (β = € 0.38) whereas that of facilitating condition was insignificant, with a p-value of 0.49. Table 5 shows the extracted text view output of the standardized summaries of the path diagram and the hypotheses explanation.

Table 5: Implication of the Hypothesis Basing on the Path Diagram Summary



Path Coefficient

β - value




Results implication






Less significant














FC Actual Usage of ITS




Not Significant


AW Actual Usage of ITS












BI Actual Usage of ITS





Analysis from the Semi-structured Interviews

Most participants indicated that most organization involve line managers in the adoption process, and some managers initiates the adoption process of ITS. They also noted that organizations provide training mostly at the inception of ITS and that most universities in particular, appoint full time support staff specifically for ITS support.

The directorate of ICT suggested that our university should acquire an ERP system. I was a member of the committee that was appointed to oversee the bidding, awarding of tender and the implementation process. (4 different financial managers from different universities)

Different people in an organization come from different academic and cultural backgrounds. It is my role as a senior manager to guide and advise them on such new changes otherwise they can resist it. (Senior financial manager at a university)

I am the ITS manager, I have four people working under me, from time to time users log calls and I appoint one of my staff to go and handle the matter. Even though we are few, we are capable of solving the problems as in most cases the problems are trivial ranging from forgetting passwords to improper following of the procedure. (ITS manager at one of the university campus)

The above arguments supports hypothesis one (H1: Organisational contextual factors such as cultural, social and organisational influences line mangers adoption and use of ITS).

Still it was noted that if an organization doesn't make enough awareness campaign and proper training some managers will not be comfortable with system hence may not be all that effective.

Some managers in organizations have limited computer skills background they hence fear that they will get embarrassed if the fail to use the system (first member of ITS software solution)

We normally make a follow up in the organizations were we sell our products. But in most cases we wait for the organizations to invite us to carry out training on job. If this is not done users tend to forget. This is a key role an organization needs to play. (Second member of ITS solutions)

We always encourage organizations to make recommendations for the necessary changes they feel would be effected. When we come for this annual conference we discuss the suggested changes and we effect them in the next version (third member of ITS solutions)

Analysis supports H1.

All interviewee agreed that ITS vendors' and consultants' support is more critical at the deployment stage. They noted that at this stage there is a general lack of technical knowledge of the system, however, they pointed it out that people with prior experience with web-based tools adopt and use the system more quicker than their other counterparts.

ITS found me at my place of work and the solution developers trained us before it was fully implemented. Because I had earlier on used Facebook and yahoo messenger I had little problem learning it. (a user from a university)

Initially I was working in bank, and our system was online to which all branches were connected so I found no problem learning to use ITS. (One financial manager)

The above analysis supports hypothesis 9. (H9: A line manager who finds ITS at the place of work will adopt and use it more quickly)

As noted by Davis (1989), it becomes mandatory for those people who find a system already being used at their place of work to adopt and use it. Participants in this study show that line mangers will be compelled to quickly adopt and use the system because of their status as team leaders.

I found ITS being used so I was eager to learn how to use it. So that I don't show my subordinates that I am an amateur. (University administrative manager)

This is in support with hypothesis 10. (H10: Managers who have experience with web-based tools will adopt and use ITS more quickly)

Conclusion and Recommendation

Results of this study suggest that organizational, social, and cultural factors are vital in explaining line managers behavioral intention to adopt and use of ITS. Statistical results were in line with what researchers (Pu-Li, and Kishore, 2005; Cody-Allen and Kishore, 2006; Tibenderana and Ogao, 2008) suggested that UTAUT's may best be put to use if its constructs are modified to fit the issue at hand. Much as this study didn't include the moderating factors in its quantitative analysis, the qualitative analysis didn't show strong support to UTUAT that suggests that the age and gender contribute highly on behavioral intention. This may be because many of the participants were above the age of 30 and the line managers responsibilities and challenges overshadow their age and gender.


This study targeted ITS annual conference participants. As always the mood of conferences, participants want to relax and visit new places and also due to 2010 World Cup gig this conference arranged world cup mock matches and other celebration events. This caused a high non-respondent rate as respondents were ever involved in these activities. Much as in structural equation modeling we fixed the loading factor of every first indicator of a construct to 1, so as to make the model admissible by the system the sample size of 64 was below the recommended minimum of 100 (Heir et al., 2006). Also the time frame for data collection was limited as the conference lasted for only 3 days.

6 Acknowledgment

The authors wish thank all participants of the ITS user group conference. Special thanks goes to Ms. Jack Ann, the ITS users group administrator who arranged a stall for exhibition of study materials and questionnaire, Mr. David Kiwana the ITS Manager Makerere University who helped in the collection of the questionnaires and, Mr. Nicholas Masango senior recruiter postgraduate TUT for moral and financial support. May God bless you all!