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trends leading researchers to seek solutions in human behavior (Odedra, 2003). Firms in .... of perceived usefulness with small business users in New Zealand.
FACTORS AFFECTING USER ACCEPTANCE OF INFORMATION TECHNOLOGY SYSTEMS IN KENYA Francis N. Gituru, MBA student. School of Business. United States International University Email:. [email protected] & Jimmy K. Macharia, Assistant Professor of Information Systems, School of Business. United States International University PO BOX 14634 Nairobi 00800 Kenya. Tel. 254-20-3606299 254-0722-779770 Email: [email protected]

ABSTRACT Prior research has established that the root cause of failure in Information Technology projects is found in users perceptions and attitudes. However, the various models and concepts that have evolved in the developed countries to explain and predict user acceptance behavior have not been validated in developing nations such as Kenya. This research study sought to test the application of the Unified Theory of Acceptance and Use of Technology (UTAUT) model by Venkatesh, Morris, Davis, G. and Davis, F. (2003) in Kenya. The UTAUT model established performance expectancy, effort expectancy, social influence and facilitating conditions as the four critical determinants of user acceptance

The objective of the research study was to find out the extent to which these factors influence user satisfaction in Kenya. Additionally, the effect of local factors of exposure and security on user acceptance was investigated. The research study was in the form of a descriptive design. Cluster sampling was used to pick 143 subjects for a cross-sectional survey of employees in 21 branches of a local bank. A 42-item questionnaire was used to collect data from the respondents. T-tests, correlation and linear regression analysis were performed to determine the relationships between variables.

In addition to the UTAUT factors, this research study established that security concern and IT experience are major determinants of user acceptance in Kenya. It is therefore important for managers to address system performance, interaction, security issues and user training needs in IT projects. Additionally, appropriate facilities should be provided and senior management involved in championing technology changes. It is recommended, among other things, that longitudinal studies be done across firms to examine the causal relationships on the factors identified in this research study.

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1.0 INTRODUCTION 1.1

Background of the Problem

The adoption of many promising technologies in Kenya is hampered by a poor understanding of the challenges faced by users of the technologies (Nyangena, 2004). Subsequently, prior research has established that there is considerable variation in adoption patterns and benefits that firms derive from technology deployment (Kinyanjui and McCormick, 2002). These researchers found that within Kenyan firms, users ignorance and perceptions have led to gross underutilization of systems such as email and Internet.

Technology accounts for the wide economic development discrepancy observed between African countries and the developed countries (Pohjola, 2003). According to this author, Information and Communications Technology (ICT) is a key driver of economic growth and yet ICT access remains a serious problem throughout Africa. Surprisingly, underutilization of ICT in the region has persisted despite a major growth in installed IT base in line with global trends leading researchers to seek solutions in human behavior (Odedra, 2003). Firms in Africa must therefore improve their technology capabilities and maximize utilization in order to enhance their competitiveness (Davidson, Vogel, Harris and Jones, 2000).

The pressure to improve business performance is not limited to Africa but is world wide and has forced firms to enhance their technical capabilities in terms of manufacturing, marketing and the product quality in order to remain competitive (Lin and Wu, 2004). Previous research has shown that businesses use computer technology to get better financial performance and higher service quality (Zhao, 2002).

Adopting new technologies is therefore essential for sustained competitive advantage in all industries and produces tangible market benefits (Edmondson, Bohmer, and Pisano, 2001). Today, Information Technology is more of a necessity rather than an option or luxury (Jungwoo, 2004), and plays a strategic role in the long-term survival of a firm (Adamson and Shine, 2003).

It is estimated that over a half of all capital investment by firms is going into information technology systems (Venkatesh, et al., 2003). However, according to Edmondson et al. (2001) adoption of new technology is hampered by a requirement for new routines and behavior. Apart from underutilization, these requirements may lead to technology rejection and nonuse of information technology, especially software. This frequently results in failure 2

to meet objectives and frustration on the part of senior managers sometimes even when the technology is functioning as planned (Lassila and Brancheau, 1999). Understanding how and why variations occur will help firms avoid the common and expensive failures witnessed in technology adoption projects (Zablah, Bellenger and Johnson, 2004).

The reasoned action theory (TRA) is a well-established model and has been broadly used to explain user acceptance behavior (Lin and Wu, 2004). Davis, (1989) proposed the technology acceptance model (TAM) derived from TRA. This has been further developed into the Unified Theory of Acceptance and Use of technology, UTAUT model (Venkatesh et al. 2003). This work represents the most recent framework for understanding employee technology usage behavior (Zablah et al, 2004).

1.2

Statement of the Problem

Deployment and use of new information technology systems is a key determinant of competitiveness. Globally, investments in Information Technology are however characterized by uncertainty over expected benefits and huge irreversible costs (Fichman, 2004). The flow of technology from the West into Africa has been on the rise and over the years, there has been growing concern over low returns and failed technology implementations in the continent (Odedra, 1993).

Blind deployment of technology without complete evaluation of factors that influence user acceptance behavior can therefore be perilous for firms in Africa because of the regions unique culture. Research has shown that social cultural settings are an important factor in technology acceptance and account for variation in user behavior across regions (Evers and Day, 1997). The existing literature does not indicate that the Unified Theory of Acceptance and Use of Technology (UTAUT) model has been tested in Kenya.

There is therefore a need to identify factors affecting acceptance and usage of Information Technology systems in Kenya.

1.3

Purpose of the Study

The purpose of this research study was to identify factors affecting user acceptance and usage of Information Technology systems in Kenya. The research study sought to extend the Unified Theory of Acceptance and Use of Technology –UTAUT by Venkatesh et al. (2003) to Kenya.

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1.4

Research Questions

The UTAUT model identifies four critical factors to technology usage behavior namely: Performance expectancy, effort expectancy, social influence and facilitating conditions moderated by age, gender and experience. The research questions were therefore formulated from the hypothesis developed under UTAUT (Venkatesh et al. 2003) as follows: •

Do expectations in performance and effort influence the acceptance and use of new technology in Kenya? ;



Does social influence have a significant effect on acceptance and use of new technology in Kenya? ;



Do facilitating conditions have a significant influence on acceptance and use of new technology in Kenya? ; and



What are the local issues that have a significant influence on acceptance and use of new technology in Kenya?

2.0

LITERATURE REVIEW

2.1

Introduction

Liberalization of the ICT sector in Kenya in the last few years has led to a rapid growth in technology deployment in the country (Oyelaran-Oyeyinka and Adeya, 2004). However, these researchers found that the low density of ICT infrastructure, congestion and costs are significant impediments to technology usage in Kenya. As a result, ICT access and user exposure to technology are limited such that a majority of the users depend on public Cyber Cafes or institutions for access to Internet and email.

Apart from the inadequate ICT framework, what little technology is deployed in Kenya is underutilized. For instance, although a majority of firms have an infrastructure that can support e-commerce, this potential is not utilized and instead, such facilities are largely used for Internet surfing and email (Kinyanjui and McCormick, 2002). These researchers found that in the few firms where managers are willing to accept the new technologies, only partial implementation has been done. For instance orders are received over the Internet but the rest of the process such as order processing, invoicing and other functions are all manual.

Sub-Saharan Africa has a very low density of personal computers and Internet deployment compared to other regions (Pohjola, 2003). The spending on ICT is low due to other pressing 4

needs that take national priority such as famines (Odedra, 1993). According to this author, information technology is perceived as foreign and there is little attention paid to factors that affect adoption such as lack of infrastructure and skills. This has led to wide inter-country disparities in IT usage arising from a weak regulatory platform, low education levels, and social economic differences (Oyelaran-Oyeyinka and Adeya, 2004).

According to Jungwoo (2004), prior research has established a list of factors that affect information technology acceptance decisions and attitudes. The technology acceptance model (TAM) developed by Davis (1989) is the most widely used model for explaining factors affecting user acceptance of new information technologies (Suh and Han, 2003).

TAM was especially developed from the Theory of Reasoned Action (TRA) to apply for information systems and the model has been properly validated by subsequent studies (Lin and Wu, 2004). Behavioral attitudes and norms in TRA are replaced with perceived ease of use and perceived usefulness in TAM. The TAM model was later adjusted to include intraorganizational and extra-organizational factors that include computing support, training and management support (Igbaria, Zinatelli, Cragg, and Cavaye, 1997).

The TAM model has been validated by many studies in various contexts and user profile settings (Hong, Thong, Wong and Tam, 2002). In particular, these researchers validated the use of TAM in predicting user behavior in a modern digital library. TAM has also been used to explain Internet Surfers use of electronic commerce and electronic commerce acceptance (Pavlou, 2003; Suh and Han, 2003; Keat and Mohan, 2004).

Venkatesh and Davis (2000) developed TAM2 from TAM. TAM2 extended TAM to include social influence, voluntariness and image. In TAM2 model, these factors are indirect determinants of user acceptance that operate through the perceived usefulness construct of TAM.

Venkatesh et al (2003) formulated the Unified Theory of Acceptance and Use of Technology (UTAUT) based on TAM and seven other existing models. They identified four direct determinants of user acceptance and usage behavior i.e. performance expectancy, effort expectancy, social influence and facilitating conditions. They used data collected from employees in organizations to examine both voluntary and mandatory contexts and focused on more complex technologies. The UTAUT model is currently the most aggressive unified framework for understanding users technology acceptance behaviors (Zablah et al. 2005). 5

2.2

Performance and Effort Expectations

2.2.1 Performance Expectations Educating users on the benefits of technology is as important as teaching them how the technology works (Duflo, Kremer and Robinson, 2005). According to these researchers, ignorance about the technology benefits is a contributing factor to failed technology implementations in Kenya. Supporting this view, Kinyanjui and McCormick (2002) found that there is little use of Wide Area Networks (WAN), extranets and high-speed digital lines in Kenya partly because of poor understanding of the performance gains that these technologies can provide.

In Kenya, performance perceptions vary with gender. In a recent study conducted among women in Kenya, Kvasny and Chong (2006) found that women believed information technology skills provided a competitive advantage in the job market. The researchers observed that women in Kenya expect to integrate IT skills to achieve better performance either in jobs or in their own private businesses.

In a study to identify factors influencing adoption of Internet by university academic staff in Kenya and Nigeria, Oyelaran-Oyeyinka and Adeya (2004) established that content is a major determinant of adoption. According to these researchers, perceived value, which is an aspect of performance expectations, is a key determinant of Internet adoption in the two countries.

Studies in the developed world have indicated a positive correlation between deployment of information technologies and rise of productivity at the firm and macroeconomic level, but not in Africa (Pohjola, 2003). Although there has been a growth in the installation of systems in Africa and despite the articulation of benefits, under-utilization has persisted (Odedra, 1993). These researchers have argued that additional training and support is required if performance is to improve.

Organizations in societies with large power distances and high uncertainty avoidance such as those found in Africa and Japan tend to be vertical with centralized decision-making (Veiga, Floyd and Dechant, 2001). Users operating within such structures perceive new information systems primarily as upward reporting, a view that tends to lower their expectations on performance (Braa et al, 2001). Within South Africa, the value of new information systems is diminished by perceptions that the technology primary function is to support existing patterns 6

rather than a new way of doing things (Kinyanjui and McCormick, 2002). This is typical behavior in societies with high uncertainty avoidance.

In a study in Nigeria, Anandarajan et al. (2002) established that performance as measured by the construct of perceived usefulness in TAM played a lesser role in African culture compared to the Western culture. They noted that effort expectation is the dominant factor in predicting user acceptance behavior in Africa in contrast to the Western where performance expectation factor is the main predictor of adoption behavior.

In a review of the various models, Venkatesh et al (2003) concluded, “Within each individual model, performance expectancy is the strongest predictor of intention”. They also concluded that performance expectancy varies with both gender and age with stronger influence on younger men. These findings were consistent with previous model tests.

However, prior research comparing the effect of performance expectation and effort expectation relative to each other is inconclusive although the majority supports the view that performance expectation is dominant (Hong et al, 2004). For instance, Igbaria et al (1997) found that the effect of perceived ease of use or effort expectation was greater than the effect of perceived usefulness with small business users in New Zealand.

TAM2 included social influence as a factor that operates through performance expectation constructed as perceived usefulness. In this model, Morris and Venkatesh (2000) argue that persuasive social information based on expert power and credibility changes perceptions of usefulness towards new systems. Additionally, status elevation within a group leads to greater productivity and positive perceptions on performance. Job relevance and output quality are also significant determinants of performance expectation (Agarwal and Prasad, 1997).

2.2.2 Effort Expectations Effort expectation is a consideration in user acceptance and use of IT systems in Kenya. According to Kinyanjui and McCormic (2002), users prefer email systems to postal service because email is easier to use. They observed that users of e-commerce prefer to send email photo attachments to potential customers rather than send samples through DHL International Ltd partly because it takes less effort to send emails than parcels.

Although a system may be easy to use, Duflo et al, (2005) argue that the users should be taught about the system benefits for sustained change in usage behavior. This suggests that 7

that performance and effort expectations are correlated. For example Kinyanjui and McCormic (2002) observed that email is more readily accepted and used because both telephone and courier services are expensive. Therefore other than the ease of use, the performance benefits achieved through cost savings tend to increase usage and satisfaction with the technology in Kenya.

According to findings of a research study in South Africa, perceived ease of use is the main predictor of both usage and usefulness in the African context (Brown, 2002). The African cultures are characterized by more structures and are relatively simple compared to those of the West. This creates a more favorable view for a new technology (Anandarajan et al., 2002) and the effort expectation rather than performance is the main determinant of user adoption behavior.

Phelps and Mok (1999) concluded that the easier the interaction, the greater the user’s sense of personal control and ability to operate the system. Additionally, they argue that effort expectations determine perceptions of system usefulness since less effort is likely to increase usage and therefore performance. In a study of small businesses adoption of Internet technologies by Jungwoo (2004), it was observed that effort expectation is significant only when dealing with innovative technology. When dealing with popular Internet technologies such as e-mail, it was noted that acceptance of these technologies is regarded as socially expected irrespective of the effort required.

Venkatesh et al (2003) found that effort expectancy in all models is significant in both mandatory and voluntary user contexts, especially in the early stages of adoption. They also found that like in performance, effort expectation is moderated by gender, age and experience with stronger effects on young women at early stages of experience.

2.3

Social Influence

According to Duflo et al (2005), the extent to which people are able to learn from one another is critical to adoption of new technologies in developing countries such as Kenya. They found that social learning takes place with “information neighbors”. In an earlier study, Nyangena (2004) established that users in Kenya observe and seek information from close friends and noted that co-operation within groups and development of trust at informal levels is therefore an important factor in user acceptance behavior.

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According to Miguel and Kremer (2003), social influence in Kenya affects technology adoption through information transmission. These researchers argued that overly optimistic beliefs lead to favorable acceptance but the beliefs are revised downwards with more information and experience and therefore the effect on usage tend to be of short-term nature. More recent research findings have confirmed that positive social learning effect tends to have a short-term influence on usage behavior unless performance gain is demonstrated as well (Duflo et al 2005).

The traditional cultural discrimination against women coupled with a large power distance in Kenya has a significant social influence on acceptance and usage of technology (OyelaranOyeyinka and Adeya, 2004). According to Veiga et al (2001), personal computers were withdrawn from women secretaries in some government departments in Kenya once it dawned on their managers that the computers were more than advanced typewriters. The women secretaries seemed to have acquired new power and status at the expense of their mainly male managers.

It is therefore not surprising that women in Kenya perceive new technology as important in leveling the playing ground for women entry into new businesses and jobs (Kvasny and Chong, 2006). According to these researchers, adoption of new technology provides a sense of belonging among the women in Kenya.

Oyelaran-Oyeyinka and Adeya (2004) further found that age is a significant moderator of social influence in Kenya and Nigeria. According to these researchers, the senior positions tend to be filled by older workers who are presumably wiser within the African cultural context. Such cultural bias limits the ability of the older workers to learn since they do not want to be seen struggling.

In general, technology developed for Western market is not appropriately customized to the African culture (Odedra, 1993). More recently, researchers have argued that technology is developed for use within a social context and therefore there are challenges in implementing technologies from developed countries within Africa (Davidson et al, 2000). The major difference between the Western cultures and African cultures is in the culture dimension of uncertainty avoidance and large power distance (Brown, 2002).

Information technology systems tend to increase power among the skilled workers at the expense of the managers. Subsequently the technology is likely to be favorably received by 9

the lower cadres within an organization and resisted by their immediate managers in African societies that are characterized by large power distance (Veiga et al, 2001). In these societies, the lower cadres are likely to adopt the opinions of their superiors without questioning (Brown, 2002).

In Africa, tribal affiliation is an important cultural aspect and is manifested in firms through personal influence and tribal patronage in personnel recruitment processes (Odedra, 1993). The author argues that hiring of expatriates, as often happens with new technologies, and the efficiencies that are meant to come with the new systems threaten existing social and power structure at the firm leading to resistance and underutilization.

Prior research has established that users tend to be more satisfied with “technology adopted to their culture” (Keat and Mohan, 2004). Even in the West, new technologies redefine job activities and therefore interactions and power relationships within an organization (Adamson and Shine, 2003). According to these researchers, such changes in relations affect user acceptance and use of the new technology.

Social influence is significant in mandatory settings during the early stages of implementation (Agarwal and Prasad 1997; Hartwick and Barki, 1994). Social influence has a significant direct effect on acceptance over and above performance and effort expectations (Venkatesh and Davis, 2000). The social influence has a stronger effect on women particularly older women (Venkatesh et al, 2003).

2.4

Facilitating Conditions

In Kenya, the government controls a very large segment of the telephone, postal and electronic infrastructure (Kinyanjui and McCormic, 2002). According to these researchers, such control has tended to limit the reach, density and quality of supporting ICT infrastructure and has subsequently affected the adoption of technologies such as Internet. However, they note that there have been recent gains after the main carrier in Kenya was restructured and new players were allowed to enter the market in response to calls to liberalize the industry.

Growth in communication infrastructure leads to improvements in sharing of knowledge and trust within social groups that makes it easier to accept new technologies (Nyangena, 2004). However, supporting facilities such as telecommunications remain expensive for most users

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in Kenya and access to system such as email and Internet is limited within the employers’ facilities (Oyelaran-Oyeyinka and Adeya, 2004).

Davidson et al (2000) have noted the absence of an enabling regulatory and policy environment as an impediment to technology acceptance in Africa. Infrastructure development for information technology is expensive and has lagged behind in Africa due to the existence of more pressing needs in most African countries (Odedra. 1993). Since imitation is much cheaper than innovation, Pohjola (2003) has argued that it is in the interest of African countries to create an enabling ICT infrastructure, which will allow for easy diffusion of technology through imitation.

Facilitating conditions can influence users perception of effort expectation. In their research, Oyelaran-Oyeyinka and Adeya (2004) found that users in Nigeria were more concerned with the facilitating conditions than with effort expectations whereas the users in Kenya, which had better facilities, were more concerned with effort expectations. In general, better facilities will therefore lead to higher usage and satisfaction.

Within Mozambique, Braa et al (2001) identified poor infrastructure, lack of skills and inadequate network support as factors impeding the spread of technology. The researchers attributed serious underutilization of IT systems at district and provincial levels to a lack of training. They observed that computers were used primarily as advanced typewriters. They found very few trainers with requisite general IT skills for effective training and specialized knowledge was completely lacking.

Facilitating conditions will also influence social learning conditions. In Mozambique, the poor formal support infrastructure has led users to collaborating in informal networks of support that enable users to learn new skills (Braa et al, 2001). Such challenges have led to wide variations in usage and adoption along demographic factors such as education level and economic status in the continent (Oyelaran-Oyeyinka and Adeya, 2004).

Prior research has established that support issues are included within the effort expectancy construct. Facilitating conditions are predictive of behavior intention when effort expectancy is absent otherwise they are non-significant (Morris and Venkatesh, 2000). The effect of facilitating conditions on behavior is stronger on older people and decrease with usage as users find multiple alternative sources of support with technology diffusion in the organization (Venkatesh et al, 2003). 11

2.5

Local Factors

In a study of e-commerce in the garment industry in Kenya, Kinyanjui and McCormic (2002) observed that there are other local factors besides the poor infrastructure that account for the underutilization of technology. They found wide variations in adoption and use of ecommerce between firms operating within the same environment in terms of infrastructure, regulations and policy framework. They therefore concluded that the degree of comfort of key personnel with the technology is an important indicator of acceptance levels.

In Kenya, the lack of important skills and relevant experience is linked to low usage levels (Duflo et al, 2005). The lack of awareness and appropriate skills is major cause of underutilization and non-use of available technologies such as WAN and extranets (Kinyanjui and McCormic, 2002). These researchers found that users in Kenya were not aware of the availability of high-speed digital lines and continued to use unreliable low speed dial-up connections. This suggests that exposure to technology is an important factor in user acceptance and use of technology.

In societies like Kenya that are characterized by high uncertainty avoidance and large power distance, technologies that are reliable and proven are accepted more readily (Veiga et al, 2001). New technologies are therefore likely to increase uncertainty and security concerns with a negative effect on user satisfaction. Kinyanjui and McCormic (2002) observed that key personnel were particularly concerned about network security and were therefore reluctant to adopt e-commerce even when the infrastructure is available.

Underutilization of systems in Africa is widespread (Odedra, 1993). According to this author, this is partly attributable to the fact that the technology is brought into Africa on the assumption that a certain level of knowledge and skills is available locally. This is not always the case and at times, due to lack of prior exposure, the users are unable to relate the available system knowledge to the local environment and therefore the systems are regarded as alien. The perceptions that the technology is foreign tend to increase uncertainty and lead to underutilization (Veiga et al, 2001).

The various models on user acceptance have been extended in various ways to take into account additional factors (Pavlou, 2003). This author noted that in extending a model, it is important to ensure consistency between the old and new variables. Subsequently, by proving

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a consistency between security issues and the other factors, the author successfully incorporated security concerns constructed as perceived risk within the TAM model.

Security concerns have been shown to influence user acceptance behavior in the case of introduction of technologies that are considered radically new in business such as ecommerce (Suh and Han, 2003). A key factor in security concerns is trust. Users do not trust unfamiliar technologies such as recent mobile payment solutions leading to low levels of user acceptance and adoption (Keat and Mohan, 2004).

2.6

System Usage and User Satisfaction

System usage has been demonstrated as a primary indicator of acceptance and is of practical value to organizations in assessing the impact of a new technology (Igbaria et al, 1997). Agarwal and Prasad (1997) found a significant correlation between usage intention and results demonstration.

In a mandatory setting, user satisfaction is more relevant than level of usage as a measure of system acceptance (Adamson and Shine, 2003). These researchers tested the validity of the TAM model in a banking environment with two banks. They argued that end user satisfaction is likely to lead to acceptance and increased usage and thus justify the systems cost to the organization. However even in mandatory setting, user acceptance behavior and satisfaction has been shown to be variable (Hartwick and Barki, 1994).

End user computing satisfaction is defined as the overall effective evaluation of an end user experience with an information system (Chin and Lee, 2000). Doll and Torkzadeh (1989) developed a twelve point end user computing satisfaction (EUCS) model consisting of five factors i.e. information content, format, accuracy, ease of use, and timeliness. The 12 point summed scale provides equivalent measures across cultures but the structural weight for the ease of use factor varies across cultural regions (Deng et al, 2003).

However Doll et al (2004) found variations in user satisfaction scores across subgroups. In particular, they found that accuracy scores vary between managerial and clerical subgroups. They attribute the variations to the different purposes for which the subgroups need the information systems.

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3.0

RESEARCH METHODOLOGY

The research was conducted in form of a quantitative descriptive study of the employees of a Kenyan bank with over 100 branches and over 2000 employees who used various IT systems. The sample was taken from 21 of 50 branches that were on a newly implemented IT system. Cluster sampling technique was used to provide regional representation in the sample.

A 42 item attitudinal self administered questionnaire was developed for data collection. A 51% response rate was achieved with 176 responses received from a target of 345 users. Frequency statistics on pie charts were used to describe the demographic characteristics of the respondents. T-tests were used to check for moderating influence of the demographic variables. Correlation and regression analysis were used to determine associations between the factors and EUCS. 3.1

Pilot

A pilot test was conducted with selected subjects at KCB KICC branch and IT personnel in head office in order to determine that the questions are relevant and are eliciting meaningful responses. Feedback from end users and IT personnel was incorporated to further refine the instrument. The pilot testing also helped set the expected time of 8 to 10 minutes needed to complete the questionnaire. 3.2 Questionnaire Administration A visit was made to the target branches with copies of the attitudinal self-report questionnaire. In 6 of the branches, the questionnaire was sent as an email attachment after discussions on phone with the branch managers. The manager was requested to print and circulate the questionnaire to the branch staff. The questionnaires were distributed by walking around users desks. The subjects were requested to complete the questionnaire early in the morning or later in the evening to avoid disrupting customer service.

Completed questionnaires were received or collected by either the branch manager or a designated person at the branch. The submitted questionnaires were mailed at the end of the day to head office through the bank’s internal mailbag system and were received in the morning the following day. Follow up was done on phone to the managers or the designated persons to monitor responses for a period of two weeks.

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3.3

Data Analysis

The returned questionnaires were centrally edited for any missing information. Questionnaires that had more than one incomplete construct were no acceptable. Also discarded were questionnaires with entire pages left blank. From the 176 submitted questionnaires, 143 were retained for the analysis after editing. The Likert scales were summed up and used to derive the values for subsequent analysis. The data analysis was done using SSPS computer package. Descriptive statistics were derived to determine the sample characteristics in terms of gender, age, experience, years of employment and employment cadre. A reliability analysis was done on each set of construct measures. Cronbach alpha values were used to ascertain internal consistency of the scale used. Convergent validity for the constructs was estimated using principal component analysis (PCA) across the items measures.

The scales for each factor were summed to derive the mean value of each independent variable. T-test and Chi-square tests were done to compare results between groups based on the demographic profile and local factor of exposure. To determine the effect of the factors in accounting for satisfaction, correlation coefficients were calculated between the factors and the EUCS measure. Linear regression analysis was then used to estimate the explanatory power of the factors on EUCS.

4.0

RESULTS AND FINDINGS

4.1

Demographics

4.1.1 Sample Characteristics The sample was gender balanced with 45% female and 55% male respondents but biased in age with 71% of the respondents below 40 years of age. Similarly a majority of 72% of the respondents had more than 3 years of IT experience. Experience with the new BEAM system was nearly evenly distributed in 3 segments of up to 5 months, 6 to 9 months and 10 months and above of experience. The distribution between supervisory and clerical staff was typical of any organization with 40% representing supervisory staff and 60% clerks. The results are shown in Table 1.

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Table1: Summary - Reliability Values Frequency

Percent

Gender

Female Male Total

64 77 141

45.4 54.6 100.0

Age

Less than 25 years Between 25 and 40 years Over 40 years Total

4 97 41 142

2.8 68.3 28.9 100.0

IT Experience

Less than 1 year 1 to 3 years Over 3 years Total

13 27 103 143

9.1 18.9 72.0 100.0

BEAM11 Experience

upto 5 Months 6 to 9 moths 10 months and above Total

41 53 43 137

29.9 38.7 31.4 100.0

Years Employed

Less than 5 years 5 to 10 years Over 10 years Total

32 17 89 138

23.2 12.3 64.5 100.0

Cadre

Clerical Supervisory Total

85 53 138

61.6 38.4 100.0

4.1.2 Local Factors-Characteristics There were 5 questions used to measure the local factors. Two of the items were on exposure and level of usage. The remaining were scale items used as a measure of the security concern construct. 4.1.2.1 Exposure The exposure item was used to generate the profile of the users. There were 133 valid responses. The larger percentage of 53% used the computer only within the company business. The remaining 47% had access to computers outside the bank offices. The results indicate a low level of exposure to technology outside the bank as shown in Figure 1.

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Figure1: Distribution by Area of Use

Local and out

Local only

47%

53%

4.1.2.2 Level of Use There were 140 valid responses from the total sample. Seventeen percent of the respondents used only one application, 20% used two applications 23% used three applications and the majority of 40% used all the four applications. This means that most of the users are using more than one application in the bank. The results are shown in Figure 2. Figure 2: Distribution by Level of Use

1 Application

17% 4 Applications 40% 20% 2 Applications

23%

3 Applications

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4.2

Model Reliability and Convergence Validity

4.2.1 Scale Reliability A reliability analysis was done on each set of construct measures. Cronbach (1951) alpha values were used to ascertain internal consistency of the scale used. All the constructs had alpha values above 0.70 and therefore demonstrated acceptable reliability for the measures except for security concerns construct. The minimum acceptable alpha value is generally 0.60 (Phelps and Mok, 1999) but a lower level of 0.50 is acceptable in early stages of research (Moore and Benbasat, 1991). Table2: Summary - Reliability Values

Factor Performance expectations Effort expectations Social Influence Facilitating conditions Security Content Accuracy Format Timeliness Overall Derived EUCS

No of Items

Reliability Coefficient (Alpha)

6 4 5 4 3 3 3 3 2 1 12

0.7 0.86 0.84 0.75 0.52 0.88 0.86 0.82 0.84 N/A 0.90

Security concerns construct with 3 items had an alpha value of 0.52. This was accepted on account of the fact that the items were few and newly developed for this study. The Cronbach alpha values are shown in Table 1.The alpha values for the 12 items in EUCS were above 0.80. The EUCS scale items were therefore sufficiently reliable as shown in Table 2.

4.2.2 Convergent Validity Convergent validity for the constructs was estimated using principal component analysis (PCA) across the item measures. All the 22 item measures for the independent variables of performance expectations, effort expectations, social influence, facilitating conditions and security concerns loaded freely in distinctive patterns in a five factor solution with Eigenvalues above 1.00. The five factors accounted for 60% of the variance. The factor loadings for all the constructs are shown in Table 3

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Table 3: Summary - Factor Loading Values 1

Performance Expectations

Effort Expectations

Social Influence

Facilitating Conditions

Security Concerns

Content

Accuracy

Format Timeliness Overall

Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q22 Q23 Q24 Q25 Q26 Q27 Q28 Q29 Q30 Q31 Q32 Q33 Q34 Q35 Q36

2 .397 .359

.534 .763 .855 .790

Component 3 .706 .621 .483 .694 .432 .610 .310

4

5

.425 .450 .389

.782 .836 .739 .582 .837 .785 .609 .708 .583

.360 .442

.714 .685 .489

.336 .724 .860 .860

.408

.874 .820 .789

.302 .839 .808 .806

.396

.855 .758 .893

All the items loaded highest on the target factors other than the item on maintenance within performance factor that loaded marginally higher (0.450) on the facilitating conditions component compared to the loading on performance expectations (0.430). This is not surprising since the facilitating conditions construct included items relating to support which conceptually includes maintenance. The minimum acceptable loading value is 0.450 (Moore and Benbasat, 1991). Although a couple of other items had cross-loadings with other factors, the cross loadings values were below the minimum limit. The scale items were therefore sufficiently convergent.

The items for EUCS loaded in a specified five factor solutions and accounted for 82% of the observed variance although the timeliness and overall satisfaction constructs loaded with Eigenvalues less than 1.00. The items loaded in a distinctive pattern with highest loading on the target factors. The lowest loading item was in content with a value of 0.724, well within the acceptable minimum value of 0.60. The EUCS scale items therefore demonstrated convergent validity. The loadings are shown in Table 3. 19

4.3

Moderating Factors

Statistical tests were carried out to determine whether there were any significant differences in performance expectations between groups. T-tests were used to compare the means between the various groups along demographic variables of gender, age, cadre and experience as well as the local variable of area of uses i.e. exposure. Cross tabulation was used to test for effect of experience on effort expectations. The results are shown in Table 4.

Gender is a significant moderating factor on performance expectations with a p value of 0.001. The other demographic variables are not significant moderators on performance expectations because their p values are above the acceptable level of 0.05 at 95% confidence level as shown in Table 4.

There was no significant moderating factor for effort expectations. As shown in Table 4, the p values were all above 0.05 and therefore the factors are not significant at 95% confidence level.

Outside exposure to technology is a significant moderating factor for social influence with a p value of 0.017. The other factors are not significant moderators of social influence at 95% confidence level because their p values are above the acceptable limit of 0.05 as shown in Table 4.

BEAM experience is a significant moderating factor for facilitating conditions with a p value of 0.022. The significance of outside exposure is indeterminate since the p value of 0.05 is on the borderline of acceptable level. The other factors are not significant moderators of facilitating conditions at 95% confidence level because their p values are above the acceptable limit of 0.05 as shown in Table 4.

Exposure is a significant moderating factor for security with a p value of 0.042. Similarly, employee cadre is a significant moderating factor on security concerns with a p value of 0.048l. The other factors are not significant moderators of security concerns at 95% confidence level because their p values are above the acceptable limit of 0.05 as shown in Table 4.

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Table 4: Moderating factors Mean Scores Exposure Outside Variable

No

Yes

p

Performance Expectation 3.53 3.58 0.261 Effort Expectation 4.24 4.52 0.094 Social Influence 3.27 3.60 0.017 Facilitating Conditions 3.76 3.99 0.050 Security Concerns 3.06 3.12 0.042

4.4

Gender Female Male

Age

p

40

p

Cadre Manager Clerical

BEAM

p

p

3.56

3.64 0.001 3.61 3.54 0.239

3.70

3.52

0.194 0.450

4.40

4.40 0.238 4.46 4.30 0.876

4.44

4.37

0.323 0.123

3.40

3.46 0.195 3.45 3.42 0.183

3.32

3.51

0.309 0.935

3.95

3.83 0.762 3.87 3.92 0.479

3.91

3.86

0.323 0.022

3.11

3.09 0.906 3.14 3.02 0.321

3.01

3.18

0.048 0.863

Structural Equation Modeling

4.4.1 Correlation Coefficients To analyze the effect of the factors identified in the research questions on EUCS, correlation coefficients were calculated between the factors and EUCS. The factors of performance expectations, effort expectations, social influence and facilitating conditions were included in the correlation analysis. Local factors are accounted for through the inclusion of security concern measure and both IT and BEAM experience in the analysis.

Correlation calculations between the independent variables in the research questions and EUCS showed that all the factors were significant and positively correlated with EUCS at 99% (at p=0.001) confidence level. IT experience was also found to be significant at 95% (at p=0.05) confidence level. A high level of correlation was observed between the factors themselves. There was a higher correlation between social influence and performance expectations (0.506) than between social influence and EUCS (0.379) suggesting that social influence was less of a direct factor in EUCS but its effect was largely accounted for through performance expectations construct. Facilitating conditions had a slightly higher correlation with effort expectations (0.525) than with EUCS (0.513). This means that facilitating conditions have a direct effect on EUCS and an indirect effect through performance expectations. Performance expectations had the highest direct impact on EUCS (0.664) and then effort expectations (0.541). Therefore performance is the most important determinant of EUCS. Correlation coefficients are shown in Table 5.

4.4.2 Linear Regression Multiple linear regression analysis was used to determine the explanatory power of the factors and their associated beta coefficients. Performance expectations, effort expectations, 21

social influence and facilitating conditions were regressed against EUCS. Local factors were presented by security concerns and both IT and BEAM experience in the analysis. Length of employment was included as a factor for completeness. Table 5: Correlation and Linear Regression Analysis Pearson Correlations

All factors Model

Re-modeled

R2=0.615 Standardized Standardized EUCS Sig. Sig. Coefficients Coefficients Beta Beta .327 .105 2

R = 611

Factors

PE

EE

(Constant) Performance 1.000 .519** .664** Expectations Effort Expectations .519** 1.000 .541** Social Influence .506** .358** .379** Facilitating Conditions .443** .525** .513** Security Concerns .303** .254 .448** IT Experience .038 .088 .189* Local Level of Use -1.00 0.23 -.064 Beam Experience -.099 .023 -.118 Years Employed -.081 -.094 -.087 PE – Performance Expectations; EE – Effort Expectations ** Correlation is significant at the 0.01 level (2-tailed). Correlation is significant at the 0.05 level (2-tailed).

.403

.000

.432

.000

.165 .048 .169 .214 .165 -.002 -.089 -.008

.030 .480 .021 .001 .006 .979 .145 .898

.169

.018

.160 .224 .161

.020 .000 .004

-.099

.078

These factors accounted for 61% of the variation in EUCS. The p values were significant at 95% confidence level for performance expectations, effort expectations, facilitating conditions, security concern and IT experience. The three items with the highest p values were dropped and the equation remodeled with the remaining factors. The factors of performance expectations, effort expectations, facilitating conditions security concern, IT and BEAM experience accounted for 61.5% of the variance in EUCS on the re-estimated model. All the factors except BEAM experience were significant at 95% confidence level and had positive beta values. Beta coefficients and p values for the initial and repeat multiple linear regression analysis is shown in Table 5.

4.5

Factors Affecting User Acceptance

4.5.1 Performance and Effort Expectations 4.5.1.1 Performance Expectations Six questions were used to measure the performance expectation variable. Overall, the mean score on the performance expectations was 3.6% with a maximum score of 5.00 and a minimum score of 1.33.

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Performance expectation is the dominant factor (b=0.43) in determining the end user satisfaction with IT systems in Kenya as shown in Table 5. The results are consistent with prior research in this field (Thompson, Higgins and Howell, 1991; Venkatesh et al, 2003; Bhattacherjee and Premkumar, 2004) and prior models of TAM and TAM2. The dominance of performance expectation over effort expectation (b=0.17) contradicts prior research findings in Africa supporting the view that effort expectation is the more significant factor due to greater social cultural influence on the continent (Anandarajan et al, 2002).

A possible explanation for the dominant role of performance expectations is the usage context. The BEAM11 application is used in a mandatory setting. The users depend on the system to perform the bulk of their daily tasks and would be paralyzed without the system. Prior research has established that where the users performance critically depends on the system, performance plays a dominant role in shaping behavior towards the system (Hong et al, 2002).

In addition to the mandatory setting, another factor that may contribute to the dominant role of performance expectations is job relevance. Some of the users’ jobs such as cashier’s functions were defined by the system and therefore the system was directly relevant to the job. Moreover, additional functions such as cheques processing and foreign currency transactions were performed through the new system. Job relevance is an indirect determinant of user satisfaction through the performance expectation construct (Morris and Venkatesh, 2000).

Consistent with the UTAUT model, performance expectations are significantly (p=0.001) moderated by gender as shown in Table 4. This is consistent with prior research and is because men are more task-oriented than women (Venkatesh et al, 2003). The effect of age and experience were however not significant (p>0.05) as shown in Table 4. A possible explanation is that the sample was heavily biased towards one age category making it difficult to make meaningful comparisons. There were only two categories in age, one below 40 years, which was the dominant group constituting of 71% of the sample and the other small group of those over 40 years.

4.5.1.2 Effort Expectations There were 4 questions used to measure the effort expectations factor. The overall mean score was quite high at 4.4 with a maximum score of 5.00 and a minimum of 1.50. In general, the system usage was free of effort. 23

The significant direct role of effort expectations in determining user acceptance is shown in Table 5 (p=0.018) and is consistent with the, TAM TAM2 and UTAUT models.. However, as a factor, effort expectation was significantly correlated with both performance expectations (0.519) and facilitating conditions (0.525) as shown in Table 4. This is not surprising since effort expectations have long been found to have strong direct effect on usage and also a strong direct effect on performance expectations (Igbaria, 1997).

The correlation with facilitating conditions is also expected and consistent with prior research views that facilitating conditions may have a direct effect on effort expectations depending on the context and how the constructs are operationalized (Thomson et al, 1991). The easier it is to use the system, the more useful the system is likely to be perceived by the end users. This indirect influence of effort expectations on performance expectations has led to inconsistencies across studies on the role of effort expectations on user acceptance (Morris and Venkatesh, 2000).

Compared to the other constructs, effort expectations had the highest mean score of 4.39 with a maximum of 5.00 and a minimum of 1.50. All the other factors had mean scores less than 4.00. A possible explanation is that this was an upgrade from the DOS system to the Windows system that allowed greater interaction with the user. Users are likely to have compared the upgrade with the previous version and found that the newer version was easier to use. Prior research has established that higher perceptions of ease of use tend to diminish the significance of effort expectations in determining user acceptance (Pavlou, 2003).

There were no significant moderating factors for effort expectations as shown in Table 4 (p>0.05). This view is inconsistent with the UTAUT model that postulates that effort expectations are moderated by gender, age and experience. The absence of the moderators may be explained by the fact that effort expectations tend to wane with time. The UTAUT model is a predictor of behavior prior to adoption. This research study was conducted at least several weeks and in most cases several months after deployment of the new system and therefore users were already well familiar with the system. 4.5.2 Social Influence The mean score on social influence was 3.43 with a maximum of 5.00 and a minimum of 1.00. This is a low score compared to effort and performance expectations. It reflects general lack of support from relatives and friends, which is expected because this is a specialized banking system and is not likely to be understood by outsiders. 24

There was no empirical support to the view that social influence is a direct determinant of user satisfaction as shown in Table 5 (p>0.05). This finding is inconsistent with the UTAUT model. Instead, our results show a strong indirect effect through performance expectations. There is a stronger correlation between social influence and performance expectations (0.506) compared to the correlation between social influence and EUCS (0.379) as shown in Table 4 . This finding is supported by prior research (Igbaria et al 1997; Phelps and Mok, 1999) where social influence is operationalized as management support.

In this research study, the majority of respondents had more than 3 months experience of the new system as shown in Figure 4. The time lapse could therefore account for the observed absence of social influence as a direct determinant of user acceptance. In particular, prior research has shown that social influence is insignificant after 3 months when the opinions of others are replaced with direct experience (Hartwick and Barki, 1994).

However, there is an aspect of social influence that persists over time irrespective of context (Morris and Venkatesh, 2000). It is an identification influence from more power through group membership that is perceived to lead to greater productivity. Since the earlier version of the system was operational only in 34 of the branches in Nairobi and major urban centers, this could have created a perception of a privileged social group and is manifested through greater performance expectations reported on the new application.

Additionally, as shown in Table 4, there was no empirical support (p>0.05) to the UTAUT view that social influence is moderated by gender and experience. As noted earlier, the time lapse may explain the generally weak direct effect of social influence in this study. In this case social influence is primarily manifested through performance expectation and in turn, performance expectation is significantly influenced by gender.

Social influence factor was found to significantly (p=0.017) differ between the two groups of those with access to outside computers and those without such exposure. Social influence is stronger among those with access to external computer systems because they constitute an independent social group defined by a set of information transmission norms. In this age of widespread Internet usage we suggest that external access to computers is primarily for Internet surfing and email, which enhances information transmission and therefore greater social influence and technology acceptance behavior (Miguel and Kremer, 2003).

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4.5.3 Facilitating Conditions There were 4 scale items used to measure the facilitating conditions construct. The mean score on facilitating conditions was 3.88 with a maximum of 5.00 and a minimum of 1.25. This is a fairly high score, second only to effort expectations suggesting a generally good support infrastructure for the system.

There is strong empirical support (p=0.020) for facilitating conditions as a direct determinant of user satisfaction as shown in Table 5 in line with the UTAUT model. Facilitating conditions have also a strong indirect effect on user satisfaction through the construct of effort expectations as shown by the high correlation (0.525) between the two factors in Table 4. This view is consistent with prior findings (Morris and Venkatesh, 2000; OyelaranOyeyinka and Adeya, 2004). This is not surprising because conceptually, the presence of an enabling environment and infrastructure support would make it easier for users to operate the new system.

There was no empirical support (p>0.05) of age as a significant moderating factor on facilitating conditions as shown in Table 4. But this can be attributed to the fact that the sample was heavily weighted in favor of one of two age categories making it difficult to detect differences along the age categories. There is empirical evidence (p=0.022) as shown in Table 4, that experience has a significant moderating factor on facilitating conditions in line with the UTAUT model. With growing experience and learning, users overcome limitations of facilitating conditions through multiple alternative sources of support (Venkatesh et al, 2003). This view is further supported by the findings in Table 4 (p=0.050) that those with external access of computers reported higher scores on facilitating conditions than the group without access.

Interestingly, BEAM experience had a negative coefficient (-0.099) when regressed against EUCS as shown in Table 5. Prior research has found that initial high expectations of performance tend to be disconfirmed with actual experience leading to a revision of perceptions that tend to lower performance ratings (Bhattacherjee and Premkumar, 2004).

4.5.4 Security Concerns Three items were used to measure the construct of security concern. The mean score for the summated scale of security concerns was 3.11 with a maximum of 5.00 and a minimum of 1.00. This is the lowest mean score among all the variables suggesting a strong concern over security of the system. 26

This research study identified security concerns as a significant (p=0.00) determinant of user acceptance in Kenya as shown in Table 5 .The findings are consistent with prior research that has established risk concerns as a determinant of user satisfaction (Suh and Han, 2003; Pavlou, 2003).

Users will not generally trust unfamiliar technologies (Keat and Mohan, 2004). This position is likely to be particularly pronounced within Africa due to the high uncertainty avoidance dimension of culture prevalent in the region (Brown, 2002). We suggest that this partly explains the low mean score (3.11) achieved in security concerns. Security concerns had the lowest mean score of 3.11. It could also explain why those with external exposure reported significantly high scores on security compared to those without any external exposure. The group with external exposure was more familiar with the windows environment and was therefore more trusting and less concerned with security.

In this research study, the users were generally uncertain about security as shown in Table 6. This may be explained by the fact that prior to the introduction of the new system, the users were in control of the signature database in the branch. Only the users in the domicile branch could view signatures and other complete details of the account. Details of customers of other branches had to be confirmed on phone and fax. Additionally, functions were clearly distinguished at the branch with separate users handling cheques and cash. The new system with centralized signatures lessened user physical control and required more trust and delegation of security to the system.

Exposure and cadre are moderators of security as shown in Table 4 (p